title: ORCHID: Thai Part-Of-Speech Tagged Corpus

abstract: This paper presents a procedure in building a Thai part-of-speech (POS) tagged corpus named ORCHID [1]. It is a collaboration project between Communications Research Laboratory (CRL) of Japan and National Electronics and Computer Technology Center (NECTEC) of Thailand. We proposed a new tagset based on the previous research on Thai parts-of-speech for using in a multi-lingual machine translation project. We marked the corpus in three levels:paragraph, sentence and word. The corpus keeps text information in text information line and numbering line which are necessary in retrieving process. Since there are no explicit word/sentence boundary, punctuation and inflection in Thai text, we have to separate a paragraph into sentences before tagging the POS. We applied a probabilistic trigram model for simultaneously word segmenting and POS tagging. Rule for syllable construction is additionally used to reduce the number of candidates for computing the probability. The problems in POS assignment are formalized to reduce the ambiguity occurring in case of the similar POSs.


title: Testing the law of one price in gram, mash and masoor markets of Pakistan

abstract: This study examines the market integration of three pulses explicitly known as gram, masoor and mash in Pakistan. The study develops a critical estimation of market integration in pulses market of Pakistan. Market integration provides valuable information about the dynamics of market adjustment and has implications for a sustainable agriculture development. The government intervention might be justified by market integration as this phenomenon raises a question whether there are present market imperfections. This study used the monthly wholesale price (Rs. /40kg) data from January 1991 to September 2010 of gram, mash and masoor in logarithmic form and empirically estimated the degree of integration in the markets of the three pulses. The study is employing an econometric technique of co-integration analysis. Co-integration results show that all gram, masoor and mash markets are highly co-integrated in the long run. The high degree of market integration observed in this case is consistent with the view that Pakistan’s pulses markets are quite competitive and provide little justification for extensive and costly government intervention designed to improve competitiveness to enhance market efficiency.


title: TiSi(Ge) Contacts Formed at Low Temperature Achieving Around $2 \,\, \times \,\, 10^{-{9}}~\Omega $ cm2 Contact Resistivities to p-SiGe

abstract: This paper reports ultralow contact resistivities ( $\rho _{c})$ achieved on highly doped p-SiGe with two low-temperature contact formation methods. One method combines precontact amorphization implantation with ~500 °C rapid thermal processing (RTP)-based Ti germano-silicidation; $\rho _{c}$ achieved was $\sim 2.9\times 10^{-9}~\Omega \cdot $ cm2. The other method combines codeposited TiSi—Ti:Si =1:1—with ~450 °C RTP-based Ti silicidation; $\rho _{c}$ achieved was $\sim 1.7\times 10^{-9}~\Omega \cdot $ cm2. When $\rho _{c}$ reaches minimum, the TiSi(Ge) alloy is generally amorphous with embedded small crystallites, similar to the previous observations on pure Si substrates.


title: Semantic mechanisms may be responsible for developing synesthesia

abstract: Currently, little is known about how synesthesia develops and which aspects of synesthesia can be acquired through a learning process. We review the increasing evidence for the role of semantic representations in the induction of synesthesia, and argue for the thesis that synesthetic abilities are developed and modified by semantic mechanisms. That is, in certain people semantic mechanisms associate concepts with perception-like experiences—and this association occurs in an extraordinary way. This phenomenon can be referred to as “higher” synesthesia or ideasthesia. The present analysis suggests that synesthesia develops during childhood and is being enriched further throughout the synesthetes’ lifetime; for example, the already existing concurrents may be adopted by novel inducers or new concurrents may be formed. For a deeper understanding of the origin and nature of synesthesia we propose to focus future research on two aspects: (i) the similarities between synesthesia and ordinary phenomenal experiences based on concepts; and (ii) the tight entanglement of perception, cognition and the conceptualization of the world. Importantly, an explanation of how biological systems get to generate experiences, synesthetic or not, may have to involve an explanation of how semantic networks are formed in general and what their role is in the ability to be aware of the surrounding world.


title: Sum-product networks: A new deep architecture

abstract: The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are general conditions under which the partition function is tractable? The answer leads to a new kind of deep architecture, which we call sum-product networks (SPNs). SPNs are directed acyclic graphs with variables as leaves, sums and products as internal nodes, and weighted edges. We show that if an SPN is complete and consistent it represents the partition function and all marginals of some graphical model, and give semantics to its nodes. Essentially all tractable graphical models can be cast as SPNs, but SPNs are also strictly more general. We then propose learning algorithms for SPNs, based on backpropagation and EM. Experiments show that inference and learning with SPNs can be both faster and more accurate than with standard deep networks. For example, SPNs perform image completion better than state-of-the-art deep networks for this task. SPNs also have intriguing potential connections to the architecture of the cortex.


title: Determinants of healthy eating in children and youth.

abstract: This review outlines the state of knowledge and research gaps in the area of determinants of healthy eating among children and youth. The article is structured around individual and collective determinants that affect healthy eating in children and youth. We defined healthy eating as "eating practices and behaviours that are consistent with improving, maintaining and/or enhancing health." Relevant databases were searched for papers published between January 1992 and March 2003 that focussed on children or youth and reported at least one factor relevant to healthy eating. Among collective factors, familial factors and the nature of foods available in the physical environment, including at home, schools and in fast-food establishments, stand out as significant influences on healthy eating in children and youth. The media, particularly television, also have an enormous potential influence and can overshadow familial influences. Individual factors identified include knowledge, attitudes and food preferences; only the latter have been identified as a strong determinant of healthy eating in both children and adolescents. The results of the review identified a significant body of literature in the area of determinants of healthy eating in children and youth; however, very little of this research has taken place in Canada. Only a few determinants, such as economic factors and food security, the content of media nutritional messages, and the issue of flavours, neophobia and food preferences, have undergone some examination by Canadian researchers. Research priorities for Canada in the area of determinants of healthy eating and surveillance of eating behaviours are identified.


Using Deep Learning to Predict Customer Churn in a Mobile Telecommunication Network


title: Learning to Incentivize: Eliciting Effort via Output Agreement

abstract: In crowdsourcing when there is a lack of verification for contributed answers, output agreement mechanisms are often used to incentivize participants to provide truthful answers when the correct answer is hold by the majority. In this paper, we focus on using output agreement mechanisms to elicit effort, in addition to eliciting truthful answers, from a population of workers. We consider a setting where workers have heterogeneous cost of effort exertion and examine the data requester's problem of deciding the reward level in output agreement for optimal elicitation. In particular, when the requester knows the cost distribution, we derive the optimal reward level for output agreement mechanisms. This is achieved by first characterizing Bayesian Nash equilibria of output agreement mechanisms for a given reward level. When the cost distribution is unknown to the requester, we develop sequential mechanisms that combine learning the cost distribution with incentivizing effort exertion to approximately determine the optimal reward level.


title: Conversational gaze aversion for humanlike robots

abstract: Gaze aversion—the intentional redirection away from the face of an interlocutor—is an important nonverbal cue that serves a number of conversational functions, including signaling cognitive effort, regulating a conversation’s intimacy level, and managing the conversational floor. In prior work, we developed a model of how gaze aversions are employed in conversation to perform these functions. In this paper, we extend the model to apply to conversational robots, enabling them to achieve some of these functions in conversations with people. We present a system that addresses the challenges of adapting human gaze aversion movements to a robot with very different affordances, such as a lack of articulated eyes. This system, implemented on the NAO platform, autonomously generates and combines three distinct types of robot head movements with different purposes: face-tracking movements to engage in mutual gaze, idle head motion to increase lifelikeness, and purposeful gaze aversions to achieve conversational functions. The results of a human-robot interaction study with 30 participants show that gaze aversions implemented with our approach are perceived as intentional, and robots can use gaze aversions to appear more thoughtful and effectively manage the conversational floor.Categories and Subject DescriptorsH.1.2 [Models and Principles]: User/Machine Systems —human factors, software psychology; H.5.2 [Information Interfaces and Presentation]: User Interfaces — evaluation/ methodology, usercentered design


title: Impact of informational factors on online recommendation credibility

abstract: This study investigates the moderating effect of recommendation source credibility on the causal relationships between informational factors and recommendation credibility, as well as its moderating effect on the causal relationship between recommendation credibility and recommendation adoption. Using data from 199 responses from a leading online consumer discussion forum in China, we find that recommendation source credibility significantly moderates two informational factors' effects on readers' perception of recommendation credibility, each in a different direction. Further, we find that source credibility negatively moderates the effect of recommendation credibility on recommendation adoption. We probe source credibility's moderating role in the model.Source credibility has moderating effect with both informational factors.Source credibility has moderating effect with recommendation credibility.


