title: 2D/3D image registration using regression learning

abstract: In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object's 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projection(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region's motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method's application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.


title: Predictive data mining in clinical medicine: Current issues and guidelines

abstract: Background: The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as ‘data mining’ offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments. Purpose: The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine. Methods: We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons. Results: The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this


title: Suspicious activity reporting using dynamic bayesian networks

abstract: Abstract Suspicious activity reporting has been a crucial part of anti-money laundering systems. Financial transactions are considered suspicious when they deviate from the regular behavior of their customers. Money launderers pay special attention to keep their transactions as normal as possible to disguise their illicit nature. This may deceive the classical deviation based statistical methods for finding anomalies. This study presents an approach, called SARDBN (Suspicious Activity Reporting using Dynamic Bayesian Network), that employs a combination of clustering and dynamic Bayesian network (DBN) to identify anomalies in sequence of transactions. SARDBN applies DBN to capture patterns in a customer’s monthly transactional sequences as well as to compute an anomaly index called AIRE (Anomaly Index using Rank and Entropy). AIRE measures the degree of anomaly in a transaction and is compared against a pre-defined threshold to mark the transaction as normal or suspicious. The presented approach is tested on a real dataset of more than 8 million banking transactions and has shown promising results.


title: Argument Mining with Structured SVMs and RNNs

abstract: We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.


title: Airborne Communication Networks: A Survey

abstract: Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, high-altitude platforms (HAPs), and low-altitude platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand for the seamless integration of heterogeneous networks such that the network quality-of-service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. To solve these challenges, extensive research has been conducted. The objective of this special issue is to disseminate the contributions in the field of ACNs. To present this special issue with the necessary background and offer an overall view of this field, three key areas of ACNs are covered. Specifically, this paper covers LAP-based communication networks, HAP-based communication networks, and integrated ACNs. For each area, this paper addresses the particular issues and reviews major mechanisms. This paper also points out future research directions and challenges.


title: Dietary polyphenols as potential nutraceuticals in management of diabetes: a review

abstract: In recent years, there is growing evidence that plant-foods polyphenols, due to their biological properties, may be unique nutraceuticals and supplementary treatments for various aspects of type 2 diabetes mellitus. In this article we have reviewed the potential efficacies of polyphenols, including phenolic acids, flavonoids, stilbenes, lignans and polymeric lignans, on metabolic disorders and complications induced by diabetes. Based on several in vitro, animal models and some human studies, dietary plant polyphenols and polyphenol-rich products modulate carbohydrate and lipid metabolism, attenuate hyperglycemia, dyslipidemia and insulin resistance, improve adipose tissue metabolism, and alleviate oxidative stress and stress-sensitive signaling pathways and inflammatory processes. Polyphenolic compounds can also prevent the development of long-term diabetes complications including cardiovascular disease, neuropathy, nephropathy and retinopathy. Further investigations as human clinical studies are needed to obtain the optimum dose and duration of supplementation with polyphenolic compounds in diabetic patients.


title: Designing for Depth Perceptions in Augmented Reality

abstract: Augmented reality technologies allow people to view and interact with virtual objects that appear alongside physical objects in the real world. For augmented reality applications to be effective, users must be able to accurately perceive the intended real world location of virtual objects. However, when creating augmented reality applications, developers are faced with a variety of design decisions that may affect user perceptions regarding the real world depth of virtual objects. In this paper, we conducted two experiments using a perceptual matching task to understand how shading, cast shadows, aerial perspective, texture, dimensionality (i.e., 2D vs. 3D shapes) and billboarding affected participant perceptions of virtual object depth relative to real world targets. The results of these studies quantify trade-offs across virtual object designs to inform the development of applications that take advantage of users' visual abilities to better blend the physical and virtual world.


title: Deep learning for face anti-spoofing: An end-to-end approach

abstract: The importance of face anti-spoofing algorithms in biometric authentication systems is becoming indispensable. Recently, the success of Convolution Neural Networks (CNN) in key application areas of computer vision has encouraged its use in face biometrics for face anti-spoofing and verification applications. However, small training data has restricted the use of deep CNN architectures for face anti-spoofing applications. In this paper, we develop an end-to-end CNN architecture for face anti-spoofing application. i.e. a deep CNN architecture which directly map the raw input face images to the corresponding output classes. Additionally, an efficient training strategy has been proposed to enable the use of deeper CNN structures for face anti-spoofing applications and to enable the growth of training data in autonomous way. For training a CNN architecture, we propose a 50RS-30SeC-1E (50 Random Samples-30 Sub-epochs Count-1Epoch) training strategy. The training data is randomly sampled during each forward-pass through the CNN architecture and 30 such passes counts for 1 complete epoch. An 11-layer VGG network with 2 derived VGG-11 networks have been trained for face anti-spoofing on CASIA-FASD dataset. Experimental results show significant improvement on various face-spoofing scenarios. A 3% improvement over state of the art approaches has been reported for Overall Test (OT) while achieving a lowest EER of 5%.


title: An Accelerator for High Efficient Vision Processing

abstract: In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are convolutional neural networks (CNNs), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is highly energy-efficient. We present a single-core implementation down to the layout at 65 nm, with a modest footprint of 5.94mm $^{\boldsymbol {2}}$ and consuming only 336mW, but still about $\boldsymbol {30\times }$ faster than high-end GPUs. For visual processing with higher resolution and frame-rate requirements, we further present a multicore implementation with elevated performance.


title: Archaeological Evidence for the Emergence of Language, Symbolism, and Music—An Alternative Multidisciplinary Perspective

abstract: In recent years, there has been a tendency to correlate the origin of modern culture and language with that of anatomically modern humans. Here we discuss this correlation in the light of results provided by our first hand analysis of ancient and recently discovered relevant archaeological and paleontological material from Africa and Europe. We focus in particular on the evolutionary significance of lithic and bone technology, the emergence of symbolism, Neandertal behavioral patterns, the identification of early mortuary practices, the anatomical evidence for the acquisition of language, the development of conscious symbolic storage, the emergence of musical traditions, and the archaeological evidence for the diversification of languages during the Upper Paleolithic. This critical reappraisal contradicts the hypothesis of a symbolic revolution coinciding with the arrival of anatomically modern humans in Europe some 40,000 years ago, but also highlights inconsistencies in the anatomically–culturally modern equation and the potential contribution of anatomically “pre-modern” human populations to the emergence of these abilities. No firm evidence of conscious symbolic storage and musical traditions are found before the Upper Paleolithic. However, the oldest known European objects that testify to these practices already show a high degree of complexity and geographic variability suggestive of possible earlier, and still unrecorded, phases of development.


title: ‘Sense of presence’ experiences in bereavement and their relationship to mental health: A critical examination of a continuing controversy

abstract: The experience of sensing the presence of the deceased is a common occurrence following bereavement. Although this experience tends to be reported as positive and meaningful by perceivers, for most of the twentieth century it has been described in pathologising terms in the bereavement literature. Recent decades have seen the publication of numerous studies that point to normality of this experience and its potential benefits for the bereaved perceivers' wellbeing. However, there is an ongoing debate within bereavement scholarship regarding the nature and healthiness of this experience. This chapter critically examines the extant literature concerning 'sense of presence' experiences and draws attention to the diversity of definitions and conceptualisations within which this experience can be interpreted. Research from a variety of perspectives, including attachment theory and the continuing bonds perspective, is discussed and emerging evidence is reported which suggests that those who can make sense of their experience within culturally-sanctioned (spiritual) conceptual frameworks enjoy greater benefits as a result. The discussion then focuses on meaning-making concerns with regard to this phenomenon and concludes with relevant practice recommendations.


title: An Improved DRFM System Based on Digital Channelized Receiver

abstract: An improved Digital Radio Frequency Memory (DRFM) system based on digital channelized receiver is stated, which employs digital channelized receiver to slow down the high speed input data flow, and uses Digital Up Converter (DUC) to generate digital intermediate frequency (IF) signal from digital baseband signal. This system can achieve broad instantaneous frequency coverage, reduce the demand for the memory capacity in DRFM system, and achieve real-time jamming information modulation in radar jamming applications. The common DRFM system is first discussed, then the realization structure for this improved DRFM is stated, the realization model of digital channelized receiver and high speed DUC are also proposed.


