title: Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World

abstract: This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment. For example, given an image, LSP can map the statement “blue mug on the table” to the set of image segments showing blue mugs on tables. LSP learns physical representations for both categorical (“blue,” “mug”) and relational (“on”) language, and also learns to compose these representations to produce the referents of entire statements. We further introduce a weakly supervised training procedure that estimates LSP’s parameters using annotated referents for entire statements, without annotated referents for individual words or the parse structure of the statement. We perform experiments on two applications: scene understanding and geographical question answering. We find that LSP outperforms existing, less expressive models that cannot represent relational language. We further find that weakly supervised training is competitive with fully supervised training while requiring significantly less annotation effort.


Hierarchical Reinforcement Learning with Parameters.


title: Mälardalen Högskola Research Planning Course Intelligent Human Robot Interaction (iHRI)

abstract: In a preferred embodiment, an apparatus for detecting the presence of, and providing for the release of, a person in a vehicle trunk compartment, including: a presence detector system to detect motion in the vehicle trunk compartment; and upon detection of motion in the vehicle trunk compartment, the presence detector system causes the activation of one or more functions selected from the group consisting of: a vehicle operator alert, vehicle headlights, a vehicle horn, a vehicle alarm, a self-contained alarm, a trunk lid release and, a trunk light.


title: Application of End-to-End Deep Learning in Wireless Communications Systems

abstract: Deep learning is a potential paradigm changer for the design of wireless communications systems (WCS), from conventional handcrafted schemes based on sophisticated mathematical models with assumptions to autonomous schemes based on the end-to-end deep learning using a large number of data. In this article, we present a basic concept of the deep learning and its application to WCS by investigating the resource allocation (RA) scheme based on a deep neural network (DNN) where multiple goals with various constraints can be satisfied through the end-to-end deep learning. Especially, the optimality and feasibility of the DNN based RA are verified through simulation. Then, we discuss the technical challenges regarding the application of deep learning in WCS.


Low Crest Factor Modulation Techniques for Orthogonal Frequency Division Multiplexing ( OFDM )


Ownership and values: nurses, nursing and higher education


An Assessment of Technology Skills and Classroom Technology Integration Experience in Preservice and Practicing Teachers


title: Patterns of eye movements when male and female observers judge female attractiveness, body fat and waist-to-hip ratio

abstract: Behavioural studies of the perceptual cues for female physical attractiveness have suggested two potentially important features: body fat distribution [the waist-to-hip ratio (WHR)] and overall body fat [often estimated by the body mass index (BMI)]. However, none of these studies tell us directly which regions of the stimulus images inform observers' judgments. Therefore, we recorded the eye movements of three groups of 10 male observers and three groups of 10 female observers, when they rated a set of 46 photographs of female bodies. The first sets of observers rated the images for attractiveness, the second sets rated for body fat and the third sets for WHR. If either WHR and/or body fat is used to judge attractiveness, then observers rating attractiveness should look at those areas of the body which allow assessment of these features, and they should look in the same areas when they are directly asked to estimate WHR and body fat. So we are able to compare the fixation patterns for the explicit judgments with those for attractiveness judgments and infer which features were used for attractiveness. Prior to group analysis of the eye-movement data, the locations of individual eye fixations were transformed into a common reference space to permit comparisons of fixation density at high resolution across all stimuli. This manipulation allowed us to use spatial statistical analysis techniques to show the following: (1) Observers' fixations for attractiveness and body fat clustered in the central and upper abdomen and chest, but not the pelvic or hip areas, consistent with the finding that WHR had little influence over attractiveness judgments. (2) The pattern of fixations for attractiveness ratings was very similar to the fixation patterns for body fat judgments. (3) The fixations for WHR ratings were significantly different from those for attractiveness and body fat.


title: Virtual communities: A marketing perspective

abstract: Increasingly, consumers interact through the Internet to share their knowledge, experiences, and opinions. Consequently, 'word-of-mouse' has become a significant market force that influences consumer decision-making. On the basis of extensive quantitative and qualitative research, the authors sketch how consumers make use of virtual communities as social and information networks, and how this affects their decision-making processes. We present three studies that address (i) determinants and effects of virtual community influence on the consumer decision process; (ii) virtual community participation patterns; and (iii) discussion practices of the most active community members. Key implications for managers, marketers, and market researchers are discussed.


title: Acquiring Cyber Threat Intelligence through Security Information Correlation

abstract: Cyber Physical Systems (CPS) operating in modern critical infrastructures (CIs) are increasingly being targeted by highly sophisticated cyber attacks. Threat actors have quickly learned of the value and potential impact of targeting CPS, and numerous tailored multi-stage cyber-physical attack campaigns, such as Advanced Persistent Threats (APTs), have been perpetrated in the last years. They aim at stealthily compromising systems' operations and cause severe impact on daily business operations such as shutdowns, equipment damage, reputation damage, financial loss, intellectual property theft, and health and safety risks. Protecting CIs against such threats has become as crucial as complicated. Novel distributed detection and reaction methodologies are necessary to effectively uncover these attacks, and timely mitigate their effects. Correlating large amounts of data, collected from a multitude of relevant sources, is fundamental for Security Operation Centers (SOCs) to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of attacks. In our previous work we introduced three methods for security information correlation. In this paper we define metrics and benchmarks to evaluate these correlation methods, we assess their accuracy, and we compare their performance. We finally demonstrate how the presented techniques, implemented within our cyber threat intelligence analysis engine called CAESAIR, can be applied to support incident handling tasks performed by SOCs.


title: On the Automated Classification of Web Sites

abstract: In this paper we discuss several issues related to automated text classification of web sites. We analyze the nature of web content and metadata in relation to requirements for text features. We find that HTML metatags are a good source of text features, but are not in wide use despite their role in search engine rankings. We present an approach for targeted spidering including metadata extraction and opportunistic crawling of specific semantic hyperlinks. We describe a system for automatically classifying web sites into industry categories and present performance results based on different combinations of text features and training data. This system can serve as the basis for a generalized framework for automated metadata creation.


title: Persuasive Technology for Improving Information Security Awareness and Behavior: Literature Review

abstract: The use of Persuasive Technology in various fields is rapidly increasing. It can be applied in many fields such as computing, marketing, sales, environment, education, and health. Persuasive Technology has been found effective in bringing a required change in users' behaviors and attitudes. However, the use of persuasive technology is scarce in the field of Information Security awareness. This paper reviews extensive literature review which focuses on a perspective on how to create awareness among users for good information security practices by applying Persuasive Technology techniques and approaches. The conceptual findings suggest there is a tremendous potential of Persuasive Technology to be applied to persuade users to change their behavior and perception toward Information Security practices.


title: Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization

abstract: Landmark/pose estimation in single monocular images have received much effort in computer vision due to its important applications. It remains a challenging task when input images severe occlusions caused by, e.g., adverse camera views. Under such circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity. To address the problem, by incorporating priors about the structure of pose components, we propose a novel structure-aware fully convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, inspired by how human identifies implausible poses, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator G generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on three pose-related tasks: 2D single human pose estimation, 2D facial landmark estimation and 3D single human pose estimation. The proposed approach significantly outperforms the state-of-the-art methods and almost always generates plausible pose predictions, demonstrating the usefulness of implicit learning of structures using GANs.


