title: Effectiveness of a Service-oriented Router in Future Content Delivery Networks

abstract: Content Delivery Networks (CDNs) constitute a major portion of Internet traffic. To cope with increasing demand for content, CDNs have deployed distributed infrastructures on Internet Service Providers (ISPs') networks. Most CDN systems optimize their traffic flow using Domain Name Systems. However, they do not collaborate with the ISPs, and the lack of collaboration limits performance such as end-user latency. Meanwhile, in future networks, it is anticipated that network routers will be equipped with more processing power and storage modules for providing most effective end-user services. From this viewpoint, a Service-oriented Router (SoR) is introduced to accelerate content-based services. In this paper, the benefits of introducing an SoR to an ISP network for maintaining ISP-CDN collaboration is outlined. Furthermore, a prototype design of the proposed system is presented. Simulations clearly demonstrate the effectiveness of the proposed ISP-CDN collaboration, which yields a 30–50% reduction in end-user latency.


title: Far-infrared pedestrian detection for advanced driver assistance systems using scene context

abstract: Pedestrian detection is one of the most critical but challenging components in advanced driver assistance systems. Far-infrared (FIR) images are well-suited for pedestrian detection even in a dark environment. However, most current detection approaches just focus on pedestrian patterns themselves, where robust and real-time detection cannot be well achieved. We propose a fast FIR pedestrian detection approach, called MAP-HOGLBP-T, to explicitly exploit the scene context for the driver assistance system. In MAP-HOGLBP-T, three algorithms are developed to exploit the scene contextual information from roads, vehicles, and background objects of high homogeneity, and we employ the Bayesian approach to build a classifier learner which respects the scene contextual information. We also develop a multiframe approval scheme to enhance the detection performance based on spatiotemporal continuity of pedestrians. Our empirical study on real-world datasets has demonstrated the efficiency and effectiveness of the proposed method. The performance is shown to be better than that of state-of-the-art low-level feature-based approaches.


title: A dual frequency microstrip patch antenna for high-precision GPS applications

abstract: Since the Global Positioning System (GPS) was launched, significant progress has been made in GPS receiver technology but the multipath error remains an unsolved problem. As solutions based on signal processing are not adequate, the most effective approach to discriminate between direct and multipath waves is to specify new and more restrictive criteria in the design of the receiving antenna. An innovative low profile, lightweight dual band (L1+L2) GPS radiator with a high multipath-rejection capability is presented. The proposed solution has been realized by two stacked shorted annular elliptical patch antennas. In what follows, a detailed account of the design process and antenna performances is given, presenting both simulated and experimental results.


title: A Ciphertext-Policy Attribute-Based Proxy Re-encryption with Chosen-Ciphertext Security

abstract: Cipher text-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE) extends the traditional Proxy Re-Encryption (PRE) by allowing a semi-trusted proxy to transform a cipher text under an access policy to the one with the same plaintext under another access policy (i.e. attribute-based re-encryption). The proxy, however, learns nothing about the underlying plaintext. CP-ABPRE has many real world applications, such as fine-grained access control in cloud storage systems and medical records sharing among different hospitals. Previous CP-ABPRE schemes leave how to be secure against Chosen-Cipher text Attacks (CCA) as an open problem. This paper, for the first time, proposes a new CP-ABPRE to tackle the problem. The new scheme supports attribute-based re-encryption with any monotonic access structures. Despite our scheme is constructed in the random oracle model, it can be proved CCA secure under the decisional q-parallel bilinear Diffie-Hellman exponent assumption.


title: Empirical Analysis of Predictive Algorithms for Collaborative Filtering

abstract: Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. ::: ::: Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metr rics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.


title: Learning to Predict Human Behavior in Crowded Scenes

abstract: Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social Forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behavior learned by our model. Moreover, we introduce a new characterization that describes the “social sensitivity” at which two targets interact. We use this characterization to define “navigation styles” and improve both forecasting models and state-of-the-art multi-target tracking – whereby the learned forecasting models help the data association step.


title: An intrusion detection method for securing in-vehicle CAN bus

abstract: Controller area network (CAN) bus has become the most used protocol in automotive network since its robustness and efficiency. However, CAN bus does not have enough security prosperities to protect the whole automotive system even to protect its network. So, security mechanism to protect CAN bus became an emergency need. One of the efficient methods for securing CAN bus, is Intrusion Detection System (IDS). In this work, a simple intrusion detection method for CAN bus is proposed. Our algorithm does not require any modification in standard procedure of CAN bus nor to be implemented in each calculators of network.


title: Areas of brain activation in males and females during viewing of erotic film excerpts

abstract: Various lines of evidence indicate that men generally experience greater sexual arousal (SA) to erotic stimuli than women. Yet, little is known regarding the neurobiological processes underlying such a gender difference. To investigate this issue, functional magnetic resonance imaging was used to compare the neural correlates of SA in 20 male and 20 female subjects. Brain activity was measured while male and female subjects were viewing erotic film excerpts. Results showed that the level of perceived SA was significantly higher in male than in female subjects. When compared to viewing emotionally neutral film excerpts, viewing erotic film excerpts was associated, for both genders, with bilateral blood oxygen level dependant (BOLD) signal increases in the anterior cingulate, medial prefrontal, orbitofrontal, insular, and occipitotemporal cortices, as well as in the amygdala and the ventral striatum. Only for the group of male subjects was there evidence of a significant activation of the thalamus and hypothalamus, a sexually dimorphic area of the brain known to play a pivotal role in physiological arousal and sexual behavior. When directly compared between genders, hypothalamic activation was found to be significantly greater in male subjects. Furthermore, for male subjects only, the magnitude of hypothalamic activation was positively correlated with reported levels of SA. These findings reveal the existence of similarities and dissimilarities in the way the brain of both genders responds to erotic stimuli. They further suggest that the greater SA generally experienced by men, when viewing erotica, may be related to the functional gender difference found here with respect to the hypothalamus. Hum. Brain Mapping 16:1–13, 2002. © 2002 Wiley-Liss, Inc.


title: Brief review on snubber circuits

abstract: This paper presents a summary of the main types of snubber circuits; generally classified as dissipative and non-dissipative or active and passive snubbers. This type of circuits are commonly used because of they can suppress electrical spikes, allowing a better performance on the main electrical circuit. This article intent to describe the currently snubber circuits and its applications without getting into their design.


title: A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

abstract: How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on in this paper, known as the cold-start problem. In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information? Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming that a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS. In comparison with three baselines, PdMS improves the performance as measured by the nDCG. These improvements are demonstrated on real, public datasets.


title: A COMPARISON OF ERP-SUCCESS MEASUREMENT APPROACHES

abstract: ERP projects are complex purposes which influence main internal and external operations of companies. There are different research approaches which try to develop models for IS / ERP success measurement or IT-success measurement in general. Each model has its own area of application and sometimes a specific measurement approach based, for instance, on different systems or different stakeholders involved. This research paper shows some of the most important models developed in the literature and an overview of the different approaches of the models. An analysis which shows the strengths, weaknesses and the cases in which the specific model could be used is made.


title: ECG signal denoising by wavelet transform thresholding

abstract: In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. In this paper a new approach based on the threshold value of ECG signal determination is proposed using Wavelet Transform coefficients. Electrocardiography has had a profound influence on the practice of medicine. The electrocardiogram signal contains an important amount of information that can be exploited in different manners. The ECG signal allows for the analysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals are used to verify the proposed method using MATLAB software. Method presented in this paper is compared with the Donoho's method for signal denoising meanwhile better results are obtained for ECG signals by the proposed algorithm.


