title: Probabilistic machine learning and artificial intelligence

abstract: How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.


Content-Based Movie Recommendation Using Different Feature Sets


title: Termination of Linear Programs

abstract: We show that termination of a class of linear loop programs is decidable. Linear loop programs are discrete-time linear systems with a loop condition governing termination, that is, a while loop with linear assignments. We relate the termination of such a simple loop, on all initial values, to the eigenvectors corresponding to only the positive real eigenvalues of the matrix defining the loop assignments. This characterization of termination is reminiscent of the famous stability theorems in control theory that characterize stability in terms of eigenvalues.


title: The Science and Psychology Behind Music and Emotion

abstract: Music has been used for thousands of years as a means of emotional expression. The goals of this paper are to (a) review current literature on how music induces emotion (b) explore the mechanisms of how this happens both physiologically and psychologically and (c) to look at the role of desired effect and musical preference to move towards a general conclusion of what drives listeners’ musical choices. This paper approaches this by looking at structural theories of music including those of Krumhansl (1997) that music has inherent qualities that instill specific responses in the listener. The paper then continues by addressing a Jungian perspective often employed in music therapy. Here, music is used to express what is otherwise inexpressible. The Behavioral Perspective section postulates that music can prime listeners by making them predisposed through associations to feel positive or negative emotions. This theory is carried over to an analysis of music and consumerism where emotional priming can serve as a bridge to an association with a product. The Physiological Effects section explores research on music’s somatic connection indicating that pleasant music reduces stress and may decrease the body’s post-stress responses. The Music and Performance section analyzes the Mozart effect and its potential relationship to the arousal and mood hypothesis, stating that the improved spatial IQ scores recorded in the Mozart effect may have more to do with the arousal generated by all classical music rather than Mozart’s music itself. The paper concludes with an analysis of what drives listeners and the Arnett (1991a; 1991b; 1992) heavy metal studies, which show that music is the way adolescents deal with emotional upheaval and how music can be used as a means of achieving catharsis.


title: Options Discovery with Budgeted Reinforcement Learning

abstract: We consider the problem of learning hierarchical policies for Reinforcement Learning able to discover options, an option corresponding to a sub-policy over a set of primitive actions. Different models have been proposed during the last decade that usually rely on a predefined set of options. We specifically address the problem of automatically discovering options in decision processes. We describe a new RL learning framework called Bi-POMDP, and a new learning model called Budgeted Option Neural Network (BONN) able to discover options based on a budgeted learning objective. Since Bi-POMDP are more general than POMDP, our model can also be used to discover options for classical RL tasks. The BONN model is evaluated on different classical RL problems, demonstrating both quantitative and qualitative interesting results.


title: FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery

abstract: We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy. Through extensive experiments, we show that FineGAN achieves the desired disentanglement to generate realistic and diverse images belonging to fine-grained classes of birds, dogs, and cars. Using FineGAN's automatically learned features, we also cluster real images as a first attempt at solving the novel problem of unsupervised fine-grained object category discovery. Our code/models/demo can be found at https://github.com/kkanshul/finegan


title: Comparing algorithms for diffeomorphic registration: Stationary LDDMM and Diffeomorphic Demons

abstract: The stationary parameterization of diffeomorphisms is be- ing increasingly used in computational anatomy. In certain applications it provides similar results to the non-stationary parameterization alle- viating the computational charge. With this characterization for diffeo- morphisms, two different registration algorithms have been recently pro- posed: stationary LDDMM and diffeomorphic Demons. To our knowl- edge, their theoretical and practical differences have not been analyzed yet. In this article we provide a comparison between both algorithms in a common framework. To this end, we have studied the differences in the elements of both registration scenarios. We have analyzed the sen- sitivity of the regularization parameters in the smoothness of the final transformations and compared the performance of the registration re- sults. Moreover, we have studied the potential of both algorithms for the computation of essential operations for further statistical analysis. We have found that both methods have comparable performance in terms of image matching although the transformations are qualitatively different in some cases. Diffeomorphic Demons shows a slight advantage in terms of computational time. However, it does not provide as stationary LD- DMM the vector field in the tangent space needed to compute statistics or exact inverse transformations.


title: Demystifying the design of mobile augmented reality applications

abstract: This research proposes a set of interaction design principles for the development of mobile augmented reality (MAR) applications. The design recommendations adopt a user-centered perspective and, thus, they focus on the necessary actions to ensure high-quality MAR user experiences. To formulate our propositions we relied on theoretical grounding and an evaluation of eight MAR applications that provide published records of their design properties. The design principles have then been applied to guide the development of a MAR travel application. We performed a field study with 33 tourists in order to elicit whether our design choices effectively lead to enhanced satisfaction and overall user experience. Results suggest that the proposed principles contribute to ensuring high usability and performance of the MAR application as well as evoking positive feelings during user and system interactions. Our prescriptions may be employed either as a guide during the initial stages of the design process (ex-ante usage) or as a benchmark to assess the performance (ex-post usage) of MAR applications.


title: Diagnostics in Semantic Segmentation

abstract: Over the past years, computer vision community has contributed to enormous progress in semantic image segmentation, a per-pixel classification task, crucial for dense scene understanding and rapidly becoming vital in lots of real-world applications, including driverless cars and medical imaging. Most recent models are now reaching previously unthinkable numbers (e.g., 89% mean iou on PASCAL VOC, 83% on CityScapes), and, while intersection-over-union and a range of other metrics provide the general picture of model performance, in this paper we aim to extend them into other meaningful and important for applications characteristics, answering such questions as 'how accurate the model segmentation is on small objects in the general scene?', or 'what are the sources of uncertainty that cause the model to make an erroneous prediction?'. Besides establishing a methodology that covers the performance of a single model from different perspectives, we also showcase several extensions that can be worth pursuing in order to further improve current results in semantic segmentation.


title: Cross-Validation Based Single Response Adaptive Design of Experiments for Deterministic Computer Simulations

abstract: A new approach for single response adaptive design of deterministic computer experiments is presented. The approach is called SFCVT, for Space-Filling Cross-Validation Tradeoff. SFCVT uses metamodeling to obtain an estimate of cross-validation errors, which is then maximized, combined with a space filling criterion (treated as a constraint) to judiciously allocate sample points in the design space. The proposed method is compared, using a set of twenty four numerical examples, with two DOE methods from the literature and then applied to an air-to-refrigerant heat exchanger problem. Based on the comparison results for the numerical examples, it is shown that SFCVT performs better than an existing adaptive and a non-adaptive DOE method for the majority of the examples.


title: An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic

abstract: This paper proposes a new method in construction fuzzy neural network to forecast travel speed for multi-step ahead based on 2-min travel speed data collected from three remote traffic microwave sensors located on a southbound segment of a fourth ring road in Beijing City. The first-order Takagi–Sugeno system is used to complete the fuzzy inference. To train the evolving fuzzy neural network (EFNN), two learning processes are proposed. First, a $K$ -means method is employed to partition input samples into different clusters and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated. Second, a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi–Sugeno type fuzzy rules. Furthermore, a trigonometric regression function is introduced to capture the periodic component in the raw speed data. Specifically, the predicted performance between the proposed model and six traditional models are compared, which are artificial neural network, support vector machine, autoregressive integrated moving average model, and vector autoregressive model. The results suggest that the prediction performances of EFNN are better than those of traditional models due to their strong learning ability. As the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.


title: Multi-task Coupled Attentions for Category-specific Aspect and Opinion Terms Co-extraction

abstract: In aspect-based sentiment analysis, most existing methods either focus on aspect/opinion terms extraction or aspect terms categorization. However, each task by itself only provides partial information to end users. To generate more detailed and structured opinion analysis, we propose a finer-grained problem, which we call category-specific aspect and opinion terms extraction. This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms. To this end, we propose an end-to-end multi-task attention model, where each task corresponds to aspect/opinion terms extraction for a specific category. Our model benefits from exploring the commonalities and relationships among different tasks to address the data sparsity issue. We demonstrate its state-of-the-art performance on three benchmark datasets.


title: Universal Dependencies for Finnish

abstract: There has been substantial recent interest in annotation schemes that can be applied consistently to many languages. Building on several recent efforts to unify morphological and syntactic annotation, the Universal Dependencies (UD) project seeks to introduce a cross-linguistically applicable part-of-speech tagset, feature inventory, and set of dependency relations as well as a large number of uniformly annotated treebanks. We present Universal Dependencies for Finnish, one of the ten languages in the recent first release of UD project treebank data. We detail the mapping of previously introduced annotation to the UD standard, describing specific challenges and their resolution. We additionally present parsing experiments comparing the performance of a stateof-the-art parser trained on a languagespecific annotation schema to performance on the corresponding UD annotation. The results show improvement compared to the source annotation, indicating that the conversion is accurate and supporting the feasibility of UD as a parsing target. The introduced tools and resources are available under open licenses from http://bionlp.utu.fi/ud-finnish.html.


