A Use Case Identification Framework and Use Case Canvas for identifying and exploring relevant Blockchain opportunities. Blockchain is a new, foundational technology with a vast amount of application possibilities. However, practitioners might not be aware of which use cases in their own business model might benefit from blockchain technology. To aid them in analyzing their business regarding blockchain suitability, this paper introduces a use case identification framework for blockchain and a use case canvas. In the development process they have been evaluated with internal and external reviews in order to offer the best possible guidance. In combination they offer an analysis framework to help practitioners decide which use cases they should take into account for blockchain technology, which characteristics these blockchain implementations would have, and which specific advantages they would offer.

Question: What is a use case identification framework?


Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments. Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running network-intensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPU-intensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPU-intensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last

Question: What is the difference between a cloud and a cloud computing?


Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP]. The purpose of this article is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work conducted in this area that is of direct relevance to signal processing. We also point out, in our view, the future research directions that may attract interests of and require efforts from more signal processing researchers and practitioners in this emerging area for advancing signal and information processing technology and applications.

Question: What is deep learning?


2018 Low-Power Image Recognition Challenge. The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short execution time and low energy consumption) and accurately (high precision). Over the four years, the winners' scores have improved more than 24 times. As computer vision is widely used in many battery-powered systems (such as drones and mobile phones), the need for low-power computer vision will become increasingly important. This paper summarizes LPIRC 2018 by describing the three different tracks and the winners' solutions.

Question: What is the low-power image recognition challenge?


Recognition of thalidomide defects.. Thalidomide: a brief history Thalidomide (alpha-phthalimido-glutarimide) was developed by the German firm Chemie Grunenthal as an anticonvulsant drug. Early trials showed it to be unsuitable for this purpose but indicated that it had sedative properties. Furthermore, it had one remarkable property: overdoses simply caused prolonged sleep, not death. The drug was first marketed in Germany in 1957 under the name Contergan, and in the UK in April 1958 as Distaval. Later, compound preparations which combined thalidomide with other drugs were marketed for a wide variety of indications: Asmaval for asthma, Tensival for hypertension, Valgraine for migraine, and so forth. The promotion of these products laid great stress on the safety of thalidomide, based on the remarkable property described above. German paediatricians and geneticists began to see children with gross limb malformations of a most unusual pattern. When two cases were shown at a paediatric meeting in Kassel by Kosenow and Pfeiffer in October 1960, few people present had ever seen similar limb defects. Wiedemann in 1961 described 13 affected infants who had been referred to him over a period of 10 months, and noted that this amounted to an epidemic. He drew attention to a number of associated malformations in these children, including congenital heart disease, microphthalmos and coloboma, intestinal atresia, renal malformations, abnormal pinnae, and facial naevus. In November 1961, Lenz suggested that these deformities resulted from the mothers having taken thalidomide. By a remarkable coincidence, the same suggestion was made at much the same time by McBride in Australia. Confirmation of this suggestion came rapidly from all parts of the British Isles, Kenya, Japan, Sweden, Belgium, Switzerland, Lebanon, Israel, Peru, Canada, Brazil, the Netherlands, and the USA. The drug had been released only for clinical trials in the USA because of concerns following reports from Euro

Question: What is thalidomide?


Large eddy simulations of the HyShot II scramjet. The present work is part of a broad effort toward predictive simulations of complex flows at high Reynolds numbers. The main objective of the Stanford PSAAP Center (Pre-dictive Science Academic Alliance Program) is to predict the reacting flow in the HyShot II scramjet experiment carried out in the HEG facility of the German Aerospace Agency DLR (cf. Laurence et al. 2011). Specifically, the objective is to predict the best-estimate of the flow, to estimate the uncertainties from a range of sources in this prediction, and, finally , to quantify the margin to engine unstart. Unstart occurs in supersonic internal flows when the flow becomes choked (e.g., due to excessive friction or heat addition), which initiates an unsteady unstart process that eventually leads to subsonic flow throughout. The unstart process involves a propagation of shock waves upstream in the combustor, the speed of which depends strongly on the amount of heat addition. The experiments by Laurence et al. (2011) indicate propagation velocities of 31 m/s at equivalence ratio (ER) of 0.5, 93 m/s at ER=0.7, and above 200 m/s at ER=1.1. For this problem with fixed mass flux of incoming air, the equivalence ratio is proportional to the fuel mass flux. The length of the HyShot combustor is about 300 mm; thus the unstart process takes about 10 ms at ER=0.5. The experiments were carried out in a shock tube with a test time of about 2.5 ms; thus the unstart process is significantly longer than the test time near the unstart boundary. For this reason, it is clear that shock tube experiments can not give a precise unstart bound. An alternative approach is to use a validated large eddy simulation (LES) method to explore and determine the unstart bound. This is the approach taken here, and the motivation of the work described in this brief. The complexity of the flow (turbulence, shocks, mixing, combustion) implies that a careful multi-stage validation plan is n

Question: What is the unstart bound?


Deep-learning: A potential method for tuberculosis detection using chest radiography. Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.

Question: What is tuberculosis?


Parallel and Distributed Data Pipelining with KNIME. In recent years a new category of data analysis applications have evolved, known as data pipelining tools, which enable even nonexperts to perform complex analysis tasks on potentially huge amounts of data. Due to the complex and computing intensive analysis processes and methods used, it is often neither sufficient nor possible to simply rely on the increase of performance of single processors. Promising solutions to this problem are parallel and distributed approaches that can accelerate the analysis process. In this paper we discuss the parallel and distribution potential of pipelining tools by demonstrating several parallel and distributed implementations in the open source pipelining platform KNIME. We verify the practical applicability in a number of real world experiments.

Question: What is the difference between parallel and distributed data pipelining?


NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.

Question: What is the size of the dataset?


Recurrent Continuous Translation Models. We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.

Question: What is the difference between a recurrent continuous translation model and a recurrent continuous translation model?


Limbic-cortical dysregulation: a proposed model of depression.. A working model of depression implicating failure of the coordinated interactions of a distributed network of limbic-cortical pathways is proposed. Resting state patterns of regional glucose metabolism in idiopathic depressed patients, changes in metabolism with antidepressant treatment, and blood flow changes with induced sadness in healthy subjects were used to test and refine this hypothesis. Dorsal neocortical decreases and ventral paralimbic increases characterize both healthy sadness and depressive illness; concurrent inhibition of overactive paralimbic regions and normalization of hypofunctioning dorsal cortical sites characterize disease remission. Normal functioning of the rostral anterior cingulate, with its direct connections to these dorsal and ventral areas, is postulated to be additionally required for the observed reciprocal compensatory changes, since pretreatment metabolism in this region uniquely predicts antidepressant treatment response. This model is offered as an adaptable framework to facilitate continued integration of clinical imaging findings with complementary neuroanatomical, neurochemical, and electrophysiological studies in the investigation of the pathogenesis of affective disorders.