title: Transparency Enhancing Tools (TETs): An Overview

abstract: As the amount of users' information collected and exchanged on the Internet is growing, so are, consequently, the users' concerns that their privacy might be violated. Some studies have shown that a large number of users avoid engaging in online services due to privacy concerns. It has been suggested that increased transparency of privacy related mechanisms may promote users' trust. This paper reviews the relationship between users' privacy concerns, transparency enhancing and privacy enhancing mechanisms on the one hand, and users' trust on the other, based on the existing literature. Our literature review demonstrates that previous studies have produced inconsistent results, implying this relationship should be re-examined in future work. Impact of higher transparency on users' trust has been insufficiently studied. Current research seems to suggest that the increase of the understanding of privacy issues increases importance of privacy for trust. Use of privacy enhancing mechanisms by service provider also seems to promote the trust, but this may only hold when these mechanisms are understood by the user. A need for tools that would provide users with this kind of knowledge has also been repeatedly recognized. Additionally, this paper provides an overview and description of the currently available transparency enhancing tools. To the best of our knowledge, no such overview has been available to this end. We demonstrate that the majority of tools promote awareness. Most of them attempt to provide a better understanding of privacy policies, or provide insight in the third party tracking behavior. Two tools have been identified that provide some insight in the collected user's data. No tool providing specific information on, or access to, processing logic has been identified.


title: Unsupervised Person Re-identification by Deep Learning Tracklet Association

abstract: Mostexistingpersonre-identification(re-id)methods relyon supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment due to the lack of exhaustive identity labelling of image positive and negative pairs for every camera pair. In this work, we address this problem by proposing an unsupervised re-id deep learning approach capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data from videos in an end-to-end model optimisation. We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL) framework characterised by jointly learning per-camera (within-camera) tracklet association (labelling) and cross-camera tracklet correlation by maximising the discovery of most likely tracklet relationships across camera views. Extensive experiments demonstrate the superiority of the proposed TAUDL model over the state-of-the-art unsupervised and domain adaptation re- id methods using six person re-id benchmarking datasets.


title: MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR T2 mapping

abstract: Quantitative mapping of magnetic resonance (MR) parameters have been shown as valuable methods for improved assessment of a range of diseases. Due to the need to image an anatomic structure multiple times, parameter mapping usually requires long scan times compared to conventional static imaging. Therefore, accelerated parameter mapping is highly-desirable and remains a topic of great interest in the MR research community. While many recent deep learning methods have focused on highly efficient image reconstruction for conventional static MR imaging, applications of deep learning for dynamic imaging and in particular accelerated parameter mapping have been limited. The purpose of this work was to develop and evaluate a novel deep learning-based reconstruction framework called Model-Augmented Neural neTwork with Incoherent k-space Sampling (MANTIS) for efficient MR parameter mapping. Our approach combines end-to-end CNN mapping with k-space consistency using the concept of cyclic loss to further enforce data and model fidelity. Incoherent k-space sampling is used to improve reconstruction performance. A physical model is incorporated into the proposed framework, so that the parameter maps can be efficiently estimated directly from undersampled images. The performance of MANTIS was demonstrated for the spin-spin relaxation time (T2) mapping of the knee joint. Compared to conventional reconstruction approaches that exploited image sparsity, MANTIS yielded lower errors and higher similarity with respect to the reference in the T2 estimation. Our study demonstrated that the proposed MANTIS framework, with a combination of end-to-end CNN mapping, signal model-augmented data consistency, and incoherent k-space sampling, represents a promising approach for efficient MR parameter mapping. MANTIS can potentially be extended to other types of parameter mapping with appropriate models.


title: 6.3 A 40-to-56Gb/s PAM-4 receiver with 10-tap direct decision-feedback equalization in 16nm FinFET

abstract: The increasing bandwidth demand in data centers and telecommunication infrastructures had prompted new electrical interface standards capable of operating up to 56Gb/s per-lane. The CEI-56G-VSR-PAM4 standard [1] defines PAM-4 signaling at 56Gb/s targeting chip-to-module interconnect. Figure 6.3.1 shows the measured S 21 of a channel resembling such interconnects and the corresponding single-pulse response after TX-FIR and RX CTLE. Although the S 21 is merely ∼10dB at 14GHz, the single-pulse response exhibits significant reflections from impedance discontinuities, mainly between package and PCB traces. These reflections are detrimental to PAM-4 signaling and cannot be equalized effectively by RX CTLE and/or a few taps of TX feed-forward equalization. This paper presents the design of a PAM-4 receiver using 10-tap direct decision-feedback equalization (DFE) targeting such VSR channels.


title: Sentiment analysis on facebook group using lexicon based approach

abstract: Internet is one of the primary sources of Big Data. Rise of the social networking platforms are creating enormous amount of data in every second where human emotions are constantly expressed in real-time. The sentiment behind each post, comments, likes can be found using opinion mining. It is possible to determine business values from these objects and events if sentiment analysis is done on the huge amount of data. Here, we have chosen FOODBANK which is a very popular Facebook group in Bangladesh; to analyze sentiment of the data to find out their market values.


title: Transcranial magnetic stimulation of left prefrontal cortex impairs working memory

abstract: Objectives: Several lines of evidence suggest that the prefrontal cortex is involved in working memory. Our goal was to determine whether transient functional disruption of the dorsolateral prefrontal cortex (DLPFC) would impair performance in a sequential-letter working memory task. Methods: Subjects were shown sequences of letters and asked to state whether the letter just displayed was the same as the one presented 3-back. Single-pulse transcranial magnetic stimulation (TMS) was applied over the DLPFC between letter presentations. Results: TMS applied over the left DLPFC resulted in increased errors relative to no TMS controls. TMS over the right DLPFC did not alter working memory performance. Conclusion: Our results indicate that the left prefrontal cortex has a crucial role in at least one type of working memory. q 2001 Elsevier Science Ireland Ltd. All rights reserved.


title: An FSS-Backed Reflection/Transmission Reconfigurable Array Antenna

abstract: Reflectarray and transmitarray antennas have been widely used in radar detection systems and satellite communications. Simultaneous realization of reflection and transmission of electromagnetic waves under the same antenna aperture will undoubtedly save more space and cost, which is beneficial to their broader applications. In this paper, an antenna element based on active frequency selective surface (FSS) is proposed, which can realize band-pass and band-stop conversion by controlling the bias of the loaded PIN diode. Then, a reconfigurable antenna array including $9\times 9$ elements and fed through a horn antenna is designed and demonstrated. Simulation and measurement results show that the designed antenna can realize the conversion of reflection and transmission at the frequency of 11 GHz, and the measured peak gain of reflection and transmission reaches 18.2 dBi and 20.9 dBi, respectively.


