title: Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research

abstract: Introduction When analyzing educational videogames, researchers can observe that games, which cannot be described as designed specifically for educational purposes (Connolly, Boyle, MacArthur, Hainey, & Boyle, 2012; Ritzhaupt, Gunter, & Jones, 2010), have been used. Off-the-shelf games like Civilization III (Squire, 2004; Squire, DeVane, & Durga, 2008), Making History (Watson, Mong, & Harris, 2011), Massively Multiplayer Online-Role-Playing Games like World of Warcraft or EVE Online (Clark, Nelson, Sengupta, & D'Angelo, 2009; Rausch, Fasshauer, & Martens, 2012; Reeves, Malone, & O Driscoll, 2008), simulation-based videogames like Sim City 2000 (Tanes & Cemalcilar, 2010), and narrative-focused adventures like The Walking Dead (Staaby, 2014) have been used in education or educational research. Even software that was never intended to be used as a game at all (e.g., Power Point, Siko & Barbour, 2013; Siko, Barbour, & Toker, 2011) appears in this context. These examples show the growing interest of researchers and teachers in using very different software as educational tools. Thus, we conclude that educational videogames are not only games that have been intended to be educational tools in the first place but also games that can be utilized as such. This can be elucidated even further when observing the impressive success of Quizkampen (http://www.quizkampen.se/), a mobile-based trivia game that includes online duels between friends and strangers. The players have to answer questions that address very different topics such as politics, geography, movies, or film stars. Thus, a stable learning process within topics that are not entirely entertainment-based is needed to improve in the competitive scenario. The German version Quizduell has been downloaded more than 10 million times (SpotOn, 2014) and was extended to other media since the game is the center of a nationwide TV show (Das Erste, 2015). The success of a game that combines educational elements and competitive videogame mechanics is an appropriate example of the potential of videogames with entertainment-focused intent. Another videogame, Minecraft (https://minecraft.net/), had a significant impact not only on videogames in general but also on education and research. More than 19 million copies (Mojang, 2015a) have been sold for PC, 12 million copies for XBOX360 (4J Studios, 2014), and more than 21 million copies for mobile phones (Bergensten, 2014), which places the game on the all-time best-sellers list. Since its developer Mojang has been taken over by Microsoft for $2.5 billion (Owen, 2014), the game is known to the public as this was addressed in news around the world. In addition, millions of hours of gameplay footage and let's plays are available for interested users who are either too young or do not have the technical or financial capacities to play the game themselves. What is Minecraft? In 2009, independent developer Zachary Barth created Infiniminer (http://www.zachtronics.com/infiniminer/) in his spare time. Within this game, he invented a new way of representing the game world with simple independent, block- shaped entities and added a procedural generation of the environment. Users who enter this randomly generated game world mine and place blocks, and thus recreate anything that can be constructed within this block-based structure. This process can be compared to a square pixel and low-resolution pictures. Based on a simple element, like a pixel, one can create almost every image he or she wants. Although the game includes player versus player gameplay, the simple but ingenious building mechanics and the world creation method inspired Markus "Notch" Persson to create his own Java-based version that eventually became Minecraft. This multiplayer sandbox building game (Ekaputra, Lim, & Eng, 2013) is simply "a game about braking and placing blocks" (Mojang, 2015a), but the game's complexity has drastically increased since then (for a more extensive description see: Duncan, 2011). …


title: Towards An Architecture-Centric Approach to Manage Variability of Cloud Robotics

abstract: Cloud robotics is a field of robotics that attempts to invoke Cloud technologies such as Cloud computing, Cloud storage, and other Internet technologies centered around the benefits of converged infrastructure and shared services for robotics. In a few short years, Cloud robotics as a newly emerged field has already received much research and industrial attention. The use of the Cloud for robotics and automation brings some potential benefits largely ameliorating the performance of robotic systems. However, there are also some challenges. First of all, from the viewpoint of architecture, how to model and describe the architectures of Cloud robotic systems? How to manage the variability of Cloud robotic systems? How to maximize the reuse of their architectures? In this paper, we present an architecture approach to easily design and understand Cloud robotic systems and manage their variability.


title: Improving network intrusion detection system performance through quality of service configuration and parallel technology

abstract: This paper outlines an innovative software development that utilises Quality of Service (QoS) and parallel technologies in Cisco Catalyst Switches to increase the analytical performance of a Network Intrusion Detection and Protection System (NIDPS) when deployed in high-speed networks. We have designed a real network to present experiments that use a Snort NIDPS. Our experiments demonstrate the weaknesses of NIDPSs, such as inability to process multiple packets and propensity to drop packets in heavy traffic and high-speed networks without analysing them. We tested Snort's analysis performance, gauging the number of packets sent, analysed, dropped, filtered, injected, and outstanding. We suggest using QoS configuration technologies in a Cisco Catalyst 3560 Series Switch and parallel Snorts to improve NIDPS performance and to reduce the number of dropped packets. Our results show that our novel configuration improves performance.


title: In-Memory Big Data Management and Processing: A Survey

abstract: Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.


title: Are Dynamic Capabilities the Missing Link Between the IS Strategy and Performance Relationship?: A Model and Exploratory Test at Three Levels of Environmental Dynamism

abstract: This study examines two dynamic capabilities which are possible intervening mechanisms by which an information systems strategy relates to information systems performance. It also explores the relevance of the dynamic capabilities absorptive capacity and agility at three different levels of environmental dynamism. Responses from a sample of over 200 CIOs suggest that absorptive capacity and agility are indeed intervening mechanisms by which information systems strategy is linked to performance, particularly for innovative strategies and in environments that are highly dynamic. It suggests that pursuit of the strategy that is able to develop and maintain the highest levels of dynamic capabilities would be the one to likewise gain the most ground on performance. The findings also bring to light the fact that even in relatively stable environments, dynamic capabilities still play major roles in effectuating performance.


title: Soft Robots Manufacturing: A Review

abstract: The growing interest in soft robots comes from the new possibilities offered by these systems to cope with problems that cannot be addressed by robots built from rigid bodies. This is in particular true when physical interaction between a robot and its environment is involved. Many innovative solutions have been developed in recent years to design soft components and systems. In this development, soft robotics has remained very closely dependent on advanced manufacturing processes, a topic which deserves special attention. This review aims at giving an insight on the current State of the Art in soft robotics, with a deeper understanding of the methods currently used to manufacture such systems.


title: Teaching Critical Thinking and Problem Solving Skills.

abstract: Abstract Critical thinking is a learned skill that requires instruction and practice. Business education instructors at both the secondary and post-secondary levels can enhance students' critical thinking skills by (1) using instructional strategies that actively engage students in the learning process rather than relying on lecture and rote memorization, (2) focusing instruction on the process of learning rather than solely on the content, and (3) using assessment techniques that provide students with an intellectual challenge rather than memory recall. Several barriers can impede critical thinking instruction. Lack of training, limited resources, biased preconceptions, and time constraints conspire to negate learning environments that promote critical thinking. However, actively engaging students in project-based or collaborative activities can encourage students' critical thinking development if instructors model the thinking process, use effective questioning techniques, and guide students' critical thinking processes. The examples provided challenge instructors to think of students as users of information rather than receivers of information. "It is possible to store the mind with a million facts and still be entirely uneducated. " -Alec Bourne Introduction What is critical thinking, and why is it so important? The Critical Thinking Community defined critical thinking as "the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action" (Scriven & Paul, 2007, p. 1). Critical thinking has also been referred to as metacognition (Tempelaar, 2006) or the process of "thinking about thinking" as defined and originally purposed by Flavell (1979). Critical thinking skills are important because they enable students "to deal effectively with social, scientific, and practical problems" (Shakirova, 2007, p. 42). Simply put, students who are able to think critically are able to solve problems effectively. Merely having knowledge or information is not enough. To be effective in the workplace (and in their personal lives), students must be able to solve problems to make effective decisions; they must be able to think critically. Critical thinking is not a new concept. "Throughout nearly 300 years of policymaking in the United States, educators have promoted eight broad goals of schooling: basic academic skills, critical thinking and problem solving, social skills and work ethic, citizenship, physical health, emotional health, the arts and literature, and preparation for skilled employment" (Rothstein, Wilder, & Jacobsen, 2007, p. 8). Business education directly addresses work ethic and the preparation for skilled employment as well as critical thinking and problem solving. Yet many teachers continually struggle to engage students in critical thinking activities (Tempelaar, 2006), and students seldom use critical thinking skills to solve complex, real-world problems (Bartlett, 2002; Rippin, Booth, Bowie, & Jordan, 2002). Why? The answer may be in our instructional methods. Two quotes that are often cited together reflect this supposition (as cited by Schaf ersman, 1991). First, Clement (1979) stated that "we should be teaching students how to think. Instead, we are teaching them what to think" (p. 1). Second, Norman (1981) noted that "it is strange that we expect students to learn, yet seldom teach them anything about learning" (p. 1). Although content is important, the process of how students learn the material is equally important. Therefore, the purpose of this article is to analyze and synthesize secondary research to provide best practices for incorporating critical thinking instructional methods into business education classrooms at both the secondary and post-secondary levels. …


title: Very deep convolutional neural network based image classification using small training sample size

abstract: Since Krizhevsky won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 competition with the brilliant deep convolutional neural networks(D-CNNs), researchers have designed lots of D-CNNs. However, almost all the existing very deep convolutional neural networks are trained on the giant ImageNet datasets. Small datasets like CIFAR-10 has rarely taken advantage of the power of depth since deep models are easy to overfit. In this paper, we proposed a modified VGG-16 network and used this model to fit CIFAR-10. By adding stronger regularizer and using Batch Normalization, we achieved 8.45% error rate on CIFAR-10 without severe overfitting. Our results show that the very deep CNN can be used to fit small datasets with simple and proper modifications and don't need to re-design specific small networks. We believe that if a model is strong enough to fit a large dataset, it can also fit a small one.


