Chain : Operator Scheduling for Memory Minimization in Data Stream Systems. In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams --- adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the run-time system memory usage. We then present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing run-time memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams, and multiple queries of the above types. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling, compare it with competing scheduling strategies, and validate our analytical conclusions.

Question: What is the difference between a data stream and a data flow?


Passive Self-Interference Suppression for Full-Duplex Infrastructure Nodes. Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the overall self-interference suppression is often due to passive suppression, i.e., isolation of the transmit and receive antennas. We present a measurement-based study of the capabilities and limitations of three key mechanisms for passive self-interference suppression: directional isolation, absorptive shielding, and cross-polarization. The study demonstrates that more than 70 dB of passive suppression can be achieved in certain environments, but also establishes two results on the limitations of passive suppression: (1) environmental reflections limit the amount of passive suppression that can be achieved, and (2) passive suppression, in general, increases the frequency selectivity of the residual self-interference signal. These results suggest two design implications: (1) deployments of full-duplex infrastructure nodes should minimize near-antenna reflectors, and (2) active cancellation in concatenation with passive suppression should employ higher-order filters or per-subcarrier cancellation.

Question: What is the difference between active and passive self-interference suppression?


ExerLink: enabling pervasive social exergames with heterogeneous exercise devices. We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

Question: How do you design a game controller?


Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery. This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The final building regions are detected by GrabCut partitioning approach. In this paper, the input requirements of the GrabCut partitioning are automatically extracted from the previously determined shadow and landscape regions, so that the approach gained an efficient fully automated behavior for the detection of buildings. Extensive experiments performed on 20 test sites selected from a set of QuickBird and Geoeye-1 VHR images showed that the proposed approach accurately detects buildings with arbitrary shapes and sizes in complex environments. The tests also revealed that even under challenging environmental and illumination conditions, reasonable building detection performances could be achieved by the proposed approach.

Question: How does the approach work?


Content-boosted Matrix Factorization Techniques for Recommender Systems. Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable.

Question: What is the difference between content-based and collaborative filtering?


Regenerative braking for electric vehicle based on fuzzy logic control strategy. In order to recycle more energy in the process of regenerative braking, we design a regenerative braking force calculation controller based on fuzzy logic. The sugeno's interface fuzzy logic controller has three-inputs including the driver's brake requirements, vehicle speed and batteries' SOC and one-output which is the regenerative braking force. To ensure charging safety, the influence of batteries' temperature is also taken into consideration. Besides, we modify the original braking force distribution regulations in the simulation software--ADVISOR. The simulation results verify that the method can not only recycle more energy from braking but also ensure the safety of braking and the batteries.

Question: What is fuzzy logic?


On the minimum node degree and connectivity of a wireless multihop network. This paper investigates two fundamental characteristics of a wireless multi -hop network: its minimum node degree and its k--connectivity. Both topology attributes depend on the spatial distribution of the nodes and their transmission range. Using typical modeling assumptions :--- :a random uniform distribution of the nodes and a simple link model :--- :we derive an analytical expression that enables the determination of the required range r0 that creates, for a given node density ρ, an almost surely k--connected network. Equivalently, if the maximum r0 of the nodes is given, we can find out how many nodes are needed to cover a certain area with a k--connected network. We also investigate these questions by various simulations and thereby verify our analytical expressions. Finally, the impact of mobility is discussed.The results of this paper are of practical value for researchers in this area, e.g., if they set the parameters in a network--level simulation of a mobile ad hoc network or if they design a wireless sensor network.

Question: How many nodes are needed to create a k--connected network?


Application of Computational Intelligence to Improve Education in Smart Cities. According to UNESCO, education is a fundamental human right and every nation's citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student's knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives' achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.

Question: What is the difference between a model and a recommendation system?


Forecasting Real Estate Prices ∗. This chapter reviews the evidence of predictability in US residential and commercial real estate markets. First, we highlight the main methodologies used in the construction of real estate indices, their underlying assumptions and their impact on the stochastic properties of the resultant series. We then survey the key empirical findings in the academic literature, including short-run persistence and long-run reversals in the log changes of real estate prices. Next, we summarize the ability of local as well as aggregate variables to forecast real estate returns. We illustrate a number of these results by relying on six aggregate indexes of the prices of unsecuritized (residential and commercial) real estate and REITs. The effect of leverage and monetary policy is also discussed.

Question: What is the effect of leverage on real estate prices?


Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.. Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, with heterogeneous distributions of node degree and community size. An increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links, which require suitable benchmark graphs for testing. In this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built-in community structure. We also consider the possibility that nodes belong to more communities, a feature occurring in real systems, such as social networks. As a practical application, we show how modularity optimization performs on our benchmark.

Question: How to test community detection algorithms on directed and weighted graphs with overlapping communities?


Chiron: a parallel engine for algebraic scientific workflows. Large-scale scientific experiments based on computer simulations are typically modeled as scientific workflows, which eases the chaining of different programs. These scientific workflows are defined, executed, and monitored by scientific workflowmanagement systems (SWfMS). As these experiments manage large amounts of data, it becomes critical to execute them in high-performance computing environments, such as clusters, grids, and clouds. However, few SWfMS provide parallel support. The ones that do so are usually labor-intensive for workflow developers and have limited primitives to optimize workflow execution. To address these issues, we developed workflow algebra to specify and enable the optimization of parallel execution of scientific workflows. In this paper, we show how the workflow algebra is efficiently implemented in Chiron, an algebraic based parallel scientific workflow engine. Chiron has a unique native distributed provenance mechanism that enables runtime queries in a relational database. We developed two studies to evaluate the performance of our algebraic approach implemented in Chiron; the first study compares Chiron with different approaches, whereas the second one evaluates the scalability of Chiron. By analyzing the results, we conclude that Chiron is efficient in executing scientific workflows, with the benefits of declarative specification and runtime provenance support. Copyright © 2013 John Wiley & Sons, Ltd.

Question: What is the difference between a scientific workflow and a scientific workflow management system?


Korean pop takes off! Social media strategy of Korean entertainment industry. The Korean Wave (K-wave), or Hallyu, referred as "the phenomenon of Korean pop culture, such as TV dramas, films, pop music, fashion, and online games being widely embraced and shared among the people of Japan, China, Hong Kong, Taiwan, and other Asian countries". After the drama-oriented craze of the first K-wave, Korean pop music (shortly K-pop) took the integral part of the second K-wave. In addition, with the rapid spread of social media like YouTube and Twitter, K-pop has expanded its fandom outside of Asia to the West. The world-wide success of K-pop contributes to improve the `Korea' image and make a positive impact on Korean economy. It is reported that around 1,000 entertainment agencies are active in Korea, while there exist the "big three" record labels and entertainment agencies: SM Entertainment, YG Entertainment and JYP Entertainment. In this case, we address the world-wide phenomenon of K-pop popularity and the role of social media on recent boom of K-pop. Focusing the major agencies above, we present lessons on how to manage social media strategically: align strategic business model with social media; maximize various social media channels; engage customers with on-and offline promotions; and stimulate audience with exclusive contents.