title: Menstrual hygiene management and school absenteeism among female adolescent students in Northeast Ethiopia

abstract: Adolescence in girls has been recognized as a special period marked with the onset of menarche. Even though menstruation is a natural process, it is associated with misconceptions, malpractices and challenges among girls in developing countries. However, much is not documented; school-absenteeism and dropout are a common problem among girls in rural Ethiopia. Focusing among school girls, this study has examined knowledge about menstruation, determinants of menstrual management and its influence on school-attendance in Northeast Ethiopia. We conducted a mixed-method research combining quantitative and qualitative methods in Northeast Ethiopia. The quantitative study was conducted among 595 randomly selected adolescent school girls. Nine in-depth interviews; five school-dropout girls and four female teachers, and four focus group discussions among school girls were conducted in 2013. The mean age at menarche was 13.98 (±1.17) years. About 51% of girls had knowledge about menstruation and its management. Only a third of the girls used sanitary napkins as menstrual absorbent during their last menstruation. Girls from urban areas, had mothers of secondary and above education and, families of higher monthly expenditure had more chance of using sanitary napkins than their counterparts. More than half of the girls reported to have been absent from school during their menstruation period. Those who did not use sanitary napkins were more likely to be absent from school [AOR-95% C.I: 5.37 (3.02 - 9.55)]. Fifty eight percent of girls reported that their school-performance had declined after they had menarche. In addition, the qualitative study indicated that school-dropout was common among girls who experienced teasing and humiliation by classmates when their clothes were stained with blood as they do not use sanitary napkins. Though there is an effort to increase girls’ school enrollment, lack of basic needs, like sanitary napkins that facilitate routine activates of girls at early adolescence are observed to deter girls’ school-attendance in rural Ethiopia. Special support for girl students, especially when they have their first menstruation and separate functioning sanitary facilities are necessities that should be in school at all times if gender equality and girls empowerment is to be achieved.


3 Data Mining for Web Personalization


Optimized Edge Detection Algorithm for Face Recognition


title: Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data

abstract: We present a new algorithm to explore and visualize multivariate time-varying data sets. We identify important trend relationships among the variables based on how the values of the variables change over time and how those changes are related to each other in different spatial regions and time intervals. The trend relationships can be used to describe the correlation and causal effects among the different variables. To identify the temporal trends from a local region, we design a new algorithm called SUBDTW to estimate when a trend appears and vanishes in a given time series. Based on the beginning and ending times of the trends, their temporal relationships can be modeled as a state machine representing the trend sequence. Since a scientific data set usually contains millions of data points, we propose an algorithm to extract important trend relationships in linear time complexity. We design novel user interfaces to explore the trend relationships, to visualize their temporal characteristics, and to display their spatial distributions. We use several scientific data sets to test our algorithm and demonstrate its utilities.


title: Negative Association Rules

abstract: Mining association rules associates events that took place together. In market basket analysis, these discovered rules associate items purchased together. Items that are not part of a transaction are not considered. In other words, typical association rules do not take into account items that are part of the domain but that are not together part of a transaction. Association rules are based on frequencies and count the transactions where items occur together. However, counting absences of items is prohibitive if the number of possible items is very large, which is typically the case. Nonetheless, knowing the relationship between the absence of an item and the presence of another can be very important in some applications. These rules are called negative association rules. We review current approaches for mining negative association rules and we discuss limitations and future research directions.


title: Patch-based Terrain Synthesis

abstract: In this paper, we present an intuitive and controllable patch-based technique for terrain synthesis. Our method is based on classical patch-based texture synthesis approaches. It generates a new terrain model by combining patches extracted from a given set of exemplars, providing a control performed by a low frequency guide, a categorization of exemplars, and a map for distributing these categories. Furthermore, we propose criteria to validate the input, some structures to accelerate the patch choice, and a metric based on the process, these structures, and some specificities of the data.


title: An ECG measurement IC using driven-right-leg circuit

abstract: In this paper, an electrocardiographic (ECG) signal processing IC, which is used for portable biomedical application, was designed using continuous-time technique. The circuit consists of an instrumentation amplifier (INA) with driven-right-leg circuit (DRL), a 5th order G/sub m/-C low pass filter (G/sub m/-C LPF) operating in sub-threshold mode, and amplifiers. DRL circuit is used to detect small amplitude signal in the presence of large common-mode voltage from the human body. The CMRR of the INA is 78 dB and the G/sub m/-C LPF has a cutoff frequency of 18 Hz. As a result of using the DRL, a small signal can be detected in the presence of large common-mode differential. The circuit consumes 1.23 mW when operating from with a supply voltage of /spl plusmn/1.5-V and occupies a core area of 0.94 mm/sup 2/. The circuit was designed in a 0.35/spl mu/m CMOS process and simulation results have successfully demonstrated the functionalities.


Incorporating Side Information into Recurrent Neural Network Language Models


abstract: We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.


title: Approximate pattern matching with k-mismatches in packed text

abstract: Given strings $P$ of length $m$ and $T$ of length $n$ over an alphabet of size $\sigma$, the string matching with $k$-mismatches problem is to find the positions of all the substrings in $T$ that are at Hamming distance at most $k$ from $P$. If $T$ can be read only one character at the time the best known bounds are $O(n\sqrt{k\log k})$ and $O(n + n\sqrt{k/w}\log k)$ in the word-RAM model with word length $w$. In the RAM models (including $AC^0$ and word-RAM) it is possible to read up to $\floor{w / \log \sigma}$ characters in constant time if the characters of $T$ are encoded using $\ceil{\log \sigma}$ bits. The only solution for $k$-mismatches in packed text works in $O((n \log\sigma/\log n)\ceil{m \log (k + \log n / \log\sigma) / w} + n^{\varepsilon})$ time, for any $\varepsilon > 0$. We present an algorithm that runs in time $O(\frac{n}{\floor{w/(m\log\sigma)}} (1 + \log \min(k,\sigma) \log m / \log\sigma))$ in the $AC^0$ model if $m=O(w / \log\sigma)$ and $T$ is given packed. We also describe a simpler variant that runs in time $O(\frac{n}{\floor{w/(m\log\sigma)}}\log \min(m, \log w / \log\sigma))$ in the word-RAM model. The algorithms improve the existing bound for $w = \Omega(\log^{1+\epsilon}n)$, for any $\epsilon > 0$. Based on the introduced technique, we present algorithms for several other approximate matching problems.


title: Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning

abstract: Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems and autonomous driving. When longer prediction horizons are needed, e.g., in decision making or motion planning, the uncertainty induced by incomplete environment perception and stochastic situation development over time cannot be neglected without sacrificing robustness and safety. Building consistent probabilistic models of drivers interactions with the environment, the road network and other traffic participants poses a complex problem. In this paper, we model the decision making process of drivers by building a hierarchical Dynamic Bayesian Model that describes physical relationships as well as the driver's behaviors and plans. This way, the uncertainties in the process on all abstraction levels can be handled in a mathematically consistent way. As drivers behaviors are difficult to model, we present an approach for learning continuous, non-linear, context-dependent models for the behavior of traffic participants. We propose an Expectation Maximization (EM) approach for learning the models integrated in the DBN from unlabeled observations. Experiments show a significant improvement in estimation and prediction accuracy over standard models which only consider vehicle dynamics. Finally, a novel approach to tactical decision making for autonomous driving is outlined. It is based on a continuous Partially Observable Markov Decision Process (POMDP) that uses the presented model for prediction.


title: Music Source Separation Using Stacked Hourglass Networks

abstract: In this paper, we propose a simple yet effective method for multiple music source separation using convolutional neural networks. Stacked hourglass network, which was originally designed for human pose estimation in natural images, is applied to a music source separation task. The network learns features from a spectrogram image across multiple scales and generates masks for each music source. The estimated mask is refined as it passes over stacked hourglass modules. The proposed framework is able to separate multiple music sources using a single network. Experimental results on MIR-1K and DSD100 datasets validate that the proposed method achieves competitive results comparable to the state-of-the-art methods in multiple music source separation and singing voice separation tasks.


