title: Fast Text Classification Using Randomized Explicit Semantic Analysis

abstract: Document classification or document categorization is one of the most studied areas in computer science due to its importance. The problem is to assign a document using its text to one or more classes or categories from a predefined set. We propose a new approach for fast text classification using randomized explicit semantic analysis (RS-ESA). It is based on a state of the art approach for word sense disambiguation based on Wikipedia, the largest encyclopedia in existence. Our method reduces Wikipedia repository using a random sample approach resulting in a throughput, which is an order of magnitude faster than the original explicit semantic analysis. RS-ESA approach has been implemented as part of the LITMUS project due to a need in classifying data from Social Media into relevant and irrelevant items with respect to landslide as a natural disaster. We demonstrate that our approach achieves 96% precision when classifying Social Media landslide data collected in December 2014. We also demonstrate the genericity of the proposed approach by using it for separating factual texts from fictional based on Wikipedia articles and fan fiction stories, where we achieve 97% in precision.


title: A hybrid prediction model with F-score feature selection for type II Diabetes databases

abstract: The medical data are multidimensional, and are represented by a large number of features. Hundreds of independent features (parameters) in these high dimensional databases need to be simultaneously considered and analyzed, for valuable decision-making information in medical prediction. Most data mining methods depend on a set of features that define the behavior of the learning algorithm and directly or indirectly influence the complexity of the resulting models. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. The aim of this study is to improve the diagnostic accuracy of diabetes disease by selecting informative features of Pima Indians Diabetes Dataset. This study proposes a Hybrid Prediction Model with F-score feature selection approach to identify the optimal feature subset of the Pima Indians Diabetes dataset. The features of diabetes dataset are ranked using F-score and the feature subset that gives the minimal clustering error is the optimal feature subset of the dataset. The correctly classified instances determine the pattern for diagnosis and are used for further classification process. The improved performance of the Support Vector Machine classifier measured in terms of Accuracy of the classifier, Sensitivity, Specificity and Area Under Curve (AUC) proves that the proposed feature approach indeed improves the performance of classification. The proposed prediction model achieves a predictive accuracy of 98.9427 and it is the highest predictive accuracy for diabetes dataset compared to other models in literature for this problem.


title: All Silicon Marx-bank Topology for High-voltage, High-frequency Rectangular Pulses

abstract: This paper discusses the operation of a fully integrated solid-state Marx generator circuit, which has been developed for high-frequency (kHz), high-voltage (kV) applications needing rectangular pulses. The conventional Marx generator, used for high-voltage pulsed applications, uses inductors, or resistors, to supply the charging capacitors voltage, which has the disadvantages of size, power loss and frequency limitation. The proposed circuit takes advantage of the intensive use of power semiconductor switches, replacing the passive elements in the conventional circuit, to increase the performance, strongly reducing losses and increasing the pulse repetition frequency. Also, the proposed topology enables the use of typical half-bridge semiconductor structures, while ensuring that the maximum voltage blocked by the semiconductors is the voltage of each capacitor (i.e. the power supply voltage), even with mismatches in the synchronized switching, and with fault conditions. A laboratory prototype with five stages, 5 kW peak power, of this all silicon Marx generator circuit, was constructed using 1200 V IGBTs and diodes, operating with 1000 V d-c input voltage and 10 kHz frequency, giving 5 kV pulses, with 10 mus width and 50 ns rise time


title: Design of a Solar-Harvesting Circuit for Batteryless Embedded Systems

abstract: The limited battery lifetime of modern embedded systems and mobile devices necessitates frequent battery recharging or replacement. Solar energy and small-size photovoltaic (PV) systems are attractive solutions to increase the autonomy of embedded and personal devices attempting to achieve perpetual operation. We present a battery less solar-harvesting circuit that is tailored to the needs of low-power applications. The harvester performs maximum-power-point tracking of solar energy collection under nonstationary light conditions, with high efficiency and low energy cost exploiting miniaturized PV modules. We characterize the performance of the circuit by means of simulation and extensive testing under various charging and discharging conditions. Much attention has been given to identify the power losses of the different circuit components. Results show that our system can achieve low power consumption with increased efficiency and cheap implementation. We discuss how the scavenger improves upon state-of-the-art technology with a measured power consumption of less than 1 mW. We obtain increments of global efficiency up to 80%, diverging from ideality by less than 10%. Moreover, we analyze the behavior of super capacitors. We find that the voltage across the supercapacitor may be an unreliable indicator for the stored energy under some circumstances, and this should be taken into account when energy management policies are used.


title: © 2012 INFORMS Clearance Pricing Optimization for a Fast-Fashion Retailer

abstract: Fast-fashion retailers such as Zara offer continuously changing assortments and use minimal in-season promotions. Their clearance pricing problem is thus challenging because it involves comparatively more different articles of unsold inventory with less historical price data points. Until 2007, Zara used a manual and informal decision-making process for determining price markdowns. In collaboration with their pricing team, we designed and implemented since an alternative process relying on a formal forecasting model feeding a price optimization model. As part of a controlled field experiment conducted in all Belgian and Irish stores during the 2008 Fall-Winter season, this new process increased clearance revenues by approximately 6%. Zara is currently using this process worldwide for its markdown decisions during clearance sales.


title: Efficiently mining long patterns from databases

abstract: We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnitude or more.


title: Experimental research on the residual stress in the surface of silicon nitride ceramic balls

abstract: Abstract The influence of thermal stress produced in sintering and mechanical stress formed in the lapping process on the residual stress in the surface of silicon nitride ceramic balls is discussed in this paper. By measuring the specific volume, the distribution of residual stress was analyzed and calculated. Some experimental research on the influence of the lapping process on the residual stress in surface of the balls was conducted. The surface of silicon nitride ceramic balls presented residual compressive stress after sintering and residual tensile stress after lapping. In the lapping process, the abrasive particles were finer and the load acting on the lapped balls was larger, the amplitude of the residual tensile stress in the surface of the silicon nitride balls being greater.


title: Structured Depth Prediction in Challenging Monocular Video Sequences

abstract: In this paper, we tackle the problem of estimating the depth of a scene from a monocular video sequence. In particular, we handle challenging scenarios, such as non-translational camera motion and dynamic scenes, where traditional structure from motion and motion stereo methods do not apply. To this end, we first study the problem of depth estimation from a single image. In this context, we exploit the availability of a pool of images for which the depth is known, and formulate monocular depth estimation as a discrete-continuous optimization problem, where the continuous variables encode the depth of the superpixels in the input image, and the discrete ones represent relationships between neighboring superpixels. The solution to this discrete-continuous optimization problem is obtained by performing inference in a graphical model using particle belief propagation. To handle video sequences, we then extend our single image model to a two-frame one that naturally encodes short-range temporal consistency and inherently handles dynamic objects. Based on the prediction of this model, we then introduce a fully-connected pairwise CRF that accounts for longer range spatio-temporal interactions throughout a video. We demonstrate the effectiveness of our model in both the indoor and outdoor scenarios.


title: Current-mode sensorless control of single-phase brushless DC fan motors

abstract: This paper proposes a new control scheme for the implementation of a low-cost and high efficiency sensorless speed control IC for single-phase brushless dc (BLDC) fan motors. The proposed control scheme detects the zero-crossing-point (ZCP) of the measured back-EMF to generate commutation signals without Hall sensor. A current mode soft switching scheme is used to smooth the current spikes induced by the reduction of the back-EMF when the rotor is crossing the commutation boundary. An adaptive blanking time control strategy is used to adjust the time interval to ensure correct detection of the ZCP. An open-loop constant Ampere/Hertz ramping control scheme is developed for the startup control from zero speed and switching to sensorless mode once the ZCP is detected. The proposed sensorless control scheme has been verified by using computer simulation based on a developed single-phase BLDC fan motor model. Experimental verification of the proposed control scheme has been carried out by using digital implementation technique. Experimental results reveal the superior performance of the proposed control scheme.


title: Inferring privacy policies for social networking services

abstract: Social networking sites have come under criticism for their poor privacy protection track record. Yet, there is an inherent difficulty in deciding which principals should have access to user's information or actions, without requiring them to constantly manage their privacy settings. We propose to extract automatically such privacy settings, based on the policy that information produced within a social context should remain in that social context, both to ensure privacy as well as maximising utility. A machine learning approach is used to extract automatically such social contexts, as well as a tentative evaluation. Copyright 2009 ACM.


title: Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter

abstract: The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor's stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.