title: Dual-band 8-element MIMO antenna with short neutral line for 5G mobile handset

abstract: A design of compact dual band 8-element MIMO antenna using folded monopole structures is proposed. The MIMO antenna consists of 8 folded monopole antenna elements, each two of them are symmetrically placed on the orthogonal frame corners of the substrate, and closely located to each other with no clearance on the ground. The folded monopole arms are used as radiation elements to resonate at two different bands of 3.4–3.6 GHz and 4.55–4.75 GHz. Each closely placed two antenna elements are connected by a 3 mm short neutral line which can be used to reduce the mutual coupling at both bands. By optimizing the antenna structure and neutral line, dual band decoupling can be achieved. The isolation and efficiency of the MIMO antenna is improved and the calculated ergodic channel capacities with a 20 dB SNR at both bands reach to 41.2 bps/Hz and 40.3 bps/Hz, respectively, very close to the ideal case of 8×8 MIMO system.


title: Connectionist Temporal Classification

abstract: This chapter introduces the connectionist temporal classification (CTC) output layer for recurrent neural networks (Graves et al., 2006). As its name suggests, CTC was specifically designed for temporal classification tasks; that is, for sequence labelling problems where the alignment between the inputs and the target labels is unknown. Unlike the hybrid approach described in the previous chapter, CTC models all aspects of the sequence with a single neural network, and does not require the network to be combined with a hidden Markov model. It also does not require presegmented training data, or external postprocessing to extract the label sequence from the network outputs. Experiments on speech and handwriting recognition show that a BLSTM network with a CTC output layer is an effective sequence labeller, generally outperforming standardHMMsandHMM-neural network hybrids, as well asmore recent sequence labelling algorithms such as large margin HMMs (Sha and Saul, 2006) and conditional random fields (Lafferty et al., 2001).


abstract: Printed atmospheric spark gaps as ESD-protection on PCBs are examined. At first an introduction to the physic behind spark gaps. Afterward the time lag (response time) vs. voltage is measured with high load impedance. The dependable clamp voltage (will be defined later) is measured as a function of the load impedance and the local field in the air gap is simulated with FIT simulation software. At last the observed results are discussed on the basic of the physic and the simulations.


title: Breakthrough candidaemias during empirical therapy with fluconazole in non-cancer and non-HIV adults caused by in vitro-susceptible Candida spp.: report of 33 cases.

abstract: The objective of this study was to assess risk factors and the outcome of breakthrough fungaemias (BFs) occurring during fluconazole (FLU) therapy in non-cancer and non-HIV individuals. Thirty-three fungaemias occurring during therapy with FLU among a total of 310 fungaemias observed within a 10-y national survey were analysed. The agar disk diffusion method was used for antifungal susceptibility testing and the Vitek system for species identification. Univariate and multivariate analysis was performed to determine risk factors for BF. All BFs were due to species known to be susceptible to FLU: Candida albicans (25/33), C. parapsilosis (6/33) and C. guillermondii (2/33). The mean number of positive blood cultures per episode was 2.4. The MIC of Candida spp. to FLU was 0.5-8 mg/ml (all strains were susceptible in vitro). Neonatal age (< 4 weeks), very low birth weight, prior surgery, central venous catheter placement, artificial ventilation, total parenteral nutrition and C. parapsilosis were significan...


title: Online tuning fuzzy PID controller using robust extended Kalman filter

abstract: Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.


title: Deep Learning Scaling is Predictable, Empirically

abstract: Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would like a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements to advance the state-of-the-art. This paper presents a large scale empirical characterization of generalization error and model size growth as training sets grow. We introduce a methodology for this measurement and test four machine learning domains: machine translation, language modeling, image processing, and speech recognition. Our empirical results show power-law generalization error scaling across a breadth of factors, resulting in power-law exponents---the"steepness"of the learning curve---yet to be explained by theoretical work. Further, model improvements only shift the error but do not appear to affect the power-law exponent. We also show that model size scales sublinearly with data size. These scaling relationships have significant implications on deep learning research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and underscore the importance of continued computational scaling.


title: An Application of Latent Dirichlet Allocation to Analyzing Software Evolution

abstract: We develop and apply unsupervised statistical topic models, in particular latent Dirichlet allocation, to identify functional components of source code and study their evolution over multiple project versions. We present results for two large, open source Java projects, Eclipse and Argo UML, which are well-known and well-studied within the software mining community. Our results demonstrate the effectiveness of probabilistic topic models in automatically summarizing the temporal dynamics of software concerns, with direct application to project management and program understanding. In addition to detecting the emergence of topics on the release timeline which represent integration points for key source code functionality, our techniques can also be used to pinpoint refactoring events in the underlying software design, as well as to identify general programming concepts whose prevalence is dependent only of the size of the code base to be analyzed. Complete results are available from our supplementary materials website at http://sourcerer.ics.uci.edu/icmla2008/software_evolution.html.


title: A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems

abstract: This paper presents a similarity-based approach for estimating the Remaining Useful Life (RUL) in prognostics. The approach is especially suitable for situations in which abundant run-to-failure data for an engineered system are available. Data from multiple units of the same system are used to create a library of degradation patterns. When estimating the RUL of a test unit, the data from it will be matched to those patterns in the library and the actual life of those matched units will be used as the basis of estimation. This approach is used to tackle the data challenge problem defined by the 2008 PHM Data Challenge Competition, in which, run-to-failure data of an unspecified engineered system are provided and the RUL of a set of test units will be estimated. Results show that the similarity-based approach is very effective in performing RUL estimation.


title: Towards cyberactivism 2.0? Understanding the use of social media and other information technologies for political activism and social movements

abstract: Abstract Information technologies are increasingly important for political and social activism. In particular, web 2.0 tools and social media applications have recently played a significant role in influencing government decision making and shaping the relationships between governments, citizens, politicians, and other social actors. After the Arab Spring and the uprisings that have led to significant political changes in Egypt, Tunisia, and Iran, commentators argue that information technologies have the potential to strengthen social movements and ultimately transform society. However, this influence is not new. There were movements in the 90s, using the new technologies of e-mail and websites, which were able to gather significant social attention and generate political pressure. Based on three Mexican social and political movements that span close to 20 years, this article identifies key similarities and differences in the use of information technologies and proposes a framework to understand the evolution of cyberactivism. Initially, activists used information technologies to promote a movement's main ideas and gain global support. More recently, a single tool or application, such as Twitter, has been the technological basis for certain social and political movements. However, there is a trend towards a more integrated use of social media tools and applications, generating what could be called cyberactivism 2.0. In addition, there are some distinguishable stages in the development of social protests using information technologies; this evolutionary model seems to be useful to understand very different social and political movements using very different levels of technological sophistication.


title: Mapping Texts: Combining Text-Mining and Geo-Visualization To Unlock The Research Potential of Historical Newspapers

abstract: Paper on mapping texts and combining text-mining and geo-visualization to unlock the research potential of historical newspapers.


title: A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

abstract: The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.


title: An image preprocessing method for fingernail segmentation in microscopy image

abstract: The digital image processing plays an important part in medical image. In traditional Chinese medicine, the surface growth status of nails reflects the physiological condition of human body. The diseases can be predicted and prevented effectively by nail diagnosis. In the nail structure, the lunula and nail plate are important reference frames. The proportion of lunula to the nail plate reflects the human health status. Therefore, this paper proposes an image preprocessing method, trying to segment different parts of nail: lunula and nail plate. In the data of poor image quality, the lunula may not be presented clearly. In order to maintain the nail image quality, this paper uses microscope to capture nail image. Besides lunula and nail plate, the nail details, such as free edge, cross striation and longitudinal striation, can be seen clearly in the image captured by microscope. More details are displayed, meaning the complexity of image processing is increased. At the present stage, this paper only implements a series of image preprocessing steps for the separation of lunula and nail plate. The experimental results show that the effect is significant. It can be used in medical diagnostic system, biometric authentication or other biometric applications in the future.


