Requirements Elicitation for an Inter-Organizational Business Intelligence System for Small and Medium Retail Enterprises. Business Intelligence (BI) is on everyone's lips nowadays, since it provides businesses with the possibility to analyze their business practices and improve them. However, Small and Medium Enterprises (SME) often cannot leverage the positive effects of BI because of missing resources like personnel, knowledge, or money. Since SME pose a major form of business organization, this fact has to be overcome. As the retail industry is a substantial part of the SME branch, we propose an inter-organizational approach for a BI system for retail SME, which allows them to collaboratively collect data and perform analysis task. The aim of our ongoing research effort is the development of such a system following the Design Science Research Methodology. Within this article, the status quo of current BI practices in SME in the retail industry is analyzed through qualitative interviews with ten SME managers. Afterwards, adoption and success factors of BI systems and Inter-organizational Information Systems are worked out in a comprehensive structured literature review. Based on the status quo and the adoption and success factors, first requirements for the acceptance of an inter-organizational BI system are identified and validated in another round of qualitative interviews. This leads to nine functional requirements and three non-functional requirements, which can be used for designing and implementing an inter-organizational BI system for SME in the following research efforts.

Question: What is the status quo of current BI practices in SME in the retail industry?


Measuring the Similarity between Automatically Generated Topics. Previous approaches to the problem of measuring similarity between automatically generated topics have been based on comparison of the topics' word probability distributions. This paper presents alternative approaches, including ones based on distributional semantics and knowledgebased measures, evaluated by comparison with human judgements. The best performing methods provide reliable estimates of topic similarity comparable with human performance and should be used in preference to the word probability distribution measures used previously.

Question: How do you measure the similarity between topics?


Detection of data injection attack in industrial control system using long short term memory recurrent neural network. In 2010, the outbreak of Stuxnet sounded a warning in the field of industrial control.security. As the major attack form of Stuxnet, data injection attack is characterized by high concealment and great destructiveness. This paper proposes a new method to detect data injection attack in Industrial Control Systems (ICS), in which Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is a temporal sequences predictor. We then use the Euclidean detector to identify attacks in a model of a chemical plant. With simulation and evaluation in Tennessee Eastman (TE) process, we show that this method is able to detect various types of data injection attacks.

Question: How to detect data injection attack in industrial control system?


Bayesian Grasp Planning. We present a Bayesian framework for grasp planning that takes into account uncertainty in object shape or pose, as well as robot motion error. When trying to grasp objects based on noisy sensor data, a common problem is errors in perception, which cause the geometry or pose of the object to be uncertain. For each hypothesis about the geometry or pose of the object to be grasped, different sets of grasps can be planned. Of the resulting grasps, some are likely to work only if particular hypotheses are true, but some may work on most or even all hypotheses. Likewise, some grasps are easily broken by small errors in robot motion, but other grasps are robust to such errors. Our probabilistic framework takes into account all of these factors while trying to estimate the overall probability of success of each grasp, allowing us to select grasps that are robust to incorrect object recognition as well as motion error due to imperfect robot calibration. We demonstrate our framework while using the PR2 robot to grasp common household objects.

Question: What is a Bayesian framework for grasp planning?


Understanding motor events: a neurophysiological study. Neurons of the rostral part of inferior premotor cortex of the monkey discharge during goal-directed hand movements such as grasping, holding, and tearing. We report here that many of these neurons become active also when the monkey observes specific, meaningful hand movements performed by the experimenters. The effective experimenters' movements include among others placing or retrieving a piece of food from a table, grasping food from another experimenter's hand, and manipulating objects. There is always a clear link between the effective observed movement and that executed by the monkey and, often, only movements of the experimenter identical to those controlled by a given neuron are able to activate it. These findings indicate that premotor neurons can retrieve movements not only on the basis of stimulus characteristics, as previously described, but also on the basis of the meaning of the observed actions.

Question: What is the meaning of the word "observed"?


Comparisons of centerline extraction methods for liver blood vessels in ImageJ and 3D slicer. In this paper, we introduce the centerline extraction processes of the liver vessel systems by using the plug-ins in ImageJ and 3D Slicer. The Skeletonize (2D/3D) plug-in in ImageJ is easy to use, and only based on the volumetric data. It performs fast and require no user interactions in terms of seed points selection. The VMTK toolkit in 3D slicer is more powerful. It uses the surface model and can provide more smooth and consistent results. Due to its complexities, it requires more computational resources and user specified seed points. Some possible improvements for these two methods are discussed.

Question: How to extract centerline of liver blood vessel?


The Visual Hull Concept for Silhouette-Based Image Understanding. Many algorithms for both identifying and reconstructing a 3-D object are based on the 2-D silhouettes of the object. In general, identifying a nonconvex object using a silhouettebased approach implies neglecting some features of its surface as identification clues. The same features cannot be reconstructed by volume intersection techniques using multiple silhouettes of the object. This paper addresses the problem of finding which parts of a nonconvex object are relevant for silhouette-based image understanding. For this purpose, the geometric concept of visual hull of a 3-D object is introduced. The visual hull of a 3-D object S is the closest approximation of S that can be obtained with the volume intersection approach. An equivalent statement, relative to object identification, is that the visual hull of S is the maximal object silhouette-equivalent to S, i.e., which can be substituted for S without affecting any silhouette. Only the parts of the surface of S that also lie on the surface of the visual hull can be reconstructed or identified using silhouette-based algorithms. The visual hull of an object depends not only on the object itself but also on the region allowed to the viewpoint. Two main viewing regions can be considered, resulting in the external and internal visual hull. In the former case the viewing region is related to the convex hull of S, in the latter it is bounded by S itself. The internal visual hull also admits an interpretation not related to silhouettes: the features of the surface of S that is not coincident with the surface of the internal visual hull cannot be observed from any viewpoint lying outside the convex hull. After a general discussion of the visual hull and its properties, algorithms for computing the internal and external visual hulls of 2-D objects and 3-D planar face objects are presented and their complexity analyzed. In general, the visual hull of a 3-D plana

Question: What is the visual hull of a 3-D object?


community2vec: Vector representations of online communities encode semantic relationships. Vector embeddings of words have been shown to encode meaningful semantic relationships that enable solving of complex analogies. This vector embedding concept has been extended successfully to many different domains and in this paper we both create and visualize vector representations of an unstructured collection of online communities based on user participation. Further, we quantitatively and qualitatively show that these representations allow solving of semantically meaningful community analogies and also other more general types of relationships. These results could help improve community recommendation engines and also serve as a tool for sociological studies of community relatedness.

Question: What is a vector representation of an online community?


