High gain antipodal tapered slot antenna With sine-shaped corrugation and fermi profile substrate slotted cut-out for MMW 5G. A high gain, high-efficiency antipodal tapered slot antenna with sine-shaped corrugation and a Fermi profile substrate cut-out has been developed for 5G millimeter wave (MMW) communications. A parametric study of a new substrate cutout with Fermi profile is demonstrated to reduce the sidelobe level at the E-plane and H-plane as well as to increase antenna efficiency by an average of 88% over a band of 20-40 GHz. A low-cost printed circuit board (PCB) is processed simply with a CNC Milling machine to fabricate the proposed antenna with Fermi profile substrate cut-out. The measured reflection coefficient is found to be less than -14 dB over a frequency range of 20-40 GHz. Furthermore, the measured gain of the proposed antenna is 17 dB at 30 GHz and the measured radiation pattern and gain is almost constant within the wide bandwidth from 30-40 GHz. Therefore, this antenna is proposed for use in an H-plane array structure such as for point-to-point communication systems, a switched-beam system.

Question: What is the gain of the antenna?


Design for Reliability of Low-voltage, Switched-capacitor Circuits. Design for Reliability of Low-voltage, Switched-capacitor Circuits by Andrew Masami Abo Doctor of Philosophy in Engineering University of California, Berkeley Professor Paul R. Gray, Chair Analog, switched-capacitor circuits play a critical role in mixed-signal, analogto-digital interfaces. They implement a large class of functions, such as sampling, filtering, and digitization. Furthermore, their implementation makes them suitable for integration with complex, digital-signal-processing blocks in a compatible, low-cost technology–particularly CMOS. Even as an increasingly larger amount of signal processing is done in the digital domain, this critical, analogto-digital interface is fundamentally necessary. Examples of some integrated applications include camcorders, wireless LAN transceivers, digital set-top boxes, and others. Advances in CMOS technology, however, are driving the operating voltage of integrated circuits increasingly lower. As device dimensions shrink, the applied voltages will need to be proportionately scaled in order to guarantee long-term reliability and manage power density. The reliability constraints of the technology dictate that the analog circuitry operate at the same low voltage as the digital circuitry. Furthermore, in achieving low-voltage operation, the reliability constraints of the technology must not be violated. This work examines the voltage limitations of CMOS technology and how analog circuits can maximize the utility of MOS devices without degrading relia-

Question: What is the voltage of a CMOS device?


Character-based Neural Machine Translation. We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.

Question: What is character-based neural machine translation?


Create Your Own Internet of Things: A survey of IoT platforms.. We live in the age of After Google (AG), where information is just one click away and talking just one touch away. The near future of the AG age is the Internet of Things (IoT), where physical things connected over a network will take part in Internet activities to exchange information about themselves and their surroundings. In other words, the IoT is nothing but a computing concept in which everyday objects with embedded hardware/devices are connected to a network or are simply online.

Question: What is the Internet of Things?


Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system. Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines. Biologically important behavioural activities in housed dairy cows can be classified accurately using a simple decision-tree algorithm applied to data collected from a neck-mounted tri-axial accelerometer. The algorithm could form part of a real-ti

Question: How accurate is the algorithm?


Scaling HDFS to More Than 1 Million Operations Per Second with HopsFS. HopsFS is an open-source, next generation distribution of the Apache Hadoop Distributed File System(HDFS) that replaces the main scalability bottleneck in HDFS, single node in-memory metadata service, with a no-sharedstate distributed system built on a NewSQL database. By removing the metadata bottleneck in Apache HDFS, HopsFS enables significantly larger cluster sizes, more than an order of magnitude higher throughput, and significantly lower clientlatencies for large clusters. In this paper, we detail the techniques and optimizations that enable HopsFS to surpass 1 million file system operations per second - at least 16 times higher throughput than HDFS. In particular, we discuss how we exploit recent high performance features from NewSQL databases, such as application defined partitioning, partition-pruned index scans, and distribution aware transactions. Together with more traditional techniques, such as batching and write-ahead caches, we show how many incremental optimizations have enabled a revolution in distributed hierarchical file system performance.

Question: What is the highest throughput file system?


Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.

Question: What is the size of the canadian military?


Learning Bound for Parameter Transfer Learning. We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in selftaught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning.

Question: What is the learning bound for self-taught learning?


An Improved Algorithm for Harris Corner Detection. Corner points are formed from two or more edges and edges usually define the boundary between two different objects or parts of the same objects. In this novel we discuss the theory of the Harris corner detection and indicate its disadvantage. Then it proposes an improved algorithm of Harris detection algorithm based on the neighboring point eliminating method. It reduces the time of the detection, and makes the corners distributing more homogenous so that avoids too many corners stay together. Experimental results show that the algorithm can detect corner more equality distributing, and can be used in some fact applications such as image registration well. Key words-Corner point; Harris corner; neighboring point; homogenous distributing 1. Introduce Corner point is a maximum curvature point of the two-dimensional image brightness change or the edge of sharp curves in a image, which haves widely used in such as the camera calibration, virtual scene reconstruction, motion estimation, image registration and more computer vision tasks. These points keep important features of image and can also be effective in reducing the amount of data information, allow real-time processing possible. Now, there are two corner detection methods: based on the image edge method and based on the image gray scale method. The former always need to encode the image edge, which is heavily dependent on image segmentation and edge extraction, have large difficulty and calculate, and if the detected area haves some change, it will lead to the failure of the operation, therefore, this method is not used widely. The latter method detect corner by calculating the curvature and gradient, it avoid these disadvantage. In currently, it haves Moravec operator, Harris operator, Susan operator and so on. Experimental shows that Harris corner detection method haves the best result in above mentioned. But in fact applications, Harris algorithm must to give

Question: What is the difference between Harris corner detection and Harris corner detection?


Zero-Shot Learning Posed as a Missing Data Problem. This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way - our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. Following the transductive setting, we leverage unlabeled data to refine the initial estimation. In experiments, our method achieves the highest classification accuracies on two popular datasets, namely, 96.00% on AwA and 60.24% on CUB.

Question: What is zero-shot learning?


Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy. This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.

Question: What is a domain adaptation support vector machine?