Toward Multi-domain Language Generation using Recurrent Neural Networks


title: Lymphatic System in Cardiovascular Medicine

abstract: The mammalian circulatory system comprises both the cardiovascular system and the lymphatic system. In contrast to the blood vascular circulation, the lymphatic system forms a unidirectional transit pathway from the extracellular space to the venous system. It actively regulates tissue fluid homeostasis, absorption of gastrointestinal lipids, and trafficking of antigen-presenting cells and lymphocytes to lymphoid organs and on to the systemic circulation. The cardinal manifestation of lymphatic malfunction is lymphedema. Recent research has implicated the lymphatic system in the pathogenesis of cardiovascular diseases including obesity and metabolic disease, dyslipidemia, inflammation, atherosclerosis, hypertension, and myocardial infarction. Here, we review the most recent advances in the field of lymphatic vascular biology, with a focus on cardiovascular disease.


title: A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge

abstract: Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance rating. The focus of this work is to construct a corn yield predictor at the county scale. Corn yield (forecasting) depends on a complex, interconnected set of variables that include economic, agricultural, management and meteorological factors. Conventional forecasting is either knowledge-based computer programs (that simulate plant-weather-soil-management interactions) coupled with targeted surveys or statistical model based. The former is limited by the need for painstaking calibration, while the latter is limited to univariate analysis or similar simplifying assumptions that fail to capture the complex interdependencies affecting yield. In this paper, we propose a data-driven approach that is"gray box"i.e. that seamlessly utilizes expert knowledge in constructing a statistical network model for corn yield forecasting. Our multivariate gray box model is developed on Bayesian network analysis to build a Directed Acyclic Graph (DAG) between predictors and yield. Starting from a complete graph connecting various carefully chosen variables and yield, expert knowledge is used to prune or strengthen edges connecting variables. Subsequently the structure (connectivity and edge weights) of the DAG that maximizes the likelihood of observing the training data is identified via optimization. We curated an extensive set of historical data (1948-2012) for each of the 99 counties in Iowa as data to train the model.


Rationalizing Sentiment Analysis in Tensorflow


title: Quantized deep neural networks for energy efficient hardware-based inference

abstract: Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification accuracy, with custom hardware implementations great candidates for high-speed, accurate inference. While progress in achieving large scale, highly accurate DNNs has been made, significant energy and area are required due to massive memory accesses and computations. Such demands pose a challenge to any DNN implementation, yet it is more natural to handle in a custom hardware platform. To alleviate the increased demand in storage and energy, quantized DNNs constrain their weights (and activations) from floating-point numbers to only a few discrete levels. Therefore, storage is reduced, thereby leading to less memory accesses. In this paper, we provide an overview of different types of quantized DNNs, as well as the training approaches for them. Among the various quantized DNNs, our LightNN (Light Neural Network) approach can reduce both memory accesses and computation energy, by filling the gap between classic, full-precision and binarized DNNs. We provide a detailed comparison between LightNNs, conventional DNNs and Binarized Neural Networks (BNNs), with MNIST and CIFAR-10 datasets. In contrast to other quantized DNNs that trade-off significant amounts of accuracy for lower memory requirements, LightNNs can significantly reduce storage, energy and area while still maintaining a test error similar to a large DNN configuration. Thus, LightNNs provide more options for hardware designers to trade-off accuracy and energy.


title: A Continuous Identity Authentication Scheme Based on Physiological and Behavioral Characteristics

abstract: Wearable devices have flourished over the past ten years providing great advantages to people and, recently, they have also been used for identity authentication. Most of the authentication methods adopt a one-time authentication manner which cannot provide continuous certification. To address this issue, we present a two-step authentication method based on an own-built fingertip sensor device which can capture motion data (e.g., acceleration and angular velocity) and physiological data (e.g., a photoplethysmography (PPG) signal) simultaneously. When the device is worn on the user’s fingertip, it will automatically recognize whether the wearer is a legitimate user or not. More specifically, multisensor data is collected and analyzed to extract representative and intensive features. Then, human activity recognition is applied as the first step to enhance the practicability of the authentication system. After correctly discriminating the motion state, a one-class machine learning algorithm is applied for identity authentication as the second step. When a user wears the device, the authentication process is carried on automatically at set intervals. Analyses were conducted using data from 40 individuals across various operational scenarios. Extensive experiments were executed to examine the effectiveness of the proposed approach, which achieved an average accuracy rate of 98.5% and an F1-score of 86.67%. Our results suggest that the proposed scheme provides a feasible and practical solution for authentication.


title: Gene expression correlated with delay in shell formation in larval Pacific oysters (Crassostrea gigas) exposed to experimental ocean acidification provides insights into shell formation mechanisms

abstract: BACKGROUND ::: Despite recent work to characterize gene expression changes associated with larval development in oysters, the mechanism by which the larval shell is first formed is still largely unknown. In Crassostrea gigas, this shell forms within the first 24 h post fertilization, and it has been demonstrated that changes in water chemistry can cause delays in shell formation, shell deformations and higher mortality rates. In this study, we use the delay in shell formation associated with exposure to CO2-acidified seawater to identify genes correlated with initial shell deposition. ::: ::: ::: RESULTS ::: By fitting linear models to gene expression data in ambient and low aragonite saturation treatments, we are able to isolate 37 annotated genes correlated with initial larval shell formation, which can be categorized into 1) ion transporters, 2) shell matrix proteins and 3) protease inhibitors. Clustering of the gene expression data into co-expression networks further supports the result of the linear models, and also implies an important role of dynein motor proteins as transporters of cellular components during the initial shell formation process. ::: ::: ::: CONCLUSIONS ::: Using an RNA-Seq approach with high temporal resolution allows us to identify a conceptual model for how oyster larval calcification is initiated. This work provides a foundation for further studies on how genetic variation in these identified genes could affect fitness of oyster populations subjected to future environmental changes, such as ocean acidification.


title: Investigations on phosphorus recovery from aqueous solutions by biochars derived from magnesium-pretreated cypress sawdust

abstract: The ability of biochars, derived from the pyrolysis at 400 °C; 500 °C and 600 °C of pretreated cypress sawdust with 20 wt% magnesium chloride (MgCl2) solutions, in recovering phosphorus from aqueous solutions was investigated under various experimental conditions in batch mode. The experimental results indicated that cypress sawdust pretreatment with MgCl2 induced important modifications of the physical and chemical biochars' properties favoring phosphorus recovery from the used synthetic solutions. Moreover, phosphorus recovery efficiency increased with the increase of the used pyrolysis temperature. Indeed, for an aqueous pH of 5.2 and a phosphorus concentration of 75 mg L-1, the recovered amounts increased from 19.2 mg g-1 to 33.8 mg g-1 when the used pyrolysis temperature was raised from 400 °C to 600 °C. For all the tested biochars, the phosphorus recovery kinetics data were well fitted by the pseudo-second-order model, and the equilibrium state was obtained after 180 min of contact time. Furthermore, the phosphorus recovery data at equilibrium were well described by the Langmuir model with a maximal recovery capacity of 66.7 mg g-1 for the magnesium pretreated biochar at 600 °C. Phosphorus recovery by the used biochars occurred probably through adsorption onto biochars' active sites as well as precipitation with magnesium ions as magnesium phosphates components. All these results suggested that biochars derived from MgCl2 pretreated cypress sawdust could be considered as promising materials for phosphorus recovery from wastewaters for a possible further subsequent use in agriculture as amendments.


title: Time-Aware Click Model

abstract: Click-through information is considered as a valuable source of users’ implicit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users’ examination behavior, most existing click models are position based, assuming that users examine results from top to bottom in a linear fashion. Although these click models have been successful, most do not take temporal information into account. As many existing studies have shown, click dwell time and click sequence information are strongly correlated with users’ perceived relevance and search satisfaction. Incorporating temporal information may be important to improve performance of user click models for Web searches. In this article, we investigate the problem of properly incorporating temporal information into click models. We first carry out a laboratory eye-tracking study to analyze users’ examination behavior in different click sequences and find that the user common examination path among adjacent clicks is linear. Next, we analyze the user dwell time distribution in different search logs and find that we cannot simply use a click dwell time threshold (e.g., 30 seconds) to distinguish relevant/irrelevant results. Finally, we propose a novel time-aware click model (TACM), which captures the temporal information of user behavior. We compare the TACM to several existing click models using two real-world search engine logs. Experimental results show that the TACM outperforms other click models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG).


title: A High Performance Pneumatic Force Actuator System Part 2-Nonlinear Controller Design ∗

abstract: In this article we present two nonlinear force controllers based on the sliding mode control theory. For this purpose we use the detailed mathematical model of the pneumatic system developed in the first part of the paper. The first controller is based on the complete model, and exhibits superior performance both in the numerical simulation and experiments, but requires complex online computations for the control law. The second controller neglects the valve dynamics and the time delay due to connecting tubes. The performance of this controller exhibits slight degradation for configurations with relatively short tubes, and at frequencies up to 20 Hz. At higher frequencies or when long connecting tubes are used, however, the performance exhibits significant degradation compared to the one provided by the full order controller.@S0022-0434~00!00703-6#


title: Effects of Novel Skewed Rotor in Squirrel-Cage Induction Motor on Electromagnetic Force

abstract: Axial endplay problems often occur in electrical machines even in the conventional skew motor. In order to solve these problems, an improved skew rotor is proposed to weaken the ill-effect of the conventional skew motor by skewing the slots toward reverse directions. The space distributions of magnetic flux field and the Maxwell stress tensor on the rotor surfaces are analyzed by an analytical method. The time-step finite-element 3-D whole model of a novel skew squirrel-cage induction motor is presented for verification. The results indicate that the radial and the axial forces decrease, but the rotary torque remains unchanged. The validity of the improved method is verified by means of the comparison with the conventional one.