title: AJ-FS9 monoclonal antibody detects masked antigens within the human sperm tail fibrous sheath.

abstract: AJ-FS9 is one of a new series of monoclonal antibodies (mAbs) raised by immunizing mice with isolated human sperm tail fibrous sheath (FS). Using indirect immunofluorescence (IIF) of human spermatozoa dried onto slides, the AJ-FS9 mAb reacted with the principal piece of occasional spermatozoa. Following their enzymatic treatment with trypsin, dispase or collagenase, but not sulphatase, all the spermatozoa were stained at their principal piece. The ultrastructural localization of the antigens to the FS was established by immunogold electron microscopy, which showed the distribution of gold particles on the FS outer surface of spermatozoa sequentially treated with 1% Triton and dispase; spermatozoa demembranated by Triton alone showed no reaction. For biochemical characterization, spermatozoa were lysed with 1% Triton, and the sperm pellet was run through a reducing sodium dodecyl sulphate-polyacrylamide gel electrophoresis, Western blotted and immunostained with AJ-FS9. The results showed the reaction of the antibody with three protein bands with molecular masses ranging between 46 and 56 kDa. IIF screening of human testicular cryostat sections with AJ-FS9 mAb showed its reactivity with occasional sperm tails; but following their dispase treatment, all spermatozoa were stained. The restricted staining of the assembled FS of maturing sperm tails indicated the late appearance of the antigens during spermatogenesis. The antibody did not react with sperm cell precursors or other cell types within/without the seminiferous tubules. Untreated and dispase-treated frozen sections of skin, oesophagus, tongue, liver, kidney, stomach, ileum or their blood vessels showed no reaction.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Speaker identification features extraction methods: A systematic review

abstract: Abstract Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.


title: Attention-deficit/hyperactivity disorder in adults: an overview

abstract: Abstract To assess the validity of adult attention-deficit/hyperactivity disorder (ADHD), we reviewed clinical, family, psychopharmacologic, neurobiological, and outcome studies. We found multiple reports describing adults with clinical features highly reminiscent of the childhood ADHD. These adults, who are impulsive, inattentive, and restless, have the clinical “look and feel” of ADHD children. As with their childhood counterparts, many adults with ADHD suffer from antisocial, depressive, and anxiety disorders. They also show clinically significant impairments—histories of school failure, occupational problems, and traffic accidents. Studies of biological features show correspondences between child and adult cases of ADHD. Both show familial aggregation and a characteristic profile of neuropsychologic deficits; an emerging neuroimaging literature suggests that abnormalities in the same brain regions underlie both the child and adult forms of the disorder. Although these converging lines of evidence support the validity of ADHD in adults, follow-up studies of ADHD children have yielded ambiguous results. This ambiguity is in part due to differences in how researchers define the persistence of ADHD, a problem that suggests future research focus on how best to diagnose ADHD in adulthood.


title: DBpedia -- A Linked Data Hub and Data Source for Web and Enterprise Applications

abstract: The DBpedia project has extracted a rich knowledge base from Wikipedia and serves this knowledge base as Linked Data on the Web. DBpedia’s knowledge base currently provides 274 million pieces of information about 2.6 million concepts. As DBpedia covers a wide range of domains and has a high degree of conceptual overlap with various openlicense datasets that are already available on the Web, an increasing number of data publishers has started to set data links from their data sources to DBpedia, making DBpedia one of the central interlinking hubs of the emerging Web of Data. This paper gives an overview about the DBpedia project and describes how application developers can make use of DBpedia knowledge within their applications.


title: Environmental Data Mining and Modelling Based on Machine Learning Algorithms and Geostatistics

abstract: Abstract The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model—machine learning (ML) residuals sequential simulations—MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. ML algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process.


title: Connected Components for a Fast and Robust 2D Lidar Data Segmentation

abstract: The paper presents a novel segmentation approach applied to a two-dimensional point-cloud extracted by a LIDAR device. The most common approaches perform well in outdoor environments where usually furniture and other objects are rather big and are composed of smooth surfaces. However, these methods fail to segment uneven, rough surfaces. In this paper we propose a novel range data segmentation algorithm that is based on the popular one-pass version of the Connected Components algorithm. Our algorithm outperforms most commonly used approaches, while keeping the low computational complexity. The algorithm is used as a part of control and perception system in our unmanned ground vehicle where real-time response time is required. We presented experimental results obtained indoors and outdoors. The latter experiment was conducted in the real test field while the vehicle was autonomously driven.


title: Increasing Carotenoid Bioaccessibility from Yellow Peppers Using Excipient Emulsions: Impact of Lipid Type and Thermal Processing.

abstract: Many phytochemicals from fruits and vegetables exert biological activities that may be beneficial to human health, but these benefits are not fully realized because of their poor oral bioavailability. The objective of this research was to establish the potential of excipient emulsions to increase carotenoid bioaccessibility from raw and cooked yellow peppers using a gastrointestinal model that included oral, gastric, and intestine phases. The influence of oil type (medium chain triglycerides, MCT; long chain triglycerides, LCT; and, indigestible orange oil, OO) on microstructural changes, particle properties, lipid digestibility, and carotenoid bioaccessibility was investigated. Oil type had a major impact, with carotenoid bioaccessibility decreasing in the following order: LCT > MCT > OO > control (no oil). Conversely, thermal treatment (raw versus boiled) had little influence on carotenoid bioaccessibility. These results will facilitate the rational design of excipient emulsions that boost the bioavaila...


Generation of Large-Scale Simulated Utterances in Virtual Rooms to Train Deep-Neural Networks for Far-Field Speech Recognition in Google Home


title: Graph Exploration: From Users to Large Graphs

abstract: The increasing interest in social networks, knowledge graphs, protein-interaction, and many other types of networks has raised the question how users can explore such large and complex graph structures easily. Current tools focus on graph management, graph mining, or graph visualization but lack user-driven methods for graph exploration. In many cases graph methods try to scale to the size and complexity of a real network. However, methods miss user requirements such as exploratory graph query processing, intuitive graph explanation, and interactivity in graph exploration. While there is consensus in database and data mining communities on the definition of data exploration practices for relational and semi-structured data, graph exploration practices are still indeterminate. In this tutorial, we will discuss a set of techniques, which have been developed in the last few years for independent purposes, within a unified graph exploration taxonomy. The tutorial will provide a generalized definition of graph exploration in which the user interacts directly with the system either providing feedback or a partial query. We will discuss common, diverse, and missing properties of graph exploration techniques based on this definition, our taxonomy, and multiple applications for graph exploration. Concluding this discussion we will highlight interesting and relevant challenges for data scientists in graph exploration.


title: Survey of Word Sense Disambiguation Approaches

abstract: Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural language processing (NLP). Hundreds of WSD algorithms and systems are available, but less work has been done in regard to choosing the optimal WSD algorithms. This paper summarizes the various knowledge sources used for WSD and classifies existing WSD algorithms according to their techniques. The rationale, tasks, performance, knowledge sources used, computational complexity, assumptions, and suitable applications for each class of WSD algorithms are also discussed. This paper will provide users with general knowledge for choosing WSD algorithms for their specific applications or for further adaptation.


title: A Statistical Approach To The Semantics Of Verb-Particles

abstract: This paper describes a distributional approach to the semantics of verb-particle constructions (e.g. put up, make off). We report first on a framework for implementing and evaluating such models. We then go on to report on the implementation of some techniques for using statistical models acquired from corpus data to infer the meaning of verb-particle constructions.