Why Did the Chicken Cross the Road? : a Homily on Liminality and Vocation


title: Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes

abstract: Wikipedia is a major source of information for many people. However, false information on Wikipedia raises concerns about its credibility. One way in which false information may be presented on Wikipedia is in the form of hoax articles, i.e., articles containing fabricated facts about nonexistent entities or events. In this paper we study false information on Wikipedia by focusing on the hoax articles that have been created throughout its history. We make several contributions. First, we assess the real-world impact of hoax articles by measuring how long they survive before being debunked, how many pageviews they receive, and how heavily they are referred to by documents on the Web. We find that, while most hoaxes are detected quickly and have little impact on Wikipedia, a small number of hoaxes survive long and are well cited across the Web. Second, we characterize the nature of successful hoaxes by comparing them to legitimate articles and to failed hoaxes that were discovered shortly after being created. We find characteristic differences in terms of article structure and content, embeddedness into the rest of Wikipedia, and features of the editor who created the hoax. Third, we successfully apply our findings to address a series of classification tasks, most notably to determine whether a given article is a hoax. And finally, we describe and evaluate a task involving humans distinguishing hoaxes from non-hoaxes. We find that humans are not particularly good at the task and that our automated classifier outperforms them by a big margin.


title: Evaluating the developmental impact of e-governance initiatives: An exploratory framework

abstract: India is faced with a large number of challenges that are typical of developing countries. The Indian government has for the past 3 decades widely acknowledged that expanded use of ICT in the public sector can offer important benefits such as improved planning and monitoring mechanisms, cost savings through rationalisation, and more effective administration and delivery of certain public services. Today, wide ranges of e-governance projects are being implemented in different parts of the country including projects aimed at reaching areas and people that had traditionally not been connected to the outside world. ::: ::: This paper uses Sen?s notion of capabilities as an evaluative space for e-governance assessment. Such an emphasis demands a change in the way e-governance is assessed. The capabilities framework allows us to focus on the developmental impact of such projects. We base our evaluation on the FRIENDS and AKSHAYA e-governance projects in the South Indian state of Kerala.


title: Content matters: a study of hate groups detection based on social networks analysis and web mining

abstract: In recent years, with rapid growth of social networking websites, users are very active in these platforms and large amount of data are aggregated. Among those social networking websites, Facebook is the most popular website that has most users. However, in Facebook, the abusing problem is a very critical issue, such as Hate Groups. Therefore, many researchers are devoting on how to detect potential hate groups, such as using the techniques of social networks analysis. However, we believe content is also a very important factors for hate groups detection. Thus, in this paper, we will propose an architecture to for hate groups detection which is based on the technique of Social Networks Analysis and Web Mining (Text Mining; Natural Language Processing). From the experiment result, it shows that content plays an critical role for hate groups detection and the performance is better than the system that just applying social networks analysis.


title: Scheduling and Routing Algorithms for AGVs: a Survey

abstract: Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even container handling applications. In the past few decades, much research has been devoted to the technology of AGV systems and rapid progress has been witnessed. As one of the enabling technologies, scheduling and routing of AGVs have attracted considerable attention. Many algorithms for the scheduling and routing of AGVs have been proposed. However, most of the existing results are applicable to systems with a small number of AGVs, offering a low degree of concurrency. With a drastically increased number of AGVs in recent applications (e.g. in the order of a hundred in a container handling system), efficient algorithms are needed to resolve the increased contention of resources (e.g. path, loading and unloading buffers) among AGVs. This survey paper first gives an account of the emergence of the problem of AGV scheduling and routing. It then differentiates it from seve...


title: Machine Learning and Social Robotics for Detecting Early Signs of Dementia

abstract: This paper presents the EACare project, an ambitious multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.


title: Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

abstract: A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r 2 ) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.


title: Dynamic thermal management for FinFET-based circuits exploiting the temperature effect inversion phenomenon

abstract: Due to limits on the availability of the energy source in many mobile user platforms (ranging from handheld devices to portable electronics to deeply embedded devices) and concerns about how much heat can effectively be removed from chips, minimizing the power consumption has become a primary driver for system-on-chip designers. Because of their superb characteristics, FinFETs have emerged as a promising replacement for planar CMOS devices in sub-20nm CMOS technology nodes. However, based on extensive simulations, we have observed that the delay vs. temperature characteristics of FinFET-based circuits are fundamentally different from that of the conventional bulk CMOS circuits, i.e., the delay of a FinFET circuit decreases with increasing temperature even in the super-threshold supply voltage regime. Unfortunately, the leakage power dissipation of the FinFET-based circuits increases exponentially with the temperature. These two trends give rise to a tradeoff between delay and leakage power as a function of the chip temperature, and hence, lead to the definition of an optimum chip temperature operating point (i.e., one that balances concerns about the circuit speed and power efficiency.) This paper presents the results of our investigations into the aforesaid temperature effect inversion (TEI) and proposes a novel dynamic thermal management (DTM) algorithm, which exploits this phenomenon to minimize the energy consumption of FinFET-based circuits without any appreciable performance penalty. Experimental results demonstrate 40% energy saving (with no performance penalty) can be achieved by the proposed TEI-aware DTM approach compared to the best-in-class DTMs that are unaware of this phenomenon.


title: Low-DC Voltage-Controlled Steering-Antenna Radome Utilizing Tunable Active Metamaterial

abstract: In this paper, we experimentally realized a steering antenna using a type of active metamaterial with tunable refractive index. The metamaterial is realized by periodically printed subwavelength metallic resonant patterns with embedded microwave varactors. The effective refractive index can be controlled by low direct-current (dc) bias voltage applied to the varactors. In-phase electromagnetic waves transmitting in different zones of such metamaterial slab experience different phase delays, and, consequently, the output direction of the transmitted wave can be steered with progressive phase shift along the interface. This antenna has a simple structure, is very easy to configure the beam direction, and has a low cost. Compared with conventional phased-array antennas, the radome approach has more flexibility to operate with different feeding antennas for various applications.


title: Comparative study of voice conversion framework with line spectral frequency and Mel-Frequency Cepstral Coefficients as features using artficial neural networks

abstract: This paper is intended to formulate the mapping function using Feed-forward Neural Networks on Line Spectral Frequency and Mel Frequency Cepstral Coefficient and to compare their outcomes to decipher the better solution to the spectral mapping impediment. The experimentation is confined to the augmentation of spectral and excitation (glottal) domains of speech. LSF and MFCC are used to represent the spectrum and as input predictor variables to the above mentioned neural networks. It contains the use of neural network in the voice conversion framework. The function of artificial neural network is to map the spectral characteristics of a source speaker to the target speaker so as to obtain an authenticated voice conversion model. The temporal alignment of the speech uttered by source and the target is attained using Dynamic Time Warping (Dynamic Programming). The excitation mapping is accomplished using Residual Selection method. The performances of these Voice Conversion systems are assessed using subjective and objective measures which assure the genuineness of the conversion system design.


Automatic Deobfuscation and Reverse Engineering of Obfuscated Code


Foundation of Structural Geology


title: Selenium Tool: A Web based Automation Testing Framework

abstract: The commercialization of I.T. sector and opportunity of new technologies, in web based system undergo frequent and rapid changes. The classical way of testing web application was based on protocol level, in which the tester created an HTTP request and then send it to the web site which was under test, further response was analyzed. So to do manually testing in most of the cases was time consuming, expensive as well as hectic job. To save time, money and also for the better business purpose an auto testing is required. But sometime there may be a problem arising to do automation testing due to improper use of testing framework. So a proper testing framework is to be chosen for a better result. In this paper we will discuss about selenium. Selenium is an open source automation testing tool and is a type of software testing framework for web application. It is one of the best tools for automation testing and provides testers with different framework for different test cases. It also uses different platform and framework according to the programming language used by the developers


title: Class D Amplifier with Ripple Steering

abstract: This paper shows the application of ripple steering to a Class D audio power amplifier for the reduction of the switching ripple residual. Several topologies are presented for use in different applications. The ripple steering enhances the performance with respect to electromagnetic compatibility, allows reduced switching losses, and enables more accurate audio test and measurement. A method is presented to further linearize and extend the bandwidth of the Class D audio power amplifier which is made practical by switching ripple reduction.


