Elastic Scheduling of Scientific Workflows under Deadline Constraints in Cloud Computing Environments. Scientific workflow applications are collections of several structured activities and fine-grained computational tasks. Scientific workflow scheduling in cloud computing is a challenging research topic due to its distinctive features. In cloud environments, it has become critical to perform efficient task scheduling resulting in reduced scheduling overhead, minimized cost and maximized resource utilization while still meeting the user-specified overall deadline. This paper proposes a strategy, Dynamic Scheduling of Bag of Tasks based workflows (DSB), for scheduling scientific workflows with the aim to minimize financial cost of leasing Virtual Machines (VMs) under a user-defined deadline constraint. The proposed model groups the workflow into Bag of Tasks (BoTs) based on data dependency and priority constraints and thereafter optimizes the allocation and scheduling of BoTs on elastic, heterogeneous and dynamically provisioned cloud resources called VMs in order to attain the proposed method's objectives. The proposed approach considers pay-as-you-go Infrastructure as a Service (IaaS) clouds having inherent features such as elasticity, abundance, heterogeneity and VM provisioning delays. A trace-based simulation using benchmark scientific workflows representing real world applications, demonstrates a significant reduction in workflow computation cost while the workflow deadline is met. The results validate that the proposed model produces better success rates to meet deadlines and cost efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

Question: What is the difference between elastic and dynamic scheduling?


Secure routing for internet of things: A survey. The Internet of Things (IoT) could be described as the pervasive and global network which aids and provides a system for the monitoring and control of the physical world through the collection, processing and analysis of generated data by IoT sensor devices. It is projected that by 2020 the number of connected devices is estimated to grow exponentially to 50 billion. The main drivers for this growth are our everyday devices such as cars, refrigerators, fans, lights, mobile phones and other operational technologies including the manufacturing infrastructures which are now becoming connected systems across the world. It is apparent that security will pose a fundamental enabling factor for the successful deployment and use of most IoT applications and in particular secure routing among IoT sensor nodes thus, mechanisms need to be designed to provide secure routing communications for devices enabled by the IoT technology. This survey analyzes existing routing protocols and mechanisms to secure routing communications in IoT, as well as the open research issues. We further analyze how existing approaches ensure secure routing in IoT, their weaknesses, threats to secure routing in IoT and the open challenges and strategies for future research work for a better secure IoT routing.

Question: What is the Internet of Things?


Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes. We introduce a novel framework for option discovery and learning transfer in complex domains that are represented as object-oriented Markov decision processes (OO-MDPs) [Diuk et al., 2008]. Our framework, Portable Option Discovery (POD), extends existing option discovery methods, and enables transfer across related but different domains by providing an unsupervised method for finding a mapping between object-oriented domains with different state spaces. The framework also includes heuristic approaches for increasing the efficiency of the mapping process. We present the results of applying POD to Pickett and Barto's [2002] PolicyBlocks and MacGlashan's [2013] Option-Based Policy Transfer in two application domains. We show that our approach can discover options effectively, transfer options among different domains, and improve learning performance with low computational overhead.

Question: What is the name of the framework?


Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine. Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.

Question: How to remove artifacts from EEG?


A Review on Deep Learning Techniques Applied to Semantic Segmentation. Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.

Question: What is the state of the art of semantic segmentation using deep learning techniques?


Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation. Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit →0. Monte Carlo methods can then draw samples from the approximate posterior to approximate predictions or error bars on parameters. These algorithms critically slow down as →0, and in practice draw samples from a broader distribution than the posterior. We propose a new approach to likelihood-free inference based on Bayesian conditional density estimation. Preliminary inferences based on limited simulation data are used to guide later simulations. In some cases, learning an accurate parametric representation of the entire true posterior distribution requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.

Question: How to approximate the posterior distribution of a model?


Don't Get Caught in the Cold, Warm-up Your JVM: Understand and Eliminate JVM Warm-up Overhead in Data-Parallel Systems. Many widely used, latency sensitive, data-parallel distributed systems, such as HDFS, Hive, and Spark choose to use the Java Virtual Machine (JVM), despite debate on the overhead of doing so. This paper analyzes the extent and causes of the JVM performance overhead in the above mentioned systems. Surprisingly, we find that the warm-up overhead, i.e., class loading and interpretation of bytecode, is frequently the bottleneck. For example, even an I/O intensive, 1GB read on HDFS spends 33% of its execution time in JVM warm-up, and Spark queries spend an average of 21 seconds in warm-up. The findings on JVM warm-up overhead reveal a contradiction between the principle of parallelization, i.e., speeding up long running jobs by parallelizing them into short tasks, and amortizing JVM warm-up overhead through long tasks. We solve this problem by designing HotTub, a new JVM that amortizes the warm-up overhead over the lifetime of a cluster node instead of over a single job by reusing a pool of already warm JVMs across multiple applications. The speed-up is significant. For example, using HotTub results in up to 1.8X speedups for Spark queries, despite not adhering to the JVM specification in edge cases.

Question: What is the overhead of the JVM?


Pomegranate: fast and flexible probabilistic modeling in python. We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with—or outperform—other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code. The code is available at https://github.com/jmschrei/pomegranate

Question: What is the pomegranate?


A Syntactic Resource for Thai: CG Treebank. This paper presents Thai syntactic resource: Thai CG treebank, a categorial approach of language resources. Since there are very few Thai syntactic resources, we designed to create treebank based on CG formalism. Thai corpus was parsed with existing CG syntactic dictionary and LALR parser. The correct parsed trees were collected as preliminary CG treebank. It consists of 50,346 trees from 27,239 utterances. Trees can be split into three grammatical types. There are 12,876 sentential trees, 13,728 noun phrasal trees, and 18,342 verb phrasal trees. There are 17,847 utterances that obtain one tree, and an average tree per an utterance is 1.85.

Question: What is the size of the Thai treebank?


Unsupervised Pattern Discovery in Speech. We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.

Question: How does unsupervised pattern discovery in speech work?


Automatic prediction of frustration. Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying ''I'm frustrated.'' The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy ðchance 1⁄4 58%Þ. The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response. r 2007 Elsevier Ltd. All rights reserved.

Question: How can we predict when a person might be frustrated?


Systematic Thinking Fostered by Illustrations in Scientific Text. In 2 experiments, students who lacked prior knowledge about car mechanics read a passage about vehicle braking systems that either contained labeled illustrations of the systems, illustrations without labels, labels without illustrations, or no labeled illustrations. Students who received passages that contained labeled illustrations of braking systems recalled more explanative than nonexplanative information as compared to control groups, and performed better on problem solving transfer but not on verbatim recognition as compared to control groups. Results support a model of meaningful learning in which illustrations can help readers to focus their attention on explanative information in text and to reorganize the information into useful mental models.

