title: Phoneme-specific speech separation

abstract: Speech separation or enhancement algorithms seldom exploit information about phoneme identities. In this study, we propose a novel phoneme-specific speech separation method. Rather than training a single global model to enhance all the frames, we train a separate model for each phoneme to process its corresponding frames. A robust ASR system is employed to identify the phoneme identity of each frame. This way, the information from ASR systems and language models can directly influence speech separation by selecting a phoneme-specific model to use at the test stage. In addition, phoneme-specific models have fewer variations to model and do not exhibit the data imbalance problem. The improved enhancement results can in turn help recognition. Experiments on the corpus of the second CHiME speech separation and recognition challenge (task-2) demonstrate the effectiveness of this method in terms of objective measures of speech intelligibility and quality, as well as recognition performance.


title: Offline recognition of handwritten Bangla characters: an efficient two-stage approach

abstract: The present work deals with recognition of handwritten characters of Bangla, a major script of the Indian sub-continent. The main contributions presented here are (a) generation of a database of handwritten basic characters of Bangla and (b) development of a handwritten character recognition scheme suitable for scripts like Bangla consisting of many similar shaped characters for the benchmark results. The present database is a pioneering development in the context of recognition of off-line handwritten characters of this script. It has 37,858 handwritten samples and accommodates a large spectrum of handwriting style by Bangla speaking population. This database will be made available ( http://www.isical.ac.in/~ujjwal/download/Banglabasiccharacter.html ) free of cost to researchers for further studies. Also, we identified two major factors affecting high recognition accuracies for the present character samples, namely, (a) erratic nature of the presence of headline (shapes of Bangla characters usually contain a horizontal line in its upper part) and (b) existence of several pairs of similar shaped characters. The proposed recognition approach takes care of the above factors. It identifies any confusion in the first stage classification between a pair of similar shaped character classes and resolves the same in the second stage classification by extracting a feature vector based on a non-uniform grid.


Quantum machine learning: a classical perspective


title: Fully aligned via integration for extendibility of interconnects to beyond the 7 nm node

abstract: A fully aligned via (FAV) integration scheme is introduced and demonstrated at 36 nm metal pitch, with extendibility to beyond the 7 nm node. Selective chemistries were developed to recess Cu and W wires and their associated barrier liner materials, so as to create local topography with no adverse effects on these wiring levels or their dielectrics. Dielectric cap layers were optimized for excellent via RIE selectivity, to act as via guiding structures during subsequent level pattern definition. This combination mitigates via overlay and critical dimension (CD) errors. FAV integration can enable line/via area scaling for 70% lower line resistances and 30% larger via contact areas at the same node. Concurrently, FAV improves TDDB reliability through increased minimum insulator spacing, and EM reliability by maximizing via/wire contact area.


Mystery behind labial and oral melanotic macules: Clinical, dermoscopic and pathological aspects of Laugier-Hunziker syndrome


Smart Menu Ordering System in Restaurant Prema


title: Skin Ageing: Natural Weapons and Strategies

abstract: The fact that the skin is the most visible organ makes us aware of the ageing process every minute. The use of plant extracts and herbs has its origins in ancient times. Chronological and photo-ageing can be easily distinguished clinically, but they share important molecular features. We tried to gather the most interesting evidence based on facts about plants and plant extracts used in antiaging products. Our main idea was to emphasize action mechanisms of these plant/herbal products, that is, their "strategies" in fighting skin ageing. Some of the plant extracts have the ability to scavenge free radicals, to protect the skin matrix through the inhibition of enzymatic degradation, or to promote collagen synthesis in the skin. There are some plants that can affect skin elasticity and tightness. Certainly, there is a place for herbal principles in antiaging cosmetics. On the other hand, there is a constant need for more evaluation and more clinical studies in vivo with emphasis on the ingredient concentration of the plant/herbal products, its formulation, safety, and duration of the antiaging effect.


Internet of Things in Industry 4.0 Case Study : Fluid Distribution Monitoring System


title: A Convolutional Neural Network Fully Implemented on FPGA for Embedded Platforms

abstract: Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capability is highly requested in the embedded system domain for video processing applications such as video surveillance and homeland security. Moreover, with the increasing requirement of portable and ubiquitous processing, power consumption is a key issue to be accounted for.In this paper, we present an FPGA implementation of CNN designed for addressing portability and power efficiency. Performance characterization results show that the proposed implementation is as efficient as a general purpose 16-core CPU, and almost 15 times faster than a SoC GPU for mobile application. Moreover, external memory footprint is reduced by 84% with respect to a standard CNN software application.


title: Cytomegalovirus Reactivation in Critically Ill Immunocompetent Patients

abstract: Context Cytomegalovirus (CMV) infection is associated with adverse clinical outcomes in immunosuppressed persons, but the incidence and association of CMV reactivation with adverse outcomes in critically ill persons lacking evidence of immunosuppression have not been well defined. Objective To determine the association of CMV reactivation with intensive care unit (ICU) and hospital length of stay in critically ill immunocompetent persons. Design, Setting, and Participants We prospectively assessed CMV plasma DNAemia by thrice-weekly real-time polymerase chain reaction (PCR) and clinical outcomes in a cohort of 120 CMV-seropositive, immunocompetent adults admitted to 1 of 6 ICUs at 2 separate hospitals at a large US tertiary care academic medical center between 2004 and 2006. Clinical measurements were assessed by personnel blinded to CMV PCR results. Risk factors for CMV reactivation and association with hospital and ICU length of stay were assessed by multivariable logistic regression and proportional odds models. Main Outcome Measures Association of CMV reactivation with prolonged hospital length of stay or death. Results The primary composite end point of continued hospitalization (n = 35) or death (n = 10) by 30 days occurred in 45 (35%) of the 120 patients. Cytomegalovirus viremia at any level occurred in 33% (39/120; 95% confidence interval [CI], 24%-41%) at a median of 12 days (range, 3-57 days) and CMV viremia greater than 1000 copies/mL occurred in 20% (24/120; 95% CI, 13%-28%) at a median of 26 days (range, 9-56 days). By logistic regression, CMV infection at any level (adjusted odds ratio [OR], 4.3; 95% CI, 1.6-11.9; P = .005) and at greater than 1000 copies/mL (adjusted OR, 13.9; 95% CI, 3.2-60; P 10 copies per milliliter (adjusted OR, 2.1; 95% CI, 1.3-3.2; P Conclusions These preliminary findings suggest that reactivation of CMV occurs frequently in critically ill immunocompetent patients and is associated with prolonged hospitalization or death. A controlled trial of CMV prophylaxis in this setting is warranted.


title: Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

abstract: This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.


title: The functional anatomy of basal ganglia disorders

abstract: Abstract Basal ganglia disorders are a heterogeneous group of clinical syndromes with a common anatomic locus within the basal ganglia. To account for the variety of clinical manifestations associated with insults to various parts of the basal ganglia we propose a model in which specific types of basal ganglia disorders are associated with changes in the function of subpopulations of striatal projection neurons. This model is based on a synthesis of experimental animal and post-mortem human anatomic and neurochemical data. Hyperkinetic disorders, which are characterized by an excess of abnormal movements, are postulated to result from the selective impairment of striatal neurons projecting to the lateral globus pallidus. Hypokinetic disorders, such as Parkinson's disease, are hypothesized to result from a complex series of changes in the activity of striatal projection neuron subpopulations resulting in an increase in basal ganglia output. This model suggests that the activity of subpopulations of striatal projection neurons is differentially regulated by striatal afferents and that different striatal projection neuron subpopulations may mediate different aspects of motor control.