title: Molecular Generation with Recurrent Neural Networks (RNNs)

abstract: The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approximately 10^8 compounds. This discrepancy has led to an interest in generating virtual libraries using hand crafted chemical rules and fragment based methods to cover a larger area of chemical space and generate chemical libraries for use in in silico drug discovery endeavors. Here it is explored to what extent a recurrent neural network with long short term memory cells can figure out sensible chemical rules and generate synthesizable molecules by being trained on existing compounds encoded as SMILES. The networks can to a high extent generate novel, but chemically sensible molecules. The properties of the molecules are tuned by training on two different datasets consisting of fragment like molecules and drug like molecules. The produced molecules and the training databases have very similar distributions of molar weight, predicted logP, number of hydrogen bond acceptors and donors, number of rotatable bonds and topological polar surface area when compared to their respective training sets. The compounds are for the most cases synthesizable as assessed with SA score and Wiley ChemPlanner.


title: A discrete robust adaptive control of a tilt-rotor UAV for an enlarged flight envelope

abstract: This work presents the modeling and control of a tilt-rotor UAV with tail controlled surfaces for path tracking with improved forward flight performance. A nonlinear dynamic model is obtained through Euler-Lagrange formulation and linearized around a reference trajectory in order to obtain a linear parameter-varying model. The forward velocity is treated as an uncertain parameter, and the linearized system is represented as a set of polytopes with nonempty intersection regarding the forward velocity. Feedback gains are computed for each of the vertices of the polytopes using a discrete mixed control approach with pole placement constraints strategy. The resultant feedback gain, which is able to control the system inside a given polytope, is obtained using an adaptive law through an optimal convex combination of the vertices' gains. Finally, an adaptive mixing scheme is used to smoothly schedule the feedback gains between the polytopes.


title: IoTAbench: an Internet of Things Analytics Benchmark

abstract: The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This ``Internet-of-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer ``Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform ``apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.


title: Speed Estimation and Abnormality Detection from Surveillance Cameras

abstract: Motivated by the increasing industry trends towards autonomous driving, vehicles, and transportation we focus on developing a traffic analysis framework for the automatic exploitation of a large pool of available data relative to traffic applications. We propose a cooperative detection and tracking algorithm for the retrieval of vehicle trajectories in video surveillance footage based on deep CNN features that is ultimately used for two separate traffic analysis modalities: (a) vehicle speed estimation based on a state of the art fully automatic camera calibration algorithm and (b) the detection of possibly abnormal events in the scene using robust optical flow descriptors of the detected vehicles and Fisher vector representations of spatiotemporal visual volumes. Finally we measure the performance of our proposed methods in the NVIDIA AI CITY challenge evaluation dataset.


title: Effects of a counselling intervention to improve contraception in deprived neighbourhoods: a randomized controlled trial

abstract: Background: This study aims to evaluate the effects of a community-based counselling intervention to improve contraception use among immigrant and native residents in deprived neighbourhoods. : Randomized controlled trial. Women aged 14-49 years and men aged 14-39 years from two low-income neighbourhoods with high proportion of immigration in Barcelona (Catalonia Spain) who had not undergone irreversible contraception and were not planning a pregnancy were recruited (2011-13). A culturally developed and theoretically based brief counselling intervention was delivered in community settings. The primary outcome was the consistent use of effective contraceptive methods (optimal use). Secondary outcomes were the incorrect use of effective methods and the use of less effective methods stratified by sex and migrant status. Differences within subgroups from baseline to the 3-month follow-up were analysed by intention to treat and per protocol. The effects were assessed with adjusted robust Poisson regressions. : The study enrolled and randomized 746 eligible participants. There were no differences between the intervention and control groups in demographic characteristics. Optimal use significantly increased in men women immigrants and natives in the intervention group with no changes in the control group. In the intervention group inconsistent use of effective methods decreased by 54.9% and that of less effective methods by 47.2%. The overall adjusted prevalence ratio of optimal use in the intervention group versus the control group was 1.138 (95% CI: 1.010-1.284). : This brief counselling intervention increased the consistent use of effective contraception in low-income neighbourhoods with a high proportion of immigration.


Retrace: Collecting execution trace with virtual machine deterministic replay


Achieving a Billion Requests Per Second Throughput on a Single Key-Value Store Server Platform via Full Stack Architecting


title: Compact SIW bandpass filter for wideband millimeter-wave applications

abstract: a compact substrate intergrated waveguide (SIW) bandpass filter (BPF) is proposed for millimeter-wave (mmW) application. Four vias are located inside the SIW cavity in the form of rotationally symmetrical structure to provide transmission zeros at upper-stopband. As design examples, one substrate integrated waveguide bandpass filter prototype using Low Temperature Co-fired Ceramic (LTCC) on single layer substrate is designed and experimentally examined. The measured results show that the filter achieves a passband insertion loss of 0.8 dB at 50 GHz, return loss below -13dB and 3-dB fractional bandwidth of 30%.


Electronic Band Structure of Transparent Conductor: Nb-Doped Anatase TiO2


title: Young attitudes towards F-commerce advertising

abstract: Facebook provides the social needs of people to interact with each other. Hence, the companies can advertise their products as long as keeping in touch with their customers on Facebook. The issue in this study is the young consumer's attitude towards advertising and the trust in e-commerce as the advertisement publisher and Facebook itself. This study used a purposive sampling method and selected 300 respondents to respond the questionnaires. Pearson Correlation measured the strength and relation between demographic factors (gender, age and education) and dependent variable. A multiple regression analysis examined the relationship between independent and dependent variables. The result showed that gender and age had a positive correlation with the attitude towards F-commerce advertising. The variable of entertainment, irritation, trust in Facebook, and trust in e-commerce on Facebook had the significant impact on attitude towards F-commerce advertising. Research limitations and further research directions are presented at the end of the paper.


title: Design of Wearable Patch Antenna for Wireless Body Area Networks

abstract: Wireless body area networks are being widely used due to the increase in the use of wireless networks and various electrical devices. A Wearable Patch antenna is used for enhancement of various applications for WBAN. In this paper, a low profile wearable microstrip patch antenna is designed and suggested for constant observation of human vital signs such as blood pressure, pulse rate and body temperature using wireless body area network (WBAN) technology. The operating frequency of the antenna is taken as 2.45 GHz which lies in industrial, scientific and medical (ISM) frequency band. Polyester textile fabric with a relative permittivity of 1.44 and thickness of 2.85 mm is used as a substrate material. The proposed antenna is designed to achieve better return loss, VSWR, gain and low value of specific absorption rate (SAR) as compare to other existing wearable antenna. The achieved antenna return loss at 2.45 GHz is about -10.52 dB and gain of 7.81 dB. The VSWR value achieved at 2.45 GHz is 1.84, which is good in terms of good impedance matching. Other antenna field parameters like 2D and 3D gain, radiation pattern, and SAR value have been calculated. High-Frequency Structure Simulator (HFSS) is used to design and simulate the proposed antenna.


title: Analog Circuit Design Optimization based on Symbolic Simulation and Simulated Annealing

abstract: A methodology for the automatic design optimization of analog integrated circuits is presented. A non-fixed-topology approach is realized by combining the optimization program OPTIMAN with the symbolic simulator ISAAC. After selecting a circuit topology, the user invokes ISAAC to model the circuit. ISAAC generates both exact and simplified analytic expressions, describing the circuit's behavior. The model is then passed to the design optimization program OPTIMAN. This program is based on a generalized formulation of the analog design problem. For the selected topology, the independent design variables are automatically extracted and OPTIMAN sizes all elements to satisfy the performance constraints, thereby optimizing a user-defined design objective. The global optimization method used on the analytic circuit models is simulated annealing. Practical examples show that OPTIMAN quickly designs analog circuits, closely meeting the specifications, and that it is a flexible and reliable design and exploration tool. >


title: A Hardware Platform for Evaluating Low-Energy Multiprocessor Embedded Systems Based on COTS Devices

abstract: Embedded systems are usually energy constrained. Moreover, in these systems, increased productivity and reduced time to market are essential for product success. To design complex embedded systems while reducing the development time and cost, there is a great tendency to use commercial off-the-shelf (“COTS”) devices. At system level, dynamic voltage and frequency scaling (DVFS) is one of the most effective techniques for energy reduction. Nonetheless, many widely used COTS processors either do not have DVFS or apply DVFS only to processor cores. In this paper, an easy-to-implement COTS-based evaluation platform for low-energy embedded systems is presented. To achieve energy saving, DVFS is provided for the whole microcontroller (including core, phase-locked loop, memory, and I/O). In addition, facilities are provided for experimenting with fault-tolerance techniques. The platform is equipped with energy measurement and debugging equipment. Physical experiments show that applying DVFS on the whole microcontroller provides up to 47% and 12% energy saving compared with the sole use of dynamic power management and applying DVFS only on the core, respectively. Although the platform is designed for ARM-based embedded systems, our approach is general and can be applied to other types of systems.