Question: What is the difference between explanative and nonexplanative information?


Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text. Biomedical named entity recognition (bio-NER) is a crucial and basic step in many biomedical information extraction tasks. However, traditional NER systems are mainly based on complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper, we construct Recurrent Neural Network to identify entity names with word embeddings input rather than hand-designed features. Our contributions mainly include three aspects: 1) we adapt a deep learning architecture Recurrent Neural Network (RNN) to entity names recognition; 2) based on the original RNNs such as Elman-type and Jordan-type model, an improved RNN model is proposed; 3) considering that both past and future dependencies are important information, we combine bidirectional recurrent neural networks based on information entropy at the top layer. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF and deep neural networks (DNN), furthermore, the improved RNN model performs better than two original RNN models and the combined method is effective.

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


Global consequences of land use.. Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Question: What are the global consequences of land use?


Handwriting development, competency, and intervention.. Failure to attain handwriting competency during the school-age years often has far-reaching negative effects on both academic success and self-esteem. This complex occupational task has many underlying component skills that may interfere with handwriting performance. Fine motor control, bilateral and visual-motor integration, motor planning, in-hand manipulation, proprioception, visual perception, sustained attention, and sensory awareness of the fingers are some of the component skills identified. Poor handwriting may be related to intrinsic factors, which refer to the child's actual handwriting capabilities, or extrinsic factors which are related to environmental or biomechanical components, or both. It is important that handwriting performance be evaluated using a valid, reliable, standardized tool combined with informal classroom observation and teacher consultation. Studies of handwriting remediation suggest that intervention is effective. There is evidence to indicate that handwriting difficulties do not resolve without intervention and affect between 10 and 30% of school-aged children. Despite the widespread use of computers, legible handwriting remains an important life skill that deserves greater attention from educators and health practitioners.

Question: What is the difference between handwriting and typing?


To Lock, Swap, or Elide: On the Interplay of Hardware Transactional Memory and Lock-Free Indexing. The release of hardware transactional memory (HTM) in commodity CPUs has major implications on the design and implementation of main-memory databases, especially on the architecture of highperformance lock-free indexing methods at the core of several of these systems. This paper studies the interplay of HTM and lockfree indexing methods. First, we evaluate whether HTM will obviate the need for crafty lock-free index designs by integrating it in a traditional B-tree architecture. HTM performs well for simple data sets with small fixed-length keys and payloads, but its benefits disappear for more complex scenarios (e.g., larger variable-length keys and payloads), making it unattractive as a general solution for achieving high performance. Second, we explore fundamental differences between HTM-based and lock-free B-tree designs. While lock-freedom entails design complexity and extra mechanism, it has performance advantages in several scenarios, especially high-contention cases where readers proceed uncontested (whereas HTM aborts readers). Finally, we explore the use of HTM as a method to simplify lock-free design. We find that using HTM to implement a multi-word compare-and-swap greatly reduces lockfree programming complexity at the cost of only a 10-15% performance degradation. Our study uses two state-of-the-art index implementations: a memory-optimized B-tree extended with HTM to provide multi-threaded concurrency and the Bw-tree lock-free B-tree used in several Microsoft production environments.

Question: What is the difference between a memory-optimized B-tree and a lock-free B-tree?


Grasping the Intentions of Others: The Perceived Intentionality of an Action Influences Activity in the Superior Temporal Sulcus during Social Perception. An explication of the neural substrates for social perception is an important component in the emerging field of social cognitive neuroscience and is relevant to the field of cognitive neuroscience as a whole. Prior studies from our laboratory have demonstrated that passive viewing of biological motion (Pelphrey, Mitchell, et al., 2003; Puce et al., 1998) activates the posterior superior temporal sulcus (STS) region. Furthermore, recent evidence has shown that the perceived context of observed gaze shifts (Pelphrey, Singerman, et al., 2003; Pelphrey et al., 2004) modulates STS activity. Here, using event-related functional magnetic resonance imaging at 4 T, we investigated brain activity in response to passive viewing of goal- and non-goal- directed reaching-to-grasp movements. Participants viewed an animated character making reaching-to-grasp movements either toward (correct) or away (incorrect) from a blinking dial. Both conditions evoked significant posterior STS activity that was strongly right lateralized. By examining the time course of the blood oxygenation level-dependent response from areas of activation, we observed a functional dissociation. Incorrect trials evoked significantly greater activity in the STS than did correct trials, while an area posterior and inferior to the STS (likely corresponding to the MT/V5 complex) responded equally to correct and incorrect movements. Parietal cortical regions, including the superior parietal lobule and the anterior intraparietal sulcus, also responded equally to correct and incorrect movements, but showed evidence for differential responding based on the hand and arm (left or right) of the animated character used to make the reaching-to-grasp movement. The results of this study further suggest that a region of the right posterior STS is involved in analyzing the

Question: What is the difference between the superior temporal sulcus and the posterior superior temporal gyrus?


Characterizing debate performance via aggregated twitter sentiment. Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.

Question: How can we use twitter to analyze the debate?


A support vector machine classifier with automatic confidence and its application to gender classification. In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the e ff ctiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.

Question: What is a support vector machine?


FRPAs and High-Gain Directional Antennas. Many types of GNSS antennas have been developed in recent years to make them suitable for different applications. Since their designs and performance requirements vary depending on their application, they have been grouped into six different categories in this book: 1. The design and performance of each type of antenna will be described here and throughout the rest of the book. This chapter discusses the design of the first two varieties: FRPAs and high-gain directional antennas. Figure 2.1 shows a representative sample of these antennas. FRPAs are the most popular and widely used of all GNSS antennas. There are many different types of these antennas; hence they will need two chapters—this chapter and Chapter 3—to fully cover all the important types. This chapter will discuss the following FRPA designs: microstrip patch antennas, which are the most ubiquitous of all GNSS antennas, and quadrifilar helix antennas (QHAs), which are also popular for handheld receivers and crossed " drooping " bow-type dipoles, which provide relatively wider bandwidths and good circular polarization. Spiral antennas such as the Archimedean spiral antenna will be discussed in Chapter 3 under multiband antennas since they are ultrawideband and can cover the entire GNSS band from 1.1 to 1.6 GHz. The conical spiral antenna is also a high-gain di

Question: What is the difference between a FRPA and a high-gain directional antenna?


A generalized maximum entropy approach to bregman co-clustering and matrix approximation. Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an information-theoretic co-clustering approach applicable to empirical joint probability distributions was proposed. In many situations, co-clustering of more general matrices is desired. In this paper, we present a substantially generalized co-clustering framework wherein any Bregman divergence can be used in the objective function, and various conditional expectation based constraints can be considered based on the statistics that need to be preserved. Analysis of the co-clustering problem leads to the minimum Bregman information principle, which generalizes the maximum entropy principle, and yields an elegant meta algorithm that is guaranteed to achieve local optimality. Our methodology yields new algorithms and also encompasses several previously known clustering and co-clustering algorithms based on alternate minimization.

