Haptic Feedback in Room-Scale VR. Virtual reality (VR) is now becoming a mainstream medium. Current systems like the HTC Vive offer accurate tracking of the HMD and controllers, which allows for highly immersive interactions with the virtual environment. The interactions can be further enhanced by adding feedback. As an example, a controller can vibrate when it is close to a grabbable ball. As such interactions are not exhaustingly researched, we conducted a user study. Specifically, we examine: 1. grabbing and throwing with controllers in a simple basketball game. 2. the influence of haptic and optical feedback on performance, presence, task load, and usability. 3. the advantages of VR over desktop for point-cloud editing. Several new techniques emerged from the point-cloud editor for VR. The bi-manual pinch gesture, which extends the handlebar metaphor, is a novel viewing method used to translate, rotate, and scale the point-cloud. Our new rendering technique uses the geometry shader to draw sparse point clouds quickly. The selection volumes at the controllers are our new technique to efficiently select points in point clouds. The resulting selection is visualized in real time. The results of the user study show that: 1. grabbing with a controller button is intuitive but throwing is not. Releasing a button is a bad metaphor for releasing a grabbed virtual object in order to throw it. 2. any feedback is better than none. Adding haptic, optical, or both feedback types to the grabbing improves the user performance and presence. However, only sub-scores like accuracy and predictability are significantly improved. Usability and task load are mostly unaffected by feedback. 3. the point-cloud editing is significantly better in VR with the bi-manual pinch gesture and selection volumes than on the desktop with the orbiting camera and lasso selections.

Question: What is the bi-manual pinch gesture?


A novel approach for efficient supergraph query processing on graph databases. In recent years, large amount of data modeled by graphs, namely graph data, have been collected in various domains. Efficiently processing queries on graph databases has attracted a lot of research attentions. Supergraph query is a kind of new and important queries in practice. A supergraph query, q, on a graph database D is to retrieve all graphs in D such that q is a supergraph of them. Because the number of graphs in databases is large and subgraph isomorphism testing is NP-complete, efficiently processing such queries is a big challenge. This paper first proposes an optimal compact method for organizing graph databases. Common subgraphs of the graphs in a database are stored only once in the compact organization of the database, in order to reduce the overall cost of subgraph isomorphism testings from stored graphs to queries during query processing. Then, an exact algorithm and an approximate algorithm for generating significant feature set with optimal order are proposed to construct indices on graph databases. The optimal order on the feature set is to reduce the number of subgraph isomorphism testings during query processing. Based on the compact organization of graph databases, a novel algorithm of testing subgraph isomorphisms from multiple graphs to one graph is presented. Finally, based on all these techniques, a query processing method is proposed. Analytical and experimental results show that the proposed algorithms outper-form the existing similar algorithms by one to two orders of magnitude.

Question: What is the optimal order on the feature set?


Maximum entropy models for natural language ambiguity resolution. MAXIMUM ENTROPY MODELS FOR NATURAL LANGUAGE AMBIGUITY RESOLUTION Adwait Ratnaparkhi Supervisor Professor Mitch Marcus This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state of the art accuracies using a single statistical modeling technique based on the principle of maximum entropy We discuss the problems of sentence boundary detection part of speech tagging prepo sitional phrase attachment natural language parsing and text categorization under the maximum entropy framework In practice we have found that maximum entropy models o er the following advantages State of the art Accuracy The probability models for all of the tasks discussed per form at or near state of the art accuracies or outperform competing learning algo rithms when trained and tested under similar conditions Methods which outperform those presented here require much more supervision in the form of additional human involvement or additional supporting resources Knowledge Poor Features The facts used to model the data or features are linguis tically very simple or knowledge poor but yet succeed in approximating complex linguistic relationships Reusable Software Technology The mathematics of the maximum entropy framework

Question: What is the maximum entropy framework?


Design and implementation of intrusion detection system using convolutional neural network for DoS detection. Nowadays, network is one of the essential parts of life, and lots of primary activities are performed by using the network. Also, network security plays an important role in the administrator and monitors the operation of the system. The intrusion detection system (IDS) is a crucial module to detect and defend against the malicious traffics before the system is affected. This system can extract the information from the network system and quickly indicate the reaction which provides real-time protection for the protected system. However, detecting malicious traffics is very complicating because of their large quantity and variants. Also, the accuracy of detection and execution time are the challenges of some detection methods. In this paper, we propose an IDS platform based on convolutional neural network (CNN) called IDS-CNN to detect DoS attack. Experimental results show that our CNN based DoS detection obtains high accuracy at most 99.87%. Moreover, comparisons with other machine learning techniques including KNN, SVM, and Naïve Bayes demonstrate that our proposed method outperforms traditional ones.

Question: How to detect DoS attack?


Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014. This paper discusses the thematic backdrop for Spanish library and information science output. It draws from Web of Science records on papers authored by researchers at Spanish institutions and published under the category 'Information Science & Library Science' between 1985 and 2014. Two analytical techniques were used, one based on co-keyword and the other on document co-citation networks. Burst detection was applied to noun phrases and references of the intellectual base. Co-citation analysis identified nine research fronts: 'digital rights management', 'citation analysis', 'translation services', 'bibliometric analysis', 'co-authorship', 'electronic books', 'webometrics', 'information systems' and 'world wide web'. The most recent trends in the subject areas addressed in Spain were found to lie in metrics-related sub-specialities: the h-index, scientific collaboration, journal bibliometric indicators, rankings, universities and webometrics.

Question: What is the most recent trend in the subject areas addressed in Spain?


Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information. The possibility of automatic understanding of customers' shopping behavior and acting according to their needs is relevant in the marketing domain, attracting a lot of attention lately. In this work, we focus on the task of automatic assessment of customers' shopping behavior, by proposing a multilevel framework. The framework is supported at low-level by different types of cameras, which are synchronized, facilitating efficient processing of information. A fish-eye camera is used for tracking, while a high-definition one serves for the action recognition task. The experiments are performed on both laboratory and real-life recordings in a supermarket. From the video recordings, we extract features related to the spatio-temporal behavior of trajectories, the dynamics and the time spent in each region of interest (ROI) in the shop and regarding the customer-products interaction patterns. Next ∗Corresponding Author. Phone: +31(0)15 2781537. Fax: +31(0)15 2787141. Email address: m.c.popa@tudelft.nl (M.C. Popa) Preprint submitted to Pattern Recognition Letters December 24, 2011 we analyze the shopping sequences using a Hidden Markov Model (HMM). We conclude that it is possible to accurately classify trajectories (93%), discriminate between different shopping related actions (91.6%), and recognize shopping behavioral types by means of the proposed reasoning model in 95% of the cases.

Question: What is the difference between a trajectory and a shopping sequence?


Directly Fabricating Soft Robotic Actuators With an Open-Source 3-D Printer. 3-D printing silicone has been a long sought for goal by roboticists. Fused deposition manufacturing (FDM) is a readily accessible and simple 3-D printing scheme that could hold the key to printing silicone. This study details an approach to 3-D print silicone elastomer through use of a thickening additive and heat curing techniques. We fabricated an identical control actuator using molding and 3-D printing techniques for comparison. By comparing the free space elongation and fixed length force of both actuators, we were able to evaluate the quality of the print. We observed that the 3-D printed linear actuator was able to perform similarly to the molded actuator, with an average error of 5.08% in actuator response, establishing the feasibility of such a system. We envision that further development of this system would contribute to the way soft robotic systems are fabricated.

