Learning how to match fresco fragments. One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.

Question: How to match fresco fragments?


Evidence for sensory prediction deficits in schizophrenia.. OBJECTIVE Patients with schizophrenia experiencing delusions and hallucinations can misattribute their own actions to an external source. The authors test the hypothesis that patients with schizophrenia have defects in their ability to predict the sensory consequences of their actions.   METHOD The authors measured sensory attenuation of self-produced stimuli by patients with schizophrenia and by healthy subjects.   RESULTS Patients with schizophrenia demonstrated significantly less sensory attenuation than healthy subjects.   CONCLUSIONS Patients with a diagnosis of schizophrenia have a dysfunction in their predictive mechanisms.

Question: What is sensory attenuation?


Reconfigurable RF MEMS Phased Array Antenna Integrated Within a Liquid Crystal Polymer (LCP) System-on-Package. For the first time, a fully integrated phased array antenna with radio frequency microelectromechanical systems (RF MEMS) switches on a flexible, organic substrate is demonstrated above 10 GHz. A low noise amplifier (LNA), MEMS phase shifter, and 2 times 2 patch antenna array are integrated into a system-on-package (SOP) on a liquid crystal polymer substrate. Two antenna arrays are compared; one implemented using a single-layer SOP and the second with a multilayer SOP. Both implementations are low-loss and capable of 12deg of beam steering. The design frequency is 14 GHz and the measured return loss is greater than 12 dB for both implementations. The use of an LNA allows for a much higher radiated power level. These antennas can be customized to meet almost any size, frequency, and performance needed. This research furthers the state-of-the-art for organic SOP devices.

Question: What is a phased array antenna?


Causality in thought.. Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.

Question: What is the difference between causal and probabilistic knowledge?


ViewDroid: towards obfuscation-resilient mobile application repackaging detection. In recent years, as mobile smart device sales grow quickly, the development of mobile applications (apps) keeps accelerating, so does mobile app repackaging. Attackers can easily repackage an app under their own names or embed advertisements to earn pecuniary profits. They can also modify a popular app by inserting malicious payloads into the original app and leverage its popularity to accelerate malware propagation. In this paper, we propose ViewDroid, a user interface based approach to mobile app repackaging detection. Android apps are user interaction intensive and event dominated, and the interactions between users and apps are performed through user interface, or views. This observation inspires the design of our new birthmark for Android apps, namely, feature view graph, which captures users' navigation behavior across app views. Our experimental results demonstrate that this birthmark can characterize Android apps from a higher level abstraction, making it resilient to code obfuscation. ViewDroid can detect repackaged apps at a large scale, both effectively and efficiently. Our experiments also show that the false positive and false negative rates of ViewDroid are both very low.

Question: How can we detect repackaged apps?


Universal Stanford dependencies: A cross-linguistic typology. Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology. We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing.

Question: What is the universal taxonomy of Stanford dependencies?


Data association for multi-object Tracking-by-Detection in multi-camera networks. Multi-object tracking is still a challenging task in computer vision. We propose a robust approach to realize multi-object tracking using multi-camera networks. Detection algorithms are utilized to detect object regions with confidence scores for initialization of individual particle filters. Since data association is the key issue in Tracking-by-Detection mechanism, we present an efficient greedy matching algorithm considering multiple judgments based on likelihood functions. Furthermore, tracking in single cameras is realized by a greedy matching method. Afterwards, 3D geometry positions are obtained from the triangulation relationship between cameras. Corresponding objects are tracked in multiple cameras to take the advantages of multi-camera based tracking. Our algorithm performs online and does not need any information about the scene, no restrictions of enter-and-exit zones, no assumption of areas where objects are moving on and can be extended to any class of object tracking. Experimental results show the benefits of using multiple cameras by the higher accuracy and precision rates.

Question: What is the difference between multi-object tracking and tracking-by-detection?


Process integration of a 27nm, 16Gb Cu ReRAM. A 27nm 16Gb Cu based NV Re-RAM chip has been demonstrated. Novel process introduction to enable this technology include a Damascene Cell, Line-SAC Digit Lines filled with Cu, exhumed-silicided array contacts, raised epitaxial arrays, and high-drive buried access devices.

Question: What is a damascene cell?


MANAGING HEALTH CARE SUPPLY CHAIN : TRENDS , ISSUES , AND SOLUTIONS FROM A LOGISTICS PERSPECTIVE. The U.S. healthcare industry is a large enterprise accounting for over 14.1% of the national economic output in 2001. It has been under pressure for cost containment and providing quality health care services to consumers. Its record of investing heavily on development of sophisticated drugs and diagnostic systems does not match that of technologies to manage its day-to-day operations. In order to achieve improved performance, healthcare supply chain must be efficient and integrated. The driver for this integration is logistics and supply chain management. This paper describes trends, issues and some solutions for logistics management for Health Care Supply Chain with concepts drawn from Industrial Engineering, and Operations Research disciplines applied to specific domains. A healthcare supply chain template utilizing Ecommerce strategy is presented. Use of simulation, optimization, and information sharing techniques are demonstrated to optimize purchasing and inventory policies. (

Question: What is the difference between a healthcare supply chain and a logistics supply chain?


Perceptually based brush strokes for nonphotorealistic visualization. An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.

Question: How to create a nonphotorealistic visualization of a large dataset?


Predicting the Semantic Orientation of Adjectives. We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. Evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90% for adjectives that occur in a modest number of conjunctions in the corpus. 1 I n t r o d u c t i o n The semantic orientation or polarity of a word indicates the direction the word deviates from the norm for its semantic group or lezical field (Lehrer, 1974). It also constrains the word's usage in the language (Lyons, 1977), due to its evaluative characteristics (Battistella, 1990). For example, some nearly synonymous words differ in orientation because one implies desirability and the other does not (e.g., simple versus simplisfic). In linguistic constructs such as conjunctions, which impose constraints on the semantic orientation of their arguments (Anscombre and Ducrot, 1983; Elhadad and McKeown, 1990), the choices of arguments and connective are mutually constrained, as illustrated by: The tax proposal was simple and well-received } simplistic but well-received *simplistic and well-received by the public. In addition, almost all antonyms have different semantic orientations3 If we know that two words relate to the same property (for example, members of the same scalar group such as hot and cold) but have different orientations, we can usually infer that they are antonyms. Given that semantically similar words can be identified

Question: What is the semantic orientation of adjectives?