ENTREPRENEURSHIP AND THE BIG FIVE PERSONALITY TRAITS: A BEHAVIORAL GENETICS PERSPECTIVE


Effects of energy drinks on the cardiovascular system


Application of deep learning to the classification of images from colposcopy


IL-15 Overcomes Hepatocellular Carcinoma-Induced NK Cell Dysfunction


title: Deep Kalman Filters

abstract: Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters. Of particular interest is the use of temporal generative models for counterfactual inference. We investigate the efficacy of such models for counterfactual inference, and to that end we introduce the"Healing MNIST"dataset where long-term structure, noise and actions are applied to sequences of digits. We show the efficacy of our method for modeling this dataset. We further show how our model can be used for counterfactual inference for patients, based on electronic health record data of 8,000 patients over 4.5 years.


title: Optimizing non-decomposable measures with deep networks

abstract: We present a class of algorithms capable of directly training deep neural networks with respect to popular families of task-specific performance measures for binary classification such as the F-measure, QMean and the Kullback–Leibler divergence that are structured and non-decomposable. Our goal is to address tasks such as label-imbalanced learning and quantification. Our techniques present a departure from standard deep learning techniques that typically use squared or cross-entropy loss functions (that are decomposable) to train neural networks. We demonstrate that directly training with task-specific loss functions yields faster and more stable convergence across problems and datasets. Our proposed algorithms and implementations offer several advantages including (i) the use of fewer training samples to achieve a desired level of convergence, (ii) a substantial reduction in training time, (iii) a seamless integration of our implementation into existing symbolic gradient frameworks, and (iv) assurance of convergence to first order stationary points. It is noteworthy that the algorithms achieve this, especially point (iv), despite being asked to optimize complex objective functions. We implement our techniques on a variety of deep architectures including multi-layer perceptrons and recurrent neural networks and show that on a variety of benchmark and real data sets, our algorithms outperform traditional approaches to training deep networks, as well as popular techniques used to handle label imbalance.


title: Deep Meta-Learning: Learning to Learn in the Concept Space

abstract: Few-shot learning remains challenging for meta-learning that learns a learning algorithm (meta-learner) from many related tasks. In this work, we argue that this is due to the lack of a good representation for meta-learning, and propose deep meta-learning to integrate the representation power of deep learning into meta-learning. The framework is composed of three modules, a concept generator, a meta-learner, and a concept discriminator, which are learned jointly. The concept generator, e.g. a deep residual net, extracts a representation for each instance that captures its high-level concept, on which the meta-learner performs few-shot learning, and the concept discriminator recognizes the concepts. By learning to learn in the concept space rather than in the complicated instance space, deep meta-learning can substantially improve vanilla meta-learning, which is demonstrated on various few-shot image recognition problems. For example, on 5-way-1-shot image recognition on CIFAR-100 and CUB-200, it improves Matching Nets from 50.53% and 56.53% to 58.18% and 63.47%, improves MAML from 49.28% and 50.45% to 56.65% and 64.63%, and improves Meta-SGD from 53.83% and 53.34% to 61.62% and 66.95%, respectively.


title: Local Deep-Feature Alignment for Unsupervised Dimension Reduction

abstract: This paper presents an unsupervised deep-learning framework named Local Deep-Feature Alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local Stacked Contractive Auto-encoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.


title: Survey on device-to-device communications: Challenges and design issues

abstract: Device-to-Device (D2D) communications has been proposed to cellular networks in order to optimize spectrum efficiency and resources utilization. In D2D-assisted networks, the traditional cellular communication techniques are not applicable but rather need to be redesigned to fulfill new requirements. Therefore, new communication techniques are proposed to take the advantages of the D2D connectivity in the network. In this paper, we describe the concept of D2D communications. Then, we review the main challenges and design issues of D2D-assisted networks. This survey focuses on cooperative communications in D2D-assisted networks and addresses challenges which limit the performance of the cooperative D2D-assisted networks such as relay selection, power consumption and multi-casting. Moreover, design issues and approaches to overcome these limitations are explained.


title: Analysis of polarimetric multistatic human micro-Doppler classification of armed/unarmed personnel

abstract: Human micro-Doppler radar signatures have been investigated to classify different types of activities and to identify potential armed personnel in the context of security and surveillance applications. In this paper the use of multistatic micro-Doppler signatures to distinguish between unarmed and armed personnel moving is described. The effect of polarimetry on the classification accuracy is evaluated. Real radar data from a multistatic radar (NetRAD) has been analyzed as part of this work. Suitable features are extracted from the spectrograms generated from the data and then used as input to a classifier. The impact of polarization diversity on the classification performance is investigated, in particular the use of co-polarized or cross-polarized data or their multistatic combination.


title: Deep MR to CT Synthesis using Unpaired Data

abstract: MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images.


title: Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors

abstract: Breast cancer has the highest incidence and second highest mortality rate for women in the US. Our study aims to utilize deep learning for benign/malignant classification of mammogram tumors using a subset of cases from the Digital Database of Screening Mammography (DDSM). Though it was a small dataset from the view of Deep Learning (about 1000 patients), we show that currently state of the art architectures of deep learning can find a robust signal, even when trained from scratch. Using convolutional neural networks (CNNs), we are able to achieve an accuracy of 85% and an ROC AUC of 0.91, while leading hand-crafted feature based methods are only able to achieve an accuracy of 71%. We investigate an amalgamation of architectures to show that our best result is reached with an ensemble of the lightweight GoogLe Nets tasked with interpreting both the coronal caudal view and the mediolateral oblique view, simply averaging the probability scores of both views to make the final prediction. In addition, we have created a novel method to visualize what features the neural network detects for the benign/malignant classification, and have correlated those features with well known radiological features, such as spiculation. Our algorithm significantly improves existing classification methods for mammography lesions and identifies features that correlate with established clinical markers.


title: iBOAT: Isolation-Based Online Anomalous Trajectory Detection

abstract: Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose an online method that is able to detect anomalous trajectories “on-the-fly” and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43 800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) ≥ 0.99.


title: Good Word Attacks on Statistical Spam Filters

abstract: Unsolicited commercial email is a significant problem for users and providers of email services. While statistical spam filters have proven useful, senders of spam are learning to bypass these filters by systematically modifying their email messages. In a good word attack, one of the most common techniques, a spammer modifies a spam message by inserting or appending words indicative of legitimate email. In this paper, we describe and evaluate the effectiveness of active and passive good word attacks against two types of statistical spam filters: naive Bayes and maximum entropy filters. We find that in passive attacks without any filter feedback, an attacker can get 50% of currently blocked spam past either filter by adding 150 words or fewer. In active attacks allowing test queries to the target filter, 30 words will get half of blocked spam past either filter.


title: Alkali-soluble and insoluble fluoride in erupted and unerupted human enamel from a high fluoride area with a low fluorosis score.

