title: Attitude Estimation by Multiple-Mode Kalman Filters

abstract: This letter proposes a multiple-mode Kalman filter for one-dimensional attitude estimation using low-cost accelerometer and gyroscope. The nonlinearity and time-varying parameters are partitioned into several modes; for each mode, a linear time-invariant Kalman filter is selected. Experimental results are given to verify the proposed Kalman filter


title: Optimal volt/var control strategy for distribution system with multiple voltage regulating devices

abstract: This paper studies the volt/var control strategy for smart distribution system by formulating and solving system optimization problems. In the study, solid state transformers (SST) are used for continuous var compensation, while voltage regulators (VR) are for voltage regulation. The objectives are to minimize total system power loss while maintaining voltages along the feeder within acceptable limits. A two step optimization volt/var control method for multiple, discrete/continuous mixed regulating device is introduced in this paper. Simulation results demonstrate that the proposed technique is able to achieve the objective efficiently.


title: A practical guide to controlled experiments of software engineering tools with human participants

abstract: Empirical studies, often in the form of controlled experiments, have been widely adopted in software engineering research as a way to evaluate the merits of new software engineering tools. However, controlled experiments involving human participants actually using new tools are still rare, and when they are conducted, some have serious validity concerns. Recent research has also shown that many software engineering researchers view this form of tool evaluation as too risky and too difficult to conduct, as they might ultimately lead to inconclusive or negative results. In this paper, we aim both to help researchers minimize the risks of this form of tool evaluation, and to increase their quality, by offering practical methodological guidance on designing and running controlled experiments with developers. Our guidance fills gaps in the empirical literature by explaining, from a practical perspective, options in the recruitment and selection of human participants, informed consent, experimental procedures, demographic measurements, group assignment, training, the selecting and design of tasks, the measurement of common outcome variables such as success and time on task, and study debriefing. Throughout, we situate this guidance in the results of a new systematic review of the tool evaluations that were published in over 1,700 software engineering papers published from 2001 to 2011.


title: Bandwidth and Gain Enhancement of Ridge Gap Waveguide H-Plane Horn Antennas Using Outer Transitions

abstract: In this communication, solutions are developed for gain and bandwidth enhancement of a ridge gap waveguide (RGW) H-plane horn antenna. Intrinsic features of RGW allow us to introduce a simple transition at the antenna aperture making its impedance matching with outer space easier, leading to antenna bandwidth enhancement. Two transitions, stepped and tapered, are introduced. The proposed transitions are fully designed as a part of horn flared part and do not introduce any expansion to the antenna length, contributing to its compactness. Furthermore, the modified form of the proposed RGW H-plane horn in which the antenna aperture is squinted is also presented. In this form, the backward radiation is reduced leading to 2 dB gain enhancement.


title: Monitoring eating habits using a piezoelectric sensor-based necklace

abstract: Maintaining appropriate levels of food intake and developing regularity in eating habits is crucial to weight loss and the preservation of a healthy lifestyle. Moreover, awareness of eating habits is an important step towards portion control and weight loss. In this paper, we introduce a novel food-intake monitoring system based around a wearable wireless-enabled necklace.The proposed necklace includes an embedded piezoelectric sensor, small Arduino-compatible microcontroller, Bluetooth LE transceiver, and Lithium-Polymer battery. Motion in the throat is captured and transmitted to a mobile application for processing and user guidance. Results from data collected from 30 subjects indicate that it is possible to detect solid and liquid foods, with an F-measure of 0.837 and 0.864, respectively, using a naive Bayes classifier. Furthermore, identification of extraneous motions such as head turns and walking are shown to significantly reduce the false positive rate of swallow detection. HighlightsPiezoelectric sensors in the lower trachea are viable for detection of food intake.Accelerometers for activity recognition can reduce the false positive rate of detected swallows.Classification can be performed between several food types using Bayes classifiers.


title: Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection

abstract: Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods.


title: An Approach to Solve Winograd Schema Challenge Using Automatically Extracted Commonsense Knowledge

abstract: The Winograd Schema Challenge has recently been proposed as an alternative to the Turing test. A Winograd Schema consists of a sentence and question pair such that the answer to the question depends on the resolution of a definite pronoun in the sentence. The answer is fairly intuitive for humans but is difficult for machines because it requires commonsense knowledge about words or concepts in the sentence. In this paper we propose a novel technique which semantically parses the text, hunts for the needed commonsense knowledge and uses that knowledge to answer the given question.


How to Analyze Change from Baseline : Absolute or Percentage Change ? June


Learning Testing a Conception of How School Leadership Influences Student


title: Teaching Surgical Skills — Changes in the Wind

abstract: Traditionally, surgeons have been trained and evaluated on the basis of their performance of surgical procedures in live patients. This article in the Medical Education series explores the use of mechanical devices for the teaching and evaluation of surgical skills.


Computation of a well–conditioned dynamic stiffness matrix for elastic layers overlying a half–space


title: A STRUCTURAL ANALYSIS OF THE DEFAULT SWAP MARKET — PART 1 (CALIBRATION)

abstract: We analyze the default swap market with the two factor I2 structural model, which is driven by firm value and firm leverage. As we show empirically, the default swap market incorporates these risks differentially over time, by region, by industry, and by coarse quality. This leads us to pool firms with similar characteristics into calibration groups whose parameters are used to align model and market sensitivities to the risk factors. We include equity factor returns to account for contagion and momentum effects for industries or credits that have suffered recent downturns. The close alignment of our model spreads with the market enables us to extract systematic effects reflected in the dynamics of average levels of model inputs and outputs, and discern relative value among credits by analyzing model errors. Applications of our model include assessment of relative value, pricing of illiquid names, cross market hedging and monitoring credit portfolios. A rich-cheap portfolio construction strategy based on our model shows consistent profits in most calibration groups between January 2004 and May 2006.


title: Threat detection in online discussions

abstract: This paper investigates the effect of various types of linguistic features (lexical, syntactic and semantic) for training classifiers to detect threats of violence in a corpus of YouTube comments. Our results show that combinations of lexical features outperform the use of more complex syntactic and semantic features for this task.


title: A high range resolution 9.4/18.8 GHz harmonic radar for bees searching

abstract: This paper demonstrates a 9.4/18.8 GHz harmonic radar for bee searching to investigate the phenomenon of colony collapse disorder (CCD). The radar system transmits a pulsed coarse/acquisition code (C/A code) signal with 0° and 90° shift key modulation and then receives the C/A code signal from transponders attached on the bees in BPSK modulation. Using the technique of C/A code positioning, a distance resolution of 0.15 m is achieved. The transponders are designed to minimize the effect from the bodies of bees. The outdoor experiments indicate the sensitivity of the radar system to be −95 dBm and with the distance of 65 m with 6-W peak power. With 3-kW peak power, the distance at which the bees can be located is expected to be 450 m.


title: Intelligent automatic overtaking system using vision for vehicle detection

abstract: Highlights? We have implemented an autonomous overtaking system in a commercial car. ? The system performs the maneuver as humans do, i.e., depending on the leading vehicle. ? Vision system is use to detect obstacles and to determine its length and width. ? Fuzzy logic was used as control technique to design lateral and longitudinal control. ? Real experiments show the ability of the system to manage any overtaking maneuver. There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous man?uvres involving vehicles - overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking man?uvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle's actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroen car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles - a motorbike, car, and truck - with encouraging results.