Socially compliant mobile robot navigation via inverse reinforcement learning. Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot's current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrate that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which makes it also applicable to fields such as behavioral science or computer graphics.

Question: How to navigate in an office environment in the presence of humans?


Effectiveness of a multi-layer foam dressing in preventing sacral pressure ulcers for the early acute care of patients with a traumatic spinal cord injury: comparison with the use of a gel mattress.. Individuals with spinal cord injury are at risk of sacral pressure ulcers due to, among other reasons, prolonged immobilisation. The effectiveness of a multi-layer foam dressing installed pre-operatively in reducing sacral pressure ulcer occurrence in spinal cord injured patients was compared to that of using a gel mattress, and stratified analyses were performed on patients with complete tetraplegia and paraplegia. Socio-demographic and clinical data were collected from 315 patients admitted in a level-I trauma centre following a spinal cord injury between April 2010 and March 2016. Upon arrival to the emergency room and until surgery, patients were transferred on a foam stretcher pad with a viscoelastic polymer gel mattress (before 1 October 2014) or received a multi-layer foam dressing applied to their sacral-coccygeal area (after 1 October 2014). The occurrence of sacral pressure ulcer during acute hospitalisation was similar irrespective of whether patients received the dressing or the gel mattress. It was found that 82% of patients with complete tetraplegia receiving the preventive dressing developed sacral ulcers as compared to only 36% of patients using the gel mattress. Although multi-layer dressings were suggested to improve skin protection and decrease pressure ulcer occurrence in critically ill patients, such preventive dressings are not superior to gel mattresses in spinal cord injured patients and should be used with precaution, especially in complete tetraplegia.

Question: What is the effectiveness of a multi-layer foam dressing in preventing sacral pressure ulcers for the early acute care of patients with a traumatic spinal cord injury?


An event-based conceptual model for context-aware movement analysis. Current tracking technologies enable collection of data describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods, etc. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach.

Question: What is a conceptual model for context-aware movement analysis?


The Software Concordance: A User Interface for Advanced Software Documents. The Software Concordance is a hypermedia software development environment exploring how document technology and versioned hypermedia can improve software document management. The Software Concordance's central tool is a document editor that integrates program analysis and hypermedia services for both source code and multimedia documentation in XML. The editor allows developers to embed inline multimedia documentation, including images and audio clips, into their program sources and to bind them to any program fragment. Web style hyperlinks are also supported. The developers are able to move seamlessly between source code and the documentation that describes its motivation, design, correctness and use without disrupting the integrated program analysis services, which include lexing, parsing and type checking. This paper motivates the need for environments like the Software Concordance, describes the key features and architecture of its document editor and discusses future work to improve its functionality.

Question: What is the Software Concordance?


Comprehensive review of epidemiology, scope, and impact of spinal pain.. Persistent pain interfering with daily activities is common. Chronic pain has been defined in many ways. Chronic pain syndrome is a separate entity from chronic pain. Chronic pain is defined as, "pain that persists 6 months after an injury and beyond the usual course of an acute disease or a reasonable time for a comparable injury to heal, that is associated with chronic pathologic processes that cause continuous or intermittent pain for months or years, that may continue in the presence or absence of demonstrable pathologies; may not be amenable to routine pain control methods; and healing may never occur." In contrast, chronic pain syndrome has been defined as a complex condition with physical, psychological, emotional, and social components. The prevalence of chronic pain in the adult population ranges from 2% to 40%, with a median point prevalence of 15%. Among chronic pain disorders, pain arising from various structures of the spine constitutes the majority of the problems. The lifetime prevalence of spinal pain has been reported as 54% to 80%. Studies of the prevalence of low back pain and neck pain and its impact in general have shown 23% of patients reporting Grade II to IV low back pain (high pain intensity with disability) versus 15% with neck pain. Further, age related prevalence of persistent pain appears to be much more common in the elderly associated with functional limitations and difficulty in performing daily life activities. Chronic persistent low back and neck pain is seen in 25% to 60% of patients, one-year or longer after the initial episode. Spinal pain is associated with significant economic, societal, and health impact. Estimates and patterns of productivity losses and direct health care expenditures among individuals with back and neck pain in the United States continue to escalate. Recent studies have shown significant increases in the prevalence of various pain probl

Question: What is the prevalence of spinal pain?


Three Decades of Driver Assistance Systems: Review and Future Perspectives. This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.

Question: What is the future of driver assistance systems?


A VoIP emergency services architecture and prototype. Providing emergency services in VoIP networks are vital to the success of VoIP. It not only presents design and implementation challenges, but also gives an opportunity to enhance the existing emergency call handling infrastructure. We propose an architecture to deliver emergency services in SIP-based VoIP networks, which can accommodate PSTN calls through PSTN to SIP gateways. Our architecture addresses the issues of identifying emergency calls, determining callers' locations, routing emergency calls to appropriate public safety answering points (PSAPs), and presenting required information to emergency call takers. We have developed a prototype implementation to prove our architecture's feasibility and scalability. We expect to undertake a pilot project at a working PSAP with our implementation once it is thoroughly tested.

Question: What is a VoIP emergency services architecture?


Compositional Analysis Framework Using EDP Resource Models. Compositional schedulability analysis of hierarchical scheduling frameworks is a well studied problem, as it has wide-ranging applications in the embedded systems domain. Several techniques, such as periodic resource model based abstraction and composition, have been proposed for this problem. However these frameworks are sub-optimal because they incur bandwidth overhead. In this work, we introduce the explicit deadline periodic (EDP) resource model, and present compositional analysis techniques under EDF and DM. We show that these techniques are bandwidth optimal, in that they do not incur any bandwidth overhead in abstraction or composition. Hence, this framework is more efficient when compared to existing approaches.

Question: What is the compositional analysis framework for hierarchical scheduling frameworks?


Integrating Grid-Based and Topological Maps for Mobile Robot Navigation. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms—grid-based and topological—, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room envi-

Question: What is the difference between grid-based and topological maps?