title: BENEFITING FROM MARKETS FOR IDEAS — AN INVESTIGATION ACROSS DIFFERENT TYPOLOGIES

abstract: Acquiring and selling knowledge through markets for ideas (MFIs) is a growing trend for organisations embracing the open innovation paradigm. However, the literature has showed that more in-depth studies about how MFIs can be increasingly beneficial for both seekers and owners are needed. Thus, the present study aims at filling this gap by investigating the main characteristics of successful markets through the application of a multiple case studies methodology. Adopting two differentiation dimensions, as closed versus open and internal versus external, four distinct typologies of MFIs were identified and analysed. Accordingly, seven propositions regarding different characteristics of knowledge owners, knowledge seekers, and ideas were posed. The propositions are also referred to the different typologies of MFIs, thus highlighting similarities and differences across the markets. Hence, this work offers a deepening of the understanding of MFIs, by providing implications both to increase the theoretical comprehension of the topic and promote the definition of suggestions and guidelines for seekers and owners.


title: A virtual environment and model of the eye for surgical simulation

abstract: An anatomically detailed 3-D computer graphic model of the eye and surrounding face within a virtual environment has been implemented for use in a surgical simulator. The simulator forms part of a teleoperated micro-surgical robotic system being developed for eye surgery. The model has been designed to both visually and mechanically simulate features of the human eye by coupling computer graphic realism with finite element analysis. ::: The paper gives an overview of the system with emphasis on the graphical modelling techniques and a computationally efficient framework for representing anatomical details of the eye and for finite element analysis of the mechanical properties. Examples of realistic images coupled to large deformation finite element model of the cornea are presented. These images can be rendered sufficiently fast for the virtual reality application.


title: Towards a Blended Learning Model for Teaching and Learning Computer Programming: A Case Study

abstract: Blended learning is becoming an attractive model in higher education as new innovative information technologies are becoming increasingly available. However, just blending face-to-face learning with information technologies cannot provide effective teaching and efficient solutions for learning. To be successful, blended learning must rely on solid learning theory and pedagogical strategies. In addition, there is a need for a design-based research approach to explore blending learning through successive cycles of experimentations, where the shortcomings of each cycle are identified, redesigned, and reevaluated. This paper reports on a study conducted on a blended learn- ing model in Java programming at the introductory level. It presents the design, implementation, and evaluation of the model and its implications for the learning of introductory computer program- ming.


title: A rational analysis of the effects of memory biases on serial reproduction

abstract: Abstract Many human interactions involve pieces of information being passed from one person to another, raising the question of how this process of information transmission is affected by the cognitive capacities of the agents involved. Bartlett (1932) explored the influence of memory biases on the “serial reproduction” of information, in which one person’s reconstruction of a stimulus from memory becomes the stimulus seen by the next person. These experiments were done using relatively uncontrolled stimuli, but suggested that serial reproduction could transform information in a way that reflected the biases inherent in memory. We formally analyze serial reproduction using a Bayesian model of reconstruction from memory, giving a general result characterizing the effect of memory biases on information transmission. We then test the predictions of this account in four experiments using simple one-dimensional stimuli. Our results provide theoretical and empirical justification for the idea that serial reproduction reflects memory biases.


title: Text Summarization of Turkish Texts using Latent Semantic Analysis

abstract: Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algorithms produces the best scores.


title: Correlated Q-Learning

abstract: This paper introduces Correlated-Q (CE-Q) learning, a multiagent Q-learning algorithm based on the correlated equilibrium (CE) solution concept. CE-Q generalizes both Nash-Q and Friend-and-Foe-Q: in general-sum games, the set of correlated equilibria contains the set of Nash equilibria; in constant-sum games, the set of correlated equilibria contains the set of minimax equilibria. This paper describes experiments with four variants of CE-Q, demonstrating empirical convergence to equilibrium policies on a testbed of general-sum Markov games.


title: TV logo classification based on convolutional neural network

abstract: This paper presents region-based, convolutional neural network for accurate and efficient TV logo classification. Although many previous methods have been applied to TV logo classification, most of them only process the video images. For images captured by smartphone, these methods often can't achieve satisfactory performance. Three contributions are given to the problem of TV logo detection and classification. Firstly, maximally stable extremal region (MSER) is used as a method of generating candidate boxes per image. Geometry constraint is introduced to filter certain boxes which are obviously different from TV logo shape in geometry space. Secondly, we design an efficient Convolutional Neural Network (CNN) for classifying TV logo. Thirdly, we create a large TV logo dataset, all of which are collected from video streaming. Some augmentation methods have been taken to improve the diversity of TV logo images. Experimental results show that the proposed method outperformed previous methods.


title: Analysis of Deep Convolutional Neural Network Architectures

abstract: In computer vision many tasks are solved using machine learning. In the past few years, state of the art results in computer vision have been achieved using deep learning. Deeper machine learning architectures are better capable in handling complex recognition tasks, compared to previous more shallow models. Many architectures for computer vision make use of convolutional neural networks which were modeled after the visual cortex. Currently deep convolutional neural networks are the state of the art in computer vision. Through a literature survey an overview is given of the components in deep convolutional neural networks. The role and design decisions for each of the components is presented, and the diculties involved in training deep neural networks are given. To improve deep learning architectures an analysis is given of the activation values in four dierent architectures using various activation functions. Current state of the art classiers use dropout, max-pooling as well as the maxout activation function. New components may further improve the architecture by providing a better solution for the diminishing gradient problem.


title: Automated spatiotemporal change detection in digital aerial imagery

abstract: Handling change within integrated geospatial environments is a challenge of dual nature. It comprises automatic change detection, and the fundamental issue of modeling/representing change. In this paper we present a novel approach for automated change detection which allows us to handle change more efficiently than commonly available approaches. More specifically, we focus on the detection of building boundary changes within a spatiotemporal GIS environment. We have developed a novel approach, as an extension of least-squares based matching. Previous spatial states of an object are compared to its current representation in a digital image, and decisions are automatically made as to whether or not change at the outline has occurred. Older object information is used to produce templates for comparison with the representation of the same object in a newer image. Semantic information extracted through an analysis of template edge geometry, and estimates of accuracy are used to enhance our model. This template matching approach allows us to integrate in a single operation object extraction from digital imagery with change detection. By decomposing a complete outline into smaller elements and applying template matching along these locations we are able to detect precisely even small changes in building outlines. In this paper we present an overview of our approach, theoretical models, certain implementation issues like template selection and weight coefficient assignment, and experimental results.


title: EEG-based automatic emotion recognition: Feature extraction, selection and classification methods

abstract: Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a noncontinuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity.


title: IntentSearch: Capturing User Intention for One-Click Internet Image Search

abstract: Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved by text-based search are reranked based on both visual and textual content. Our key contribution is to capture the users' search intention from this one-click query image in four steps. 1) The query image is categorized into one of the predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a specific weight schema is used to combine visual features adaptive to this kind of image to better rerank the text-based search result. 2) Based on the visual content of the query image selected by the user and through image clustering, query keywords are expanded to capture user intention. 3) Expanded keywords are used to enlarge the image pool to contain more relevant images. 4) Expanded keywords are also used to expand the query image to multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to further improve content-based image reranking. All these steps are automatic, without extra effort from the user. This is critically important for any commercial web-based image search engine, where the user interface has to be extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in Internet image search are designed. Experimental evaluation shows that our approach significantly improves the precision of top-ranked images and also the user experience.


title: Inductorless wideband LNA with improved input matching using feedforward technique

abstract: This paper presents the design of an inductorless wideband LNA for multistandard wireless applications including WiFi, Bluetooth, GSM and WCDMA. The circuit uses a current reuse shunt-shunt feedback architecture with a feedforward path in order to improve the input matching, gain and noise figure (NF) simultaneously. The amplifier has been fabricated in standard 65nm CMOS technology and operates under a 1.2 V supply voltage. The achievable peak gain is 16 dB with a −3 dB bandwidth between 100 MHz and 5 GHz, while the input matching provides a Su dB and IIP3 of −17 dBm and −6 dBm respectively. The power consumption is only 6.3 mW. The area of the chip occupies only 0.1 mm2 with pads included.


title: A new approach to interdomain routing based on secure multi-party computation

abstract: Interdomain routing involves coordination among mutually distrustful parties, leading to the requirements that BGP provide policy autonomy, flexibility, and privacy. BGP provides these properties via the distributed execution of policy-based decisions during the iterative route computation process. This approach has poor convergence properties, makes planning and failover difficult, and is extremely difficult to change. To rectify these and other problems, we propose a radically different approach to interdomain-route computation, based on secure multi-party computation (SMPC). Our approach provides stronger privacy guarantees than BGP and enables the deployment of new policy paradigms. We report on an initial exploration of this idea and outline future directions for research.