title: Restaurant Rating: Industrial Standard and Word-of-Mouth -- A Text Mining and Multi-dimensional Sentiment Analysis

abstract: AAA Restaurant Diamond Rating Guidelines (which is regarded as industry standards) rate a restaurant in three aspects: food, service, and decor/ambience. Drawing upon extant literature, we argue that special contexts and pricing are two other major aspects in restaurant rating in addition to aforementioned three aspects. We tested our hypotheses based on our text mining and sentiment analysis of 268, 442 customer reviews of 7,508 restaurants on Yelp.com, A form of digital word-of-mouth. Results from fitting a multilevel model showed that the sentiments about each of these five aspects alone explained about 28% of the explainable between-restaurant variances, and 12% of the explainable within-restaurant variances of the restaurants' star ratings. With other level and control variables, the multilevel model can explain more than 53% between-restaurant variances and 28% within-restaurant variances.


title: High-performance complex event processing over hierarchical data

abstract: While Complex Event Processing (CEP) constitutes a considerable portion of the so-called Big Data analytics, current CEP systems can only process data having a simple structure, and are otherwise limited in their ability to efficiently support complex continuous queries on structured or semistructured information. However, XML-like streams represent a very popular form of data exchange, comprising large portions of social network and RSS feeds, financial feeds, configuration files, and similar applications requiring advanced CEP queries. In this article, we present the XSeq language and system that support CEP on XML streams, via an extension of XPath that is both powerful and amenable to an efficient implementation. Specifically, the XSeq language extends XPath with natural operators to express sequential and Kleene-* patterns over XML streams, while remaining highly amenable to efficient execution. In fact, XSeq is designed to take full advantage of the recently proposed Visibly Pushdown Automata (VPA), where higher expressive power can be achieved without compromising the computationally attractive properties of finite state automata. Besides the efficiency and expressivity benefits, the choice of VPA as the underlying model also enables XSeq to go beyond XML streams and be easily applicable to any data with both sequential and hierarchical structures, including JSON messages, RNA sequences, and software traces. Therefore, we illustrate the XSeq's power for CEP applications through examples from different domains and provide formal results on its expressiveness and complexity. Finally, we present several optimization techniques for XSeq queries. Our extensive experiments indicate that XSeq brings outstanding performance to CEP applications: two orders of magnitude improvement is obtained over the same queries executed in general-purpose XML engines.


INTERNET DATING ADS : SEX , ETHNICITY , AGE-RELATED DIFFERENCES , AND SUPPORT FOR EVOLUTIONARY THEORY


title: The effect of pinealectomy and leptin hormone on the proliferation and apoptosis activation in Syrian hamster testis in different photoperiods.

abstract: The effects of pinealectomy and leptin hormone on proliferative and apoptotic processes in the epithelia of testicular seminiferous tubules of Syrian hamsters have been investigated. Proliferative and apoptotic processes were assessed semi-quantitatively by proliferating cell nuclear antigen (PCNA) and caspase-3 immune stainings. Animals used in the study were divided into four groups; control, pinealectomy (PinX), leptin-treated (10 microg/mL/day/kg body weight, intraperitoneally) and pinealectomy + leptin groups. Half of the hamsters in each group were exposed to short and the other half to long photoperiods for 8 weeks. In short photoperiod, PCNA activity especially in spermatogonia was significantly higher in the pinealectomy and leptin-treated groups compared with the control group. Histological score (HSCORE) value of PCNA in the PinX + leptin group was lower than those of PinX and leptin-treated groups. HSCORE value of caspase-3 in PinX and PinX + leptin groups was increased. In the long photoperiod, PCNA activation in the PinX group was significantly lower than the control group while the differences between the controls and other groups were not significant. The difference between the increases in caspase-3 activity in the PinX and control groups was significant. Thus, it was observed that photoperiods had no effect on the proliferation activity in the control groups. The inhibiting effect of short photoperiod on testis was not observed throughout 8 weeks. PinX eliminated the inhibiting effect of short photoperiod but did not alter the stimulating effect of long photoperiod. Leptin did not show any effect in long photoperiod but decreased proliferation by stimulating melatonin in short photoperiod.


title: User-defined gestures for augmented reality

abstract: Recently there has been an increase in research of hand gestures for interaction in the area of Augmented Reality (AR). However this research has focused on developer designed gestures, and little is known about user preference and behavior for gestures in AR. In this paper, we present the results of a guessability study focused on hand gestures in AR. A total of 800 gestures have been elicited for 40 selected tasks from 20 partic-ipants. Using the agreement found among gestures, a user-defined gesture set was created to guide design-ers to achieve consistent user-centered gestures in AR.


title: Economic Analysis of Cryptocurrency Backed Money Laundering

abstract: This paper reports on our research towards an economic analysis of money laundering schemes utilizing cryptocurrencies, which are convertible decentralized virtual currencies based on cryptographic operations. They gain ground as means to offer enterprises and its customers new payment methods, investing opportunities and some are even intended as substitutes for centrally controlled governmentissued fiat currencies. Our starting point is the observation that their increasing popularity attracts the attention of practitioners and scholars, particularly because of raising anti-money laundering concerns. Consequently, work has already been conducted in this area, mainly focusing on implications on anti-money laundering efforts. However, we argue that the potential benefits for criminal individuals are an important, yet neglected factor in the dissemination of cryptocurrencies as money laundering instrument. Addressing this issue, the paper firstly presents the structure of the money laundering process and introduces prevailing anti money-laundering controls. This forms the basis for the subsequent analysis of contextual and transactional factors with respect to their influence on the incentives of criminals to utilize cryptocurrencies for money laundering. This aims at providing an answer to the open question, whether cryptocurrencies constitute a driver for money laundering.


title: Demystifying Neural Style Transfer

abstract: Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. Specifically, we theoretically show that matching the Gram matrices of feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with the second order polynomial kernel. Thus, we argue that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. To further support our standpoint, we experiment with several other distribution alignment methods, and achieve appealing results. We believe this novel interpretation connects these two important research fields, and could enlighten future researches.


title: New generation 6.5 kV SiC power MOSFET

abstract: Following the recent advances in the production of 3.3 kV and 10 kV SiC power MOSFETs, Wolfspeed launched an effort to develop a new generation 6.5 kV SiC power MOSFET to fill the medium-to high-voltage fast switching device void in applications such as rail traction and mediumvoltage motor drives. The characteristics of the new generation 6.5 kV SiC power MOSFET rated at a drain current of 30 A, are presented for the first time. The device shows a typical R DS, on th of 3.6 V (at I D =1 mA) measured at room temperature. Our 6.5 kV SiC power MOSFET includes a useable body diode with low V f and reverse-recovery charge at operating temperature. The use of the body diode as a freewheeling device is demonstrated, eliminating the need for an external anti-parallel freewheeling SiC JBS diode, hence, reducing size and cost of a system. The effects of changing the 6.5 kV SiC power MOSFET gate runner design on the internal on-chip gate resistance and the MOSFET switching capability are presented.