Synchronous neural oscillations and cognitive processes. The central problem for cognitive neuroscience is to describe how cognitive processes arise from brain processes. This review summarizes the recent evidence that synchronous neural oscillations reveal much about the origin and nature of cognitive processes such as memory, attention and consciousness. Memory processes are most closely related to theta and gamma rhythms, whereas attention seems closely associated with alpha and gamma rhythms. Conscious awareness may arise from synchronous neural oscillations occurring globally throughout the brain rather than from the locally synchronous oscillations that occur when a sensory area encodes a stimulus. These associations between the dynamics of the brain and cognitive processes indicate progress towards a unified theory of brain and cognition.

Question: What is the relationship between neural oscillations and cognitive processes?


The Internet of Things in an Enterprise Context. This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.

Question: What is the future of the Internet of Things?


Comparison of SQL, NoSQL and NewSQL databases for internet of things. The Internet of Things (IoT) in all its essentiality is a collection of sensors bridged together tightly. The present day development in the technology industry has thus elevated the emphasis of large amounts of data. IoT is expected to generate and collect an enormous amount of data from varied locations very quickly. The concerns for features like storage and performance factors are coercing the databases to outperform themselves. Traditional relational databases have time and again proved to be efficient. NoSQL and more recently NewSQL databases having gained an impetus are asserted to perform better than the classic SQL counterpart. This paper compares the performance of three databases each from the said technologies; SQL (MySQL), NoSQL (MongoDB) and NewSQL (VoltDB) respectively for sensor readings. The sensor data handled ranges in vast (MB to GB) size and tested against: single write, single read, single delete and multi write operations.

Question: What is the difference between SQL, NoSQL and NewSQL databases?


Entropy-Based Subword Mining with an Application to Word Embeddings. Recent literature has shown a wide variety of benefits to mapping traditional onehot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have longtail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropybased subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.

Question: What is the difference between a word embedding and a word2vec?


Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach. In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.

Question: How many 3D objects can be detected in real time?


Nonlinear controller design for a magnetic levitation device. Various applications of micro-robotic technology suggest the use of new actuator systems which allow motions to be realized with micrometer accuracy. Conventional actuation techniques such as hydraulic or pneumatic systems are no longer capable of fulfilling the demands of hi-tech micro-scale areas such as miniaturized biomedical devices and MEMS production equipment. These applications pose significantly different problems from actuation on a large scale. In particular, large scale manipulation systems typically deal with sizable friction, whereas micro manipulation systems must minimize friction to achieve submicron precision and avoid generation of static electric fields. Recently, the magnetic levitation technique has been shown to be a feasible actuation method for microscale applications. In this paper, a magnetic levitation device is recalled from the authors' previous work and a control approach is presented to achieve precise motion control of a magnetically levitated object with sub-micron positioning accuracy. The stability of the controller is discussed through the Lyapunov method. Experiments are conducted and showed that the proposed control technique is capable of performing a positioning operation with rms accuracy of 16 lm over a travel range of 30 mm. The nonlinear control strategy proposed in this paper showed a significant improvement in comparison with the conventional control strategies for large gap magnetic levitation systems.

Question: How to control a magnetic levitation device?


Performance Sensitivity of Space Sharing Processor Scheduling in Distributed-Memory Multicomputers. Processor scheduling in distributed-memory systems has received considerable attention in recent years. Several commercial distributed-memory systems use spacesharing processor scheduling. In space-sharing, the set of processors in a system is partitioned and each partition is assigned for the exclusive use of a job. Space-sharing policies can be divided into fixed, static, or dynamic categories. For distributed-memory systems, dynamic policies incur high overhead. Thus, static policies are considered as these policies provide a better performance than the fixed policies. Several static policies have been proposed in the literature. In a previously proposed adaptive static policy, the partition size is a function of the number of queued jobs. This policy, however, tends to underutilize the system resources. To improve the performance of this policy, we propose a new policy in which the partition size is a function of the total number of jobs in the system, as opposed to only the queued jobs. The results presented here demonstrate that the new policy performs substantially better than the original policy for the various workload and system parameters. Another major contribution is the evaluation of the performance sensitivity to job structure, variances in inter-arrival times and job service times, and network topology.

Question: What is the performance sensitivity of space sharing processor scheduling in distributed-memory multicomputers?


A Fusion Approach for Efficient Human Skin Detection. A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.

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


LETOR : Benchmark Dataset for Research on Learning to Rank for Information Retrieval. This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. Unfortunately, there was no benchmark dataset that could be used in comparison of existing learning algorithms and in evaluation of newly proposed algorithms, which stood in the way of the related research. To deal with the problem, we have constructed a benchmark dataset referred to as LETOR and distributed it to the research communities. Specifically we have derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data. The two collections contain queries, the contents of the retrieved documents, and human judgments on the relevance of the documents with respect to the queries. We have extracted features from the datasets, including both conventional features, such as term frequency, inverse document frequency, BM25, and language models for IR, and features proposed recently at SIGIR, such as HostRank, feature propagation, and topical PageRank. We have then packaged LETOR with the extracted features, queries, and relevance judgments. We have also provided the results of several state-ofthe-arts learning to rank algorithms on the data. This paper describes in details about LETOR.

Question: What is the LETOR dataset?


World ocean simulation system (WOSS): a simulation tool for underwater networks with realistic propagation modeling. Network simulators are a fundamental tool for the performance evaluation of protocols and applications in complex scenarios, which would be too expensive or infeasible to realize in practice.  With the aim to provide a shared environment for the simulation of underwater networks we have adapted the ns2 network simulator to provide a detailed reproduction of the propagation of sound in water (i.e., by means of ray tracing instead of empirical relations). This has been tied to formerly available simulation frameworks (such as the MIRACLE extensions to ns2) to provide a completely customizable tool, including acoustic propagation, physical layer modeling, and cross-layer specification of networking protocols. In this paper, we describe our tool, and use it for a case study involving the comparison of three MAC protocols for underwater networks over different kinds of physical layers. Our results compare the transmission coordination approach chosen by each protocol, and show when it is better to rely on random access, as opposed to loose or tight coordination.

Question: What is the difference between loose and tight coordination?