title: Factors influencing enterprise to improve data quality in information systems application —An empirical research on 185 enterprises through field study

abstract: Management of data quality has become the bottleneck for enterprises to upgrade their application of information technology (IT). Based on the theory of total quality management, this paper presents five influencing factors of data quality from the perspective of organizational management, namely top management support, capability on regulation and process management, business-IT alignment, staff participation and integration of information systems (IS). At the same time, this paper establishes a process model of influencing factors, and conducts an empirical analysis through a field study of 185 enterprises. The results show that the influence of five factors presents transfer characteristics clearly. Top management support has an effect upon the capability on regulation and process management, and business-IT alignment directly, while indirectly on staff participation and integration of IS. Capability on regulation and process management, and business-IT alignment exert a direct impact on staff participation and integration of IS, while indirectly on data quality. Staff participation and integration of IS shows a direct impact on data quality. Finally, this paper presents some suggestions on improving data quality.


title: PCA-based dictionary building for accurate facial expression recognition via sparse representation

abstract: Abstract Sparse representation is a new approach that has received significant attention for image classification and recognition. This paper presents a PCA-based dictionary building for sparse representation and classification of universal facial expressions. In our method, expressive facials images of each subject are subtracted from a neutral facial image of the same subject. Then the PCA is applied to these difference images to model the variations within each class of facial expressions. The learned principal components are used as the atoms of the dictionary. In the classification step, a given test image is sparsely represented as a linear combination of the principal components of six basic facial expressions. Our extensive experiments on several publicly available face datasets (CK+, MMI, and Bosphorus datasets) show that our framework outperforms the recognition rate of the state-of-the-art techniques by about 6%. This approach is promising and can further be applied to visual object recognition.


title: A 6-Gbit/s Hybrid Voltage-Mode Transmitter With Current-Mode Equalization in 90-nm CMOS

abstract: Low-power (LP) high-speed serial I/O transmitters which include equalization to compensate for channel frequency-dependent loss are required to meet the aggressive link energy-efficiency targets of future systems. This brief presents an LP serial-link-transmitter design that utilizes an output stage which combines a voltage-mode driver, which offers low static-power dissipation, and current-mode equalization, which offers low complexity and dynamic-power dissipation. The utilization of current-mode equalization decouples the equalization settings and termination impedance, allowing for a significant reduction in predriver complexity relative to segmented voltage-mode drivers. Proper transmitter series termination is set with an impedance control loop which adjusts the on-resistance of the output transistors in the driver voltage-mode portion. Further reductions in dynamic-power dissipation are achieved through scaling the serializer and local clock distribution supply with data rate. Fabricated in a 1.2-V 90-nm LP CMOS process, the transmitter supports an output swing range of 100–400 $\hbox{mV}_{{\rm ppd}}$ and up to 6 dB of equalization and includes output-duty-cycle control. The transmitter achieves 6-Gbit/s operation at 1.26-pJ/bit energy efficiency with 300- $\hbox{mV}_{{\rm ppd}}$ output swing and 3.72-dB equalization.


title: Semi-Supervised Spam Detection in Twitter Stream

abstract: Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. In this paper, we propose a semi-supervised spam detection (S3D) framework for spam detection at tweet-level. The proposed framework consists of two main modules: spam detection module operating in real-time mode and model update module operating in batch mode. The spam detection module consists of four lightweight detectors: 1) blacklisted domain detector to label tweets containing blacklisted URLs; 2) near-duplicate detector to label tweets that are near-duplicates of confidently prelabeled tweets; 3) reliable ham detector to label tweets that are posted by trusted users and that do not contain spammy words; and 4) multiclassifier-based detector labels the remaining tweets. The information required by the detection module is updated in batch mode based on the tweets that are labeled in the previous time window. Experiments on a large-scale data set show that the framework adaptively learns patterns of new spam activities and maintain good accuracy for spam detection in a tweet stream.


title: Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems

abstract: In this work, we propose a locally connected deep learning framework for recommender systems, which reduces the complexity of deep neural network (DNN) by two to three orders of magnitude. We further extend the framework using the idea of the recently proposed Wide&Deep model. Experiments on industrial-scale datasets show that our methods could achieve good results with much shorter runtime.


title: Design and Implementation of a Novel Multilevel DC–AC Inverter

abstract: In this paper, a novel multilevel dc–ac inverter is proposed. The proposed multilevel inverter generates seven-level ac output voltage with the appropriate gate signals’ design. Also, the low-pass filter is used to reduce the total harmonic distortion of the sinusoidal output voltage. The switching losses and the voltage stress of power devices can be reduced in the proposed multilevel inverter. The operating principles of the proposed inverter and the voltage balancing method of input capacitors are discussed. Finally, a laboratory prototype multilevel inverter with 400-V input voltage and output $220\ {\text{V}}_{\text{rms}}/2\,{\text{kW}}$ is implemented. The multilevel inverter is controlled with sinusoidal pulse-width modulation (SPWM) by TMS320LF2407 digital signal processor (DSP). Experimental results show that the maximum efficiency is 96.9% and the full load efficiency is 94.6%.


title: Improved QO-STBC OFDM System Using Null Interference Elimination

abstract: The quasi-orthogonal space time block coding (QO-STBC) over orthogonal frequency division multiplexing (OFDM) is investigated. Traditionally, QO-STBC does not achieve full diversity since the detection matrix of QO-STBC scheme is not a diagonal matrix. In STBC, the decoding matrix is a diagonal matrix which enables linear decoding whereas the decoding matrix in traditional QO-STBC does not enable linear decoding. In this paper it is shown that there are some interfering terms in terms of non-diagonal elements that result from the decoding process which limit the linear decoding. As a result, interference from the application of the QO-STBC decoding matrix depletes the performance of the scheme such that full diversity is not attained. A method of eliminating this interference in QO-STBC is investigated by nulling the interfering terms towards full diversity for an OFDM system. It was found that the interference reduction technique permits circa 2dB BER performance gain in QO-STBC. The theoretical and simulation results are presented, for both traditional QO-STBC and interference-free QO-STBC applying OFDM


Predicting Customer Churn in Telecom Industry using Multilayer Preceptron Neural Networks : Modeling and Analysis


title: Two systems for empathy: a double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions

abstract: Recent evidence suggests that there are two possible systems for empathy: a basic emotional contagion system and a more advanced cognitive perspective-taking system. However, it is not clear whether these two systems are part of a single interacting empathy system or whether they are independent. Additionally, the neuroanatomical bases of these systems are largely unknown. In this study, we tested the hypothesis that emotional empathic abilities (involving the mirror neuron system) are distinct from those related to cognitive empathy and that the two depend on separate anatomical substrates. Subjects with lesions in the ventromedial prefrontal (VM) or inferior frontal gyrus (IFG) cortices and two control groups were assessed with measures of empathy that incorporate both cognitive and affective dimensions. The findings reveal a remarkable behavioural and anatomic double dissociation between deficits in cognitive empathy (VM) and emotional empathy (IFG). Furthermore, precise anatomical mapping of lesions revealed Brodmann area 44 to be critical for emotional empathy while areas 11 and 10 were found necessary for cognitive empathy. These findings are consistent with these cortices being different in terms of synaptic hierarchy and phylogenetic age. The pattern of empathy deficits among patients with VM and IFG lesions represents a first direct evidence of a double dissociation between emotional and cognitive empathy using the lesion method.


title: Cognitive behavioral therapy for public-speaking anxiety using virtual reality for exposure

abstract: This study used an open clinical trial to test a cognitive-behavioral treatment for public-speaking anxiety that utilized virtual reality as a tool for exposure therapy. Treatment was completed by participants (n=10) meeting the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV criteria for social phobia, or panic disorder with agoraphobia in which public speaking was the predominantly feared stimulus. Treatment was conducted by a licensed psychologist in an outpatient clinic. Treatment consisted of eight individual therapy sessions, including four sessions of anxiety management training and four sessions of exposure therapy using a virtual audience, according to a standardized treatment manual. Participants completed standardized self-report questionnaires assessing public-speaking anxiety at pre-treatment, post-treatment, and 3-month follow-up. Participants were asked to give a speech to an actual audience at pre- and post-treatment. Results showed decreases on all self-report measures of public-speaking anxiety from pre- to post-treatment, which were maintained at follow-up (n=8; all P<.05). Participants were no more likely to complete a speech post-treatment than at pre-treatment. This study provides preliminary evidence that a cognitive-behavioral treatment using virtual reality for exposure to public speaking may reduce public-speaking anxiety and suggests that further research with a controlled design is needed. Depression and Anxiety 22:156–158, 2005. © 2005 Wiley-Liss, Inc.


title: Development of Parametric Drawing Program Based on AutoCAD VBA

abstract: This paper is written to exemplify the basic design idea and show the design procedure of a parameter drawing. Use of AutoCAD ActiveX Automation to access AutoCAD objects and ADO to build up data communication between AutoCAD and Access the parametric drawing software program based on AutoCAD VBA is poised for realization. In the course of exploration of this subject, great improvements can be made in functionality of integration, interaction and automation of CAD system, fully meeting the requirements of engineering practice.


title: A 28GHz quadrature fractional-N synthesizer for 5G mobile communication with less than 100fs jitter in 65nm CMOS

abstract: A 26–32GHz quadrature cascaded phase locked loop (PLL) is presented. The PLL is implemented in 65nm bulk CMOS, consuming 27mW and has less than 100fsec integrated jitter with −114.4 and −112.6dBc/Hz phase noise at 1MHz offset for integer and fractional modes, respectively.