Portfolio selection in stochastic environments, Working Paper


title: Extended Kalman filter based grid synchronization in the presence of voltage unbalance for smart grid

abstract: In this paper, grid synchronization for gird-connected power generation systems in the presence of voltage unbalance and frequency variation is considered. A new extended Kalman filter based synchronization algorithm is proposed to track the phase angle of the utility network. Instead of processing the three-phase voltage signal in the abc natural reference frame and resorting to the symmetrical component transformation as in the traditional way, the proposed algorithm separates the positive and negative sequences in the transformed αβ stationary reference frame. Based on the obtained expressions in αβ domain, an EKF is developed to track both the in-phase and quadrature sinusoidal signals together with the unknown frequency. An estimate of the phase angle of the positive sequence is then obtained. As a by-product, estimates of phase angle of negative sequence and grid frequency are also computed. Compared to the commonly used scheme, the proposed algorithm has a simplified structure. The good performance is supported by computer simulations.


title: Stereological and Histopathological Study of Ovarian Tissue after Hydro-alcoholic Extract of Artemisia Plants Compared with Metformin in Diabetic Rats

abstract: Introduction: Diabetes is an endocrine and metabolic disorder, a variety of effects on the structure and function of many body systems leaves. Reproductive system, the rule is different. The aim of this study was to investigate the effects of the herb Artemisia extracts of ovarian tissue is improved due to diabetes complications. Materials and Methods: 50 rats, adult female aged approximately 10 weeks of Wistar rats randomly divided into 5 groups: control, diabetic (streptozotocin 50 mg/kg), diabetic + extract of Artemisia (200 & 300 mg/kg) and a positive control (metformin 250mg/kg) were included. The streptozotocin was administered intraperitoneally, and other materials as gavage. Treated mice continued for 8 weeks. At the beginning and end of the experiment, mice were weighed and blood glucose was measured, so the mice were dissected and the right ovary weighed after fixation in 3% formaldehyde and cutting and preparing for special studies were conducted respectively. Mason with three more chrome slides, were stained with hematoxylin eosin and periodic acid Schiff. Few studies include estimates of mean ovarian volume and follicle determines the quality and histopathological studies were performed. The results indicate an increase in blood sugar levels, ovarian volume, the percentage of atretic follicles quantitatively and cause congestion, hyperemia, and increased thickness of the germinal epithelium and fibrosis in diabetic rats was qualitatively, in the other groups, these parameters showed a decreasing trend (p ≤ 0.05). Conclusion: This study suggests that as plant extract could significantly reduce the complications of diabetes in female reproductive system changes. Key words: diabetes, ovarian, moose, mountain sagebrush, stereology, histopathology


title: Correlated changes in body shape after five generations of selection to improve growth rate in a breeding program for Nile tilapia Oreochromis niloticus in Brazil

abstract: Body shape is a commercial trait of great interest as it impacts profit and productivity of aquaculture enterprises. In the present study, we examined correlated changes in two measures of body shape (depth to length ratio, DL-R and ellipticity of mid sagittal plane, EL-H) from a selection program for high daily weight gain in a Nile tilapia population reared in freshwater cages in Brazil. Genetic parameters for body shape and its genetic association with growth traits (body weight and daily gain) were also estimated from 8,725 individuals with growth performance recorded over five generations from 2008 to 2013. Mixed model analysis showed that the selection program resulted in substantial improvement in growth performance (about 4 % genetic gain per generation or per year) and also brought about trivial changes in body shape. The heritabilities ranged from 0.470 to 0.564 for growth traits and 0.180 to 0.289 for body shape. The common family effects were low for all traits studied, accounting for only 3–11 % of total phenotypic variance. The genetic correlations between body shape and growth traits were weak, i.e., −0.385 between EL-H and growth traits and 0.28 between DL-R and body weight or daily gain. Strong and negative genetic association was found between the two body shape traits (rg = -−0.955). Harvest body weight and daily gain are essentially the same traits, as indicated by the close to one genetic correlations between the two characters. Our results demonstrated that the selection process to increase growth rate had small, but slowly constant effect in body shape traits; and in the long term, the fish would have become rotund.


title: Overview of the NLPCC-ICCPOL 2016 Shared Task: Open Domain Chinese Question Answering

abstract: In this paper, we give the overview of the open domain Question Answering (or open domain QA) shared task in the NLPCC-ICCPOL 2016. We first review the background of QA, and then describe two open domain Chinese QA tasks in this year’s NLPCC-ICCPOL, including the construction of the benchmark datasets and the evaluation metrics. The evaluation results of submissions from participating teams are presented in the experimental part.


title: A Dual-Band Circularly Polarized Stub Loaded Microstrip Patch Antenna for GPS Applications

abstract: A single-feed low-profile and easy to fabricate circularly polarized microstrip patch antenna has been developed for GPS applications. For dual frequency operation, four slots are etched near edges of the patch and a crossed slot etched in the center for generating circular polarization. In order to reducing the frequency ratio of two frequency bands of the antenna, the patch is loaded by four short circuit microstrip stubs. The paper reports several simulation results that confirm the desired characteristics of the antenna. Using stub loading, the frequency ratio of two bands of the antenna can be, even, reduced to 1.1.


title: POI Recommendation: A Temporal Matching between POI Popularity and User Regularity

abstract: Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user's availability. While there are some prior studies which included the temporal effect into POI recommendations, they overlooked the compatibility between time-varying popularity of POIs and regular availability of users, which we believe has a non-negligible impact on user decision-making. To this end, in this paper, we present a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we first profile the temporal popularity of POIs to show when a POI is popular for visit by mining the spatio-temporal human mobility and POI category data. Secondly, we propose latent user regularities to characterize when a user is regularly available for exploring POIs, which is learned with a user-POI temporal matching function. Finally, results of extensive experiments with real-world POI check-in and human mobility data demonstrate that our proposed user-POI temporal matching method delivers substantial advantages over baseline models for POI recommendation tasks.


title: Feature extraction and classification of EEG for automatic seizure detection

abstract: One of the many challenges in the automated detection of epileptic seizures is to draw a line of demarcation between seizure activity and non-seizure activity. To accomplish this task, identification of related features and there extraction from the EEG plays a key role. The work presented in this paper is a part of an overall effort going on to develop a new method for automated detection of seizures. A wavelet based feature extraction technique has been adopted. Statistical features, Inter-quartile range (IQR) and Median Absolute Deviation (MAD) also form part of the feature vector. The algorithm was evaluated on 23 subjects with 195 seizures. The results gave an average detection accuracy of 96.5%. The database used is the CHB-MIT scalp EEG database. All the calculations were performed on Matlab.


title: Defining Mobile Learning in the Higher Education Landscape

abstract: Introduction The evolution of handheld portable devices and wireless technology has resulted in radical changes in the social and economic lifestyles of modern people. Today, many technological devices are produced in portable form and people have become accustomed to them. These devices are reshaping users daily lives in different ways. But the development of digital technologies has so far been limited to social communication and few people have regarded mobile learning as a core pedagogical activity in higher institutions of learning. Although this model has been used as a minor adjunct to learning activities such as lectures and assignments, it is still not the primary mode of delivery in higher education. Currently, the instructional technology transmitted by means of mobile technology is mainly social and, to a lesser extent, economic. Advanced mobile devices such as "smart" cellular telephones are very popular among people primarily because they are wireless and portable. These functionalities enable users to communicate while on the move. The popularity of these devices is therefore a consequent of their ability to function at multiple levels. Moreover, the intense commercial competitiveness in the mobile device industry is forcing manufacturers to be very innovative, constantly striving to introduce new features that can give them a competitive edge. Against this backdrop, visionary educators, designers and developers should begin to consider the implications of these devices for the modern teaching and learning environment. In such an environment, contents and services can be relayed to a university student by personal wireless mobile devices. This will add another layer to the personal computer-based model of teaching and learning. This also means e-learning will take place in conditions that will be radically different from those educators and learners are familiar with. Providing university students with services, content instruction and information outside the traditional learning space is becoming more acceptable among education providers who predicate their services on the routine use of advanced information and communication technologies. This article seeks to provide a comprehensive definition of mobile learning and attempts to understand why actual learning practices are changing very rapidly while the learning theories that support educational practices are not. To find viable answers, the article will describe the different components of mobile learning that reflect on the increasing mobility of learners, learning and learner technology. The emergence of revolutionary technologies has had a significant impact on educational technology. It has increased the potential of e-learning as a mode of delivery in education. By definition, mobile learning (or "m- learning") is learning by means of wireless technological devices that can be pocketed and utilised wherever the learner's device is able to receive unbroken transmission signals (Attewell & Savill-Smith, 2005). For example, Laouris and Eteokleous (2005) have reiterated the need for a definition of mobile learning that takes into account all the aspects of the mobile learning process Nyir (2002) has also contributed to a philosophy of mobile learning that relies on Dewey's insights into democracy and education. Nyir and his contemporaries argue that mobile devices are responsible for undermining and, in many cases, eliminating the fixity of traditional classrooms such as lecture halls, laboratories and all the paraphernalia of traditional education. For decades, these traditional spaces have depended on static models of communication and devices for subject delivery. Significantly, mobile devices are revolutionary because they transcend the boundaries of the structural stasis of classrooms and lecture halls and their associated modes of communication--they do not have to be confined to one particular place in order to be effective. …