Sub-Nyquist Radar via Doppler Focusing. We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here, we describe a sub-Nyquist sampling and recovery approach called Doppler focusing, which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size, which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement, which scales linearly with P, obtaining good detection performance even at SNR as low as - 25 dB. The recovery is based on the Xampling framework, which allows reduction of the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling radar prototype.

Question: What is a monostatic pulse-Doppler radar?


The Tri-Wheel: A novel wheel-leg mobility concept. The Tri-Wheel is a novel wheel-leg locomotion concept inspired by work with first responders. Through its two modes of operation-Driving Mode and Tumbling Mode- this mechanism is able to both drive quickly on smooth surfaces at roughly 1.7 times desired speed and climb objects as tall as 67% of the diameter of the mechanism. The unique gearing configuration that facilitates these dual capabilities is described, and testing quantifies that this nonprecision gearing system is roughly 81% efficient in a worst-case scenario of loading. This work introduces the Tri-Wheel concept and provides preliminary testing to validate its predicted operating characteristics.

Question: What is the Tri-Wheel?


From "Bonehead" to "cLoNehEAd": Nicknames, Play and Identity on Internet Relay Chat.

Question: What is the difference between nicknames and handles?


User-Oriented Context Suggestion. Recommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' preferences across contexts, and suggesting items that are appropriate in different contexts. In this paper, we present a novel recommendation task, "Context Suggestion", whereby the system recommends contexts in which items may be selected. We introduce the motivations behind the notion of context suggestion and discuss several potential solutions. In particular, we focus specifically on user-oriented context suggestion which involves recommending appropriate contexts based on a user's profile. We propose extensions of well-known context-aware recommendation algorithms such as tensor factorization and deviation-based contextual modeling and adapt them as methods to recommend contexts instead of items. In our empirical evaluation, we compare the proposed solutions to several baseline algorithms using four real-world data sets.

Question: What is the motivation behind the notion of context suggestion?


Intangible benefits valuation in ERP projects. The development, implementation and ownership of information systems, especially large-scale systems such as enterprise resource planning (ERP), has become progressively longer in duration and more cost intensive. As a result, IS managers are being required to justify projects financially based on their return. Historically, information systems have been difficult to quantify in monetary terms because of the intangible nature of many of the derived benefits, e.g. improved customer service. Using the case study methodology, this paper examines an attempt by a large computer manufacturer to incorporate intangibles into traditional cost–benefit analysis in an ERP project. The paper reviews the importance of intangibles, lists intangible benefits that are important in ERP projects and demonstrates the use of a scheme through which they can be incorporated into traditional evaluation techniques.

Question: What is the importance of intangibles in ERP projects?


Learning Fair Representations. We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.

Question: What is a fair representation?


Neural Probabilistic Model for Non-projective MST Parsing. In this paper, we propose a probabilistic parsing model that defines a proper conditional probability distribution over nonprojective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTMCNNs, which automatically benefits from both wordand character-level representations, by using a combination of bidirectional LSTMs and CNNs. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over nonprojective trees. By exploiting Kirchhoff's Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straightforward end-to-end model training procedure via back-propagation. We evaluate our model on 17 different datasets, across 14 different languages. Our parser achieves state-of-the-art parsing performance on nine datasets.

Question: What is the neural network architecture?


On the use of words and n-grams for Chinese information retrieval. In the processing of Chinese documents and queries in information retrieval (IR), one has to identify the units that are used as indexes. Words and n-grams have been used as indexes in several previous studies, which showed that both kinds of indexes lead to comparable IR performances. In this study, we carry out more experiments on different ways to segment documents and queries, and to combine words with n-grams. Our experiments show that a combination of the longest-matching algorithm with single characters is the best choice.

Question: What is the best way to segment documents and queries for Chinese information retrieval?


The RC5 Encryption Algorithm. This document describes the RC encryption algorithm a fast symmetric block cipher suitable for hardware or software imple mentations A novel feature of RC is the heavy use of data dependent rotations RC has a variable word size a variable number of rounds and a variable length secret key The encryption and decryption algorithms are exceptionally simple

Question: What is the RC5 encryption algorithm?


Learning with Taxonomies: Classifying Documents and Words. Automatically extracting semantic information about word meaning and document topic from text typically involves an extensive number of classes. Such classes may represent predefined word senses, topics or document categories and are often organized in a taxonomy. The latter encodes important information, which should be exploited in learning classifiers from labeled training data. To that extent, this paper presents an extension of multiclass Support Vector Machine learning which can incorporate prior knowledge about class relationships. The latter can be encoded in the form of class attributes, similarities between classes or even a kernel function defined over the set of classes. The paper also discusses how to specify and optimize meaningful loss functions based on the relative position of classes in the taxonomy. We include experimental results for text categorization and for word sense classification.

Question: How to classify documents based on taxonomies?


Recognition and identification of the clothes in the photo or video using neural networks. We introduce our solution of the problem of clothing recognition and identification. The cause of taking convolutional neural network as a mathematic model for the further research was the result of an experimental survey of methods of keypoints detection and computing their descriptors. The convolutional neural network model was developed and trained on the Apparel Classification with Style clothing image dataset. The training image dataset was expanded with the help of augmentation methods. The computational experiment aimed at determination of neural network efficiency and stability proved its productivity. Moreover, we developed clothing color recognition, which let us perform clothing identification by 2 parameters: type and color.

Question: How to recognize clothes?


Methods for the prevention, detection and removal of software security vulnerabilities. Over the past decade, the need to build secure software has become a dominant goal in software development. Consequently, software researchers and practitioners have identified ways that malicious users can exploit software and how developers can fix the vulnerabilities. They have also built a variety of source code security checking software applications to partially automate the task of performing a security analysis of a program. Although great advances have been made in this area, the core problem of how the security vulnerabilities occur still exists. An answer to this problem could be a paradigm shift from imperative to functional programming techniques. This may hold the key to removing software vulnerabilities altogether.

Question: What is a paradigm shift?