abstract: The amounts of fluoroapatite and 'CaF2-like' fluoride (F) were determined in enamel of unerupted and erupted teeth that had been exposed in vivo to 1.8-2.6 parts/10(6) F in the drinking water and to brushing with F dentifrice at least once a day, and occasionally to a F mouth-rinse (0.022% F). Enamel was sampled by acid-etching and the F levels were measured with an adapted F ion-selective electrode. More F was built into the deeper enamel in the high-F area than in a similar low-F area. Unerupted enamel did not etch significantly (p > 0.05) deeper than erupted enamel. No significant differences (p > 0.05) were found in the F concentrations amongst the following: alkali-washed erupted, unwashed erupted, alkali-washed unerupted and unwashed unerupted at the outer most enamel (approx. 6 microns). However the erupted enamel (alkali-washed or not) showed higher F levels than unerupted enamel (alkali-washed or not) between approx. 6 microns and greater than 100 microns. The increase of F for this high-F area was about 100% in the deeper enamel while for a low-F area it was approx. 78% in the most outer enamel with no increase after a depth of about 20 microns. In contrast to a similar low-F area (water F < 0.10 parts/10(6)), no significant 'CaF2-like' F could be detected in erupted or unerupted enamel for the high-F area.


title: Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

abstract: This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM). A Pyramid Dilated Convolution (PDC) module is first designed for simultaneously extracting spatial features at multiple scales. These spatial features are then concatenated and fed into an extended Deeper Bidirectional ConvLSTM (DB-ConvLSTM) to learn spatiotemporal information. Forward and backward ConvLSTM units are placed in two layers and connected in a cascaded way, encouraging information flow between the bi-directional streams and leading to deeper feature extraction. We further augment DB-ConvLSTM with a PDC-like structure, by adopting several dilated DB-ConvLSTMs to extract multi-scale spatiotemporal information. Extensive experimental results show that our method outperforms previous video saliency models in a large margin, with a real-time speed of 20 fps on a single GPU. With unsupervised video object segmentation as an example application, the proposed model (with a CRF-based post-process) achieves state-of-the-art results on two popular benchmarks, well demonstrating its superior performance and high applicability.


title: On fast and accurate detection of unauthorized wireless access points using clock skews

abstract: We explore the use of clock skew of a wireless local area network access point (AP) as its fingerprint to detect unauthorized APs quickly and accurately. The main goal behind using clock skews is to overcome one of the major limitations of existing solutions - the inability to effectively detect Medium Access Control (MAC) address spoofing. We calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization Function (TSF) timestamps sent out in the beacon/probe response frames. We use two different methods for this purpose - one based on linear programming and the other based on least square fit. We supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. We collect TSF timestamp data from several APs in two different residential settings. Using our measurement data as well as data obtained from a large conference setting, we find that clock skews remain consistent over time for the same AP but vary significantly across APs. Furthermore, we improve the resolution of received timestamp of the frames and show that with this enhancement our methodology can find clock skews very quickly, using 50-100 packets in most of the cases. We also discuss and quantify the impact of various external factors including temperature variation, virtualization, and NTP synchronization on clock skews. Our results indicate that the use of clock skews appears to be an efficient and robust method for detecting fake APs in wireless local area networks.


title: Design and implementation of floating point multiplier based on Vedic Multiplication Technique

abstract: In this paper, Vedic Multiplication Technique is used to implement IEEE 754 Floating point multiplier. The Urdhva-triyakbhyam sutra is used for the multiplication of Mantissa. The underflow and over flow cases are handled. The inputs to the multiplier are provided in IEEE 754, 32 bit format. The multiplier is implemented in VHDL and Virtex-5 FPGA is used.


title: Attribute Adaptation for Personalized Image Search

abstract: Current methods learn monolithic attribute predictors, with the assumption that a single model is sufficient to reflect human understanding of a visual attribute. However, in reality, humans vary in how they perceive the association between a named property and image content. For example, two people may have slightly different internal models for what makes a shoe look "formal", or they may disagree on which of two scenes looks "more cluttered". Rather than discount these differences as noise, we propose to learn user-specific attribute models. We adapt a generic model trained with annotations from multiple users, tailoring it to satisfy user-specific labels. Furthermore, we propose novel techniques to infer user-specific labels based on transitivity and contradictions in the user's search history. We demonstrate that adapted attributes improve accuracy over both existing monolithic models as well as models that learn from scratch with user-specific data alone. In addition, we show how adapted attributes are useful to personalize image search, whether with binary or relative attributes.


title: Millimeter Wave Communications for Future Mobile Networks

abstract: Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to rates of multiple Gbps (gigabit per second) per user. Though mmWave can be readily used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output (MIMO) transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.


title: LoRa Backscatter : Enabling The Vision of Ubiquitous Connectivity

abstract: The vision of embedding connectivity into billions of everyday objects runs into the reality of existing communication technologies -- there is no existing wireless technology that can provide reliable and long-range communication at tens of microwatts of power as well as cost less than a dime. While backscatter is low-power and low-cost, it is known to be limited to short ranges. This paper overturns this conventional wisdom about backscatter and presents the first wide-area backscatter system. Our design can successfully backscatter from any location between an RF source and receiver, separated by 475 m, while being compatible with commodity LoRa hardware. Further, when our backscatter device is co-located with the RF source, the receiver can be as far as 2.8 km away. We deploy our system in a 4,800 ft2 (446 m2) house spread across three floors, a 13,024 ft2 (1210 m2) office area covering 41 rooms, as well as a one-acre (4046 m2) vegetable farm and show that we can achieve reliable coverage, using only a single RF source and receiver. We also build a contact lens prototype as well as a flexible epidermal patch device attached to the human skin. We show that these devices can reliably backscatter data across a 3,328 ft2 (309 m2) room. Finally, we present a design sketch of a LoRa backscatter IC that shows that it costs less than a dime at scale and consumes only 9.25 &mgr;W of power, which is more than 1000x lower power than LoRa radio chipsets.


PyDial: A Multi-domain Statistical Dialogue System Toolkit


title: A method of spatial calibration for camera and radar

abstract: The technique of multi-sensor fusion has already been used widely in intelligent vehicle environment perception. The spatial calibration of the radar and camera is the basis for road detection with information fusion real-timely. So, a method of spatial calibration of radar and the camera has been proposed to implement the spatial calibration of the two sensors. The non-linear distortion of the camera is considered in the paper. Coordinate transformation between the coordinates relevant to the radar and camera is introduced as the constraints. After transforming the feature between the related coordinates of image and the relative coordinates of radar, the calibration parameters are determined with the least square error function. Experiment results indicate that this method is simple and easy to realize, which has the high accuracy that satisfy requirement of the system.


title: The challenges of building mobile underwater wireless networks for aquatic applications

abstract: The large-scale mobile underwater wireless sensor network (UWSN) is a novel networking paradigm to explore aqueous environments. However, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from ground-based wireless sensor networks. The novel networking paradigm poses interdisciplinary challenges that will require new technological solutions. In particular, in this article we adopt a top-down approach to explore the research challenges in mobile UWSN design. Along the layered protocol stack, we proceed roughly from the top application layer to the bottom physical layer. At each layer, a set of new design intricacies is studied. The conclusion is that building scalable mobile UWSNs is a challenge that must be answered by interdisciplinary efforts of acoustic communications, signal processing, and mobile acoustic network protocol design.


title: Integrated analytic hierarchy process and its applications - A literature review

abstract: Due to its wide applicability and ease of use, the analytic hierarchy process (AHP) has been studied extensively for the last 20 years. Recently, it is observed that the focus has been confined to the applications of the integrated AHPs rather than the stand-alone AHP. The five tools that commonly combined with the AHP include mathematical programming, quality function deployment (QFD), meta-heuristics, SWOT analysis, and data envelopment analysis (DEA). This paper reviews the literature of the applications of the integrated AHPs. Related articles appearing in the international journals from 1997 to 2006 are gathered and analyzed so that the following three questions can be answered: (i) which type of the integrated AHPs was paid most attention to? (ii) which area the integrated AHPs were prevalently applied to? (iii) is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the integrated AHPs are better than the stand-alone AHP, but also aids the researchers and decision makers in applying the integrated AHPs effectively.


title: ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

abstract: Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real-world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging “synthetic-2-real” set-ups and show that the approach can also be used for detection.