Question: What is the difference between depression and sadness?


The attention system of the human brain.. The concept of attention as central to human performance extends back to the start of experimental psychology (James 1890), yet even a few years ago, it would not have been possible to outline in even a preliminary form a functional anatomy of the human attentional system. New developments in neuroscience (Hillyard & Picton 1987, Raichle 1983, Wurtz et al 1980) have opened the study of higher cognition to physiological analysis, and have revealed a system of anatomical areas that appear to be basic to the selection of information for focal (conscious) processing. The importance of attention is its unique role in connecting the mental level of description of processes used in cognitive science with the anatomical level common i neuroscience. Sperry (1988, p. 609) describes the central role that mental concepts play in understanding brain function as follows:

Question: What is the central role of attention in the human brain?


Power Quality Control and Design of Power Converter for Variable-Speed Wind Energy Conversion System with Permanent-Magnet Synchronous Generator. The control strategy and design of an AC/DC/AC IGBT-PMW power converter for PMSG-based variable-speed wind energy conversion systems (VSWECS) operation in grid/load-connected mode are presented. VSWECS consists of a PMSG connected to a AC-DC IGBT-based PWM rectifier and a DC/AC IGBT-based PWM inverter with LCL filter. In VSWECS, AC/DC/AC power converter is employed to convert the variable frequency variable speed generator output to the fixed frequency fixed voltage grid. The DC/AC power conversion has been managed out using adaptive neurofuzzy controlled inverter located at the output of controlled AC/DC IGBT-based PWM rectifier. In this study, the dynamic performance and power quality of the proposed power converter connected to the grid/load by output LCL filter is focused on. Dynamic modeling and control of the VSWECS with the proposed power converter is performed by using MATLAB/Simulink. Simulation results show that the output voltage, power, and frequency of VSWECS reach to desirable operation values in a very short time. In addition, when PMSG based VSWECS works continuously with the 4.5 kHz switching frequency, the THD rate of voltage in the load terminal is 0.00672%.

Question: How to design a power converter for a wind energy system?


Power consumption estimation in CMOS VLSI chips. Power consumption from logic circuits, interconnections, dock distribution, on chip memories, and off chip driving in CMOS VLSI is estimated. Estimation methods are demonstrated and verified. An estimate tool is created. Power consumption distribution between ~ I I ~ I T O M ~ C ~ ~ O I W , clock distribution, logic gates, memories, and off chip driving are analyzed by examples. Comparisons are done between cell library, gate array, and full custom design. Also comparisons between static and dynamic logic are given. Results show that the power consumption of all interconnections and off chip driving can be up to 20% and 65% of the total power consumption respectively. Compared to cell library design, gate array designed chips consume about 10% more power, and power reduction in full custom designed chips could be 15%.

Question: How much power does a CMOS VLSI chip consume?


Fast Feature Pyramids for Object Detection. Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).

Question: What is the difference between a pyramid and a pyramid?


A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network. Efficient enumeration of all maximal cliques in a given graph has many applications in graph theory, data mining and bio informatics. However, the exponentially increasing computation time of this problem confines the scale of the graph. Meanwhile, recent researches show that many networks in our world are complex networks involving massive data. To solve the maximal clique problem in the real-world scenarios, this paper presents a parallel algorithm Peamc (parallel enumeration of all maximal cliques) which exploits several new and effective techniques to enumerate all maximal cliques in a complex network. Furthermore, we provide a performance study on a true-life call graph with up to 2,423,807 vertices and 5,317,183 edges. The experimental results show that Peamc can find all the maximal cliques in a complex network with high efficiency and scalability

Question: What is a call graph?


Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention. Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.

Question: What is the difference between topic models and network embedding?


Comparative study on power conversion methods for wireless battery charging platform. In this paper, four different power conversion methods (voltage control, duty-cycle control, frequency control and phase-shift control) are compared for wireless power transfer applications by considering the energy transfer efficiency, electromagnetic interference, stability, and implementation complexity. The phase-shift control is found to be the optimal scheme with good efficiency and low electromagnetic interference. Its constant frequency feature is also within the framework of the new international wireless charging standard called 'Qi'. A high system efficiency of 72% for 5W wireless charging applications has been achieved practically.

Question: What is the optimal power conversion method for wireless battery charging?


Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine. Human often wants to listen to music that fits best his current emotion. A grasp of emotions in songs might be a great help for us to effectively discover music. In this paper, we aimed at automatically classifying moods of songs based on lyrics and metadata, and proposed several methods for supervised learning of classifiers. In future, we plan to use automatically identified moods of songs as metadata in our music search engine. Mood categories in a famous contest about Audio Music Mood Classification (MIREX 2007) are applied for our system. The training data is collected from a LiveJournal blog site in which each blog entry is tagged with a mood and a song. Then three kinds of machine learning algorithms are applied for training classifiers: SVM, Naive Bayes and Graph-based methods. The experiments showed that artist, sentiment words, putting more weight for words in chorus and title parts are effective for mood classification. Graph-based method promises a good improvement if we have rich relationship information among songs.

Question: What is the mood of this song?


A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges. As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

Question: What is the next generation mobile cloud computing applications?


Managing flood disasters on the built environment in the rural communities of Zimbabwe: Lessons learnt. This article is about managing flood disasters affecting the built environment in the rural communities of Zimbabwe. Using Tsholotsho district in Matabeleland North province as a case study, the authors argue that flooding has adversely impacted the built environment through destroying infrastructure. The principal objectives of this study were to establish the impact of flood disasters on the built environment, to demarcate factors that perpetuate communities' vulnerabilities to flooding and to delineate challenges that negate the management of flood disasters in the built environment. This qualitative study was based on a purposive sample of 40 participants. Data were collected through semi-structured interviews and observation methods. The findings were that floods can damage human shelter, roads, bridges and dams. Locating homesteads near rivers and dams, using poor-quality construction materials, and lack of flood warning were found to perpetuate vulnerability to flooding. Poverty and costs of rebuilding infrastructure, lack of cooperation between the communities and duty-bearers, and failure to use indigenous knowledge were found to be impeding the management of flood disasters. The study concluded that flood disasters can wipe out community development gains accumulated over many years. Further, community vulnerability to flooding in the built environment is socially constructed. The study posits that addressing the root causes, reducing flood vulnerability and avoiding risk creation are viable options to development in the built environment. Lastly, reconstruction following flood disasters is arduous and gruelling, and not an easy exercise.