Fast and Robust Joint Models for Biomedical Event Extraction. Extracting biomedical events from literature has attracted much recent attention. The bestperforming systems so far have been pipelines of simple subtask-specific local classifiers. A natural drawback of such approaches are cascading errors introduced in early stages of the pipeline. We present three joint models of increasing complexity designed to overcome this problem. The first model performs joint trigger and argument extraction, and lends itself to a simple, efficient and exact inference algorithm. The second model captures correlations between events, while the third model ensures consistency between arguments of the same event. Inference in these models is kept tractable through dual decomposition. The first two models outperform the previous best joint approaches and are very competitive with respect to the current state-of-theart. The third model yields the best results reported so far on the BioNLP 2009 shared task, the BioNLP 2011 Genia task and the BioNLP 2011 Infectious Diseases task.

Question: What is the best performing joint model for biomedical event extraction?


Snooping Keystrokes with mm-level Audio Ranging on a Single Phone. This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.

Question: How many phones are needed to locate keystrokes?


A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation. The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

Question: How to design a wheel slip controller for an antilock braking system?


Scalable Distributed Monte-Carlo Tree Search. Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS has been notoriously difficult to scale well, especially in distributed environments. For a distributed parallel search, transposition-table driven scheduling (TDS) is known to be efficient in several domains. We present a massively parallel MCTS algorithm, that applies the TDS parallelism to the Upper Confidence bound Applied to Trees (UCT) algorithm, which is the most representative MCTS algorithm. To drastically decrease communication overhead, we introduce a reformulation of UCT called Depth-First UCT. The parallel performance of the algorithm is evaluated on clusters using up to 1,200 cores in artificial game-trees. We show that this approach scales well, achieving 740-fold speedups in the best case.

Question: What is the most representative MCTS algorithm?


Predicting driver drowsiness using vehicle measures: recent insights and future challenges.. INTRODUCTION Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.   METHOD The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.   RESULTS Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.   CONCLUSIONS Further research is necessary to examine more complex functions, as well as individual differences between drivers.   IMPACT ON INDUSTRY A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.

Question: What is the goal of this review?


Bicycle dynamics and control: adapted bicycles for education and research. In this paper, the dynamics of bicycles is analyzed from the perspective of control. Models of different complexity are presented, starting with simple ones and ending with more realistic models generated from multibody software. Models that capture essential behavior such as self-stabilization as well as models that demonstrate difficulties with rear wheel steering are considered. Experiences using bicycles in control education along with suggestions for fun and thought-provoking experiments with proven student attraction are presented. Finally, bicycles and clinical programs designed for children with disabilities are described.

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


EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis. The domain name service (DNS) plays an important role in the operation of the Internet, providing a two-way mapping between domain names and their numerical identifiers. Given its fundamental role, it is not surprising that a wide variety of malicious activities involve the domain name service in one way or another. For example, bots resolve DNS names to locate their command and control servers, and spam mails contain URLs that link to domains that resolve to scam servers. Thus, it seems beneficial to monitor the use of the DNS system for signs that indicate that a certain name is used as part of a malicious operation. In this paper, we introduce EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity. We use 15 features that we extract from the DNS traffic that allow us to characterize different properties of DNS names and the ways that they are queried. Our experiments with a large, real-world data set consisting of 100 billion DNS requests, and a real-life deployment for two weeks in an ISP show that our approach is scalable and that we are able to automatically identify unknown malicious domains that are misused in a variety of malicious activity (such as for botnet command and control, spamming, and phishing).

Question: What is the difference between passive and active DNS analysis?


Compliance with Information Security Policies: An Empirical Investigation. Information security was the main topic in this paper. An investigation of the compliance to information security policies were discussed. The author mentions that the insignificant relationship between rewards and actual compliance with information security policies does not make sense. Quite possibly this relationship results from not applying rewards for security compliance. Also mentions that based on the survey conducted, careless employee behavior places an organization's assets and reputation in serious jeopardy. The major threat to information security arises from careless employees who fail to comply with organizations' information security policies and procedures.

Question: What is the relationship between rewards and actual compliance with information security policies?


Deception through telling the truth ? ! : Experimental evidence from individuals and teams. Informational asymmetries abound in economic decision making and often provide an incentive for deception through telling a lie or misrepresenting information. In this paper I use a cheap-talk sender-receiver experiment to show that telling the truth should be classified as deception too if the sender chooses the true message with the expectation that the receiver will not follow the sender's (true) message. The experimental data reveal a large degree of 'sophisticated' deception through telling the truth. The robustness of my broader definition of deception is confirmed in an experimental treatment where teams make decisions. JEL-classification: C72, C91, D82

Question: What is the difference between telling the truth and telling a lie?


A Decision Framework for Blockchain Platforms for IoT and Edge Computing. Blockchains started as an enabling technology in the area of digital currencies with the introduction of Bitcoin. However, blockchains have emerged as a technology that goes beyond financial transactions by providing a platform supporting secure and robust distributed public ledgers. We think that the Internet of Things (IoT) can also benefit from blockchain technology, especially in the areas of security, privacy, fault tolerance, and autonomous behavior. Here we present a decision framework to help practitioners systematically evaluate the potential use of blockchains in an IoT context.

Question: What is a decision framework for blockchain platforms?


Progressive skyline computation in database systems. The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.

Question: What is the nearest neighbor search?


Algorithmic Complexity in Coding Theory and the Minimum Distance Problem. We startwithan overviewof algorithmiccomplexity problemsin coding theory We then show that the problemof computing the minimumdiktanceof a binaryIinwr code is NP-hard,and the correspondingdeci~"onproblemis W-complete. Thisconstitutes a proof of the conjecture Bedekamp, McEliece,vanTilborg, dating back to 1978. Extensionsand applicationsof this result to other problemsin codingtheqv are discussed.

Question: What is the minimum distance of a binary code?


Psychological first-aid: a practical aide-memoire.. Despite advances made in recent years in medical first aid, psychiatric intervention, survival training and equipment design, many people still perish quickly during and immediately following a disastrous event. In this study, individuals and groups of survivors of life-threatening events were debriefed and the behavior of those who coped well during such a threat to life were compared with those who did not. The behaviors of those who coped well were distilled into a set of principles for psychological first aid; that is, a series of simple actions for use within a disaster which serves to recover victims to functional behavior as quickly as possible, thus increasing their chance for survival. These principles of psychological first aid have recently been introduced into basic first aid and survival training courses for both military and civilian units.

Question: What is psychological first aid?


Evaluation of Different Control Strategies for Teams of Unmanned Marine Vehicles ( UMVs ) Comparing the Requirements of Underwater Communication and Navigation in MATLAB ® Simulations. Thomas Glotzbach, TU Ilmenau; Fraunhofer Center for Applied Systems Technology, Ilmenau/Germany, thomas.glotzbach@tu-ilmenau.de Andrea Picini, INNOVA S.p.A., Rome/Italy, a.picini@innova-eu.net Antonio Zangrilli, INNOVA S.p.A., Rome/Italy, a.zangrilli@innova-eu.net Mike Eichhorn, TU Ilmenau/Germany, mike.eichhorn@tu-ilmenau.de Peter Otto, TU Ilmenau/Germany, peter.otto@tu-ilmenau.de Matthias Schneider, TU Ilmenau/Germany, schneider.matthias@tu-ilmenau.de

Question: How to evaluate different control strategies for teams of unmanned marine vehicles?


