Research and implementation of RSA algorithm for encryption and decryption. Cryptographic technique is one of the principal means to protect information security. Not only has it to ensure the information confidential, but also provides digital signature, authentication, secret sub-storage, system security and other functions. Therefore, the encryption and decryption solution can ensure the confidentiality of the information, as well as the integrity of information and certainty, to prevent information from tampering, forgery and counterfeiting. Encryption and decryption algorithm's security depends on the algorithm while the internal structure of the rigor of mathematics, it also depends on the key confidentiality. Key in the encryption algorithm has a pivotal position, once the key was leaked, it means that anyone can be in the encryption system to encrypt and decrypt information, it means the encryption algorithm is useless. Therefore, what kind of data you choose to be a key, how to distribute the private key, and how to save both data transmission keys are very important issues in the encryption and decryption algorithm. This paper proposed an implementation of a complete and practical RSA encrypt/decrypt solution based on the study of RSA public key algorithm. In addition, the encrypt procedure and code implementation is provided in details.

Question: What is the RSA algorithm?


Efficient lineage tracking for scientific workflows. Data lineage and data provenance are key to the management of scientific data. Not knowing the exact provenance and processing pipeline used to produce a derived data set often renders the data set useless from a scientific point of view. On the positive side, capturing provenance information is facilitated by the widespread use of workflow tools for processing scientific data. The workflow process describes all the steps involved in producing a given data set and, hence, captures its lineage. On the negative side, efficiently storing and querying workflow based data lineage is not trivial. All existing solutions use recursive queries and even recursive tables to represent the workflows. Such solutions do not scale and are rather inefficient. In this paper we propose an alternative approach to storing lineage information captured as a workflow process. We use a space and query efficient interval representation for dependency graphs and show how to transform arbitrary workflow processes into graphs that can be stored using such representation. We also characterize the problem in terms of its overall complexity and provide a comprehensive performance evaluation of the approach.

Question: How to store and query lineage information captured as a workflow process?


Low-Voltage Wilson Current Mirrors in CMOS. In this paper, we describe three simple low-voltage CMOS analogs of the Wilson current mirror that function well at all current levels, ranging from weak inversion to strong inversion. Each of these current mirrors can operate on a low power-supply voltage of a diode drop plus two saturation voltages and features a wide output-voltage swing with a cascode-type incremental output impedance. Two of the circuits requires an input voltage of a diode drop plus a saturation voltage while the third one features a low input voltage of a saturation voltage. We present experimental results from versions of these three current mirrors that were fabricated in a 0.5-mum CMOS process through MOSIS, comparing them with CMOS implementations of the conventional Wilson and super-Wilson current mirrors.

Question: What is the difference between a CMOS and a MOSIS process?


A single neuron PID control for twin rotor MIMO system. This paper presents an intelligent control scheme which utilizes a single neuron PID controller to an experimental propeller setup called the twin rotor multi-input multi-output system (TRMS). The control objective is to make the TRMS move quickly and accurately to the desired attitudes. The pitch angle and the azimuth angle in the conditions of decoupled and cross-coupled between vertical and horizontal axes are considered. It is difficult to design a suitable controller because of the influence between two axes and nonlinear movement. For easy demonstrate the movement on the vertical plane and horizontal plane separately, the TRMS is decoupled first by the main rotor and tail rotor. After successful simulations in decoupled condition, the more difficult simulations of cross-coupled condition are performed. Simulation results show that the new approach can improve the tracking performance.

Question: How to control a twin rotor MIMO system?


Structural equations modeling : Fit Indices , sample size , and advanced topics. This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth. © 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.

Question: What is the difference between structural equations modeling and structural equation modeling?


Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data. Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.

Question: What is the cost of health insurance fraud?


Forecasting the weather of Nevada: A deep learning approach. This paper compares two approaches for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. We describe our data and our representation and compare a standard neural network against a deep learning network. Our empirical results indicate that a deep neural network with Stacked Denoising Auto-Encoders (SDAE) outperforms a standard multilayer feed forward network on this noisy time series prediction task. In addition, predicting air temperature from historical air temperature data alone can be improved by employing related weather variables like barometric pressure, humidity and wind speed data in the training process.

Question: What is a deep learning network?


A Novel Term Weighting Scheme Model. The use of textual data has increased exponentially in recent years due to the networking infrastructure such as Facebook, Twitter, Wikipedia, Blogs, and so one. Analysis of this massive textual data can help to automatically categorize and label new content. Before classification process, term weighting scheme is the crucial step for representing the documents in a way suitable for classification algorithms. In this paper, we are conducting a survey on the term weighting schemes and we propose an efficient term weighting scheme that provide a better classification accuracy than those obtained with the famous TF-IDF, the recent IF-IGM and the others term weighting schemes in the literature.

Question: What is a term weighting scheme?


Microwave Breast Imaging With a Monostatic Radar-Based System: A Study of Application to Patients. A prototype microwave breast imaging system is used to scan a small group of patients. The prototype implements a monostatic radar-based approach to microwave imaging and utilizes ultra-wideband signals. Eight patients were successfully scanned, and several of the resulting images show responses consistent with the clinical patient histories. These encouraging results motivate further studies of microwave imaging for breast health assessment.

Question: What is a monostatic radar-based approach?


GPUs and the Future of Parallel Computing. This article discusses the capabilities of state-of-the art GPU-based high-throughput computing systems and considers the challenges to scaling single-chip parallel-computing systems, highlighting high-impact areas that the computing research community can address. Nvidia Research is investigating an architecture for a heterogeneous high-performance computing system that seeks to address these challenges.

Question: What is a GPU?


BOSS: Building Operating System Services. Commercial buildings are attractive targets for introducing innovative cyber-physical control systems, because they are already highly instrumented distributed systems which consume large quantities of energy. However, they are not currently programmable in a meaningful sense because each building is constructed with vertically integrated, closed subsystems and without uniform abstractions to write applications against. We develop a set of operating system services called BOSS, which supports multiple portable, fault-tolerant applications on top of the distributed physical resources present in large commercial buildings. We evaluate our system based on lessons learned from deployments of many novel applications in our test building, a four-year-old, 140,000sf building with modern digital controls, as well as partial deployments at other sites.

Question: What is the building that we are evaluating?


Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks. In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members' similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.

Question: What is the difference between a deep belief network and a regular belief network?


Measuring User Engagement. User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus "in the wild"), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users' motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon at

Question: How do you measure user engagement?


An Image-based Feature Extraction Approach for Phishing Website Detection. Phishing website creators and anti-phishing defenders are in an arms race. Cloning a website is fairly easy and can be automated by any junior programmer. Attempting to recognize numerous phishing links posted in the wild e.g. on social media sites or in email is a constant game of escalation. Automated phishing website detection systems need both speed and accuracy to win. We present a new method of detecting phishing websites and a prototype system LEO (Logo Extraction and cOmparison) that implements it. LEO uses image feature recognition to extract "visual hotspots" of a webpage, and compare these parts with known logo images. LEO can recognize phishing websites that has different layout from the original websites, or logos embedded in images. Comparing to existing visual similaritybased methods, our method has a much wider application range and higher detection accuracy. Our method successfully recognized 24 of 25 random URLs from PhishTank that previously evaded detection of other visual similarity-based methods.