Question: What is the impact of flood disasters on the built environment?


A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment. Recent progress in autonomous and semiautonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of different sensor technologies is essential. Consequently, the availability of synchronized multi-sensory data streams are necessary to promote the development of fusion based algorithms for low, mid and high level semantic tasks. In this paper, we provide a comprehensive description of our heavily sensorized, panoramic testbed capable of providing high quality data from a slew of synchronized and calibrated sensors such as cameras, LIDARs, radars, and the IMU/GPS. The vehicle has recorded over 100 hours of real world data for a very diverse set of weather, traffic and daylight conditions. All captured data is accurately calibrated and synchronized using timestamps, and stored safely in high performance servers mounted inside the vehicle itself. Details on the testbed instrumentation, sensor layout, sensor outputs, calibration and synchronization are described in this paper.

Question: What is a multimodal, full-surround vehicular testbed for naturalistic studies and benchmarking?


A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection. Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods of SAR ship detection are difficult to detect small scale ships and avoid the interference of inshore complex background. Deep learning detection methods have shown great performance on various object detection tasks recently but using deep learning methods for SAR ship detection does not show an excellent performance it should have. One of the important reasons is that there is no effective model to handle the detection of multiscale ships in multiresolution SAR images. Another important reason is it is difficult to handle multiscene SAR ship detection including offshore and inshore, especially it cannot effectively distinguish between inshore complex background and ships. In this paper, we propose a densely connected multiscale neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection. Instead of using a single feature map to generate proposals, we densely connect one feature map to every other feature maps from top to down and generate proposals from each fused feature map. In addition, we propose a training strategy to reduce the weight of easy examples in the loss function, so that the training process more focus on the hard examples to reduce false alarm. Experiments on expanded public SAR ship detection dataset, verify the proposed method can achieve an excellent performance on multiscale SAR ship detection in multiscene.

Question: What is a densely connected neural network?


A systematic review of the impact of the use of social networking sites on body image and disordered eating outcomes.. A large body of literature has demonstrated mass media effects on body image and disordered eating. More recently, research in this area has turned to 'new' forms of media, such as the Internet, and particularly Social Networking Sites (SNSs). A systematic search for peer-reviewed articles on SNS use and body image and eating disorders resulted in 20 studies meeting specific inclusion criteria. As a whole, these articles demonstrated that use of SNSs is associated with body image and disordered eating. Specific SNS activities, such as viewing and uploading photos and seeking negative feedback via status updates, were identified as particularly problematic. A small number of studies also addressed underlying processes and found that appearance-based social comparison mediated the relationship between SNS use and body image and eating concerns. Gender was not found to be a moderating factor. It was concluded that, although there is a good deal of correlational research supporting the maladaptive effect of SNS use on body image and disordered eating, more longitudinal and experimental studies are needed.

Question: What is the relationship between social networking sites and body image?


Lossy compression on IoT big data by exploiting spatiotemporal correlation. As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics. Motivated by this, we present a feasibility analysis of lossy compression on agricultural sensor data by comparing fidelity of reconstructed data from various signal processing algorithms and temporal difference encoding. Specifically, we evaluated five real-world sensor data from weather stations as one of major IoT applications. Our experimental results indicate that Discrete Cosine Transform (DCT) and Fast Walsh-Hadamard Transform (FWHT) generate higher compression ratios than others. In terms of information loss, Lossy Delta Encoding (LDE) significantly outperforms others nonetheless. We also observe that, as compression factor is increased, error rates for all compression algorithms also increase. However, the impact of introduced error is much severe in DCT and FWHT while LDE was able to maintain a relatively lower error rate than other methods.

Question: What is the difference between lossy compression and lossless compression?


Iterative and incremental developments. a brief history. Although many view iterative and incremental development as a modern practice, its application dates as far back as the mid-1950s. Prominent software-engineering thought leaders from each succeeding decade supported IID practices, and many large projects used them successfully. These practices may have differed in their details, but all had a common theme-to avoid a single-pass sequential, document-driven, gated-step approach.

Question: What is iterative and incremental development?


Cloud RAN challenges and solutions. In this paper we take an overall look at key technical challenges in the evolution of the Radio Access Network (RAN) architecture towards Cloud RAN, and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of General-Purpose Processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. Keywords—Cloud RAN, fronthaul, real-time, pooling, elastic scaling, multi-cell coordination, orchestration, NFV, 5G.

Question: What is the cloud management technology used in cloud RAN?


AAAI: An Argument Against Artificial Intelligence. The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI.

Question: What is the argument against artificial intelligence?


ITU-T G.729.1: AN 8-32 Kbit/S Scalable Coder Interoperable with G.729 for Wideband Telephony and Voice Over IP. This paper describes the scalable coder - G.729.1 - which has been recently standardized by ITU-T for wideband telephony and voice over IP (VoIP) applications. G.729.1 can operate at 12 different bit rates from 32 down to 8 kbit/s with wideband quality starting at 14 kbit/s. This coder is a bitstream interoperable extension of ITU-T G.729 based on three embedded stages: narrowband cascaded CELP coding at 8 and 12 kbit/s, time-domain bandwidth extension (TDBWE) at 14 kbit/s, and split-band MDCT coding with spherical vector quantization (VQ) and pre-echo reduction from 16 to 32 kbit/s. Side information - consisting of signal class, phase, and energy - is transmitted at 12, 14 and 16 kbit/s to improve the resilience and recovery of the decoder in case of frame erasures. The quality, delay, and complexity of G.729.1 are summarized based on ITU-T results.

Question: What is the bit rate of G.729.1?


Distributed Incremental Pattern Matching on Streaming Graphs. Big data has shifted the computing paradigm of data analysis. While some of the data can be treated as simple texts or independent data records, many other applications have data with structural patterns which are modeled as a graph, such as social media, road network traffic and smart grid, etc. However, there is still limited amount of work has been done to address the velocity problem of graph processing. In this work, we aim to develop a distributed processing system for solving pattern matching queries on streaming graphs where graphs evolve over time upon the arrives of streaming graph update events. To achieve the goal, we proposed an incremental pattern matching algorithm and implemented it on GPS, a vertex centric distributed graph computing framework. We also extended the GPS framework to support streaming graph, and adapted a subgraphcentric data model to further reduce communication overhead and system performance. Our evaluation using real wiki trace shows that our approach achieves a 3x -- 10x speedup over the batch algorithm, and significantly reduces network and memory usage.