title: Analysis and simulation of hybrid electric vehicles for sedan vehicle

abstract: This paper presents a modeling and simulation of two different vehicles topologies (parallel hybrid electric vehicle and series hybrid electric vehicle) using the ADVISOR (ADvanced Vehicle SimulatOR). An accurate analysis of the performance of a hybrid electric vehicle, as well as of its consumption and pollution level, requires a dynamic analysis of its behavior. Two kinds of simulation tools for electric and hybrid electric vehicles exist: steady state and dynamic tools. An accurate analysis of the vehicle performance requires a dynamic model that includes many components such as its electric motor, its batteries and its motor controller. The central controller of this sedan consists of I/O Board, Adapter and Microcontroller of motor drive. In a first step, the vehicle components are sized, using a power flow analysis, to meet the requirements of energy and power of a typical “Sedan vehicle”. In a second step, simulation results are presented and discussed to analysis complexity and performance.


title: Reconstruction of Personalized 3D Face Rigs from Monocular Video

abstract: We present a novel approach for the automatic creation of a personalized high-quality 3D face rig of an actor from just monocular video data (e.g., vintage movies). Our rig is based on three distinct layers that allow us to model the actor’s facial shape as well as capture his person-specific expression characteristics at high fidelity, ranging from coarse-scale geometry to fine-scale static and transient detail on the scale of folds and wrinkles. At the heart of our approach is a parametric shape prior that encodes the plausible subspace of facial identity and expression variations. Based on this prior, a coarse-scale reconstruction is obtained by means of a novel variational fitting approach. We represent person-specific idiosyncrasies, which cannot be represented in the restricted shape and expression space, by learning a set of medium-scale corrective shapes. Fine-scale skin detail, such as wrinkles, are captured from video via shading-based refinement, and a generative detail formation model is learned. Both the medium- and fine-scale detail layers are coupled with the parametric prior by means of a novel sparse linear regression formulation. Once reconstructed, all layers of the face rig can be conveniently controlled by a low number of blendshape expression parameters, as widely used by animation artists. We show captured face rigs and their motions for several actors filmed in different monocular video formats, including legacy footage from YouTube, and demonstrate how they can be used for 3D animation and 2D video editing. Finally, we evaluate our approach qualitatively and quantitatively and compare to related state-of-the-art methods.


title: A symbolic-connectionist theory of relational inference and generalization

abstract: The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization.


title: On the relation between brain potentials and the awareness of voluntary movements

abstract: We investigated the relation between neural events and the perceived time of voluntary actions or the perceived time of initiating those actions using the method of Libet. No differences were found in either movement-related potentials or perceived time of motor events between a fixed movement condition, where subjects made voluntary movements of a single finger in each block, and a free movement condition, in which subjects chose whether to respond with the left or the right index finger on each trial. We next calculated both the readiness potential (RP) and lateralised readiness potential (LRP) for trials with early and late times of awareness. The RP tended to occur later on trials with early awareness of movement initiation than on trials with late awareness, ruling out the RP as a cause of our awareness of movement intiation. However, the LRP occurred significantly earlier on trials with early awareness than on trials with late awareness, suggesting that the processes underlying the LRP may cause our awareness of movement initiation.


title: Mammalian small stress proteins protect against oxidative stress through their ability to increase glucose-6-phosphate dehydrogenase activity and by maintaining optimal cellular detoxifying machinery.

abstract: The protective activity of small stress proteins (sHsp) against H2O2-mediated cell death in the highly sensitive murine L929 fibroblast has been analyzed. We report here that the human Hsp27- and murine Hsp25-mediated rise in glutathione (GSH) levels as well as the maintenance of this redox modulator in its reduced form was directly responsible for the protection observed at the level of cell morphology and mitochondrial membrane potential. sHsp expression also buffered the increase in protein oxidation following H2O2 treatment and protected several key enzymes against inactivation. In this case, however, the protection necessitated both an increase in GSH and the presence of sHsp per se since the pattern of protection against protein oxidation mediated by a simple GSH increase was different from that induced by sHsp expression. Among the enzymes analyzed, we noticed that sHsp significantly increased glucose-6-phosphate dehydrogenase (G6PD) activity and to a lesser extent glutathione reductase and glutathione transferase activities. Moreover, an increased GSH level was observed in G6PD-overexpressing L929 cell clones. Taken together our results suggest that sHsp protect against oxidative stress through a G6PD-dependent ability to increase and uphold GSH in its reduced form and by using this redox modulator as an essential parameter of their in vivo chaperone activity against oxidized proteins.


title: A BRDF Representing Method Based on Gaussian Process

abstract: In recent years, digital reconstruction of cultural heritage provides an effective way of protecting historical relics, in which the modeling of surface reflection of historical heritage with high fidelity places a very important role. In this paper Gaussian process (GP) regression based approach is proposed to model the reflection properties of real materials, in which the simulation data generated by the existing model are both used as the training data and the proof that Gaussian process model can be used to describe the material reflection. Matusik’s MERL database is also adopted to perform training and inference and obtain the reflection model of the real material. Simulation results show that the proposed GP regression approach can achieve a good fitting of the reflection properties of certain materials, greatly reduce the BRDF measurement time and ensure high realistic rendering at the same time.


title: Design of an embedded based control system for efficient sorting of waste plastics using Near Infrared Spectroscopy

abstract: In this paper we present an embedded system solution to automate the sorting of different types of plastic by using the concepts of Near Infrared Spectroscopy (NIRS). Plastics are everyday used non-biodegradable materials when not disposed properly has adverse effects on the environment. For recycling of plastics different types of plastics (polymers) have to be identified and segregated. For economic reasons plastics must be identified and sorted instantaneously. The NIRS technique has been used for the instantaneous identification of plastics. Measurements made by NIRS are quite accurate and fast. To realize the above, a low cost Raspberry Pi based Control System, for efficient sorting of waste plastics has been developed. The necessary software required to interface the spectrometer with the Raspberry Pi as well as to process the NIRS data to obtain information on the polymer category have been written on the general-purpose, high-level programming language Python.


title: Higher Order Recurrent Neural Networks

abstract: In this paper, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units to keep track of more preceding states in recurrent neural networks (RNNs), which are all recurrently fed to the hidden layers as feedback through different weighted paths. By extending the popular recurrent structure in RNNs, we provide the models with better short-term memory mechanism to learn long term dependency in sequences. Analogous to digital filters in signal processing, we call these structures as higher order RNNs (HORNNs). Similar to RNNs, HORNNs can also be learned using the back-propagation through time method. HORNNs are generally applicable to a variety of sequence modelling tasks. In this work, we have examined HORNNs for the language modeling task using two popular data sets, namely the Penn Treebank (PTB) and English text8 data sets. Experimental results have shown that the proposed HORNNs yield the state-of-the-art performance on both data sets, significantly outperforming the regular RNNs as well as the popular LSTMs.


title: Domain specificity versus expertise: factors influencing distinct processing of faces

abstract: To explore face specificity in visual processing, we compared the role of task-associated strategies and expertise on the N170 event-related potential (ERP) component elicited by human faces with the ERPs elicited by cars, birds, items of furniture, and ape faces. In Experiment 1, participants performed a car monitoring task and an animacy decision task. In Experiment 2, participants monitored human faces while faces of apes were the distracters. Faces elicited an equally conspicuous N170, significantly larger than the ERPs elicited by non-face categories regardless of whether they were ignored or had an equal status with other categories (Experiment 1), or were the targets (in Experiment 2). In contrast, the negative component elicited by cars during the same time range was larger if they were targets than if they were not. Furthermore, unlike the posterior-temporal distribution of the N170, the negative component elicited by cars and its modulation by task were more conspicuous at occipital sites. Faces of apes elicited an N170 that was similar in amplitude to that elicited by the human face targets, albeit peaking 10 ms later. As our participants were not ape experts, this pattern indicates that the N170 is face-specific, but not specie-specific, i.e. it is elicited by particular face features regardless of expertise. Overall, these results demonstrate the domain specificity of the visual mechanism implicated in processing faces, a mechanism which is not influenced by either task or expertise. The processing of other objects is probably accomplished by a more general visual processor, which is sensitive to strategic manipulations and attention.


title: A Unified Approximation Framework for Deep Neural Networks

abstract: Deep neural networks (DNNs) have achieved significant success in a variety of real world applications. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various compression and acceleration techniques have been investigated, among which low-rank filters and sparse filters are heavily studied. In this paper we propose a unified framework to compress the convolutional neural networks by combining these two strategies, while taking the nonlinear activation into consideration. The filer of a layer is approximated by the sum of a sparse component and a low-rank component, both of which are in favor of model compression. Especially, we constrain the sparse component to be structured sparse which facilitates acceleration. The performance of the network is retained by minimizing the reconstruction error of the feature maps after activation of each layer, using the alternating direction method of multipliers (ADMM). The experimental results show that our proposed approach can compress VGG-16 and AlexNet by over 4X. In addition, 2.2X and 1.1X speedup are achieved on VGG-16 and AlexNet, respectively, at a cost of less increase on error rate.