title: A case study on how the Apple Watch can benefit medical heart research

abstract: The medical health industry is entering a new era and technology will play a great role in this area. Equipment in hospitals is in many cases strictly dependent on technology that works. However, t ...


title: Iterative Cross Learning on Noisy Labels

abstract: To address the problem of incorrect labels in training data for deep learning, we propose a novel and simple training strategy, Iterative Cross Learning (ICL), that significantly improves the classification accuracy of neural networks with training data that has noisy labels. We randomly partition the noisy training data into multiple separate subsets, each of which is used to train an independent network. After these independent networks predict labels for the original data, if the labels agree, we update the label with the predicted result for that data point, but otherwise we update the label with a random label, a key to the success of our proposed method. The process is repeated, possibly with several stages, to gradually improve the performance. Testing our method on MNIST and CIFAR-10 with partially shuffled labels, ICL significantly improves the classification accuracy of existing methods when the data labels have noise, especially in heavy noise situations. Moreover, the proposed method doesn't require any change to the underlying neural networks' structure or loss function, so it can also easily be combined with other existing methods that address noisy labels, improving their performance.


title: A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data

abstract: Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function, these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state, which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step toward an algorithmic theory of neural computation.


title: Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses

abstract: Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse highdimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.


title: Multiview registration of 3D scenes by minimizing error between coordinate frames

abstract: This paper addresses the problem of large scale multiview registration of range images captured from unknown viewing directions. To reduce the computational burden, we decouple the local problem of pairwise registration on neighboring views from the global problem of distribution of accumulated errors. We define the global problem over the graph of neighboring views, and we show that this graph can be decomposed into a set of cycles such that the optimal transformation parameters for each cycle can be solved in closed form. We then describe an iterative procedure that can be used to integrate the solutions for the set of cycles across the graph. This method for error distribution does not require point correspondences between views, and therefore can be used together with robot odometry or any method of pairwise registration. Experimental results demonstrate the effectiveness of this technique on range images of an indoor facility.


title: Atypical clinical presentation of visceral leishmaniasis: a rare case without splenomegaly

abstract: Clinical manifestations of visceral leishmaniasis (VL) usually include splenomegaly. We report a case of a woman from an endemic area with fever but normal splenic size. This is rare, especially in patients not immunocompromised.


title: Joint optimization of LCMV beamforming and acoustic echo cancellation

abstract: Full-duplex hands-free acoustic human/machine interfaces often require the combination of acoustic echo cancellation and speech enhancement in order to suppress acoustic echoes, local interference, and noise. In order to optimally exploit positive synergies between acoustic echo cancellation and speech enhancement, we present in this contribution a combined least-squares (LS) optimization criterion for the integration of acoustic echo cancellation and adaptive linearly-constrained minimum variance (LCMV) beamforming. Based on this optimization criterion, we derive a computationally efficient system based on the generalized sidelobe canceller (GSC), which effectively deals with scenarioes with time-varying acoustic echo paths and simultaneous presence of double-talk of acoustic echoes, local interference, and desired speakers.


title: Design consideration of active clamp flyback converter with highly nonlinear junction capacitance

abstract: Active clamped flyback converter performs zero voltage switching (ZVS) and recycling the leakage energy of transformer to boost the efficiency in higher frequency operation. Although it is well known that the switch-node capacitance determines the circulating energy for ZVS, the capacitance-nonlinearity impacts from each of the two primary-side switches and from the secondary synchronous rectifier have not been well understood. In this paper, the design tradeoff and system performance with different nonlinearity of junction capacitance from each switching device are investigated across the full load to light load operations, and then control strategies to overcome the capacitance nonlinearity are proposed. Then, analytical equations and design procedures are developed, considering the nonlinearity impact. Finally, the above studies and control method are supported with the experimental verifications on a 30W charger and a 45W adapter using state-of-the-art GaN and silicon FETs.


title: Design Case Studies Interactivity Attributes for Expression-oriented Interaction Design

abstract: This study proposes a new way of conceptualizing and designing interactive artifacts that emphasizes the importance of articulating sophisticated qualities of interactivity for promoting the design of aesthetic interaction. To make this possible, we introduce a set of attributes that solely describe the quality of interactivity, which is dynamic and invisible unlike other visual properties of an interactive artifact, and we call them interactivity attributes. In order to examine and explore the effects of applying interactivity attributes in interaction design, we conducted a series of studies in which we observed design students designing a set of interactive artifacts with and without the introduced interactivity attributes. We found that sensitizing the design students with these attributes changed their ways of approaching the design of interactivity of the artifacts. Designs became more expressive, quality descriptions were more sophisticated, both input- and output-behavior concepts of the interactive artifacts were considered consciously, and new materials and interaction styles were considered and applied.


title: Effect of 3D product presentation on consumer preference in e-commerce

abstract: Current electronic commerce offerings to the end customer have become a globally accessible mainstream purchasing pattern through the Internet technologies. E-commerce customer interaction through the web offering is largely preferred over the traditional brick and mortar purchasing interactions. With the advancement of rich content and multi modal user interaction support through web, the online customer experience has reached to a point where physical exploration and tactile information about the product are not crucial for purchases of generic goods; However there is a vital research question to be answered: do e-commerce customers prefer to have more infusion of product related information to make better purchase choice with confidence. To answer this, we have researched 3D support for product exploration. The goal of this research is to study the effects of 3D product presentations on consumer experience, value perception, purchase decisions and other factors in e-commerce environments under different platforms. This paper describes two experiments conducted to investigate these effects using 360 degree product views with varying degree of freedom in control and quality. The results indicate interesting positive effects on consumers using computers and mixed effects on consumers using mobile phones. This paper suggests using carefully designed interaction models to minimize those challenging effects and yield the positive effects; it also highlights the major challenges that need to be addressed for more engaging and convenient mobile based e-commerce experience as further studies.


title: Projective texture mapping

abstract: Projective texture mapping is a method of texture mapping described by Segal [3] that allows the texture image to be projected onto the scene as if by a slide projector. Figure 1 shows some example screen shots from the projspot demo, available in the NVIDIA OpenGL SDK. Projective texture mapping is useful in a variety of lighting techniques, including shadow mapping [4]. This document provides some background and describes the steps involved in projective texture mapping in OpenGL.


Data Compression Techniques in Wireless Sensor Networks


title: New Half-Hemispherical Dielectric Resonator Antenna for Broadband Monopole-Type Radiation

abstract: A new half-hemispherical dielectric resonator antenna (h-HDRA) is proposed and a two-element h-HDRA configuration is employed to design a broadband monopole-type radiator. Two half sections of a hemispherical DRA are employed to enhance the impedance bandwidth by introducing an additional resonant mode. Since the new geometry of an h-HDRA is studied here for the first time, it is characterized using simulation and experimental studies. Two closely spaced resonant modes, which in a two-element h-HDRA generate monopole-type radiation are investigated. Results for both the single and two-element h-HDRAs are presented. As much as 35% impedance bandwidth (S11<-10dB) with more than 5 dBi peak gain, 99.08% efficiency and monopole-type radiation pattern has been demonstrated using a prototype occupying a compact volume measuring 0.48lambda0 by 0.2lambda0 approximately, lambda0 being the wave length corresponding to the center of the impedance bandwidth


title: Autonomous Multisensor System Powered by a Solar Thermoelectric Energy Harvester With Ultralow-Power Management Circuit

abstract: An autonomous multisensor system powered by an energy harvester fabricated with a flat-panel solar thermoelectric generator with an ultralow-power management circuit is presented. The multisensor system was tested in an agricultural application, where every 15 min the values of the temperature, air humidity, and solar radiation have to be measured and stored in a mass memory device (a Secure Digital card), with their respective time stamp. The energy-harvesting switching dc-dc converter is based on a low-input-voltage commercial integrated circuit (LTC3108), which charges a 1.65-F supercapacitor up to 5.0 V. A novel ultralow-power management circuit was developed to replace the internal power management circuitry of the LTC3108, and using this circuit, the operation of the system when no energy can be harvested from the environment is extended from 136 h to more than 266 h. The solar thermoelectric generator used for the energy harvesting is composed of a bismuth telluride thermoelectric generator with a 110-mV/°C Seebeck coefficient sandwiched between a 40 cm $\times $ 40 cm anodized aluminum flat panel and an aluminum heatsink. On a sunny winter day in the southern hemisphere (12 August 2014, at Campinas, SP—Brazil, Latitude: 22° 54’), the energy supplied by the harvesting system to the supercapacitor was 7 J.