MOS PARAMETER EXTRACTION AND OPTIMIZATION WITH GENETIC ALGORITHM. Extracting an optimal set of parameter values for a MOS device is great importance in contemporary technology is a complex problem. Traditional methods of parameter extraction can produce far from optimal solutions because of the presence of local optimum in the solution space. Genetic algorithms are well suited for finding near optimal solutions in irregular parameter spaces. In this study*, We have applied a genetic algorithm to the problem of device model parameter extraction and are able to produce models of superior accuracy in much less time and with less reliance on human expertise. MOS transistor's parameters have been extracted and optimized with genetic algorithm. 0.35μm fabricated by C35 process have been used for the results of experimental studies of parameter extraction. Extracted parameters' characteristic data results have been compared with measurement results. Different values of parameters of genetic algorithm, such as population size, crossover rate , and generation size are compared by different tests.

Question: What is the difference between a genetic algorithm and a genetic programming?


The prerepresentational self and its affective core..

Question: What is the prerepresentational self?


Solving the Multiple Instance Problem with Axis-Parallel Rectangles. The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.

Question: What is the multiple instance problem?


Progressive Geometric Algorithms. Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms for two geometric problems: computing the convex hull of a planar point set, and finding popular places in a set of trajectories.

Question: What is a progressive algorithm?


SelCSP: A Framework to Facilitate Selection of Cloud Service Providers. With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients' data. If loss of data occurs due to an outage, the customer's business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider's SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.

Question: How to select a cloud service provider?


Software metrics : usability and evaluation of software quality. It is difficult to understand, let alone improve, the quality of software without the knowledge of its software development process and software products. There must be some measurement process to predict the software development, and to evaluate the software products. This thesis provides a brief view on Software Quality, Software Metrics, and Software Metrics methods that will predict and measure the specified quality factors of software. It further discusses about the Quality as given by the standards such as ISO, principal elements required for the Software Quality and Software Metrics as the measurement technique to predict the Software Quality. This thesis was performed by evaluating a source code developed in Java, using Software Metrics, such as Size Metrics, Complexity Metrics, and Defect Metrics. Results show that, the quality of software can be analyzed, studied and improved by the usage of software metrics.

Question: What is the size of the canadian military?


What do online behavioral advertising privacy disclosures communicate to users?. Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's online activities, has led to privacy concerns. In an attempt to mitigate these privacy concerns, the online advertising industry has proposed the use of OBA disclosures: icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures. The disclosures failed to clearly notify participants about OBA and inform them about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. When shown the disclosures again, the majority mistakenly believed that ads would pop up if they clicked on disclosures, and more participants incorrectly thought that clicking the disclosures would let them purchase advertisements than correctly understood that they could then opt out of OBA. "AdChoices", the most commonly used tagline, was particularly ineffective at communicating notice and choice. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We dicuss challenges in crafting disclosures and provide suggestions for improvement.

Question: What do online behavioral advertising privacy disclosures communicate to users?


Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications. Transfer learning allows practitioners to recognize and apply knowledge learned in previous tasks (source task) to new tasks or new domains (target task), which share some commonality. The two important factors impacting the performance of transfer learning models are: (a) the size of the target dataset, and (b) the similarity in distribution between source and target domains. Thus far, there has been little investigation into just how important these factors are. In this paper, we investigate the impact of target dataset size and source/target domain similarity on model performance through a series of experiments. We find that more data is always beneficial, and model performance improves linearly with the log of data size, until we are out of data. As source/target domains differ, more data is required and fine tuning will render better performance than feature extraction. When source/target domains are similar and data size is small, fine tuning and feature extraction renders equivalent performance. Our hope is that by beginning this quantitative investigation on the effect of data volume and domain similarity in transfer learning we might inspire others to explore the significance of data in developing more accurate statistical models.

Question: How much data is needed to train a model?


Evaluating Induced CCG Parsers on Grounded Semantic Parsing. We compare the effectiveness of four different syntactic CCG parsers for a semantic slotfilling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at https://github.com/ sivareddyg/graph-parser.

Question: What is the difference between a CCG parser and a semantic parser?


A Counterexample to Theorems of Cox and Fine. Cox s well known theorem justifying the use of probability is shown not to hold in nite domains The counterexample also suggests that Cox s assumptions are insu cient to prove the result even in in nite domains The same counterexample is used to disprove a result of Fine on comparative conditional probability

Question: What is a counterexample to a theorem?


QuickCut: An Interactive Tool for Editing Narrated Video. We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.

Question: What is an interactive video editing tool?


Query optimization techniques for partitioned tables. Table partitioning splits a table into smaller parts that can be accessed, stored, and maintained independent of one another. From their traditional use in improving query performance, partitioning strategies have evolved into a powerful mechanism to improve the overall manageability of database systems. Table partitioning simplifies administrative tasks like data loading, removal, backup, statistics maintenance, and storage provisioning. Query language extensions now enable applications and user queries to specify how their results should be partitioned for further use. However, query optimization techniques have not kept pace with the rapid advances in usage and user control of table partitioning. We address this gap by developing new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. Our techniques are designed for easy incorporation into bottom-up query optimizers that are in wide use today. We have prototyped these techniques in the PostgreSQL optimizer. An extensive evaluation shows that our partition-aware optimization techniques, with low optimization overhead, generate plans that can be an order of magnitude better than plans produced by current optimizers.

Question: How to partition a table?


Advanced Message Queuing Protocol. The advanced message queuing protocol (AMQP) working group's goal is to create an open standard for an interoperable enterprise-scale asynchronous messaging protocol. AMQP is finally addressing the lack of enterprise messaging interoperability standards. This relatively simple yet compellingly powerful enterprise messaging protocol is thus poised to open up a bright new era for enterprise messaging

Question: What is the goal of the AMQP working group?


High Performance Emulation of Quantum Circuits. As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and debugging of algorithms as well as exploring hardware-software co-design to guide the development of quantum hardware and architectures. The simulation of quantum computers entails multiplications of sparse matrices with very large dense vectors of dimension 2n, where n denotes the number of qubits, making this a memory-bound and network bandwidth-limited application. We introduce the concept of a quantum computer emulator as a component of a software framework for quantum computing, enabling a significant performance advantage over simulators by emulating quantum algorithms at a high level rather than simulating individual gate operations. We describe various optimization approaches and present benchmarking results, establishing the superiority of quantum computer emulators in terms of performance.

Question: What is a quantum computer emulator?