Question: What is a generalized maximum entropy approach to bregman co-clustering and matrix approximation?


A review of ontologies for describing scholarly and scientific documents. Several ontologies have been created in the last years for the semantic annotation of scholarly publications and scientific documents. This rich variety of ontologies makes it difficult for those willing to annotate their documents to know which ones they should select for such activity. This paper presents a classification and description of these state-of-the-art ontologies, together with the rationale behind the different approaches. Finally, we provide an example of how some of these ontologies can be used for the annotation of a scientific document.

Question: What ontologies are available for the semantic annotation of scholarly and scientific documents?


Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation. We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.

Question: How to learn a pose-aware, compact embedding?


Design of a new Dual Rotor Radial Flux BLDC motor with Halbach array magnets for an electric vehicle. This paper presents a novel topology of dual rotor radial flux-BLDC motor with Halbach array magnets as an efficient solution for drives. Energy efficient motors are the need of day for electric vehicles, industrial and consumer products. Especially this concept is arrived to cater for the requirements of electrical vehicle. Due to advent of high permeability, high saturation magnetic materials, high energy product permanent magnets, BLDC motors are suitable candidate for electric vehicle propulsion. Traditionally, outer rotor BLDC motor is a wide choice which is being used in commercially available electric vehicles. New topologies of BLDC motors are being proposed by research activities which aim at improvement of torque density of motors. Dual rotor brushless DC machine (DR-BLDC) is one of the consequences of such research and claimed to have very high torque density and power density. In this paper, a new topology of Dual Rotor Halbach (DR-HB) BLDC motor is proposed which is aimed to increase the torque density of motor. The complete design of DR-HB BLDC motor is carried out and compared with DR-BLDC of the same specifications and volume. The obtained performance characteristics of the proposed design are very much encouraging in terms of torque density.

Question: What is the difference between a brushless DC motor and a brushless DC motor?


Spatial Reference in Linguistic Human-Robot Interaction: Iterative, Empirically Supported Development of a Model of Projective Relations. The aim of our research is to enable spontaneous and efficient spatial reference to objects in human-robot interaction situations. This paper presents the iterative, empirically based design of a robotic system that uses a computational model for identifying objects on the basis of a range of spatial reference systems. The efficiency of the system is evaluated by two successive empirical studies involving uninformed users. The linguistic analysis points to the striking variability in speakers' spontaneous strategies and preferences, and it motivates a number of modifications of the computational model.

Question: What is the aim of the research?


Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature. Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.

Question: How to detect ships from satellite images?


Polyhedral Extraction Tool. We present a new library for extracting a polyhedral model from C source. The library is based on clang, the LLVM C frontend, and isl, a library for manipulating quasi-affine sets and relations. The use of clang for parsing the C code brings advanced diagnostics and full support for C99. The use of isl allows for an easy construction and a powerful and compact representation of the polyhedral model. Besides allowing arbitrary piecewise quasi-affine index expressions and conditions, the library also supports some data dependent constructs and has special treatment for unsigned integers. The library has been successfully used to obtain polyhedral models for use in an equivalence checker, a tool for constructing polyhedral process networks, a parallelizer targeting GPUs and an interactive polyhedral environment.

Question: What is a polyhedral model?


Privacy Aware Learning. We study statistical risk minimization problems under a privacy model in which the data is kept confidential even from the learner. In this local privacy framework, we establish sharp upper and lower bounds on the convergence rates of statistical estimation procedures. As a consequence, we exhibit a precise tradeoff between the amount of privacy the data preserves and the utility, as measured by convergence rate, of any statistical estimator or learning procedure.

Question: What is the tradeoff between privacy and utility?


Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs). The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.

Question: What is the best accuracy result?


Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms. Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informal conversational settings, but despite its evident impact on event coreference resolution and extraction, there has been relatively little work on computational methods for identifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative machine learning techniques trained on features extracted from an automatically parsed, publicly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identification that uses the Margin-Infused-RelaxedAlgorithm, which shows promising results.

Question: What is the canadian military?


Entity Extraction, Linking, Classification, and Tagging for Social Media: A Wikipedia-Based Approach. Many applications that process social data, such as tweets, must extract entities from tweets (e.g., "Obama" and "Hawaii" in "Obama went to Hawaii"), link them to entities in a knowledge base (e.g., Wikipedia), classify tweets into a set of predefined topics, and assign descriptive tags to tweets. Few solutions exist today to solve these problems for social data, and they are limited in important ways. Further, even though several industrial systems such as OpenCalais have been deployed to solve these problems for text data, little if any has been published about them, and it is unclear if any of the systems has been tailored for social media. In this paper we describe in depth an end-to-end industrial system that solves these problems for social data. The system has been developed and used heavily in the past three years, first at Kosmix, a startup, and later at WalmartLabs. We show how our system uses a Wikipedia-based global "real-time" knowledge base that is well suited for social data, how we interleave the tasks in a synergistic fashion, how we generate and use contexts and social signals to improve task accuracy, and how we scale the system to the entire Twitter firehose. We describe experiments that show that our system outperforms current approaches. Finally we describe applications of the system at Kosmix and WalmartLabs, and lessons learned.

Question: What is a knowledge base?


Multilabel SVM active learning for image classification. Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.

Question: What is the advantage of proposed method?


Magnetic Equivalent Circuit Modeling of the AC Homopolar Machine for Flywheel Energy Storage. This paper develops a magnetic equivalent circuit model suitable to the design and optimization of the synchronous ac homopolar machine. The ac homopolar machine is of particular interest in the application of grid-based flywheel energy storage, where it has the potential to significantly reduce self-discharge associated with magnetic losses. The ac homopolar machine features both axial and radial magnetizing flux paths, which requires finite element analysis to be conducted in 3-D. The computation time associated with 3-D finite element modeling is highly prohibitive in the design process. The magnetic equivalent circuit model developed in this paper is shown to be a viable alternative for calculating several design performance parameters and has a computation time which is orders of magnitude less than that of 3-D finite element analysis. Results obtained from the developed model are shown to be in good agreement with finite element and experimental results for varying levels of saturation.

Question: How to calculate the magnetic flux density of a homopolar machine?


A high frequency high voltage power supply. Novel compact high frequency high voltage (HV) power supply is introduced for small volume, lightweight, ultra fast high voltage output application. The two key factors high frequency HV transformer and voltage multiplier diodes are evaluated in this paper. The HV resonant tank size is only 1L for 400kHz∼500kHz 35kV 2kW output, the rise time for output HV is around 100us by lab prototype experiment results.

