title: Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective

abstract: Based on stimulus-organism-response model, the purpose of this paper is to develop an integrated model to explore the effects of six marketing-mix components (stimuli) on consumer loyalty (response) through consumer value (organism) in social commerce (SC).,In order to target online social buyers, a web-based survey was employed. Structural equation modeling with partial least squares (PLS) is used to analyze valid data from 599 consumers who have repurchase experience via Facebook.,The results from PLS analysis show that all components of SC marketing mix (SCMM) have significant effects on SC consumer value. Moreover, SC customer value positively influences SC customer loyalty (CL).,The data for this study are collected from Facebook only and the sample size is limited; thus, replication studies are needed to improve generalizability and data representativeness of the study. Moreover, longitudinal studies are needed to verify the causality among the constructs in the proposed research model.,SC sellers should implement more effective SCMM strategies to foster SC CL through better SCMM decisions.,The SCMM components represent the collective benefits of social interaction, exemplifying the importance of effective communication and interaction among SC customers.,This study develops a parsimonious model to explain the over-arching effects of SCMM components on CL in SC mediated by customer value. It confirms that utilitarian, hedonic, and social values can be applied to online SC and that SCMM can be leveraged to achieve these values.


title: Using Visual Speech Information in Masking Methods for Audio Speaker Separation

abstract: This paper examines whether visual speech information can be effective within audio-masking-based speaker separation to improve the quality and intelligibility of the target speech. Two visual-only methods of generating an audio mask for speaker separation are first developed. These use a deep neural network to map the visual speech features to an audio feature space from which both visually derived binary masks and visually derived ratio masks are estimated, before application to the speech mixture. Second, an audio ratio masking method forms a baseline approach for speaker separation which is extended to exploit visual speech information to form audio-visual ratio masks. Speech quality and intelligibility tests are carried out on the visual-only, audio-only, and audio-visual masking methods of speaker separation at mixing levels from $-$ 10 to +10 dB. These reveal substantial improvements in the target speech when applying the visual-only and audio-only masks, but with highest performance occurring when combining audio and visual information to create the audio-visual masks.


title: Why pay?: exploring how financial incentives are used for question & answer

abstract: Electronic commerce has enabled a number of online pay-for-answer services. However, despite commercial interest, we still lack a comprehensive understanding of how financial incentives support question asking and answering. Using 800 questions randomly selected from a pay-for-answer site, along with site usage statistics, we examined what factors impact askers' decisions to pay. We also explored how financial rewards affect answers, and if question pricing can help organize Q&A exchanges for archival purposes. We found that askers' decisions are two-part--whether or not to pay and how much to pay. Askers are more likely to pay when requesting facts and will pay more when questions are more difficult. On the answer side, our results support prior findings that paying more may elicit a higher number of answers and answers that are longer, but may not elicit higher quality answers (as rated by the askers). Finally, we present evidence that questions with higher rewards have higher archival value, which suggests that pricing can be used to support archival use.


title: Human Talent Prediction in HRM using C4.5 Classification Algorithm

abstract: In HRM, among the challenges for HR professionals is to manage an organization's talents, especially to ensure the right person for the right job at the right time. Human talent prediction is an alternative to handle this issue. Due to that reason, classification and prediction in data mining which is commonly used in many areas can also be implemented to human talent. There are many classification techniques in data mining techniques such as Decision Tree, Neural Network, Rough Set Theory, Bayesian theory and Fuzzy logic. Decision tree is among the popular classification techniques, which can produce the interpretable rules or logic statement. The generated rules from the selected technique can be used for future prediction. In this article, we present the study on how the potential human talent can be predicted using a decision tree classifier. By using this technique, the pattern of talent performance can be identified through the classification process. In that case, the hidden and valuable knowledge discovered in the related databases will be summarized in the decision tree structure. In this study, we use decision tree C4.5 classification algorithm to generate the classification rules for human talent performance records. Finally, the generated rules are evaluated using the unseen data in order to estimate the accuracy of the prediction result. Keywords-Human talent; Classification, Prediction; Decision Tree; C4.5 classifier; Classification Algorithm


title: Engineering Quality Requirements in Large Scale Distributed Agile Environment

abstract: Agile software development methods have become increasingly popular in the last years. However, agile methods don’t specify explicitly how to deal with the quality requirements. Moreover there is little known about how organizations currently deal with this shortcoming. Based on several case studies this research will investigate real-world large-scale distributed agile projects to understand the challenges agile teams face regarding quality requirements and the approach they are currently using to cope with these challenges. After that a set of good practices will be introduced to exp licitly integrate quality requirements in agile processes. Other case studies will be conducted to validate the suggested good practices.


Survey of Keyword Extraction Techniques


title: UIMA Ruta Workbench: Rule-based Text Annotation

abstract: UIMA Ruta is a rule-based system designed for information extraction tasks, but it is also applicable for many natural language processing use cases. This demonstration gives an overview of the UIMA Ruta Workbench, which provides a development environment and tooling for the rule language. It was developed to ease every step in engineering rule-based applications. In addition to the full-featured rule editor, the user is supported by explanation of the rule execution, introspection in results, automatic validation and rule induction. Furthermore, the demonstration covers the usage and combination of arbitrary components for natural language processing.


title: T-SKIRT: Online Estimation of Student Proficiency in an Adaptive Learning System

abstract: We develop T-SKIRT: a temporal, structured-knowledge, IRT-based method for predicting student responses online. By explicitly accounting for student learning and employing a structured, multidimensional representation of student proficiencies, the model outperforms standard IRT-based methods on an online response prediction task when applied to real responses collected from students interacting with diverse pools of educational content.


title: Generalized simulated annealing

abstract: We discuss and illustrate a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so-called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and turns out to be quicker than both.


title: Low-cost compact MEMS scanning ladar system for robotic applications

abstract: Future robots and autonomous vehicles require compact low-cost Laser Detection and Ranging (LADAR) systems for ::: autonomous navigation. Army Research Laboratory (ARL) had recently demonstrated a brass-board short-range eye-safe ::: MEMS scanning LADAR system for robotic applications. Boeing Spectrolab is doing a tech-transfer (CRADA) of this ::: system and has built a compact MEMS scanning LADAR system with additional improvements in receiver sensitivity, ::: laser system, and data processing system. Improved system sensitivity, low-cost, miniaturization, and low power ::: consumption are the main goals for the commercialization of this LADAR system. The receiver sensitivity has been ::: improved by 2x using large-area InGaAs PIN detectors with low-noise amplifiers. The FPGA code has been updated to ::: extend the range to 50 meters and detect up to 3 targets per pixel. Range accuracy has been improved through the ::: implementation of an optical T-Zero input line. A compact commercially available erbium fiber laser operating at 1550 ::: nm wavelength is used as a transmitter, thus reducing the size of the LADAR system considerably from the ARL brassboard ::: system. The computer interface has been consolidated to allow image data and configuration data (configuration ::: settings and system status) to pass through a single Ethernet port. In this presentation we will discuss the system ::: architecture and future improvements to receiver sensitivity using avalanche photodiodes.


title: Making the Best of Mifare Classic Update

abstract: What would you do if you would be instructed to make a secure application built on the Mifare Classic? Arguably, due to the vulnerabilities shown in [5] and hinted at on [6], this is rather dicult and it may be easier to use to another chip. This document explores what the best is you can get, if the only option is Mifare Classic. We propose countermeasures against state restoration and against cloning. The eectiveness of these countermeasures depends on the absense of other vulnerabilities of the Mifare Classic.


title: Low-Profile Dual-Band Textile Antenna With Artificial Magnetic Conductor Plane

abstract: A dual-band textile antenna loaded with an artificial magnetic conductor (AMC) plane is proposed for WLAN applications. Its dual-band operation is enabled by a rectangular patch in the 2.4 GHz band and a patch-etched slot dipole in the 5 GHz band. Since the AMC approaches a perfect magnetic conductor (PMC) in the 5 GHz band, the slot dipole can be located close to the ground. The proposed antenna is fully fabricated using textiles except for a feeding connector used for testing purposes and a via. Simulations and experiments agree well and validate that this low profile antenna operates with a good reflection coefficient and a high front-to-back ratio (FBR) within the desired bands.