title: PIkit: A New Kernel-Independent Processor-Interconnect Rootkit

abstract: The goal of rootkit is often to hide malicious software running on a compromised machine. While there has been significant amount of research done on different rootkits, we describe a new type of rootkit that is kernel-independent – i.e., no aspect of the kernel is modified and no code is added to the kernel address space to install the rootkit. In this work, we present PIkit – Processor-Interconnect rootkit that exploits the vulnerable hardware features within multi-socket servers that are commonly used in datacenters and high-performance computing. In particular, PIkit exploits the DRAM address mapping table structure that determines the destination node of a memory request packet in the processorinterconnect. By modifying this mapping table appropriately, PIkit enables access to victim’s memory address region without proper permission. Once PIkit is installed, only user-level code or payload is needed to carry out malicious activities. The malicious payload mostly consists of memory read and/or write instructions that appear like “normal” user-space memory accesses and it becomes very difficult to detect such malicious payload. We describe the design and implementation of PIkit on both an AMD and an Intel x86 multi-socket servers that are commonly used. We discuss different malicious activities possible with PIkit and limitations of PIkit, as well as possible software and hardware solutions to PIkit.


FUZZY Based PID Controller for Speed Control of D . C . Motor Using LabVIEW


title: Infusing computational thinking into the middle- and high-school curriculum

abstract: In recent years there have been significant efforts to revamp undergraduate and K-12 curricula to emphasize computational thinking, a term popularized by Jeannette Wing in 2006. We describe work introducing and enhancing computational thinking activities and assessments in the middle- and high-school curriculum at the University of Chicago Lab Schools. In total six courses were altered as a part of the Computational Thinking across the Curriculum Project: middle-school and high-school computer science, and high-school Latin, graphic arts, English, and history. We detail the modifications to the curriculum and discuss the successes and challenges of the project.


Value of Head CT Scan in the Emergency Department in Patients with Vertigo without Focal Neurological Abnormalities


title: Multi-entity Sentiment Scoring

abstract: We present a compositional framework for modelling entity-level sentiment (sub)contexts, and demonstrate how holistic multi-entity polarity scoring emerges as a by-product of compositional sentiment parsing. A data set of five annotators’ multi-entity judgements is presented, and a human ceiling is established for the challenging new task. The accuracy of an initial implementation, which includes both supervised learning and heuristic distance-based scoring methods, is 5.6∼6.8 points below the human ceiling amongst sentences and 8.1∼8.7 points amongst phrases.


title: Review of Anatomy, Evaluation, and Treatment of Musculoskeletal Pelvic Floor Pain in Women

abstract: Objective The purpose of this review is 2-fold. The first is to provide a review for physiatrists already providing care for women with musculoskeletal pelvic floor pain and a resource for physiatrists who are interested in expanding their practice to include this patient population. The second is to describe how musculoskeletal dysfunctions involving the pelvic floor can be approached by the physiatrist using the same principles used to evaluate and treat others dysfunctions in the musculoskeletal system. This discussion clarifies that evaluation and treatment of pelvic floor pain of musculoskeletal origin is within the scope of practice for physiatrists. The authors review the anatomy of the pelvic floor, including the bony pelvis and joints, muscle and fascia, and the peripheral and autonomic nervous systems. Pertinent history and physical examination findings are described. The review concludes with a discussion of differential diagnosis and treatment of musculoskeletal pelvic floor pain in women. Improved recognition of pelvic floor dysfunction by healthcare providers will reduce impairment and disability for women with pelvic floor pain. A physiatrist is in the unique position to treat the musculoskeletal causes of this condition because it requires an expert grasp of anatomy, function, and the linked relationship between the spine and pelvis. Further research regarding musculoskeletal causes and treatment of pelvic floor pain will help validate these concepts and improve awareness and care for women limited by this condition.


title: Improved Style Transfer by Respecting Inter-layer Correlations

abstract: A popular series of style transfer methods apply a style to a content image by controlling mean and covariance of values in early layers of a feature stack. This is insufficient for transferring styles that have strong structure across spatial scales like, e.g., textures where dots lie on long curves. This paper demonstrates that controlling inter-layer correlations yields visible improvements in style transfer methods. We achieve this control by computing cross-layer, rather than within-layer, gram matrices. We find that (a) cross-layer gram matrices are sufficient to control within-layer statistics. Inter-layer correlations improves style transfer and texture synthesis. The paper shows numerous examples on"hard"real style transfer problems (e.g. long scale and hierarchical patterns); (b) a fast approximate style transfer method can control cross-layer gram matrices; (c) we demonstrate that multiplicative, rather than additive style and content loss, results in very good style transfer. Multiplicative loss produces a visible emphasis on boundaries, and means that one hyper-parameter can be eliminated.


An Anti-Phishing Password Authentication Protocol


title: 3D Pose Estimation for Fine-Grained Object Categories

abstract: Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new large dataset to benchmark pose estimation for fine-grained objects, thanks to the availability of both 2D and 3D fine-grained data recently. Specifically, we augment two popular fine-grained recognition datasets (StanfordCars and CompCars) by finding a fine-grained 3D CAD model for each sub-category and manually annotating each object in images with 3D pose. We show that, with enough training data, a full perspective model with continuous parameters can be estimated using 2D appearance information alone. We achieve this via a framework based on Faster/Mask R-CNN. This goes beyond previous works on category-level pose estimation, which only estimate discrete/continuous viewpoint angles or recover rotation matrices often with the help of key points. Furthermore, with fine-grained 3D models available, we incorporate a dense 3D representation named as location field into the CNN-based pose estimation framework to further improve the performance. The new dataset is available at www.umiacs.umd.edu/~wym/3dpose.html.


Acoustic Modeling with DFSMN-CTC and Joint CTC-CE Learning


title: DNN Dataflow Choice Is Overrated

abstract: Many DNN accelerators have been proposed and built using different microarchitectures and program mappings. To fairly compare these different approaches, we modified the Halide compiler to produce hardware as well as CPU and GPU code, and show that Halide's existing scheduling language has enough power to represent all existing dense DNN accelerators. Using this system we can show that the specific dataflow chosen for the accelerator is not critical to achieve good efficiency: many different dataflows yield similar energy efficiency with good performance. However, finding the best blocking and resource allocation is critical, and we achieve a 2.6X energy savings over Eyeriss system by reducing the size of the local register file. Adding an additional level in the memory hierarchy saves an additional 25%. Based on these observations, we develop an optimizer that automatically finds the optimal blocking and storage hierarchy. Compared with Eyeriss system, it achieves up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs) respectively.


Surface Reconstruction of Water Waves Using Thermographic Stereo Imaging


title: An Efficient Meta Heuristic Algorithm for POS-tagging

abstract: Tagging is an increasingly important task in natural language processing domains. As there are many natural language processing tasks which can be improved by applying disambiguation to the text, fast and high quality tagging algorithms are a crucial task in information retrieval and question answering. Tagging aims to assigning to each word of a text its correct tag according to the context in which the word is used. Part Of Speech (POS) tagging is a difficult problem by itself, since many words has a number of possible tags associated to it. In this paper we present a novel algorithm that deals with POS-tagging problem based on Harmony Search (HS) optimization method. This paper analyzes the relative advantages of HS metaheuristic approache to the well-known natural language processing problem of POS-tagging. In the experiments we conducted, we applied the proposed algorithm on linguistic corpora and compared the results obtained against other optimization methods such as genetic and simulated annealing algorithms. Experimental results reveal that the proposed algorithm provides more accurate results compared to the other algorithms.


title: Application of a General Machine Learning Approach �

abstract: Because the World Wide Web consists primarily of text, information extraction is central to any effort that would use the Web as a resource for knowledge discovery. We show how information extraction can be cast as a standard machine learning problem, and argue for the suitability of relational learning in solving it. The implementation of a general-purpose relational learner for information extraction, SRV, is described. In contrast with earlier learning systems for information extraction, SRV makes no assumptions about document structure and the kinds of information available for use in learning extraction patterns. Instead, structural and other information is supplied as input in the form of an extensible token-oriented feature set. We demonstrate the effectiveness of this approach by adapting SRV for use in learning extraction rules for a domain consisting of university course and research project pages sampled from the Web. Making SRV Web-ready only involves adding several simple HTML-specific features to its basic feature set.