Question: How do you 3-D print silicone?


Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks. This paper proposes a self-organized power allocation technique to solve the interference problem caused by a femtocell network operating in the same channel as an orthogonal frequency division multiple access cellular network. We model the femto network as a multi-agent system where the different femto base stations are the agents in charge of managing the radio resources to be allocated to their femtousers. We propose a form of real-time multi-agent reinforcement learning, known as decentralized Q-learning, to manage the interference generated to macro-users. By directly interacting with the surrounding environment in a distributed fashion, the multi-agent system is able to learn an optimal policy to solve the interference problem. Simulation results show that the introduction of the femto network increases the system capacity without decreasing the capacity of the macro network.

Question: How does the femtocell network work?


A Comparative Analysis of Personality-Based Music Recommender Systems. This article describes a preliminary study on considering information about the target user's personality in music recommender systems (MRSs). For this purpose, we devised and implemented four MRSs and evaluated them on a sample of real users and real-world datasets. Experimental results show that MRSs that rely on purely users' personality information are able to provide performance comparable with those of a state-of-the-art MRS, even better in terms of the diversity of the suggested items.

Question: What is a personality-based music recommender system?


Keyword Search on Spatial Databases. Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.

Question: What is the nearest business to a specified location that contains a set of keywords?


Impact of an animal-assisted therapy programme on physiological and psychosocial variables of paediatric oncology patients. The objective of this study was to propose an intervention and safety protocol for performing animal-assisted therapy (AAT) and evaluating its efficacy in children under outpatient oncological treatment based on psychological, physiological, and quality of life indicators for the children and caregivers. The sample consisted of 24 children diagnosed with leukaemia and solid tumours (58% girls with a mean age of 8.0 years) who underwent an AAT programme consisting of three 30-min sessions in an open group. Two dogs (one Labrador retriever and one golden retriever) were used, and activities such as sensory stimulation, gait training, and socialization were conducted. The exclusion criteria were severe mental problems, inability to answer the questions included in the instruments used, allergy to animals, unavailability/lack of interest, isolation precaution, surgical wound, use of invasive devices, ostomy, no current blood count for evaluation, neutropaenia, infection, fever, diarrhoea, vomiting, respiratory symptoms at the beginning of the intervention or 1 week before the intervention, hospitalization or scheduled surgery, and non-completion of the AAT programme. The variables analysed using validated self or other evaluations were stress, pain, mood, anxiety, depression, quality of life, heart rate, and blood pressure. A quasi-experimental study design was used. We observed a decrease in pain (p = 0.046, d = -0.894), irritation (p = 0.041, d = -0.917), and stress (p = 0.005; d = -1.404) and a tendency towards improvement of depressive symptoms (p = 0.069; d = -0.801). Among the caregivers, an improvement was observed in anxiety (p = 0.007, d = -1.312), mental confusion (p = 0.006, d = -1.350), and tension (p = 0.006, d = -1.361). Therefore, the selection criteria and care protocols used for the AAT programme in the oncological context were ade

Question: What is the difference between a quasi-experimental study and a randomized controlled trial?


Metadata Embeddings for User and Item Cold-start Recommendations. I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations.

Question: What is a hybrid matrix factorisation model?


DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving. Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations.

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


Compiling Bayesian Networks Using Variable Elimination. Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of compiling based on variable elimination (VE) and Algebraic Decision Diagrams (ADDs). The approach is important for the following reasons. First, it exploits local structure much more effectively than previous techniques based on VE. Second, the approach allows any of the many VE variants to compute answers to multiple queries simultaneously. Third, the approach makes a large body of research into more structured representations of factors relevant in many more circumstances than it has been previously. Finally, experimental results demonstrate that VE can exploit local structure as effectively as state–of–the–art algorithms based on conditioning on the networks considered, and can sometimes lead to much faster compilation times.

Question: What is the name of the algorithm?


A framework for cognitive Internet of Things based on blockchain. Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.

Question: What is a framework for cognitive Internet of Things based on blockchain?


Interaction control of an UAV endowed with a manipulator. In this paper, we present the design, simulation and experimental validation of a control architecture for an unmanned aerial vehicle endowed with a manipulation system and interacting with a remote environment. The goal of this work is to show that the interaction control allows the manipulator to track a desired force, normal to a vertical wall, while still maintaining the possibility of moving on the wall. The control strategy has been implemented and validated in simulations and experiments on the manipulator standalone, i.e., attached to a fixed base, and on the manipulator attached to the aerial vehicle.

Question: How to control a manipulator attached to an aerial vehicle?


The bright-side and the dark-side of CEO personality: examining core self-evaluations, narcissism, transformational leadership, and strategic influence.. This article reports on an examination of the relationships between chief executive officer (CEO) personality, transformational and transactional leadership, and multiple strategic outcomes in a sample of 75 CEOs of Major League Baseball organizations over a 100-year period. CEO bright-side personality characteristics (core self-evaluations) were positively related to transformational leadership, whereas dark-side personality characteristics (narcissism) of CEOs were negatively related to contingent reward leadership. In turn, CEO transformational and contingent reward leadership were related to 4 different strategic outcomes, including manager turnover, team winning percentage, fan attendance, and an independent rating of influence. CEO transformational leadership was positively related to ratings of influence, team winning percentage, and fan attendance, whereas contingent reward leadership was negatively related to manager turnover and ratings of influence.

Question: What is the relationship between CEO personality and transformational leadership?


SLAM and Static Driver Verifier: Technology Transfer of Formal Methods inside Microsoft. A tutorial introduction to designs in unifying theories of programming p. 40 An integration of program analysis and automated theorem proving p. 67 Verifying controlled components p. 87 Efficient CSP[subscript Z] data abstraction p. 108 State/event-based software model checking p. 128 Formalising behaviour trees with CSP p. 148 Generating MSCs from an integrated formal specification language p. 168 UML to B : formal verification of object-oriented models p. 187 Software verification with integrated data type refinement for integer arithmetic p. 207

Question: What is the difference between a formal specification language and a formal verification language?


An Empirical Evaluation of True Online TD({\lambda}). The true online TD(λ) algorithm has recently been proposed (van Seijen and Sutton, 2014) as a universal replacement for the popular TD(λ) algorithm, in temporal-difference learning and reinforcement learning. True online TD(λ) has better theoretical properties than conventional TD(λ), and the expectation is that it also results in faster learning. In this paper, we put this hypothesis to the test. Specifically, we compare the performance of true online TD(λ) with that of TD(λ) on challenging examples, random Markov reward processes, and a real-world myoelectric prosthetic arm. We use linear function approximation with tabular, binary, and non-binary features. We assess the algorithms along three dimensions: computational cost, learning speed, and ease of use. Our results confirm the strength of true online TD(λ): 1) for sparse feature vectors, the computational overhead with respect to TD(λ) is minimal; for non-sparse features the computation time is at most twice that of TD(λ), 2) across all domains/representations the learning speed of true online TD(λ) is often better, but never worse than that of TD(λ), and 3) true online TD(λ) is easier to use, because it does not require choosing between trace types, and it is generally more stable with respect to the step-size. Overall, our results suggest that true online TD(λ) should be the first choice when looking for an efficient, general-purpose TD method.

Question: What is the true online TD(λ)?


