title: Indirect Speech Acts

abstract: In this paper, we address several puzzles concerning speech acts,particularly indirect speech acts. We show how a formal semantictheory of discourse interpretation can be used to define speech actsand to avoid murky issues concerning the metaphysics of action. Weprovide a formally precise definition of indirect speech acts, includingthe subclass of so-called conventionalized indirect speech acts. Thisanalysis draws heavily on parallels between phenomena at the speechact level and the lexical level. First, we argue that, just as co-predicationshows that some words can behave linguistically as if they're `simultaneously'of incompatible semantic types, certain speech acts behave this way too.Secondly, as Horn and Bayer (1984) and others have suggested, both thelexicon and speech acts are subject to a principle of blocking or ``preemptionby synonymy'': Conventionalized indirect speech acts can block their`paraphrases' from being interpreted as indirect speech acts, even ifthis interpretation is calculable from Gricean-style principles. Weprovide a formal model of this blocking, and compare it withexisting accounts of lexical blocking.


title: Complication of acute stroke: A study in ten Asian countries

abstract: Background and Objective: There is a paucity of studies looking into the frequency of complications after stroke among Asians. We sought to determine the frequency and rate of complications among Asians after acute stroke. Methods: Consecutive patients with acute stroke among 10 participating Asian countries were included in the study. The frequency and timing of pre-determined complications, and their relation to area of admission were noted. Results: Of the 1,153 patients included in the study, 423 (41.9%) developed complications within the first 2 weeks of stroke. Recurrent stroke, chest infections and urinary tract infections were most commonly encountered, and were most frequent within the first week of stroke onset. A lower rate of complications was noted among patients admitted at an organized stroke unit. Conclusion: There is a similar rate of frequency and timing of complications after acute stroke among Asians as compared with other populations.


title: A Probabilistic Framework for Reserve Scheduling and ${\rm N}-1$ Security Assessment of Systems With High Wind Power Penetration

abstract: We propose a probabilistic framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation. We also identify a reserve strategy according to which we deploy the reserves in real-time operation, which serves as a corrective control action. To achieve this, we formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To incorporate a reserve decision scheme, we take into account the steady-state behavior of the secondary frequency controller and, hence, consider the deployed reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program. To achieve tractability, we propose a convex reformulation and a heuristic algorithm, whereas to deal with the chance constraint we use a scenario-based-approach and an approach that considers only the quantiles of the stationary distribution of the wind power error. To quantify the effectiveness of the proposed methodologies and compare them in terms of cost and performance, we use the IEEE 30-bus network and carry out Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain-based model.


title: Effects of Zika Virus Strain and Aedes Mosquito Species on Vector Competence

abstract: In the Western Hemisphere, Zika virus is thought to be transmitted primarily by Aedes aegypti mosquitoes. To determine the extent to which Ae. albopictus mosquitoes from the United States are capable of transmitting Zika virus and the influence of virus dose, virus strain, and mosquito species on vector competence, we evaluated multiple doses of representative Zika virus strains in Ae. aegypti and Ae. albopictus mosquitoes. Virus preparation (fresh vs. frozen) significantly affected virus infectivity in mosquitoes. We calculated 50% infectious doses to be 6.1-7.5 log 10 PFU/mL; minimum infective dose was 4.2 log 10 PFU/mL. Ae. albopictus mosquitoes were more susceptible to infection than Ae. aegypti mosquitoes, but transmission efficiency was higher for Ae. aegypti mosquitoes, indicating a transmission barrier in Ae. albopictus mosquitoes. Results suggest that, although Zika virus transmission is relatively inefficient overall and dependent on virus strain and mosquito species, Ae. albopictus mosquitoes could become major vectors in the Americas.


title: An equivalent circuit model of Frequency Selective Surfaces embedded within dielectric layers

abstract: A general equivalent circuit model for the analysis of Frequency Selective Surfaces has been presented. The lumped parameters representing the periodic surface are explained and commented. Influence of thin dielectric substrates on FSS behavior is analyzed determining a simple model to correct the lumped parameters. FSS periodicity can be changed by scaling all lumped parameters by the initial and actual periodicity ratio.


title: Data Recombination for Neural Semantic Parsing

abstract: Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a high-precision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sampled from this grammar, thereby teaching the model about these structural properties. Data recombination improves the accuracy of our RNN model on three semantic parsing datasets, leading to new state-of-the-art performance on the standard GeoQuery dataset for models with comparable supervision.


title: Identifying Exceptional Patterns in Multi-Databases

abstract: Abstract- In multi-database mining, there can be many localpatterns (frequent itemsets or association rules) in each database.At the end of multi-database mining, it is necessary to analyzethese local patterns to gain global patterns, when putting all thedata from the databases into a single dataset can destroy impor-tant information that reflect the distribution of global patterns.This paper develops an algorithm for synthesizing local patternsin multi-database is proposed. This approach is particularly fitto find potentially useful exceptions. The proposed method hasbeen evaluated experimentally. The experimental results haveshown that this method is efficient and appropriate to identifyingexceptional patterns. jlldex Terms- multi-database mining; local pattern evaluation;local pattern; global patteru; exceptional patternL INTRODUCTIONWith the increasing development and application of distributeddatabase technique and computer network, there exist many distributeddatabases in a business or financial organization. For example, a largecompany has many subsidiary companies, and each subsidiary com-pany has its own database, all of the databases from each subsidiarycompany are relevant or irrelevant in logic, but they are distributed indifferent places. Different subsidiary company has different functionsin helping the head company to make decisions. To make decisionsfor the development of company, the decision maker of the head com-pany needs to know every database's interesting pattern or regulationand then synthetically evaluate these local patterns to generate globalpatterns.It would appear to be unrealistic to collect data from differentbranches for centralized processing because of the potentially volumeof data [20]. For example, different branches of Wal-Mart collect 20million transactions per day. This is more than the rate at which datacan feasibly be collected and analyzed by using today's computingpower.On the other hand, because of data privacy and related issues, it ispossible that some databases of an organization can share their asso-ciation rules but not their original data. Therefore, mining associationrules from different databases and forwarding the rules (rather thanthe original raw data) to the central company headquarter provides afeasible way dealing with multiple database problems [19].However, current data mining researches focus on mining in mono-database, but mono-database mining is different from multi-databasemining because of their different data structure. So we need to comeup with other solutions to analyze the data in multi-databases insteadof using the technique in mono-database mining. This paper mainlydiscusses the pattern evaluation process at the end of data miningprocess and presents a method for identifying exceptional patterns.


title: Noise Reduction by Fuzzy Image Filtering

abstract: A new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions. The filter can be applied iteratively to effectively reduce heavy noise. In particular, the shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. A statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions. Experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.


title: Linear work suffix array construction

abstract: Suffix trees and suffix arrays are widely used and largely interchangeable index structures on strings and sequences. Practitioners prefer suffix arrays due to their simplicity and space efficiency while theoreticians use suffix trees due to linear-time construction algorithms and more explicit structure. We narrow this gap between theory and practice with a simple linear-time construction algorithm for suffix arrays. The simplicity is demonstrated with a Cpp implementation of 50 effective lines of code. The algorithm is called DC3, which stems from the central underlying concept of difference cover. This view leads to a generalized algorithm, DC, that allows a space-efficient implementation and, moreover, supports the choice of a space--time tradeoff. For any v ∈ l1,nr, it runs in O(vn) time using O(n/v) space in addition to the input string and the suffix array. We also present variants of the algorithm for several parallel and hierarchical memory models of computation. The algorithms for BSP and EREW-PRAM models are asymptotically faster than all previous suffix tree or array construction algorithms.


