Multi-labeled Relation Extraction with Attentive Capsule Network. To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works. Introduction Relation extraction plays a crucial role in many natural language processing (NLP) tasks. It aims to identify relation facts for pairs of entities in a sentence to construct triples like [Arthur Lee, place born, Memphis]. Relation extraction has received renewed interest in the neural network era, when neural models are effective to extract semantic meanings of relations. Compared with traditional approaches which focus on manually designed features, neural methods such as Convolutional Neural Network (CNN) (Liu et al. 2013; Zeng et al. 2014) and Recurrent Neural Network (RNN) (Zhang and Wang 2015; Zhou et al. 2016) have achieved significant improvement in relation classification. However, previous neural models are unlikely to scale in the scenario where a sentence has multiple relation labels and face the challenges in extracting highly overlapped and discrete relation fea

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


Classification of Substitution Ciphers using Neural Networks. Most of the time of a cryptanalyst is spent on finding the cipher technique used for encryption rather than the finding the key/ plaintext of the received cipher text. The Strength of the classical substitution cipher"s success lie on the variety of characters employed to represent a single character. More, the characters employed more the complexity. Thus, in order to reduce the work of the cryptanalyst, neural network based identification is done based on the features of the cipher methods. In this paper, classical substitution ciphers, namely, Playfair, Vigenère and Hill ciphers are considered. The features of the cipher methods under consideration were extracted and a backpropagation neural network was trained. The network was tested for random texts with random keys of various lengths. The cipher text size was fixed as 1Kb. The results so obtained were encouraging.

Question: What is a neural network?


Generalized velocity obstacles. We address the problem of real-time navigation in dynamic environments for car-like robots. We present an approach to identify controls that will lead to a collision with a moving obstacle at some point in the future. Our approach generalizes the concept of velocity obstacles, which have been used for navigation among dynamic obstacles, and takes into account the constraints of a car-like robot. We use this formulation to find controls that will allow collision free navigation in dynamic environments. Finally, we demonstrate the performance of our algorithm on a simulated car-like robot among moving obstacles.

Question: How to navigate among moving obstacles?


Stippling by Example. In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.

Question: How do you create a stipple texture?


Photobook: Content-based manipulation of image databases. We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We discuss three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with text-based descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.

Question: What is the difference between a photobook and a photo album?


Group-Sensitive Triplet Embedding for Vehicle Reidentification. The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation "group" between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database "PKU-Vehicle," consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.

Question: What is the largest vehicle database?


Nanoseconds Switching for High Voltage Circuit Using Avalanche Transistors. The avalanche transistor is suitable for switching high voltage in kilovolts region. In this paper, a simple switching circuit consists of avalanche transistor is demonstrated. Sixteen of ZTX 415 avalanche transistors were used to switch a high voltage circuit up to 4.5 kV. A PIC16F84 microcontroller was utilized as control unit to generate input trigger. The result shows that the developed circuit was able to switch applied voltage up-to 4.5 kV with an average falling time is 2.89 ns.

Question: What is the voltage of a kilovolt?


Inner Product Similarity Search using Compositional Codes. This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group M -selection algorithm that selects M elements from M source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets (1M and 1B SIFT features, 1M linear models and Netflix) demonstrate the superiority of the proposed approach.

Question: What is the nearest neighbor search problem?


A systematic analysis of textual variability modeling languages. Industrial variability models tend to grow in size and complexity due to ever-increasing functionality and complexity of software systems. Some authors report on variability models specifying several thousands of variabilities. However, traditional variability modeling approaches do not seem to scale adequately to cope with size and complexity of such models. Recently, textual variability modeling languages have been advocated as one scalable solution.  In this paper, we provide a systematic analysis of the capabilities of current textual variability modeling languages, in particular regarding variability management in the large. Towards this aim, we define a classification schema consisting of five dimensions, classify ten different textual variability modeling languages using the classification schema and provide an analysis. In summary, some textual variability modeling languages go beyond textual representations of traditional variability modeling approaches and provide sophisticated modeling concepts and constraint languages. Three textual variability modeling approaches already support mechanisms for large-scale variability modeling such as model composition, modularization, or evolution support.

Question: What is a textual variability modeling language?


Zerocash: Decentralized Anonymous Payments from Bitcoin. Bit coin is the first digital currency to see widespread adoption. While payments are conducted between pseudonyms, Bit coin cannot offer strong privacy guarantees: payment transactions are recorded in a public decentralized ledger, from which much information can be deduced. Zero coin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment's origin. Yet, it still reveals payments' destinations and amounts, and is limited in functionality. In this paper, we construct a full-fledged ledger-based digital currency with strong privacy guarantees. Our results leverage recent advances in zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs). First, we formulate and construct decentralized anonymous payment schemes (DAP schemes). A DAP scheme enables users to directly pay each other privately: the corresponding transaction hides the payment's origin, destination, and transferred amount. We provide formal definitions and proofs of the construction's security. Second, we build Zero cash, a practical instantiation of our DAP scheme construction. In Zero cash, transactions are less than 1 kB and take under 6 ms to verify - orders of magnitude more efficient than the less-anonymous Zero coin and competitive with plain Bit coin.

Question: What is the difference between Zerocash and Bit coin?


Glomosim: a scalable network simulation environment. Large-scale hybrid networks that include wireless, wired, and satellite based communications are becoming common in both military and commercial situations. This paper describes a scalable simulation environment called GloMoSim (for Global Mobile Information System Simulator) that effectively utilizes parallel execution to reduce the simulation time of detailed high-fidelity models of large communication networks. The paper also presents a set of case studies that evaluate the performance of large wireless networks with thousands of nodes and compares the impact of different lower layer protocols on the performance of typical applications.

Question: What is a scalable network simulation environment?


Machine learning approach for automated screening of malaria parasite using light microscopic images.. The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification.

Question: What is the difference between Bayesian and SVM?


LUBM: A benchmark for OWL knowledge base systems. We describe our method for benchmarking Semantic Web knowledge base systems with respect to use in large OWL applications. We present the Lehigh University Benchmark (LUBM) as an example of how to design such benchmarks. The LUBM features an on-tology for the university domain, synthetic OWL data scalable to an arbitrary size, fourteen extensional queries representing a variety of properties, and several performance metrics. The LUBM can be used to evaluate systems with different reasoning capabilities and storage mechanisms. We demonstrate this with an evaluation of two memory-based systems and two systems with persistent storage.

Question: What is the LUBM?


Applications of soft union sets to hemirings via SU-h-ideals. The aim of this article is to lay a foundation for providing a soft algebraic tool in considering many problems that contain uncertainties. In order to provide these soft algebraic structures, we introduce the concepts of soft union hemirings (soft union h-ideals) of hemirings by soft intersection-union product and obtain some related results. Finally, we investigate some characterizations of h-hemiregular hemirings by soft union h-ideals.

Question: What is a soft union hemi-ring?