Question: What is a phishing website?


Long-Term Amnesia: A Review and Detailed Illustrative Case Study. Long-term amnesia is a slowly developing form of anterograde amnesia accompanied by retrograde amnesia of variable severity (Kapur, 1996; 1997) often associated with damage to the anterior temporal neocortex and epileptic seizures. The precise neural and functional deficits that underlie this condition are unknown. A patient, JL, who has this condition following a closed-head injury, is described in detail. Her injury caused bilateral anterior temporal neocortex damage that was more extensive on the left and right-sided damage to the perirhinal and orbitofrontal cortices. The hippocampus appeared to be intact bilaterally. Epilepsy developed within two years of JL's injury. Apart from her memory impairments, JL's cognitive functions, including high-level visual perception, attention, semantic memory and executive functions were well preserved. Her memory also seemed well preserved for at least 30 minutes following encoding. The one exception was the patient's relatively greater impairment at difficult visual recognition tests for which verbalization may not have been an effective strategy. This problem may have been caused by JL's right-sided perirhinal and orbitofrontal cortex damage. Her recall and recognition was clearly impaired after a three-week delay. She also showed a retrograde amnesia, which appeared to be milder than her remote post-morbid memory deficit. JL's remote memory was preserved for information first encountered in either the pre- or post-morbid period provided the information had received sufficient rehearsal over long periods of time. Her long-term amnesia may have been caused by anterior temporal neocortex damage, possibly in association with her epileptic seizures. Whether the condition is heterogeneous, involves a deficit in slow consolidation, disruption of unconsolidated memories, or blockage of maintenance or disruption of insufficiently rehearsed memories whether or not the

Question: What is the difference between remote and long-term amnesia?


An Improved Dijkstra Algorithm for Firefighting. In this paper, briefly the efforts going toward a mathematical calculation to validate a new-Dijkstra algorithm from the original normal Dijkstra algorithm [1] as an improvised Dijkstra. The result of this improvised Dijkstra was used to find the shortest path for firefighting unit to reach the location of the fire exactly. The idea depends on the influence of turns on the path, while there are two equal paths', the more number of turns take more time to pass through it and the less number turns takes less time to pass through it. To apply this scenario practically we take a small real area in south Khartoum. The result gives strong justification that proves and verifies our methodology with a clear contribution in Improved Dijkstra algorithm for firefighting like Geo-Dijkstra. Furthermore, an evaluation of the above-mentioned algorithms has been done showing very promising and realistic results.

Question: What is the result of the algorithm?


Improving neural networks by preventing co-adaptation of feature detectors. When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition. A feedforward, artificial neural network uses layers of non-linear hidden units between its inputs and its outputs. By adapting the weights on the incoming connections of these hidden units it learns feature detectors that enable it to predict the correct output when given an input vector [15]. If the relationship between the input and the correct output is complicated and the network has enough hidden units to model it accurately, there will typically be many different settings of the weights that can model the training set almost perfectly, especially if there is only a limited amount of labeled training data. Each of these weight vectors will make different predictions on held-out test data and almost all of them will do worse on the test data than on the training data because the feature detectors have been tuned to work well together on the training data but not on the test data. Overfitting can be reduced by using "dropout" to prevent complex co-adaptations on the training data. On each presentation of each training case, each hidden unit is randomly omitted from the network with a probability of 0.5, so a hidden unit cannot rely on other hidden units being present. Another way to view the dropout procedur

Question: How to prevent overfitting in neural networks?


How data science can advance mental health research. Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial. Russ et al. discuss the broad applications of data science to mental health research and consider future ways that big data can improve detection, diagnosis, treatment, healthcare provision and disease management.

Question: How can data science improve mental health research?


Natural Language Interface to Database-An Introduction. Information plays an important role in our lives .it is required numerous applications. One of the major sources of information is databases. Databases and database technology are growing rapidly and are having major impact on the growing use of computers. Computers are used in accessing of information from database. Almost all IT applications are storing, retrieving organizing, accessing analyzing, and information from databases. using the internet. Retrieving information from database requires knowledge of database languages like Structured Query Language (SQL) However, not everybody is able to write SQL queries as they may not be aware of the syntax of SQL structure of the database,. This has lead to the developing such a system where non-expert users compose their questions in their natural language and obtain the results in the form of database. So instead of working with the SQL one can query relational databases in their natural language. This new idea is provoked to develop new type of processing system called natural language interface to database Natural language interface to database enhances the users performance and is a flexible approach querying in database. This paper is an introduction to natural language interfaces to databases (NLIDBS). Covers a brief overview of the NLIDB its components its advantages disadvantages, approaches and techniques .

Question: What is a natural language interface to database?


Green Internet of Things for Smart World. Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

Question: What is the green information and communications technologies (ICTs) enabling green IoT?


Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.

Question: What is a binary partition tree?


Single-step assembly of a gene and entire plasmid from large numbers of oligodeoxyribonucleotides.. Here, we describe assembly PCR as a method for the synthesis of long DNA sequences from large numbers of oligodeoxyribonucleotides (oligos). The method, which is derived from DNA shuffling [Stemmer, Nature 370 (1994a) 389-391], does not rely on DNA ligase but instead relies on DNA polymerase to build increasingly longer DNA fragments during the assembly process. A 1.1-kb fragment containing the TEM-1 beta-lactamase-encoding gene (bla) was assembled in a single reaction from a total of 56 oligos, each 40 nucleotides (nt) in length. The synthetic gene was PCR amplified and cloned in a vector containing the tetracycline-resistance gene (TcR) as the sole selectable marker. Without relying on ampicillin (Ap) selection, 76% of the TcR colonies were ApR, making this approach a general method for the rapid and cost-effective synthesis of any gene. We tested the range of assembly PCR by synthesizing, in a single reaction vessel containing 134 oligos, a high-molecular-mass multimeric form of a 2.7-kb plasmid containing the bla gene, the alpha-fragment of the lacZ gene and the pUC origin of replication. Digestion with a unique restriction enzyme, followed by ligation and transformation in Escherichia coli, yielded the correct plasmid. Assembly PCR is well suited for several in vitro mutagenesis strategies.

Question: How to assemble a gene from a large number of oligos?


Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. The algorithm often outperforms the well-known ReliefF attribute estimator when used as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees. It performs more feature selection than ReliefF does—reducing the data dimensionality by fifty percent in most cases. Also, decision and model trees built from the preprocessed data are often significantly smaller.

Question: What is a correlation-based feature selection algorithm?


Automatic indexing: an approach using an index term corpus and combining linguistic and statistical methods. This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with

Question: What is the contribution of this thesis to this problem?