Multiple Skip Connections and Dilated Convolutions for Semantic Segmentation


title: Effort, duration and cost estimation in agile software development

abstract: Estimation is very important and integral part of software development life cycle. Without estimation of effort, duration and cost, software cannot be developed. It is important to do accurate estimation as much as possible. Today in Information technology Industry Estimation in agile software development is mostly based on heuristic approaches like expert judgment and planning poker. In absence of agile experts, it is very difficult to estimate in agile software development. There is need of a algorithmic method in agile software development which can do accurate estimates. In this paper, we propose a simple algorithmic estimation method for agile software development and compare the results given by proposed method to check the feasibility of algorithmic method in real time.


title: Micromechanical behavior and thermal stability of a dual-phase α+α’ titanium alloy produced by additive manufacturing

abstract: ABSTRACT In order to improve the tensile properties of additively manufactured Ti-6Al-4V parts, specific heat treatments have been developed. Previous work demonstrated that a sub-transus thermal treatment at 920 °C followed by water quenching generates a dual-phase α+α' microstructure with a high work-hardening capacity inducing a desirable increase in both strength and ductility. The present study investigates the micromechanical behavior of this α+α' material as well as the thermal stability of the metastable α’ martensite. To that end, annealing of the α+α' microstructure is performed and the resulting microstructural evolution is analyzed, along with its impact on the tensile properties. A deeper understanding of the micromechanics of the multiphase microstructure both before and after annealing is achieved by performing in-situ tensile testing within a SEM, together with digital image correlation for full-field local strain measurements. This approach allows the strain partitioning to be quantified at a microscale and highlights a significant mechanical contrast between the two phases. In the α+α' microstructure, the α' phase is softer than the α phase, which is confirmed by nanoindentation measurements. Partial decomposition of the martensite during annealing induces a substantial hardening of the α' phase, which is attributed to fine-scale precipitation and solution strengthening. A scale transition model based on the iso-work assumption and describing the macroscopic tensile behavior of the material depending on the individual mechanical behavior of each phase is also proposed. This model enables to provide insights into the underlying deformation and work-hardening mechanisms.


ImageNet Classification with Deep Convolutional Neural


Deployable helical antennas for cubesats


title: Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

abstract: In this paper, we propose an online learning algorithm based on a Rao-Blackwellized particle filter for spatial concept acquisition and mapping. We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in the theoretical framework of the Bayesian generative model. The proposed method can simultaneously learn place categories and lexicons while incrementally generating an environmental map. Furthermore, the proposed method has scene image features and a language model added to SpCoA. In the experiments, we tested online learning of spatial concepts and environmental maps in a novel environment of which the robot did not have a map. Then, we evaluated the results of online learning of spatial concepts and lexical acquisition. The experimental results demonstrated that the robot was able to more accurately learn the relationships between words and the place in the environmental map incrementally by using the proposed method.


The mechanisms of adrenergic modulation of excitatory postsynaptic currents in pyramidal neurons of rat cerebral cortex


title: Seeing what the system thinks you know: visualizing evidence in an open learner model

abstract: User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.


title: Multimodal Sentiment Analysis of Spanish Online Videos

abstract: Using multimodal sentiment analysis, the presented method integrates linguistic, audio, and visual features to identify sentiment in online videos. In particular, experiments focus on a new dataset consisting of Spanish videos collected from YouTube that are annotated for sentiment polarity.


title: Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices

abstract: We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than n-best list or lattice rescoring as the neural decoder is not restricted to the SMT search space. We show an efficient and simple way to integrate risk estimation into the NMT decoder which is suitable for word-level as well as subword-unit-level NMT. We test our method on English-German and Japanese-English and report significant gains over lattice rescoring on several data sets for both single and ensembled NMT. The MBR decoder produces entirely new hypotheses far beyond simply rescoring the SMT search space or fixing UNKs in the NMT output.


title: SOME EVIDENCE FOR ‘SPEECH’ AS AN ACOUSTIC FEATURE

abstract: Under conditions of serial recall of auditorily presented lists of digits, recall of the last item has been shown to be adversely affected by the presence of a redundant item following the list. This is known as ‘the suffix effect’ (Crowder & Morton, 1969). In a series of experiments it is shown that the size of this effect is not influenced by the phonological complexity of the suffix. Non-speech sounds, on the other hand, produce no suffix effect even when the subjects are forced to process them. Certain speech sounds were also found to produce no effect. It is concluded that these sounds lacked properties which are characteristic of speech sounds and so were classified as ‘non-speech’ and that as a result, these sounds are processed by a separate system from the speech sounds.


title: A comprehensive survey on safe reinforcement learning

abstract: Safe Reinforcement Learning can be defined as the process of learning policies that maximize the expectation of the return in problems in which it is important to ensure reasonable system performance and/or respect safety constraints during the learning and/or deployment processes. We categorize and analyze two approaches of Safe Reinforcement Learning. The first is based on the modification of the optimality criterion, the classic discounted finite/infinite horizon, with a safety factor. The second is based on the modification of the exploration process through the incorporation of external knowledge or the guidance of a risk metric. We use the proposed classification to survey the existing literature, as well as suggesting future directions for Safe Reinforcement Learning.


title: A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences

abstract: The problem of ground truth generation is fundamental for many approaches of computer vision and image processing. In order to test algorithms for object segmentation, object tracking, object interactions, it is necessary to have image sequences in which the ground truth is determined in an objective way. In the context of visual surveillance where many people moves in the scene occluding each other, it could be very complex and hard the work of generating for each image the position of all the moving objects and maintain this information for all the period in which they remain in the scene. In this paper we propose a semi-automatic system that generates an initial ground truth estimation, and then provides a user-friendly interface to manually validate or correct the track results. The proposed system has been tested on some soccer video sequences that have been published on-line for being available to the scientific community, but it can be used also in other surveillance contexts.


title: Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation

abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting locations, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, a growing line of research has exploited the temporal effect, geographical-social influence, content effect and word-of-mouth effect. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the issue of data-sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work. In light of the above, we propose a joint probabilistic generative model to mimic user check-in behaviors in a process of decision making, which strategically integrates the above factors to effectively overcome the data sparsity, especially for out-of-town users. To demonstrate the applicability and flexibility of our model, we investigate how it supports two recommendation scenarios in a unified way, i.e., home-town recommendation and out-of-town recommendation. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets in terms of both recommendation effectiveness and efficiency, and the experimental results show its superiority over other competitors.


title: Cross-domain semantic transfer from large-scale social media

abstract: With the rapid development of location-based social networks (LBSNs), more and more media data are unceasingly uploaded by users. The asynchrony between the visual and textual information has made it extremely difficult to manage the multimodal information for manual annotation-free retrieval and personalized recommendation. Consequently the automated image semantic discovery of multimedia location-related user-generated contents (UGCs) for user experience has become mandatory. Most of the literatures leverage single-modality data or correlated multimedia data for image semantic detection. However, the intrinsically heterogeneous UGCs in LBSNs are usually independent and uncorrelated. It is hard to build correlation between textual information and visual information. In this paper, we propose a cross-domain semantic modeling method for automatic image annotation for visual information from social network platforms. First, we extract a set of hot topics from the collected textual information for image dataset preparation. Then the proposed noisy sample filtering is implemented to remove low-relevance photos. Finally, we leverage cross-domain datasets to discover the common knowledge of each semantic concept from UGCs and boost the performance of semantic annotation by semantic transfer. The comparison experiments on cross-domain datasets were conducted to demonstrate the superiority of the proposed method.


title: Intelligent logistics: Involving the customer

abstract: HighlightsWe propose a conceptual model for customer orientation in intelligent logistics.We provide a set of structured requirements for a successful customer-oriented intelligent logistics system.We illustrate the model and structured requirements in the context using example from our current research. The role of logistics in effective supply chain management is increasingly critical, and researchers and practitioners have recently focused their attention in designing more intelligent systems to address today's challenges. In this paper, we focus on one such challenge concerning improving the role of the customer in logistics operations. In particular, we identify specific developments in the systems governing core logistics operations, which will enhance the customer experience. This paper proposes a conceptual model for customer orientation in intelligent logistics and describes a number of specific developments the authors are involved in.