title: Host Identity Protocol (HIP): Connectivity, Mobility, Multi-Homing, Security, and Privacy over IPv4 and IPv6 Networks

abstract: The Host Identity Protocol (HIP) is an inter-networking architecture and an associated set of protocols, developed at the IETF since 1999 and reaching their first stable version in 2007. HIP enhances the original Internet architecture by adding a name space used between the IP layer and the transport protocols. This new name space consists of cryptographic identifiers, thereby implementing the so-called identifier/locator split. In the new architecture, the new identifiers are used in naming application level end-points (sockets), replacing the prior identification role of IP addresses in applications, sockets, TCP connections, and UDP-based send and receive system calls. IPv4 and IPv6 addresses are still used, but only as names for topological locations in the network. HIP can be deployed such that no changes are needed in applications or routers. Almost all pre-compiled legacy applications continue to work, without modifications, for communicating with both HIP-enabled and non-HIP-enabled peer hosts. The architectural enhancement implemented by HIP has profound consequences. A number of the previously hard networking problems become suddenly much easier. Mobility, multi-homing, and baseline end-to-end security integrate neatly into the new architecture. The use of cryptographic identifiers allows enhanced accountability, thereby providing a base for easier build up of trust. With privacy enhancements, HIP allows good location anonymity, assuring strong identity only towards relevant trusted parties. Finally, the HIP protocols have been carefully designed to take middle boxes into account, providing for overlay networks and enterprise deployment concerns. This article provides an in-depth look at HIP, discussing its architecture, design, benefits, potential drawbacks, and ongoing work.


title: Face Recognition Under Occlusions and Variant Expressions With Partial Similarity

abstract: Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. In particular, few researchers have addressed the challenge to recognize noncooperative or even uncooperative subjects who try to cheat the recognition system by deliberately changing their facial appearance through such tricks as variant expressions or disguise (e.g., by partial occlusions). This paper addresses these problems within the framework of similarity matching. A novel perception-inspired nonmetric partial similarity measure is introduced, which is potentially useful in dealing with the concerned problems because it can help capture the prominent partial similarities that are dominant in human perception. Two methods, based on the general golden section rule and the maximum margin criterion, respectively, are proposed to automatically set the similarity threshold. The effectiveness of the proposed method in handling large expressions, partial occlusions, and other distortions is demonstrated on several well-known face databases.


title: Generalized Continuous Time Bayesian Networks and their GSPN Semantics

abstract: We present an extension to Continuous Time Bayesian Networks (CTBN) called Generalized CTBN (GCTBN). The formalism allows one to model continuous time delayed variables (with exponentially distributed transition rates), as well as non delayed or “immediate” variables, which act as standard chance nodes in a Bayesian Network. The usefulness of this kind of model is discussed through an example concerning the reliability of a simple component-based system. The interpretation of GCTBN is proposed in terms of Generalized Stochastic Petri Nets (GSPN); the purpose is twofold: to provide a well-defined semantics for GCTBNin terms of the underlying stochastic process, and to provide an actual mean to perform inference (both prediction and smoothing) on GCTBN.


title: The complete chloroplast genome of threatened Magnolia laevifolia, a rare ornamental shrub with strong aromatic flowers

abstract: In this study, we assembled the complete chloroplast (cp) genome of Magnolia laevifolia Y.W. Law & Y. F. Figlar endemic to Yunnan, China. The species is a rare and threatened evergreen shrub with great important for horticulture because of its attractive strong aromatic flowers. The cp genome of M. laevifolia was 160,120 bp in length and contained a pair of inverted repeat (IR, 26,588 bp) regions, which were separated by the small single copy (SSC, 18,799 bp) and the large single copy (LSC, 88,145 bp) regions. The cp genome encoded 131 genes including 86 protein-coding genes, 37 tRNA genes, and eight rRNA ribosomal genes. The overall AT content of M. laevifolia cp genome is 60.8%. The maximum likelihood phylogenetic analysis revealed that M. laevifolia was closely related to M. odora. This result will be helpful for the conservation, taxonomy, and phylogeny programs of the genus Magnolia.


title: Motivation Factors of Consumers’ Food Choice

abstract: Consumer perception of food products is a very complex phenomenon that is influenced by a wide range of characteristics. The major motivation for food science and nutrition should be sensual features, cost/price balance, and consumer health (sufficient/balanced nutrition). However, there are important differences between theory and reality. Food choice is a complex process influenced by a number of factors related to the product, the consumer, and the consumption context. The role of the consumer in determining the market success of a product is of maximum relevance. Consumer perceptions and preferences are in motion and in change. Understanding and analyzing consumers’ motivation factors, perception and preferences are important both food industry and also governments. In this study, some of these factors were discussed and aimed to identify them with reasons.


title: A Bank of Maximum A Posteriori (MAP) Estimators for Target Tracking

abstract: Nonlinear estimation problems, such as range-only and bearing-only target tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions. In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides relinearization of the entire state trajectory, multihypothesis tracking, and an efficient hypothesis generation scheme. Each estimator in the bank is initialized using a locally optimal state estimate for the current time step. Every time a new measurement becomes available, we relax the original batch-MAP problem and solve it incrementally. More specifically, we convert the relaxed one-step-ahead cost function into polynomial or rational form and compute all the local minima analytically . These local minima generate highly probable hypotheses for the target's trajectory and hence greatly improve the quality of the overall MAP estimate. Additionally, pruning of least probable hypotheses and marginalization of old states are employed to control the computational cost. Monte Carlo simulation and real-world experimental results show that the proposed approach significantly outperforms the standard EKF, the batch-MAP estimator, and the particle filter.


title: An Ontology Based Text Analytics on Social Media

abstract: The amount of digital information that is created and used is progressively rising along with the growth of sophisticated hardware and software. In addition, real-world data come in a diversity of forms and can be tremendously bulky. This has augmented the need for powerful algorithms that can deduce and dig out appealing facts and useful information from these data. Text Mining (TM), which is a very complex process; has been successfully used for this purpose. Text mining alternately referred to as text data mining, more or less equivalent to text analytics, can be defined as the process of extracting high-quality information from text. Text mining involves the process of structuring the input data, deriving patterns within the structured data and lastly interpretation and revelation of the output. This paper provides outline on text analytics and social media analytics. At the end, this paper presents our proposed work based on ontology framework to cope up with excessive social media textual data.


title: Embedded Non-destructive Evaluation for Structural Health Monitoring, Damage Detection, and Failure Prevention

abstract: In this paper we review the state of the art in an emerging new technology: embedded ultrasonic non-destructive evaluation (NDE). Embedded ultrasonic NDE permits active structural health monitoring, i.e. the on-demand interrogation of the structure to determine its current state of structural health. The enabling element of embedded ultrasonic NDE is the piezoelectric wafer active sensor (PWAS). We begin by reviewing the guided wave theory in plate, tube, and shell structures, with special attention to Lamb waves. The mechanisms of Lamb wave excitation and detection with embeddable PWAS transducers is presented. It is shown analytically and verified experimentally that Lamb wave mode tuning can be achieved by the judicious combination of PWAS dimensions, frequency value, and Lamb mode characteristics. Subsequently, we address in turn the use of pitch-catch, pulse-echo, and phased array ultrasonic methods for Lambwave damage detection. In each case, the conventional ultrasonic NDE results are contrasted with embedded NDE results. Detection of cracks, disbonds, delaminations, and diffuse damage in metallic and composite structures are exemplified. Other techniques, such as the time reversal method and the migration technique, are also presented. The paper ends with conclusions and suggestions for further work.


title: Faculty Participation in Planning Activities and it's Relationship with their Vision of the Institution

abstract: This work took place among academicians in a public state university, it analyzes the posible relation of the academicians’ level of participation in the formulation process of the Comprehensive Institucional Strengthening Program (Programa Integral de Fortalecimiento Institucional, PIFI , in Spanish) and/or the Program for Strengthening High Education Institutions (Programa de Fortaleciemiento de la Dependencia de Educacion Superior, PRODES , also in Spanish), with their level of information, their perception of the working environment, administration, decision-making and influence, institutional image and sense of belonging, and work satisfaction. The information used for this analysis was provided by the Organizational Environment Annual Survey ( Encuesta Anual de Ambiente Organizacional , EAAO , name in Spanish), which is applied every year to said institution. Results show that academicians who have higher levels of participation also appear to have, in general, a better image of the institution, particularly regarding working environment, level of information and decision-making. Also, those who have a better perception of the institution’s administration, image and pride as well as work satisfaction appear to have higher levels of participation, though the difference is minor. These results are discussed according to the value that the strategic and contextual planning gives to the actors of the organization.


title: A partition-based method for string similarity joins with edit-distance constraints

abstract: As an essential operation in data cleaning, the similarity join has attracted considerable attention from the database community. In this article, we study string similarity joins with edit-distance constraints, which find similar string pairs from two large sets of strings whose edit distance is within a given threshold. Existing algorithms are efficient either for short strings or for long strings, and there is no algorithm that can efficiently and adaptively support both short strings and long strings. To address this problem, we propose a new filter, called the segment filter. We partition a string into a set of segments and use the segments as a filter to find similar string pairs. We first create inverted indices for the segments. Then for each string, we select some of its substrings, identify the selected substrings from the inverted indices, and take strings on the inverted lists of the found substrings as candidates of this string. Finally, we verify the candidates to generate the final answer. We devise efficient techniques to select substrings and prove that our method can minimize the number of selected substrings. We develop novel pruning techniques to efficiently verify the candidates. We also extend our techniques to support normalized edit distance. Experimental results show that our algorithms are efficient for both short strings and long strings, and outperform state-of-the-art methods on real-world datasets.