An Overview of Virtual Machine Architectures. hen early computer systems were being developed, hardware was designed first and software written specifically for that hardware followed. Each system was essentially handcrafted with its own instruction set, and software was developed for that specific instruction set. This included operating system software, assemblers (later compilers), and application programs. With a small user community, relatively simple programs, and fixed hardware, this tightly integrated approach worked quite well, especially while the basic concepts of the stored program computer were still evolving. But user communities grew, operating systems became more complex, and the number of application programs rapidly expanded, so that re-writing and distributing software for each new computer system became a major burden.

Question: What is a virtual machine?


Feature-Oriented Software Product Lines. Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

Question: What is the best way to improve my performance?


A multimodal adaptive session manager for physical rehabilitation exercising. Physical exercising is an essential part of any rehabilitation plan. The subject must be committed to a daily exercising routine, as well as to a frequent contact with the therapist. Rehabilitation plans can be quite expensive and time-consuming. On the other hand, tele-rehabilitation systems can be really helpful and efficient for both subjects and therapists. In this paper, we present ReAdapt, an adaptive module for a tele-rehabilitation system that takes into consideration the progress and performance of the exercising utilizing multisensing data and adjusts the session difficulty resulting to a personalized session. Multimodal data such as speech, facial expressions and body motion are being collected during the exercising and feed the system to decide on the exercise and session difficulty. We formulate the problem as a Markov Decision Process and apply a Reinforcement Learning algorithm to train and evaluate the system on simulated data.

Question: What is a multimodal adaptive session manager for physical rehabilitation?


Modeling and Signal Processing of Acoustic Gunshot Recordings. Audio recordings of gunshots can provide information about the gun location with respect to the microphone(s), the speed and trajectory of the projectile, and in some cases the type of firearm and ammunition. Recordings obtained under carefully controlled conditions can be well-modeled by geometrical acoustics. Special acoustic processing systems for real time gunshot detection and localization are used by the military and law enforcement agencies for sniper detection. Forensic analysis of audio recordings is also used to provide evidence in criminal and civil cases. This paper reviews the distinctive features and limitations of acoustic gunshot analysis using DSP techniques

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


A simple intrusion detection method for controller area network. The Controller Area Network (CAN) is established as the main communication channel inside vehicles. CAN relies on frame broadcast to share data between different microcontrollers managing critical or comfort functions such as cruise control or air conditioning. CAN is distinguished by its simplicity, its real-time application compatibility and its low deployment cost. However, CAN major drawback is its lack of security support. That is, CAN fails to provide protections against attacks such as intrusion, denial of service or impersonation. We propose in this work a simple intrusion detection method for CAN. Our main idea is to make each microcontroller monitor the CAN bus to detect malicious frames.

Question: What is the main drawback of CAN?


A Hardware-Assisted Realtime Attack on A5/2 Without Precomputations. A5/2 is a synchronous stream cipher that is used for protecting GSM communication. Recently, some powerful attacks [2,10] on A5/2 have been proposed. In this contribution we enhance the ciphertext-only attack [2] by Barkan, Biham, and Keller by designing special-purpose hardware for generating and solving the required systems of linear equations. For realizing the LSE solver component, we use an approach recently introduced in [5,6] describing a parallelized hardware implementation of the Gauss-Jordan algorithm. Our hardware-only attacker immediately recovers the initial secret state of A5/2 which is sufficient for decrypting all frames of a session using a few ciphertext frames without any precomputations and memory. More precisely, in contrast to [2] our hardware architecture directly attacks the GSM speech channel (TCH/FS and TCH/EFS). It requires 16 ciphertext frames and completes the attack in about 1 second. With minor changes also input from other GSM channels (e.g., SDCCH/8) can be used to mount the attack.

Question: What is the hardware-assisted realtime attack on A5/2?


C4: the continuously concurrent compacting collector. C4, the Continuously Concurrent Compacting Collector, an updated generational form of the Pauseless GC Algorithm [7], is introduced and described, along with details of its implementation on modern X86 hardware. It uses a read barrier to support concur- rent compaction, concurrent remapping, and concurrent incremental update tracing. C4 differentiates itself from other generational garbage collectors by supporting simultaneous-generational concurrency: the different generations are collected using concurrent (non stop-the-world) mechanisms that can be simultaneously and independently active. C4 is able to continuously perform concurrent young generation collections, even during long periods of concurrent full heap collection, allowing C4 to sustain high allocation rates and maintain the efficiency typical to generational collectors, without sacrificing response times or reverting to stop-the-world operation. Azul systems has been shipping a commercial implementation of the Pauseless GC mechanism, since 2005. Three successive generations of Azul's Vega series systems relied on custom multi-core processors and a custom OS kernel to deliver both the scale and features needed to support Pauseless GC. In 2010, Azul released its first software-only commercial implementation of C4 for modern commodity X86 hardware, using Linux kernel enhancements to support the required feature set. We discuss implementa- tion details of C4 on X86, including the Linux virtual and physical memory management enhancements that were used to support the high rate of virtual memory operations required for sustained pauseless operation. We discuss updates to the collector's manage- ment of the heap for efficient generational collection and provide throughput and pause time data while running sustained workloads.

Question: What is the difference between a generational collector and a concurrent collector?


Who, where, when and what: discover spatio-temporal topics for twitter users. Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.

Question: What is the location of a user given a tweet posted by the user and the posting time?


The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.

Question: What is the sound of APALM clapping?


qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.

Question: What is qBase?


Clinical human factors: the need to speak up to improve patient safety.. This article aims to inspire nurses to recognise how human factors affect individual and team performance. Use of a case study and learning derived from a subsequent independent inquiry exposes the dynamics that can affect teamwork and inhibit effective communication with devastating consequences. The contribution of situational awareness and the importance of nurses speaking up when they have concerns are demonstrated as vital components in the delivery of safe patient care.

Question: How can nurses improve patient safety?


Modeling Dominance in Group Conversations Using Nonverbal Activity Cues. Dominance - a behavioral expression of power - is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audiovisual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in social computing. In this paper, we present a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multi-camera, multi-microphone setting. We investigate efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities. Unsupervised and supervised approaches for dominance modeling are also investigated. Activity cues and models are objectively evaluated on a set of dominance-related classification tasks, derived from an analysis of the variability of human judgment of perceived dominance in group discussions. Our investigation highlights the power of relatively simple yet efficient approaches and the challenges of audiovisual integration. This constitutes the most detailed study on automatic dominance modeling in meetings to date.

Question: How to model dominance in group conversations?


Microphone Array Processing for Distant Speech Recognition: From Close-Talking Microphones to Far-Field Sensors. Distant speech recognition (DSR) holds the promise of the most natural human computer interface because it enables man-machine interactions through speech, without the necessity of donning intrusive body- or head-mounted microphones. Recognizing distant speech robustly, however, remains a challenge. This contribution provides a tutorial overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beam forming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Moreover, we present an emerging technology in the area of far-field audio and speech processing based on spherical microphone arrays. Performance comparisons of spherical and linear arrays reveal that a spherical array with a diameter of 8.4 cm can provide recognition accuracy comparable or better than that obtained with a large linear array with an aperture length of 126 cm.

