title: Point-of-Interest Demand Modeling with Human Mobility Patterns

abstract: Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.


title: Group Blind Digital Signatures: A Scalable Solution to Electronic Cash

abstract: In this paper we construct a practical group blind signature scheme. Our scheme combines the already existing notions of blind signatures and group signatures. It is an extension of Camenisch and Stadler's Group Signature Scheme [5] that adds the blindness property. We show how to use our group blind signatures to construct an electronic cash system in which multiple banks can securely distribute anonymous and untraceable e-cash. Moreover, the identity of the e-cash issuing bank is concealed, which is conceptually novel. The space, time, and communication complexities of the relevant parameters and operations are independent of the group size.


title: Privacy-Aware Smart Metering: A Survey

abstract: The increasing share of renewables creates new challenges for the existing electrical grid. To deal with these challenges, various efforts are being made to transform the existing power grid into a so-called smart grid. Part of this process is the deployment of an advanced metering infrastructure, which provides novel high-frequency two-way communication between consumers and producers. But as useful as the access to high-frequency measurements may be for energy suppliers, this also poses a major threat to the privacy of the customers. In this survey we present approaches to the problem of customer privacy-protection in the smart grid. We show that the privacy problem in smart grids can be further divided into the problems metering for billing and metering for operations. For each of these problems we identify generic approaches to solve them.


title: Automatic visual tracking and firing system for anti aircraft machine gun

abstract: Visual tracking is one of the most important field of computer vision. It has immense number of applications ranging from surveillance to hi-fi military applications. This paper is based on the application developed for automatic visual tracking and fire control system for anti-aircraft machine gun (AAMG). Our system mainly consists of camera, as visual sensor; mounted on a 2D-moving platform attached with 2GHz embedded system through RS-232 and AAMG mounted on the same moving platform. Camera and AAMG are both bore-sighted. Correlation based template matching algorithm has been used for automatic visual tracking. This is the algorithm used in civilian and military automatic target recognition, surveillance and tracking systems. The algorithm does not give robust performance in different environments, especially in clutter and obscured background, during tracking. So, motion and prediction algorithms have been integrated with it to achieve robustness and better performance for real-time tracking. Visual tracking is also used to calculate lead angle, which is a vital component of such fire control systems. Lead is angular correction needed to compensate for the target motion during the time of flight of the projectile, to accurately hit the target. Although at present lead computation is not robust due to some limitation as lead calculation mostly relies on gunner intuition. Even then by the integrated implementation of lead angle with visual tracking and control algorithm for moving platform, we have been able to develop a system which detects tracks and destroys the target of interest.


title: SCADA Systems in the Railway Domain: Enhancing Reliability through Redundant MultipathTCP

abstract: Reliability is a key research issue to promote the role of rail in the transportation system. An important set of critical and vital railway services, such as Traction Power Supply and Supervision Systems, relay currently on modern Supervisory Control and Data Acquisition (SCADA) systems. At the same time, these large scale and networked SCADA systems are deployed over heterogeneous wide area networks. Current strategies to warrant reliability on these systems focus on costly equipment redundancy or retransmission techniques with associated delay penalization. In this paper, we propose a MultiPath strategy with redundant policy to increase reliability in railway SCADA over TCP/IP systems. We built a hybrid simulation tool with real Modbus over TCP/IP equipment and validate our approach comparing the obtained results with three alternative strategies (default TCP/IP, backup and default delivery policy).


title: Hybrid Consensus: Efficient Consensus in the Permissionless Model

abstract: Consensus, or state machine replication is a foundational building block of distributed systems and modern cryptography. Consensus in the classical, "permissioned" setting has been extensively studied in the 30 years of distributed systems literature. Recent developments in Bitcoin and other decentralized cryptocurrencies popularized a new form of consensus in a "permissionless" setting, where anyone can join and leave dynamically, and there is no a-priori knowledge of the number of consensus nodes. So far, however, all known permissionless consensus protocols assume network synchrony, i.e., the protocol must know an upper bound of the network's delay, and transactions confirm slower than this a-priori upper bound. ::: ::: We initiate the study of the feasibilities and infeasibilities of achieving responsiveness in permissionless consensus. In a responsive protocol, the transaction confirmation time depends only on the actual network delay, but not on any a-priori known upper bound such as a synchronous round. Classical protocols in the partial synchronous and asynchronous models naturally achieve responsiveness, since the protocol does not even know any delay upper bound. Unfortunately, we show that in the permissionless setting, consensus is impossible in the asynchronous or partially synchronous models. ::: ::: On the positive side, we construct a protocol called Hybrid Consensus by combining classical-style and blockchain-style consensus. Hybrid Consensus shows that responsiveness is nonetheless possible to achieve in permissionless consensus (assuming proof-of-work) when 1) the protocol knows an upper bound on the network delay; 2) we allow a non-responsive warmup period after which transaction confirmation can become responsive; 3) honesty has some stickiness, i.e., it takes a short while for an adversary to corrupt a node or put it to sleep; and 4) less than 1/3 of the nodes are corrupt. We show that all these conditions are in fact necessary - if only one of them is violated, responsiveness would have been impossible. Our work makes a step forward in our understanding of the permissionless model and its differences and relations to classical consensus.


title: Denial of Service Attacks in Wireless Networks: The Case of Jammers

abstract: The shared nature of the medium in wireless networks makes it easy for an adversary to launch a Wireless Denial of Service (WDoS) attack. Recent studies, demonstrate that such attacks can be very easily accomplished using off-the-shelf equipment. To give a simple example, a malicious node can continually transmit a radio signal in order to block any legitimate access to the medium and/or interfere with reception. This act is called jamming and the malicious nodes are referred to as jammers. Jamming techniques vary from simple ones based on the continual transmission of interference signals, to more sophisticated attacks that aim at exploiting vulnerabilities of the particular protocol used. In this survey, we present a detailed up-to-date discussion on the jamming attacks recorded in the literature. We also describe various techniques proposed for detecting the presence of jammers. Finally, we survey numerous mechanisms which attempt to protect the network from jamming attacks. We conclude with a summary and by suggesting future directions.


Survey on Skin Tone Detection using Color Spaces


title: ProppML: A Complete Annotation Scheme for Proppian Morphologies

abstract: We give a preliminary description of ProppML, an annotation scheme designed to capture all the components of a Proppian-style morphological analysis of narratives. This work represents the first fully complete annotation scheme for Proppian morphologies, going beyond previous annotation schemes such as PftML, ProppOnto, Bod et al., and our own prior work. Using ProppML we have annotated Propp's morphology on fifteen tales (18,862 words) drawn from his original corpus of Russian folktales. This is a significantly larger set of data than annotated in previous studies. This pilot corpus was constructed via double annotation by two highly trained annotators, whose annotations were then combined after discussion with a third highly trained adjudicator, resulting in gold standard data which is appropriate for training machine learning algorithms. Agreement measures calculated between both annotators show very good agreement (F_1>0.75, kappa>0.9 for functions; F_1>0.6 for moves; and F_1>0.8, kappa>0.6 for dramatis personae). This is the first robust demonstration of reliable annotation of Propp's system.


title: Garment Replacement in Monocular Video Sequences

abstract: We present a semi-automatic approach to exchange the clothes of an actor for arbitrary virtual garments in conventional monocular video footage as a postprocess. We reconstruct the actor's body shape and motion from the input video using a parameterized body model. The reconstructed dynamic 3D geometry of the actor serves as an animated mannequin for simulating the virtual garment. It also aids in scene illumination estimation, necessary to realistically light the virtual garment. An image-based warping technique ensures realistic compositing of the rendered virtual garment and the original video. We present results for eight real-world video sequences featuring complex test cases to evaluate performance for different types of motion, camera settings, and illumination conditions.


title: Comprehensive study of single feeding techniques on circularly polarized microstrip antenna radiation pattern

abstract: This paper presents a novel design of circularly polarized near circular microstrip antenna using 3 different single feeding techniques. In order to achieve Circular polarization with single feed perturbation segments are used which makes it more difficult to optimize the antenna parameters. The results of the 3 feeding techniques are compared in terms of axial ratio and return loss.


title: Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine

abstract: Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).