title: Compact and Broadband CPW-to-RWG Transition Using Stub Resonators

abstract: This paper proposes a broadband coplanar waveguide (CPW)-to-rectangular waveguide (RWG) transition using stub resonators. The reflection coefficient of the proposed transition is less than −20 dB in the frequency band ranging from 8.6 to 12.6 GHz, which almost covers the whole X-band (8.2–12.4 GHz). As compared with the miniaturized CPW-to-RWG transition using the inductance-compensated slotline, the reflection coefficient is reduced from −15 to −20 dB and the size is maintained at 8.6 mm. In order to reduce the circuit size, a compact CPW-to-RWG transition using right-angled stub resonators is proposed. The circuit size is reduced from 8.6 to 7.8 mm while the broadband response of the −20-dB reflection coefficient is maintained. In order to verify the simulation results, three back-to-back CPW-to-RWG transitions are fabricated and measured by which the measurement results agree with the simulation results.


title: Regularized locality preserving indexing via spectral regression

abstract: We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing (LSI) which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. Specifically, the computation of LPI involves eigen-decompositions of two dense matrices which is expensive. In this paper, we propose a new algorithm called Regularized Locality Preserving Indexing (RLPI). Benefit from recent progresses on spectral graph analysis, we cast the original LPI algorithm into a regression framework which enable us to avoid eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizers can be naturally incorporated into our algorithm which makes it more flexible. Extensive experimental results show that RLPI obtains similar or better results comparing to LPI and it is significantly faster, which makes it an efficient and effective data preprocessing method for large scale text clustering, classification and retrieval.


title: Data, DIKW, Big Data and Data Science

abstract: Abstract In this paper we discuss the relationship between data and DIKW, that the data only evolves to knowledge, which may have some value, but if without the wisdom we still could let the knowledge be really useful to people. Now the big data occupies much attention in some extent for his volume, velocity, and variety. But in practical use the value plays more important role. Finally to judge the value for data not necessary for big, in some cases the small data also may lead to big value. So we appreciate the data science, which may consider more inherent value from data.


title: On Trajectory Prediction in Indoor Retail Environments for Mobile Advertising Using Selected Self-Histories

abstract: In this paper, we propose a new trajectory prediction algorithm, 3 Indoor Trajectory Analysis (3-ITA), to analyze the different trajectories of a shopping centre visitor, and to predict the trajectory with high probability that the visitor could use on a specific date. Here, a trajectory is a sequence of stores visited by a user, not necessarily the actual physical path/walk taken by the user when visiting the stores. 3-ITA is capable of predicting how many stores will be visited by that visitor, what those stores are, and the order of visiting each one of the predicted stores. Moreover, 3-ITA is working on an individual level. Every trajectory-pattern analysis and every predicted trajectory is exclusively related to the profile of a registered visitor. In addition, we developed a prototype system called Ad4Me. Ad4Me is a personal trajectory-pattern-aware pervasive system for mobile advertising in retail environments. Ad4Me will use the predicted stores from 3-ITA to detect the interests of a shopping centre visitor, in order to generate an adaptive list of relevant ads to that visitor. The experimental results show the high prediction accuracy of our algorithm.


title: A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data

abstract: Display Omitted Differences in patient progression can significantly impact outcomes.We present a methodology for interactive pattern mining and analysis of patient data.Our approach combines ad hoc visual queries, mining, and interactive visualization.Our methods uncover key event patterns and their associations with outcome over time.Prototype implementation applied to population of 32,000 cardiology patients. Patients' medical conditions often evolve in complex and seemingly unpredictable ways. Even within a relatively narrow and well-defined episode of care, variations between patients in both their progression and eventual outcome can be dramatic. Understanding the patterns of events observed within a population that most correlate with differences in outcome is therefore an important task in many types of studies using retrospective electronic health data. In this paper, we present a method for interactive pattern mining and analysis that supports ad hoc visual exploration of patterns mined from retrospective clinical patient data. Our approach combines (1) visual query capabilities to interactively specify episode definitions, (2) pattern mining techniques to help discover important intermediate events within an episode, and (3) interactive visualization techniques that help uncover event patterns that most impact outcome and how those associations change over time. In addition to presenting our methodology, we describe a prototype implementation and present use cases highlighting the types of insights or hypotheses that our approach can help uncover.


title: Performance Analysis of Modified SEPIC Converter with Low Input Voltage 1

abstract: The paper presents performance analysis of modified SEPIC dc-dc converter with low input voltage and wide output voltage range. The operational analysis and the design is done for the 380W power output of the modified converter. The simulation results of modified SEPIC converter are obtained with PI controller for the output voltage. The results obtained with the modified converter are compared with the basic SEPIC converter topology for the rise time, peak time, settling time and steady state error of the output response for open loop. Voltage tracking curve is also shown for wide output voltage range. Dc-dc converters are widely used in regulated switched mode dc power supplies and in dc motor drive applications. The input to these converters is often an unregulated dc voltage, which is obtained by rectifying the line voltage and it will therefore fluctuate due to variations of the line voltages. Switched mode dc-dc converters are used to convert this unregulated dc input into a controlled dc output at a desired voltage level. The recent growth of battery powered applications and low voltage storage elements are increasing the demand of efficient step-up dc-dc converters. Typical applications are in adjustable speed drives, switch-mode power supplies, uninterrupted power supplies, and utility interface with nonconventional energy sources, battery energy storage systems, battery charging for electric vehicles, and power supplies for telecommunication systems etc.. These applications demand high step-up static gain, high efficiency and reduced weight, volume and cost. The step-up stage normally is the critical point for the design of high efficiency converters due to the operation with high input current and high output voltage (1). The boost converter topology is highly effective in these applications but at low line voltage in boost converter, the switching losses are high because the input current has the maximum value and the highest step-up conversion is required. The inductor has to be oversized for the large current at low line input. As a result, a boost converter designed for universal-input applications is heavily oversized compared to a converter designed for a narrow range of input ac line voltage (2). However, recently new non-isolated dc-dc converter topologies with basic boost are proposed, showing that it is possible to obtain high static gain, low voltage stress and low losses, improving the performance with respect to the classical topologies. Some single stage high power factor rectifiers are presented in (3-6). A new alternative for the implementation of high step-up structures is proposed in this paper with the use of the voltage multiplier cells integrated with basic non-isolated dc-dc converters. The uses of the voltage multiplier in the basic dc-dc converters add new operation characteristics, becoming the resultant structure well suited to implement high-static gain step-up converters (7). The use of high static gain and low-switch voltage topologies can improve the efficiency operating with low input voltage, as presented in (8-10). II. Overview of Circuit and Operation The modified SEPIC converter is accomplished by including of the diode DM and the capacitor CM in basic SEPIC converter. The voltage multiplier technique is used to increase the static gain of single-phase boost dc-dc converters. An adaptation of the voltage multiplier technique with the SEPIC converter is presented in fig. 1. Many operational characteristics of the basic SEPIC converter are changed with the proposed modification.


title: Resilience in children threatened by extreme adversity: Frameworks for research, practice, and translational synergy

abstract: This article delineates parallel frameworks that grew out of the research on risk and resilience over the past four decades, a framework for research and a framework for practice, and then discusses the promise of an emerging synthesis. The research framework defined the meaning, models, and methods that successfully guided four waves of research to date on the nature and processes involved in human resilience. The applied framework emerged in response to urgent needs of children and families faced by adversity and those charged with helping them, resulting in guidelines for translating the unfolding but incomplete research evidence into action. The application of a resilience approach transformed practice in many fields concerned with promoting resilience in people at risk for problems, revolutionizing the mission, models, measures, and methods of practice to align with the emphasis on positive adaptation and strengths defining a resilience-based approach. Yet these interventions rarely translated back to inform and refine resilience theory in ways that would accelerate progress to promote resilience more effectively. The concluding section on translational synergy discusses the potential for a synthesis of basic and applied resilience frameworks as the next steps toward realizing the original objective and promise of resilience science.


title: Stress and multitasking in everyday college life: an empirical study of online activity

abstract: While HCI has focused on multitasking with information workers, we report on multitasking among Millennials who grew up with digital media - focusing on college students. We logged computer activity and used biosensors to measure stress of 48 students for 7 days for all waking hours, in their in situ environments. We found a significant positive relationship with stress and daily time spent on computers. Stress is positively associated with the amount of multitasking. Conversely, stress is negatively associated with Facebook and social media use. Heavy multitaskers use significantly more social media and report lower positive affect than light multitaskers. Night habits affect multitasking the following day: late-nighters show longer duration of computer use and those ending their activities earlier in the day multitask less. Our study shows that college students multitask at double the frequency compared to studies of information workers. These results can inform designs for stress management of college students.