Question: What is the difference between a microphone array and a microphone?


Differentially private aggregation of distributed time-series with transformation and encryption. We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users collect temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data.  To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier Perturbation Algorithm (FPAk). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Θ(n) to each query answer, making the answers practically useless if n is large. Our FPAk algorithm perturbs the Discrete Fourier Transform of the query answers. For answering n queries, FPAk improves the expected error from Θ(n) to roughly Θ(k) where k is the number of Fourier coefficients that can (approximately) reconstruct all the n query answers. Our experiments show that k << n for many real-life data-sets resulting in a huge error-improvement for FPAk.  To deal with the absence of a trusted central server, we propose the Distributed Laplace Perturbation Algorithm (DLPA) to add noise in a distributed way in order to guarantee differential privacy. To the best of our knowledge, DLPA is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of users.

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


Unfriending on Facebook: Friend Request and Online/Offline Behavior Analysis. Objectives: Determine the role of the friend request in unfriending decisions. Find factors in unfriending decisions and find differences in the perception of online and offline behaviors that vary depending on the unfriending decision. Method: Survey research conducted online. 690 surveys about unfriending were analyzed using exploratory statistical techniques. Results: The research results show that the initiator of the friend request has more than their expected share of unfriends compared to those who receive the friend request. There are online and offline factors for unfriending decisions; the research identified six constructs to evaluate unfriending decisions. There are 4 components for online behaviors (unimportant/frequent posts, polarizing posts, inappropriate posts and everyday life posts) and 2 offline components (disliked behavior and changes in the relationship). Survey respondents who said they unfriend for online reasons were more likely to agree that the person posted too frequently about unimportant topics, polarizing topics, and inappropriate topics compared to those who unfriended for offline reasons.

Question: What is the role of the friend request in unfriending decisions?


ISO 9241-11 Revised: What Have We Learnt About Usability Since 1998?. A revision is currently being undertaken of ISO 9241-11, published in 1998 to provide guidance on usability. ISO-9241-11 defines usability in terms of effectiveness, efficiency and satisfaction in a particular context of use. The intention was to emphasise that usability is an outcome of interaction rather than a property of a product. This is now widely accepted. However, the standard also places emphasis on usability measurement and it is now appreciated that there is more to usability evaluation than measurement. Other developments include an increasing awareness of the importance of the individual user's emotional experience as discretionary usage of complex consumer products and use of the World Wide Web have became more widespread. From an organisational perspective, it is now appreciated that usability plays an important role in managing the potentials risks that can arise from inappropriate outcomes of interaction. The revision of ISO 9241-11 takes account of these issues and other feedback.

Question: What have we learnt about usability since 1998?


Finding social roles in Wikipedia. This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

Question: What is the difference between a social networker and a social networker?


How to Use the SOINN Software: User's Guide (Version 1.0). The Self-Organizing Incremental Neural Network (SOINN) is an unsupervised classifier that is capable of online incremental learning. Studies have been performed not only for improving the SOINN, but also for applying it to various problems. Furthermore, using the SOINN, more intelligent functions are achieved, such as association, reasoning, and so on. In this paper, we show how to use the SOINN software and to apply it to the above problems.

Question: How to use the SOINN software?


Vectorized total variation defined by weighted L infinity norm for utilizing inter channel dependency. Vectorized total variation (VTV) is very successful convex regularizer to solve various color image recovery problems. Despite the fact that color channels of natural color images are closely related, existing variants of VTV can not utilize this prior efficiently. We proposed L∞-VTV as a convex regularizer can penalize the violation of such inter-channel dependency by employing weighted L∞ (L-infty) norm. We also introduce an effective algorithm for an image denoising problem using L∞-VTV. Experimental results shows that our proposed method can outperform the conventional methods.

Question: What is the difference between L∞-VTV and L∞-TV?


The Painting Fool Sees! New Projects with the Automated Painter. We report the most recent advances in The Painting Fool project, where we have integrated machine vision capabilities from the DARCI system into the automated painter, to enhance its abilities before, during and after the painting process. This has enabled new art projects, including a commission from an Artificial Intelligence company, and we report on this collaboration, which is one of the first instances in Computational Creativity research where creative software has been commissioned directly. The new projects have advanced The Painting Fool as an independent artist able to produce more diverse styles which break away from simulating natural media. The projects have also raised a philosophical question about whether software artists need to see in the same way as people, which we discuss briefly.

Question: What is the painting fool?


Understanding Intra-Class Knowledge Inside CNN. Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization [8, 10, 13]. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers. To invert the intraclass knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN visualization models [8, 10]. With it, we show how different "styles" of templates for an object class are organized by CNN in terms of location and content, and represented in a hierarchical and ensemble way. Moreover, such intra-class knowledge can be used in many interesting applications, e.g. style-based image retrieval and style-based object completion.

Question: How to visualize the intra-class knowledge inside CNN?


A Different Approach to the Management of Osteoarthritis in the Knee: Ultrasound Guided Genicular Nerve Block.. A 61-year-old female was referred to our tertiary physical medicine and rehabilitation outpatient clinic with a 7-year history of severe left knee pain. The patient was a nonsmoker and obese, with no comorbidities, and reported that the left knee pain worsened when walking and descending stairs and particularly when climbing stairs. Radiography of the left knee revealed KellgrenLawrence grade 3 osteoarthritis (OA) with multiple osteophytes and definite joint space narrowing.

Question: What is the difference between osteoarthritis and osteoarthrosis?


Robotics Component Verification on ISS ROKVISS - Preliminary Results for Telepresence. ROKVISS, Germany's newest space robotics technology experiment, was successfully installed outside at the Russian Service Module of the International Space Station (ISS) during an extravehicular space walk at the end of January 2005. Since February 2005 a two joint manipulator is operated from ground via a direct radio link. The aim of ROKVISS is the in flight verification of highly integrated modular robotic joints as well as the demonstration of different control modes, reaching from high system autonomy to force feedback teleoperation (telepresence mode). The experiment will be operated for at least one year in free space to evaluate and qualify intelligent light weight robotics components under realistic circumstances for maintenance and repair tasks as foreseen in upcoming manned and unmanned space applications in near future. This paper focuses in the telepresence control mode, its technology and first results from the space experiment ROKVISS

Question: What is the aim of ROKVISS?


Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization. Conversion prediction and click prediction are two important and intertwined problems in display advertising, but existing approaches usually look at them in isolation. In this paper, we aim to predict the conversion response of users by jointly examining the past purchase behavior and the click response behavior. Additionally, we model the temporal dynamics between the click response and purchase activity into a unified framework. In particular, a novel matrix factorization approach named dynamic collective matrix factorization (DCMF) is proposed to address this problem. Our model considers temporal dynamics of post-click conversions and also takes advantages of the side information of users, advertisements, and items. Experiments on a real-world marketing dataset show that our model achieves significant improvements over several baselines.