Gualzru's Path to the Advertisement World. This paper describes the genesis of Gualzru, a robot commissioned by a large Spanish technological company to provide advertisement services in open public spaces. Gualzru has to stand by at an interactive panel observing the people passing by and, at some point, select a promising candidate and approach her to initiate a conversation. After a small verbal interaction, the robot is supposed to convince the passerby to walk back to the panel, leaving the rest of the selling task to an interactive software embedded in it. The whole design and building process took less than three years of team composed of five groups at different geographical locations. We describe here the lessons learned during this period of time, from different points of view including the hardware, software, architectural decisions and team collaboration issues.

Question: What is the genesis of Gualzru?


Increased corpus callosum size in musicians. Using in-vivo magnetic resonance morphometry it was investigated whether the midsagittal area of the corpus callosum (CC) would differ between 30 professional musicians and 30 age-, sex- and handedness-matched controls. Our analyses revealed that the anterior half of the CC was significantly larger in musicians. This difference was due to the larger anterior CC in the subgroup of musicians who had begun musical training before the age of 7. Since anatomic studies have provided evidence for a positive correlation between midsagittal callosal size and the number of fibers crossing through the CC, these data indicate a difference in interhemispheric communication and possibly in hemispheric (a)symmetry of sensorimotor areas. Our results are also compatible with plastic changes of components of the CC during a maturation period within the first decade of human life, similar to those observed in animal studies.

Question: What is the corpus callosum?


Miniature Continuous Coverage Antenna Array for GNSS Receivers. This letter presents a miniature conformal array that provides continuous coverage and good axial ratio from 1100-1600 MHz. Concurrently, it maintains greater than 1.5 dBic RHCP gain and return loss less than -10 dB. The four element array is comprised of two-arm slot spirals with lightweight polymer substrate dielectric loading and a novel termination resistor topology. Multiple elements provide the capability to suppress interfering signals common in GNSS applications. The array, including feeding network, is 3.5" times 3.5" times 0.8" in size and fits into the FRPA-3 footprint and radome.

Question: What is a conformal array?


Gaming frequency and academic performance. There are numerous claims that playing computer and video games may be educationally beneficial, but there has been little formal investigation into whether or not the frequency of exposure to such games actually affects academic performance. This paper explores the issue by analysing the relationships between gaming frequency – measured as the amount of time undergraduate students spend playing games in their free time – and their academic performance as measured by their examination marks. Using a sample of 713 students, correlation analyses between gaming frequency and examination performance were conducted for students of varying gaming frequency, study discipline, gender, and general attitudes towards gaming and study. The results reveal that examination marks are in fact negatively correlated with gaming frequency – i.e. frequent gamers generally achieve lower marks than less frequent gamers.

Question: What is the relationship between gaming and academic performance?


Recurrent Memory Array Structures Technical Report. The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves stateof-the-art performance on character level text prediction achieving 1.402 BPC† on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.

Question: What is the enwik8 dataset?


Study of bi-directional buck-boost converter with different control methods. In this paper, four types control methods have been proposed for buck-boost cascade topology. This converter used in hybrid electric vehicle can step the battery voltage level either up or down. The target is to find the most appropriate control method for the converter with certain specifications. Critical conduction mode control, fixed frequency control, the buck/boost control and the buck + boost control have been mentioned. The inductor design is presented according to the given parameters. The relationship among the parameters for buck/boost mode, buck mode and boost mode in critical conduction mode are proposed. The efficiency comparison for four control methods is given. The loss breakdown of different control methods is also presented by mathematical calculation.

Question: What is the difference between the buck and boost mode?


Applying a tendency to be well retweeted to false information detection. While a lot of useful information can be found in SNS, false information also diffuses through it, thereby confusing many people sometimes. In this paper, we predict a tendency of tweets to be well retweeted and consider applying the tendency to false information detection. The tendency prediction can be implemented with simple features of tweets. We examine the effect of the tendency when it is used in false information detection empirically. Our experimental results indicate that it would be valuable to take the tendency into account for the detection. We also discuss findings when applying them to tweets in Japanese.

Question: How to detect false information in SNS?


TRESNEI, a Matlab trust-region solver for systems of nonlinear equalities and inequalities. The Matlab implementation of a trust-region Gauss-Newton method for boundconstrained nonlinear least-squares problems is presented. The solver, called TRESNEI, is adequate for zero and small-residual problems and handles the solution of nonlinear systems of equalities and inequalities. The structure and the usage of the solver are described and an extensive numerical comparison with functions from the Matlab Optimization Toolbox is carried out.

Question: What is a trust-region solver?


Strength-based interventions : Their importance in application to the gifted. Positive psychology has revived psychology's abandoned interest in the study of morally positively valued traits (the so-called character strengths) and virtues. We review literature generated on strength-based approaches and focus on applications in the so-called positive psychology interventions. There seems to be great potential in this approach for research in the field of giftedness and, of course, also when practically working with gifted children and adolescents. We highlight some ideas for future research directions. DOI: https://doi.org/10.1177/0261429416640334 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-128345 Accepted Version Originally published at: Proyer, René T; Gander, Fabian; Tandler, Nancy (2017). Strength-based interventions: Their importance in application to the gifted. Gifted Education International, 33(2):118-130. DOI: https://doi.org/10.1177/0261429416640334 Strength-based interventions: Their importance in application to the gifted René T. Proyer Martin-Luther University Halle-Wittenberg, Germany University of Zurich, Switzerland Fabian Gander University of Zurich, Switzerland Nancy Tandler Martin-Luther University Halle-Wittenberg, Germany René T. Proyer is at the Department of Psychology at the Martin-Luther University Halle-Wittenberg (Germany) and the Department of Psychology at the University of Zurich (Switzerland). Fabian Gander is at Department of Psychology at the University of Zurich (Switzerland). Nany Tandler is at the Department of Psychology at the Martin-Luther University Halle-Wittenberg (Germany) The preparation of this paper has been facilitated by research grants of the Swiss National Science Foundation (SNSF; 100014_132512 and 100014_149772) awarded to RTP. Correspondence concerning this article should be addressed to René Proyer, MartinLuther University Halle-Wittenberg (Germany),

Question: Strength-based interventions: Their importance in application to the gifted


An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows. Thepickup and delivery problem with time windows is the problem of serving a number of transportation requestsusing a limited amount of vehicles. Each request involves mo ving a number of goods from a pickup location to a delivery location. Our task is to constr uc routes that visit all locations such that corresponding pickups and deliveries are placed on the same rout and such that a pickup is performed before the corresponding delivery. The routes must also sat isfy time window and capacity constraints. This paper presents a heuristic for the problem based on an ex te sion of theLarge Neighborhood Search heuristic previously suggested for solving the vehicle rou ting problem with time windows. The proposed heuristic is composed of a number of competing sub-heuristi cs which are used with a frequency corresponding to their historic performance. This general framework i s denotedAdaptive Large Neighborhood Search . The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for mor e than 50% of the problems. The computational experiments indicate that it is advantag eous to use several competing sub-heuristics instead of just one. We believe that the proposed heuristic i very robust and is able to adapt to various instance characteristics.