title: Wisdom in the social crowd: an analysis of quora

abstract: Efforts such as Wikipedia have shown the ability of user communities to collect, organize and curate information on the Internet. Recently, a number of question and answer (Q&A) sites have successfully built large growing knowledge repositories, each driven by a wide range of questions and answers from its users community. While sites like Yahoo Answers have stalled and begun to shrink, one site still going strong is Quora, a rapidly growing service that augments a regular Q&A system with social links between users. Despite its success, however, little is known about what drives Quora's growth, and how it continues to connect visitors and experts to the right questions as it grows. In this paper, we present results of a detailed analysis of Quora using measurements. We shed light on the impact of three different connection networks (or graphs) inside Quora, a graph connecting topics to users, a social graph connecting users, and a graph connecting related questions. Our results show that heterogeneity in the user and question graphs are significant contributors to the quality of Quora's knowledge base. One drives the attention and activity of users, and the other directs them to a small set of popular and interesting questions.


title: SPAM: Signal Processing to Analyze Malware

abstract: In this article, we explored orthogonal methods to analyze malware motivated by signal and image processing. Malware samples are represented as images or signals. Image and signal-based features are extracted to characterize malware. Our extensive experiments demonstrate the efficacy of our methods on malware classification and retrieval. We believe that our techniques will open the scope of signal-and image-based methods to broader fields in computer security.


title: Color CENTRIST: a color descriptor for scene categorization

abstract: We design a method to incorporate color information into the framework of CENsus Transform histogram (CENTRIST), a state-of-the-art visual descriptor for scene categorization. The newly proposed color CENTRIST descriptor describes global shape information by not only gradient derived from intensity values but also color variations between pixels in local image patches. Through extensive evaluations on various datasets, we demonstrate that the color CENTRIST descriptor is not only easily to be implemented, but also reliably achieves performance over that of CENTRIST.


title: Information theory for data management

abstract: We explore the use of information theory as a tool to express and quantify notions of information content and information transfer for representing and analyzing data, using examples from database design, data integration and data anonymization. We also examine the computational challenges associated with information-theoretic primitives, indicating how they might be computed efficiently.


title: Using big data to study the link between human mobility and socio-economic development

abstract: Big Data offer nowadays the potential capability of creating a digital nervous system of our society, enabling the measurement, monitoring and prediction of relevant aspects of socio-economic phenomena in quasi real time. This potential has fueled, in the last few years, a growing interest around the usage of Big Data to support official statistics in the measurement of individual and collective economic well-being. In this work we study the relations between human mobility patterns and socioeconomic development. Starting from nation-wide mobile phone data we extract a measure of mobility volume and a measure of mobility diversity for each individual. We then aggregate the mobility measures at municipality level and investigate the correlations with external socio-economic indicators independently surveyed by an official statistics institute. We find three main results. First, aggregated human mobility patterns are correlated with these socio-economic indicators. Second, the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits the strongest correlation with the external socio-economic indicators. Third, the volume of mobility and the diversity of mobility show opposite correlations with the socioeconomic indicators. Our results, validated against a null model, open an interesting perspective to study human behavior through Big Data by means of new statistical indicators that quantify and possibly "nowcast" the socio-economic development of our society.


title: False data injection attacks against state estimation in electric power grids

abstract: A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers. In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.


title: Integrating the Supply Chain

abstract: Common to all manufacturing companies is the need to control the flow of material from suppliers, through the value adding processes and distribution channels, to customers. The supply chain is the connected series of activities which is concerned with planning, co‐ordinating and controlling material, parts and finished goods from supplier to customer. Traditionally, the flow of material has been considered only at an operational level. No longer, however, can the potential of integrating the supply chain be ignored. Companies that manage the supply chain as a single entity and ensure the appropriate use of tools and techniques in order to meet the needs of the market, will not get left behind in the fight for survival.


title: An induction furnace employing with half bridge series resonant inverter

abstract: In this paper, an induction furnace employing with half bridge series resonant inverter built around IGBT as its switching devices suitable for melting 500 grams of brass is presented. Melting times of 10 minutes were achieved at a power level of approximately 4 kW. The operating frequency is automatically tracked to maintain a small constant lagging phase angle using dual phase lock loop when brass is melted. The coil voltage is controlled to protect the resonant capacitors. The experimental results are presented.


title: A study of near-field direct antenna modulation systems using convex optimization

abstract: This paper studies the constellation diagram design for a class of communication systems known as near-field direct antenna modulation (NFDAM) systems. The modulation is carried out in a NFDAM system by means of a control unit that switches among a number of pre-designed passive controllers such that each controller generates a desired voltage signal at the far field. To find an optimal number of signals that can be transmitted and demodulated reliably in a NFDAM system, the coverage area of the signal at the far field should be identified. It is shown that this coverage area is a planar convex region in general and simply a circle in the case when no constraints are imposed on the input impedance of the antenna and the voltage received at the far field. A convex optimization method is then proposed to find a polygon that is able to approximate the coverage area of the signal constellation diagram satisfactorily. A similar analysis is provided for the identification of the coverage area of the antenna input impedance, which is beneficial for designing an energy-efficient NFDAM system.


Transfer of Corpus-Specific Dialogue Act Annotation to ISO Standard: Is it worth it?


title: Augmenting the science centre and museum experience

abstract: Recent advances in computer graphics and interactive techniques have increased the visual quality and flexibility of Augmented Reality (AR) applications. This, in turn has increased the viability of applying AR to educational exhibits for use in Science Centres, Museums, Libraries and other education centres. This article outlines a selection of five projects developed at the Human Interface Technology Laboratory in New Zealand (HIT Lab NZ) that have explored different techniques for applying AR to educational exhibits.These exhibits have received very positive feedback and appear to have educational benefits involving spatial, temporal and contextual conceptualisation and provide kinaesthetic, explorative and knowledge-challenging stimulus. The controls available to a user of turning a page, moving an AR marker, moving their head and moving a slider provide sufficient freedom to create many interaction scenarios that can serve educative outcomes. While the use of virtual media provides many advantages, creating new content is still quite difficult, requiring specialist software and skills. Useability observations are shared.


title: A standardized approach to qualitative content analysis of focus group discussions from different countries

abstract: Objective: To describe the methodological procedures of a multi-centre focus group research for obtaining content categories also suitable for categorical statistical analyses. Methods: Inductive content analyses were performed on a subsample of 27 focus groups conducted in three different countries, the Netherlands (Utrecht), the UK (Liverpool) and Italy (Verona). The analyses of the subsample of focus group discussions were performed in five steps: (1) independent development of content categories in each of the participating centres, (2) obtaining consensus categories, (3) creation of a manual with coding rules and defining criteria for categories and subcategories, (4) assessment of inter-rater reliability to identify unreliable categories to be revised, and (5) repetition of inter-rater reliability assessment. Results: The resulting coding system considers five areas: non verbal communication, process oriented expression, task oriented or problem focused expressions, affective or emotional expressions, and physician's personal characteristics. It contains 12 categories of acceptable inter-rater reliability and 41 subcategories. Conclusion: The coding procedures show how focus group data, obtained in an international multi-centre study can be analysed in a systematic way combining scientific rigour with the richness of data obtainable from qualitative methodologies. Practice implication: The applied procedures may be helpful for multi-centre focus group research on other topics.


title: Ethical Issues in Neuromarketing: “I Consume, Therefore I am!”

abstract: Neuromarketing is a recent interdisciplinary field which crosses traditional boundaries between neuroscience, neuroeconomics and marketing research. Since this nascent field is primarily concerned with improving marketing strategies and promoting sales, there has been an increasing public aversion and protest against it. These protests can be exemplified by the reactions observed lately in Baylor School of Medicine and Emory University in the United States. The most recent attempt to stop ongoing neuromarketing research in France is also remarkable. The pertaining ethical issues have been continuously attracting much attention, especially since the number of neuromarketing companies has exceeded 300 world-wide. This paper begins with a brief introduction to the field of neurotechnology by presenting its current capabilities and limitations. Then, it will focus on the ethical issues and debates most related with the recent applications of this technology. The French Parliament’s revision of rules on bioethics in 2004 has an exemplary role in our discussion. The proposal by Murphy et al. (2008) has attracted attention to the necessity of ethical codes structuring this field. A code has recently been declared by the Neuromarketing Science and Business Association. In this paper, it is argued that these technologies should be sufficiently discussed in public spheres and its use on humans should be fully carried out according to the ethical principles and legal regulations designed in line with human rights and human dignity. There is an urgent need in the interdisciplinary scientific bodies like ethics committees monitoring the research regarding the scientific and ethical values of nonmaleficence, beneficence, autonomy, confidentiality, right to privacy and protection of vulnerable groups.


title: Knowledge Management: An Introduction and Perspective

abstract: Leaders of successful organizations are consistently searching for better ways to improve performance and results. Frequent disappointments with past management initiatives have motivated managers to gain new understandings into the underlying, but complex mechanisms ‐ such as knowledge ‐ which govern an enterprise’s effectiveness. Knowledge Management, far from being a management “fad”, is broad, multi‐dimensional and covers most aspects of the enterprise’s activities. To be competitive and successful, experience shows that enterprises must create and sustain a balanced intellectual capital portfolio. They need to set broad priorities and integrate the goals of managing intellectual capital and the corresponding effective knowledge processes. This requires systematic Knowledge Management. With knowledge as the major driving force behind the “economics of ideas”, we can expect that the emphasis on knowledge creation, development, organization and leverage will continue to be the prime focus for improving society.