Biomimetic Materials in Our World: A Review.


title: Performance evaluation of corner detectors using consistency and accuracy measures

abstract: This paper evaluates the performance of several popular corner detectors using two newly defined criteria. The majority of authors of published corner detectors have not used theoretical criteria to measure the consistency and accuracy of their algorithms. They usually only illustrate their results on a few test images and may compare the results visually to the results of other corner detectors. Some authors have proposed various criteria for performance evaluation of corner detection algorithms but those criteria have a number of shortcomings. We propose two new criteria to evaluate the performance of corner detectors. Our proposed criteria are consistency and accuracy. These criteria were measured using several test images and experiments such as rotation, uniform scaling, non-uniform scaling and affine transforms. To measure accuracy, we created ground truth based on majority human judgement. The results show that the enhanced CSS corner detector performs better according to these criteria.


title: A survey on visual surveillance of object motion and behaviors

abstract: Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.


title: Machine Learning Techniques for Intrusion Detection

abstract: An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used in today's IDS are not able to deal with the dynamic and complex nature of cyber attacks on computer networks. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. In this paper, we study several such schemes and compare their performance. We divide the schemes into methods based on classical artificial intelligence (AI) and methods based on computational intelligence (CI). We explain how various characteristics of CI techniques can be used to build efficient IDS.


title: Cross-Domain Internet of Things Application Development: M3 Framework and Evaluation

abstract: Internet of Things (IoT) applications are becoming more and more popular but not interoperable with each other. In this paper, we propose the Machine-to-Machine Measurement (M3) framework to: (1) build IoT applications, (2) assist users in interpreting sensor measurements, and (3) combine domains with each other. The M3 framework is based on semantic web technologies to explicitly describe the meaning of sensor measurements in an unified way to ease the interpretation of sensor data and to combine domains.


Adversarial Active Learning for Sequences Labeling and Generation


title: Pangloss: Fast Entity Linking in Noisy Text Environments

abstract: Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph construction, and question answering. Traditionally, this work has focused on text that has been well-formed, like news articles, but in common real world datasets such as messaging, resumes, or short-form social media, non-grammatical, loosely-structured text adds a new dimension to this problem. This paper presents Pangloss, a production system for entity disambiguation on noisy text. Pangloss combines a probabilistic linear-time key phrase identification algorithm with a semantic similarity engine based on context-dependent document embeddings to achieve better than state-of-the-art results (>5% in F1) compared to other research or commercially available systems. In addition, Pangloss leverages a local embedded database with a tiered architecture to house its statistics and metadata, which allows rapid disambiguation in streaming contexts and on-device disambiguation in low-memory environments such as mobile phones.


title: An experimental study of people creating spreadsheets

abstract: Nine experienced users of electronic spreadsheets each created three spreadsheets. Although participants were quite confident that their spreadsheets were accurate, 44 percent of the spreadsheets contained user-generated programming errors. With regard to the spreadsheet creation process, we found that experienced spreadsheet users spend a large percentage of their time using the cursor keys, primarily for the purpose of moving the cursor around the spreadsheet. Users did not spend a lot of time planning before launching into spreadsheet creation, nor did they spend much time in a separate, systematic debugging stage. Participants spent 21 percent of their time pausing, presumably reading and/or thinking, prior to the initial keystrokes of spreadsheet creation episodes.


title: Identifying Conditional Causal Effects

abstract: This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution.


title: A probabilistic roadmap planner for flexible objects with a workspace medial-axis-based sampling approach

abstract: Probabilistic roadmap planners have been used with success to plan paths for flexible objects such as metallic plates or plastic flexible pipes. This paper improves the performance of these planners by using the medial axis of the workspace to guide the random sampling. At a preprocessing stage, the medial axis of the workspace is computed using a recent efficient algorithm. Then the flexible object is fitted at random points along the medial axis. The energy of all generated configurations is minimized and the planner proceeds to connect them with low-energy quasi-static paths in a roadmap that captures the connectivity of the free space. Given an initial and a final configuration, the planner connects these to the roadmap and searches the roadmap for a path. Our experimental results show that the new sampling scheme is successful in identifying critical deformations of the object along solution paths which results in a significant reduction of the computation time. Our work on planning for flexible objects has applications in industrial settings, virtual reality environments, and medicine.


title: Internet-based e-shopping and consumer attitudes: an empirical study

abstract: Abstract This paper analyses consumer attitudes towards Internet-based e-shopping. It aims to provide a theoretically and empirically grounded initial reference position, against which later research can explore and interpret the effects of changes in variables representing consumer preferences and shifts in these preferences on the success or failure of B2C e-commerce over the Internet. Because of the opportunity to sample at the outset and of ceteris paribus conditions following from the tendency for other factors such as e-transactions cost to remain small and constant, Singapore data were employed. Regression analysis shows that the life content of products, transactions security, price, vendor quality, IT education and Internet usage significantly affect the initial willingness of Singaporeans to e-shop on the Internet. Generalising, we suggest that Internet-based B2C e-commerce can profitably be introduced or promoted along similar dimensions in socio-geographically and technologically similar situations.


title: Tracking food insecurity from tweets using data mining techniques

abstract: Data mining algorithms can be applied to extract useful patterns from social media conversations to monitor disasters such as tsunami, earth quakes and nuclear power accidents. While food insecurity has persistently remained a world concern, its monitoring with this strategy has received limited attention. In attempt to address this concern, UN Global Pulse demonstrated that tweets reporting food prices from Indonesians can aid in predicting actual food price increase. For regions like Kenya and Uganda where use of tweets is considered low, this option can be problematic. Using Uganda as a case study, this study takes an alternative of using tweets from all over the world with mentions of; (1) uganda +food, (2) uganda + hunger, and (3) uganda + famine for years 2014, 2015 and 2016. The study however utilized tweets on food insecurity instead of tweets on food prices. In the first step, five data mining algorithms (D-tree, SVM, KNN, Neural Networks and N-Bayes) were trained to identify tweets conversations on food insecurity. Algorithmic performance were found comparable with human labeled tweet on the same subject. In step two, tweets reporting food insecurity were generated into trends. Comparing with trends from Uganda Bureau of Statistics, promising findings have been obtained with correlation coefficients of 0.56 and 0.37 for years 2015 and 2016 respectively. The study provides a strategy to generate information about food insecurity for stakeholders such as World Food Program in Uganda for mitigation action or further investigation depending on the situation. To improve performance, future work can; (1) aggregate tweets with other datasets, (2) ensemble algorithms, and (3) apply unexplored algorithms.