title: An active battery cell balancing topology without using external energy storage elements

abstract: Cell balancing circuits are important to extent life-cycle of batteries and to extract maximum power from the batteries. A lot of power electronics topology has been tried for cell balancing in the battery packages. Active cell balancing topologies transfer energy from the cells showing higher performance to the cells showing lower performance to balance voltages across the cells of the battery using energy storage elements like combination of inductor-capacitor or transformer-capacitor or switched capacitor or switched inductor. In this study an active balancing topology without using any energy storage element is proposed. The idea is similar to the switched capacitor topology in which a capacitor or capacitor banks is switched across the cells of battery to balance the voltages. Since a basic battery cell model includes capacitance because of capacitive effect of the cell, this capacitive effect can be utilized in cell balancing. Hence the equalizer capacitors in switched capacitor topology can be eliminated and the cells of battery can be switched with each other. This allows faster energy transfer and hence results in quick equalization. The proposed topology removes the need of extra energy storage elements like capacitors which frequently fails in power electronic circuits, reduces the losses inserted by extra energy storage elements and cost and volume of the circuits and simplifies control algorithm. The proposed balancing circuit can be implemented according to the application requirement. The proposed topology is simulated in MATLAB/Simulink environment and showed better results in terms of balancing speed in comparison to switched capacitor topologies.


title: Affect, generalization, and the perception of risk.

abstract: Experimental manipulations of affect induced by a brief newspaper report of a tragic event produced a pervasive increase in subjects' estimates of the frequency of many risks and other undesirable events. Contrary to expectation, the effect was independent of the similarity between the report arid the estimated risk. An account of a fatal stabbing did not increase the frequency estimate of a closely related risk, homicide, more than the estimates of unrelated risks such as natural hazards. An account of a happy event that created positive affect produced a comparable global decrease in judged frequency of risks. As a society, we have never been more concerned with the assessment, the management, and the regulation of risk. Because public reaction to hazards from pesticides, nuclear power, or food additives appears to influence the regulation and management of these technologies, it is important to understand how the lay person perceives and evaluates risks. This is particularly true for hazards such as terrorism, nuclear power, or genetic engineering for which the available statistical data are very limited and where the assessments of the risks are based on subjective and intuitive judgments. Indeed, psychologists and other researchers have shown increasing interest in the manner in which people perceive and estimate the severity of various risks. Lichtenstein, Slovic, Fischhoff, Layman, and Combs (1978) asked lay people to estimate the number of deaths per year that are due to various hazards. These investigators found that the subjective estimates were consistent across several methods of elicitation and correlated reasonably well (median r = .7) with actuarial estimates of frequency. A comparison of objective and subjective estimates revealed two


title: An area efficient 64-bit square root carry-select adder for low power applications

abstract: The carry-select method has deemed to be a good compromise between cost and performance in carry propagation adder design. However, the conventional carry-select adder (CSL) is still area-consuming due to the dual ripple-carry adder structure. The excessive area overhead makes CSL relatively unattractive but this has been circumvented by the use of an add-one circuit introduced recently. In this paper, an area efficient square root CSL scheme based on a new first zero detection logic is proposed. The proposed CSL witnesses a notable power-delay and area-delay performance improvement by virtue of proper exploitation of logic structure and circuit technique. For 64-bit addition, our proposed CSL requires 44% fewer transistors than the conventional one. Simulation results indicate that our proposed CSL can complete 64-bit addition in 1.50 ns and dissipate only 0.35 mW at 1.8V in TSMC 0.18 /spl mu/m CMOS technology.


title: Feed-Net: Fully End-to-End Dehazing

abstract: This paper proposes an image dehazing model built with a fully convolutional neural network (CNN), called Fully End-to-End Dehazing Network (FEED-Net). In contrast to estimate the transmission map and the atmospheric light separately as most previous deep learning methods, FEED-Net recovers the hazy-free image directly from a hazy image via a light-weight CNN. In addition, we introduce contextual information into dehazing via dilated convolution and use dense skip connection for feature fusion, which makes end-to-end dehazing possible. Experimental results show our method outperforms the state-of-the-art algorithms on both synthetic dataset and real-world images.


title: Continuous Fraud Detection in Enterprise Systems through Audit Trail Analysis

abstract: Enterprise systems, real time recording and real time reporting pose new and significant challenges to the accounting and auditing professions. This includes developing methods and tools for continuous assurance and fraud detection. In this paper we propose a methodology for continuous fraud detection that exploits security audit logs, changes in master records and accounting audit trails in enterprise systems. The steps in this process are: (1) threat monitoring-surveillance of security audit logs for ‘red flags’, (2) automated extraction and analysis of data from audit trails, and (3) using forensic investigation techniques to determine whether a fraud has actually occurred. We demonstrate how mySAP, an enterprise system, can be used for audit trail analysis in detecting financial frauds, Afterwards we use a case study of a suspected fraud to illustrate how to implement the methodology.


title: Performance analysis of Kalman filter, fuzzy Kalman filter and wind driven optimized Kalman filter for tracking applications

abstract: State space estimation approaches are one of the finest approaches to predict the future behavior of real time systems like tracking, navigation, guidance systems etc. Kalman filter is employed for the estimation of system dynamics. It is very important to tune the parameters observation noise (measurement noise) and plant noise (process noise) for better estimation of dynamics of the system especially in tracking applications. Tuning of measurement noise and plant noise can be done through Nature Inspired Optimization techniques. Fuzzy logic is another approach to tune these parameters. In this paper the authors tried to estimate target motion parameters using Kalman filter and the performance is analyzed using the tuning methods Wind Driven Optimization (WDO) and fuzzy logic.


TOWARD AN ECOLOGICAL THEORY OF ADAPTATION AND AGING


title: Incremental Parsing Models for Dialog Task Structure

abstract: In this paper, we present an integrated model of the two central tasks of dialog management: interpreting user actions and generating system actions. We model the interpretation task as a classification problem and the generation task as a prediction problem. These two tasks are interleaved in an incremental parsing-based dialog model. We compare three alternative parsing methods for this dialog model using a corpus of human-human spoken dialog from a catalog ordering domain that has been annotated for dialog acts and task/subtask information. We contrast the amount of context provided by each method and its impact on performance.


title: Effect of Carbonate Chemistry Alteration on the Early Embryonic Development of the Pacific Oyster (Crassostrea gigas)

abstract: Ocean acidification, due to anthropogenic CO2 absorption by the ocean, may have profound impacts on marine biota. Calcareous organisms are expected to be particularly sensitive due to the decreasing availability of carbonate ions driven by decreasing pH levels. Recently, some studies focused on the early life stages of mollusks that are supposedly more sensitive to environmental disturbances than adult stages. Although these studies have shown decreased growth rates and increased proportions of abnormal development under low pH conditions, they did not allow attribution to pH induced changes in physiology or changes due to a decrease in aragonite saturation state. This study aims to assess the impact of several carbonate-system perturbations on the growth of Pacific oyster (Crassostrea gigas) larvae during the first 3 days of development (until shelled D-veliger larvae). Seawater with five different chemistries was obtained by separately manipulating pH, total alkalinity and aragonite saturation state (calcium addition). Results showed that the developmental success and growth rates were not directly affected by changes in pH or aragonite saturation state but were highly correlated with the availability of carbonate ions. In contrast to previous studies, both developmental success into viable D-shaped larvae and growth rates were not significantly altered as long as carbonate ion concentrations were above aragonite saturation levels, but they strongly decreased below saturation levels. These results suggest that the mechanisms used by these organisms to regulate calcification rates are not efficient enough to compensate for the low availability of carbonate ions under corrosive conditions.


title: Spectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey

abstract: Dynamic spectrum access and cognitive radio (CR) are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. They have been receiving tremendous amount of well-deserved attention in the literature. One of the challenging problems in dynamic spectrum access for cognitive radios is to sense the spectrum in wide band regime of heterogeneous networks to identify the spectrum opportunities. Our main goal in this paper is to present the state-of-the-art research results in signal processing techniques for spectrum sensing in CR network to make automatic and wise decision by unlicensed secondary users to adapt their transmission parameters according to their operating RF environment and respect the primary users. Specifically, we present dynamic spectrum sharing models for spectrum access and signal processing techniques for spectrum sensing by categorizing them into five basic groups. We also present the comparison of different signal processing techniques from different perspectives including networking metrics for the evaluation of the methods e.g. how accurate and how fast can sense spectrum utilization given the various methods and how this will affect networking functionality, and pros and cons of all the methods in the context of networking systems. We also present the status, research challenges and perspectives of signal processing/detection techniques for spectrum sensing in cognitive radio networks.