title: Global Locator, Local Locator, and Identifier Split (GLI-Split)

abstract: The locator/identifier split is an approach for a new addressing and routing architecture to make routing in the core of the Internet more scalable. Based on this principle, we developed the GLI-Split framework, which separates the functionality of current IP addresses into a stable identifier and two independent locators, one for routing in the Internet core and one for edge networks. This makes routing in the Internet more stable and provides more flexibility for edge networks. GLI-Split can be incrementally deployed and it is backward-compatible with the IPv6 Internet. We describe its architecture, compare it to other approaches, present its benefits, and finally present a proof-of-concept implementation of GLI-Split.


title: Finite Element Analysis on Temperature Field of Insulated Cabin for LNG Carrier Based on ANSYS

abstract: The paper analyses heat insulation cavity of insulated cabin and brings forward heat exchanging mathematic models, boundary conditions and treat with cabin correctly. It makes 3D finite element model of 1/4 cargo cabin for 125000 m3 LNG carrier with ANSYS, calculate it with APDL program , gains temperature field distributing for insulated cabin in all conditions and total quantity of exchange heat, BOR and so on. The analysis results come up to vessel reference, it shows that model and simulation calculation are feasible.


title: Stacked cylindrical dielectric resonator antenna with metamaterial as a superstrate for enhancing the bandwidth and gain

abstract: A dual segment cylindrical dielectric resonator antenna with metamaterial superstrate is presented in this paper. The metamaterial cover acting as a converging lens is sharply increasing the gain of the proposed antenna and is also responsible for increasing the bandwidth. The proposed antenna is having a better impedance matching (less than -10 dB return loss) in a wide frequency band with a bandwidth of 2.12 GHz (8.49-10.61 GHz) and peak gain of 12.86 dB. The proposed antenna is presented with parametric analysis of some key parameters. Broadside radiation pattern is obtained without metamaterial, while directive radiation pattern with narrow beamwidth is obtained with metamaterial using as a superstrate. The gain characteristics of the proposed antenna are also presented.


title: Tourist Satisfaction and Destination Loyalty intention: A Structural and Categorical Analysis

abstract: This study explores the relationship between travel satisfaction and destination loyalty intention. The research was conducted with 486 tourists visiting Arade, a Portuguese tourist destination. Taking as the basis the use of structural equation modelling (SEM), the results substantiate the importance of tourism satisfaction as a determinant of destination loyalty. Also, a categorical principal components analysis (CATPCA) provides a detailed analysis of this cause-effect relationship by establishing that greater levels of satisfaction (measured by overall satisfaction in terms of holiday experience, destination attributes and met expectations) result in increased likelihood of future repeat visits and a keen willingness to recommend the destination to others. Clusters of tourists were also identified and characterized in relation to satisfaction levels and loyalty intentions. These analyses provide a useful background in the planning of future tourist marketing strategies.


title: Localized climatic responses during the 11 July 1991 eclipse: Phoenix AZ

abstract: Measurements taken from a variety of land use environments in and around the Phoenix AZ metropolitan area were compared to determine the effects of various land use characteristics on the thermal response to a reduction of 68% in solar radiation during the solar eclipse of 11 July 1991. The results include: (a) The magnitude of the change in ambient air temperature during the eclipse is related to land use. Heavily asphalted military sites and irrigated golf courses recorded small decreases in temperature while natural desert terrain experienced the largest decreases. Variations in thermal response are more closely related to the albedo of the specific land type than to other factors such as thermal admittance but this high correlation is probably an artifact of the combination of other variables such as moisture. (b) Due to the complex mix of land uses and timing of eclipse, marked wind speed changes associated with the eclipse do not appear to be linked directly to land use changes, and, (c) Temperature minima at residential/commercial stations occurred in general, before the minima at stations in agricultural/golf terrains. Because typical albedo differences are not significant between these two land uses, land use characteristics other than albedo are most likely controlling the timing of the temperature minima. Opportunities to empirically examine concepts previously testable only through numerical modeling such as solar reduction experiments should continue to be exploited.


title: Consumer acceptance of nutrigenomics-based personalised nutrition

abstract: Nutrigenomics is a new and promising development in nutritional science which aims to understand the fundamental molecular processes affected by foods. Despite general agreement on its promise for better understanding diet-health relationships, less consensus exists among experts on the potential of spin-offs aimed at the consumer such as personalised nutrition. Research into consumer acceptance of such applications is scarce. The present study develops a set of key hypotheses on public acceptance of personalised nutrition and tests these in a representative sample of Dutch consumers. An innovative consumer research methodology is used in which consumers evaluate short films which are systematically varied scenarios for the future of personalised nutrition. Consumer evaluations of these films, which are pre-tested in a pilot study, allow a formal test of how consumer perceptions of personalised nutrition drive consumer acceptance and through which fundamental psychological processes these effects are mediated. Public acceptance is enhanced if consumers can make their genetic profile available free at their own choice, if the actual spin-off products provide a clearly recognisable advantage to the consumer, and are easy to implement into the daily routine. Consumers prefer communication on nutrigenomics and personalised nutrition by expert stakeholders to be univocal and aimed at building support with consumers and their direct environments for this intriguing new development. Additionally, an exploratory segmentation analysis indicated that people have different focal points in their preferences for alternative scenarios of personalised nutrition. The insights obtained from the present study provide guidance for the successful further development of nutrigenomics and its applications.


Text-Based Deep Learning for Artificial Jazz Improvisation


title: A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

abstract: Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.


title: Non-functional Requirements in Architectural Decision Making

abstract: Software architects often must work with incomplete or ill-specified non-functional requirements (NFRs) and use them to make decisions. Through this process, existing NFRs are refined or modified and new ones emerge. Although much research has centered on how software architects treat NFRs, no empirical studies have investigated the state of the practice. A survey based on interviews with 13 software architects addressed two fundamental issues: how do architects face NFRs from an engineering perspective, and how do NFRs influence their decision-making? The survey revealed that architects usually elicit NFRs themselves in an iterative process; they usually don't document the NFRs and only partially validate them.


title: An analysis of the Burrows—Wheeler transform

abstract: The Burrows—Wheeler Transform (also known as Block-Sorting) is at the base of compression algorithms that are the state of the art in lossless data compression. In this paper, we analyze two algorithms that use this technique. The first one is the original algorithm described by Burrows and Wheeler, which, despite its simplicity outperforms the Gzip compressor. The second one uses an additional run-length encoding step to improve compression. We prove that the compression ratio of both algorithms can be bounded in terms of the k th order empirical entropy of the input string for any k ≥ 0. We make no assumptions on the input and we obtain bounds which hold in the worst case that is for every possible input string. All previous results for Block-Sorting algorithms were concerned with the average compression ratio and have been established assuming that the input comes from a finite-order Markov source.


title: Circularly polarized tag antenna on an AMC substrate for wearable UHF RFID applications

abstract: This paper presents a circularly polarized tag on an AMC to achieve longer read range for UHF RFID on-body applications. In the conference paper, two asymmetrically orthogonal cross-dipoles printed on FR4 substrate are combined to generate a degenerate mode for circularly polarization. A symmetric T-matching transformer is employed to conjugate matching with a Monza 4 microchip. The body effect is very significant as the tag is designed for on-body applications. An artificial magnetic conductor (AMC) is placed on the back plane of the cross-dipole tag in order to insulate the influence from a human body. The tag with the AMC can increase antenna gain and achieve over 15 m read range as it is pasted on the human body.


title: "Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding"

abstract: An overview on state of the art automotive radar usage is presented and the changing requirements from detection and ranging towards radar based environmental understanding for highly automated and autonomous driving deduced. The traditional segmentation in driving, manoeuvering and parking tasks vanishes at the driver less stage. Situation assessment and trajectory/manoeuver planning need to operate in a more thorough way. Hence, fast situational up-date, motion prediction of all kind of dynamic objects, object dimension, ego-motion estimation, (self)-localisation and more semantic/classification information, which allows to put static and dynamic world into correlation/context with each other is mandatory. All these are new areas for radar signal processing and needs revolutionary new solutions. The article outlines the benefits that make radar essential for autonomous driving and presents recent approaches in radar based environmental perception.


title: Riluzole for amyotrophic lateral sclerosis (ALS)/motor neuron disease (MND).