Diagnosis and debugging of programmable logic controller control programs by neural networks. Ladder logic diagram (LLD) as the interfacing programming language of programmable logic controllers (PLCs) is utilized in modern discrete event control systems. However, LLD is hard to debug and maintain in practice. This is due to many factors such as non-structured nature of LLD, the LLD programmers' background, and the huge sizes of real world LLD. In this paper, we introduce a recurrent neural network (RNN) based technique for PLC program diagnosis. A manufacturing control system example has been presented to illustrate the applicability of the proposed algorithm. This method could be very advantageous in reducing the complexity in PLC control programs diagnosis because of the ease of use of the RNN compared to debugging the LLD code.

Question: How to debug a programmable logic controller?


THE STRUCTURE ( S ) OF PARTICLE VERBS. • complex heads are formed either in the lexicon (cf. Booij 1990 , Johnson 1991 , Koizumi 1993, Neeleman 1994, Neeleman & Weerman 1993, Stiebels 1996, Stiebels & Wunderlich 1994, Wiese 1996, Ackerman & Webelhuth 1998 ) or by some kind of overt or covert incorporation of the particle into the verb (cf. van Riemsdijk 1978, Baker 1988, Koopman 1995, Olsen 1995a, 1995b, Zeller 1997a, 1997b, 1998) • small clause structure (cf. Kayne 1985, Guéron 1987 , 1990, Hoekstra 1988, Grewendorf 1990, Bennis 1992, Mulder 1992 , den Dikken 1992, 1995 , Zwart 1997 among others) 1

Question: What is the structure of particle verbs?


Support-Vector Networks. The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

Question: What is the support-vector network?


Overview of the High Efficiency Video Coding (HEVC) Standard. High Efficiency Video Coding (HEVC) is currently being prepared as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The main goal of the HEVC standardization effort is to enable significantly improved compression performance relative to existing standards-in the range of 50% bit-rate reduction for equal perceptual video quality. This paper provides an overview of the technical features and characteristics of the HEVC standard.

Question: What is the high efficiency video coding standard?


Detecting epistasis in human complex traits. Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

Question: What is the purpose of this review?


Hopping of a monopedal robot with a biarticular muscle driven by electromagnetic linear actuators. The compliance of muscles with external forces and the structural stability given by biarticular muscles are important features of animals to realize dynamic whole body motions such as running and hopping in various environments. For this reason, we have been studying an electromagnetic linear actuator. This actuator can emulate the behavior of a human muscle such as the spring-damper characteristics by quick control of the output force (i.e. impedance control) and it is expected to be used as an artificial muscle. In this paper, we develop a monopedal robot possessing bi- and mono-articular muscles implemented by linear actuators. Thanks to the biarticular muscle, the bouncing direction of the robot can be controlled by changing the stiffness ellipse at the endpoint (i.e. foot) of the robot. We confirm that the bouncing direction of the robot can be controlled and hopping can be achieved by changing the stiffness ellipse.

Question: What is a monopedal robot?


Online Convex Programming and Generalized Infinitesimal Gradient Ascent. Convex programming involves a convex set F ⊆ R and a convex function c : F → R. The goal of convex programming is to find a point in F which minimizes c. In this paper, we introduce online convex programming. In online convex programming, the convex set is known in advance, but in each step of some repeated optimization problem, one must select a point in F before seeing the cost function for that step. This can be used to model factory production, farm production, and many other industrial optimization problems where one is unaware of the value of the items produced until they have already been constructed. We introduce an algorithm for this domain, apply it to repeated games, and show that it is really a generalization of infinitesimal gradient ascent, and the results here imply that generalized infinitesimal gradient ascent (GIGA) is universally consistent.

Question: What is online convex programming?


Noncontact Proximity Vital Sign Sensor Based on PLL for Sensitivity Enhancement. In this paper, a noncontact proximity vital sign sensor, using a phase locked loop (PLL) incorporated with voltage controlled oscillator (VCO) built-in planar type circular resonator, is proposed to enhance sensitivity in severe environments. The planar type circular resonator acts as a series feedback element of the VCO as well as a near-field receiving antenna. The frequency deviation of the VCO related to the body proximity effect ranges from 0.07 MHz/mm to 1.8 MHz/mm (6.8 mV/mm to 205 mV/mm in sensitivity) up to a distance of 50 mm, while the amount of VCO drift is about 21 MHz in the condition of 60 °C temperature range and discrete component tolerance of ±5%. Total frequency variation occurs in the capture range of the PLL which is 60 MHz. Thus, its loop control voltage converts the amount of frequency deviation into a difference of direct current (DC) voltage, which is utilized to extract vital signs regardless of the ambient temperature. The experimental results reveal that the proposed sensor placed 50 mm away from a subject can reliably detect respiration and heartbeat signals without the ambiguity of harmonic signals caused by respiration signal at an operating frequency of 2.4 GHz.

Question: What is the sensitivity of the sensor?


A study on human gait analysis. Human gait is one of the most important biometric which has so far been neglected for use in medical diagnostics. In this paper, we make a feasibility study on human gait acquired from a wearable sensor based biometric suit called as Intelligent Gait Oscillation Detector (IGOD). This suit measures simultaneous gait oscillation from eight major joints (two knees, two hips, two elbows and two shoulders) of a human body. Techniques for analyzing and understanding the human gait patterns were developed. Variance in the gait oscillation was studied with respect to gait speed varying from 3km/hr to 5km/hr. Gender variance (male/female) gait oscillation has also been studied. A comprehensive analysis on human gait affected by knee joint movement and hip joint oscillation has been addressed with the arm swing effects. This analysis will provide us with an insight on human bipedal locomotion and its stability. We plan to create a repository of human gait oscillations which could extensively be analyzed for person identification and detecting walking problems in patients, which is detection of disease in the medical field.

Question: What is the difference between gait and walking?


A Teachable-Agent Arithmetic Game's Effects on Mathematics Understanding, Attitude and Self-efficacy. A teachable-agent arithmetic game is presented and evaluated in terms of student performance, attitude and self-eff icacy. An experimental prepost study design was used, enrolling 153 3 rd and 5 grade students in Sweden. The playing group showed significantly larger gains i math performance and self-efficacy beliefs, but not in general attitude towards math, compared to control groups. The contributions in relation to pr evious work include a novel educational game being evaluated, and an emphasis o n elf-efficacy in the study as a strong predictor of math achievements.

Question: What is a teachable-agent arithmetic game?


Sediment quality criteria in use around the world. There have been numerous sediment quality guidelines (SQGs) developed during the past 20 years to assist regulators in dealing with contaminated sediments. Unfortunately, most of these have been developed in North America. Traditionally, sediment contamination was determined by assessing the bulk chemical concentrations of individual compounds and often comparing them with background or reference values. Since the 1980s, SQGs have attempted to incorporate biological effects in their derivation approach. These approaches can be categorized as empirical, frequency-based approaches to establish the relationship between sediment contamination and toxic response, and theoretically based approaches that attempt to account for differences in bioavailability through equilibrium partitioning (EqP) (i.e., using organic carbon or acid volatile sulfides). Some of these guidelines have been adopted by various regulatory agencies in several countries and are being used as cleanup goals in remediation activities and to identify priority polluted sites. The original SQGs, which compared bulk chemical concentrations to a reference or to background, provided little insight into the ecosystem impact of sediment contaminants. Therefore, SQGs for individual chemicals were developed that relied on field sediment chemistry paired with field or laboratory-based biological effects data. Although some SQGs have been found to be relatively good predictors of significant site contamination, they also have several limitations. False positive and false negative predictions are frequently in the 20% to 30% range for many chemicals and higher for others. The guidelines are chemical specific and do not establish causality where chemical mixtures occur. Equilibrium-based guidelines do not consider sediment ingestion as an exposure route. The guidelines do not consider spatial and temporal variability, and they may not apply in dynamic or larger-grai

Question: What is the difference between sediment quality guidelines and sediment quality criteria?