title: MUSIC: Mutation-based SQL Injection Vulnerability Checking

abstract: SQL injection is one of the most prominent vulnerabilities for web-based applications. Exploitation of SQL injection vulnerabilities (SQLIV) through successful attacks might result in severe consequences such as authentication bypassing, leaking of private information etc. Therefore, testing an application for SQLIV is an important step for ensuring its quality. However, it is challenging as the sources of SQLIV vary widely, which include the lack of effective input filters in applications, insecure coding by programmers, inappropriate usage of APIs for manipulating databases etc. Moreover, existing testing approaches do not address the issue of generating adequate test data sets that can detect SQLIV. In this work, we present a mutation-based testing approach for SQLIV testing. We propose nine mutation operators that inject SQLIV in application source code. The operators result in mutants, which can be killed only with test data containing SQL injection attacks. By this approach, we force the generation of an adequate test data set containing effective test cases capable of revealing SQLIV. We implement a MUtation-based SQL Injection vulnerabilities Checking (testing) tool (MUSIC) that automatically generates mutants for the applications written in Java Server Pages (JSP) and performs mutation analysis. We validate the proposed operators with five open source web-based applications written in JSP. We show that the proposed operators are effective for testing SQLIV.


title: Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory

abstract: For dialogue response generation, traditional generative models generate responses solely from input queries. Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways. Consequentially, those models tend to output generic and dull responses, impeding the generation of informative utterances. Recently, researchers have attempted to fill the information gap by exploiting information retrieval techniques. When generating a response for a current query, similar dialogues retrieved from the entire training data are considered as an additional knowledge source. While this may harvest massive information, the generative models could be overwhelmed, leading to undesirable performance. In this paper, we propose a new framework which exploits retrieval results via a skeleton-then-response paradigm. At first, a skeleton is generated by revising the retrieved responses. Then, a novel generative model uses both the generated skeleton and the original query for response generation. Experimental results show that our approaches significantly improve the diversity and informativeness of the generated responses.


title: OTPaaS—One Time Password as a Service

abstract: Conventional password-based authentication is considered inadequate by users as many online services started to affect each other. Online credentials are used to recover other credentials and complex attacks are directed to the weakest one of many of these online credentials. As researchers are looking for new authentication techniques, one time passwords, which is a two-factor authentication scheme, looks like a natural enhancement over conventional username/password schemes. The manuscript places the OTP verifier to the cloud to ease adoption of its usage by cloud service providers. When the OTP verifier is placed on the cloud as a service, other cloud service providers could outsource their OTP deployments as well as cloud users could activate their respective account on the OTP provider on several cloud services. This enables them to use several cloud services without the difficulty of managing several OTP accounts for each cloud service. On the other hand, OTP service provision saves inexperienced small to medium enterprises from spending extra costs for OTP provisioning hardware, software, and employers. The paper outlines architecture to build a secure, privacy-friendly, and sound OTP provider in the cloud to outsource the second factor of authentication. Cloud user registration to OTP provider, service provider activation, and authentication phases are inspected. The security and privacy considerations of the proposed architecture are defined and analyzed. Attacks from outsiders, unlinkability properties of user profiles, attacks from curious service providers or OTP verifiers are mitigated within the given assumptions. The proposed solution, which locates the OTP provider in the cloud, is rendered robust and sound as a result of the analysis.


title: A wide stopband CPW low pass filter using quarter wavelength stepped impedance resonators

abstract: A novel wide stopband coplanar waveguide (CPW) low pass filter (LPF) using quarter wavelength stepped impedance resonators (SIR) is presented. The bandstop characteristics of the quarter wavelength short circuited stubs SIR are demonstrated. By cascading three tapered quarter wavelength short circuited stubs SIR and a CPW spurline resonator, the fabricated low pass filter has sharp rejection with cutoff frequency of 3.5 GHz, and deep attenuation from 3.7 GHz to 11 GHz lower than −24 dB. Good agreement between simulated and measured results is demonstrated.


title: The deficiency of galectin-3 in stromal cells leads to enhanced tumor growth and bone marrow metastasis

abstract: BackgroundGalectin-3 is a multifunctional β-galactoside-binding lectin that once synthesized, is expressed in the nucleus, cytoplasm, cell surface and in the extracellular environment. Because of its unique structure, galectin-3 can oligomerize forming lattice upon binding to multivalent oligossacharides and influence several pathologic events such as tumorigenesis, invasion and metastasis.MethodsIn our study, balb/c Lgals3+/+ and Lgals3−/− female mice were inoculated in the fourth mammary fat pad with 4T1 breast cancer cell line. The primary tumor, inguinal lymph nodes and iliac bone marrow were evaluated 15, 21 and 28 days post-injection. The primary tumor growth was evaluated by measuring the external diameter, internal growth by ultrasound and weight of the excised tumor. The presence of cancer cells in the draining lymph nodes and iliac crest bone marrow were performed by immunohistochemistry, PCR and clonogenic metastatic assay.ResultsIn this study we demonstrated that the deletion of galectin-3 in the host affected drastically the in vivo growth rate of 4T1 tumors. The primary tumors in Lgals3−/− mice displayed a higher proliferative rate (p < 0,05), an increased necrotic area (p < 0,01) and new blood vessels with a wider lumen in comparison with tumors from Lgals3+/+ mice (P < 0,05). Moreover, we detected a higher number of 4T1-derived metastatic colonies in the lymph nodes and the bone marrow of Lgals3−/− mice (p < 0,05). Additionally, healthy Lgals3−/− control mice presented an altered spatial distribution of CXCL12 in the bone marrow, which may explain at least in part the initial colonization of this organ in Lgals3−/− injected with 4T1 cells.ConclusionsTaken together, our results demonstrate for the first time that the absence of galectin-3 in the host microenvironment favors the growth of the primary tumors, the metastatic spread to the inguinal lymph nodes and bone marrow colonization by metastatic 4T1 tumor cells.


title: Neural Representations of Self versus Other: Visual-Spatial Perspective Taking and Agency in a Virtual Ball-tossing Game

abstract: Human self-consciousness relies on the ability to distinguish between oneself and others. We sought to explore the neural correlates involved in self-other representations by investigating two critical processes: perspective taking and agency. Although recent research has shed light on the neural processes underlying these phenomena, little is known about how they overlap or interact at the neural level. In a two-factorial functional magnetic resonance imaging (fMRI) experiment, participants played a ball-tossing game with two virtual characters (“avatars”). During an active/agency (ACT) task, subjects threw a ball to one of the avatars by pressing a button. During a passive/nonagency (PAS) task, they indicated which of the other avatars threw the ball. Both tasks were performed from a first-person perspective (1PP), in which subjects interacted from their own perspective, and a third-person perspective (3PP), in which subjects interacted from the perspective of an avatar with another location in space. fMRI analyses revealed overlapping activity in medial prefrontal regions associated with representations of one's own perspective and actions (1PP and ACT), and overlapping activity in temporal-occipital, premotor, and inferior frontal, as well as posterior parietal regions associated with representation of others' perspectives and actions (3PP and PAS). These findings provide evidence for distinct neural substrates underlying representations of the self and others and provide support for the idea that the medial prefrontal cortex crucially contributes to a neural basis of the self. The lack of a statistically significant interaction suggests that perspective taking and agency represent independent constituents of self-consciousness.


title: MEMS Laser Scanners: A Review

abstract: Laser scanners have been an integral part of MEMS research for more than three decades. During the last decade, miniaturized projection displays and various medical-imaging applications became the main driver for progress in MEMS laser scanners. Portable and truly miniaturized projectors became possible with the availability of red, green, and blue diode lasers during the past few years. Inherent traits of the laser scanning technology, such as the very large color gamut, scalability to higher resolutions within the same footprint, and capability of producing an always-in-focus image render it a very viable competitor in mobile projection. Here, we review the requirements on MEMS laser scanners for the demanding display applications, performance levels of the best scanners in the published literature, and the advantages and disadvantages of electrostatic, electromagnetic, piezoelectric, and mechanically coupled actuation principles. Resonant high-frequency scanners, low-frequency linear scanners, and 2-D scanners are included in this review.