Question: How to predict user behavior in display advertising?


iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos. Fig. 1. We present iMapper, a method that reasons about the interactions of humans with objects, to recover both a plausible scene arrangement and human motions, that best explain an input monocular video (see inset). We fit characteristic interactions called scenelets (e.g., A, B, C) to the video and use them to reconstruct a plausible object arrangement and human motion path (left). The key challenge is that reliable fitting requires information about occlusions, which are unknown (i.e., latent). (Right) We show an overlay (from top-view) of our result over manually annotated groundtruth object placements. Note that object meshes are placed based on estimated object category, location, and size information.

Question: What is the name of the method?


Psychological testing and assessment in the 21st century.. As spin-offs of the current revolution in the cognitive and neurosciences, clinical neuropsychologists in the 21st century will be using biological tests of intelligence and cognition that record individual differences in brain functions at the neuromolecular, neurophysiologic, and neurochemical levels. Assessment of patients will focus more on better use of still intact functions, as well as rehabilitating or bypassing impaired functions, than on diagnosis, as is the focus today. Better developed successors to today's scales for assessing personal competency and adaptive behavior, as well as overall quality of life, also will be in wide use in clinical settings. With more normal individuals, use of new generations of paper-and-pencil inventories, as well as biological measures for assessing differences in interests, attitudes, personality styles, and predispositions, is predicted.

Question: What is the difference between a paper-and-pencil inventory and a biological measure?


Deblurring Face Images with Exemplars. The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.

Question: How to deblur face images?


Real-Time Semantic Segmentation with Label Propagation. Despite of the success of convolutional neural networks for semantic image segmentation, CNNs cannot be used for many applications due to limited computational resources. Even efficient approaches based on random forests are not efficient enough for real-time performance in some cases. In this work, we propose an approach based on superpixels and label propagation that reduces the runtime of a random forest approach by factor 192 while increasing the segmentation accuracy.

Question: What is the runtime of a random forest approach?


Incorporating Unsupervised Learning in Activity Recognition. Users are constantly involved in a multitude of activities in ever-changing context. Analyzing activities in contextrich environments has become a great challenge in contextawareness research. Traditional methods for activity recognition, such as classification, cannot cope with the variety and dynamicity of context and activities. In this paper, we propose an activity recognition approach that incorporates unsupervised learning. We analyze the feasibility of applying subspace clustering—a specific type of unsupervised learning— to high-dimensional, heterogeneous sensory input. Then we present the correspondence between clustering output and classification input. This approach has the potential to discover implicit, evolving activities, and can provide valuable assistance to traditional classification based methods. As sensors become prevalent means in context detection and information channels proliferate to make context sharing easier, it is increasingly challenging to interpret context and analyze its effects on the activities (Lim and Dey 2010). We argue that applying traditional approaches to activity recognition may become more and more difficult to apply in context and activity-rich environments. In the literature, context attributes used for learning activities are chosen by either empirical assumption or dimension reduction to render a small set of features (Krause, Smailagic, and Siewiorek 2006). These approaches are infeasible in face of a broad spectrum of context information. The most significant drawback is that they fail to acknowledge the large variety of features needed to describe different activities. For activity recognition, most previous works applied supervised learning approaches that aimed at predicting activities among a set of known classes(Ferscha et al. 2004). These approaches, however, are also challenged when coping with new and fast evolving activities. Unsupervised learning

Question: What is unsupervised learning?


Wafer level fabrication method of hemispherical reflector coupled micro-led array stimulator for optogenetics. We report a monolithic wafer-level fabrication method for a hemispherical reflector coupled light-emitting-diode (LED) array using isotropic etching of silicon. These neural stimulators collect the backside as well as the front side emission of the μ-LEDs and thus provide higher intensity, which is imperative for opsin expressions in optogenetics experiments. Aluminum was used as the reflective layer and the planarization of polymer on the reflector cavity was done using polydimethylsiloxane (PDMS). The lateral and vertical profiles of silicon etching were measured and the light intensity increase due to the reflector was investigated. It was found that the intensity increases by a minimum of 49% and maximum of 65% when coupling a reflector with the μ-LEDs.

Question: How does the reflector affect the light intensity?


A neural network students' performance prediction model (NNSPPM). In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic achievement at semester eight. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. Students' results for the fundamental subjects in the first semester were used as independent variables or input predictor variables while CGPA at semester eight was used as the output or the dependent variable. The study was done for two different entry points namely Matriculation and Diploma intakes. Performances of the models were measured using the coefficient of Correlation R and Mean Square Error (MSE). The outcomes from the study showed that fundamental subjects at semester one and three have strong influence in the final CGPA upon graduation.

Question: What is the difference between a neural network and a neural network students' performance prediction model?


Online Product Quantization. Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behavior of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.

Question: What is the approximate nearest neighbor search?


Pokémon Go: Impact on Yelp Restaurant Reviews. Pokémon Go, the popular Augmented Reality based mobile application, launched in July of 2016. The game's meteoric rise in usage since that time has had an impact on not just the mobile gaming industry, but also the physical activity of players, where they travel, where they spend their money, and possibly how they interact with other social media applications. In this paper, we studied the impact of Pokémon Go on Yelp reviews. For restaurants near PokéStops, we found a slight drop in the number of online reviews.

Question: What is the impact of Pokémon Go on Yelp reviews?


Sentiment Analysis of Twitter Data. We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the use of a tree kernel to obviate the need for tedious feature engineering. The new features (in conjunction with previously proposed features) and the tree kernel perform approximately at the same level, both outperforming the state-of-the-art baseline.

Question: What is the tree kernel?


Introduction of optical camera communication for Internet of vehicles (IoV). In this paper, we have introduced optical camera communication (OCC) as a new technology for internet of vehicles (IoV). OCC has already been established in many areas which can also be used in vehicular communication. There have some researches in the field of OCC-based vehicular communication but these are not mature enough. Here, we have proposed a new system which will provide great advantages to the vehicular system. We proposed a combination of OCC and cloud-based communication for IoV which will ensure secure, stable, and system. We have also proposed an algorithm to provide cloud based IoV service.

Question: What is the difference between optical camera communication and internet of vehicles?


A bidirectional soft pneumatic fabric-based actuator for grasping applications. THIS paper presents the development of a bidirectional fabric-based soft pneumatic actuator requiring low fluid pressurization for actuation, which is incorporated into a soft robotic gripper to demonstrate its utility. The bidirectional soft fabric-based actuator is able to provide both flexion and extension. Fabrication of the fabric actuators is simple as compared to the steps involved in traditional silicone-based approach. In addition, the fabric actuators are able to generate comparably larger vertical grip resistive force at lower operating pressure than elastomeric actuators and 3D-printed actuators, being able to generate resistive grip force up to 20N at 120 kPa. Five of the bidirectional soft fabric-based actuators are deployed within a five-fingered soft robotic gripper, complete with five casings and a base. It is capable of grasping a variety of objects with maximum width or diameter closer to its bending curvature. A cutting task involved bimanual manipulation was demonstrated successfully with the gripper. To incorporate intelligent control for such a task, a soft force made completely of compliant material was attached to the gripper, which allows determination of whether the cutting task is completed. To the authors' knowledge, this work is the first study which incorporates two soft robotic grippers for bimanual manipulation with one of the grippers sensorized to provide closed loop control.