title: Print this Meridian Article Video Games: Why Kids Play and What They Learn

abstract: This paper provides an overview of the relationship children and youth have with video games, giving special attention to concepts of “fun” and “learning.” Video games are introduced as a new media, and rationale is given for studying the relationship kids have with them. Motivation and learning are discussed in relation to video games, and the question is asked “Can an experience (like playing video games) be both fun and educative?” Imagine a ten year old girl who comes home from school each day and devotes two to three hours to her favorite activity. If this activity is playing music, we might say the child has a passion. But if the child is playing video games, would we be more likely to call it an obsession? Is it only our perception that changes, or is there a fundamental problem with how children relate to video games? Video games are a relatively new media and, similar to television, they have attracted both negative and positive attention from scholars. While some scholars see video games as harbingers of educational utopia, saying “the young people of today understand instinctively that their games are their very best teachers” (Prensky, 2003, p. 3), others worry that we are Amusing ourselves to Death (Postman, 1985). These conflicting views of video games may be due to the ways children relate to the games. This paper explores the relationship children and youth have to video games. In this exploration, video games are first introduced as a new media, and a rationale is given for studying the relationship kids have with them. Second, the appeal of video games is discussed including what it means to play and the motivational power of video games. A discussion of if and what children learn from video games follows. Finally, the relationship between fun and learning is explored. What is the Rationale for Studying how Kids Relate to Video Games?


title: Towards High Performance Video Object Detection

abstract: There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. ::: Built upon the recent works, this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection.


title: MRI BRAIN IMAGE SEGMENTATION TECHNIQUES - A REVIEW

abstract: Brain tumour is one of the most dangerous disease occurring commonly among human beings. The chances of survival can be increased if the tumour is detected correctly at its early stage. MRI brain imaging technique is widely used to visualize the anatomy and structure of the brain. The images produced by MRI are high in tissue contrast and have fewer artifacts. It has several advantages over other imaging techniques, providing high contrast between soft tissues. However, the amount of data is far too much for manual analysis, which has been one of the biggest obstacles in the effective use of MRI. The detection of tumour requires several processes on MRI images which includes image preprocessing, feature extraction, image enhancement and classification. The final classification process concludes that a person is diseased or not. Although numerous efforts and promising results are obtained in medical imaging area, reproducible segmentation and classification of abnormalities are still a challenging task because of the different shapes, locations and image intensities of different types of tumours. In this paper, various approaches of MRI brain image segmentation algorithms are reviewed and their advantages, disadvantages are discussed.


title: In the Eye of the Beholder: A Survey of Models for Eyes and Gaze

abstract: Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.


title: Molecular dynamics simulations of large macromolecular complexes.

abstract: Connecting dynamics to structural data from diverse experimental sources, molecular dynamics simulations permit the exploration of biological phenomena in unparalleled detail. Advances in simulations are moving the atomic resolution descriptions of biological systems into the million-to-billion atom regime, in which numerous cell functions reside. In this opinion, we review the progress, driven by large-scale molecular dynamics simulations, in the study of viruses, ribosomes, bioenergetic systems, and other diverse applications. These examples highlight the utility of molecular dynamics simulations in the critical task of relating atomic detail to the function of supramolecular complexes, a task that cannot be achieved by smaller-scale simulations or existing experimental approaches alone.


title: A performance study of Ethernet Audio Video Bridging (AVB) for Industrial real-time communication

abstract: This paper investigates the potential of the emerging Ethernet Audio Video Bridging (AVB) in Industrial networks. An analytical model of AVB is described, and AVB's priority queuing (PQ) and credit-based shaper algorithms (CBQ) are evaluated and compared with Standard Ethernet.


title: PDFMEF: A Multi-Entity Knowledge Extraction Framework for Scholarly Documents and Semantic Search

abstract: We introduce PDFMEF, a multi-entity knowledge extraction framework for scholarly documents in the PDF format. It is implemented with a framework that encapsulates open-source extraction tools. Currently, it leverages PDFBox and TET for full text extraction, the scholarly document filter described in [5] for document classification, GROBID for header extraction, ParsCit for citation extraction, PDFFigures for figure and table extraction, and algorithm extraction [27]. While it can be run as a whole, the extraction tool in each module is highly customizable. Users can substitute default extractors with other extraction tools they prefer by writing a thin wrapper to implement the abstracts. The framework is designed to be scalable and is capable of running in parallel using a multi-processing technique in Python. Experiments indicate that the system with default setups is CPU bounded, and leaves a small footprint in the memory, which makes it best to run on a multi-core machine. The best performance using a dedicated server of 16 cores takes 1.3 seconds on average to process one PDF document. It is used to index extracted information and help users to quickly locate relevant results in published scholarly documents and to efficiently construct a large knowledge base in order to build a semantic scholarly search engine. Part of it is running on CiteSeerX digital library search engine.


title: Garbage Collection Techniques for Flash-Resident Page-Mapping FTLs

abstract: Storage devices based on flash memory have replaced hard disk drives (HDDs) due to their superior performance, increasing density, and lower power consumption. Unfortunately, flash memory is subject to challenging idiosyncrasies like erase-before-write and limited block lifetime. These constraints are handled by a flash translation layer (FTL), which performs out-of-place updates, wear-leveling and garbage-collection behind the scene, while offering the application a virtualization of the physical address space. ::: A class of relevant FTLs employ a flash-resident page-associative mapping table from logical to physical addresses, with a smaller RAM-resident cache for frequently mapped entries. In this paper, we address the problem of performing garbage-collection under such FTLs. We observe two problems. Firstly, maintaining the metadata needed to perform garbage-collection under these schemes is problematic, because at write-time we do not necessarily know the physical address of the before-image. Secondly, the size of this metadata must remain small, because it makes RAM unavailable for caching frequently accessed entries. We propose two complementary techniques, called Lazy Gecko and Logarithmic Gecko, which address these issues. Lazy Gecko works well when RAM is plentiful enough to store the GC metadata. Logarithmic Gecko works well when RAM isn't plentiful and efficiently stores the GC metadata in flash. Thus, these techniques are applicable to a wide range of flash devices with varying amounts of embedded RAM.


title: Horizon-split ambient occlusion

abstract: The method includes receiving a plurality of graphics primitives for rendering at a GPU of a computer system and rendering graphics primitives into pixel parameters of the pixels of a display, wherein the parameters include pixel depth values and pixel normal values. For each pixel of the display, an ambient occlusion process is performed. The algorithm takes as input a ND-buffer containing pixel depth values and pixel normals. Based on the pixel 3-D position and the pixel normal vector, horizon heights are computed by sampling the ND-buffer and an occlusion term is computed for each pixel based on the horizon heights. Based on the pixel 3-D position, the pixel normal vector, a normal occlusion term is computed by sampling the ND-buffer above the horizon in multiple directions. An ambient occlusion illumination value is computed by combining the horizon occlusion term and the normal occlusion term.