Question: What is a high frequency high voltage power supply?


What video games have to teach us about learning and literacy. Good computer and video games like System Shock 2, Deus Ex, Pikmin, Rise of Nations, Neverwinter Nights, and Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won't get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so to speak. But most gamers don't want short and easy games. Thus, designers face and largely solve an intriguing educational dilemma, one also faced by schools and workplaces: how to get people, often young people, to learn and master something that is long and challenging--and enjoy it, to boot.

Question: What video games have to teach us about learning and literacy?


Numerical methods for least square problems.

Question: What is a numerical methods for least square problems?


Language Independent Single Document Image Super-Resolution using CNN for improved recognition. Recognition of document images have important applications in restoring old and classical texts. The problem involves quality improvement before passing it to a properly trained OCR to get accurate recognition of the text. The image enhancement and quality improvement constitute important steps as subsequent recognition depends upon the quality of the input image. There are scenarios when high resolution images are not available and our experiments show that the OCR accuracy reduces significantly with decrease in the spatial resolution of document images. Thus the only option is to improve the resolution of such document images. The goal is to construct a high resolution image, given a single low resolution binary image, which constitutes the problem of single image super-resolution. Most of the previous work in super-resolution deal with natural images which have more information-content than the document images. Here, we use Convolution Neural Network to learn the mapping between low and the corresponding high resolution images. We experiment with different number of layers, parameter settings and non-linear functions to build a fast end-to-end framework for document image super-resolution. Our proposed model shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images, resulting in a 3% improvement of word level accuracy by the OCR. It takes less time than the recent sparse based natural image super-resolution technique, making it useful for real-time document recognition applications.

Question: How to improve the accuracy of OCR?


GQ ( λ ) : A general gradient algorithm for temporal-difference prediction learning with eligibility traces. A new family of gradient temporal-difference learning algorithms have recently been introduced by Sutton, Maei and others in which function approximation is much more straightforward. In this paper, we introduce the GQ(λ) algorithm which can be seen as extension of that work to a more general setting including eligibility traces and off-policy learning of temporally abstract predictions. These extensions bring us closer to the ultimate goal of this work—the development of a universal prediction learning algorithm suitable for learning experientially grounded knowledge of the world. Eligibility traces are essential to this goal because they bridge the temporal gaps in cause and effect when experience is processed at a temporally fine resolution. Temporally abstract predictions are also essential as the means for representing abstract, higher-level knowledge about courses of action, or options. GQ(λ) can be thought of as an extension of Q-learning. We extend existing convergence results for policy evaluation to this setting and carry out a forward-view/backwardview analysis to derive and prove the validity of the new algorithm. Introduction One of the main challenges in artificial intelligence (AI) is to connect the low-level experience to high-level representations (grounded world knowledge). Low-level experience refers to rich signals received back and forth between the agent and the world. Recent theoretical developments in temporal-difference learning combined with mathematical ideas developed for temporally abstract options, known as intra-option learning, can be used to address this challenge (Sutton, 2009). Intra-option learning (Sutton, Precup, and Singh, 1998) is seen as a potential method for temporalabstraction in reinforcement learning. Intra-option learning is a type of off-policy learning. Off-policy learning refers to learning about a target poli

Question: What is the difference between Q-learning and GQ?


Radio frequency identification: technologies, applications, and research issues. A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers' interference, and security issue. Copyright # 2006 John Wiley & Sons, Ltd.

Question: What is the difference between RFID and barcode?


"Teach Me–Show Me"—End-User Personalization of a Smart Home and Companion Robot. Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in "smart-home" environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and "nontechnical." To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human-robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.

Question: How do you teach a robot to do things?


Paving green passage for emergency vehicle in heavy traffic: Real-time motion planning under the connected and automated vehicles environment. This paper describes a real-time multi-vehicle motion planning (MVMP) algorithm for the emergency vehicle clearance task. To address the inherent limitations of human drivers in perception, communication, and cooperation, we require that the emergency vehicle and the surrounding normal vehicles are connected and automated vehicles (CAVs). The concerned MVMP task is to find cooperative trajectories such that the emergency vehicle can efficiently pass through the normal vehicles ahead. We use an optimal-control based formulation to describe the MVMP problem, which is centralized, straightforward, and complete. For the online solutions, the centralized MVMP formulation is converted into a multi-period and multi-stage version. Concretely, each period consists of two stages: the emergency vehicle and several normal CAVs ahead try to form a regularized platoon via acceleration or deceleration (stage 1); when a regularized platoon is formed, these vehicles act cooperatively to make way for the emergency vehicle until the emergency vehicle becomes the leader in this local platoon (stage 2). When one period finishes, the subsequent period begins immediately. This sequential process continues until the emergency vehicle finally passes through all the normal CAVs. The subproblem at stage 1 is extremely easy because nearly all the challenging nonlinearity gathers only in stage 2; typical solutions to the subproblem at stage 2 can be prepared offline, and then implemented online directly. Through this, our proposed MVMP algorithm avoids heavy online computations and thus runs in real time.

Question: How to make way for an emergency vehicle?


Engaging with massive online courses. The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood.  In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course.  We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.

Question: What is a MOOC?


Preventing Location-Based Identity Inference in Anonymous Spatial Queries. The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.

Question: How to prevent location-based identity inference?


Inferring Painting Style with Multi-Task Dictionary Learning. Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.

Question: What is the DART dataset?


Blue Screen Matting. A classical problem of imaging—the matting problem—is separation of a non-rectangular foreground image from a (usually) rectangular background image—for example, in a film frame, extraction of an actor from a background scene to allow substitu tion of a different background. Of the several attacks on this diff cult and persistent problem, we discuss here only the special ca of separating a desired foreground image from a background of constant, or almost constant, backing color. This backing color has often been blue, so the problem, and its solution, have bee called blue screen matting . However, other backing colors, such as yellow or (increasingly) green, have also been used, so we o ten generalize to constant color matting . The mathematics of constant color matting is presented and proven to be unsolvable as generally practiced. This, of course, flies in the face of the fact that the technique is commonly used in film and video, so we demonstrate constraints on the general problem that lead to so tions, or at least significantly prune the search space of solution We shall also demonstrate that an algorithmic solution is possib by allowing the foreground object to be shot against two constant backing colors—in fact, against two completely arbitrary backin so long as they differ everywhere.

Question: What is the difference between blue screen matting and constant color matting?


