title: A Recommendation Engine Using Apache Spark

abstract: The volume of structured and unstructured data has grown at exponential scale in recent days. As a result of this rapid data growth, we are always inundated with plethora of choices in any product or service. It is very natural to get lost in the amazon of such choices and finding hard to make decisions. The project aims at addressing this problem by using entity recommendation. The two main aspects that the project concentrates on are implementing and presenting more accurate entity recommendations to the user and another is dealing with vast amount of data. The project aims at presenting recommendation results according to user’s query with efficiency and accuracy. Project makes use of ListNet ranking algorithm to rank the recommendation results. Query independent features and query dependent features are used to come up with ranking scores. Ranking scores decide the order in which the recommendation results are presented to the user. Project makes use of Apache Spark, a distributed bigdata processing framework. Spark gives the advantage of handling iterative and interactive algorithms with efficiency and minimal processing time as compared to traditional mapreduce paradigm. We performed the experiments for recommendation engine using DBPedia as the dataset and tested the results for movie domain. We used both queryindependent (pagerank) and querydependent (clicklogs) features for ranking purposes. We observed that ListNet algorithm performs really well by making use of Apache Spark as the RDDs provide faster way for iterative algorithms to execute. We also observed that the results of recommendation engine are accurate and the entities are well ranked.


title: Predicting neuronal responses during natural vision

abstract: A model that fully describes the response properties of visual neurons must be able to predict their activity during natural vision. While many models have been proposed for the visual system, few have ever been tested against this criterion. To address this issue, we have developed a general framework for fitting and validating nonlinear models of visual neurons using natural visual stimuli. Our approach derives from linear spatiotemporal receptive field (STRF) analysis, which has frequently been used to study the visual system. However, prior to the linear filtering stage typical of STRFs, a linearizing transformation is applied to the stimulus to account for nonlinear response properties. We used this approach to compare two models for neurons in primary visual cortex: a nonlinear Fourier power model, which accounts for spatial phase invariant tuning, and a traditional linear model. We characterized prediction accuracy in terms of the total explainable variance, given intrinsic experimental noise. On average, Fourier power STRFs predicted 40% of explainable variance while linear STRFs were able to predict only 21% of explainable variance. The performance of the Fourier power model provides a benchmark for evaluating more sophisticated models in the future.


title: A Stator Winding Insulation Condition Monitoring Technique for Inverter-Fed Machines

abstract: Stator insulation quality assessment is an important issue for pulsewidth modulation inverter-fed machines, since the stator insulation is exposed to increased thermal/electrical stresses. In this paper, a new approach for monitoring the condition of the stator insulation for inverter-fed machines is proposed. The main concept of the proposed approach is to apply various types of test voltages to the motor stator insulation using the inverter, to perform standard offline insulation tests whenever the motor is not operating. Two methods for testing the insulation with modifications to the inverter circuit are proposed and verified experimentally. The results show that the proposed technique provides a simple low cost solution for detecting stator insulation degradation at an early stage for reliable operation of inverter-fed machine systems, since the insulation condition can be monitored frequently. This not only helps prevent forced outages and safety risks due to insulation breakdown, but also helps perform maintenance in an efficient manner since individual motor maintenance can be prioritized and scheduled based on its present condition.


title: Implementation of a fuzzy logic speed controller for a permanent magnet dc motor using a low-cost Arduino platform

abstract: Implementation of a Fuzzy Logic speed controller for a Permanent Magnet DC (PMDC) motor using a low-cost Arduino interfaced with a standard xPC Target MATLAB environment is demonstrated. The Arduino solution is acceptable at kHz sampling frequencies. The study of speed control of PMDC motor based on Arduino and L298N H-Bridge using an artificial intelligence control method, such as Fuzzy PID with only four rules, has been validated by experiments on the physical system.


title: ENTREPRENEURIAL ACTION AND THE ROLE OF UNCERTAINTY IN THE THEORY OF THE ENTREPRENEUR.

abstract: By considering the amount of uncertainty perceived and the willingness to bear uncertainty concomitantly, we provide a more complete conceptual model of entrepreneurial action that allows for examination of entrepreneurial action at the individual level of analysis while remaining consistent with a rich legacy of system-level theories of the entrepreneur. Our model not only exposes limitations of existing theories of entrepreneurial action but also contributes to a deeper understanding of important conceptual issues, such as the nature of opportunity and the potential for philosophical reconciliation among entrepreneurship scholars.


title: Energy management and control for islanded microgrid using multi-agents

abstract: This paper presents a multi-agent system for realtime operation of simulated microgrid using the Smart-Grid Test Bed at Washington State University. The multi-agent system (MAS) was developed in JADE (Java Agent DEvelopment Framework) which is a Foundation for Intelligent Physical Agents (FIPA) compliant open source multi-agent platform. The proposed operational strategy is mainly focused on using an appropriate energy management and control strategies to improve the operation of an islanded microgrid, formed by photovoltaic (PV) solar energy, batteries and resistive and rotating machines loads. The focus is on resource management and to avoid impact on loads from abrupt variations or interruption that changes the operating conditions. The management and control of the PV system is performed in JADE, while the microgrid model is simulated in RSCAD/RTDS (Real-Time Digital Simulator). Finally, the outcome of simulation studies demonstrated the feasibility of the proposed multi-agent approach for real-time operation of a microgrid.


title: Phoenix: DGA-Based Botnet Tracking and Intelligence

abstract: Modern botnets rely on domain-generation algorithms (DGAs) to build resilient command-and-control infrastructures. Given the prevalence of this mechanism, recent work has focused on the analysis of DNS traffic to recognize botnets based on their DGAs. While previous work has concentrated on detection, we focus on supporting intelligence operations. We propose Phoenix, a mechanism that, in addition to telling DGA- and non-DGA-generated domains apart using a combination of string and IP-based features, characterizes the DGAs behind them, and, most importantly, finds groups of DGA-generated domains that are representative of the respective botnets. As a result, Phoenix can associate previously unknown DGA-generated domains to these groups, and produce novel knowledge about the evolving behavior of each tracked botnet. We evaluated Phoenix on 1,153,516 domains, including DGA-generated domains from modern, well-known botnets: without supervision, it correctly distinguished DGA- vs. non-DGA-generated domains in 94.8 percent of the cases, characterized families of domains that belonged to distinct DGAs, and helped researchers “on the field” in gathering intelligence on suspicious domains to identify the correct botnet.


title: An efficient data mining classification approach for detecting lung cancer disease

abstract: Background: Automated disease classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. Proposed model is a classification based an efficient approach in which machine learning concepts are used for the detection of Lung cancer diseases. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to compare the proposed work model working. Results: We developed user friendly disease prediction model based on PCA and LDA. To validate the method, the proposed method is applied in MATLAB 2014a to achieve high accuracy performance metric and then comparison has been made with ICA and SURF method. Conclusions: The proposed approach offers improved user-friendliness, as feature extraction is performed in an easily editable. As a direct implication, intermediate results are more easily accessible.


title: Case Study on Measurement of SpO2 from PPG Signals in the Presence of Motion Artifact

abstract: In recent years, researchers have presented a number of new methods for removing the motion artifacts from the PPG signal during the measurement of SpO2 parameters using a pulse oximeter. The ubiquity in the measurement of SpO2 due to motion artifacts presents the possibility for many new hybrid algorithms. In this paper, we present a review of the work on photoplethysmographic (PPG) signal with respect to motion artifacts and their open issues and research gap. This review specifically focuses on the state-of-the-art in measurement of SpO2 in the presence of motion artifacts where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of measurement of SpO2 in the presence of motion artifact research.


title: Asymmetric Algorithms and Symmetric Algorithms: A Review

abstract: In these days securing a network is an important issue. Many techniques are provided to secure network. Cryptographic is a technique of transforming a message into such form which is unreadable, and then retransforming that message back to its original form. Cryptography works in two techniques: symmetric key also known as secret-key cryptography algorithms and asymmetric key also known as public-key cryptography algorithms. In this paper we are reviewing different symmetric and asymmetric algorithms.


title: Warpage and Thermal Characterization of Fan-Out Wafer-Level Packaging

abstract: In this study, the warpage and thermal performances of fan-out wafer-level packaging (FOWLP) are investigated. Emphasis is placed on the characterization of the effects of FOWLP important parameters, such as chip size, chip thickness, package/chip area ratio, epoxy molding compound (EMC), chip EMC cap, carrier material and thickness, and die-attach film, on the warpage after post mold cure (PMC) and backgrinding of the EMC. The simulation results are compared with the experimental measurements. Also, the thermal performance (junction-to-ambient thermal resistance) of FOWLP with various chip thicknesses is characterized. Finally, some FOWLP important parameters affecting the warpage and thermal performances are recommended.


title: Question/Answer matching for CQA system via combining lexical and sequential information

abstract: Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.