title: MapAffil: A Bibliographic Tool for Mapping Author Affiliation Strings to Cities and Their Geocodes Worldwide

abstract: Bibliographic records often contain author affiliations as free-form text strings. Ideally one would be able to automatically identify all affiliations referring to any particular country or city such as Saint Petersburg, Russia. That introduces several major linguistic challenges. For example, Saint Petersburg is ambiguous (it refers to multiple cities worldwide and can be part of a street address) and it has spelling variants (e.g., St. Petersburg, Sankt-Peterburg, and Leningrad, USSR). We have designed an algorithm that attempts to solve these types of problems. Key components of the algorithm include a set of 24,000 extracted city, state, and country names (and their variants plus geocodes) for candidate look-up, and a set of 1.1 million extracted word n-grams, each pointing to a unique country (or a US state) for disambiguation. When applied to a collection of 12.7 million affiliation strings listed in PubMed, ambiguity remained unresolved for only 0.1%. For the 4.2 million mappings to the USA, 97.7% were complete (included a city), 1.8% included a state but not a city, and 0.4% did not include a state. A random sample of 300 manually inspected cases yielded six incompletes, none incorrect, and one unresolved ambiguity. The remaining 293 (97.7%) cases were unambiguously mapped to the correct cities, better than all of the existing tools tested: GoPubMed got 279 (93.0%) and GeoMaker got 274 (91.3%) while MediaMeter CLIFF and Google Maps did worse. In summary, we find that incorrect assignments and unresolved ambiguities are rare (< 1%). The incompleteness rate is about 2%, mostly due to a lack of information, e.g. the affiliation simply says "University of Illinois" which can refer to one of five different campuses. A search interface called MapAffil has been developed at the University of Illinois in which the longitude and latitude of the geographical city-center is displayed when a city is identified. This not only helps improve geographic information retrieval but also enables global bibliometric studies of proximity, mobility, and other geo-linked data.


title: Field Experiments on 5G Radio Access Using Multi-Point Transmission

abstract: This paper presents outdoor field experimental results to clarify the 4x4 MIMO throughput performance from applying multi-point transmission in the 15 GHz frequency band in the downlink of 5G cellular radio access system. The experimental results in large-cell scenario shows that up to 30 % throughput gain compared to non-multi-point transmission is achieved although the difference for the RSRP of two TPs is over 10 dB, so that the improvement for the antenna correlation is achievable and important aspect for the multi-point transmission in the 15 GHz frequency band as well as the improvement of the RSRP. Furthermore in small-cell scenario, the throughput gain of 70% and over 5 Gbps are achieved applying multi-point transmission in the condition of two different MIMO streams transmission from a single TP as distributed MIMO instead of four MIMO streams transmission from a single TP.


title: Colorectal Cancer Detection using Image Processing based on IGVF Model

abstract: Colorectal cancer is the third most common cancer in both men and women. Computed Tomography (CT) Colonography is a valid technique for detecting and screening colorectal cancers. In order to accomplish an effective way to identify colon cancer at an early stage, digital images of colon polyps have been investigated. In this paper, an improved gradient vector flow (IGVF) is considered as an essential method to segment an image appropriately. This new algorithm, IGVF can improve GVF snake model's ability to capture thin boundary indentation like the boundary of cancer images. The segmented candidates are typically characterized by features describing like the polyp shape and its internal intensity distribution. Such features will serve as input for the classification system. Classification is performed by Support Vector Machine (SVM).Finally, measure the overlap between the manual and the algorithm segmentation to test the accuracy of frame work. It is expected to obtain greater accuracy and to produce good segmentation results of colonic polyps.


title: Laser-based finger tracking system suitable for MOEMS integration

abstract: This paper describes a proof-of-principle demonstration of a novel and simple active tracking mechanism using a laser diode, steering mirrors, and a single non-imaging photodetector. Tracking is based on the analysis of a temporal signal corresponding to the amount of backscattered light produced during a rapid, local circular scan (or saccade) around the presumed object position. The simplicity of the system is such that, using state-of-the-art Micro-Opto-Electro-Mechanical-System (MOEMS) technology, it would be possible to integrate the whole system on a chip, making it an interesting input interface for portable computing devices.


title: Topology and Data

abstract: An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.


title: LFADS - Latent Factor Analysis via Dynamical Systems

abstract: Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS is a sequential model based on a variational auto-encoder. By making a dynamical systems hypothesis regarding the generation of the observed data, LFADS reduces observed spiking to a set of low-dimensional temporal factors, per-trial initial conditions, and inferred inputs. We compare LFADS to existing methods on synthetic data and show that it significantly out-performs them in inferring neural firing rates and latent dynamics.


title: Efficient text summarization using lexical chains

abstract: The rapid growth of the Internet has resulted in enormous amounts of information that has become more difficult to access efficiently. Internet users require tools to help manage this vast quantity of information. The primary goal of this research is to create an efficient and effective tool that is able to summarize large documents quickly. This research presents a linear time algorithm for calculating lexical chains which is a method of capturing the “aboutness” of a document. This method is compared to previous, less efficient methods of lexical chain extraction. We also provide alternative methods for extracting and scoring lexical chains. We show that our method provides similar results to previous research, but is substantially more efficient. This efficiency is necessary in Internet search applications where many large documents may need to be summarized at once, and where the response time to the end user is extremely important.


title: Face localization using fuzzy classifier with wavelet-localized focus color features and shape features

abstract: This paper proposes a new fuzzy classifier (FC)-based face localization approach. The FC used is a self-organizing TS-type fuzzy network with support vector learning (SOTFN-SV). The SOTFN-SV learns consequent parameters using a linear support vector machine to improve generalization ability. The FC is first applied to segment human skin pixels in scaled hue and saturation (hS) color space, after which connected skin-color regions are regarded as face candidates. The FC is then applied to detect and localize faces from the candidates. The proposed FC-based face localization approach uses shape and wavelet-localized focus color features. A best fitting ellipse of each face candidate is found to obtain shape features. Focus color features are extracted from four focus regions, including the two eyes, the mouth, and the face skin-color region. To find these focus color regions, the Haar-wavelet transformation is first applied to the face candidates in the YCb color space to localize all possible pairs of eye candidates. The mouth region is then localized according to its geometric relationship with the eyes. The hS color features of the located eyes, mouth, and face skin are extracted. These focus color features, together with shape features, serve as inputs to another FC for final face localization. Comparisons with various classifiers and face detection methods demonstrate the advantage of the FC-based skin color segmentation and face localization method.


A STUDY ON AODV BASED CLUSTERING IN MOBILE ADHOC NETWORKS


title: Cricket Inspired Flow-Sensor Arrays

abstract: We report current developments in biomimetic flow-sensors based on mechanoreceptive sensory hairs of crickets. These filiform hairs are highly perceptive to low-frequency sound with energy sensitivities close to thermal threshold. In this work we describe hair-sensors fabricated by a combination of sacrificial poly-silicon technology, to form silicon-nitride suspended membranes, and SU8 polymer processing for fabrication of hairs with diameters of about 50 mum and up to 1 mm length. The membranes have thin chromium electrodes on top forming variable capacitors with the substrate allowing for capacitive read-out. Previously these sensors have been shown to exhibit acoustic sensitivity. Based on a hydrodynamic - mechanical interaction model we derive a figure of merit. We present optical measurements on acoustically excited hair-sensors. Experimental data and the derived models are shown to exhibit good correspondence.


title: The Children’s Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP INTEND): Test development and reliability

abstract: The motor skills of patients with spinal muscular atrophy, type I (SMA-I) are very limited. It is difficult to quantify the motor abilities of these patients and as a result there is currently no validated measure of motor function that can be utilized as an outcome measure in clinical trials of SMA-I. We have developed the Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders ("CHOP INTEND") to evaluate the motor skills of patients with SMA-I. The test was developed following the evaluation of 26 infants with SMA-I mean age 11.5 months (1.4-37.9 months) with the Test of Infant Motor Performance and The Children's Hospital of Philadelphia Test of Strength in SMA, a newly devised motor assessment for SMA. Items for the CHOP INTEND were selected by an expert panel based on item mean and standard deviation, item frequency distribution, and Chronbach's alpha. Intra-rater reliability of the resulting test was established by test-retest of 9 infants with SMA-I over a 2 month period; Intraclass correlation coefficient (ICC) (3,1)=0.96. Interrater reliability was by video analysis of a mixed group of infants with neuromuscular disease by 4 evaluators; ICC (3,4)=0.98 and in a group of 8 typically developing infants by 5 evaluators ICC (3,5)=0.93. The face validity of the CHOP INTEND is supported by the use of an expert panel in item selection; however, further validation is needed. The CHOP INTEND is a reliable measure of motor skills in patients with SMA-I and neuromuscular disorders presenting in infancy.