Algorithms for Rendering in Artistic Styles


title: Block Ciphers – Focus on the Linear Layer (feat. PRIDE )

abstract: The linear layer is a core component in any substitution-permutation network block cipher. Its design significantly influences both the security and the efficiency of the resulting block cipher. Surprisingly, not many general constructions are known that allow to choose trade-offs between security and efficiency. Especially, when compared to Sboxes, it seems that the linear layer is crucially understudied. In this paper, we propose a general methodology to construct good, sometimes optimal, linear layers allowing for a large variety of trade-offs. We give several instances of our construction and on top underline its value by presenting a new block cipher. PRIDE is optimized for 8-bit micro-controllers and significantly outperforms all academic solutions both in terms of code size and cycle count.


title: A Hybrid Model for Identity Obfuscation by Face Replacement

abstract: As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments, we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.


title: Information Hiding Using Least Significant Bit Steganography and Cryptography

abstract: Steganalysis is the art of detecting the message's existence and blockading the covert communication. Various steganography techniques have been proposed in literature. The Least Significant Bit (LSB) steganography is one such technique in which least significant bit of the image is replaced with data bit. As this method is vulnerable to steganalysis so as to make it more secure we encrypt the raw data before embedding it in the image. Though the encryption process increases the time complexity, but at the same time provides higher security also. This paper uses two popular techniques Rivest, Shamir, Adleman (RSA) algorithm and Diffie Hellman algorithm to encrypt the data. The result shows that the use of encryption in Steganalysis does not affect the time complexity if Diffie Hellman algorithm is used in stead of RSA algorithm.


title: A 0.3–1.4 GHz All-Digital Fractional-N PLL With Adaptive Loop Gain Controller

abstract: A 0.3-1.4 GHz all-digital phase locked loop (ADPLL) with an adaptive loop gain controller (ALGC), a 1/8-resolution fractional divider and a frequency search block is presented. The ALGC reduces the nonlinearity of the bang-bang phase-frequency detector (BBPFD), reducing output jitter. The fractional divider partially compensates for the large input phase error caused by fractional-N frequency synthesis. A fast frequency search unit using the false position method achieves frequency lock in 6 iterations that correspond to 192 reference clock cycles. A prototype ADPLL using a BBPFD with a dead-zone-free retimer, an ALGC, a fractional divider, and a digital logic implementation of a frequency search algorithm was fabricated in a 0.13-μm CMOS logic process. The core occupies 0.2 mm2 and consumes 16.5 mW with a 1.2-V supply at 1.35-GHz. Measured RMS and peak-to-peak jitter with activating the ALGC are 3.7 ps and 32 ps respectively.


title: Adtributor: revenue debugging in advertising systems

abstract: Advertising (ad) revenue plays a vital role in supporting free websites. When the revenue dips or increases sharply, ad system operators must find and fix the root-cause if actionable, for example, by optimizing infrastructure performance. Such revenue debugging is analogous to diagnosis and root-cause analysis in the systems literature but is more general. Failure of infrastructure elements is only one potential cause; a host of other dimensions (e.g., advertiser, device type) can be sources of potential causes. Further, the problem is complicated by derived measures such as costs-per-click that are also tracked along with revenue. ::: ::: Our paper takes the first systematic look at revenue debugging. Using the concepts of explanatory power, succinctness, and surprise, we propose a new multi-dimensional root-cause algorithm for fundamental and derived measures of ad systems to identify the dimension mostly likely to blame. Further, we implement the attribution algorithm and a visualization interface in a tool called the Adtributor to help troubleshooters quickly identify potential causes. Based on several case studies on a very large ad system and extensive evaluation, we show that the Adtributor has an accuracy of over 95% and helps cut down troubleshooting time by an order of magnitude.


title: Multimodal Local Perception Bilinear Pooling for Visual Question Answering

abstract: Visual question answering is a challenging multimodal task, which has received increasing attention in recent years. One key solution to visual question answering is how to fuse the visual and textual features extracted from the image and questions, and thus, we can comprehensively employ the information from both modals and deliver correct answers. Bilinear pooling has been a powerful fusion approach owing to its exhausting interaction of each element of two modals, but its overuse of parameters limits its practical application. In this paper, we aim to retain the advantages of bilinear pooling for feature interaction and propose a novel multimodal feature fusion approach named multimodal local perception bilinear (MLPB) pooling, which can retain the second-order interactions between visual and textual features with limited learning parameters. To be specific, the MLPB utilizes local perception mechanism, which transforms the bilinear pooling between two high-dimensional raw features into multiple low-dimensional part features. To further reduce the computational cost, we propose to share the learning parameters of each local bilinear pooling. In this way, MLPB can achieve the complex interactions of the bilinear pooling without taking up too much computational resource. Extensive experiments show that the proposed method can achieve competitive or better performance than the state of the art.


Multitask Sequence-to-Sequence Models for Grapheme-to-Phoneme Conversion.


title: Sexual arousal: The correspondence of eyes and genitals

abstract: Men's, more than women's, sexual responses may include a coordination of several physiological indices in order to build their sexual arousal to relevant targets. Here, for the first time, genital arousal and pupil dilation to sexual stimuli were simultaneously assessed. These measures corresponded more strongly with each other, subjective sexual arousal, and self-reported sexual orientation in men than women. Bisexual arousal is more prevalent in women than men. We therefore predicted that if bisexual-identified men show bisexual arousal, the correspondence of their arousal indices would be more female-typical, thus weaker, than for other men. Homosexual women show more male-typical arousal than other women; hence, their correspondence of arousal indices should be stronger than for other women. Findings, albeit weak in effect, supported these predictions. Thus, if sex-specific patterns are reversed within one sex, they might affect more than one aspect of sexual arousal. Because pupillary responses reflected sexual orientation similar to genital responses, they offer a less invasive alternative for the measurement of sexual arousal.


title: Using Markov decision process in cognitive radio networks towards the optimal reward

abstract: The Learning is an indispensable phase in the cognition cycle of cognitive radio network. It corresponds between the executed actions and the estimated rewards. Based on this phase, the agent learns from past experiences to improve his actions in the next interventions. In the literature, there are several methods that treat the artificial learning. Among them, we cite the reinforcement learning that look for the optimal policy, for ensuring the maximum reward. The present work exposes an approach, based on a model of reinforcement learning, namely Markov decision process, to maximize the sum of transfer rates of all secondary users. Such conception defines all notions relative to an environment with finite set of states, including: the agent, all states, the allowed actions with a given state, the obtained reward after the execution of an action and the optimal policy. After the implementation, we remark a correlation between the started policy and the optimal policy, and we improve the performances by referring to a previous work.


title: Recent Advances in Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.


title: The impact of social isolation on delayed hospital discharges of older hip fracture patients and associated costs

abstract: Delayed discharges represent an inefficient use of acute hospital beds. Social isolation and referral to a public-funded rehabilitation unit were significant predictors of delayed discharges while admission from an institution was a protective factor for older hip fracture patients. Preventing delays could save between 11.2 and 30.7 % of total hospital costs for this patient group.Delayed discharges of older patients from acute care hospitals are a major challenge for administrative, humanitarian, and economic reasons. At the same time, older people are particularly vulnerable to social isolation which has a detrimental effect on their health and well-being with cost implications for health and social care services. The purpose of the present study was to determine the impact and costs of social isolation on delayed hospital discharge.A prospective study of 278 consecutive patients aged 75 or older with hip fracture admitted, as an emergency, to the Orthopaedics Department of Hospital Universitario de Santa Maria, Portugal, was conducted. A logistic regression model was used to examine the impact of relevant covariates on delayed discharges, and a negative binomial regression model was used to examine the main drivers of days of delayed discharges. Costs of delayed discharges were estimated using unit costs from national databases.Mean age at admission was 85.5 years and mean length of stay was 13.1 days per patient. Sixty-two (22.3 %) patients had delayed discharges, resulting in 419 bed days lost (11.5 % of the total length of stay). Being isolated or at a high risk of social isolation, measured with the Lubben social network scale, was significantly associated with delayed discharges (odds ratio (OR) 3.5) as was being referred to a public-funded rehabilitation unit (OR 7.6). These two variables also increased the number of days of delayed discharges (2.6 and 4.9 extra days, respectively, holding all else constant). Patients who were admitted from an institution were less likely to have delayed discharges (OR 0.2) with 5.5 fewer days of delay. Total costs of delayed discharges were between 11.2 and 30.7 % of total costs (€2352 and €9317 per patient with delayed discharge) conditional on whether waiting costs for placement in public-funded rehabilitation unit were included.High risk of social isolation, social isolation and referral to public-funded rehabilitation units increase delays in patients' discharges from acute care hospitals.