Control of lower limb exoskeleton for elderly assistance on basic mobility tasks. This paper presents the modelling, simulation and control of a lower limbs exoskeleton devices whose aim is to assist elderly people during standing-up and walking tasks. A humanoid and actuated exoskeleton frames were modelled in Solid Works and assembled in Visual Nastran 4D virtual environment. Control of exoskeletons actuators by means of PID and fuzzy controllers, together with a finite state machine, was designed. Simulations of the humanoid wearing an exoskeleton to perform the mentioned tasks, using controllers with set and adaptive orientation references as input, were performed. It is assumed that the humanoid, representing an old person, is only capable to provide 70% of the required torque, therefore the exoskeleton must supply 30% to complete the motions. With the aid of exoskeleton devices, the elderly will be able to perform motions of a physically able people. Optimization of fuzzy controller parameters for walking motion was done using SDA. The exoskeleton succeeded on assisting standing-up and walking motions but research is still needed to improve the performance and adaptability of the system.

Question: How to control a lower limb exoskeleton for elderly assistance on basic mobility tasks?


Usability Analysis of Visual Programming Environments: A 'Cognitive Dimensions' Framework. The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other. The purpose of this paper is to propose the framework as an evaluation technique for visual programming environments. We apply it to two commercially-available dataflow languages (with further examples from other systems) and conclude that it is effective and insightful; other HCI-based evaluation techniques focus on different aspects and would make good complements. Insofar as the examples we used are representative, current VPLs are successful in achieving a good 'closeness of match', but designers need to consider the 'viscosity' (resistance to local change) and the 'secondary notation' (possibility of conveying extra meaning by choice of layout, colour, etc.).

Question: What is a cognitive dimensions framework?


MODELING AND CONTROL OF BALL AND BEAM SYSTEM USING MODEL BASED AND NON-MODEL BASED CONTROL APPROACHES. The ball and beam system is a laboratory equipment with high nonlinearity in its dynamics. The aims of this research are to model the ball and beam system considering nonlinear factors and coupling effect and to design controllers to control the ball position. The LQR is designed considering two Degrees-of-Freedom and

Question: What is the ball and beam system?


Security Ontology: Simulating Threats to Corporate Assets. Threat analysis and mitigation, both essential for corporate security, are time consuming, complex and demand expert knowledge. We present an approach for simulating threats to corporate assets, taking the entire infrastructure into account. Using this approach effective countermeasures and their costs can be calculated quickly without expert knowledge and a subsequent security decisions will be based on objective criteria. The ontology used for the simulation is based on Landwehr's [ALRL04] taxonomy of computer security and dependability.

Question: What is the ontology used for the simulation?


Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings. The placement of the complete baseband processing in a centralized pool results in high data rate requirement and inflexibility of the fronthaul network, which challenges the energy and cost effectiveness of the cloud radio access network (C-RAN). Recently, redesign of the C-RAN through functional split in the baseband processing chain has been proposed to overcome these challenges. This paper evaluates, by mathematical and simulation methods, different splits with respect to network level energy and cost efficiency having in the mind the expected quality of service. The proposed mathematical model quantifies the multiplexing gains and the trade-offs between centralization and decentralization concerning the cost of the pool, fronthaul network capacity and resource utilization. The event-based simulation captures the influence of the traffic load dynamics and traffic type variation on designing an efficient fronthaul network. Based on the obtained results, we derive a principle for fronthaul dimensioning based on the traffic profile. This principle allows for efficient radio access network with respect to multiplexing gains while achieving the expected users' quality of service.

Question: How to evaluate the performance of the fronthaul network?


Design methodology using inversion coefficient for low-voltage low-power CMOS voltage reference. This paper presents an analog design methodology, using the selection of inversion coefficient of MOS devices, to design low voltage and low-power (LVLP) CMOS voltage references. These circuits often work under subthreshold operation. Hence, there is a demand for analog design methods that optimize the sizing process of transistors working in weak and moderate inversion. The advantage of the presented method -- compared with the traditional approach to design circuits -- is the reduction of design cycle time and minimization of trial-and-error simulations, if the proposed equations are used. As a case study, a LVLP voltage reference based on subthreshold MOSFETs with supply voltage of 0.7 V was designed for 0.18-¼m CMOS technology.

Question: How to design a low voltage and low power CMOS voltage reference?


Man-At-The-End attacks: Analysis, taxonomy, human aspects, motivation and future directions. Man-At-The-End (MATE) attacks and fortifications are difficult to analyze, model, and evaluate predominantly for three reasons: firstly, the attacker is human and, therefore, utilizes motivation, creativity, and ingenuity. Secondly, the attacker has limitless and authorized access to the target. Thirdly, all major protections stand up to a determined attacker till a certain period of time. Digital assets range from business to personal use, from consumer devices to home networks, the public Internet, the cloud, and the Internet of Things – where traditional computer and network security are inadequate to address MATE attacks. MATE is fundamentally a hard problem. Much of the extant focus to deal with MATE attacks is purely technical; though security is more than just a technical issue. The main objective of the paper is to mitigate the consequences of MATE attacks through the human element of security and highlight the need for this element to form a part of a holistic security strategy alongside the necessary techniques and technologies. This paper contributes by taking software protection (SP) research to a new realm of challenges. Moreover, the paper elaborates the concept of MATE attacks, the different forms, and the analysis of MATE versus insider threats to present a thematic taxonomy of a MATE attack. The ensuing paper also highlights the fundamental concept of digital assets, and the core protection mechanisms and their qualitative comparison against MATE attacks. Finally, we present state-of-the-art trends and cutting-edge future research directions by taking into account only the human aspects for young researchers and professionals. & 2014 Elsevier Ltd. All rights reserved.

Question: What is the difference between a man-at-the-end attack and a man-in-the-middle attack?


ZoomNet: Deep Aggregation Learning for High-Performance Small Pedestrian Detection. It remains very challenging for a single deep model to detect pedestrians of different sizes appears in an image. One typical remedy for the small pedestrian detection is to upsample the input and pass it to the network multiple times. Unfortunately this strategy not only exponentially increases the computational cost but also probably impairs the model effectiveness. In this work, we present a deep architecture, refereed to as ZoomNet, which performs small pedestrian detection by deep aggregation learning without up-sampling the input. ZoomNet learns and aggregates deep feature representations at multiple levels and retains the spatial information of the pedestrian from different scales. ZoomNet also learns to cultivate the feature representations from the classification task to the detection task and obtains further performance improvements. Extensive experimental results demonstrate the state-of-the-art performance of ZoomNet. The source code of this work will be made public available to facilitate further studies on this problem.

Question: How to detect small pedestrians?


A new approach to developing and implementing eager database replication protocols. Database replication is traditionally seen as a way to increase the availability and performance of distributed databases. Although a large number of protocols providing data consistency and fault-tolerance have been proposed, few of these ideas have ever been used in commercial products due to their complexity and performance implications. Instead, current products allow inconsistencies and often resort to centralized approaches which eliminates some of the advantages of replication. As an alternative, we propose a suite of replication protocols that addresses the main problems related to database replication. On the one hand, our protocols maintain data consistency and the same transactional semantics found in centralized systems. On the other hand, they provide flexibility and reasonable performance. To do so, our protocols take advantage of the rich semantics of group communication primitives and the relaxed isolation guarantees provided by most databases. This allows us to eliminate the possibility of deadlocks, reduce the message overhead and increase performance. A detailed simulation study shows the feasibility of the approach and the flexibility with which different types of bottlenecks can be circumvented.

