title: Harmonized authentication based on ThumbStroke dynamics on touch screen mobile phones

abstract: Abstract The pervasive and prevalent use of touch screen mobile phones in both work and daily life has generated more and more private and sensitive information on those devices. Accordingly, there is an ever-increasing need to improve the security of mobile phones. Recent advances in mobile user authentication technologies mainly focus on entry-point authentication. Although post-log-in continuous authentication has attracted increasing attention from researchers, none of the previous studies addressed mobile user authentication at both stages simultaneously. In addition, extant authentication systems are subject to the common trade-off between security and usability. To address the above limitations, we propose Harmonized Authentication based on ThumbStroke dynamics (HATS) that supports both entry-point and post-log-in mobile user authentication. HATS integrates password, gesture, keystroke, and touch dynamics-based authentication methods to address the vulnerabilities of individual methods to certain security attacks. Moreover, HATS supports one-handed thumb stroke-based interaction with touch screen mobile phones to improve the usability of authentication systems. We empirically evaluated HATS through controlled lab experiments. The results provide strong evidence that HATS improved both security and usability of mobile user authentication compared with keystroke dynamics based user authentication.


title: Metrics for Linking Marketing to Financial Performance

abstract: Marketers have been inundated with performance measures that reflect the impact of their marketing expenditures. These measures often reflect impact with the customer ::: or the channel, such as awareness, preference, purchase intent, customer satisfaction and loyalty, share of requirements, shelf space, ACV (all commodity volume), and the like. ::: Unfortunately, these tend to be interim measures and do not reflect the financial impact on the firm. Marketers are being asked to translate marketing performance ::: measures into financial consequences. For example, what is a point of customer satisfaction worth? Pressure is being placed on marketing to justify expenditures and to ::: translate their measures into financial outcomes, which is the language used by the rest of the firm. ::: This paper will explore methods to better link marketing expenditures to financial outcomes. In the process, we discuss both what we know about the linkage between ::: marketing and financial outcomes as well as what remains to be uncovered.


title: Analysis of Ethernet-switch traffic shapers for in-vehicle networking applications

abstract: Switched Ethernet has been proposed as network technology for automotive and industrial applications. IEEE AVB is a collection of standards that specifies (among other elements) a set of network traffic shaping mechanisms (i.e., rules to regulate the traffic flow) to have guaranteed Quality of Service for Audio/Video traffic. However, in-vehicle control applications like advanced driver-assistance systems require much lower latencies than provided by this standard. Within the context of IEEE TSN (Time Sensitive Networking), three new traffic shaping mechanisms are considered, named Burst Limiting, Time Aware and Peristaltic shaper respectively. In this paper we explain and compare these shapers, we examine their worst case end-to-end latencies analytically and we investigate their behavior through a simulation of a particular setup. We show that the shapers hardly satisfy the requirements for 100Mbps Ethernet, but can come close under further restrictions. We also show the impact the shapers have on AVB traffic.


title: Scalable network traffic visualization using compressed graphs

abstract: The visualization of complex network traffic involving a large number of communication devices is a common yet challenging task. Traditional layout methods create the network graph with overwhelming visual clutter, which hinders the network understanding and traffic analysis tasks. The existing graph simplification algorithms (e.g. community-based clustering) can effectively reduce the visual complexity, but lead to less meaningful traffic representations. In this paper, we introduce a new method to the traffic monitoring and anomaly analysis of large networks, namely Structural Equivalence Grouping (SEG). Based on the intrinsic nature of the computer network traffic, SEG condenses the graph by more than 20 times while preserving the critical connectivity information. Computationally, SEG has a linear time complexity and supports undirected, directed and weighted traffic graphs up to a million nodes. We have built a Network Security and Anomaly Visualization (NSAV) tool based on SEG and conducted case studies in several real-world scenarios to show the effectiveness of our technique.


title: Roller: a novel approach to Web information extraction

abstract: The research regarding Web information extraction focuses on learning rules to extract some selected information from Web documents. Many proposals are ad hoc and cannot benefit from the advances in machine learning; furthermore, they are likely to fade away as the Web evolves, and their intrinsic assumptions are not satisfied. Some authors have explored transforming Web documents into relational data and then using techniques that got inspiration from inductive logic programming. In theory, such proposals should be easier to adapt as the Web evolves because they build on catalogues of features that can be adapted without changing the proposals themselves. Unfortunately, they are difficult to scale as the number of documents or features increases. In the general field of machine learning, there are propositio-relational proposals that attempt to provide effective and efficient means to learn from relational data using propositional techniques, but they have seldom been explored regarding Web information extraction. In this article, we present a new proposal called Roller: it relies on a search procedure that uses a dynamic flattening technique to explore the context of the nodes that provide the information to be extracted; it is configured with an open catalogue of features, so that it can adapt to the evolution of the Web; it also requires a base learner and a rule scorer, which helps it benefit from the continuous advances in machine learning. Our experiments confirm that it outperforms other state-of-the-art proposals in terms of effectiveness and that it is very competitive in terms of efficiency; we have also confirmed that our conclusions are solid from a statistical point of view.


title: Radar tomography for the generation of three-dimensional images

abstract: Computer-aided tomography is normally a process by which a 2D cross-sectional image of an object is obtained by illuminating it from many different directions in a plane. For the case of radar imaging, microwave energy reflected by the object is processed to produce an image which maps the object's radar cross-section (RCS) density into the image plane. Each observation provides a 1D projection of the RCS density. The Fourier slice theorem states that the Fourier transform (FT) of each projection is equal to the functional value of the 2D FT of the RCS density along a related projection. By accumulating the FT of many 1D projections, it is possible to accumulate a sample representation of the FT of the RCS density. The image can then be obtained using the backprojection algorithm by taking the inverse FT of the sampled transform function. The authors extend the tomographic technique to the generation of 3D images from 1D range profiles. It is seen that the Fourier slice theorem, the backprojection image generation algorithm, and the backprojected function are useful concepts in the interpretation of 3D imagery. Point spread functions (PSFs) for various radar and observation parameters are illustrated.