Question: What is the role of social media in the Korean entertainment industry?


A Maximum-Entropy-Inspired Parser. We present a new parser for parsing down to Penn tree-bank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less, and 89.5% for sentences of length 100 and less when trMned and tested on the previously established [5,9,10,15,17] "standard" sections of the Wall Street Journal treebank. This represents a 13% decrease in error rate over the best single-parser results on this corpus [9]. The major technical innovation is tire use of a "ma~ximum-entropy-inspired" model for conditioning and smoothing that let us successfully to test and combine many different conditioning events. We also present some partial results showing the effects of different conditioning information, including a surprising 2% improvement due to guessing the lexical head's pre-terminal before guessing the lexical head.

Question: What is maximum entropy?


Digital Mosaic Frameworks - An Overview. Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we survey in a unified framework several methods to transform raster input images into good quality mosaics. For each of the major different approaches in literature the paper reports a short description and a discussion of the most relevant issues. To complete the survey comparisons among the different techniques both in terms of visual quality and computational complexity are provided.

Question: What is the difference between a mosaic and a mosaic framework?


Treacher Collins syndrome: clinical implications for the paediatrician—a new mutation in a severely affected newborn and comparison with three further patients with the same mutation, and review of the literature. Treacher Collins syndrome (TCS) is the most common and well-known mandibulofacial dysostosis caused by mutations in at least three genes involved in pre-rRNA transcription, the TCOF1, POLR1D and POLR1C genes. We present a severely affected male individual with TCS with a heterozygous de novo frameshift mutation within the TCOF1 gene (c.790_791delAG,p.Ser264GlnfsX7) and compare the clinical findings with three previously unpublished, milder affected individuals from two families with the same mutation. We elucidate typical clinical features of TCS and its clinical implications for the paediatrician and mandibulofacial surgeon, especially in severely affected individuals and give a short review of the literature. Conclusion:The clinical data of these three families illustrate that the phenotype associated with this specific mutation has a wide intra- and interfamilial variability, which confirms that variable expressivity in carriers of TCOF1 mutations is not a simple consequence of the mutation but might be modified by the combination of genetic, environmental and stochastic factors. Being such a highly complex disease treatment of individuals with TCS should be tailored to the specific needs of each individual, preferably by a multidisciplinary team consisting of paediatricians, craniofacial surgeons and geneticists.

Question: What is the clinical implications of Treacher Collins syndrome?


Database updating through user feedback in fingerprint-based Wi-Fi location systems. Wi-Fi fingerprinting is a technique which can provide location in GPS-denied environments, relying exclusively on Wi-Fi signals. It first requires the construction of a database of "fingerprints", i.e. signal strengths from different access points (APs) at different reference points in the desired coverage area. The location of the device is then obtained by measuring the signal strengths at its location, and comparing it with the different reference fingerprints in the database. The main disadvantage of this technique is the labour required to build and maintain the fingerprints database, which has to be rebuilt every time a significant change in the wireless environment occurs, such as installation or removal of new APs, changes in the layout of a building, etc. This paper investigates a new method to utilise user feedback as a way of monitoring changes in the wireless environment. It is based on a system of "points" given to each AP in the database. When an AP is switched off, the number of points associated with that AP will gradually reduce as the users give feedback, until it is eventually deleted from the database. If a new AP is installed, the system will detect it and update the database with new fingerprints. Our proposed system has two main advantages. First it can be used as a tool to monitor the wireless environment in a given place, detecting faulty APs or unauthorised installation of new ones. Second, it regulates the size of the database, unlike other systems where feedback is only used to insert new fingerprints in the database.

Question: How does the fingerprinting system work?


SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services. In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.

Question: What is the trade-off between location privacy and query performance?


Toward Rapid Development of Multi-Party Virtual Human Negotiation Scenarios. This paper reports on an ongoing effort to enable the rapid development of multi-party virtual human negotiation scenarios. We present a case study in which a new scenario supporting negotiation between two human role players and two virtual humans was developed over a period of 12 weeks. We discuss the methodology and development process that were employed, from storyline design through role play and iterative development of the virtual humans' semantic and task representations and natural language processing capabilities. We analyze the effort, expertise, and time required for each development step, and discuss opportunities to further streamline the development process.

Question: How to develop a virtual human negotiation scenario?


Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition. The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.

Question: How does gait recognition work?


Breathing at a rate of 5.5 breaths per minute with equal inhalation-to-exhalation ratio increases heart rate variability.. OBJECTIVES Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation-to-exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns.   METHODS Forty-seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6). The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns.   RESULTS The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline.   CONCLUSION The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.

Question: What is the difference between the breathing patterns?


BER analysis of optical eU-OFDM transmission over AWGN. Enhanced unipolar orthogonal frequency-division multiplexing (eU-OFDM) was recently introduced to improve the power-spectral efficiency trade-off of intensity-modulated direct-detection (IM/DD) optical OFDM transmission schemes. This is accomplished by superimposing unipolar signals over several layers. Bit-error-rate (BER) analysis for each layer however, is eluded by error-propagation from previous layers in the eU-OFDM receiver. This paper presents a general mathematical model for eU-OFDM transmission. From the model, we introduce successive interference cancellation using soft-symbol estimates, which leads to slight performance improvement compared to the original hard-decision counterpart. More importantly, by using soft-symbol estimates we derive analytically tractable expressions that accurately predict the system's performance.

Question: What is the difference between unipolar and bipolar?


An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach. A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

Question: What is the diagnosis of Parkinson's disease?


Artificial neural networks: fundamentals, computing, design, and application.. Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.

Question: What is the difference between neural networks and expert systems?


How GaN Power Transistors Drive High-Performance Lidar: Generating ultrafast pulsed power with GaN FETs. Light detection and ranging (lidar) is a versatile light-based remote sensing technology that recently has been the subject of great attention. It has shown up in a number of media venues and has even led to public debate about the engineering choices of a well-known electric car company, Tesla Motors [1]. While this article is not going to enter the fray, it will provide some background on lidar and discuss its strong connection to power electronics technologies.

Question: What is a lidar?


Social collaborative filtering for cold-start recommendations. We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users.

Question: What is the cold-start recommendation task?


Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E-commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

Question: What is the difference between collaborative filtering, content-based filtering and hybrid recommendation system?


Utility max-min fair resource allocation for communication networks with multipath routing. This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max-min fairness is achieved, which is essential for balancing QoS (Quality of Service) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max-min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.

Question: What is the utility max-min fair resource allocation for communication networks with multipath routing?


A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models. The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content-based image matching. Though the Earth Mover's Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We first present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more efficient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy-or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.