Comparison of eSports and Traditional Sports Consumption Motives


title: A Survey on Wireless Multimedia Sensor Networks

abstract: The availability of low-cost hardware such as CMOS cameras and microphones has fostered the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., networks of wirelessly interconnected devices that are able to ubiquitously retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, the state of the art in algorithms, protocols, and hardware for wireless multimedia sensor networks is surveyed, and open research issues are discussed in detail. Architectures for WMSNs are explored, along with their advantages and drawbacks. Currently off-the-shelf hardware as well as available research prototypes for WMSNs are listed and classified. Existing solutions and open research issues at the application, transport, network, link, and physical layers of the communication protocol stack are investigated, along with possible cross-layer synergies and optimizations.


title: No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

abstract: For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units. ::: In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phonemes. We also compare grapheme and phoneme-based approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple dialects.


title: DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

abstract: This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. ::: ::: The objective of this survey is to summarize the existing methodologies, compare their features, and propose a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements.


title: A novel driving scheme for synchronous rectifier in MHz CRM flyback converter with GaN devices

abstract: Synchronous rectifier (SR) is widely used in flyback converter to reduce the output side conduction loss in order to meet the system conversion efficiency requirement. The conventional SR driving methods are not suitable for high frequency (>500kHz) application. This paper proposes a novel SR driving method for MHz flyback converter operating at critical conduction mode (CRM). A RC network is in parallel with SR to emulate the magnetizing current of flyback converter. The sensing signal is clean and immune to the parasitic ringing caused by the leakage inductance and parasitic capacitors. SR gate signal is accurately generated based on a simple signal processing circuit and the conduction loss of SR body diode is minimized. The theoretical analysis is validated by simulation and experiment.


title: Praise for intelligence can undermine children's motivation and performance.

abstract: Praise for ability is commonly considered to have beneficial effects on motivation. Contrary to this popular belief, six studies demonstrated that praise for intelligence had more negative consequences for students' achievement motivation than praise for effort. Fifth graders praised for intelligence were found to care more about performance goals relative to learning goals than children praised for effort. After failure, they also displayed less task persistence, less task enjoyment, more lowability attributions, and worse task performance than children praised for effort. Finally, children praised for intelligence described it as a fixed trait more than children praised for hard work, who believed it to be subject to improvement. These findings have important implications for how achievement is best encouraged, as well as for more theoretical issues, such as the potential cost of performance goals and the socialization of contingent self-worth.


title: Process Modeling using UML

abstract: The Unified Modeling Language (UML)1 is a visual, object-oriented, and multipurpose modeling language. Primarily designed for modeling software systems, it can also be used for business process modeling. Since the early 1970s, a large variety of languages for data and software modeling like entity-relationship diagrams [2], message sequence charts [5, 10], state charts [9], and so on, have been developed, each of them focusing on a different aspect of software structure or behavior. In the early 1990s, object-oriented design approaches gained increasing attention, for instance, in the work of James Rumbaugh (Object Modeling Technique or OMT [21]), Grady Booch [1], and Ivar Jacobson [12]. The UML emerged from the intention of Rumbaugh, Booch, and Jacobson to find a common framework for their approaches and notations. Furthermore, the language was also influenced by other object-oriented approaches like that of Coad and Yourdon [3]. The first version, UML 1.0 [20], was released in 1997 and accepted as a standard by the Object Management Group (OMG)2 the same year. The OMG, which took over the responsibility for the evolution of the UML from then on, is a consortium from both industry and academia and is also responsible for other wellknown initiatives like CORBA, MDA, and XMI. OMG specifications have to undergo a sophisticated adoption process before being agreed upon as a standard by the OMG members. Since many important tool builders and influential software companies are involved in the OMG, UML has quickly been accepted by the software industry, especially since version UML 1.3 appeared in 1999. When writing this book, the current UML version was UML 2.0 [18], a major revision of the language. UML is a conglomeration of various diagram types. Therefore, the challenge is to provide a uniform framework for all these heterogeneous diagram types and accounting for relationships between them. In UML, this is solved by a common meta-model that formally defines the abstract syntax of all diagram types. The


title: An updated survey of GA-based multiobjective optimization techniques

abstract: After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.


title: On Vectorization of Deep Convolutional Neural Networks for Vision Tasks

abstract: We recently have witnessed many ground-breaking results in machine learning and computer vision, generated by using deep convolutional neural networks (CNN). While the success mainly stems from the large volume of training data and the deep network architectures, the vector processing hardware (e.g. GPU) undisputedly plays a vital role in modern CNN implementations to support massive computation. Though much attention was paid in the extent literature to understand the algorithmic side of deep CNN, little research was dedicated to the vectorization for scaling up CNNs. In this paper, we studied the vectorization process of key building blocks in deep CNNs, in order to better understand and facilitate parallel implementation. Key steps in training and testing deep CNNs are abstracted as matrix and vector operators, upon which parallelism can be easily achieved. We developed and compared six implementations with various degrees of vectorization with which we illustrated the impact of vectorization on the speed of model training and testing. Besides, a unified CNN framework for both high-level and low-level vision tasks is provided, along with a vectorized Mat-lab implementation with state-of-the-art speed performance.


title: Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder.

abstract: Both feedback of ratings and trust relationships can be used to reveal user preference to improve recommendation performance, especially for cold users. However, the high-order correlations between tow kind of data are always ignored by existing works. Towards this problem, we propose a Correlative Denoising Autoencoder (CoDAE) model to learn correlations from both rating and trust data for Top-N recommendation. First, a novel deep learning model CoDAE, in which two mid-layers from separate stack denoising autoencoders are fused into one shared layer. Advancing previous works which utilize these data in shallow level, this model can effectively extract high-order correlations from low-level representations of these data for recommendation. Second, to further learn implicit corrections between these two autoencoders, we develop a novel correlative regulation to build the relations between other hidden layers of the two separate autoencoders. In this way, this model can learn correlations more effectively and thus improve the recommendation quality. Comprehensive experiments on two public datasets demonstrate that the CoDAE significantly outperforms other state-of-the-art approaches in top-N recommendation task.


title: An adaptive algorithm for selecting profitable keywords for search-based advertising services

abstract: Increases in online search activities have spurred the growth of search-based advertising services offered by search engines. These services enable companies to promote their products to consumers based on their search queries. In most search-based advertising services, a company sets a daily budget, selects a set of keywords, determines a bid price for each keyword, and designates an ad associated with each selected keyword. When a consumer searches for one of the selected keywords, search engines then display the ads associated with the highest bids for that keyword on the search result page. A company whose ad is displayed pays the search engine only when the consumer clicks on the ad. If the company's spending has exceeded its daily budget, however, its ads will not be displayed. With millions of available keywords and a highly uncertain clickthru rate associated with the ad for each keyword, identifying the most profitable set of keywords given the daily budget constraint becomes challenging for companies wishing to promote their goods and services via search-based advertising. Motivated by these challenges, we formulate a model of keyword selection in search-based advertising services. We develop an algorithm that adaptively identifies the set of keywords to bid on based on historical performance. The algorithm prioritizes keywords based on a prefix ordering-- sorting of keywords in a descending order of profit-tocost ratio (or "bang-per-buck"). We show that the average expected profit generated by the algorithm converges to near-optimal profits. Furthermore, the convergence rate is independent of the number of keywords and scales gracefully with the problem's parameters. Extensive numerical simulations show that our algorithm outperforms existing methods, increasing profits by about 7%.