Question: What is the pickup and delivery problem with time windows?


A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activation function is simple, differentiable, and parameterized so that it can be trained as the rest of the network is trained. We hypothesize that soft exponential has the potential to improve neural network learning, as it can exactly calculate many natural operations that typical neural networks can only approximate, including addition, multiplication, inner product, distance, and sinusoids.

Question: What is the soft exponential activation function?


Analysis and Design of an Ultrabroadband Stacked Power Amplifier in CMOS Technology. This brief presents the analysis and design of a two-stage stacked power amplifier (PA) with very broadband gain frequency response and power performance in a small chip size. The broadband load impedance match is realized using modified stacked field-effect transistors (FETs) with a resistive feedback by analyzing the matching condition of the source input impedance of the stacked FET. In order to further improve the broadband gain frequency response, the effectiveness of a gain expansion from a stacked driver amplifier is demonstrated to compensate the gain compression of the last-stage amplifier. To verify the design concept, a two-stage three-stacked PA has been implemented in a 0.18-μm CMOS technology. The PA achieves a saturated output power of 22-24.3 dBm and a power added efficiency of 13%-20% within a 194% fractional bandwidth from 0.1 to 6.5 GHz. It also demonstrates better than 11-dB input return loss (RL) and better than 5.1-dB output RL. This PA occupies a chip size of 0.64 mm2 including pads.

Question: What is the size of the chip?


User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop. Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.

Question: What is the scalability of collaborative filtering?


Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization. Automatic text summarization is a major area of research in the domain of information systems. Most of the methods requires domain knowledge in order to produce a coherent and meaningful summary. In Extractive text summarization, sentences are scored on some features. A large number of feature based scoring methods have been proposed for extractive automatic text summarization by researchers. This paper reviews features for sentence scoring. The results on combinations of various features for scoring are discussed. ROUGE-N is used to evaluate generated summary with abstractive summary of DUC 2002 dataset.

Question: What is the difference between extractive and abstractive summarization?


LEARNING TO SOLVE CIRCUIT-SAT: AN UNSUPERVISED DIFFERENTIABLE APPROACH. Recent efforts to combine Representation Learning with Formal Methods, commonly known as Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem. Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure, and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem. The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.

Question: What is the circuit satisfaction problem?


The impact of data replication on job scheduling performance in the Data Grid. In the Data Grid environment, the primary goal of data replication is to shorten the data access time experienced by the job and consequently reduce the job turnaround time. After introducing a Data Grid architecture that supports efficient data access for the Grid job, the dynamic data replication algorithms are put forward. Combined with different Grid scheduling heuristics, the performances of the data replication algorithms are evaluated with various simulations. The simulation results demonstrate f shortest lacement data e ists, d the ten-

Question: What is the impact of data replication on job scheduling performance in the Data Grid?


Exploring multicriteria decision strategies in GIS with linguistic quantifiers: A case study of residential quality evaluation. Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decision support systems. We propose to extend a previous implementation of OWA with linguistic quantifiers to simplify the definition of decision strategies and to facilitate an exploratory analysis of multiple criteria. The linguistic quantifier-guided OWA procedure is illustrated using a dataset for evaluating residential quality of neighborhoods in London, Ontario.

Question: How to use linguistic quantifiers in GIS?


LAN security: problems and solutions for Ethernet networks. Despite many research and development efforts in the area of data communications security, the importance of internal Ž . local area network LAN security is still underestimated. This paper discusses why many traditional approaches to network Ž . security e.g. firewalls, the modern IPSec or various application level protocols are not so effective in local networks and proposes a prospective solution for building of secure encrypted Ethernet LANs. The architecture presented allows for employment of the existing office network infrastructure, does not require changes to workstations' software, and provides high level of protection. The core idea is to apply security measures in the network interface devices connecting individual Ž Ž .. computers to the network i.e. network interface cards NICs . This eliminates the physical possibility of sending unprotected information through the network. Implementation details are given for data and key exchange, network management, and interoperability issues. An in-depth security analysis of the proposed architecture is presented and some conclusions are drawn. q 2000 Elsevier Science B.V. All rights reserved.

Question: What is the difference between a firewall and a router?


A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues. The best-matching segment is then assigned to this groundtruth bounding box for the evaluation metric described in our paper, as well as for the metric described below. Some previous works have evaluated 3D segmentation using the intersection-over-union metric on 3D points [5]. Note that our method segments the entire scene, as opposed to the method of Wang et al. [5], so the evaluation metric from Wang et al. [5] does not directly apply. However, we could modify the intersection-over-union metric [5] as follows: we can compute the fraction of ground-truth bounding boxes which have an intersection-over-union score less than a threshold τIOU , as

Question: What is the best-matching segment?


Graph-based forensic investigation of Bitcoin transactions. This thesis illustrates forensic research work on Bitcoin, an innovative Internet based global transaction system that attracts ascending popularity during the recent few years. As an open, public and scalable distributed payment system, Bitcoin brings forward significant economic and technological impact to our world. Meanwhile, a new notion of virtual currency, "Bitcoin" comes into existence such that Bitcoin currency can be "mined" from all over world complying with specific algorithms. Mined bit "coins" has practical monetary values that turn the Bitcoin system into a digital currency circulation system. Due to Bitcoin's decentralized semantics, Bitcoin transaction and currency are not subject to control and censorship from any single authority. Therefore, Bitcoin brings out various security concerns about its application as a long-term reliable system. The research in the thesis focuses on forensic study on Bitcoin. It covers experimental study on the Bitcoin network as a peer-to-peer system and a graph-based forensic approach against Bitcoin's transaction data. Major contributions include network data evaluation and transaction history analysis. In case of forensic investigation is needed against criminal incidents such as fraud, false transactions and money theft, which are commonly seen in commonly used digital payment systems, the research provides a guidance of efficient information collection and framework of evidence data processing and extraction.

Question: What is a peer-to-peer system?


Improving Online Multiple Object tracking with Deep Metric Learning. Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how deep metric learning can be used to improve three aspects of tracking by detection. We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset offline. It is then used to improve the online performance of tracking while retaining a high frame rate. We use this learned appearance metric to robustly build estimates of pedestrian's trajectories in the MOT16 dataset. In breaking with the tracking by detection model, we use our appearance metric to propose detections using the predicted state of a tracklet as a prior in the case where the detector fails. This method achieves competitive results in evaluation, especially among online, realtime approaches. We present an ablative study showing the impact of each of the three uses of our deep appearance metric.

Question: How to improve online multiple object tracking?