title: Acoustic model selection for recognition of regional accented speech

abstract: Accent is cited as an issue for speech recognition systems. Our experiments showed that the ASR word error rate is up to seven times greater for accented speech compared with standard British English. The main objective of this research is to develop Automatic Speech Recognition (ASR) techniques that are robust to accent variation. We applied different acoustic modelling techniques to compensate for the effects of regional accents on the ASR performance. For conventional GMM-HMM based ASR systems, we showed that using a small amount of data from a test speaker to choose an accent dependent model using an accent identification system, or building a model using the data from N neighbouring speakers in AID space, will result in superior performance compared to that obtained with unsupervised or supervised speaker adaptation. In addition we showed that using a DNN-HMM rather than a GMM-HMM based acoustic model would improve the recognition accuracy considerably. Even if we apply two stages of accent followed by speaker adaptation to the GMM-HMM baseline system, the GMM-HMM based system will not outperform the baseline DNN-HMM based system. For more contemporary DNN-HMM based ASR systems we investigated how adding different types of accented data to the training set can provide better recognition accuracy on accented speech. Finally, we proposed a new approach for visualisation of the AID feature space. This is helpful in analysing the AID recognition accuracies and analysing AID confusion matrices.


title: Generating Steganographic Images via Adversarial Training

abstract: Adversarial training has proved to be competitive against supervised learning methods on computer vision tasks. However, studies have mainly been confined to generative tasks such as image synthesis. In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic algorithm. Steganography is a collection of techniques for concealing the existence of information by embedding it within a non-secret medium, such as cover texts or images. We show that adversarial training can produce robust steganographic techniques: our unsupervised training scheme produces a steganographic algorithm that competes with state-of-the-art steganographic techniques. We also show that supervised training of our adversarial model produces a robust steganalyzer, which performs the discriminative task of deciding if an image contains secret information. We define a game between three parties, Alice, Bob and Eve, in order to simultaneously train both a steganographic algorithm and a steganalyzer. Alice and Bob attempt to communicate a secret message contained within an image, while Eve eavesdrops on their conversation and attempts to determine if secret information is embedded within the image. We represent Alice, Bob and Eve by neural networks, and validate our scheme on two independent image datasets, showing our novel method of studying steganographic problems is surprisingly competitive against established steganographic techniques.


title: Usage Bibliometrics

abstract: Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.


title: Information extraction from legal documents

abstract: Legal TRUTHS (TuRning Unstructured Texts to Helpful Structure) is a system that extracts relevant information from Philippine Supreme Court decisions, specifically on criminal cases. We describe here the processes involved in the development of Legal TRUTHS focusing on the issues relating to the domain and the geographical setting of the source documents, and the performance evaluation results are also presented. Pertinent information to be extracted for criminal cases such as the crime, the date and time of commission, the plaintiff, and the penalty were determined from a sample set of documents. Sections of these documents were identified for initial segmentation of the data. Automatic filtering of the data was involved in drawing out relevant information from the texts. From 25 training documents and also the same set for testing, performance showed over-all precision at 91.7%, recall at 99.5%, and F-measure at 95.6%. Testing on another 50 documents showed over-all precision at 84.3%, recall at 95.8%, and F-measure at 91.0%.


title: Distributed agent architecture for port automation

abstract: In the near future, container ports will no longer be able to expand into the surrounding land and will thus be unable to meet the storage requirements due to the boom in world trade. A solution to this problem is to increase the container throughput of the port by reducing the amount of time necessary to load and unload a ship. This paper presents a distributed agent architecture to achieve this task. Under such architecture, an intelligent planning algorithm is continuously optimised by the dynamic and co-operative rescheduling of yard resources such as quay cranes and container vehicles.


title: The Nencki Affective Picture System (NAPS): Introduction to a novel, standardized, wide-range, high-quality, realistic picture database

abstract: Selecting appropriate stimuli to induce emotional states is essential in affective research. Only a few standardized affective stimulus databases have been created for auditory, language, and visual materials. Numerous studies have extensively employed these databases using both behavioral and neuroimaging methods. However, some limitations of the existing databases have recently been reported, including limited numbers of stimuli in specific categories or poor picture quality of the visual stimuli. In the present article, we introduce the Nencki Affective Picture System (NAPS), which consists of 1,356 realistic, high-quality photographs that are divided into five categories (people, faces, animals, objects, and landscapes). Affective ratings were collected from 204 mostly European participants. The pictures were rated according to the valence, arousal, and approach–avoidance dimensions using computerized bipolar semantic slider scales. Normative ratings for the categories are presented for each dimension. Validation of the ratings was obtained by comparing them to ratings generated using the Self-Assessment Manikin and the International Affective Picture System. In addition, the physical properties of the photographs are reported, including luminance, contrast, and entropy. The new database, with accompanying ratings and image parameters, allows researchers to select a variety of visual stimulus materials specific to their experimental questions of interest. The NAPS system is freely accessible to the scientific community for noncommercial use by request at http://naps.nencki.gov.pl.


title: Type-2 Fuzzy Logic for Edge Detection of Gray Scale Images

abstract: Image processing is characterized by a procedure of information processing for which both the input and output are images, such as photographs or frames of video [Jain’86]. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.[webpage1] Fuzzy Image Processing (FIP) is a collection of different fuzzy approaches to image processing. Nevertheless, the following definition can be regarded as an attempt to determine the boundaries. Fuzzy image processing includes all approaches that understand, represent and process the images, their segments and features as fuzzy sets. [webpage2]The representation and processing of the images depend on the selected fuzzy technique and on the problem to be solved [Jang’95]. Here is a list of general observations about fuzzy logic:  Fuzzy logic is conceptually easy to understand.


title: Capacitor Detection in PCB Using YOLO Algorithm

abstract: Optical inspection is an important task of PCB manufacturing. Once PCB manufactured in small batch production, it needs a fast way to teach and adjust the automatic optical inspection (AOI) system for the inspection of the batch of product. This paper proposes a capacitor detection method based on YOLO algorithm for printed circuit board (PCB) assembly. YOLO is a kind of fast object detection method based on convolutional neural network (CNN). The deep network architecture of CNN can detect discrimination features from all of the input images, so we do not need experts to define image features. To verify the effectiveness of the proposed approach, samples of PCB images with nine kinds of capacitors are collected and trained by YOLO. Experimental results show all the types of capacitors in PCB can be detected and the average detection time is less than 0.3 second. The detection time is fast enough to develop an on-line PCB assembly inspection.


title: A Model Seeker: Extracting Global Constraint Models From Positive Examples

abstract: We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, which finds a ranked list of matching constraints given one or more sample call patterns. ::: ::: We have tested the modeler with 230 examples, ranging from 4 to 6,500 variables, using between 1 and 7,000 samples. These examples come from a variety of domains, including puzzles, sports-scheduling, packing & placement, and design theory. When comparing against manually specified "canonical" models for the examples, we achieve a hit rate of 50%, processing the complete benchmark set in less than one hour on a laptop. Surprisingly, in many cases the system finds usable candidate lists even when working with a single, positive example.


title: A software framework for matchmaking based on semantic web technology

abstract: An important objective of the Semantic Web is to make Electronic Commerce interactions more flexible and automated. To achieve this, standardization of ontologies, message content and message protocols will be necessary.In this paper we investigate how Semantic and Web Services technologies can be used to support service advertisement and discovery in e-commerce. In particular, we describe the design and implementation of a service matchmaking prototype which uses a DAML-S based ontology and a Description Logic reasoner to compare ontology based service descriptions. We also present the results of initial experiments testing the performance of this prototype implementation in a realistic agent based e-commerce scenario.


title: Intracellular Protein Degradation: From a Vague Idea, through the Lysosome and the Ubiquitin-Proteasome System, and onto Human Diseases and Drug Targeting Nobel Lecture

abstract: Between the 1950s and 1980s, scientists were focusing mostly on how the genetic code is transcribed to RNA and translated to proteins, but how proteins are degraded has remained a neglected research area. With the discovery of the lyso-some by Christian de Duve it was assumed that cellular proteins are degraded within this organelle. Yet, several independent lines of experimental evidence strongly suggested that intracellular proteolysis is largely non-lysosomal, but the mechanisms involved had remained obscure. The discovery of the ubiquitin-proteasome system resolved this enigma. We now recognize that ubiquitin- and proteasome-mediated degradation of intracellular proteins is involved in regulation of a broad array of cellular processes, such as cell cycle and division, regulation of transcription factors, and assurance of the cellular quality control. Not surprisingly, aberrations in the system have been implicated in the pathogenesis of many human diseases, malignancies and neurodegenerative disorders among them, which led subsequently to an increasing effort to develop mechanism-based drugs; one is already in use.


