Bilevel Model-Based Discriminative Dictionary Learning for Recognition. Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the  $\ell _{0}$  or  $\ell _{1}$  norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn–Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

Question: What is the bilevel model-based discriminative dictionary learning method for recognition?


Merging thermal and visual images by a contrast pyramid. J. M. Valeton I nstitute for Perception TNO Kampweg 5 3769 DE Soesterberg, The Netherlands Abstract. Integration of images from different sensing modalities can produce information that cannot be obtained by viewing the sensor outputs separately and consecutively. This paper introduces a hierarchical i mage merging scheme based on a multiresolution contrast decomposition (the ratio of a low-pass pyramid). The composite images produced by this scheme preserve those details from the input images that are most relevant to visual perception. The method is tested by merging parallel registered thermal and visual images. The results show that the fused images present a more detailed representation of the depicted scene. Detection, recognition, and search tasks may therefore benefit from this new image representation.

Question: How to merge thermal and visual images?


Concept Decompositions for Large Sparse Text Data Using Clustering. Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors–a few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical k-means algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain "fractal-like" and "self-similar" behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectors; these decompositions are obtained by taking the least-squares approximation onto the linear subspace spanned by all the concept vectors. We empirically establish that the approximation errors of the concept decompositions are close to the best possible, namely, to truncated singular value decompositions. As our third contribution, we show that the concept vectors are localized in the word space, are sparse, and tend towards orthonormality. In contrast, the singular vectors are global in the word space and are dense. Nonetheless, we observe the surprising fact that the linear subspaces spanned by the concept vectors and the leading singular vectors are quite close in the sense of small principal angles between them. In conclusion, the concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized "basis" for text data sets.

Question: What is the difference between a singular vector and a concept vector?


Using the WordNet Ontology in the GeoCLEF Geographical Information Retrieval Task. This paper describes how we managed to use the WordNet ontology for the GeoCLEF 2005 English monolingual task. Both a query expansion method, based on the expansion of geographical terms by means of WordNet synonyms and meronyms, and a method based on the expansion of

Question: How to use the WordNet ontology in the GeoCLEF Geographical Information Retrieval Task.


Statistical and Machine Learning approach in forex prediction based on empirical data. This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals.

Question: What is the difference between statistical and machine learning?


Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. We investigate the problem of generating fast approximate a nswers to queries posed to large sparse binary data sets. We focus in particular on probabilistic mode l-based approaches to this problem and develop a number of techniques that are significantly more accurate t han a baseline independence model. In particular, we introduce two techniques for building probabil ist c models from frequent itemsets: the itemset maximum entropy method, and the itemset inclusion-exclusi on model. In the maximum entropy method we treat itemsets as constraints on the distribution of the q uery variables and use the maximum entropy principle to build a joint probability model for the query at tributes online. In the inclusion-exclusion model itemsets and their frequencies are stored in a data structur e alled an ADtree that supports an efficient implementation of the inclusion-exclusion principle in orde r to answer the query. We empirically compare these two itemset-based models to direct querying of the ori ginal data, querying of samples of the original data, as well as other probabilistic models such as the indep endence model, the Chow-Liu tree model, and the Bernoulli mixture model. These models are able to handle high-dimensionality (hundreds or thousands of attributes), whereas most other work on this topic has foc used on relatively low-dimensional OLAP problems. Experimental results on both simulated and realwor d transaction data sets illustrate various fundamental tradeoffs between approximation error, model complexity, and the online time required to compute a query answer.

Question: What is the maximum entropy model?


DARPA's explainable artificial intelligence (XAI) program. The DARPA's Explainable Artificial Intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. This talk will summarize the XAI program and present highlights from these Phase 1 evaluations.

Question: What is the DARPA's explainable artificial intelligence (XAI) program?


Simplified markov random fields for efficient semantic labeling of 3D point clouds. In this paper, we focus on 3D point cloud classification by assigning semantic labels to each point in the scene. We propose to use simplified Markov networks to model the contextual relations between points, where the node potentials are calculated from point-wise classification results using off-the-shelf classifiers, such as Random Forest and Support Vector Machines, and the edge potentials are set by physical distance between points. Our experimental results show that this approach yields comparable if not better results with improved speed compared with state-of-the-art methods. We also propose a novel robust neighborhood filtering method to exclude outliers in the neighborhood of points, in order to reduce noise in local geometric statistics when extracting features and also to reduce number of false edges when constructing Markov networks. We show that applying robust neighborhood filtering improves the results when classifying point clouds with more object categories.

Question: What is the size of the canadian military?


Mechanism of collapse of tall steel moment frame buildings under earthquake excitation. The mechanism of collapse of tall steel moment frame buildings is explored through three-dimensional nonlinear analyses of two 18-story steel moment frame buildings under earthquake excitation. Both fracture-susceptible as well as perfect-connection conditions are investigated. Classical energy balance analysis shows that only longperiod excitation imparts energy to tall buildings large enough to cause collapse. Under such long-period motion, the shear beam analogy alludes to the existence of a characteristic mechanism of collapse or a few preferred mechanisms of collapse for these buildings. Numerical evidence from parametric analyses of the buildings under a suite of idealized sawtooth-like ground motion time histories, with varying period (T ), amplitude (peak ground velocity, PGV ), and duration (number of cycles, N ), is presented to support this hypothesis. Damage localizes to form a "quasi-shear" band over a few stories. When the band is destabilized, sidesway collapse is initiated and gravity takes over. Only one to five collapse mechanisms occur out of a possible 153 mechanisms in either principal direction of the buildings considered. Where two or more preferred mechanisms do exist, they have significant story-overlap, typically separated by just one story. It is shown that a simple work-energy relation applied to all possible quasi-shear bands, combined with plastic analysis principles can systematically identify all the preferred collapse mechanisms.

Question: What is the mechanism of collapse of tall steel moment frame buildings under earthquake excitation?


Learning under Feature Drifts in Textual Streams. Huge amounts of textual streams are generated nowadays, especially in social networks like Twitter and Facebook. As the discussion topics and user opinions on those topics change drastically with time, those streams undergo changes in data distribution, leading to changes in the concept to be learned, a phenomenon called concept drift. One particular type of drift, that has not yet attracted a lot of attention is feature drift, i.e., changes in the features that are relevant for the learning task at hand. In this work, we propose an approach for handling feature drifts in textual streams. Our approach integrates i) an ensemble-based mechanism to accurately predict the feature/word values for the next time-point by taking into account the different features might be subject to different temporal trends and ii) a sketch-based feature space maintenance mechanism that allows for a memory-bounded maintenance of the feature space over the stream. Experiments with textual streams from the sentiment analysis, email preference and spam detection demonstrate that our approach achieves significantly better or competitive performance compared to baselines.

Question: How to handle feature drift in textual streams?