Question: What is a database replication protocol?


Natural language question answering over RDF: a graph data driven approach. RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a national language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.

Question: What is the cost of disambiguation?


Go with the Flow: When Listeners Use Music as Technology. Music has been shown to have a profound effect on listeners' internal states as evidenced by neuroscience research. Listeners report selecting and listening to music with specific intent, thereby using music as a tool to achieve desired psychological effects within a given context. In light of these observations, we argue that music information retrieval research must revisit the dominant assumption that listening to music is only an end unto itself. Instead, researchers should embrace the idea that music is also a technology used by listeners to achieve a specific desired internal state, given a particular set of circumstances and a desired goal. This paper focuses on listening to music in isolation (i.e., when the user listens to music by themselves with headphones) and surveys research from the fields of social psychology and neuroscience to build a case for a new line of research in music information retrieval on the ability of music to produce flow states in listeners. We argue that interdisciplinary collaboration is necessary in order to develop the understanding and techniques necessary to allow listeners to exploit the full potential of music as psychological technology.

Question: What is the difference between listening to music and listening to music with headphones?


Breakthrough in improving the skin sagging with focusing on the subcutaneous tissue structure , retinacula cutis. Skin sagging is one of the most prominent aging signs and a concerning issue for people over middle age. Although many cosmetic products are challenging the reduction of the skin sagging by improving the dermal elasticity, which decreases with age, the effects are insufficient for giving drastic changes to the facial morphology. This study focused on subcutaneous tissue for investigating a skin sagging mechanism. Subcutaneous tissue consists of predominantly adipose tissue with fibrous network structures, called retinacula cutis (RC), which is reported to have a possibility to maintain the soft tissue structure and morphology. This study investigated the effect of subcutaneous tissue physical-property alteration due to RC-deterioration on the skin sagging. For evaluating RC structure noninvasively, the tomographic images of faces were obtained by magnetic resonance (MR) imaging. Subcutaneous tissue network structures observed by MR imaging were indicated to be RC by comparing MR images and the histological specimens of human skin. The density of RC was measured by image analysis. For evaluating sagging degree and physical properties of the skin, sagging scoring and the measurement of elasticity of deeper skin layers were performed. The density of RC was correlated with the elasticity data of deeper skin layers, and the sagging scores tended to increase with decreasing the density. These results suggested that the sparse RC structure gave a decrease in the elasticity of subcutaneous tissue layer, which consequently would be the cause of facial sagging. This study would be a pathfinder for the complete elimination of skin sagging.

Question: What is the cause of skin sagging?


Graph Database Applications and Concepts with Neo4j. Graph databases (GDB) are now a viable alternative to Relational Database Systems (RDBMS). Chemistry, biology, semantic web, social networking and recommendation engines are all examples of applications that can be represented in a much more natural form. Comparisons will be drawn between relational database systems (Oracle, MySQL) and graph databases (Neo4J) focusing on aspects such as data structures, data model features and query facilities. Additionally, several of the inherent and contemporary limitations of current offerings comparing and contrasting graph vs. relational database implementations will be explored.

Question: What is a graph database?


Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking. Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.

Question: What is hyperspectral image?


User Experience Design and Agile Development : From Theory to Practice. We used existing studies on the integration of user experience design and agile methods as a basis to develop a framework for integrating UX and Agile. We performed a field study in an ongoing project of a medium-sized company in order to check if the proposed framework fits in the real world, and how some aspects of the integration of UX and Agile work in a real project. This led us to some conclusions situating contributions from practice to theory and back again. The framework is briefly described in this paper and consists of a set of practices, artifacts, and techniques derived from the literature. By combining theory and practice we were able to confirm some thoughts and identify some gaps—both in the company process and in our proposed framework—and drive our attention to new issues that need to be addressed. We believe that the most important issues in our case study are: UX designers cannot collaborate closely with developers because UX designers are working on multiple projects and that UX designers cannot work up front because they are too busy with too many projects at the same time.

Question: How can UX designers collaborate with developers?


A Solution to Separation and Multicollinearity in Multiple Logistic Regression.. In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Question: How to solve the problem of multicollinearity in logistic regression?


DESIGN AND IMPLEMENTATION OF A VIRTUAL CLASSROOM SYSTEM. In the last few decades, education has witnessed some advances in technologies involving computeraided learning that promises to drastically change the methods of teaching and learning. The World Wide Web has played a major role in information storage and dissemination in the educational community. Conventional classroom based teaching involves the delivery of course materials by the lecturer in a particular place at a defined time. Hence it imposes a constraint of time and place on both the instructor and the student. Due to human factor arising from the traditional classroom method, the lecturer may not always be able to put in optimum effort towards preparing and delivering course materials. There may also be inconsistencies in the pedagogy and learning style due to repetitive nature of teaching/learning. The objective of this paper is to develop a virtual classroom system to enhance learning on campus. The system was developed using PHP and MySQL as server side programming and database respectively. The web-based virtual classroom provides a web enabled interactive model for e-learning in which the course material is presented using multimedia and hypermedia.

Question: How does a virtual classroom system work?


Model-Driven Artificial Intelligence for Online Network Optimization. Future 5G wireless networks will rely on agile and automated network management, where the usage of diverse resources must be jointly optimized with surgical accuracy. A number of key wireless network functionalities (e.g., traffic steering, energy savings) give rise to hard optimization problems. What is more, high spatio-temporal traffic variability coupled with the need to satisfy strict per slice/service SLAs in modern networks, suggest that these problems must be constantly (re-)solved, to maintain close-to-optimal performance. To this end, in this paper we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, adaptive, and AI-based techniques. Since the mathematical tools and the studied regimes vary widely among these methodologies, a theoretical comparison is often out of reach. Therefore, the important question "what is the right ONO technique?" remains open to date. In this paper, we discuss the pros and cons of each technique and further attempt a direct quantitative comparison for a specific use case, using real data. Our results suggest that carefully combining the insights of problem modeling with state-of-the-art AI techniques provides significant advantages at reasonable complexity.

Question: What is the right online network optimization technique?


An algorithmic overview of surface registration techniques for medical imaging. This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration can be roughly partitioned into three issues: choice of transformation, elaboration of surface representation and similarity criterion, and matching and global optimization. The first issue concerns the assumptions made about the nature of relationships between the two modalities, e.g. whether a rigid-body assumption applies, and if not, what type and how general a relation optimally maps one modality onto the other. The second issue determines what type of information we extract from the 3D surfaces, which typically characterizes their local or global shape, and how we organize this information into a representation of the surface which will lead to improved efficiency and robustness in the last stage. The last issue pertains to how we exploit this information to estimate the transformation which best aligns local primitives in a globally consistent manner or which maximizes a measure of the similarity in global shape of two surfaces. Within this framework, this paper discusses in detail each surface registration issue and reviews the state-of-the-art among existing techniques.

Question: What is a surface registration?