title: Progress of automatic drug delivery in anaesthesia-The ‘Rostock assistant system for anaesthesia control (RAN)’

abstract: The main goals of general anaesthesia should include (not exclusively) adequate hypnosis, analgesia and maintenance of vital functions. In addition, neuromuscular blockade (NMB) may be needed for a number of surgical procedures. Patient safety and cost reductions via the minimization of drug consumption and the shortening of post-operative recovery represent some of the main issues and motivations of automation of anaesthesia. ::: ::: ::: ::: Since the early eighties engineers and physicians joined efforts towards the development of sophisticated closed-loop control systems for drug delivery in the operating theatre and post-operatively. ::: ::: ::: ::: First, this paper should be seen as a review about the automatic drug delivery in anaesthesia. A summary is given about the methods of measurement, modelling and general progress of closed-loop control systems in anaesthesia. In particular, the development of the ‘Rostock assistant system for anaesthesia control (RAN)’ is also described. This system has been developed during the last 15 years at the University of Rostock and various systems and control-based ideas have been integrated since. With this system the multiple-input–multiple-output (MIMO) control of the depth of hypnosis and NMB has been shown to be possible as well as the closed-loop control of a deep arterial hypotension. Promising results have already been obtained from a first study, which has so far included as many as 22 patients using MIMO control. Copyright © 2008 John Wiley & Sons, Ltd.


title: Antimicrobial mechanisms of phagocytes and bacterial evasion strategies

abstract: Professional phagocytes have a vast and sophisticated arsenal of microbicidal features. They are capable of ingesting and destroying invading organisms, and can present microbial antigens on their surface, eliciting acquired immune responses. To survive this hostile response, certain bacterial species have developed evasive strategies that often involve the secretion of effectors to co-opt the cellular machinery of the host. In this Review, we present an overview of the antimicrobial defences of the host cell, with emphasis on macrophages, for which phagocytosis has been studied most extensively. In addition, using Mycobacterium tuberculosis, Listeria monocytogenes, Legionella pneumophila and Coxiella burnetii as examples, we describe some of the evasive strategies used by bacteria.


title: A team of robotic agents for surveillance

abstract: ABSTRACT This paper presents the hardware and software omponents of a roboti team designed for se urity and surveillan e appli ations. The team onsists of two types of roboti agents. The rst type is a larger, heavy-duty roboti platform, alled the \ranger." Rangers are used to transport, deploy, and supervise a number of small, mobile sensor platforms alled \s outs," the se ond type of roboti agent. In an example s enario, the s outs are deployed into an oAE e/lab environment, navigate towards dark areas, and position themselves to dete t moving obje ts using their ameras. A ranger ommuni ates with ea h of the s outs and determines whether there are obje ts of potential interest within the observed area. The paper also in ludes experimental results for individual s out and ranger-s out a tivities.


title: Privacy preserving multi-factor authentication with biometrics

abstract: An emerging approach to the problem of reducing the identity theft is represented by the adoption of biometric authentication systems. Such systems however present however several challenges, related to privacy, reliability, security of the biometric data. Inter-operability is also required among the devices used for the authentication. Moreover, very often biometric authentication in itself is not sufficient as a conclusive proof of identity and has to be complemented with multiple other proofs of identity like passwords, SSN, or other user identifiers. Multi-factor authentication mechanisms are thus required to enforce strong authentication based on the biometric and identifiers of other nature.In this paper we provide a two-phase authentication mechanism for federated identity management systems. The first phase consists of a two-factor biometric authentication based on zero knowledge proofs. We employ techniques from vector-space model to generate cryptographic biometric keys. These keys are kept secret, thus preserving the confidentiality of the biometric data, and at the same time exploit the advantages of a biometric authentication. The second authentication combines several authentication factors in conjunction with the biometric to provide a strong authentication. A key advantage of our approach is that any unanticipated combination of factors can be used. Such authentication system leverages the information of the user that are available from the federated identity management system.


title: An Automated Approach for Complementing Ad Blockers’ Blacklists

abstract: Abstract Privacy in the Web has become a major concern resulting in the popular use of various tools for blocking tracking services. Most of these tools rely on manually maintained blacklists, which need to be kept up-to-date to protect Web users’ privacy efficiently. It is challenging to keep pace with today’s quickly evolving advertisement and analytics landscape. In order to support blacklist maintainers with this task, we identify a set of Web traffic features for identifying privacyintrusive services. Based on these features, we develop an automatic approach that learns the properties of advertisement and analytics services listed by existing blacklists and proposes new services for inclusion on blacklists. We evaluate our technique on real traffic traces of a campus network and find in the order of 200 new privacy-intrusive Web services that are not listed by the most popular Firefox plug-in Adblock Plus. The proposed Web traffic features are easy to derive, allowing a distributed implementation of our approach.


title: A Systematic Analysis of Software Architecture Visualization Techniques

abstract: The visualization of software systems allows a software developer to build a mental model of the program supporting her to better understand its design and functionality. This research aims at studying current visualization techniques and practices to propose a set of principles for designing effective software architecture visualization techniques, focusing on their support for program comprehension. The research will be carried out in three main phases. First, we will complement current works by proposing a taxonomy of visualization techniques. The second contribution will be to identify different requirements and characteristics of architecture visualization techniques. Finally, to evaluate software visualization's usability and effectiveness in practice, we will measure the performance of developers in terms of their percentage of the correct answers and effort developers spend to answer given questions. To compute the developer's effort, we will use eye-tracker's data.


title: I Publish in I Edit? - Do Editorial Board Members of Urologic Journals Preferentially Publish Their Own Scientific Work?

abstract: Scientists who are members of an editorial board have been accused of preferentially publishing their scientific work in the journal where they serve as editor. Reputation and academic standing do depend on an uninterrupted flow of published scientific work and the question does arise as to whether publication mainly occurs in the self-edited journal. This investigation was designed to determine whether editorial board members of five urological journals were more likely to publish their research reports in their own rather than in other journals. A retrospective analysis was conducted for all original reports published from 2001-2010 by 65 editorial board members nominated to the boards of five impact leading urologic journals in 2006. Publications before editorial board membership, 2001-2005, and publications within the period of time as an editorial board member, 2006-2010, were identified. The impact factors of the journals were also recorded over the time period 2001-2010 to see whether a change in impact factor correlated with publication locality. In the five journals as a whole, scientific work was not preferentially published in the journal in which the scientists served as editor. However, significant heterogeneity among the journals was evident. One journal showed a significant increase in the amount of published papers in the 'own' journal after assumption of editorship, three journals showed no change and one journal showed a highly significant decrease in publishing in the 'own' journal after assumption of editorship.


title: A Novel Six-Axis Force/Torque Sensor for Robotic Applications

abstract: This paper presents a novel six-axis force/torque (F/T) sensor for robotic applications that is self-contained, rugged, and inexpensive. Six capacitive sensor cells are adopted to detect three normal and three shear forces. Six sensor cell readings are converted to F/T information via calibrations and transformation. To simplify the manufacturing processes, a sensor design with parallel and orthogonal arrangements of sensing cells is proposed, which achieves the large improvement of the sensitivity. Also, the signal processing is realized with a single printed circuit board and a ground plate, and thus, we make it possible to build a lightweight six-axis F/T sensor with simple manufacturing processes at extremely low cost. The sensor is manufactured and its performances are validated by comparing them with a commercial six-axis F/T sensor.


Language-independent Techniques for Automated Text Summarization


title: Investigation of portability of space docking techniques for autonomous underwater docking

abstract: This document presents a study of space docking strategies and their applicability for autonomous underwater docking. While autonomous docking is already used in space, there are only a few examples in the underwater environment. This study analyses current space docking techniques and identifies the key areas, which are portable in the underwater domain. Space and underwater environments are quite different, and this implies a great difference in the sensors which can be used. However, it is shown that some ideas and some techniques can be transferred, with the necessary adaptations, from the space to the underwater environment.


title: What's congress doing on twitter?

abstract: As Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand how officials use these communication tools. Using data from 380 members of Congress' Twitter activity during the winter of 2012, we find that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. We highlight a number of differences in communication frequency between men and women, Senators and Representatives, Republicans and Democrats. We provide groundwork for future research examining the behavior of public officials online and testing the predictive power of officials' social media behavior.


title: Immersive Environments and Virtual Reality: Systematic Review and Advances in Communication, Interaction and Simulation

abstract: Today, virtual reality and immersive environments are lines of research which can be applied to numerous scientific and educational domains. Immersive digital media needs new approaches regarding its interactive and immersive features, which means the design of new narratives and relationships with users. Additionally, ICT (information and communication theory) evolves through more immersive and interactive scenarios, it being necessary to design and conceive new forms of representing information and improving users’ interaction with immersive environments. Virtual reality and technologies associated with the virtuality continuum, such as immersive and digital environments, are emerging media. As a medium, this approach may help to build and represent ideas and concepts, as well as developing new languages. This review analyses the cutting-edge expressive, interactive and representative potential of immersive digital technologies. It also considers future possibilities regarding the evolution of these immersive technologies, such as virtual reality, in coming years, in order to apply them to diverse scientific, artistic or informational and educational domains. We conclude that virtual reality is an ensemble of technological innovations, but also a concept, and propose models to link it with the latest in other domains such as UX (user experience), interaction design. This concept can help researchers and developers to design new experiences and conceive new expressive models that can be applied to a wide range of scientific lines of research and educational dynamics.