Entity Matching in Online Social Networks. In recent years, Online Social Networks (OSNs) have essentially become an integral part of our daily lives. There are hundreds of OSNs, each with its own focus and offers for particular services and functionalities. To take advantage of the full range of services and functionalities that OSNs offer, users often create several accounts on various OSNs using the same or different personal information. Retrieving all available data about an individual from several OSNs and merging it into one profile can be useful for many purposes. In this paper, we present a method for solving the Entity Resolution (ER), problem for matching user profiles across multiple OSNs. Our algorithm is able to match two user profiles from two different OSNs based on machine learning techniques, which uses features extracted from each one of the user profiles. Using supervised learning techniques and extracted features, we constructed different classifiers, which were then trained and used to rank the probability that two user profiles from two different OSNs belong to the same individual. These classifiers utilized 27 features of mainly three types: name based features (i.e., the Soundex value of two names), general user info based features (i.e., the cosine similarity between two user profiles), and social network topological based features (i.e., the number of mutual friends between two users' friends list). This experimental study uses real-life data collected from two popular OSNs, Facebook and Xing. The proposed algorithm was evaluated and its classification performance measured by AUC was 0.982 in identifying user profiles across two OSNs.

Question: Entity Matching in Online Social Networks


Estimation of human body shape and cloth field in front of a kinect. This paper describes an easy-to-use system to estimate the shape of a human body and his/her clothes. The system uses a Kinect to capture the human's RGB and depth information from different views. Using the depth data, a non-rigid deformation method is devised to compensate motions between different views, thus to align and complete the dressed shape. Given the reconstructed dressed shape, the skin regions are recognized by a skin classifier from the RGB images, and these skin regions are taken as a tight constraints for the body estimation. Subsequently, the body shape is estimated from the skin regions of the dressed shape by leveraging a statistical model of human body. After the body estimation, the body shape is non-rigidly deformed to fit the dressed shape, so as to extract the cloth field of the dressed shape. We demonstrate our system and the therein algorithms by several experiments. The results show the effectiveness of the proposed method. & 2014 Elsevier B.V. All rights reserved.

Question: How to estimate the human body shape and cloth field in front of a kinect?


Digital array radar: Technology and trends. This paper presents the architecture of digital array radar and analysis the key technologies, digital T/R modules, waveform generation and amplitude-phase control module based on DDS, frequency up/down convert, high efficiency power amplifier, hybrid digital/microwave multilayer circuit and high performance computing are described as the main technologies. The correlation between the microsystems technologies and digital array architectures trends is also discussed.

Question: What is the architecture of digital array radar?


Measured Gene-Environment Interactions in Psychopathology: Concepts, Research Strategies, and Implications for Research, Intervention, and Public Understanding of Genetics.. There is much curiosity about interactions between genes and environmental risk factors for psychopathology, but this interest is accompanied by uncertainty. This article aims to address this uncertainty. First, we explain what is and is not meant by gene-environment interaction. Second, we discuss reasons why such interactions were thought to be rare in psychopathology, and argue instead that they ought to be common. Third, we summarize emerging evidence about gene-environment interactions in mental disorders. Fourth, we argue that research on gene-environment interactions should be hypothesis driven, and we put forward strategies to guide future studies. Fifth, we describe potential benefits of studying measured gene-environment interactions for basic neuroscience, gene hunting, intervention, and public understanding of genetics. We suggest that information about nurture might be harnessed to make new discoveries about the nature of psychopathology.

Question: What is the difference between gene-environment interaction and gene-environment correlation?


Distributed Compressed Sensing off the Grid. This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery.

Question: What is the difference between atomic norm and concatenated atomic norm?


Compact Planar Microstrip Branch-Line Couplers Using the Quasi-Lumped Elements Approach With Nonsymmetrical and Symmetrical T-Shaped Structure. A class of the novel compact-size branch-line couplers using the quasi-lumped elements approach with symmetrical or nonsymmetrical T-shaped structures is proposed in this paper. The design equations have been derived, and two circuits using the quasi-lumped elements approach were realized for physical measurements. This novel design occupies only 29% of the area of the conventional approach at 2.4 GHz. In addition, a third circuit was designed by using the same formula implementing a symmetrical T-shaped structure and occupied both the internal and external area of the coupler. This coupler achieved 500-MHz bandwidth while the phase difference between S21 and S31 is within 90degplusmn1deg. Thus, the bandwidth is not only 25% wider than that of the conventional coupler, but occupies only 70% of the circuit area compared to the conventional design. All three proposed couplers can be implemented by using the standard printed-circuit-board etching processes without any implementation of lumped elements, bonding wires, and via-holes, making it very useful for wireless communication systems

Question: What is the bandwidth of the coupler?


I know what you did on your smartphone: Inferring app usage over encrypted data traffic. Smartphones and tablets are now ubiquitous in many people's lives and are used throughout the day in many public places. They are often connected to a wireless local area network (IEEE 802.11 WLANs) and rely on encryption protocols to maintain their security and privacy. In this paper, we show that even in presence of encryption, an attacker without access to encryption keys is able to determine the users' behavior, in particular, their app usage. We perform this attack using packet-level traffic analysis in which we use side-channel information leaks to identify specific patterns in packets regardless of whether they are encrypted or not. We show that just by collecting and analyzing small amounts of wireless traffic, one can determine what apps each individual smartphone user in the vicinity is using. Furthermore, and more worrying, we show that by using these apps the privacy of the user is more at risk compared to using online services through browsers on mobile devices. This is due to the fact that apps generate more identifiable traffic patterns. Using random forests to classify the apps we show that we are able to identify individual apps, even in presence of noise, with great accuracy. Given that most online services now provide native apps that may be identified by this method, these attacks represent a serious threat to users' privacy.

Question: What apps are used by the canadian military?


Genetic Programming + Unfolding Embryology in Automated Layout Planning. ABSTRACT Automated layout planning aims to the implementation of computational methods for the generation and the optimization of floor plans, considering the spatial configuration and the assignment of activities. Sophisticated strategies such as Genetic Algorithms have been implemented as heuristics of good solutions. However, the generative forces that derive from the social structures have been often neglected. This research aims to illustrate that the data that encode the layout's social and cultural generative forces, can be implemented within an evolutionary system for the design of residential layouts. For that purpose a co-operative system was created, which is composed of a Genetic Programming algorithm and an agent-based unfolding embryology procedure that assigns activities to the spaces generated by the GP algorithm. The assignment of activities is a recursive process which follows instructions encoded as permeability graphs. Furthermore, the Ranking Sum Fitness evaluation method is proposed and applied for the achievement of multiobjective optimization. Its efficiency is tested against the Weighted-Sum Fitness function. The system's results, both numerical and spatial, are compared to the results of a conventional evolutionary approach. This comparison showed that, in general, the proposed system can yield better solutions.