DSD: Dense-Sparse-Dense Training for Deep Neural Networks. Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance. In the first D (Dense) step, we train a dense network to learn connection weights and importance. In the S (Sparse) step, we regularize the network by pruning the unimportant connections with small weights and retraining the network given the sparsity constraint. In the final D (re-Dense) step, we increase the model capacity by removing the sparsity constraint, re-initialize the pruned parameters from zero and retrain the whole dense network. Experiments show that DSD training can improve the performance for a wide range of CNNs, RNNs and LSTMs on the tasks of image classification, caption generation and speech recognition. On ImageNet, DSD improved the Top1 accuracy of GoogLeNet by 1.1%, VGG-16 by 4.3%, ResNet-18 by 1.2% and ResNet-50 by 1.1%, respectively. On the WSJ'93 dataset, DSD improved DeepSpeech and DeepSpeech2 WER by 2.0% and 1.1%. On the Flickr-8K dataset, DSD improved the NeuralTalk BLEU score by over 1.7. DSD is easy to use in practice: at training time, DSD incurs only one extra hyper-parameter: the sparsity ratio in the S step. At testing time, DSD doesn't change the network architecture or incur any inference overhead. The consistent and significant performance gain of DSD experiments shows the inadequacy of the current training methods for finding the best local optimum, while DSD effectively achieves superior optimization performance for finding a better solution. DSD models are available to download at https://songhan.github.io/DSD.

Question: What is DSD?


Correspondence-free pose estimation for 3D objects from noisy depth data. Estimating the pose of objects from depth data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from noisy 2.5D depth images obtained from a consumer depth sensor. Initialized with a coarsely accurate pose, the proposed approach applies a hypothesize-and-test scheme that combines stochastic optimization and graphics-based rendering to refine the supplied initial pose, so that it accurately accounts for a sensed depth image. Pose refinement employs particle swarm optimization to minimize an objective function that quantifies the misalignment between the acquired depth image and a rendered one that is synthesized from a hypothesized pose with the aid of an object mesh model. No explicit correspondences between the depth data and the model need to be established, whereas pose hypothesis rendering and objective function evaluation are efficiently performed on the GPU. Extensive experimental results demonstrate the superior performance of the proposed approach compared to the ICP algorithm, which is typically used for pose refinement in depth images. Furthermore, the experiments indicate the graceful degradation of its performance to limited computational resources and its robustness to noisy and reduced polygon count models, attesting its suitability for use with automatically scanned object models and common graphics hardware.

Question: How accurate is the pose estimation?


Exploring simultaneous keyword and key sentence extraction: improve graph-based ranking using wikipedia. Summarization and Keyword Selection are two important tasks in NLP community. Although both aim to summarize the source articles, they are usually treated separately by using sentences or words. In this paper, we propose a two-level graph based ranking algorithm to generate summarization and extract keywords at the same time. Previous works have reached a consensus that important sentence is composed by important keywords. In this paper, we further study the mutual impact between them through context analysis. We use Wikipedia to build a two-level concept-based graph, instead of traditional term-based graph, to express their homogenous relationship and heterogeneous relationship. We run PageRank and HITS rank on the graph to adjust both homogenous and heterogeneous relationships. A more reasonable relatedness value will be got for key sentence selection and keyword selection. We evaluate our algorithm on TAC 2011 data set. Traditional term-based approach achieves a score of 0.255 in ROUGE-1 and a score of 0.037 and ROUGE-2 and our approach can improve them to 0.323 and 0.048 separately.

Question: How to improve graph-based ranking using wikipedia?


Computer Architecture with Associative Processor Replacing Last-Level Cache and SIMD Accelerator. This study presents a computer architecture, where a last-level cache and a SIMD accelerator are replaced by an associative processor. Associative processor combines data storage and data processing, and functions as a massively parallel SIMD processor and a memory at the same time. An analytic performance model of this computer architecture is introduced. Comparative analysis supported by cycle-accurate simulation and emulation shows that this architecture may outperform a conventional computer architecture comprising a SIMD coprocessor and a shared last-level cache while consuming less power.

Question: What is a massively parallel SIMD processor?


Flattening Gamma: Radiometric Terrain Correction for SAR Imagery. Enabling intercomparison of synthetic aperture radar (SAR) imagery acquired from different sensors or acquisition modes requires accurate modeling of not only the geometry of each scene, but also of systematic influences on the radiometry of individual scenes. Terrain variations affect not only the position of a given point on the Earth's surface but also the brightness of the radar return as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover induced backscatter differences, and comparison of backscatter from multiple satellites, modes, or tracks loses meaning. The ASAR & PALSAR sensors provide state vectors and timing with higher absolute accuracy than was previously available, allowing them to directly support accurate tie-point-free geolocation and radiometric normalization of their imagery. Given accurate knowledge of the acquisition geometry of a SAR image together with a digital height model (DHM) of the area imaged, radiometric image simulation is applied to estimate the local illuminated area for each point in the image. Ellipsoid-based or sigma naught (σ0) based incident angle approximations that fail to reproduce the effect of topographic variation in their sensor model are contrasted with a new method that integrates terrain variations with the concept of gamma naught (γ0) backscatter, converting directly from beta naught (β0) to a newly introduced terrain-flattened γ0 normalization convention. The interpretability of imagery treated in this manner is improved in comparison to processing based on conventional ellipsoid or local incident angle based σ0 normalization.

Question: How do you flatten gamma?


DCA for bot detection. Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.

Question: How does the dendritic cell algorithm work?


Clinical and microbiologic characteristics of vulvovaginitis in Korean prepubertal girls, 2009–2014: a single center experience. OBJECTIVE To update information on the clinical and microbiologic characteristics of pediatric vulvovaginitis in Korean prepubertal girls.   METHODS A total of 120 girls (aged 0 to 9 years) with culture-confirmed pediatric vulvovaginitis, diagnosed between 2009 and 2014, were enrolled in the study. The epidemiologic and microbiologic characteristics, and clinical outcomes were assessed. Patients with sexual precocity, as well as those who were referred for suspected sexual abuse, were excluded.   RESULTS Girls aged 4 to 6 years were at the highest risk of pediatric vulvovaginitis. Seasonal distribution indicated obvious peaks in summer and winter. Of the 120 subjects, specific pathogens were identified in the genital specimens in only 20 cases (16.7%). Streptococcus pyogenes (n=12, 60%) was the leading cause of specific vulvovaginitis. Haemophilus influenzae was isolated in one patient. No cases presented with enteric pathogens, such as Shigella or Yersinia. A history of recent upper respiratory tract infection, swimming, and bubble bath use was reported in 37.5%, 15.8%, and 10.0% of patients, respectively. Recent upper respiratory tract infection was not significantly correlated with the detection of respiratory pathogens in genital specimens (P>0.05). Of 104 patients who underwent perineal hygienic care, 80 (76.9%) showed improvement of symptoms without antibiotic treatment. Furthermore, the efficacy of hygienic care was not significantly different between patients with or without specific pathogens (P>0.05).   CONCLUSION Specific pathogens were only found in 16.7% of pediatric vulvovaginitis cases. Our results indicate an excellent outcome with hygienic care, irrespective of the presence of specific pathogens.