Mikro Bloglardaki Finans Toplulukları için Kullanıcı Ağırlıklandırılmış Duygu Analizi Yöntemi. Nowadays, sentiment analysis is a popular research area in computer science. which aims to determine a person's or a group's mood, behaviour and opinion about any topic with using textual documents. With the proliferation of social micro-blogging sites, digital opinion text data is increased. Thus, many sentiment analysis researches are performed on these public data in different sociological fields, such as finance, economy and politics. In this paper, a novel sentiment analysis method is proposed on micro-blogging sites which uses new user metrics. Proposed method is used to measure financial community's sentiment polarity on micro-blogging sites. In addition to that we analyze the correlation between the mood of financial community and the behavior of the Borsa Istanbul 100 index weekly by Pearson correlation coefficient method. Our test results show that this novel sentiment analysis method improves to the accuracy of linear relationship between the behavior of the stock market and the sentiment polarity of the financial community.

Question: What is the mood of the financial community?


Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint. Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms.

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


Mr. DLib's Living Lab for Scholarly Recommendations. We introduce the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab ́s API in platforms such as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib's living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%.

Question: What is a living lab?


A unified approach for motion and force control of robot manipulators: The operational space formulation. A framework for the analysis and control of manipulator systems with respect to the dynamic behavior of their end-effectors is developed. First, issues related to the description of end-effector tasks that involve constrained motion and active force control are discussed. The fundamentals of the operational space formulation are then presented, and the unified approach for motion and force control is developed. The extension of this formulation to redundant manipulator systems is also presented, constructing the end-effector equations of motion and describing their behavior with respect to joint forces. These results are used in the development of a new and systematic approach for dealing with the problems arising at kinematic singularities. At a singular configuration, the manipulator is treated as a mechanism that is redundant with respect to the motion of the end-effector in the subspace of operational space orthogonal to the singular direction.

Question: What is the unified approach for motion and force control?


Energy-Efficient Communication Protocol for Wireless Microsensor Networks. Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adap tive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

Question: What is the energy dissipation of a wireless microsensor network?


Evaluation of Talent Management on Employees Performance in Beverage Industry : A Case of Delmonte Kenya Limited. Talent management practice within organization is an international human resource strategy that seeks to identify, develop, deploy and retain talented and high potential employees. The objectives of the study were to determine the effect of talent retention on employees performance, assess how talent attraction impacts, effect of learning and development on employees performance in beverage industry in Kenya. The study adopted a descriptive research design in which the target population of 2,500 employees of Del Monte Kenya. The study used stratified sampling method to select 83 employees according to their job cadres. Descriptive statistics such as the standard deviation, percentages and frequency distribution were used. The study established that the job retention motivated the employees of Del Monte leading to ultimate performance. The study recommended that the management should ensure the work environment was attractive to the employees so as to motivate, thus leading to better performance.

Question: How to evaluate the performance of employees in beverage industry?


Parallel geometric algorithms for multi-core computers. Computers with multiple processor cores using shared memory are now ubiquitous. In this paper, we present several parallel geometric algorithms that specifically target this environment, with the goal of exploiting the additional computing power. The d-dimensional algorithms we describe are (a) spatial sorting of points, as is typically used for preprocessing before using incremental algorithms, (b) kd-tree construction, (c) axis-aligned box intersection computation, and finally (d) bulk insertion of points in Delaunay triangulations for mesh generation algorithms or simply computing Delaunay triangulations. We show experimental results for these algorithms in 3D, using our implementations based on the Computational Geometry Algorithms Library (CGAL, http://www.cgal.org/). This work is a step towards what we hope will become a parallel mode for CGAL, where algorithms automatically use the available parallel resources without requiring significant user intervention.

Question: What is the d-dimensional algorithms for multi-core computers?


Multi-view point clouds registration and stitching based on SIFT feature. In order to solve multi-view point clouds registration in large non-feature marked scenes, a new registration and stitching method was proposed based on 2D SIFT(Scale-invariant feature transform) features. Firstly, we used texture mapping method to generate 2D effective texture image, and then extracted and match SIFT features, obtained accurate key points and registration relationship between the effective texture images. Secondly, we reflected the SIFT key points and registration relationship to the 3D point clouds data, obtained key points and registration relationship of multi-view point clouds, we can achieve multi-view point clouds stitching. Our algorithm used texture mapping method to generate 2D effective texture image, it can eliminate interference of the holes and ineffective points, and can eliminate unnecessary mistake matching. Our algorithm used correct extracted matching point pairs to stitch, avoiding stepwise iterated of ICP algorithm, so our algorithm is simple to calculate, and it's matching precision and matching efficiency are improved to some extent. We carried experiments on two-view point clouds in two large indoor; the experiment results verified the validity of our algorithm.

Question: How to register multi-view point clouds?


Hyper-parameter selection in non-quadratic regularization-based radar image formation. We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence there is interest in developing methods for automatic parameter choice. We investigate Stein's unbiased risk estimator (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) "Backhoe Data Dome," to demonstrate and discuss the effectiveness of these methods.

Question: What is the radar image formation technique?


A fast and efficient sift detector using the mobile GPU. Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.

Question: What is a heterogeneous dataflow scheme?


Aggregation and Correlation of Intrusion-Detection Alerts. This paper describes an aggregation and correlation algorithm used in the design and implementation of an intrusion-detection console built on top of the Tivoli Enterprise Console (TEC). The aggregation and correlation algorithm aims at acquiring intrusion-detection alerts and relating them together to expose a more condensed view of the security issues raised by intrusion-detection systems.

Question: What is the aggregation and correlation algorithm used in the design and implementation of an intrusion-detection console built on top of the Tivoli Enterprise Console (TEC)?


Explanation-Based Generalization: A Unifying View. The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.

Question: What is the unifying view?


Real AdaBoost : boosting for credit scorecards and similarity to WOE logistic regression. Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning and the method has well-established empirical and theoretical support spanning 15 years. Practically speaking, Real AdaBoost is able to produce readable credit scorecards and offers attractive features including variable interaction and adaptive, stage-wise binning. We will contrast Real AdaBoost to the dominant methodology for creating credit scorecards: stepwise weight of evidence logistic regression (SWOELR). Real AdaBoost is remarkably similar to SWOELR and is well positioned to serve as a benchmark for SWOELR models; it may even offer a statistical framework by which we can understand the power of SWOELR. We offer a macro to generate Real AdaBoost models in SAS. INTRODUCTION Financial institutions (FIs) must develop a wide range of models for marketing, fraud detection, loan adjudication, etc. Modeling has undergone a recent renaissance as machine learning has exploded – spurned by the availability of advanced statistical techniques, the ubiquity of powerful computers to execute these techniques, and the well-publicized successes of the companies who have embraced these methods (Parloff 2016). Modeling departments within some FIs face opposing demands: executives want some of the famed value of advanced methods, while government regulators, internal deployment teams and front-line staff want models that are easy to implement, interpret and understand. In this paper we review Real AdaBoost, a machine learning technique that may offer a middle-ground between powerful, but opaque machine

Question: What is a macro to generate real adaboost models in SAS?