Modeling of Bulk Current Injection ( BCI ) Setups for Virtual Automotive IC Tests


title: Teaching and learning robotic arm model

abstract: In this paper, the design and development of a robotic arm controller that enables use of a robotic arms as practical laboratory model for teaching and learning of robot arm algorithms is presented. The proposed system consists of OWI robotic arm with 5-Degrees Of Freedom (DOF) with DC motors for each joint movement, Arduino based control unit for control of arm motion and a MATLAB based Graphical User Interface (GUI) with kinematics algorithm at the backend providing user friendly environment for controlling the end-effector of robot arm remotely. The robotic arm is equipped with accelerometers to provide feedback to the microcontroller. The kinematics algorithm is implemented in MATLAB. The MATLAB based GUI, Arduino controller and accelerometers together with the robotic arm provide the practical laboratory model of teaching learning robotic arm platform. This robot arm also provides stand alone operation for pick and place application with the use of Arduino controller and accelerometers.


title: More Text Please! Understanding and Supporting the Use of Visualization for Clinical Text Overview

abstract: Clinical practice is heavily reliant on the use of unstructured text to document patient stories due to its expressive and flexible nature. However, a physician's capacity to recover information from text for clinical overview is severely affected when records get longer and time pressure increases. Data visualization strategies have been explored to aid in information retrieval by replacing text with graphical summaries, though often at the cost of omitting important text features. This causes physician mistrust and limits real-world adoption. This work presents our investigation into the role and use of text in clinical practice, and reports on efforts to assess the best of both worlds---text and visualization---to facilitate clinical overview. We report on insights garnered from a field study, and the lessons learned from an iterative design process and evaluation of a text-visualization prototype, MedStory, with 14 medical professionals. The results led to a number of grounded design recommendations to guide visualization design to support clinical text overview.


title: A Single-Phase Single-Stage High Step-Up AC–DC Matrix Converter Based on Cockcroft–Walton Voltage Multiplier With PFC

abstract: This paper proposes a high-performance transformerless single-stage high step-up ac-dc matrix converter based on Cockcroft-Walton (CW) voltage multiplier. Deploying a four-bidirectional-switch matrix converter between the ac source and CW circuit, the proposed converter provides high quality of line conditions, adjustable output voltage, and low output ripple. The matrix converter is operated with two independent frequencies. One of which is associated with power factor correction (PFC) control, and the other is used to set the output frequency of the matrix converter. Moreover, the relationship among the latter frequency, line frequency, and output ripple will be discussed. This paper adopts one-cycle control method to achieve PFC, and a commercial control IC associating with a preprogrammed complex programmable logic device is built as the system controller. The operation principle, control strategy, and design considerations of the proposed converter are all detailed in this paper. A 1.2-kV/500-W laboratory prototype of the proposed converter is built for test, measurement, and evaluation. At full-load condition, the measured power factor, the system efficiency, and the output ripple factor are 99.9%, 90.3%, and 0.3%, respectively. The experimental results demonstrate the high performance of the proposed converter and the validity for high step-up ac-dc applications.


title: Business model innovation through big data

abstract: Research depicts how big data analytics techniques have enabled the new business models which were either hard to shape or impossible to render. Big data platforms hold the promise of improved operational efficiencies and generation of greater revenues with enhanced business growth. Efficiency of big data techniques have been analyzed in the research uses the perspective that it delivers greater understanding of business value and ensures business growth. Value addition ensuring phenomenon has been explained through graphical method for business growth through big data techniques.


title: Eye movement analysis and cognitive processing: detecting indicators of conversion to Alzheimer’s disease

abstract: A great amount of research has been developed around the early cognitive impairments that best predict the onset of Alzheimer’s disease (AD). Given that mild cognitive impairment (MCI) is no longer considered to be an intermediate state between normal aging and AD, new paths have been traced to acquire further knowledge about this condition and its subtypes, and to determine which of them have a higher risk of conversion to AD. It is now known that other deficits besides episodic and semantic memory impairments may be present in the early stages of AD, such as visuospatial and executive function deficits. Furthermore, recent investigations have proven that the hippocampus and the medial temporal lobe structures are not only involved in memory functioning, but also in visual processes. These early changes in memory, visual, and executive processes may also be detected with the study of eye movement patterns in pathological conditions like MCI and AD. In the present review, we attempt to explore the existing literature concerning these patterns of oculomotor changes and how these changes are related to the early signs of AD. In particular, we argue that deficits in visual short-term memory, specifically in iconic memory, attention processes, and inhibitory control, may be found through the analysis of eye movement patterns, and we discuss how they might help to predict the progression from MCI to AD. We add that the study of eye movement patterns in these conditions, in combination with neuroimaging techniques and appropriate neuropsychological tasks based on rigorous concepts derived from cognitive psychology, may highlight the early presence of cognitive impairments in the course of the disease.


title: Influence of car panorama glass roofs on Car2Car communication (poster)

abstract: The Car2Car communication will use a reserved frequency band at 5.9 GHz. One of the key requirements for a Car2Car antenna is that the gain is concentrated in the horizontal plane, which is a problem due to the limited ground plane of a car roof. The influence of roof curvature, roof racks and panorama glass roofs were investigated to quantify them. To the authors knowledge such measurements have not been published yet. Three different antennas were simulated and measured on round ground planes and real car roofs in different configurations. The configuration with a panorama glass roof showed an unexpected high loss of gain in the direction of the glass roof in the range of 15 to 20 dB which result in a drastically reduced communication range to the front direction. This phenomenon is still under investigation.


title: Lexical surprisal as a general predictor of reading time

abstract: Probabilistic accounts of language processing can be psychologically tested by comparing word-reading times (RT) to the conditional word probabilities estimated by language models. Using surprisal as a linking function, a significant correlation between unlexicalized surprisal and RT has been reported (e.g., Demberg and Keller, 2008), but success using lexicalized models has been limited. In this study, phrase structure grammars and recurrent neural networks estimated both lexicalized and unlexicalized surprisal for words of independent sentences from narrative sources. These same sentences were used as stimuli in a self-paced reading experiment to obtain RTs. The results show that lexicalized surprisal according to both models is a significant predictor of RT, outperforming its un-lexicalized counterparts.


Pre-Processing Very Noisy Text


title: Intensive care nursing students' perceptions of simulation for learning confirming communication skills: A descriptive qualitative study.

abstract: AIM ::: The aim of this study was to explore intensive care nursing students experiences with confirming communication skills training in a simulation-based environment. ::: ::: ::: RESEARCH METHODOLOGY ::: The study has a qualitative, exploratory and descriptive design. The participants were students in a post-graduate program in intensive care nursing, that had attended a one day confirming communication course. Three focus group interviews lasting between 60 and 80min were conducted with 14 participants. The interviews were transcribed verbatim. Thematic analysis was performed, using Braun & Clark's seven steps. ::: ::: ::: FINDINGS ::: The analysis resulted in three main themes: "awareness", "ice-breaker" and "challenging learning environment". The participants felt that it was a challenge to see themselves on the video-recordings afterwards, however receiving feedback resulted in better self-confidence in mastering complex communication. ::: ::: ::: CONCLUSION ::: The main finding of the study is that the students reported improved communication skills after the confirming communication course. However; it is uncertain how these skills can be transferred to clinical practice improving patient outcomes.


title: Language Models for Image Captioning: The Quirks and What Works

abstract: Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-ofthe-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments.


title: Data Envelopment Analysis with application to a business simulation game

abstract: Up to now, we have seen a great variety of applications of DEA (Data Envelopment Analysis) for evaluating the efficiency of many different kinds of activities of organizations called DMU (Decision Making Unit). This study is concerned with efficiency analysis through data produced from a business simulation game. We concentrate on two factors, namely selecting of evaluating item and improving of range for efficiency. The efficient methods to select proper items and improve efficiency range are proposed based on the financial ratio analysis and the sensitivity analysis, respectively. The objective of this study is to propose a method that can make up for shortfalls of conventional financial ratio analysis methods and the DEA model (CCR) with quantitative and non-quantitative data produced from production, sales, financing and other business operations on a business simulation game.