abstract: BACKGROUND ::: Riluzole 100 mg probably prolongs survival in patients with amyotrophic lateral sclerosis by about two months and the safety of the drug is not a major concern. The evidence from randomized controlled trials indicates that patients taking riluzole probably survive longer than patients taking placebo. The beneficial effects are very modest and the drug is expensive. Adverse effects from riluzole are relatively minor and for the most part reversible after stopping the drug. Riluzole has been approved for treatment of patients with amyotrophic lateral sclerosis in many countries but not all. Questions persist about its clinical utility because of high cost, modest efficacy and concern over adverse effects. ::: ::: ::: OBJECTIVES ::: To examine the efficacy of riluzole in prolonging survival, and in delaying the use of surrogates (tracheostomy and mechanical ventilation) to sustain survival. ::: ::: ::: SEARCH STRATEGY ::: Search of the Cochrane Neuromuscular Disease Group Register for randomized trials and enquiry from authors of trials, Aventis (manufacturer of riluzole) and other experts in the field. The most recent search was November 2002. ::: ::: ::: SELECTION CRITERIA ::: Randomized trials of adults with diagnosis of amyotrophic lateral sclerosis (ALS), treated with riluzole or placebo. Types of outcome measures: Primary: pooled hazard ratio of tracheostomy-free survival over all time points with riluzole 100 mg. Secondary: per cent mortality as a function of time with riluzole 100 mg and other doses of riluzole; neurologic function, quality of life, muscle strength and adverse events. ::: ::: ::: DATA COLLECTION & ANALYSIS ::: We identified four eligible randomized trials. Each reviewer graded them for methodological quality. Data extraction was performed by a single reviewer and checked by two others. We obtained some missing data from investigators and regulatory agencies. We performed meta-analyses with Review Manager 4.1 software using a fixed effects model. A test of drug efficacy was based on the Parmar pooled hazard ratio. ::: ::: ::: RESULTS ::: The three trials examining tracheostomy-free survival included a total of 876 riluzole treated patients and 406 placebo treated patients. The data for tracheostomy-free survival was not available from the fourth trial. The methodological quality was acceptable and the three trials were easily comparable, although one trial included older patients in more advanced stages of amyotrophic lateral sclerosis. Riluzole 100 mg per day provided a benefit for the homogeneous group of patients in the first two trials (p=0.039, hazard ratio 0.80, 95% confidence interval 0.64 to 0.99) and there was no evidence of heterogeneity (p=0.33). When the third trial (which included older and more seriously affected patients) is added, there is evidence of heterogeneity (p<0.0001) and the random effects model, which takes this into account results in the overall treatment effect estimate falling just short of significance (p=0.056, hazard ratio 0.84, 95% confidence interval 0.70 to 1.01). This represents a 9% gain in the probability of surviving one year (57% in the placebo and 66% in the riluzole group). In secondary analyses of survival at separate time points, there was a significant survival advantage with riluzole 100 mg at six, nine, 12 and 15 months, but not at three or 18 months. There was a small beneficial effect on both bulbar and limb function, but not on muscle strength. There were no data on quality of life, but patients treated with riluzole remained in a more moderately affected health state significantly longer than placebo-treated patients (weighted mean difference 35.5 days, 95% confidence interval 5.9 to 65.0). A threefold increase in serum alanine transferase was more frequent in riluzole treated patients than controls (weighted mean difference 2.69, 95% confidence interval 1.65 to 4.38). ::: ::: ::: CONCLUSIONS ::: Riluzole 100 mg daily is reasonably safe and probably prolongs survival by about two months in patients with ALS. More studies are needed, especially to clarify its effect in older patients (over 75 years), and those with more advanced disease.


title: Affective Color in Visualization

abstract: Communicating the right affect, a feeling, experience or emotion, is critical in creating engaging visual communication. We carried out three studies examining how different color properties (lightness, chroma and hue) and different palette properties (combinations and distribution of colors) contribute to different affective interpretations in information visualization where the numbers of colors is typically smaller than the rich palettes used in design. Our results show how color and palette properties can be manipulated to achieve affective expressiveness even in the small sets of colors used for data encoding in information visualization.


title: Winning the darpa grand challenge with an ai robot

abstract: This paper describes the software architecture of Stanley, an autonomous land vehicle developed for high-speed desert driving without human intervention. The vehicle recently won the DARPA Grand Challenge, a major robotics competition. The article describes the software architecture of the robot, which relied pervasively on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning.


The food and feeding of Atlantic salmon (Salmo salar L.) during feeding and spawning migrations in Icelandic coastal waters.


title: A Frequency-Reconfigurable Dual-Band Low-Profile Monopolar Antenna

abstract: A concept of a low-profile monopolar antenna operating in two independently reconfigurable frequency bands is introduced in this paper. For the lower band, the design utilizes a center-fed patch with shorting rods at its edges, forcing the patch to radiate as an equivalent magnetic-current loop. For the upper band, another magnetic current loop is created by adding four symmetrical resonant slots on the patch, which radiates almost independently of the shorted patch. To allow coverage of a larger bandwidth, two sets of varactor diodes are used to independently control the resonance frequencies of the two bands. As a validation of the proposed concept, two antenna prototypes, with and without reconfigurabilities, have been optimized and fabricated. Measurement results show that the low-profile reconfigurable monopolar antenna achieves two independent tunable bands, with −10-dB-tuning ranges of 31% and 22% centered at about 0.9 and 1.7 GHz, respectively. The antenna height is only $0.020\lambda _{\text {min}}$ , where $\lambda _{\text {min}}$ is the free-space wavelength at the minimum operating frequency. Importantly, stable omnidirectional patterns and vertical polarization are consistently achieved across both tuning ranges.


title: Recommendations for next generation air traffic control training

abstract: This paper investigates the current uses of simulation in FAA Academy ATC training in an effort to identify potential improvement areas to the current training program in the areas of simulation and course content. Once identified, recommendations for changes to the current training program can be made. A thorough literature review of current training techniques used at the FAA Academy and training centers was conducted. The primary findings of this research revealed that training is predominantly accomplished via traditional classroom-based instruction, with complementing low-, medium-, and high-fidelity simulation labs. Recommendations made regarding the FAA ATC training process include the incorporation of web-based training technologies, the addition of voice recognition and synthesis technologies to current simulators, and updating current simulators to include recording and playback features.


title: TheR-function method in boundary-value problems with geometric and physical symmetry

abstract: This paper is devoted to developing constructive methods of the theory of R-functions. It gives the first discussion of methods of constructing the equations of loci with symmetry of translation type for duplicated domains not separated by lines and with point symmetry of cyclic type for regions both possessing and lacking axial symmetry. The possible applications are illustrated by numerous examples carried out using the POLE system, including solutions of boundary-value problems involving the influence of cyclically located circular and star-shaped insulators on an electric field.


title: An IoT Enabled Real-Time Communication and Location Tracking System for Vehicular Emergency

abstract: Internet of Things is an emerging technology having the ability to change the way we live. In IoT vision, each and every 'thing' has the ability of talking to each other that brings the idea of Internet of Everything in reality. Numerous IoT services can make our daily life easier, smarter, and even safer. Using IoT in designing some special services can make a lifesaver system. In this paper, we have presented an IoT enabled approach that can provide emergency communication and location tracking services in a remote car that meets an unfortunate accident or any other emergency situation. Immediately after an accident or an emergency, the system either starts automatically or may be triggered manually. Depending upon type of emergency (police and security, fire and rescue, medical, or civil) it initiates communication and shares critical information e.g. location information, a set of relevant images taken from prefixed angles etc. with appropriate server / authority. Provision of interactive real-time multimedia communication, real-time location tracking etc. have also been integrated to the proposed system to monitor the exact condition in real-time basis. The system prototype has been designed with Raspberry Pi 3 Model B and UMTS-HSDPA communication protocol.


title: Hungarian Layer: Logics Empowered Neural Architecture

abstract: Neural architecture is a purely numeric framework, which fits the data as a continuous function. However, lacking of logic flow (e.g. \textit{if, for, while}), traditional algorithms (e.g. \textit{Hungarian algorithm, A$^*$ searching, decision tress algorithm}) could not be embedded into this paradigm, which limits the theories and applications. In this paper, we reform the calculus graph as a dynamic process, which is guided by logic flow. Within our novel methodology, traditional algorithms could empower numerical neural network. Specifically, regarding the subject of sentence matching, we reformulate this issue as the form of task-assignment, which is solved by Hungarian algorithm. First, our model applies BiLSTM to parse the sentences. Then Hungarian layer aligns the matching positions. Last, we transform the matching results for soft-max regression by another BiLSTM. Extensive experiments show that our model outperforms other state-of-the-art baselines substantially.