title: Dynamical Evolutionary Psychology: Individual Decision Rules and Emergent Social Norms

abstract: A new theory integrating evolutionary and dynamical approaches is proposed. Following evolutionary models, psychological mechanisms are conceived as conditional decision rules designed to address fundamental problems confronted by human ancestors, with qualitatively different decision rules serving different problem domains and individual differences in decision rules as a function of adaptive and random variation. Following dynamical models, decision mechanisms within individuals are assumed to unfold in dynamic interplay with decision mechanisms of others in social networks. Decision mechanisms in different domains have different dynamic outcomes and lead to different sociospatial geometries. Three series of simulations examining trade-offs in cooperation and mating decisions illustrate how individual decision mechanisms and group dynamics mutually constrain one another, and offer insights about gene– culture interactions.


title: An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset

abstract: Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers. In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are data preprocessing, outlier analysis, and outlier rank. First, we investigate a linear interpolation method to convert assembled outliers into isolated ones. Second, a detection mechanism based on the cross-correlation analysis is proposed for translating the high-dimensional data sets into 1-D cross-correlation function, according to which the isolated outlier is determined. Finally, a multilevel Otsu’s method is adopted to help us select the rank thresholds adaptively and output the abnormal samples at different levels. To illustrate the effectiveness of the ODCA algorithm, four experiments are performed using several high-dimensional time series data sets, which include two small-scale sets and two large-scale sets. Furthermore, we compare the proposed algorithm with the detection methods based on wavelet analysis, bilateral filtering, particle swarm optimization, auto-regression, and extreme learning machine. In addition, we discuss the robustness of the ODCA algorithm. The statistical results show that the ODCA algorithm is much better than existing mainstream methods in both effectiveness and time complexity.


title: UAV-Aided Secure Communications With Cooperative Jamming

abstract: This paper investigates unmanned aerial vehicle (UAV) enabled secure communication systems where a mobile UAV wishes to send confidential messages to multiple ground users. To improve the security performance, a cooperative UAV is additionally considered that transmits the jamming signal. In this system, we maximize the minimum secrecy rate among the ground users by jointly optimizing the trajectory and the transmit power of the UAVs as well as the user scheduling. To efficiently solve this nonconvex problem, we adopt block successive upper bound minimization techniques, which address a sequence of approximated convex problems for each block of variables. Numerical results verify that the proposed algorithm outperforms baseline methods.


title: Binary adder circuit design using emerging MIGFET devices

abstract: Multiple independent-gate field effect transistors (MIGFETs) have great potential for digital integrated circuits. In this work, we demonstrate that conventional binary adder architectures may benefit from the use of MIGFET devices. As case studies, we have designed ripple-carry adders (RCA) and parallel-prefix adders (PPA), where circuit area and performance optimizations are explored. Different versions of adders have been built using MIGFET and compared to adder topologies based on single-gate transistors.


title: Super-twisting algorithm devoted to control the greenhouse system

abstract: A Super-Twisting Algorithm (STA) is proposed for the tracking control of a greenhouse system. This proposed technique, ensure that the occurrence of the sliding motion in finite time and the trajectory of the tracking error converge to the equilibrium point. Furthermore, High Sliding Mode is in the but to approximate the unknown nonlinear functions of the system model and moreover to approximate the switching control term of the sliding control to resolve the chattering problem. The convergence and stability of the proposed control schema are proved using Lyapunov's method. Finally, many simulation results for the greenhouse are given to illustrate the good tracking performances.


The Semantic Web: ESWC 2017 Satellite Events


title: Rethinking Skin Lesion Segmentation in a Convolutional Classifier

abstract: Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion. Ostensibly, segmenting a target skin lesion will remove inessential information, non-lesion skin, and artifacts to aid in classification. Our results indicate that segmentation border enlargement produces, to a certain degree, better results across all metrics of interest when using a convolutional based classifier built using the transfer learning paradigm. Consequently, preprocessing methods which produce borders larger than the actual lesion can potentially improve classifier performance, more than both perfect segmentation, using dermatologist created ground truth masks, and no segmentation altogether.


title: Affordance++: Allowing Objects to Communicate Dynamic Use

abstract: We propose extending the affordance of objects by allowing them to communicate dynamic use, such as (1) motion (e.g., spray can shakes when touched), (2) multi-step processes (e.g., spray can sprays only after shaking), and (3) behaviors that change over time (e.g., empty spray can does not allow spraying anymore). Rather than enhancing objects directly, however, we implement this concept by enhancing the user. We call this affordance++. By stimulating the user's arms using electrical muscle stimulation, our prototype allows objects not only to make the user actuate them, but also perform required movements while merely approaching the object, such as not to touch objects that do not "want" to be touched. In our user study, affordance++ helped participants to successfully operate devices of poor natural affordance, such as a multi-functional slicer tool or a magnetic nail sweeper, and to stay away from cups filled with hot liquids.


title: From the Internet of Computers to the Internet of Things

abstract: This paper discusses the vision, the challenges, possible usage scenarios and technological building blocks of the "Internet of Things". In particular, we consider RFID and other important technological developments such as IP stacks and web servers for smart everyday objects. The paper concludes with a discussion of social and governance issues that are likely to arise as the vision of the Internet of Things becomes a reality.


title: "On the internet, nobody knows you're a dog": a twitter case study of anonymity in social networks

abstract: Twitter does not impose a Real-Name policy for usernames, giving users the freedom to choose how they want to be identified. This results in some users being Identifiable (disclosing their full name) and some being Anonymous (disclosing neither their first nor last name). In this work we perform a large-scale analysis of Twitter to study the prevalence and behavior of Anonymous and Identifiable users. We employ Amazon Mechanical Turk (AMT) to classify Twitter users as Highly Identifiable, Identifiable, Partially Anonymous, and Anonymous. We find that a significant fraction of accounts are Anonymous or Partially Anonymous, demonstrating the importance of Anonymity in Twitter. We then select several broad topic categories that are widely considered sensitive--including pornography, escort services, sexual orientation, religious and racial hatred, online drugs, and guns--and find that there is a correlation between content sensitivity and a user's choice to be anonymous. Finally, we find that Anonymous users are generally less inhibited to be active participants, as they tweet more, lurk less, follow more accounts, and are more willing to expose their activity to the general public. To our knowledge, this is the first paper to conduct a large-scale data-driven analysis of user anonymity in online social networks.


title: Towards scalable RDF graph analytics on MapReduce

abstract: In order to exploit the growing amount of RDF data in decision-making, there is an increasing demand for analytics-style processing of such data. RDF data is modeled as a labeled graph that represents a collection of binary relations (triples). In this context, analytical queries can be interpreted as consisting of three main constructs namely pattern matching, grouping and aggregation, and require several join operations to reassemble them into n-ary relations relevant to the given query, unlike traditional OLAP systems where data is suitably organized. MapReduce-based parallel processing systems like Pig have gained success in processing scalable analytical workloads. However, these systems offer only relational algebra style operators which would require an iterative n-tuple reassembly process in which intermediate results need to be materialized. This leads to high I/O costs that negatively impacts performance. In this paper, we propose UDFs that (i) re-factor analytical processing on RDF graphs in a way that enables more parallelized processing (ii) perform a look-ahead processing to reduce the cost of subsequent operators in the query execution plan. These functions have been integrated into the Pig Latin function library and the experimental results show up to 50% improvement in execution times for certain classes of queries. An important impact of this work is that it could serve as the foundation for additional physical operators in systems such as Pig for more efficient graph processing.


title: On Inductive Abilities of Latent Factor Models for Relational Learning

abstract: Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.