title: IT-user-aligned Business Model Innovation (ITUA) in the Sharing Economy: A Dynamic Capabilities Perspective

abstract: We introduce the sharing economy as an IT-enabled phenomenon that is distinct from existing socioeconomic modes of interaction. While research on the consumer perspective is growing, studies examining the complementary organizational perspective seem to be lagging behind. Our study therefore aims to investigate the firm stance by analyzing how successful sharing economy organizations manage their business models. We choose the business model as a unit of analysis, as it allows incorporating IT as an integral component of organizational activities into our research. Our case study uses multiple data sources and elicitation methods and provides detailed rigorously collected information that is not typical for case studies. Our findings suggest that consumer needs in the sharing economy lead to conflicting requirements for business model configuration: innovativeness and acceptability. To carefully balance the two, firms need to constantly integrate novel technologies into their business model while not deviating too heavily from already existing models. We term our emergent theory ITuser-aligned business model innovation (ITUA). It offers the unique contribution of conceptualizing the process of business model innovation as a dynamic capability. Our study further highlights the fundamental role of IT in enabling a potential reconciliation of economic and societal value creation.


title: Reconfigurable and Tunable S-Shaped Split-Ring Resonators and Application in Band-Notched UWB Antennas

abstract: This paper proposes a compact reconfigurable (bandstop/bandpass) and frequency-tunable structure based on S-shaped split-ring resonators (S-SRRs). It is known that an S-SRR coupled to a coplanar waveguide (CPW) provides a stopband in the transmission characteristic of the line. It is shown here that this behavior of the S-SRR can be switched between fundamental resonance and second harmonic response by introduction of a p-i-n diode in the center segment of the S-SRR. Alternatively, if the S-SRR is loaded with a varactor diode instead of a switch, the frequency of the stopband can be continuously tuned from the S-SRRs fundamental resonance frequency to its second harmonic. Furthermore, it is shown that if a pair of shunt p-i-n diodes are introduced across the slots of the host CPW, the structure can be reconfigured from a bandstop to a bandpass structure. Thus, the proposed resonator structure can be used as the building block of reconfigurable (bandstop/bandpass) filters with tunable operating frequency. Finally, to demonstrate a practical application of the proposed structure, an ultrawideband antenna with a tunable band notch is designed and experimentally validated.


title: Restricted Boltzmann machines for collaborative filtering

abstract: Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies. We present efficient learning and inference procedures for this class of models and demonstrate that RBM's can be successfully applied to the Netflix data set, containing over 100 million user/movie ratings. We also show that RBM's slightly outperform carefully-tuned SVD models. When the predictions of multiple RBM models and multiple SVD models are linearly combined, we achieve an error rate that is well over 6% better than the score of Netflix's own system.


title: Programmable self-assembly in a thousand-robot swarm

abstract: When individuals swarm, they must somehow communicate to direct collective motion. Swarms of robots need to deal with outliers, such as robots that move more slowly than the rest. Rubenstein et al. created a large swarm of programmed robots that can form collaborations using only local information. The robots could communicate only with nearby members, within about three times their diameter. They were able to assemble into complex preprogrammed shapes. If the robots' formation hit snags when they bumped into one another or because of an outlier, additional algorithms guided them to rectify their collective movements. ::: ::: Science , this issue p. [795][1] ::: ::: [1]: /lookup/volpage/345/795?iss=6198


title: Audio and face video emotion recognition in the wild using deep neural networks and small datasets

abstract: This paper presents the techniques used in our contribution to Emotion Recognition in the Wild 2016’s video based sub-challenge. The purpose of the sub-challenge is to classify the six basic emotions (angry, sad, happy, surprise, fear & disgust) and neutral. Compared to earlier years’ movie based datasets, this year’s test dataset introduced reality TV videos containing more spontaneous emotion. Our proposed solution is the fusion of facial expression recognition and audio emotion recognition subsystems at score level. For facial emotion recognition, starting from a network pre-trained on ImageNet training data, a deep Convolutional Neural Network is fine-tuned on FER2013 training data for feature extraction. The classifiers, i.e., kernel SVM, logistic regression and partial least squares are studied for comparison. An optimal fusion of classifiers learned from different kernels is carried out at the score level to improve system performance. For audio emotion recognition, a deep Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) is trained directly using the challenge dataset. Experimental results show that both subsystems individually and as a whole can achieve state-of-the art performance. The overall accuracy of the proposed approach on the challenge test dataset is 53.9%, which is better than the challenge baseline of 40.47% .


title: Volume visualization using sparse nonparametric support vector machines and harmoniccolors

abstract: In Direct Volume Rendering (DVR), the Transfer Function (TF) to map voxel values to color and opacity values is difficult to obtain. Existing TF design tools are complex and non-intuitive for the end user, who is more likely to be a medical professional than an expert in image processing. In this paper, we propose a volume visualization method where the user directly works on the volume data to simply select the parts he/she would like to visualize. The user's work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, all the voxels are classified using our Sparse Nonparametric Support Vector Machine (SN-SVM) classifier, which combines both local and near-global distributional information of the training data to obtain accurate results. The voxel classes are then mapped to color and opacity values using the concept of harmonic colors, which provides easily distinguishable and aesthetically pleasing results. Experimental results on several benchmark datasets show the effectiveness of the proposed method.


title: Evaluation of a Fully Automatic Cooperative Persuasive Dialogue System

abstract: In this paper, we construct and evaluate a fully automated text-based cooperative persuasive dialogue system, which is able to persuade the user to take a specific action while maintaining user satisfaction. In our previous works, we created a dialogue management module for cooperative persuasive dialogue (Hiraoka et al., Reinforcement learning of cooperative persuasive dialogue policies using framing, Proceedings of international conference on computational linguistics (COLING), 2014), but only evaluated it in a wizard-of-Oz setting, as we did not have the capacity for natural language generation (NLG) and natural language understanding (NLU). In this work, the main technical contribution is the design of the NLU and the NLG modules which allows us to remove this bottleneck and create the first fully automatic cooperative persuasive dialogue system. Based on this system, we performed an evaluation with real users. Experimental results indicate that the learned policy is able to effectively persuade the users: the reward of the proposed model is much higher than baselines and almost the same as a dialogue manager controlled by a human. This tendency is almost the same as our previous evaluation using a wizard-of-Oz framework (Hiraoka et al., Reinforcement learning of cooperative persuasive dialogue policies using framing, Proceedings of international conference on computational linguistics (COLING), 2014), demonstrates that the proposed NLU and NLG modules are effective for cooperative persuasive dialogue.


title: Bolt: Accelerated Data Mining with Fast Vector Compression

abstract: Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors. We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x. Because it can encode over 2GB of vectors per second, it makes vector quantization cheap enough to employ in many more circumstances. For example, using our technique to compute approximate dot products in a nested loop can multiply matrices faster than a state-of-the-art BLAS implementation, even when our algorithm must first compress the matrices. In addition to showing the above speedups, we demonstrate that our approach can accelerate nearest neighbor search and maximum inner product search by over 100x compared to floating point operations and 10x compared to other vector quantization methods. Our approximate Euclidean distance and dot product computations are not only faster than those of related algorithms with slower encodings, but also faster than Hamming distance computations, which have direct hardware support on the tested platforms. We also assess the errors of our algorithm's approximate distances and dot products, and find that it is competitive with existing, slower vector quantization algorithms.


title: A Spellchecker for Dyslexia

abstract: Poor spelling is a challenge faced by people with dyslexia throughout their lives. Spellcheckers are therefore a crucial tool for people with dyslexia, but current spellcheckers do not detect real-word errors, which are a common type of errors made by people with dyslexia. Real-word errors are spelling mistakes that result in an unintended but real word, for instance, form instead of from. Nearly 20% of the errors that people with dyslexia make are real-word errors. In this paper, we introduce a system called Real Check that uses a probabilistic language model, a statistical dependency parser and Google n-grams to detect real-world errors. We evaluated Real Check on text written by people with dyslexia, and showed that it detects more of these errors than widely used spellcheckers. In an experiment with 34 people (17 with dyslexia), people with dyslexia corrected sentences more accurately and in less time with Real Check.


title: Determining Uses and Gratifications for Indian Internet Users

abstract: Uses and gratification theory aids in the assessment of how audiences use a particular medium and the gratifications they derive from that use. In this paper this theory has been applied to derive Internet uses and gratifications for Indian Internet users. This study proceeds in four stages. First, six first-order gratifications namely self development, wide exposure, user friendliness, relaxation, career opportunities, and global exchange were identified using an exploratory factor analysis. Then the first order gratifications were subjected to first-order confirmatory factor analysis. Third, using second-order confirmatory factor analysis three types of second-order gratifications were obtained, namely process gratifications, content gratifications and social gratifications. Finally, with the use of t-tests the study has shown that males and females differ significantly on the gratification factors “self development”, “user friendliness”, “wide exposure” and “relaxation.” The intended audience consists of masters’ level students and doctoral students who want to learn exploratory factor analysis and confirmatory factor analysis. This case study can also be used to teach the basics of structural equation modeling using the software AMOS.