Author ' s personal copy EEG applications for sport and performance


title: ABSTRACT Batch and On-line Spam Filter Comparison

abstract: In the TREC 2005 Spam Evaluation Track, a number of popular spam filters – all owing their heritage to Graham’s A Plan for Spam – did quite well. Machine learning techniques reported elsewhere to perform well were hardly represented in the participating filters, and not represented at all in the better results. A non-traditional technique Prediction by Partial Matching (PPM) – performed exceptionally well, at or near the top of every test. Are the TREC results an anomaly? Is PPM really the best method for spam filtering? How are these results to be reconciled with others showing that methods like Support Vector Machines (SVM) are superior? We address these issues by testing implementations of five different classification methods on the TREC public corpus using the online evaluation methodology introduced in TREC. These results are complemented with cross validation experiments, which facilitate a comparison of the methods considered in the study under different evaluation schemes, and also give insight into the nature and utility of the evaluation regimens themselves. For comparison with previously published results, we also conducted cross validation experiments on the Ling-Spam and PU1 datasets. These tests reveal substantial differences attributable to different test assumptions, in particular batch vs. on-line training and testing, the order of classification, and the method of tokenization. Notwithstanding these differences, the methods that perform well at TREC also perform well using established test methods and corpora. Two previously untested methods – one based on Dynamic Markov Compression and one using logistic regression – compare favorably with competing approaches.


title: CodeSum: Translate Program Language to Natural Language

abstract: During software maintenance, programmers spend a lot of time on code comprehension. Reading comments is an effective way for programmers to reduce the reading and navigating time when comprehending source code. Therefore, as a critical task in software engineering, code summarization aims to generate brief natural language descriptions for source code. In this paper, we propose a new code summarization model named CodeSum. CodeSum exploits the attention-based sequence-to-sequence (Seq2Seq) neural network with Structure-based Traversal (SBT) of Abstract Syntax Trees (AST). The AST sequences generated by SBT can better present the structure of ASTs and keep unambiguous. We conduct experiments on three large-scale corpora in different program languages, i.e., Java, C#, and SQL, in which Java corpus is our new proposed industry code extracted from Github. Experimental results show that our method CodeSum outperforms the state-of-the-art significantly.


title: Security Analysis of an Open Car Immobilizer Protocol Stack

abstract: An increasing number of embedded security applications—which traditionally have been heavily reliant on secret and/or proprietary solutions—apply the principle of open evaluation. A recent example is the specification of an open security protocol stack for car immobilizer applications by Atmel, which has been presented at ESCAR 2010. This stack is primarily intended to be used in conjunction with automotive transponder chips of this manufacturer, but could in principle be deployed on any suitable type of transponder chip. In this paper we re-evaluate the security of this protocol stack. We were able to uncover a number of security vulnerabilities. We show that an attacker with a cheap standard reader close to such a car key can track it, lock sections of its EEPROM, and even render its immobilizer functionality completely useless. After eavesdropping on a genuine run of the authentication protocol between the car key and the car, an attacker is enabled to read and write the memory of the car key. Furthermore, we point out the threats of relay attacks and session hijacking, which require slightly more elaborate attack setups. For each of the indicated attacks we propose possible fixes and discuss their overhead.


Variational Bayesian Approach to Movie Rating Prediction


title: Lean manufacturing: context, practice bundles, and performance

abstract: Management literature has suggested that contextual factors may present strong inertial forces within organizations that inhibit implementations that appear technically rational [R.R. Nelson, S.G. Winter, An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA, 1982]. This paper examines the effects of three contextual factors, plant size, plant age and unionization status, on the likelihood of implementing 22 manufacturing practices that are key facets of lean production systems. Further, we postulate four “bundles” of inter-related and internally consistent practices; these are just-in-time (JIT), total quality management (TQM), total preventive maintenance (TPM), and human resource management (HRM). We empirically validate our bundles and investigate their effects on operational performance. The study sample uses data from IndustryWeek’s Census of Manufacturers. The evidence provides strong support for the influence of plant size on lean implementation, whereas the influence of unionization and plant age is less pervasive than conventional wisdom suggests. The results also indicate that lean bundles contribute substantially to the operating performance of plants, and explain about 23% of the variation in operational performance after accounting for the effects of industry and contextual factors. © 2002 Elsevier Science B.V. All rights reserved.


title: Multilayer modeling and analysis of human brain networks

abstract: Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer's or Parkinson's, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain.


title: Relationship between depression and physical activity, disability, burden, and health-related quality of life among patients with arthritis.