Question: What is the name of the algorithm?


On the rate of channel polarization. A bound is given on the rate of channel polarization. As a corollary, an earlier bound on the probability of error for polar coding is improved. Specifically, it is shown that, for any binary-input discrete memoryless channel W with symmetric capacity I(W) and any rate R ≪ I(W), the polar-coding block-error probability under successive cancellation decoding satisfies P<inf>e</inf>(N, R) ≤ 2<sup>−Nβ</sup> for any β ≪ 1/2 when the block-length N is large enough.

Question: What is the rate of channel polarization?


A Fuzzy Logic Controller for Autonomous Wheeled Vehicles. Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).

Question: What is a fuzzy logic controller?


CANAuth - A Simple, Backward Compatible Broadcast Authentication Protocol for CAN bus. The Controller-Area Network (CAN) bus protocol [1] is a bus protocol invented in 1986 by Robert Bosch GmbH, originally intended for automotive use. By now, the bus can be found in devices ranging from cars and trucks, over lightning setups to industrial looms. Due to its nature, it is a system very much focused on safety, i.e., reliability. Unfortunately, there is no build-in way to enforce security, such as encryption or authentication. In this paper, we investigate the problems associated with implementing a backward compatible message authentication protocol on the CAN bus. We show which constraints such a protocol has to meet and why this eliminates, to the best of our knowledge, all the authentication protocols published so far. Furthermore, we present a message authentication protocol, CANAuth, that meets all of the requirements set forth and does not violate any constraint of the CAN bus. Keywords—CAN bus, embedded networks, broadcast authentication, symmetric cryptography

Question: What is the CAN bus?


Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different for different languages, the feature transformations can be shared across languages. In this paper we propose a shared-hidden-layer multilingual DNN (SHL-MDNN), in which the hidden layers are made common across many languages while the softmax layers are made language dependent. We demonstrate that the SHL-MDNN can reduce errors by 3-5%, relatively, for all the languages decodable with the SHL-MDNN, over the monolingual DNNs trained using only the language specific data. Further, we show that the learned hidden layers sharing across languages can be transferred to improve recognition accuracy of new languages, with relative error reductions ranging from 6% to 28% against DNNs trained without exploiting the transferred hidden layers. It is particularly interesting that the error reduction can be achieved for the target language that is in different families of the languages used to learn the hidden layers.

Question: How to transfer knowledge across languages?


Differential Power Analysis. are the property of their respective owners. The information contained in this presentation is provided for illustrative purposes only, and is provided without any guarantee or warranty whatsoever, and does not necessarily represent official opinions of CRI or its partners. Confidential-unauthorized copying, use or redistribution is prohibited.

Question: What is the property of their respective owners?


Sensation seeking and internet dependence of Taiwanese high school adolescents. The present study examined excessive Internet use of Taiwanese adolescents and a psychological aspect of users, sensation seeking, thus to differentiate motivation of Internet dependents and non-dependents. Seven hundred and fifty three Taiwanese high school students were selected using cluster sampling and 88 of them were categorized as Internet dependent users. Results indicated that Internet dependents spent more time on-line than non-dependents. While Internet dependents perceived significantly more negative Internet influences on daily routines, school performance, and parental relation than non-dependents, both Internet dependents and non-dependents viewed Internet use as enhancing peer relations. Making friends through the Internet has become a popular activity among adolescents, potentially leading to its excessive use. Internet dependents scored significantly higher on overall sensation seeking and disinhibition than Internet non-dependents. However, both groups did not differ in the life experience seeking subscale and thrill and adventure seeking subscale. This finding contradicts that of Lavin, Marvin, McLarney, Nola, and Scott [CyberPsychol. Behav. 2 (2000) 425]. Possible reasons for this discrepancy and for the relation between Internet dependence and disinhibition in Taiwanese adolescents are also discussed. # 2002 Elsevier Science Ltd. All rights reserved.

Question: What is the relation between sensation seeking and internet dependence?


Modular equalization architecture using inter-module and switchless intra-module equalizer for energy storage system. This paper proposes a novel modular equalization architecture using inter-module and intra-module equalizers for energy storage systems comprising multiple cells/modules connected in series. In the proposed equalization architecture, cells in a module is equalized by a series-resonant voltage multiplier (SRVM)-based intra-module equalizer while module voltages are unified by a switched capacitor converter (SCC)-based inter-module equalizer. A square wave voltage generated at a switching node of the SCC-based inter-module equalizer is utilized to drive the SRVM-based intra-module equalizer so that the SRVM-based intra-module equalizer can be totally switchless topology. The required switch count of the proposed equalization architecture can be dramatically reduced compared to conventional architectures, allowing simplified circuitry. The proposed equalization architecture is modular and allows different equalizer topologies to be used for inter- and intra-module equalizers, offering good modularity and design flexibility. The derivation procedure of the proposed equalization architecture is explained, followed by separate analyses for the SCC-based inter-module equalizer and SRVM-based intra-module equalizer. An experimental equalization test for three supercapacitor (SC) modules, each consisting of four cells in series, was performed from an initially voltage-imbalanced condition. Module and cell voltages were equalized at different rates, and the equalization performance of the proposed modular equalization architecture was successfully demonstrated.

Question: What is the difference between a voltage multiplier and a voltage doubler?


Issues and Perspectives in Meditation Research: In Search for a Definition. Despite the growing interest in the neurobiological correlates of meditation, most research has omitted to take into account the underlying philosophical aspects of meditation and its wider implications. This, in turn, is reflected in issues surrounding definition, study design, and outcomes. Here, I highlight the often ignored but important aspect of definition in the existing scholarship on neuroscience and meditation practice. For a satisfactory account of a neuroscience of meditation, we must aim to retrieve an operational definition that is inclusive of a traditional ontological description as well as the modern neurocognitive account of the phenomena. Moving beyond examining the effects of meditation practice, to take a potential step forward in the direction to establish how meditation works, it becomes crucial to appraise the philosophical positions that underlie the phenomenology of meditation in the originating traditions. This endeavor may challenge our intuitions and concepts in either directions, but issues pertaining to definition, design, and validity of response measures are extremely important for the evolution of the field and will provide a much-needed context and framework for meditation based interventions.

Question: What is the definition of meditation?


Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization.. This two-part article provides an overview of the global burden of atherothrombotic cardiovascular disease. Part I initially discusses the epidemiologic transition which has resulted in a decrease in deaths in childhood due to infections, with a concomitant increase in cardiovascular and other chronic diseases; and then provides estimates of the burden of cardiovascular (CV) diseases with specific focus on the developing countries. Next, we summarize key information on risk factors for cardiovascular disease (CVD) and indicate that their importance may have been underestimated. Then, we describe overarching factors influencing variations in CVD by ethnicity and region and the influence of urbanization. Part II of this article describes the burden of CV disease by specific region or ethnic group, the risk factors of importance, and possible strategies for prevention.

Question: What is the burden of cardiovascular disease?


Predicting Player Churn in Multiplayer Games using Goal-Weighted Empowerment. The extent to which players feel compelled to play a game is a primary factor in determining that game's success. Using ideas from self-determination theory, we propose that the drive to play games is related to player's ability to exercise their mastery drive. In self-determination theory, increasing one's control is intrinsically rewarding, however it is difficult to instantiate this theory to make concrete predictions of when a player will quit a game early in favor of another activity. The problem of predicting within-game churn events involves modeling and predicting a player's motivational state to remain in a game. We combine new motivation theory ideas with machine learning methods to develop a computational model that postulates that player's satisfaction is directly related to their perceived locus of control, extrinsic vs. intrinsic, and hypothesize a set of measurable signals that mediate a player's locus of control. An agent will continue goal pursuit within the game based on its "empowerment", which is essentially its ability to predict the future based on its own actions. This indicator of locus of control accounts for both the feasibility of the goal and whether expected progress toward a selected goal is being achieved. A player's behavior can be used to estimate their empowerment by correlating their continuation of goal pursuit with measures of their expectations of progress and goal-achievability. We apply these concepts and build a model to predict player when players will quit a game early for the multiplayer game, Dota2.

Question: How do you predict player churn in multiplayer games?


Neural correlates of cognitive processing in monolinguals and bilinguals.. Here, we review the neural correlates of cognitive control associated with bilingualism. We demonstrate that lifelong practice managing two languages orchestrates global changes to both the structure and function of the brain. Compared with monolinguals, bilinguals generally show greater gray matter volume, especially in perceptual/motor regions, greater white matter integrity, and greater functional connectivity between gray matter regions. These changes complement electroencephalography findings showing that bilinguals devote neural resources earlier than monolinguals. Parallel functional findings emerge from the functional magnetic resonance imaging literature: bilinguals show reduced frontal activity, suggesting that they do not need to rely on top-down mechanisms to the same extent as monolinguals. This shift for bilinguals to rely more on subcortical/posterior regions, which we term the bilingual anterior-to-posterior and subcortical shift (BAPSS), fits with results from cognitive aging studies and helps to explain why bilinguals experience cognitive decline at later stages of development than monolinguals.

Question: What is the neural correlates of cognitive processing in monolinguals and bilinguals?


A review of eye-tracking applications as tools for training. Substantial literature exists regarding how eye-tracking systems can be used to measure cognitive load and how these measurements can be useful for adapting training in real time. Much of the published literature discusses the applications and limitations of the research and typically provides recommendations for improvement. This review assesses these articles collectively to provide a clearer solution for implementing eye-tracking systems into a training environment. Although limitations exist for using eye tracking as an interface tool, gaze and pupillary response have been successfully used to reflect changes in cognitive load and are starting to be incorporated into adaptive training systems, although issues are still present with differentiating pupil responses from simultaneous psychological effects. Additionally, current eye-tracking systems and data analysis software have proven accurate enough for general use, but issues including system cost and software integration prevent this technology from becoming commercialized for use in common instructional settings.

Question: What is the difference between a review and a solution?


Cause related marketing campaigns and consumer purchase intentions : The mediating role of brand awareness and corporate image. The purpose of this research is to investigate the kind of relationship between Cause Related Marketing (CRM) campaigns, brand awareness and corporate image as possible antecedents of consumer purchase intentions in the less developed country of Pakistan. An initial conceptualization was developed from mainstream literature to be validated through empirical research. The conceptualization was then tested with primary quantitative survey data collected from 203 students studying in different universities of Rawalpindi and Islamabad. Correlation and regression analysis were used to test the key hypothesis derived from literature positing brand awareness and corporate image as mediating the relationship between CRM and consumer purchase intentions. The findings indicate that consumer purchase intentions are influenced by the cause related marketing campaigns. Furthermore it was observed that the brand awareness and corporate image partially mediate the impact of CRM campaigns on consumer purchase intentions. The data was gathered from universities situated in Rawalpindi and Islamabad only. Hence, future research could extend these findings to other cities in Pakistan to test their generalizability. Further research can be carried out through data collection from those people who actually participated in cause related marketing campaigns to identify the original behavior of customers instead of their purchase intentions. This research and the claims made are limited to the FMCG industry. The key implications cause related marketing of these findings for marketing managers lend support for the use of campaigns in Pakistan. The findings also suggest some measures which can be taken in to consideration in order to enhance brand awareness and to improve corporate image as both variables mediate the impact of CRM campaigns on consumer purchase inten

Question: What is the relationship between cause related marketing campaigns and consumer purchase intentions?


Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network. The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.

Question: What is the wealth distribution of the bitcoin network?


Introduction to the talking points project. A number of systems, technological and otherwise, exist for helping blind and visually impaired users navigate from place to place. [5, 3] However, in focusing only on the destination, these systems often neglect the journey. We propose to rectify this, with a device to improve a user's peripheral awareness of their surroundings. This is the motivation and the primary goal of this project. The Talking Points project aims to create a system for attaching information to places and objects, in a format that can be easily converted to speech. It allows blind and visually impaired users to get information about the places and objects they are walking by, and can also be used to provide digital information to other passersby. We have created a proof-of-concept of this system. The system reads passive RFID tags from a mobile reader. It looks up a tag's ID number to find associated text, then uses text-to-speech software to read that speech to the user.

Question: What is the motivation of the talking points project?