title: Multi-document abstractive summarization using chunk-graph and recurrent neural network

abstract: Automatic multi-document abstractive summarization system is used to summarize several documents into a short one with generated new sentences. Many of them are based on word-graph and ILP method, and lots of sentences are ignored because of the heavy computation load. To reduce computation and generate readable and informative summaries, we propose a novel abstractive multi-document summarization system based on chunk-graph (CG) and recurrent neural network language model (RNNLM). In our approach, A CG which is based on word-graph is constructed to organize all information in a sentence cluster, CG can reduce the size of graph and keep more semantic information than word-graph. We use beam search and character-level RNNLM to generate readable and informative summaries from the CG for each sentence cluster, RNNLM is a better model to evaluate sentence linguistic quality than n-gram language model. Experimental results show that our proposed system outperforms all baseline systems and reach the state-of-art systems, and the system with CG can generate better summaries than that with ordinary word-graph.


title: Adaptive Semantics-Aware Malware Classification

abstract: Automatic malware classification is an essential improvement over the widely-deployed detection procedures using manual signatures or heuristics. Although there exists an abundance of methods for collecting static and behavioral malware data, there is a lack of adequate tools for analysis based on these collected features. Machine learning is a statistical solution to the automatic classification of malware variants based on heterogeneous information gathered by investigating malware code and behavioral traces. However, the recent increase in variety of malware instances requires further development of effective and scalable automation for malware classification and analysis processes. ::: ::: In this paper, we investigate the topic modeling approaches as semantics-aware solutions to the classification of malware based on logs from dynamic malware analysis. We combine results of static and dynamic analysis to increase the reliability of inferred class labels. We utilize a semi-supervised learning architecture to make use of unlabeled data in classification. Using a nonparametric machine learning approach to topic modeling we design and implement a scalable solution while maintaining advantages of semantics-aware analysis. The outcomes of our experiments reveal that our approach brings a new and improved solution to the reoccurring problems in malware classification and analysis.


title: Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

abstract: Research in automatic analysis of sign language has largely focused on recognizing the lexical (or citation) form of sign gestures as they appear in continuous signing, and developing algorithms that scale well to large vocabularies. However, successful recognition of lexical signs is not sufficient for a full understanding of sign language communication. Nonmanual signals and grammatical processes which result in systematic variations in sign appearance are integral aspects of this communication but have received comparatively little attention in the literature. In this survey, we examine data acquisition, feature extraction and classification methods employed for the analysis of sign language gestures. These are discussed with respect to issues such as modeling transitions between signs in continuous signing, modeling inflectional processes, signer independence, and adaptation. We further examine works that attempt to analyze nonmanual signals and discuss issues related to integrating these with (hand) sign gestures. We also discuss the overall progress toward a true test of sign recognition systems?dealing with natural signing by native signers. We suggest some future directions for this research and also point to contributions it can make to other fields of research. Web-based supplemental materials (appendicies) which contain several illustrative examples and videos of signing can be found at www.computer.org/publications/dlib.


title: Link Prediction across Heterogeneous Social Networks: A Survey

abstract: Online social networks have gained great success in recent years. Some online social networks only involving users and social links among users can be represented as homogeneous networks. Meanwhile, some other social networks containing abundant information, which include multiple kinds of nodes and complex relationships, can be denoted as heterogeneous networks. Predicting the missing links or links that will be formed in the future based on a snapshot of social networks is formally defined as the link prediction problem. Link prediction problems have extensive applications in real-world social networks and many concrete social services can be cast as link prediction tasks, e.g., friend and location recommendations can all be solved as the problem of predicting social links among users and the location links between users and locations. Link prediction problems have been an important research topic for many years and a large number of different methods have been proposed so far. This article summaries the existing link prediction methods for both homogeneous and heterogeneous networks, which include various unsupervised link predicators, random walk based link prediction methods, methods based on matrix factorization techniques, supervised link prediction methods and meta paths based link prediction methods. Meanwhile, as proposed in recent works, people are usually involved in multiple social networks simultaneously nowadays and networks sharing common users are formally defined as the aligned networks. In this article, we will also introduce the latest progress of link prediction problems across multiple aligned heterogeneous networks. The link prediction problems across aligned networks can include anchor link prediction problem and link transfer across aligned heterogeneous networks. We will introduce the newly proposed methods to solve these problems in details and, finally, we will conclude this survey with a discussion about the future link prediction research works.


title: Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

abstract: While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.


title: Kocuria sediminis sp. nov., isolated from a marine sediment sample

abstract: A Gram-positive, pinkish-orange pigmented, coccoid strain, FCS-11(T) was isolated from a marine sediment sample taken from Kochi fort area, Kerala, India and subjected to polyphasic taxonomic study. The 16S rRNA gene sequence of the strain was determined and the results of 16S rRNA gene sequence analysis showed that the strain FCS-11(T) should be assigned to the genus Kocuria. The chemotaxonomic data supported this taxonomic placement i.e. menaquinones MK-7(H(2)), MK-8(H(2)) and MK-9(H(2)); major fatty acids anteiso C15:0 and iso-C15:0 and phosphatidylglycerol (PG) and diphosphatidylglycerol (DPG) as major polar lipids. Further phylogenetic analysis of the 16S rRNA gene sequence confirmed that the strain FCS-11(T) belonged to the genus Kocuria and is closely related to Kocuria turfanensis MTCC 10790(T) (99.4%) followed by Kocuria polaris MTCC 3702(T) (98.2%), Kocuria rosea MTCC 2522(T) (98.2%), Kocuria flava MTCC 10971(T) (98.2%), Kocuria aegyptia MTCC 10791(T) (98.0%), Kocuria himachalensis MTCC 7020(T) (97.5%) and Kocuria atrinae MTCC 10972(T) (97.1%). However, the DNA-DNA hybridisation values obtained between strain FCS-11(T) and other related strains were well below the threshold that is required for the proposal of a novel species. The G+C content of the genomic DNA was 60.7 mol%. The phenotypic and genotypic data showed that the strain FCS-11(T) merits the recognition as a representative of a novel species of the genus Kocuria. It is proposed that the isolate should be classified in the genus Kocuria as a novel species, Kocuria sediminis sp. nov. The type strain is FCS-11(T) (= MTCC 10969(T) = JCM 17929(T)).


Chapter 2 The Impact of Culture on Neuropsychological Test Performance


title: A comparison of the steroidogenic acute regulatory protein-related lipid transfer (START) domain-containing 6 on the brain and testes between young and aged rats

abstract: The START domain-containing 6 (StarD6) was originally reported to play a role during male germ cell maturation. We have since reported on StarD6 in the developing hypothyroid rat brain. Therefore, we investigated qualitative and quantitative changes of StarD6 in the aging rat brain and testes of male Sprague-Dawley rats. Serum testosterone levels decreased with aging and total protein levels of StarD6 in the testes decreased. While the immunolocalization of StarD6 in the spermatocytes decreased, cytoplasmic localization appeared in the aged testes. Compared with young rats, aged rats showed decreased StarD6 in the cerebrum and cerebellum without changes in immunolocalization in the cortical neurons of the cerebral cortex and Purkinje cells of the cerebellar cortex. Aged rats also showed increases in StarD6 in the hippocampus with changes in its immunolocalization from the Stratum pyramidale to the Stratum radiatum and Stratum lacunosum-moleculare. Taken together, StarD6 decreased with aging in the testes, which implies that StarD6 might play a role in impaired spermatogenesis in the aged rat. StarD6 decreased in the cerebrum and the cerebellum, but slightly increased in the hippocampus, which suggests that StarD6 might also play a role for neurosteroidogenesis in the hippocampus of aged rats.


title: An application-level solution for the TCP-incast problem in data center networks

abstract: Data centers have become very popular for storing large volumes of data. In particular, companies like Amazon, Google, and Yahoo! routinely use data centers for storage, Web search, and large-scale computations. The main characteristics of a data center network are high-speed links, low propagation delays, and limited-size switch buffers. In addition, the data for a given client application are usually striped (spread) over many servers, for increased reliability and performance (i.e., parallelism). Recent research efforts have resulted in several architectures of data centers [1].