abstract: This study purports to examine the relationship of depression with physical activity, disability, arthritis-attributable burden (joint limitation, work limitation, social activity limitation, and joint pain), and health-related quality of life (HRQOL) among arthritis patients. Data from the 2011 Behavioral Risk Factor Surveillance System, a nationally representative sample of noninstitutionalized adults in the United States, was used for the purpose of this study. Multivariable logistic regression was employed to address the study objectives. The final study sample included 167,068 arthritis patients, 45,459 of whom had comorbid depression. Arthritis patients with depression had lower odds of engaging in physical activity (odds ratio [OR]=1.070, confidence interval [CI] 1.006-1.139) and higher odds of being disabled (OR=1.411, CI 1.306-1.524). Arthritis patients with depression also had greater odds of arthritis-attributable joint limitations (OR=1.551, CI 1.460-1.648), work limitations (OR=1.506, CI 1.414-1.604), social activity limitations (OR=1.647, CI 1.557-1.742), and pain (OR=1.438, CI 1.364-1.517) as compared to those without depression. Arthritis patients with versus without depression had greater odds of poor general health status (OR=1.698, CI 1.586-1.819), physical HRQOL (OR=1.592, CI 1.486-1.704), mental HRQOL (OR=6.225, CI 5.768-6.718), and activity limitations (OR=2.345, CI 2.168-2.537). Study results indicate toward a negative functional impact of depression among arthritis patients. Policy makers should consider incorporating screening and management of depression into routine clinical care of arthritis patients.


title: Practice-Based Interdisciplinary Approach and Environmental Research

abstract: Interdisciplinary researchers and educators, as community members, creators of knowledge, and environmental activists and practitioners, have a responsibility to build a bridge between community practice, academic scholarship, and professional contributions aimed at establishing environmental sustainability. In this paper, I focus on an undervalued area of environmental politics, practices, and often unarticulated assumptions which underlie human–environmental relations. This article challenges interdisciplinary studies that are not connected with practice by reconfiguring the meaning of a community-based, interdisciplinary approach. Drawing from works by Foucault, Latour, and Haraway, this paper first shows how to reconfigure the meaning of an interdisciplinary approach. Second, using Bourdieu and Brightman’s ethnographic studies as a framework, the paper situates practice as central to our efforts to deconstruct and replace current interdisciplinary initiatives with a practice-based approach. Through a practice-based interdisciplinary approach (PIA), environmental educators and researchers gain an awareness of and learn to make an investment in sustainable communities. As teams of environmental researchers practising in the local community, they are meaningfully involved with the community, with each other, and with the environment.


title: Embodiment as a unifying perspective for psychology

abstract: A basic claim of the embodiment framework is that all psychological processes are influenced by body morphology, sensory systems, motor systems, and emotions. As such, the framework holds the promise of providing a unifying perspective for psychological research. This article begins with a sketch of several arguments, from evolution to philosophy, as to why the embodiment framework is a good bet. These arguments are followed by a review of approaches to embodiment, including those from cognitive linguistics, perceptual symbol theory, and action-based theories. Finally, examples are provided for how a unifying perspective might work for cognition (including language and memory), cognitive and social development, social psychology, neuroscience, clinical psychology, and psychology applied to education. Copyright © 2010 John Wiley & Sons, Ltd. ::: ::: For further resources related to this article, please visit the WIREs website


title: JSME: a free molecule editor in JavaScript

abstract: A molecule editor, i.e. a program facilitating graphical input and interactive editing of molecules, is an indispensable part of every cheminformatics or molecular processing system. Today, when a web browser has become the universal scientific user interface, a tool to edit molecules directly within the web browser is essential. One of the most popular tools for molecular structure input on the web is the JME applet. Since its release nearly 15 years ago, however the web environment has changed and Java applets are facing increasing implementation hurdles due to their maintenance and support requirements, as well as security issues. This prompted us to update the JME editor and port it to a modern Internet programming language - JavaScript. The actual molecule editing Java code of the JME editor was translated into JavaScript with help of the Google Web Toolkit compiler and a custom library that emulates a subset of the GUI features of the Java runtime environment. In this process, the editor was enhanced by additional functionalities including a substituent menu, copy/paste, drag and drop and undo/redo capabilities and an integrated help. In addition to desktop computers, the editor supports molecule editing on touch devices, including iPhone, iPad and Android phones and tablets. In analogy to JME the new editor is named JSME. This new molecule editor is compact, easy to use and easy to incorporate into web pages. A free molecule editor written in JavaScript was developed and is released under the terms of permissive BSD license. The editor is compatible with JME, has practically the same user interface as well as the web application programming interface. The JSME editor is available for download from the project web page http://peter-ertl.com/jsme/


title: Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches

abstract: Abstract A correct diagnosis of tuberculosis (TB) can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining(DM) solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients without waiting the exact medical test results or not. In this research, we proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. 667 different patient records which are obtained from a clinic are used in the entire process of this research. Each of the patient records consist of 30 separate input parameters. ANFIS model is generated by using 500 of those records. We also implemented a multilayer perceptron and PART model using the same data set. The ANFIS model classifies the instances with an RMSE of 18% whereas Multilayer Perceptron does the same classification with an RMSE of % 19 and PART algorithm with an RMSE of % 20. ANFIS is an accurate and reliable method when compared with Multilayer Perceptron and PART algorithms for classification of tuberculosis patients. This study has contribution on forecasting patients before the medical tests.


title: Hominin interbreeding and the evolution of human variation

abstract: Mitochondrial Eve confirms the “out of Africa” theory, but the evidence also supports interbreeding between Homo sapiens and other hominins: Neanderthals, Denisovans, and Homo heidelbergensis. This article explains how interbreeding between early H. sapiens and archaic hominins occurred. The availability of edible insects in East Asia aided the spread of the unaggressive, highly cooperative Neanderthals, who interbred with H. sapiens in Asia, resulting in a higher admixture of Neanderthal DNA in East Asian populations. Geographical variation in degree of interbreeding between H. sapiens and Neanderthals likely contributed to neurological and behavioral differences in modern humans. Similarly, people with Denisovan genetic admixture were better able to dwell in mountainous regions, allowing their genetic legacy to cross the Himalayas and persist in Southeast Asian and Oceanian H. sapiens. In the Sub-Saharan region, unaffected by Denisovan or Neanderthal interbreeding, H. sapiens interbred with H. heidelbergensis, because high humidity militated against fire-making and allowed the survival of these non-fire-making hominins.