title: The changing nature of user involvement in information system development projects

abstract: This paper presents an investigation into the changing nature of user involvement in Information System Development (ISD) projects. This analysis shows the representative papers that have been published on user involvement in ISD projects and identifies areas requiring further investigation and research. Considering different project contexts the need for a new approach where an ISD project is steeped within social complexity is proposed. The specific phase of requirements elicitation and the need to consider more than the technical requirements for an ISD project are clearly presented. The systems approach is proposed for considering the “whole” rather than the separate social and technical systems as well as considering the business processes that are associated with the ISD project. It is proposed that creating a shared understanding between users and developers at the very beginning stages of an ISD project will influence a projects success.


title: Cross-Language Text Classification Using Structural Correspondence Learning

abstract: We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling. ::: ::: We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas.


title: Chemosensitivity prediction by transcriptional profiling

abstract: In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells’ tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible.


title: Facial expression editing in video using a temporally-smooth factorization

abstract: We address the problem of editing facial expression in video, such as exaggerating, attenuating or replacing the expression with a different one in some parts of the video. To achieve this we develop a tensor-based 3D face geometry reconstruction method, which fits a 3D model for each video frame, with the constraint that all models have the same identity and requiring temporal continuity of pose and expression. With the identity constraint, the differences between the underlying 3D shapes capture only changes in expression and pose. We show that various expression editing tasks in video can be achieved by combining face reordering with face warping, where the warp is induced by projecting differences in 3D face shapes into the image plane. Analogously, we show how the identity can be manipulated while fixing expression and pose. Experimental results show that our method can effectively edit expressions and identity in video in a temporally-coherent way with high fidelity.


title: Broadband directional Moreno coupler for high-performance air-filled SIW-based Substrate Integrated Systems

abstract: A broadband directional Moreno coupler consisting of a high performance Air-Filled Substrate Integrated Waveguide (AFSIW) coupled to a top Dielectric-Filled Substrate Integrated Waveguide (DFSIW) is introduced. It is intended for high performance and low-cost millimeter-wave substrate integrated systems based on multilayer Printed Circuit Board (PCB) process. The bottom layer may make use of an extremely low-cost standard substrate such as FR-4 on which base-band or digital circuits can be implemented so to obtain a self-packaged and compact millimeter-wave integrated system. Design of the proposed Moreno coupler is detailed. To the authors' knowledge, it is the first time that a Moreno coupler based on different dielectric-loaded waveguides is reported. It is also the first proposed AFSIW device taking advantage of a multilayer structure and the first AFSIW directional coupler reported in the literature. For demonstration purposes, a broadband 20 dB coupler operating over Ka-band has been designed and fabricated. It achieves a measured insertion loss, coupling and isolation of 0.6 ±0.2 dB, 18.7 ±1.3 dB and 44.7 ±13.7 dB, respectively. This coupler is of particular interest for monitoring applications, for example, in radar transmitters.


title: BER Performance Evaluation of Tail-Biting Convolution Coding Applied to Companded QPSK Mobile WiMax

abstract: A Bit-Error-Rate (BER) performance evaluation is presented for mu-Law companded QPSK modulated Mobile WiMax (IEEE802.16e) employing tail-biting convolutional coding (TBCC). The Down Link (DL) Partially Used Subcarrier (PUSC) deployment has been considered for this investigation. Equalized symbol power transmissions for uncompanded and mu-Law companded WiMax scenarios employing mu = 0.1, 1, 3, 10, 30, 100, 255, 1000, and 3000 has been undertaken. It is shown that for 30 clusters each carrying 48 bits of random data, the improvement in SNR for TBCC using the convolutional code (7, 171, 133) is best for uncompanded WiMax and deteriorates slightly for companded WiMax situations. The maximum improvement in SNR at the 0.001 BER probability level is ~1.7dB, whilst at the 0.0001 BER probability level the improvement is ~2.9dB. As companded BER rates for smaller values of mu are only slightly degraded over normal WiMax BER rates, then TBCC for small values of mu may be used to provide improved SNR performance over normal convolutional coding (CC) WiMax situations. Importantly, small values of mu can also provide improved Peak-to-Average Power Ratio (PAPR) but for larger values of mu, where significant PAPR reduction can be obtained, the improved performance of TBCC does not provide sufficient SNR gain to improve upon the standard CC WiMax performance.


title: The essence of command injection attacks in web applications

abstract: Web applications typically interact with a back-end database to retrieve persistent data and then present the data to the user as dynamically generated output, such as HTML web pages. However, this interaction is commonly done through a low-level API by dynamically constructing query strings within a general-purpose programming language, such as Java. This low-level interaction is ad hoc because it does not take into account the structure of the output language. Accordingly, user inputs are treated as isolated lexical entities which, if not properly sanitized, can cause the web application to generate unintended output. This is called a command injection attack, which poses a serious threat to web application security. This paper presents the first formal definition of command injection attacks in the context of web applications, and gives a sound and complete algorithm for preventing them based on context-free grammars and compiler parsing techniques. Our key observation is that, for an attack to succeed, the input that gets propagated into the database query or the output document must change the intended syntactic structure of the query or document. Our definition and algorithm are general and apply to many forms of command injection attacks. We validate our approach with SqlCheckS , an implementation for the setting of SQL command injection attacks. We evaluated SqlCheckS on real-world web applications with systematically compiled real-world attack data as input. SqlCheckS produced no false positives or false negatives, incurred low runtime overhead, and applied straightforwardly to web applications written in different languages.


Coarse-and Fine-Grained Sentiment Analysis of Social Media Text


title: ANAPSID: an adaptive query processing engine for SPARQL endpoints

abstract: Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-thenexecute paradigm, may timeout as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traffic is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.


title: Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

abstract: This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This paper starts with a discussion of the rationale for FDIR, and then investigates different areas of the FDIR problem. Recently reported approaches are compared and related to discussions on current practices. This paper then addresses some of the often-cited associated technical, environmental, and economic challenges of implementing self-healing for the distribution grid. The review concludes by pointing toward the need and directions for future research.


title: Spatial structure affects temporal judgments: Evidence for a synchrony binding code

abstract: Visual binding is the process by which the brain groups the elements belonging to one object, whilst segregating them from other scene elements. A computationally parsimonious mechanism of visual binding is the binding-by-synchrony (BBS) hypothesis. According to this hypothesis, detectors that respond to elements of a single object fire in synchrony, while detectors that respond to elements of different objects do not. Current psychophysical and physiological evidence are inconclusive about the role of BBS in the visual integration process. Here we provide psychophysical and computational evidence suggesting that the visual system implements a mechanism that synchronizes response onsets to object parts and attenuates or cancels their latency differences. In three experiments, observers had to judge the synchrony of two flickering Gabor patches embedded in a static Gabor contour, passing through fixation. We found that a smooth contour, as compared to a jagged one, impedes judgments of temporal synchrony between the targets, whilst facilitating non-temporal judgments of contrast on the same targets. We show that the results are consistent with a simple computational model that implements synchronization of responses via lateral interactions, enabling the visual system to pick up objects by synchrony among a temporally diverse background. Language: en


title: HOW TO EVOLVE AUTONOMOUS ROBOTS: DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS

abstract: A methodology for evolving the control systems of autonomous robots has not yet been well established. In this paper we will show different examples of applications of evolutionary robotics to real robots by describing three different approaches to develop neural controllers for mobile robots. In all the experiments described real robots are involved and are indeed the ultimate means of evaluating the success and the results of the procedures employed. Each approach will be compared with the others and the relative advantages and drawbacks will be discussed. Last, but not least, we will try to tackle a few important issues related to the design of the hardware and of the evolutionary conditions in which the control system of the autonomous agent should evolve.