Real-time omnidirectional and panoramic stereo


title: A robust eye gaze tracking method based on a virtual eyeball model

abstract: Gaze positions can provide important cues for natural computer interfaces. In this paper, we describe a new gaze estimation method based on a three dimensional analysis of the human eye which can be used in head-mounted display (HMD) environments. This paper presents four advantages over previous works. First, in order to obtain accurate gaze positions, we used a virtual eyeball model based on the 3D characteristics of the human eyeball. Second, we calculated the 3D position of the virtual eyeball and gaze vector by using a camera and three collimated IR-LEDs. Third, three reference frames (the camera, the monitor and the eye reference frames) were unified, which simplified the complex 3D converting calculations and allowed for calculation of the 3D eye position and gaze position on a HMD monitor. Fourth, a simple user-dependent calibration method was proposed by gazing at one position based on Kappa compensation. Experimental results showed that the eye gaze estimation error of the proposed method was lower than 1°.


title: Efficient Harmful Email Identification Using Neural Network

abstract: DOI: 10.9734/BJMCS/2015/15279 Editor(s): (1) Xiaodi Li, School of Mathematical Sciences, Shandong Normal University Ji'nan, 250014, Shandong, P.R. China. Reviewers: (1) Ravi Narayan, Computer Science & Engineering Department, Thapar University, India. (2) K. Gnana Sheela, Anna University, Regional Centre, Coimbatore, India. (3) Anonymous, Jordan. (4) Manu Pratap Singh, Department of Computer science, Dr. B. R. Ambedkar University, Agra, India. Complete Peer review History: http://www.sciencedomain.org/review-history.php?iid=932&id=6&aid=7977


title: Efficient Computation in Adaptive Artificial Spiking Neural Networks

abstract: Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of communication. This contrasts sharply with biological neurons that communicate sparingly and efficiently using binary spikes. While artificial Spiking Neural Networks (SNNs) can be constructed by replacing the units of an ANN with spiking neurons, the current performance is far from that of deep ANNs on hard benchmarks and these SNNs use much higher firing rates compared to their biological counterparts, limiting their efficiency. Here we show how spiking neurons that employ an efficient form of neural coding can be used to construct SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on important benchmarks, while requiring much lower average firing rates. For this, we use spike-time coding based on the firing rate limiting adaptation phenomenon observed in biological spiking neurons. This phenomenon can be captured in adapting spiking neuron models, for which we derive the effective transfer function. Neural units in ANNs trained with this transfer function can be substituted directly with adaptive spiking neurons, and the resulting Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up to an order of magnitude fewer spikes compared to previous SNNs. Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention. AdSNNs thus hold promise as a novel and efficient model for neural computation that naturally fits to temporally continuous and asynchronous applications


title: Embeddedness in the making of financial capital: How social relations and networks benefit firms seeking financing

abstract: I investigate how social embeddedness affects an organization’s acquisition and cost of financial capital in middle-market banking—a lucrative but understudied financial sector. Using existing theory and original fieldwork, I develop a framework to explain how embeddedness can influence which firms get capital and at what cost. I then statistically examine my claims using national data on small-business lending. At the level of dyadic ties, I find that firms that embed their commercial transactions with their lender in social attachments receive lower interest rates on loans. At the network level, firms are more likely to get loans and to receive lower interest rates on loans if their network of bank ties has a mix of embedded ties and arm’s-length ties. These network effects arise because embedded ties motivate network partners to share private resources, while arm’s-length ties facilitate access to public information on market prices and loan opportunities so that the benefits of different types of ties are optimized within one network. I conclude with a discussion of how the value produced by a network is at a premium when it creates a bridge that links the public information of markets with the private resources of relationships.


title: Integrating nature-inspired optimization algorithms to K-means clustering

abstract: Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization algorithms inspired by biology, including the Wolf, Firefly, Cuckoo, Bat and Ant algorithms, simulate swarm behavior in which peers are attracted while steering towards a global objective. It is found that these bio-inspired algorithms have their own virtues and could be logically integrated into K-means clustering to avoid local optima during iteration to convergence. In this paper, the constructs of the integration of bio-inspired optimization methods into K-means clustering are presented. The extended versions of clustering algorithms integrated with bio-inspired optimization methods produce improved results. Experiments are conducted to validate the benefits of the proposed approach.


title: A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors

abstract: Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field.


title: Advantages of Monte Carlo Confidence Intervals for Indirect Effects

abstract: Monte Carlo simulation is a useful but underutilized method of constructing confidence intervals for indirect effects in mediation analysis. The Monte Carlo confidence interval method has several distinct advantages over rival methods. Its performance is comparable to other widely accepted methods of interval construction, it can be used when only summary data are available, it can be used in situations where rival methods (e.g., bootstrapping and distribution of the product methods) are difficult or impossible, and it is not as computer-intensive as some other methods. In this study we discuss Monte Carlo confidence intervals for indirect effects, report the results of a simulation study comparing their performance to that of competing methods, demonstrate the method in applied examples, and discuss several software options for implementation in applied settings.


title: Rectangular Waveguide Filters With Meandered Topology

abstract: In this paper, a new topology for rectangular waveguide bandpass and low-pass filters is presented. A simple, accurate, and robust design technique for these novel meandered waveguide filters is provided. The proposed filters employ a concatenation of ±90° $E$ -plane mitered bends (±90° EMBs) with different heights and lengths, whose dimensions are consecutively and independently calculated. Each ±90° EMB satisfies a local target reflection coefficient along the device so that they can be calculated separately. The novel structures allow drastically reduce the total length of the filters and embed bends if desired, or even to provide routing capabilities. Furthermore, the new meandered topology allows the introduction of transmission zeros above the passband of the low-pass filter, which can be controlled by the free parameters of the ±90° EMBs. A bandpass and a low-pass filter with meandered topology have been designed following the proposed novel technique. Measurements of the manufactured prototypes are also included to validate the novel topology and design technique, achieving excellent agreement with the simulation results.


title: Parsing Geometry Using Structure-Aware Shape Templates

abstract: Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality. Structure typically appears in the form of individual parts and their arrangement. Knowing about object structure can be an important cue for object recognition and scene understanding - a key goal for various AR and robotics applications. However, commodity RGB-D sensors used in these scenarios only produce raw, unorganized point clouds, without structural information about the captured scene. Moreover, the generated data is commonly partial and susceptible to artifacts and noise, which makes inferring the structure of scanned objects challenging. In this paper, we organize large shape collections into parameterized shape templates to capture the underlying structure of the objects. The templates allow us to transfer the structural information onto new objects and incomplete scans. We employ a deep neural network that matches the partial scan with one of the shape templates, then match and fit it to complete and detailed models from the collection. This allows us to faithfully label its parts and to guide the reconstruction of the scanned object. We showcase the effectiveness of our method by comparing it to other state-of-the-art approaches.


title: Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI

abstract: Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooling) layers, which in turn reduces segmentation accuracy for smaller structures. In this paper, we propose a 3D CNN for volumetric segmentation of the left atrial chamber in LGE-MRI. Our network is based on the well known U-Net architecture. We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge. The results show that including global context through the use of dilated convolutions, helps in domain adaptation, and the overall segmentation accuracy is improved in comparison to a 3D U-Net.


title: A Transformerless Hybrid Active Filter Using a Three-Level Pulsewidth Modulation (PWM) Converter for a Medium-Voltage Motor Drive

abstract: This paper presents a transformerless hybrid active filter integrated into a medium-voltage motor drive for energy savings. This hybrid filter is intended for line harmonic-current mitigation of the three-phase diode rectifier used as the front end of the motor drive. It is based on direct connection of a passive filter tuned to the seventh-harmonic frequency in series with an active filter using a three-level pulsewidth modulated (PWM) converter. This paper provides a theoretical discussion on voltage-balancing control of two split dc capacitors of the active filter. The 400-V 15-kW motor drive system is designed, constructed, and tested, which can be considered as a downscaled model from a medium-voltage motor drive without regenerative braking. Experimental results verify that the hybrid filter has the capability of satisfactory harmonic filtering and stable voltage balancing in all the load conditions.