title: Photoredox-Catalyzed Hydrodifluoroalkylation of Alkenes Using Difluorohaloalkyl Compounds and a Hantzsch Ester

abstract: Photoredox-catalyzed hydrodifluoroalkylation of alkenes proceeded smoothly in the presence of a Hantzsch ester as a hydrogen source under visible light irradiation. The reaction was also applicable to the hydrodifluoroalkylation of alkynes, and a continuous photo flow reaction was also successful.


title: The Disease of Addiction: Origins, Treatment, and Recovery

abstract: ddiction can be defined as the continued use of mood-altering addicting ubstances or behaviors (e.g., gambling, compulsive sexual behaviors) espite adverse consequences. We have learned that alcoholism is a rimary, chronic disease with genetic, psychosocial, and environmental actors influencing its development and manifestations. It is characterized y continuous or periodic impaired control over drinking, preoccupation ith the alcohol, use of alcohol despite adverse consequences, and istortions in thinking, most notably denial. This is a definition forarded in JAMA in 1992, and includes the thinking of the American ociety of Addiction Medicine and the National Council on Alcoholism nd Drug Dependencies. Since that time, continued exploration of the ature of addiction includes other mood-altering substances aside from lcohol, as well as a number of highly reinforcing behaviors. The common pathways in reward circuitry that affect memory and earning, motivation, control, and decision making are also involved in he addictive process. With the more global understanding of addiction ome more treatment strategies, such as meditation and mindfulness raining, psychosocial interventions, and pharmacologic approaches. Inerestingly, our growing understanding of addiction as a disease has not iminished the value of the spiritually driven approaches, such as 2-step-oriented treatments, that are outlined in this article. An undertanding of the disease of chemical dependency, and the multiple pproaches to treatment, can assist the primary care physician (PCP) in he treatment of addiction. The PCP is an essential member of the xtended treatment team and at the forefront of patient care. Substance abuse negatively impacts public safety, reduces workers’ roductivity, and contributes to higher healthcare costs, premature deaths, nd disability for millions of Americans. Despite this massive health


title: Constructing an Interaction Behavior Model for Web Image Search

abstract: User interaction behavior is a valuable source of implicit relevance feedback. In Web image search a different type of search result presentation is used than in general Web search, which leads to different interaction mechanisms and user behavior. For example, image search results are self-contained, so that users do not need to click the results to view the landing page as in general Web search, which generates sparse click data. Also, two-dimensional result placement instead of a linear result list makes browsing behaviors more complex. Thus, it is hard to apply standard user behavior models (e.g., click models) developed for general Web search to Web image search. In this paper, we conduct a comprehensive image search user behavior analysis using data from a lab-based user study as well as data from a commercial search log. We then propose a novel interaction behavior model, called grid-based user browsing model (GUBM), whose design is motivated by observations from our data analysis. GUBM can both capture users' interaction behavior, including cursor hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1) It is based on an unsupervised learning method and does not need manually annotated data for training. (2) It is based on user interaction features on search engine result pages (SERPs) and is easily transferable to other scenarios that have a grid-based interface such as video search engines. We conduct extensive experiments to test the performance of our model using a large-scale commercial image search log. Experimental results show that in terms of behavior prediction (perplexity), and topical relevance and image quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms state-of-the-art baseline models as well as the original ranking. We make the implementation of GUBM and related datasets publicly available for future studies.


title: Software Engineering in Industrial Automation: State-of-the-Art Review

abstract: This paper presents one perspective on recent developments related to software engineering in the industrial automation sector that spans from manufacturing factory automation to process control systems and energy automation systems. The survey's methodology is based on the classic SWEBOK reference document that comprehensively defines the taxonomy of software engineering domain. This is mixed with classic automation artefacts, such as the set of the most influential international standards and dominating industrial practices. The survey focuses mainly on research publications which are believed to be representative of advanced industrial practices as well.


title: Automatically classifying database workloads

abstract: The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS, and then use the model to identify any change in the workload type. We construct a workload classifier from the Browsing and Ordering profiles of the TPC-W benchmark. Experiments with an industry-supplied workload show that our classifier accurately identifies the mix of OLTP and DSS work within an application workload.


title: Pink frilly dresses and the avoidance of all things "girly": children's appearance rigidity and cognitive theories of gender development

abstract: Many young children pass through a stage of gender appearance rigidity; girls insist on wearing dresses, often pink and frilly, whereas boys refuse to wear anything with a hint of femininity. In 2 studies, we investigated the prevalence of this apparent hallmark of early gender development and its relation to children’s growing identification with a gender category. Study 1a examined the prevalence of this behavior and whether it would exhibit a developmental pattern of rigidity followed by flexibility, consistent with past research on identity-related cognitions. Interviews with 76 White, middle-class parents and their 3- to 6-year-old children revealed that about two thirds of parents of 3- and 4-year-old girls and almost half (44%) of parents of 5- and 6-year-old boys reported that their children had exhibited a period of rigidity in their gender-related appearance behavior. Appearance rigidity was not related to parents’ preferences for their children’s gender-typed clothing. Study 1b examined whether cognitive theories of identity development could shed light on gender appearance rigidity. The more important and positive children considered their gender and the more children understood that gender categories remain stable over time (gender stability), the more likely children were to wear gender-typed outfits. In Study 2, we extended this research to a more diverse population and found that gender appearance rigidity was also prevalent in 267 4-year-old children in the United States from African American, Chinese, Dominican, and Mexican immigrant low-income backgrounds. Results suggest that rigid gender-related appearance behavior can be seen among young children from different backgrounds and might reflect early developing cognitions about gender identity.