title: Real-time image-based 6-DOF localization in large-scale environments

abstract: We present a real-time approach for image-based localization within large scenes that have been reconstructed offline using structure from motion (Sfm). From monocular video, our method continuously computes a precise 6-DOF camera pose, by efficiently tracking natural features and matching them to 3D points in the Sfm point cloud. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the need for online extraction of scale-invariant features. Instead, offline we construct an indexed database containing multiple DAISY descriptors per 3D point extracted at multiple scales. The key to the efficiency of our method lies in invoking DAISY descriptor extraction and matching sparingly during localization, and in distributing this computation over a window of successive frames. This enables the algorithm to run in real-time, without fluctuations in the latency over long durations. We evaluate the method in large indoor and outdoor scenes. Our algorithm runs at over 30 Hz on a laptop and at 12 Hz on a low-power, mobile computer suitable for onboard computation on a quadrotor micro aerial vehicle.


title: Blue Brain - The Magic of Man

abstract: What is it that differentiates man and puts him on the throne of the entire animal kingdom? Yes, it's the human brain, and not the human, which is the most valuable creation of God. But the brain, all its knowledge and power are destroyed after the death of man. Is it possible to model a brain and upload the contents of the real natural brain into it thereby propagating life even after death? Yes, indeed. Today, organ donation is giving a new lease of life to many ailing patients, however can brain be added to the list of organs that can be donated? Imagine the marvels that would be back into existence if we could replicate and restore the brains of geniuses like Sir Albert Einstein or Newton or even our great leaders of the past like Mahatma Gandhi. This paper presents a literature survey on the Blue Brain technology that promptly answers all these sparkling questions. Scientists today are in research to take artificial intelligence to a level beyond everything, to create a virtual brain that could think, react, make decisions and also keep everything in some form of memory to do everything that a normal human brain can. To achieve this, the main approach is to upload an actual brain in a virtual brain. So that even after death the person's conscience would be able to function exactly as a normal brain in the form of a machine. IBM along with scientists at BMI (Brain and Mind Institute) Ecole Polytechnique Federale de Lausanne (EPFL) Research University in Switzerland are simulating the brain's biological systems into a 3D model, which would recreate the electrochemical interactions that take place inside the brain. If this simulation results in a success, the modeling would extend to different parts of the brain and make it function with all the abilities of a natural brain, thereby eliminating the chances of any brain malfunctions such as psychiatric disorders like depression and autism, which are possible in the normal brain. IBM names this project as the 'Blue Brain' project.


title: An Integrated Hybrid Silicon Multiwavelength AWG Laser

abstract: The first integrated multiwavelength laser based on an arrayed waveguide grating (AWG) fabricated on a silicon-on-insulator wafer is presented. It consists of Fabry-Perot cavities integrated with hybrid silicon amplifiers and an intracavity filter in the form of an AWG with a channel spacing of 360 GHz. Four-channel lasing operation is shown. Single-sided fiber-coupled output powers as high as 35 μW are measured. The device shows subnanosecond rise and fall times, and direct modulation at 1 GHz gives an open eye with an extinction ratio of 7.7 dB.


title: Using Heart Rate Monitors to Detect Mental Stress

abstract: This article describes an approach to detecting mental stress using unobtrusive wearable sensors. The approach relies on estimating the state of the autonomic nervous system from an analysis of heart rate variability. Namely, we use a non-linear system identification technique known as principal dynamic modes (PDM) to predict the activation level of the two autonomic branches: sympathetic (i.e. stress-inducing) and parasympathetic (i.e. relaxation-related). We validate the method on a discrimination problem with two psychophysiological conditions, one associated with mental tasks and one induced by relaxation exercises. Our results indicate that PDM features are more stable and less subject-dependent than spectral features, though the latter provide higher classification performance within subjects. When PDM and spectral features are combined, our system discriminates stressful events with a success rate of 83% within subjects (69% between subjects).


title: Ultrastructure of neuro-spirocyte synapses in the sea anemone Aiptasia pallida (Cnidaria, Anthozoa, Zoantharia).

abstract: Using transmission electron microscopy of serially sectioned tentacles from the sea anemone Aiptasia pallida, we located and characterized two types of neuro-spirocyte synapses. Clear vesicles were observed at 10 synapses and dense-cored vesicles at five synapses. The diameters of vesicles at each neuro-spirocyte synapse were averaged; clear vesicles ranged from 49-89 nm in diameter, whereas the dense-cored vesicles ranged from 97-120 nm in diameter. One sequential pair of synapses included a neuro-spirocyte synapse with clear vesicles (81 nm) and a neuro-neuronal synapse with dense-cored vesicles (168 nm). A second synapse on the same cell had dense-cored vesicles (103 nm). An Antho-RFamide-labeled ganglion cell and three different neurites were observed adjacent to spirocytes, but no neuro-spirocyte synapses were present. Many of the spirocytes also were immunoreactive to Antho-RFamide. The presence of sequential neuro-neuro-spirocyte synapses suggests that synaptic modulation may be involved in the neural control of spirocyst discharge. The occurrence of either dense-cored or clear vesicles at neuro-spirocyte synapses suggests that at least two types of neurotransmitter substances control the discharge of spirocysts in sea anemones.


title: A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

abstract: Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.


title: The Effect of Violent and Nonviolent Video Games on Heart Rate Variability, Sleep, and Emotions in Adolescents With Different Violent Gaming Habits

abstract: Objective To study cardiac, sleep-related, and emotional reactions to playing violent (VG) versus nonviolent video games (NVG) in adolescents with different gaming habits.Methods Thirty boys (aged ...


title: BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning.

abstract: We introduce a novel method to train agents of reinforcement learning (RL) by sharing knowledge in a way similar to the concept of using a book. The recorded information in the form of a book is the main means by which humans learn knowledge. Nevertheless, the conventional deep RL methods have mainly focused either on experiential learning where the agent learns through interactions with the environment from the start or on imitation learning that tries to mimic the teacher. Contrary to these, our proposed book learning shares key information among different agents in a book-like manner by delving into the following two characteristic features: (1) By defining the linguistic function, input states can be clustered semantically into a relatively small number of core clusters, which are forwarded to other RL agents in a prescribed manner. (2) By defining state priorities and the contents for recording, core experiences can be selected and stored in a small container. We call this container as ‘BOOK’. Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.


title: A round- and computation-efficient three-party authenticated key exchange protocol

abstract: In three-party authenticated key exchange protocols, each client shares a secret only with a trusted server with assists in generating a session key used for securely sending messages between two communication clients. Compared with two-party authenticated key exchange protocols where each pair of parties must share a secret with each other, a three-party protocol does not cause any key management problem for the parties. In the literature, mainly there exist three issues in three-party authenticated key exchange protocols are discussed that need to be further improved: (1) to reduce latency, communication steps in the protocol should be as parallel as possible; (2) as the existence of a security-sensitive table on the server side may cause the server to become compromised, the table should be removed; (3) resources required for computation should be as few as possible to avoid the protocol to become an efficiency bottleneck. In various applications over networks, a quick response is required especially by light-weight clients in the mobile e-commerce. In this paper, a round- and computation-efficient three-party authenticated key exchange protocol is proposed which fulfils all of the above mentioned requirements.


title: PTEN promotes intervertebral disc degeneration by regulating nucleus pulposus cell behaviors.

abstract: Intervertebral disc degeneration (IDD) is induced by multiple factors including increased apoptosis, decreased survival, and reduced extracellular matrix (ECM) synthesis in the nucleus pulposus (NP) cells. The tumor suppressor PTEN is the only known lipid phosphatase counteracting the PI3K/AKT pathway. Loss of PTEN leads to activated PI3K/AKT signaling, which plays a key role in a variety of cancers. However, the role of PTEN/PI3K/AKT signaling nexus in IDD remains unknown. Here, we report that PTEN is overexpressed in degenerative NP, which correlates with inactivated AKT. Using PTEN knockdown approach by lentivirus-mediated siRNA gene transfer technique, we report that PTEN decreases survival but induces apoptosis and senescence of NP cells. PTEN also inhibits expression and production of ECM components including collagen II, aggrecan and proteoglycan. Furthermore, PTEN modulates expression of ECM regulatory molecules SOX-9 and matrix metalloproteinase-3 (MMP-3). Using small molecule AKT inhibitor GDC-0068, we confirm that PTEN regulates NP cell behaviors through its direct targeting of PI3K/AKT. These findings demonstrate for the first time that PTEN/PI3K/AKT signaling axis plays an important role in pathogenesis of IDD. Targeting PTEN using gene therapy may represent a promising therapeutic approach against disc degenerative diseases. This article is protected by copyright. All rights reserved.