title: IrukaTact: Submersible Haptic Search Glove

abstract: IrukaTact is an open source assistive underwater glove that translates ultrasonic range finding data into haptic feedback. This paper describes the development of new models for underwater haptic actuation and the ongoing prototype development process necessary for creating this tool. Our objective is to create a system that detects underwater topographies to assist the location of sunken objects in flooded areas by sending haptic signals to wearer's fingertips produced by micropumps that propel water of varying pressures. This feedback system extends current haptic technologies by providing hybrid actuation including pressure and vibration underwater, while preserving the wearer's natural ability to grasp objects. This technology has many potential applications beyond underwater echohaptic location, such as new interfaces for virtual reality object simulation in aqueous environments.


title: OpenML Benchmarking Suites and the OpenML100

abstract: We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. Major distinguishing features of OpenML benchmark suites are (a) ease of use through standardized data formats, APIs, and existing client libraries; (b) machine-readable meta-information regarding the contents of the suite; and (c) online sharing of results, enabling large scale comparisons. As a first such suite, we propose the OpenML100, a machine learning benchmark suite of 100~classification datasets carefully curated from the thousands of datasets available on OpenML.org.


title: A Head Pose-free Approach for Appearance-based Gaze Estimation

abstract: T o infer human gaze from eye appearance, various methods have been proposed. However, most of them assume a fixed head pose because allowing free head motion adds 6 degrees of freedom to the problem and requires a prohibitively large number of training samples. In this paper, we aim at solving the appearance-based gaze estimation problem under free head motion without significantly increasing the cost of training. The idea is to decompose the problem into subproblems, including initial estimation under fixed head pose and subsequent compensations for estimation biases caused by head rotation and eye appearance distortion. Then each subproblem is solved by either learning-based method or geometric-based calculation. Specifically, the gaze estimation bias caused by eye appearance distortion is learnt effectively from a 5-seconds video clip. Extensive experiments were conducted to verify the effectiveness of the proposed approach.


title: Markov chain Monte Carlo without likelihoods

abstract: Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte Carlo method for generating observations from a posterior distribution without the use of likelihoods. It can also be used in frequentist applications, in particular for maximum-likelihood estimation. The approach is illustrated by an example of ancestral inference in population genetics. A number of open problems are highlighted in the discussion.


Cluster ensemble selection


From the inevitable to the undesirable: The experience of the stigma of the aging process in the contemporary world


title: The Re-identification Challenge

abstract: For making sense of the vast quantity of visual data generated by the rapid expansion of large-scale distributed multi-camera systems, automated person re-identification is essential. However, it poses a significant challenge to computer vision systems. Fundamentally, person re-identification requires to solve two difficult problems of ‘finding needles in haystacks’ and ‘connecting the dots’ by identifying instances and associating the whereabouts of targeted people travelling across large distributed space–time locations in often crowded environments. This capability would enable the discovery of, and reasoning about, individual-specific long-term structured activities and behaviours. Whilst solving the person re-identification problem is inherently challenging, it also promises enormous potential for a wide range of practical applications, ranging from security and surveillance to retail and health care. As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. This chapter introduces the re-identification problem, highlights the difficulties in building person re-identification systems, and presents an overview of recent progress and the state-of-the-art approaches to solving some of the fundamental challenges in person re-identification, benefiting from research in computer vision, pattern recognition and machine learning, and drawing insights from video analytics system design considerations for engineering practical solutions. It also provides an introduction of the contributing chapters of this book. The chapter ends by posing some open questions for the re-identification challenge arising from emerging and future applications.


title: Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices

abstract: The pedestrian inertial navigation systems are generally based on Pedestrian Dead Reckoning (PDR) algorithm. Considering the physiological characteristics of pedestrian movement, we use the cyclical characteristics and statistics of acceleration waveform and features which are associated with the walking speed to estimate the stride length. Due to the randomness of the pedestrian hand-held habit, the step events cannot always be detected by using the periods of zero velocity updates (ZUPTs). Furthermore, the signal patterns of the sensor could differ significantly depending on the carrying modes and the user's hand motion. Hence, the step detection and the associated adaptive step length model using a handheld device equipped with accelerometer is required to obtain high-accurate measurements. To achieve this goal, a compositional algorithm of empirical formula and Back-propagation neural network by using handheld devices is proposed to estimate the step length, as well as achieve the accuracy of step detection higher than 98%. Furthermore, the proposed joint step detection and adaptive step length estimation algorithm can help much in the development of Pedestrian Navigation Devices (PNDs) based on the handheld inertial sensors. Index Terms—Pedestrian inertial navigation; handheld devices; step detection; step length estimation; accelerometer


title: Robust Adaptive Segmentation of Range Images

abstract: We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squares of residuals. The optimal value of k is determined from the data, and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques. The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods.


title: The affective and cognitive empathic nature of the dark triad of personality

abstract: Abstract The dark triad represents the most prominent, socially aversive personalities (viz., Psychopathy, Narcissism, and Machiavellianism) characterised by a common underlying deficit in empathy. Although, evidence shows that empathy can be further divided into cognitive and affective systems, this two-dimensional conceptualisation had not been considered when examining the empathic impairments of the complete dark triad. The present study aimed to determine whether the dark triad is associated with deficits in cognitive or affective empathy as measured through self-reports and facial expressions tasks. The sample comprised 139 university students. All dark triad personalities were associated with deficits in affective empathy, but showed little evidence of impairment in cognitive empathy. The facial expression tasks provided further support for the affective nature of the dark triad’s empathic deficits. Finally, the results emphasised the importance of primary psychopathy, as the main predictor of empathic deficits within the dark triad.


title: Web application security assessment by fault injection and behavior monitoring

abstract: As a large and complex application platform, the World Wide Web is capable of delivering a broad range of sophisticated applications. However, many Web applications go through rapid development phases with extremely short turnaround time, making it difficult to eliminate vulnerabilities. Here we analyze the design of Web application security assessment mechanisms in order to identify poor coding practices that render Web applications vulnerable to attacks such as SQL injection and cross-site scripting. We describe the use of a number of software-testing techniques (including dynamic analysis, black-box testing, fault injection, and behavior monitoring), and suggest mechanisms for applying these techniques to Web applications. Real-world situations are used to test a tool we named the Web Application Vulnerability and Error Scanner (WAVES, an open-source project available at http://waves.sourceforge.net) and to compare it with other tools. Our results show that WAVES is a feasible platform for assessing Web application security.


title: Intelligent sensor attack detection and identification for automotive cyber-physical systems

abstract: This paper addresses the problem of detection and identification of the sensor attacks when most sensors are attacked. Sensors can play a key role to improve safety and convenience in automotive Cyber-Physical Systems (CPS). A dramatic increase in connectivity and openness of the automotive CPS brings high security risks. If multiple and heterogeneous sensors equipped for braking and steering provides false sensing information for their controllers under deception attacks, it might cause catastrophic situations during driving. If the existing machine learning approaches are applied for sensor attacks while the majority of sensors is attacked, it cannot guarantee to identify deceptions as cyber-physical attacks. To address this problem, we propose an intelligent sensor attack detection and identification method based on Deep Neural Network (DNN) techniques, called deep learning, without a prior knowledge about the deception attacks modifying sensing data in time. We investigate an autonomous vehicle with Inertial Measurement Unit (IMU) and wheel encoder sensors under conditions of uncertainty and nonlinearity during driving. We firstly identify all possible attacks category on the sensors of it, choose what model to use and then systematically design its architecture on which the performance of deep learning highly depends. We train and then validate the proposed method's performance on real measurement data obtained from an unmanned ground vehicle. Finally, we show analytically the superiority of our method in terms of accuracy, precision, and computation time, including the worst situation where two among three sensors are simultaneously attacked.