A hardware CABAC encoder for HEVC. This paper presents a hardware design of context-based adaptive binary arithmetic coding (CABAC) for the emerging High efficiency video coding (HEVC) standard. While aiming at higher compression efficiency, the CABAC in HEVC also invests a lot of effort in the pursuit of parallelism and reducing hardware cost. Simulation results show that our design processes 1.18 bins per cycle on average. It can work at 357 MHz with 48.940K gates targeting 0.13 μm CMOS process. This processing rate can support real-time encoding for all sequences under common test conditions of HEVC standard conforming to the main profile level 6.1 of main tier or main profile level 5.1 of high tier.

Question: What is the hardware cost of a hardware CABAC encoder for HEVC?


A Wide-band Circular Polarization Stacked Patch Antenna for the Wireless Communication Applications. A wide-band 'corners-truncated rectangular' stacked patch antenna for use in the circular polarization applications was proposed. The antenna proposed in this paper an axial ratio of less than 3 dB and a VSWR of less than 2 : 1 were shown to be achievable over a 25% bandwidth for use in the wireless communication applications, and this antenna can achieves higher gain, lower side lobes and wider bandwidth compared to the traditional microstrip patch antenna.

Question: What is a wide-band circular polarization stacked patch antenna?


Software Design and Architecture The once and future focus of software engineering. The design of software has been a focus of software engineering research since the field's beginning. This paper explores key aspects of this research focus and shows why design will remain a principal focus. The intrinsic elements of software design, both process and product, are discussed: concept formation, use of experience, and means for representation, reasoning, and directing the design activity. Design is presented as being an activity engaged by a wide range of stakeholders, acting throughout most of a system's lifecycle, making a set of key choices which constitute the application's architecture. Directions for design research are outlined, including: (a) drawing lessons, inspiration, and techniques from design fields outside of computer science, (b) emphasizing the design of application "character" (functionality and style) as well as the application's structure, and (c) expanding the notion of software to encompass the design of additional kinds of intangible complex artifacts.

Question: What is the future of software design?


Security Keys: Practical Cryptographic Second Factors for the Modern Web. "Security Keys" are second-factor devices that protect users against phishing and man-in-the-middle attacks. Users carry a single device and can self-register it with any online service that supports the protocol. The devices are simple to implement and deploy, simple to use, privacy preserving, and secure against strong attackers. We have shipped support for Security Keys in the Chrome web browser and in Google's online services. We show that Security Keys lead to both an increased level of security and user satisfaction by analyzing a two year deployment which began within Google and has extended to our consumer-facing web applications. The Security Key design has been standardized by the FIDO Alliance, an organization with more than 250 member companies spanning the industry. Currently, Security Keys have been deployed by Google, Dropbox, and GitHub. An updated and extended tech report is available at https://github.com/google/u2fref-code/docs/SecurityKeys_TechReport.pdf.

Question: What is a second factor?


Rethink fronthaul for soft RAN. In this article we discuss the design of a new fronthaul interface for future 5G networks. The major shortcomings of current fronthaul solutions are first analyzed, and then a new fronthaul interface called next-generation fronthaul interface (NGFI) is proposed. The design principles for NGFI are presented, including decoupling the fronthaul bandwidth from the number of antennas, decoupling cell and user equipment processing, and focusing on high-performancegain collaborative technologies. NGFI aims to better support key 5G technologies, in particular cloud RAN, network functions virtualization, and large-scale antenna systems. NGFI claims the advantages of reduced bandwidth as well as improved transmission efficiency by exploiting the tidal wave effect on mobile network traffic. The transmission of NGFI is based on Ethernet to enjoy the benefits of flexibility and reliability. The major impact, challenges, and potential solutions of Ethernet-based fronthaul networks are also analyzed. Jitter, latency, and time and frequency synchronization are the major issues to overcome.

Question: What is the difference between fronthaul and backhaul?


Hybrid sampling Bayesian Occupancy Filter. Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter [1] is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30×30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices.

Question: How to model the velocity of a cell?


Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Graphs are widely used to model complicated data semantics in many applications. In this paper, we aim to develop efficient techniques to retrieve graphs, containing a given query graph, from a large set of graphs. Considering the problem of testing subgraph isomorphism is generally NP-hard, most of the existing techniques are based on the framework of filtering-and-verification to reduce the precise computation costs; consequently various novel feature-based indexes have been developed. While the existing techniques work well for small query graphs, the verification phase becomes a bottleneck when the query graph size increases. Motivated by this, in the paper we firstly propose a novel and efficient algorithm for testing subgraph isomorphism, QuickSI. Secondly, we develop a new feature-based index technique to accommodate QuickSI in the filtering phase. Our extensive experiments on real and synthetic data demonstrate the efficiency and scalability of the proposed techniques, which significantly improve the existing techniques.

Question: What is the size of the canadian military?


BCS: A Binary Cuckoo Search algorithm for feature selection. Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.

Question: What is a binary cuckoo search?


Human activity recognition with wearable sensors. This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information – others include the user's location or the state of his environment – which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors – mainly accelerometers – to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the a

Question: What is the difference between a wearable sensor and a wearable computer?


OpenSense: open community driven sensing of environment. This paper outlines a vision for community-driven sensing of our environment. At its core, community sensing is a dynamic new form of mobile geosensor network. We believe that community sensing networks, in order to be widely deployable and sustainable, need to follow utilitarian approaches towards sensing and data management. Current projects exploring community sensing have paid less attention to these underlying fundamental principles. We illustrate this vision through OpenSense -- a large project that aims to explore community sensing driven by air pollution monitoring.

Question: What is a community sensing network?


Trellis: Privilege Separation for Multi-user Applications Made Easy. Operating systems provide a wide variety of resource isolation and access control mechanisms, ranging from traditional user-based security models to fine-grained permission systems as found in modern mobile operating systems. However, comparatively little assistance is available for defining and enforcing access control policies within multiuser applications. These applications, often found in enterprise environments, allow multiple users to operate at different privilege levels in terms of exercising application functionality and accessing data. Developers of such applications bear a heavy burden in ensuring that security policies over code and data in this setting are properly expressed and enforced. We present Trellis, an approach for expressing hierarchical access control policies in applications and enforcing these policies during execution. The approach enhances the development toolchain to allow programmers to partially annotate code and data with simple privilege level tags, and uses a static analysis to infer suitable tags for the entire application. At runtime, policies are extracted from the resulting binaries and are enforced by a modified operating system kernel. Our evaluation demonstrates that this approach effectively supports the development of secure multi-user applications with modest runtime performance overhead.

Question: What is the difference between privilege separation and separation of duties?