Question: What is the novel earth mover's distance methodology for image matching with gaussian mixture models?


Toward Delay-Efficient Game-Aware Data Centers for Cloud Gaming. Gaming on demand is an emerging service that has recently started to garner prominence in the gaming industry. Cloud-based video games provide affordable, flexible, and high-performance solutions for end-users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients. Despite its advantages, cloud gaming's Quality of Experience (QoE) suffers from high and varying end-to-end delay. Since the significant part of computational processing, including game rendering and video compression, is performed in data centers, controlling the transfer of information within the cloud has an important impact on the quality of cloud gaming services. In this article, a novel method for minimizing the end-to-end latency within a cloud gaming data center is proposed. We formulate an optimization problem for reducing delay, and propose a Lagrangian Relaxation (LR) time-efficient heuristic algorithm as a practical solution. Simulation results indicate that the heuristic method can provide close-to-optimal solutions. Also, the proposed model reduces end-to-end delay and delay variation by almost 11% and 13.5%, respectively, and outperforms the existing server-centric and network-centric models. As a byproduct, our proposed method also achieves better fairness among multiple competing players by almost 45%, on average, in comparison with existing methods.

Question: How to reduce latency in cloud gaming?


A Log-Logistic Model-Based Interpretation of TF Normalization of BM25. The effectiveness of BM25 retrieval function is mainly due to its sub-linear term frequency (TF) normalization component, which is controlled by a parameter k1. Although BM25 was derived based on the classic probabilistic retrieval model, it has been so far unclear how to interpret its parameter k1 probabilistically, making it hard to optimize the setting of this parameter. In this paper, we provide a novel probabilistic interpretation of the BM25 TF normalization and its parameter k1 based on a log-logistic model for the probability of seeing a document in the collection with a given level of TF. The proposed interpretation allows us to derive different approaches to estimation of parameter k1 based solely on the current collection without requiring any training data, thus effectively eliminating one free parameter from BM25. Our experiment results show that the proposed approaches can accurately predict the optimal k1 without requiring training data and achieve better or comparable retrieval performance to a well-tuned BM25 where k1 is optimized based on training data.

Question: How to interpret the parameter k1 of BM25?


PIP Loss : A Unitary-invariant Metric for Understanding Functionality and Dimensionality of Vector Embeddings. In this paper, we present a theoretical framework for understanding vector embedding, a fundamental building block of many deep learning models, especially in NLP. We discover a natural unitary-invariance in vector embeddings, which is required by the distributional hypothesis. This unitary-invariance states the fact that two embeddings are essentially equivalent if one can be obtained from the other by performing a relative-geometry preserving transformation, for example a rotation. This idea leads to the Pairwise Inner Product (PIP) loss, a natural unitary-invariant metric for the distance between two embeddings. We demonstrate that the PIP loss captures the difference in functionality between embeddings, and that it is tightly connect to two fundamental properties, namely similarity and compositionality. By formulating the embedding training process as matrix factorization under noise, we reveal a fundamental bias-variance tradeoff in dimensionality selection. With tools from perturbation and stability theory, we provide an upper bound on the PIP loss using the signal spectrum and noise variance, both of which can be readily inferred from data. Our framework sheds light on many empirical phenomena, including the existence of an optimal dimension, and the robustness of embeddings against over-parametrization. The bias-variance tradeoff of PIP loss explicitly answers the fundamental open problem of dimensionality selection for vector embeddings. 1 ar X iv :1 80 3. 00 50 2v 3 [ st at .M L ] 1 2 A pr 2 01 8

Question: What is the difference between a vector embedding and a vector space?


Grasping Without Squeezing: Design and Modeling of Shear-Activated Grippers. Grasping objects that are too large to envelop is traditionally achieved using friction that is activated by squeezing. We present a family of shear-activated grippers that can grasp such objects without the need to squeeze. When a shear force is applied to the gecko-inspired material in our grippers, adhesion is turned on; this adhesion in turn results in adhesion-controlled friction, a friction force that depends on adhesion rather than a squeezing normal force. Removal of the shear force eliminates adhesion, allowing easy release of an object. A compliant shear-activated gripper without active sensing and control can use the same light touch to lift objects that are soft, brittle, fragile, light, or very heavy. We present three grippers, the first two designed for curved objects, and the third for nearly any shape. Simple models describe the grasping process, and empirical results verify the models. The grippers are demonstrated on objects with a variety of shapes, materials, sizes, and weights.

Question: How can a gripper grasp an object without squeezing?


Quantitative Evaluation of Software Quality. The study reported in this paper establishes a conceptual framework and some key initial results in the analysis of the characteristics of software quality. Its main results and conclusions are:  • Explicit attention to characteristics of software quality can lead to significant savings in software life-cycle costs.  • The current software state-of-the-art imposes specific limitations on our ability to automatically and quantitatively evaluate the quality of software.  • A definitive hierarchy of well-defined, well-differentiated characteristics of software quality is developed. Its higher-level structure reflects the actual uses to which software quality evaluation would be put; its lower-level characteristics are closely correlated with actual software metric evaluations which can be performed.  • A large number of software quality-evaluation metrics have been defined, classified, and evaluated with respect to their potential benefits, quantifiability, and ease of automation.  •Particular software life-cycle activities have been identified which have significant leverage on software quality.  Most importantly, we believe that the study reported in this paper provides for the first time a clear, well-defined framework for assessing the often slippery issues associated with software quality, via the consistent and mutually supportive sets of definitions, distinctions, guidelines, and experiences cited. This framework is certainly not complete, but it has been brought to a point sufficient to serve as a viable basis for future refinements and extensions.

Question: What is the difference between a conceptual framework and a conceptual model?


Viraliency: Pooling Local Virality. In our overly-connected world, the automatic recognition of virality – the quality of an image or video to be rapidly and widely spread in social networks – is of crucial importance, and has recently awaken the interest of the computer vision community. Concurrently, recent progress in deep learning architectures showed that global pooling strategies allow the extraction of activation maps, which highlight the parts of the image most likely to contain instances of a certain class. We extend this concept by introducing a pooling layer that learns the size of the support area to be averaged: the learned top-N average (LENA) pooling. We hypothesize that the latent concepts (feature maps) describing virality may require such a rich pooling strategy. We assess the effectiveness of the LENA layer by appending it on top of a convolutional siamese architecture and evaluate its performance on the task of predicting and localizing virality. We report experiments on two publicly available datasets annotated for virality and show that our method outperforms state-of-the-art approaches.

Question: What is the average size of the support area to be averaged?