title: Towards a Stochastic Model for Integrated detection and filtering of DoS attacks in Cloud environments

abstract: Cloud Data Center (CDC) security remains a major challenge for business organizations and takes an important concern with research works. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs. As a result, Distributed Denial of Service (DDoS) attacks have appeared as the most popular attack. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to users. This paper provides a defending system in order to mitigate the Denial of Service (DoS) attack in CDC environment. Basically it outlines the different techniques of DoS attacks and its countermeasures by combining the filtering and detection mechanisms. We presented an analytical model based on queueing model to evaluate the impact of flooding attack on cloud environment regarding service availability and QoS performance. Consequently, we have plotted the response time, throughput, drop rate and resource computing utilization varying the attack arrival rate. We have used JMT (Java Modeling Tool) simulator to validate the analytical model. Our approach was appeared powerful for attacks mitigation in the cloud environment.


title: Privacy-preserving k-means clustering over vertically partitioned data

abstract: Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for k-means clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.


title: Heart-lung and lung transplantation for cystic fibrosis.

abstract: End-stage lung disease in Cystic Fibrosis (CF) now is considered to be one of the indications for heart-lung or double lung transplantation. Results of this surgery for 50 or so CF patients in the US and Europe are about the same as for other diseases, although there are some postoperative problems specific for this diagnosis. These include: need for higher oral dosages of cyclosporine, likelihood of precipitation of diabetes mellitus with high dosage corticosteroid therapy for acute lung rejection, constant threat of pathogens remaining in the sinuses, increased likelihood of drug toxicity to the liver and kidneys, and need to make a psychological transition from a patient with a fatal disease to one with optimism about the future. Although improved postoperative management likely will improve postoperative mortality and morbidity, scarcity of donor organs and the high cost of the procedure will limit the impact of this procedure on the general CF population.


title: Controller design and fault tolerance analysis of 4-phase floating interleaved boost converter for fuel cell electric vehicles

abstract: Owing to the energy shortage and the increasingly serious environmental pollution, fuel cell electric vehicles (FCEV) with zero-emission and high-efficiency have been expected to be the most potential candidate to substitute the conventional vehicles. The DC/DC converter is the interface between the fuel cell (FC) and the driveline of FCEV. It not only needs high voltage gain to convert the wide FC voltage into an appropriate voltage level, but also needs the capacity of fault tolerance to enhance the reliability of the system. For this reason, floating interleaved boost converters (FIBC) seem to be the optimal selection. Despite this topology can continue operating without interruption under the action of the proper control scheme in the case of power switch open circuit fault (OCF), operating in degraded mode has adverse impacts on the component stress and the input current ripple. Hence, this paper aims to design an effective controller to maintain the dc bus voltage constant and to demonstrate thorough theoretical analysis and simulation verification of these undesirable effects.


title: Overcoming Security Challenges in Microservice Architectures

abstract: The microservice architectural style is an emerging trend in software engineering that allows building highly scalable and flexible systems. However, current state of the art provides only limited insight into the particular security concerns of microservice system. With this paper, we seek to unravel some of the mysteries surrounding microservice security by: providing a taxonomy of microservices security; assessing the security implications of the microservice architecture; and surveying related contemporary solutions, among others Docker Swarm and Netflix security decisions. We offer two important insights. On one hand, microservice security is a multi-faceted problem that requires a layered security solution that is not available out of the box at the moment. On the other hand, if these security challenges are solved, microservice architectures can improve security; their inherent properties of loose coupling, isolation, diversity, and fail fast all contribute to the increased robustness of a system. To address the lack of security guidelines this paper describes the design and implementation of a simple security framework for microservices that can be leveraged by practitioners. Proof-of-concept evaluation results show that the performance overhead of the security mechanisms is around 11%.


title: Impact of Spreading Factor Imperfect Orthogonality in LoRa Communications

abstract: In this paper we study the impact of imperfect-orthogonality in LoRa spreading factors (SFs) in simulation and real-world experiments. First, we analyze LoRa modulation numerically and show that collisions between packets of different SFs can indeed cause packet loss if the interference power received is strong enough. Second, we validate such findings using commercial devices, confirming our numerical results. Third, we modified and extended LoRaSim, an open-source LoRa simulator, to measure the impact of inter-SF collisions and fading (which was not taken into account previously in the simulator). Our results show that non-orthogonality of the SFs can deteriorate significantly the performance especially of higher SFs (10 to 12) and that fading has virtually no impact when multiple gateways are available in space diversity.


title: GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

abstract: Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using the Gumbel-softmax distribution, which is a continuous approximation to a multinomial distribution parameterized in terms of the softmax function. In this work, we evaluate the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements.


title: Robust object tracking via online multiple instance metric learning

abstract: This paper presents a novel object tracking method using online multiple instance metric learning to adaptively capture appearance variations. More specifically, we seek for an appropriate metric via online metric learning to match the different appearances of an object and simultaneously separate the object from the background. The drift problem caused by potentially misaligned training examples is alleviated by performing online metric learning under the multiple instance setting. Both qualitative and quantitative evaluations on various challenging sequences are discussed.


title: C-Store: A Column-oriented DBMS

abstract: This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.


title: Extensive study of text based methods for opinion mining

abstract: Opining mining is widely used in most of real time applications. The opinion features are described by the contents and an attribute of contents are used by users to express their opinion such as negative reviews or positive reviews. The accurate identification of content features gathered from the unstructured textual data is major research challenge. Therefore, it is a must to design an efficient technique which will be able to identify such features from the unstructured datasets. There are number of state-of-art techniques reported recently. There are three main components of opinion mining and analysis methods such as pre-processing, feature extraction and classification. There are only few methods considering the pre-processing of input text data. After pre-processing step, the next step is feature extraction. Effective feature extraction method leads to accurate prediction based on user's feedback. Some techniques which present the extraction of opinion based feature in dataset mining patterns are used by single review corpus and ignored by the non-trivial disparity in the word of distributional characteristics of opinion feature. Such methods are not sufficient to predict the user feedback correctly. After the feature extraction, last phase is the prediction model which is based on classifiers. This paper presents the study on recent opinion mining methods and its comparative analysis with an aim of identifying the current research problems and their significance. The outcome of this paper is research gaps in present solutions.


title: Foreground segmentation using adaptive mixture models in color and depth

abstract: Segmentation of novel or dynamic objects in a scene, often referred to as "background subtraction" or foreground segmentation", is a critical early in step in most computer vision applications in domains such as surveillance and human-computer interaction. All previously described, real-time methods fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-reflections, similarity of foreground color to background and non-static backgrounds (e.g. active video displays or trees waving in the wind). The advent of hardware and software for real-time computation of depth imagery makes better approaches possible. We propose a method for modeling the background that uses per-pixel, time-adaptive, Gaussian mixtures in the combined input space of depth and luminance-invariant color. This combination in itself is novel, but we further improve it by introducing the ideas of (1) modulating the background model learning rate based on scene activity, and (2) making color-based segmentation criteria dependent on depth observations. Our experiments show that the method possesses much greater robustness to problematic phenomena than the prior state-of-the-art, without sacrificing real-time performance, making it well-suited for a wide range of practical applications in video event detection and recognition.


title: Ensemble learning for credit card fraud detection

abstract: Timely detection of fraudulent credit card transactions is a business critical and challenging problem in Financial Industry. Specifically, we must deal with the highly skewed nature of the dataset, that is, the ratio of fraud to normal transactions is very small. In this work, we present an ensemble machine learning approach as a possible solution to this problem. Our observation is that Random Forest is more accurate in detecting normal instances, and Neural Network is for detecting fraud instances. We present an ensemble method - based on a combination of random forest and neural network - which keeps the best of both worlds, and is able to predict with high accuracy and confidence the label of a new sample. We experimentally validate our observations on real world datasets.