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


Locality Sensitive Hashing based deepmatching for optical flow estimation. DeepMatching (DM) is one of the state-of-art matching algorithms to compute quasi-dense correspondences between images. Recent optical flow methods use DeepMatching to find initial image correspondences and achieves outstanding performance. However, the key building block of DeepMatching, the correlation map computation, is time-consuming. In this paper, we propose a new algorithm, LSHDM, which addresses the problem by employing Locality Sensitive Hashing (LSH) to DeepMatching. The computational complexity is greatly reduced for the correlation map computation step. Experiments show that image matching can be accelerated by our approach in ten times or more compared to DeepMatching, while retaining comparable accuracy for optical flow estimation.

Question: How to accelerate optical flow estimation?


The Media ' s Influence on Body Image Disturbance and Eating Disorders : We ' ve Reviled Them , Now Can We Rehabilitate Them ?. Survey, correlational, randomized control, and covariance structure modeling investigations indicate that the media are a significant factor in the development and maintenance of eating and shape-related disorders. One specific individual difference variable, intemalization of societal pressures regarding prevailing standards of attractiveness, appears to moderate or even mediate the media's effects on women's body satisfaction and eating dysfunction. Problematic media messages inherent in existing media portrayals of eating disorders are apparent, leading researchers to pinpoint intervention strategies that might counteract such viewpoints. Social activism and social marketing approaches are suggested as methods for fighting negative media messages. The media itself is one potential vehicle for communicating productive, accurate, and deglamorized messages about eating and shape-related disorders.

Question: What is the media's influence on body image disturbance and eating disorders?


CTR prediction for contextual advertising: learning-to-rank approach. Contextual advertising is a textual advertising displayed within the content of a generic web page. Predicting the probability that users will click on ads plays a crucial role in contextual advertising because it influences ranking, filtering, placement, and pricing of ads. In this paper, we introduce a click-through rate prediction algorithm based on the learning-to-rank approach. Focusing on the fact that some of the past click data are noisy and ads are ranked as lists, we build a ranking model by using partial click logs and then a regression model on it. We evaluated this approach offline on a data set based on logs from an ad network. Our method is observed to achieve better results than other baselines in our three metrics.

Question: What is contextual advertising?


ESUR prostate MR guidelines 2012. The aim was to develop clinical guidelines for multi-parametric MRI of the prostate by a group of prostate MRI experts from the European Society of Urogenital Radiology (ESUR), based on literature evidence and consensus expert opinion. True evidence-based guidelines could not be formulated, but a compromise, reflected by "minimal" and "optimal" requirements has been made. The scope of these ESUR guidelines is to promulgate high quality MRI in acquisition and evaluation with the correct indications for prostate cancer across the whole of Europe and eventually outside Europe. The guidelines for the optimal technique and three protocols for "detection", "staging" and "node and bone" are presented. The use of endorectal coil vs. pelvic phased array coil and 1.5 vs. 3 T is discussed. Clinical indications and a PI-RADS classification for structured reporting are presented. Key Points • This report provides guidelines for magnetic resonance imaging (MRI) in prostate cancer. • Clinical indications, and minimal and optimal imaging acquisition protocols are provided. • A structured reporting system (PI-RADS) is described.

Question: What is the difference between a minimal and an optimal protocol?


A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences. In this paper, we propose a sequential neural encoder with latent structured description SNELSD for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory LSTM units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.

Question: What is a sequential neural encoder with latent structured description?


The Earth Mover's Distance as a Metric for Image Retrieval. We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.

Question: What is the earth mover distance?


Twitter Geolocation and Regional Classification via Sparse Coding. Experimental Methodology ! Three types of embedding schemes (binary bagof-words, word-counts, word-sequence) ! For a fair comparison, we follow the identical experimental methodology as Eisenstein et al. (2010) ! Twitter's location service enables users to add location information to their Tweets ! Location information of message is useful (e.g., consumer marketing) Ø However, < 3% of messages are geotagged† ! Machine learning can geolocate based on message content Ø Best results are from supervised modelbased approaches ! We focus on unsupervised data-driven approach, namely sparse coding, to take advantage of abundance of unlabeled messages to geolocate Twitter

Question: What is the best way to geolocate twitter messages?


Design and Implementation of a Fast Dynamic Packet Filter. This paper presents Swift, a packet filter for high-performance packet capture on commercial off-the-shelf hardware. The key features of the Swift include: 1) extremely lowfilter update latency for dynamic packet filtering, and 2) gigabits-per-second high-speed packet processing. Based on complex instruction set computer (CISC) instruction set architecture (ISA), Swift achieves the former with an instruction set design that avoids the need for compilation and security checking, and the latter by mainly utilizing single instruction, multiple data (SIMD). We implement Swift in the Linux 2.6 kernel for both i386 and ×86-64 architectures and extensively evaluate its dynamic and static filtering performance on multiple machines with different hardware setups. We compare Swift to BPF (the BSD packet filter)--the de facto standard for packet filtering in modern operating systems--and hand-coded optimized C filters that are used for demonstrating possible performance gains. For dynamic filtering tasks, Swift is at least three orders of magnitude faster than BPF in terms of filter update latency. For static filtering tasks, Swift outperforms BPF up to three times in terms of packet processing speed and achieves much closer performance to the optimized C filters. We also show that Swift can harness the processing power of hardware SIMD instructions by virtue of its SIMD-capable instruction set.

Question: What is the difference between a packet filter and a packet analyzer?


One Model To Learn Them All. Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.

Question: What is a single model that yields good results on a number of problems spanning multiple domains?


Angry, disgusted, or afraid? Studies on the malleability of emotion perception.. Current theories of emotion perception posit that basic facial expressions signal categorically discrete emotions or affective dimensions of valence and arousal. In both cases, the information is thought to be directly "read out" from the face in a way that is largely immune to context. In contrast, the three studies reported here demonstrated that identical facial configurations convey strikingly different emotions and dimensional values depending on the affective context in which they are embedded. This effect is modulated by the similarity between the target facial expression and the facial expression typically associated with the context. Moreover, by monitoring eye movements, we demonstrated that characteristic fixation patterns previously thought to be determined solely by the facial expression are systematically modulated by emotional context already at very early stages of visual processing, even by the first time the face is fixated. Our results indicate that the perception of basic facial expressions is not context invariant and can be categorically altered by context at early perceptual levels.

Question: How can we change the perception of facial expressions?


Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.

Question: What is the fisher linear discriminant model?


OPC Unified Architecture: A Service-oriented Architecture for Smart Grids. In this paper, the OPC UA is introduced as a key technology for realizing a variety of Smart Grid use cases enabling relevant tasks of automation and control. OPC UA is the successor of the established Classic OPC specification and state of the art regarding information exchange in the industrial automation branch. One of its key improvements over the Classic OPC is that the area of application is no longer restricted to industrial automation but OPC UA can be applied almost in every domain facing challenges in automated control. This improvement stems from a more generic and object-oriented approach. For the adoption of OPC UA to Smart Grids, two of the most important data models -- the Common Information Model (CIM) and the IEC 61850 -- have been identified to be integrated into OPC UA communication. In this contribution, basic OPC UA features and functionalities (information modeling, communication services, and information security) are introduced and discussed in the context of Smart Grids.

Question: What is the common information model?