title: Local text reuse detection

abstract: Text reuse occurs in many different types of documents and for many different reasons. One form of reuse, duplicate or near-duplicate documents, has been a focus of researchers because of its importance in Web search. Local text reuse occurs when sentences, facts or passages, rather than whole documents, are reused and modified. Detecting this type of reuse can be the basis of new tools for text analysis. In this paper, we introduce a new approach to detecting local text reuse and compare it to other approaches. This comparison involves a study of the amount and type of reuse that occurs in real documents, including TREC newswire and blog collections.


title: Aspect Extraction through Semi-Supervised Modeling

abstract: Aspect extraction is a central problem in sentiment analysis. Current methods either extract aspects without categorizing them, or extract and categorize them using unsupervised topic modeling. By categorizing, we mean the synonymous aspects should be clustered into the same category. In this paper, we solve the problem in a different setting where the user provides some seed words for a few aspect categories and the model extracts and clusters aspect terms into categories simultaneously. This setting is important because categorizing aspects is a subjective task. For different application purposes, different categorizations may be needed. Some form of user guidance is desired. In this paper, we propose two statistical models to solve this seeded problem, which aim to discover exactly what the user wants. Our experimental results show that the two proposed models are indeed able to perform the task effectively.


title: Feature selection for support vector machines with RBF kernel

abstract: Linear kernel Support Vector Machine Recursive Feature Elimination (SVM-RFE) is known as an excellent feature selection algorithm. Nonlinear SVM is a black box classifier for which we do not know the mapping function $${\Phi}$$ explicitly. Thus, the weight vector w cannot be explicitly computed. In this paper, we proposed a feature selection algorithm utilizing Support Vector Machine with RBF kernel based on Recursive Feature Elimination(SVM-RBF-RFE), which expands nonlinear RBF kernel into its Maclaurin series, and then the weight vector w is computed from the series according to the contribution made to classification hyperplane by each feature. Using $${w_i^2}$$ as ranking criterion, SVM-RBF-RFE starts with all the features, and eliminates one feature with the least squared weight at each step until all the features are ranked. We use SVM and KNN classifiers to evaluate nested subsets of features selected by SVM-RBF-RFE. Experimental results based on 3 UCI and 3 microarray datasets show SVM-RBF-RFE generally performs better than information gain and SVM-RFE.


title: 3D Printed Helical Soft Pneumatic Actuators

abstract: Nature abounds with complex, three-dimensional shapes, and helices which are some of the most common structures in nature. This article describes a new soft pneumatic actuator that generates bending and twisting motions. The actuator consists of several angled chambers arranged in a line that can be actuated simultaneously using one power source (air pressure). The bending and twisting trajectory of the actuator has been characterized by changing the length of the actuator, the number of chambers, and the angle of chambers using the finite element analysis (FEA). The 3D printing method of fused filament fabrication (FFF) type was employed to directly manufacture the thin-walled, airtight, and low cost helical actuators, without requiring any post-manufacturing process. Based on the above design and fabrication, an entire soft helical actuator was fabricated, and used as a gripper to handle slender objects.


title: Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture

abstract: This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train.


title: Black-Box Analysis of the Block-Cipher-Based Hash-Function Constructions from PGV

abstract: Preneel, Govaerts, and Vandewalle [6] considered the 64 most basic ways to construct a hash function H: {0, 1}* ? {0, 1}n from a block cipher E: {0, 1}n × {0, 1}n ? {0, 1}n. They regarded 12 of these 64 schemes as secure, though no proofs or formal claims were given. The remaining 52 schemes were shown to be subject to various attacks. Here we provide a formal and quantitative treatment of the 64 constructions considered by PGV. We prove that, in a black-box model, the 12 schemes that PGV singled out as secure really are secure: we give tight upper and lower bounds on their collision resistance. Furthermore, by stepping outside of the Merkle-Damgard approach to analysis, we show that an additional 8 of the 64 schemes are just as collision resistant (up to a small constant) as the first group of schemes. Nonetheless, we are able to differentiate among the 20 collision-resistant schemes by bounding their security as one-way functions. We suggest that proving black-box bounds, of the style given here, is a feasible and useful step for understanding the security of any block-cipher-based hash-function construction.


title: Clozapine induced myocarditis: A rare but fatal complication

abstract: Abstract The need to consider myocarditis as one cause of flu-like symptoms in patients taking Clozapine should be well entrenched. Suspicion should be heightened if the symptoms develop during the first 6–8 weeks of therapy. The FDA and the drug's manufacturer have strengthened warnings to include that a potentially fatal myocarditis may occur when taking Clozapine. We aim to highlight the side effects of Clozapine and increase awareness of this condition associated with the usage of the drug.


title: An Over-60dB True Rail-to-Rail Performance Using Correlated Level Shifting and an Opamp with 30dB Loop Gain

abstract: This work introduces correlated level shifting (CLS) that simultaneously decreases the error due to finite opamp gain and allows true rail-to-rail operation. An extra phase is needed, but, surprisingly, there is no speed penalty when compared to a high-gain opamp solution because CLS does not require full settling during the next-to-last phase. In fact, the increased signal swing means that the same SNR can be achieved using smaller sampling capacitors. Thus, it could be argued that CLS can provide accurate results at a higher speed than using a traditional 2-phase operation with a high-gain opamp. For example, with CLS, the performance is maintained over the whole 0.9V supply, whereas a standard configuration has only a 0.6V swing. Thus, the standard configuration requires 2.25x larger sampling capacitors to achieve the same signal to kT/C noise ratio.


SERVICE ROBOTIC SYSTEMS FOR GLASS CURTAIN WALLS CLEANING ON THE HIGH-RISE BUILDINGS


A comparative look at model transformation languages


title: A Video-Based Method for Traffic Flow Detection of Multi-lane Road

abstract: A novel algorithm for video-based traffic monitoring of multi-lane road using multiple statistical parameters is proposed to tackle the illumination condition change and adjacent moving shadow problems. This algorithm uses normalized cross correlation (NCC) of the traffic video image as the main detecting feature along with the homogeneity based parameter as the secondary parameter. The traffic flow is calculated based on the decrease-increase variation of the main detecting feature in the observation window. Blocking processing and homogeneity feature are employed to distinguish the shadow and vehicle. The results of the video test suggest that the proposed algorithm has higher efficiency and accuracy as well as better robustness against illumination condition change and adjacent moving shadow comparing to the traditional approach.


title: Better Approximation of Betweenness Centrality

abstract: Estimating the importance or centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications, e.g., social network analysis or route planning. Since exact computation is prohibitive for large networks, approximation algorithms are important. In this paper, we propose a framework for unbiased approximation of betweenness that generalizes a previous approach by Brandes. Our best new schemes yield significantly better approximation than before for many real world inputs. In particular, we also get good approximations for the betweenness of unimportant nodes.