Question: What is the prevalence of specific pathogens in pediatric vulvovaginitis?


Generating regular expression signatures for network traffic classification in trusted network management. Network traffic classification is a critical foundation for trusted network management and security systems. Matching application signatures in traffic payload is widely considered to be the most reliable classifying method. However, deriving accurate and efficient signatures for various applications is not a trivial task, for which current practice is mostly manual thus error-prone and of low efficiency. In this paper, we tackle the problem of automatic signature generation. In particular, we focus on generating regular expression signatures with a certain subset of standard syntax rules, which are of sufficient expressive power and compatible with most practical systems. We propose a novel approach that takes as input a labeled training data set and produces a set of signatures for matching the application classes presented in the data. The approach involves four procedures: pre-processing to extract application session payload, tokenization to find common substrings and incorporate position constraints, multiple sequence alignment to find common subsequences, and signature construction to transform the results into regular expressions. A real life full payload traffic trace is used to evaluate the proposed system, and signatures for a range of applications are automatically derived. The results indicate that the signatures are of high quality, and exhibit low false negatives and false positives. & 2011 Elsevier Ltd. All rights reserved.

Question: How to generate regular expression signatures for network traffic classification in trusted network management?


A computational approach to motion perception. In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.

Question: How to compute the optical flow?


Analysis and reduction of quadrature errors in the material point method ( MPM ). The material point method (MPM) has demonstrated itself as a computationally effective particle method for solving solid mechanics problems involving large deformations and/or fragmentation of structures, which are sometimes problematic for finite element methods (FEMs). However, similar to most methods that employ mixed Lagrangian (particle) and Eulerian strategies, analysis of the method is not straightforward. The lack of an analysis framework for MPM, as is found in FEMs, makes it challenging to explain anomalies found in its employment and makes it difficult to propose methodology improvements with predictable outcomes. In this paper we present an analysis of the quadrature errors found in the computation of (material) internal force in MPM and use this analysis to direct proposed improvements. In particular, we demonstrate that lack of regularity in the grid functions used for representing the solution to the equations of motion can hamper spatial convergence of the method. We propose the use of a quadratic B-spline basis for representing solutions on the grid, and we demonstrate computationally and explain theoretically why such a small change can have a significant impact on the reduction in the internal force quadrature error (and corresponding 'grid crossing error') often experienced when using MPM. Copyright q 2008 John Wiley & Sons, Ltd.

Question: What is the material point method?


USER INTERFACE DESIGN OF VOICE CONTROLLED CONSUMER ELECTRONICS. Today speech recognition of a small vocabulary can be realized so costeffectively that the technology can penetrate into consumer electronics. But, as first applications that failed on the market show, it is by no means obvious how to incorporate voice control in a user interface. This paper addresses the issue of how to design a voice control so that the user perceives it as a benefit. User interface guidelines that are adapted or specific to voice control are presented. Then the process of designing a voice control in the user-centred approach is described. By means of two examples, the car stereo and the telephone answering machine, it is shown how this is turned into practice.

Question: How to design a voice control?


Path Planning with Real Time Obstacle Avoidance. One of the most important areas of research on mobile robots is that of their moving from one point in a space to the other and that too keeping aloof from the different objects in their path i. e. real time obstacle detection. This is the basic problem of path planning through a continuous domain. For this a large number of algorithms have been proposed of which only a few are really good as far as local and global path planning are concerned as to some extent a trade of has to be made between the efficiency of the algorithm and its accuracy. In this project an integrated approach for both local as well as global path planning of a robot has been proposed. The primary algorithm that has been used for path planning is the artificial Potential field approach [1] and a* search algorithm has been used for finding the most optimum path for the robot. Obstacle detection for collision avoidance (a high level planning problem) can be effectively done by doing complex robot operations in real time and distributing the whole problem between different control levels. This project proposes the artificial potential field algorithm not only for static but also for moving obstacles using real time potential field values by creating sub-goals which eventually lead to the main goal of the most optimal complete path found by the A* search algorithm. Apart from these scan line and convex hull techniques have been used to improve the robustness of the algorithm. To some extent the shape and size of a robot has also been taken into consideration. The effectiveness of these algorithms has been

Question: What is the difference between local and global path planning?


On the Cache Behavior of SPLASH-2 Benchmarks on ARM and ALPHA Processors in Gem5 Full System Simulator. Today cache size and hierarchy level of caches play an important role in improving computer performance. By using full system simulations of gem5, the variation in memory bandwidth, system bus throughput, L1 and L2 cache size misses are measured by running SPLASH-2 Benchmarks on ARM and ALPHA Processors. In this work we calculate cache misses, memory bandwidth and system bus throughput by running SPLASH2 benchmarks on gem5 Full System Mode. Our results show that L1 cache misses decrease as L1 cache size is varied from 16KB to 64KB. L1 cache misses are independent of L2 cache size after the program data resides in L2 cache. The memory bandwidth and system bus throughput decreases as L1 and L2 cache size increases.

Question: How does cache size affect cache misses?


Assessing the veracity of identity assertions via OSNs. Anonymity is one of the main virtues of the Internet, as it protects privacy and enables users to express opinions more freely. However, anonymity hinders the assessment of the veracity of assertions that online users make about their identity attributes, such as age or profession. We propose FaceTrust, a system that uses online social networks to provide lightweight identity credentials while preserving a user's anonymity. Face-Trust employs a "game with a purpose" design to elicit the opinions of the friends of a user about the user's self-claimed identity attributes, and uses attack-resistant trust inference to assign veracity scores to identity attribute assertions. FaceTrust provides credentials, which a user can use to corroborate his assertions. We evaluate our proposal using a live Facebook deployment and simulations on a crawled social graph. The results show that our veracity scores strongly correlate with the ground truth, even when a large fraction of the social network users is dishonest and employs the Sybil attack.

Question: How can we assess the veracity of identity assertions?


Survey of clustering algorithms for MANET. Many clustering schemes have been proposed for ad hoc networks. A systematic classification of these clustering schemes enables one to better understand and make improvements. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance. Protocols try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control protocol. The clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. A large variety of approaches for ad hoc clustering have been developed by researchers which focus on different performance metrics. This paper presents a survey of different clustering schemes.

Question: What is a clustering scheme?