title: Malware detection by text and data mining

abstract: Cyber frauds are a major security threat to the banking industry worldwide. Malware is one of the manifestations of cyber frauds. Malware authors use Application Programming Interface (API) calls to perpetrate these crimes. In this paper, we propose a static analysis method to detect Malware based on API call sequences using text and data mining in tandem. We analyzed the dataset available at CSMINING group. First, we employed text mining to extract features from the dataset consisting a series of API calls. Further, mutual information is invoked for feature selection. Then, we resorted to over-sampling to balance the data set. Finally, we employed various data mining techniques such as Decision Tree (DT), Multi Layer Perceptron (MLP), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Group Method for Data Handling (GMDH). We also applied One Class SVM (OCSVM). Throughout the paper, we used 10-fold cross validation technique for testing the techniques. We observed that SVM and OCSVM achieved 100% sensitivity after balancing the dataset.


title: Video steganography: a comprehensive review

abstract: Steganography is the art and science of secret communication, concealing the very existence of a communication. Modern cover types can take many forms such as text documents, audio tracks, digital images, and video streams. Extensive research has been done on image steganography in the previous decade due to their popularity on the internet. Nowadays, video files are drawing much more attention. They are transmitted more and more frequent on internet websites such as Facebook and YouTube imposing a larger practical significance on video steganography. Information hiding in video has a variety of techniques, each of which has its strengths and weaknesses. This paper intends to provide an up-to-date comprehensive review on the various video steganographic methods found in the literature in the last 5 years. Furthermore, since security and robustness are very important issues in designing a good steganographic algorithm, some relevant attacks and steganalysis techniques are also surveyed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.


title: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture

abstract: The hypothesis is presented that "cognitive synergy" – proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory – may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.


title: On Symmetric and Asymmetric LSHs for Inner Product Search

abstract: We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. However, we show there does exist a simple symmetric LSH that enjoys stronger guarantees and better empirical performance than the asymmetric LSH they suggest. We also show a variant of the settings where asymmetry is in-fact needed, but there a different asymmetric LSH is required.


title: Fear conditioning induces associative long-term potentiation in the amygdala.

abstract: Long-term potentiation (LTP) is an experience-dependent form of neural plasticity believed to involve mechanisms that underlie memory formation. LTP has been studied most extensively in the hippocampus, but the relation between hippocampal LTP and memory has been difficult to establish. Here we explore the relation between LTP and memory in fear conditioning, an amygdala-dependent form of learning in which an innocuous conditioned stimulus (CS) elicits fear responses after being associatively paired with an aversive unconditioned stimulus (US). We have previously shown that LTP induction in pathways that transmit auditory CS information to the lateral nucleus of the amygdala (LA) increases auditory-evoked field potentials in this nucleus. Now we show that fear conditioning alters auditory CS-evoked responses in LA in the same way as LTP induction. The changes parallel the acquisition of CS-elicited fear behaviour, are enduring, and do not occur if the CS and US remain unpaired. LTP-like associative processes thus occur during fear conditioning, and these may underlie the long-term associative plasticity that constitutes memory of the conditioning experience.


title: Deconstruct and Reconstruct: Using Topic Modeling on an Analytics Corpus

abstract: The question posed by the 2014 LAK Data Challenge is “What do analytics on learning analytics tell us?” The authors looked to take a two-pronged approach to this challenge. First, the authors wanted to use advanced analytical techniques on the corpus to make the “eat your own dog food” point. Since many of the EDM/LAK submissions explain advanced statistical or semantic analytic approaches, we wanted to utilize those same methods for our analysis. To that end, we used two natural language processing (NLP) tools to analyze the corpus of papers. First we used Latent Dirichlet allocation (LDA) to extract clusters of terms from the content. Second, we used Turbo Topics to convert the LDA output into phrases (bi-grams and tri-grams). The use of these NLP tools allowed us to execute the second part of our approach to the challenge. Once the corpus was aggregated as topics, we used Tableau to visually inspect the corpus for trends. In addition to standard descriptive visualizations, we were able to identify trends in the corpus topics from 2008 through 2013. Most interesting is that with both EDM and LAK, we noticed a trend of topic convergence after three years. Also, we were able to easily discern topic trends such as the increased popularity of "social" and "network," over the last three years, and the consistent appearance of ‘Cognitive Tutor’ related topics (e.g. intelligent tutoring, concept map). While these findings may not be unexpected, we believe that the ability to extract and visualize these outcomes is unique.


Microcontroller Based Anti-theft Security System Using GSM Networks with Text Message as Feedback


title: Design patterns for the industrial Internet of Things

abstract: The Internet of Things (IoT) is a vast collection of interconnected sensors, devices, and services that share data and information over the Internet with the objective of leveraging multiple information sources to optimize related systems. The technologies associated with the IoT have significantly improved the quality of many existing applications by reducing costs, improving functionality, increasing access to resources, and enhancing automation. The adoption of IoT by industries has led to the next industrial revolution: Industry 4.0. The rise of the Industrial IoT (IIoT) promises to enhance factory management, process optimization, worker safety, and more. However, the rollout of the IIoT is not without significant issues, and many of these act as major barriers that prevent fully achieving the vision of Industry 4.0. One major area of concern is the security and privacy of the massive datasets that are captured and stored, which may leak information about intellectual property, trade secrets, and other competitive knowledge. As a way forward toward solving security and privacy concerns, we aim in this paper to identify common input-output (I/O) design patterns that exist in applications of the IIoT. These design patterns enable constructing an abstract model representation of data flow semantics used by such applications, and therefore better understand how to secure the information related to IIoT operations. In this paper, we describe communication protocols and identify common I/O design patterns for IIoT applications with an emphasis on data flow in edge devices, which, in the industrial control system (ICS) setting, are most often involved in process control or monitoring.


title: Congestion control policies for IP-based CDMA radio access networks

abstract: As CDMA-based cellular networks mature, the current point-to-point links used in connecting base stations to network controllers will evolve to an IP-based radio access network (RAN) for reasons of lower cost due to statistical multiplexing gains, better scalability and reliability, and the projected growth in data applications. In this paper, we study the impact of congestion in a best-effort IP RAN on CDMA cellular voice networks. We propose and evaluate three congestion control mechanisms, admission control, diversity control, and router control, to maximize network capacity while maintaining good voice quality. We first propose two new enhancements to CDMA call admission control that consider a unified view of both IP RAN and air interface resources. Next, we introduce a novel technique called diversity control that exploits the soft-handoff feature of CDMA networks and drops selected frames belonging to multiple soft-handoff legs to gracefully degrade voice quality during congestion. Finally, we study the impact of router control where an active queue management technique is used to reduce delay and minimize correlated losses. Using simulations of a large mobile network, we show that the three different control mechanisms can help gracefully manage 10-40% congestion overload in the IP RAN.


title: Mixed MOSFET-IGBT bridge for high-efficient Medium-Frequency Dual-Active-Bridge converter in Solid State Transformers

abstract: High power DC-DC conversion is a key element within the Solid-State-Transformer concept. In order to reduce the switching losses of the Medium-Voltage side semiconductors, a Triangular-Current-Mode modulation scheme presents an attractive option. This modulation scheme, however introduces considerable challenges in the design of the low-voltage side power electronic bridges, which need to deal with high conducted and high switched currents. In order to increase the converter's efficiency, a combination of IGBTs and MOSFETs in a full-bridge configuration is considered. Practical hardware realizations are utilized in order to quantify the improvements introduced by the combination of these switches. Furthermore, the MOSFET's current conduction phase is supported by parallel connected IGBTs, which are used in order to further increase the full-bridge's efficiency, as shown by the provided experimental verification.