title: International virtual networking capabilities and firm performance: A study of international entrepreneurial Australian SMEs

abstract: The Internet is a critical resource for a new generation of small and medium sized enterprise. Specifically, the Internet is important for small entrepreneurial firms in pursuing international opportunities through increased digital integration. As such, the Internet has been identified as a key enabler of international entrepreneurship (Reuber & Fischer, 2011). By facilitating international business for many entrepreneurial SMEs, the Internet has the ability to increase the quality and speed of communications, lower transaction costs, and facilitate the development of international networks. Although the Internet has been found to play a pivotal role in the creation of international relationships and is a mechanism for the creation of international growth opportunities in SMEs (Mathews & Healy, 2008), the role of the international entrepreneurial decision-maker in the development of international virtual networks for leveraging opportunities in internationalisation remains unclear. The findings of this research indicate that developing an ‘international virtual network capability’ forms an important part of the firm’s resource and more specifically dynamic capability base, which is just one component of a firm’s resource bundle that builds towards successful internationalisation via an Internet platform.


title: Experiences in strategic information systems planning

abstract: Strategic information systems planning (SISP) remains a top concern of many organizations. Accordingly, researchers have investigated SISP practice and proposed both formal methods and principles of good practice. SISP cannot be understood by considering formal methods alone. The processes of planning and the implementation of plans are equally important. However, there have been very few field investigations of these phenomena. This study examines SISP experience in 27 companies and, unusually, relies on interviews not only with IS managers but also with general managers and line managers. By adopting this broader perspective, the investigation reveals companies were using five different SISP approaches: Business-Led, Method-Driven, Administrative, Technological, and Organizational. Each approach has different characteristics and, therefore, a different likelihood of success. The results show that the Organizational Approach appears to be most effective. The taxonomy of the five approaches potentially provides a diagnostic tool for analyzing and evaluating an organization's experience with SISP.


Deep Density Destructors


title: Entailment Graphs for Text Analytics in the Excitement Project

abstract: In the last years, a relevant research line in Natural Language Processing has focused on detecting semantic relations among portions of text, including entailment, similarity, temporal relations, and, with a less degree, causality. The attention on such semantic relations has raised the demand to move towards more informative meaning representations, which express properties of concepts and relations among them. This demand triggered research on “statement entailment graphs”, where nodes are natural language statements (propositions), comprising of predicates with their arguments and modifiers, while edges represent entailment relations between nodes.


title: Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It

abstract: One hallmark of natural organisms is their significant evolvability, i.e.,their increased potential for further evolution. However, reproducing such evolvability in artificial evolution remains a challenge, which both reduces the performance of evolutionary algorithms and inhibits the study of evolvable digital phenotypes. Although some types of selection in evolutionary computation indirectly encourage evolvability, one unexplored possibility is to directly select for evolvability. To do so, we estimate an individual's future potential for diversity by calculating the behavioral diversity of its immediate offspring, and select organisms with increased offspring variation. While the technique is computationally expensive, we hypothesized that direct selection would better encourage evolvability than indirect methods. Experiments in two evolutionary robotics domains confirm this hypothesis: in both domains, such Evolvability Search produces solutions with higher evolvability than those produced with Novelty Search or traditional objective-based search algorithms. Further experiments demonstrate that the higher evolvability produced by Evolvability Search in a training environment also generalizes, producing higher evolvability in a new test environment without further selection. Overall, Evolvability Search enables generating evolvability more easily and directly, facilitating its study and understanding, and may inspire future practical algorithms that increase evolvability without significant computational overhead.


title: Studying illicit drug trafficking on Darknet markets: Structure and organisation from a Canadian perspective

abstract: Cryptomarkets are online marketplaces that are part of the Dark Web and mainly devoted to the sale of illicit drugs. They combine tools to ensure anonymity of participants with the delivery of products by mail to enable the development of illicit drug trafficking. Using data collected on eight cryptomarkets, this study provides an overview of the Canadian illicit drug market. It seeks to inform about the most prevalent illicit drugs vendors offer for sale and preferred destination countries. Moreover, the research gives an insight into the structure and organisation of distribution networks existing online. In particular, we provide information about how vendors are diversifying and replicating across marketplaces. We inform on the number of listings each vendor manages, the number of cryptomarkets they are active on and the products they offer. This research demonstrates the importance of online marketplaces in the context of illicit drug trafficking. It shows how the analysis of data available online may elicit knowledge on criminal activities. Such knowledge is mandatory to design efficient policy for monitoring or repressive purposes against anonymous marketplaces. Nevertheless, trafficking on Dark Net markets is difficult to analyse based only on digital data. A more holistic approach for investigating this crime problem should be developed. This should rely on a combined use and interpretation of digital and physical data within a single collaborative intelligence model.Copyright © 2016 Elsevier Ireland Ltd. All rights reserved. Language: en


title: Memristor Bridge Synapse-Based Neural Network and Its Learning

abstract: Analog hardware architecture of a memristor bridge synapse-based multilayer neural network and its learning scheme is proposed. The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations. To compensate for the spatial nonuniformity and nonideal response of the memristor bridge synapse, a modified chip-in-the-loop learning scheme suitable for the proposed neural network architecture is also proposed. In the proposed method, the initial learning is conducted in software, and the behavior of the software-trained network is learned by the hardware network by learning each of the single-layered neurons of the network independently. The forward calculation of the single-layered neuron learning is implemented on circuit hardware, and followed by a weight updating phase assisted by a host computer. Unlike conventional chip-in-the-loop learning, the need for the readout of synaptic weights for calculating weight updates in each epoch is eliminated by virtue of the memristor bridge synapse and the proposed learning scheme. The hardware architecture along with the successful implementation of proposed learning on a three-bit parity network, and on a car detection network is also presented.


title: The Brain's Default Mode Network

abstract: The brain's default mode network consists of discrete, bilateral and symmetrical cortical areas, in the medial and lateral parietal, medial prefrontal, and medial and lateral temporal cortices of the human, nonhuman primate, cat, and rodent brains. Its discovery was an unexpected consequence of brain-imaging studies first performed with positron emission tomography in which various novel, attention-demanding, and non-self-referential tasks were compared with quiet repose either with eyes closed or with simple visual fixation. The default mode network consistently decreases its activity when compared with activity during these relaxed nontask states. The discovery of the default mode network reignited a longstanding interest in the significance of the brain's ongoing or intrinsic activity. Presently, studies of the brain's intrinsic activity, popularly referred to as resting-state studies, have come to play a major role in studies of the human brain in health and disease. The brain's default mode network p...


title: The DARPA Urban Challenge

abstract: This volume, edited by Martin Buehler, Karl Iagnemma and Sanjiv Singh, presents a unique and comprehensive collection of the scientific results obtained by finalist teams that participated in the DARPA Urban Challenge in November 2007, in the mock city environment of the George Air Force base in Victorville, California. This book is the companion of a previous volume by the same editors which was devoted to the Grand Challenge, which took place in the Nevada desert during October 2005, and was the second in the series of autonomous vehicle races sponsored by DARPA. The Urban Challenge demonstrated how cutting-edge perception, control, and motion planning techniques can allow intelligent autonomous vehicles not only to travel significant distances in off-road terrain, but also to operate in urban scenarios. Beyond the value for future military applications--which motivated DARPA to sponsor the race--the expected impact in the commercial sector for automotive manufacturers is equally, if not more, important: autonomous sensing and control constitute key technologies for vehicles of the future, and might help save thousands of lives that are now lost in traffic accidents. As with the previous STAR volume, the original papers collected in this book were initially published in special issues of the Journal of Field Robotics. Our series is proud to collect them in an archival publication as a special STAR volume!