title: Summarization of Films and Documentaries Based on Subtitles and Scripts

abstract: We study the behavior of automatic summarization for films and documentaries.Well-known extractive summarization algorithms are ranked for this task.Assessment of strategies for effective extractive summarization in these domains.Quantitative results are presented for relevant experiments.Qualitative assessment is also provided (concerning the best approaches). We assess the performance of generic text summarization algorithms applied to films and documentaries, using extracts from news articles produced by reference models of extractive summarization. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries against news abstracts, plot summaries, and synopses. We show that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films. Despite the different nature of films and documentaries, their relative behavior is in accordance with that obtained for news articles.


title: Row-wise tiling for the Myers' bit-parallel approximate string matching algorithm

abstract: Given a text T[1..n] and a pattern P[1..m] the classic dynamic programming algorithm for computing the edit distance between P and every location of T runs in time O(nm). The bit-parallel computation of the dynamic programming matrix [6] runs in time \(O(n \left\lceil m/w \right\rceil)\), where w is the number of bits in computer word. We present a new method that rearranges the bit-parallel computations, achieving time \(O(\left\lceil n/w \right\rceil (m+\sigma\log_2(\sigma))+n)\), where σ is the size of the alphabet. The algorithm is then modified to solve the k differences problem. The expected running time is \(O(\left\lceil n/w \right\rceil (L(k)+\sigma\log_2(\sigma))+R)\), where L(k) depends on k, and R is the number of occurrences. The space usage is O(σ + m). It is in practice much faster than the existing \(O(n \left\lceil k/w \right\rceil)\) algorithm [6]. The new method is applicable only for small (e.g. dna) alphabets, but this becomes the fastest algorithm for small m, or moderate k/m. If we want to search multiple patterns in a row, the method becomes attractive for large alphabet sizes too. We also consider applying 128-bit vector instructions for bit-parallel computations.


title: RHadoop-based fuzzy data mining: Architecture, design and system implementation

abstract: Data mining is a challenge for end-users, which requires knowledge and skills on business domains, data mining algorithms and software development. In response to the challenge, we have proposed, designed and implemented a novel data mining system named RFDM (RHadoop-based Fuzzy Data Mining), which supports fuzzy data mining process and experience with user convenience and reduced cost. The system is capable of supporting fully-automated data mining life-cycle activities, with limited user interactions in dataset uploading and data mining configuration. In addition, a RHadoop-based framework has been integrated, which meets the requirements of large-scale datasets in data mining. Experiments have indicated that the RFDM system achieves enhanced performance while supporting fuzzy data mining.


title: A complete U-V-disparity study for stereovision based 3D driving environment analysis

abstract: Reliable understanding of the 3D driving environment is vital for obstacle detection and adaptive cruise control (ACC) applications. Laser or millimeter wave radars have shown good performance in measuring relative speed and distance in a highway driving environment. However the accuracy of these systems decreases in an urban traffic environment as more confusion occurs due to factors such as parked vehicles, guardrails, poles and motorcycles. A stereovision based sensing system provides an effective supplement to radar-based road scene analysis with its much wider field of view and more accurate lateral information. This paper presents an efficient solution using a stereovision based road scene analysis algorithm which employs the "U-V-disparity" concept. This concept is used to classify a 3D road scene into relative surface planes and characterize the features of road pavement surfaces, roadside structures and obstacles. Real-time implementation of the disparity map calculation and the "U-V-disparity" classification is also presented.


Integrated Manufacture of Exoskeletons and Sensing Structures for Folded Millirobots


title: HIS based dual band dual polarized Minkowski fractal patch antenna

abstract: A compact single probe feed Minkowski fractal boundary patch antenna based on HIS (High Impedance Surface) is proposed for dual band dual polarization operation. Dual band operation with widened bandwidth at each resonating frequency and dual polarization can be achieved by using the combination of HIS and Minkowski fractal curves. The 10-dB return loss bandwidth of lower and upper frequency bands are 13.01% and 4.95% respectively. The 3-dB Axial Ratio bandwidth is 3.4%.


title: A decade of FBAR success and what is needed for another successful decade

abstract: July 31st, 2001, Avago (then Agilent) shipped its first 25,000 FBAR duplexers (in tape & reel) for nCDMA mobile phones. The value proposition was size (relative to the ceramic duplexers used at that time). At the time, it was supposed that FBAR technology using AlN as the active piezo material would not last beyond a few years. SAW technology, already entrenched in the lower frequencies and with better economies of scale and lower technology barrier to commercialization should have quickly made FBAR a ‘short-lived’ technology. Although still a possibility, FBAR has persevered for over 10 years due to several reasons; high Q, small size, ability to form an all-silicon package (utilizing silicon-fab technology) and reliability. However, the next 10 years pose a challenge to FBAR and serious innovation is necessary so that a similar talk about the success of FBAR covering 20 years can be given. Innovation must cover the ability to go differential, temperature compensation, continued Q enhancement, and chip-scale packages to spread the cost of manufacturing and research. This talk will touch on each of these subjects as well as giving an overview of the unique set of circumstances that made FBAR as successful as it is.


title: Smart video surveillance - Signal Processing Magazine, IEEE

abstract: Situation awareness is the key to security. Awareness requires information that spans multiple scales of space and time. Smart video surveillance systems are capable of enhancing situational awareness across multiple scales of space and time. However, at the present time, the component technologies are evolving in isolation. To provide comprehensive, nonintrusive situation awareness, it is imperative to address the challenge of multiscale, spatiotemporal tracking. This article explores the concepts of multiscale spatiotemporal tracking through the use of real-time video analysis, active cameras, multiple object models, and long-term pattern analysis to provide comprehensive situation awareness.


title: Integrating Social Media Communications into the Rapid Assessment of Sudden Onset Disasters

abstract: Recent research on automatic analysis of social media data during disasters has given insight into how to provide valuable and timely information to formal response agencies—and members of the public—in these safety-critical situations. For the most part, this work has followed a bottom-up approach in which data are analyzed first, and the target audience’s needs are addressed later.


title: UNDERSTANDING AESTHETICS DESIGN FOR E-COMMERCE WEB SITES: A COGNITIVE-AFFECTIVE FRAMEWORK

abstract: Despite its centrality to human thoughts and practices, aesthetics has largely been ignored in Web site design. Until recent years, studies begin to show that task-unrelated (aesthetic) qualities such as color, graphics and music can play an important role in enhancing both usage and enjoyment of information systems as well as improving work quality. However, research progress has been greatly impeded by the lack of a conceptual framework for understanding aesthetics as well as empirically verified instrument to measure Web site aesthetics. Our research objective is to provide a sound theoretical framework of Web site aesthetics as well as a validated instrument to measure it. Based on extensive literature review on aesthetics in environmental psychology and Human-Computer Interaction (HCI), we propose a two-dimensional structure of Web site aesthetics concept in a cognitive-affective framework and develop a Web site aesthetics instrument in the context of electronic shopping. Initial item development, statistical analyses, and validity assessment (reliability, convergent validity, and discriminant validity) are described here in details. The contribution of this effort is twofold: First, it integrates theories from different domains to help us understand the concept of Web site aesthetics. Second, an instrument is provided for Web site aesthetics, which will facilitate future research in this field.


title: Finger-vein pattern identification using SVM and neural network technique

abstract: Abstract This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.


title: EmotionSensing: Predicting Mobile User Emotion

abstract: User emotions are important contextual features in building context-aware pervasive applications. In this paper, we explore the question of whether it is possible to predict user emotions from their smartphone activities. To get the ground truth data, we have built an Android app that collects user emotions along with a number of features including their current location, activity they are engaged in, and smartphones apps they are currently running. We deployed this app for over a period of three months and collected a large amount of useful user data. We describe the details of this data in terms of statistics and user behaviors, provide a detailed analysis in terms of correlations between user emotions and other features, and finally build classifiers to predict user emotions. Performance of these classifiers is quite promising with high accuracy. We describe the details of these classifiers along with the results.


title: Distinguishing Round from Square Pegs: Predicting Hiring Based on Pre-hire Language Use

abstract: This article examines how cultural matching relates to a job applicant's likelihood of getting hired into an organization and identifies the components of cultural similarity that matter most for hiring success. Cultural compatibility at the hiring stage can forecast an individual's post-hire productivity but is difficult to reliably measure in the selection process. As a consequence, cultural matching is often subject to various informational and identity-based biases. We develop a language-based model that provides a means for directly assessing job candidates' cultural similarity. Based on variegated data from a mid-sized technology firm--including job applicants' free text responses at the pre-hire stage, applicant characteristics, applicant-interviewer assignments, and hiring outcomes--we find that linguistic similarity with previously hired employees increases a job candidate's chances of being hired, even after controlling for the applicant's human and social capital. We further find that, although all three forms of cultural fit that we assess--fit based on work preferences, lifestyles, and ideology--predict hiring in between-interviewer models, only work preferences fit predicts hiring in within-interviewer models. Supplemental analyses indicate that pre-hire cultural fit is also predictive of successful enculturation in the firm over the first six months of employment. Together, these results indicate that cultural matching leads to sorting on attributes that are both relevant and potentially irrelevant for job success.