How We Transmit Memories to Other Brains: Constructing Shared Neural Representations Via Communication. Humans are able to mentally construct an episode when listening to another person's recollection, even though they themselves did not experience the events. However, it is unknown how strongly the neural patterns elicited by mental construction resemble those found in the brain of the individual who experienced the original events. Using fMRI and a verbal communication task, we traced how neural patterns associated with viewing specific scenes in a movie are encoded, recalled, and then transferred to a group of naïve listeners. By comparing neural patterns across the 3 conditions, we report, for the first time, that event-specific neural patterns observed in the default mode network are shared across the encoding, recall, and construction of the same real-life episode. This study uncovers the intimate correspondences between memory encoding and event construction, and highlights the essential role our common language plays in the process of transmitting one's memories to other brains.

Question: How do we transmit memories to other brains?


Rapidly-Exploring Random Trees: A New Tool for Path Planning.

Question: What is a random tree?


Virtual reality using games for improving physical functioning in older adults: a systematic review. The use of virtual reality through exergames or active video game, i.e. a new form of interactive gaming, as a complementary tool in rehabilitation has been a frequent focus in research and clinical practice in the last few years. However, evidence of their effectiveness is scarce in the older population. This review aim to provide a summary of the effects of exergames in improving physical functioning in older adults. A search for randomized controlled trials was performed in the databases EMBASE, MEDLINE, PsyInfo, Cochrane data base, PEDro and ISI Web of Knowledge. Results from the included studies were analyzed through a critical review and methodological quality by the PEDro scale. Thirteen studies were included in the review. The most common apparatus for exergames intervention was the Nintendo Wii gaming console (8 studies), followed by computers games, Dance video game with pad (two studies each) and only one study with the Balance Rehabilitation Unit. The Timed Up and Go was the most frequently used instrument to assess physical functioning (7 studies). According to the PEDro scale, most of the studies presented methodological problems, with a high proportion of scores below 5 points (8 studies). The exergames protocols and their duration varied widely, and the benefits for physical function in older people remain inconclusive. However, a consensus between studies is the positive motivational aspect that the use of exergames provides. Further studies are needed in order to achieve better methodological quality, external validity and provide stronger scientific evidence.

Question: What is the effect of exergames on physical functioning in older adults?


Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory. Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.

Question: How does the emotional chatting machine work?


Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems. To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.

Question: How can weather forecasts be used to improve energy harvesting sensor systems?


A PRINCIPAL AXIS TRANSFORMATION FOR NON-HERMITIAN MATRICES CARL ECKART AND GALE YOUNG The availability of the principal axis transformation for hermitian. each two nonparallel elements of G cross each other. Obviously the conclusions of the theorem do not hold. The following example will show that the condition that no two elements of the collection G shall have a complementary domain in common is also necessary. In the cartesian plane let M be a circle of radius 1 and center at the origin, and iVa circle of radius 1 and center at the point (5, 5). Let d be a collection which contains each continuum which is the sum of M and a horizontal straight line interval of length 10 whose left-hand end point is on the circle M and which contains no point within M. Let G2 be a collection which contains each continuum which is the sum of N and a vertical straight line interval of length 10 whose upper end point is on the circle N and which contains no point within N. Let G = Gi+G2 . No element of G crosses any other element of G, but uncountably many have a complementary domain in common with some other element of the collection. However, it is evident that no countable subcollection of G covers the set of points each of which is common to two continua of the collection G. I t is not known whether or not the condition that each element of G shall separate some complementary domain of every other one can be omitted.

Question: What is the difference between a hermitian matrix and a non-hermitian matrix?


The Effect of Using E-Learning Tools in Online and Campus-based Classrooms on Student Performance. Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual's perception of how the adoption of a new technology will increase their efficiency, and the individual's perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance.

Question: What is the effect of using e-learning tools in online and campus-based classrooms on student performance?


Centrality based Document Ranking. In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to rerank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.

Question: How to rank documents using centrality based approach?


Exploring Synergies between Machine Learning and Knowledge Representation to Capture Scientific Knowledge. In this paper we explore synergies between the machine learning and knowledge representation fields by considering how scientific knowledge is represented in these areas. We illustrate some of the knowledge obtained through machine learning methods, providing two contrasting examples of such models: probabilistic graphical models (aka Bayesian networks) and artificial neural networks (including deep learning networks). From knowledge representation, we give an overview of ontological representations, qualitative reasoning, and planning. Then we discuss potential synergies that would benefit both areas.

Question: What is the difference between machine learning and knowledge representation?


Omni-Directional Mobility Using Active Split Offset Castors. An omni-directional mobility platform design concept using two active split offset cas (ASOC) and one or more conventional castors is presented. An ASOC module cons two coaxial conventional wheels driven independently and connected to the platfor an offset link. The kinematics and implementation of the omni-directional platform described and analyzed. Particular attention is paid to the system performance on u floors. The fundamental mechanics of the ASOC wheel scrubbing, which is critic system wear and energy use, is analyzed and compared to conventional active designs. The effectiveness of the design is shown experimentally using an inte mobility aid for the elderly.@DOI: 10.1115/1.1767181 #

Question: What is the difference between a conventional wheel and an omni-directional wheel?


Optimising Results from Minimal Access Cranial Suspension Lifting (MACS-Lift). Between November 1999 and February 2005, 450 minimal access cranial suspension (MACS) lifts were performed. Starting with the idea of suspension for sagging soft tissues using permanent purse-string sutures, a new comprehensive approach to facial rejuvenation was developed in which the vertical vector appeared to be essential. The neck is corrected by extended submental liposuction and strong vertical traction on the lateral part of the platysma by means of a first vertical purse-string suture. The volume of the jowls and the cheeks is repositioned in a cranial direction with a second, slightly oblique purse-string suture. The descent of the midface is corrected by suspending the malar fat pad in a nearly vertical direction. In 23 cases (5.1%), the result in the neck was unsatisfactory, and additional work had to be done secondarily, or in later cases, primarily. The problem that appeared was unsatisfactory correction of platysmal bands (resolved with an additional anterior cervicoplasty) or vertical skin folds that appeared in the infralobular region (corrected with an additional posterior cervicoplasty). This article describes two ancillary procedures that, although not frequently necessary, can optimise the result of MACS lifting.

Question: What is the difference between a cervicoplasty and a platysmaplasty?


Cyber Security Analysis of Substation Automation System. The automation of substation is increasing in the modern world. The implementation of SCADA system is necessary for Substation Automation (SA) systems. Generally Substation Automation Systems uses Intelligent Electronic devices (IED) for monitoring, control and protection of substation. Standard protocols used by the Substation Automation systems are IEC 60870-5-104, DNP, IEC 61850, IEC 60870-5-101. In this paper, Modbus protocol is used as communication protocol. Cyber attack is critical issue in SCADA systems. This paper deals with the monitoring of substation and cyber security analysis of SCADA systems.

Question: What is cyber security analysis of SCADA systems?


Hairstyle recommendation system for women. A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.