title: 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients

abstract: High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50 % patients in 1–2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features, we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9 % We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time, which provides valuable insights into functional neuro-oncological applications.


title: Analysis and Design of Current-Fed Half-Bridge (C)(LC)–( LC) Resonant Topology for Inductive Wireless Power Transfer Application

abstract: This paper proposes and analyzes a new power electronics circuit topology for wireless inductive power transfer application using current-fed half-bridge converter with CCL-LC resonant network. The major focus is analysis and implementation of a new current-fed resonant topology with current-sharing and voltage doubling features. Generally, inductive power transfer circuits with current fed converter use parallel CL resonant tank to transfer power effectively through air gap. However, in medium power application, this topology suffers from a major limitation of high voltage stress across the inverter semiconductor devices owing to high reactive power consumed by loosely coupled coil. In proposed topology, this is mitigated by adding a capacitor in series with the coil developing series-parallel CCL tank. The power flow is controlled through variable frequency modulation. Soft-switching of the devices is obtained irrespective of the load current. For grid-to-vehicle or solar-to-vehicle, the converter is analyzed and detailed design procedure is illustrated. Experimental results are presented to verify the analysis and demonstrate the performance.


title: The hybrid nested/hierarchical dirichlet process and its application to topic modeling with word differentiation

abstract: The hierarchical Dirichlet process (HDP) is a powerful nonparametric Bayesian approach to modeling groups of data which allows the mixture components in each group to be shared. However, in many cases the groups themselves are also in latent groups (categories) which may impact the modeling a lot. In order to utilize the unknown category information of grouped data, we present the hybrid nested/ hierarchical Dirichlet process (hNHDP), a prior that blends the desirable aspects of both the HDP and the nested Dirichlet Process (NDP). Specifically, we introduce a clustering structure for the groups. The prior distribution for each cluster is a realization of a Dirichlet process. Moreover, the set of cluster-specific distributions can share part of atoms between groups, and the shared atoms and specific atoms are generated separately. We apply the hNHDP to document modeling and bring in a mechanism to identify discriminative words and topics. We derive an efficient Markov chain Monte Carlo scheme for posterior inference and present experiments on document modeling.


Wearable sensors/systems and their impact on biomedical engineering


title: A Simple Named Entity Extractor Using AdaBoost

abstract: This paper presents a Named Entity Extraction (NEE) system for the CoNLL-2003 shared task competition. As in the past year edition (Carreras et al., 2002a), we have approached the task by treating the two main sub–tasks of the problem, recognition (NER) and classification (NEC), sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary and multiclass AdaBoost classifiers. Named Entity recognition is performed as a greedy sequence tagging procedure under the well–known BIO labelling scheme. This tagging process makes use of three binary classifiers trained to be experts on the recognition of B, I, and O labels, respectively. Named Entity classification is viewed as a 4–class classification problem (with LOC, PER, ORG, and MISC class labels), which is straightforwardly addressed by the use of a multiclass learning algorithm. The system presented here consists of a replication, with some minor changes, of the system that obtained the best results in the CoNLL-2002 NEE task. Therefore, it can be considered as a benchmark of the state–of–the– art technology for the current edition, and will allow also to make comparisons about the training corpora of both editions.


title: Measuring tie-strength in virtual social networks

abstract: Tie-strength has been in the focus of social science research for decades, yet the use of measurement tools or scales has been relatively scarce. The aim of this study was to fill the gap and provide a tool that is able to provide a quantitative and continuous measure of tie strength in social networks. The focus was on virtual communities because the fast expansion of Internet use and the constant growth of on-line communities provide today’s researchers with an excellent opportunity for effective and speedy data collection regarding tie-strength measures in these virtual social groups. The Virtual Tie-Strength Scale (VTS-Scale) consist of 11 questions and it was developed on a sample of 56 people (3080 asymmetric ties) and tested for reliability of smaller sample of 16 (204 asymmetric ties) independent sample participation regularly on a Hungarian discussion board like forum. Reliability coefficients were reassuringly high for both samples, Cronbach alphas of 0.92 and 0.86, respectively. Data triangulation offered evidence for scale validity. In summary, the VTS-Scale and its scoring method seem to provide a valid and reliable measure of tie strength in virtual communities. Although the aim of the research was to develop a tool that measures tie-strength in virtual communities, the tool can be easily modified for off-line social groups. The VTS-Scale is also capable of distinguishing between two components of tie-strength: acquaintances and friendship. However, the content of each component needs further investigation.


title: Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks

abstract: A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One approach to such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable of establishing a common focus of attention and be able to use and integrate spoken instructions, visual perception, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and a modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.


title: Natural Language Metaphors Covertly Influence Reasoning

abstract: Metaphors pervade discussions of social issues like climate change, the economy, and crime. We ask how natural language metaphors shape the way people reason about such social issues. In previous work, we showed that describing crime metaphorically as a beast or a virus, led people to generate different solutions to a city's crime problem. In the current series of studies, instead of asking people to generate a solution on their own, we provided them with a selection of possible solutions and asked them to choose the best ones. We found that metaphors influenced people's reasoning even when they had a set of options available to compare and select among. These findings suggest that metaphors can influence not just what solution comes to mind first, but also which solution people think is best, even when given the opportunity to explicitly compare alternatives. Further, we tested whether participants were aware of the metaphor. We found that very few participants thought the metaphor played an important part in their decision. Further, participants who had no explicit memory of the metaphor were just as much affected by the metaphor as participants who were able to remember the metaphorical frame. These findings suggest that metaphors can act covertly in reasoning. Finally, we examined the role of political affiliation on reasoning about crime. The results confirm our previous findings that Republicans are more likely to generate enforcement and punishment solutions for dealing with crime, and are less swayed by metaphor than are Democrats or Independents.