The Post-Development Cautionary Note for Africa: Will the Free Sudan from Female Genital Mutilation/Cutting do Things Differently?


title: Nitrogen-fixing and cellulose-producing Gluconacetobacter kombuchae sp . nov . , isolated from Kombucha tea

abstract: A few members of the family Acetobacteraceae are cellulose-producers, while only six members fix nitrogen. Bacterial strain RG3T, isolated from Kombucha tea, displays both of these characteristics. A high bootstrap value in the 16S rRNA gene sequence-based phylogenetic analysis supported the position of this strain within the genus Gluconacetobacter, with Gluconacetobacter hansenii LMG 1527T as its nearest neighbour (99.1 % sequence similarity). It could utilize ethanol, fructose, arabinose, glycerol, sorbitol and mannitol, but not galactose or xylose, as sole sources of carbon. Single amino acids such as L-alanine, L-cysteine and L-threonine served as carbon and nitrogen sources for growth of strain RG3T. Strain RG3T produced cellulose in both nitrogen-free broth and enriched medium. The ubiquinone present was Q-10 and the DNA base composition was 55.8 mol% G+C. It exhibited low values of 5.2-27.77 % DNA-DNA relatedness to the type strains of related gluconacetobacters, which placed it within a separate taxon, for which the name Gluconacetobacter kombuchae sp. nov. is proposed, with the type strain RG3T (=LMG 23726T=MTCC 6913T).


title: Gestural interaction on the steering wheel: reducing the visual demand

abstract: Cars offer an increasing number of infotainment systems as well as comfort functions that can be controlled by the driver. In our research, we investigate new interaction techniques that aim to make it easier to interact with these systems while driving. We suggest utilizing the steering wheel as an additional interaction surface. In this paper, we present two user studies conducted with a working prototype of a multi-touch steering wheel. In the first, we developed a user-defined steering wheel gesture set, and in the second, we applied the identified gestures and compared their application to conventional user interaction with infotainment systems in terms of driver distraction. The main outcome was that driver's visual demand is reduced significantly by using gestural interaction on the multi-touch steering wheel.


title: Applying Data Mining Techniques to Intrusion Detection in Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSNs) have become a hot research topic in recent years. They have many potential applications for both civil and military tasks. However, the unattended nature of WSNs and the limited computational and energy resources of their nodes make them susceptible to many types of attacks. Intrusion detection is one of the major and efficient defence methods against attacks in a network infrastructure. Intrusion Detection Systems can be seen as the second line of defence and they complement the security primitives that are adopted in order to prevent attacks against the computer network being protected. The peculiar features of a wireless sensor network pose stringent requirements to the design of intrusion detection systems. In this paper, we propose a hybrid, lightweight, distributed Intrusion Detection System (IDS) for wireless sensor networks. This IDS uses both misuse-based and anomaly-based detection techniques. It is composed of a Central Agent, which performs highly accurate intrusion detection by using data mining techniques, and a number of Local Agents running lighter anomaly-based detection techniques on the motes. Decision trees have been adopted as classification algorithm in the detection process of the Central Agent and their behaviour has been analysed in selected attacks scenarios. The accuracy of the proposed IDS has been measured and validated through an extensive experimental campaign. This paper presents the results of these experimental tests.


title: A c-mu Rule for Two-Tiered Parallel Servers

abstract: The cμ rule is known to be optimal in many queueing systems with memoryless service and inter-arrival times. We establish a cμ rule for a parallel flexible server system with two tiers. Multiple job classes arrive and wait in separate queues. The first tier contains classes that can only be served by one server each, and the second tier contains a class that can be served by all servers. The cμ preferences give priority to the first tier. Sequencing decisions are made to minimize linear holding costs.


title: Compressing Word Embeddings via Deep Compositional Code Learning

abstract: Natural language processing (NLP) models often require a massive number of parameters for word embeddings, resulting in a large storage or memory footprint. Deploying neural NLP models to mobile devices requires compressing the word embeddings without any significant sacrifices in performance. For this purpose, we propose to construct the embeddings with few basis vectors. For each word, the composition of basis vectors is determined by a hash code. To maximize the compression rate, we adopt the multi-codebook quantization approach instead of binary coding scheme. Each code is composed of multiple discrete numbers, such as (3, 2, 1, 8), where the value of each component is limited to a fixed range. We propose to directly learn the discrete codes in an end-to-end neural network by applying the Gumbel-softmax trick. Experiments show the compression rate achieves 98% in a sentiment analysis task and 94% ~ 99% in machine translation tasks without performance loss. In both tasks, the proposed method can improve the model performance by slightly lowering the compression rate. Compared to other approaches such as character-level segmentation, the proposed method is language-independent and does not require modifications to the network architecture.


title: Quantitatively Evaluating GANs With Divergences Proposed for Training

abstract: Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants are being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used only for training. We observe consistency across the various proposed metrics and, interestingly, the test-time metrics do not favour networks that use the same training-time criterion. We also compare the proposed metrics to human perceptual scores.


title: A Method for Obtaining Force Law Information by Applying the Heisenberg Uncertainty Principle

abstract: Assuming only that the electromagnetic force between two charged bodies is mediated by “virtual” photons, and that the “strength” of the electromagnetic interaction scales as qα1/2, Coulomb's law is derived directly from Heisenberg's uncertainty relation between time and energy. Extension of this treatment to the nuclear force, assuming mediation by virtual mesons, yields a relation for the maximum range of the nuclear force, r<ƛc(meson). That is, the nuclear force acts only over distances less than the Compton wavelength of the mediating particle. Examination of the gravitational force law yields the results that the graviton must be massless and move at speed c and that the gravitational interaction should scale as im′(Gm02/ħc)1/2. These examples demonstrate how the application of the uncertainty principle may be used to yield information on the force laws due to the basic interactions.


title: Robust Multirate Control Scheme With Predictive Virtual Lanes for Lane-Keeping System of Autonomous Highway Driving

abstract: In this paper, we propose a new approach for a robust multirate lane-keeping control scheme with predictive virtual lanes. First, the multirate lane-keeping control scheme is proposed to improve the lane-keeping performance and to reduce the ripple in the yaw rate. To improve the lane-keeping performance on a curved road, the integral of the lateral offset error is added to the state feedback controller. A multirate Kalman filter (KF) has been developed to resolve the problems caused by slow lane detection due to the vision processing system. This multirate KF estimates vehicle states at a fast rate using a microprocessor. Utilizing the estimated states, the linear quadratic state feedback control operates at the same fast update rate of the microprocessor. Thus, a multirate control scheme can reduce the ripple in the yaw rate. Second, we propose a virtual lane prediction method that compensates for the momentary failure of lane detection from unexpected problems. If the camera sensor momentarily fails while obtaining lane information, the predicted virtual lane can be substituted for the lane detection using the camera sensor in the proposed control scheme. Thus, the proposed control scheme can normally operate when the lane information is momentarily unavailable. The performance of the proposed method was evaluated via experiments.


title: Efficient VLSI Implementation of Neural Networks With Hyperbolic Tangent Activation Function

abstract: Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions. Accurate implementation of these transfer functions in digital networks faces certain challenges. In this paper, an efficient approximation scheme for hyperbolic tangent function is proposed. The approximation is based on a mathematical analysis considering the maximum allowable error as design parameter. Hardware implementation of the proposed approximation scheme is presented, which shows that the proposed structure compares favorably with previous architectures in terms of area and delay. The proposed structure requires less output bits for the same maximum allowable error when compared to the state-of-the-art. The number of output bits of the activation function determines the bit width of multipliers and adders in the network. Therefore, the proposed activation function results in reduction in area, delay, and power in VLSI implementation of artificial neural networks with hyperbolic tangent activation function.


title: Minjingu phosphate rock applications increase the population of phosphate solubilising microorganisms with a positive impact on crop yields in a Kenyan Ferralsol

abstract: Soil microbes such as plant growth promoting rhizobacteria play significant roles in the solubilisation of inorganic phosphorus (P), mineralization of organic P and in improving plant P uptake. It is known that phosphate solubilising microorganisms (PSM) populations largely vary depending on the ecosystems, the cropping systems or the soil management. The capacity of Minjingu phosphate rock (PR) to enhance the populations of native PSM under three cereal–legume rotation systems was assessed in the third season of rotation. Triple super phosphate (TSP) was used as a positive control. In comparison to the negative control, application of Minjingu PR increased the total fungal diversity and phosphate solubilising bacteria (PSB) population by 67–90 % while high rates of TSP significantly (p < 0.05) reduced bacterial diversity and populations of PSB by 46–69 %. Minjingu PR also resulted in both crop and legume yields increase (41–104 % compared to the control), which were similar to those obtained with TSP application. Cropping systems incorporating sparingly soluble P sources such as Minjingu PR into soils can stimulate the populations of native PSB and agronomic productivity. They may represent a promising way of minimizing the utilization of mineral P fertilizers.