title: Reducing the Rank in Relational Factorization Models by Including Observable Patterns

abstract: Tensor factorization has become a popular method for learning from multi-relational data. In this context, the rank of the factorization is an important parameter that determines runtime as well as generalization ability. To identify conditions under which factorization is an efficient approach for learning from relational data, we derive upper and lower bounds on the rank required to recover adjacency tensors. Based on our findings, we propose a novel additive tensor factorization model to learn from latent and observable patterns on multi-relational data and present a scalable algorithm for computing the factorization. We show experimentally both that the proposed additive model does improve the predictive performance over pure latent variable methods and that it also reduces the required rank — and therefore runtime and memory complexity — significantly.


title: Data mining techniques for autonomous exploration of large volumes of geo-referenced crime data

abstract: We incorporate two knowledge discovery techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns. This tool is an autonomous pattern detector to reveal plausible cause-effect associations between layers of point and area data. We present two methods for this exploratory analysis and we detail algorithms to effectively explore geo-referenced data. We illustrate the algorithms with real crime data. We demonstrate our approach to a new type of analysis of the spatio-temporal dimensions of records of criminal events. We hope this will lead to new approaches in the exploration of large volumes of spatio-temporal data.


title: Ischemic priapism in South-East Nigeria: Presentation, management challenges, and aftermath issues.

abstract: CONTEXT ::: Ischemic priapism is the more common variety of priapism and often presents late. Outcome is largely dependent on the duration of ischemia. ::: ::: ::: AIMS ::: To determine the etiology, presentation, management, and outcome of ischemic priapism. ::: ::: ::: SETTINGS AND DESIGN ::: Retrospective analysis of consecutive cases presenting to three hospitals offering specialist urological services in South-East Nigeria from January 2000 to December 2010. ::: ::: ::: PATIENTS AND METHODS ::: Fifteen patients were assessed for clinical data and outcome. ::: ::: ::: STATISTICAL ANALYSIS USED ::: The data were analyzed descriptively and inferentially using Statistical Package for Social Sciences (SPSS version 16, SPSS Inc., Chicago IL, USA) with P < 0.05. ::: ::: ::: RESULTS ::: Mean age was 30.5 years (standard deviation [SD] =1.63), range: 14-79 years. Onset to presentation interval ranged from 6 h to 28 days. Eight patients (53.3%) had sickle cell disease (SCD). Four patients (26.7%) had unidentified causes. The 8 SCD patients had stuttering priapism on several occasions previously. Six patients (40%) had taken oral herbal medications as treatment prior to presentation. Initial resuscitative measures were intravenous hydration, aspiration, and irrigation with normal saline in 13 patients. Glanulo-cavernous shunt (Al-Ghorab) was performed in all the patients. Detumescence was immediate in 14 and delayed in 1 patient. Three patients had transient recurrence of tumescence, while one had to be reshunted. Erectile dysfunction (ED) occurred in 7 patients (46.7%). Occurrence of ED increased significantly in patients presenting 24 h after onset of symptoms ([P = 0.032] Fishers exact test). Mean duration of follow-up was 21.9 weeks (SD = 4.1), range: 3-156 weeks. ::: ::: ::: CONCLUSIONS ::: Low flow priapism is common in our environment, and approximately half will occur in SCD patients who have had stuttering priapism previously. Timely diagnosis and treatment will reduce the probability of severe ED. In our experience, the Al-Ghorab shunt provides rapid relief. Enlightenment is vital in reducing ischemia time. Emphasis on preventive measures in SCD patients is vital.


title: DM EMI Noise Prediction for Constant On-Time, Critical Mode Power Factor Correction Converters

abstract: Critical conduction mode (CRM) power factor correction (PFC) converters are widely used in industries. For CRM PFC converters, high inductor current ripples generate high differential mode (DM) electromagnetic interference (EMI) noise, which calls for big EMI filters. On the other hand, the switching frequency varies during a half line cycle. The characteristics of the EMI in CRM PFC converters have not been carefully investigated and the worst case DM EMI noise has not been identified. The design of an EMI filter is difficult. In this paper, a mathematical model based on the principle of quasi-peak noise detection is proposed to predict the EMI noise in CRM PFC converters. The developed model is verified by experimental results. Based on this model, the worst case DM EMI noise can be predicted. This will greatly help in the design of EMI filters for CRM PFC converters.


title: ARDB—Antibiotic Resistance Genes Database

abstract: The treatment of infections is increasingly compromised by the ability of bacteria to develop resistance to antibiotics through mutations or through the acquisition of resistance genes. Antibiotic resistance genes also have the potential to be used for bio-terror purposes through genetically modified organisms. In order to facilitate the identification and characterization of these genes, we have created a manually curated database—the Antibiotic Resistance Genes Database (ARDB)—unifying most of the publicly available information on antibiotic resistance. Each gene and resistance type is annotated with rich information, including resistance profile, mechanism of action, ontology, COG and CDD annotations, as well as external links to sequence and protein databases. Our database also supports sequence similarity searches and implements an initial version of a tool for characterizing common mutations that confer antibiotic resistance. The information we provide can be used as compendium of antibiotic resistance factors as well as to identify the resistance genes of newly sequenced genes, genomes, or metagenomes. Currently, ARDB contains resistance information for 13 293 genes, 377 types, 257 antibiotics, 632 genomes, 933 species and 124 genera. ARDB is available at http://ardb.cbcb.umd.edu/.


title: A brief survey of quantum programming languages

abstract: This article is a brief and subjective survey of quantum programming language research. 1 Quantum Computation Quantum computing is a relatively young subject. It has its beginnings in 1982, when Paul Benioff and Richard Feynman independently pointed out that a quantum mechanical system can be used to perform computations [11, p.12]. Feynman’s interest in quantum computation was motivated by the fact that it is computationally very expensive to simulate quantum physical systems on classical computers. This is due to the fact that such simulation involves the manipulation is extremely large matrices (whose dimension is exponential in the size of the quantum system being simulated). Feynman conceived of quantum computers as a means of simulating nature much more efficiently. The evidence to this day is that quantum computers can indeed perform certain tasks more efficiently than classical computers. Perhaps the best-known example is Shor’s factoring algorithm, by which a quantum computer can find the prime factors of any integer in probabilistic polynomial time [15]. There is no known classical probabilistic algorithm which can solve this problem in polynomial time. In the ten years since the publication of Shor’s result, there has been an enormous surge of research in quantum algorithms and quantum complexity theory. 2 Quantum Programming Languages Quantum physics involves phenomena, such as superposition and entanglement, whose properties are not always intuitive. These same phenomena give quantum computation its power, and are often at the heart of an interesting quantum algorithm. However, there does not yet seem to be a unifying set of principles by which quantum algorithms are developed; each new algorithm seems to rely on a unique set of “tricks” to achieve its particular goal. One of the goals of programming language design is to identify and promote useful “high-level” concepts — abstractions or paradigms which allow humans 2 to think about a problem in a conceptual way, rather than focusing on the details of its implementation. With respect to quantum programming, it is not yet clear what a useful set of abstractions would be. But the study of quantum programming languages provides a setting in which one can explore possible language features and test their usefulness and expressivity. Moreover, the definition of prototypical programming languages creates a unifying formal framework in which to view and analyze existing quantum algorithm. 2.1 Virtual Hardware Models Advances in programming languages are often driven by advances in compiler design, and vice versa. In the case of quantum computation, the situation is complicated by the fact that no practical quantum hardware exists yet, and not much is known about the detailed architecture of any future quantum hardware. To be able to speak of “implementations”, it is therefore necessary to fix some particular, “virtual” hardware model to work with. Here, it is understood that future quantum hardware may differ considerably, but the differences should ideally be transparent to programmers and should be handled automatically by the compiler or operating system. There are several possible virtual hardware models to work with, but fortunately all of them are equivalent, at least in theory. Thus, one may pick the model which fits one’s computational intuitions most closely. Perhaps the most popular virtual hardware model, and one of the easiest to explain, is the quantum circuit model. Here, a quantum circuit is made up from quantum gates in much the same way as a classical logic circuit is made up from logic gates. The difference is that quantum gates are always reversible, and they correspond to unitary transformations over a complex vector space. See e.g. [3] for a succinct introduction to quantum circuits. Of the two basic quantum operations, unitary transformations and measurements, the quantum circuit model emphasizes the former, with measurements always carried out as the very last step in a computation. Another virtual hardware model, and one which is perhaps even better suited for the interpretation of quantum programming languages, is the QRAM model of Knill [9]. Unlike the quantum circuit model, the QRAM models allows unitary transformations and measurements to be freely interleaved. In the QRAMmodel, a quantum device is controlled by a universal classical computer. The quantum device contains a large, but finite number of individually addressable quantum bits, much like a RAM memory chip contains a multitude of classical bits. The classical controller sends a sequence of instructions, which are either of the form “apply unitary transformation U to qubits i and j” or “measure qubit i”. The quantum device carries out these instruction, and responds by making the results of the measurements available. A third virtual hardware model, which is sometimes used in complexity theory, is the quantum Turing machine. Here, measurements are never performed, and the entire operation of the machine, which consists of a tape, head, and finite control, is assumed to be unitary. While this model is theoretically equivalent