Revisiting Cryptographic Accumulators, Additional Properties and Relations to Other Primitives. Cryptographic accumulators allow to accumulate a finite set of values into a single succinct accumulator. For every accumulated value, one can efficiently compute a witness, which certifies its membership in the accumulator. However, it is computationally infeasible to find a witness for any nonaccumulated value. Since their introduction, various accumulator schemes for numerous practical applications and with different features have been proposed. Unfortunately, to date there is no unifying model capturing all existing features. Such a model can turn out to be valuable as it allows to use accumulators in a black-box fashion. To this end, we propose a unified formal model for (randomized) cryptographic accumulators which covers static and dynamic accumulators, their universal features and includes the notions of undeniability and indistinguishability. Additionally, we provide an exhaustive classification of all existing schemes. In doing so, it turns out that most accumulators are distinguishable. Fortunately, a simple, light-weight generic transformation allows to make many existing dynamic accumulator schemes indistinguishable. As this transformation, however, comes at the cost of reduced collision freeness, we additionally propose the first indistinguishable scheme that does not suffer from this shortcoming. Finally, we employ our unified model for presenting a black-box construction of commitments from indistinguishable accumulators as well as a black-box construction of indistinguishable, undeniable universal accumulators from zero-knowledge sets. Latter yields the first universal accumulator construction that provides indistinguishability.

Question: What is a cryptographic accumulator?


Product Classification in E-Commerce using Distributional Semantics. Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.

Question: What is the taxonomy tree?


In-Store Gamification: Testing a Location-Based Treasure Hunt App in a Real Retailing Environment. Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.

Question: What is gamification?


Identifying the Scan and Attack Infrastructures Behind Amplification DDoS Attacks. Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic.  In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\% certainty.

Question: How to identify the source of an amplification attack?


From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation. We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.

Question: What is the relationship between lexical spaces and contextually-defined conceptual spaces?


Numba: a LLVM-based Python JIT compiler. Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].

Question: What is LLVM?


Automatic Readability Classification of Crowd-Sourced Data based on Linguistic and Information-Theoretic Features. This paper presents a classifier of text readability based on information-theoretic features. The classifier was developed based on a linguistic approach to readability that explores lexical, syntactic and semantic features. For this evaluation we extracted a corpus of 645 articles from Wikipedia together with their quality judgments. We show that information-theoretic features perform as well as their linguistic counterparts even if we explore several linguistic levels at once.

Question: What is the readability of the following text?


Occurrence of partenites in mollusks and the influence that metacercaria of Apophallus muehlingi (Jagerskiold, 1898) and Posthodiplostomum cuticola (Nordmann, 1832) has on some biochemical parameters in fish. A heavy parasitic load from trematode partenites of Apophallus muehlingi (Jagerskiold, 1898) was observed in the mollusk population of Lithoglyphus naticoides (Pfeiffer) after its natural invasion into the Rybinsk Reservoir. An elevated concentration of glycogen was detected in the muscles of infected fish, both in the natural populations and under experimental conditions. Interspecific differences were found for the protein concentrations when elevated concentrations were observed in the muscles of the infected roach, and no differences were discovered for the goldfish (both infected and parasite-free). The ties between the biochemical parameters and motor activity are discussed for the infected fish.

Question: What is the difference between the biochemical parameters and motor activity?


Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.

Question: What is the efficiency of the market?


Lifted Relational Neural Networks. We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method.

Question: What is a lifted neural network?


Why We Need New Evaluation Metrics for NLG. The majority of NLG evaluation relies on automatic metrics, such as BLEU. In this paper, we motivate the need for novel, systemand data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is dataand system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.

Question: What is the need for new evaluation metrics for NLG?


Multiuser Millimeter Wave Communications With Nonorthogonal Beams. Recently, millimeter-wave (mmWave) and even terahertz wireless (with higher frequency) networks have attracted significant research interests as alternatives to support the formidable growth of wireless communication services. Normally, directional beamforming (BF) is shown as a promising technique to compensate its high path loss. We focus on mmWave communications and assume that both mmWave base stations (MBSs) and user equipments (UEs) can support directional BF. As mmWave spectrum has short wavelength, massive antenna arrays can be deployed at MBSs to form multiple directional beams through BF training. Then, an MBS can transmit simultaneously to multiple UEs (SUEs) with different beams in the networks. However, the beams that serve different SUEs may transmit (almost) in the same path, especially when SUEs are distributed densely. Thus, they are not in perfect orthogonal beams. Due to the leakage of transmission power, the interference among these beams may be severe. To address this problem, typically the MBS could serve these SUEs in time division multiplex. This will degrade the spectral efficiency. In this context, we investigate the effect of nonorthogonal beam interference and then propose two novel solutions (i.e., dynamic beam switching and static beam selection) to coordinate the transmitting beams effectively. Then, an improved downlink multiuser simultaneous transmission scheme is introduced. In the scheme, an MBS can serve multiple SUEs simultaneously with multiple orthogonal and/or nonorthogonal beams to guarantee SUEs' Quality of Service. The theoretical and numerical results have shown that our scheme can largely improve the performance of the achievable rate and, meanwhile, can serve lots of SUEs simultaneously.

Question: How to coordinate the transmitting beams effectively?


Question/Answer Matching for CQA System via Combining Lexical and Sequential Information. Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.

Question: What is the architecture of the proposed system?


The effects of Zumba training on cardiovascular and neuromuscular function in female college students.. The present study examined the effects of Zumba training (group fitness based on salsa and aerobics) on endurance, trunk strength, balance, flexibility, jumping performance and quality of life (QoL) in female college students. Thirty female participants were randomly assigned (strata: age, BMI and physical activity) to an intervention (INT, n = 15: age: 21.0 ± 2.3 years; BMI: 21.8 ± 3.0 kg/m(2); physical activity (PA): 7.6 ± 4.6 h/week) or control group (CON, n = 14: age: 21.0 ± 2.8 years; BMI: 21.0 ± 2.1 kg/m(2); PA: 7.3 ± 3.6 h/week). Instructed Zumba training was provided twice a week for 8 weeks (training attendance: 100%). QoL was assessed using the WHO-QoL-BREF questionnaire. Endurance was measured with the 6-min walking test (6MWT). Trunk flexibility was assessed with the stand-and-reach-test and lower-extremity strength with the jump-and-reach-test. The star excursion balance test (SEBT) was employed to assess dynamic balance. Trunk strength endurance was examined using the Swiss global trunk strength test in prone and lateral (left, right) positions. All testings were performed before and after the training period. We observed large statistically significant between-group effects of total QoL score (INT: +9.8%, CON: +0.4%, p < 0.001; partial eta squared [Formula: see text]), 6MWT distance (INT: +21%, CON: -2%, p < 0.001, [Formula: see text]), trunk strength endurance (prone, INT: +48%, CON: +11%, p = 0.04, [Formula: see text]; lateral-left, INT: +71%, CON: +11%, p = 0.01, [Formula: see text], lateral-right, INT: +54%, CON: +11%, p = 0.01, [Formula: see text]) and dynamic balance (all eight reaching distances of the SEBT, INT: +11-26%, CON: +1.1-3.8%, 0.001 < p < 0.04, 0.14 < [Formula: see text]) with significantly larger improvements for INT. Flexibility and jump performance were not relevantly affected (p > 0.05). Instructed Zumba training can be applied

Question: What is the difference between a Zumba class and a salsa class?