title: An empirical study of the mechanisms of mindfulness in a mindfulness-based stress reduction program

abstract: S. L. Shapiro and colleagues (2006) have described a testable theory of the mechanisms of mindfulness and how it affects positive change. They describe a model in which mindfulness training leads to a fundamental change in relationship to experience (reperceiving), which leads to changes in self-regulation, values clarification, cognitive and behavioral flexibility, and exposure. These four variables, in turn, result in salutogenic outcomes. Analyses of responses from participants in a mindfulness-based stress-reduction program did not support the mediating effect of changes in reperceiving on the relationship of mindfulness with those four variables. However, when mindfulness and reperceiving scores were combined, partial support was found for the mediating effect of the four variables on measures of psychological distress. Issues arising in attempts to test the proposed theory are discussed, including the description of the model variables and the challenges to their assessment. © 2009 Wiley Periodicals, Inc. J Clin Psychol 65: 1–14, 2009.


title: Ultra-broadband GaN power amplifier utilizing planar Guanella transformer

abstract: In this work, the design concept of a highly efficient and ultra-broadband GaN power amplifier is presented. The amplifier uses a novel concept for realizing the impedance matching networks with multi octave bandwidth. These matching networks are based on planar, broadside coupled micro-strip lines that are interconnected to a 4:1 Guanella impedance transformer. To achieve an even mode impedance as high as possible, the ground metallization underneath the coupled lines is removed. A high even mode impedance is the key for achieving wide bandwidth. The designed amplifier, deploying a commercial 25 W GaN HEMT from CREE™ achieved a CW output power of more than 42 dBm over a frequency range from 0.4 to 2.8 GHz with a minimum PAE of 45% over the whole bandwidth and peak PAE-values of more than 65%.


title: Neuro-cognitive mechanisms of conscious and unconscious visual perception: From a plethora of phenomena to general principles

abstract: Psychological and neuroscience approaches have promoted much progress in elucidating the cognitive and neural mechanisms that underlie phenomenal visual awareness during the last decades. In this article, we provide an overview of the latest research investigating important phenomena in conscious and unconscious vision. We identify general principles to characterize conscious and unconscious visual perception, which may serve as important building blocks for a unified model to explain the plethora of findings. We argue that in particular the integration of principles from both conscious and unconscious vision is advantageous and provides critical constraints for developing adequate theoretical models. Based on the principles identified in our review, we outline essential components of a unified model of conscious and unconscious visual perception. We propose that awareness refers to consolidated visual representations, which are accessible to the entire brain and therefore globally available. However, visual awareness not only depends on consolidation within the visual system, but is additionally the result of a post-sensory gating process, which is mediated by higher-level cognitive control mechanisms. We further propose that amplification of visual representations by attentional sensitization is not exclusive to the domain of conscious perception, but also applies to visual stimuli, which remain unconscious. Conscious and unconscious processing modes are highly interdependent with influences in both directions. We therefore argue that exactly this interdependence renders a unified model of conscious and unconscious visual perception valuable. Computational modeling jointly with focused experimental research could lead to a better understanding of the plethora of empirical phenomena in consciousness research.


The Quest to Replace Passwords : a Framework for Comparative Evaluation of Web Authentication Schemes


title: Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic

abstract: Rapidly-exploring random trees (RRTs) are pop- ular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only inefficient but also inconsistent with their single-query nature. For problems seeking to minimize path length, the subset of states that can improve a solution can be described by a prolate hyperspheroid. We show that unless this subset is sam- pled directly, the probability of improving a solution becomes arbitrarily small in large worlds or high state dimensions. In this paper, we present an exact method to focus the search by directly sampling this subset. The advantages of the presented sampling technique are demonstrated with a new algorithm, Informed RRT*. This method retains the same probabilistic guarantees on complete- ness and optimality as RRT* while improving the convergence rate and final solution quality. We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms. We show exper- imentally that it outperforms RRT* in rate of convergence, final solution cost, and ability to find difficult passages while demonstrating less dependence on the state dimension and range of the planning problem.


title: Collaboratively Training Sentiment Classifiers for Multiple Domains

abstract: We propose a collaborative multi-domain sentiment classification approach to train sentiment classifiers for multiple domains simultaneously. In our approach, the sentiment information in different domains is shared to train more accurate and robust sentiment classifiers for each domain when labeled data is scarce. Specifically, we decompose the sentiment classifier of each domain into two components, a global one and a domain-specific one. The global model can capture the general sentiment knowledge and is shared by various domains. The domain-specific model can capture the specific sentiment expressions in each domain. In addition, we extract domain-specific sentiment knowledge from both labeled and unlabeled samples in each domain and use it to enhance the learning of domain-specific sentiment classifiers. Besides, we incorporate the similarities between domains into our approach as regularization over the domain-specific sentiment classifiers to encourage the sharing of sentiment information between similar domains. Two kinds of domain similarity measures are explored, one based on textual content and the other one based on sentiment expressions. Moreover, we introduce two efficient algorithms to solve the model of our approach. Experimental results on benchmark datasets show that our approach can effectively improve the performance of multi-domain sentiment classification and significantly outperform baseline methods.


title: SASI: A New Ultralightweight RFID Authentication Protocol Providing Strong Authentication and Strong Integrity

abstract: As low-cost RFIDs become more and more popular, it is imperative to design ultralightweight RFID authentication protocols to resist all possible attacks and threats. However, all of the previous ultralightweight authentication schemes are vulnerable to various attacks. In this paper, we propose a new ultralightweight RFID authentication protocol that provides strong authentication and strong integrity protection of its transmission and of updated data. The protocol requires only simple bit-wise operations on the tag and can resist all the possible attacks. These features make it very attractive to low-cost RFIDs and very low-cost RFIDs.


title: A Compact and Broadband 90-Degree Waveguide Twist Transformer for Integrated Waveguide Subsystems

abstract: A novel 90-degree waveguide twist is presented. The design features compactness by utilizing a central triple-mode quarter-wave transformer region between input and output waveguides of different cross sections. Return-loss values in excess of 30 dB are obtained over an operating bandwidth close to 20 percent. Larger bandwidths are obtained by using additional quarter-wave steps preceding and/or succeeding the central transformer region. Several different design examples at 4, 6, 19 and 26 GHz are presented for this unique 90-degree twist transformer approach. Experimental verification is provided in the 6 and 19 GHz range.


title: Marketing the academic library with online social network advertising

abstract: Purpose – The purpose of this paper is to investigate the relative effectiveness of different forms of Facebook advertising in promoting the collections and services of academic libraries.Design/methodology/approach – The results of several Facebook advertising campaigns conducted by an academic library in Hong Kong are presented and analyzed. Statistics were gathered from the advertising application integrated into the social networking platform. Conclusions are drawn based on a comparison of the performance metrics of the different advertising approaches that were employed.Findings – In line with an earlier study, the results provide further support for the view that social network advertising has the potential to be a cost‐effective means of marketing academic libraries. It was also found that advertising strategies that draw upon the social features of Facebook were more effective than traditional approaches.Research limitations/implications – Since only the experience of a single institution is consi...


title: QoS for Mobile WiMAX Networks: Call Admission Control and Bandwidth Allocation

abstract: A total QoS solution including dynamic call admission control scheme and bandwidth allocation algorithm is proposed for IEEE 802.16e mobile WiMAX. In this paper, the relationship between the channel utilization, the dropping and blocking probability versus traffic loads are investigated. The proposed WiMAX scheme supports voice, data and multimedia services with differentiated QoS.


title: Identifying user habits through data mining on call data records

abstract: In this paper we propose a frameworks for identifying patterns and regularities in the pseudo-anonymized Call Data Records (CDR) pertaining a generic subscriber of a mobile operator. We face the challenging task of automatically deriving meaningful information from the available data, by using an unsupervised procedure of cluster analysis and without including in the model any a priori knowledge on the applicative context. Clusters mining results are employed for understanding users' habits and to draw their characterizing profiles. We propose two implementations of the data mining procedure; the first is based on a novel system for clusters and knowledge discovery called LD-ABCD, capable of retrieving clusters and, at the same time, to automatically discover for each returned cluster the most appropriate dissimilarity measure (local metric). The second approach instead is based on PROCLUS, the well-know subclustering algorithm. The dataset under analysis contains records characterized only by few features and, consequently, we show how to generate additional fields which describe implicit information hidden in data. Finally, we propose an effective graphical representation of the results of the data-mining procedure, which can be easily understood and employed by analysts for practical applications.