BGP-RCN: improving BGP convergence through root cause notification. This paper presents a new mechanism, called BGP with root cause notification (BGP-RCN), that provides an upper bound of O(d) on routing convergence delay for BGP, where d is the network diameter as measured by the number of AS hops. BGP-RCN lets each routing update message carry the information about the specific cause which triggered the update message. Once a node v receives the first update message triggered by a link failure, v can avoid using any paths that have been obsoleted by the same failure. The basic approach in BGP-RCN is applicable to path vector routing protocols in general. Our analysis and simulation show that BGP-RCN can achieve substantial reduction in both BGP convergence time and the total number of intermediate route changes. 2004 Elsevier B.V. All rights reserved.

Question: What is the diameter of the network?


The slot-coupled hemispherical dielectric resonator antenna with a parasitic patch: applications to the circularly polarized antenna and wide-band antenna. The aperture-coupled hemispherical dielectric resonator antenna (DRA) with a parasitic patch is studied rigorously. Using the Green's function approach, integral equations for the unknown patch and slot currents are formulated and solved using the method of moments. The theory is utilized to design a circularly polarized (CP) DRA and a wide-band linearly polarized (LP) DRA. In the former, the CP frequency and axial ratio (AR) can easily be controlled by the patch location and patch size, respectively, with the impedance matched by varying the slot length and microstrip stub length. It is important that the AR will not be affected when the input impedance is tuned, and the CP design is therefore greatly facilitated. For the wide-band LP antenna, a maximum bandwidth of 22% can be obtained, which is much wider than the previous bandwidth of 7.5% with no parasitic patches. Finally, the frequency-tuning characteristics of the proposed antenna are discussed. Since the parasitic patch can be applied to any DRAs, the method will find applications in practical DRA designs.

Question: What is the frequency-tuning characteristics of the proposed antenna?


Feature Extraction and Classification for Automatic Speaker Recognition System – A Review. Automatic speaker recognition (ASR) has found immense applications in the industries like banking, security, forensics etc. for its advantages such as easy implementation, more secure, more user friendly. To have a good recognition rate is a pre-requisite for any ASR system which can be achieved by making an optimal choice among the available techniques for ASR. In this paper, different techniques for the system have been discussed such as MFCC, LPCC, LPC, Wavelet decomposition for feature extraction and VQ, GMM, SVM, DTW, HMM for feature classification. All these techniques are also compared with each other to find out best suitable candidate among them. On the basis of the comparison done, MFCC has upper edge over other techniques for feature extraction as it is more consistent with human hearing. GMM comes out to be the best among classification models due to its good classification accuracy and less memory usage. Keywords— Automatic Speaker Recognition, Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients, Gaussian Mixture Model ( GMM), Vector Quantization ( VQ), Dynamic Time Warping (DTW), Hidden Markov Model ( HMM), Wavelet decomposition

Question: What is the best feature extraction technique for automatic speaker recognition?


Detecting Singleton Review Spammers Using Semantic Similarity. Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).

Question: How to detect fake reviews written by the same person using multiple names?


Controlling Home Appliances Remotely through Voice Command. the main concern in systems development is the integration of technologies to increase customer satisfaction. Research presented in this paper focuses mainly in three things first to understand the speech or voice of user second is to control the home appliances through voice call and third is to finds intrusion in the house. The user can make a voice call in order to perform certain actions such as switching lights on/off, getting the status of any appliance etc. And when system finds intrusion it sends an alert voice message to preconfigured cell when the user is away from the place. The proposed system is implemented using voice Global System for Mobile Communications (GSM) and wireless technology based on .NET framework and Attention (AT) commands. Microsoft speech reorganization engine, speech SDK 5.1 is used to understand the voice command of user. As it is wireless so more cost effective and easy to use. The GSM technology used in system provide the everywhere access of the system for security. Experimental results show that the system is more secure and cost effective as compared to existing systems. We conclude that this system provides solution for the problems faced by home owner in daily life and make their life easy and comfortable by proposing cost effective and reliable solution. Keywords-component; Voice GSM; Voice message; radio friquencey (RF); AT commands.

Question: How to control home appliances remotely through voice call?


A Smart Fridge with an Ability to Enhance Health and Enable Better Nutrition. Intelligent appliances with multimedia capability have been emerging into our daily life, thanks to the fast advance of computing technology and the wide use of the Interne. Smart home is one of the most prominent areas of intelligent appliances. Kitchen is one of the places where such intelligent appliances have been used. Since modern life style is driving people spending less time on cooking healthy food at home, an enjoyable and healthy life style can be assisted with an intelligent kitchenware such as a smart fridge. In this paper we introduce a novel idea of the smart fridge which can enable better nutrition and enhance health. It is designed for managing items stored in it and advising its users with cooking methods depending on what kind of food is stored. More importantly, it can perform other actions such as dietary control, nutrition monitoring, eating habit analysis, etc. The characteristics, functions and design details of the smart fridge are presented in details. We are confident that such a smart fridge will be an important component in future smart home.

Question: What is the smart fridge?


Minimum Near-Convex Shape Decomposition.. Shape decomposition is a fundamental problem for part-based shape representation. We propose the Minimum Near-Convex Decomposition (MNCD) to decompose arbitrary shapes into minimum number of "near-convex" parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of non-intersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via Binary Integer Linear Programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts, and thus leads to robust shape representation.

Question: What is the minimum near-convex shape decomposition?


Situation models in language comprehension and memory.. This article reviews research on the use of situation models in language comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.

Question: What is a situation model?


Detecting sources of computer viruses in networks: theory and experiment. We provide a systematic study of the problem of finding the source of a computer virus in a network. We model virus spreading in a network with a variant of the popular SIR model and then construct an estimator for the virus source. This estimator is based upon a novel combinatorial quantity which we term rumor centrality. We establish that this is an ML estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has non-trivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops in different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding virus sources in networks which are not tree-like.

Question: How to detect the source of a computer virus in a network?


Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease. Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method

Question: What is the difference between the Sugihara causality analysis and Granger causality analysis?


Copy Detection Mechanisms for Digital Documents. In a digital library system, documents are available in digital form and therefore are more easily copied and their copyrights are more easily violated. This is a very serious problem, as it discourages owners of valuable information from sharing it with authorized users. There are two main philosophies for addressing this problem: prevention and detection. The former actually makes unauthorized use of documents difficult or impossible while the latter makes it easier to discover such activity.In this paper we propose a system for registering documents and then detecting copies, either complete copies or partial copies. We describe algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security). We also describe a working prototype, called COPS, describe implementation issues, and present experimental results that suggest the proper settings for copy detection parameters.

Question: What is a digital library system?


A survey and analysis of ontology-based software tools for semantic interoperability in IoT and WoT landscapes. The current Internet of Things (IoT) ecosystem consists of non-interoperable products and services. The Web of Things (WoT) advances the IoT by allowing consumers to interact with the IoT ecosystem through the open and standard web technologies. But the Web alone does not solve the interoperability issues. It is widely acknowledged that Semantic Web Technologies hold the potential of achieving data and platform interoperability in both the IoT and WoT landscapes. In this context, the paper attempts to review and analyze the current state of ontology-based software tools for semantic interoperability.