title: Real-time moving vehicle detection, tracking, and counting system implemented with OpenCV

abstract: Moving vehicle detection, tracking, and counting are very critical for traffic flow monitoring, planning, and controlling. Video-based solution, comparing to other techniques, does not disturb traffic flow and is easily installed. By analyzing the traffic video sequence recorded from a video camera, this paper presents a video-based solution applied with adaptive subtracted background technology in combination with virtual detector and blob tracking technologies. Experimental results, implemented in Visual C++ code with OpenCV development kits, indicate that the proposed method can detect, track, and count moving vehicles accurately.


title: ConRec: A Software Framework for Context-Aware Recommendation Based on Dynamic and Personalized Context

abstract: Contextual information is proven helpful to recommender system. And context-aware recommender system(CARS) has been applied in various applications. To improve the accuracy of context-aware recommendation and make recommender application development easier, we develop a lightweight software framework named ConRec, which introduces a dynamic context oriented approach to extend traditional reduction based recommender. This framework takes the dynamic nature of context into full consideration from different aspects to get better recommendation result. The dynamism of context exists in the process of context modeling, the computation of context weight and the handling of newly emergent context. In ConRec, context is dynamically modeled by clustering similar context values into one set automatically, rather than statically predefined by domain experts. Users' preferences to different types of context are explicitly measured through context weighting function based on real dataset. Moreover, ConRec supports incrementally adding new type of context to recommendation process, which reduces much cost of re-building the whole recommender model. Based on our improved reduction-based algorithm, ConRec is built as a highly scalable and reusable software framework for developing context-aware recommender applications. Finally, we evaluate our proposed approach on public datasets and get more accurate recommendation than traditional methods.


title: Nmbox$^3$ - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format

abstract: Extracting Linked Data following the Semantic Web principle from unstructured sources has become a key challenge for scientific research. Named Entity Recognition and Disambiguation are two basic operations in this extraction process. One step towards the realization of the Semantic Web vision and the development of highly accurate tools is the availability of data for validating the quality of processes for Named Entity Recognition and Disambiguation as well as for algorithm tuning. This article presents three novel, manually curated and annotated corpora (N). All of them are based on a free license and stored in the NLP Interchange Format to leverage the Linked Data character of our datasets.


title: Real-Time EEG-Based Human Emotion Recognition and Visualization

abstract: Emotions accompany everyone in the daily life, playing a key role in non-verbal communication, and they are essential to the understanding of human behavior. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of “inner” emotions from electroencephalogram (EEG) signals as humans could control their facial expressions or vocal intonation. The need and importance of the automatic emotion recognition from EEG signals has grown with increasing role of brain computer interface applications and development of new forms of human-centric and human-driven interaction with digital media. We propose fractal dimension based algorithm of quantification of basic emotions and describe its implementation as a feedback in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called “emotion dimension” to human computer interfaces.


title: An Efficient Signature Verification Method Based on an Interval Symbolic Representation and a Fuzzy Similarity Measure

abstract: In this paper, an efficient offline signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of local binary pattern-based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual’s handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature data set (GPDS-300) and a large data set (BHSig260) composed of Bangla and Hindi offline signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.


title: Improved Component Carrier Selection Method for Non-Continuous Carrier Aggregation in LTE-Advanced Systems

abstract: In order to meet the requirement of wider bandwidth in IMT-Advanced, carrier aggregation (CA) is introduced by 3GPP to support bandwidth up to 100MHz in its LTE-Advanced standards. An LTE Rel.10 UE can aggregate up to 5 component carriers (CCs) simultaneously, but may be assigned only part of the total available CCs depending on its capability or traffic load. Thus, a proper CC selection method is necessary. First, two traditional CC selection methods respectively named Random and Round Robin CC selection are investigated, which can well balance the load across different CCs. However, neither of them is suitable for non-continuous CA scenarios through our analysis. To solve this problem, an improved CC selection method for non-continuous CA is proposed in this paper. Simulation results demonstrate that the proposed method outperforms both of the traditional ones.


title: Spatial and Temporal Influences on Bacterial Profiling of Forensic Soil Samples

abstract: Bacterial content may be helpful in differentiating forensic soil samples; however, the effectiveness of bacterial profiling depends on several factors, including uniqueness among different habitat types, the level of heterogeneity within a habitat, and changes in bacterial communities over time. To examine these, soils from five diverse habitats were tested over a 1 year period using terminal restriction fragment length polymorphism (TRFLP) analysis. Soil samples were collected at central locations monthly, and 10 feet in cardinal directions quarterly. Similarity indices were found to be least related among habitats, while the greatest bacterial similarities existed among collection locations within a habitat. Temporally, however, bacterial content varied considerably, and there was substantial overlap in similarity indices among habitats during different parts of the year. Taken together, the results indicate that while bacterial DNA profiling may be useful for forensic soil analysis, certain variables, particularly time, must be considered.


title: Serious Games: A New Paradigm for Education?

abstract: This chapter explores the context for the new paradigm of learning emerging in education, in relation to key critical concepts that centre around gamification, immersion, interface and social interactivity. The chapter provides an extensive literature review as part of the context for the paradigm shift, including considering serious games and gamification, and social learning as key constructs for considering the changes to educational practices and infrastructure faced by educationalists and instructors over the coming years. The chapter also provides an historical background section and highlights some of the conceptual work that has been done already to frame the changes, firstly in relation to the notion of ‘gamification’ through the lens of an historical overview of serious games and secondly in a section exploring the need for an overall model for serious game design based upon four models and frameworks developed in past research work including the four dimensional framework, exploratory learning model, multimodal interface architecture model and the game-based learning framework. The chapter aims to set out the key conceptual territory for serious game design and bring together the main theoretical areas under consideration for future development of effective serious game content.


Automated News Item Categorization


title: Cell cycle control in acute myeloid leukemia.

abstract: Acute myeloid leukemia (AML) is the result of a multistep transforming process of hematopoietic precursor cells (HPCs) which enables them to proceed through limitless numbers of cell cycles and to become resistant to cell death. Increased proliferation renders these cells vulnerable to acquiring mutations and may favor leukemic transformation. Here, we review how deregulated cell cycle control contributes to increased proliferation in AML and favors genomic instability, a prerequisite to confer selective advantages to particular clones in order to adapt and independently proliferate in the presence of a changing microenvironment. We discuss the connection between differentiation and proliferation with regard to leukemogenesis and outline the impact of specific alterations on response to therapy. Finally, we present examples, how a better understanding of cell cycle regulation and deregulation has already led to new promising therapeutic strategies.