title: Reckless Behavior in Adolescence: A Developmental Perspective

abstract: Abstract A developmental theory of reckless behavior among adolescents is presented, in which sensation seeking and adolescent egocentrism are especially prominent factors. Findings from studies of automobile driving, sex without contraception, illegal drug use, and minor criminal activity are presented in evidence of this. The influence of peers is then discussed and reinterpreted in the light of sensation seeking and adolescent egocentrism. Socialization influences are considered in interaction with sensation seeking and adolescent egocentrism, and the terms narrow and broad socialization are introduced. Factors that may be responsible for the decline of reckless behavior with age are discussed.


title: Bayesian Blind Deconvolution with General Sparse Image Priors

abstract: We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.


title: Structure preserving embedding

abstract: Structure Preserving Embedding (SPE) is an algorithm for embedding graphs in Euclidean space such that the embedding is low-dimensional and preserves the global topological properties of the input graph. Topology is preserved if a connectivity algorithm, such as k-nearest neighbors, can easily recover the edges of the input graph from only the coordinates of the nodes after embedding. SPE is formulated as a semidefinite program that learns a low-rank kernel matrix constrained by a set of linear inequalities which captures the connectivity structure of the input graph. Traditional graph embedding algorithms do not preserve structure according to our definition, and thus the resulting visualizations can be misleading or less informative. SPE provides significant improvements in terms of visualization and lossless compression of graphs, outperforming popular methods such as spectral embedding and Laplacian eigen-maps. We find that many classical graphs and networks can be properly embedded using only a few dimensions. Furthermore, introducing structure preserving constraints into dimensionality reduction algorithms produces more accurate representations of high-dimensional data.


title: Design Principles for a Family of Direct-Drive Legged Robots

abstract: This letter introduces Minitaur, a dynamically running and leaping quadruped, which represents a novel class of direct-drive (DD) legged robots. We present a methodology that achieves the well-known benefits of DD robot design (transparency, mechanical robustness/efficiency, high-actuation bandwidth, and increased specific power), affording highly energetic behaviors across our family of machines despite severe limitations in specific force. We quantify DD drivetrain benefits using a variety of metrics, compare our machines' performance to previously reported legged platforms, and speculate on the potential broad-reaching value of “transparency” for legged locomotion.


title: Soft Combination for Cooperative Spectrum Sensing in Fading Channels

abstract: In this paper, we study the distributed energy-based detectors for spectrum sensing in cognitive radio networks. We assume that the sensing channel includes both small-scale and large-scale fading. The small-scale fading is modeled as Nakagami- $m$ and independent for different cooperating cognitive users, while the large-scale fading is assumed to be known (or can be estimated) by the cognitive users, due to their slowly changing nature. Furthermore, we assume that the channel gains are constant in one observation interval and vary independently in different intervals. Based on the Bayesian rule, we derive the optimal energy combining rule, i.e., the average likelihood ratio (ALR) detector. We also suggest two solutions: 1) mixture of gamma (MoG)-based ALR detector and 2) generalized Gauss–Laguerre formula (GLF)-based ALR detector, to overcome the problem of the intractable integrals in the optimal rule, and we propose two novel suboptimal but practical combining rules: 1) GLF-based linear combining detector, which can be implemented by linear functions and a comparator with negligible performance degradation and 2) GLF-based weighted-energy detector applicable for the low SNR regime. The simulation results reveal that with MoG and GLF detectors, the ALR detector can be implemented almost precisely with lower complexity. Moreover, all the proposed detectors outperform the conventional ones, especially when large-scale channel gains differ for different cognitive users.


title: A Systematic Mapping Study of Software Development With GitHub

abstract: Context: GitHub, nowadays the most popular social coding platform, has become the reference for mining Open Source repositories, a growing research trend aiming at learning from previous software projects to improve the development of new ones. In the last years, a considerable amount of research papers have been published reporting findings based on data mined from GitHub. As the community continues to deepen in its understanding of software engineering thanks to the analysis performed on this platform, we believe that it is worthwhile to reflect on how research papers have addressed the task of mining GitHub and what findings they have reported. Objective: The main objective of this paper is to identify the quantity, topic, and empirical methods of research works, targeting the analysis of how software development practices are influenced by the use of a distributed social coding platform like GitHub. Method: A systematic mapping study was conducted with four research questions and assessed 80 publications from 2009 to 2016. Results: Most works focused on the interaction around coding-related tasks and project communities. We also identified some concerns about how reliable were these results based on the fact that, overall, papers used small data sets and poor sampling techniques, employed a scarce variety of methodologies and/or were hard to replicate. Conclusions: This paper attested the high activity of research work around the field of Open Source collaboration, especially in the software domain, revealed a set of shortcomings and proposed some actions to mitigate them. We hope that this paper can also create the basis for additional studies on other collaborative activities (like book writing for instance) that are also moving to GitHub.


title: Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning

abstract: Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD. Existing methods analyzing CT images from lung cancer screening for prediction of CVD events or mortality use engineered features extracted from the images combined with patient information. In this work we propose a method that automatically predicts 5-year cardiovascular mortality directly from chest CT scans without the need for hand-crafting image features. A set of 1,583 participants of the National Lung Screening Trial was included (1,188 survivors, 395 nonsurvivors). Low-dose chest CT images acquired at baseline were analyzed and the follow-up time was 5 years. To limit the analysis to the heart region, the heart was first localized by our previously developed algorithm for organ localization exploiting convolutional neural networks. Thereafter, a convolutional autoencoder was used to encode the identified heart region. Finally, based on the extracted encodings subjects were classified into survivors or non-survivors using a neural network. The performance of the method was assessed in eight cross-validation experiments with 1,433 images used for training, 50 for validation and 100 for testing. The method achieved a performance with an area under the ROC curve of 0.73. The results demonstrate that prediction of cardiovascular mortality directly from low-dose screening chest CT scans, without hand-crafted features, is feasible, allowing identification of subjects at risk of fatal CVD events.


Examining the Relationships among Job Satisfaction , Organizational Commitment , and Turnover Intention : An Empirical Study


title: Pesticide Use and Self-Reported Symptoms of Acute Pesticide Poisoning among Aquatic Farmers in Phnom Penh, Cambodia

abstract: Organophosphates and carbamates (OPs/CMs) are known for their acetylcholinesterase inhibiting character. A cross-sectional study of pesticide handling practices and self-perceived symptoms of acute pesticide poisoning was conducted using questionnaire-based interviews with 89 pesticide sprayers in Boeung Cheung Ek (BCE) Lake, Phnom Penh, Cambodia. The study showed that 50% of the pesticides used belonged to WHO class I + II and personal protection among the farmers were inadequate. A majority of the farmers (88%) had experienced symptoms of acute pesticide poisoning, and this was significantly associated with the number of hours spent spraying with OPs/CMs (OR = 1.14, CI 95%: 1.02–1.28). The higher educated farmers reduced their risk of poisoning by 55% for each extra personal protective measure they adapted (OR = 0.45, CI 95%: 0.22–0.91). These findings suggest that improving safe pesticide management practices among the farmers and enforcing the effective banning of the most toxic pesticides will considerably reduce the number of acute pesticide poisoning episodes.


title: Regression Trees and Random forest based feature selection for malaria risk exposure prediction

abstract: This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction , and the CPU time point of view than those obtained by GLM-Lasso method.


title: Elliptical head tracking using intensity gradients and color histograms

abstract: An algorithm for tracking a person's head is presented. The head's projection onto the image plane is modeled as an ellipse whose position and size are continually updated by a local search combining the output of a module concentrating on the intensity gradient around the ellipse's perimeter with that of another module focusing on the color histogram of the ellipse's interior. Since these two modules have roughly orthogonal failure modes, they serve to complement one another. The result is a robust, real-time system that is able to track a person's head with enough accuracy to automatically control the camera's pan, tilt, and zoom in order to keep the person centered in the field of view at a desired size. Extensive experimentation shows the algorithm's robustness with respect to full 360-degree out-of-plane rotation, up to 90-degree tilting, severe but brief occlusion, arbitrary camera movement, and multiple moving people in the background.


title: Flexible Linked Axes for Multivariate Data Visualization

abstract: Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.