title: Diffeomorphic Demons: Efficient Non-parametric Image Registration

abstract: We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.


title: Beyond Sentiment Analysis: Mining Defects and Improvements from Customer Feedback

abstract: Customer satisfaction is considered as one the key performance indicators within businesses. In the current competitive marketplace where businesses compete for customers, managing customer satisfaction is very essential. One of the important sources of customer feedback is product reviews. Sentiment analysis on customer reviews has been a very hot topic in the last decade. While early works were mainly focused on identifying the positiveness and negativeness of reviews, later research tries to extract more detailed information by estimating the sentiment score of each product aspect/feature. In this work, we go beyond sentiment analysis by extracting actionable information from customer feedback. We call a piece of information actionable (in the sense of customer satisfaction) if the business can use it to improve its product. We propose a technique to automatically extract defects (problem/issue/bug reports) and improvements (modification/upgrade/enhancement requests) from customer feedback. We also propose a method for summarizing extracted defects and improvements. Experimental results showed that without any manual annotation cost, the proposed semi-supervised technique can achieve comparable accuracy to a fully supervised model in identifying defects and improvements.


title: BLISS: Balancing Performance, Fairness and Complexity in Memory Access Scheduling

abstract: In a multicore system, applications running on different cores interfere at main memory. This inter-application interference degrades overall system performance and unfairly slows down applications. Prior works have developed application-aware memory request schedulers to tackle this problem. State-of-the-art application-aware memory request schedulers prioritize memory requests of applications that are vulnerable to interference, by ranking individual applications based on their memory access characteristics and enforcing a total rank order. In this paper, we observe that state-of-the-art application-aware memory schedulers have two major shortcomings. First, such schedulers trade off hardware complexity in order to achieve high performance or fairness, since ranking applications individually with a total order based on memory access characteristics leads to high hardware cost and complexity. Such complexity could prevent the scheduler from meeting the stringent timing requirements of state-of-the-art DDR protocols. Second, ranking can unfairly slow down applications that are at the bottom of the ranking stack, thereby sometimes leading to high slowdowns and low overall system performance. To overcome these shortcomings, we propose the Blacklisting Memory Scheduler (BLISS) , which achieves high system performance and fairness while incurring low hardware cost and complexity. BLISS design is based on two new observations. First, we find that, to mitigate interference, it is sufficient to separate applications into only two groups, one containing applications that are vulnerable to interference and another containing applications that cause interference, instead of ranking individual applications with a total order. Vulnerable-to-interference group is prioritized over the interference-causing group. Second, we show that this grouping can be efficiently performed by simply counting the number of consecutive requests served from each application. We evaluate BLISS across a wide variety of workloads and system configurations and compare its performance and hardware complexity (via RTL implementations), with five state-of-the-art memory schedulers. Our evaluations show that BLISS achieves 5 percent better system performance and 25 percent better fairness than the best-performing previous memory scheduler while greatly reducing critical path latency and hardware area cost of the memory scheduler (by 79 and 43 percent, respectively), thereby achieving a good trade-off between performance, fairness and hardware complexity.


Learning factorized transforms for unsupervised adaptation of LSTM-RNN acoustic models


title: νZ - Maximal Satisfaction with Z3

abstract: Satisability Modulo Theories, SMT, solvers are used in many applications. These applications benet from the power of tuned and scalable theorem proving technologies for supported logics and specialized theory solvers. SMT solvers are primarily used to determine whether formulas are satisable. Furthermore, when formulas are satisable, many applications need models that assign values to free variables. Yet, in many cases arbitrary assignments are insucient, and what is really needed is an optimal assignment with respect to objective functions. So far, users of Z3, an SMT solver from Microsoft Research, build custom loops to achieve objective values. This is no longer necessary with Z (new-Z, or max-Z), an extension within Z3 that lets users formulate objective functions directly with Z3. Under the hood there is a portfolio of approaches for solving linear optimization problems over SMT formulas, MaxSMT, and their combinations. Objective functions are combined as either Pareto fronts, lexicographically, or each objective is optimized independently.


title: Requirements analysis of presence: Insights from a RPG game

abstract: Virtual worlds are computer-based simulations intended to give its users the impression of being in another place. Presence, or the sense of “being there,” is a major design requirement for virtual environments where users inhabit an artificial reality in the form of two or three-dimensional graphical representations. Promoting this subjective experience has always been one of the major concerns of designers, but this complex and difficult task requires the awareness of other design requirements and their effects on presence. This article aims to define various psychological and technological aspects of presence based on virtual environment design requirements defined by Stuart [2001]. Previous research tried to define hypothesized factors of presence by using subjective user responses obtained from questionnaires. This study incorporates a different approach to define potential components of presence, specifying the individual design requirements for virtual worlds based on the conceptual framework designed by Stuart. This framework has not been applied to the analysis of the concept of presence before, and it defines possible factors that contribute to a sense of presence, some of which have not been included in previous work. In order to decompose presence into its components, researchers should also be aware of the design requirements delineated in this framework. Detailed analysis of design requirements will focus on a computer role-playing game (RPG), giving examples from one of the best titles in the genre. Since role-playing games are social and interactive worlds where players assume the role of a virtual character that can be subjectively defined as a second-self, they are highly relevant to presence research. Thus, selected design requirements will be discussed from a computer-gaming perspective by defining how each relevant requirement is addressed on the selected RPG, and how they should be addressed by game designers.


title: The Effects of Diversity in Global, Distributed Collectives: A Study of Open Source Project Success

abstract: Diversity is a defining characteristic of global collectives facilitated by the Internet. Though substantial evidence suggests that diversity has profound implications for a variety of outcomes including performance, member engagement, and withdrawal behavior, the effects of diversity have been predominantly investigated in the context of organizational workgroups or virtual teams. We use a diversity lens to study the success of nontraditional virtual work groups exemplified by open source software (OSS) projects. Building on the diversity literature, we propose that three types of diversity (separation, variety, and disparity) influence two critical outcomes for OSS projects: community engagement and market success. We draw on the OSS literature to further suggest that the effects of diversity on market success are moderated by the application development stage. We instantiate the operational definitions of three forms of diversity to the unique context of open source projects. Using archival data from 357 projects hosted on SourceForge, we find that disparity diversity, reflecting variation in participants' contribution-based reputation, is positively associated with success. The impact of separation diversity, conceptualized as culture and measured as diversity in the spoken language and country of participants, has a negative impact on community engagement but an unexpected positive effect on market success. Variety diversity, reflected in dispersion in project participant roles, positively influences community engagement and market success. The impact of diversity on market success is conditional on the development stage of the project. We discuss how the study's findings advance the literature on antecedents of OSS success, expand our theoretical understanding of diversity, and present the practical implications of the results for managers of distributed collectives.