Sustainable Power Supply Solutions for Off-Grid Base Stations. The telecommunication sector plays a significant role in shaping the global economy and the way people share information and knowledge. At present, the telecommunication sector is liable for its energy consumption and the amount of emissions it emits in the environment. In the context of off-grid telecommunication applications, offgrid base stations (BSs) are commonly used due to their ability to provide radio coverage over a wide geographic area. However, in the past, the off-grid BSs usually relied on emission-intensive power supply solutions such as diesel generators. In this review paper, various types of solutions (including, in particular, the sustainable solutions) for powering BSs are discussed. The key aspects in designing an ideal power supply solution are reviewed, and these mainly include the pre-feasibility study and the thermal management of BSs, which comprise heating and cooling of the BS shelter/cabinets and BS electronic equipment and power supply components. The sizing and optimization approaches used to design the BSs' power supply systems as well as the operational and control strategies adopted to manage the power supply systems are also reviewed in this paper.

Question: What is the role of the telecommunication sector in shaping the global economy?


Culture and diverging views of social events.. The authors compared East Asians' and Americans' views of everyday social events. Research suggests that Americans tend to focus more on the self and to have a greater sense of personal agency than East Asians. The authors assessed whether, compared to East Asians, Americans emphasize main characters even when events do not involve the self and whether they see more agency or intentionality in actions, even when the actions are not their own. Whether East Asians would observe more emotions in everyday scenarios than would Americans also was investigated. In Study 1, Chinese and Americans read alleged diary entries of another person. Americans did focus more on main characters and on characters' intentionality. Study 2 replicated these results comparing Taiwanese and Americans on free recall of events concerning the self and of narratives and videos concerning others. Study 2 also found that Taiwanese made more comments about the emotional states of characters.

Question: What is the difference between East Asians and Americans?


Today the Earwig, Tomorrow Man?. Kirsh, D., Today the earwig, tomorrow man?, Artificial Intelligence 47 (1991) 161-184. A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied.

Question: What is the limit of a creature without concepts?


3D laser scan classification using web data and domain adaptation. Over the last years, object recognition has become a more and more active field of research in robotics. An important problem in object recognition is the need for sufficient labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google's 3D Warehouse to train classifiers for 3D laser scans collected by a robot navigating through urban environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled 3D laser scans and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real laser scans.

Question: How to classify 3D laser scans using web data and domain adaptation?


Reliability of lower limb motor evoked potentials in stroke and healthy populations: How many responses are needed?. OBJECTIVE To determine the intra- and inter-session reliability of motor evoked potential (MEP) size parameters in the lower limb of patients with stroke, focussing on the number of MEPs collected and the method of measuring MEP size.   METHODS Transcranial magnetic stimulation was used to elicit MEPs in the soleus muscle of patients with stroke (n=13) and age-matched healthy participants (n=13) during low level muscle activation. Two sets of 10 responses were collected in the first session and a further 10 responses collected in a second session held 7 days later. Four MEP size measurements were made using 4, 6, 8, or all 10 of the MEPs collected. Intra- and inter-session reliability was examined using intraclass correlation coefficients (ICC) and typical percentage error.   RESULTS Intrasession ICC statistics using 6 or more MEPs were >0.85 in the stroke group but intersession values were all <0.5. Reliability was best when measuring parameters from individual MEPs rather than averaged responses.   CONCLUSIONS Reliability of intrasession MEP size is excellent in the lower limb of patients with stroke using as few as 6 MEPs but intersession reliability is poor.   SIGNIFICANCE Comparing MEP size measures across two or more sessions is questionable in the lower limb of patients with stroke.

Question: How many responses are needed to measure MEP size in the lower limb of patients with stroke?


Natural Language Processing for Question Answering ( NLP 4. The recent developments in Question Answering have kept with open-domain questions and collections, sometimes argued as being more di fficult than narrow domain-focused questions and corpora. The biomedical field is indeed a specialized domain; however, its scope is fairly broad, so that considering a biomedical QA task is not necessarily such a simplification over open-domain QA as represented in the recent TREC evaluations. We shall try to characterize salient aspects of biomedical QA as well as to give a short review of useful resources to address this task.

Question: What is the scope of biomedical question answering?


An Ethics Evaluation Tool for Automating Ethical Decision-Making in Robots and Self-Driving Cars. As we march down the road of automation in robotics and artificial intelligence, we will need to automate an increasing amount of ethical decision-making in order for our devices to operate independently from us. But automating ethical decision-making raises novel questions for engineers and designers, who will have to make decisions about how to accomplish that task. For example, some ethical decisionmaking involves hard moral cases, which in turn requires user input if we are to respect established norms surrounding autonomy and informed consent. The author considers this and other ethical considerations that accompany the automation of ethical decision-making. He proposes some general ethical requirements that should be taken into account in the design room, and sketches a design tool that can be integrated into the design process to help engineers, designers, ethicists, and policymakers decide how best to automate certain forms of ethical decision-making.

Question: What is the author's opinion on the ethics of automating ethical decision-making?


Machine Learning for High-Throughput Stress Phenotyping in Plants.. Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

Question: What is a taxonomy of machine learning tools for high-throughput stress phenotyping in plants?


Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Retaining users and facilitating continuance usage are crucial to the success of mobile social network services (SNS). This research examines the continuance usage of mobile SNS in China by integrating both the perspectives of social influence and privacy concern. Social influence includes three processes: compliance, identification and internalization, which are respectively represented by subjective norm, social identity, and group norm. The results indicate that these three factors and privacy concern have significant effects on continuance usage. The results suggest that service providers should address the issues of social influence and privacy concern to encourage mobile SNS continuance usage. 2014 Elsevier Ltd. All rights reserved.

Question: What is the difference between social influence and privacy concern?


A Survey of Distributed Mining of Data Streams. With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.

Question: What is the current state of the field?


Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology. Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-theart techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Given that the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the nonlinear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting engine, we propose a systematic pre-processing framework to overcome the challenges in an E-commerce setting. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart.com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming stateof-the-art techniques.

Question: What is the sales demand forecast in e-commerce?


Can e-learning replace classroom learning?. In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.

Question: Can e-learning replace classroom learning?


Applied spatial data analysis with R. Do you need the book of Applied Spatial Data Analysis with R pdf with ISBN of 9781461476177? You will be glad to know that right now Applied Spatial Data Analysis with R pdf is available on our book collections. This Applied Spatial Data Analysis with R comes PDF and EPUB document format. If you want to get Applied Spatial Data Analysis with R pdf eBook copy, you can download the book copy here. The Applied Spatial Data Analysis with R we think have quite excellent writing style that make it easy to comprehend.

Question: What is the book of Applied Spatial Data Analysis with R pdf?