title: Contributing to Discourse

abstract: For people to contribute to discourse, they must do more than utter the right sentence at the right time. The basic requirement is that they odd to their common ground in on orderly way. To do this, we argue, they try to establish for each utterance the mutual belief that the addressees hove understood what the speaker meant well enough for current purposes. This is accomplished by the collective actions of the current contributor and his or her partners, and these result in units of conversation called contributions. We present a model of contributions and show how it accounts for o variety of features of everyday conversations. People take part in conversation in order to plan, debate, discuss, gossip, and carry out other social processes. When they do take part, they could be said to contribute to the discourse. But how do they contribute? At first the answer seems obvious. A discourse is a sequence of utterances produced as the participants proceed turn by turn. All that participants have to do to contribute is utter the right sentence at the right time. They may make errors, but once they have corrected them, they are done. The other participants have merely to listen and understand. This is the view subscribed to in most discourse theories in psychology, linguistics, philosophy; and artificial intelligence. A closer look at actual conversations, however, suggests that they are much more than sequences of utterances produced turn by turn. They are highly coordinated activities in which the current speaker tries to make sure he or she is being attended to, heard, and understood by the other participants, and they in turn try to let the speaker know when he or she has succeeded. Contributing to a discourse, then, appears to require more than just uttering the right words at the right time. It seems to consist of collective acts performed by the participants working together. In this paper we describe a model of contributions as parts of collective acts. We first describe the need for such a model, next present the model


Comparing Digital Badges-and-Points with Classroom Token Systems: Effects on Elementary School ESL Students' Classroom Behavior and English Learning


title: Towards Automatic Persona Generation Using Social Media

abstract: The use of personas is an interactive design technique with considerable potential for product and content development. However, personas have typically been viewed as fairly static. In this research, we implement an approach for creating personas in real time, based on automated analysis of actual social media data, integrating data from Facebook, Twitter, and YouTube channels for a large commercial organization. From Twitter, we gather user insights representing interests and viewpoints, leveraging approximately 195,000 follower profiles. From YouTube, we gather demographic data and topical interests, leveraging more than 188,000 subscriber profiles and millions of user interactions. From Facebook, we collect instances of hundreds of thousands of link sharing by more than 54,000 social media followers, specifically examining the domains these users share. We integrate the social media data from all three platforms in order to demonstrating that this data can be used to develop personas in real-time. The research results provide insights into competitive marketing, topical interests, and preferred system features for the users of the online news medium. Research implications are that personas can be generated in real-time, instead of being the result of a laborious, time-consuming development process.


title: A Personal Agent Application for the Semantic Web

abstract: The Semantic Web is a vision to simplify and improve knowledge reuse on the Web. It is all set to alter the way humans benefit from the web from active interaction to somewhat passive utilization through the proliferation of software agents and in particular personal assistants that can better function and thrive on the Semantic Web than the conventional web. Agents can parse, understand and reason about information available on Semantic Web pages in an attempt to use it to meet users’ needs. Such personal assistants will be driven by rules , axioms and the internal model or profile that the agents have inside them for the user. An intrinsic and important pre-requisite for a personal assistant or rather any agent is to manipulate information available on the Semantic Web in the form of ontologies, axioms, and rules written in various semantic markup languages. In this paper, a model architecture for such a personal assistant dealing with real-world semantic markup is described. The agent reasons with semantic markup written in DAML+OIL, using the Java Expert System Shell (JESS) as the reasoning engine. This software assistant views information providers on the Semantic Web as recommender agents that have a limited view of the user’s preferences and provides a improved notion of personalization by collaborating with peer personal assistants (what are referred to as buddy agents) within communities that the user has identified as trusted parties to exchange information with. Collaboration is achieved through simple solicitation and recommendation of information with these buddy agents.


title: Emerging 5G applications over mmWave: Hands-on assessment of WiGig radios

abstract: Nowadays, many emerging technologies, such as Augmented and Virtual reality, require extremely high-rate data transmissions. This imposes an increasing demand on the network throughput, which currently surpasses the capabilities of commercially available wireless communication systems. To address this constraint, some companies are considering the implementation of high-throughput wired technologies, such as optical fibers, as part of their products. This approach is effective in terms of communication link capacity, but on the other hand may bring disadvantages and constraints in terms of user mobility (i.e., the limited length of cables). Therefore, we are currently witnessing much faster development of novel high-rate wireless technologies, which are considered to be enablers for future 5G applications. This paper offers an evaluation of the emerging IEEE 802.11ad (WiGig) wireless technology capable of delivering multi-gigabit data transfer rates. This hands-on assessment aims at real-world experimentation as well as simulation-based study of selected scenarios to assess the usability of the millimeter-wave technology in prospective 5G applications. All of our practical measurements were conducted on the commercially available WiGig-ready Dell D5000 hardware platforms. The obtained data was comprehensively compared with the corresponding simulation scenario in Network Simulator 3.


title: Icwsm – a great catchy name: Semi-supervised recognition of sarcastic sentences in product reviews

abstract: Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semisupervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.


title: COMMENT Why g Is Not an Adaptation: A Comment on Kanazawa (2004)

abstract: In S. Kanazawa's evolutionary theory of general intelligence (g), g is presented as a species-typical information-processing mechanism. This conceptualization of g departs radically from the accepted conceptualization of g as a source of individual differences that is manifest in the positive manifold. Kanazawa's theory is thus problematic in the sense that it concerns a purely hypothetical, and empirically unsupported, conceptualization of g. The authors argue that an evolutionary account of g should address it as a source of individual differences--that is, in a manner that is consistent with the empirical support for g.


oftware ecosystems – A systematic literature review


title: Design of Intelligent Parking Management System Based on License Plate Recognition

abstract: License Plate Recognition (LPR) which has been extensively studied recently is becoming one important part of Intelligent Transportation Systems (ITS). It is not only applied to public security monitoring, but also applied to a red light shot, speeding violation paparazzi, parking system and so on. The present paper introduces an intelligent parking management system based on LPR, recognizing license plate automatically at the parking lot access and realizing vehicle information integrated management conveniently. The software part is developed with VC++, QT and Microsoft Office Access Database. In the system, LPR is carried out through video streaming frame by frame to choose the best result with a high speed. After accurate LPR, the vehicle information, such as LP number, LP color, car type and access time, will be stored in system database. In addition, the system interface is designed very friendly and simple for administrators to operate, which can largely improve the whole performance of LPR system. Experimental results show that this parking management system can achieve high correct rate at 95%, and can be applied to real-time implementation


title: Classroom relationship qualities and social-cognitive correlates of defending and passive bystanding in school bullying in Sweden: A multilevel analysis

abstract: Abstract Using the social-ecological and social cognitive theories as integrated guiding frameworks, the present study examined whether moral disengagement and defender self-efficacy at the individual level, and moral disengagement, quality of teacher–student relationships and quality of student–student relationships at the classroom level were associated with passive bystanding and defending in bullying situations. Participants were 900 Swedish students from 43 classrooms, ranging in age from 9 to 13 years. Multilevel regression analyses revealed that passive reactions by bystanders were associated with greater moral disengagement and less defender self-efficacy. Defending, in turn, was associated with less moral disengagement and greater defender self-efficacy and classroom student–student relationship quality. Furthermore, students who scored high in moral disengagement were even less prone to defend victims when the classroom student–student relationship quality was low, but more prone to act as defenders when the classroom student–student relationship quality was high. In addition, the negative association between defender self-efficacy and passive bystanding was stronger both in classrooms with higher student–student relationship quality and in those with lower class moral disengagement. Implications for prevention are discussed.


title: A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking

abstract: This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian-sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.