Design procedure for two-stage CMOS operational amplifiers employing current buffer. The design procedure of the two-stage CMOS operational amplifiers employing Miller capacitor in conjunction with the common-gate current buffer is presented. Unlike the previously reported design strategy of the opamp of this type, which results in the opamp with a pair of nondominant complex conjugate poles and a finite zero, the proposed procedure is based upon the design strategy that results in the opamp with only one dominant pole. Design example of the proposed procedure is given.

Question: How to design a CMOS operational amplifier?


TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data. For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.

Question: How to track clusters in dynamic text data?


On the limited memory BFGS method for large scale optimization. We study the numerical performance of a limited memory quasi Newton method for large scale optimization which we call the L BFGS method We compare its performance with that of the method developed by Buckley and LeNir which combines cyles of BFGS steps and conjugate direction steps Our numerical tests indicate that the L BFGS method is faster than the method of Buckley and LeNir and is better able to use additional storage to accelerate convergence We show that the L BFGS method can be greatly accelerated by means of a simple scaling We then compare the L BFGSmethod with the partitioned quasi Newton method of Griewank and Toint a The results show that for some problems the partitioned quasi Newton method is clearly superior to the L BFGS method However we nd that for other problems the L BFGS method is very competitive due to its low iteration cost We also study the convergence properties of the L BFGS method and prove global convergence on uniformly convex problems

Question: What is the L BFGS method?


Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students: A Cross-Sectional Designed Study. BACKGROUND AND AIMS Internet addiction (IA) could be a major concern in university medical students aiming to develop into health professionals. The implications of this addiction as well as its association with sleep, mood disorders and self-esteem can hinder their studies, impact their long-term career goals and have wide and detrimental consequences for society as a whole. The objectives of this study were to: 1) Assess potential IA in university medical students, as well as factors associated with it; 2) Assess the relationships between potential IA, insomnia, depression, anxiety, stress and self-esteem.   METHODS Our study was a cross-sectional questionnaire-based survey conducted among 600 students of three faculties: medicine, dentistry and pharmacy at Saint-Joseph University. Four validated and reliable questionnaires were used: the Young Internet Addiction Test, the Insomnia Severity Index, the Depression Anxiety Stress Scales (DASS 21), and the Rosenberg Self Esteem Scale (RSES).   RESULTS The average YIAT score was 30 ± 18.474; Potential IA prevalence rate was 16.8% (95% confidence interval: 13.81-19.79%) and it was significantly different between males and females (p-value = 0.003), with a higher prevalence in males (23.6% versus 13.9%). Significant correlations were found between potential IA and insomnia, stress, anxiety, depression and self-esteem (p-value < 0.001); ISI and DASS sub-scores were higher and self-esteem lower in students with potential IA.   CONCLUSIONS Identifying students with potential IA is important because this addiction often coexists with other psychological problems. Therefore, interventions should include not only IA management but also associated psychosocial stressors such as insomnia, anxiety, depression, stress, and self-esteem.

Question: What is the prevalence of internet addiction in university students?


Airport capacity: representation, estimation, optimization.

Question: What is the capacity of an airport?


Video semantic object segmentation by self-adaptation of DCNN. This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods. c © 2016 Elsevier Ltd. All rights reserved.

Question: What is the name of the method?


BAP: A Binary Analysis Platform. BAP is a publicly available infrastructure for performing program verification and analysis tasks on binary (i.e., executable) code. In this paper, we describe BAP as well as lessons learned from previous incarnations of binary analysis platforms. BAP explicitly represents all side effects of instructions in an intermediate language (IL), making syntaxdirected analysis possible. We have used BAP to routinely generate and solve verification conditions that are hundreds of megabytes in size and encompass 100,000's of assembly instructions.

Question: What is a binary analysis platform?


Why did my car just do that ? Explaining semi-autonomous driving actions to improve driver understanding , trust , and performance. This study explores, in the context of semiautonomous driving, how the content of the verbalized message accompanying the car's autonomous action affects the driver's attitude and safety performance. Using a driving simulator with an auto-braking function, we tested different messages that provided advance explanation of the car's imminent autonomous action. Messages providing only "how" information describing actions (e.g., "The car is braking") led to poor driving performance, whereas "why" information describing reasoning for actions (e.g., "Obstacle ahead") was preferred by drivers and led to better driving performance. Providing both "how and why" resulted in the safest driving performance but increased negative feelings in drivers. These results suggest that, to increase overall safety, car makers need to attend not only to the design of autonomous actions but also to the right way to explain these actions to the drivers.

Question: How does the content of the verbalized message affect the driver's attitude and safety performance?


ADE: A Framework for Robust Complex Robotic Architectures. Robots that can interact naturally with humans require the integration and coordination of many different components with heavy computational demands. We argue that an architecture framework with facilities for dynamic, reliable, fault-recovering, remotely accessible, distributed computing is needed for the development and operation of applications that support and enhance human activities and capabilities. We describe a robotic architecture development system, called ADE, that is built on top of a multi-agent system in order to provide all of the above features. Specifically, we discuss support for autonomic computing in ADE, briefly comparing it to related features of other commonly used robotic systems. We also report our experiences with ADE in the development of an architecture for an intelligent robot assistant and provide experimental results demonstrating the system's utility

Question: What is an architecture framework for robust complex robotic architectures?


A Hierarchical Recurrent Neural Network for Symbolic Melody Generation. In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty for designing a good model. In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time. Specifically, the three subnetworks generate bar profiles, beat profiles and notes in turn, and the output of the high-level subnetworks are fed into the low-level subnetworks, serving as guidance for generating the finer time-scale melody components in low-level subnetworks. Two human behavior experiments demonstrate the advantage of this structure over the single-layer LSTM which attempts to learn all hidden structures in melodies. Compared with the state-of-the-art models MidiNet (Yang, Chou, and Yang 2017) and MusicVAE (Roberts et al. 2018), the hierarchical recurrent neural network produces better melodies evaluated by humans. Introduction Automatic music generation using neural networks has attracted much attention. There are two classes of music generation approaches, symbolic music generation (Hadjeres, Pachet, and Nielsen 2017)(Waite et al. 2016)(Yang, Chou, and Yang 2017) and audio music generation (van den Oord et al. )(Mehri et al. 2016). In this study, we focus on symbolic melody generation, which requires learning from sheet music. Many music genres such as pop music consist of melody and harmony. Since usually beautiful harmonies can be ensured by using legitimate chord progressions which have been summarized by musicians, we only focus on melody generation, similar to some recent studies (Waite et al. 2016)(Yang, Chou, and Yang 2017)(Colombo, Seeholzer, and Gerstner 2017)(Roberts et al. 2018). This greatly simplifies the melody generation problem. Melody is a linear succession of musical notes along time

Question: What is a melody?