Semi-Supervised AUC Optimization Without Guessing Labels of Unlabeled Data. Semi-supervised learning, which aims to construct learners that automatically exploit the large amount of unlabeled data in addition to the limited labeled data, has been widely applied in many real-world applications. AUC is a well-known performance measure for a learner, and directly optimizing AUC may result in a better prediction performance. Thus, semi-supervised AUC optimization has drawn much attention. Existing semi-supervised AUC optimization methods exploit unlabeled data by explicitly or implicitly estimating the possible labels of the unlabeled data based on various distributional assumptions. However, these assumptions may be violated in many real-world applications, and estimating labels based on the violated assumption may lead to poor performance. In this paper, we argue that, in semi-supervised AUC optimization, it is unnecessary to guess the possible labels of the unlabeled data or prior probability based on any distributional assumptions. We analytically show that the AUC risk can be estimated unbiasedly by simply treating the unlabeled data as both positive and negative. Based on this finding, two semi-supervised AUC optimization methods named SAMULT and SAMPURA are proposed. Experimental results indicate that the proposed methods outperform the existing methods. Introduction In many real-world applications, collecting a large amount of unlabeled data is relatively easy, while obtaining the labels for the collected data is rather expensive since much human effort and expertise is required for labeling. Semisupervised learning (Chapelle, Schlkopf, and Zien 2006; Zhu et al. 2009), aiming to construct learners that automatically exploit the large amount of unlabeled data in addition to the limited labeled data in the purpose of improving the learning performance, has drawn significant attention. Many semi-supervised learning methods have been proposed. To effectively exploit

Question: What is the difference between semi-supervised learning and supervised learning?


An intelligent decision support system for skin cancer detection from dermoscopic images. It is challenging to develop an intelligent agent-based or robotic system to conduct long-term automatic health monitoring and robust efficient disease diagnosis as autonomous e-Carers in real-world applications. In this research, we aim to deal with such challenges by presenting an intelligent decision support system for skin lesion recognition as the initial step, which could be embedded into an intelligent service robot for health monitoring in home environments to promote early diagnosis. The system is developed to identify benign and malignant skin lesions using multiple steps, including pre-processing such as noise removal, segmentation, feature extraction from lesion regions, feature selection and classification. After extracting thousands of raw shape, colour and texture features from the lesion areas, a Genetic Algorithm (GA) is used to identify the most discriminating significant feature subsets for healthy and cancerous cases. A Support Vector Machine classifier has been employed to perform benign and malignant lesion recognition. Evaluated with 1300 images from the Dermofit dermoscopy image database, the empirical results indicate that our approach achieves superior performance in comparison to other related research reported in the literature.

Question: What is the difference between benign and malignant skin lesions?


Multiple HOG templates for gait recognition. In gait recognition field, template-based approaches such as Gait Energy Image (GEI) and Chrono-Gait Image (CGI) can achieve good recognition performance with low computational cost. Meanwhile, CGI can preserve temporal information better than GEI. However, they pay less attention to the local shape features. To preserve temporal information and generate more abundant local shape features, we generate multiple HOG templates by extracting Histogram of Oriented Gradients (HOG) of GEI and CGI templates. Experiments show that compared with several published approaches, our proposed multiple HOG templates achieve better performance for gait recognition.

Question: How to generate multiple HOG templates?


MaCow: Masked Convolutional Generative Flow. Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MACOW), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MACOW enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models.

Question: What is a generative model?


Enriching textbooks with images. Textbooks have a direct bearing on the quality of education imparted to the students. Therefore, it is of paramount importance that the educational content of textbooks should provide rich learning experience to the students. Recent studies on understanding learning behavior suggest that the incorporation of digital visual material can greatly enhance learning. However, textbooks used in many developing regions are largely text-oriented and lack good visual material. We propose techniques for finding images from the web that are most relevant for augmenting a section of the textbook, while respecting the constraint that the same image is not repeated in different sections of the same chapter. We devise a rigorous formulation of the image assignment problem and present a polynomial time algorithm for solving the problem optimally. We also present two image mining algorithms that utilize orthogonal signals and hence obtain different sets of relevant images. Finally, we provide an ensembling algorithm for combining the assignments. To empirically evaluate our techniques, we use a corpus of high school textbooks in use in India. Our user study utilizing the Amazon Mechanical Turk platform indicates that the proposed techniques are able to obtain images that can help increase the understanding of the textbook material.

Question: How can we enrich textbooks with images?


Iterative thresholding based image segmentation using 2D improved Otsu algorithm. Threshold based segmentation methods can effectively separate objects from the background. The conventional Otsu's method can achieve good result only when the histogram of an image has two distinct peaks. In this paper, an iterative thresholding based on 2D improved Otsu method using a novel threshold value recognition function is proposed which is used to find the optimum threshold value in different types of histograms and separate into two classes. For the two classes separated by the above threshold value, mean values are computed. Based on the threshold value and the two mean values, the histogram of an image is divided iteratively into three regions, namely, the Foreground (FG) region, Background (BG) region and To-Be-Processed (TBP) region. The pixels greater than the larger mean belongs to FG region, the pixel which are lesser than the smaller mean belongs to BG region. The pixel that lies between the two mean values belongs to the TBP region and only this region is processed at next iteration, the FG and BG region are not processed further. Again for that TBP region, the novel threshold value recognition function is used to find a new threshold value and then the two mean values are computed to separate into three classes, namely, the FG, BG and TBP region. Then the new TBP region is processed in a similar manner iteratively until the two mean values computed at successive iterations are not changed. Finally all the previously determined foreground regions are combined separately and the background regions are combined separately to give the segmented image. The experimental results show that the proposed method performs well in detecting the objects and has better anti-noise performance compared with the existing methods.

Question: What is the difference between the iterative thresholding based image segmentation using 2D improved Otsu algorithm and the iterative thresholding based image segmentation using 2D improved Otsu algorithm?


Analysis of Procrastination and Flow Experiences.

Question: What is the analysis of procrastination and flow experiences?


Establishment of tumor-specific copy number alterations from plasma DNA of patients with cancer. With the increasing number of available predictive biomarkers, clinical management of cancer is becoming increasingly reliant on the accurate serial monitoring of tumor genotypes. We tested whether tumor-specific copy number changes can be inferred from the peripheral blood of patients with cancer. To this end, we determined the plasma DNA size distribution and the fraction of mutated plasma DNA fragments with deep sequencing and an ultrasensitive mutation-detection method, i.e., the Beads, Emulsion, Amplification, and Magnetics (BEAMing) assay. When analyzing the plasma DNA of 32 patients with Stage IV colorectal carcinoma, we found that a subset of the patients (34.4%) had a biphasic size distribution of plasma DNA fragments that was associated with increased circulating tumor cell numbers and elevated concentration of mutated plasma DNA fragments. In these cases, we were able to establish genome-wide tumor-specific copy number alterations directly from plasma DNA. Thus, we could analyze the current copy number status of the tumor genome, which was in some cases many years after diagnosis of the primary tumor. An unexpected finding was that not all patients with progressive metastatic disease appear to release tumor DNA into the circulation in measurable quantities. When we analyzed plasma DNA from 35 patients with metastatic breast cancer, we made similar observations suggesting that our approach may be applicable to a variety of tumor entities. This is the first description of such a biphasic distribution in a surprisingly high proportion of cancer patients which may have important implications for tumor diagnosis and monitoring.

Question: What is the size distribution of plasma DNA?