Question: What is the current state of ontology-based software tools for semantic interoperability in IoT and WoT landscapes?


Modeling Interestingness with Deep Neural Networks. This paper presents a deep semantic similarity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target documents to be of interest to a user based on a source document that she is reading. We observe, identify, and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent space in such a way that the distance between source documents and their corresponding interesting targets in that space is minimized. The effectiveness of the DSSM is demonstrated using two interestingness tasks: automatic highlighting and contextual entity search. The results on large-scale, real-world datasets show that the semantics of documents are important for modeling interestingness and that the DSSM leads to significant quality improvement on both tasks, outperforming not only the classic document models that do not use semantics but also state-of-the-art topic models.

Question: What is a deep semantic similarity model?


Modeling the Diffuse Paintings of 'Sumie'.

Question: What is the name of the painting?


Thermal characterisation of a copper-clip-bonded IGBT module with double-sided cooling. In pursuing higher power density power electronic equipment, the copper clip bonding concept is one of the double-sided cooling approaches under investigation for power semiconductors. This paper presents a comprehensive thermal assessment of the copper clip concept by undertaking comparative studies on one bespoke copper-clip-bonded IGBT power module and a conventional wire-bonded counterpart. A series of single-sided cooling tests and double-sided cooling tests have been undertaken on these two modules. Reductions are reported in the junction to case thermal resistance of up to 23% for one individual die and around 18% for the parallel operation of two dies in the single-sided cooling tests. Whilst in the double-sided cooling experiments, an additional average 18% thermal improvement is achieved due to the addition of a top fan-cooled heatsink mounted onto the copper clip. The results show clearly that the copper clip technology could provide significant benefits in the heat removal for power semiconductors and enable more power dense equipment.

Question: What is the thermal characterisation of a copper-clip-bonded IGBT module with double-sided cooling?


Design and Implementation of T-junction Triangular Microstrip Patch Antenna Array for Wireless Applications. Abstract— This paper describes 2 x 2 triangular microstrip patch antenna using T-junction with quarter wave transformer. By regulating the distance in patch antenna and adjusting feed position, bandwidth can be obtained and by using an array, directivity is enhanced. The requirement of large bandwidth, high directivity, and minimum size leads to the design of 2 x 2 triangular microstrip patch antenna array feeding with T-junction network operate at 5.5 GHz. An antenna designed on an FR4 substrate that had a dielectric constant (r) 4.4, a loss tangent 0.02 and thickness of 1.6 mm. Simulated results showed that designed antenna has directivity 12.91 dB and bandwidth 173 MHz with VSWR 1.07 using T-junction feeding network. The proposed 2 x 2 triangular array has the benefit of light weight, simplicity of fabrication, single layer structure, and high directivity. Keyword Bandwidth, Corporate feeding, Return Loss, T-junction, VSWR.

Question: What is the benefit of using a T-junction feeding network?


Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.

Question: How to translate images?


Interactive Visualization of Small World Graphs. Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example

Question: What is a small world graph?


Wasserstein Generative Adversarial Network. Recent advances in deep generative models give us new perspective on modeling highdimensional, nonlinear data distributions. Especially the GAN training can successfully produce sharp, realistic images. However, GAN sidesteps the use of traditional maximum likelihood learning and instead adopts an two-player game approach. This new training behaves very differently compared to ML learning. There are still many remaining problem of GAN training. In this thesis, we gives a comprehensive review of recently published methods or analysis on GAN training, especially the Wasserstein GAN and FlowGAN model. We also discuss the limitation of the later model and use this as the motivation to propose a novel generator architecture using mixture models. Furthermore, we also modify the discriminator architecture using similar ideas to allow 'personalized' guidance. We refer the generator mixture model as Mixflow and mixture of discriminators as 'personalized GAN' (PGAN). In experiment chapter, we demonstrate their performance advantages using toy examples compared to single flow model. In the end, we test their performance on MNIST dataset and the Mixflow model not only achieves the best log likelihood but also produce reasonable images compared to state-of-art DCGAN generation.

Question: What is the difference between GAN and Wasserstein GAN?


Formal Models for Computer Security. Efforts to build "secure" computer systems have now been underway for more than a decade. Many designs have been proposed, some prototypes have been constructed, and a few systems are approaching the production stage. A small number of systems are even operating in what the Department of Defense calls the "multilevel" mode some information contained m these computer systems may have a clasmfication higher than the clearance of some of the users of those systems. This paper revmws the need for formal security models, describes the structure and operation of military security controls, considers how automation has affected security problems, surveys models that have been proposed and applied to date, and suggests possible d~rectlons for future models

Question: What is a formal security model?


Internet security architecture. Fear of security breaches has been a major r eason f or the business world' s reluctance to embrace the Inter net as a viable means of communication. A widely adopted solution consists of physically separating private networks from the rest of Internet using firewalls. This paper discusses the curr ent cryptographic security measures available for the Internet infrastructur e as an alternative to physical segregation. First the IPsec ar chitecture including security protocols in the Internet Layer and the related key management pr oposals are introduced. The transport layer security protocol and security issues in the netw ork control and management are then presented. The paper is addr essed to r eaders with a basic understanding of common security mechanisms including encryption, authentication and key exchange techniques.

Question: What is the current security architecture of the internet?


APD: the Antimicrobial Peptide Database. An antimicrobial peptide database (APD) has been established based on an extensive literature search. It contains detailed information for 525 peptides (498 antibacterial, 155 antifungal, 28 antiviral and 18 antitumor). APD provides interactive interfaces for peptide query, prediction and design. It also provides statistical data for a select group of or all the peptides in the database. Peptide information can be searched using keywords such as peptide name, ID, length, net charge, hydrophobic percentage, key residue, unique sequence motif, structure and activity. APD is a useful tool for studying the structure-function relationship of antimicrobial peptides. The database can be accessed via a web-based browser at the URL: http://aps.unmc.edu/AP/main.html.

Question: What is the URL for the Antimicrobial Peptide Database?


TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry. TanDEM-X (TerraSAR-X add-on for digital elevation measurements) is an innovative spaceborne radar interferometer that is based on two TerraSAR-X radar satellites flying in close formation. The primary objective of the TanDEM-X mission is the generation of a consistent global digital elevation model (DEM) with an unprecedented accuracy, which is equaling or surpassing the HRTI-3 specification. Beyond that, TanDEM-X provides a highly reconfigurable platform for the demonstration of new radar imaging techniques and applications. This paper gives a detailed overview of the TanDEM-X mission concept which is based on the systematic combination of several innovative technologies. The key elements are the bistatic data acquisition employing an innovative phase synchronization link, a novel satellite formation flying concept allowing for the collection of bistatic data with short along-track baselines, as well as the use of new interferometric modes for system verification and DEM calibration. The interferometric performance is analyzed in detail, taking into account the peculiarities of the bistatic operation. Based on this analysis, an optimized DEM data acquisition plan is derived which employs the combination of multiple data takes with different baselines. Finally, a collection of instructive examples illustrates the capabilities of TanDEM-X for the development and demonstration of new remote sensing applications.