title: A High Throughput/Gate AES Hardware Architecture by Compressing Encryption and Decryption Datapaths

abstract: This paper proposes a highly efficient AES hardware architecture that supports both encryption and decryption for the CBC mode. Some conventional AES architectures employ pipelining techniques to enhance the throughput and efficiency. However, such pipelined architectures are frequently unfit because many practical cryptographic applications work in the CBC mode, where block-wise parallelism is not available for encryption. In this paper, we present an efficient AES encryption/decryption hardware design suitable for such block-chaining modes. In particular, new operation-reordering and register-retiming techniques allow us to unify the inversion circuits for encryption and decryption (i.e., SubBytes and InvSubBytes) without any delay overhead. A new unification technique for linear mappings further reduces both the area and critical delay in total. Our design employs a common loop architecture and can therefore efficiently perform even in the CBC mode. We also present a shared key scheduling datapath that can work on-the-fly in the proposed architecture. To the best of our knowledge, the proposed architecture has the shortest critical path delay and is the most efficient in terms of throughput per area among conventional AES encryption/decryption architectures with tower-field S-boxes. We evaluate the performance of the proposed and some conventional datapaths by logic synthesis results with the TSMC 65-nm standard-cell library and NanGate 45- and 15-nm open-cell libraries. As a result, we confirm that our proposed architecture achieves approximately 53–72 % higher efficiency (i.e., a higher bps/GE) than any other conventional counterpart.


title: Analysis of source code metrics from ns-2 and ns-3 network simulators

abstract: Abstract Ns-2 and its successor ns-3 are discrete-event simulators which are closely related to each other as they share common background, concepts and similar aims. Ns-3 is still under development, but it offers some interesting characteristics for developers while ns-2 still has a large user base. While other studies have compared different network simulators, focusing on performance measurements, in this paper we adopted a different approach by focusing on technical characteristics and using software metrics to obtain useful conclusions. We chose ns-2 and ns-3 for our case study because of the popularity of the former in research and the increasing use of the latter. This reflects the current situation where ns-3 has emerged as a viable alternative to ns-2 due to its features and design. The paper assesses the current state of both projects and their respective evolution supported by the measurements obtained from a broad set of software metrics. By considering other qualitative characteristics we obtained a summary of technical features of both simulators including, architectural design, software dependencies or documentation policies.


title: Web-application development using the Model/View/Controller design pattern

abstract: The Model/View/Controller design pattern is very useful for architecting interactive software systems. This design pattern is partition-independent, because it is expressed in terms of an interactive application running in a single address space. Applying the Model/View/Controller design pattern to web-applications is therefore complicated by the fact that current technologies encourage developers to partition the application as early as in the design phase. Subsequent changes to that partitioning require considerable changes to the application's implementation-despite the fact that the application logic has not changed. This paper introduces the concept of Flexible Web-Application Partitioning, a programming model and implementation infrastructure, that allows developers to apply the Model/View/Controller design pattern in a partition-independent manner Applications are developed and tested in a single address-space; they can then be deployed to various client/server architectures without changing the application's source code. In addition, partitioning decisions can be changed without modifying the application.


title: Critical Success Factors in New Product Development

abstract: New product development (NPD) is essential for outstanding corporate performance, and research about what leads to new product success has been carried out for both goods and services frequently. Despite extensive documentation on how to achieve success, NPD remains a high risk venture. Recent studies showed that the overall rate of success for newly commercialized products has remained stable at less than 60 %, indicating that substantial resources continue to be devoted to new product development efforts that fail in the marketplace. Therefore, In this research, to prevent the unsuccessful resource use that allocate to new product development, critical success factors that affect new product performance are presented. Key Words: New product development (NPD), critical success factors Ozet: Firmalarin performanslarinin artirilmasinda yeni urun gelistirme onemli bir etkiye sahiptir. Bundan dolayi gerek mal gerekse hizmet uretiminde yeni urun gelistirmede etkili olan basari faktorleri siklikla ele alinmaktadir. Bu cabalar sonucu yeni urun gelistirme hakkinda yiginla bilgi bulunmasina ragmen hala yeni urun gelistirme cabalari riskli bir macera olarak gorulmektedir.Yeni ticarilestirilen urunlerin genel basari ortalamasinin % 60’in altinda olmasi, yeni urun gelistirmeye ayrilan kaynaklarin basarisiz kullanildiginin bir gostergesidir. Bundan dolayi bu arastirmada, yeni urun gelistirmeye ayrilan basarisiz kaynak kullanimini engelleyici kritik basari faktorleri ele alinacaktir. Anahtar Kelimeler: Yeni urun gelistirme, kritik basari faktorleri


title: Kinematically Redundant Manipulators.

abstract: The disclosure relates to a process for separating acetone from reaction mixtures originating from the reaction of methyl acetate and/or dimethylether with carbon monoxide and optionally hydrogen in the presence of a catalyst system consisting of carbonyl complexes of noble metals of group VIII of the Periodic System of the elements, acetic acid, an organophosphorus or organonitrogen compound, methyl iodide and optionally carbonyl-yielding compounds of common metals. To this end, the disclosure provides for the reaction mixture containing acetic anhydride, acetic acid, ethylidene diacetate, methyl iodide, acetone and methyl acetate which is distilled off from the catalyst solution, or just its low boiler fraction consisting of methyl acetate, methyl iodide and acetone to be subjected wholly or partially to an extractive distillation with acetic acid and for pure methyl iodide to be distilled off; for an acetone/methyl acetate mixture to be distilled off from the acetic acid extract; for this mixture to be separated in known fashion and for the acetic acid extractant to be recycled into the extraction stage, if desired after separation of the final reaction products comprised of acetic anhydride; ethylidene diacetate and acetic acid.


title: Why Put FPGAs in your CPU socket

abstract: Summary form only given. Ever since FPGAs were invented, there has been great interest in using them as computing devices, and with the logic densities of today's devices, many interesting functions have been shown to have significant performance and energy benefits when implemented in FPGAs. However, when an application requires the combination of a high-performance CPU and an FPGA accelerator, the effectiveness of the FPGA is highly determined by the latency and bandwidth between the CPU, the CPU memory system and the FPGA and its memory system. Putting FPGAs into the CPU socket is one way to address this issue. This talk will present the history, the advantages and disadvantages, the challenges, architectures, programming models and applications of "insocket" accelerator systems.


title: Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment

abstract: Photo aesthetic quality evaluation is a challenging task in multimedia and computer vision fields. Conventional approaches suffer from the following three drawbacks: 1) the deemphasized role of semantic content that is many times more important than low-level visual features in photo aesthetics; 2) the difficulty to optimally fuse low-level and high-level visual cues in photo aesthetics evaluation; and 3) the absence of a sequential viewing path in the existing models, as humans perceive visually salient regions sequentially when viewing a photo. To solve these problems, we propose a new aesthetic descriptor that mimics humans sequentially perceiving visually/semantically salient regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space. Thereafter, each graphlet can be described by multiple types of visual features, both at low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent graphlets in a photo. They are sequentially linked into a path that simulates the process of humans actively viewing. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos that are marked as aesthetically pleasing by multiple users. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data; and 2) our photo aesthetic measure outperforms many of its competitors.


title: An Experimental Comparison of Pregel-like Graph Processing Systems

abstract: The introduction of Google's Pregel generated much interest in the field of large-scale graph data processing, inspiring the development of Pregel-like systems such as Apache Giraph, GPS, Mizan, and GraphLab, all of which have appeared in the past two years. To gain an understanding of how Pregel-like systems perform, we conduct a study to experimentally compare Giraph, GPS, Mizan, and GraphLab on equal ground by considering graph and algorithm agnostic optimizations and by using several metrics. The systems are compared with four different algorithms (PageRank, single source shortest path, weakly connected components, and distributed minimum spanning tree) on up to 128 Amazon EC2 machines. We find that the system optimizations present in Giraph and GraphLab allow them to perform well. Our evaluation also shows Giraph 1.0.0's considerable improvement since Giraph 0.1 and identifies areas of improvement for all systems.