Proposal Manipulation of 3 D Objects in Immersive Virtual Environments. Interactions within virtual environments (VE) often require manipulation of 3D virtual objects. For this purpose, several research have been carried out focusing on mouse, touch and gestural input. While existing mid-air gestures in immersive VE (IVE) can offer natural manipulations, mimicking interactions with physical objects, they cannot achieve the same levels of precision attained with mouse-based applications for computer-aided design (CAD). Following prevailing trends in previous research, such as degrees-offreedom (DOF) separation, virtual widgets and scaled user motion, we intend to explore techniques for IVEs that combine the naturalness of mid-air gestures and the precision found in traditional CAD systems. In this document we survey and discuss the state-of-the-art in 3D object manipulation. With challenges identified, we present a research proposal and corresponding work plan.

Question: How to manipulate 3D objects in virtual environments?


MATCHINGS AND PAIRED DOMINATION IN FUZZY GRAPHS USING STRONG ARCS. The concepts of covering and matching in fuzzy graphs using strong arcs are introduced and obtained the relationship between them analogous to Gallai's results in graphs. The notion of paired domination in fuzzy graphs using strong arcs is also studied. The strong paired domination number γspr of complete fuzzy graph and complete bipartite fuzzy graph is determined and obtained bounds for the strong paired domination number of fuzzy graphs. An upper bound for the strong paired domination number of fuzzy graphs in terms of strong independence number is also obtained.

Question: What is the strong paired domination number of fuzzy graphs?


Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models. Abnormal event detection is one of the important objectives in research and practical applications of video surveillance. However, there are still three challenging problems for most anomaly detection systems in practical setting: limited labeled data, ambiguous definition of "abnormal" and expensive feature engineering steps. This paper introduces a unified detection framework to handle these challenges using energy-based models, which are powerful tools for unsupervised representation learning. Our proposed models are firstly trained on unlabeled raw pixels of image frames from an input video rather than hand-crafted visual features; and then identify the locations of abnormal objects based on the errors between the input video and its reconstruction produced by the models. To handle video stream, we develop an online version of our framework, wherein the model parameters are updated incrementally with the image frames arriving on the fly. Our experiments show that our detectors, using Restricted Boltzmann Machines (RBMs) and Deep Boltzmann Machines (DBMs) as core modules, achieve superior anomaly detection performance to unsupervised baselines and obtain accuracy comparable with the state-of-the-art approaches when evaluating at the pixel-level. More importantly, we discover that our system trained with DBMs is able to simultaneously perform scene clustering and scene reconstruction. This capacity not only distinguishes our method from other existing detectors but also offers a unique tool to investigate and understand how the model works.

Question: What is the difference between a generative model and a discriminative model?


Design of Hybrid Excited Synchronous Machine for Electrical Vehicles. This paper deals with the optimal design of a new synchronous motor particularly suitable for electric vehicles. The unique advantage of this motor is its possibility to realize field weakening at high speeds, which is the most important demand for such drives. The main design goal is to identify the dimensions of certain crucial regions of the machine in order to avoid saturation of the iron and to minimize the mass to volume ratio, while simultaneously maximizing the torque. The originality of the contribution is manifold: formulating the design problem in multiobjective terms, solving it by means of evolutionary computing, solving the associated direct problem based on a three-dimensional finite element analysis (3-D-FEA) field model, considering a reduced set of design variables by means of response surface method, referring to a real-life problem.

Question: What is the difference between a synchronous motor and an induction motor?


User Models for Adaptive Hypermedia and Adaptive Educational Systems. One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user

Question: What is the difference between the overlay and uncertainty-based approaches to user model representation and maintenance?


A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence. Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers – one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.

Question: What is a general optimization framework for multi-document summarization?


IoT based smart emergency response system for fire hazards. The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Smart city vision, Urban IoT design plans exploit added value services for citizens as well as administration of the city with the most advanced communication technologies. To make emergency response real time, IoT enhances the way first responders and provides emergency managers with the necessary up-to-date information and communication to make use those assets. IoT mitigates many of the challenges to emergency response including present problems like a weak communication network and information lag. In this paper it is proposed that an emergency response system for fire hazards is designed by using IoT standardized structure. To implement this proposed scheme a low-cost Espressif wi-fi module ESP-32, Flame detection sensor, Smoke detection sensor (MQ-5), Flammable gas detection sensor and one GPS module are used. The sensors detects the hazard and alerts the local emergency rescue organizations like fire departments and police by sending the hazard location to the cloud-service through which all are connected. The overall network utilizes a light weighted data oriented publish-subscribe message protocol MQTT services for fast and reliable communication. Thus, an intelligent integrated system is designed with the help of IoT.

Question: What is IoT?


Beyond engagement analytics: which online mixed-data factors predict student learning outcomes?. This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 % of grade variance in an undergraduate business course.

Question: What is the difference between qualitative and quantitative data?


MusicMiner : Visualizing timbre distances of music as topographical maps. Timbre distances and similarities are an expression of the phenomenon that some music appears similar while other songs sound very different to us. The notion of genre is often used to categorize music, but songs from a single genre do not necessarily sound similar and vice versa. Instead we aim at a visualization of timbre similarities of sound within a music collection. We analyzed and compared a large amount of different audio features and psychoacoustic variants thereof for the purpose of modelling timbre distance of sound. The sound of polyphonic music is commonly described by extracting audio features on short time windows during which the sound is assumed to be stationary. The resulting down sampled time series are aggregated to form a high level feature vector describing the music. We generated high level features by systematically applying static and temporal statistics for aggregation. Especially the temporal structure of features has previously been largely neglected. A novel supervised feature selection method is applied to the huge set of possible features. Distances between vectors of the selected features correspond to timbre differences in music. The features show few redundancies and have high potential for explaining possible clusters. They outperform seven other previously proposed feature sets on several datasets w.r.t. the separation of the known groups of timbrally different music. Clustering and visualization based on these feature vectors can discover emergent structures in collections of music. Visualization based on Emergent Self-Organizing Maps in particular enables the unsupervised discovery of timbrally consistent clusters that may or may not correspond to musical genres and artists. We demonstrate the visualizations capabilities of the U-Map and related methods based on the new audio features. An intuitive browsing of large music collections is offered based on th

Question: What is the difference between static and temporal statistics?


Web question answering: is more always better?. This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Most question answering systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. We use this redundancy to simplify the query rewrites that we need to use, and to support answer mining from returned snippets. Our system performs quite well given the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching passages. Simple passage ranking and n-gram extraction techniques work well in our system making it efficient to use with many backend retrieval engines.

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


Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. OBJECTIVE Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies.   METHODS From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations.   RESULTS We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use ca

Question: What is the difference between owlready and owlready2?


Building Hierarchical Representations for Oracle Character and Sketch Recognition. In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.

Question: What is the oldest hieroglyphs in China?