Toward a Neural-Symbolic Sigma: Introducing Neural Network Learning


title: An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging Database

abstract: The FG-NET aging database was released in 2004 in an attempt to support research activities related to facial aging. Since then a number of researchers used the database for carrying out research in various disciplines related to facial aging. Based on the analysis of published work where the FG-NET aging database was used, conclusions related to the type of research carried out in relation to the impact of the dataset in shaping up the research topic of facial aging, are presented. In particular we focus our attention on the topic of age estimation that proved to be the most popular among users of the FG-NET aging database. Through the review of key papers in age estimation and the presentation of benchmark results the main approaches/directions in facial aging are outlined and future trends, requirements and research directions are drafted.


title: Semantically Rich Application-Centric Security in Android

abstract: Smartphones are now ubiquitous. However, the security requirements of these relatively new systems and the applications they support are still being understood. As a result, the security infrastructure available in current smartphone operating systems is largely underdeveloped. In this paper, we consider the security requirements of smartphone applications and augment the existing Android operating system with a framework to meet them. We present Secure Application INTeraction (Saint), a modified infrastructure that governs install-time permission assignment and their run-time use as dictated by application provider policy. An in-depth description of the semantics of application policy is presented. The architecture and technical detail of Saint is given, and areas for extension, optimization, and improvement explored. As we show through concrete example, Saint provides necessary utility for applications to assert and control the security decisions on the platform.


title: The femoral hypoplasia-unusual facies syndrome.

abstract: A series of thirteen persons with bilateral femoral hypoplasia are presented. Six of these had facial features compatible with a diagnosis of femoral hypoplasia-unusual facies syndrome. One was attributable to severe fetal constraint secondary to oligohydramnios, three were associated with maternal diabetes, and two were idiopathic. All thirteen cases were sporadic.


title: Campus emergency notification systems: an examination of factors affecting compliance with alerts

abstract: The increasing number of campus-related emergency incidents, in combination with the requirements imposed by the Clery Act, have prompted college campuses to develop emergency notification systems to inform community members of extreme events that may affect them. Merely deploying emergency notification systems on college campuses, however, does not guarantee that these systems will be effective; student compliance plays a very important role in establishing such effectiveness. Immediate compliance with alerts, as opposed to delayed compliance or noncompliance, is a key factor in improving student safety on campuses. This paper investigates the critical antecedents that motivate students to comply immediately with messages from campus emergency notification systems. Drawing on Etzioni's compliance theory, a model is developed. Using a scenario-based survey method, the model is tested in five types of events--snowstorm, active shooter, building fire, health-related, and robbery--and with more than 800 college students from the Northern region of the United States. The results from this study suggest that subjective norm and information quality trust are, in general, the most important factors that promote immediate compliance. This research contributes to the literature on compliance, emergency notification systems, and emergency response policies.


title: A Voltage and Frequency Droop Control Method for Parallel Inverters

abstract: In this paper, a new control method for the parallel operation of inverters operating in an island grid or connected to an infinite bus is described. Frequency and voltage control, including mitigation of voltage harmonics, are achieved without the need for any common control circuitry or communication between inverters. Each inverter supplies a current that is the result of the voltage difference between a reference ac voltage source and the grid voltage across a virtual complex impedance. The reference ac voltage source is synchronized with the grid, with a phase shift, depending on the difference between rated and actual grid frequency. A detailed analysis shows that this approach has a superior behavior compared to existing methods, regarding the mitigation of voltage harmonics, short-circuit behavior and the effectiveness of the frequency and voltage control, as it takes the R to X line impedance ratio into account. Experiments show the behavior of the method for an inverter feeding a highly nonlinear load and during the connection of two parallel inverters in operation.


title: Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

abstract: Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant featuretransform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in complex environments such as the City of San Francisco. Deep convolutional neural networks (DNNs) can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DNN has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. In this letter, we present a hybrid DNN (HDNN), by dividing the maps of the last convolutional layer and the max-pooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes, to enable the HDNN to extract variable-scale features. Comparative experimental results indicate that our proposed HDNN significantly outperforms the traditional DNN on vehicle detection.


Virtualization : Benefits , Challenges , and Solutions


title: Hybrid Full-Bridge Three-Level LLC Resonant Converter- A Novel DC-DC Converter Suitable for Fuel Cell Power System

abstract: This paper proposes a novel hybrid full-bridge (H-FB) three-level (TL) LLC resonant converter. It integrates the advantages of the H-FB TL converter and the LLC resonant converter. It can operate under both three-level mode and two-level mode, so it is very suitable for wide-input-voltage-range applications, such as fuel-cell power systems. Compared with the traditional full-bridge converter, the input current ripple and output filter can be reduced. In addition, all the switches can realize zero-voltage switching from nearly zero to full load, and the switches of the TL leg sustain only half of the input voltage. Moreover, the rectifier diodes can achieve zero-current switching, and the voltage stress across them can be minimized to the output voltage. A prototype of 200-400-V input and 360-V/4-A output is built in our laboratory to verify the operation principle of the proposed converter


title: BERG, BELHUMEUR: TOM-VS-PETE CLASSIFIERS AND IDENTITY-PRESERVING ALIGNMENT 1 Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification

abstract: We propose a method of face verification that takes advantage of a reference set of faces, disjoint by identity from the test faces, labeled with identity and face part locations. The reference set is used in two ways. First, we use it to perform an “identity-preserving” alignment, warping the faces in a way that reduces differences due to pose and expression but preserves differences that indicate identity. Second, using the aligned faces, we learn a large set of identity classifiers, each trained on images of just two people. We call these “Tom-vs-Pete” classifiers to stress their binary nature. We assemble a collection of these classifiers able to discriminate among a wide variety of subjects and use their outputs as features in a same-or-different classifier on face pairs. We evaluate our method on the Labeled Faces in the Wild benchmark, achieving an accuracy of 93.10%, significantly improving on the published state of the art.


title: The Movies Productive Players , Commodification and Transformative Play

abstract: / This article examines the contested issue of player productivity, with a focus on the multifaceted relations between players and the game industry. These relations are explored using the movie studio simulation The Movies (Lionhead Studios, 2005) as a case study. Firstly, through an examination of the game, some of the prevailing game industry's practices, and their relations to the ones applied amongst other creative industries (particularly the film industry) are introduced. Secondly, the game, and especially the accompanying moviemaking editor, is analysed from the perspective of `transformative play'. Further, the reasons behind, and consequences of, allowing the players to make and distribute short movies of their own are contemplated. Finally the article addresses the mechanisms of control and reward that are applied by the corporate actors in relation to The Movies. The article suggests that in the age of commodified play, players face trade-offs all the time. Therefore, the study of rules that h...


title: A Cascaded Coupled Inductor-Reverse High Step-Up Converter Integrating Three-Winding Coupled Inductor and Diode–Capacitor Technique

abstract: This paper introduces a cascaded high step-up converter realized with a tightly coupled three-winding coupled inductor. Compared with existing high step-up converters, the proposed converter features that the smaller the turns ratio, the larger the conversion gain. So, the name coupled inductor-reverse is given to represent reverse-coupled inductor principle of operation. In addition, diode–capacitor circuit is introduced to not only recycle leakage energy to output, but also further lift voltage conversion gain. This paper illustrates operation principle of the proposed converter, discusses effect of leakage inductance on voltage gain, and deduces voltage and current stresses of components. Finally, a prototype rated at 400 W has been established, and experimental results verify correctness of the above theoretical analysis.


title: Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

abstract: Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, the state-of-the-art approach is to rely on the programmer to specify this mapping manually and statically. This approach is not only labor intensive but also not adaptable to changes in runtime environments like problem sizes and hardware/software configurations. In this study, we propose adaptive mapping, a fully automatic technique to map computations to processing elements on a CPU+GPU machine. We have implemented it in our experimental heterogeneous programming system called Qilin. Our results show that, by judiciously distributing works over the CPU and GPU, automatic adaptive mapping achieves a 25% reduction in execution time and a 20% reduction in energy consumption than static mappings on average for a set of important computation benchmarks. We also demonstrate that our technique is able to adapt to changes in the input problem size and system configuration.


title: Frequency compression of SR for detecting rotor's incipient fault signal

abstract: Stochastic resonance system is only for small parameter(small amplitude and low frequency) system.However,the frequency of the fault signal of the rotor is higher than the stochastic resonance system.According to the basic theory of stochastic resonance systems,the limit of frequency range of the measure signal of the stochastic resonance system was eliminated by introducing the time-frequency compression algorithm,and stochastic resonance system was extended to the whole band.Theoretical and experimental results show that: by continuous compression transformation,an appropriate input signal was obtained to stochastic resonance system.The unknown frequencies of the original signal can be obtained,according to the change of resonant peaks and transformation operations.The algorithm is 6 orders of magnitude faster than the traditional algorithm of frequency sweep,and it can detect the fault signal under the limit SNR(-50dB).


title: Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs

abstract: In recent years convolutional neural networks (CNNs) have been successfully applied to various applications that are appropriate for deep learning, from image and video processing to speech recognition. The advancements in both hardware (e.g. more powerful GPUs) and software (e.g. deep learning models, open-source frameworks and supporting libraries) have significantly improved the accuracy and training time of CNNs. However, the high speed and accuracy are at the cost of energy consumption, which has been largely ignored in previous CNN design. With the size of data sets grows exponentially, the energy demand for training such data sets increases rapidly. It is highly desirable to design deep learning frameworks and algorithms that are both accurate and energy efficient. In this paper, we conduct a comprehensive study on the power behavior and energy efficiency of numerous well-known CNNs and training frameworks on CPUs and GPUs, and we provide a detailed workload characterization to facilitate the design of energy efficient deep learning solutions.


title: Enterprise Architecture at Work: Modelling, Communication and Analysis

abstract: Providing concrete instruments to improve architectural practice, this book presents an enterprise modelling language that captures the complexity of architectural domains and their relations for construction of integrated enterprise architecture models.