Speech and Language Processing Dependency Parsing


title: Matrix Completion à la Dempster by the Principle of Parsimony

abstract: Dempster's covariance selection method is extended first to general nonsingular matrices and then to full rank rectangular matrices. Dempster observed that his completion solved a maximum entropy problem. We show that our generalized completions are also solutions of a suitable entropy-like variational problem.


title: Task planning for human-robot interaction

abstract: Human-robot interaction requires explicit reasoning on the human environment and on the robot capacities to achieve its tasks in a collaborative way with a human partner.This paper focuses on organization of the robot decisional abilities and more particularly on the management of human interaction as an integral part of the robot control architecture. Such an architecture should be the framework that will allow the robot to accomplish its tasks but also produce behaviors that support its engagement vis-a-vis its human partner and interpret similar behaviors from him.Together and in coherence with this framework, we intend to develop and experiment various task planners and interaction schemes, that will allow the robot to select and perform its tasks while taking into account explicitly the constraints imposed by the presence of humans, their needs and preferences.We have considered a scheme where the robot plans for itself and for the human in order not only (1) to assess the feasibility of the task (at a certain level) before performing it, but also (2) to share the load between the robot and the human and (3) to explain/illustrate a possible course of action.


title: Mamdani Model Based Adaptive Neural Fuzzy Inference System and its Application in Traffic Level of Service Evaluation

abstract: Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.


title: Enriching Literature Reviews with Computer-Assisted Research Mining. Case: Profiling Group Support Systems Research

abstract: In this paper we discuss and demonstrate how traditional literature reviews may be enriched by computer-assisted research profiling. Research profiling makes use of sophisticated text mining tools designed for structured science and technology information resources, such as the ISI Web of science, INSPEC or ABI/INFORM ProQuest. Besides aiding in summarizing and visualizing knowledge domains, these research mining tools act as interactive decision support systems for researchers. We illustrate research profiling with 2.000 publications on group support systems between years 1982-2005


title: Identifying books in library using line segment detector and contour clustering

abstract: In this paper, a new method is presented to automatically identify books based on recognition of call number label on the book spine captured by a smartphone in library. In previous methods for book spine segmentation, Canny edge detector results in curved or broken lines instead of detecting straight lines formed by book spine boundary. Moreover, line detection by Hough transform is prone to interference of texts on the book spine. We address these issues by employing color segmentation with line segment detector to separate every individual book. For call number extraction, characters on the label cannot be easily distinguished from texts like the title and author's name of the book. To solve this problem, we adopt contour clustering to group connected components of characters on the edge map according to their geometrical features. The experimental results demonstrate that our method is effective for the exaction and recognition of books used by existing automated library robotic systems or mobile devices.


title: 30.10 A 1TOPS/W analog deep machine-learning engine with floating-gate storage in 0.13μm CMOS

abstract: Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog or mixed-mode signal processors have been reported to yield much higher energy efficiency than DSP [1-4], presenting the means of overcoming these limitations. However, the use of volatile digital memory in [1-3] precludes their use in intermittently-powered devices, and the required interfacing and internal A/D/A conversions add power and area overhead. Nonvolatile storage is employed in [4], but the lack of learning capability requires task-specific programming before operation, and precludes online adaptation.


title: One-class document classification via Neural Networks

abstract: Automated document retrieval and classification is of central importance in many contexts; our main motivating goal is the efficient classification and retrieval of ''interests'' on the internet when only positive information is available. In this paper, we show how a simple feed-forward neural network can be trained to filter documents under these conditions, and that this method seems to be superior to modified methods (modified to use only positive examples), such as Rocchio, Nearest Neighbor, Naive-Bayes, Distance-based Probability and One-Class SVM algorithms. A novel experimental finding is that retrieval is enhanced substantially in this context by carrying out a certain kind of uniform transformation (''Hadamard'') of the information prior to the training of the network.


title: Anonymous Cardinality Estimation in RFID Systems with Multiple Readers

abstract: In this paper, we study the anonymous cardinality estimation problem in radio frequency identification (RFID) systems. To preserve privacy and anonymity, each tag only transmits a portion of its ID to the reader when it is being queried. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. The cardinality estimation problem is to estimate the total number of tags (or the tag population) in an RFID system. We first propose an exclusive estimator to estimate the number of tags that are exclusively located in the interrogation zone of a selected reader. We then present a multiple-reader tag estimation (MRTE) algorithm that can accurately estimate the tag population using the measurement from different readers and the exclusive estimator. The accuracy of our proposed algorithm and the approximation are validated via simulations. We compare our proposed MRTE algorithm with the enhanced zero-based (EZB) and maximum a posteriori tag estimation (MPTE) algorithms. Although the mean of the estimation error for all three algorithms approaches zero under certain circumstances, the variance of the estimation error for MRTE algorithm increases linearly with the number of readers while it increases exponentially for EZB and MPTE algorithms.


title: On the log-normal self-adaptation of the mutation rate in binary search spaces

abstract: This paper discusses the adoption of self-adaptation for Evolutionary Algorithms operating in binary spaces using a direct encoding of the mutation rate. In particular, it focuses on the log-normal update rule for adapting the mutation rate, incorporated in a (mu, lambda)-strategy. Although it is well known that this update rule requires a lower boundary of the mutation rate to prevent it from collapsing to zero, the naive approach of enforcing a fixed lower boundary has undesirable side-effects. This paper studies the dynamics of the fixed lower boundary approach in depth and proposes a simple alternative for dealing with the lower boundary issue.


ECG data classification with deep learning tools


title: Person re-identification using CNN features learned from combination of attributes

abstract: This paper presents fine-tuned CNN features for person re-identification. Recently, features extracted from top layers of pre-trained Convolutional Neural Network (CNN) on a large annotated dataset, e.g., ImageNet, have been proven to be strong off-the-shelf descriptors for various recognition tasks. However, large disparity among the pre-trained task, i.e., ImageNet classification, and the target task, i.e., person image matching, limits performances of the CNN features for person re-identification. In this paper, we improve the CNN features by conducting a fine-tuning on a pedestrian attribute dataset. In addition to the classification loss for multiple pedestrian attribute labels, we propose new labels by combining different attribute labels and use them for an additional classification loss function. The combination attribute loss forces CNN to distinguish more person specific information, yielding more discriminative features. After extracting features from the learned CNN, we apply conventional metric learning on a target re-identification dataset for further increasing discriminative power. Experimental results on four challenging person re-identification datasets (VIPeR, CUHK, PRID450S and GRID) demonstrate the effectiveness of the proposed features.


title: Relative Wulst volume is correlated with orbit orientation and binocular visual field in birds

abstract: In mammals, species with more frontally oriented orbits have broader binocular visual fields and relatively larger visual regions in the brain. Here, we test whether a similar pattern of correlated evolution is present in birds. Using both conventional statistics and modern comparative methods, we tested whether the relative size of the Wulst and optic tectum (TeO) were significantly correlated with orbit orientation, binocular visual field width and eye size in birds using a large, multi-species data set. In addition, we tested whether relative Wulst and TeO volumes were correlated with axial length of the eye. The relative size of the Wulst was significantly correlated with orbit orientation and the width of the binocular field such that species with more frontal orbits and broader binocular fields have relatively large Wulst volumes. Relative TeO volume, however, was not significant correlated with either variable. In addition, both relative Wulst and TeO volume were weakly correlated with relative axial length of the eye, but these were not corroborated by independent contrasts. Overall, our results indicate that relative Wulst volume reflects orbit orientation and possibly binocular visual field, but not eye size.