Question: What is a hairstyle recommendation system for women?


Post-Stroke Rehabilitation with the Rutgers Ankle System: A Case Study. The Rutgers Ankle is a Stewart platform-type haptic interface designed for use in rehabilitation. The system supplies six-degree-of-freedom (DOF) resistive forces on the patient's foot, in response to virtual reality-based exercises. The Rutgers Ankle controller contains an embedded Pentium board, pneumatic solenoid valves, valve controllers, and associated signal conditioning electronics. The rehabilitation exercise used in our case study consists of piloting a virtual airplane through loops. The exercise difficulty can be selected based on the number and placement of loops, the airplane speed in the virtual environment, and the degree of resistance provided by the haptic interface. Exercise data is stored transparently, in real time, in an Oracle database. These data consist of ankle position, forces, and mechanical work during an exercise, and over subsequent rehabilitation sessions. The number of loops completed and the time it took to do that are also stored online. A case study is presented of a patient nine months post-stroke using this system. Results showed that, over six rehabilitation sessions, the patient improved on clinical measures of strength and endurance, which corresponded well with torque and power output increases measured by the Rutgers Ankle. There were also substantial improvements in task accuracy and coordination during the simulation and the patient's walking and stair-climbing ability.

Question: What is a case study?


Perpetual development: A model of the Linux kernel life cycle. Software evolution is widely recognized as an important and common phenomenon, whereby the system follows an ever-extending development trajectory w ith intermittent releases. Nevertheless there have been only few lifecycle models that attempt to por tray such evolution. We use the evolution of the Linux kernel as the basis for the formulation of such a model, integrating the progress in time with growth of the codebase, and differentiating bet ween development of new functionality and maintenance of production versions. A unique elem nt of the model is the sequence of activities involved in releasing new production versions, and how this has changed with the growth of Linux. In particular, the release follow-up phase before th forking of a new development version, which was prominent in early releases of production ve rsions, has been eliminated in favor of a concurrent merge window in the release of 2.6.x versions . We also show that a piecewise linear model with increasing slopes provides the best descr iption of the growth of Linux. The perpetual development model is used as a framework in which c ommonly recognized benefits of incremental and evolutionary development may be demonstra ted, nd to comment on issues such as architecture, conservation of familiarity, and failed p rojects. We suggest that this model and variants thereof may apply to many other projects in additio n to Linux.

Question: What is the difference between the Linux kernel and the Linux kernel life cycle?


The principles of software QRS detection. The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

Question: What is the QRS complex?


Autonomous Battery Exchange of UAVs with a Mobile Ground Base. This paper presents the autonomous battery exchange operation for small scale UAVs, using a mobile ground base that carries a robotic arm and a service station containing the battery exchange mechanism. The goal of this work is to demonstrate the means to increase the autonomy and persistence of robotic systems without requiring human intervention. The design and control of the system and its components are presented in detail, as well as the collaborative software framework used to plan and execute complex missions. Finally, the results of autonomous outdoor experiments are presented, in which the ground rover successfully localizes, retrieves, services, and deploys the landed UAV, proving its capacity to extend and enhance autonomous operations.

Question: How to design a mobile ground base?


Isolation and characterization of microsatellite loci in the black pepper, Piper nigrum L. (piperaceae). The black pepper, Piper nigrum L., which originated in Índia, is the World's most important commercial spice. Brazil has a germplasm collection of this species preserved at the Brazilian Agricultural Research Corporation (Embrapa—Eastern Amazonia) where efforts are being made to generation information on the patterns of genetic variation and develop strategies for conservation and management of black pepper. Molecular markers of the SSR type are powerful tools for the description of material preserved in genetic resources banks, due to characteristics such as high levels of polymorphism, codominance and Mendelian segregation. Given this, we developed nine microsatellite markers from an enriched library of Piper nigrum L. Twenty varieties clonal from the Brazilian germplasm collection were analyzed, and observed and expected heterozygosity values ranged over 0.11–1.00 and 0.47–0.87, respectively. The nine microsatellite loci characterized here will contribute to studies of genetic diversity and conservation of Piper nigrum L.

Question: What is the black pepper?


Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task. We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing "keywords" (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded "understanding" of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys. The paper makes several contributions: an effective and extensible mechanism for generating decoys from (humancreated) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the endto-end task of image captioning.

Question: What is the name of the dataset?


Development of a robotic finger with an active dual-mode twisting actuation and a miniature tendon tension sensor. A robot finger is developed with enhanced grasping force and speed using active dual-mode twisting actuation, which is a type of twisted string actuation. This actuation system has two twisting modes (Speed Mode and Force Mode) with different radii of the twisted string, and the twisting mode can be changed automatically. Therefore, the actuator operates like an automatic transmission having two transmission ratios, and the robot finger can generate a large grasping force or fast motion depending on the twisting mode. In addition, a miniature tendon tension sensor is developed and embedded in the fingertip for automatic mode change and grasping force control. A relation between the fingertip force and the tension of the tendon is derived by kinematic and kinetic analyses of the robot finger. The performance of the robotic finger and the tension sensor are verified by experimental results.

Question: What is a robot finger?


Multi-target threat assessment for automotive applications. Auto-brake systems have been on the market for a few years, but in order to continue improving their performance, multi-target threat assessment is a key component. The goal is to early be able to say that a collision can no longer be avoided by a steering maneuver, and thus braking can be started earlier. This is done by looking at all objects in the road scene, including other vehicles and barriers, and then looking for escape paths. The paper presents a computationally efficient way of determining whether an escape path can be found in a scenario with generally positioned objects, given the limited dynamics of the vehicle.

Question: What is a multi-target threat assessment?


Transfer Learning in Multi-Agent Reinforcement Learning Domains. Transfer learning refers to the process of reusing knowledge from past tasks in order to speed up the learning procedure in new tasks. In reinforcement learning, where agents often require a considerable amount of training, transfer learning comprises a suitable solution for speeding up learning. Transfer learning methods have primarily been applied in single-agent reinforcement learning algorithms, while no prior work has addressed this issue in the case of multi-agent learning. This work proposes a novel method for transfer learning in multi-agent reinforcement learning domains. We test the proposed approach in a multiagent domain under various setups. The results demonstrate that the method helps to reduce the learning time and increase the asymptotic performance.

Question: What is the asymptotic performance?


An airborne experimental test platform: From theory to flight. This paper provides an overview of the experimental flight test platform developed by the University of Minnesota Unmanned Aerial Vehicle Research Group. Key components of the current infrastructure are highlighted, including the flight test system, high-fidelity nonlinear simulations, software-and hardware-in-the-loop simulations, and the real-time flight software. Recent flight control research and educational applications are described to showcase the advanced capabilities of the platform. A view towards future expansion of the platform is given in the context of upcoming research projects.