Improving Neural Translation Models with Linguistic Factors.


title: Adaptive Distance Estimation and Localization in WSN using RSSI Measures

abstract: Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.


title: The Structure of Working Memory From 4 to 15 Years of Age.

abstract: The structure of working memory and its development across the childhood years were investigated in children 4-15 years of age. The children were given multiple assessments of each component of the A. D. Baddeley and G. Hitch (1974) working memory model. Broadly similar linear functions characterized performance on all measures as a function of age. From 6 years onward, a model consisting of 3 distinct but correlated factors corresponding to the working memory model provided a good fit to the data. The results indicate that the basic modular structure of working memory is present from 6 years of age and possibly earlier, with each component undergoing sizable expansion in functional capacity throughout the early and middle school years to adolescence.


title: Measuring the Implications of Sales and Consumer Inventory Behavior

abstract: Temporary price reductions (sales) are common for many goods and naturally result in large increases in the quantity sold. Demand estimation based on temporary price reductions may mis-measure the long run responsiveness to prices. In this paper we quantify the extent of the problem and assess its economic implications. We structurally estimate a dynamic model of consumer choice using two years of scanner data on the purchasing behavior of a panel of households. The results suggest that static demand estimates, which neglect dynamics: (i) overestimate own price elasticities by 30 percent; (ii) underestimate cross-price elasticities to other products by up to a factor of 5; and (iii) overestimate the substitution to the no purchase, or outside option, by over 200 percent.


title: Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers

abstract: Abstract Objectives The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase. Design Observational. Methods Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSD mean ) as well as the coefficient of variation (lnRMSSD cv ) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition. Results Competition preparation resulted in improved race times ( p Moderate decreases in lnRMSSD mean , and Large to Very Large increases in lnRMSSD cv , perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP ( p mean and lnRMSSD cv were Very Large at BL and OL ( p Moderate at TP ( p > 0.05). Conclusions OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.


title: A study of dielectric resonator antenna array applied to 5G communication system

abstract: In this paper, the application of dielectric resonator antenna (DRA) array in 5G communication system is studied. Firstly, an 8-way Wilkinson power divider with fold structure is designed, whose port phase and transmission efficiency are applicable for a feed network. Secondly, a kind of semi cylindrical DRA unit operated on 28 GHz is presented. It is fed by aperture coupled microstrip line. The 8-unit uniform linear array is made up of the unit. The simulation result is given in this paper, which shows that both bandwidth and gain can better meet the requirements of 5G communication system.


title: A Neurodynamic Optimization Approach to Bilevel Quadratic Programming

abstract: This paper presents a neurodynamic optimization approach to bilevel quadratic programming (BQP). Based on the Karush–Kuhn–Tucker (KKT) theorem, the BQP problem is reduced to a one-level mathematical program subject to complementarity constraints (MPCC). It is proved that the global solution of the MPCC is the minimal one of the optimal solutions to multiple convex optimization subproblems. A recurrent neural network is developed for solving these convex optimization subproblems. From any initial state, the state of the proposed neural network is convergent to an equilibrium point of the neural network, which is just the optimal solution of the convex optimization subproblem. Compared with existing recurrent neural networks for BQP, the proposed neural network is guaranteed for delivering the exact optimal solutions to any convex BQP problems. Moreover, it is proved that the proposed neural network for bilevel linear programming is convergent to an equilibrium point in finite time. Finally, three numerical examples are elaborated to substantiate the efficacy of the proposed approach.


title: You Are What You Like! Information Leakage Through Users’ Interests

abstract: Suppose that a Facebook user, whose age is hidden or missing, likes Britney Spears. Can you guess his/her age? Knowing that most Britney fans are teenagers, it is fairly easy for humans to answer this question. Interests (or "likes") of users is one of the highly-available on-line information. In this paper, we show how these seemingly harmless interests (e.g., music interests) can leak privacy sensitive information about users. In particular, we infer their undisclosed (private) attributes using the public attributes of other users sharing similar interests. In order to compare user-defined interest names, we extract their semantics using an ontologized version of Wikipedia and measure their similarity by applying a statistical learning method. Besides self-declared interests in music, our technique does not rely on any further information about users such as friend relationships or group belongings. Our experiments, based on more than 104K public profiles collected from Facebook and more than 2000 private profiles provided by volunteers, show that our inference technique efficiently predicts attributes that are very often hidden by users. To the best of our knowledge, this is the first time that user interests are used for profiling, and more generally, semantics-driven inference of private data is addressed.


title: Exploiting efficient data mining techniques to enhance intrusion detection systems

abstract: Security is becoming a critical part of organizational information systems. Intrusion detection system (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. Data mining is being used to clean, classify, and examine large amount of network data to correlate common infringement for intrusion detection. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. The amount of data accumulated each day by a network is huge. Several data mining techniques such as clustering, classification, and association rules are proving to be useful for gathering different knowledge for intrusion detection. This paper presents the idea of applying data mining techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems.


title: INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES Automatic Optimization Computational Method for Unconventional S.W.A.T.H. Ships Resistance

abstract: The paper illustrates the main theoretical and computational aspects of an automatic computer based procedure for the parametric shape optimization of a particular unconventional hull typology: that for a catamaran S.W.A.T.H. ship. The goal of the integrated computational procedure is to find the best shape of the submerged hulls of a new U.S.V. (Unmanned Surface Vehicle) S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel, in terms of minimum wave pattern resistance. After dealing with the theoretical aspects the papers presents the numerical aspects of the main software module of the automatic procedure, which integrates a parametric generation routine for innovative and unconventional S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel geometry, a multi-objective, globally convergent and constrained, optimization algorithm and a Computational Fluid Dynamic (C.F.D.) solver. The integrated process is able to find the best shape of the submerged hull of the vessel, subject to the total displaced volume constraint. The hydrodynamic computation is carried out by means of a free surface potential flow method and it is addressed to find the value of wave resistance of each hull variant. Results of the application of the described computational procedure are presented for two optimization cases and the obtained best shapes are compared with a conventional one, featuring a typical torpedo-shaped body, proving the effectiveness of the method in reducing the resistance by a considerable extent, in the order of 40 percent.


title: TCA: An Efficient Two-Mode Meta-Heuristic Algorithm for Combinatorial Test Generation (T)

abstract: Covering arrays (CAs) are often used as test suites for combinatorial interaction testing to discover interaction faults of real-world systems. Most real-world systems involve constraints, so improving algorithms for covering array generation (CAG) with constraints is beneficial. Two popular methods for constrained CAG are greedy construction and meta-heuristic search. Recently, a meta-heuristic framework called two-mode local search has shown great success in solving classic NP-hard problems. We are interested whether this method is also powerful in solving the constrained CAG problem. This work proposes a two-mode meta-heuristic framework for constrained CAG efficiently and presents a new meta-heuristic algorithm called TCA. Experiments show that TCA significantly outperforms state-of-the-art solvers on 3-way constrained CAG. Further experiments demonstrate that TCA also performs much better than its competitors on 2-way constrained CAG.


title: Using goal-driven deep learning models to understand sensory cortex

abstract: Recent computational neuroscience developments have used deep neural networks to model neural responses in higher visual areas. This Perspective describes key algorithmic underpinnings in computer vision and artificial intelligence that have contributed to this progress and outlines how deep networks could drive future improvements in understanding sensory cortical processing.


title: A Brief Review of Real-World Color Image Denoising

abstract: Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution. However, the rapid development of camera lens pos- es greater demands on image denoising in terms of both efficiency and effectiveness. Currently, the most widely accepted framework employs the combination of transform domain techniques and nonlocal similarity characteristics of natural images. Based on this framework, many competitive methods model the correlation of R, G, B channels with pre-defined or adaptively learned transforms. In this chapter, a brief review of related methods and publicly available datasets is presented, moreover, a new dataset that includes more natural outdoor scenes is introduced. Extensive experiments are performed and discussion on visual effect enhancement is included.


title: Pitch Dynamics of Unmanned Aerial Vehicles

abstract: Dynamic stability requirements for manned aircraft have been in place for many years. However, we cannot expect stability constraints for UAVs to match those for manned aircraft; and dynamic stability requirements specific to UAVs have not been developed. The boundaries of controllability for both remotely-piloted and auto-piloted aircraft must be established before UAV technology can reach its full potential. The development of dynamic stability requirements specific to UAVs could improve flying qualities and facilitate more efficient UAV designs to meet specific mission requirements. As a first step to developing UAV stability requirements in general, test techniques must be established that will allow the stability characteristics of current UAVs to be quantified. This paper consolidates analytical details associated with procedures that could be used to experimentally determine the pitch stability boundaries for good UAV flying qualities. The procedures require determining only the maneuver margin and pitch radius of gyration and are simple enough to be used in an educational setting where resources are limited. The premise is that these procedures could be applied to UAVs now in use, in order to characterize the longitudinal flying qualities of current aircraft. This is but a stepping stone to the evaluation of candidate metrics for establishing flying-quality constraints for unmanned aircraft.