A PATTERN LANGUAGE FOR COMPUTER GRAPHICS


title: Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos

abstract: Recent years have witnessed the popularity of using recurrent neural network (RNN) for action recognition in videos. However, videos are of high dimensionality and contain rich human dynamics with various motion scales, which makes the traditional RNNs difficult to capture complex action information. In this paper, we propose a novel recurrent spatial-temporal attention network (RSTAN) to address this challenge, where we introduce a spatial-temporal attention mechanism to adaptively identify key features from the global video context for every time-step prediction of RNN. More specifically, we make three main contributions from the following aspects. First, we reinforce the classical long short-term memory (LSTM) with a novel spatial-temporal attention module. At each time step, our module can automatically learn a spatial-temporal action representation from all sampled video frames, which is compact and highly relevant to the prediction at the current step. Second, we design an attention-driven appearance-motion fusion strategy to integrate appearance and motion LSTMs into a unified framework, where LSTMs with their spatial-temporal attention modules in two streams can be jointly trained in an end-to-end fashion. Third, we develop actor-attention regularization for RSTAN, which can guide our attention mechanism to focus on the important action regions around actors. We evaluate the proposed RSTAN on the benchmark UCF101, HMDB51 and JHMDB data sets. The experimental results show that, our RSTAN outperforms other recent RNN-based approaches on UCF101 and HMDB51 as well as achieves the state-of-the-art on JHMDB.


title: Towards automatic tweet generation: A comparative study from the text summarization perspective in the journalism genre

abstract: In recent years, Twitter has become one of the most important microblogging services of the Web 2.0. Among the possible uses it allows, it can be employed for communicating and broadcasting information in real time. The goal of this research is to analyze the task of automatic tweet generation from a text summarization perspective in the context of the journalism genre. To achieve this, different state-of-the-art summarizers are selected and employed for producing multi-lingual tweets in two languages (English and Spanish). A wide experimental framework is proposed, comprising the creation of a new corpus, the generation of the automatic tweets, and their assessment through a quantitative and a qualitative evaluation, where informativeness, indicativeness and interest are key criteria that should be ensured in the proposed context. From the results obtained, it was observed that although the original tweets were considered as model tweets with respect to their informativeness, they were not among the most interesting ones from a human viewpoint. Therefore, relying only on these tweets may not be the ideal way to communicate news through Twitter, especially if a more personalized and catchy way of reporting news wants to be performed. In contrast, we showed that recent text summarization techniques may be more appropriate, reflecting a balance between indicativeness and interest, even if their content was different from the tweets delivered by the news providers.


A Multi-Agent System Based Approach to Fight Financial Fraud: An Application to Money Laundering


title: Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics

abstract: For an effective interpretation of spatio-temporal patterns of crime clusters/hotspots, we explore the possibility of three-dimensional mapping of crime events in a space-time cube with the aid of space-time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch-and-run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space-time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter-cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space-time statistical approaches, we conclude that combining these approaches in a space-time cube display is particularly valuable for a spatio-temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space-time crime events.


title: Protein and fat composition of mare's milk: some nutritional remarks with reference to human and cow's milk

abstract: Abstract Milk composition of mammallian species varies widely with reference to genetic, physiological and nutritional factors and environmental conditions. In this survey, the composition of mare's milk is reviewed and compared to human and cow's milk, considering principal protein fractions and fatty acid content. Protein content in mare's milk is higher than in human milk and lower than in cow's milk; casein concentration in mare's milk is intermediate between the other two milks. Fat content is lower in mare's milk compared to human and cow's milk. Distribution of di- and tri-glycerides in mare's and women’ milk is similar. The proportion of polyunsaturated fatty acids in mare's and human milk is remarkably higher than in cow's milk. Mare's milk shows some structural and functional peculiarities that make it more suitable for human nourishment than cow's milk.


title: An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

abstract: Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.


title: The effect of video game competition and violence on aggressive behavior: Which characteristic has the greatest influence?

abstract: Objective: This study is the first to our knowledge to isolate the effect of video game violence and competitiveness on aggressive behavior. Method: In Pilot Study 1, a violent and nonviolent video game were matched on competitiveness, difficulty, and pace of action, and the effect of each game on aggressive behavior was then compared using an unambiguous measure of aggressive behavior (i.e., the Hot Sauce Paradigm) in Experiment 1. In Pilot Study 2, competitiveness was isolated by matching games on difficulty and pace of action, and systematically controlling for violence. The effect of video game competition on aggressive behavior was then examined in Experiment 2. Results: We found that video game violence was not sufficient to elevate aggressive behavior compared with a nonviolent video game, and that more competitive games produced greater levels of aggressive behavior, irrespective of the amount of violence in the games. Conclusion: It appears that competition, not violence, may be the video game characteristic that has the greatest influence on aggressive behavior. Future research is needed to explore the mechanisms through which video game competitiveness influences aggressive behavior, as well as whether this relation holds in the long-term.


Neural Response Generation for Customer Service based on Personality Traits


title: A COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS FOR CREDIT CARD APPROVAL USING WEKA

abstract: The growing requirement of data mining applications based on classification in financial and other areas has shown the need that data mining algorithms need to be applied for better decision making. The increasing volume of data is also becoming a challenge for data mining community. In this paper, four classification algorithms have been comparatively tested to find the optimum algorithm for classification. The Credit Card Approval dataset has been used for experimental purposes that contain 690 instances with 15 attributes and 1 class attribute to test and justify the differences among classification algorithms. The four classification algorithms are – Decision Tree (DT), Naive Bayes‟ (NB), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The WEKA (Waikato Environment for Knowledge Analysis) tool which is open source software is used for simulation. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in financial applications.


Change Theory : Towards a Better Understanding of Software Maintenance


title: On stereo-rectification of pushbroom images

abstract: Image stereo pairs obtained from pinhole cameras can be stereo-rectified, thus permitting to test and use the many standard stereo matching algorithms of the literature. Yet, it is well-known that pushbroom Earth observation satellites produce image pairs that are not stereo-rectifiable. Nevertheless, we show that by a new and adequate use of the satellite calibration data, one can perform a precise local stereo-rectification of large Earth images. Based on this we built a fully automatic 3D reconstruction chain for the new Pleiades Earth observation satellite. It produces 1/10 pixel accurate Earth image stereo pairs at a high resolution. Examples will be made available online to the computer vision community.


title: Learning in the Presence of Concept Drift and Hidden Contexts

abstract: On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and re-using them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift.


title: Suicidal patients’ experiences regarding their safety during psychiatric in-patient care: a systematic review of qualitative studies

abstract: BACKGROUND ::: In-patient suicide prevention is a high priority in many countries, but its practice remains poorly understood. Patients in a suicidal crisis who receive psychiatric care can provide valuable insight into understanding and improving patient safety. The aim of this paper was therefore to summarize the qualitative literature regarding suicidal patients' in-patient care experiences. The following question guided the review: How can we describe suicidal patients' experiences regarding safety during psychiatric in-patient care? ::: ::: ::: METHODS ::: Systematic searches were conducted in the MEDLINE, Academic Search Premier, CINAHL, SOCINDEX and PsycINFO databases, identifying 20 qualitative studies on suicidal patients and their psychiatric in-patient care experiences. These studies were systematically reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, synthesized via thematic analysis and subjected to quality appraisals. ::: ::: ::: RESULTS ::: Patients described safety as "feeling safe", and three components, i.e., connection, protection and control, were vital to their experiences of safety. Fulfilling these needs was essential to patients recovering from suicidal crises, feeling safe during encounters with health care professionals and feeling safe from suicidal impulses. Unmet needs for connection, protection and control left patients feeling unsafe and increased their suicidal behaviour. ::: ::: ::: CONCLUSION ::: Our review addresses the importance of adopting a wider perspective of patient safety than considering safety solely in technical and physical terms. Safety for the suicidal patient is highly dependent on patients' perceptions of their psychological safety and the fulfilment of their needs. The three patient-identified factors mentioned above - connection, protection and control - should be considered an integral part of patient safety practices and should form the basis of future efforts to understand the safety of suicidal patients during psychiatric in-patient care.