title: Learning to Play Othello with N-Tuple Systems

abstract: This paper investigates the use of n-tuple systems as position value functions for the game of Othello. The architecture is described, and then evaluated for use with temporal difference learning. Performance is compared with previously de-veloped weighted piece counters and multi-layer perceptrons. The n-tuple system is able to defeat the best performing of these after just five hundred games of self-play learning. The conclusion is that n-tuple networks learn faster and better than the other more conventional approaches.


title: Gamification-based incentive mechanism for participatory sensing

abstract: Since participatory sensing relies on users' active participation, several monetary incentive mechanisms for attracting users' participation have been proposed. However, to make users participate in “heavy” sensing tasks (either physically or mentally), stronger incentive is required. Thus, the total amount of rewards paid by the client will quickly rise. In this paper, we propose a novel incentive mechanism based on gamification for participatory sensing to reduce the total amount of rewards paid by the client. The proposed incentive mechanism incorporates a status level scheme depending on earned reward points like airline's mileage services, so that users with higher status can earn more reward points. We also introduce a ranking scheme among users and a badge scheme based on gamification so that users are attracted by getting not only monetary reward points but also sense of accomplishment. We formulate the problem of sensing given PoI (points of interest) with minimal reward points and devise a heuristic algorithm for deriving the set of users to which requests are sent and appropriate reward points for each request. The algorithm requires the participation probability distribution for each tuple of a user, reward points, and the burden of a sensing task. To obtain the probability distribution, we implemented a prototype of a participatory sensing system with the proposed incentive mechanism and conducted an experiment with 18 users for 30 days. As a result, we confirmed that the gamification mechanism increased participation probability from 53% (without gamification) to 73%.


title: Prolog - the language and its implementation compared with Lisp

abstract: Prolog is a simple but powerful programming language founded on symbolic logic. The basic computational mechanism is a pattern matching process (“unification”) operating on general record structures (“terms” of logic). We briefly review the language and compare it especially with pure Lisp. The remainder of the paper discusses techniques for implementing Prolog efficiently; in particular we describe how to compile the patterns involved in the matching process. These techniques are as incorporated in our DECsystem-10 Prolog compiler (written in Prolog). The code it generates is comparable in speed with that produced by existing DEC10 Lisp compilers. We argue that pattern matching is a better method for expressing operations on structured data than conventional selectors and constructors - both for the user and for the implementor.


title: Tactile sensory substitution : Models for enaction in HCI

abstract: To apply enactive principles within human-computer interaction poses interesting challenges to the way that we design and evaluate interfaces, particularly those that possess a strong sensorimotor character. This article surveys the field of tactile sensory substitution, an area of science and engineering that lies at the intersection of such research domains as neuroscience, haptics, and sensory prosthetics. It is argued that this area of research is of high relevance to the design and understanding of enactive interfaces that make use of touch, and is also a fertile arena for revealing fundamental issues at stake in the design and implementation of enactive interfaces, ranging from engineering, to human sensory physiology, and the function and plasticity of perception. A survey of these questions is provided, alongside a range of current and historical examples.


An Algerian Arabic-French Code-Switched Corpus


title: THE IMPACT OF REFERRAL CHANNELS IN ONLINE CUSTOMER JOURNEY

abstract: This study scrutinizes the predictive relationship between three referral channels, search engine, social medial, and third-party advertising, and online consumer search and purchase. The results derived from vector autoregressive models suggest that the three channels have differential predictive relationship with sale measures. The predictive power of the three channels is also considerably different in referring customers among competing online shopping websites. In the short run, referrals from all three channels have a significantly positive predictive relationship with the focal online store’s sales amount and volume, but having no significant relationship with conversion. Only referrals from search engines to the rival website have a significantly negative predictive relationship with the focal website’s sales and volume. In the long run, referrals from all three channels have a significant positive predictive relationship with the focal website’s sales, conversion and sales volume. In contrast, referrals from all three channels to the competing online stores have a significant negative predictive relationship with the focal website’s sales, conversion and sales volume. Our results also show that search engine referrals explains the most of the variance in sales, while social media referrals explains the most of the variance in conversion and third party ads referrals explains the most of the variance in sales volume. This study offers new insights for IT and marketing practitioners in respect to better and deeper understanding on marketing attribution and how different channels perform in order to optimize the media mix and overall performance.


title: Clinical usefulness of the two-site Semmes-Weinstein monofilament test for detecting diabetic peripheral neuropathy.

abstract: The present study was done to validate the two-site Semmes-Weinstein (SW) monofilament test in identifying patients at risk of lower-extremity complications in clinical setting. The SW monofilament test and nerve conduction study were conducted on type 2 diabetic patients (n=37) at Pusan National University Hospital in Korea. As the duration of diabetes mellitus was longer, neuropathy identified by nerve conduction study and complications of diabetes were more severe (p<0.01). The number of sites unable to perceive SW monofilament (p<0.001) was larger in patients with lower-extremity neuropathy symptoms than those without symptoms. Sensitivity and specificity at two sites (the third and fifth metatarsal head sites) were 93% and 100%, respectively. In conclusion, the two-site SW monofilament test was a sensitive, specific, simple, and inexpensive screening tool for identifying diabetic peripheral neuropathy in clinical setting.


title: Crowdsourcing, Open Innovation and Collective Intelligence in the Scientific Method - A Research Agenda and Operational Framework

abstract: The lonely researcher trying to crack a problem in her office still plays an important role in fundamental research. However, a vast exchange, often with participants from different fields is taking place in modern research activities and projects. In the "Research Value Chain" (a simplified depiction of the Scientific Method as a process used for the analyses in this paper), interactions between researchers and other individuals (intentional or not) within or outside their respective institutions can be regarded as occurrences of Collective Intelligence. "Crowdsourcing" (Howe 2006) is a special case of such Collective Intelligence. It leverages the wisdom of crowds (Surowiecki 2004) and is already changing the way groups of people produce knowledge, generate ideas and make them actionable. A very famous example of a Crowdsourcing outcome is the distributed encyclopedia "Wikipedia". Published research agendas are asking how techniques addressing "the crowd" can be applied to non-profit environments, namely universities, and fundamental research in general. This paper discusses how the non-profit "Research Value Chain" can potentially benefit from Crowdsourcing. Further, a research agenda is proposed that investigates a) the applicability of Crowdsourcing to fundamental science and b) the impact of distributed agent principles from Artificial Intelligence research on the robustness of Crowdsourcing. Insights and methods from different research fields will be combined, such as complex networks, spatially embedded interacting agents or swarms and dynamic networks. Although the ideas in this paper essentially outline a research agenda, preliminary data from two pilot studies show that nonscientists can support scientific projects with high quality contributions. Intrinsic motivators (such as "fun") are present, which suggests individuals are not (only) contributing to such projects with a view to large monetary rewards.


title: Keyword based searching in social networks

abstract: The main purpose of analyzing the social network data is to observe the behaviors and trends that are followed by people. How people interact with each other, what they usually share, what are their interests on social networks, so that analysts can focus new trends for the provision of those things which are of great interest for people so in this paper an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result it will be analyzed that, what people are focusing most in politics.


Intelligent Transportation Systems using IoT Service for Vehicular Data Cloud


title: Non-isolated Bidirectional Soft-Switching Current-Fed LCL Resonant DC/DC Converter to Interface Energy Storage in DC Microgrid

abstract: This paper proposes a non-isolated soft-switching bidirectional dc/dc converter for interfacing energy storage in DC microgrid. The proposed converter employs a half-bridge boost converter at input port followed by a LCL resonant circuit to assist in soft-switching of switches and diodes, and finally a voltage doubler circuit at output port to enhance the voltage gain by 2x. The LCL resonant circuit may also add a suitable voltage gain to the converter. Therefore, overall high voltage gain of the converter is obtained without transformer or large number of multiplier circuit. For buck operation, the high side voltage is first divided by half with capacitive divider to gain higher step down ratio. Converter is operated at high frequency to obtain low output voltage ripple and reduced magnetics and filters. Zero voltage turn-on is achieved for all switches and zero current turnon and turn-off is achieved for all diodes for both buck/boost operation. Voltage stress across switches and diode is clamped naturally without external snubber circuit. An experimental prototype rated at 350 W has been designed, built and tested in the laboratory to verify the analysis, design and demonstrate the performance of proposed converter.