title: Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization

abstract: PURPOSE ::: The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. ::: ::: ::: METHODS ::: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of "metal shadows" in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. ::: ::: ::: RESULTS ::: The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. ::: ::: ::: CONCLUSIONS ::: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general "missing data" image reconstruction problems.


title: Indicator selection for daily equity trading with recurrent reinforcement learning

abstract: Recurrent reinforcement learning (RRL), a machine learning technique, is very successful in training high frequency trading systems. When trading analysis of RRL is done with lower frequency financial data, e.g. daily stock prices, the decrease of autocorrelation in prices may lead to a decrease in trading profit. In this paper, we propose a RRL trading system which utilizes the price information, jointly with the indicators from technical analysis, fundamental analysis and econometric analysis, to produce long/short signals for daily trading. In the proposed trading system, we use a genetic algorithm as a pre-screening tool to search suitable indicators for RRL trading. Moreover, we modify the original RRL parameter update scheme in the literature for out-of-sample trading. Empirical studies are conducted based on data sets of 238 S&P stocks. It is found that the trading performance concerning the out-of sample daily Sharpe ratios turns better: the number of companies with a positive and significant Sharpe ratio increases after feeding the selected indicators jointly with prices information into the RRL system.


title: Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics

abstract: To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a driver's expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the level of sensitivity to errors, i.e., low, middle, and high. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to driver's path-following characteristics, reducing the driver's workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in CarSim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed-speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, as compared with the classical control strategy of steering ratio, can improve task performance by about 7% and reduce the driver's physical workload and mental workload by about 35% and 50%, respectively, when following the given path.


title: A broadband circularly polarized cross-dipole antenna for GNSS applications

abstract: A broadband circularly polarized (CP) cross-dipole antenna is proposed for GNSS applications. The antenna has a very simple structure and a small size but has a very broad impedance bandwidth from 1.15 to 1.75 GHz (a fractional bandwidth of 41.4%) for S11 < −10 dB. The CP bandwidth is from 1.37 to 1.5 GHz for axial ratio < 3 dB. A computer simulation is conducted to optimize its dimensions and predict its performance. The optimized antenna is fabricated and measured. The simulated and measured results are in good agreement and have demonstrated that the proposed antenna is indeed a very good candidate for GNSS applications.


title: How to detect the Cuckoo Sandbox and to Strengthen it?

abstract: Nowadays a lot of malware are analyzed with virtual machines. The Cuckoo sandbox (Cuckoo DevTeam: Cuckoo sandbox. http://www.cuckoosandbox.org, 2013) offers the possibility to log every actions performed by the malware on the virtual machine. To protect themselves and to evande detection, malware need to detect whether they are in an emulated environment or in a real one. With a few modifications and tricks on Cuckoo and the virtual machine we can try to prevent malware to detect that they are under analyze, or at least make it harder. It is not necessary to apply all the modifications, because it may produce a significant overhead and if malware checks his execution time, it may detect an anomaly and consider that it is running in a virtual machine. The present paper will show how a malware can detect the Cuckoo sandbox and how we can counter that.


title: Graphene and Graphene Oxide: Synthesis, Properties, and Applications

abstract: There is intense interest in graphene in fields such as physics, chemistry, and materials science, among others. Interest in graphene's exceptional physical properties, chemical tunability, and potential for applications has generated thousands of publications and an accelerating pace of research, making review of such research timely. Here is an overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.


title: Reconfigurable Software Defined Radio and Its Applications

abstract: Currently, most Software Defined Radio (SDR) related products and researches focus on analog communication and voice transmission. In this paper, we propose a SDR platform with digital data communication capability. This platform consists of Field Programmable Gate Array (FPGA) based radio hardware and open source SDR software modules. The main features include: 1) Radio Spectrum Sensing; 2) Reconfigurable Radio Modules; 3) Link for Digital Data Communication. Based on the proposed SDR platform, we could easily reconfigure its radio modules and discover the spectrum hole to achieve better communication quality. These features are important basis to accomplish Cognitive Radio (CR) technologies.


title: Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs

abstract: This paper presents a generic approach for tactical decision-making under uncertainty in the context of driving. The complexity of this task mainly stems from the fact that rational decision-making in this context must consider several sources of uncertainty: The temporal evolution of situations cannot be predicted without uncertainty because other road users behave stochastically and their goals and plans cannot be measured. Even more important, road users are only able to perceive a tiny part of the current situation with their sensors because measurements are noisy and most of the environment is occluded. In order to anticipate the consequences of decisions a probabilistic approach, considering both forms of uncertainty, is necessary. The authors address this by formulating the task of driving as a continuous Partially Observable Markov Decision Process (POMDP) that can be automatically optimized for different scenarios. As driving is a continuous-space problem, the belief space is infinite-dimensional. The authors do not use a symbolic representation or discretize the state space a priori because there is no representation of the state space that is optimal for every situation. Instead, they employ a continuous POMDP solver that learns a good representation of the specific situation.


title: Analysis of a balanced analog multiplier for an arbitrary number of signed inputs

abstract: We present an extension of the double-balanced current-commutating analog multiplier (also known as the Gilbert cell) that enables the multiplication of an arbitrary number of signed differential input voltages. A general analysis of the circuit for an arbitrary device nonlinearity is provided, and simulations on a bulk CMOS process as well as measurement results of a discrete bipolar implementation are reported.


title: INTEL-TUT Dataset for Camera Invariant Color Constancy Research

abstract: In this paper, we provide a novel dataset designed for camera invariant color constancy research. Camera invariance corresponds to the robustness of an algorithm's performance when run on images of the same scene taken by different cameras. Accordingly, images in the database correspond to several lab and field scenes each of which are captured by three different cameras with minimal registration errors. The lab scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the lab light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation and testing partitions. Accordingly, we evaluate a recently proposed convolutional neural network based color constancy algorithm as a baseline for future research. As a side contribution, this dataset also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well.


title: Environmental Detectives – The Development of an Augmented Reality Platform for Environmental Simulations.

abstract: The form factors of handheld computers make them increasingly popular among K-12 educators. Although some compelling examples of educational software for handhelds exist, we believe that the potential of this platform are just being discovered. This paper reviews innovative applications for mobile computing for both education and entertainment purposes, and then proposes a framework for approaching handheld applications we call “augmented reality educational gaming.” We then describe our development process in creating a development platform for augmented reality games that draws from rapid prototyping, learner-centered software, and contemporary game design methodologies. We provide a narrative case study of our development activities spread across five case studies with classrooms, and provide a design narrative explaining this development process and articulate an approach to designing educational software on emerging technology platforms. Pedagogical, design, and technical conclusions and implications are discussed.


title: Gender Identification on Twitter Using the Modified Balanced Winnow

abstract: With the rapid growth of web-based social networking technologies in recent years, author identification and analysis have proven increasingly useful. Authorship analysis provides information about a document’s author, often including the author’s gender. Men and women are known to write in distinctly different ways, and these differences can be successfully used to make a gender prediction. Making use of these distinctions between male and female authors, this study demonstrates the use of a simple stream-based neural network to automatically discriminate gender on manually labeled tweets from the Twitter social network. This neural network, the Modified Balanced Winnow, was employed in two ways; the effectiveness of data stream mining was initially examined with an extensive list of n-gram features. Feature selection techniques were then evaluated by drastically reducing the feature list using WEKA’s attribute selection algorithms. This study demonstrates the effectiveness of the stream mining approach, achieving an accuracy of 82.48%, a 20.81% increase above the baseline prediction. Using feature selection methods improved the results by an additional 16.03%, to an accuracy of 98.51%.