title: Energy Management for Microgrids Using a Hierarchical Game-Machine Learning Algorithm

abstract: This paper presents an energy management strategy for microgrids using a multiagent game-learning algorithm. This microgrid is powered by photovoltaic (PV) systems equipped with batteries and is intended to be operating in islanded mode. The proposed energy management strategy is applied to wireless communication networks by addressing the tradeoff between the communication signal's quality of service (QoS) and energy availability. A two-layer algorithm combining multiagent-game and reinforcement learning (RL) is designed for base stations (BSs) in order to accomplish the goal mentioned above. The proposed method shows improvement in the microgrid's performance and has a higher converging speed compared to a direct RL approach. The designed energy management algorithm was tested in multiple case studies.


title: Metrological Characterization of a Method for Blood Pressure Estimation Based on Arterial Lumen Area Model

abstract: Accuracy of blood pressure (BP) measurement is a challenging issue in oscillometry. Most of the automated noninvasive BP monitors estimate BP from envelope of the measured oscillometric pulses. The peak and the trough of the oscillometric pulses are very sensitive to noise caused by breathing, heart-rate variability, motion artifacts, muscle contraction, and environmental noise. Therefore, accuracy of the estimated BP based on the oscillometric waveform envelopes is not reliable in some cases. Recently, employing a modeling approach to estimate BP, we obtained the accurate results for a set of healthy subjects. The method is based on the lumen area oscillations model and estimates BP by comparing the actual and corresponding simulated waveforms. The method’s accuracy worsened when we tested it on a broader range of healthy subjects, while a significant drop was observed when the method was used for patients with chronic cardiovascular diseases. The work presented in this paper represents an improved version of our previous approach. We tested the proposed method on both healthy subjects and patients with chronic cardiovascular diseases, and compared the results to two popular BP estimation algorithms: maximum amplitude algorithm and maximum/minimum slope algorithm. We observed up to 56.7% and 57.3% improvements in mean absolute error, 98.9% and 64.4% improvements in mean error, 50% and 59% improvements in standard deviation of errors, and up to 57.6% and 55.8% in measurement uncertainty for the estimated systolic and diastolic pressures, respectively.


title: A tunable filter based on miniature SIR coaxial resonators

abstract: This paper presents a method of electric tunability using varactor diodes installed on SIR coaxial resonators and associated filters. Using varactor diodes connected in parallel, in combination with the SIR coaxial resonator, makes it possible, by increasing the number of varactor diodes, to expand the tuning range and maintain the unloaded quality factor of the resonator. A second order filter, tunable in center frequency, was built with these resonators, providing a very large tuning range.


title: Homicide in the western family and background factors of a perpetrator

abstract: AIMS ::: Familicide is a multiple-victim homicide incident in which the killer's spouse and one or more children are slain. A systematic review was conducted to reveal the background factors of western homicide perpetrators. ::: ::: ::: METHODS ::: The systematic search was performed in the Arto, Medic, Cinahl, Medline, EBSCOhost Academic Search Premier and Social Services abstracts databases. The keywords were familicide, family homicide, familicide-suicide, filicide-suicide, extended suicide, child, murder, family, filicide and infanticide. The searches revealed 4139 references from the databases. The references were filtered and 32 peer-reviewed research articles revealed in years 2004-2014 were selected as data. The articles were analysed using inductive content analysis, by finding all possible background factors related to homicide. ::: ::: ::: RESULTS ::: The factors were described as percentages of the range. The background factors of familicide perpetrators were categorised as follows: perpetrators who had committed homicide of a child and intimate partner and possibly committed suicide; a father had who killed a child; a mother who had killed a child; a father who had committed a filicide-suicide; and a mother who had committed a filicide-suicide. ::: ::: ::: CONCLUSIONS ::: Psychological instability, violence and crime were found in all these categories of familicides. Perpetrators who had committed a suicide in addition to the familicide had more often been diagnosed with depression, but they sought treatment for mental health problems less often and had violence and self-destructiveness less often in their background than in other familicide categories. Social and healthcare professionals should be more sensitive to emerging family problems and be prepared for intervention.


title: Applying frequency analysis techniques to dag-based workflows to benchmark and predict resource behavior on non-dedicated clusters

abstract: Today, scientific workflows on high-end non-dedicated clusters increasingly resemble directed acyclic graphs (DAGs). The execution trace analysis of the associated DAG-based workflows can provide valuable insights into the system behavior in general, and the occurrences of events like idle times in particular, thereby opening avenues for optimized resource utilization. In this paper, we propose a bipartite tool that uses frequency analysis techniques to benchmark and predict event occurrences in DAG-based workflows; highlighting the system behavior for a given cluster configuration. Using an empirically determined prediction window, the tool parses real-time traces to generate the cumulative distribution function (CDF) of the event occurrences. The CDF is then queried to predict the likelihood of a given number of event instances on the cluster resources in a future time frame. Our results yield average prediction hit-rates as high as 94%. The proposed research enables a runtime system to identify unfavorable event occurrences, thereby allowing for preventive scheduling strategies that maximize system utilization.


title: Fashion Landmark Detection in the Wild

abstract: Visual fashion analysis has attracted many attentions in the recent years. Previous work represented clothing regions by either bounding boxes or human joints. This work presents fashion landmark detection or fashion alignment, which is to predict the positions of functional key points defined on the fashion items, such as the corners of neckline, hemline, and cuff. To encourage future studies, we introduce a fashion landmark dataset (The dataset is available at http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html.) with over 120K images, where each image is labeled with eight landmarks. With this dataset, we study fashion alignment by cascading multiple convolutional neural networks in three stages. These stages gradually improve the accuracies of landmark predictions. Extensive experiments demonstrate the effectiveness of the proposed method, as well as its generalization ability to pose estimation. Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.


title: Toward Streaming Synapse Detection with Compositional ConvNets

abstract: Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated the use of convolutional neural networks (ConvNets) for segmenting cell membranes to individuate neurons. However, there has been comparatively little success in high-throughput identification of the intercellular synaptic connections required for deriving connectivity graphs. ::: In this study, we take a compositional approach to segmenting synapses, modeling them explicitly as an intercellular cleft co-located with an asymmetric vesicle density along a cell membrane. Instead of requiring a deep network to learn all natural combinations of this compositionality, we train lighter networks to model the simpler marginal distributions of membranes, clefts and vesicles from just 100 electron microscopy samples. These feature maps are then combined with simple rules-based heuristics derived from prior biological knowledge. ::: Our approach to synapse detection is both more accurate than previous state-of-the-art (7% higher recall and 5% higher F1-score) and yields a 20-fold speed-up compared to the previous fastest implementations. We demonstrate by reconstructing the first complete, directed connectome from the largest available anisotropic microscopy dataset (245 GB) of mouse somatosensory cortex (S1) in just 9.7 hours on a single shared-memory CPU system. We believe that this work marks an important step toward the goal of a microscope-pace streaming connectomics pipeline.


iTACITUS – Novel Interaction and Tracking Paradigms for Mobile AR


title: Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words

abstract: Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query, SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The experimental results reveal that this system performs well on two different standard datasets.


title: Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning

abstract: Detection of glaucoma eye disease is still a challenging task for computer-aided diagnostics (CADx) systems. During eye screening process, the ophthalmologists measures the glaucoma by structure changes in optic disc (OD), loss of nerve fibres (LNF) and atrophy of the peripapillary region (APR). In retinal images, the automated CADx systems are developed to assess this eye disease through segmentation-based hand-crafted features. Therefore in this paper, the convolutional neural network (CNN) unsupervised architecture was used to extract the features through multilayer from raw pixel intensities. Afterwards, the deep-belief network (DBN) model was used to select the most discriminative deep features based on the annotated training dataset. At last, the final decision is performed by softmax linear classifier to differentiate between glaucoma and non-glaucoma retinal fundus image. This proposed system is known as Glaucoma-Deep and tested on 1200 retinal images obtained from publically and privately available datasets. To evaluate the performance of Glaucoma-Deep system, the sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRC) statistical measures were utilized. On average, the SE of 84.50%, SP of 98.01%, ACC of 99% and PRC of 84% values were achieved. Comparing to state-of-the-art systems, the Nodular-Deep system accomplished significant higher results. Consequently, the Glaucoma-Deep system can easily recognize the glaucoma eye disease to solve the problem of clinical experts during eye-screening process on large-scale environments.


title: Why sex matters for neuroscience

abstract: A rapidly burgeoning literature documents copious sex influences on brain anatomy, chemistry and function. This article highlights some of the more intriguing recent discoveries and their implications. Consideration of the effects of sex can help to explain seemingly contradictory findings. Research into sex influences is mandatory to fully understand a host of brain disorders with sex differences in their incidence and/or nature. The striking quantity and diversity of sex-related influences on brain function indicate that the still widespread assumption that sex influences are negligible cannot be justified, and probably retards progress in our field.