Designing Neural Networks Using Genetic Algorithms with Graph Generation System. We present a new method of designing neural networks using the genetic algorithm. Recently there have been several reports claiming attempts to design neural networks using genetic algorithms were successful. However, these methods have a problem in scalability, i.e., the convergence characteristic degrades significantly as the size of the network increases. This is because these methods employ direct mapp ing of chromosomes into network connectivities. As an alternative approach, we propose a graph grammatical encoding that will encode graph generation grammar to the chromosome so that it generates more regular connectivity patterns with shorter chromosome length. Experimental results support that our new scheme provides magnitude of speedup in convergence of neural network design and exhibits desirable scaling property.

Question: How to design neural networks using genetic algorithms?


Repeatability, reproducibility and rigor in systems research. Computer systems research spans sub-disciplines that include embedded and real-time systems, compilers, networking, and operating systems. Our contention is that a number of structural factors inhibit quality research and decrease the velocity of science. We highlight some of the factors we have encountered in our work and observed in published papers and propose solutions that, if widely adopted, could both increase the productivity of researchers and the quality of their output.

Question: What are some structural factors that inhibit quality research?


An accident prediction approach based on XGBoost. As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.

Question: What is the difference between a model and a method?


Using thematic analysis in psychology. Thematic analysis is a poorly demarcated, rarely acknowledged, yet widely used qualitative analytic method within psychology. In this paper, we argue that it offers an accessible and theoretically flexible approach to analysing qualitative data. We outline what thematic analysis is, locating it in relation to other qualitative analytic methods that search for themes or patterns, and in relation to different epistemological and ontological positions. We then provide clear guidelines to those wanting to start thematic analysis, or conduct it in a more deliberate and rigorous way, and consider potential pitfalls in conducting thematic analysis. Finally, we outline the disadvantages and advantages of thematic analysis. We conclude by advocating thematic analysis as a useful and flexible method for qualitative research in and beyond psychology. Qualitative Research in Psychology 2006; 3: 77 /101

Question: What is thematic analysis?


Comparison of complex network analysis software: Citespace, SCI2 and Gephi. Big Data Analysis (BDA) has attracted considerable interest and curiosity from scientists of various fields recently. As big size and complexity of big data, it is pivotal to uncover hidden patterns, bursts of activity, correlations and laws of it. Complex network analysis could be effective method for this purpose, because of its powerful data organization and visualization ability. Besides the general complex software (Gephi), some bibliometrics software (Citespace and SCI2) could also be used for this analysis, due to their powerful data process and visualization functions. This paper presents a comparison of Citespace, SCI2 and Gephi from the following three aspects: (1) Data preprocessing. Citespace is time-consuming and laborious on merging synonyms. SCI2 is efficient and suitable for massive data. Both of them could remove duplicate records. However, Gephi lacks these. (2) Network extraction. Citespace pays more attention on temporal analysis, but SCI2 and Gephi pay less attention on it. Besides, Citespace and SCI2 could provide pruning algorithms highlight the main structure of social network, but Gephi lacks this. (3) Visualization. In Citespace, co-occurrence network could present time, frequency and betweenness centrality simultaneously; cluster view could label the clusters with phrases. However, SCI2 and Gephi provide more various layout algorithms to present network. Besides, they have a better edit-ability than Citespace on generated network.

Question: What is the difference between Citespace, SCI2 and Gephi?


Brand followers' retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth. Twitter, the popular microblogging site, has received increasing attention as a unique communication tool that facilitates electronic word-of-mouth (eWOM). To gain greater insight into this potential, this study investigates how consumers' relationships with brands influence their engagement in retweeting brand messages on Twitter. Data from a survey of 315 Korean consumers who currently follow brands on Twitter show that those who retweet brand messages outscore those who do not on brand identification, brand trust, community commitment, community membership intention, Twitter usage frequency, and total number of postings. 2014 Elsevier Ltd. All rights reserved.

Question: How do brand followers' relationships with brands influence their engagement in retweeting brand messages on Twitter?


Fast Cryptographic Computation on Intel ® Architecture Processors Via Function Stitching. Cryptographic applications often run more than one independent algorithm such as encryption and authentication. This fact provides a high level of parallelism which can be exploited by software and converted into instruction level parallelism to improve overall performance on modern super-scalar processors. We present fast and efficient methods of computing such pairs of functions on IA processors using a method called " function stitching ". Instead of computing pairs of functions sequentially as is done today in applications/libraries, we replace the function calls by a single call to a composite function that implements both algorithms. The execution time of this composite function can be made significantly shorter than the sums of the execution times for the individual functions and, in many cases, close to the execution time of the slower function. Function stitching is best done at a very fine grain, interleaving the code for the individual algorithms at an instruction-level granularity. This results in excellent utilization of the execution resources in the processor core with a single thread. We show how stitching pairs of functions together in a fine-grained manner results in excellent performance on IA processors. Currently, applications perform the functions sequentially. We demonstrate performance gains of 1.4X-1.9X with stitching over the best sequential function performance. We show performance results achieved by this method on the Intel ® processors based on the Westmere architecture.

Question: How to compute pairs of functions on IA processors?


How can machine learning help stock investment ?. The million-dollar question for stock investors is if the price of a stock will rise or not. The fluctuation of stock market is violent and there are many complicated financial indicators. Only people with extensive experience and knowledge can understand the meaning of the indicators, use them to make good prediction to get fortune. Most of other people can only rely on lucky to earn money from stock trading. Machine learning is an opportunity for ordinary people to gain steady fortune from stock market and also can help experts to dig out the most informative indicators and make better prediction.

Question: How can machine learning help stock investment?