title: On the limits of mutation reduction strategies

abstract: Although mutation analysis is considered the best way to evaluate the effectiveness of a test suite, hefty computational cost often limits its use. To address this problem, various mutation reduction strategies have been proposed, all seeking to reduce the number of mutants while maintaining the representativeness of an exhaustive mutation analysis. While research has focused on the reduction achieved, the effectiveness of these strategies in selecting representative mutants, and the limits in doing so have not been investigated, either theoretically or empirically. We investigate the practical limits to the effectiveness of mutation reduction strategies, and provide a simple theoretical framework for thinking about the absolute limits. Our results show that the limit in improvement of effectiveness over random sampling for real-world open source programs is a mean of only 13.078%. Interestingly, there is no limit to the improvement that can be made by addition of new mutation operators. Given that this is the maximum that can be achieved with perfect advance knowledge of mutation kills, what can be practically achieved may be much worse. We conclude that more effort should be focused on enhancing mutations than removing operators in the name of selective mutation for questionable benefit.


title: Reconsidering happiness : the costs of distinguishing between hedonics and eudaimonia

abstract: In recent years, well-being researchers have distinguished between eudaimonic happiness (e.g., meaning and purpose; taking part in activities that allow for the actualization of one's skills, talents, and potential) and hedonic happiness (e.g., high frequencies of positive affect, low frequencies of negative affect, and evaluating life as satisfying). Unfortunately, this distinction (rooted in philosophy) does not necessarily translate well to science. Among the problems of drawing too sharp a line between ‘types of happiness’ is the fact that eudaimonia is not well-defined and lacks consistent measurement. Moreover, empirical evidence currently suggests that hedonic and eudaimonic well-being overlap conceptually, and may represent psychological mechanisms that operate together. In this article, we outline the problems and costs of distinguishing between two types of happiness, and provide detailed recommendations for a research program on well-being with greater scientific precision. The purpose of life ...


The Benefits of High-Order Interface Advection Schemes?


title: Advances in Light Microscopy for Neuroscience

abstract: Since the work of Golgi and Cajal, light microscopy has remained a key tool for neuroscientists to observe cellular properties. Ongoing advances have enabled new experimental capabilities using light to inspect the nervous system across multiple spatial scales, including ultrastructural scales finer than the optical diffraction limit. Other progress permits functional imaging at faster speeds, at greater depths in brain tissue, and over larger tissue volumes than previously possible. Portable, miniaturized fluorescence microscopes now allow brain imaging in freely behaving mice. Complementary progress on animal preparations has enabled imaging in head-restrained behaving animals, as well as time-lapse microscopy studies in the brains of live subjects. Mouse genetic approaches permit mosaic and inducible fluorescence-labeling strategies, whereas intrinsic contrast mechanisms allow in vivo imaging of animals and humans without use of exogenous markers. This review surveys such advances and highlights emerging capabilities of particular interest to neuroscientists.


title: Non-Parametric Ecological Regression and Spatial Variation

abstract: Ecological studies aim to analyse the variation of disease risk in relation to exposure variables that are measured at an area unit level. In practice it is rarely possible to use the exposure variables themselves, either because the corresponding data are not available or because the causes of the disease are not fully understood. It is therefore quite common to use crude proxies of the real exposure to the disease in question. These proxies are rarely able to explain the disease variation and hence additional area level random effects are introduced to account for the residual variation. In this paper we investigate the possibility to model the effect of ecological covariates non-parametrically, with and without additional random effects for the residual spatial variation. We illustrate the issues arising through analyses of simulated and real data on larynx cancer mortality in Germany, during the years of 1986 to 1990, where we use the corresponding lung cancer rates as a proxy for smoking consumption.


title: Health at hand: A systematic review of smart watch uses for health and wellness

abstract: INTRODUCTION ::: Smart watches have the potential to support health in everyday living by: enabling self-monitoring of personal activity; obtaining feedback based on activity measures; allowing for in-situ surveys to identify patterns of behavior; and supporting bi-directional communication with health care providers and family members. However, smart watches are an emerging technology and research with these devices is at a nascent stage. ::: ::: ::: METHODS ::: We conducted a systematic review of smart watch studies that engaged people in their use by searching PubMed, Embase, IEEE XPlore and ACM Digital libraries. Participant demographics, device features, watch applications and methods, and technical challenges were abstracted from included studies. ::: ::: ::: RESULTS ::: Seventy-three studies were returned in the search. Seventeen studies published were included. Included studies were published from 2014 to 2016, with the exception of one published in 2011. Most studies employed the use of consumer-grade smart watches (14/17, 82%). Patient-related studies focused on activity monitoring, heart rate monitoring, speech therapy adherence, diabetes self-management, and detection of seizures, tremors, scratching, eating, and medication-taking behaviors. Most patient-related studies enrolled participants with few exclusion criteria to validate smart watch function (10/17, 58%). Only studies that focused on Parkinson's disease, epilepsy, and diabetes management enrolled persons living with targeted conditions. One study focused on nursing work in the ICU and one focused on CPR training for laypeople. ::: ::: ::: CONCLUSION ::: Consumer-grade smart watches have penetrated the health research space rapidly since 2014. Smart watch technical function, acceptability, and effectiveness in supporting health must be validated in larger field studies that enroll actual participants living with the conditions these devices target.


title: PathShuffle: Credit Mixing and Anonymous Payments for Ripple

abstract: Abstract The I owe you (IOU) credit network Ripple is one of the most prominent alternatives in the burgeoning field of decentralized payment systems. Ripple’s path-based transactions set it apart from cryptocurrencies such as Bitcoin. Its pseudonymous nature, while still maintaining some regulatory capabilities, has motivated several financial institutions across the world to use Ripple for processing their daily transactions. Nevertheless, with its public ledger, a credit network such as Ripple is no different from a cryptocurrency in terms of weak privacy; recent demonstrative deanonymization attacks raise important concerns regarding the privacy of the Ripple users and their transactions. However, unlike for cryptocurrencies, there is no known privacy solution compatible with the existing credit networks such as Ripple. In this paper, we present PathShuffle, the first path mixing protocol for credit networks. PathShuffle is fully compatible with the current credit networks. As its essential building block, we propose PathJoin, a novel protocol to perform atomic transactions in credit networks. Using PathJoin and the P2P mixing protocol DiceMix, PathShuffle is a decentralized solution for anonymizing path-based transactions. We demonstrate the practicality of PathShuffle by performing path mixing in Ripple.


title: DC/DC Converter Design for Supercapacitor and Battery Power Management in Hybrid Vehicle Applications—Polynomial Control Strategy

abstract: This paper presents supercapacitor (SCAP) and battery modeling with an original energy management strategy in a hybrid storage technology. The studied dc power supply is composed of SCAPs and batteries. SCAPs are dimensioned for peak power requirement, and batteries provide the power in steady state. A bidirectional dc/dc converter is used between SCAPs and the dc bus. Batteries are directly connected to the dc bus. The originality of this study is focused on SCAP behavior modeling and energy management strategy. The proposed strategy is based on a polynomial (RST) controller. For reasons of cost and existing components (not optimized) such as batteries and semiconductors, the experimental test benches are designed in reduced scale. The characterized packs of SCAPs include two modules of ten cells in series for each one and present a maximum voltage of 27 V. The proposed strategy is implemented on a PIC18F4431 microcontroller for two dc/dc converter topology controls. Experimental and simulation results obtained from the polynomial control strategy are presented, analyzed, and compared with that of classical proportional-integral control.


title: A hierarchical Bayesian network for event recognition of human actions and interactions

abstract: Recognizing human interactions is a challenging task due to the multiple body parts of interacting persons and the concomitant occlusions. This paper presents a method for the recognition of two-person interactions using a hierarchical Bayesian network (BN). The poses of simultaneously tracked body parts are estimated at the low level of the BN, and the overall body pose is estimated at the high level of the BN. The evolution of the poses of the multiple body parts are processed by a dynamic Bayesian network (DBN). The recognition of two-person interactions is expressed in terms of semantic verbal descriptions at multiple levels: individual body-part motions at low level, single-person actions at middle level, and two-person interactions at high level. Example sequences of interacting persons illustrate the success of the proposed framework.


title: Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images

abstract: Abstract We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully-supervised annotation cost.