title: A Broadband Dual Circularly Polarized Square Slot Antenna

abstract: A novel coplanar waveguide-fed broadband dual circularly polarized square slot antenna is presented. The antenna consists of a square slot, two asymmetric T-shaped feed lines in orthogonal direction protrude from signal lines, and an inverted-L grounded strip with three straight strips at the corner of the slot adjacent to both the feed lines. The circular polarization is obtained due to orthogonal feed lines. Axial ratio (AR) bandwidth is significantly enhanced because of inverted-L grounded strip with attached three straight strips. The 3-dB AR bandwidth of the antenna is about 60% in which return loss, and isolation are better than 10 and 15 dB, respectively.


title: Eye images increase generosity, but not for long: the limited effect of a false cue

abstract: Abstract People are more cooperative when explicitly observed, and simply exposing people to images of eyes or faces has been shown to increase cooperation of various types and in various contexts, albeit with notable, if controversial, exceptions. This ‘eyes effect' is important both for its potential real-world applications and for its implications regarding the role of reputation in the evolution and maintenance of human cooperation. Based on the general principle that organisms eventually cease responding to uninformative stimuli, we predicted that the eyes effect would be eliminated by prolonged exposure. A novel experiment confirmed that participants exposed briefly to an eye-like image gave more money in an economic game than those in a longer exposure condition and those in a control condition. There was no generosity difference between the long exposure and control conditions. Furthermore, a meta-analysis of 25 eyes effects experiments confirmed that the effect emerges reliably after short exposures to eye images, but not after long exposures. An understanding of the limits of false cues on behaviour helps resolve empirical discrepancies regarding the eyes effect and exonerates the importance of reputation even in anonymous, one-shot interactions.


title: The human sexual response cycle: Brain imaging evidence linking sex to other pleasures

abstract: Sexual behavior is critical to species survival, yet comparatively little is known about the neural mechanisms in the human brain. Here we systematically review the existing human brain imaging literature on sexual behavior and show that the functional neuroanatomy of sexual behavior is comparable to that involved in processing other rewarding stimuli. Sexual behavior clearly follows the established principles and phases for wanting, liking and satiety involved in the pleasure cycle of other rewards. The studies have uncovered the brain networks involved in sexual wanting or motivation/anticipation, as well as sexual liking or arousal/consummation, while there is very little data on sexual satiety or post-orgasmic refractory period. Human sexual behavior also interacts with other pleasures, most notably social interaction and high arousal states. We discuss the changes in the underlying brain networks supporting sexual behavior in the context of the pleasure cycle, the changes to this cycle over the individual's life-time and the interactions between them. Overall, it is clear from the data that the functional neuroanatomy of sex is very similar to that of other pleasures and that it is unlikely that there is anything special about the brain mechanisms and networks underlying sex. (C) 2012 Elsevier Ltd. All rights reserved.


1 CRYPTOGRAPHIC HASH FUNCTIONS : AN OVERVIEW


title: A Topic Model for Word Sense Disambiguation

abstract: We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously disambiguating a corpus and learning the domains in which to consider each word. Using the WORDNET hierarchy, we embed the construction of Abney and Light (1999) in the topic model and show that automatically learned domains improve WSD accuracy compared to alternative contexts.


title: Entanglement-assisted quantum error-correcting codes from units

abstract: Entanglement-assisted quantum error-correcting codes (EAQECCs) to desired rate, error-correcting capability and maximum shared entanglement are constructed. Thus for a required rate $R$, required error-correcting capability to correct $t$ errors, mds (maximum distance separable) EAQECCs of the form $[[n,r,d;c]]$ with $R=\frac{r}{n}, d\geq (2t+1), c = (n-r), d= (n-r+1)$ are constructed. Series of such codes may be constructed where the rate and the relative distance approach non-zero constants as $n$ approaches infinity. The codes may also be constructed over prime order fields in which modular arithmetic may be employed.


title: ViType: A Cost Efficient On-Body Typing System through Vibration

abstract: Nowadays, smart wristbands have become one of the most prevailing wearable devices as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to extend the input. Yet by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which firstly leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rate, ViType designs a set of novel mechanisms, including an artificial neural network to process the vibration signals, and a runtime calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust to fight against various confounding factors. The average recognition accuracy is 94.8% with an initial training sample size of 20 for each key, which is 1.52 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98% on average during one month.


title: LBPH based improved face recognition at low resolution

abstract: Automatic individual face recognition is the most challenging query from the past decade in computer vision. However, the law enforcement agencies are inadequate to identify and recognize any person through the video monitoring cameras further efficiently; the blur conditions, illumination, resolution, and lighting are still the major problems in face recognition. Our proposed system operates better at the minimum low resolution of 35px to identify the human face in various angles, side poses and tracking the face during human motion. We have designed the dataset (LR500) for training and classification. This paper employs the Local Binary Patterns Histogram (LBPH) algorithm architecture to address the human face recognition in real time at the low level of resolution.


title: A Heuristic Approach to the Discovery of Macro-Operators

abstract: This paper describes a heuristic approach to the discovery of useful macro-operators (macros) in problem solving. The approach has been implemented in a program, MACLEARN, that has three parts: macro-proposer, static filter, and dynamic filter. Learning occurs during problem solving, so that performance improves in the course of a single problem trial. Primitive operators and macros are both represented within a uniform representational framework that is closed under composition. This means that new macros can be defined in terms of others, which leads to a definitional hierarchy. The representation also supports the transfer of macros to related problems. MACLEARN is embedded in a supporting system that carries out best-first search. Experiments in macro learning were conducted for two classes of problems: peg solitaire (generalized “Hi-Q puzzle”), and tile sliding (generalized “Fifteen puzzle”). The results indicate that MACLEARN'S filtering heuristics all improve search performance, sometimes dramatically. When the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems.


title: Cooperative transport by ants and robots

abstract: In several species of ants, workers cooperate to retrieve large prey. Usually, one ant finds a prey item, tries to move it, and, when unsucessful for some time, recuits nestmates through direct contact or chemical marking. When a group of ants tries to move large prey, the ants change position and alignment until the prey can be moved toward the nest. A robotic implementation of this phenomenon is described. Although the robotic system may not appear to be very efficient, it is an interesting example of decentralized problem-solving by a group of robots, and it provides the first formalized model of cooperative transport in ants. Published in Robotics and Autonomous Systems30 (2000), 85-101.


title: Design of an adaptive sliding mode controller for a novel spherical rolling robot

abstract: This paper presents a novel spherical rolling robot and the design procedure of an adaptive sliding mode controller for the proposed robot. Towards containing the uncertainties, the sliding mode controller is utilized and simulation tests represent the effectiveness of this method. However, the chattering phenomenon in the robot operation is the main insufficiency of this controller. Hence, in order to present a more accurate controller, sliding mode controller is equipped with an identification method which works in an online manner for identifying the exact model of the robot. The provided identification procedure is based on Recursive Least Square approach, which is one of the most promising approaches toward identifying the model. It should be also noted that, this adaptive controller identify the model without taking into account any data from the derived model.


title: Modeling, simulation and control study for the quad-copter UAV

abstract: In the recent years UAV (Unmanned Aerial Vehicles) having quad-copter helicopter i.e. quad-copter configuration have been receiving increasing attention amongst the global researchers due to its wide-range of applications such as surveillance in military, civilian and disaster management applications. This paper presents our investigations on the modeling, simulation, altitude model validation and comparison of some popular quad-copter control schemes. Quad-copter consists of two pairs of counter rotating rotors situated at the ends of a cross frame, symmetric about the centre of gravity, which coincides with the origin of the reference systems used. The model used in our work is based on the Euler Lagrange method used to derive the defining equations of motions of the six degree of freedom system with minor changes to consider variable air-density. This modified model is then simulated in matlab-simulink framework to dynamically compute the quad-copter altitude and attitude. Also, some commonly used control algorithms like PD, PID and back-stepping control are compared using our modified model simulation framework computing certain well-known performance metrics, like ISE, IAE, for the closed loop altitude and attitude control.