Millimeter-wave CMOS design. This paper describes the design and modeling of CMOS transistors, integrated passives, and circuit blocks at millimeter-wave (mm-wave) frequencies. The effects of parasitics on the high-frequency performance of 130-nm CMOS transistors are investigated, and a peak f/sub max/ of 135 GHz has been achieved with optimal device layout. The inductive quality factor (Q/sub L/) is proposed as a more representative metric for transmission lines, and for a standard CMOS back-end process, coplanar waveguide (CPW) lines are determined to possess a higher Q/sub L/ than microstrip lines. Techniques for accurate modeling of active and passive components at mm-wave frequencies are presented. The proposed methodology was used to design two wideband mm-wave CMOS amplifiers operating at 40 GHz and 60 GHz. The 40-GHz amplifier achieves a peak |S/sub 21/| = 19 dB, output P/sub 1dB/ = -0.9 dBm, IIP3 = -7.4 dBm, and consumes 24 mA from a 1.5-V supply. The 60-GHz amplifier achieves a peak |S/sub 21/| = 12 dB, output P/sub 1dB/ = +2.0 dBm, NF = 8.8 dB, and consumes 36 mA from a 1.5-V supply. The amplifiers were fabricated in a standard 130-nm 6-metal layer bulk-CMOS process, demonstrating that complex mm-wave circuits are possible in today's mainstream CMOS technologies.

Question: What is the difference between a millimeter wave and a microwave?


Using Data Mining Techniques for Sentiment Shifter Identification. Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent.

Question: What is a sentiment shifter?


An MDP-based Recommender System. Typical Recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. To succeed in practice, an MDP-based Recommender system must employ a strong initial model; and the bulk of this paper is concerned with the generation of such a model. In particular, we suggest the use of an n-gram predictive model for generating the initial MDP. Ourn-gram model induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models. We describe our predictive model in detail and evaluate its performance on real data. In addition, we show how the model can be used in an MDP-based Recommender system.

Question: What is an MDP-based Recommender System?


Duty-cycling buildings aggressively: The next frontier in HVAC control. Buildings are known to be the largest consumers of electricity in the United States, and often times the dominant energy consumer is the HVAC system. Despite this fact, in most buildings the HVAC system is run using primitive static control algorithms based on fixed work schedules causing wasted energy during periods of low occupancy. In this paper we present a novel control architecture that uses occupancy sensing to guide the operation of a building HVAC system. We show how we can enable aggressive duty-cycling of building HVAC systems — that is, turn them ON or OFF — to save energy while meeting building performance requirements using inexpensive sensing and control methods. We have deployed our occupancy sensor network across an entire floor of a university building and our data shows several periods of low occupancy with significant opportunities to save energy over normal HVAC schedules. Furthermore, by interfacing with the building Energy Management System (EMS) directly and using real-time occupancy data collected by our occupancy nodes, we measure electrical energy savings of 9.54% to 15.73% and thermal energy savings of 7.59% to 12.85% for the HVAC system by controlling just one floor of our four floor building.

Question: How can we save energy by turning off the HVAC system?


Effective Crowd Annotation for Relation Extraction. Can crowdsourced annotation of training data boost performance for relation extraction over methods based solely on distant supervision? While crowdsourcing has been shown effective for many NLP tasks, previous researchers found only minimal improvement when applying the method to relation extraction. This paper demonstrates that a much larger boost is possible, e.g., raising F1 from 0.40 to 0.60. Furthermore, the gains are due to a simple, generalizable technique, Gated Instruction, which combines an interactive tutorial, feedback to correct errors during training, and improved screening.

Question: How to improve relation extraction?


A Graduated Assignment Algorithm for Graph Matching. A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(/m), where land mare the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 node random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.

Question: What is the name of the algorithm?


Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial. Robots increasingly have the potential to interact with people in daily life. It is believed that, based on this ability, they will play an essential role in human society in the not-so-distant future. This article examined the proposition that robots could form relationships with children and that children might learn from robots as they learn from other children. In this article, this idea is studied in an 18-day field trial held at a Japanese elementary school. Two English-speaking "Robovie" robots interacted with firstand sixth-grade pupils at the perimeter of their respective classrooms. Using wireless identification tags and sensors, these robots identified and interacted with children who came near them. The robots gestured and spoke English with the children, using a vocabulary of about 300 sentences for speaking and 50 words for recognition. The children were given a brief picture–word matching English test at the start of the trial, af62 KANDA, HIRANO, EATON, ISHIGURO

Question: What is the difference between a robot and a robot?


Unhealthy region of citrus leaf detection using image processing techniques. Producing agricultural products are difficult task as the plant comes to an attack from various micro-organisms, pests and bacterial diseases. The symptoms of the attacks are generally distinguished through the leaves, steams or fruit inspection. The present paper discusses the image processing techniques used in performing early detection of plant diseases through leaf features inspection. The objective of this work is to implement image analysis and classification techniques for extraction and classification of leaf diseases. Leaf image is captured and then processed to determine the status of each plant. Proposed framework is model into four parts image preprocessing including RGB to different color space conversion, image enhancement; segment the region of interest using K-mean clustering for statistical usage to determine the defect and severity areas of plant leaves, feature extraction and classification. texture feature extraction using statistical GLCM and color feature by means of mean values. Finally classification achieved using SVM. This technique will ensure that chemicals only applied when plant leaves are detected to be effected with the disease.

Question: What is the difference between image processing and image analysis?


An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS. Building a text corpus suitable to be used in corpus-based speech synthesis is a time-consuming process that usually requires some human intervention to select the desired phonetic content and the necessary variety of prosodic contexts. If an emotional text-to-speech (TTS) system is desired, the complexity of the corpus generation process increases. This paper presents a study aiming to validate or reject the use of a semantically neutral text corpus for the recording of both neutral and emotional (acted) speech. The use of this kind of texts would eliminate the need to include semantically emotional texts into the corpus. The study has been performed for Basque language. It has been made by performing subjective and objective comparisons between the prosodic characteristics of recorded emotional speech using both semantically neutral and emotional texts. At the same time, the performed experiments allow for an evaluation of the capability of prosody to carry emotional information in Basque language. Prosody manipulation is the most common processing tool used in concatenative TTS. Experiments of automatic recognition of the emotions considered in this paper (the "Big Six emotions") show that prosody is an important emotional indicator, but cannot be the only manipulated parameter in an emotional TTS system-at least not for all the emotions. Resynthesis experiments transferring prosody from emotional to neutral speech have also been performed. They corroborate the results and support the use of a neutral-semantic-content text in databases for emotional speech synthesis

Question: What is the role of prosody in emotional TTS?


A unifying computational framework for motor control and social interaction.. Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.

Question: What is the computational framework for motor control?


Lattice Attacks on Digital Signature Schemes. We describe a lattice attack on the Digital Signature Algorithm (DSA) when used to sign many messages, mi, under the assumption that a proportion of the bits of each of the associated ephemeral keys, yi, can be recovered by alternative techniques.

Question: What is a lattice attack?


The ODD protocol : a review and first update. The 'ODD' (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.