Plant Disease Prediction using Image Processing Techniques-A Review. Agriculture has become much more than simply a means to feed ever growing populations. It is very important where in more than 70% population depends on agriculture in India. That means it feeds great number of people. The plant diseases effect the humans directly or indirectly by health or also economically. To detect these plant diseases we need a fast automatic way. Diseases are analyzed by different digital image processing techniques. In this paper, we have done survey on different digital image processing techniques to detect the plant diseases.

Question: What is the plant disease prediction using image processing techniques?


The Interplay of Cross-Situational Word Learning and Sentence-Level Constraints. A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner (cross-situational word learning, CSWL). Equally, they can exploit sentential contexts, relying on top-down information such as verb-argument relations and world knowledge, offering immediate constraints on meaning (word learning based on sentence-level constraints, SLCL). When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal that learners prioritize SLCL over CSWL. CSWL is applied in addition to SLCL only if SLCL is not perfectly disambiguating, thereby complementing or competing with it. These studies demonstrate the importance of investigating word-learning mechanisms simultaneously, revealing important characteristics of their interaction in more naturalistic learning environments.

Question: How do learners prioritize cross-situational word learning over sentence-level constraints?


Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers. Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.

Question: What is the stock market classification model using sentiment analysis on twitter based on hybrid naive bayes classifiers?


User experience: challenges and opportunities. User expectations, motivations and feelings when using a product or encountering a system have urged the need to investigate beyond the traditional functionality and usability concerns by assessing and designing for the user experience. Having this realized, we can ensure positive user experience as well as desirable products. This paper aims at providing an overview of the main concepts and principles related to user experience. The topics discussed in this paper cover both theories from the academia as well as empirical studies from the industry. Due to misconception of user experience, it was found that industry depends on and uses the traditional usability methods in the product development as user experience practices. As a result, this paper discusses and elaborates the relationship between conventional usability and user experience in addition to the variety of methods which can be used for user experience evaluation during different time spans. Furthermore, user experience extends over different areas such as human computer interaction, and product design and development. Knowledge and concepts related to user experience are scattered due to the dynamic concepts of user experience. In other words, authors tend to discuss the user experience from their own perspective and interest. Hence, this paper is trying to provide and establish a holistic understanding of the user experience by covering different topics related to user experience. The output of this paper may provide and assist researchers and practitioners to establish a base to aid in proper understanding of user experience, its concepts and principles.

Question: What is user experience?


A Multi-Theoretical Literature Review on Information Security Investments using the Resource-Based View and the Organizational Learning Theory. The protection of information technology (IT) has become and is predicted to remain a key economic challenge for organizations. While research on IT security investment is fast growing, it lacks a theoretical basis for structuring research, explaining economictechnological phenomena and guide future research. We address this shortcoming by suggesting a new theoretical model emerging from a multi-theoretical perspective adopting the Resource-Based View and the Organizational Learning Theory. The joint application of these theories allows to conceptualize in one theoretical model the organizational learning effects that occur when the protection of organizational resources through IT security countermeasures develops over time. We use this model of IT security investments to synthesize findings of a large body of literature and to derive research gaps. We also discuss managerial implications of (closing) these gaps by providing practical examples.

Question: What is the difference between a multi-theoretical and a theoretical model?


An Analysis of Mobile Banking User Behavior Using Customer Segmentation. This study proposed an integrated data mining and customer behavior scoring model to manage existing mobile banking users in an Iranian bank. This segmentation model was developed to identify groups of customers based on transaction history, recency, frequency, monetary background. It classified mobile banking users into six groups. This study demonstrated that identifying customers by a behavioral scoring facilitates marketing strategy assignment. Then the bank can develop its marketing actions. Thus, the bank can attract more customers, maintain its customers, and keep high customers' satisfaction. Keywords—Data mining; mobile data, mobile banking; customer segmentation Introduction Online banking is increasingly common. Financial institutions deliver online services via various electronic channels, subsequently diminishing the importance of conventional branch networks. The newly emerging channels of online banking and rapidly increasing penetration rates of mobile phones motivate this study (C. S. Chen, 2013). The internet has had a significant impact on financial institutions, allowing consumers to access many bank facilities 24 hours a day, while allowing banks to significantly cut their costs. Research has shown that online banking is the cheapest delivery channel for many banking services (KoenigLewis, Palmer, & Moll, 2010; Robinson, 2000). A number of studies have identified advantages to bank customers, including cost and time savings as well as spatial independence benefits (Koenig-Lewis et al., 2010). According to Gartner's prediction of leading trends of 2012 in mobile applications, mobile commerce (m-commerce) remains the most important one. Gartner further forecasts that mobile devices will replace PCs as the main device to access the internet. As for the third quarter of 2012, IPSOS indicated that "The era of Multi-Screen has come, and smartphones account for the purchasing beha

Question: What is the difference between mobile banking and online banking?


Robust Face Recognition via Sparse Representation. We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

Question: What is the difference between a face and a face recognition system?


Physiological Computing: Interfacing with the Human Nervous System. This chapter describes the physiological computing paradigm where electrophysiological changes from the human nervous system are used to interface with a computer system in real time. Physiological computing systems are categorized into five categories: muscle interfaces, brain-computer interfaces, biofeedback, biocybernetic adaptation and ambulatory monitoring. The differences and similarities of each system are described. The chapter also discusses a number of fundamental issues for the design of physiological computing system, these include: the inference between physiology and behaviour, how the system represents behaviour, the concept of the biocybernetic control loop and ethical issues.

Question: What is physiological computing?


Multiple String LED Driver With Flexible and High-Performance PWM Dimming Control. The main objectives in driving multiple LED strings include achieving uniform current control and high-performance pulse width modulation (PWM) dimming for all strings. This study proposes a new multiple string LED driver to achieve not only current balance, but also flexible and wide range PWM dimming ratio for each string. A compact single-inductor multiple-output topology is adopted in the driver, accompanied by synchronous integrators and variable dimming frequency, to achieve both high-efficiency and high-performance dimming. By using the proposed variable dimming frequency scheme, high dimming frequency is applied to a string with high dimming ratio, which helps us to maintain the deviation of LED string current in an acceptable range, while low dimming frequency is applied to a string with low dimming ratio, which helps us to achieve rectangular LED current waveform. Meanwhile, the new time multiplexing control scheme automatically optimizes the LED strings' bus voltages, thus minimizes each string's power loss. A three-string LED driver prototype is constructed to validate the effectiveness of the proposed control scheme, where the three strings can have different dimming ratios between 4% and 100%.

Question: How to control multiple strings of LEDs?


Problem-solving methods in artificial intelligence. Well, someone can decide by themselves what they want to do and need to do but sometimes, that kind of person will need some problem solving methods in artificial intelligence references. People with open minded will always try to seek for the new things and information from many sources. On the contrary, people with closed mind will always think that they can do it by their principals. So, what kind of person are you?

Question: What kind of person are you?