Question: What is the TanDEM-X mission?


Inverse reinforcement learning for interactive systems. Human machine interaction is a field where machine learning is present at almost any level, from human activity recognition to natural language generation. The interaction manager is probably one of the latest components of an interactive system that benefited from machine learning techniques. In the late 90's, sequential decision making algorithms like reinforcement learning have been introduced in the field with the aim of making the interaction more natural in a measurable way. Yet, these algorithms require providing the learning agent with a reward after each interaction. This reward is generally handcrafted by the system designer who introduces again some expertise in the system. In this paper, we will discuss a method for learning a reward function by observing expert humans, namely inverse reinforcement learning (IRL). IRL will then be applied to several steps of the spoken dialogue management design such as user simulation and clustering but also to co-adaptation of human user and machine.

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


CTrigger: exposing atomicity violation bugs from their hiding places. Multicore hardware is making concurrent programs pervasive. Unfortunately, concurrent programs are prone to bugs. Among different types of concurrency bugs, atomicity violation bugs are common and important. Existing techniques to detect atomicity violation bugs suffer from one limitation: requiring bugs to manifest during monitored runs, which is an open problem in concurrent program testing.  This paper makes two contributions. First, it studies the interleaving characteristics of the common practice in concurrent program testing (i.e., running a program over and over) to understand why atomicity violation bugs are hard to expose. Second, it proposes CTrigger to effectively and efficiently expose atomicity violation bugs in large programs. CTrigger focuses on a special type of interleavings (i.e., unserializable interleavings) that are inherently correlated to atomicity violation bugs, and uses trace analysis to systematically identify (likely) feasible unserializable interleavings with low occurrence-probability. CTrigger then uses minimum execution perturbation to exercise low-probability interleavings and expose difficult-to-catch atomicity violation.  We evaluate CTrigger with real-world atomicity violation bugs from four sever/desktop applications (Apache, MySQL, Mozilla, and PBZIP2) and three SPLASH2 applications on 8-core machines. CTrigger efficiently exposes the tested bugs within 1--235 seconds, two to four orders of magnitude faster than stress testing. Without CTrigger, some of these bugs do not manifest even after 7 full days of stress testing. In addition, without deterministic replay support, once a bug is exposed, CTrigger can help programmers reliably reproduce it for diagnosis. Our tested bugs are reproduced by CTrigger mostly within 5 seconds, 300 to over 60000 times faster than stress testing.

Question: What is atomicity violation?


Comprehensive Experimental Analyses of Automotive Attack Surfaces. Modern automobiles are pervasively computerized, and hence potentially vulnerable to attack. However, while previous research has shown that the internal networks within some modern cars are insecure, the associated threat model — requiring prior physical access — has justifiably been viewed as unrealistic. Thus, it remains an open question if automobiles can also be susceptible to remote compromise. Our work seeks to put this question to rest by systematically analyzing the external attack surface of a modern automobile. We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft. Finally, we discuss the structural characteristics of the automotive ecosystem that give rise to such problems and highlight the practical challenges in mitigating them.

Question: What is the attack surface of a modern automobile?


Gut microbes and the brain: paradigm shift in neuroscience.. The discovery of the size and complexity of the human microbiome has resulted in an ongoing reevaluation of many concepts of health and disease, including diseases affecting the CNS. A growing body of preclinical literature has demonstrated bidirectional signaling between the brain and the gut microbiome, involving multiple neurocrine and endocrine signaling mechanisms. While psychological and physical stressors can affect the composition and metabolic activity of the gut microbiota, experimental changes to the gut microbiome can affect emotional behavior and related brain systems. These findings have resulted in speculation that alterations in the gut microbiome may play a pathophysiological role in human brain diseases, including autism spectrum disorder, anxiety, depression, and chronic pain. Ongoing large-scale population-based studies of the gut microbiome and brain imaging studies looking at the effect of gut microbiome modulation on brain responses to emotion-related stimuli are seeking to validate these speculations. This article is a summary of emerging topics covered in a symposium and is not meant to be a comprehensive review of the subject.

Question: What is the gut microbiome?


Measures of retaining digital evidence to prosecute computer-based cyber-crimes. With the rapid growth of computer and network systems in recent years, there has also been a corresponding increase in cyber-crime. Cybercrime takes many forms and has garnered much attention in the media, making information security a more urgent and important priority. In order to fight cyber-crime, criminal evidence must be gathered from these computer-based systems. This is quite different from the collection of conventional criminal evidence and can confuse investigators attempting to deal with the forensics of cyber-crime, highlighting the importance of computer forensics. In this paper, we offer solutions to guard against cyber-crime through the implementation of software toolkits for computerbased systems. In this way, those who engage in criminal acts in cyber-space can be more easily apprehended. © 2006 Elsevier B.V. All rights reserved.

Question: What is the difference between cyber-crime and computer forensics?


THE THREE-DIMENSIONAL EXTENSION OF SPACE SYNTAX 048. The purpose of this study is to develop a new space syntax technique by adding third dimension in analysis of urban structure that is important to understand the formation of the cities. The theoretical framework of the study is put forward by integrating space syntax with the theory of the image of city. This entails the essential elucidation of spatial cognition pattern and image and the integration of space syntax and the image of city. The paper analyzes and demonstrates the prerequisite and feasibility of the mutual complementarily between space syntax and the image of city. The threedimensional space syntax integrated with the image of city is different from the traditional syntax that is only limited to non-figurative two-dimensional spatial cognition. It also emphasizes the important influences of the three-dimensional image points on people's behaviour in space and it is quite close to the accurate description of "natural movement". Based on the three-dimensional theory, a concept model of the threedimensional syntax is constructed, and its content includes how to describe forming process of the three-dimensional spatial image pots during the cognition activity, how to construct the objective evaluation model for the three-dimensional image pots and how to put forward the concept of weighted-integration to calculate the concept mathematical model. In the end, the paper carries through three experimental analyses. The paper argues that the extended three-dimensional syntactical model is much closer to people's spatial illation and cognition. It should be more accurate to use the model to describe and grasp the urban configuration.

Question: What is the purpose of this study?


Comparison of vision-based and sensor-based systems for joint angle gait analysis. Gait analysis has become recently a popular research field and been widely applied to clinical diagnosis of neurodegenerative diseases. Various low-cost sensor-based and vision-based systems are developed for capturing the hip and knee joint angles. However, the performances of these systems have not been validated and compared between each other. The purpose of this study is to set up an experiment and compare the performances of a sensor-based system with multiple inertial measurement units (IMUs), a vision-based gait analysis system with marker detection, and a markerless vision-based system on capturing the hip and knee joint angles during normal walking. The obtained measurements were validated with the data acquired from goniometers as ground truth measurement. The results indicate that the IMUs-based sensor system gives excellent performance with small errors, while vision systems produce acceptable results with slightly larger errors.