Partial-parallel-repair (PPR): a distributed technique for repairing erasure coded storage. With the explosion of data in applications all around us, erasure coded storage has emerged as an attractive alternative to replication because even with significantly lower storage overhead, they provide better reliability against data loss. Reed-Solomon code is the most widely used erasure code because it provides maximum reliability for a given storage overhead and is flexible in the choice of coding parameters that determine the achievable reliability. However, reconstruction time for unavailable data becomes prohibitively long mainly because of network bottlenecks. Some proposed solutions either use additional storage or limit the coding parameters that can be used. In this paper, we propose a novel distributed reconstruction technique, called Partial Parallel Repair (PPR), which divides the reconstruction operation to small partial operations and schedules them on multiple nodes already involved in the data reconstruction. Then a distributed protocol progressively combines these partial results to reconstruct the unavailable data blocks and this technique reduces the network pressure. Theoretically, our technique can complete the network transfer in ⌈(log2(k + 1))⌉ time, compared to k time needed for a (k, m) Reed-Solomon code. Our experiments show that PPR reduces repair time and degraded read time significantly. Moreover, our technique is compatible with existing erasure codes and does not require any additional storage overhead. We demonstrate this by overlaying PPR on top of two prior schemes, Local Reconstruction Code and Rotated Reed-Solomon code, to gain additional savings in reconstruction time.

Question: What is the difference between erasure codes and replication?


Construction and evaluation of a robust multifeature speech/music discriminator. We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on system performance and the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound.

Question: How to build a robust speech/music discriminator?


Exploiting the use of recurrent neural networks for driver behavior profiling. Driver behavior affects traffic safety, fuel/energy consumption and gas emissions. The purpose of driver behavior profiling is to understand and have a positive influence on driver behavior. Driver behavior profiling tasks usually involve an automated collection of driving data and the application of computer models to classify what characterizes the aggressiveness of drivers. Different sensors and classification methods have been employed for this task, although low-cost solutions, high performance and collaborative sensing remain open questions for research. This paper makes an investigation with different Recurrent Neural Networks (RNN), aiming to classify driving events employing data collected by smartphone accelerometers. The results show that specific configurations of RNN upon accelerometer data provide high accuracy results, being a step towards the development of safer transportation systems.

Question: What is driver behavior profiling?


An Improved Algorithm for Incremental Induction of Decision Trees. This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called`slewing' is introduced. Finally, a non-incremental method is given for nding a decision tree based on a direct metric of a candidate tree.

Question: What is an algorithm for incremental induction of decision trees?


Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model. The General Data Protection Regulations (GDPR) imposes greater restrictions on obtaining valid user consents involving the use of personal data. A semantic model of consent can make the concepts of consent explicit, establish a common understanding and enable re-use of consent. Therefore, forming a semantic model of consent will satisfy the GDPR requirements of specificity and unambiguity and is an important step towards ensuring compliance. In this paper, we discuss obtaining an open vocabulary of expressing consent leveraging existing semantic models of provenance, processes, permission and obligations. We also present a reference architecture for the management of data processing according to consent permission. This data management model utilizes the open vocabulary of consent and incorporates the change of context into the data processing activity. By identifying and incorporating changes to the relational context between data controllers and data subjects into the data processing model, it aims to improve the integration of data management across different information systems specifically adhering to the GDPR and helping controllers to demonstrate compliance.

Question: What is the semantic model of consent?


Adolescent self-compassion : Associations with narcissism , self-esteem , aggression , and internalizing symptoms in at-risk males. Self-compassion is an attitude toward oneself that involves perceiving one's experiences as an opportunity for self-awareness and improvement, as well as limited self-judgment after failure. Self-compassion has not been extensively studied in adolescence, a time when self-perception and self-appraisals regarding success and failure take on notable importance. This study considered the connection between self-compassion, narcissism, self-esteem, aggression, and internalizing problems in a sample of 251 male adolescents, ages 16–18, attending a residential program. Self-compassion was negatively correlated with aggression and vulnerable narcissism and positively correlated with self-esteem. In general, selfcompassion did not exhibit the hypothesized protective effect on the relation between narcissism and aggression. Findings indicate that, as expected, self-compassion is indicative of a relatively secure, positive sense of self in adolescents. 2014 Elsevier Ltd. All rights reserved.

Question: What is the difference between self-compassion and self-esteem?


Learning concepts and categories: is spacing the "enemy of induction"?. Inductive learning -- that is, learning a new concept or category by observing exemplars -- happens constantly, for example, when a baby learns a new word or a doctor classifies x-rays. What influence does the spacing of exemplars have on induction? Compared with massing, spacing enhances long-term recall, but we expected spacing to hamper induction by making the commonalities that define a concept or category less apparent. We asked participants to study multiple paintings by different artists, with a given artist's paintings presented consecutively (massed) or interleaved with other artists' paintings (spaced). We then tested induction by asking participants to indicate which studied artist (Experiments 1a and 1b) or whether any studied artist (Experiment 2) painted each of a series of new paintings. Surprisingly, induction profited from spacing, even though massing apparently created a sense of fluent learning: Participants rated massing as more effective than spacing, even after their own test performance had demonstrated the opposite.

Question: What is the effect of spacing on induction?


Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning. Distantly supervised approaches have become popular in recent years as they allow training relation extractors without textbound annotation, using instead known relations from a knowledge base and a large textual corpus from an appropriate domain. While state of the art distant supervision approaches use off-theshelf named entity recognition and classification (NERC) systems to identify relation arguments, discrepancies in domain or genre between the data used for NERC training and the intended domain for the relation extractor can lead to low performance. This is particularly problematic for "non-standard" named entities such as album which would fall into the MISC category. We propose to ameliorate this issue by jointly training the named entity classifier and the relation extractor using imitation learning which reduces structured prediction learning to classification learning. We further experiment with Web features different features and compare against using two off-the-shelf supervised NERC systems, Stanford NER and FIGER, for named entity classification. Our experiments show that imitation learning improves average precision by 4 points over an one-stage classification model, while removing Web features results in a 6 points reduction. Compared to using FIGER and Stanford NER, average precision is 10 points and 19 points higher with our imitation learning approach.

Question: How do you extract relations between non-standard entities?


Evaluating the Effect of Total Quality Management Practices on Business Performance : A Study of Manufacturing Firms of Pakistan. The present study has evaluated the effect of TQM practices on business performance of manufacturing firms in Pakistan. A sample of 65 managers working in the quality assurance department was taken from these manufacturing firms and their responses were collected using a structured questionnaire. Descriptive statistics were used to demonstrate the effect of different levels of TQM practices on business performance in terms of financial performance, product quality performance, customer satisfaction. The study had also considered organizational level variables like size of firm, year of existence, top management commitment as moderating variables, the results have shown that TQM was implemented only at the first three levels i.e. quality control ,quality assurance and continuous improvement in the selected firms and that top management commitment is the most important variable effecting on TQM implementation which then directly effect on business performance in these manufacturing firms.

Question: What is the effect of TQM on business performance?


Moving beyond untagging: photo privacy in a tagged world. Photo tagging is a popular feature of many social network sites that allows users to annotate uploaded images with those who are in them, explicitly linking the photo to each person's profile. In this paper, we examine privacy concerns and mechanisms surrounding these tagged images. Using a focus group, we explored the needs and concerns of users, resulting in a set of design considerations for tagged photo privacy. We then designed a privacy enhancing mechanism based on our findings, and validated it using a mixed methods approach. Our results identify the social tensions that tagging generates, and the needs of privacy tools to address the social implications of photo privacy management.

Question: What are the social tensions that tagging generates?