title: Fundamentals of scatter search and path relinking

abstract: Abstract —The evolutionary approach called Scatter Search, and its generalized form called Path Relinking, have proved unusually effective for solving a diverse array of optimization problems from both classical and real world settings. Scatter Search and Path Relinking differ from other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions (in both Euclidean and neighborhood spaces) and by utilizing strategic designs where other approaches resort to randomization. Scatter Search and Path Relinking are also intimately related to the Tabu Search metaheuristic, and derive additional advantages by making use of adaptive memory and associated memory-exploiting mechanisms that are capable of being adapted to particular contexts. We describe the features of Scatter Search and Path Relinking that set them apart from other evolutionary approaches, and that offer opportunities for creating increasingly more versatile and effective methods in the future.


title: Accelerating Convolutional Neural Networks for Mobile Applications

abstract: Convolutional neural networks (CNNs) have achieved remarkable performance in a wide range of computer vision tasks, typically at the cost of massive computational complexity. The low speed of these networks may hinder real-time applications especially when computational resources are limited. In this paper, an efficient and effective approach is proposed to accelerate the test-phase computation of CNNs based on low-rank and group sparse tensor decomposition. Specifically, for each convolutional layer, the kernel tensor is decomposed into the sum of a small number of low multilinear rank tensors. Then we replace the original kernel tensors in all layers with the approximate tensors and fine-tune the whole net with respect to the final classification task using standard backpropagation. \\ Comprehensive experiments on ILSVRC-12 demonstrate significant reduction in computational complexity, at the cost of negligible loss in accuracy. For the widely used VGG-16 model, our approach obtains a 6.6$\times$ speed-up on PC and 5.91$\times$ speed-up on mobile device of the whole network with less than 1\% increase on top-5 error.


title: Fuzzy Clustering Algorithms — Review of the Applications

abstract: Fuzzy clustering is an alternative method to conventional or hard clustering algorithms, which makes partitions of data containing similar subjects. The tendency of adopting machine learning, big data science, cloud computation in various industries depends on unsupervised learning on data structures to tell the story about consumers' behavior, fraud detection, and market segmentation. Fuzzy clustering contrasts with hard clustering by its nonlinear nature and discipline of flexibility in grouping massive data. It provides more accurate and close-to-nature solutions for partitions and herein implies more possibility of solutions for decision-making. In the specific matter of computation, fuzzy clustering has its roots in fuzzy logic and indicates the likelihood or degrees of one data point belonging to more than one group. This paper focuses on the study of models of fuzzy clustering in various cases. Uniquely designed algorithms enhance the accuracy of outcomes and are worth studying to assist future work. In some case scenarios, modeling processes are data-driven and place emphasis on the distances between points and new centers of clusters. In some other cases, which aim at market segmentation or evaluation of patients by healthcare records, membership degree is a key element in the algorithm. This paper surveys a wide-range of research that has well-designed mathematic models for fuzzy clustering, some of which include genetic algorithms and neural networks. The last section introduces open sources of Python and displays sample results from hands-on practice with these packages.


title: A New Gradient Based Character Segmentation Method for Video Text Recognition

abstract: The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute gradient feature matrix of the video text line image. Union of the text cluster with the output of Canny operation of the input video text line is proposed to restore missing text candidates. Then a run length algorithm is applied on the text candidate image for identifying word gaps. We propose a new idea for segmenting characters from the restored word image based on the fact that the text height difference at the character boundary column is smaller than that of the other columns of the word image. We have conducted experiments on a large dataset at two levels (word and character level) in terms of recall, precision and f-measure. Our experimental setup involves 3527 characters of English and Chinese, and this dataset is selected from TRECVID database of 2005 and 2006.


title: Deep Learning for the Classification of Lung Nodules

abstract: Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.


Fingerprint Recognition using MatLab


title: A quaternion-based unscented Kalman filter for orientation tracking

abstract: This paper describes a Kalman filter for the real-time estimation of a rigid body orientation from mea- surements of acceleration, angular velocity and magnetic field strength. A quaternion representation of the orienta- tion is computationally effective and avoids problems with singularities. The nonlinear relationship between estimated Orientation and expected measurement prevent the usage of a classical Kalman filter: This problem is solved by an Un- scented Kalman filter which allows nonlinear process and measurement models and is more accurate and less costly than the common Extended Kalman filter: Several exten- sions to the original Unscented Kalman filter are necessary to treat the inherent properties of unit quaternions. Results with simulated and measured data are discussed.


title: Test data generation for programming exercises with symbolic execution in Java PathFinder

abstract: Automatic assessment of programming exercises is typically based on testing approach. Most automatic assessment frameworks execute tests and evaluate test results automatically, but the test data generation is not automated. No matter that such test data generation techniques and tools are available. We have researched how the Java PathFinder software model checker can be adopted to the specific needs of test data generation in automatic assessment. Practical problems considered are: How to derive test data directly from students' programs (i.e. without annotation) and how to visualize and how to abstract test data automatically for students? Interesting outcomes of our research are that with minor refinements generalized symbolic execution with lazy initialization (a test data generation algorithm implemented in PathFinder) can be used to construct test data directly from students' programs without annotation, and that intermediate results of the same algorithm can be used to provide novel visualizations of the test data.