title: Automatic Tagging Of Arabic Text: From Raw Text To Base Phrase Chunks

abstract: To date, there are no fully automated systems addressing the community's need for fundamental language processing tools for Arabic text. In this paper, we present a Support Vector Machine (SVM) based approach to automatically tokenize (segmenting off clitics), part-of-speech (POS) tag and annotate base phrases (BPs) in Arabic text. We adapt highly accurate tools that have been developed for English text and apply them to Arabic text. Using standard evaluation metrics, we report that the SVM-TOK tokenizer achieves an Fβ=1 score of 99.12, the SVM-POS tagger achieves an accuracy of 95.49%, and the SVM-BP chunker yields an Fβ=1 score of 92.08.


title: Analysis of sliding of a soft fingertip embedded with a novel micro force/moment sensor: Simulation, experiment, and application

abstract: We have investigated the deformation of a soft fingertip when it slides. This process was first simulated using the non-linear Finite Element Analysis (FEA) method. Based on the results of this simulation, we designed experiments to observe the sliding and object grasping of a soft fingertip, in which a 3-DOF (degree of freedom) micro force/moment sensor was embedded. With this sensor, forces and moments acting in the fingertip are measured based on the piezoresistive effect. These measurements provide information on the status of contact and sliding of a soft fingertip on a surface. Based on these results, incipient slip, which has an important role in object gripping by a robot manipulator, can be realized. Textile's texture recognition experiments were also conducted to assess potentials of the fingertip in tactile and texture perception.


Integrating Software Security into Agile-Scrum Method


title: Human-Like Motion Generation and Control for Humanoid's Dual Arm Object Manipulation

abstract: The robot manipulation in human environment is a challenging issue because the human environment is complex, dynamic, unstructured, and difficult to perceive reliably. In order to implement promising robot applications in our daily lives, robots need to perform manipulation tasks within human environment. Particularly for a humanoid robot, the manipulability of the objects is essential to assist the humans in human environment. This paper presents a method for manipulating an object with both arms of a humanoid robot. We focus on the generation of human-like movements by using the human motion capture data. Then, the control method based on the virtual dynamics model is proposed to control both the motion and force under the uniform control system. This method empowers the robot to perform the object manipulation task including reaching, grasping, and moving an object in sequence. The proposed algorithm is implemented on a humanoid robot with independent joint controller at each motor; its performance is demonstrated by manipulating an object with both arms.


title: pH effect on the assembly of metal–organic architectures

abstract: Crystal engineering is the rational design and assembly of solid-state structures with desired properties via the manipulation of intermolecular interactions, hydrogen bonding and metal–ligand complexation in particular. The heart of crystal engineering is to control the ordering of the building blocks, be they molecular or ionic, toward a specific disposition in the solid state. The relatively weak strength of intermolecular forces with respect to chemical bonding renders the assembly of supramolecular constructs sensitive to external physical and chemical stimuli, with pH condition of the reaction mixture being arguably the most prominent and extensively observed. Using selected examples of constructing metal–organic architectures from recent literature, the influences of pH on the specific ligand forms, the generation and metal coordination of hydroxo ligands, ligand transformation promoted by pH condition changes, pH-dependent kinetics of crystallization of a number of metal–organic architectures are discussed. Current status of this particular areas of research in supramolecular chemistry and materials are assessed and personal perspectives as to toward what directions should this chemistry head are elaborated.


title: Similarity Based Classification

abstract: This paper reviews and extends the field of similarity-based classification, presenting new analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich collection of classification problems. Specifically, the generalizability of using similarities as features is analyzed, design goals and methods for weighting nearest-neighbors for similarity-based learning are proposed, and different methods for consistently converting similarities into kernels are compared. Experiments on eight real data sets compare eight approaches and their variants to similarity-based learning.


title: Intelligent stock trading system by turning point confirming and probabilistic reasoning

abstract: Financial engineering such as trading decision is an emerging research area and also has great commercial potentials. A successful stock buying/selling generally occurs near price trend turning point. Traditional technical analysis relies on some statistics (i.e. technical indicators) to predict turning point of the trend. However, these indicators can not guarantee the accuracy of prediction in chaotic domain. In this paper, we propose an intelligent financial trading system through a new approach: learn trading strategy by probabilistic model from high-level representation of time series-turning points and technical indicators. The main contributions of this paper are two-fold. First, we utilize high-level representation (turning point and technical indicators). High-level representation has several advantages such as insensitive to noise and intuitive to human being. However, it is rarely used in past research. Technical indicator is the knowledge from professional investors, which can generally characterize the market. Second, by combining high-level representation with probabilistic model, the randomness and uncertainty of chaotic system is further reduced. In this way, we achieve great results (comprehensive experiments on S&P500 components) in a chaotic domain in which the prediction is thought impossible in the past.


title: Customer behavior in electronic commerce: The moderating effect of e-purchasing experience

abstract: This study analyzes the perceptions which induce customers to purchase over the Internet, testing the moderating effect of e-purchasing experience. We distinguish between two groups: (1) potential e-customers, who are considering making their first e-purchase, and (2) experienced e-customers, who have made at least one e-purchase and are thinking about continuing to do so. The perceptions that induce individuals to purchase online for the first time may not be the same as those that produce repurchasing behavior. Our findings demonstrate that customer behavior does not remain stable because the experience acquired from past e-purchases means that perceptions evolve. The relationships between perceptions of e-commerce change with purchasing experience, whilst the influence of Internet experience is stable for all users. The implications are especially interesting for e-commerce providers whose business models depend on e-customer behavior.


Workshop track-ICLR 2018 A SPECT-BASED Q UESTION G ENERATION


title: Unified activity management: supporting people in e-business

abstract: Meshing formal business processes with informal human collaborations is needed to support business activities.


title: A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

abstract: Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.


title: Virtual interference study for FMCW and PMCW radar

abstract: Mutual interference of radar systems has been identified as one of the major challenges for future automotive radar systems. In this work the interference of frequency (FMCW) and phase modulated continuous wave (PMCW) systems is investigated by means of simulations. All twofold combinations of the aforementioned systems are considered. The interference scenario follows a typical use-case from the well-known MOre Safety for All by Radar Interference Mitigation (MOSARIM) study. The investigated radar systems operate with similar system parameters to guarantee a certain comparability, but with different waveform durations, and chirps with different slopes and different phase code sequences, respectively. Since the effects in perfect synchrony are well understood, we focus on the cases where both systems exhibit a certain asynchrony. It is shown that the energy received from interferers can cluster in certain Doppler bins in the range-Doppler plane when systems exhibit a slight asynchrony.


title: Gamification usage ecology

abstract: Gamification is applied in academia and industry and is being studied in the last years. Gamification is nowadays applied to several areas, such as learning, computer science, business, marketing, tourism, among others. However, we need to understand to what extent it is studied in academic context. We present a bibliographic study on gamification research, by identifying publications trends. This paper presents the gamification related areas, result from a publication keyword analysis of the scientific publications on gamification. In this paper, we also identify the game elements, game mechanics, and game dynamics, used as part of any gamification implementation. In this study are also present the most used gamification theoretical models.


title: Consensus problems in networks of agents with switching topology and time-delays

abstract: In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results.


title: Cloud Computing: Issues and Challenges

abstract: Many believe that Cloud will reshape the entire ICT industry as a revolution. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms - Service-Oriented Computing and Grid computing, and their relationships with Cloud computing We then identify several challenges from the Cloud computing adoption perspective. Last, we will highlight the Cloud interoperability issue that deserves substantial further research and development.


title: A Slow-Wave Microstrip Line With a High-Q and a High Dielectric Constant for Millimeter-Wave CMOS Application

abstract: A slow-wave microstrip line (S-MSL) designed in 0.13 μm CMOS technology with a high-Q and a high dielectric constant is proposed in this letter. Grounded metal strips with two metal layers are located in zigzags in order to prevent penetration of the electric field into the silicon substrate. These metal strips equate the potentials of the ground planes and thus eliminate the parasitic coupled slotline mode without requiring additional air-bridges. Also, locating the ground planes in the SiO2 layers instead of on the top metal reduces the size of the gap between the signal line and ground planes relative to the conventional structure. This allows relaxation of the metal density rule of the CMOS processes. Measured results for the proposed S-MSL show that the relative permittivity is 25 and the quality factor ranges from 18 to 37.7 between 20 and 60 GHz. The wavelength of the measured 253 μm S-MSL is 4/λ at 60 GHz.