title: Constrained Laplacian Eigenmap for dimensionality reduction

abstract: Dimensionality reduction is a commonly used tool in machine learning, especially when dealing with high dimensional data. We consider semi-supervised graph based dimensionality reduction in this paper, and a novel dimensionality reduction algorithm called constrained Laplacian Eigenmap (CLE) is proposed. Suppose the data set contains r classes, and for each class we have some labeled points. CLE maps each data point into r different lines, and each map i tries to separate points belonging to class i from others by using label information. CLE constrains the solution space of Laplacian Eigenmap only to contain embedding results that are consistent with the labels. Then, each point is represented as a r-dimensional vector. Labeled points belonging to the same class are merged together, labeled points belonging to different classes are separated, and similar points are close to one another. We perform semi-supervised document clustering using CLE on two standard corpora. Experimental results show that CLE is very effective.


title: Happiness in the face of adversity: Reformulating the dynamic and modular bases of subjective well-being.

abstract: Addressing diversity and apparent contradictions in manifestations of happiness, this article delineates subjective well-being (SWB) as a dynamic system in the face of possible adversity. SWB constitutes a favorable psychological environment that regulates the hostile-world scenario, defined as one’s image of actual or potential threats to one’s life or integrity. SWB operates in various modules: experiential, wherein private awareness of SWB dwells on relevant core themes; declarative, wherein public selfreports of SWB function as social behavior; differential, wherein synchronic dimensions of SWB form well-being types; and narrative, wherein diachronic valences of SWB construct trajectories along one’s life story. By explicating the regulatory and configurational nature of SWB, the present conceptualization emphasizes the process, rather than the outcome, of pursuing happiness.


title: Creating state of the art, next-generation Virtual Reality exposure therapies for anxiety disorders using consumer hardware platforms: design considerations and future directions

abstract: AbstractDecades of research and more than 20 randomized controlled trials show that Virtual Reality exposure therapy (VRET) is effective in reducing fear and anxiety. Unfortunately, few providers or patients have had access to the costly and technical equipment previously required. Recent technological advances in the form of consumer Virtual Reality (VR) systems (e.g. Oculus Rift and Samsung Gear), however, now make widespread use of VRET in clinical settings and as self-help applications possible. In this literature review, we detail the current state of VR technology and discuss important therapeutic considerations in designing self-help and clinician-led VRETs, such as platform choice, exposure progression design, inhibitory learning strategies, stimuli tailoring, gamification, virtual social learning and more. We illustrate how these therapeutic components can be incorporated and utilized in VRET applications, taking full advantage of the unique capabilities of virtual environments, and showcase some...


title: Bilinear deep learning for image classification

abstract: Image classification is a well-known classical problem in multimedia content analysis. This paper proposes a novel deep learning model called bilinear deep belief network (BDBN) for image classification. Unlike previous image classification models, BDBN aims to provide human-like judgment by referencing the architecture of the human visual system and the procedure of intelligent perception. Therefore, the multi-layer structure of the cortex and the propagation of information in the visual areas of the brain are realized faithfully. Unlike most existing deep models, BDBN utilizes a bilinear discriminant strategy to simulate the "initial guess" in human object recognition, and at the same time to avoid falling into a bad local optimum. To preserve the natural tensor structure of the image data, a novel deep architecture with greedy layer-wise reconstruction and global fine-tuning is proposed. To adapt real-world image classification tasks, we develop BDBN under a semi-supervised learning framework, which makes the deep model work well when labeled images are insufficient. Comparative experiments on three standard datasets show that the proposed algorithm outperforms both representative classification models and existing deep learning techniques. More interestingly, our demonstrations show that the proposed BDBN works consistently with the visual perception of humans.


title: How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation

abstract: The creation of golden standard datasets is a costly business. Optimally more than one judgment per document is obtained to ensure a high quality on annotations. In this context, we explore how much annotations from experts differ from each other, how different sets of annotations influence the ranking of systems and if these annotations can be obtained with a crowdsourcing approach. This study is applied to annotations of images with multiple concepts. A subset of the images employed in the latest ImageCLEF Photo Annotation competition was manually annotated by expert annotators and non-experts with Mechanical Turk. The inter-annotator agreement is computed at an image-based and concept-based level using majority vote, accuracy and kappa statistics. Further, the Kendall τ and Kolmogorov-Smirnov correlation test is used to compare the ranking of systems regarding different ground-truths and different evaluation measures in a benchmark scenario. Results show that while the agreement between experts and non-experts varies depending on the measure used, its influence on the ranked lists of the systems is rather small. To sum up, the majority vote applied to generate one annotation set out of several opinions, is able to filter noisy judgments of non-experts to some extent. The resulting annotation set is of comparable quality to the annotations of experts.


title: Field-guided registration for feature-conforming shape composition

abstract: We present an automatic shape composition method to fuse two shape parts which may not overlap and possibly contain sharp features, a scenario often encountered when modeling man-made objects. At the core of our method is a novel field-guided approach to automatically align two input parts in a feature-conforming manner. The key to our field-guided shape registration is a natural continuation of one part into the ambient field as a means to introduce an overlap with the distant part, which then allows a surface-to-field registration. The ambient vector field we compute is feature-conforming; it characterizes a piecewise smooth field which respects and naturally extrapolates the surface features. Once the two parts are aligned, gap filling is carried out by spline interpolation between matching feature curves followed by piecewise smooth least-squares surface reconstruction. We apply our algorithm to obtain feature-conforming shape composition on a variety of models and demonstrate generality of the method with results on parts with or without overlap and with or without salient features.


title: Competitive algorithms for VWAP and limit order trading

abstract: We introduce new online models for two important aspectsof modern financial markets: Volume Weighted Average Pricetrading and limit order books. We provide an extensivestudy of competitive algorithms in these models and relatethem to earlier online algorithms for stock trading.


title: Synonymous paraphrasing using wordnet and internet

abstract: We propose a method of synonymous paraphrasing of a text based on WordNet synonymy data and Internet statistics of stable word combinations (collocations). Given a text, we look for words or expressions in it for which WordNet provides synonyms, and substitute them with such synonyms only if the latter form valid collocations with the surrounding words according to the statistics gathered from Internet. We present two important applications of such synonymous paraphrasing: (1) style-checking and correction: automatic evaluation and computer-aided improvement of writing style with regard to various aspects (increasing vs. decreasing synonymous variation, conformistic vs. individualistic selection of synonyms, etc.) and (2) steganography: hiding of additional information in the text by special selection of synonyms. A basic interactive algorithm of style improvement is outlined and an example of its application to editing of newswire text fragment in English is traced. Algorithms of style evaluation and information hiding are also proposed.


title: Evolution and classification of the CRISPR–Cas systems

abstract: The CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) modules are adaptive immunity systems that are present in many archaea and bacteria. These defence systems are encoded by operons that have an extraordinarily diverse architecture and a high rate of evolution for both the cas genes and the unique spacer content. Here, we provide an updated analysis of the evolutionary relationships between CRISPR-Cas systems and Cas proteins. Three major types of CRISPR-Cas system are delineated, with a further division into several subtypes and a few chimeric variants. Given the complexity of the genomic architectures and the extremely dynamic evolution of the CRISPR-Cas systems, a unified classification of these systems should be based on multiple criteria. Accordingly, we propose a 'polythetic' classification that integrates the phylogenies of the most common cas genes, the sequence and organization of the CRISPR repeats and the architecture of the CRISPR-cas loci.


Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts


title: Pauses, gaps and overlaps in conversations

abstract: This paper explores durational aspects of pauses gaps and overlaps in three different conversational corpora with a view to challenge claims about precision timing in turn-taking Distributions of p ...


title: A Process for Data Protection Impact Assessment Under the European General Data Protection Regulation

abstract: With the General Data Protection Regulation there will be a legal obligation for controllers to conduct a Data Protection Impact Assessment for the first time. This paper examines the new provisions in detail and examines ways for their successful implementation. It proposes a process which operationalizes established requirements ensuring the appropriate attention to fundamental rights as warranted by the GDPR, incorporates the legislation’s new requirements and can be adapted to suit the controller’s needs.


title: Analysis and optimization of combined equalizer for high speed serial link

abstract: Many factors influence the choice of equalization for high speed serial link. This paper presents the analysis and optimization of combined equalizer for high speed serial link based on the ADS simulation. By modeling the high speed serial link, which mainly includes the equalization for the loss of channel, the performance of various equalizers including pre-emphasis in transmitter, CTLE/DFE in receiver and some combined equalizers are analyzed for different channels. The results show that much better performance can be obtained by using some combined equalization architectures compared to using CTLE or DFE separately, and the cost is only a little increase in complexity.


title: A phenomenological model of EEG based on the dynamics of a stochastic Duffing-van der Pol oscillator network

abstract: Abstract In this work, we propose a novel phenomenological model of the EEG signal based on the dynamics of a coupled Duffing-van der Pol oscillator network. An optimization scheme is adopted to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions. It is shown that a coupled system of two Duffing-van der Pol oscillators with optimized parameters yields signals with characteristics that match those of the EEG in both the EO and EC cases. The results, which are reinforced using statistical analysis, show that the EEG recordings under EC and EO resting conditions are clearly distinct realizations of the same underlying model occurring due to parameter variations with qualitatively different nonlinear dynamic characteristics. In addition, the interplay between noise and nonlinearity is addressed and it is shown that, for appropriately chosen values of noise intensity in the model, very good agreement exists between the model output and the EEG in terms of the power spectrum as well as Shannon entropy. In summary, the results establish that an appropriately tuned stochastic coupled nonlinear oscillator network such as the Duffing-van der Pol system could provide a useful framework for modeling and analysis of the EEG signal. In turn, design of algorithms based on the framework has the potential to positively impact the development of novel diagnostic strategies for brain injuries and disorders.


title: Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

abstract: Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.


title: Automatic construction and evaluation of a large semantically enriched wikipedia

abstract: The hyperlink structure of Wikipedia constitutes a key resource for many Natural Language Processing tasks and applications, as it provides several million semantic annotations of entities in context. Yet only a small fraction of mentions across the entire Wikipedia corpus is linked. In this paper we present the automatic construction and evaluation of a Semantically Enriched Wikipedia (SEW) in which the overall number of linked mentions has been more than tripled solely by exploiting the structure of Wikipedia itself and the wide-coverage sense inventory of BabelNet. As a result we obtain a sense-annotated corpus with more than 200 million annotations of over 4 million different concepts and named entities. We then show that our corpus leads to competitive results on multiple tasks, such as Entity Linking and Word Similarity.


title: Learning to rank for spatiotemporal search

abstract: In this article we consider the problem of mapping a noisy estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. Despite the poor accuracy of GPS on current mobile devices and the relatively high density of places in urban areas, it is possible to predict a user's location with considerable precision by explicitly modeling both places and users and by combining a variety of signals about a user's current context. Places are often simply modeled as a single latitude and longitude when in fact they are complex entities existing in both space and time and shaped by the millions of people that interact with them. Similarly, models of users reveal complex but predictable patterns of mobility that can be exploited for this task. We propose a novel spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. We evaluate a variety of techniques and demonstrate that machine learning algorithms for ranking and spatiotemporal models of places and users offer significant improvement over common methods for location search based on distance and popularity.


title: Low stiffness design and hysteresis compensation torque control of SEA for active exercise rehabilitation robots

abstract: Rehabilitation robots for active exercise requires compliant but consistent torque (or force) assistance (or imposition) while passive rehabilitation robots are programmed to execute certain movements for patients. This kind of torque assistance in the rehabilitation system can be provided by using SEA (Series Elastic Actuator). In this paper, low stiffness SEA and spring hysteresis compensation are proposed for the robust and accurate torque control of the rehabilitation robot. C-DSSAS (Compact Dual Spiral Spring Actuation System) is developed to implement the low stiffness SEA and a hysteresis compensation method is proposed for robust accurate torque control. Pros and cons of low stiffness SEAs are dealt with in order to explain validity of proposed application. In addition, proposed hysteresis compensation torque controller has backlash-based polynomial model and passivity-based control. Experiments of active exercise of knee were performed wearing the knee rehabilitation device using the C-DSSAS with hysteresis control. The experimental results demonstrate its improved performance of the robot in terms of the robustness and accuracy.


title: A social reinforcement learning agent

abstract: We report on our reinforcement learning work on Cobot, a software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot~\cite{cobotaaai} provided him with the ability to collect {\em social statistics\/} and report them to users in a reactive manner. Here we describe our application of reinforcement learning to allow Cobot to proactively take actions in this complex social environment, and adapt his behavior from multiple sources of human reward. After 5 months of training, Cobot received 3171 reward and punishment events from 254 different Lambda\-MOO users, and learned nontrivial preferences for a number of users. Cobot modifies his behavior based on his current state in an attempt to maximize reward. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.


title: Wireless ad-hoc networks with high node density for home AV transmission

abstract: Wireless ad-hoc networks are a key technology for the set up of home AV distribution system and the services supported to the consumer. Broad market penetration of such networks leads to high densities of active transmission links. The paper describes related problems and a solution approach based on the physical layer of the IEEE 802.11 standard. The concept of cooperative nodes introduced in the paper might be helpful to set up self organizing networks with high spectral efficiency for consumer homes.


title: Numerical Analysis of the Power Balance of an Electrical Machine With Rotor Eccentricity

abstract: The power balance in the numerical simulation of a cage induction machine with eccentric rotor has been studied. The asymmetrical air-gap flux density distribution caused by the non-uniform air gap due to eccentricity produced forces that play an important role in the rotor dynamic stability. These forces act both in the radial and the tangential directions. The tangential force together with the whirling motion produces additional power in the shaft. If the power balance of the simulation satisfies, the power due to the whirling can be calculated from the power balance. This could also give a new approach to compute the forces due to eccentricity or verify the existing force computation methods. The error in the power balance of an eccentric machine has been calculated and the sources of the errors have been illustrated and discussed.


title: Adjustable properties of visual representations: Improving the quality of human-information interaction

abstract: Complex cognitive activities, such as analytical reasoning, problem solving, and sense making, are often performed through the mediation of interactive computational tools. Examples include visual analytics, decision support, and educational tools. Through interaction with visual representations of information at the visual interface of these tools, a joint, coordinated cognitive system is formed. This partnership results in a number of relational properties-those depending on both humans and tools-that researchers and designers must be aware of if such tools are to effectively support the performance of complex cognitive activities. This article presents 10 properties of interactive visual representations that are essential and relational and whose values can be adjusted through interaction. By adjusting the values of these properties, better coordination between humans and tools can be effected, leading to higher quality performance of complex cognitive activities. This article examines how the values of these properties affect cognitive processing and visual reasoning and demonstrates the necessity of making their values adjustable-all of which is situated within a broader theoretical framework concerned with human-information interaction in complex cognitive activities. This framework can facilitate systematic research, design, and evaluation in numerous fields including information visualization, health informatics, visual analytics, and educational technology.


title: A Five-Level Design Framework for Bicluster Visualizations

abstract: Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.


title: Design of passive UHF RFID circulators/duplexers using directional couplers

abstract: The design of passive circulators/duplexers for RFID applications in the UHF band is presented. The design idea is based on the use of simple passive directional couplers, offering simplicity and low cost compared to ferrite devices or active architectures. The Tx/Rx isolation problem is solved exploiting a mismatched load, which creates a cancelling signal on the Rx port. Additional impedance matching is applied at the Tx port, in order to balance the effects of the mismatch load at the input (Tx). Both branch-line and ring couplers are alternatively used, designed mainly for unequal power division between output ports. A variety of design examples is presented, including miniaturized topologies, aiming at the reduction of size and cost. Design frequency is chosen at 900MHz, trying to cover both 860MHz and 910MHz bands.


title: Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs

abstract: In order to match shoppers with desired products and provide personalized promotions, whether in online or offline shopping worlds, it is critical to model both consumer preferences and price sensitivities simultaneously. Personalized preferences have been thoroughly studied in the field of recommender systems, though price (and price sensitivity) has received relatively little attention. At the same time, price sensitivity has been richly explored in the area of economics, though typically not in the context of developing scalable, working systems to generate recommendations. In this study, we seek to bridge the gap between large-scale recommender systems and established consumer theories from economics, and propose a nested feature-based matrix factorization framework to model both preferences and price sensitivities. Quantitative and qualitative results indicate the proposed personalized, interpretable and scalable framework is capable of providing satisfying recommendations (on two datasets of grocery transactions) and can be applied to obtain economic insights into consumer behavior.