title: Mediation and Interaction with Application to Survival After Myocardial Infarction

abstract: Previous studies have found that the socioeconomic risk factors education, income and family type were independently associated with mortality after first myocardial infarction (MI). In this popula ...


title: Modified two-stage procedures for the treatment of gingival recession.

abstract: BACKGROUND ::: Unfavorable conditions at the soft tissues adjacent to a recession defect may preclude performing pedicle flaps (advanced or rotational) both as a root coverage procedure, and as a covering flap for a connective tissue graft. Free gingival grafts may not be recommended because of the low root coverage predictability and the poor esthetic outcome. The goal of the present case report is to suggest modifications of the two-stage surgical technique aimed at improving root coverage and esthetic outcomes, and reducing patient morbidity. ::: ::: ::: METHOD ::: In the first case report, a Miller class II gingival recession, associated with a deep buccal probing depth, affecting a lower central incisor was treated. In the first step of the surgery an epithelized graft with an apical-coronal dimension equal to the keratinized tissue height of the adjacent teeth was sutured apical to the bone dehiscence. Four months later, a coronally advanced flap was performed to cover the root exposure. In the second case report, a Miller class III gingival recession, complicated with a deep buccal probing depth affecting the mesial root of the first lower molar was treated. In the first step of the surgery, a free gingival graft was positioned mesially to the root exposure to create keratinized tissue lateral to the recession defect. This was adequate to perform the laterally moved, coronally advanced flap that was used as a second-step root coverage surgical procedure. ::: ::: ::: RESULTS ::: In the first case report complete root coverage, an increase (4 mm) in keratinized tissue height and realignment of the mucogingival line were achieved 1 year after the surgery. The reduced dimension of the graft permitted to minimize patient's discomfort and to obtain good esthetics of mucogingival tissues. These successful outcomes were well maintained for 5 years. In the second case report successful root coverage, increase (3 mm) in keratinized tissue height and good harmony of mucogingival tissues were achieved 1 year after the surgery. These outcomes were well maintained 5 years after the surgery. ::: ::: ::: CONCLUSIONS ::: The present study suggested that modifications of the two-stage procedure by minimizing the dimension of the graft and by standardizing the surgical techniques allowed successful results to be achieved in the treatment of gingival recessions characterized by local conditions that otherwise preclude the use of one-step root coverage surgical techniques.


title: Description of Pallisentis (Brevitritospinus) punctati n. sp. (Acanthocephala: Quadrigyridae) from Channa punctatus in Bareilly, Uttar Pradesh, India

abstract: Background: The genus Pallisentis is an endoparasitic acanthocephalan inhabiting the intestinal walls. Hooks and spines of the worm are significant taxonomical and adaptive tools. Methods: The parasites were fixed, dehydrated and examined under Olympus BX 53 Microscope with DIC attachment, digital camera and CELLSENS imaging system [Light microscopy (LM)] and ﬁxed in 2% glutaraldehyde in 0.1 M cacodylate buffer, dehydrated, rotary-coated with gold palladium in NeoCoater 100-240V and examined in Neo JCM-6000 [scanning electron microscopy (SEM)]. Results: P. punctati n. sp. (prevalence 65.51%; mean intensity 3-6 par/host) is described. Females almost twice as large as males; proboscis hooks small; collar spine rows similar [16] and constant in both sexes but number of spines per row greater in females [22] than males [14]; trunk spine rows 28-39 (spines per row 14-18) in females and 20-26 (spines per row 10-12) in males. spine length of females almost twice as long as males, spines extend up to posterior testis in males and ¾ of total body length in females, Saefftigen’s pouch present, nuclei in cement gland 10-11, seminal vesicle, bursa and egg size small. SEM indicated lack of micro sculptures, and spines embedded on pre-trunk and trunk. Sex-based differences apparent (hook sizes, greater number of spines per row and longer spines in pre-trunk and trunk of females). Male trunk spine was narrower and of lateral spine with characteristic hooked appearance. Conclusion: A new species of Pallisentis based on LM and SEM is described, sexual diversity in hook and spine structure is reported.


title: Social determinants of under-5 child health: A qualitative study in Wolkayit Woreda, Tigray Region, Ethiopia

abstract: Despite the significant reductions seen in under-5 child mortality in Ethiopia over the last two decades, more than 10,000 children still die each year in Tigray Region alone, of whom 75% die from preventable diseases. Using an equity lens, this study aimed to investigate the social determinants of child health in one particularly vulnerable district as a means of informing the health policy decision-making process. An exploratory qualitative study design was adopted, combining focus group discussions and qualitative interviews. Seven Focus Group Discussions with mothers of young children, and 21 qualitative interviews with health workers were conducted in Wolkayit district in May-June 2015. Data were subjected to thematic analysis. Mothers' knowledge regarding the major causes of child mortality appeared to be good, and they also knew about and trusted the available child health interventions. However, utilization and practice of these interventions was limited by a range of issues, including cultural factors, financial shortages, limited female autonomy on financial resources, seasonal mobility, and inaccessible or unaffordable health services. Our findings pointed to the importance of a multi-sectoral strategy to improve child health equity and reduce under-5 mortality in Wolkayit. Recommendations include further decentralizing child health services to local-level Health Posts, and increasing the number of Health Facilities based on local topography and living conditions.


title: Trains of Thought: Generating Information Maps

abstract: When information is abundant, it becomes increasingly difficult to fit nuggets of knowledge into a single coherent picture. Complex stories spaghetti into branches, side stories, and intertwining narratives. In order to explore these stories, one needs a map to navigate unfamiliar territory. We propose a methodology for creating structured summaries of information, which we call metro maps. Our proposed algorithm generates a concise structured set of documents maximizing coverage of salient pieces of information. Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development. We first formalize characteristics of good maps and formulate their construction as an optimization problem. Then we provide efficient methods with theoretical guarantees for generating maps. Finally, we integrate user interaction into our framework, allowing users to alter the maps to better reflect their interests. Pilot user studies with a real-world dataset demonstrate that the method is able to produce maps which help users acquire knowledge efficiently.


title: Multi-Objective Resource Allocation for Secure Communication in Cognitive Radio Networks with Wireless Information and Power Transfer

abstract: In this paper, we study resource allocation for multiuser multiple-input single-output secondary communication systems with multiple system design objectives. We consider cognitive radio networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multi-objective optimization framework for the design of a Pareto optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference power leakage-to-transmit power ratio minimization. The proposed framework takes into account a quality of service requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The adopted multi-objective optimization problem is non-convex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP relaxed problem. Besides, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes, but also unveil an interesting trade-off between the considered conflicting system design objectives.