title: Construction of a hexapod robot with cockroach kinematics benefits both robotics and biology

abstract: Any device that moves by actuating several multi-segmented legs must solve fundamental problems in mechanics and control regardless of whether that device is made out of living tissue or metal. With this in mind, we believe that advances can be made both in the design of legged robots and in understanding how legged animals locomote by working on these issues in tandem. This basic philosophy has led us to build a hexapod robot with kinematics that are remarkably similar to those of the death head cockroach Blaberus discoidalis . This eff ort has required us to make detailed neurobiological and kinematic observations of cockroaches walking on a treadmill and climbing over barriers. The data acquired in this way were then input to our dynamic simulation tool to determine mechanically accurate parameters for the design of the robot. The resulting vehicle captures the cockroach kinematics so well that issues that are being addressed in controlling it are providing important notions regarding the neural contro...


title: Personality and motivations associated with Facebook use

abstract: Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered.


title: Selective sampling on graphs for classification

abstract: Selective sampling is an active variant of online learning in which the learner is allowed to adaptively query the label of an observed example. The goal of selective sampling is to achieve a good trade-off between prediction performance and the number of queried labels. Existing selective sampling algorithms are designed for vector-based data. In this paper, motivated by the ubiquity of graph representations in real-world applications, we propose to study selective sampling on graphs. We first present an online version of the well-known Learning with Local and Global Consistency method (OLLGC). It is essentially a second-order online learning algorithm, and can be seen as an online ridge regression in the Hilbert space of functions defined on graphs. We prove its regret bound in terms of the structural property (cut size) of a graph. Based on OLLGC, we present a selective sampling algorithm, namely Selective Sampling with Local and Global Consistency (SSLGC), which queries the label of each node based on the confidence of the linear function on graphs. Its bound on the label complexity is also derived. We analyze the low-rank approximation of graph kernels, which enables the online algorithms scale to large graphs. Experiments on benchmark graph datasets show that OLLGC outperforms the state-of-the-art first-order algorithm significantly, and SSLGC achieves comparable or even better results than OLLGC while querying substantially fewer nodes. Moreover, SSLGC is overwhelmingly better than random sampling.


title: Multitouch Interface for Audio Mixing

abstract: Audio mixing is the adjustment of relative volumes, panning and other parameters corresponding to dierent sound sources, in order to create a technically and aesthetically adequate sound sum. To do this, audio engineers employ \panpots" and faders, the standard controls in audio mixers. The design of such devices has remained practically unchanged for decades since their introduction. At the time, no usability studies seem to have been conducted on such devices, so one could question if they are really optimized for the task they are meant for. This paper proposes a new set of controls that might be used to simplify and/or improve the performance of audio mixing tasks, taking into account the spatial characteristics of modern mixing technologies such as surround and 3D audio and making use of multitouch interface technologies. A preliminary usability test has shown promising results.


title: Model of Differentiation between Normal and Abnormal Heart Sounds in Using the Discrete Wavelet Transform

abstract: Today, modern technology has provided more powerful tools to evaluate the information related to heart sounds that traditional tools like stethoscope cannot achieve. One of the most common methods used for listening and tracking the heart sounds is to record them with special devices. The recorded heart sounds is known as PCG (phonocardiogram) signal. It is a particularly useful diagnosis tool since it contains different timings and relative intensities of heart beat sounds which are directly related to heart activity. The objective of this paper we develop a simple model for analysing the PCG signal in order to distinguish between normal and abnormal heart sounds. This analysis is carried out by using discrete wavelet transform. By using the discrete wavelet transform (DWT) the PCG signal is decomposed in to 7 stages. The average standard deviation of the detailed coefficients at each stage is calculated for each signal. The slopes of these curves for each case are obtained by plotting the average standard deviation of the detailed coefficients at each level detail. The analysis of these slopes shown that the discrimination between the normal signals from abnormal is possible


title: Safe Control under Uncertainty

abstract: Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments.


title: Detecting Protein Function and Protein-Protein Interactions from Genome Sequences

abstract: A computational method is proposed for inferring protein interactions from genome sequences on the basis of the observation that some pairs of interacting proteins have homologs in another organism fused into a single protein chain. Searching sequences from many genomes revealed 6809 such putative protein-protein interactions in Escherichia coli and 45,502 in yeast. Many members of these pairs were confirmed as functionally related; computational filtering further enriches for interactions. Some proteins have links to several other proteins; these coupled links appear to represent functional interactions such as complexes or pathways. Experimentally confirmed interacting pairs are documented in a Database of Interacting Proteins.


title: Analysis of Bounded Cable Tensions in Cable-Actuated Parallel Manipulators

abstract: Cable-actuated parallel manipulators (CPMs) rely on cables instead of rigid links to manipulate the moving platform in the taskspace. Upper and lower bounds imposed on the cable tensions limit the force capability in CPMs and render certain forces infeasible at the end effector. This paper presents a geometrical analysis of the problems to 1) determine whether a CPM is capable of balancing a given wrench within the cable tension limits (feasibility check); 2) minimize the 2-norm of the cable tensions that balance feasible wrenches; and 3) check for the existence of an all-positive nullspace vector, which is a necessary condition to have a wrench-closure configuration in CPMs. The unified approach used in this analysis is systematic and geometrically intuitive that is based on the formulation of the static force equilibrium problem as an intersection between two convex sets and the application of Dykstra's alternating projection algorithm to find the projection of a point onto that intersection. In the case of infeasible wrenches, the algorithm can determine whether the infeasibility is because of the cable tension limits or the non-wrench-closure configuration. For the former case, a method was developed by which this algorithm can be used to extend the cable tension limits to balance infeasible wrenches. In addition, the performance of the algorithm is explained in the case of incompletely restrained cable-driven manipulators and the case of manipulators at singular poses. This paper also discusses the algorithm convergence and termination rule. This geometrical and systematic approach is intended for use as a convenient tool for cable tension analysis during design.


title: Visual aesthetics and human preference.

abstract: Human aesthetic preference in the visual domain is reviewed from definitional, methodological, empirical, and theoretical perspectives. Aesthetic science is distinguished from the perception of art and from philosophical treatments of aesthetics. The strengths and weaknesses of important behavioral techniques are presented and discussed, including two-alternative forced-choice, rank order, subjective rating, production/adjustment, indirect, and other tasks. Major findings are reviewed about preferences for colors (single colors, color combinations, and color harmony), spatial structure (low-level spatial properties, shape properties, and spatial composition within a frame), and individual differences in both color and spatial structure. Major theoretical accounts of aesthetic response are outlined and evaluated, including explanations in terms of mere exposure effects, arousal dynamics, categorical prototypes, ecological factors, perceptual and conceptual fluency, and the interaction of multiple components. The results of the review support the conclusion that aesthetic response can be studied rigorously and meaningfully within the framework of scientific psychology.


Identifying Informative Messages in Disasters using Convolutional Neural Networks


title: A Probabilistic Model for Time-Aware Entity Recommendation

abstract: In recent years, there has been an increasing effort to develop techniques for related entity recommendation, where the task is to retrieve a ranked list of related entities given a keyword query. Another trend in the area of information retrieval (IR) is to take temporal aspects of a given query into account when assessing the relevance of documents. However, while this has become an established functionality in document search engines, the significance of time has not yet been recognized for entity recommendation. In this paper, we address this gap by introducing the task of time-aware entity recommendation. We propose the first probabilistic model that takes time-awareness into consideration for entity recommendation by leveraging heterogeneous knowledge of entities extracted from different data sources publicly available on the Web. We extensively evaluate the proposed approach and our experimental results show considerable improvements compared to time-agnostic entity recommendation approaches.


title: Road Detection From Remote Sensing Images by Generative Adversarial Networks

abstract: Road detection with high-precision from very high resolution remote sensing imagery is very important in a huge variety of applications. However, most existing approaches do not automatically extract the road with a smooth appearance and accurate boundaries. To address this problem, we proposed a novel end-to-end generative adversarial network. In particular, we construct a convolutional network based on adversarial training that could discriminate between segmentation maps coming either from the ground truth or generated by the segmentation model. The proposed method could improve the segmentation result by finding and correcting the difference between ground truth and result output by the segmentation model. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods greatly on the performance of segmentation map.