CMOS Amperometric Instrumentation and Packaging for Biosensor Array Applications. An integrated CMOS amperometric instrument with on-chip electrodes and packaging for biosensor arrays is presented. The mixed-signal integrated circuit supports a variety of electrochemical measurement techniques including linear sweep, constant potential, cyclic and pulse voltammetry. Implemented in 0.5 μm CMOS, the 3 × mm2 chip dissipates 22.5 mW for a 200 kHz clock. The highly programmable chip provides a wide range of user-controlled stimulus rate and amplitude settings with a maximum scan range of 2 V and scan rates between 1 mV/sec and 400 V/sec. The amperometric readout circuit provides ±500 fA linear resolution and supports inputs up to ±47 μA. A 2 × 2 gold electrode array was fabricated on the surface of the CMOS instrumentation chip. An all-parylene packaging scheme was developed for compatibility with liquid test environments as well as a harsh piranha electrode cleaning process. The chip was tested using cyclic voltammetry of different concentrations of potassium ferricyanide at 100 mV/s and 200 mV/s, and results were identical to measurements using commercial instruments.

Question: What is the difference between a biosensor and an amperometric instrument?


Influence and passivity in social media. The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.

Question: How to determine influence in social media?


Optimal Multi-taxi Dispatch for Mobile Taxi-Hailing Systems. Traditional taxi-hailing systems through wireless networks in metropolitan areas allow taxis to compete for passengers chaotically and accidentally, which generally result in inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new Mobile Taxi-hailing System (called MTS) based on optimal multi-taxi dispatch, which can be used by taxi service companies (TSCs). Different from the competition modes used in traditional taxi-hailing systems, MTS assigns vacant taxis to taxi-hailing passengers proactively. For the taxi dispatch problem in MTS, we define a system utility function, which involves the total net profits of taxis and waiting time of passengers. Moreover, in the utility function, we take into consideration the various classes of taxis with different resource configurations, and the cost associated with taxis' empty travel distances. Our goal is to maximize the system utility function, restricted by the individual net profits of taxis and the passengers' requirements for specified classes of taxis. To solve this problem, we design an optimal algorithm based on the idea of Kuhn-Munkres (called KMBA), and prove the correctness and optimality of the proposed algorithm. Additionally, we demonstrate the significant performances of our algorithm through extensive simulations.

Question: What is the optimal multi-taxi dispatch for mobile taxi-hailing systems?


Why-type Question Classification in Question Answering System. The fundamental requisite to acquire information on any topic has become increasingly important. The need for Question Answering Systems (QAS) prevalent nowadays, replacing the traditional search engines stems from the user requirement for the most accurate answer to any question or query. Thus, interpreting the information need of the users is quite crucial for designing and developing a question answering system. Question classification is an important component in question answering systems that helps to determine the type of question and its corresponding type of answer. In this paper, we present a new way of classifying Why-type questions, aimed at understanding a questioner's intent. Our taxonomy classifies Why-type questions into four separate categories. In addition, to automatically detect the categories of these questions by a parser, we differentiate them at lexical level.

Question: What is the difference between a parser and a question answering system?


Are educational computer microgames engaging and effective for knowledge acquisition at high-schools ? A quasi-experimental study. Curricular schooling can benefit from the usage of educational computer games, but it is difficult to integrate them in the formal schooling system. Here, we investigate one possible approach to this integration, which capitalizes on using a micro-game that can be played with a teacher's guidance as a supplement after a traditional expository lecture followed by a debriefing. The game's purpose is to reinforce and integrate part of the knowledge learnt during the lecture. We investigated feasibility of this approach in a quasi-experimental study in 70 min long seminars on the topic of animal learning at 5 classes at 4 different high-schools in the Czech Republic. Each class was divided to two groups randomly. After an expository lecture, the game group played a game called Orbis Pictus Bestialis while the control group received an extra lecture that used media-rich materials. The time allotment was the same in both groups. We investigated the immediate and one month delayed effects of the game on students' knowledge reinforced and integrated by the game as well as on knowledge learnt during the expository lecture but not strengthened by the game. We also investigated students' overall appeal towards the seminar and its perceived educational value. Data from 100 students were analysed. The results showed that a) the game-playing is comparable to the traditional form of teaching concerning immediate knowledge gains and has a significant medium positive effect size regarding retention, b) the gameplaying is not detrimental to information transmitted in the expository lecture but not strengthened by the game, c) perceived educational value and the overall appeal were high in the game group, nevertheless the perceived educational value was slightly lower in the game group comparing to the traditional group. Our results suggest that the propos

Question: What is the effect of educational computer games on knowledge acquisition at high-schools?


Robot Modeling and Control [Book Review]. The field of robotics began in the 1960s and 1970s with the design and control of general purpose robotic manipulators. During the 1980s the expense of integrating robotics into manufacturing lines and the difficulty of performing seemingly easy tasks, such as manipulating flexible objects or washing a window, led to disenchantment among many researchers and funding agencies. Since the 1990s the popularity of robotics has resurged with many new exciting areas of application particularly in the medical, defense, and service sectors. Robotics has grown from a field that is primarily concerned with problems such as the manipulation of rigid bodies in well-structured environments to a field that has tackled challenging problems such as extraterrestrial exploration and remote minimally invasive surgery. I began my involvement in the field of robotics as a graduate student studying control theory. My project was to design a control algorithm to induce stable walking in a biped robot. As with many control problems, before formulating a solution I had to determine a suitable model for the plant. Since I was an electrical engineering student with little background in the kinematics and dynamics of rigid-body mechanical systems, this task was daunting. With some guidance from a fellow student, I fumbled my way through Lagrange's method to derive the robot's equations of motion. I had little idea at the time why forming the Lagrangian and taking certain derivatives resulted in the right equations. For insight I was pointed to various texts on classical mechanics. While these texts provided the desired insight, I was still left with questions: Given an arbitrary system, how do I systematically construct the Lagrangian so that I can derive the system's equation of motion? What are the most effective techniques for robot control? What special aspects of the robot's dynamics should be considered in developing controllers? Five years after

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


A Software-Based Sonar Ranging Sensor for Smart Phones. We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone's microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.

Question: What is the accuracy of the sonar sensor?


Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach. Business analytics has evolved from being a novelty used by a select few, to an accepted facet of conducting business. Recommender systems form a cri tical component of the business analytics toolkit and, by enabling firms to effectively target custom ers with products and services, are helping alter t h ecommerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association ru le based methods being the most common. In this pap er, we propose a method to improve the quality of recom mendations made using association rules. This is accomplished by combining rules when possible, and stands apart from existing rule-combination methods in that it is strongly grounded in probabil ity theory. Combining rules requires the identifica t on of the best combination of rules from the many comb inations that might exist, and we use a maximumlikelihood framework to compare alternative combina tions. As it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context – the best solution corresponds to the set of rules that leads to the h ighest sum of mutual information associated with th e rules. Through a variety of experiments that evalua te the quality of recommendations made using the proposed approach, we show that (i) a greedy heuris tic u ed to solve the maximum likelihood estimation problem is very effective, providing results compar able to those from using the optimal set partitioni ng solution, (ii) the recommendations made by our appr oach are more accurate than those made by a variety of state-of-the-art benchmarks, including collabora tive filtering and matrix factorization, and (iii) the recommendations can be made in a fraction of a seco n

Question: What is the best way to combine association rules?