Dual wideband CPW-fed split-ring monopole antenna for WLAN applications. This paper presents a dual wideband CPW-fed monopole antenna based on split-ring elements for WLAN (2.4/5 GHz) applications. The proposed split-ring monopole antenna (SRMA) has a compact novel design which provides 13.8% and 55.5% impedance bandwidth at operational frequency bands. Moreover, the antenna exhibits good radiation pattern with predicted gains of 4–6 dBi in the related bands. The analysis and design of the proposed microstrip antenna have been carried out by means of Ansoft HFSS and an antenna prototype was then constructed and tested. Numerical analyses using the simulator and measurement results are also presented in this paper.

Question: What is the dual wideband CPW-fed split-ring monopole antenna based on?


Information Privacy Awareness (IPA): A Review of the Use, Definition and Measurement of IPA. Despite the acknowledged importance of awareness in the information privacy (IP) literature, we lack a consistent and thorough understanding of information privacy awareness (IPA). Drawing on Endsley's model of Situation Awareness, we propose a multidimensional model of IPA and define each of its dimensions. We then conducted a thorough review of the IP literature's use of awareness and synthesize our findings using our proposed model. This paper makes significant contributions by 1) distinguishing between IP knowledge, literacy and awareness 2) consolidating the IP literature's definitions of awareness and providing a new detailed definition 3) proposing a new IPA model that future authors can reference when using or measuring IPA.

Question: What is the difference between information privacy awareness and situation awareness?


Delicate manipulations with compliant mechanism and electrostatic adhesion. Traditional rigid robotic hand manipulator has been used in many field nowadays due to its advantages of large gripping force and stable performance. However, this kind of rigid manipulator is not suitable for gripping fragile objects since it is motorized and force control can be a problem. It is also not suitable to grip object with different shapes since the manipulator is rigid and not compliant. In this study, a novel manipulator with gripping capability is designed and fabricated. The manipulator combines electrostatic adhesion actuation with soft manipulators. The manipulator has high flexibility and can be compliant to different shapes due to the property of the materials. It is very promising to do delicate manipulations in industry field and biomedical field.

Question: What is the difference between a rigid manipulator and a compliant manipulator?


Development of Generalized Photovoltaic Model Using MATLAB / SIMULINK. temperature into consideration, the output current and power characteristics of PV model are simulated and optimized using the proposed model. This enables the dynamics of PV power system to be easily simulated, analyzed, and optimized.

Question: How to simulate the dynamics of a PV power system?


Familial hypertrichosis cubiti: hairy elbows syndrome.. Genetically determined hypertrichosis is uncommon, but several forms of familial hairiness have been described. In some kindreds the unusual hairiness has been generalized, while in others, the hypertrichosis has been confined to a specific site on the body. The purpose of this paper is to report the occurrence of undue hairiness of the elbow regions in members of an Amish family, and to discuss the genetic significance of the condition. No previous descriptions of hypertrichosis of this distribution could be found in the literature, and it is therefore suggested that the condition be termed familial hypertrichosis cubiti or the hairy elbows syndrome.

Question: What is the genetic significance of the condition?


SCNN: An accelerator for compressed-sparse convolutional neural networks. Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.

Question: What is a convolutional neural network?


Neuro-fuzzy rule generation: survey in soft computing framework. The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

Question: What is a neuro-fuzzy rule generation?


A Penalty-Function Approach for Pruning Feedforward Neural Networks. This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.

Question: What is a penalty function?


AUTOMATIC BLOOD VESSEL SEGMENTATION IN COLOR IMAGES OF RETINA. Automated image processing techniques have the ability to assist in the early detection of diabetic retinopathy disease which can be regarded as a manifestation of diabetes on the retina. Blood vessel segmentation is the basic foundation while developing retinal screening systems, since vessels serve as one of the main retinal landmark features. This paper proposes an automated method for identification of blood vessels in color images of the retina. For every image pixel, a feature vector is computed that utilizes properties of scale and orientation selective Gabor filters. The extracted features are then classified using generative Gaussian mixture model and discriminative support vector machines classifiers. Experimental results demonstrate that the area under the receiver operating characteristic (ROC) curve reached a value 0.974, which is highly comparable and, to some extent, higher than the previously reported ROCs that range from 0.787 to 0.961. Moreover, this method gives a sensitivity of 96.50% with a specificity of 97.10% for identification of blood vessels. Keywords– Retinal blood vessels, Gabor filters, support vector machines, vessel segmentation

Question: What is the area under the ROC curve?


Hand Gesture Recognition Based on Perceptual Shape Decomposition with a Kinect Camera. In this paper, we propose the Perceptual Shape Decomposition (PSD) to detect fingers for a Kinect-based hand gesture recognition system. The PSD is formulated as a discrete optimization problem by removing all negative minima with minimum cost. Experiments show that our PSD is perceptually relevant and robust against distortion and hand variations, and thus improves the recognition system performance. key words: Kinect camerta, hand gesture recognition, perceptual decomposition, finger detection

Question: How to detect fingers for a Kinect-based hand gesture recognition system?


Opportunities and advances in ultra-wideband electronically scanned arrays. This paper discusses bandwidth and polarization challenges in modern dual-polarized ultra-wideband (UWB) electronically scanned array (ESA) aperture design and two recent solution advances aiming to remedy such issues: a 6:1 Planar Ultrawideband Modular Antenna (PUMA) array and a 10:1 Sliced Notch Antenna (SNA) array. The proposed arrays enable wide bandwidth, low cross-polarization, and high efficiency from both low-profile and high-profile array research directions to appeal to a wider variety of applications and design embodiments. The proposed UWB-ESAs are analyzed alongside a conventional 10:1 all-metal Vivaldi array to shed light upon modern design trends and implications.

Question: What is the difference between a planar and a sliced notch antenna?


Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.

Question: What is the difference between a gated recurrent unit and a recurrent unit?