title: Developing DSLs using combinators. A design pattern

abstract: The development of domain-specific languages (DSLs) is considered by many authors as a hard task. To simplify the design of DSLs we describe a design pattern based on the combinators technique, which can also provide guidelines for previous domain analysis phase because it is based on equational reasoning over the domain knowledge. Combinators is a common technique from functional programming to write programs. It was used many times to implement domain-specific embedded languages (DSELs) but that implementation approach is not the only one. In this paper we present the pattern together with the underlying and basic ideas behind it. We also show benefits of using it and illustrate the use of this pattern with some examples.


title: Background Subtraction With Real-Time Semantic Segmentation

abstract: Accurate and fast foreground (FG) object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel BGS framework with the real-time semantic segmentation. Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background (BG) models and segments FG objects. The real-time semantic segmenter S is used to refine the FG segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It, the BGS segmenter B computes a preliminary FG/BG mask B ::: t ::: . At the same time, the real-time semantic segmenter S extracts the object-level semantics S ::: t ::: . Then, some specific rules are applied on B ::: t ::: and S ::: t ::: to generate the final detection D ::: t ::: . Finally, the refined FG/BG mask D ::: t ::: is fed back to update the BG model. The comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves the state-of-the-art performance among all unsupervised BGS methods while operating at the real-time and even performs better than some deep learning-based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.


title: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit

abstract: Recommender systems research is being slowed by the difficulty of replicating and comparing research results. Published research uses various experimental methodologies and metrics that are difficult to compare. It also often fails to sufficiently document the details of proposed algorithms or the evaluations employed. Researchers waste time reimplementing well-known algorithms, and the new implementations may miss key details from the original algorithm or its subsequent refinements. When proposing new algorithms, researchers should compare them against finely-tuned implementations of the leading prior algorithms using state-of-the-art evaluation methodologies. With few exceptions, published algorithmic improvements in our field should be accompanied by working code in a standard framework, including test harnesses to reproduce the described results. To that end, we present the design and freely distributable source code of LensKit, a flexible platform for reproducible recommender systems research. LensKit provides carefully tuned implementations of the leading collaborative filtering algorithms, APIs for common recommender system use cases, and an evaluation framework for performing reproducible offline evaluations of algorithms. We demonstrate the utility of LensKit by replicating and extending a set of prior comparative studies of recommender algorithms --- showing limitations in some of the original results --- and by investigating a question recently raised by a leader in the recommender systems community on problems with error-based prediction evaluation.


title: Bharatanatyam hand gesture recognition using normalized chain codes and oriented distances

abstract: We propose a system to identify Bharatanatyam hand gestures or Mudras. This involves a preprocessing stage which does a skin based segmentation to obtain the hand boundary. The feature extraction stage involves obtaining the chaincode of the entire contour of the hand followed by normalization. It also includes extracting the Euclidean distance from the centroid to the outermost boundary of the hand along 360 degrees. Extracted features from the training images are used to build four recognition models Naive Bayes, KNN, Logistic Regression, Multiclass SVM. The system shows an accuracy of 88.47%, 87.06%, 89.83%, 92.3% using Naive Bayes, KNN, Logistic Regression, Multiclass SVM respectively. Multiclass SVM classifier shows the best performance. This system provides an interface for newbies to learn Bharatanatyam Dance form.


title: Poverty and Economic Decision-Making: Evidence from Changes in Financial Resources at Payday

abstract: We study the effect of financial resources on decision-making. Low-income US households are randomly assigned to receive an online survey before or after payday. The survey collects measures of cognitive function and administers risk and intertemporal choice tasks. The study design generates variation in cash, checking and savings balances, and expenditures. Before-payday participants behave as if they are more present-biased when making intertemporal choices about monetary rewards but not when making intertemporal choices about nonmonetary real-effort tasks. Nor do we find before-after differences in risk-taking, the quality of decision-making, the performance in cognitive function tasks, or in heuristic judgments. (JEL C83, D14, D81, D91, I32)


title: EFFECTS OF SMALL-SIDED GAMES ON PHYSICAL CONDITIONING AND PERFORMANCE IN YOUNG SOCCER PLAYERS

abstract: The purpose of this study was to examine, first, the movement actions performed during two different small-sided games and, second, their effects on a series of field endurance and technical tests. Thirty-four young soccer players (age: 13 ± 0.9 yrs; body mass: 62.3 ± 15.1 kg; height: 1.65 ± 0.06 m) participated in the study. Small-sided games included three-a-side (3 versus 3 players) and six-a-side (6 versus 6 players) games consisting of 10 bouts of 4 min duration with 3 min active recovery between bouts. Soccer player performance was evaluated using five field tests: a) 30m sprint, b) throw-in for distance, c) Illinois Agility Test, d) dribbling the ball and e) horizontal jump before, in the middle and after the implementation of both game situations. Heart rate was monitored during the entire testing session. Each game was also filmed to measure soccer movements within the game. The ANOVA analysis indicated that the three-a-side games displayed significantly higher heart rate values compared with the six-a-side games (p < 0.05). The number of short passes, kicks, tackles, dribbles and scoring goals were significantly higher during the three-a-side compared with the six-aside game condition (p < 0.05) while players performed more long passes and headed the ball more often during the six-a-side (p < 0.05). After the three-a-side games, there was a significant decline in sprint and agility performance (p < 0.05), while after both game conditions significant alterations in the throw-in and the horizontal jump performance were observed (p < 0.05). The results of the present study indicated that three-a-side games provide higher stimulus for physical conditioning and technical improvement than six-a-side games and their use for training young soccer players is recommended.


title: Enhancing Neural Disfluency Detection with Hand-Crafted Features

abstract: In this paper, we apply a bidirectional Long Short-Term Memory with a Conditional Random Field to the task of disfluency detection. Long-range dependencies is one of the core problems for disfluency detection. Our model handles long-range dependencies by both using the Long Short-Term Memory and hand-crafted discrete features. Experiments show that utilizing the hand-crafted discrete features significantly improves the model’s performance by achieving the state-of-the-art score of 87.1 % on the Switchboard corpus.


Handling Heterogeneous Constraints in Revision Ordering Heuristics


title: Calibrating multiple cameras with non-overlapping views using coded checkerboard targets

abstract: This paper presents an approach for combined intrinsic and extrinsic calibration of multi-camera rigs using coded targets. It is suited for conventional mono or stereo camera setups as well as for arrangements of multiple cameras with non-overlapping fields of view. The authors use a static scene consisting of multiple checkerboard targets and a sequence of images. This gives us a large amount of different observations and leads to precise calibration results. To solve the association problem of checkerboard corners over time and between different cameras, the authors use binary patterns that surround each checkerboard. A sparse nonlinear least squares solver is finally used to estimate the optimal parameter set. The parameter set contains intrinsic and extrinsic parameters, time series of the multi-camera rig pose, board poses and a description of board deformation.


title: Analysis of different architectures of counter based Wallace multipliers

abstract: Multiplication is one of the most commonly used operations in the signal processing algorithms. Multipliers based on Wallace reduction tree provide an area-efficient strategy for high speed multiplication. A number of modifications are proposed in the literature to optimize the speed and area of the Wallace multiplier. Counter based Wallace multipliers are proved to provide faster operation as compared to the traditional Wallace multipliers. This work proposes a number of architectures for the counter based Wallace multipliers to analyse their performance for various bit lengths. Designs are synthesized using Synopsys Design Compiler in 90 nm process technology and the post synthesis delay and power results are obtained by using Synopsys Prime Time. The proposed counter based Wallace multipliers are also compared with traditional Wallace multiplier to evaluate the energy per operation of both designs. The synthesis results shows that the Power-Delay Product of the counter based Wallace multiplier is up to 17% lower as compared to the traditional Wallace multiplier.


title: Data quality issues in implementing an ERP

abstract: Data quality is a critical issue during the implementation of an enterprise resource planning (ERP) system. Data quality problems can have a significant impact on an organisation’s information system. Therefore, it is essential to understand data quality issues to ensure success in implementing ERP systems. This paper uses SAP as an example of an ERP system and describes a study, which explores data quality problems with existing systems, and identifies critical success factors that impact data quality. The study resulted in the development of a framework for understanding data quality issues in implementing an ERP, and application of this framework in a case study in two large Australian organisations. The findings of the study suggest that the importance of data quality needs to be widely understood in implementing an ERP, as well as providing recommendations that may be useful to practitioners.