title: Non-bianisotropic split ring resonator based CPW-fed dual band antenna

abstract: A compact coplanar waveguide (CPW) fed dual band antenna is designed by metamaterial inspired Split ring resonator. The radiating element Hexagonal Closed Ring Resonator (Hex-CRR) and a Non Bianisotropic split-ring resonator (NB-SRR) are connected together. The overall antenna dimension is 31.7 × 25 × 1.6 mm3. The prototype antenna covers bandwidth of 36 MHz (2.677-2.713 GHz) and 2057 MHz (5.513-7.57 GHz) with center frequency of 2.7 GHz and 6 GHz, respectively. In order to improve the impedance matching of the antenna, CPW-fed line with tapered impedance transformer line and sunk in the ground plane are introduced. Hex-CRR is used to create a lower frequency band and NB-SRR offers higher frequency band. To miniaturize the size of the antenna, inner NB-SRR dimensions are taken as smaller than the resonant wavelength. The prototype antenna has consistent Omnidirectional (H-Plane) radiation pattern, and dipole (E-Plane) radiation pattern of both resonant frequencies.


title: A hybrid and learning agent architecture for network intrusion detection

abstract: A hybrid learning software agent for network intrusion detection is proposed.The agent combines a reactive and a case-based reasoning performance components.A learning component allows to improve the performance by learning reactive rules.The performance and effectiveness of the agent were evaluated.Evaluation results show that the agent is well-suited for dynamic environments. Learning is an effective way for automating the adaptation of systems to their environment. This ability is especially relevant in dynamic environments as computer networks where new intrusions are constantly emerging, most of them having similarities and occurring frequently. Traditional intrusion detection systems still have limitations of adaptability because they are just able to detect intrusions previously set in system design. This paper proposes HyLAA a software agent architecture that combines case-based reasoning, reactive behavior and learning. Through its learning mechanism, HyLAA can adapt itself to its environment and identify new intrusions not previously specified in system design. This is done by learning new reactive rules by observing recurrent good solutions to the same perception from the case-based reasoning system, which will be stored in the agent knowledge base. The effectiveness of HyLAA to detect intrusions using case-based reasoning behavior, the accuracy of the classifier learned by the learning component and both the performance and effectiveness of HyLAA to detect intrusions using hybrid behavior with learning and without learning were evaluated, respectively, by conducting four experiments. In the first experiment, HyLAA exhibited good effectiveness to detect intrusions. In the second experiment the classifiers learned by the learning component presented high accuracy. Both the hybrid agent behavior with learning and without learning (third and fourth experiment, respectively) presented greater effectiveness and a balance between performance and effectiveness, but only the hybrid behavior showed better effectiveness and performance as long as the agent learns.


title: Controlling Racial Prejudice Social-Cognitive Goals Affect Amygdala and Stereotype Activation

abstract: The malleability of stereotyping matters in social psychology and in society. Previous work indicates rapid amygdala and cognitive responses to racial out-groups, leading some researchers to view these responses as inevitable. In this study, the methods of social-cognitive neuroscience were used to investigate how social goals control prejudiced responses. Participants viewed photographs of unfamiliar Black and White faces, under each of three social goals: social categorization (by age), social individuation (vegetable preference), and simple visual inspection (detecting a dot). One study recorded brain activity in the amygdala using functional magnetic resonance imaging, and another measured cognitive activation of stereotypes by lexical priming. Neither response to photos of the racial out-group was inevitable; instead, both responses depended on perceivers' current social-cognitive goal.


title: Computer Self-Efficacy, Computer Anxiety, and Attitudes toward the Internet: A Study among Undergraduates in Unimas

abstract: Eighty-one female and sixty-seven male undergraduates at a Malaysian university, from seven faculties and a Center for Language Studies completed a Computer Self-Efficacy Scale, Computer Anxiety Scale, and an Attitudes toward the Internet Scale and give information about their use of the Internet. This survey research investigated undergraduates’ computer anxiety, computer self-efficacy, and reported use of and attitudes toward the Internet. This study also examined differences in computer anxiety, computer selfefficacy, attitudes toward the Internet and reported use of the Internet for undergraduates with different demographic variables. The findings suggest that the undergraduates had moderate computer anxiousness, medium attitudes toward the Internet, and high computer self-efficacy and used the Internet extensively for educational purposes such as doing research, downloading electronic resources and e-mail communications. This study challenges the long perceived male bias in the computer environment and supports recent studies that have identified greater gender equivalence in interest, use, and skills levels. However, there were differences in undergraduates’ Internet usage levels based on the discipline of study. Furthermore, higher levels of Internet usage did not necessarily translate into better computer self-efficacy among the undergraduates. A more important factor in determining computer self-efficacy could be the discipline of study and undergraduates studying computer related disciplines appeared to have higher self-efficacy towards computers and the Internet. Undergraduates who used the Internet more often may not necessarily feel more comfortable using them. Possibly, other factors such as the types of application used, the purpose for using, and individual satisfaction could also influence computer self-efficacy and computer anxiety. However, although Internet usage levels may not have any impact on computer self-efficacy, higher usage of the Internet does seem to decrease the levels of computer anxiety among the undergraduates. Undergraduates with lower computer anxiousness demonstrated more positive attitudes toward the Internet in this study.