Origami Pop-up Card Generation from 3D Models Using a Directed Acyclic Graph. Origami is a paper art used to create three-dimensional (3D) objects by cutting and folding a single sheet of paper. Origami is labor-intensive and requires a high skill level to generate two-dimensional (2D) objects that pop-up into realistic 3D objects. However, this special feature makes designing an origami architecture procedure challenging. This paper provides a novel algorithm to create an origami paper card from a 3D model with a user-specified folding line. The algorithm segments the 2D shape from a 3D model and creates layers of the pop-up paper card using a directed acyclic graph. After applying layers to the layout, the algorithm creates connections between layers, and then outputs the origami layout. Based on a directed acyclic graph, the algorithm computes a class of paper architectures containing two sets of layers and connections that approximate the input geometry while guaranteeing that a pop up card is physically realizable. The proposed method is demonstrated with a number of paper pop-ups, and physical experimental results are presented.

Question: How to generate origami pop-up card from 3D model?


Multi-Player Alpha-Beta Pruning. Korf, R.E., Multi-player alpha-beta pruning (Research Note), Artificial Intelligence 48 (1991) 99-111. We consider the generalization of minimax search with alpha-beta pruning to non-cooperative, perfect-information games with more than two players. The minimax algorithm was generalized in [2] to the maxn algorithm applied to vectors of n-tuples representing the evaluations for each of the players. If we assume an upper bound on the sum of the evaluations for each player, and a lower bound on each individual evaluation, then shallow alpha-beta pruning is possible, but not deep pruning. In the best case, the asymptotic branching factor is reduced to (1 + 4bv'~b-"s~-3)/2. In the average case, however, pruning does not reduce the asymptotic branching factor. Thus, alpha-beta pruning is found to be effective only in the special case of two-player games. In addition, we show that it is an optimal directional algorithm for two players.

Question: What is the asymptotic branching factor for alpha-beta pruning?


Automatic Acquisition of Hyponyms from Large Text Corpora. We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexicosyntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.

Question: What is the lexicosyntactic pattern that indicates the lexical relation of interest?


Leveraging stereopsis for saliency analysis. Stereopsis provides an additional depth cue and plays an important role in the human vision system. This paper explores stereopsis for saliency analysis and presents two approaches to stereo saliency detection from stereoscopic images. The first approach computes stereo saliency based on the global disparity contrast in the input image. The second approach leverages domain knowledge in stereoscopic photography. A good stereoscopic image takes care of its disparity distribution to avoid 3D fatigue. Particularly, salient content tends to be positioned in the stereoscopic comfort zone to alleviate the vergence-accommodation conflict. Accordingly, our method computes stereo saliency of an image region based on the distance between its perceived location and the comfort zone. Moreover, we consider objects popping out from the screen salient as these objects tend to catch a viewer's attention. We build a stereo saliency analysis benchmark dataset that contains 1000 stereoscopic images with salient object masks. Our experiments on this dataset show that stereo saliency provides a useful complement to existing visual saliency analysis and our method can successfully detect salient content from images that are difficult for monocular saliency analysis methods.

Question: What is the stereo saliency analysis benchmark dataset?


Digital image integrity - a survey of protection and verification techniques. We are currently on a verge of a revolution in digital photography. Developments in computational imaging and adoption of artificial intelligence have spawned new editing techniques that give impressive results in astonishingly short timeframes. The advent of multi-sensor and multi-lens cameras will further challenge many existing integrity verification techniques. As a result, it will be necessary to re-evaluate our notion of image authenticity and look for new techniques that could work efficiently in this new reality. The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity. In contrast to other recent surveys, the discussion covers the most important developments both in active protection and in passive forensic analysis techniques. Existing approaches are analyzed with respect to their capabilities, fundamental limitations, and prospective attack vectors. Whenever possible, the discussion is supplemented with real operation examples and a list of available implementations. Finally, the paper reviews resources available in the research community, including public data-sets and commercial or open-source software. The paper concludes by discussing relevant developments in computational imaging and highlighting future challenges and open research problems.

Question: What is the difference between a digital image and a digital photograph?


On HMM static hand gesture recognition. A real time static isolated gesture recognition application using a Hidden Markov Model approach with features extracted from gestures' silhouettes is presented. Nine different hand poses with various degrees of rotation are considered. The system, both simple and effective, uses color images of the hands to be recognized directly from the camera and is capable of processing 23 frames per second on a Quad Core Intel Processor.

Question: What is a hand pose?


Review and Comparative Study of Clustering Techniques. Clustering is an automatic learning technique which aims at grouping a set of objects into clusters so that objects in the same clusters should be similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in other clusters. Document clustering aims to group in an unsupervised way, a given document set into clusters such that documents within each clusters are more similar between each other than those in different clusters. Cluster analysis aims to organize a collection of patterns into clusters based on similarity. This paper focuses on survey of various clustering techniques. These techniques can be divided into several categories: Partitional algorithms, Hierarchical algorithms, Density based, and comparison of various algorithm is surveyed and shows how Hierarchical Clustering can be better than other techniques.

Question: What is a clustering technique?


Parallel Coordinate Descent for L1-Regularized Loss Minimization. We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict near-linear speedups, up to a problemdependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential for parallelism closely match behavior on real data. Shotgun outperforms other published solvers on a range of large problems, proving to be one of the most scalable algorithms for L1.

Question: What is the difference between Lasso and L1 regularized loss minimization?


Predictive Control in Power Electronics and Drives. Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.

Question: What is predictive control?


Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database. To provide insurance on the resistance of a system against side-channel analysis, several national or private schemes are today promoting an evaluation strategy, common in classical cryptography, which is focussing on the most powerful adversary who may train to learn about the dependency between the device behaviour and the sensitive data values. Several works have shown that this kind of analysis, known as Template Attacks in the side-channel domain, can be rephrased as a classical Machine Learning classification problem with learning phase. Following the current trend in the latter area, recent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in Machine Learning and (2) it does not allow for the reproducibility of the presented results. This paper aims to address these limitations in several ways. First, completing recent works, we propose a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, we address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign

Question: What is the difference between a template attack and a deep learning attack?


Millimeter-wave Gbps Broadband Evolution towards 5G: Fixed Access and Backhaul. — As wireless communication evolves towards 5G, both fixed broadband and mobile broadband will play a crucial part in providing the Gbps infrastructure for a connected society. This paper proposes a Millimeter-wave Gbps Broadband (MGB) system as the solution to two critical problems in this evolution: last-mile access for fixed broadband and small cell backhaul for mobile broadband. The key idea is to use spectrum that is already available in the millimeter wave bands for fixed wireless access with optimized dynamic beamforming and massive MIMO infrastructure to achieve high capacity with wide area coverage. This paper explains the MGB concept and describes potential array architectures for realizing the system. Simulations demonstrate that with 500 MHz of bandwidth (at 39 GHz band) and 28 dBm transmission power (55 dBm EIRP), it is possible to provide more than 11 Gbps backhaul capacity for 96 small cells within 1-km radius.

Question: What is the millimeter wave?