title: Bio-inspired computing with resistive memories — models, architectures and applications

abstract: The traditional Von Neumann architecture has constrained the potential for applying massively parallel architecture to embedded high performance computing where we must optimize the size, weight and power of the system. Inspired by highly parallel biological systems, such as the human brain, the neuromorphic architecture offers a promising novel computing paradigm for compact and energy efficient platforms. The discovery of memristor devices provided the element we need with unprecedented efficiency in realizing such a computing architecture. There are still many challenges left to meet our goal of a fully functional bio-inspired computer. Here we will discuss our research in memristor crossbar based architecture, adaptation of this architecture for cogent confabulation models, and potential applications of the bio-inspired computer.


title: Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems

abstract: For any space mission, safety and reliability are the most important issues. To tackle this problem, we have studied anomaly detection and fault diagnosis methods for spacecraft systems based on machine learning (ML) and data mining (DM) technology. In these methods, the knowledge or model which is necessary for monitoring a spacecraft system is (semi-)automatically acquired from the spacecraft telemetry data. In this paper, we first overview the anomaly detection/diagnosis problem in the spacecraft systems and conventional techniques such as limit-check, expert systems and model-based diagnosis. Then we explain the concept of ML/DM-based approach to this problem, and introduce several anomaly detection/diagnosis methods which have been developed by us.


title: Back-End Deposited Silicon Photonics for Monolithic Integration on CMOS

abstract: We present the vision of back-end deposited silicon photonics (BDSP) and review works that have been done in this field. Individual aspects of BDSP platform including excimer-laser-annealed polycrystalline silicon, low-loss plasma-enhanced chemical vapor deposition silicon nitride waveguide, modulator, detector, electrical interface, back-end CMOS compatibility, and benefits of the platform are discussed in detail.


title: Design and analysis of kinematically redundant parallel manipulators with configurable platforms

abstract: Redundancy can, in general, improve the ability and performance of parallel manipulators by implementing the redundant degrees of freedom to optimize a secondary objective function. Almost all published researches in the area of parallel manipulators redundancy were focused on the design and analysis of redundant parallel manipulators with rigid (nonconfigurable) platforms and on grasping hands to be attached to the platforms. Conventional grippers usually are not appropriate to grasp irregular or large objects. Very few studies focused on the idea of using a configurable platform as a grasping device. This paper highlights the idea of using configurable platforms in both planar and spatial redundant parallel manipulators, and generalizes their analysis. The configurable platform is actually a closed kinematic chain of mobility equal to the degree of redundancy of the manipulator. The additional redundant degrees of freedom are used in reconfiguring the shape of the platform itself. Several designs of kinematically redundant planar and spatial parallel manipulators with configurable platform are presented. Such designs can be used as a grasping device especially for irregular or large objects or even as a micro-positioning device after grasping the object. Screw algebra is used to develop a general framework that can be adapted to analyze the kinematics of any general-geometry planar or spatial kinematically redundant parallel manipulator with configurable platform.


A Case Report of Autonucleation and Psychosis


title: Investigation, Evaluation, and Optimization of Stray Inductance in Laminated Busbar

abstract: Wiring inductance has a critical effect on electrical, thermal, and electromagnetic compatibility (EMC) performances in inverters. Therefore, the low stray inductance laminated busbar, as a state-of-the-art pathway interface, is widely used in high-power inverters. However, the asymmetrical stray inductances of the laminated busbar may cause different voltage and thermal stresses for semiconductor devices of the same power stage. In order to achieve low and symmetrical stray inductances in the laminated busbar, this paper presents the investigation, evaluation, and optimization of the stray inductances of the laminated busbar. Based on the current commutation loop (CCL) analysis, the investigation of two-layer, three-layer, and multilayer laminated busbars is proposed through three-dimensional (3-D) finite element analysis (FEA) simulations. It can be found that the skin effect, mutual effect, CCL length, CCL separation distance, and split plate significantly influence the stray inductance of laminated busbars. Furthermore, the multilayer laminated busbar structure for multilevel neutral point clamped (NPC) inverter is derived. On the basis of the theoretical analysis, CCL stray inductances of a three-layer laminated busbar are extracted by means of FEA simulation and an improved impedance resonant measurement in a single-phase H-bridge high-power inverter. Insulated gate bipolar transistor (IGBT) voltage overshoot waveforms further validate the stray inductance asymmetry of the laminated busbar. A compromise between the stray inductance and symmetry is proposed in the three-layer laminated busbar. Finally, several feasible guidelines for the optimization of the laminated busbar design are introduced.


title: On the authentication of devices in the Internet of things

abstract: Reaping the benefits of the Internet of things (IoT) system is contingent upon developing IoT-specific security and privacy solutions. Conventional security solutions fail to meet the IoT security requirements due to the computationally limited and portable nature of IoT objects. In this paper, an object authentication framework is proposed to exploit device-specific information, called fingerprints, to authenticate objects in the IoT. The proposed framework is shown to effectively track the effects of physical environment on objects' fingerprints via a transfer learning tool to differentiate between security attacks and normal change in fingerprints. Simulation results show that the proposed framework improves the authentication accuracy.


title: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

abstract: The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.


title: A puppet interface for retrieval of motion capture data

abstract: Intuitive and efficient retrieval of motion capture data is essential for effective use of motion capture databases. In this paper, we describe a system that allows the user to retrieve a particular sequence by performing an approximation of the motion with an instrumented puppet. This interface is intuitive because both adults and children have experience playacting with puppets and toys to express particular behaviors or to tell stories with style and emotion. The puppet has 17 degrees of freedom and can therefore represent a variety of motions. We develop a novel similarity metric between puppet and human motion by computing the reconstruction errors of the puppet motion in the latent space of the human motion and those of the human motion in the latent space of the puppet motion. This metric works even for relatively large databases. We conducted a user study of the system and subjects could find the desired motion with reasonable accuracy from a database consisting of everyday, exercise, and acrobatic behaviors.


title: Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

abstract: This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) a novel fog densification method to densify the fog in real foggy scenes without using depth; and 4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images under dense fog. Our experiments show that 1) our fog simulation and fog density estimator outperform their state-of-the-art counterparts with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The datasets and code are available at the project website.


Security Architecture for Federated Mobile Cloud Computing


COSMECEUTICALS AND HERBAL DRUGS : PRACTICAL USES


title: An Adaptive Variable Structure Control methodology for attitude and position control of a Vectored Thrust Aerial Vehicle

abstract: This paper presents an Adaptive Variable Structure Control (AVSC) methodology to autonomously control an UAV platform known as the Vectored Thrust Aerial Vehicle (VTAV). The proposed control algorithm eliminates chattering present in Sliding Mode Control (SMC), and improves the settling time of the VTAV system by driving the error states in a parabolic trajectory towards the origin of error space instead of constraining the error states to a predefined sliding manifold. The proposed methodology is also robust to external disturbances and parametric uncertainties. Simulation results are presented to demonstrate the successful operation of the designed control algorithm on the VTAV.


title: LPV modelling and control of a Twin Rotor MIMO System

abstract: This paper describes the linear parameter varying (LPV) modelling and control of the Twin Rotor MIMO System (TRMS) developed by Feedback Instruments Limited. The nonlinear model of the TRMS is transformed into a quasi-LPV system and then approximated in a polytopic way. Using the LPV LMI pole placement approach, a LPV observer/controller for the TRMS is designed. The proposed modelling and control approach is demonstrated in simulation to prove its effectiveness and performance.


title: A New Rule for Cost Reassignment in Adaptive Steganography

abstract: In steganography schemes, the distortion function is used to define modification costs on cover elements, which is distinctly vital to the security of modern adaptive steganography. There are several successful rules for reassigning the costs defined by a given distortion function, which can promote the security level of the corresponding steganographic algorithm. In this paper, we propose a novel cost reassignment rule, which is applied to not one but a batch of existing distortion functions. We find that the costs assigned on some pixels by several steganographic methods may be very different even though these methods exhibit close security levels. We call such pixels “controversial pixel” . Experimental results show that steganalysis features are not sensitive to controversial pixels; therefore, these pixels are suitable to carry more payloads. We name this rule the controversial pixels prior (CPP) rule. Following the rule, we propose a cost reassignment scheme. Through extensive experiments on several kinds of stego algorithms, steganalysis features, and cover databases, we demonstrate that the CPP rule can improve the security of the state-of-the-art steganographic algorithms for spatial images.