A review of “ Asymmetric Numeral Systems ”


title: A Survey of Generative Adversarial Networks

abstract: Generative adversarial networks(GANs) coming from the game theory allow machines to learn deep representations without extra training data. By training two adversarial networks, including a generator and a discriminator, GANs could get the distribution of the real samples. This capability makes it a prospect learning method in image synthesis, image recognition, image translation etc. In this paper, we survey the state of the art of GANs by categorizing the GANs into four classifications on the basis of GANs' functions and list two application domains: vision computing & natural language processing(NLP) regarding to GANs' applications.


title: Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year.

abstract: On the basis of a new model of motivation, we examined the effects of 3 dimensions of teacher (n = 14) behavior (involvement, structure, and autonomy support) on 144 children's (Grades 3-5) behavioral and emotional engagement across a school year. Correlational and path analyses revealed that teacher involvement was central to children's experiences in the classroom and that teacher provision of both autonomy support and optimal structure predicted children's motivation across the school year. Reciprocal effects of student motivation on teacher behavior were also found. Students who showed higher initial behavioral engagement received subsequently more of all 3 teacher behaviors. These findings suggest that students who are behaviorally disengaged receive teacher responses that should further undermine their motivation. The importance of the student-teacher relationship, especially interpersonal involvement, in optimizing student motivation is highlighted. What are the factors that motivate children to learn? Educators and parents value motivation in school for its own sake as well as for its long-term contribution to children's learning and self-esteem. Highly motivated children are easy to identify: They are enthusiastic, interested, involved, and curious; they try hard and persist; and they actively cope with challenges and setbacks. These are the children who should stay in school longer, learn more, feel better about themselves, and continue their education after high school. Recent research has borne this out (Ames & Ames, 1984, 1985; Pintrich, 1991; Stipek, 1988). Although motivated students are easy to recognize, they are difficult to find. Research shows that across the preschool to high school years, children's intrinsic motivation decreases and they feel increasingly alienated from learning (Harter, 1981). Why is it so difficult to optimize student motivation? Decades of psychological and educational research


title: A qualitative analysis of an ontology based issue resolution system for cyber attack management

abstract: Cyber-attacks are increasing at an alarming rate and the attackers have progressively improved in devising attacks towards specific targets. To further develop the area of cyber-attack communication, we propose an ontology based issue resolution system used to identify and defend against cyber-attacks. The issue resolution system (IRS) facilitates attack discovery and suggestive defenses for a small to medium sized organizations. We validate our IRS Ontology using qualitative study of security expert professionals and highlight future work intended to simulate the IRS in a virtual attack test environment.


title: Airmed-cardio: a GSM and Internet services-based system for out-of-hospital follow-up of cardiac patients

abstract: A platform built around three information entities (patient, health-care/spl I.bar/agent, and central/spl I.bar/station) was designed to enable patients with chronic heart disease (in stable condition; emergency situations were excluded deliberately) to complete specifically defined protocols for out-of-hospital follow-up and monitoring. The patients belonged to one of four specific risk groups: arterial hypertension, malignant arrhythmias, heart failure, and postinfarction rehabilitation. They were provided with portable recording equipment and a cellular phone that supported data transmission [electrocardiogram (ECG)] and wireless application protocol (WAP) (remaining parameters and ad hoc questionnaires). The central station was an automatized platform, with no human operator. The information received was organized chronologically in patient folders. The health-care/spl I.bar/agents had continuous and secure access to the patient folders, through tools based on the world wide web and WAP, and to short messages sent by their patients. A pilot project was conducted with 89 patients (mean length of participation: 50.1 days). A total of 2168 ECGs (mean duration transmission=2 min/30 s; network errors < 0.1%) and 4011 short messages (none lost, in 95% of cases 30 s<delay<1 min) were transmitted; 6083 WAP sessions (mean duration=3 min 11 s; network failures < 0.1%) were held. The functionality of the platform was also evaluated, analyzing the subjective component of usability, showing the evolution of patient acceptance over time.


title: Identification of Photovoltaic Source Models

abstract: This paper presents a photovoltaic (PV) model estimation method from the PV module data. The model is based on a single-diode model of a PV cell. The cell model of a module is extended to estimate the parameters of arrays. The variation of the parameters with change in temperature and irradiance is also studied. Finally, estimation of the maximum power point from the estimated parameters under varying environmental conditions is presented.


Relevance of mtDNA analysis for forensic applications


title: Failure Behavior of Plain Weave Fabric Laminates under On-Axis Uniaxial Tensile Loading: I—Analytical Predictions

abstract: A two-dimensional woven fabric composite strength model is presented for the prediction of failure strength of two-dimensional orthogonal plain weave fabric laminates under on-axis uniaxial static tensile loading. Different stages of failure such as warp strand transverse failure, fill strand shear/transverse failure, pure matrix block failure and the failure of matrix and fiber in the fill strand in longitudinal tension are considered. Material and geometrical nonlinearities have been considered for predicting the stress-strain behavior. The studies were carried out for three idealized laminate configurations. The possible shift of layers with respect to each other along x-, Y- and -directions were considered for the laminates. A comprehensive experimental program was carried out on different material systems. A very good correlation is observed between the predicted and the experimental results.


title: Distributed containment control of multi‐agent systems with general linear dynamics in the presence of multiple leaders

abstract: SUMMARY ::: This paper considers the containment control problems for both continuous-time and discrete-time multi-agent systems with general linear dynamics under directed communication topologies. Distributed dynamic containment controllers based on the relative outputs of neighboring agents are constructed for both continuous-time and discrete-time cases, under which the states of the followers will asymptotically converge to the convex hull formed by those of the leaders if, for each follower, there exists at least one leader that has a directed path to that follower. Sufficient conditions on the existence of these dynamic controllers are given. Static containment controllers relying on the relative states of neighboring agents are also discussed as special cases. Copyright © 2011 John Wiley & Sons, Ltd.


How to Conduct a Heuristic Evaluation


title: Cloud Computing

abstract: The paper discussed the emerging technologies that can help address the challenges of trust in cloud computing. Cloud computing provides many opportunities for enterprises by offering a range of computing services. In today's competitive environment, the service dynamism, elasticity, and choices offered by this highly scalable technology are too attractive for enterprises to ignore. These opportunities, however, don't come without challenges. Cloud computing has opened up a new frontier of challenges by introducing a different type of trust scenario. Today, the problem of trusting cloud computing is a paramount concern for most enterprises. It's not that the enterprises don't trust the cloud providers' intentions; rather, they question cloud computing's capabilities.


title: Bluetooth adaptive frequency hopping and scheduling

abstract: In this paper, we investigate the use of adaptive frequency hopping (AFH) techniques aimed at modifying the Bluetooth frequency hopping sequence in the presence of WLAN direct sequence spread spectrum devices. We examine the conditions such as the applications, topologies, and scenarios under which AFH techniques improve performance that is measured in terms of packet loss, TCP delay, and channel efficiency. We also compare the results obtained with AFH to others obtained using a scheduling technique that consist in delaying the transmission of a Bluetooth packet until the medium is "idle". Our results show that an obvious performance improvement with AFH is in terms of delay and throughput. AFH brings the delay down to the same level than when no interference is present. On the other hand, AFH is rather slow in responding to changes in the environment and the packet loss is more significant than with the scheduling. This is probably due to the limitations imposed by the communication overhead. The main difficulty for AFH is having to dynamically communicate the changes to all slaves in the piconet in order to keep the synchronization.


title: Green OFDMA resource allocation in cache-enabled CRAN

abstract: Cloud radio access network (CRAN), in which remote radio heads (RRHs) are deployed to serve users in a target area, and connected to a central processor (CP) via limited-capacity links termed the fronthaul, is a promising candidate for the next-generation wireless communication systems. Due to the content-centric nature of future wireless communications, it is desirable to cache popular contents beforehand at the RRHs, to reduce the burden on the fronthaul and achieve energy saving through cooperative transmission. This motivates our study in this paper on the energy efficient transmission in an orthogonal frequency division multiple access (OFDMA)-based CRAN with multiple RRHs and users, where the RRHs can prefetch popular contents. We consider a joint optimization of the user-SC assignment, RRH selection and transmit power allocation over all the SCs to minimize the total transmit power of the RRHs, subject to the RRHs' individual fronthaul capacity constraints and the users' minimum rate constraints, while taking into account the caching status at the RRHs. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We compare the minimum transmit power required by the proposed algorithm with different caching strategies against the case without caching by simulations, which show the significant energy saving with caching.


title: Rate adaptation for adaptive HTTP streaming

abstract: Recently, HTTP has been widely used for the delivery of real-time multimedia content over the Internet, such as in video streaming applications. To combat the varying network resources of the Internet, rate adaptation is used to adapt the transmission rate to the varying network capacity. A key research problem of rate adaptation is to identify network congestion early enough and to probe the spare network capacity. In adaptive HTTP streaming, this problem becomes challenging because of the difficulties in differentiating between the short-term throughput variations, incurred by the TCP congestion control, and the throughput changes due to more persistent bandwidth changes. In this paper, we propose a novel rate adaptation algorithm for adaptive HTTP streaming that detects bandwidth changes using a smoothed HTTP throughput measured based on the segment fetch time (SFT). The smoothed HTTP throughput instead of the instantaneous TCP transmission rate is used to determine if the bitrate of the current media matches the end-to-end network bandwidth capacity. Based on the smoothed throughput measurement, this paper presents a receiver-driven rate adaptation method for HTTP/TCP streaming that deploys a step-wise increase/ aggressive decrease method to switch up/down between the different representations of the content that are encoded at different bitrates. Our rate adaptation method does not require any transport layer information such as round trip time (RTT) and packet loss rates which are available at the TCP layer. Simulation results show that the proposed rate adaptation algorithm quickly adapts to match the end-to-end network capacity and also effectively controls buffer underflow and overflow.