title: Diphallus with Imperforate Anus and Complete Duplication of Recto-Sigmoid Colon and Lower Urinary Tract

abstract: Background: Diphallus is a rare anomaly and accompanying anomalies vary from bifid scrotum, bladder exstrophy, imperforate anus and colo‐rectal anomaly such as duplication, and other associated anomalies. Case Presentation: A 2‐day old infant is reported with imperforate anus and complete duplication of recto‐sigmoid colon, re ctal pouch, doubling of the genitalia with completely formed penis (diphallus), double bladder, urethra and hypospadias. No family history of abnormalities was noted. The patient underwent several operations: laparatory and colostomy at 3rd day of life, and after clinical and paraclinical investigations, cystoplasty, ureteral reimplantation and resection of left phallus were carried out when 4 months old. At the age of 1 year, after colostogram and total colon evaluation, laparatomy, resection of duplicated recto‐ sigmoid colon, and pull‐through was carried out; 3 months later c olostomy closure was performed and the patient discharged without complications. Conclusion: The patients with diphallus have to be examined carefully because of the high incidence of other systemic anomalies. Treatment of diphallus usually includes excision of the duplicated penile structure, its urethra, and repair of associated anomalies.


title: A big data processing methods for visualization

abstract: The current visualization has been difficult to adapt to the growing of big data. The paper presents method to use binned aggregation to reduce big data for visualize a variety of data types. Givens a data storage scheme of the dimensionality reduction block. Then for interactive visualization in binned plots through multivariate data tiles. For a large range of data, we cut into a lot of small pieces. When carrying out a range query, just use the divided pieces of data for this range be spliced. The big data through reduction into data block and be interactive checking among binned plots through many multivariate data tiles.


title: Speech Technology for Information Access: a South African Case Study

abstract: Telephone-based information access has the potential to deliver a significant positive impact in the developing world. We discuss some of the most important issues that must be addressed in order to realize this potential, including matters related to resource development, automatic speech recognition, text-to-speech systems, and user-interface design. Although our main focus has been on the eleven official languages of South Africa, we believe that many of these same issues will be relevant for the application of speech technology throughout the developing world.


title: Design of Neural Network PID Controller Based on Brushless DC Motor

abstract: For the performance and accuracy requirements of brushless DC motor speed control system, this paper integrates organically neural network and the traditional PID to constitute brushless DC motor speed control system based on BP neural network self-tuning parameters PID control. The traditional PID controller is used in the beginning several seconds, and then another parameter self-tuning PID controller based on BP neural network is converted to after training for seconds. Simulation model was established in Matlab/Simulink. The simulation results indicate that the neutral network PID controller can improve the robustness of the system and has better adaptabilities to the model and environments compared with the traditional PID controller.


title: Recognition of Plants by Leaves Digital Image and Neural Network

abstract: To identify different plants by leaves digital image is one key problem in precision farming. By the combination of image processing and neural network, Most of the image blocks of different plants could be correctly classified. Firstly, the image enhancement processing can make objects in the source image clear. Secondly, due to the different shapes and sizes of image blocks of leaves, they could be separated and extracted from sources. Then, by using image analysis tools from Matlab, these characters such as sizes, radius, perimeters, solidity, and eccentricity could be calculated. Then, using them as input data, create a radial basis function neural networks. Divide the input data into two parts. Select one part to train the network and the other to check the validity of the model. Finally, input data from other image frames under the same condition could be used to check the model. In this work, the total accuracy is about 80%. These methods was simple and highly effective, So they could easily be integrated into auto machines in the field, which can largely saving labor and enhance productivity.


title: From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach

abstract: In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.


title: Privacy Engineering for Digital Rights Management Systems

abstract: Internet-based distribution of mass-market content provides great opportunities for producers, distributors, and consumers, but it may seriously threaten users’ privacy. Some of the paths to loss of privacy are quite familiar (e.g., mining of credit-card data), but some are new or much more serious than they were in earlier distribution regimes. We examine the contributions that digital-rights-management (DRM) technology can make to both compromising and protecting users’ privacy. We argue that the privacy-enhancing technology (e.g., encryption, anonymity, and pseudonymity) that absorbs most of the attention of the security R&D community cannot by itself solve the privacy problems raised by DRM, although it can play a role in various solutions. Finally, we provide a list of “privacy engineering” principles for DRM systems, some of which are easy to implement and potentially quite effective.


title: The Internet of Things: A survey

abstract: This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.


title: Named Entity Recognition: Fallacies, challenges and opportunities

abstract: Abstract Named Entity Recognition serves as the basis for many other areas in Information Management. However, it is unclear what the meaning of Named Entity is, and yet there is a general belief that Named Entity Recognition is a solved task. In this paper we analyze the evolution of the field from a theoretical and practical point of view. We argue that the task is actually far from solved and show the consequences for the development and evaluation of tools. We discuss topics for further research with the goal of bringing the task back to the research scenario.


title: Payment Applications Make E-Commerce Mobile

abstract: Because vendors see a profitable opportunity in applications that let users pay for goods and services via mobile devices, they are releasing easier-to-use mobile-payment applications.


title: SiC MOSFETs for future motor drive applications

abstract: This paper investigates the switching performance of six-pack SiC MOSFET and Si IGBT modules for motor drive applications. Both the modules have same packaging and voltage rating (1.2 kV). The three bridge legs of the modules are paralleled forming a single half-bridge configuration for achieving higher output power. Turn-on and turn-off switching energy losses are measured using a standard double pulse methodology. The conduction losses from the datasheet and the switching energy losses obtained from the laboratory measurements are used as a look up table input when simulating the detailed inverter losses in a three-phase motor drive inverter. The total inverter loss is plotted for different switching frequencies in order to illustrate the performance improvement that SiC MOSFETs can bring over Si IGBTs for a motor drive inverter from the efficiency point of view. The overall analysis gives an insight into how SiC MOSFET outperforms Si IGBT over all switching frequency ranges with the advantages becoming more pronounced at higher frequencies and temperatures.


title: Visual cortex inspired features for object detection in X-ray images

abstract: Visual cortex inspired features mimic what we know of the brain's visual cortex, which is still the best existing object detection system regarding speed and accuracy. For this paper we benchmarked two prominent implementations of these features, Mutch and Lowe's SLF-HMAX and Pinto et al.'s V1-like, against the popular local invariant features SIFT and PHOW in combination with the bag of visual words approach. The benchmark task was the detection of illicit objects in X-ray images of luggage. X-ray inspection is one of the main means of preventing the transport of illicit objects into sensitive areas. The visual cortex inspired features performed superior to the conventional features, probably owing to the textureless nature of X-ray images and the encoding of geometric information.


title: A novel analog 360° phase shifter design in Ku and Ka bands

abstract: This article presents a new reflection-type 360° phase shifter, for Ku- and Ka-bands applications.At 12 GHz, the measured prototype has more than 360° phase shift with insertion loss of 3.3 ± 0.5 dB. For the Ka-band circuit, the lowest insertion loss is obtained at 30.3 GHz (3.6 dB ± 1.8 dB) and the phase shift is 315°. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1733–1736, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25307


title: Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

abstract: Unobtrusive and long-term monitoring of human vital signs are essential requirements for early diagnosis and prophylaxis due to many reasons, one of the most important being improving the quality of life. Currently, vital signs are continuously monitored through sensors attached to the body, such as multiple electrodes for measuring electrical activity of the heart. Such methods may be undesirable, especially for elderly, infants and other groups of people. In this paper, we introduce an improved technique for measuring heart rate from noisy ballistocardiogram signals acquired from 50 human volunteers in a sitting position using a massage chair. The signals are unobtrusively collected from a microbend fiber optic sensor embedded within the headrest of the chair, and then transmitted to a computer through a Bluetooth connection. The heart rate is computed using the multiresolution analysis of the maximal overlap discrete wavelet transform. The error between the proposed method and the reference ECG is estimated in beats per minute using the mean absolute error, where the system achieved relatively good results (7.31 ± 1.60) despite the large amount of motion artifacts produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. Unlike the complete ensemble empirical mode decomposition algorithm, previously employed for heart rate estimation, the suggested system is much faster. Hence, it can be used in real-time applications.


title: Network diversity and economic development.

abstract: Social networks form the backbone of social and economic life. Until recently, however, data have not been available to study the social impact of a national network structure. To that end, we combined the most complete record of a national communication network with national census data on the socioeconomic well-being of communities. These data make possible a population-level investigation of the relation between the structure of social networks and access to socioeconomic opportunity. We find that the diversity of individuals' relationships is strongly correlated with the economic development of communities.