Question: What is the ODD protocol?


Toward an integrated knowledge discovery and data mining process model. Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests

Question: What is the difference between a knowledge discovery and data mining process model and a knowledge discovery and data mining process?


Google Web 1T 5-Grams Made Easy (but not for the computer). This paper introduces Web1T5-Easy, a simple indexing solution that allows interactive searches of the Web 1T 5-gram database and a derived database of quasi-collocations. The latter is validated against co-occurrence data from the BNC and ukWaC on the automatic identification of non-compositional VPC.

Question: What is a quasi-collocation?


WAIS-IV visual puzzles in a mixed clinical sample.. Little is known about which cognitive functions underlie the new Visual Puzzles subtest of the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV). The purpose of this study was to investigate relationships between Visual Puzzles and common neuropsychological measures in a mixed clinical sample. A total of 44 veterans (75% men) were administered the WAIS-IV as part of a neuropsychological evaluation. Average age was 47.4 years (SD = 11.8), and average education was 13.8 years (SD = 2.3). Correlations were conducted to examine relationships between Visual Puzzles, demographic variables, and neuropsychological measures. Hierarchical regression analysis was used to determine which measures contributed the most variance to Visual Puzzles. Visual Puzzles correlated significantly with measures of visuospatial reasoning, verbal learning and recall, mental flexibility, processing speed, and naming, which accounted for 50% of the variance in Visual Puzzles performance. The results indicate that Visual Puzzles is not a pure measure of visuoperceptual reasoning, at least in a mixed clinical sample, because memory, mental flexibility, processing speed, and language abilities also contribute to successful performance of the task. Thus it may be important to consider other aspects of cognitive functioning when interpreting Visual Puzzles performance.

Question: What is the purpose of the visual puzzles subtest of the wechsler adult intelligence scale?


Hierarchical Self-Organization in Genetic programming. This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation onthe-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibilityof approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem's fitness function. Preliminary empirical results are presented.

Question: What is a genetic programming?


Optimizing Paxos with batching and pipelining. Paxos is probably the most popular state machine replication protocol. Two optimizations that can greatly improve its performance are batching and pipelining. However, tuning these two optimizations to achieve high-throughput can be challenging, as their effectiveness depends on many parameters like the network latency and bandwidth, the speed of the nodes, and the properties of the application. We address this question by presenting an analytical model of the performance of Paxos, and showing how it can be used to determine configuration values for the batching and pipelining optimizations that result in high-throughput. We then validate the model, by presenting the results of experiments where we investigate the interaction of these two optimizations both in LAN and WAN environments, and comparing these resultswith the prediction from themodel. The results confirm the accuracy of the model, with the predicted values being usually very close to the ones that provide the highest performance in the experiments. Furthermore, both the model and the experiments give useful insights into the relative effectiveness of batching and pipelining. They show that although batching by itself provides the largest gains in all scenarios, in most cases combining it with pipelining provides a very significant additional increase in throughput, with this being true not only on high-latencyWANbut also inmany LAN scenarios. © 2012 Elsevier B.V. All rights reserved.

Question: What is the difference between batching and pipelining?


Active Reinforcement Learning with Monte-Carlo Tree Search. Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost. This subtle change makes exploration substantially more challenging. Powerful principles in RL like optimism, Thompson sampling, and random exploration do not help with ARL. We relate ARL in tabular environments to BayesAdaptive MDPs. We provide an ARL algorithm using Monte-Carlo Tree Search that is asymptotically Bayes optimal. Experimentally, this algorithm is near-optimal on small Bandit problems and MDPs. On larger MDPs it outperforms a Q-learner augmented with specialised heuristics for ARL. By analysing exploration behaviour in detail, we uncover obstacles to scaling up simulation-based algorithms for ARL.

Question: What is the difference between active reinforcement learning and reinforcement learning?


PMLAB: An Scripting Environment for Process Mining. In a decade of process mining research, several algorithms have been proposed to solve particular process mining tasks. At the same pace, tools have appeared both in the academic and the commercial domains. These tools have enabled the use of process mining practices to a rather limited extent. In this paper we advocate for a change in the mentality: process mining may be an exploratory discipline, and PMLAB – a Python-based scripting environment supporting this – is proposed. This demo presents the main features of the PMLAB environment

Question: What is a scripting environment for process mining?


Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis. This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.

Question: How to segment the nucleus and cytoplasm of white blood cells?


Adaptive Convolutional Filter Generation for Natural Language Understanding. Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP are not expressive enough, in the sense that all input sentences share the same learned (and static) set of filters. Motivated by this problem, we propose an adaptive convolutional filter generation framework for natural language understanding, by leveraging a meta network to generate inputaware filters. We further generalize our framework to model question-answer sentence pairs and propose an adaptive question answering (AdaQA) model; a novel two-way feature abstraction mechanism is introduced to encapsulate co-dependent sentence representations. We investigate the effectiveness of our framework on document categorization and answer sentence-selection tasks, achieving state-of-the-art performance on several

Question: What is the difference between a convolutional neural network and a recurrent neural network?


Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.

Question: How to normalize a neural network?


Joint Optimization of Resource Provisioning in Cloud Computing. Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.

Question: How to optimize resource provisioning in cloud computing?


Onboard Reconfigurable Battery Charger for Electric Vehicles With Traction-to-Auxiliary Mode. This paper proposes a single-phase reconfigurable battery charger for an electric vehicle (EV) that operates in three different modes: grid-to-vehicle (G2V) mode, in which the traction batteries are charged from the power grid; vehicle-to-grid (V2G) mode, in which the traction batteries deliver part of the stored energy back to the power grid; and traction-to-auxiliary (T2A) mode, in which the auxiliary battery is charged from the traction batteries. When connected to the power grid, the battery charger works with a sinusoidal current in the ac side, for both G2V and V2G modes, and regulates the reactive power. When the EV is disconnected from the power grid, the control algorithms are modified, and the full-bridge ac-dc bidirectional converter works as a full-bridge isolated dc-dc converter that is used to charge the auxiliary battery of the EV, avoiding the use of an additional charger to accomplish this task. To assess the behavior of the proposed reconfigurable battery charger under different operation scenarios, a 3.6-kW laboratory prototype has been developed, and experimental results are presented.

Question: How does the battery charger work?