Online Keyword Spotting with a Character-Level Recurrent Neural Network. In this paper, we propose a context-aware keyword spotting m odel employing a character-level recurrent neural network (RNN) for spoken term detection in co tinuous speech. The RNN is end-toend trained with connectionist temporal classification (CT C) to generate the probabilities of character and word-boundary labels. There is no need for the phonetic t ranscription, senone modeling, or system dictionary in training and testing. Also, keywords can easi ly be added and modified by editing the text based keyword list without retraining the RNN. Moreover, th e unidirectional RNN processes an infinitely long input audio streams without pre-segmentation and keyw ords are detected with low-latency before the utterance is finished. Experimental results show that th e proposed keyword spotter significantly outperforms the deep neural network (DNN) and hidden Markov m del (HMM) based keyword-filler model even with less computations.

Question: What is the difference between a character-level recurrent neural network and a deep neural network?


Support vector regression with modified firefly algorithm for stock price forecasting. The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.

Question: What is the difference between the firefly algorithm and the support vector regression?


Personalized machine learning for robot perception of affect and engagement in autism therapy. Robots have the potential to facilitate future therapies for children on the autism spectrum. However, existing robots are limited in their ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Their inference challenge is made even harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in children with autism, we used the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children's affective states and engagement during robot-assisted autism therapy. Instead of using the traditional one-size-fits-all ML approach, we personalized our framework to each child using their contextual information (demographics and behavioral assessment scores) and individual characteristics. We evaluated this framework on a multimodal (audio, video, and autonomic physiology) data set of 35 children (ages 3 to 13) with autism, from two cultures (Asia and Europe), and achieved an average agreement (intraclass correlation) of ~60% with human experts in the estimation of affect and engagement, also outperforming nonpersonalized ML solutions. These results demonstrate the feasibility of robot perception of affect and engagement in children with autism and have implications for the design of future autism therapies.

Question: How can we improve the robot's ability to perceive affect and engagement?


Distributed Multi-Robot Localization. This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.

Question: How many robots are in the group?


On approximately fair allocations of indivisible goods. We study the problem of fairly allocating a set of indivisible goods to a set of people from an algorithmic perspective. fair division has been a central topic in the economic literature and several concepts of fairness have been suggested. The criterion that we focus on is envy-freeness. In our model, a monotone utility function is associated with every player specifying the value of each subset of the goods for the player. An allocation is envy-free if every player prefers her own share than the share of any other player. When the goods are divisible, envy-free allocations always exist. In the presence of indivisibilities, we show that there exist allocations in which the envy is bounded by the maximum marginal utility, and present a simple algorithm for computing such allocations. We then look at the optimization problem of finding an allocation with minimum possible envy. In the general case the problem is not solvable or approximable in polynomial time unless P = NP. We consider natural special cases (e.g.additive utilities) which are closely related to a class of job scheduling problems. Approximation algorithms as well as inapproximability results are obtained. Finally we investigate the problem of designing truthful mechanisms for producing allocations with bounded envy.

Question: What is the difference between indivisible goods and divisible goods?


Designing Graph Database Models from Existing Relational Databases. In this paper, a method for transforming a relational database to a graph database model is described. In this approach, the dependency graphs for the entities in the system are transformed into star graphs. This star graph model is transformed into a hyper graph model for the relational database, which, in turn, can be used to develop the domain relationship model that can be converted in to a graph database model.

Question: How to transform a relational database to a graph database model?


Improving Social Inclusion Using NLP : Tools and Resources. This paper discusses the issue of accessibility to being online and using social media for people with a learning disability, and the challenges to using a co-production approach in an accessible technology project. While an increasing number of daily living tasks are now completed online, people with a learning disability frequently experience digital exclusion due to limited literacy and IT skills. The Able to Include project sought to engage people with a learning disability as active partners to test and feedback on the use and development of a pictogram app used to make social media more accessible. The challenges mainly related to the feedback needing to be sent electronically to the partners; there was only minimal contact with them and no face to face contact. The paper also outlines how other challenges were overcome to enable genuine and meaningful co-production. These included addressing online safety and ethical issues regarding anonymity.

Question: How to overcome the challenges of co-production?


On the effectiveness of address-space randomization. Address-space randomization is a technique used to fortify systems against buffer overflow attacks. The idea is to introduce artificial diversity by randomizing the memory location of certain system components. This mechanism is available for both Linux (via PaX ASLR) and OpenBSD. We study the effectiveness of address-space randomization and find that its utility on 32-bit architectures is limited by the number of bits available for address randomization. In particular, we demonstrate a <i>derandomization attack</i> that will convert any standard buffer-overflow exploit into an exploit that works against systems protected by address-space randomization. The resulting exploit is as effective as the original exploit, although it takes a little longer to compromise a target machine: on average 216 seconds to compromise Apache running on a Linux PaX ASLR system. The attack does not require running code on the stack.  We also explore various ways of strengthening address-space randomization and point out weaknesses in each. Surprisingly, increasing the frequency of re-randomizations adds at most 1 bit of security. Furthermore, compile-time randomization appears to be more effective than runtime randomization. We conclude that, on 32-bit architectures, the only benefit of PaX-like address-space randomization is a small slowdown in worm propagation speed. The cost of randomization is extra complexity in system support.

Question: How effective is address-space randomization?


Determinants of Successful Knowledge Management Programs. The main objective of this paper is to investigate and identify the main determinants of successful knowledge management (KM) programs. We draw upon the institutional theory and the theory of technology assimilation to develop an integrative model of KM success that clarifies the role of information technology (IT) in relation to other important KM infrastructural capabilities and to KM process capabilities. We argue that the role of IT cannot be studied in isolation and that the effect of IT on KM success is fully mediated by KM process capabilities. The research model is tested with a survey study involving 191 KM practitioners. The empirical results provided strong support for the model. In addition to its theoretical contributions, this study also presents important practical implications through the identification of specific infrastructural capabilities leading to KM success.

Question: What are the main determinants of successful knowledge management programs?


MULTISCALE ENTANGLEMENT RENORMALIZATION ANSATZ. The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERAlayers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.

Question: How to use tensor trains for neural networks?


Data-Confined HTML5 Applications. Rich client-side applications written in HTML5 proliferate diverse platforms such as mobile devices, commodity PCs, and the web platform. These client-side HTML5 applications are increasingly accessing sensitive data, including users' personal and social data, sensor data, and capability-bearing tokens. To fulfill their security and privacy guarantees, these applications need to maintain certain data-confinement invariants. These invariants are not explicitly stated in today's HTML5 applications and are enforced using ad-hoc mechanisms. The complexity of web applications, coupled with hard-to-analyze client-side languages, leads to low-assurance data-confinement mechanisms in which the whole application needs to be in the TCB to ensure data-confinement in-

Question: What is the complexity of web applications?