Predicting hospital admission at emergency department triage using machine learning. OBJECTIVE To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.   METHODS This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.   RESULTS A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).   CONCLUSION Machine learning can robustly predict hospital admissi

Question: Predicting hospital admission at emergency department triage using machine learning.


Sequence-discriminative training of deep neural networks. Sequence-discriminative training of deep neural networks (DNNs) is investigated on a standard 300 hour American English conversational telephone speech task. Different sequencediscriminative criteria — maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI — are compared. Two different heuristics are investigated to improve the performance of the DNNs trained using sequence-based criteria — lattices are regenerated after the first iteration of training; and, for MMI and BMMI, the frames where the numerator and denominator hypotheses are disjoint are removed from the gradient computation. Starting from a competitive DNN baseline trained using cross-entropy, different sequence-discriminative criteria are shown to lower word error rates by 7-9% relative, on average. Little difference is noticed between the different sequencebased criteria that are investigated. The experiments are done using the open-source Kaldi toolkit, which makes it possible for the wider community to reproduce these results.

Question: What is the difference between the different sequence-discriminative criteria?


Neuroimaging: Decoding mental states from brain activity in humans. Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.

Question: What is the difference between decoding and reading?


A WL-SPPIM Semantic Model for Document Classification. In this paper, we explore SPPIM-based text classification method, and the experiment reveals that the SPPIM method is equal to or even superior than SGNS method in text classification task on three international and standard text datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although SPPMI provides a better solution, it is not necessarily better than SGNS in text classification tasks.. Based on our analysis, SGNS takes into the consideration of weight calculation during decomposition process, so it has better performance than SPPIM in some standard datasets. Inspired by this, we propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows that WL-SPPIM approach has better classification and higher scalability in the text classification task compared with LDA, SGNS and SPPIM approaches.

Question: What is the difference between SGNS and SPPIM?


A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification. We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume fixeddimensional feature spaces. To address these challenges, we propose an uncertaintyaware classification framework based on a special computational layer we refer to as the Gaussian process adapter that can connect irregularly sampled time series data to any black-box classifier learnable using gradient descent. We show how to scale up the required computations based on combining the structured kernel interpolation framework and the Lanczos approximation method, and how to discriminatively train the Gaussian process adapter in combination with a number of classifiers end-to-end using backpropagation.

Question: What is a Gaussian process?


Probabilistic Subgraph Matching Based on Convex Relaxation. We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. For the resulting quadratic integer program, a mathematically tight relaxation is derived by exploiting the degrees of freedom of the embedding space of positive semidefinite matrices. We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all close-to-optimal combinatorial matchings within the original solution space. As a result, the approach can even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.

Question: What is the name of the approach?


Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring. The promising potential of the emerging Internet of Things (IoT) technologies for interconnected medical devices and sensors has played an important role in the next-generation healthcare industry for quality patient care. Because of the increasing number of elderly and disabled people, there is an urgent need for a real-time health monitoring infrastructure for analyzing patients' healthcare data to avoid preventable deaths. Healthcare Industrial IoT (HealthIIoT) has significant potential for the realization of such monitoring. HealthIIoT is a combination of communication technologies, interconnected apps, Things (devices and sensors), and people that would function together as one smart system to monitor, track, and store patients' healthcare information for ongoing care. This paper presents a HealthIIoT-enabled monitoring framework, where ECG and other healthcare data are collected by mobile devices and sensors and securely sent to the cloud for seamless access by healthcare professionals. Signal enhancement, watermarking, and other related analytics will be used to avoid identity theft or clinical error by healthcare professionals. The suitability of this approach has been validated through both experimental evaluation, and simulation by deploying an IoT-driven ECG-based health monitoring service in the cloud. © 2016 Elsevier B.V. All rights reserved.

Question: What is the cloud-assisted IIoT framework for health monitoring?


Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks.. BACKGROUND AND PURPOSE Waist circumference has been shown to be a better predictor of cardiovascular risk than body mass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to the risk of stroke and transient ischemic attacks (TIA).   METHODS We recruited 1137 participants: 379 cases with stroke/TIA and 758 regional controls matched for age and sex. Associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference and waist-to-stature ratio) and risk of stroke/TIA were assessed by using conditional logistic regression adjusted for other risk factors.   RESULTS BMI showed a positive association with cerebrovascular risk which became nonsignificant after adjustment for physical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottom tertile). Markers of abdominal adiposity were strongly associated with the risk of stroke/TIA. For the waist-to-hip ratio, adjusted odds ratios for every successive tertile were greater than that of the previous one (2nd tertile: 2.78, 1.57 to 4.91; 3rd tertile: 7.69, 4.53 to 13.03). Significant associations with the risk of stroke/TIA were also found for waist circumference and waist-to-stature ratio (odds ratio 4.25, 2.65 to 6.84 and odds ratio 4.67, 2.82 to 7.73, top versus bottom tertile after risk adjustment, respectively).   CONCLUSIONS Markers of abdominal adiposity showed a graded and significant association with risk of stroke/TIA, independent of other vascular risk factors. Waist circumference and related ratios can better predict cerebrovascular events than BMI.

Question: What is the difference between stroke and transient ischemic attack?


Investigating Language Universal and Specific Properties in Word Embeddings. Recently, many NLP tasks have benefited from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent architecture. Here we investigate three representative models on a large set of language samples by mapping dense embedding to sparse linguistic property space. Experiment results reveal the language universal and specific properties encoded in various word representation. Additionally, strong evidence supports the utility of word form, especially for inflectional languages.

Question: What is the language universal and specific properties encoded in word embedding?