Question: What is the difference between a soft fabric-based actuator and a soft elastomeric actuator?


Modulation, control and capacitor voltage balancing of alternate arm modular multilevel converter with DC fault blocking capability. This paper provides an overview of DC side fault tolerance issues of VSC based HVDC system and the need for fault tolerant converters. The working principle and DC fault ride through capability of recently introduced Alternate Arm Modular Multilevel Converter(AAMMC) has been discussed. The capacitor voltage balancing issues of AAMMC is analyzed and a novel scheme for balancing capacitor voltages of the wave shaping circuit is presented in this paper. The voltage balancing of capacitors of wave shaping circuits in the arm is done by introducing an overlap period during zero voltage period. Using the proposed scheme, the magnitude and direction of the current during the overlap period can be controlled by varying the switching pattern. It helps in charging or discharging of the submodule capacitors to bring them to their reference value. At the end of the overlap period, the arm current is brought to zero before opening the director switch so as to avoid the spike across the arm inductor. The efficacy of the proposed control scheme has been validated using simulation study done in PSCAD/EMTDC.

Question: How to control the capacitor voltage of a wave shaping circuit?


Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke?. BACKGROUND AND PURPOSE Motor dysfunction after unilateral deafferentation in primates can be overcome by restraining the unaffected limb. We asked whether a constraint-induced movement (CIM) program could be implemented within 2 weeks after stroke and whether CIM is more effective than traditional upper-extremity (UE) therapies during this period.   METHODS Twenty-three persons were enrolled in a pilot randomized, controlled trial that compared CIM with traditional therapies. A blinded observer rated the primary end point, the Action Research Arm Test (ARA). Inclusion criteria were the following: ischemic stroke within 14 days, persistent hemiparesis, evidence of preserved cognitive function, and presence of a protective motor response. Differences between the groups were compared by using Student's t tests, ANCOVA, and Mann-Whitney U: tests.   RESULTS Twenty subjects completed the 14-day treatment. Two adverse outcomes, a recurrent stroke and a death, occurred in the traditional group; 1 CIM subject met rehabilitation goals and was discharged before completing 14 inpatient days. The CIM treatment group had significantly higher scores on total ARA and pinch subscale scores (P:<0.05). Differences in the mean ARA grip, grasp, and gross movement subscale scores did not reach statistical significance. UE activities of daily living performance was not significantly different between groups, and no subject withdrew because of pain or frustration.   CONCLUSIONS A clinical trial of CIM therapy during acute rehabilitation is feasible. CIM was associated with less arm impairment at the end of treatment. Long-term studies are needed to determine whether CIM early after stroke is superior to traditional therapies.

Question: What is the difference between constraint-induced movement therapy and traditional therapies?


DeltaIoT: A Self-Adaptive Internet of Things Exemplar. Internet of Things (IoT) consists of networked tiny embedded computers (motes) that are capable of monitoring and controlling the physical world. Examples range from building security monitoring to smart factories. A central problem of IoT is minimising the energy consumption of the motes, while guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance or inefficiencies. Enhancing the IoT network with self-adaptation can automate these tasks. This paper presents DeltaIoT, an exemplar that enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in IoT. DeltaIoT is the first exemplar for research on self-adaptation that provides both a simulator for offline experimentation and a physical setup that can be accessed remotely for real-world experimentation.

Question: What is a self-adaptive internet of things exemplar?


Anomaly detection of malicious users' behaviors for web applications based on web logs. With more and more online services developed into web applications, security problems based on web applications become more serious now. Most intrusion detection systems are based on every single request to find the cyber-attack instead of users' behaviors, and these systems can only protect web application from known vulnerability rather than some zero-day attacks. In order to detect newly developed attacks, we analyze web logs from web servers and define users' behaviors to divide them into normal and malicious ones. The result shows that by using the feature of web resources to define users' behaviors, a higher accuracy rate and lower false alarm rate of intrusion detection can be obtained.

Question: What is anomaly detection?


Dielectric properties of human normal, malignant and cirrhotic liver tissue: in vivo and ex vivo measurements from 0.5 to 20 GHz using a precision open-ended coaxial probe.. Hepatic malignancies have historically been treated with surgical resection. Due to the shortcomings of this technique, there is interest in other, less invasive, treatment modalities, such as microwave hepatic ablation. Crucial to the development of this technique is the accurate knowledge of the dielectric properties of human liver tissue at microwave frequencies. To this end, we characterized the dielectric properties of in vivo and ex vivo normal, malignant and cirrhotic human liver tissues from 0.5 to 20 GHz. Analysis of our data at 915 MHz and 2.45 GHz indicates that the dielectric properties of ex vivo malignant liver tissue are 19 to 30% higher than normal tissue. The differences in the dielectric properties of in vivo malignant and normal liver tissue are not statistically significant (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 16% higher than normal). Also, the dielectric properties of in vivo normal liver tissue at 915 MHz and 2.45 GHz are 16 to 43% higher than ex vivo. No statistically significant differences were found between the dielectric properties of in vivo and ex vivo malignant tissue (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 28% higher than normal). We report the one-pole Cole-Cole parameters for ex vivo normal, malignant and cirrhotic liver tissue in this frequency range. We observe that wideband dielectric properties of in vivo liver tissue are different from the wideband dielectric properties of ex vivo liver tissue, and that the in vivo data cannot be represented in terms of a Cole-Cole model. Further work is needed to uncover the mechanisms responsible for the observed wideband trends in the in vivo liver data.

Question: What is the difference between in vivo and ex vivo liver tissue?


A fast method of fog and haze removal. Fog and haze degrade the quality of preview and captured image by reducing the contrast and saturation. As a result the visibility of scene or object degrades. The objective of the present work is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. We propose a method that uses single frame for enhancing foggy images using multilevel transmission map. The method is fast and free from noise or artifacts that generally arise in such enhancement techniques. A comparison with existing methods shows that the proposed method performs better in terms of both processing time and quality. The proposed method works in real time for VGA resolution. The proposed work also presents a scheme to remove fog, rain and snow in real-time.

Question: How to remove fog and haze in real-time?


Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition. This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with state-of-theart method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the "MediaEval2015 emotion in music" dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.

Question: What is the best result reported on this dataset?


A 0.18μm CMOS fully integrated RFDAC and VGA for WCDMA transmitters. A digital IF to RF converter architecture with an RF VGA targeted for WCDMA transmitters is presented. The RFDAC consists of a 1.5-bits current steering DAC with an embedded semi-digital FIR filter and a mixer with an LC load. The VGA controls the output power in 10 dB steps. The prototype is fabricated in 0.18 mum CMOS technology and achieves 0 dBm output power at 2 GHz and 33 dB image rejection. The measured rms EVM and ACPR show the system fulfills the standard specifications for the WCDMA. The chip draws 123.9 mA from 1.8 V supply. The die area is 0.8 times 1.6 mm2.