A General Lower Bound on the Number of Examples Needed for Learning. We prove a lower bound of ( ln + VCdim(C) ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and and are the accuracy and con dence parameters. This improves the previous best lower bound of ( ln + VCdim(C)), and comes close to the known general upper bound of O( ln + VCdim(C) ln ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor. A. Ehrenfeucht was supported by NSF grant MCS-8305245, D. Haussler by ONR grant N00014-86-K-0454, M. Kearns by ONR grant N00014-86-K-0454 and an A.T. & T. Bell Laboratories Scholarship, and L. Valiant by grants ONR-N00014-85-K-0445, NSF-DCR-8600379, DAAL03-86-K-0171 and by the SERC of the U.K. Part of this research was done while M. Kearns was visiting U.C. Santa Cruz.

Question: What is the lower bound on the number of examples needed for learning?


Attributive Concept Descriptions with Complements. Schmidt-SchaulL M. and G. Smolka, Attributive concept descriptions with complements, Artificial Intelligence 48 (1991) 1-26. We investigate the consequences of adding unions and complements to attributive concept descriptions employed in terminological knowledge representation languages. It is shown that deciding coherence and subsumption of such descriptions are PSPACE-complete problems that can be decided with linear space.

Question: What is the complexity of deciding coherence and subsumption of concept descriptions?


Does the Fragrance of Essential Oils Alleviate the Fatigue Induced by Exercise? A Biochemical Indicator Test in Rats. Objective To study the effect of the essential oils of Citrus sinensis L., Mentha piperita L., Syzygium aromaticum L., and Rosmarinus officinalis L. on physical exhaustion in rats.   Methods Forty-eight male Wistar rats were randomly divided into a control group, a fatigue group, an essential oil mixture (EOM) group, and a peppermint essential oil (PEO) group. Loaded swimming to exhaustion was used as the rat fatigue model. Two groups were nebulized with EOM and PEO after swimming, and the others were nebulized with distilled water. After continuous inhalation for 3 days, the swimming time, blood glucose, blood lactic acid (BLA), blood urea nitrogen (BUN), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX), and malondialdehyde (MDA) in blood were determined.   Results While an increased time to exhaustion and SOD activity were apparent in both the EOM and PEO groups, the BLA and MDA were lower in both groups, in comparison with the fatigue group, and the changes in the EOM group were more dramatic. Additionally, the EOM group also showed marked changes of the rise of blood glucose and the decrease of BUN and GSH-PX.   Conclusion The results suggested that the inhalation of an essential oil mixture could powerfully relieve exercise-induced fatigue.

Question: What is the effect of essential oils on fatigue?


Cooperative Inverse Reinforcement Learning. For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partialinformation game with two agents, human and robot; both are rewarded according to the human's reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.

Question: What is the difference between cooperative inverse reinforcement learning and inverse reinforcement learning?


Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review. Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.

Question: What is the difference between deep learning and machine learning?


Schedulability analysis of periodic fixed priority systems. Feasibility analysis of fixed priority systems has been widely studied in the real-time literature and several acceptance tests have been proposed to guarantee a set of periodic tasks. They can be divided in two main classes: polynomial time tests and exact tests. Polynomial time tests can efficiently be used for online guarantee of real-time applications, where tasks are activated at runtime. These tests introduce a negligible overhead, when executed upon a new task arrival, however provide only a sufficient schedulability condition, which may cause a poor processor utilization. On the other hand, exact tests, which are based on response time analysis, provide a necessary and sufficient schedulability condition, but are too complex to be executed on line for large task sets. As a consequence, for large task sets, they are often executed off line. This paper proposes a novel approach for analyzing the schedulability of periodic task sets on a single processor under an arbitrary fixed priority assignment: Using this approach, we derive a new schedulability test which can be tuned through a parameter to balance complexity versus acceptance ratio, so that it can be used on line to better exploit the processor, based on the available computational power. Extensive simulations show that our test, when used in its exact form, is significantly faster than the current response time analysis methods. Moreover the proposed approach, for its elegance and compactness, offers an explanation of some known phenomena of fixed priority scheduling and could be helpful for further work on schedulability analysis.

Question: What is the difference between a polynomial time test and an exact test?


Using Markov decision process in cognitive radio networks towards the optimal reward. The Learning is an indispensable phase in the cognition cycle of cognitive radio network. It corresponds between the executed actions and the estimated rewards. Based on this phase, the agent learns from past experiences to improve his actions in the next interventions. In the literature, there are several methods that treat the artificial learning. Among them, we cite the reinforcement learning that look for the optimal policy, for ensuring the maximum reward.  The present work exposes an approach, based on a model of reinforcement learning, namely Markov decision process, to maximize the sum of transfer rates of all secondary users. Such conception defines all notions relative to an environment with finite set of states, including: the agent, all states, the allowed actions with a given state, the obtained reward after the execution of an action and the optimal policy. After the implementation, we remark a correlation between the started policy and the optimal policy, and we improve the performances by referring to a previous work.

Question: What is the optimal policy?


Design of Kernels in Convolutional Neural Networks for Image Classification. Despite the effectiveness of convolutional neural networks (CNNs) for image classification, our understanding of the effect of shape of convolution kernels on learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define receptive fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we present a feature visualization method for visualization of pixel-wise classification score maps of learned features. Motivated by our experimental results, and observations reported in the literature for modeling of visual systems, we propose a novel design of shape of kernels for learning of representations in CNNs. In the experimental results, the proposed models achieved an outstanding performance in the classification task, comparing to a base CNN models that introduces more parameters and computational time. Additionally, we examined the region of interest (ROI) of different models in the classification task and analyzed the robustness of the proposed method to occluded images. Our results indicate the effectiveness of the proposed approach.

Question: What is the relationship between shape of kernels and image classification?


A SUMMARY REVIEW OF VIBRATION-BASED DAMAGE IDENTIFICATION METHODS. This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based vs. non-model-based methods and linear vs. nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.

Question: What is vibration-based damage identification?


Efficient region search for object detection. We propose a branch-and-cut strategy for efficient region-based object detection. Given an oversegmented image, our method determines the subset of spatially contiguous regions whose collective features will maximize a classifier's score. We formulate the objective as an instance of the prize-collecting Steiner tree problem, and show that for a family of additive classifiers this enables fast search for the optimal object region via a branch-and-cut algorithm. Unlike existing branch-and-bounddetection methods designed for bounding boxes, our approach allows scoring of irregular shapes — which is especially critical for objects that do not conform to a rectangular window. We provide results on three challenging object detection datasets, and demonstrate the advantage of rapidly seeking best-scoring regions rather than subwindow rectangles.

Question: What is the branch-and-cut strategy for efficient region-based object detection?