title: Validity and reliability of the Spanish version of the DN4 (Douleur Neuropathique 4 questions) questionnaire for differential diagnosis of pain syndromes associated to a neuropathic or somatic component

abstract: BackgroundThis study assesses the validity and reliability of the Spanish version of DN4 questionnaire as a tool for differential diagnosis of pain syndromes associated to a neuropathic (NP) or somatic component (non-neuropathic pain, NNP).MethodsA study was conducted consisting of two phases: cultural adaptation into the Spanish language by means of conceptual equivalence, including forward and backward translations in duplicate and cognitive debriefing, and testing of psychometric properties in patients with NP (peripheral, central and mixed) and NNP. The analysis of psychometric properties included reliability (internal consistency, inter-rater agreement and test-retest reliability) and validity (ROC curve analysis, agreement with the reference diagnosis and determination of sensitivity, specificity, and positive and negative predictive values in different subsamples according to type of NP).ResultsA sample of 164 subjects (99 women, 60.4%; age: 60.4 ± 16.0 years), 94 (57.3%) with NP (36 with peripheral, 32 with central, and 26 with mixed pain) and 70 with NNP was enrolled. The questionnaire was reliable [Cronbach's alpha coefficient: 0.71, inter-rater agreement coefficient: 0.80 (0.71–0.89), and test-retest intra-class correlation coefficient: 0.95 (0.92–0.97)] and valid for a cut-off value ≥ 4 points, which was the best value to discriminate between NP and NNP subjects.DiscussionThis study, representing the first validation of the DN4 questionnaire into another language different than the original, not only supported its high discriminatory value for identification of neuropathic pain, but also provided supplemental psychometric validation (i.e. test-retest reliability, influence of educational level and pain intensity) and showed its validity in mixed pain syndromes.


title: Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

abstract: This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.


title: Dynamic path planning and replanning for mobile robots using RRT

abstract: It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT∗ is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method.


title: Effect of SiO2 and SiO2/SiNx Passivation on the Stability of Amorphous Indium-Gallium Zinc-Oxide Thin-Film Transistors Under High Humidity

abstract: We studied the environmental stability of amorphous indium-gallium-zinc-oxide (a-IGZO) thin-film transistors (TFTs) with single-layer (SiO2) and bilayer (SiO2/SiN x ) passivation under high-humidity (80%) storage. During the 30 days of investigation, all single-layer passivated TFTs showed negative turn-ON voltage shifts ( $\Delta V_{\mathrm{{\scriptstyle ON}}}$ ), the size of which increased with storing time. The negative $\Delta ~V_{\mathrm{{\scriptstyle ON}}}$ is attributed to donor generation inside the active a-IGZO caused by the diffusion of ambient hydrogen/water molecules passing through the SiO2 passivation layer. The X-ray photoelectron spectroscopy depth profile for the SiO2 passivated structures confirms that the concentration of oxygen vacancies, which is initially larger at the a-IGZO/SiO2 interface, compared with the bulk a-IGZO, decreases after 30 days of storage under high humidity. This can be explained as the passivation of oxygen vacancies by diffused hydrogen. On the other hand, all bilayer passivated TFTs showed good air stability at room temperature and high humidity (80%).


title: Management of persistent coccydynia with transrectal manipulation: results of a combined procedure

abstract: We compared the results of manual therapy combined with steroid injection with single steroid injection in the treatment of persistent coccydynia. Combined therapy was performed in 21 patients (Group 1) and steroid injection in 23 patients (Group 2). We compared two groups and investigated the combined therapy group in details. Patients were classified according to the underlying cause, BMI, anatomic type of coccyx and duration of symptoms (< 6 or ≥ 6 months). Mean age was 30.5 years at the time of procedures. Mean followup time was 27.8 months. VAS scores were decreased in both groups but combined therapy group had much more better results. Complete pain relief was achieved in 61.9% of patients in Group 1 whereas it was only 17.4% in Group 2. In 23.8% of Group 1, the VAS score was significantly decreased but the feeling of uncomfortability persisted. This was 73.9% in Group 2. We had no relapse in Group 1 but in Group 2 the relapse rate was 56.5%. Underlying cause, body mass index, anatomic type of coccyx and duration of symptoms had no effect on results. Manual therapy combined with steroid injection would be an alternative method in case of persistent coccydynia. It is a safe and easy option before surgical treatment.


title: Algorithm for Efficient Attendance Management: Face Recognition based approach

abstract: Students attendance in the classroom is very important task and if taken manually wastes a lot of time. There are many automatic methods available for this purpose i.e. biometric attendance. All these methods also waste time because students have to make a queue to touch their thumb on the scanning device. This work describes the efficient algorithm that automatically marks the attendance without human intervention. This attendance is recorded by using a camera attached in front of classroom that is continuously capturing images of students, detect the faces in images and compare the detected faces with the database and mark the attendance. The paper review the related work in the field of attendance system then describes the system architecture, software algorithm and results.


title: Speed-up techniques for solving large-scale bTSP with the Two-Phase Pareto Local Search

abstract: We first present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first phase of the method, an initial population composed of an approximation of the extreme supported efficient solutions is generated. We use as second phase a Pareto Local Search method applied to all solutions of the initial population. We show that using the combination of these two techniques: good initial population generation and Pareto Local Search gives good results, without numerical parameters. As the computational time of the second phase grows exponentially according to the instances size, speed-up techniques are used to considerably reduce the computational time of the second phase. It makes it possible to find good approximation of the efficient set of large-scale biobjective traveling salesman problems, in a reasonable resolution time.


title: Autonomous UAV path planning and estimation

abstract: Unmanned aerial vehicles (UAVs) have shown promise in recent years for autonomous sensing. UAVs systems have been proposed for a wide range of applications such as mapping, surveillance, search, and tracking operations. The recent availability of low-cost UAVs suggests the use of teams of vehicles to perform sensing tasks. To leverage the capabilities of a team of vehicles, efficient methods of decentralized sensing and cooperative path planning are necessary. The goal of this work is to examine practical control strategies for a team of fixed-wing vehicles performing cooperative sensing. We seek to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions. Sensing goals are posed from an information theoretic standpoint to design strategies that explicitly minimize uncertainty. This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning.


title: Information sharing and security in dynamic coalitions

abstract: Today, information sharing is critical to almost every institution. There is no more critical need for information sharing than during an international crisis, when international coalitions dynamically form. In the event of a crisis, whether it is humanitarian relief, natural disaster, combat operations, or terrorist incidents, international coalitions have an immediate need for information. These coalitions are formed with international cooperation, where each participating country offers whatever resources it can muster to support the given crisis. These situations can occur suddenly, simultaneously, and without warning. Often times, participants are coalition partners in one crisis and adversaries in another, raising difficult security issues with respect to information sharing. Our specific interest is in the Dynamic Coalition Problem (DCP), with an emphasis on the information sharing and security risks when coalitions are formed in response to a crisis. This paper defines the DCP and explores its intricate, challenging, and complex information and resource sharing, and security issues, utilizing real-world situations, which are drawn from a military domain.


title: A Survey and Typology of Human Computation Games

abstract: Human computation games (HCGs) can be seen as a paradigm that makes use of human brain power to solve computational problems as a byproduct of game play. In recent years, HCGs are increasing in popularity within various application areas which aim to harvest human problem solving skills by seamlessly integrating computational tasks into games. Thus, a proper system of describing such games is necessary in order to obtain a better understanding of the current body of work and identify new opportunities for future research directions. As a starting point, in this paper, we review existing literature on HCGs and present a typology consisting of 12 dimensions based on the key features of HCGs. With this typology, all HCGs could be classified and compared in a systematic manner.


title: Fast Low-Rank Matrix Learning with Nonconvex Regularization

abstract: Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows the use of power method to approximate the SVD efficiently. Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace. Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed. Extensive experiments are performed on matrix completion and robust principal component analysis. The proposed method achieves significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.


title: Tracing traitors

abstract: We give cryptographic schemes that help trace the source of leaks when sensitive or proprietary data is made available to a large set of parties. A very relevant application is in the context of pay television, where only paying customers should be able to view certain programs. In this application, the programs are normally encrypted, and then the sensitive data is the decryption keys that are given to paying customers. If a pirate decoder is found, it is desirable to reveal the source of its decryption keys. We describe fully resilient schemes which can be used against any decoder which decrypts with nonnegligible probability. Since there is typically little demand for decoders which decrypt only a small fraction of the transmissions (even if it is nonnegligible), we further introduce threshold tracing schemes which can only be used against decoders which succeed in decryption with probability greater than some threshold. Threshold schemes are considerably more efficient than fully resilient schemes.


Learning topic models --- provably and efficiently


Corpus Based Urdu Lexicon Development


title: Secure Border Gateway Protocol (Secure-BGP

abstract: The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet's routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This document describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture with other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.