title: DyNet: The Dynamic Neural Network Toolkit

abstract: We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives. In DyNet's dynamic declaration strategy, computation graph construction is mostly transparent, being implicitly constructed by executing procedural code that computes the network outputs, and the user is free to use different network structures for each input. Dynamic declaration thus facilitates the implementation of more complicated network architectures, and DyNet is specifically designed to allow users to implement their models in a way that is idiomatic in their preferred programming language (C++ or Python). One challenge with dynamic declaration is that because the symbolic computation graph is defined anew for every training example, its construction must have low overhead. To achieve this, DyNet has an optimized C++ backend and lightweight graph representation. Experiments show that DyNet's speeds are faster than or comparable with static declaration toolkits, and significantly faster than Chainer, another dynamic declaration toolkit. DyNet is released open-source under the Apache 2.0 license and available at this http URL.


title: Wavelets on graphs with application to transportation networks

abstract: The technological advancements in Intelligent Transport Systems have made it possible to acquire large amounts of traffic data in real-time. As a result, various data-mining techniques are being used to extract useful traffic patterns. The research presented in this article focuses on the detection of disruptive traffic events such as congestion. In most transportation studies, traffic parameters are typically modeled as time series. However, these techniques fail to incorporate the spatial dependencies between different traffic variables. In this work, the traffic quantities such as speeds are considered as the signals defined at the vertices of a network line graph. Furthermore, the graph wavelet operators are applied to the spatial signals to generate the wavelet coefficients at different wavelet scales. By analyzing these wavelet coefficients, useful information such as origin, propagation, and the span of traffic congestion are inferred. For analysis, we consider two major expressways in Singapore. The analysis shows that the abrupt changes in the speed can be captured by using the wavelet coefficients at the higher scales. On the other hand, the high magnitude coefficients at the lower wavelet scales reflect the smooth flow of the traffic across the network. I. INTRODUCTION Intelligent Transport Systems (ITS) can play a vital role in developing sophisticated control strategies for optimal usage of the road infrastructure of a land-scarce city like Singapore. The technological advancements in ITS and sensor developments enabled the availability of extensive data related to the on ground traffic conditions. Consequently, data driven approaches are being widely used for applications such as traffic sensing, congestion control, traffic forecasting, and route guidance (1)-(5). In this work, we focus on detecting disruptive traffic events such as unexpected traffic speed fluctuations, traffic slowdown, and congestion that hinder normal traffic flow. The early detection of such traffic events can be useful in issuing early warnings that will eventually help the drivers to plan alternate routes. Previous studies have proposed various methods to detect congestion and other disruptive events. These methods include Principal Component Analysis (PCA), Robust Principal Component Analysis (RPCA), Fourier transform, and wavelets. However, such approaches typically model


title: BRIEF REPORT Chess Masters Show a Hallmark of Face Processing With Chess

abstract: Face processing has several distinctive hallmarks that researchers have attributed either to face-specific mechanisms or to extensive experience distinguishing faces. Here, we examined the face-processing hallmark of selective attention failure—as indexed by the congruency effect in the composite paradigm—in a domain of extreme expertise: chess. Among 27 experts, we found that the congruency effect was equally strong with chessboards and faces. Further, comparing these experts with recreational players and novices, we observed a trade-off: Chess expertise was positively related to the congruency effect with chess yet negatively related to the congruency effect with faces. These and other findings reveal a case of expertise-dependent, facelike processing of objects of expertise and suggest that face and expert-chess recognition share common processes.


title: A CMOS nested-chopper instrumentation amplifier with 100-nV offset

abstract: Offset and 1/f noise have been major enemies for analog IC designers since the enormous increase of the market for digital applications forced them to develop analog front ends in standard CMOS technology. Because static techniques like trimming cannot reduce 1/f noise, solutions had to be found in dynamic offset cancellation. The current work presents an improvement of the chopper amplifier concept, called the nested chopper amplifier. It is shown that with this architecture the residual offset is reduced to <100 nV, while retaining minimal thermal noise in the signal band.


title: Algorithms For Bigram And Trigram Word Clustering

abstract: Abstract In this paper, we describe an efficient method for obtaining word classes for class language models. The method employs an exchange algorithm using the criterion of perplexity improvement. The novel contributions of this paper are the extension of the class bigram perplexity criterion to the class trigram perplexity criterion, the description of an efficient implementation for speeding up the clustering process, the detailed computational complexity analysis of the clustering algorithm, and, finally, experimental results on large text corpora of about 1, 4, 39 and 241 million words including examples of word classes, test corpus perplexities in comparison to word language models, and speech recognition results.


title: Reducing children's television-viewing time: a qualitative study of parents and their children.

abstract: OBJECTIVES ::: The American Academy of Pediatrics recommends that children over age 2 years spend < or = 2 hours per day with screen media, because excessive viewing has been linked to a plethora of physical, academic, and behavioral problems. The primary goal of this study was to qualitatively explore how a recommendation to limit television viewing might be received and responded to by a diverse sample of parents and their school-age children. ::: ::: ::: METHODS ::: The study collected background data about media use, gathered a household media inventory, and conducted in-depth individual and small group interviews with 180 parents and children ages 6 to 13 years old. ::: ::: ::: RESULTS ::: Most of the children reported spending approximately 3 hours per day watching television. The average home in this sample had 4 television sets; nearly two thirds had a television in the child's bedroom, and nearly half had a television set in the kitchen or dining room. Although virtually all of the parents reported having guidelines for children's television viewing, few had rules restricting the time children spend watching television. Data from this exploratory study suggest several potential barriers to implementing a 2-hour limit, including: parents' need to use television as a safe and affordable distraction, parents' own heavy television viewing patterns, the role that television plays in the family's day-to-day routine, and a belief that children should spend their weekend leisure time as they wish. Interviews revealed that for many of these families there is a lack of concern that television viewing is a problem for their child, and there remains confusion about the boundaries of the recommendation of the American Academy of Pediatrics. ::: ::: ::: CONCLUSIONS ::: Parents in this study expressed interest in taking steps toward reducing children's television time but also uncertainty about how to go about doing so. Results suggest possible strategies to reduce the amount of time children spend in front of the screen.


title: Development of Large Earth-Station Reflector Antennas in Japan

abstract: This paper reports an overview of the development of large Earth-station reflector antennas for satellite communication systems in Japan. KDD (now, KDDI) developed a Cassegrain reflector antenna 20 m in diameter at KDD Ibaraki Space Communication Laboratory for Japan's first Earth station in 1963. This antenna was the first Earth station using a Cassegrain antenna in the world. It was awarded an IEEE Milestone as the “First Transpacific Reception of a Television (TV) Signal via Satellite, 1963” on November 23, 2009. The authors describe design technologies for large Earth-station reflector antennas, including beam-waveguide feed systems and reflector-shaping techniques. These design technologies have contributed tremendously to the popularization and advancement of satellite communication systems. The shaped Cassegrain antenna, fed by a four-reflector beam-waveguide feed, is symbolic of the standard Earth-station antenna all over the world.


title: A flexible attitude system for wireless Micro-Ball endoscopy

abstract: To meet the needs of reducing miss rate of wireless Micro-Ball endoscopy and assisting in gastrointestinal (GI) tract images 2D representation in post-processing, a small size, low power attitude system is designed. The system, which consist of 3-axis magnetic and accelerate sensors, calculates the orientation of the wireless Micro-Ball helping select the most suitable camera to capture GI images and provide information for image registration. A demonstration system based on MCU is implemented and is verified in the Micro-Ball endoscopy system. The total power consumption of the hardware implementation is about 1.5mW at 50Hz output data rate (ODR). And the relative errors of pitch and yaw are within 2.5°, roll is within 3°.


title: Correlations among prototypical usability metrics: evidence for the construct of usability

abstract: Correlations between prototypical usability metrics from 90 distinct usability tests were strong when measured at the task-level (r between .44 and .60). Using test-level satisfaction ratings instead of task-level ratings attenuated the correlations (r between .16 and .24). The method of aggregating data from a usability test had a significant effect on the magnitude of the resulting correlations. The results of principal components and factor analyses on the prototypical usability metrics provided evidence for an underlying construct of general usability with objective and subjective factors.


Part of Speech Tagging for Bengali with Hidden Markov Model