THE DEVELOPMENT OF THE BALANCED SCORECARD AS A STRATEGIC MANAGEMENT TOOL. The Balanced Scorecard is a widely adopted performance management framework first described in the early 1990s. More recently it has been proposed as the basis for a 'strategic management system'. This paper describes its evolution, recognising three distinct generations of Balanced Scorecard design. The paper relates the empirically driven developments in Balanced Scorecard thinking with literature concerning strategic management within organisations. It concludes that developments to date have been worthwhile, highlights potential areas for further refinement, and sets out some possible topics for future research into the field. The Balanced Scorecard and its development The Balanced Scorecard was first introduced in the early 1990s through the work of Robert Kaplan and David Norton of the Harvard Business School. Since then, the concept has become well known and its various forms widely adopted across the world (Rigby, 2001). By combining financial measures and non-financial measures in a single report, the Balanced Scorecard aims to provide managers with richer and more relevant information about activities they are managing than is provided by financial measures alone. To aid clarity and utility, Kaplan and Norton proposed that the number of measures on a Balanced Scorecard should also be constrained in number, and clustered into four groups (Kaplan and Norton, 1992, 1993). Beyond this, the original definition of Balanced Scorecard was sparse. But from the outset it was clear that the selection of measures, both in terms of filtering (organisations typically had access to many more measures than were needed to populate the Balanced Scorecard) and clustering (deciding which measures should appear in which perspectives) would be a key activity. Kaplan and Norton proposed that measure selection should focus on information relevant to the implementation of strategic plans, and that simple att

Question: What is the balanced scorecard?


i-JEN: Visual Interactive Malaysia Crime News Retrieval System. Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crimebased event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes.

Question: What is the purpose of the system?


Streaming End-to-end Speech Recognition For Mobile Devices. End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.

Question: What is the difference between a CTC and an RNN transducer?


Automatic emotional speech classification. Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The sequential forward selection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the cross-validated correct classification score of one of the following classifiers: nearest mean and Bayes classifier where class pdf are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% /spl plusmn/ 3% correct classification rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those two-class emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them.

Question: What is the purpose of the paper?


Adaptive figure-ground classification. We propose an adaptive figure-ground classification algorithm to automatically extract a foreground region using a user-provided bounding-box. The image is first over-segmented with an adaptive mean-shift algorithm, from which background and foreground priors are estimated. The remaining patches are iteratively assigned based on their distances to the priors, with the foreground prior being updated online. A large set of candidate segmentations are obtained by changing the initial foreground prior. The best candidate is determined by a score function that evaluates the segmentation quality. Rather than using a single distance function or score function, we generate multiple hypothesis segmentations from different combinations of distance measures and score functions. The final segmentation is then automatically obtained with a voting or weighted combination scheme from the multiple hypotheses. Experiments indicate that our method performs at or above the current state-of-the-art on several datasets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds. In addition, this improvement in accuracy is achieved with low computational cost.

Question: What is the adaptive mean-shift algorithm?


Realizing underwater communication through magnetic induction. The majority of the work on underwater communication has mainly been based on acoustic communication. Acoustic communication faces many known problems, such as high propagation delays, very low data rates, and highly environment-dependent channel behavior. In this article, to address these shortcomings, magnetic induction is introduced as a possible communication paradigm for underwater applications. Accordingly, all research challenges in this regard are explained. Fundamentally different from the conventional underwater communication paradigm, which relies on EM, acoustic, or optical waves, the underwater MI communications rely on the time varying magnetic field to covey information between the transmitting and receiving parties. MI-based underwater communications exhibit several unique and promising features such as negligible signal propagation delay, predictable and constant channel behavior, sufficiently long communication range with high bandwidth, as well as silent and stealth underwater operations. To fully utilize the promising features of underwater MI-based communications, this article introduces the fundamentals of underwater MI communications, including the MI channel models, MI networking protocols design, and MI-based underwater localization.

Question: What is the difference between acoustic and magnetic induction communication?


Use of physiological signals to predict cybersickness. http://dx.doi.org/10.1016/j.displa.2016.07.002 0141-9382/ 2016 Elsevier B.V. All rights reserved. q This paper was recommended for publication by Richard H.Y. So. ⇑ Corresponding author at: University of California, Irvine, 2201 Social & Behavioral Sciences Gateway Building, Irvine, CA 92697-5100, USA. E-mail addresses: mdenniso@uci.edu (M.S. Dennison), awisti@uci.edu (A.Z. Wisti), mdzmura@uci.edu (M. D'Zmura). Mark S. Dennison ⇑, A. Zachary Wisti, Michael D'Zmura

Question: What is the corresponding author?


A 600MS/s 30mW 0.13µm CMOS ADC array achieving over 60dB SFDR with adaptive digital equalization. At high conversion speed, time interleaving provides a viable way of achieving analog-to-digital conversion with low power consumption, especially when combined with the successive-approximation-register (SAR) architecture that is known to scale well in CMOS technology. In this work, we showcase a digital background-equalization technique to treat the path-mismatch problem as well as individual ADC nonlinearities in time-interleaved SAR ADC arrays. This approach was first introduced to calibrate the linearity errors in pipelined ADCs [1], and subsequently extended to treat SAR ADCs [2]. In this prototype, we demonstrate the effectiveness of this technique in a compact SAR ADC array, which achieves 7.5 ENOB and a 65dB SFDR at 600MS/s while dissipating 23.6mW excluding the on-chip DLL, and exhibiting one of the best conversion FOMs among ADCs with similar sample rates and resolutions [3].

Question: What is the best ADC for a 0.13µm CMOS process?


Quranic Verse Extraction base on Concepts using OWL-DL Ontology. In recent years, there has been a global growing demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about a number of automated applications that ease the retrieval of knowledge from the Holy Book, being the major source of Knowledge in Islam. However, the current retrieval methods in the Quranic domain lack adequate semantic search capabilities; they are mostly based on the keywords matching approach. There is a lack of adequate linked data to provide a better description of concepts found in the Holy Quran. In this study we propose an Ontology assisted semantic search system in the Qur'an domain. The system makes use of Quran ontology and various relationships and restrictions. This will enable the user to semantically search for verses related to their query in Al-Quran. The system has improved the search capability of the Holy Quran knowledge to 95 percent accuracy level.

Question: What is the accuracy of the system?


The LED Block Cipher. We present a new block cipher LED. While dedicated to compact hardware implementation, and offering the smallest silicon footprint among comparable block ciphers, the cipher has been designed to simultaneously tackle three additional goals. First, we explore the role of an ultra-light (in fact non-existent) key schedule. Second, we consider the resistance of ciphers, and LED in particular, to related-key attacks: we are able to derive simple yet interesting AES-like security proofs for LED regarding relatedor single-key attacks. And third, while we provide a block cipher that is very compact in hardware, we aim to maintain a reasonable performance profile for software implementation.

Question: What is the smallest block cipher?


Extending the Hierarchical Deep Reinforcement Learning framework. iv

Question: What is the hierarchical deep reinforcement learning framework?


High Beam Steering in Fabry–Pérot Leaky-Wave Antennas. A high-gain low-profile Fabry-Perot (FP) leaky-wave antenna (LWA) presenting one-dimensional high beam steering properties is proposed in this letter. The structure consists of a ground plane and a varying inductive partially reflective surface (PRS). A microstrip patch antenna is embedded into the cavity to act as the primary feed. As design examples, antennas are designed to operate at 9.5 GHz. Subwavelength FP cavities with fixed overall thickness of λ0 /6 (where λ0 is the free-space operating wavelength) are fabricated and measured. The impact of varying the PRS inductance is analyzed. It is shown that a high beam steering angle from broadside toward endfire direction close to 60 can be obtained when judiciously designing the inductive grid of the PRS.