Shear Wave Velocity Measurements at Municipal Solid Waste Landfills in Michigan


Spatial Queries on a Hierarchical Terrain Model


title: Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark

abstract: Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users' devices, and increases the workload of market maintainers. In the space of six years, the research around this specific issue has produced 57 approaches which do not readily scale to millions of apps or are only evaluated on private datasets without, in general, tool support available to the community. Through a systematic literature review of the subject, we argue that the research is slowing down, where many state-of-the-art approaches have reported high-performance rates on closed datasets, which are unfortunately difficult to replicate and to compare against. In this work, we propose to reboot the research in repackaged app detection by providing a literature review that summarises the challenges and current solutions for detecting repackaged apps and by providing a large dataset that supports replications of existing solutions and implications of new research directions. We hope that these contributions will re-activate the direction of detecting repackaged apps and spark innovative approaches going beyond the current state-of-the-art.


Association between CYP 1 A 1 polymorphisms and cervical cancer risk : a meta-analysis in the Chinese population


title: Comparison and combination of iris matchers for reliable personal authentication

abstract: The personal identification approaches using iris images are receiving increasing attention in the biometrics literature. Several methods have been presented in the literature and those based on the phase encoding of texture information are suggested to be the most promising. However, there has not been any attempt to combine these approaches to achieve further improvement in the performance. This paper presents a comparative study of the performance from the iris authentication using Log-Gabor, Haar wavelet, DCT and FFT based features. Our experimental results suggest that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work. Therefore, the combination of these two matchers is most promising, both in terms of performance and the computational complexity. Our experimental results from the all 411 users (CASIA v3) and 224 users (IITD v1) database illustrate significant improvement in the performance which is not possible with either of these approaches individually.


title: Cross-lingual Transfer of Semantic Role Labeling Models

abstract: Semantic Role Labeling (SRL) has become one of the standard tasks of natural language processing and proven useful as a source of information for a number of other applications. We address the problem of transferring an SRL model from one language to another using a shared feature representation. This approach is then evaluated on three language pairs, demonstrating competitive performance as compared to a state-of-the-art unsupervised SRL system and a cross-lingual annotation projection baseline. We also consider the contribution of different aspects of the feature representation to the performance of the model and discuss practical applicability of this method. 1 Background and Motivation


Phishing Attack Detection , Classification and Proactive Prevention using Fuzzy Logic and Data Mining Algorithm


title: Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (Extended Abstract)

abstract: Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation.


title: A dual-layer estimator architecture for long-term localization

abstract: In this paper, we present a localization algorithm for estimating the 3D position and orientation (pose) of a moving vehicle based on visual and inertial measurements. The main advantage of the proposed method is that it provides precise pose estimates at low computational cost. This is achieved by introducing a two-layer estimation architecture that processes measurements based on their information content. Inertial measurements and feature tracks between consecutive images are processed locally in the first layer (multi-state-constraint Kalman filter) providing estimates for the motion of the vehicle at a high rate. The second layer comprises a bundle adjustment iterative estimator that operates intermittently so as to (i) reduce the effect of the linearization errors, and (ii) update the state estimates every time an area is re-visited and features are re-detected (loop closure). Through this process reliable state estimates are available continuously, while the estimation errors remain bounded during long-term operation. The performance of the developed system is demonstrated in large-scale experiments, involving a vehicle localizing within an urban area.


title: Societal Controversies in Wikipedia Articles

abstract: Collaborative content creation inevitably reaches situations where different points of view lead to conflict. We focus on Wikipedia, the free encyclopedia anyone may edit, where disputes about content in controversial articles often reflect larger societal debates. While Wikipedia has a public edit history and discussion section for every article, the substance of these sections is difficult to phantom for Wikipedia users interested in the development of an article and in locating which topics were most controversial. In this paper we present Contropedia, a tool that augments Wikipedia articles and gives insight into the development of controversial topics. Contropedia uses an efficient language agnostic measure based on the edit history that focuses on wiki links to easily identify which topics within a Wikipedia article have been most controversial and when.


title: Two-stage image denoising by principal component analysis with local pixel grouping

abstract: This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.


title: Properties and Construction of Polar Codes

abstract: Recently, Ar{\i}kan introduced the method of channel polarization on which one can construct efficient capacity-achieving codes, called polar codes, for any binary discrete memoryless channel. In the thesis, we show that decoding algorithm of polar codes, called successive cancellation decoding, can be regarded as belief propagation decoding, which has been used for decoding of low-density parity-check codes, on a tree graph. On the basis of the observation, we show an efficient construction method of polar codes using density evolution, which has been used for evaluation of the error probability of belief propagation decoding on a tree graph. We further show that channel polarization phenomenon and polar codes can be generalized to non-binary discrete memoryless channels. Asymptotic performances of non-binary polar codes, which use non-binary matrices called the Reed-Solomon matrices, are better than asymptotic performances of the best explicitly known binary polar code. We also find that the Reed-Solomon matrices are considered to be natural generalization of the original binary channel polarization introduced by Ar{\i}kan.


title: A stereo depth recovery method using belief propagation

abstract: Stereo vision refers to the ability to infer information on the 3D structure of scene from two or more images taken from different viewpoints. This paper describes procedure for depth map creating using rectified stereo images and segmentation algorithm belief propagation (BP). Very necessary steps to creating depth map are camera calibration and image rectification of the image pairs. Calibration of the stereoscopic cameras consists from a two parameters of a stereo system: intrinsic parameters, which characterize the transformation mapping an image point from camera to pixel coordinates in each camera and extrinsic parameters, which describe the relative position and orientation of the two cameras. The depth recovery is important problem of image analysis in the study of compute vision and is optimized by using the belief propagation techniques.


title: Using machine learning techniques for grapheme to phoneme transcription.

abstract: The renewed interest in grapheme to phoneme conversion (G2P), due to the need of developing multilingual speech synthesizers and recognizers, suggests new approaches more efficient than the traditional rule&exception ones. A number of studies have been performed to investigate the possible use of machine learning techniques to extract phonetic knowledge in a automatic way starting from a lexicon. In this paper, we present the results of our experiments in this research field. Starting from the state of art, our contribution is in the development of a language-independent learning scheme for G2P based on Classification and Regression Trees (CART). To validate our approach, we realized G2P converters for the following languages: British English, American English, French and Brazilian Portuguese.


title: Comparing textual descriptions to process models - The automatic detection of inconsistencies

abstract: Many organizations maintain textual process descriptions alongside graphical process models. The purpose is to make process information accessible to various stakeholders, including those who are not familiar with reading and interpreting the complex execution logic of process models. Despite this merit, there is a clear risk that model and text become misaligned when changes are not applied to both descriptions consistently. For organizations with hundreds of different processes, the effort required to identify and clear up such conflicts is considerable. To support organizations in keeping their process descriptions consistent, we present an approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach detects cases where the two process representations describe activities in different orders and detect process model activities not contained in the textual description. A quantitative evaluation with 53 real-life model-text pairs demonstrates that our approach accurately identifies inconsistencies between model and text. HighlightsWe propose an approach to detect conflicts between textual and model-based process descriptions.The approach is fully automatic based on tailored natural language processing techniques.Quantitative evaluation demonstrates the applicability of the approach on real-life data.


title: Damage to the prefrontal cortex increases utilitarian moral judgements

abstract: The psychological and neurobiological processes underlying moral judgement have been the focus of many recent empirical studies. Of central interest is whether emotions play a causal role in moral judgement, and, in parallel, how emotion-related areas of the brain contribute to moral judgement. Here we show that six patients with focal bilateral damage to the ventromedial prefrontal cortex (VMPC), a brain region necessary for the normal generation of emotions and, in particular, social emotions, produce an abnormally 'utilitarian' pattern of judgements on moral dilemmas that pit compelling considerations of aggregate welfare against highly emotionally aversive behaviours (for example, having to sacrifice one person's life to save a number of other lives). In contrast, the VMPC patients' judgements were normal in other classes of moral dilemmas. These findings indicate that, for a selective set of moral dilemmas, the VMPC is critical for normal judgements of right and wrong. The findings support a necessary role for emotion in the generation of those judgements.