Insecticide Resistance Alleles Affect Vector Competence of Anopheles gambiae s.s. for Plasmodium falciparum Field Isolates. The widespread insecticide resistance raises concerns for vector control implementation and sustainability particularly for the control of the main vector of human malaria, Anopheles gambiae sensu stricto. However, the extent to which insecticide resistance mechanisms interfere with the development of the malignant malaria parasite in its vector and their impact on overall malaria transmission remains unknown. We explore the impact of insecticide resistance on the outcome of Plasmodium falciparum infection in its natural vector using three An. gambiae strains sharing a common genetic background, one susceptible to insecticides and two resistant, one homozygous for the ace-1(R) mutation and one for the kdr mutation. Experimental infections of the three strains were conducted in parallel with field isolates of P. falciparum from Burkina Faso (West Africa) by direct membrane feeding assays. Both insecticide resistant mutations influence the outcome of malaria infection by increasing the prevalence of infection. In contrast, the kdr resistant allele is associated with reduced parasite burden in infected individuals at the oocyst stage, when compared to the susceptible strain, while the ace-1 (R) resistant allele showing no such association. Thus insecticide resistance, which is particularly problematic for malaria control efforts, impacts vector competence towards P. falciparum and probably parasite transmission through increased sporozoite prevalence in kdr resistant mosquitoes. These results are of great concern for the epidemiology of malaria considering the widespread pyrethroid resistance currently observed in Sub-Saharan Africa and the efforts deployed to control the disease.

Question: What is the impact of insecticide resistance on vector competence?


Broadband design of substrate integrated waveguide to stripline interconnect. A broadband design of substrate integrated waveguide (SIW) to stripline interconnects is presented for the first time. The transition shows a wideband performance and, contrary to microstrip and coplanar waveguide circuitry, interconnects two planar transmission-line media that are both capable of medium-level power handling capabilities. Over a bandwidth of 18 GHz to 28 GHz (43.4 percent), the single interconnect shows a worst-case return loss of 24 dB and maximum insertion losses of 0.44 dB. For a back-to-back connection, the return loss reduces to 19 dB and insertion loss increases to 0.87 dB. All dimensional parameters are specified, and the design is validated by two commercially available field-solver packages. Moreover, field plots are presented that highlight the step-by-step mode conversion from SIW to stripline.

Question: What is a broadband design of substrate integrated waveguide to stripline interconnect?


Neural Clinical Paraphrase Generation with Attention. Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoderdecoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.

Question: What is the first large-scale Canadian peacekeeping mission?


Concept-Based Information Retrieval Using Explicit Semantic Analysis. Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keyword-based text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new text features automatically, and we have found that high-quality feature selection becomes crucial in this setting to make the retrieval more focused. However, due to the lack of labeled data, traditional feature selection methods cannot be used, hence we propose new methods that use self-generated labeled training data. The resulting system is evaluated on several TREC datasets, showing superior performance over previous state-of-the-art results.

Question: What is the difference between a thesaurus and a dictionary?


Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios. Traditional acoustic echo cancellation (AEC) works by identifying an acoustic impulse response using adaptive algorithms. We formulate AEC as a supervised speech separation problem, which separates the loudspeaker signal and the nearend signal so that only the latter is transmitted to the far end. A recurrent neural network with bidirectional long short-term memory (BLSTM) is trained to estimate the ideal ratio mask from features extracted from the mixtures of near-end and farend signals. A BLSTM estimated mask is then applied to separate and suppress the far-end signal, hence removing the echo. Experimental results show the effectiveness of the proposed method for echo removal in double-talk, background noise, and nonlinear distortion scenarios. In addition, the proposed method can be generalized to untrained speakers.

Question: How does deep learning work?


Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task. The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from the

Question: How to decode EEG signals?


Efficient Algorithms for Citation Network Analysis. In the paper very efficient, linear in number of arcs, algorithms for determining Hummon and Doreian's arc weights SPLC and SPNP in citation network are proposed, and some theoretical properties of these weights are presented. The nonacyclicity problem in citation networks is discussed. An approach to identify on the basis of arc weights an important small subnetwork is proposed and illustrated on the citation networks of SOM (self organizing maps) literature and US patents.

Question: What is the nonacyclicity problem in citation networks?


Discrete-time signal processing (2nd ed.).

Question: What is discrete-time signal processing?


Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks. Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pheromone update by particle swarm optimization. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm. Keywordssocial network analysis; clique problem; ACO; PSO.

Question: How to find a maximum clique in a social network?


A fully balanced pseudo-differential OTA with common-mode feedforward and inherent common-mode feedback detector. A pseudo-differential fully balanced fully symmetric CMOS operational transconductance amplifier (OTA) architecture with inherent common-mode detection is proposed. Through judicious arrangement, the common-mode feedback circuit can be economically implemented. The OTA achieves a third harmonic distortion of 43 dB for 900 mVpp at 30 MHz. The OTA, fabricated in 0.5m CMOS process, is used to design a 100-MHz fourth-order linear phase filter. The measured filter's group delay ripple is 3% for frequencies up to 100 MHz, and the measured dynamic range is 45 dB for a total harmonic distortion of 46 dB. The filter consumes 42.9 mW per complex pole pair while operating from a 1.65-V power supply.

Question: What is a pseudo-differential OTA?


Efficient aircraft spare parts inventory management under demand uncertainty. In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.

Question: How to minimize the total cost of aircraft maintenance?


Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease. The prediction of survival of Coronary heart disease (CHD) has been a challenging research problem for medical society. The goal of this paper is to develop data mining algorithms for predicting survival of CHD patients based on 1000 cases .We carry out a clinical observation and a 6-month follow up to include 1000 CHD cases. The survival information of each case is obtained via follow up. Based on the data, we employed three popular data mining algorithms to develop the prediction models using the 502 cases. We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results indicated that the SVM is the best predictor with 92.1 % accuracy on the holdout sample artificial neural networks came out to be the second with91.0% accuracy and the decision trees models came out to be the worst of the three with 89.6% accuracy. The comparative study of multiple prediction models for survival of CHD patients along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data.

Question: What is the goal of this paper?


ITGovA: Proposition of an IT governance Approach. I To cope with issues related to optimization, rationalization, risk management, economic value of technology and information assets, the implementation of appropriate IT governance seems an important need in public and private organizations. It's one of these concepts that suddenly emerged and became an important issue in the information technology area. Many organizations started with the implementation of IT governance to achieve a better alignment between business and IT, however, there is no method to apply the IT governance principles in companies. This paper proposes a new approach to implement IT governance based on five iterative phases. This approach is a critical business process that ensures that the business meets its strategic objectives and depending on IT resources for execution. Keywords—Information technology, IT governance, lifecycle, strategic alignment, value

Question: What is the IT governance approach?