title: High-Performance Shielded Coplanar Waveguides for the Design of CMOS 60-GHz Bandpass Filters

abstract: This paper presents optimized very high performance CMOS slow-wave shielded CPW transmission lines (S-CPW TLines). They are used to realize a 60-GHz bandpass filter, with T-junctions and open stubs. Owing to a strong slow-wave effect, the longitudinal length of the S-CPW is reduced by a factor up to 2.6 compared to a classical microstrip topology in the same technology. Moreover, the quality factor of the realized S-CPWs reaches 43 at 60 GHz, which is about two times higher than the microstrip one and corresponds to the state of the art concerning S-CPW TLines with moderate width. For a proof of concept of complex passive device realization, two millimeter-wave filters working at 60 GHz based on dual-behavior-resonator filters have been designed with these S-CPWs and measured up to 110 GHz. The measured insertion loss for the first-order (respectively, second-order) filter is -2.6 dB (respectively, -4.1 dB). The comparison with a classical microstrip topology and the state-of-the-art CMOS filter results highlights the very good performance of the realized filters in terms of unloaded quality factor. It also shows the potential of S-CPW TLines for the design of high-performance complex CMOS passive devices.


title: Single-cell systems biology by super-resolution imaging and combinatorial labeling

abstract: Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral overlap between fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using fluorescence in situ hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured mRNA levels of 32 genes simultaneously in single Saccharomyces cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells is a natural approach to bring systems biology into single cells.


title: Combining syntax and semantics for automatic extractive single-document summarization

abstract: The goal of automated summarization is to tackle the "information overload" problem by extracting and perhaps compressing the most important content of a document. Due to the difficulty that single-document summarization has in beating a standard baseline, especially for news articles, most efforts are currently focused on multi-document summarization. The goal of this study is to reconsider the importance of single-document summarization by introducing a new approach and its implementation. This approach essentially combines syntactic, semantic, and statistical methodologies, and reflects psychological findings that pinpoint specific selection patterns as humans construct summaries. Successful summary evaluation results and baseline out-performance are demonstrated when our system is executed on two separate datasets: the Document Understanding Conference (DUC) 2002 data set and a scientific magazine article set. These results have implications not only for extractive and abstractive single-document summarization, but could also be leveraged in multi-document summarization.


title: Transformation Pursuit for Image Classification

abstract: A simple approach to learning invariances in image clas- sification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a com- pact subset is challenging. Indeed, all transformations are not equally informative and adding uninformative transfor- mations increases training time with no gain in accuracy. We propose a principled algorithm--Image Transformation Pursuit (ITP)--for the automatic selection of a compact set of transformations. ITP works in a greedy fashion, by se- lecting at each iteration the one that yields the highest accuracy gain. ITP also allows to efficiently explore complex transformations, that combine basic transformations. We report results on two public benchmarks: the CUB dataset of bird images and the ImageNet 2010 challenge. Using Fisher Vector representations, we achieve an improvement from 28.2% to 45.2% in top-1 accuracy on CUB, and an im- provement from 70.1% to 74.9% in top-5 accuracy on Im- ageNet. We also show significant improvements for deep convnet features: from 47.3% to 55.4% on CUB and from 77.9% to 81.4% on ImageNet.


title: FingARtips: gesture based direct manipulation in Augmented Reality

abstract: This paper presents a technique for natural, fingertip-based interaction with virtual objects in Augmented Reality (AR) environments. We use image processing software and finger- and hand-based fiducial markers to track gestures from the user, stencil buffering to enable the user to see their fingers at all times, and fingertip-based haptic feedback devices to enable the user to feel virtual objects. Unlike previous AR interfaces, this approach allows users to interact with virtual content using natural hand gestures. The paper describes how these techniques were applied in an urban planning interface, and also presents preliminary informal usability results.


title: Microwave Breast Phantom Measurement System With Compact Side Slotted Directional Antenna

abstract: In this paper, a new, complete, and comprehensive breast phantom measurement system is presented. A side slotted vivaldi antenna is used for breast phantom measurement. The radiating fins are modified by etching six side slots to enhance the electrical length and produce stronger directive radiation with higher gain. This approach reduces the lower operating frequency and increases the gain and efficiency without compromising the size of the antenna. The overall size of the antenna is 8.8 ( ${L}$ ) $\times7.5$ ( ${W}$ ) cm $^{{ {2}}}$ or approximately $0.4\lambda \times 0.5\lambda $ at the first resonant frequency of 1.79 GHz. The results show that the antenna has a fractional bandwidth of approximately 127% from 1.54 to 7 GHz for return loss less than 10 dB with a directional radiation pattern. The average gain of the proposed prototype is 8.5 dBi, and the radiation efficiency is approximately 92% on average over the operating bandwidth. The fidelity factor for face to face is 0.98, and that for side by side is 0.4479, which proves the directionality and lower distortion of the signal. The prototype is successfully simulated, fabricated, and analyzed. The radiating fins of the proposed prototype are optimized to achieve the desired properties for breast phantom measurement. The antenna is used as the transceiver in a breast phantom measurement system to detect unwanted tumor cells inside the breast. An automated electromechanical imaging system with the necessary data post processing makes it an easy and suitable tool for microwave imaging to detect breast tumors.


title: Effects of resistance vs. aerobic training combined with an 800 calorie liquid diet on lean body mass and resting metabolic rate.

abstract: OBJECTIVE ::: Utilization of very-low-calorie diets (VLCD) for weight loss results in loss of lean body weight (LBW) and a decrease in resting metabolic rate (RMR). The addition of aerobic exercise does not prevent this. The purpose of this study was to examine the effect of intensive, high volume resistance training combined with a VLCD on these parameters. ::: ::: ::: METHODS ::: Twenty subjects (17 women, three men), mean age 38 years, were randomly assigned to either standard treatment control plus diet (C+D), n = 10, or resistance exercise plus diet (R+D), n = 10. Both groups consumed 800 kcal/day liquid formula diets for 12 weeks. The C+D group exercised 1 hour four times/week by walking, biking or stair climbing. The R+D group performed resistance training 3 days/week at 10 stations increasing from two sets of 8 to 15 repetitions to four sets of 8 to 15 repetitions by 12 weeks. Groups were similar at baseline with respect to weight, body composition, aerobic capacity, and resting metabolic rate. ::: ::: ::: RESULTS ::: Maximum oxygen consumption (Max VO2) increased significantly (p<0.05) but equally in both groups. Body weight decreased significantly more (p<0.01) in C+D than R+D. The C+D group lost a significant (p<0.05) amount of LBW (51 to 47 kg). No decrease in LBW was observed in R+D. In addition, R+D had an increase (p<0.05) in RMR O2 ml/kg/min (2.6 to 3.1). The 24 hour RMR decreased (p<0.05) in the C+D group. ::: ::: ::: CONCLUSION ::: The addition of an intensive, high volume resistance training program resulted in preservation of LBW and RMR during weight loss with a VLCD.


title: MaaS and GDPR: an overview

abstract: In MaaS (Mobility as a Service), means of transport are virtualized in mobility resources and provided to users using the Internet. From a legal perspective, this model of ITS (Intelligent Transport System) raises several concerns with regard to data protection. This contribution, after a short description of MaaS and an introduction to the issues of data protection in ITS, explores the impact of GDPR (General Data Protection Regulation) in the European Union, detecting possible threats and remedies and suggesting a plausible approach.


title: Anterior Prefrontal Function and the Limits of Human Decision-Making

abstract: The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly limited, which suggests that the FPC is efficient for protecting the execution of long-term mental plans from immediate environmental demands and for generating new, possibly more rewarding, behavioral or cognitive sequences, rather than for complex decision-making and reasoning.


title: Issues in evaluating semantic spaces using word analogies

abstract: The offset method for solving word analogies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method's reliance on cosine similarity conflates offset consistency with largely irrelevant neighborhood structure, and propose simple baselines that should be used to improve the utility of the method in vector space evaluation.


title: Arrow: Generating signatures to detect drive-by downloads

abstract: A drive-by download attack occurs when a user visits a webpage which attempts to automatically download malware without the user's consent. Attackers sometimes use a malware distribution network (MDN) to manage a large number of malicious webpages, exploits, and malware executables. In this paper, we provide a new method to determine these MDNs from the secondary URLs and redirect chains recorded by a high-interaction client honeypot. In addition, we propose a novel drive-by download detection method. Instead of depending on the malicious content used by previous methods, our algorithm first identifies and then leverages the URLs of the MDN's central servers, where a central server is a common server shared by a large percentage of the drive-by download attacks in the same MDN. A set of regular expression-based signatures are then generated based on the URLs of each central server. This method allows additional malicious webpages to be identified which launched but failed to execute a successful drive-by download attack. The new drive-by detection system named ARROW has been implemented, and we provide a large-scale evaluation on the output of a production drive-by detection system. The experimental results demonstrate the effectiveness of our method, where the detection coverage has been boosted by 96% with an extremely low false positive rate.