StackMap: Low-Latency Networking with the OS Stack and Dedicated NICs. StackMap leverages the best aspects of kernel-bypass networking into a new low-latency Linux network service based on the full-featured TCP kernel implementation, by dedicating network interfaces to applications and offering an extended version of the netmap API as a zero-copy, lowoverhead data path while retaining the socket API for the control path. For small-message, transactional workloads, StackMap outperforms baseline Linux by 4 to 80 % in latency and 4 to 391 % in throughput. It also achieves comparable performance with Seastar, a highly-optimized user-level TCP/IP stack for DPDK.

Question: What is the stackmap API?


Authenticated Append-only Skip Lists. In this work we describe, design and analyze the security of a tamper-evident, append-only data structure for maintaining secure data sequences in a loosely coupled distributed system where individual system components may be mutually distrust-ful. The resulting data structure, called an Authenticated Append-Only Skip List (AASL), allows its maintainers to produce one-way digests of the entire data sequence, which they can publish to others as a commitment on the contents and order of the sequence. The maintainer can produce efficiently succinct proofs that authenticate a particular datum in a particular position of the data sequence against a published digest. AASLs are secure against tampering even by malicious data structure maintain-ers. First, we show that a maintainer cannot " invent " and authenticate data elements for the AASL after he has committed to the structure. Second, he cannot equivocate by being able to prove conflicting facts about a particular position of the data sequence. This is the case even when the data sequence grows with time and its maintainer publishes successive commitments at times of his own choosing. AASLs can be invaluable in reasoning about the integrity of system logs maintained by untrusted components of a loosely-coupled distributed system .

Question: What is a skip list?


Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called marginal Fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions

Question: What is the difference between supervised and unsupervised dimensionality reduction?


Visualizing the non-visual: spatial analysis and interaction with information from text documents. The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.

Question: How can I visualize text content?


Enabling Public Verifiability and Data Dynamics for Storage Security. Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.

Question: How to construct a hash tree?


Impact Of Online Advertising On Consumer Attitudes And Interests Buy Online ( Survey On Students Of Internet Users In Makassar ). The rapid development of technology today makes Internet users continues to increase. This is supported by the ease of internet users access the internet either through a PC, laptop, mobile phones, tablets and other media. The increase in Internet users this makes the internet into a proper promotion using online advertising. With a wide reach and touch the various layers of the Internet media communities may be appropriate advice for company promotion. However, an increasing number of Internet users, especially in the city of Makassar is not accompanied by an increase in the number of online purchases. Based on that it is necessary to examine how the effect of online advertising on consumer buying behavior and online, as well as how to control the behavior and subjective norms influence the attitudes and interests of consumers buy online. This study aims to analyze and test the effect of online advertising on consumer attitudes and purchase interest online, a survey conducted on students of Internet users in the city of Makassar. The study was conducted on students of public and private universities in the city of Makassar. The method used was a quantitative analysis using the technique of purposive sampling method with a sample of 340 people. Testing this hypothesis using structural equation modeling (SEM). The results showed that online advertising has an influence on consumer buying behavior and online. Dimensions interactivity of online advertising provides the highest influence on the attitudes and interests of consumers purchasing online.

Question: What is the impact of online advertising on consumer attitudes and interests?


Agile in Public Administration: Oxymoron or Reality? An Experience Report. In the last 10 years, Agile methods and practices have emerged as an alternative for software development. Different "flavors" of Agile have appeared ranging from project management to tests organization. These approaches have being gaining popularity and involve now a solid option for organizations developing software, but what about Public Administrations? Is Agile a suitable option for developing software in Public Administrations? Even if Public Administrations have been traditionally regarded as changeresistant, Agile approach can also provide them with the benefits of quick adaptation and frequent value delivery. This paper presents the results of two different projects, which use an Agile framework based on Scrum, developed by a Spanish Public Administration. Additionally, after considering the obtained results, it takes out some relevant learned lessons on the suitability of applying Agile approaches to Public Administration environments.

Question: Is agile suitable for public administration?


A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors. Article history: Available online 23 December 2014

Question: What is a plenoptic sensor?


vFPGAmanager: A Virtualization Framework for Orchestrated FPGA Accelerator Sharing in 5G Cloud Environments. Network operators are actively pushing towards the new 5G era and a crucial part to accomplish this is the Network Functions Virtualization (NFV). FPGAs and their hardware accelerators are a promising solution for NFV and 5G cloud environments because of their fast turnaround time and great speedup potential through application parallelism mapping on the reconfigurable fabric. Recently, consolidation reached a plateau in this field with lightweight virtualization techniques, that require a high overcommitment of FPGA accelerator resources to cope with numerous demands of guests. Although FPGAs can play an important role for the future 5G networks their capability to manage and control them from the upper layers of the software stack is inadequate. The lack of such support coupled with cloud integration and programmability issues can repel potential providers from utilizing FPGAs at their data centers. This paper presents the communication mechanism of the vFPGAmanager, an FPGA virtualization framework which can be orchestrated, monitored and enables accelerators overcommitment with direct guest access. These are key features to allow potential adopters of FPGA technology to include them in the next generation of NFV systems. The communication mechanism architecture is detailed and then benchmarked to show that even under heavy load on the system it demonstrates a minimal overhead to orchestrate and monitor the FPGA as a resource.

Question: What is the communication mechanism of the vFPGAmanager?


A passivity based Cartesian impedance controller for flexible joint robots - part II: full state feedback, impedance design and experiments. The paper presents a Cartesian impedance controller for flexible joint robots based on the feedback of the complete state of the system, namely the motor position, the joint torque and their derivatives. The approach is applied to a quite general robot model, in which also a damping element is considered in parallel to the joint stiffness. Since passivity and asymptotic stability of the controller hold also for varying damping matrices, some possibilities of designing those gain matrices (depending on the actual inertia matrix) are addressed. The passivity of the controller relies on the usage of only motor side measurements for the position feedback. A method is introduced, which provides the exact desired link side stiffness based on this motor position information. Experimental results are validating the proposed controller.

Question: How to design a passivity based Cartesian impedance controller for flexible joint robots?


Traffic management: a holistic approach to memory placement on NUMA systems. NUMA systems are characterized by Non-Uniform Memory Access times, where accessing data in a remote node takes longer than a local access. NUMA hardware has been built since the late 80's, and the operating systems designed for it were optimized for access locality. They co-located memory pages with the threads that accessed them, so as to avoid the cost of remote accesses. Contrary to older systems, modern NUMA hardware has much smaller remote wire delays, and so remote access costs per se are not the main concern for performance, as we discovered in this work. Instead, congestion on memory controllers and interconnects, caused by memory traffic from data-intensive applications, hurts performance a lot more. Because of that, memory placement algorithms must be redesigned to target traffic congestion. This requires an arsenal of techniques that go beyond optimizing locality. In this paper we describe Carrefour, an algorithm that addresses this goal. We implemented Carrefour in Linux and obtained performance improvements of up to 3.6 relative to the default kernel, as well as significant improvements compared to NUMA-aware patchsets available for Linux. Carrefour never hurts performance by more than 4% when memory placement cannot be improved. We present the design of Carrefour, the challenges of implementing it on modern hardware, and draw insights about hardware support that would help optimize system software on future NUMA systems.