CUSAT_NLP@DPIL-FIRE2016: Malayalam Paraphrase Detection


title: SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy.

abstract: SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.


title: Incidence of Cervical Branch Injury with ???Marginal Mandibular Nerve Pseudo-Paralysis??? in Patients Undergoing Face Lift

abstract: The anatomy of the cervical and marginal mandibular branches of the facial nerve is reviewed. In the senior author's practice, "pseudoparalysis of the marginal mandibular nerve" due to cervical branch injury occurred in 34 of 2002 superficial musculoaponeurotic system-platysma face lifts (1.7 percent) and was associated with a full recovery in 100 percent of cases within a time period ranging from 3 weeks to 6 months. Cervical branch injury can be distinguished from marginal mandibular nerve injury by the fact that the patient will be able to evert the lower lip because of a functioning mentalis muscle.


title: Deep code comment generation

abstract: During software maintenance, code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in the software projects. Developers have to infer the functionality from the source code. This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generated comments aim to help developers understand the functionality of Java methods. DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. We use a deep neural network that analyzes structural information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects from GitHub. We evaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepCom outperforms the state-of-the-art by a substantial margin.


title: Multi-faceted trust and distrust prediction for recommender systems

abstract: Many trust-aware recommender systems have explored the value of explicit trust, which is specified by users with binary values and simply treated as a concept with a single aspect. However, in social science, trust is known as a complex term with multiple facets, which has not been well exploited in prior recommender systems. In this paper, we attempt to address this issue by proposing a (dis)trust framework with considerations of both interpersonal and impersonal aspects of trust and distrust. Specifically, four interpersonal aspects (benevolence, competence, integrity and predictability) are computationally modeled based on users' historic ratings, while impersonal aspects are formulated from the perspective of user connections in trust networks. Two logistic regression models are developed and trained by accommodating these factors, and then applied to predict continuous values of users' trust and distrust, respectively. Trust information is further refined by corresponding predicted distrust information. The experimental results on real-world data sets demonstrate the effectiveness of our proposed model in further improving the performance of existing state-of-the-art trust-aware recommendation approaches. Multi-faceted trust framework considering both interpersonal and impersonal aspects is employed to model trust and distrust.Trust values refined by logistic regression models are evaluated in three representative trust-based recommendation methods.Experiments on three real datasets demonstrate both interpersonal and impersonal aspects are useful in different scenarios.Our ability of predicting the implicit trust values could bootstrap the trust network for recommender systems.


title: RGB-D flow: Dense 3-D motion estimation using color and depth

abstract: 3-D motion estimation is a fundamental problem that has far-reaching implications in robotics. A scene flow formulation is attractive as it makes no assumptions about scene complexity, object rigidity, or camera motion. RGB-D cameras provide new information useful for computing dense 3-D flow in challenging scenes. In this work we show how to generalize two-frame variational 2-D flow algorithms to 3-D. We show that scene flow can be reliably computed using RGB-D data, overcoming depth noise and outperforming previous results on a variety of scenes. We apply dense 3-D flow to rigid motion segmentation.


title: 4-Hydroxy-2-Nonenal, a Reactive Product of Lipid Peroxidation, and Neurodegenerative Diseases: A Toxic Combination Illuminated by Redox Proteomics Studies

abstract: Abstract Significance: Among different forms of oxidative stress, lipid peroxidation comprises the interaction of free radicals with polyunsaturated fatty acids, which in turn leads to the formation of highly reactive electrophilic aldehydes. Among these, the most abundant aldehydes are 4-hydroxy-2-nonenal (HNE) and malondialdehyde, while acrolein is the most reactive. HNE is considered a robust marker of oxidative stress and a toxic compound for several cell types. Proteins are particularly susceptible to modification caused by HNE, and adduct formation plays a critical role in multiple cellular processes. Recent Advances: With the outstanding progress of proteomics, the identification of putative biomarkers for neurodegenerative disorders has been the main focus of several studies and will continue to be a difficult task. Critical Issues: The present review focuses on the role of lipid peroxidation, particularly of HNE-induced protein modification, in neurodegenerative diseases. By comparing results obt...


title: Health data integration with Secured Record Linkage: A practical solution for Bangladesh and other developing countries

abstract: Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. Also, identifiable health data in electronic health repositories may produce a significant risk to patient privacy and also make the health information systems security vulnerable to hackers. In this paper, we have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh for developing national health data warehouse. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. Experimental results show that our proposed method successfully linked records with acceptable accuracy for noisy data in the absence of any standard ID like SSN.


Mobile Apps for Omnichannel Retailing: Revealing the Emerging Showroom Phenomenon


title: Heart rate monitoring: applications and limitations.

abstract: Over the last 20 years, heart rate monitors (HRMs) have become a widely used training aid for a variety of sports. The development of new HRMs has also evolved rapidly during the last two decades. In addition to heart rate (HR) responses to exercise, research has recently focused more on heart rate variability (HRV). Increased HRV has been associated with lower mortality rate and is affected by both age and sex. During graded exercise, the majority of studies show that HRV decreases progressively up to moderate intensities, after which it stabilises. There is abundant evidence from cross-sectional studies that trained individuals have higher HRV than untrained individuals. The results from longitudinal studies are equivocal, with some showing increased HRV after training but an equal number of studies showing no differences. The duration of the training programmes might be one of the factors responsible for the versatility of the results.HRMs are mainly used to determine the exercise intensity of a training session or race. Compared with other indications of exercise intensity, HR is easy to monitor, is relatively cheap and can be used in most situations. In addition, HR and HRV could potentially play a role in the prevention and detection of overtraining. The effects of overreaching on submaximal HR are controversial, with some studies showing decreased rates and others no difference. Maximal HR appears to be decreased in almost all 'overreaching' studies. So far, only few studies have investigated HRV changes after a period of intensified training and no firm conclusions can be drawn from these results. The relationship between HR and oxygen uptake (VO(2)) has been used to predict maximal oxygen uptake (VO(2max)). This method relies upon several assumptions and it has been shown that the results can deviate up to 20% from the true value. The HR-VO(2) relationship is also used to estimate energy expenditure during field conditions. There appears to be general consensus that this method provides a satisfactory estimate of energy expenditure on a group level, but is not very accurate for individual estimations. The relationship between HR and other parameters used to predict and monitor an individual's training status can be influenced by numerous factors. There appears to be a small day-to-day variability in HR and a steady increase during exercise has been observed in most studies. Furthermore, factors such as dehydration and ambient temperature can have a profound effect on the HR-VO(2) relationship.


title: A unified view of generative models for networks: models, methods, opportunities, and challenges

abstract: Research on probabilistic models of networks now spans a wide variety of fields, including physics, sociology, biology, statistics, and machine learning. These efforts have produced a diverse ecology of models and methods. Despite this diversity, many of these models share a common underlying structure: pairwise interactions (edges) are generated with probability conditional on latent vertex attributes. Differences between models generally stem from different philosophical choices about how to learn from data or different empirically-motivated goals. The highly interdisciplinary nature of work on these generative models, however, has inhibited the development of a unified view of their similarities and differences. For instance, novel theoretical models and optimization techniques developed in machine learning are largely unknown within the social and biological sciences, which have instead emphasized model interpretability. Here, we describe a unified view of generative models for networks that draws together many of these disparate threads and highlights the fundamental similarities and differences that span these fields. We then describe a number of opportunities and challenges for future work that are revealed by this view.


title: Indoor positioning: A review of indoor ultrasonic positioning systems

abstract: In order to provide location information for indoor applications and context-aware computing, a lot of research is being done since last decade for development of real-time Indoor location system. In this paper, we have investigated indoor location concepts and have focused two major technologies used in many indoor location systems i.e. RF and ultrasonic. An overview of various RF systems that use different RF properties for location estimation has been given. Ultrasonic systems have been reviewed in detail as they provide low cost fine grained location systems. A few well known ultrasonic location systems have been investigated with a comparison of the system based on performance, accuracy and limitations.