title: Time Series Segmentation of Paleoclimate Tipping Points by an Evolutionary Algorithm

abstract: Recent studies propose that some dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TP). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the segmentation of a paleoclimate time series to find segments sharing common patterns with the purpose of finding one or more kinds of segments corresponding to TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series together with a method to perform time series segmentation comparisons. Without a priori information, the method clusters together most of the TPs and avoids false positives, which is a promising result given the challenging nature of the problem.


title: TDR/TDT analysis by crosstalk in single and differential meander delay lines for high speed PCB applications

abstract: Meander (serpentine) delay lines are generally used for controlling the skew of the traces in high-speed Printed Circuit Board (PCB) applications. They consist of equal-length unit lines closely packed to each other. However the meander lines deteriorate the total time delay and the waveform distortion in the end of the lines, since each unit line is tightly coupled. In this paper, to predict accurate Time-Domain Reflectometry/Time- Domain Transmit (TDR/TDT) waveforms by crosstalk in the single and differential meander line, simple TDR/TDT equations are proposed in point of the signal integrity. The proposed TDR/TDT waveform equations are verified by using TDR/TDT measurements in the single and differential meander delay lines. Keywords-crosstalk; meander; delay; serpentine; differential line; Time-Domain Reflectometry; TDR; Time-Domain Transmit; TDT


title: Development of a flexible coupled spine mechanism for a small quadruped robot

abstract: Quadruped animals realize dynamic motions such as fast running and high and long jumping with the utilization of the flexibility of their bodies. It is known that the motions of their front and rear legs couple with the flexion and extension of their spine when running or jumping. In recent years, quadruped robots inspired by cats and cheetahs have been developed. In our group, a small quadruped robot has been developed with leg mechanisms which consist of spring and damper systems inspired by bi-articular muscle-tendon complexes of animals. The robot is able to perform various motions such as walking, running and jumping. In this paper, flexibility features as in animal bodies are to be introduced to make the robot run fast and jump long (or high) by coupling the motions of their spine and legs. In our design, the flexion and extension of the spine are realized by a spine joint with a torsion spring, and the coupling to the front and rear legs has been realized by cables. Driving the scapula or hip joints causes flexion of the spine and stores the elastic energy in the spring. The extension of the spine with the spring assists in driving the scapula or hip joints. As a result, in the jump experiment, the robot with a flexible spine can realize longer jump distance than that by a robot with a rigid spine.


European standards for the documentation of historic buildings and their relationship with CIDOC-CRM


title: Dynamic reconfigurable multicast interconnections by using radix-4 multistage networks in FPGA

abstract: To fully realize the benefits of rapid reconfiguration of embedded system, we often need a flexible interconnection system. This work proposes a low cost reconfigurable interconnection based on multistage interconnection networks (MINs) for FPGA systems. We show how radix 4 MINs can be efficiently implemented on top of 6 input LUTs. We also show that two parallel blocking MINs could behave as a non-blocking network even in presence of multicast connections. We further show how to route by using software or hardware-assistant approach. The hardware version has a low cost and a high performance. Consequently, route time can be reduced to few clock cycles. We further outline how a global interconnection based on MIN could be used for parallel datapath reconfigurable architectures.


title: Removing Shadows From Images using Retinex

abstract: The Retinex Theory first introduced by Edwin Land forty years ago has been widely used for a range of applications. It was first introduced as a model of our own visual processing but has since been used to perform a range of image processing tasks including illuminant correction, dynamic range compression, and gamut mapping. In this paper we show how the theory can be extended to perform yet another image processing task: that of removing shadows from images. Our method is founded on a simple modification to the original, path based retinex computation such that we incorporate information about the location of shadow edges in an image. We demonstrate that when the location of shadow edges is known the algorithm is able to remove shadows effectively. We also set forth a method for the automatic location of shadow edges which makes use of a 1-d illumination invariant image. In this case the location of shadow edges is imperfect but we show that even so, the algorithm does a good job of removing the shadows.


title: The ghost in the browser: Analysis of web-based malware

abstract: As more users are connected to the Internet and conduct their daily activities electronically, computer users have become the target of an underground economy that infects hosts with malware or adware for financial gain. Unfortunately, even a single visit to an infected web site enables the attacker to detect vulnerabilities in the user's applications and force the download a multitude of malware binaries. Frequently, this malware allows the adversary to gain full control of the compromised systems leading to the ex-filtration of sensitive information or installation of utilities that facilitate remote control of the host. We believe that such behavior is similar to our traditional understanding of botnets. However, the main difference is that web-based malware infections are pull-based and that the resulting command feedback loop is looser. To characterize the nature of this rising thread, we identify the four prevalent mechanisms used to inject malicious content on popular web sites: web server security, user contributed content, advertising and third-party widgets. For each of these areas, we present examples of abuse found on the Internet. Our aim is to present the state of malware on the Web and emphasize the importance of this rising threat.


title: Centralized versus distributed schedulers for multiple bag-of-task applications

abstract: Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where communication is restricted to a tree embedded in the network, and the applications consist of a large number of independent tasks that originate at the tree's root. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary for different applications. Each application is given a weight that quantifies its relative value. The goal of scheduling is to maximize throughput while executing tasks from each application in the same ratio as their weights. We can find the optimal asymptotic rates by solving a linear program that expresses all necessary problem constraints, and we show how to construct a periodic schedule. For single-level trees, the solution is characterized by processing tasks with larger communication-to-computation ratios at children with larger bandwidths. For multi-level trees, this approach requires global knowledge of all application and platform parameters. For large-scale platforms, such global coordination by a centralized scheduler may be unrealistic. Thus, we also investigate decentralized schedulers that use only local information at each participating resource. We assess their performance via simulation, and compare to a centralized solution obtained via linear programming. The best of our decentralized heuristics achieves the same performance on about two-thirds of our test cases, but is far worse in a few cases. While our results are based on simplistic assumptions and do not explore all parameters (such as buffer size), they provide insight into the important question of fairly and optimally co-scheduling heterogeneous applications on heterogeneous grids.


Night Time Vehicle Detection Using Tail Lights : A Survey


title: A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots

abstract: We have recently seen significant advancements in the development of robotic machines that are designed to assist people with their daily lives. Socially assistive robots are now able to perform a number of tasks autonomously and without human supervision. However, if these robots are to be accepted by human users, there is a need to focus on the form of human–robot interaction that is seen as acceptable by such users. In this paper, we extend our previous work, originally presented in Ruiz-Garcia et al. (in: Engineering applications of neural networks: 17th international conference, EANN 2016, Aberdeen, UK, September 2–5, 2016, proceedings, pp 79–93, 2016. https://doi.org/10.1007/978-3-319-44188-7_6), to provide emotion recognition from human facial expressions for application on a real-time robot. We expand on previous work by presenting a new hybrid deep learning emotion recognition model and preliminary results using this model on real-time emotion recognition performed by our humanoid robot. The hybrid emotion recognition model combines a Deep Convolutional Neural Network (CNN) for self-learnt feature extraction and a Support Vector Machine (SVM) for emotion classification. Compared to more complex approaches that use more layers in the convolutional model, this hybrid deep learning model produces state-of-the-art classification rate of \(96.26\%\), when tested on the Karolinska Directed Emotional Faces dataset (Lundqvist et al. in The Karolinska Directed Emotional Faces—KDEF, 1998), and offers similar performance on unseen data when tested on the Extended Cohn–Kanade dataset (Lucey et al. in: Proceedings of the third international workshop on CVPR for human communicative behaviour analysis (CVPR4HB 2010), San Francisco, USA, pp 94–101, 2010). This architecture also takes advantage of batch normalisation (Ioffe and Szegedy in Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167, 2015) for fast learning from a smaller number of training samples. A comparison between Gabor filters and CNN for feature extraction, and between SVM and multilayer perceptron for classification is also provided.