A Chopper Current-Feedback Instrumentation Amplifier With a 1 mHz $1/f$  Noise Corner and an AC-Coupled Ripple Reduction Loop. This paper presents a chopper instrumentation amplifier for interfacing precision thermistor bridges. For high CMRR and DC gain, the amplifier employs a three-stage current-feedback topology with nested-Miller compensation. By chopping both the input and intermediate stages of the amplifier, a 1 mHz 1/f noise corner was achieved at an input-referred noise power spectral density (PSD) of 15 nV/¿Hz. To reduce chopper ripple, the amplifier employs a continuous-time AC-coupled ripple reduction loop. Due to its continuous-time nature, the loop causes no noise folding to DC and hence offers improved noise performance over auto-zeroed amplifiers. The loop reduces chopper ripple by more than 60 dB, to levels below the amplifier's own input-referred noise. Furthermore, a maximum input referred offset of 5 ¿ V and a CMRR greater than 120 dB were measured at a supply current of 230 ¿A at 5 V.

Question: What is a chopper amplifier?


A Bidirectional Bridge Modular Switched-Capacitor-Based Power Electronics Transformer. This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.

Question: What is the maximum power efficiency of the converter?


Novel Word-sense Identification. Automatic lexical acquisition has been an active area of research in computational linguistics for over two decades, but the automatic identification of new word-senses has received attention only very recently. Previous work on this topic has been limited by the availability of appropriate evaluation resources. In this paper we present the largest corpus-based dataset of diachronic sense differences to date, which we believe will encourage further work in this area. We then describe several extensions to a state-of-the-art topic modelling approach for identifying new word-senses. This adapted method shows superior performance on our dataset of two different corpus pairs to that of the original method for both: (a) types having taken on a novel sense over time; and (b) the token instances of such novel senses.

Question: What is the largest corpus-based dataset of diachronic sense differences to date?


Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test. This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different agent decision controllers. Accordingly, the effectiveness of various algorithms is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors influencing the collective performance of these systems.

Question: What is the anytime intelligence test?


Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement. In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of "one size fits all" and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user's search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of "one size fit all" and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.

Question: How to design a searchable encryption scheme that supports personalized search and improves user search experience?


High throughput heavy hitter aggregation for modern SIMD processors. Heavy hitters are data items that occur at high frequency in a data set. They are among the most important items for an organization to summarize and understand during analytical processing. In data sets with sufficient skew, the number of heavy hitters can be relatively small. We take advantage of this small footprint to compute aggregate functions for the heavy hitters in fast cache memory in a single pass.  We design cache-resident, shared-nothing structures that hold only the most frequent elements. Our algorithm works in three phases. It first samples and picks heavy hitter candidates. It then builds a hash table and computes the exact aggregates of these elements. Finally, a validation step identifies the true heavy hitters from among the candidates.  We identify trade-offs between the hash table configuration and performance. Configurations consist of the probing algorithm and the table capacity that determines how many candidates can be aggregated. The probing algorithm can be perfect hashing, cuckoo hashing and bucketized hashing to explore trade-offs between size and speed.  We optimize performance by the use of SIMD instructions, utilized in novel ways beyond single vectorized operations, to minimize cache accesses and the instruction footprint.

Question: What is a heavy hitter?


How Small Labels Create Big Improvements. It is widely believed that identifying communities in an ad hoc mobile communications system, such as a pocket switched network, can reduce the amount of traffic created when forwarding messages, but there has not been any empirical evidence available to support this assumption to date. In this paper, we show through use of real experimental human mobility data, how using a small label, identifying users according to their affiliation, can bring a large improvement in forwarding performance, in term of both delivery ratio and cost.

Question: How small labels create big improvements


Increased prefrontal and parietal activity after training of working memory. Working memory capacity has traditionally been thought to be constant. Recent studies, however, suggest that working memory can be improved by training. In this study, we have investigated the changes in brain activity that are induced by working memory training. Two experiments were carried out in which healthy, adult human subjects practiced working memory tasks for 5 weeks. Brain activity was measured with functional magnetic resonance imaging (fMRI) before, during and after training. After training, brain activity that was related to working memory increased in the middle frontal gyrus and superior and inferior parietal cortices. The changes in cortical activity could be evidence of training-induced plasticity in the neural systems that underlie working memory.

Question: What is the middle frontal gyrus?


Designing an Interactive Messaging and Reminder Display for Elderly. Despite the wealth of information and communicatio n technology in society today, there appears to be a lack of accept able information services for the growing elderly population in need of care. Acc eptability is not only related to human factors such as button size and legibility , but rather relates to perceived value and harmony in relation to existing l ving patterns. This paper describes the design of an asynchronous interactive ommunication system based upon a bulletin board metaphor. A panel of en d-users was involved in various stages of the design process. To improve ea s of use, functionality exposed to elderly users is limited, while caregive rs are given extended control. A pilot field study with a working prototype showed a high degree of user acceptance. The user centered approach resulted in a design concept that was acceptable for the elderly participants.

Question: How do you design an interactive messaging and reminder display for elderly?


Exploring the Influence of Online Consumers ' Perception on Purchase Intention as Exemplified with an Online Bookstore. The purpose of this study is to use structural equation modeling (SEM) to explore the influence of online bookstore consumers' perception on their purchase intention. Through literature review, four constructs were used to establish a causal relationship between perception of online shopping and consumers' purchase intention. Questionnaires based on these four constructs were designed and distributed to the customers of an online bookstore; AMOS software as analytical tool was used to build and confirm a SEM model. Results of this study show that product perception, shopping experience, and service quality have positive and significant influence on consumers' purchase intention, but perceived risk has negative influence on consumers' purchase intention, and shopping experience is most important.

Question: What is the purpose of this study?


How To Exchange Secrets with Oblivious Transfer. As this paper puts forward the notion of "Oblivious Transfers" and is a well-known and frequently cited paper, I felt I should typeset the manuscript, and here is the result. While typesetting, I tried to stick to the original manuscript as much as possible. However, there were some cases, such as a few typos or punctuation marks, which were changed. As in many papers on cryptography, Alice and Bob play the role of participants of the given cryptographic protocols. For the sake of readability, Alice's and Bob's messages were printed in red and blue ink, respectively. This work was carefully proofread by my colleague Y. Sobhdel (sobhdel@ce.sharif.edu). Thanks also to H. M. Moghaddam for mentioning a minor mistake in an earlier version. That said, I will be thankful if you inform me of any possible mistakes.