Gender, intimate relationships and violence


Queuing theory models for computer networks


title: A Wideband Slot-Coupled Stacked-Patch Array for Wireless Communications

abstract: In this letter, a novel slot-coupling feeding technique is used to realize a dual-polarized 2 × 1 microstrip stacked patch array for mobile wireless communication systems. The array is intended as a basic module for base-station linear arrays, whose final size depending on beamwidth and gain requirements. Each array element is fed through two microstrip lines arranged on the basis of a sequential rotation technique. Each stacked square patch is excited through a square ring slot realized in the feeding network ground plane. Design procedure, simulation results and measurement data are presented for a 2 × 1 array working in the GSM 1800-1900 band (1710-1910 MHz), UMTS band (1920-2170 MHz), ISM band (2400-2484 MHz), and UMTS 3G expansion band (2500-2690 MHz) or, alternatively, WiMAX band (2300-2700 MHz), with a resulting 45% percentage bandwidth (reflection coefficient <; -10 dB). Due to both the symmetry properties of the novel slot-coupling feeding configuration and the implementation of a sequential rotation technique, good results have been obtained in terms of port isolation and cross-polar radiation patterns.


title: Producing sustainable competitive advantage through the effective management of people.

abstract: Executive Overview Achieving competitive success through people involves fundamentally altering how we think about the workforce and the employment relationship. It means achieving success by working with people, not by replacing them or limiting the scope of their activities. It entails seeing the workforce as a source of strategic advantage, not just as a cost to be minimized or avoided. Firms that take this different perspective are often able to successfully outmaneuver and outperform their rivals.


title: An adaptive control method for regression coefficient in frequency domain non-linear echo suppressor

abstract: An echo canceller, which consists of a linear echo canceller and a non-linear echo suppressor, has been proposed for mobile phones. This approach provides an efficient nonlinear echo suppressor with low computational complexity. In this method, the non-linear echo is estimated in the frequency domain from the linear echo replica. A ratio of the non-linear echo and the linear echo replica is called ‘Regression Coefficient’. In this approach, it is very important how to estimate and how to update the regression coefficient by using the residual signal after the linear echo canceller, which includes the residual non-linear echo and the near-end signal, and the linear echo replica. In this paper, we propose a new adaptive control method for the regression coefficient. The ratio of the residual signal and the replica quickly changes along frame, which is an interval of FFT. Therefore, first the average of the ratio is estimated. Second, the average is amplified in order to suppress the residual non-linear echo. In the proposed method, the regression coefficient is not updated in the double talk intervals, because effects of the near-end signal is large. The double talk intervals are detected based on the correlation coefficient between the linear echo replica and the microphone input signal, which includes the linear and non-linear echo components and the near-end signal. Simulation results show the proposed method can improve echo reduction and the segmental SNR by 13 dB and 1.5 ∼ 2.5 dB, respectively.


title: An Approach to Cold-Start Link Prediction: Establishing Connections between Non-Topological and Topological Information

abstract: Cold-start link prediction is a term for information starved link prediction where little or no topological information is present to guide the determination of whether links to a node will form. Due to the lack of topological information, traditional topology-based link prediction methods cannot be applied to solve the cold-start link prediction problem. Therefore, an effective approach is presented through establishing connections between non-topological and topological information. In the approach, topological information is first extracted by a latent-feature representation model, then a logistic model is proposed to establish the connections between topological and non-topological information, and finally the linking possibility between cold-start users and existing users is calculated. Experiments with three types of real-world social networks Weibo, Facebook, and Twitter show that the proposed approach is more effective in solving the cold-start link prediction problem and establishing connections between topological and non-topological information.


Admittance dependences of the mid-wave infrared barrier structure based on HgCdTe grown by molecular beam epitaxy


title: Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

abstract: Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.


title: Count-ception: Counting by Fully Convolutional Redundant Counting

abstract: Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016.


Trends in Quantitative Risk Assessments


title: Argumentation: Content, Structure, and Relationship with Essay Quality

abstract: In this paper, we investigate the relationship between argumentation structures and (a) argument content, and (b) the holistic quality of an argumentative essay. Our results suggest that structure-based approaches hold promise for automated evaluation of argumentative writing.


Non-Intrusive Appliance Load Identification with the Ensemble of Classifiers


title: ‘The research compass’: An introduction to research in medical education: AMEE Guide No. 56

abstract: This AMEE Guide offers an introduction to research in medical education. It is intended for those who are contemplating conducting research in medical education but are new to the field. The Guide is structured around the process of transforming ideas and problems into researchable questions, choosing a research approach that is appropriate to the purpose of the study and considering the individual researcher’s preferences and the contextual possibilities and constraints. The first section of the Guide addresses the rationale for research in medical education and some of the challenges posed by the complexity of the field. Next is a section on how to move from an idea or problem to a research question by placing a concrete idea or problem within a conceptual, theoretical framework. The following sections are structured around an overview model of approaches to medical education research, ‘The research compass’. Core to the model is the conceptual, theoretical framework that is the key to any direction. The compass depicts four main categories of research approaches that can be applied when studying medical education phenomena, ‘Explorative studies’; ‘Experimental studies’; ‘Observational studies’; and ‘Translational studies’. Future AMEE Guides in the research series will address these approaches in more detail.


title: Uncertainty, Neuromodulation, and Attention

abstract: Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological and behavioral data implicating acetylcholine an norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.


title: Performance evaluation of fingerprint verification systems

abstract: This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent fingerprint verification competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.


Power Amplifier Principles and Modern Design Techniques


title: State of the Art and Future Directions for Lower Limb Robotic Exoskeletons

abstract: Research on robotic exoskeletons has rapidly expanded over the previous decade. Advances in robotic hardware and energy supplies have enabled viable prototypes for human testing. This review paper describes current lower limb robotic exoskeletons, with specific regard to common trends in the field. The preponderance of published literature lacks rigorous quantitative evaluations of exoskeleton performance, making it difficult to determine the disadvantages and drawbacks of many of the devices. We analyzed common approaches in exoskeleton design and the convergence, or lack thereof, with certain technologies. We focused on actuators, sensors, energy sources, materials, and control strategies. One of the largest hurdles to be overcome in exoskeleton research is the user interface and control. More intuitive and flexible user interfaces are needed to increase the success of robotic exoskeletons. In the last section, we discuss promising future solutions to the major hurdles in exoskeleton control. A number of emerging technologies could deliver substantial advantages to existing and future exoskeleton designs. We conclude with a listing of the advantages and disadvantages of the emerging technologies and discuss possible futures for the field.


title: Smart Transmitters and Receivers for Underwater Free-Space Optical Communication

abstract: The number of unmanned vehicles and devices deployed underwater is increasing. New communication systems and networking protocols are required to handle this growth. Underwater free-space optical communication is poised to augment acoustic communication underwater, especially for short-range, mobile, multi-user environments in future underwater systems. Existing systems are typically point-to-point links with strict pointing and tracking requirements. In this paper we demonstrate compact smart transmitters and receivers for underwater free-space optical communications. The receivers have segmented wide field of view and are capable of estimating angle of arrival of signals. The transmitters are highly directional with individually addressable LEDs for electronic switched beamsteering, and are capable of estimating water quality from its backscattered light collected by its co-located receiver. Together they form enabling technologies for non-traditional networking schemes in swarms of unmanned vehicles underwater.