title: A 4-Port Diversity Antenna With High Isolation for Mobile Communications

abstract: A novel wideband four-port diversity antenna that is capable of exciting four different radiation patterns is presented. The antenna consists of four magnetoelectric dipoles arranged in a ring configuration above a ground plane and a vertical electric monopole at the center of the ground plane. A matching network is used to combine the signals from the four magnetoelectric dipoles to produce two orthogonal broadside modes and one conical mode with horizontal polarization. The electric monopole is used to generate a conical beam with vertical polarization in electric feed. The performance of the antenna is studied computationally. A prototype operated at around 2.4 GHz was constructed and tested. Experimentally, the antenna exhibits about 22.2% overlapped bandwidth of the four ports. The two orthogonal broadside modes have about 11 dBi average gain. Whereas for the two orthogonal conical modes, an average gain of 6 dBi is achieved. The measured radiation patterns of the four degenerate modes are stable within the operating band. The measured isolations between different ports are generally less than -26 dB within the overlapped bandwidth. The measured radiating efficiencies of the four modes are all over 80% within the operating band.


title: Phase control of single-pulse nutation of coherent biexcitons in semiconductors

abstract: Specific features of two-photon nutation in a system of coherent biexcitons in CuCl-type semiconductors are studied. It is shown that, depending on the parameters of the system, nutation represents a process of periodic conversion of photon pairs into biexcitons and vice versa. The possibility of phase control of optical nutation is predicted.


title: Rapid and multifaceted effects of second-language learning on first-language speech production

abstract: Abstract Despite abundant evidence of malleability in speech production, previous studies of the effects of late second-language learning on first-language speech production have been limited to advanced learners. This study examined these effects in novice learners, adult native English speakers enrolled in elementary Korean classes. In two acoustic studies, learners' production of English was found to be influenced by even brief experience with Korean. The effect was consistently one of assimilation to phonetic properties of Korean; moreover, it occurred at segmental, subsegmental, and global levels, often simultaneously. Taken together, the results suggest that cross-language linkages are established from the onset of second-language learning at multiple levels of phonological structure, allowing for pervasive influence of second-language experience on first-language representations. The findings are discussed with respect to current notions of cross-linguistic similarity, language development, and historical sound change.


title: Topological Volume Skeletonization and its Application to Transfer Function Design

abstract: Topological volume skeletonization is a novel approach for automating transfer function design in visualization by extracting the topological structure of a volume dataset. The skeletonization process yields a graph called a volume skeleton tree, which consists of volumetric critical points and their connectivity. The resultant graph provides critical field values whose color and opacity are accentuated in the design of transfer functions for direct volume rendering. Visually pleasing results of volume visualization demonstrate the feasibility of the present approach.


title: Recurrent Recommender Networks

abstract: Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.


title: Verifying the Microsoft Hyper-V Hypervisor with VCC

abstract: VCC is an industrial-strength verification suite for the formal verification of concurrent, low-level C code. It is being developed by Microsoft Research, Redmond, and the European Microsoft Innovation Center, Aachen. The development is driven by two applications from the Verisoft XT project: the Microsoft Hyper-V Hypervisor and SYSGO's PikeOS micro kernel. ::: ::: This paper gives a brief overview on the Hypervisor with a special focus on verification related challenges this kind of low-level software poses. It discusses how the design of VCC addresses these challenges, and highlights some specific issues of the Hypervisor verification and how they can be solved with VCC.


title: Blockchain-based traceability in Agri-Food supply chain management: A practical implementation

abstract: The recent, exponential rise in adoption of the most disparate Internet of Things (IoT) devices and technologies has reached also Agriculture and Food (Agri-Food) supply chains, drumming up substantial research and innovation interest towards developing reliable, auditable and transparent traceability systems. Current IoT-based traceability and provenance systems for Agri-Food supply chains are built on top of centralized infrastructures and this leaves room for unsolved issues and major concerns, including data integrity, tampering and single points of failure. Blockchains, the distributed ledger technology underpinning cryptocurrencies such as Bitcoin, represent a new and innovative technological approach to realizing decentralized trustless systems. Indeed, the inherent properties of this digital technology provide fault-tolerance, immutability, transparency and full traceability of the stored transaction records, as well as coherent digital representations of physical assets and autonomous transaction executions. This paper presents AgriBlockIoT, a fully decentralized, blockchain-based traceability solution for Agri-Food supply chain management, able to seamless integrate IoT devices producing and consuming digital data along the chain. To effectively assess AgriBlockIoT, first, we defined a classical use-case within the given vertical domain, namely from-farm-to-fork. Then, we developed and deployed such use-case, achieving traceability using two different blockchain implementations, namely Ethereum and Hyperledger Sawtooth. Finally, we evaluated and compared the performance of both the deployments, in terms of latency, CPU, and network usage, also highlighting their main pros and cons.


title: Keyword Extraction Through Contextual Semantic Analysis of Documents

abstract: Keywords in a text are often used to suggest the main concepts being discussed and help to index them. However, most of the traditional approaches make use of techniques that rely on analyzing just the syntactic aspect of texts, ignoring the meaning they convey and more importantly, the semantic effect of one word over another (context). This paper explores two alternative approaches to extract concept-terms based on semantic features embedded in textual documents extracted from Wikipedia. The first approach extends the concept of Word Sense Disambiguation (WSD), and the second approach enhances the theory behind traditional lexical chains. These applied techniques also consider distinct levels of abstraction with respect to the meanings of words, in addition to the context in which they appear. Our initial results show that both techniques are robust and can extract the main concepts in a document without human intervention or supervision.


title: BendSketch: modeling freeform surfaces through 2D sketching

abstract: Sketch-based modeling provides a powerful paradigm for geometric modeling. Recent research had shown, sketch based modeling methods are most effective when targeting a specific family of surfaces. A large and growing arsenal of sketching tools is available for different types of geometries and different target user populations. Our work augments this arsenal with a new and powerful tool for modeling complex freeform shapes by sketching sparse 2D strokes; our method complements existing approaches in enabling the generation of surfaces with complex curvature patterns that are challenging to produce with existing methods. To model a desired surface patch with our technique, the user sketches the patch boundary as well as a small number of strokes representing the major bending directions of the shape. Our method uses this input to generate a curvature field that conforms to the user strokes and then uses this field to derive a freeform surface with the desired curvature pattern. To infer the surface from the strokes we first disambiguate the convex versus concave bending directions indicated by the strokes and estimate the surface bending magnitude along the strokes. We subsequently construct a curvature field based on these estimates, using a non-orthogonal 4-direction field coupled with a scalar magnitude field, and finally construct a surface whose curvature pattern reflects this field through an iterative sequence of simple linear optimizations. Our framework is well suited for single-view modeling, but also supports multi-view interaction, necessary to model complex shapes portions of which can be occluded in many views. It effectively combines multi-view inputs to obtain a coherent 3D shape. It runs at interactive speed allowing for immediate user feedback. We demonstrate the effectiveness of the proposed method through a large collection of complex examples created by both artists and amateurs. Our framework provides a useful complement to the existing sketch-based modeling methods.