title: Mobile M2M communication architectures, upcoming challenges, applications, and future directions

abstract: Machine-to-machine (M2M) communication is becoming an increasingly important part of mobile traffic and thus also a topic of major interest for mobile communication research and telecommunication standardization bodies. M2M communication offers various ubiquitous services and is one of the main enablers of the vision inspired by the Internet of Things (IoT). The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability, and decreasing costs of future mobile networks. Nevertheless, M2M communications pose significant challenges to mobile networks, e.g., due to the expected large number of devices with simultaneous access for sending small-sized data, and a diverse application range. This paper provides a detailed survey of M2M communications in the context of mobile networks, and thus focuses on the latest Long-Term Evolution-Advanced (LTE-A) networks. Moreover, the end-to-end network architectures and reference models for M2M communication are presented. Furthermore, a comprehensive survey is given to M2M service requirements, major current standardization efforts, and upcoming M2M-related challenges. In addition, an overview of upcoming M2M services expected in 5G networks is presented. In the end, various mobile M2M applications are discussed followed by open research questions and directions.


title: Survey of review spam detection using machine learning techniques

abstract: Online reviews are often the primary factor in a customer’s decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or services. Because of their impact, manufacturers and retailers are highly concerned with customer feedback and reviews. Reliance on online reviews gives rise to the potential concern that wrongdoers may create false reviews to artificially promote or devalue products and services. This practice is known as Opinion (Review) Spam, where spammers manipulate and poison reviews (i.e., making fake, untruthful, or deceptive reviews) for profit or gain. Since not all online reviews are truthful and trustworthy, it is important to develop techniques for detecting review spam. By extracting meaningful features from the text using Natural Language Processing (NLP), it is possible to conduct review spam detection using various machine learning techniques. Additionally, reviewer information, apart from the text itself, can be used to aid in this process. In this paper, we survey the prominent machine learning techniques that have been proposed to solve the problem of review spam detection and the performance of different approaches for classification and detection of review spam. The majority of current research has focused on supervised learning methods, which require labeled data, a scarcity when it comes to online review spam. Research on methods for Big Data are of interest, since there are millions of online reviews, with many more being generated daily. To date, we have not found any papers that study the effects of Big Data analytics for review spam detection. The primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.


title: A Novel "Credit Union" Model of Cloud Computing

abstract: Cloud Computing is drawing people’s attention from all walks of the IT world. It promises significant reduction of cost among many other advantages as it proclaims, including increased availability, fast provision, on-demand, and pay-per-use, etc. This paper presents a novel model of Cloud Computing, named “Credit Union” model (referred to as the CU model or CUM for short). This model is motivated by the cooperative business model of the many credit unions that have been widely practiced as a type of financial institutions world-wide. The CU model aims at utilizing the vast, underutilized computing resources in homes and offices, and transforming them into a self-provisioned community cloud that mimics the business model of a credit union, i.e., membership and credits are obtained by contributing spare computing resources. Clouds built based on the CU model, referred to as CU clouds, bear the following advantageous characteristics comparing to the general clouds: complete vendor independence, improved availability (due to reduced internet-dependence), better security, and superb sustainability (green computing). This paper expounds the principles and motivations of the CU model, addresses its implementation architecture and related issues, and outlooks prospect applications.


title: Circularly Polarized Substrate-Integrated Waveguide Tapered Slot Antenna for Millimeter-Wave Applications

abstract: A low-cost wideband circularly polarized (CP) tapered slot antenna based on substrate-integrated waveguide (SIW) is presented for millimeter-wave applications. After using two linearly symmetrical slots on the two sides of the SIW, a bandwidth from 80.1 to 108 GHz for both axial ratio <; 3 dB and VSWR <; 2 can be achieved. The measuring results also show that the antenna can generate good endfire radiation patterns and gain around 7.9 dBi over this bandwidth. The mechanism for exciting CP wave of the proposed antenna and the influence of the size of the metalized via holes of SIW are also analyzed. The proposed antenna was fabricated by the standard single-layered printed circuit board (PCB) technology, and the measured results agree reasonably with the simulated ones.


title: The use and effect of video game design theory in the creation of game-based systems for upper limb stroke rehabilitation

abstract: Upper limb exercise is often neglected during post-stroke rehabilitation. Video games have been shown to be useful in providing environments in which patients can practise repetitive, functionally meaningful movements, and in inducing neuroplasticity. The design of video games is often focused upon a number of fundamental principles, such as reward, goals, challenge and the concept of meaningful play, and these same principles are important in the design of games for rehabilitation. Further to this, there have been several attempts for the strengthening of the relationship between commercial game design and rehabilitative game design, the former providing insight into factors that can increase motivation and engagement with the latter. In this article, we present an overview of various game design principles and the theoretical grounding behind their presence, in addition to attempts made to utilise these principles in the creation of upper limb stroke rehabilitation systems and the outcomes of their use....


title: Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks

abstract: In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activation only from a global perspective, our research aims to clarify how a certain input unit (dimension) collaborates with other units (dimensions) to constitute inference patterns of the neural network and thus contribute to the network output. The analysis of local contextual effects w.r.t. certain input units is of special values in real applications. Explaining the logic of the alphaGo Zero model is a typical application. In experiments, our method successfully disentangled the rationale of each move during the Go game.


title: Perceptually motivated automatic sharpness enhancement using hierarchy of non-local means

abstract: We address the problem of sharpness enhancement of images. Existing hierarchical techniques that decompose an image into a smooth image and high frequency components based on Gaussian filter and bilateral filter suffer from halo effects, whereas techniques based on weighted least squares extract low contrast features as detail. Other techniques require multiple images and are not tolerant to noise.


title: Power factor correction for AC-DC converters with cost effective inductive filtering

abstract: This paper presents a new, simple and cost effective solution for the power factor correction of the input current of a switch mode power supply, which meets the IEC limits on harmonic distortion. A sloped air-gap inductor, which adapts its inductance values to the input current conditions of the power supply, is used to shape the current to comply with the limits for harmonic distortions as defined in EN61000-3-2. It is shown that using a classical inductor design, the filter inductor is too bulky, whereas the volume of the sloped air-gap choke is as small as the switching transformer of the power (e.g. 100 kHz) supply, In fact, the volume required is no bigger than that required for an active PFC circuit, but at a fraction of the cost.


title: Thyristor Control of Shaded-Pole Induction Motors

abstract: ABSTRACT Thyristor phase control of shaded-pole motors provides a simple system for low power variable-speed drive. The performance analysis of the system is complicated by the presence of harmonics in the voltage applied and the discontinuity in the motor current. The paper presents a numerical method of solving the basic differential equations of the motor which easily takes phase control into consideration. Computed and experimental results are compared.


title: Metabolic disorders of purine metabolism affecting the nervous system.

abstract: The purines are a group of molecules used by all cells for many vital biochemical processes including energy-requiring enzymatic reactions, cofactor-requiring reactions, synthesis of DNA or RNA, signaling pathways within and between cells, and other processes. Defects in some of the enzymes of purine metabolism are known to be associated with specific clinical disorders, and neurological problems may be a presenting sign or the predominant clinical problem for several of them. This chapter describes three disorders for which the clinical features and metabolic basis are well characterized. Deficiency of adenylosuccinate-lyase (ADSL) causes psychomotor retardation, epilepsy, and autistic features. Lesch-Nyhan disease is caused by deficiency of hypoxanthine-guanine phosphoribosyltransferase (HPRT) and is characterized by hyperuricemia, motor and cognitive disability, and self-injurious behavior. Deficiency of myoadenylate deaminase (mAMPD) is associated with myopathic features. In addition to these disorders, several other disorders are briefly summarized. These include defects of phosphoribosylpyrophosphate synthase, adenosine deaminase (ADA), purine nucleoside phosphorylase (PND), deoxyguanosine kinase (dGK), or IMP dehydrogenase (IMPDH). Each of these disorders provides an unusual window on the unique importance of purine metabolism for function of different parts of the nervous system.