Question: What is a RFDAC?


Online payment system using steganography and visual cryptography. A rapid growth in E-Commerce market is seen in recent time throughout the world. With ever increasing popularity of online shopping, Debit or Credit card fraud and personal information security are major concerns for customers, merchants and banks specifically in the case of CNP (Card Not Present). This paper presents a new approach for providing limited information only that is necessary for fund transfer during online shopping thereby safeguarding customer data and increasing customer confidence and preventing identity theft. The method uses combined application of steganography and visual cryptography for this purpose.

Question: What is a CNP?


Shape, Illumination, and Reflectance from Shading. A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison-there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.

Question: What is the most likely explanation of a single image?


Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages. In this paper, we report a study that examines the relationship between image-based computational analyses of web pages and users' aesthetic judgments about the same image material. Web pages were iteratively decomposed into quadrants of minimum entropy (quadtree decomposition) based on low-level image statistics, to permit a characterization of these pages in terms of their respective organizational symmetry, balance and equilibrium. These attributes were then evaluated for their correlation with human participants' subjective ratings of the same web pages on four aesthetic and affective dimensions. Several of these correlations were quite large and revealed interesting patterns in the relationship between low-level (i.e., pixel-level) image statistics and design-relevant dimensions.

Question: How do we correlate low-level image statistics with users' aesthetic judgments of web pages?


Neuroevolution: from architectures to learning. Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these problems. New insights in both neuroscience and evolutionary biology have led to the development of increasingly powerful neuroevolution techniques over the last decade. This paper gives an overview of the most prominent methods for evolving ANNs with a special focus on recent advances in the synthesis of learning architectures.

Question: What is a neural network?


Temporal multi-hierarchy smoothing for estimating rates of rare events. We consider the problem of estimating rates of rare events obtained through interactions among several categorical variables that are heavy-tailed and hierarchical. In our previous work, we proposed a scalable log-linear model called LMMH (Log-Linear Models for Multiple Hierarchies) that combats data sparsity at granular levels through small sample size corrections that borrow strength from rate estimates at coarser resolutions. This paper extends our previous work in two directions. First, we model excess heterogeneity by fitting local LMMH models to relatively homogeneous subsets of the data. To ensure scalable computation, these subsets are induced through a decision tree, we call this Treed-LMMH. Second, the Treed-LMMH method is coupled with temporal smoothing procedure based on a fast Kalman filter style algorithm. We show that simultaneously performing hierarchical and temporal smoothing leads to significant improvement in predictive accuracy. Our methods are illustrated on a large scale computational advertising dataset consisting of billions of observations and hundreds of millions of attribute combinations(cells).

Question: How to estimate rates of rare events?


A weight-neutral versus weight-loss approach for health promotion in women with high BMI: A randomized-controlled trial. Weight loss is the primary recommendation for health improvement in individuals with high body mass index (BMI) despite limited evidence of long-term success. Alternatives to weight-loss approaches (such as Health At Every Size - a weight-neutral approach) have been met with their own concerns and require further empirical testing. This study compared the effectiveness of a weight-neutral versus a weight-loss program for health promotion. Eighty women, aged 30-45 years, with high body mass index (BMI ≥ 30 kg/m(2)) were randomized to 6 months of facilitator-guided weekly group meetings using structured manuals that emphasized either a weight-loss or weight-neutral approach to health. Health measurements occurred at baseline, post-intervention, and 24-months post-randomization. Measurements included blood pressure, lipid panels, blood glucose, BMI, weight, waist circumference, hip circumference, distress, self-esteem, quality of life, dietary risk, fruit and vegetable intake, intuitive eating, and physical activity. Intention-to-treat analyses were performed using linear mixed-effects models to examine group-by-time interaction effects and between and within-group differences. Group-by-time interactions were found for LDL cholesterol, intuitive eating, BMI, weight, and dietary risk. At post-intervention, the weight-neutral program had larger reductions in LDL cholesterol and greater improvements in intuitive eating; the weight-loss program had larger reductions in BMI, weight, and larger (albeit temporary) decreases in dietary risk. Significant positive changes were observed overall between baseline and 24-month follow-up for waist-to-hip ratio, total cholesterol, physical activity, fruit and vegetable intake, self-esteem, and quality of life. These findings highlight that numerous health benefits, even in the absence of weight loss, are achievable

Question: What is the difference between weight-neutral and weight-loss approaches?


Evolution of fashion brands on Twitter and Instagram. Social media has become a popular platform for marketing and brand advertisement especially for fashion brands. To promote their products and gain popularity, different brands post their latest products and updates as photos on different social networks. Little has been explored on how these fashion brands use different social media to reach out to their customers and obtain feedback on different products. Understanding this can help future consumers to focus on their interested brands on specific social media for better information and also can help the marketing staff of different brands to understand how the other brands are utilizing the social media. In this article we focus on the top-20 fashion brands and comparatively analyze how they target their current and potential future customers on Twitter and Instagram. Using both linguistic and deep image features, our work reveals an increasing diversification of trends accompanied by a simultaneous concentration towards a few selected trends. It provides insights about brand marketing strategies and their respective competencies. Our investigations show that the brands are using Twitter and Instagram in a distinctive manner.

Question: How to analyze fashion brands on social media?


GSA: A Gravitational Search Algorithm. In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. 2009 Elsevier Inc. All rights reserved.

Question: What is a heuristic search algorithm?


Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading. Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Question: What is the energy efficient resource allocation for mobile-edge computation offloading?


Innovative Contactless Energy Transfer Accessory for Rotary Ultrasonic Machining and Its Circuit Compensation Based on Coil Turns. An innovative concentric rotary ultrasonic machining (RUM) device based on local induction is proposed. The device is used as a machine tool accessory that can add the RUM to any machining center. Rather than using a complete ring, a portion of a ring is used as the primary core in the proposed rotary transformer, which eliminates spindle speed limits. The simplified configuration provides increased safety and is convenient for material processing applications. Additionally, a circuit compensation method based on coil turns is proposed. It takes the whole circuit, reliability of electronic components, transmission efficiency, and output power into consideration. We use this method to choose the optimal number of coil turns and compensation elements required to achieve increased and safe power transmission performance. This paper also demonstrates the performance of the device through experimentation. Finally, practicability is discussed.

Question: What is the purpose of the device?


Compressing deep neural networks using a rank-constrained topology. We present a general approach to reduce the size of feedforward deep neural networks (DNNs). We propose a rankconstrained topology, which factors the weights in the input layer of the DNN in terms of a low-rank representation: unlike previous work, our technique is applied at the level of the filters learned at individual hidden layer nodes, and exploits the natural two-dimensional time-frequency structure in the input. These techniques are applied on a small-footprint DNN-based keyword spotting task, where we find that we can reduce model size by 75% relative to the baseline, without any loss in performance. Furthermore, we find that the proposed approach is more effective at improving model performance compared to other popular dimensionality reduction techniques, when evaluated with a comparable number of parameters.