title: Augmented reality based on edge computing using the example of remote live support

abstract: Augmented Reality (AR) introduces vast opportunities to the industry in terms of time and therefore cost reduction when utilized in various tasks. The biggest obstacle for a comprehensive deployment of mobile AR is that current devices still leave much to be desired concerning computational and graphical performance. To improve this situation in this paper we introduce an AR Edge Computing architecture with the aim to offload the demanding AR algorithms over the local network to a high-end PC considering the real-time requirements of AR. As an example use case we implemented an AR Remote Live Support application. Applications like this on the one hand are strongly demanded in the industry at present, on the other hand by now mostly do not implement a satisfying tracking algorithm lacking computational resources. In our work we lay the focus on both, the possibilities our architecture offers regarding improvements of tracking and the challenges it implies in respect of real-time. We found that offloading AR algorithms in real-time is possible with available WiFi making use of standard compression techniques like JPEG. However it can be improved by future radio solutions offering higher bandwidth to avoid additional latency contributed by the coding.


title: The Dynamic Hungarian Algorithm for the Assignment Problem with Changing Costs

abstract: In this paper, we present the dynamic Hungarian algorithm, applicable to optimally solving the assignment problem in situations with changing edge costs or weights. This problem is relevant, for example, in a transportation domain where the unexpected closing of a road translates to changed transportation costs. When such cost changes occur after an initial assignment has been made, the new problem, like the original problem, may be solved from scratch using the well-known Hungarian algorithm. However, the dynamic version of the algorithm which we present solves the new problem more efficiently by repairing the initial solution obtained before the cost changes. We present proofs of the correctness and efficiency of our algorithm and present simulation results illustrating its efficiency.


title: Microorganisms with Claimed Probiotic Properties: An Overview of Recent Literature

abstract: Probiotics are defined as live microorganisms, which when administered in adequate amounts, confer a health benefit on the host. Health benefits have mainly been demonstrated for specific probiotic strains of the following genera: Lactobacillus, Bifidobacterium, Saccharomyces, Enterococcus, Streptococcus, Pediococcus, Leuconostoc, Bacillus, Escherichia coli. The human microbiota is getting a lot of attention today and research has already demonstrated that alteration of this microbiota may have far-reaching consequences. One of the possible routes for correcting dysbiosis is by consuming probiotics. The credibility of specific health claims of probiotics and their safety must be established through science-based clinical studies. This overview summarizes the most commonly used probiotic microorganisms and their demonstrated health claims. As probiotic properties have been shown to be strain specific, accurate identification of particular strains is also very important. On the other hand, it is also demonstrated that the use of various probiotics for immunocompromised patients or patients with a leaky gut has also yielded infections, sepsis, fungemia, bacteraemia. Although the vast majority of probiotics that are used today are generally regarded as safe and beneficial for healthy individuals, caution in selecting and monitoring of probiotics for patients is needed and complete consideration of risk-benefit ratio before prescribing is recommended.


title: A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization ∗

abstract: Dimension reduction is commonly defined as the process of mapping high-dimensional data to a lower-dimensional embedding. Applications of dimension reduction include, but are not limited to, filtering, compression, regression, classification, feature analysis, and visualization. We review methods that compute a point-based visual representation of high-dimensional data sets to aid in exploratory data analysis. The aim is not to be exhaustive but to provide an overview of basic approaches, as well as to review select state-of-the-art methods. Our survey paper is an introduction to dimension reduction from a visualization point of view. Subsequently, a comparison of state-of-the-art methods outlines relations and shared research foci.


title: Which clinical risk factors determine a pathological urodynamic evaluation in patients with multiple sclerosis? an analysis of 100 prospective cases

abstract: BACKGROUND ::: Urinary tract symptoms are an underestimated problem in multiple sclerosis (MS). ::: ::: ::: OBJECTIVE ::: Hundred urodynamics of MS patients have been evaluated prospectively. ::: ::: ::: DESIGN, SETTING AND PARTICIPANTS ::: In an inpatient rehabilitation, all persons with MS who also suffered from urinary tract symptoms received a voiding diary, post-void sonography and an urodynamic examination according to International Continence-Society-Standard. ::: ::: ::: RESULTS AND LIMITATIONS ::: Between 10/2009 and 3/2011, 100 patients (79 women; 21 men; mean EDSS, 4.52 ± 2.26) were examined who had primary progressive MS (9×), relapsing-remitting MS (41×), secondary progressive MS (43×) and CIS (1×). The mean duration of MS was 10.26 ± 10.09 years and mean duration of LUTS, 6.9 ± 7.75 years. Urodynamic testing showed normal findings in 22 patients, detrusor overactivity in 7, increased bladder sensation without detrusor overactivity in 21, detrusor-sphincter dyssynergia in 26, detrusor hypocontractility in 12, detrusor acontractility in 4 and unclear diagnosis in 8 patients. Statistically significant risk factors for pathological urodynamic findings were as follows: wheelchair dependency, use of more than one incontinence pad per day and a MS type other than relapsing-remitting. ::: ::: ::: CONCLUSIONS ::: The urodynamic investigation at hand showed urinary tract dysfunction in 78 of 100 MS patients with lower urinary tract symptoms (LUTS). The long latency between the occurrence of MS and/or the beginning of LUTS and the first neuro-urological evaluation indicates a deficit in treatment. Beyond national guidelines, all MS patients should at regular intervals be questioned about LUTS and receive urodynamic assessment especially according to the presented risk profile.


title: Issues of Knowledge Management in the Public Sector

abstract: The new economy not only poses challenges, but also offers opportunities for both private and public sectors alike. To meet the challenges and take the opportunities, government must take active initiatives to adopt new management tools, techniques and philosophies of the private sector and adapt to its circumstance. Knowledge management (KM) is such an area that needs to be further explored and exploited for its full benefits to be reaped. Key issues, challenges, and opportunities of KM in the public sector need to be addressed and better understood


title: Learning Transferrable Representations for Unsupervised Domain Adaptation

abstract: Supervised learning with large scale labelled datasets and deep layered models has caused a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers from generalization issues under the presence of a domain shift between the training and the test data distribution. Since unsupervised domain adaptation algorithms directly address this domain shift problem between a labelled source dataset and an unlabelled target dataset, recent papers have shown promising results by fine-tuning the networks with domain adaptation loss functions which try to align the mismatch between the training and testing data distributions. Nevertheless, these recent deep learning based domain adaptation approaches still suffer from issues such as high sensitivity to the gradient reversal hyperparameters and overfitting during the fine-tuning stage. In this paper, we propose a unified deep learning framework where the representation, cross domain transformation, and target label inference are all jointly optimized in an end-to-end fashion for unsupervised domain adaptation. Our experiments show that the proposed method significantly outperforms state-of-the-art algorithms in both object recognition and digit classification experiments by a large margin. We will make our learned models as well as the source code available immediately upon acceptance.


title: Microstrip Interdigital Hairpin Resonator With an Optimal Physical Length

abstract: In this letter, we report a novel microstrip interdigital hairpin resonator by having the parallel coupled lines in a conventional miniaturized hairpin resonator replaced by an interdigital capacitor. Equations are derived in order to design the resonator with an optimal physical length. It will be proved that the novel resonator can achieve even more size reduction than the conventional miniaturized hairpin resonator owing to the employment of the slow-wave structure. Finally, a four-pole cross-coupled bandpass filter using the optimal resonator is fabricated as verification


title: Concealed CFRP Vehicle Chassis Antenna Cavity

abstract: A concealed vehicular carbon fiber reinforced polymer cavity in the roof that contains antennas is proposed, prototyped, and measured. Compared to state-of-the-art roof-mounted shark-fin modules, this offers significantly more room for antennas and radio frequency hardware, thus enabling a smooth transition of vehicular connectivity towards 5G. Three antennas were prototyped and embedded in the cavity: two laser direct structured antennas (inverted-F antenna for 2 GHz, monopole for 5.9 GHz), as well as a broadband conical monopole antenna milled from brass that characterizes the feasible frequency range from 2 to 6 GHz by measurement. It is shown that near-omnidirectional radiation from the concealed automotive antenna cavity is achievable.