title: Foot Plantar Pressure Measurement System: A Review

abstract: Foot plantar pressure is the pressure field that acts between the foot and the support surface during everyday locomotor activities. Information derived from such pressure measures is important in gait and posture research for diagnosing lower limb problems, footwear design, sport biomechanics, injury prevention and other applications. This paper reviews foot plantar sensors characteristics as reported in the literature in addition to foot plantar pressure measurement systems applied to a variety of research problems. Strengths and limitations of current systems are discussed and a wireless foot plantar pressure system is proposed suitable for measuring high pressure distributions under the foot with high accuracy and reliability. The novel system is based on highly linear pressure sensors with no hysteresis.


title: A fine-grained access control system for XML documents

abstract: Web-based applications greatly increase information availability and ease of access, which is optimal for public information. The distribution and sharing of information via the Web that must be accessed in a selective way, such as electronic commerce transactions, require the definition and enforcement of security controls, ensuring that information will be accessible only to authorized entities. Different approaches have been proposed that address the problem of protecting information in a Web system. However, these approaches typically operate at the file-system level, independently of the data that have to be protected from unauthorized accesses. Part of this problem is due to the limitations of HTML, historically used to design Web documents. The extensible markup language (XML), a markup language promoted by the World Wide Web Consortium (W3C), is de facto the standard language for the exchange of information on the Internet and represents an important opportunity to provide fine-grained access control. We present an access control model to protect information distributed on the Web that, by exploiting XML's own capabilities, allows the definition and enforcement of access restrictions directly on the structure and content of the documents. We present a language for the specification of access restrictions, which uses standard notations and concepts, together with a description of a system architecture for access control enforcement based on existing technology. The result is a flexible and powerful security system offering a simple integration with current solutions.


title: Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

abstract: Information-centric networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current Internet protocol-address-based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, and caching the content at intermediate nodes to provide reliable, efficient data delivery, and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoT) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoT attracts both industry and research communities. IoT is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoT by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN-based caching, naming, security, and mobility approaches for IoT with appropriate classification. Furthermore, we present operating systems and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoT.


title: Synthesis of filigrees for digital fabrication

abstract: Filigrees are thin patterns found in jewelry, ornaments and lace fabrics. They are often formed of repeated base elements manually composed into larger, delicate patterns. Digital fabrication simplifies the process of turning a virtual model of a filigree into a physical object. However, designing a virtual model of a filigree remains a time consuming and challenging task. The difficulty lies in tightly packing together the base elements while covering a target surface. In addition, the filigree has to be well connected and sufficiently robust to be fabricated. We propose a novel approach automating this task. Our technique covers a target surface with a set of input base elements, forming a filigree strong enough to be fabricated. We exploit two properties of filigrees to make this possible. First, as filigrees form delicate traceries they are well captured by their skeleton. This affords for a simpler definition of operators such as matching and deformation. Second, instead of seeking for a perfect packing of the base elements we relax the problem by allowing appearance preserving partial overlaps. We optimize a filigree by a stochastic search, further improved by a novel boosting algorithm that records and reuses good configurations discovered during the process. ::: We illustrate our technique on a number of challenging examples reproducing filigrees on large objects, which we manufacture by 3D printing. Our technique affords for several user controls, such as the scale and orientation of the elements.


title: Why the high attrition rate for computer science students: some thoughts and observations

abstract: This paper investigates the possible causes for high attrition rates for Computer Science students. It is a serious problem in universities that must be addressed if the need for technologically competent professionals is to be met.


title: Catching the cuckoo: verifying TPM proximity using a quote timing side-channel

abstract: We present a Trusted Platform Module (TPM) application protocol that detects a certain man in the middle attack where an adversary captures and replaces a legitimate computing platform with an imposter that forwards platform authentication challenges to the captive over a high speed data link. This revised Cuckoo attack allows the imposter to satisfy a user's query of platform integrity, tricking the user into divulging sensitive information to the imposter. Our protocol uses an ordinary smart card to verify the platform boot integrity through TPM quote requests, and to verify TPM proximity by measuring TPM tickstamp times required to answer the quotes. Quotes not answered in an expected amount of time may indicate the presence of an imposter's data link, revealing the Cuckoo attack. We describe a timing model for the Cuckoo attack, and summarize experimental results that demonstrate the feasibility of using timing to detect the Cuckoo attack over practical levels of adversary link speeds.


title: Persistence weighted Gaussian kernel for topological data analysis

abstract: Topological data analysis (TDA) is an emerging mathematical concept for characterizing shapes in complex data. In TDA, persistence diagrams are widely recognized as a useful descriptor of data, and can distinguish robust and noisy topological properties. This paper proposes a kernel method on persistence diagrams to develop a statistical framework in TDA. The proposed kernel satisfies the stability property and provides explicit control on the effect of persistence. Furthermore, the method allows a fast approximation technique. The method is applied into practical data on proteins and oxide glasses, and the results show the advantage of our method compared to other relevant methods on persistence diagrams.


title: V/STOL Flight Test Instrumentation Requirements for Extraction of Aerodynamic Coefficients. Volume II.

abstract: Abstract : The results of a synthesis of pertinent maneuvers with a mathematical model of the XC-142 V/STOL airplane have served to establish tolerances in the measurement of flight parameters which provide the time histories used in the derivation of aerodynamic coefficients. Extracting valid aerodynamic coefficients from flight test time histories requires highly accurate instruments or detailed a priori knowledge of instrumentation errors. Furthermore, the mathematical technique used should provide the capability of extracting many coefficients simultaneously and delineating the probable error of each coefficient. The method of multiple linear regression, a least squares algorithm, provides the requisite mathematical tool with adequate statistical credentials. A state-of-the-art survey and a vendor-product-line review have provided guidelines for a system that will meet the overall accuracy requirements established. Details of equipment installations, oriented toward minimizing environmental stresses (particularly vibrations), have been studied. Conclusions drawn from this program are that the assembly and application of a precision data acquisition system based on digital 'onboard' handling techniques, with the appropriate sensing instrumentation, is entirely feasible but that limited development of measurement techniques, hardware, and software are required. (Author)


title: Understanding Image Virality

abstract: Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit1 and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different “contexts” in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.


title: Crowdsourcing Control: Moving Beyond Multiple Choice

abstract: To ensure quality results from crowdsourced tasks, requesters often aggregate worker responses and use one of a plethora of strategies to infer the correct answer from the set of noisy responses. However, all current models assume prior knowledge of all possible outcomes of the task. While not an unreasonable assumption for tasks that can be posited as multiple-choice questions (e.g. n-ary classification), we observe that many tasks do not naturally fit this paradigm, but instead demand a free-response formulation where the outcome space is of infinite size (e.g. audio transcription). We model such tasks with a novel probabilistic graphical model, and design and implement LAZYSUSAN, a decision-theoretic controller that dynamically requests responses as necessary in order to infer answers to these tasks. We also design an EM algorithm to jointly learn the parameters of our model while inferring the correct answers to multiple tasks at a time. Live experiments on Amazon Mechanical Turk demonstrate the superiority of LAZYSUSAN at solving SAT Math questions, eliminating 83.2% of the error and achieving greater net utility compared to the state-of-the-art strategy, majority-voting. We also show in live experiments that our EM algorithm outperforms majority-voting on a visualization task that we design.


title: Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation

abstract: The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may happen in a video sequence, especially long sequence videos, how to dynamically select the appropriate features is one of the key problems in this approach. To address this issue in multicue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. As a result, robust tracking performance is obtained. Experimental results on publicly available videos show that the proposed method outperforms both existing sparse representation based and fusion-based trackers.


title: The MVGC Multivariate Granger Causality Toolbox: A New Approach to Granger-causal Inference

abstract: Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, e ect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational e ciency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and e cient approach to G-causal inference.