What is Coordination Theory and How Can It Help Design Cooperative Work Systems?. It is possible to design cooperative work tools based only on "common sense" and good intuitions. But the history of technology is replete with examples of good theories greatly aiding the development of useful technology. Where, then, might we look for theories to help us design computer-supported cooperative work tools? In this paper, we will describe one possible perspective—the interdisciplinary study of coordination—that focuses, in part, on how people work together now and how they might do so differently with new information technologies. In one sense, there is little that is new about the study of coordination. Many different disciplines—including computer science, sociology, political science, management science, systems theory, economics, linguistics, and psychology—have all dealt, in one way or another, with fundamental questions about coordination. Furthermore, several previous writers have suggested that theories about coordination are likely to be important for designing cooperative work tools (e.g., [Holt88], [Wino86]). We hope to suggest here, however, that the potential for fruitful interdisciplinary connections concerning coordination is much greater than has as yet been widely appreciated. For instance, we believe that fundamentally similar coordination phenomena arise—unrecognized as such—in many of the fields listed above. Though a single coherent body of theory about coordination does not yet exist, many different disciplines could both contribute to and benefit from more general theories of coordination. Of particular interest to researchers in the field of computer-supported cooperative work is the prospect of drawing on a much richer body of existing and future work in these fields than has previously been suggested. In this paper, we will first describe what we mean by "coordination theory" and give examples of how previous research on computer-supported coope

Question: What is coordination theory and how can it help design cooperative work systems?


Attention-Based Convolutional Neural Network for Semantic Relation Extraction. Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several stateof-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.

Question: What is the attention-based convolutional neural network architecture for semantic relation extraction?


Learning in and about complex systems. Change is accelerating, and the complexity of the systems in which we live is growing. Increasingly change is the result of humanity itself. As complexity grows so do the unanticipated side effects of human action, further increasing complexity in a vicious cycle. Many scholars call for the development of 'systems thinking' to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world, and using the new information we revise the decisions we make and the mental models that motivate those decisions. Unfortunately, in the world of social action various impediments slow or prevent these learning feedbacks from functioning, allowing erroneous and harmful behaviors and beliefs to persist. The barriers to learning include the dynamic complexity of the systems themselves, inadequate and ambiguous outcome feedback, systematic 'misperceptions of feedback' where our cognitive maps omit important feedback processes, delays, stocks and flows, and nonlinearities that characterize complex systems, inability to simulate mentally the dynamics of our cognitive maps, poor interpersonal and organizational inquiry skills, and poor scientific reasoning skills. To be successful methods to enhance learning about complex systems must address all these impediments. Effective methods for learning in and about complex dynamic systems must include (1) tools to elicit participant knowledge, articulate and reframe perceptions, and create maps of the feedback structure of a problem from those perceptions; (2) simulation tools and management flight simulators to assess the dynamics of those maps and test new policies; and (3) methods to improve scientific reasoning skills, strengthen group process and overcome defensive routines for individuals and teams.

Question: How do people learn in and about complex systems?


An efficient message access quality model in vehicular communication networks. In vehicular ad hoc network (VANET), vehicles equipped with computing, sensing, and communication capabilities can exchange information within a geographical area to distribute emergency messages and achieve safety system. Then how to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service. In this paper, we propose a hierarchical access control with authentication scheme for transmitted messages with security assurance over VANET. By extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiple authorities, the scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control on the transmitted messages. Also by exploiting attribute-based signature (ABS), the scheme can authorize the vehicles that can most appropriately deal with the message efficiently. The results of efficiency analysis and comparison with the related works show that the proposed scheme is efficient and scalable in dealing with access control and message authentication for data dissemination in VANET. & 2014 Elsevier B.V. All rights reserved.

Question: How to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service.


Giving a new makeover to STEAM: Establishing YouTube beauty gurus as digital literacy educators through messages and effects on viewers.

Question: What is the effect of beauty gurus on viewers?


Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless ensemble methods in data mining improving accuracy through combining predictions synthesis lectures on data mining and knowledge discovery sources. Yeah, sources about the books from countries in the world are provided.

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


Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription. We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription.

Question: What is a probabilistic model based on distribution estimators conditioned on a recurrent neural network?


Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function. Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rank-based in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach.

Question: How to disambiguate authors?


Use of e-books in an academic and research environment: A case study from the Indian Institute of Science. Purpose: E-books are not as popular as other types of e-publications, such as e-journals and e-newspapers. The possible reasons for this could be because the technology for creating/accessing e-books (both hardware and software) is not yet matured, users' perspectives about e-books need to be changed and the awareness of e-books needs to be raised. The purpose of this study is to investigate the use and usability of e-books from users' perspectives in an academic and research environment.

Question: What is the purpose of this study?


Brain structures differ between musicians and non-musician. From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique, we found gray matter volume differences in motor, auditory, and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multiregional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.

Question: What is the difference between musicians and non-musicians?


Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions.. OBJECTIVE To present nationally representative data on lifetime prevalence and comorbidity of pathological gambling with other psychiatric disorders and to evaluate sex differences in the strength of the comorbid associations.   METHOD Data were derived from a large national sample of the United States. Some 43,093 household and group quarters residents age 18 years and older participated in the 2001-2002 survey. Prevalence and associations of lifetime pathological gambling and other lifetime psychiatric disorders are presented. The diagnostic interview was the National Institute on Alcohol Abuse and Alcoholism Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version. Fifteen symptom items operationalized the 10 pathological gambling criteria.   RESULTS The lifetime prevalence rate of pathological gambling was 0.42%. Almost three quarters (73.2%) of pathological gamblers had an alcohol use disorder, 38.1% had a drug use disorder, 60.4% had nicotine dependence, 49.6% had a mood disorder, 41.3% had an anxiety disorder, and 60.8% had a personality disorder. A large majority of the associations between pathological gambling and substance use, mood, anxiety, and personality disorders were overwhelmingly positive and significant (p < .05), even after controlling for sociodemographic and socioeconomic characteristics. Male sex, black race, divorced/separated/widowed marital status, middle age, and living in the West and Midwest were associated with increased risk for pathological gambling. Further, associations between alcohol dependence, any drug use disorder, drug abuse, nicotine dependence, major depressive episode, and generalized anxiety disorder and pathological gambling were stronger among women than men (p > .05).   CONCLUSION Pathological gambling is highly comorbid with substance

Question: What is the prevalence of pathological gambling in the united states?