title: NEPTUNE: Real Time Stream Processing for Internet of Things and Sensing Environments

abstract: Improvements in miniaturization and networking capabilities of sensors have contributed to the proliferation of Internet of Things (IoT) and continuous sensing environments. Data streams generated in such settings must keep pace with generation rates and be processed in real time. Challenges in accomplishing this include: high data arrival rates, buffer overflows, context-switches, and object creation overheads. We propose a holistic framework that addresses the CPU, memory, network, and kernel issues involved in stream processing. Our prototype, Neptune, builds on our Granules cloud runtime. The framework maximizes bandwidth utilization in the presence of small messages via the use of buffering and dynamic compactions of packets based on payload entropy. Our use of thread-pools and batched processing reduces context switches and improves effective CPU utilizations. NEPTUNE alleviates memory pressure that can lead to swapping, page faults, and thrashing through efficient reuse of objects. To cope with buffer overflows we rely on flow control and throttling the preceding stages of a processing pipeline. Our benchmarks demonstrate the suitability of the Neptune and we contrast our performance with Apache Storm, the dominant stream-processing framework developed by Twitter. At a single node, we are able to achieve a processing rate of ~2 million stream packets per-second. In a distributed setup, we achieved a rate of ~100 million packets per-second.


title: Assessing team strategy using spatiotemporal data

abstract: The "Moneyball" revolution coincided with a shift in the way professional sporting organizations handle and utilize data in terms of decision making processes. Due to the demand for better sports analytics and the improvement in sensor technology, there has been a plethora of ball and player tracking information generated within professional sports for analytical purposes. However, due to the continuous nature of the data and the lack of associated high-level labels to describe it - this rich set of information has had very limited use especially in the analysis of a team's tactics and strategy. In this paper, we give an overview of the types of analysis currently performed mostly with hand-labeled event data and highlight the problems associated with the influx of spatiotemporal data. By way of example, we present an approach which uses an entire season of ball tracking data from the English Premier League (2010-2011 season) to reinforce the common held belief that teams should aim to "win home games and draw away ones". We do this by: i) forming a representation of team behavior by chunking the incoming spatiotemporal signal into a series of quantized bins, and ii) generate an expectation model of team behavior based on a code-book of past performances. We show that home advantage in soccer is partly due to the conservative strategy of the away team. We also show that our approach can flag anomalous team behavior which has many potential applications.


title: Community detection and visualization in social networks: Integrating structural and semantic information

abstract: Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, i.e., how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles and paths from one community to another. This paper presents a novel model to use in a conjoint way, the semantic information from the social network and its structural information to, first find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divide the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.


title: Konnect: An Internet of Things(IoT) based smart helmet for accident detection and notification

abstract: The objective of the smart helmet is to provide a means and apparatus for detecting and reporting accidents. Sensors, Wi-Fi enabled processor, and cloud computing infrastructures are utilised for building the system. The accident detection system communicates the accelerometer values to the processor which continuously monitors for erratic variations. When an accident occurs, the related details are sent to the emergency contacts by utilizing a cloud based service. The vehicle location is obtained by making use of the global positioning system. The system promises a reliable and quick delivery of information relating to the accident in real time and is appropriately named Konnect. Thus, by making use of the ubiquitous connectivity which is a salient feature for the smart cities, a smart helmet for accident detection is built.


An Open Source Data Visualization System for Wireless Sensor Network


title: DVB-The Family of International Standards for Digital Video Broadcasting

abstract: This paper presents an overview of the fields of technology addressed by technical specifications created by the DVB Project (DVB). It serves as an introduction to the section of the special issue of the Proceedings of the IEEE in which a number of very recent DVB technologies will be explained in detail. The overview starts with an explanation of the wide field of application which the members of the DVB Project decided to address over the years. It then discusses the base band processing required for DVB services and looks into the specifications provided for the broadcasting over cable, satellite, and terrestrial transmitters. The concept of broadcasting generic data is explained and the interaction channels supported by DVB will be introduced. Multimedia Home Platform (MHP) facilitates a horizontal market of receivers able to run software programs (applications in DVB terminology)in a well defined way. After a short introduction into the specifications addressing the delivery of broadcast-type content over broad-band IP networks,the concept of broadcasting to handheld devices is introduced. The paper closes with a description of the newest areas of development DVB has decided to tackle.


title: Transmission performance of hybrid WDM-FSO system for using diversity multiplexing in the presence of optical nonlinearities and fading

abstract: In this paper, we have theoretically analyzed the transmission performance of wavelength division multiplexed (WDM) free-space optical communication (FSO) link in the presence of optical and wireless nonlinearities. In order to mitigate the nonlinearities due to atmospheric turbulence and optical path, spatial multiplexing technique has been utilized in conjunction with advanced modulation formats like CS-RZ, QPSK and, DSPK and QAM has been employed. A complete theoretical model and simulation study has been carried out. Our analysis shows that modulation formats QAM, DPSK and QPSK outperforms CS-RZ in the presence of strong and medium turbulence. The analysis further shows that using spatial multiplexing (MIMO), QAM has 5.1 dB advantage over CS-RZ and 1.6 dB over DPSK and QPSK modulations at FEC threshold 3.8×10−3. The WDM-FSO system can transport wireless signal upto 36 Km at the FEC threshold.


Monitoring High Nature Value Grassland in Transylvania, Romania


title: Multi-instance object segmentation with occlusion handling

abstract: We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottomup schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012 segmentation set. The proposed algorithm achieves favorable results on the joint detection and segmentation task against the state-of-the-art method both quantitatively and qualitatively.


title: Traumatic Loss and Major Disasters: Strengthening Family and Community Resilience

abstract: This article presents the core principles and value of a family and community resilience-oriented approach to recovery from traumatic loss when catastrophic events occur. In contrast to individually based, symptom-focused approaches to trauma recovery, this multisystemic practice approach contextualizes the distress in the traumatic experience and taps strengths and resources in relational networks to foster healing and posttraumatic growth. The intertwining of trauma and traumatic losses is discussed. Key family and social processes in risk and resilience in traumatic loss situations are outlined. Case illustrations, model programs, and intervention guidelines are described in situations of community violence and major disasters to suggest ways to foster family and community resilience.


title: FingerNet: Deep learning-based robust finger joint detection from radiographs

abstract: Radiographic image assessment is the most common method used to measure physical maturity and diagnose growth disorders, hereditary diseases and rheumatoid arthritis, with hand radiography being one of the most frequently used techniques due to its simplicity and minimal exposure to radiation. Finger joints are considered as especially important factors in hand skeleton examination. Although several automation methods for finger joint detection have been proposed, low accuracy and reliability are hindering full-scale adoption into clinical fields. In this paper, we propose FingerNet, a novel approach for the detection of all finger joints from hand radiograph images based on convolutional neural networks, which requires little user intervention. The system achieved 98.02% average detection accuracy for 130 test data sets containing over 1,950 joints. Further analysis was performed to verify the system robustness against factors such as epiphysis and metaphysis in different age groups.


title: The experiences of people with borderline personality disorder admitted to acute psychiatric inpatient wards: a meta-synthesis

abstract: AbstractBackground: Acute psychiatric inpatient care is recommended for people with borderline personality disorder (BPD) to manage a crisis. Qualitative research exploring service user experience ...


title: Learning to look from different perspectives – what can dental undergraduates learn from an arts and humanities-based teaching approach?