title: Statistical relational learning for document mining

abstract: A major obstacle to fully integrated deployment of many data mining algorithms is the assumption that data sits in a single table, even though most real-world databases have complex relational structures. We propose an integrated approach to statistical modelling from relational databases. We structure the search space based on "refinement graphs", which are widely used in inductive logic programming for learning logic descriptions. The use of statistics allows us to extend the search space to include richer set of features, including many which are not Boolean. Search and model selection are integrated into a single process, allowing information criteria native to the statistical model, for example logistic regression, to make feature selection decisions in a step-wise manner. We present experimental results for the task of predicting where scientific papers will be published based on relational data taken from CiteSeer. Our approach results in classification accuracies superior to those achieved when using classical "flat" features. The resulting classifier can be used to recommend where to publish articles.


title: Scheduling strategies for efficient ETL execution

abstract: Extract-transform-load (ETL) workflows model the population of enterprise data warehouses with information gathered from a large variety of heterogeneous data sources. ETL workflows are complex design structures that run under strict performance requirements and their optimization is crucial for satisfying business objectives. In this paper, we deal with the problem of scheduling the execution of ETL activities (a.k.a. transformations, tasks, operations), with the goal of minimizing ETL execution time and allocated memory. We investigate the effects of four scheduling policies on different flow structures and configurations and experimentally show that the use of different scheduling policies may improve ETL performance in terms of memory consumption and execution time. First, we examine a simple, fair scheduling policy. Then, we study the pros and cons of two other policies: the first opts for emptying the largest input queue of the flow and the second for activating the operation (a.k.a. activity) with the maximum tuple consumption rate. Finally, we examine a fourth policy that combines the advantages of the latter two in synergy with flow parallelization.


title: Clustering Semantic Spaces of Suicide Notes and Newsgroups Articles.

abstract: Historically, suicide risk assessment has relied on question-and-answer type tools. These tools, built on psychometric advances, are widely used because of availability. Yet there is no known tool based on biologic and cognitive evidence. This absence often cause a vexing clinical problem for clinicians who question the value of the result as time passes. The purpose of this paper is to describe one experiment in a series of experiments to develop a tool that combines Biological Markers (Bm) with Thought Markers (Tm), and use machine learning to compute a real-time index for assessing the likelihood repeated suicide attempt in the next six-months. For this study we focus using unsupervised machine learning to distinguish between actual suicide notes and newsgroups. This is important because it gives us insight into how well these methods discriminate between real notes and general conversation.


title: A Novel Hybrid-Fed Patch Antenna With Pattern Diversity

abstract: Abstract-In this letter, a novel hybrid-fed circular patch antenna operating with TM11 and TM01 modes is presented. By making use of a hybrid feed network, the two modes can be operated at an overlapped frequency range for 2.4-GHz WLAN applications, while TM11 mode reveals good broadside radiation patterns and TM01 mode shows monopole-like radiation patterns. The proposed antenna is fabricated and tested. The measured gain for the broadside mode is 7.1-9.1 dBi from 2.16 to 2.72 GHz (23.0%), and for the conical mode 3.4-4.3 dBi from 2.14 to 2.64 GHz (20.5%). High isolation (<; -40 dB) between the two feeding ports for the entire impedance bandwidth of the proposed antenna is achieved. The planar dual-port antenna provides different patterns and polarizations to take advantage of the pattern and polarization diversity for modern mobile communications.


title: From Template to Image: Reconstructing Fingerprints from Minutiae Points

abstract: Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line integral convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint


The Potential Value of Peer-to-Peer Energy Trading in the Australian National Electricity Market


title: Clinical and microbiologic characteristics of vulvovaginitis in Korean prepubertal girls, 2009–2014: a single center experience

abstract: To update information on the clinical and microbiologic characteristics of pediatric vulvovaginitis in Korean prepubertal girls.A total of 120 girls (aged 0 to 9 years) with culture-confirmed pediatric vulvovaginitis, diagnosed between 2009 and 2014, were enrolled in the study. The epidemiologic and microbiologic characteristics, and clinical outcomes were assessed. Patients with sexual precocity, as well as those who were referred for suspected sexual abuse, were excluded.Girls aged 4 to 6 years were at the highest risk of pediatric vulvovaginitis. Seasonal distribution indicated obvious peaks in summer and winter. Of the 120 subjects, specific pathogens were identified in the genital specimens in only 20 cases (16.7%). Streptococcus pyogenes (n=12, 60%) was the leading cause of specific vulvovaginitis. Haemophilus influenzae was isolated in one patient. No cases presented with enteric pathogens, such as Shigella or Yersinia. A history of recent upper respiratory tract infection, swimming, and bubble bath use was reported in 37.5%, 15.8%, and 10.0% of patients, respectively. Recent upper respiratory tract infection was not significantly correlated with the detection of respiratory pathogens in genital specimens (P>0.05). Of 104 patients who underwent perineal hygienic care, 80 (76.9%) showed improvement of symptoms without antibiotic treatment. Furthermore, the efficacy of hygienic care was not significantly different between patients with or without specific pathogens (P>0.05).Specific pathogens were only found in 16.7% of pediatric vulvovaginitis cases. Our results indicate an excellent outcome with hygienic care, irrespective of the presence of specific pathogens.


title: 1 Enterprise Social Media: Definition, History, and Prospects for the Study of Social Technologies in Organizations

abstract: Social media are increasingly implemented in work organizations as tools for communication among employees. It is important that we develop an understanding of how they enable and constrain the communicative activities through which work is accomplished because it is these very dynamics that constitute and perpetuate organizations. We begin by offering a definition of enterprise social media and providing a rough historical account of the various avenues through which these technologies have entered and continue to enter the workplace. We also review areas of research covered by papers in this special issue and papers on enterprise social media published elsewhere to take stock of the current state of out knowledge and to propose directions for future research.


title: A Low-Cost Search-and-Rescue Drone for Near Real-Time Detection of Missing Persons

abstract: In this work, an unmanned aerial system is implemented to search an outdoor area for an injured or missing person (subject) without requiring a connection to a ground operator or control station. The system detects subjects using exclusively on-board hardware as it traverses a predefined search path, with each implementation envisioned as a single element of a larger swarm of identical search drones. Imagery is streamed from a camera to an Odroid single-board computer, which prepares the data for inference by a Neural Compute Stick vision accelerator. A single-class TinyYolo network, trained on the Okutama-Action dataset and an original Albatross dataset, is utilized to detect subjects in the prepared frames. The detection apparatus is mounted on a drone and field tests validate the system feasibility and efficacy.


title: Fundamental Considerations of Three-Level DC–DC Converters: Topologies, Analyses, and Control

abstract: This paper discusses the basic family of three-level (TL) dc-dc converters. The origin of TL converters and their basic topological variations are described. Systematic procedures leading to improved and simplified circuit topologies are discussed. A feedforward control scheme that ensures the proper functioning of these converters is proposed. Moreover, the virtues and drawbacks of these converters as compared to conventional converters are highlighted. In particular, the advantages of TL converters include reduced voltage stress of the switches, reduced filter size, and improved dynamic response. It is shown that TL converters are highly suitable for high input/output voltage and medium-to-high-power dc-dc power conversions.


Evidence-Based Higher Education – Is the Learning Styles ‘Myth’ Important?


title: From Predictive Models to Instructional Policies

abstract: At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way to quantify whether the student has mastered a skill. A large amount of work has been done on building student models that can predict student performance on the next question. In this paper, we leverage this prior work with a new whento-stop policy that is compatible with any such predictive student model. Our results suggest that, when employed as part of our new predictive similarity policy, student models with similar predictive accuracies can suggest that substantially different amounts of practice are necessary. This suggests that predictive accuracy may not be a sufficient metric by itself when choosing which student model to use in intelligent tutoring systems.


title: Anxiety disorders and comorbid medical illness

abstract: Abstract Objective To provide an overview of the role of anxiety disorders in medical illness. Method The Anxiety Disorders Association of America held a multidisciplinary conference from which conference leaders and speakers reviewed presentations and discussions, considered literature on prevalence, comorbidity, etiology and treatment, and made recommendations for research. Irritable bowel syndrome (IBS), asthma, cardiovascular disease (CVD), cancer and chronic pain were reviewed. Results A substantial literature supports clinically important associations between psychiatric illness and chronic medical conditions. Most research focuses on depression, finding that depression can adversely affect self-care and increase the risk of incident medical illness, complications and mortality. Anxiety disorders are less well studied, but robust epidemiological and clinical evidence shows that anxiety disorders play an equally important role. Biological theories of the interactions between anxiety and IBS, CVD and chronic pain are presented. Available data suggest that anxiety disorders in medically ill patients should not be ignored and could be considered conjointly with depression when developing strategies for screening and intervention, particularly in primary care. Conclusions Emerging data offer a strong argument for the role of anxiety in medical illness and suggest that anxiety disorders rival depression in terms of risk, comorbidity and outcome. Research programs designed to advance our understanding of the impact of anxiety disorders on medical illness are needed to develop evidence-based approaches to improving patient care.