Question: What is the difference between NUMA and NUMA systems?


Discriminative learning for differing training and test distributions. We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.

Question: How to learn under covariate shift?


Conflict in Adult Close Relationships : An Attachment Perspective. Relationship researchers have focused on the frequency of conflict in couples' relationships and the manner in which couples engage in and try to resolve conflicts. conflict, under some conditions, may facilitate the development and maintenance of associated with patterns of behavior (e.g., negative affect reciprocity, demand-withdraw) and thought that tend to escalate conflict and make it more difficult to negotiate a facilitates intimacy or exacerbates distress may depend on individual differences in the way in which people interpret and respond to conflict. framework for understanding different responses to conflict. People are thought to differ in their working models of attachment, which include expectations, beliefs, and goals These working models are likely to shape people's thoughts, feelings, and behavior during conflict. For example, a person who expects close others to be generally responsive and available is likely to interpret and respond to conflict very differently from a person who expects close others to be rejecting and unavailable. Attachment theory may be able to inform the literature on 3 conflict in close relationships by suggesting how individuals might differ in how they construe conflict. At the same time, the study of relationship conflict provides a useful context for testing important aspects of attachment theory. Conflict may be particularly likely to reveal attachment processes because (a) it may act as a stressor on the relationship and thereby activate the attachment system (Simpson, Rholes, & Phillips, 1996), (b) it challenges partners' abilities to regulate their emotions and behavior (Kobak & Duemmler, 1994), which are thought to be connected to attachment processes and (c) it may trigger behaviors (e.g., personal disclosures) that typically promote intimacy, thereby providing evidence relevant to different attachment goals such as achieving intimacy or maintaining

Question: What is the difference between conflict and disagreement?


On temporal-spatial realism in the virtual reality environment. The Polhemus Isotrak is often used as an orientation and position tracking device in virtual reality environments. When it is used to dynamically determine the user's viewpoint and line of sight ( e.g. in the case of a head mounted display) the noise and delay in its measurement data causes temporal-spatial distortion, perceived by the user as jittering of images and lag between head movement and visual feedback. To tackle this problem, we first examined the major cause of the distortion, and found that the lag felt by the user is mainly due to the delay in orientation data, and the jittering of images is caused mostly by the noise in position data. Based on these observations, a predictive Kalman filter was designed to compensate for the delay in orientation data, and an anisotropic low pass filter was devised to reduce the noise in position data. The effectiveness and limitations of both approaches were then studied, and the results shown to be satisfactory.

Question: How to reduce the noise in position data?


Common Ground in Dialogue with a Gendered Humanoid Robot *. s of the 2004 Conference on Human Factors and Computing Systems, pp. 1485-1488, April 2004.

Question: What is the common ground in dialogue with a gendered humanoid robot?


Kinesthetic interaction: revealing the bodily potential in interaction design. Within the Human-Computer Interaction community there is a growing interest in designing for the whole body in interaction design. The attempts aimed at addressing the body have very different outcomes spanning from theoretical arguments for understanding the body in the design process, to more practical examples of designing for bodily potential. This paper presents Kinesthetic Interaction as a unifying concept for describing the body in motion as a foundation for designing interactive systems. Based on the theoretical foundation for Kinesthetic Interaction, a conceptual framework is introduced to reveal bodily potential in relation to three design themes --- kinesthetic development, kinesthetic means and kinesthetic disorder; and seven design parameters --- engagement, sociality, movability, explicit motivation, implicit motivation, expressive meaning and kinesthetic empathy. The framework is a tool to be utilized when analyzing existing designs, as well as developing designs exploring new ways of designing kinesthetic interactions.

Question: What is kinesthetic interaction?


Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data.. Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques.The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected.The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1-5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described.

Question: What is the best foetal heart rate estimation method?


Decision Support for Handling Mismatches between COTS Products and System Requirements. In the process of selecting commercial off-the-shelf (COTS) products, it is inevitable to encounter mismatches between COTS products and system requirements. Mismatches occur when COTS attributes do not exactly match our requirements. Many of these mismatches are resolved after selecting a COTS product in order to improve its fitness with the requirements. This paper proposes a decision support approach that aims at addressing COTS mismatches during and after the selection process. Our approach can be integrated with existing COTS selection methods at two stages: (I) When evaluating COTS candidates: our approach is used to estimate the anticipated fitness of the candidates if their mismatches are resolved. This helps to base our COTS selection decisions on the fitness that the COTS candidates will eventually have if selected. (2) After selecting a COTS product: the approach suggests alternative plans for resolving the most appropriate mismatches using suitable actions, such that the most important risk, technical, and resource constraints are met. A case study from the e-services domain is used to illustrate the method and to discuss its added value

Question: What is a decision support approach for handling mismatches between COTS products and system requirements?


Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. To describe the log-likelihood computation in our model, let us consider a two scale pyramid for the moment. Given a (vectorized) j × j image I , denote by l = d(I) the coarsened image, and h = I − u(d(I)) to be the high pass. In this section, to simplify the computations, we use a slightly different u operator than the one used to generate the images displayed in Figure 3 of the paper. Namely, here we take d(I) to be the mean over each disjoint block of 2× 2 pixels, and take u to be the operator that removes the mean from each 2× 2 block. Since u has rank 3d/4, in this section, we write h in an orthonormal basis of the range of u, then the (linear) mapping from I to (l, h) is unitary. We now build a probability density p on Rd2 by p(I) = q0(l, h)q1(l) = q0(d(I), h(I))q1(d(I)); in a moment we will carefully define the functions qi. For now, suppose that qi ≥ 0, ∫ q1(l) dl = 1, and for each fixed l, ∫ q0(l, h) dh = 1. Then we can check that p has unit integral: ∫

Question: What is the difference between the u operator used in the paper and the u operator used in this example?


The role of rumination in depressive disorders and mixed anxiety/depressive symptoms.. Several studies have shown that people who engage in ruminative responses to depressive symptoms have higher levels of depressive symptoms over time, after accounting for baseline levels of depressive symptoms. The analyses reported here showed that rumination also predicted depressive disorders, including new onsets of depressive episodes. Rumination predicted chronicity of depressive disorders before accounting for the effects of baseline depressive symptoms but not after accounting for the effects of baseline depressive symptoms. Rumination also predicted anxiety symptoms and may be particularly characteristic of people with mixed anxiety/depressive symptoms.

Question: What is the role of rumination in depressive disorders?


Diagnostic assessment of the borg MOEA for many-objective product family design problems. The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the ε-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.

Question: What is the borg MOEA?


Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.

Question: What is the difference between a deep reinforcement learning algorithm and a deep reinforcement learning algorithm?