abstract: By its nature, clinical teaching involves supporting small groups of dental students at the chairside as they treat their own patients. Scaffolding their learning in this way enables observation at close quarters of the various stages of development from early novice, just commencing clinical treatment of patients, to those approaching qualification. The students' main concerns throughout are not primarily with the technical skills required, which they have already been taught in the clinical skills laboratories, but dealing with the complex realities and ambiguities of clinical practice; the 'hidden curriculum' of decision making, judgement calls, issues of communication and what it actually means to be professional. Yet, in an already packed curriculum little time is spent helping the students develop these higher order skills. In an effort to improve clinical reasoning and interpretative skills, many medical schools in the US and a number of leading medical schools here in the UK now incorporate arts and humanities-based initiatives into their curricula. This allows for a greater balance between the objectivity of evidence-based medicine and the pluralism and subjectivity of the arts and humanities, providing a more holistic, patient-centred education that promotes a tolerance of ambiguity. In this paper, we describe a pilot programme which sought to explore the value of this approach in the context of dental education, and share early indicators that balancing interventions of this type with clinical sciences can enhance dental students' capabilities in their professional and personal development. We conclude that in today's complex world we must educate not just for competence, but for capability and that the interdisciplinarity afforded by the 'clinical humanities' is both a promising area for further educational research and potentially a valuable addition to the curriculum.


title: Epidemiology and predictors of survival of tongue cancer among Egyptians in the Delta region

abstract: Tongue cancer is one of the most aggressive oral cavity malignancies. It is more common in Southeast Asia. Epidemiology in Africa and Arab countries is not well studied. Herein the authors searched the hospital-based registry for tongue cancer patients whether surgically treated or not. The authors retrieved 156 cases eligible for the study. The tumour mainly affected older individuals with a male to female ratio of 1.04:1. Most of the patients received surgical treatment mainly partial or hemiglossectomy with either elective or therapeutic neck dissection. Overall survival was affected by age and recurrence, while disease-free survival was affected by age, grade, T stage, lymph node status, type of surgery and adjuvant therapy. In conclusion, population based registries are needed to further quantify the risk of disease in Africa and the Middle East. In addition, high treatment failure after classic treatment of tongue cancer warrants further research in identifying underlying aetiology and implementing neoadjuvant and adjuvant therapy in the treatment tools.


title: A coding approach to event correlation

abstract: This paper describes a novel approach to event correlation in networks based on coding techniques. Observable symptom events are viewed as a code that identifies the problems that caused them; correlation is performed by decoding the set of observed symptoms. The coding approach has been implemented in SMARTS Event Management System (SEMS), as server running under Sun Solaris 2.3. Preliminary benchmarks of the SEMS demonstrate that the coding approach provides a speedup at least two orders of magnitude over other published correlation systems. In addition, it is resilient to high rates of symptom loss and false alarms. Finally, the coding approach scales well to very large domains involving thousands of problems.


title: Cache'n DASH: Efficient Caching for DASH

abstract: HTTP-based video streaming services have been dominating the global IP traffic over the last few years. Caching of video content reduces the load on the content servers. In the case of Dynamic Adaptive Streaming over HTTP (DASH), for every video the server needs to host multiple representations of the same video file. These individual representations are further broken down into smaller segments. Hence, for each video the server needs to host thousands of segments out of which, the client downloads a subset of the segments. Also, depending on the network conditions, the adaptation scheme used at the client-end might request a different set of video segments (varying in bitrate) for the same video. The caching of DASH videos presents unique challenges. In order to optimize the cache hits and minimize the misses for DASH video streaming services we propose an Adaptation Aware Cache (AAC) framework to determine the segments that are to be prefetched and retained in the cache. In the current scheme, we use bandwidth estimates at the cache server and the knowledge of the rate adaptation scheme used by the client to estimate the next segment requests, thus improving the prefetching at the cache.


title: Unsupervised Sequential Outlier Detection With Deep Architectures

abstract: Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.


title: Mixed methods research synthesis: definition, framework, and potential

abstract: Literature on the combination of qualitative and quantitative research components at the primary empirical study level has recently accumulated exponentially. However, this combination is only rarely discussed and applied at the research synthesis level. The purpose of this paper is to explore the possible contribution of mixed methods research to the integration of qualitative and quantitative research at the synthesis level. In order to contribute to the methodology and utilization of mixed methods at the synthesis level, we present a framework to perform mixed methods research syntheses (MMRS). The presented classification framework can help to inform researchers intending to carry out MMRS, and to provide ideas for conceptualizing and developing those syntheses. We illustrate the use of this framework by applying it to the planning of MMRS on effectiveness studies concerning interventions for challenging behavior in persons with intellectual disabilities, presenting two hypothetical examples. Finally, we discuss possible strengths of MMRS and note some remaining challenges concerning the implementation of these syntheses.


Social Media Analytics : Definition , Benefits , and Challenges Completed


title: Keyframe-based dense planar SLAM

abstract: In this work, we develop a novel keyframe-based dense planar SLAM (KDP-SLAM) system, based on CPU only, to reconstruct large indoor environments in real-time using a hand-held RGB-D sensor. Our keyframe-based approach applies a fast dense method to estimate odometry, fuses depth measurements from small baseline images, extracts planes from the fused depth map, and optimizes the poses of the keyframes and landmark planes in a global factor graph using incremental smoothing and mapping (iSAM). Using the fast odometry estimation, correct plane correspondences may be found projectively, and the pose of each frame can be estimated accurately even without sufficient planes to fully constrain the 6 degree-of-freedom transformation. The depth map generated from the local fusion process generates higher quality reconstructions and plane segmentations by eliminating noise. Moreover, explicitly modeling plane landmarks in the fully probabilistic global optimization significantly reduces the drift that plagues other dense SLAM algorithms. We test our system on standard RGB-D benchmarks as well as additional indoor environments, demonstrating its state-of-the-art performance as a real-time dense 3D SLAM algorithm, without the use of GPU.


title: Analyzing neighborhoods of falsifying traces in cyber-physical systems

abstract: We study the problem of analyzing falsifying traces of cyber-physical systems. Specifically, given a system model and an input which is a counterexample to a property of interest, we wish to understand which parts of the inputs are "responsible" for the counterexample as a whole. Whereas this problem is well known to be hard to solve precisely, we provide an approach based on learning from repeated simulations of the system under test. Our approach generalizes the classic concept of "one-at-a-time" sensitivity analysis used in the risk and decision analysis community to understand how inputs to a system influence a property in question. Specifically, we pose the problem as one of finding a neighborhood of inputs that contains the falsifying counterexample in question, such that each point in this neighborhood corresponds to a falsifying input with a high probability. We use ideas from statistical hypothesis testing to infer and validate such neighborhoods from repeated simulations of the system under test. This approach not only helps to understand the sensitivity of these counterexamples to various parts of the inputs, but also generalizes or widens the given counterexample by returning a neighborhood of counterexamples around it. We demonstrate our approach on a series of increasingly complex examples from automotive and closed loop medical device domains. We also compare our approach against related techniques based on regression and machine learning.


title: Isolating and analyzing fraud activities in a large cellular network via voice call graph analysis

abstract: With widespread adoption and growing sophistication of mobile devices, fraudsters have turned their attention from landlines and wired networks to cellular networks. While security threats to wireless data channels and applications have attracted the most attention, voice-related fraud activities also represent a serious threat to mobile users. In particular, we have seen increasing numbers of incidents where fraudsters deploy malicious apps, e.g., disguised as gaming apps to entice users to download; when invoked, these apps automatically - and without users' knowledge - dial certain (international) phone numbers which charge exorbitantly high fees. Fraudsters also frequently utilize social engineering (e.g., SMS or email spam, Facebook postings) to trick users into dialing these exorbitant fee-charging numbers. In this paper, we develop a novel methodology for detecting voice-related fraud activities using only call records. More specifically, we advance the notion of voice call graphs to represent voice calls from domestic callers to foreign recipients and propose a Markov Clustering based method for isolating dominant fraud activities from these international calls. Using data collected over a two year period from one of the largest cellular networks in the US, we evaluate the efficacy of the proposed fraud detection algorithm and conduct systematic analysis of the identified fraud activities. Our work sheds light on the unique characteristics and trends of fraud activities in cellular networks, and provides guidance on improving and securing hardware/software architecture to prevent these fraud activities.


title: Reflecting on the DARPA Red Balloon Challenge

abstract: Finding 10 balloons across the U.S. illustrates how the Internet has changed the way we solve highly distributed problems.