Question: What is the difference between a sensor-based system and a vision-based system?


Visual Exploration of Climate Variability Changes Using Wavelet Analysis. Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Nino southern oscillation phenomenon. Beside a shift of the mean states and of extreme values of climate variables, climate change may also change the frequency or the spatial patterns of these natural climate variations. Wavelet analysis is a well established tool to investigate variability in the frequency domain. However, due to the size and complexity of the analysis results, only few time series are commonly analyzed concurrently. In this paper we will explore different techniques to visually assist the user in the analysis of variability and variability changes to allow for a holistic analysis of a global climate model data set consisting of several variables and extending over 250 years. Our new framework and data from the IPCC AR4 simulations with the coupled climate model ECHAM5/MPI-OM are used to explore the temporal evolution of El Nino due to climate change.

Question: How to visualize climate variability changes?


The emotional brain. The discipline of affective neuroscience is concerned with the neural bases of emotion and mood. The past 30 years have witnessed an explosion of research in affective neuroscience that has addressed questions such as: which brain systems underlie emotions? How do differences in these systems relate to differences in the emotional experience of individuals? Do different regions underlie different emotions, or are all emotions a function of the same basic brain circuitry? How does emotion processing in the brain relate to bodily changes associated with emotion? And, how does emotion processing in the brain interact with cognition, motor behaviour, language and motivation?

Question: What is the emotional brain?


DATS - Data Containers for Web Applications. Data containers enable users to control access to their data while untrusted applications compute on it. However, they require replicating an application inside each container - compromising functionality, programmability, and performance. We propose DATS - a system to run web applications that retains application usability and efficiency through a mix of hardware capability enhanced containers and the introduction of two new primitives modeled after the popular model-view-controller (MVC) pattern. (1) DATS introduces a templating language to create views that compose data across data containers. (2) DATS uses authenticated storage and confinement to enable an untrusted storage service, such as memcached and deduplication, to operate on plain-text data across containers. These two primitives act as robust declassifiers that allow DATS to enforce non-interference across containers, taking large applications out of the trusted computing base (TCB). We showcase eight different web applications including Gitlab and a Slack-like chat, significantly improve the worst-case overheads due to application replication, and demonstrate usable performance for common-case usage.

Question: What is the templating language?


The formation and function of plant cuticles.. The plant cuticle is an extracellular hydrophobic layer that covers the aerial epidermis of all land plants, providing protection against desiccation and external environmental stresses. The past decade has seen considerable progress in assembling models for the biosynthesis of its two major components, the polymer cutin and cuticular waxes. Most recently, two breakthroughs in the long-sought molecular bases of alkane formation and polyester synthesis have allowed construction of nearly complete biosynthetic pathways for both waxes and cutin. Concurrently, a complex regulatory network controlling the synthesis of the cuticle is emerging. It has also become clear that the physiological role of the cuticle extends well beyond its primary function as a transpiration barrier, playing important roles in processes ranging from development to interaction with microbes. Here, we review recent progress in the biochemistry and molecular biology of cuticle synthesis and function and highlight some of the major questions that will drive future research in this field.

Question: What is the function of the cuticle?


Homicides with mutilation of the victim's body.. Information on homicide offenders guilty of mutilation is sparse. The current study estimates the rate of mutilation of the victim's body in Finnish homicides and compares sociodemographic characteristics, crime history, life course development, psychopathy, and psychopathology of these and other homicide offenders. Crime reports and forensic examination reports of all offenders subjected to forensic examination and convicted for a homicide in 1995-2004 (n = 676) were retrospectively analyzed for offense and offender variables and scored with the Psychopathy Check List Revised. Thirteen homicides (2.2%) involved mutilation. Educational and mental health problems in childhood, inpatient mental health contacts, self-destructiveness, and schizophrenia were significantly more frequent in offenders guilty of mutilation. Mutilation bore no significant association with psychopathy or substance abuse. The higher than usual prevalence of developmental difficulties and mental disorder of this subsample of offenders needs to be recognized.

Question: What is the prevalence of homicide offenders with mutilation?


Human Pose Regression by Combining Indirect Part Detection and Contextual Information. In this paper, we propose an end-to-end trainable regression approach for human pose estimation from still images. We use the proposed Soft-argmax function to convert feature maps directly to joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two very challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods and comparable results to the state-of-the-art detection based approaches.

Question: How to combine indirect part detection and contextual information?


Reverse Word Order Models. In this work, we study the impact of the word order decoding direction for statistical machine translation (SMT). Both phrase-based and hierarchical phrasebased SMT systems are investigated by reversing the word order of the source and/or target language and comparing the translation results with the normal direction. Analysis are done on several components such as alignment model, language model and phrase table to see which of them accounts for the differences generated by various translation directions. Furthermore, we propose to use system combination, alignment combinations and phrase table combinations to take benefit from systems trained with different translation directions. Experimental results show improvements of up to 1.7 points in BLEU and 3.1 points in TER compared to the normal direction systems for the NTCIR9 Japanese-English and Chinese-English tasks.

Question: What is the impact of the word order decoding direction for statistical machine translation?


Applying Winnow to Context-Sensitive Spelling Correction. Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: contextsensitive spelling correction. This is the task of xing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classi ers. We nd that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. In Machine Learning: Proceedings of the 13th International Conference, Lorenza Saitta, ed., Morgan Kaufmann, San Francisco, CA, 1996, pages 182{190. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubis

Question: What is the difference between Winnow and Bayes?


The Complexity of Relational Query Languages (Extended Abstract). Two complexity measures for query languages are proposed. Data complexity is the complexity of evaluating a query in the language as a function of the size of the database, and expression complexity is the complexity of evaluating a query in the language as a function of the size of the expression defining the query. We study the data and expression complexity of logical languages - relational calculus and its extensions by transitive closure, fixpoint and second order existential quantification - and algebraic languages - relational algebra and its extensions by bounded and unbounded looping. The pattern which will be shown is that the expression complexity of the investigated languages is one exponential higher then their data complexity, and for both types of complexity we show completeness in some complexity class.

Question: What is the complexity of relational calculus?