Question: How to exchange secrets with oblivious transfer


Validation of the Ten-Item Internet Gaming Disorder Test (IGDT-10) and evaluation of the nine DSM-5 Internet Gaming Disorder criteria.. INTRODUCTION The inclusion of Internet Gaming Disorder (IGD) in the DSM-5 (Section 3) has given rise to much scholarly debate regarding the proposed criteria and their operationalization. The present study's aim was threefold: to (i) develop and validate a brief psychometric instrument (Ten-Item Internet Gaming Disorder Test; IGDT-10) to assess IGD using definitions suggested in DSM-5, (ii) contribute to ongoing debate regards the usefulness and validity of each of the nine IGD criteria (using Item Response Theory [IRT]), and (iii) investigate the cut-off threshold suggested in the DSM-5.   METHODS An online gamer sample of 4887 gamers (age range 14-64years, mean age 22.2years [SD=6.4], 92.5% male) was collected through Facebook and a gaming-related website with the cooperation of a popular Hungarian gaming magazine. A shopping voucher of approx. 300 Euros was drawn between participants to boost participation (i.e., lottery incentive). Confirmatory factor analysis and a structural regression model were used to test the psychometric properties of the IGDT-10 and IRT analysis was conducted to test the measurement performance of the nine IGD criteria. Finally, Latent Class Analysis along with sensitivity and specificity analysis were used to investigate the cut-off threshold proposed in the DSM-5.   RESULTS Analysis supported IGDT-10's validity, reliability, and suitability to be used in future research. Findings of the IRT analysis suggest IGD is manifested through a different set of symptoms depending on the level of severity of the disorder. More specifically, "continuation", "preoccupation", "negative consequences" and "escape" were associated with lower severity of IGD, while "tolerance", "loss of control", "giving up other activities" and "deception" criteria were associated with more severe levels. "Preoccupation" and "escape" p

Question: What is the cut-off threshold suggested in the DSM-5?


Improved algorithm for mining maximum frequent patterns based on FP-Tree. Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space. Keywordsdata mining;association rule;maximum frequent pattern;FP-Tree

Question: What is the name of the algorithm?


Education , politics and opinions about climate change evidence for interaction effects. U.S. public opinion regarding climate change has become increasingly polarized in recent years, as partisan think tanks and others worked to recast an originally scientific topic into a political wedge issue. Nominally "scientific" arguments against taking anthropogenic climate change seriously have been publicized to reach informed but ideologically receptive audiences. Reflecting the success of such arguments, polls have noted that concern about climate change increased with education among Democrats, but decreased with education among Republicans. These observations lead to the hypothesis that there exist interaction (non-additive) effects between education or knowledge and political orientation, net of other background factors, in predicting public concern about climate change. Two regional telephone surveys, conducted in New Hampshire (n = 541) and Michigan (n = 1, 008) in 2008, included identical climate-change questions that provide opportunities to test this hypothesis. Multivariate analysis of both surveys finds significant interactions. These empirical results fit with theoretical interpretations and several other recent studies. They suggest that the classically identified social bases of concern about the environment in general, and climate in particular, have shifted in recent years. Narrowcast media, including the many Web sites devoted to discrediting climatechange concerns, provide ideal conduits for channeling contrarian arguments to an audience predisposed to believe and electronically spread them further. Activeresponse Web sites by climate scientists could prove critical to counterbalancing contrarian arguments. L. C. Hamilton (B) Sociology Department, University of New Hampshire, Durham, NH, 03824, USA e-mail: Lawrence.Hamilton@unh.edu

Question: What is the effect of education on political orientation?


Cloud forensics: A research perspective. Cloud computing and digital forensics are both developing topics and researching these topics requires an understanding of the main aspects of both cloud computing and digital forensics. In cloud computing it is necessary not only to understand its characteristics and the different services and deployment models but also to survey the underpinning elements of cloud computing such as virtualization and the distributed computing which are important to identify its impact on current digital forensics guidelines and procedures. Unlike papers discussing the challenges and opportunities presented by cloud computing in relation to digital forensics, in this paper, we will discuss the underpinning cloud computing elements which are required to provide forensics friendly cloud services. Furthermore, we suggest a set of questions that will aid in the process of cloud forensics analysis.

Question: What is cloud computing?


Design of broadband circularly polarized horn antenna using an L-shaped probe. This paper deals with a circular horn antenna fed by an L-shaped probe. The design process for broadband matching to a 50 Omega coaxial cable, and the antenna performance in axial ratio and gain are presented. The simulation results of this paper were obtained using Ansoft HFSS 9.2

Question: What is the design of a broadband circularly polarized horn antenna?


An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.

Question: What is the difference between adaptive streaming and adaptive bitrate streaming?


Modeling and Control of a Flux-Modulated Compound-Structure Permanent-Magnet Synchronous Machine for Hybrid Electric Vehicles. The compound-structure permanent-magnet synchronous machine (CS-PMSM), comprising a double rotor machine (DRM) and a permanent-magnet (PM) motor, is a promising electronic-continuously variable transmission (e-CVT) concept for hybrid electric vehicles (HEVs). By CS-PMSM, independent speed and torque control of the vehicle engine is realized without a planetary gear unit. However, the slip rings and brushes of the conventional CS-PMSM are considered a major drawback for vehicle application. In this paper, a brushless flux-modulated CS-PMSM is investigated. The operating principle and basic working modes of the CS-PMSM are discussed. Mathematical models of the CS-PMSM system are given, and joint control of the two integrated machines is proposed. As one rotor of the DRM is mechanically connected with the rotor of the PM motor, special rotor position detection and torque allocation methods are required. Simulation is carried out by Matlab/Simulink, and the feasibility of the control system is proven. Considering the complexity of the controller, a single digital signal processor (DSP) is used to perform the interconnected control of dual machines instead of two separate ones, and a typical hardware implementation is proposed.

Question: How to control a brushless flux-modulated compound-structure permanent-magnet synchronous machine for hybrid electric vehicles?


An empirical study of the mechanisms of mindfulness in a mindfulness-based stress reduction program.. S. L. Shapiro and colleagues (2006) have described a testable theory of the mechanisms of mindfulness and how it affects positive change. They describe a model in which mindfulness training leads to a fundamental change in relationship to experience (reperceiving), which leads to changes in self-regulation, values clarification, cognitive and behavioral flexibility, and exposure. These four variables, in turn, result in salutogenic outcomes. Analyses of responses from participants in a mindfulness-based stress-reduction program did not support the mediating effect of changes in reperceiving on the relationship of mindfulness with those four variables. However, when mindfulness and reperceiving scores were combined, partial support was found for the mediating effect of the four variables on measures of psychological distress. Issues arising in attempts to test the proposed theory are discussed, including the description of the model variables and the challenges to their assessment.

Question: What is a salutogenic outcome?


Bayesian Unsupervised Topic Segmentation. This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian framework.1

Question: What is the Bayesian approach to unsupervised topic segmentation?