title: Nonlinear Neural Networks: Principles, Mechanisms, and Architectures

abstract: Abstract An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentieth-century scientific movements. The nonlinear, nonstationary, and nonlocal nature of behavioral and brain data are emphasized. Three sources of contemporary neural network research—the binary, linear, and continuous-nonlinear models—are noted. The remainder of the article describes results about continuous-nonlinear models: Many models of content-addressable memory are shown to be special cases of the Cohen-Grossberg model and global Liapunov function, including the additive, brain-state-in-a-box, McCulloch-Pitts, Boltzmann machine, Hartline-Ratliff-Miller, shunting, masking field, bidirectional associative memory, Volterra-Lotka, Gilpin-Ayala, and Eigen-Schuster models. A Liapunov functional method is described for proving global limit or oscillation theorems for nonlinear competitive systems when their decision schemes are globally consistent or inconsistent, respectively. The former case is illustrated by a model of a globally stable economic market, and the latter case is illustrated by a model of the voting paradox. Key properties of shunting competitive feedback networks are summarized, including the role of sigmoid signalling, automatic gain control, competitive choice and quantization, tunable filtering, total activity normalization, and noise suppression in pattern transformation and memory storage applications. Connections to models of competitive learning, vector quantization, and categorical perception are noted. Adaptive resonance theory (ART) models for self-stabilizing adaptive pattern recognition in response to complex real-time nonstationary input environments are compared with off-line models such as autoassociators, the Boltzmann machine, and back propagation. Special attention is paid to the stability and capacity of these models, and to the role of top-down expectations and attentional processing in the active regulation of both learning and fast information processing. Models whose performance and learning are regulated by internal gating and matching signals, or by external environmentally generated error signals, are contrasted with models whose learning is regulated by external teacher signals that have no analog in natural real-time environments. Examples from sensory-motor control of adaptive vector encoders, adaptive coordinate transformations, adaptive gain control by visual error signals, and automatic generation of synchronous multijoint movement trajectories illustrate the former model types. Internal matching processes are shown capable of discovering several different types of invariant environmental properties. These include ART mechanisms which discover recognition invariants, adaptive vector encoder mechanisms which discover movement invariants, and autoreceptive associative mechanisms which discover invariants of self-regulating target position maps.


title: Filtering Offensive Language in Online Communities using Grammatical Relations ABSTRACT

abstract: Offensive language has arisen to be a big issue to the health of both online communities and their users. To the online community, the spread of offensive language undermines its reputation, drives users away, and even directly affects its growth. To users, viewing offensive language brings negative influence to their mental health, especially for children and youth. When offensive language is detected in a user message, a problem arises about how the offensive language should be removed, i.e. the offensive language filtering problem. To solve this problem, manual filtering approach is known to produce the best filtering result. However, manual filtering is costly in time and labor thus can not be widely applied. In this paper, we analyze the offensive language in text messages posted in online communities, and propose a new automatic sentence-level filtering approach that is able to semantically remove the offensive language by utilizing the grammatical relations among words. Comparing with existing automatic filtering approaches, the proposed filtering approach provides filtering results much closer to manual filtering. To demonstrate our work, we created a dataset by manually filtering over 11,000 text comments from the YouTube website. Experiments on this dataset show over 90% agreement in filtered results between the proposed approach and manual filtering approach. Moreover, we show the overhead of applying proposed approach to user comments filtering is reasonable, making it practical to be adopted in real life applications.


title: A study of personal information in human-chosen passwords and its security implications

abstract: Though not recommended, Internet users often include parts of personal information in their passwords for easy memorization. However, the use of personal information in passwords and its security implications have not yet been studied systematically in the past. In this paper, we first dissect user passwords from a leaked dataset to investigate how and to what extent user personal information resides in a password. In particular, we extract the most popular password structures expressed by personal information and show the usage of personal information. Then we introduce a new metric called Coverage to quantify the correlation between passwords and personal information. Afterwards, based on our analysis, we extend the Probabilistic Context-Free Grammars (PCFG) method to be semantics-rich and propose Personal-PCFG to crack passwords by generating personalized guesses. Through offline and online attack scenarios, we demonstrate that Personal-PCFG cracks passwords much faster than PCFG and makes online attacks much easier to succeed.


title: Children's engagement with educational iPad apps: Insights from a Spanish classroom

abstract: This study investigates the effects of a story-making app called Our Story and a selection of other educational apps on the learning engagement of forty-one Spanish 4-5-year-olds. Children were observed interacting in small groups with the story-making app and this was compared to their engagement with a selection of construction and drawing apps. Children's engagement was analysed in two ways: it was categorised using Bangert-Drowns and Pyke's taxonomy for individual hands-on engagement with educational software, and using the concept of exploratory talk as developed by Mercer et al. to analyse peer engagement. For both approaches, quantitative and qualitative indices of children's engagement were considered. The overall findings suggested that in terms of the Bangert-Drowns and Pyke taxonomy, the quality of children's individual engagement was higher with the OS app in contrast to their engagement with other app software. The frequency of children's use of exploratory talk was similar with the OS and colouring and drawing apps, and a detailed qualitative analysis of the interaction transcripts revealed several instances of the OS and drawing apps supporting joint problem-solving and collaborative engagement. We suggest that critical indices of an app's educational value are the extent to which the app supports opportunities for open-ended content and children's independent use of increasingly difficult features.


title: On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

abstract: The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fraction of the training data, say $32$-$512$ data points, is sampled to compute an approximation to the gradient. It has been observed in practice that when using a larger batch there is a degradation in the quality of the model, as measured by its ability to generalize. We investigate the cause for this generalization drop in the large-batch regime and present numerical evidence that supports the view that large-batch methods tend to converge to sharp minimizers of the training and testing functions - and as is well known, sharp minima lead to poorer generalization. In contrast, small-batch methods consistently converge to flat minimizers, and our experiments support a commonly held view that this is due to the inherent noise in the gradient estimation. We discuss several strategies to attempt to help large-batch methods eliminate this generalization gap.


title: Contrast Enhancement of brain MRI images using histogram based techniques

abstract: MRI is an advanced medical imaging technique used to produce high quality images of human body and different parts. It gives detail information to analyse the diseases. Medical image processing plays important role to give information in more extent for such advance images. Original MRI images are generally having low contrast. It is difficult for doctors to analyse them. By increasing the contrast of an image, it will be easy for analysing because of detailed information. This increase in contrast can be done by number of ways in image processing. This paper compares different methods of enhancement of brain MRI using histogram based techniques.


title: The relationship between total quality management practices and their effects on firm performance

abstract: Abstract Recent research on total quality management (TQM) has examined the relationships between the practices of quality management and various levels of organizational performance. These studies have produced mixed results, probably because of the nature of the research designs used such as measuring TQM or performance as a single construct. Based on a comprehensive literature review, this study identifies the relationships among TQM practices and examines the direct and indirect effects of these practices on various performance levels. A proposed research model and hypotheses are tested by using cross-sectional mail survey data collected from firms operating in the US. The test of the structural model supports the proposed hypotheses. The implications of the findings for researchers and practitioners are discussed and further research directions are offered.


title: Modulation and Control of a Three-Phase Phase-Modular Isolated Matrix-Type PFC Rectifier

abstract: Three-phase phase-modular isolated power factor correction rectifiers are an interesting alternative to phase-integrated three-phase rectifiers as matrix-type phase modules allow a single-stage isolated energy conversion between the three-phase mains and a dc bus. Therefore, this paper presents a phase-modular isolated matrix-type rectifier which can be connected to the mains either in star (Y) or delta ( $\Delta$ ) configuration, enabling a wide input voltage range. Additionally this allows to select the voltage and current stresses of the phase module switches according to the used semiconductor technology, for example ${\text{650 V}}$ Si or GaN devices could be used in rectifiers powered from the ${400}$ or ${\text{480 V}}_{\text{rms}}$ mains. A detailed analysis of the operating principles and switching behavior of the converter is presented, showing that zero voltage switching can be achieved in the phase modules. Additionally a third harmonic current injection concept is proposed which allows an up to ${15} \%$ higher output voltage in $\Delta$ -mode. The concepts are validated with measurements taken on a ${\text{7.5}}\;{\text{kW}}$ , ${\text{400}}\;{\text{V}}$ dc output voltage prototype converter achieving ${97.2} \%$ efficiency and a total harmonic distortion of $ at rated power.