Use of Non-conductive Film (NCF) with Nano-Sized Filler Particles for Solder Interconnect: Research and Development on NCF Material and Process Characterization. As three-dimensional Through-Silicon Via (3D-TSV) packaging is emerging in the semiconductor market to integrate multiple functions in a system for further miniaturization, thermal compression bonding (TCB), which stacks multiple bare chips on top of each other with Cu pillar bumps with solder cap, has become an indispensable new packaging technology. The novel non-conductive film (NCF) described in this paper is an epoxy-type thermosetting material in film form, with lower density of Nano-sized silica filler particles (average particle size is 100 Nano meters). Advantages of this NCF material with Nano-sized fillers include: transparency of the NCF material so that the TCB bonder's image recognition system can easily identify fiducial marks on the chip, ability of the Nano-filler to flow out with the NCF resin during thermal compression bonding, to mitigate filler entrapment between solder joints, which is critical to ensure reliable solder connections, compatibility with fine pitch applications with extremely narrow chip-to-chip and chip-to-substrate gaps to form void-free underfill. Instead of the previous processes of die attach, fluxing, traditional oven mass re-flow, flux cleaning, and capillary underfill (CUF), the current process involves the use of NCF & TCB. The NCF is applied on a wafer in a film form by lamination, and then a wafer is diced with the pre-laminated NCF in pieces. Then they are joined by TCB, with typical parameters of 250°C for 10 seconds with 80N (solder cap on 15μm diameter Cu pillar, 40μm pitch, 1000 bumps). NCF heated by TCB is quickly liquidized to lower its viscosity after a few seconds. Other advantages of NCF is that it has a fluxing function included in the material, which eliminates the need for separate flux apply and flux clean steps, which simplifies process and costs

Question: What is the difference between a solder bump and a solder cap?


Story Cloze Ending Selection Baselines and Data Examination. This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using features based on word embeddings and semantic similarity computation. We further implement a neural LSTM system with different encoding strategies that try to model the relation between the story and the provided endings. Our experiments show that a model using representation features based on average word embedding vectors over the given story words and the candidate ending sentences words, joint with similarity features between the story and candidate ending representations performed better than the neural models. Our best model achieves an accuracy of 72.42, ranking 3rd in the official evaluation.

Question: What is the best model for the story cloze test?


Movie trailer quality evaluation using real-time human electroencephalogram. The total US box office revenue exceeds ten billion dollars a year. Inevitably, new product forecasting and diagnosis have high financially stakes. The motion picture industry has a huge incentive to perform early prediction of movie success or failure. In this paper, we present a study that evaluate viewer responses to short movie trailers using a cap instrumented with Electroencephalogram (EEG) sensors. The method can be used to evaluate prerelease movies regarding how engaging they are to viewers. The primary advantage of our approach is that responses of a viewer can be recorded without any manual inputs from the viewer. The data collected, if analyzed properly, can reveal more accurate information regarding the viewer's emotion states while watching the movie than post-viewing surveys. This approach also enables the delivery of personalized entertainment through the brain interface.

Question: How to evaluate movie trailers?


Comparison of Different Classification Techniques on PIMA Indian Diabetes Data. The development of data-mining applications such as classification and clustering has been applied to large scale data. In this research, we present comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. The algorithm or classifiers tested are Multilayer Perceptron, Bayes Network, J48graft (c4.5), Fuzzy Lattice Reasoning (FLR), NaiveBayes, JRip (RIPPER), Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference Systems(ANFIS). A fundamental review on the selected technique is presented for introduction purposes. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.

Question: What is the difference between classification and clustering?


Burnout in the Mental Health Workforce: A Review. There are enormous concerns regarding the recruitment, retention, training, and performance of the behavioral health workforce. Paramount among these concerns is turnover, which causes disruption in continuity of care, diminishes access to care while a position remains vacant, and poses financial hardship on the provider organization through costs related to recruitment, orientation, and training of a new hire. There is frequent mention of burnout within the literature and among behavioral health managers as a potential cause of turnover. However, there is no recent or comprehensive review of burnout that examines the evidence surrounding its validity, utility, and potential relationship to turnover. The purpose of this paper is to provide such a review by examining the construct of burnout, methodological and measurement issues, its prevalence in the mental health workforce, correlates of burnout, and interventions to decrease it. The implications for provider organizations and recommendations for future research are identified.

Question: What is the prevalence of burnout in the mental health workforce?


Achievements : Re ̄ ections on the Measurement of Women ' s Empowerment. This paper begins from the understanding that women's empowerment is about the process by which those who have been denied the ability to make strategic life choices acquire such an ability. Awide gap separates this processual understanding of empowerment from the more instrumentalist forms of advocacy which have required the measurement and quanti®cation of empowerment. The ability to exercise choice incorporates three inter-related dimensions: resources (de®ned broadly to include not only access, but also future claims, to both material and human and social resources); agency (including processes of decision making, as well as less measurable manifestations of agency such as negotiation, deception and manipulation); and achievements (well-being outcomes). A number of studies of women's empowerment are analysed to make some important methodological points about the measurement of empowerment. The paper argues that these three dimensions of choice are indivisible in determining the meaning of an indicator and hence its validity as a measure of empowerment. The notion of choice is further quali®ed by referring to the conditions of choice, its content and consequences. These quali®cations represent an attempt to incorporate the structural parameters of individual choice in the analysis of women's empowerment. CONCEPTUALIZING EMPOWERMENT

Question: What is the difference between empowerment and choice?


Improving the Neural Algorithm of Artistic Style. In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style [7]. While showing great results when transferring homogeneous and repetitive patterns, the original style representation often fails to capture more complex properties, like having separate styles of foreground and background. This leads to visual artifacts and undesirable textures appearing in unexpected regions when performing style transfer. We tackle this issue with a variety of approaches, mostly by modifying the style representation in order for it to capture more information and impose a tighter constraint on the style transfer result. In our experiments, we subjectively evaluate our best method as producing from barely noticeable to significant improvements in the quality of style transfer.

Question: How to improve the neural algorithm of artistic style?


A Survey of Intrusion Detection Techniques. Intrusion detection is an alternative to the situation of the security violation.Security mechanism of the network is necessary against the threat to the system. There are two types of intruders: external intruders, who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This paper describes a brief overview of various intrusion detection techniques such as fuzzy logic, neural network, pattern recognition methods, genetic algorithms and related techniques is presented. Among the several soft computing paradigms, fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming is model fast and efficient intrusion detection systems. KeywordsIntroduction, intrusion detection methods, misuse detection techniques, anomaly detection techniques, genetic algorithms.

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


Information propagation in the Bitcoin network. Bitcoin is a digital currency that unlike traditional currencies does not rely on a centralized authority. Instead Bitcoin relies on a network of volunteers that collectively implement a replicated ledger and verify transactions. In this paper we analyze how Bitcoin uses a multi-hop broadcast to propagate transactions and blocks through the network to update the ledger replicas. We then use the gathered information to verify the conjecture that the propagation delay in the network is the primary cause for blockchain forks. Blockchain forks should be avoided as they are symptomatic for inconsistencies among the replicas in the network. We then show what can be achieved by pushing the current protocol to its limit with unilateral changes to the client's behavior.

Question: What is the bitcoin network?