Question: What is the experimental flight test platform?


Successfully Managing Impending Skin Necrosis following Hyaluronic Acid Filler Injection, using High-Dose Pulsed Hyaluronidase. Facial fillers are becoming increasingly popular as aesthetic procedures to temporarily reduce the depth of wrinkles or to contour faces. However, even in the hands of very experienced injectors, there is always a small possibility of vascular complications like intra-arterial injection of filler substance. We present a case report of a patient who developed features of vascular obstruction in right infraorbital artery and tell-tale signs of impending skin necrosis, after hyaluronic acid filler injection by an experienced injector. The diagnosis of a vascular complication was made quickly with the help of clinical features like blanching, livedo reticularis, and poor capillary refill. Patient was treated promptly with "high-dose pulsed hyaluronidase protocol" comprising three 1,000-unit pulses of hyaluronidase, administered hourly. There was no further increase in size of the involved area after the first dose of hyaluronidase. All of the involved area, along with 1 cm overlapping in uninvolved skin area, was injected during each injection pulse, using a combination of cannula and needle. Complete reperfusion and good capillary filling were achieved after completion of 3 pulses, and these were taken as the end-point of high-dose pulsed hyaluronidase treatment. Immediate skin changes after filler injections, as well as after hyaluronidase injections and during the 3-week recovery period, were documented with photographs and clinical notes. Involved skin was found to have been fully recovered from this vascular episode, thus indicating that complete recovery of the ischemic skin changes secondary to possible intra-arterial injection could be achieved using high-dose pulsed hyaluronidase protocol.

Question: What is the treatment for vascular complications after hyaluronic acid filler injection?


GestureSleeve: using touch sensitive fabrics for gestural input on the forearm for controlling smartwatches. Smartwatches provide quick and easy access to information. Due to their wearable nature, users can perceive the information while being stationary or on the go. The main drawback of smartwatches, however, is the limited input possibility. They use similar input methods as smartphones but thereby suffer from a smaller form factor. To extend the input space of smartwatches, we present GestureSleeve, a sleeve made out of touch enabled textile. It is capable of detecting different gestures such as stroke based gestures or taps. With these gestures, the user can control various smartwatch applications. Exploring the performance of the GestureSleeve approach, we conducted a user study with a running application as use case. In this study, we show that input using the GestureSleeve outperforms touch input on the smartwatch. In the future the GestureSleeve can be integrated into regular clothing and be used for controlling various smart devices.

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


Monitoring road traffic with a UAV-based system. Unmanned Aerial Vehicles (UAVs) are becoming an attractive solution for road traffic monitoring because of their mobility, low cost, and broad view range. Up to now, existing traffic monitoring systems based on UAVs only use one UAV with fixed trajectory to extract information about vehicles. In this paper, we propose a road traffic monitoring system using multiple UAVs. We develop a method to generate adaptive UAVs trajectories, which is based on the tracking of moving points in the UAV field of view. Also we generate UAVs trajectories using mobility models that are usually used to model vehicles mobility. UAVs monitor the traffic on a city road, they are responsible for collecting and sending, in real time, vehicle information to a traffic processing center for traffic regulation purposes. We show that the performance of our system is better than the performance of the fixed UAV trajectory traffic monitoring system in terms of coverage rates and events detection rates.

Question: How to monitor road traffic with a UAV-based system?


RelFinder: Revealing Relationships in RDF Knowledge Bases. The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

Question: What is the graph covering relationships between the objects of interest?


Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities. This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities. First, we present our work towards the development of the EasyRead corpus, which contains easy-to-read documents created especially for people with cognitive disabilities. We then compare the EasyRead corpus to the simplified output contained in the LocalNews corpus (Feng, 2009), the accessibility of which has been evaluated through reading comprehension experiments including 20 adults with mild intellectual disability. This comparison is made on the basis of 13 disability-specific linguistic features. The comparison reveals that there are no major differences between the two corpora, which shows that the EasyRead corpus is to a similar reading level as the user-evaluated texts. We also discuss the role of Simple Wikipedia (Zhu et al., 2010) as a widely-used accessibility benchmark, in light of our finding that it is significantly more complex than both the EasyRead and the LocalNews corpora.

Question: How do you evaluate the readability of text simplification output for readers with cognitive disabilities?


3D textureless object detection and tracking: An edge-based approach. This paper presents an approach to textureless object detection and tracking of the 3D pose. Our detection and tracking schemes are coherently integrated in a particle filtering framework on the special Euclidean group, SE(3), in which the visual tracking problem is tackled by maintaining multiple hypotheses of the object pose. For textureless object detection, an efficient chamfer matching is employed so that a set of coarse pose hypotheses is estimated from the matching between 2D edge templates of an object and a query image. Particles are then initialized from the coarse pose hypotheses by randomly drawing based on costs of the matching. To ensure the initialized particles are at or close to the global optimum, an annealing process is performed after the initialization. While a standard edge-based tracking is employed after the annealed initialization, we employ a refinement process to establish improved correspondences between projected edge points from the object model and edge points from an input image. Comparative results for several image sequences with clutter are shown to validate the effectiveness of our approach.

Question: What is the special Euclidean group, SE(3)?


Fast Min-Sum Algorithms for Decoding of LDPC over GF(q). In this paper, we present a fast min-sum algorithm for decoding LDPC codes over GF(q). Our algorithm is different from the one presented by David Declercq and Marc Fossorier (2005) only at the way of speeding up the horizontal scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up the computation by reducing the number of configurations, while our algorithm uses the dynamic programming instead. Compared with the configuration reduction algorithm, the dynamic programming one is simpler at the design stage because it has less parameters to tune. Furthermore, it does not have the performance degradation problem caused by the configuration reduction because it searches the whole configuration space efficiently through dynamic programming. Both algorithms have the same level of complexity and use simple operations which are suitable for hardware implementations

Question: What is the difference between the min-sum algorithm and the configuration reduction algorithm?


Supervised Word Mover's Distance. Recently, a new document metric called the word mover's distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely unsupervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient technique to learn a supervised metric, which we call the Supervised-WMD (S-WMD) metric. The supervised training minimizes the stochastic leave-one-out nearest neighbor classification error on a perdocument level by updating an affine transformation of the underlying word embedding space and a word-imporance weight vector. As the gradient of the original WMD distance would result in an inefficient nested optimization problem, we provide an arbitrarily close approximation that results in a practical and efficient update rule. We evaluate S-WMD on eight real-world text classification tasks on which it consistently outperforms almost all of our 26 competitive baselines.

Question: What is the supervised word mover's distance?