Question: How to compress deep neural networks?


Hierarchical graph indexing. Traffic analysis, in the context of Telecommunications or Internet and Web data, is crucial for large network operations. Data in such networks is often provided as large graphs with hundreds of millions of vertices and edges. We propose efficient techniques for managing such graphs at the storage level in order to facilitate its processing at the interface level(visualization). The methods are based on a hierarchical decomposition of the graph edge set that is inherited from a hierarchical decomposition of the vertex set. Real time navigation is provided by an efficient two level indexing schema called the gkd*-tree. The first level is a variation of a kd-tree index that partitions the edge set in a way that conforms to the hierarchical decomposition and the data distribution (the gkd-tree). The second level is a redundant R-tree that indexes the leaf pages of the gkd-tree. We provide computational results that illustrate the superiority of the gkd-tree against conventional indexes like the kd-tree and the R*-tree both in creation as well as query response times.

Question: What is hierarchical graph indexing?


AN ALGORITHM TO GROUP DEFECTS ON PRINTED CIRCUIT BOARD FOR AUTOMATED VISUAL INSPECTION. Due to disadvantages in manual inspection, an automated visual inspection system is needed to eliminate subjective aspects and provides fast and quantitative assessment of printed circuit board (PCB). Up to the present, there has been a lot of work and research concentrated on PCB defect detection. PCB defects detection is necessary for verification of the characteristics of PCB to make sure it is in conformity with the design specifications. However, besides the need to detect the defects, it is also essential to classify these defects so that the source of these defects can be identified. Unfortunately, this area has been neglected and not been given enough attention. Hence, this study proposes an algorithm to group the defects found on bare PCB. Using a synthetically generated PCB image, the algorithm is able to group 14 commonly known PCB defects into five groups. The proposed algorithm includes several image processing operations such as image subtraction, image adding, logical XOR and NOT, and flood fill operator.

Question: What is a PCB?


The Seventh PASCAL Recognizing Textual Entailment Challenge. This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year's challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7.

Question: What is the seventh recognising textual entailment challenge?


Enhancing recurrent neural network-based language models by word tokenization. Introduction Statistical language models estimate the probability for a given sequence of words. Given a sentence s with n words such as s = (w1,w2 . . .wn), the language model assigns P(s). Statistical language models assess good word sequence estimations based on the sequence probability estimation. Building robust, fast and accurate language models is one of the main factors in the success of building systems such as machine translation systems and automatic speech recognition systems. Statistical language models can be estimated based on various approaches. Classical language models are estimated based on n-gram word sequences such as P(s) = P(w1,w2 . . .wn) = ∏n i=1 P(wi|wi−1) and can be approximated based on the Markov concept for shorter contexts (for example, bigram if n = 2 or trigram if n = 3 and so on). Recent researchers have applied neural networks different architectures to build and estimate language models. The classical feed-forward neural network-based language models have been continuously reporting good results among the traditional n-gram language modeling techniques [1]. It Abstract

Question: What is the difference between a neural network and a feed-forward neural network?


Optimal design methodology for LLC resonant converter. Although LLC resonant converter can achieve wide operation range with high efficiency, lack of design methodology makes it difficult to be implemented. In this paper, based on the theoretical analysis on the operation principles during normal condition and holdup time, the relationship between converter efficiency and operation range with different circuit parameters has be revealed. An optimal design methodology has been developed based on the revealed relationship. A 1MHz, 1kW LLC converter is designed to verify the proposed method.

Question: What is the optimal design methodology for LLC resonant converter?


A Novel CMOS Envelope Detector Structure. A novel high performance envelope detector is offered in this work. Proposed circuit holds the signal peaks in two periods and combines them to obtain the envelope of the signal. This way, ripple is fixed by the peak holder and tracking can be improved without the traditional compensation between keeping and tracking required in these circuits. A comparison is offered between a conventional circuit, a previous work and the proposed envelope detector. It is shown the superior performance of the latter obtaining small ripple (<1%), fast settling (0.4mus) and high linearity.

Question: What is the novel envelope detector?


Hopelessness Depression : A Theory-Based Subtype of Depression. We present a revision of the 1978 reformulated theory of helplessness and depression and call it the hopelessness theory of depression. Although the 1978 reformulation has generated a vast amount of empirical work on depression over the past 10 years and recently has been evaluated as a model of depression, we do not think that it presents a clearly articulated theory of depression. We build on the skeletal logic of the 1978 statement and (a) propose a hypothesized subtype of depression— hopelessness depression, (b) introduce hopelessness as a proximal sufficient cause of the symptoms of hopelessness depression, (c) deemphasize causal attributions because inferred negative consequences and inferred negative characteristics about the self are also postulated to contribute to the formation of hopelessness and, in turn, the symptoms of hopelessness depression, and (d) clarify the diathesisstress and causal mediation components implied, but not explicitly articulated, in the 1978 statement. We report promising findings for the hopelessness theory and outline the aspects that still need to be tested.

Question: What is the hopelessness theory of depression?


DESC: enabling secure data exchange based on smart contracts. Dear editor, As data are one of the most important factors of today's intelligent systems but are relatively scarce, people try exchanging data with other organizations or public marketplaces. However, data exchange is now lack of a secure execution mechanism to automatically and fairly protect the rights which are seriously concerned by data owners. For instance, data owners cannot limit the identity of a data transferee who is banned to take the data by any way. Moreover, data transferees are also afraid that their applications for exchange may be unfairly treated. For instance, data owners could dynamically change data price when multiple transferees submit their applications at the same time. A data marketplace can take over the role of a trusted third party during data exchange, but single point failure is always a serious problem in a large-scale distributed system. Furthermore, these marketplaces usually use contracts based on natural languages which may be maliciously misinterpreted and cannot be automatically executed. Numerous researches have been presented to provide digital rights management (DRM) [1]. For instance, Zhu et al. [2] presented a privacypreserving video subscription scheme with the limitation of expire date. However, rights of data owners are more complex than rights of digital content owners. Decentralized and trustworthy enforcement for access control policies is a long-term issue. Han et al. [3] proposed an optimized mechanism which is a trusted and decentralized access control framework for the client/server architecture. These studies still need third party authorities which are used to execute or supervise the rights control, leading to a few trust and security issues. Existing studies [4] on attribute-based access control have been applied to many fields, such as e-commerce and Internet of Things. This article introduces attribute-based access control and proposes a secure

Question: What is a secure data exchange based on smart contracts?


Internet Traffic Measurement. This tutorial article discusses the role of network traffic measurement in the design, testing, and evaluation of Internet protocols and applications. The article begins with some background information on Internet traffic measurement, and then proceeds to discuss the "tools of the trade", including examples of both hardwarebased and software-based approaches to network traffic measurement. The article concludes with a summary of the main observations from the past fifteen years of network measurement research, along with pointers to the relevant literature for more information.

Question: What is the role of network traffic measurement in the design, testing, and evaluation of Internet protocols and applications?