Multiscale Shear-Lag Analysis of Stiffness Enhancement in Polymer-Graphene Nanocomposites.. Graphene and other two-dimensional (2D) materials are of emerging interest as functional fillers in polymer-matrix composites. In this study, we present a multiscale atomistic-to-continuum approach for modeling interfacial stress transfer in graphene-high-density polyethylene (HDPE) nanocomposites. Via detailed characterization of atomic-level stress profiles in submicron graphene fillers, we develop a modified shear-lag model for short fillers. A key feature of our approach lies in the correct accounting of stress concentration at the ends of fillers that exhibits a power-law dependence on filler ("flaw") size, determined explicitly from atomistic simulations, without any ad hoc modeling assumptions. In addition to two parameters that quantify the end stress concentration, only one additional shear-lag parameter is required to quantify the atomic-level stress profiles in graphene fillers. This three-parameter model is found to be reliable for fillers with dimensions as small as ∼10 nm. Our model predicts accurately the elastic response of aligned graphene-HDPE composites and provides appropriate upper bounds for the elastic moduli of nanocomposites with more realistic randomly distributed and oriented fillers. This study provides a systematic approach for developing hierarchical multiscale models of 2D material-based nanocomposites and is of particular relevance for short fillers, which are, currently, typical of solution-processed 2D materials.

Question: What is the elastic modulus of graphene?


Algebraic MACs and Keyed-Verification Anonymous Credentials. We consider the problem of constructing anonymous credentials for use in a setting where the issuer of credentials is also the verifier, or more generally where the issuer and verifier have a shared key. In this setting we can use message authentication codes (MACs) instead of public key signatures as the basis for the credential system.  To this end, we construct two algebraic MACs in prime-order groups, along with efficient protocols for issuing credentials, asserting possession of a credential, and proving statements about hidden attributes (e.g., the age of the credential owner). We prove the security of the first scheme in the generic group model, and prove the security of the second scheme\dash using a dual-system-based approach\dash under decisional Diffie-Hellman (DDH). Our MACs are of independent interest, as they are the only uf-cmva-secure MACs with efficient proofs of knowledge.  Finally, we compare the efficiency of our new systems to two existing constructions of anonymous credentials: U-Prove and Idemix. We show that the performance of the new schemes is competitive with U-Prove (which does not have multi-show unlinkability), and many times faster than Idemix.

Question: What is the difference between algebraic MACs and public key signatures?


Classification of Text Documents. The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83 %, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.

Question: What is the best classification method for text documents?


A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines. Road safety is one of the most vital and crucial part of every countries daily economic growth. It gives so much impact in public health specially in the Philippines. Securing its safety will be a big help to a particular country's economic growth. The main objective of this paper is to analyze road accident's data to discover hidden pattern that can be used as precautionary measure to at least lessen the accident that occur yearly in Los Baños, Laguna, Philippines. Predicting algorithms such as Decision Tree, Naïve Bayes and Rule induction were used to identify factors affecting accident in Los Baños, Laguna. Using these three classifier the following are the results obtained by the researchers; for Decision Tree 92.84% accuracy occurred with 0.797 kappa while in Naïve Bayes 91.50% accuracy was generated with 0.741 kappa and 92.50% accuracy for Rule Induction and 0.783 kappa was produced. The researchers discovered that the place where an accident happened don't have significant correlation on the fatality of the victim. On the other hand, the researchers also found that the time and day play a vital role on the fatality or severity of victims of road accident particularly car collision.

Question: What is the main objective of this paper?


A Span-Extraction Dataset for Chinese Machine Reading Comprehension. Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a SpanExtraction dataset for Chinese Machine Reading Comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated by human on Wikipedia paragraphs. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the machine reading comprehension research in Chinese language.1

Question: What is the span extraction dataset for Chinese machine reading comprehension?


Leveraging Tokens in a Natural Language Query for. Natural Language Interface to Database (NLIDB) systems convert a Natural Language (NL) query into a Structured Query Language (SQL) and then use the SQL query to retrieve information from a database. The main advantage with this function of an NLIDB system is that it makes information retrieval much easier and more importantly, it also allows non-technical users to query a database. In this work, we present an effective usage of tokens in an NL query to address various problems in an NLIDB system. The conversion of an NL query to SQL query is framed as a token classification problem, using which we unveil a novel method of mapping an NL query to an SQL query. Our approach reasonably addresses domain dependency which is one of the major drawbacks of NLIDB systems. Concepts Identification is a major component of NLIDB systems (Gupta et al., 2012; Srirampur et al., 2014). We improve Concepts Identification (Srirampur et al., 2014) by making use of Stanford Parser Dependencies. Our approach is more robust than previously proposed methods. In addition, we also discuss how Concepts Identification can be applied to address Ellipsis in a dialogue process. In addition to providing results to a user, it is essential to provide a relevant and a compact set of results. We propose a new problem of generating a compact set of results to a user query. At a higher level, user-system interactions are modeled based on patterns frequently observed between a user's current query and his previous queries, while interacting with a system. Using these models, we propose a novel method of system prompting to help a user obtain a smaller and a relevant set of results. In addition to providing a compact and a relevant set of results, it is imperative that answers of an NLIDB system are comprehensible even by the people who are less familiar with a common language like English. NLIDB systems use Natural Language Generation (NLG) modules to pr

Question: What is the difference between a natural language interface to a database and a natural language interface to a database?


Visual Analytics for Explainable Deep Learning. Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.

Question: What is the state of the art in explainable deep learning?


Sampling Large Databases for Association Rules. Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a random sample, to nd using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and in those rare cases where our sampling method does not produce all association rules, the missing rules can be found in a second pass. Our experiments show that the proposed algorithms can nd association rules very e ciently in only one database pass.

Question: What is the size of the database?


The challenges of SVM optimization using Adaboost on a phoneme recognition problem. The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.

Question: What is the cognitive infocommunications?


On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article's results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.

Question: What is the simple Bayesian classifier?


How Important is Weight Symmetry in Backpropagation?. Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This "weight transport problem" [1] is thought to be one of the main reasons of BP's biological implausibility. Using 15 different classification datasets, we systematically study to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.'s demonstration [2] but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter—the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) [3] and/or a "Batch Manhattan" (BM) update rule. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. ar X iv :1 51 0. 05 06 7v 3 [ cs .L G ] 2 D ec 2 01 5

Question: How important is weight symmetry in backpropagation?


Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences. We describe a syntax-based algorithm that automatically builds Finite State Automata (word lattices) from semantically equivalent translation sets. These FSAs are good representations of paraphrases. They can be used to extract lexical and syntactic paraphrase pairs and to generate new, unseen sentences that express the same meaning as the sentences in the input sets. Our FSAs can also predict the correctness of alternative semantic renderings, which may be used to evaluate the quality of translations.