Exposure of Children and Adolescents to Alcohol Marketing on Social Media Websites. AIMS In 2011, online marketing became the largest marketing channel in the UK, overtaking television for the first time. This study aimed to describe the exposure of children and young adults to alcohol marketing on social media websites in the UK.   METHODS We used commercially available data on the three most used social media websites among young people in the UK, from December 2010 to May 2011. We analysed by age (6-14 years; 15-24 years) and gender the reach (proportion of internet users who used the site in each month) and impressions (number of individual pages viewed on the site in each month) for Facebook, YouTube and Twitter. We further analysed case studies of five alcohol brands to assess the marketer-generated brand content available on Facebook, YouTube and Twitter in February and March 2012.   RESULTS Facebook was the social media site with the highest reach, with an average monthly reach of 89% of males and 91% of females aged 15-24. YouTube had a similar average monthly reach while Twitter had a considerably lower usage in the age groups studied. All five of the alcohol brands studied maintained a Facebook page, Twitter page and YouTube channel, with varying levels of user engagement. Facebook pages could not be accessed by an under-18 user, but in most cases YouTube content and Twitter content could be accessed by those of all ages.   CONCLUSION The rise in online marketing of alcohol and the high use of social media websites by young people suggests that this is an area requiring further monitoring and regulation.

Question: What is the largest marketing channel in the UK?


Time-series detection of perspiration as a liveness test in fingerprint devices. Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets.

Question: How to detect spoofing in fingerprint scanners?


Parsimonious module inference in large networks.. We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obtain general bounds on the detectability of any prescribed block structure, given the number of nodes and edges in the sampled network. We also obtain that the maximum number of detectable blocks scales as sqrt[N], where N is the number of nodes in the network, for a fixed average degree ⟨k⟩. We also show that the simplicity of the minimum description length approach yields an efficient multilevel Monte Carlo inference algorithm with a complexity of O(τNlogN), if the number of blocks is unknown, and O(τN) if it is known, where τ is the mixing time of the Markov chain. We illustrate the application of the method on a large network of actors and films with over 10(6) edges, and a dissortative, bipartite block structure.

Question: What is the complexity of the multilevel Monte Carlo inference algorithm?


The Stanford CoreNLP Natural Language Processing Toolkit. We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.

Question: What is the Stanford CoreNLP Natural Language Processing Toolkit?


Parallelization of DC Analysis through Multiport Decomposition. Physical problems offer scope for macro level parallelization of solution by their essential structure. For parallelization of electrical network simulation, the most natural structure based method is that of multiport decomposition. In this paper this method is used for the simulation of electrical networks consisting of resistances, voltage and current sources using a distributed cluster of weakly coupled processors. At the two levels in which equations are solved in this method the authors have used sparse LU for both levels in the first scheme and sparse LU in the inner level and conjugate gradient in the outer level in the second scheme. Results are presented for planar networks, for the cases where the numbers of slave processors are 1 and 2, and for circuit sizes up to 8.2 million nodes and 16.4 million edges using 8 slave processors. The authors use a cluster of Pentium IV processors linked through a 10/100MBPS Ethernet switch

Question: How to parallelize DC analysis through multiport decomposition?


Development of Network Gateway Between CAN and FlexRay Protocols For ECU Embedded Systems. This paper presents a network gateway between the CAN and FlexRay protocols for ECU embedded systems. Flexible gateways are necessary for implementing several functions to avoiding the need for increased clock speed. This gateway system is based on a second internal control unit, which can be implemented as a RISC or a specialized finite state machine and use combined message buffer RAM. The control unit is used to lessen the interrupt burden of the main CPU and to reduce the required CPU performance, while message buffer RAM is used to store all messages transmitted and received. It requires a high system frequency to guarantee the transfer of data between two or more networks in real-time and to assure data integrity. To compensate for the limited functionality of the modules, the control unit requires greater program memory capacity, for storing the complex routing information. This gateway system is based on proven communication controllers, expanded with a gateway interface. These communication controllers are linked by an independent CPU data bus structure that enables the transfer of messages. The data flow between the modules is controlled by an application specific gateway unit that provides all control signals. This unit consists of special function registers or of an independent CPU finite state machine. Due to the special gateway interfaces, which allows concurrent CPU and gateway unit request to the same cell, semaphores and flags are not necessary. This maximizes the flexibility of the module. These Gateways play and will continue to play a major role in automotive networks such as CAN, and FlexRay

Question: What is the difference between CAN and FlexRay?


Analysis of packet loss and delay variation on QoE for H.264 andWebM/VP8 Codecs. The popularity of multimedia services over Internet has increased in the recent years. These services include Video on Demand (VoD) and mobile TV which are predominantly growing, and the user expectations towards the quality of videos are gradually increasing. Different video codecs are used for encoding and decoding. Recently Google has introduced the VP8 codec which is an open source compression format. It is introduced to compete with existing popular codec namely H.264/AVC developed by ITU-T Video Coding Expert Group (VCEG), as by 2016 there will be a license fee for H.264. In this work we compare the performance of H.264/AVC and WebM/VP8 in an emulated environment. NetEm is used as an emulator to introduce delay/delay variation and packet loss. We have evaluated the user perception of impaired videos using Mean Opinion Score (MOS) by following the International Telecommunication Union (ITU) Recommendations Absolute Category Rating (ACR) and analyzed the results using statistical methods. It was found that both video codecs exhibit similar performance in packet loss, But in case of delay variation H.264 codec shows better results when compared to WebM/VP8. Moreover along with the MOS ratings we also studied the effect of user feelings and online video watching experience impacts on their perception.

Question: What is the difference between H.264 and WebM/VP8?


Microservices validation: Mjolnirr platform case study. Microservice architecture is a cloud application design pattern that implies that the application is divided into a number of small independent services, each of which is responsible for implementing of a certain feature. The need for continuous integration of developed and/or modified microservices in the existing system requires a comprehensive validation of individual microservices and their co-operation as an ensemble with other microservices. In this paper, we would provide an analysis of existing methods of cloud applications testing and identify features that are specific to the microservice architecture. Based on this analysis, we will try to propose a validation methodology of the microservice systems.

Question: What is microservice architecture?


Serendipitous recommendation for scholarly papers considering relations among researchers. Serendipity occurs when one finds an interesting discovery while searching for something else. While search engines seek to report work relevant to a targeted query, recommendation engines are particularly well-suited for serendipitous recommendations as such processes do not need to fulfill a targeted query. Junior researchers can use such an engine to broaden their horizon and learn new areas, while senior researchers can discover interdisciplinary frontiers to apply integrative research. We adapt a state-of-the-art scholarly paper recommendation system's user profile construction to make use of information drawn from 1) dissimilar users and 2) co-authors to specifically target serendipitous recommendation.

Question: What is a serendipitous recommendation?