title: Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs

abstract: We consider a simple and overarching representation for permutation-invariant functions of sequences (or set functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This allows us to leverage the rich and mature literature on permutation-sensitive functions to construct novel and flexible permutation-invariant functions. If carried out naively, Janossy pooling can be computationally prohibitive. To allow computational tractability, we consider three kinds of approximations: canonical orderings of sequences, functions with k-order interactions, and stochastic optimization algorithms with random permutations. Our framework unifies a variety of existing work in the literature, and suggests possible modeling and algorithmic extensions. We explore a few in our experiments, which demonstrate improved performance over current state-of-the-art methods.


title: Transfer learning for time series classification

abstract: Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network’s weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network’s generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike for image recognition problems, transfer learning techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved if the model is fine-tuned from a pre-trained neural network instead of training it from scratch. In this paper, we fill this gap by investigating how to transfer deep CNNs for the TSC task. To evaluate the potential of transfer learning, we performed extensive experiments using the UCR archive which is the largest publicly available TSC benchmark containing 85 datasets. For each dataset in the archive, we pre-trained a model and then fine-tuned it on the other datasets resulting in 7140 different deep neural networks. These experiments revealed that transfer learning can improve or degrade the models predictions depending on the dataset used for transfer. Therefore, in an effort to predict the best source dataset for a given target dataset, we propose a new method relying on Dynamic Time Warping to measure inter-datasets similarities. We describe how our method can guide the transfer to choose the best source dataset leading to an improvement in accuracy on 71 out of 85 datasets.


Multi-encoder Transformer Network for Automatic Post-Editing.


title: Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

abstract: The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, these methods are susceptible to the presence of sharp road curves, challenging traffic conditions, strong shadows, and severe lighting changes. In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction. Specifically, we train our steering angle predictive model by distilling multi-layer knowledge from multiple heterogeneous auxiliary networks that perform related but different tasks, e.g., image segmentation or optical flow estimation. As opposed to multi-task learning, our method does not require expensive annotations of related tasks on the target set. This is made possible by applying contemporary off-the-shelf networks on the target set and mimicking their features in different layers after transformation. The auxiliary networks are discarded after training without affecting the runtime efficiency of our model. Our approach achieves a new state-of-the-art on Udacity and Comma.ai, outperforming the previous best by a large margin of 12.8% and 52.1%1, respectively. Encouraging results are also shown on Berkeley Deep Drive (BDD) dataset.


title: Characterizing Task Completion Latencies in Fog Computing

abstract: Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive quantitative characteristics of the properties of fog computing architectures have been conducted. In this paper we examine the properties of task completion latencies in fog computing. First, we present the results of our empirical benchmarking-based study of task completion latencies. The study covered a range of settings, and uniquely considered both traditional and serverless fog computing execution points. It demonstrated the range of execution point characteristics in different locations and the relative stability of latency characteristics for a given location. It also highlighted properties of serverless execution that are not incorporated in existing fog computing algorithms. Second, we present a framework we developed for co-optimizing task completion quality and latency, which was inspired by the insights of our empirical study. We describe fog computing task assignment problems we formulated under this framework, and present the algorithms we developed for solving them.


title: Policy Certificates: Towards Accountable Reinforcement Learning

abstract: The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about the quality of their current policy before executing it, and thus have limited use in high-stakes applications like healthcare. We address this lack of accountability by proposing that algorithms output policy certificates. These certificates bound the sub-optimality and return of the policy in the next episode, allowing humans to intervene when the certified quality is not satisfactory. We further introduce two new algorithms with certificates and present a new framework for theoretical analysis that guarantees the quality of their policies and certificates. For tabular MDPs, we show that computing certificates can even improve the sample-efficiency of optimism-based exploration. As a result, one of our algorithms is the first to achieve minimax-optimal PAC bounds up to lower-order terms, and this algorithm also matches (and in some settings slightly improves upon) existing minimax regret bounds.


title: Endurance and Strength Training for Soccer Players

abstract: Top soccer players do not necessarily have an extraordinary capacity in any of the areas of physical performance. Soccer training is largely based on the game itself, and a common recruitment pattern from player to coach and manager reinforces this tradition. New developments in understanding adaptive processes to the circulatory system and endurance performance as well as nerve and muscle adaptations to training and performance have given rise to more effective training interventions. Endurance interval training using an intensity at 90-95% of maximal heart rate in 3- to 8-minute bouts have proved to be effective in the development of endurance, and for performance improvements in soccer play. Strength training using high loads, few repetitions and maximal mobilisation of force in the concentric mode have proved to be effective in the development of strength and related parameters. The new developments in physical training have important implications for the success of soccer players. The challenge both for coaches and players is to act upon the new developments and change existing training practice.


title: Efficient and Versatile Locomotion With Highly Compliant Legs

abstract: Drawing inspiration from nature, this paper introduces and compares two compliant robotic legs that are able to perform precise joint torque and position control, enable passive adaption to the environment, and allow for the exploitation of natural dynamic motions. We report in detail on the design and control of both prototypes and elaborate specifically on the problem of precise foot placement during flight without the sacrifice of efficient energy storage during stance. This is achieved through an integrated design and control approach that incorporates series elastic actuation, series damping actuation, and active damping through torque control. The two legs are employed in efficient hopping/running motions for which they achieve performance similar to humans or animals. This paper is concluded by a comparison of the various design choices with respect to performance and applicability, as well as an outlook on the usage of these legs in a fully actuated quadruped.


title: Underwater optical communication performance for laser beam propagation through weak oceanic turbulence

abstract: In clean ocean water, the performance of a underwater optical communication system is limited mainly by oceanic turbulence, which is defined as the fluctuations in the index of refraction resulting from temperature and salinity fluctuations. In this paper, using the refractive index spectrum of oceanic turbulence under weak turbulence conditions, we carry out, for a horizontally propagating plane wave and spherical wave, analysis of the aperture-averaged scintillation index, the associated probability of fade, mean signal-to-noise ratio, and mean bit error rate. Our theoretical results show that for various values of the rate of dissipation of mean squared temperature and the temperature–salinity balance parameter, the large-aperture receiver leads to a remarkable decrease of scintillation and consequently a significant improvement on the system performance. Such an effect is more noticeable in the plane wave case than in the spherical wave case.


title: Dual-purpose non-overlapped coil sets as foreign object and vehicle location detections for wireless stationary EV chargers

abstract: For commercialization of wireless stationary EV chargers, the foreign object detection (FOD) on a power supply coil and the location detection (LOD) of electric vehicles (EVs) are needed. In this paper, a dual-purpose non-overlapped coil sets as both FOD and LOD are newly proposed. Not only the existence of conductive object debris on a power supply coil but also the location of them are determined by an induced voltage difference of the coil sets. By measuring the induced voltage of the coil sets, displacements between a power supply coil and a pick-up coil can be also found to inform drivers of their EV alignments. Moreover, the proposed FOD and LOD methods make no contribution to any power losses. The proposed non-overlapped coil sets have been demonstrated by simulations and experiments for a prototype coil set. Throughout experiments, the induced voltage difference of a coil set shows 2.19 mV (ideally zero) without foreign objects while the induced voltage difference significantly increases by 78.2 mV, which is about 35 times of the value without objects, when eight conductive coins are located on a power supply coil. Also, it is found that the LOD can be achieved by measuring the variation of the induced voltages in coil sets when a pick-up coil moves on a power supply coil.


title: Brain computer interface: A review

abstract: A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface through electrodes. Researchers primarily rely on EEG to characterise the brain activity, because it can be recorded non-invasively by using portable equipment. The EEG or the brain activity can be used in real time to control external devices via a complete BCI system. A typical BCI scheme generally consists of a data acquisition system, pre-processing of the acquired signals, feature extraction process, classification of the features, post-processing of the classifier output, and finally the control interface and device controller. The post-processed output signals are translated into appropriate commands so as to control output devices, with several applications such as robotic arms, video games, wheelchair etc.