title: Design of Low Phase-Noise Oscillator and Voltage-Controlled Oscillator Using Microstrip Trisection Bandpass Filter

abstract: In this letter, a low phase-noise oscillator using a microsrtip trisection bandpass filter is designed, fabricated, and experimentally verified. The trisection filter is mainly treated as a frequency stabilization element, and then embedded into the feedback loop of the oscillator. As the oscillation frequency is designed at group delay peak of the filter near the passband edge, the phase noise of the oscillator can be significantly reduced. In addition, by attaching a varactor on one of the resonators of the trisection filter, the developed oscillator can be easily extended to a voltage-controlled oscillator (VCO). In this letter, the oscillation frequency of the oscillator is designed at 2.46 GHz, and the measured phase noise is -144.47 dBc/Hz at 1 MHz offset frequency. Moreover, the developed VCO has a frequency turning range from 2.497 to 2.537 GHz. Over this frequency range, the measured phase noise is from -127.47 to -138.17 dBc/Hz at 1 MHz offset frequency.


title: The Relationship Between Professional Quality of Life and Caring Ability in Critical Care Nurses

abstract: BACKGROUND ::: Critical care nursing has some features that may affect the ability of critical care nurses to provide care. Professionals as critical care nurses who deal with peoples' health and life often experience some levels of stress that affects their quality of life. ::: ::: ::: OBJECTIVE ::: This study examined the relationship between professional quality of life and caring ability of critical care nurses. ::: ::: ::: METHOD ::: In this descriptive correlational study, 253 critical care nurses working in the medical and surgical intensive care units of Tehran University of Medical Sciences were recruited by convenience sampling method. The data collection tools included demographic data form, Caring Ability Inventory, and questionnaire for professional quality of life version 5. ::: ::: ::: RESULTS ::: There was a statistically significant positive relationship between the professional quality of life and the caring ability (P < .0001). A significant inverse relationship was found between burn-out subscale of compassion fatigue as one aspect of professional life quality with all 3 aspects of caring ability (knowledge, courage, and patience). Secondary traumatic stress, as another subscale of compassion fatigue (as one aspect of professional quality of life), had no statistically significant correlation with caring ability aspects of knowledge and patience. However, there was a significant inverse correlation between the secondary traumatic stress and courage aspect of caring ability (P < .0001). There was a statistically significant positive relationship between compassion satisfaction aspect of professional life quality with knowledge and patience aspects of caring ability, but there was not any relationship between the compassion satisfaction aspect of professional life quality and the courage aspect of caring ability. ::: ::: ::: DISCUSSION ::: Improvement of critical care nurses' professional quality of life may increase their caring ability, thereby leading to better and more effective nursing care. Increased awareness by critical care nurse managers of the compassion fatigue phenomenon (secondary traumatic stress and burnout) and its effect on quality of critical care would be helpful in planning more specific strategies and preventing the onset and progression of these symptoms.


title: RFID security and privacy: a research survey

abstract: This paper surveys recent technical research on the problems of privacy and security for radio frequency identification (RFID). RFID tags are small, wireless devices that help identify objects and people. Thanks to dropping cost, they are likely to proliferate into the billions in the next several years-and eventually into the trillions. RFID tags track objects in supply chains, and are working their way into the pockets, belongings, and even the bodies of consumers. This survey examines approaches proposed by scientists for privacy protection and integrity assurance in RFID systems, and treats the social and technical context of their work. While geared toward the nonspecialist, the survey may also serve as a reference for specialist readers.


title: Remaining useful life estimation for systems with non-trendability behaviour

abstract: This paper presents an approach and the presented solution of the questions raised in the IEEE PHM 2012 Conference Challenge Competition. What was given (known) is the real run-to-failure data of 6 bearings only from the three groups exposed to different operating conditions. One should use this data to estimate the Remaining Useful Life (RUL) of the given set of 11 test bearings. The main feature of the presented data is significant loss of trendability (i.e. "non-trendability") of the defined significant parameters' behavior (horizontal and vertical vibration), thus avoiding the use of well-known supervised learning RUL prediction models. New models have been developed and used; further the Cross-Entropy method has been used for control parameter optimization based on the Cross-Validation procedure. The presented solution has been recognized as a "Winner from Industry" in the above mentioned Competition. The achieved results demonstrate the effectiveness of the approach for the RUL estimation for systems parameters with the non-trendability behavior.


title: A Study on Keyframe Extraction Methods for Video Summary

abstract: In this paper we carry out a survey on key frame extraction methods for Video Summary. We also discuss the summary evaluation criteria and compare the approaches based on the method, data set and the results. Video Summary is a process of presenting an abstract of entire video within a short period of time. It aims to provide a compact video representation, while preserving the essential activities of the original video. It is an essential task in video analysis and indexing applications. Most of the video summaries are based on selection of key frames within the shots of a video. Many of them use motion features and few use visual features for extracting the key frames. The video summary quality assessment methods are based more on subjective and less on objective measures. Tong wei Ren et al has provided a framework to assess the quality of the video against a given reference summary using both subjective and objective measures. Ciocca et al used the objective measures for evaluation of summary and most of them evaluate by taking the subjective opinion of experts. A framework for automatic evalution is needed based on both subjective and objective measures without the reference summary.


title: Corroborating information from disagreeing views

abstract: We consider a set of views stating possibly conflicting facts. Negative facts in the views may come, e.g., from functional dependencies in the underlying database schema. We want to predict the truth values of the facts. Beyond simple methods such as voting (typically rather accurate), we explore techniques based on "corroboration", i.e., taking into account trust in the views. We introduce three fixpoint algorithms corresponding to different levels of complexity of an underlying probabilistic model. They all estimate both truth values of facts and trust in the views. We present experimental studies on synthetic and real-world data. This analysis illustrates how and in which context these methods improve corroboration results over baseline methods. We believe that corroboration can serve in a wide range of applications such as source selection in the semantic Web, data quality assessment or semantic annotation cleaning in social networks. This work sets the bases for a wide range of techniques for solving these more complex problems.


title: A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

abstract: Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).


title: Towards innovation measurement in the software industry

abstract: In today's highly competitive business environments with shortened product and technology life cycle, it is critical for software industry to continuously innovate. This goal can be achieved by developing a better understanding and control of the activities and determinants of innovation. Innovation measurement initiatives assess innovation capability, output and performance to help develop such an understanding. This study explores various aspects relevant to innovation measurement ranging from definitions, measurement frameworks and metrics that have been proposed in literature and used in practice. A systematic literature review followed by an online questionnaire and interviews with practitioners and academics were employed to identify a comprehensive definition of innovation that can be used in software industry. The metrics for the evaluation of determinants, inputs, outputs and performance were also aggregated and categorised. Based on these findings, a conceptual model of the key measurable elements of innovation was constructed from the findings of the systematic review. The model was further refined after feedback from academia and industry through interviews.


title: An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories

abstract: A novel variant of the familiar backpropagation-through-time approach to training recurrent networks is described. This algorithm is intended to be used on arbitrary recurrent networks that run continually without ever being reset to an initial state, and it is specifically designed for computationally efficient computer implementation. This algorithm can be viewed as a cross between epochwise backpropagation through time, which is not appropriate for continually running networks, and the widely used on-line gradient approximation technique of truncated backpropagation through time.