title: Novelty detection for time series data analysis in water distribution systems using support vector machines

abstract: The sampling frequency and quantity of time series data collected from water distribution systems has been increasing in recent years, giving rise to the potential for improving system knowledge if suitable automated techniques can be applied, in particular, machine learning. Novelty (or anomaly) detection refers to the automatic identification of novel or abnormal patterns embedded in large amounts of “normal” data. When dealing with time series data (transformed into vectors), this means abnormal events embedded amongst many normal time series points. The support vector machine is a data-driven statistical technique that has been developed as a tool for classification and regression. The key features include statistical robustness with respect to non-Gaussian errors and outliers, the selection of the decision boundary in a principled way, and the introduction of nonlinearity in the feature space without explicitly requiring a nonlinear algorithm by means of kernel functions. In this research, support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data. No use is made of past event histories collected through other information sources. The support vector regression methodology, whose robustness derives from the training error function, is applied to a case study.


title: Integrated Monitoring and Information Systems for Managing Aquatic Invasive Species in a Changing Climate

abstract: Changes in temperature, precipitation, and other climatic drivers and sea-level rise will affect populations of existing native and non-native aquatic species and the vulnerability of aquatic environments to new invasions. Monitoring surveys provide the foundation for assessing the combined effects of climate change and invasions by providing baseline biotic and environmental conditions, although the utility of a survey depends on whether the results are quantitative or qualitative, and other design considerations. The results from a variety of monitoring programs in the United States are available in integrated biological information systems, although many include only non-native species, not native species. Besides including natives, we suggest these systems could be improved through the development of standardized methods that capture habitat and physiological requirements and link regional and national biological databases into distributed Web portals that allow drawing information from multiple sources. Combining the outputs from these biological information systems with environmental data would allow the development of ecological- niche models that predict the potential distribution or abundance of native and non-native species on the basis of current environmental conditions. Environmental projections from climate models can be used in these niche models to project changes in species distributions or abundances under altered climatic conditions and to identify potential high-risk invaders. There are, however, a number of challenges, such as uncertainties associated with projections from climate and niche models and difficulty in integrating data with different temporal and spatial granularity. Even with these uncertainties, integration of biological and environmental informationsystems,nichemodels,andclimateprojectionswouldimprovemanagementofaquaticecosystems under the dual threats of biotic invasions and climate change.


title: Viscous Flow With Free Surface Motion by Least Square Finite Element Method

abstract: A method for simulation of free surface problems is presented. Based on the viscous incompressible Navier-Stokes equations, space discretization of the flow is obtained by the least square finite element method. The time evolution is obtained by the finite difference method. Lagrangian description is used to track the free surface. The results are compared with the experimental dam break results, including water collapse in a 2D rectangular section and in a 3D cylinder section. A good agreement is achieved for the distance of surge front as well as the height of the residual column.


title: Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

abstract: In this paper, we study aspects of single microphone speech enhancement SE based on deep neural networks DNNs. Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker, and the signal-to-noise ratio SNR. Furthermore, we investigate how specialized DNN-based SE systems, which have been trained to be either noise type specific, speaker specific or SNR specific, perform relative to DNN based SE systems that have been trained to be noise type general, speaker general, and SNR general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error STSA-MMSE based SE algorithm. We show that DNN-based SE systems, when trained specifically to handle certain speakers, noise types and SNRs, are capable of achieving large improvements in estimated speech quality SQ and speech intelligibility SI, when tested in matched conditions. Furthermore, we show that improvements in estimated SQ and SI can be achieved by a DNN-based SE system when exposed to unseen speakers, genders and noise types, given a large number of speakers and noise types have been used in the training of the system. In addition, we show that a DNN-based SE system that has been trained using a large number of speakers and a wide range of noise types outperforms a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI for others.


title: FloodDefender: Protecting data and control plane resources under SDN-aimed DoS attacks

abstract: The separated control and data planes in software-defined networking (SDN) with high programmability introduce a more flexible way to manage and control network traffic. However, SDN will experience long packet delay and high packet loss rate when the communication link between two planes is jammed by SDN-aimed DoS attacks with massive table-miss packets. In this paper, we propose FloodDefender, an efficient and protocol-independent defense framework for SDN/OpenFlow networks to mitigate DoS attacks. It stands between the controller platform and other controller apps, and can protect both the data and control plane resources by leveraging three new techniques: table-miss engineering to prevent the communication bandwidth from being exhausted; packet filter to identify attack traffic and save computational resources of the control plane; and flow rule management to eliminate most of useless flow entries in the switch flow table. All designs of FloodDefender conform to the OpenFlow policy, requiring no additional devices. We implement a prototype of FloodDefender and evaluate its performance in both software and hardware environments. Experimental results show that FloodDefender can efficiently mitigate the SDN-aimed DoS attacks, incurring less than 0.5% CPU computation to handle attack traffic, only 18ms packet delay and 5% packet loss rate under attacks.


title: Urban area characterization based on crowd behavioral lifelogs over Twitter

abstract: Recent location-based social networking sites are attractively providing us with a novel capability of monitoring massive crowd lifelogs in the real-world space. In particular, they make it easier to collect publicly shared crowd lifelogs in a large scale of geographic area reflecting the crowd's daily lives and even more characterizing urban space through what they have in minds and how they behave in the space. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing crowd lifelogs in urban area over the social networking sites. In order to collect crowd behavioral data, we exploit the most famous microblogging site, Twitter, where a great deal of geo-tagged micro lifelogs emitted by massive crowds can be easily acquired. We first present a model to deal with crowds' behavioral logs on the social network sites as a representing feature of urban space's characteristics, which will be used to conduct crowd-based urban characterization. Based on this crowd behavioral feature, we will extract significant crowd behavioral patterns in a period of time. In the experiment, we conducted the urban characterization by extracting the crowd behavioral patterns and examined the relation between the regions of common crowd activity patterns and the major categories of local facilities.


title: RAGuard: A Hardware Based Mechanism for Backward-Edge Control-Flow Integrity

abstract: Control-flow integrity (CFI) is considered as a general and promising method to prevent code-reuse attacks, which utilize benign code sequences to realize arbitrary computation. Current approaches can efficiently protect control-flow transfers caused by indirect jumps and function calls (forward-edge CFI). However, they cannot effectively protect control-flow caused by the function return (backward-edge CFI). The reason is that the set of return addresses of the functions that are frequently called can be very large, which might bend the backward-edge CFI. We address this backward-edge CFI problem by proposing a novel hardware-assisted mechanism (RAGuard) that binds a message authentication code to each return address and enhances security via a physical unclonable function and a hardware hash function. The message authentication codes can be stored on the program stack with return address. RAGuard hardware automatically verifies the integrity of return addresses. Our experiments show that for a subset of the SPEC CPU2006 benchmarks, RAGuard incurs 1.86% runtime overheads on average with no need for OS support.


title: SentimentAnalysis: ACombinedApproach

abstract: Sentiment analysis is an important current research area. This paper combines rule-based classification, supervised learning and machine learning into a new combined method. This method is tested on movie reviews, product reviews and MySpace comments. The results show that a hybrid classification can improve the classification effectiveness in terms of micro- and macro-averaged F1. F1 is a measure that takes both the precision and recall of a classifier’s effectiveness into account. In addition, we propose a semi-automatic, complementary approach in which each classifier can contribute to other classifiers to achieve a good level of effectiveness.


title: Online mining of temporal maximal utility itemsets from data streams

abstract: Data stream mining has become an emerging research topic in the data mining field, and finding frequent itemsets is an important task in data stream mining with wide applications. Recently, utility mining is receiving extensive attentions with two issues reconsidered: First, the utility (e.g., profit) of each item may be different in real applications; second, the frequent itemsets might not produce the highest utility. In this paper, we propose a novel algorithm named GUIDE (Generation of temporal maximal Utility Itemsets from Data strEams) which can find temporal maximal utility itemsets from data streams. A novel data structure, namely, TMUI-tree (Temporal Maximal Utility Itemset tree), is also proposed for efficiently capturing the utility of each itemset with one-time scanning. The main contributions of this paper are as follows: 1) GUIDE is the first one-pass utility-based algorithm for mining temporal maximal utility itemsets from data streams, and 2) TMUI-tree is efficient and easy to maintain. The experimental results show that our approach outperforms other existing utility mining algorithms like Two-Phase algorithm under the data stream environments.