Anticipating the Future by Constructing Human Activities using Object Affordances. An important aspect of human perception is anticipation and anticipating which activities will a human do next (and how to do them) in useful for many applications, for example, anticipation enables an assistive robot to plan ahead for reactive responses in the human environments. In this work, we present a constructive approach for generating various possible future human activities by reasoning about the rich spatial-temporal relations through object affordances. We represent each possible future using an anticipatory temporal conditional random field (ATCRF) where we sample the nodes and edges corresponding to future object trajectories and human poses from a generative model. We then represent the distribution over the potential futures using a set of constructed ATCRF particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 75.4%, 69.2% and 58.1% for an anticipation time of 1, 3 and 10 seconds respectively. 1

Question: What is the canadian military?


The Economics of State Fragmentation-Assessing the Economic Impact of Secession. This paper provides empirical evidence that declaring independence significantly lowers per capita GDP based on a large panel of countries covering the period 1950-2016. To do so, we rely on a semi-parametric identification strategy that controls for the confounding effects of past GDP dynamics, anticipation effects, unobserved heterogeneity, model uncertainty and effect heterogeneity. Our baseline results indicate that declaring independence reduces per capita GDP by around 20% in the long run. We subsequently propose a quadruple-difference procedure to demonstrate that the results are not driven by simulation and matching inaccuracies or spillover effects. A second methodological novelty consists of the development of a two-step estimator that relies on the control function approach to control for the potential endogeneity of the estimated independence payoffs and their potential determinants, to shed some light on the primary channels driving our results. We find tentative evidence that the adverse effects of independence decrease in territorial size, pointing to the presence of economies of scale, but that they are mitigated when newly independent states liberalize their trade regime or use their new-found political autonomy to democratize.

Question: What is the economic impact of secession?


Learning Plannable Representations with Causal InfoGAN. In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans – a plausible sequence of observations that transition a dynamical system from its current configuration to a desired goal state, which can later be used as a reference trajectory for control. We focus on systems with high-dimensional observations, such as images, and propose an approach that naturally combines representation learning and planning. Our framework learns a generative model of sequential observations, where the generative process is induced by a transition in a low-dimensional planning model, and an additional noise. By maximizing the mutual information between the generated observations and the transition in the planning model, we obtain a low-dimensional representation that best explains the causal nature of the data. We structure the planning model to be compatible with efficient planning algorithms, and we propose several such models based on either discrete or continuous states. Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations. We demonstrate our method on imagining plausible visual plans of rope manipulation3.

Question: How to generate a visual plan of rope manipulation?


Realtime ray tracing and interactive global illumination. Interaktive 3D-Computergraphik basiert heutzutage fast ausschliesslich auf dem Rasterisierungsverfahren. Dieses ist heute sehr effizient und günstig realisierbar, stösst aber zunehmend an seine Grenzen hinsichtlich unterstützter Szenenkomplexität und erreichbarer Darstellungsqualität. Eine Alternative zum Rasterisierungsverfahren ist das Ray Tracing Verfahren, welches zwar allgemein für bessere Bildqualität bekannt ist, aber aufgrund hoher Rechenanforderungen bisher als unvereinbar mit interaktiver Performanz galt. In dieser Arbeit geht es um die Entwicklung der Echtzeit Ray Tracing Technologie, mit der das Ray Tracing Verfahren auch für interaktive Anwendungen ermöglicht wird. Die im Rahmen dieser Dissertation entwickelten Techniken ermöglichen nun erstmalig interaktive Ray Tracing Performanz selbst auf Standard PCs. Die dazu entwickelten Methoden beinhalten unter anderem einen extrem schnellen Ray Tracing Kern, ein effizientes Parallelisierungsframework, die Unterstützung dynamischer und extrem komplexer Modelle, sowie eine geeignete Programmierschnittstelle. Darauf aufbauend wurden dann weiterhin Verfahren entwickelt, die es erstmalig ermöglichen, auch die globale Lichtausbreitung interaktiv zu simulieren. Die im Rahmen der Dissertation entwickelten Techniken formen ein komplettes Framework für Echtzeit-Ray Tracing und interaktive Beleuchtungssimulation, welches interaktive Performanz selbst für massiv komplexe Szenen liefert, sowie erstmalig physikalisch basierte Computergraphik unter Echtzeitbedingungen ermöglicht.

Question: What is the difference between ray tracing and rasterization?


Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds. We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent's environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given.

Question: How to speed up deep reinforcement learning?


Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO. This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

Question: What is a hybrid power system?


Triple Band Annular Ring Loaded Stacked Circular Patch Microstrip Antenna. In this paper a novel structure of annular ring loaded stacked circular patch microstrip antenna is theoretically analysed to observe various parameters such as return loss, input impedance, gain, directivity and radiation pattern. It is found that antenna possess three band of operation which signify the compactness and multiband operation of antenna. The antenna is resonating at three operating frequencies 1.720, 2.950, 3.060 GHz. The proposed theory is verified by simulation using Ansoft's HFSS and theoretical results are in good agreement with simulated results. The antenna is useful for multi-services operations such as WLAN, GSM, UMTS, and WiMAX services.

Question: What is the purpose of the antenna?


Taking perspective into account in a communicative task. Previous neuroimaging studies of spatial perspective taking have tended not to activate the brain's mentalising network. We predicted that a task that requires the use of perspective taking in a communicative context would lead to the activation of mentalising regions. In the current task, participants followed auditory instructions to move objects in a set of shelves. A 2x2 factorial design was employed. In the Director factor, two directors (one female and one male) either stood behind or next to the shelves, or were replaced by symbolic cues. In the Object factor, participants needed to use the cues (position of the directors or symbolic cues) to select one of three possible objects, or only one object could be selected. Mere presence of the Directors was associated with activity in the superior dorsal medial prefrontal cortex (MPFC) and the superior/middle temporal sulci, extending into the extrastriate body area and the posterior superior temporal sulcus (pSTS), regions previously found to be responsive to human bodies and faces respectively. The interaction between the Director and Object factors, which requires participants to take into account the perspective of the director, led to additional recruitment of the superior dorsal MPFC, a region activated when thinking about dissimilar others' mental states, and the middle temporal gyri, extending into the left temporal pole. Our results show that using perspective taking in a communicative context, which requires participants to think not only about what the other person sees but also about his/her intentions, leads to the recruitment of superior dorsal MPFC and parts of the social brain network.

Question: What is the difference between the superior dorsal MPFC and the superior temporal sulci?


The State of Solving Large Incomplete-Information Games, and Application to Poker. ion algorithms take as input a description of the game and output a smaller but strategically similar — or even equivalent — game. The abstraction algorithms discussed here work with any finite number of players and do not assume a zerosum game. Information Abstraction The most popular kind of abstraction is information abstraction. The game is abstracted so that the agents cannot distinguish some of the states that they can distinguish in the actual game. For example in an abstracted poker hand, an agent is not able to observe all the nuances of the cards that she would normally observe. Lossless Information Abstraction. It turns out that it is possible to do lossless information abstraction, which may seem like an oxymoron at first. The method I will describe (Gilpin and Sandholm 2007b) is for a class of games that we call games with ordered signals. It is structured, but still general enough to capture a wide range of strategic situations. A game with ordered signals consists of a finite number of rounds. Within a round, the players play a game on a directed tree (the tree can be different in different rounds). The only uncertainty players face stems from private signals the other players have received and from the unknown future signals. In other words, players observe each Articles 14 AI MAGAZINE Nash equilibrium Nash equilibrium Original game

Question: What is a directed tree?