Question: What is a high-gain low-profile Fabry-Perot (FP) leaky-wave antenna (LWA)?


A graph-based recommender system for digital library. Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, user-user and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.

Question: What is a graph-based recommender system?


Model-Based Wheel Slip Detection for Outdoor Mobile Robots. This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.

Question: How to detect wheel slip?


Random Bits Regression: a Strong General Predictor for Big Data. * Correspondence: momiao.xiong@gmail.com; Momiao.Xiong@uth.tmc.edu; yin.yao@nih.gov; lijin@fudan.edu.cn Equal contributors Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, TX, USA Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China Full list of author information is available at the end of the article Abstract

Question: What is the name of the author?


Improved Training of Generative Adversarial Networks Using Representative Features. Despite the success of generative adversarial networks (GANs) for image generation, the trade-off between visual quality and image diversity remains a significant issue. This paper achieves both aims simultaneously by improving the stability of training GANs. The key idea of the proposed approach is to implicitly regularize the discriminator using representative features. Focusing on the fact that standard GAN minimizes reverse Kullback-Leibler (KL) divergence, we transfer the representative feature, which is extracted from the data distribution using a pre-trained autoencoder (AE), to the discriminator of standard GANs. Because the AE learns to minimize forward KL divergence, our GAN training with representative features is influenced by both reverse and forward KL divergence. Consequently, the proposed approach is verified to improve visual quality and diversity of state of the art GANs using extensive evaluations.

Question: How to improve the stability of training GANs?


Mining Frequent Patterns by Pattern-Growth: Methodology and Implications. Mining frequent patterns has been a focused topic in data mining research in recent years, with the developmeht of numerous interesting algorithms for mining association, correlation, causality, sequential patterns, partial periodicity, constraint-based frequent pattern mining, associative classification, emerging patterns, etc. Most of the previous studies adopt an Apriori-like, candidate generation-and-test approach. However, based on our analysis, candidate generation and test may still be expensive, especially when encountering long and numerous patterns. A new methodology, called f r e q u e n t p a t t e r n g rowth , which mines frequent patterns without candidate generation, has been developed. The method adopts a divide-andconquer philosophy to project and partition databases based on the currently discovered frequent patterns and grow such patterns to longer ones in the projected databases. Moreover, efficient data structures have been developed for effective database compression and fast in-memory traversal. Such a methodology may eliminate or substantially reduce the number of candidate sets to be generated and also reduce the size of the database to be iteratively examined, and, therefore, lead to high performance. In this paper, we provide an overview of this approach and examine its methodology and implications for mining several kinds of frequent patterns, including association, frequent closed itemsets, max-patterns, sequential patterns, and constraint-based mining of frequent patterns. We show that frequent pattern growth is efficient at mining large databases and its further development may lead to scalable mining of many other kinds of patterns as well.

Question: What is the size of the canadian military?


Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [5], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [13].

Question: What is the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks?


Bayesian Neural Word Embedding. Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the SkipGram objective and a detailed step by step description is provided. We present experimental results that demonstrate the performance of the proposed algorithm for word analogy and similarity tasks on six different datasets and show it is competitive with the original Skip-Gram method.

Question: What is a scalable Bayesian neural word embedding algorithm?


A series elastic actuator as a new load-sensitive continuously variable transmission mechanism for control actuation systems. Electrical motors are often used to actuate mechanisms with varying speed and under varying load e.g., missile control actuation systems or robot extremities. Sizing of the motor is critical in these applications where space is limited. Varying speed and load cause poor utilization of the motor if the reduction ratio is constant. This requires employment of a larger motor than the smallest possible with ideal variable transmission. To realize better utilization of the motor, we propose a series elastic actuator that works as a load sensitive continuously variable transmission system. This is achieved by combining a pin in slot mechanism with an elastic output shaft. The proposed mechanism is modeled and simulated using example missile flight data and a typical robot finger loading scenario. Results are compared to the constant reduction ratio case, and advantages of the proposed mechanism are shown as decreased actuator size and power.

Question: What is a series elastic actuator?


A First Step towards Eye State Prediction Using EEG. In this paper, we investigate how the eye state (open or closed) can be predicted by measuring brain waves with an EEG. To this end, we recorded a corpus containing the activation strength of the fourteen electrodes of a commercial EEG headset as well as the manually annotated eye state corresponding to the recorded data. We tested 42 different machine learning algorithms on their performance to predict the eye state after training with the corpus. The best-performing classifier, KStar, produced a classification error rate of only 2.7% which is a 94% relative reduction over the majority vote of 44.9% classification error.

Question: How can we predict eye state using EEG?


Interpersonal connectedness: conceptualization and directions for a measurement instrument. Interpersonal connectedness is the sense of belonging based on the appraisal of having sufficient close social contacts. This feeling is regarded as one of the major outcomes of successful (mediated) social interaction and as such an important construct for HCI. However, the exact nature of this feeling, how to achieve it, and how to assess it remain unexplored to date. In the current paper we start the theoretical conceptualization of this phenomenon by exploring its basic origins in psychological literature and simultaneously formulate requirements for a measurement instrument to be developed in the service of exploring and testing CMC applications, in particular awareness technologies.

Question: What is the difference between interpersonal connectedness and belonging?


Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction. A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation on pairs of linked nodes. It can not only discover overlapping communities and inter-community interactions, but also predict missing edges. A simplified version omitting inter-community interactions is also provided and we reveal its interesting connections to existing models. The number of communities is automatically inferred in a nonparametric Bayesian manner, and efficient inference via Gibbs sampling is derived using novel data augmentation techniques. Experimental results on four real networks demonstrate the models' scalability and state-of-the-art performance.

Question: What is the number of communities in the above example?


Nano-Power Wireless Wake-Up Receiver With Serial Peripheral Interface. We designed, implemented, tested and measured an ultra low power Wake Up Receiver (WUR), intended for use in Wireless Body Area Networks (WBAN). Gaussian On-Off Keying (GOOK) and Pulse Width Modulation (PWM) are used to modulate and encode, respectively, the preamble signal. The receiver incorporates a decoder to enable Serial Peripheral Interface (SPI). WUR was also comprehensively tested for power consumption and robustness to RF interference from wireless devices commonly found in the vicinity of persons utilising WBAN technology. Our results and comparative evaluation demonstrate that the achieved listening power of 270nW for the Wake Up Receiver is significantly lower power consumption than for the other state-of-the-art. The proposed preamble detection scheme can significantly reduce false wake ups due to other wireless devices in a WBAN. Additionally, SPI significantly reduces the overall power consumption for packet reception and decoding.

Question: What is the power consumption of a wireless receiver?


LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two realworld engine datasets with both predictive and unpredictable behavior. We show that EncDecAD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).

Question: How to detect anomalies in time-series?