title: A simple distributed autonomous power control algorithm and its convergence

abstract: For wireless cellular communication systems, one seeks a simple effective means of power control of signals associated with randomly dispersed users that are reusing a single channel in different cells. By effecting the lowest interference environment, in meeting a required minimum signal-to-interference ratio of rho per user, channel reuse is maximized. Distributed procedures for doing this are of special interest, since the centrally administered alternative requires added infrastructure, latency, and network vulnerability. Successful distributed powering entails guiding the evolution of the transmitted power level of each of the signals, using only focal measurements, so that eventually all users meet the rho requirement. The local per channel power measurements include that of the intended signal as well as the undesired interference from other users (plus receiver noise). For a certain simple distributed type of algorithm, whenever power settings exist for which all users meet the rho requirement, the authors demonstrate exponentially fast convergence to these settings. >


title: Packet-Level Telemetry in Large Datacenter Networks

abstract: Debugging faults in complex networks often requires capturing and analyzing traffic at the packet level. In this task, datacenter networks (DCNs) present unique challenges with their scale, traffic volume, and diversity of faults. To troubleshoot faults in a timely manner, DCN administrators must a) identify affected packets inside large volume of traffic; b) track them across multiple network components; c) analyze traffic traces for fault patterns; and d) test or confirm potential causes. To our knowledge, no tool today can achieve both the specificity and scale required for this task. We present Everflow, a packet-level network telemetry system for large DCNs. Everflow traces specific packets by implementing a powerful packet filter on top of "match and mirror" functionality of commodity switches. It shuffles captured packets to multiple analysis servers using load balancers built on switch ASICs, and it sends "guided probes" to test or confirm potential faults. We present experiments that demonstrate Everflow's scalability, and share experiences of troubleshooting network faults gathered from running it for over 6 months in Microsoft's DCNs.


title: Contact graph routing in DTN space networks: overview, enhancements and performance

abstract: Delay- and Disruption Tolerant Networks (DTNs) are based on an overlay protocol and on the store-carry-forward paradigm. In practice, each DTN node can store information for a long time before forwarding it. DTNs are particularly suited to cope with the challenges imposed by the space environment. This paper is focused on routing in space DTNs, and in particular on contact graph routing (CGR) and its most representative enhancements, available in the literature, which are briefly surveyed in this work. Moreover, the applicability and the obtained performance of the DTN protocol stack and of the CGR have been evaluated by presenting results from real experimental experiences such as the Deep Impact Network experiment (employing the EPOXI space cruise), the JAXA jointly performed space link demonstrations with NASA (where the JAXA’s GEO relay satellite called Data Relay Test Satellite has been used), the Space Data Routers European Project, and the pilot operation of a DTN implementation on the International Space Station (ISS).


title: A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions

abstract: This paper presents the first attempt to use word embeddings to predict the compositionality of multiword expressions. We consider both single- and multi-prototype word embeddings. Experimental results show that, in combination with a back-off method based on string similarity, word embeddings outperform a method using count-based distributional similarity. Our best results are competitive with, or superior to, state-of-the-art methods over three standard compositionality datasets, which include two types of multiword expressions and two languages.


title: Recent trends of workflow scheduling algorithms in cloud computing under Qos constraints

abstract: Cloud computing is a sort of parallel and distributed framework to supply, utilize and conveyance of IT administrations or services through the internet. Due to the development of cloud, scheduling has become one of the major challenges. Scheduling implies to a set of strategies for controlling the requests of different types of works to be performed by resources. A good scheduler is the one that makes its scheduling strategy suitable for particular use as per changing the environment and the nature of the task. Manual scheduling consumes a lot of time along with many resources. Hence to avoid these, resources to a task can easily be assigned and managed till its completion through a workflow. We studied different scheduling algorithms and based on those, parameters being affected are enumerated in this paper.


title: Offline signature verification using classifier combination of HOG and LBP features

abstract: We present an offline signature verification system based on a signature's local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP).


title: A Stretchable Electronic Fabric Artificial Skin with Pressure‐, Lateral Strain‐, and Flexion‐Sensitive Properties

abstract: A stretchable and multiple-force-sensitive electronic fabric based on stretchable coaxial sensor electrodes is fabricated for artificial-skin application. This electronic fabric, with only one kind of sensor unit, can simultaneously map and quantify the mechanical stresses induced by normal pressure, lateral strain, and flexion.


Outlier detection using autoencoders August 19 , 2016


title: A Hybrid Blockchain Ledger for Supply Chain Visibility

abstract: Optimizing physical goods distribution by providing increased visibility to trading partners can directly impact product cost. However, current supply chain information systems often lack the ability to cost-effectively relay ground truth information in near real time to all stakeholders and most importantly to the supplier and the customer during the transport of the shipment. This paper presents a solution that addresses this gap through a peer-to-peer architecture that can support the increasing demand for visibility and timely delivery of information during the physical distribution phase of supply chain. Additional features of the proposed solution include scalability, privacy and validity of the information that is delivered to the trading partners. The solution enables small, medium and large businesses to interact in a dynamic and shipment-centric manner through a private blockchain sub-ledger that digitizes the transfer of custody chain for each shipment. Information in this private ledger is augmented by a public event ledger that reflects the movement of the shipment in near real time. Third party monitors are engaged in the validation of the geolocation of the shipments by posting their physical proximity in the form of events to the public ledger.


title: Anatomically correct testbed hand control: Muscle and joint control strategies

abstract: Human hands are capable of many dexterous grasping and manipulation tasks. To understand human levels of dexterity and to achieve it with robotic hands, we constructed an anatomically correct testbed (ACT) hand which allows for the investigation of the biomechanical features and neural control strategies of the human hand. This paper focuses on developing control strategies for the index finger motion of the ACT Hand. A direct muscle position control and a force-optimized joint control are implemented as building blocks and tools for comparisons with future biological control approaches. We show how Gaussian process regression techniques can be used to determine the relationships between the muscle and joint motions in both controllers. Our experiments demonstrate that the direct muscle position controller allows for accurate and fast position tracking, while the force-optimized joint controller allows for exploitation of actuation redundancy in the finger critical for this redundant system. Furthermore, a comparison between Gaussian processes and least squares regression method shows that Gaussian processes provide better parameter estimation and tracking performance. This first control investigation on the ACT hand opens doors to implement biological strategies observed in humans and achieve the ultimate human-level dexterity.


title: Working memory for complex figures: An fMRI comparison of letter and fractal n-back tasks

abstract: n -back letter and fractal tasks were administered to 11 participants during functional magnetic resonance imaging to test process specificity theories of prefrontal cortex (PFC) function and assess task validity. Tasks were matched on accuracy, but fractal n-back responses were slower and more conservative. Maintenance (1-back minus 0-back) activated inferior parietal and dorsolateral PFC, with additional activation in right ventrolateral PFC during letter n-back and left lingual gyrus during fractal n-back. Maintenance plus manipulation (2-back minus 0-back) activated inferior parietal, Broca’s area, insula, and dorsolateral and ventral PFC, with greater right dorsolateral PFC activation for letter n-back. Manipulation only (2-back minus 1-back) produced additional and equivalent dorsolateral PFC and anterior cingulate activation in both tasks. Results support fractal n-back validity and indicate substantial overlap in working memory functions of dorsal and ventral PFC.


title: On Divergences and Informations in Statistics and Information Theory

abstract: The paper deals with the f-divergences of Csiszar generalizing the discrimination information of Kullback, the total variation distance, the Hellinger divergence, and the Pearson divergence. All basic properties of f-divergences including relations to the decision errors are proved in a new manner replacing the classical Jensen inequality by a new generalized Taylor expansion of convex functions. Some new properties are proved too, e.g., relations to the statistical sufficiency and deficiency. The generalized Taylor expansion also shows very easily that all f-divergences are average statistical informations (differences between prior and posterior Bayes errors) mutually differing only in the weights imposed on various prior distributions. The statistical information introduced by De Groot and the classical information of Shannon are shown to be extremal cases corresponding to alpha=0 and alpha=1 in the class of the so-called Arimoto alpha-informations introduced in this paper for 0<alpha<1 by means of the Arimoto alpha-entropies. Some new examples of f-divergences are introduced as well, namely, the Shannon divergences and the Arimoto alpha-divergences leading for alphauarr1 to the Shannon divergences. Square roots of all these divergences are shown to be metrics satisfying the triangle inequality. The last section introduces statistical tests and estimators based on the minimal f-divergence with the empirical distribution achieved in the families of hypothetic distributions. For the Kullback divergence this leads to the classical likelihood ratio test and estimator