Question: How to extract paraphrases?


On self-aggrandizement and anger: a temporal analysis of narcissism and affective reactions to success and failure.. Narcissists are thought to display extreme affective reactions to positive and negative information about the self. Two experiments were conducted in which high- and low-narcissistic individuals, as defined by the Narcissistic Personality Inventory (NPI), completed a series of tasks in which they both succeeded and failed. After each task, participants made attributions for their performance and reported their moods. High-NPI participants responded with greater changes in anxiety, anger, and self-esteem. Low self-complexity was examined, but it neither mediated nor moderated affective responses. High-NPI participants tended to attribute initial success to ability, leading to more extreme anger responses and greater self-esteem reactivity to failure. A temporal sequence model linking self-attribution and emotion to narcissistic rage is discussed.

Question: What is the temporal sequence model linking self-attribution and emotion to narcissistic rage?


A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters. One of the most difficult problems in the design of an anomaly b sed intrusion detection system (IDS) that uses clustering is th at of labelling the obtained clusters, i.e. determining which of them correspond t "good" behaviour on the network/host and which to "bad" behaviour. In this pap er, a new clusters' labelling strategy, which makes use of a clustering quality index is proposed for application in such an IDS. The aim of the new labelling algor ithm is to detect compact clusters containing very similar vectors and these are highly likely to be attack vectors. Two clustering quality indexes have been te sted and compared: the Silhouette index and the Davies-Bouldin index. Experiment al results comparing the effectiveness of a multiple classifier IDS with the two in dexes implemented show that the system using the Silhouette index produces sli ghtly more accurate results than the system that uses the Davies-Bouldin index. However, the computation of the Davies-Bouldin index is much less complex th an the computation of the Silhouette index, which is a very important advantage regarding eventual real-time operation of an IDS that employs clustering.

Question: What is the Silhouette index?


Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings.. Mindfulness meditation practices (MMPs) are a subgroup of meditation practices which are receiving growing attention. The present paper reviews current evidence about the effects of MMPs on objective measures of cognitive functions. Five databases were searched. Twenty three studies providing measures of attention, memory, executive functions and further miscellaneous measures of cognition were included. Fifteen were controlled or randomized controlled studies and 8 were case-control studies. Overall, reviewed studies suggested that early phases of mindfulness training, which are more concerned with the development of focused attention, could be associated with significant improvements in selective and executive attention whereas the following phases, which are characterized by an open monitoring of internal and external stimuli, could be mainly associated with improved unfocused sustained attention abilities. Additionally, MMPs could enhance working memory capacity and some executive functions. However, many of the included studies show methodological limitations and negative results have been reported as well, plausibly reflecting differences in study design, study duration and patients' populations. Accordingly, even though findings here reviewed provided preliminary evidence suggesting that MMPs could enhance cognitive functions, available evidence should be considered with caution and further high quality studies investigating more standardized mindfulness meditation programs are needed.

Question: What is the effect of mindfulness training on cognitive functions?


Modern Recommender Systems: from Computing Matrices to Thinking with Neurons. Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: from matrix factorization methods and multi-armed bandits to deep neural networks. In this tutorial, we will cover recent algorithmic advances in recommender systems, highlight their capabilities, and their impact. We will give many examples of industrial-scale recommender systems that define the future of the recommender systems area. We will discuss related evaluation issues, and outline future research directions. The ultimate goal of the tutorial is to encourage the application of novel recommendation approaches to solve problems that go beyond user consumption and to further promote research in the intersection of recommender systems and databases.

Question: What is the future of recommender systems?


Feature-based survey of model transformation approaches. Concrete Figure 5 Features of the body of a domain: (A) patterns and (B) logic Language Paradigm Value Specification Element Creation Implicit Explicit Logic Constraint Object-Oriented Functional Procedural Logic Value Binding Imperative Assignment IBM SYSTEMS JOURNAL, VOL 45, NO 3, 2006 CZARNECKI AND HELSEN 629 operating on one model from the parts operating on other models. For example, classical rewrite rules have an LHS operating on the source model and an RHS operating on the target model. In other approaches, such as a rule implemented as a Java program, there might not be any such syntactic distinction. Multidirectionality Multidirectionality refers to the ability to execute a rule in different directions (see Figure 4A). Rules supporting multidirectionality are usually defined over in/out-domains. Multidirectional rules are available in MTF and QVT Relations. Application condition Transformation rules in some approaches may have an application condition (see Figure 4A) that must be true in order for the rule to be executed. An example is the when-clause in QVT Relations (Example 1). Intermediate structure The execution of a rule may require the creation of some additional intermediate structures (see Figure 4A) which are not part of the models being transformed. These structures are often temporary and require their own metamodel. A particular example of intermediate structures are traceability links. In contrast to other intermediate structures, traceability links are usually persisted. Even if traceability links are not persisted, some approaches, such as AGG and VIATRA, rely on them to prevent multiple ''firings'' of a rule for the same input element. Parameterization The simplest kind of parameterization is the use of control parameters that allow passing values as control flags (Figure 7). Control parameters are useful for implementing policies. For example, a transformation from class models to r

Question: What is a control parameter?


From semantics to syntax and back again: Argument structure in the third year of life. An essential part of the human capacity for language is the ability to link conceptual or semantic representations with syntactic representations. On the basis of data from spontaneous production, suggested that young children acquire such links on a verb-by-verb basis, with little in the way of a general understanding of linguistic argument structure. Here, we suggest that a receptive understanding of argument structure--including principles linking syntax and conceptual/semantic structure--appears earlier. In a forced-choice pointing task we have shown that toddlers in the third year of life can map a single scene (involving a novel causative action paired with a novel verb) onto two distinct syntactic frames (transitive and intransitive). This suggests that even before toddlers begin generalizing argument structure in their own speech, they have some representation of conceptual/semantic categories, syntactic categories, and a system that links the two.

Question: What is the difference between a transitive and an intransitive sentence?


On Networking of Internet of Things: Explorations and Challenges. Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.

Question: What is the basic model of IoT architecture?


Question: What are the two modifications of CNN?