title: Closed Loop Controlled AC-AC Converter for Induction Heating

abstract: A single-switch parallel resonant converter for induction heating is simulated and implemented. The circuit consists of input LC-filter, bridge rectifier and one controlled power switch. The switch operates in soft commutation mode and serves as a high frequency generator. Output power is controlled via switching frequency. Steady state analysis of the converter operation is presented.. A closed loop circuit model for AC to AC converted induction heating system is also proposed. Experimental results are compared with simulation results. Introduction Static frequency converters have been extensively applied in industry as a medium –frequency power supply for induction heating and melting installations. They are applied in all branches of the military, machine-building industries, jewellery, smithy heating, domestic heating cooking devices and other purposes. The ordinary circuit of an AC-AC converter for induction heating typically includes a controlled rectifier and a frequency controlled current source or a voltage source inverter. It is a well known fact that the input rectifier does not ensure a sine wave input current, and is characterized by a low power [1-3]. Recently many studies of high power factor rectifiers with a single switch have been made [4-5]. These schemes are also characterized by a close to sine wave input current. In addition, in [6] the scheme of the AC-AC converter for induction heating is described. The input circuit of the converter is constructed similarly to the input circuit in [4, 5], which also ensures a high power factor. However the inverting circuit is constructed by traditional mode with four controlled switches. The above literature does not deal with closed loop modeling and embedded implementation of AC to AC converter fed induction heater. In the present work AC to AC converter is modeled and it is implemented using an atmel microcontroller. The present problem aims to minimize the cost of induction heater system by using an embedded controller. Fig.1 Circuit Diagram In the scheme (Fig.1) of the AC-AC converter there are two main advantages: It is characterized by a high power factor and a sine wave input current. On the other hand the inverter circuit is constructed with a single controlled switch, which serves as a high-frequency generator for induction heating. Principle Of Operation The operating principles of the circuit are illustrated by Fig.2 and the theoretical waveforms Fig.2a. Mode 1 (to-t1) Mr. D.Kirubakaran has obtained M.E. degree from Bharathidasan University in 2000. He is presently doing his research in the area of ACAC converters for induction heating. He has 10 years of teaching experience. He is a life member of ISTE. Dr. S. Rama Reddy received his M.E degree from College of Engineering, Anna University, Chennai, India in 1987. He received Ph.D degree in the area of Resonant Converters from College of Engineering, Anna University, Chennai India in 1995. Presently he is working as Dean in Electrical & Electronics Dept., Jerusalem College of Engineering, Chennai. He has worked in Tata Consulting Engineers and Anna University, Chennai, India. He is fellow member of Institution of Electronics and Telecommunication Engineers (India), Life Member of Institution of Engineers (India), Member of ISTE, Member of CSI and Member of SPE, India. He has authored text books on Power Electronics, Electronic Circuits and Electromagnetic Fields. He has published 30 research papers in reputed journals. His research areas are Power Electronic Converters, Drives and FACTS.


title: Qualitative analysis of visualization: a building design field study

abstract: We conducted an ethnographic field study examining the ways in which building design teams used visual representations of data to coordinate their work. Here we describe our experience with this field study approach, including both quantitative and qualitative analysis of field study data. Conducting a field study enabled us to effectively examine real work practice of a diverse team of experts, which would have been nearly impossible in a laboratory study. We also found that structured qualitative analysis methods provided deeper insight into our results than our initial quantitative approach. Our experience suggests that field studies and qualitative analysis could have substantial benefit in visualization and could nicely complement existing quantitative laboratory studies.


title: Fingerprint imaging using CMUT impediography with glass waveguide

abstract: This paper discusses a method for fingerprint imaging, which involves applying an impedance signal to a fingerprint via ultrasonic impediography, using a capacitive micromachined ultrasonic transducer (CMUT). We explore the use of ultrasonic fingerprint imaging with impediography using a CMUT and a fingerprint phantom, and contrast the use of direct-touch and waveguide models. By using a waveguide in the middle of a wafer made of a hard material, the protection and stability of CMUT impedance signals are ensured. The measured impedance value is used to distinguish between the ridges and valleys of a fingerprint and create a fingerprint image by image processing. The images created using direct-touch and waveguide models are compared and analyzed.


Event Detection in Social Media Data


title: Unconstrained realtime facial performance capture

abstract: We introduce a realtime facial tracking system specifically designed for performance capture in unconstrained settings using a consumer-level RGB-D sensor. Our framework provides uninterrupted 3D facial tracking, even in the presence of extreme occlusions such as those caused by hair, hand-to-face gestures, and wearable accessories. Anyone's face can be instantly tracked and the users can be switched without an extra calibration step. During tracking, we explicitly segment face regions from any occluding parts by detecting outliers in the shape and appearance input using an exponentially smoothed and user-adaptive tracking model as prior. Our face segmentation combines depth and RGB input data and is also robust against illumination changes. To enable continuous and reliable facial feature tracking in the color channels, we synthesize plausible face textures in the occluded regions. Our tracking model is personalized on-the-fly by progressively refining the user's identity, expressions, and texture with reliable samples and temporal filtering. We demonstrate robust and high-fidelity facial tracking on a wide range of subjects with highly incomplete and largely occluded data. Our system works in everyday environments and is fully unobtrusive to the user, impacting consumer AR applications and surveillance.


title: Road-Departure Prevention in an Emergency Obstacle Avoidance Situation

abstract: This paper presents a driving simulator experiment, which evaluates a road-departure prevention (RDP) system in an emergency situation. Two levels of automation are evaluated: 1) haptic feedback (HF) where the RDP provides advisory steering torque such that the human and the machine carry out the maneuver cooperatively, and 2) drive by wire (DBW) where the RDP automatically corrects the front-wheels angle, overriding the steering-wheel input provided by the human. Thirty participants are instructed to avoid a pylon-confined area while keeping the vehicle on the road. The results show that HF has a significant impact on the measured steering wheel torque, but no significant effect on steering-wheel angle or vehicle path. DBW prevents road departure and tends to reduce self-reported workload, but leads to inadvertent human-initiated steering resulting in pylon collisions. It is concluded that a low level of automation, in the form of HF, does not prevent road departures in an emergency situation. A high level of automation, on the other hand, is effective in preventing road departures. However, more research may have to be done on the human response while driving with systems that alter the relationship between steering-wheel angle and front-wheels angle.


LONG-TERM PRESERVATION OF 3-D CULTURAL HERITAGE DATA RELATED TO ARCHITECTURAL SITES


title: ‘Cycle Thieves, We Are Watching You’: Impact of a Simple Signage Intervention against Bicycle Theft

abstract: Background ::: Bicycle theft is a serious problem in many countries, and there is a lack of evidence concerning effective prevention strategies. Displaying images of ‘watching eyes’ has been shown to make people behave in more socially desirable ways in a number of settings, but it is not yet clear if this effect can be exploited for purposes of crime prevention. We report the results of a simple intervention on a university campus where signs featuring watching eyes and a related verbal message were displayed above bicycle racks. ::: ::: ::: Methodology and Principal Findings ::: We installed durable signs at three locations which had experienced high levels of bicycle theft, and used the rest of the university campus as a control location. Reported thefts were monitored for 12 months before and after the intervention. Bicycle thefts decreased by 62% at the experimental locations, but increased by 65% in the control locations, suggesting that the signs were effective, but displaced offending to locations with no signs. The Odds Ratio for the effect of the intervention was 4.28 (95% confidence interval 2.04–8.98), a large effect compared to other place-based crime prevention interventions. ::: ::: ::: Conclusions and Significance ::: The effectiveness of this extremely cheap and simple intervention suggests that there can be considerable crime-reduction benefits to engaging the psychology of surveillance, even in the absence of surveillance itself. Simple interventions for high-crime locations based on this principle should be considered as an adjunct to other measures, although a possible negative consequence is displacement of offending.


title: MineBench: A Benchmark Suite for Data Mining Workloads

abstract: Data mining constitutes an important class of scientific and commercial applications. Recent advances in data extraction techniques have created vast data sets, which require increasingly complex data mining algorithms to sift through them to generate meaningful information. The disproportionately slower rate of growth of computer systems has led to a sizeable performance gap between data mining systems and algorithms. The first step in closing this gap is to analyze these algorithms and understand their bottlenecks. With this knowledge, current computer architectures can be optimized for data mining applications. In this paper, we present MineBench, a publicly available benchmark mark suite containing fifteen representative data mining applications belonging to various categories such as clustering, classification, and association rule mining. We believe that MineBench will be of use to those looking to characterize and accelerate data mining workloads.