The wisdom development scale: further validity investigations.. Researchers are gaining an interest in the concept of wisdom, a more holistic yet often ineffable educational outcome. Models of wisdom abound, but few have rigorously tested measures. This study looks at Brown's (2004a, 2004b) Model of Wisdom Development and its associated measure, the Wisdom Development Scale (WDS; Brown & Greene, 2006). The construct validity, measurement invariance, criterion validity, and reliability of scores from the WDS were assessed with over 3000 participants from two separate groups: one a sample of professionals and the other a sample of college students. Support for construct validity and reliability with these samples was found, along with measurement invariance. Latent means analyses showed predicted discrimination between the groups, and criterion validity evidence, with another measure of collegiate educational outcomes, was found.

Question: What is the wisdom development scale?


A Broadband Center-Fed Circular Patch-Ring Antenna With a Monopole Like Radiation Pattern. A center-fed circular microstrip patch antenna with a coupled annular ring is presented. This antenna has a low profile configuration with a monopole like radiation pattern. Compared to the center-fed circular patch antenna (CPA), the proposed antenna has a large bandwidth and similar radiation pattern. The proposed antenna is fabricated and tested. It resonates at 5.8 GHz, the corresponding impedance bandwidth and gain are 12.8% and 5.7 dBi, respectively. Very good agreement between the measurement and simulation for the return loss and radiation patterns is achieved.

Question: What is a monopole like radiation pattern?


SRP-PHAT methods of locating simultaneous multiple talkers using a frame of microphone array data. Two new methods for locating multiple sound sources using a single segment of data from a large-aperture microphone array are presented. Both methods employ the proven-robust steered response power using the phase transform (SRP-PHAT) as a functional. To cluster the data points into highly probable regions containing global peaks, the first method fits a Gaussian mixture model (GMM), whereas the second one sequentially finds the points with highest SRP-PHAT values that most likely represent different clusters. Then the low-cost global optimization method, stochastic region contraction (SRC), is applied to each cluster to find the global peaks. We test the two methods using real data from five simultaneous talkers in a room with high noise and reverberation. Results are presented and discussed.

Question: SRP-PHAT methods of locating simultaneous multiple talkers using a frame of microphone array data. Two new methods for locating multiple sound sources using a single segment of data from a large-aperture microphone array are presented. Both methods employ the proven-robust steered response power using the phase transform (SRP


Filing, piling, and everything in between: The dynamics of E-mail inbox management. Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e-mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e-mail, and unobtrusively explores the ongoing inbox-management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox-management behaviors in mitigating the causes of e-mail overload, and proposes a continuous index that quantifies one of these inbox-management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.

Question: How do you manage your inbox?


Semisupervised Autoencoder for Sentiment Analysis. In this paper, we investigate the usage of autoencoders in modeling textual data. Traditional autoencoders suffer from at least two aspects: scalability with the high dimensionality of vocabulary size and dealing with task-irrelevant words. We address this problem by introducing supervision via the loss function of autoencoders. In particular, we first train a linear classifier on the labeled data, then define a loss for the autoencoder with the weights learned from the linear classifier. To reduce the bias brought by one single classifier, we define a posterior probability distribution on the weights of the classifier, and derive the marginalized loss of the autoencoder with Laplace approximation. We show that our choice of loss function can be rationalized from the perspective of Bregman Divergence, which justifies the soundness of our model. We evaluate the effectiveness of our model on six sentiment analysis datasets, and show that our model significantly outperforms all the competing methods with respect to classification accuracy. We also show that our model is able to take advantage of unlabeled dataset and get improved performance. We further show that our model successfully learns highly discriminative feature maps, which explains its superior performance.

Question: What is the difference between supervised and unsupervised autoencoders?


GFlink: An In-Memory Computing Architecture on Heterogeneous CPU-GPU Clusters for Big Data. The increasing main memory capacity and the explosion of big data have fueled the development of in-memory big data management and processing. By offering an efficient in-memory parallel execution model which can eliminate disk I/O bottleneck, existing in-memory cluster computing platforms (e.g., Flink and Spark) have already been proven to be outstanding platforms for big data processing. However, these platforms are merely CPU-based systems. This paper proposes GFlink, an in-memory computing architecture on heterogeneous CPU-GPU clusters for big data. Our proposed architecture extends the original Flink from CPU clusters to heterogeneous CPU-GPU clusters, greatly improving the computational power of Flink. Furthermore, we have proposed a programming framework based on Flink's abstract model, i.e., DataSet (DST), hiding the programming complexity of GPUs behind the simple and familiar high-level interfaces. To achieve high performance and good load-balance, an efficient JVM-GPU communication strategy, a GPU cache scheme, and an adaptive locality-aware scheduling scheme for three-stage pipelining execution are proposed. Extensive experiment results indicate that the high computational power of GPUs can be efficiently utilized, and the implementation on GFlink outperforms that on the original CPU-based Flink.

Question: What is the computational power of GPUs?


Arc Flash Calculations for a 1.3-MW Photovoltaic System. When planning for the installation of a 1.3-MW photovoltaic (PV) system and its integration into an existing facility's electrical system, an arc flash study was performed to determine the best protective device settings to minimize the arc flash energy in the PV system and to determine the effect of the additional short-circuit current on arc flash calculations for the existing system. The existing system has distribution at 13.8 kV and two 10-MVA transformers to a 2.4-kV feeder system. The PV system adds the hazard of 500-Vdc subarray collection wiring and terminations. The dc arc flash exposure of PV systems, including those in the inverter enclosures, can be complicated, and this paper outlines the basic data collected for the study, the steps taken to complete the calculations, the methodology used, the results, and the team's learnings.

Question: How to calculate arc flash for a photovoltaic system?


Deep Discriminative Representation Learning with Attention Map for Scene Classification. Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.

Question: What is the center loss?


Smooth contact-aware facial blendshapes transfer. In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.

Question: How to improve the quality of the facial blendshapes synthesized using deformation transfer?


Power Waves and the Scattering Matrix. This paper discusses the physical meaning and properties of the waves defined by v%+ z%Ib ~ = V. – Z,*Ii a%= ,, 2u/Re Z,] 2<1 ReZtl where V, and 1, are the voltage at and the current flowing into the ith port of a junction and Z, is the impedance of the circuit connected to the ith port. The square of the magnitude of these waves is directly related to the exchangeable power of a source and the reflected power. For this reason, in this paper, they are called the power waves. For certain applications where the power relations are of main concern, the power waves are more suitable quantities than the conventional traveling waves. The lossless and reciprocal conditions as well as the frequency characteristics of the scattering matrix are presented. Then, the formula is given for a new scattering matrix when the 2,'s are changed. As an application, the condition under which an amplifier can be matched simultaneously at both input and output ports as well as the condition for the network to be unconditionally stable are given in terms of the scattering matrix components. Also a brief comparison is made between the traveling waves and the power waves.

Question: What is the difference between the power waves and the traveling waves?