title: Identity Mappings in Deep Residual Networks

abstract: Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62 % error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/resnet-1k-layers.


title: The global prevalence of dementia: A systematic review and metaanalysis

abstract: Abstract Background The evidence base on the prevalence of dementia is expanding rapidly, particularly in countries with low and middle incomes. A reappraisal of global prevalence and numbers is due, given the significant implications for social and public policy and planning. Methods In this study we provide a systematic review of the global literature on the prevalence of dementia (1980–2009) and metaanalysis to estimate the prevalence and numbers of those affected, aged ≥60 years in 21 Global Burden of Disease regions. Results Age-standardized prevalence for those aged ≥60 years varied in a narrow band, 5%–7% in most world regions, with a higher prevalence in Latin America (8.5%), and a distinctively lower prevalence in the four sub-Saharan African regions (2%–4%). It was estimated that 35.6 million people lived with dementia worldwide in 2010, with numbers expected to almost double every 20 years, to 65.7 million in 2030 and 115.4 million in 2050. In 2010, 58% of all people with dementia lived in countries with low or middle incomes, with this proportion anticipated to rise to 63% in 2030 and 71% in 2050. Conclusion The detailed estimates in this study constitute the best current basis for policymaking, planning, and allocation of health and welfare resources in dementia care. The age-specific prevalence of dementia varies little between world regions, and may converge further. Future projections of numbers of people with dementia may be modified substantially by preventive interventions (lowering incidence), improvements in treatment and care (prolonging survival), and disease-modifying interventions (preventing or slowing progression). All countries need to commission nationally representative surveys that are repeated regularly to monitor trends.


title: Capturing personality from Facebook photos and photo-related activities: How much exposure do you need?

abstract: Users with various personality traits set up albums and upload photos differently.Extraversion predicted having more friends, more photo uploads and more cover photos.Neuroticism was associated with extensive photo uploads and voluminous photo albums.Conscientiousness was predictive of more self-generated albums and video upload.The more Agreeable users received more 'likes' and 'comments' on profile pictures. Photo-related activities are noticeably prevalent among social media users. On Facebook, users predominantly communicate visually and manage their self-presentation. Such online behaviours tend to mimic what would be expected of individuals' offline personalities. This study sought to address the link between Facebook users' photo-related activities and the Big Five personality traits by encoding basic Facebook visual features. Content analysis on the actual profiles (n=115) and multiple regression analyses revealed many associations as a manifestation of users' characteristics. For instance, Neuroticism and Extraversion predicted more photo uploads. Conscientiousness was predictive of more self-generated albums and video uploads and Agreeableness predicted the average number of received 'likes' and 'comments' on profile pictures. Additionally, the Facebook experience in interaction with the personality factors was found to be influential on the type of photo-related activity and the level of photo participation of users. The findings provide evidence that Facebook users with various personality traits set up albums and upload photos differently. Given the uses and gratification model, users adapt the construction of their profiles and manage their interactions to gratify their psychological needs on Facebook.


title: Youth Top Problems: using idiographic, consumer-guided assessment to identify treatment needs and to track change during psychotherapy.

abstract: Objective: To complement standardized measurement of symptoms, we developed and tested an efficient strategy for identifying (before treatment) and repeatedly assessing (during treatment) the problems identified as most important by caregivers and youths in psychotherapy. Method: A total of 178 outpatient-referred youths, 7-13 years of age, and their caregivers separately identified the 3 problems of greatest concern to them at pretreatment and then rated the severity of those problems weekly during treatment. The Top Problems measure thus formed was evaluated for (a) whether it added to the information obtained through empirically derived standardized measures (e.g., the Child Behavior Checklist [CBCL; Achenbach & Rescorla, 2001] and the Youth Self-Report [YSR; Achenbach & Rescorla, 2001]) and (b) whether it met conventional psychometric standards. Results: The problems identified were significant and clinically relevant; most matched CBCL/YSR items while adding specificity. The top problems also complemented the information yield of the CBCL/YSR; for example, for 41% of caregivers and 79% of youths, the identified top problems did not correspond to any items of any narrowband scales in the clinical range. Evidence on test―retest reliability, convergent and discriminant validity, sensitivity to change, slope reliability, and the association of Top Problems slopes with standardized measure slopes supported the psychometric strength of the measure. Conclusions: The Top Problems measure appears to be a psychometrically sound, client-guided approach that complements empirically derived standardized assessment; the approach can help focus attention and treatment planning on the problems that youths and caregivers consider most important and can generate evidence on trajectories of change in those problems during treatment.


title: A wide duty cycle range wide band high frequency isolated gate driver for power converters

abstract: There are many gate driver circuits in the literature both with and without galvanic isolation. Almost all existing methods have limitations either in duty cycle and/or switching frequency. Further, most of the opto-isolated drives require multiple isolated power supplies. This paper presents a novel isolated gate driver based on dual forward converter topology that addresses the above issues. In the proposed gate drive, two forward converter transformers are paralleled at the secondary side achieving 0–100% duty cycle together with galvanic isolation and only with a single power supply. The gate driver switching devices and magnetics are designed for high frequencies independent of the main PWM frequency. At high frequencies, problems of resonance due to parasitics become significant. This paper also discusses this problem and proposes a novel solution to mitigate it. This circuit can be used for any application irrespective of switching frequency thus making it a wide band generalized gate driver. The proposed circuit is analyzed and verified experimentally.


title: Language preference and development of dementia among bilingual individuals.

abstract: In bilingual individuals, regression to a primary language may be associated with development of cognitive impairment and increased risk for development of dementia. This report describes two bilingual patients who presented with early symptoms of dementia after regression to their primary language. The results of this study may help clinicians identify aging bilingual patients who are beginning to develop cognitive impairment or dementia and suggest that further studies on the long term cognitive effects of bilingualism and interactions with the aging process are indicated.


title: Mobile technologies in medical education: AMEE Guide No. 105.

abstract: Mobile technologies (including handheld and wearable devices) have the potential to enhance learning activities from basic medical undergraduate education through residency and beyond. In order to use these technologies successfully, medical educators need to be aware of the underpinning socio-theoretical concepts that influence their usage, the pre-clinical and clinical educational environment in which the educational activities occur, and the practical possibilities and limitations of their usage. This Guide builds upon the previous AMEE Guide to e-Learning in medical education by providing medical teachers with conceptual frameworks and practical examples of using mobile technologies in medical education. The goal is to help medical teachers to use these concepts and technologies at all levels of medical education to improve the education of medical and healthcare personnel, and ultimately contribute to improved patient healthcare. This Guide begins by reviewing some of the technological changes that have occurred in recent years, and then examines the theoretical basis (both social and educational) for understanding mobile technology usage. From there, the Guide progresses through a hierarchy of institutional, teacher and learner needs, identifying issues, problems and solutions for the effective use of mobile technology in medical education. This Guide ends with a brief look to the future.


title: A Survey on Anonymity and Privacy in Bitcoin-Like Digital Cash Systems

abstract: Bitcoin is the most widely known distributed, peer-to-peer payment network without existence of a central authority. In Bitcoin, users do not use real names; instead, pseudonyms are used. Managing and verifying transactions and issuing of bitcoins are performed collectively by peers in the network. Since pseudonyms are used without providing any identity, it is perceived that Bitcoin provides anonymity. However, it is one of the most transparent payment networks since all transactions are publicly announced. Blockchain, which is the public ledger of Bitcoin, includes all transactions to prevent double-spending and to provide integrity. By using data in the blockchain, flow of bitcoins between transactions can be observed and activities of the users can be traced. When the implications obtained from the blockchain are combined with external data, identity and profile of a user can be revealed. This possibility has undesirable effects such as spending history of a user becomes accessible to other people, or cash flow of a merchant becomes exposed to competitors. There are several proposals as extensions or alternatives to Bitcoin, which improve anonymity and privacy. This survey presents an overview and detailed investigation of anonymity and privacy in Bitcoin-like digital cash systems. We examine the studies in the literature/Web in two major categories: 1) analyses of anonymity and privacy in Bitcoin and 2) extensions and alternatives to Bitcoin, which improve anonymity and privacy. We list and describe methods and outcomes for both categories and group studies according to these methods and outcomes. We also present relationships between outcomes of analyses and the improvement methods. We compare performances of the methods and show relationships between the proposals. Moreover, we present guidelines for designing an anonymity/privacy improvement and discuss future research directions.