title: Defected ground structure for bandwidth improvement of artificial magnetic conductor-based microwave absorber

abstract: In this paper, the bandwidth improvement technique applied for microwave absorber is numerically investigated. The proposed technique is carried out by defecting or making some structure on the ground surface of microwave absorber, thus it is known as defected ground structure (DGS). The absorber that is constructed based on artificial magnetic conductor (AMC) concept has narrow bandwidth due to the characteristic of AMC structure. There are 3 kinds of DGS pattern utilized in the proposed technique. Each pattern will be investigated and analyzed through the unit cell of AMC structure. From the result, it shows that the bandwidth of microwave absorber with DGS pattern and PEC boundary condition is improved up to 6% compared to the bandwidth of microwave absorber without DGS. The utilization of DGS pattern is also lowering the resonance frequency of microwave absorber from 3.62GHz to 2.98GHz for PEC boundary condition. In addition, the incorporation of external resistors with particular resistance value put between adjacent patches in parallel to the incoming wave has shown the significant contribution to increase the absorption rate of microwave absorber.


title: Transformation-Based Models of Video Sequences

abstract: In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the transformations needed for generating the next frame in a sequence, given the transformations of the past frames. This leads to sharper results, while using a smaller prediction model. ::: ::: In order to enable a fair comparison between different video frame prediction models, we also propose a new evaluation protocol. We use generated frames as input to a classifier trained with ground truth sequences. This criterion guarantees that models scoring high are those producing sequences which preserve discrim- inative features, as opposed to merely penalizing any deviation, plausible or not, from the ground truth. Our proposed approach compares favourably against more sophisticated ones on the UCF-101 data set, while also being more efficient in terms of the number of parameters and computational cost.


title: Weighted Low-Rank Approximations

abstract: We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted low-rank approximation problems, which, unlike their unweighted version, do not admit a closed-form solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstrate the utility of accommodating the weights in reconstructing the underlying low-rank representation, and extend the formulation to non-Gaussian noise models such as logistic models. Finally, we apply the methods developed to a collaborative filtering task.


title: Predicting safe employee behavior in the steel industry: Development and test of a sociotechnical model

abstract: Abstract Industrial safety is an important issue for operations managers — it has implications for cost, delivery, quality, and social responsibility. Minor accidents can interfere with production in a variety of ways, and a serious accident can shut down an entire operation. In this context, questions about the causes of workplace accidents are highly relevant. There is a popular notion that employees' unsafe acts are the primary causes of workplace accidents, but a number of authors suggest a perspective that highlights influences from operating and social systems. The study described herein addresses this subject by assessing steelworkers' responses to a survey about social, technical, and personal factors related to safe work behaviors. Results provide evidence that a chain reaction of technical and social constructs operate through employees to influence safe behaviors. These results demonstrate that safety hazards, safety culture, and production pressures can influence safety efficacy and cavalier attitudes, on a path leading to safe or unsafe work behaviors. Based on these results, we conclude with prescriptions for operations managers and others who play roles in the causal sequence.


title: Generative Temporal Models with Spatial Memory for Partially Observed Environments

abstract: In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.


title: Privacy-Preserving Machine Learning: Threats and Solutions

abstract: For privacy concerns to be addressed adequately in current machine learning systems, the knowledge gap between the machine learning and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.


title: A Systematic Review of Transcendent States Across Meditation and Contemplative Traditions

abstract: Background Across cultures and throughout history, transcendent states achieved through meditative practices have been reported. The practices to attain transcendent states vary from transcendental meditation to yoga to contemplative prayer, to other various forms of sitting meditation. While these transcendent states are ascribed many different terms, those who experience them describe a similar unitive, ineffable state of consciousness. Despite the common description, few studies have systematically examined transcendent states during meditation. Objectives The objectives of this systematic review were to: 1) characterize studies evaluating transcendent states associated with meditation in any tradition; 2) qualitatively describe physiological and phenomenological outcomes collected during transcendent states and; 3) evaluate the quality of these studies using the Quality Assessment Tool. Methods Medline, PsycINFO, CINAHL, AltHealthWatch, AMED, and the Institute of Noetic Science Meditation Library were searched for relevant papers in any language. Included studies required adult participants and the collection of outcomes before, during, or after a reported transcendent state associated with meditation. Results Twenty-five studies with a total of 672 combined participants were included in the final review. Participants were mostly male (61%; average age 39 ± 11 years) with 12.7 ± 6.6 (median 12.6; range 2–40) average years of meditation practice. A variety of meditation traditions were represented: (Buddhist; Christian; Mixed (practitioners from multiple traditions); Vedic: Transcendental Meditation and Yoga). The mean quality score was 67 ± 13 (100 highest score possible). Subjective phenomenology and the objective outcomes of electroencephalography (EEG), electrocardiography, electromyography, electrooculogram, event-related potentials, functional magnetic resonance imaging, magnetoencephalography, respiration, and skin conductance and response were measured. Transcendent states were most consistently associated with slowed breathing, respiratory suspension, reduced muscle activity and EEG alpha blocking with external stimuli, and increased EEG alpha power, EEG coherence, and functional neural connectivity. The transcendent state is described as being in a state of relaxed wakefulness in a phenomenologically different space-time. Heterogeneity between studies precluded any formal meta-analysis and thus, conclusions about outcomes are qualitative and preliminary. Conclusions Future research is warranted into transcendent states during meditation using more refined phenomenological tools and consistent methods and outcome evaluation.


title: Mindfulness-based cognitive therapy for generalized anxiety disorder

abstract: Generalized anxiety disorder (GAD) is a chronic condition characterized by intense and excessive worry and is associated with high rates of comorbidity. Cognitive behavioral therapy (CBT) has been found to be efficacious for GAD. Nonetheless, GAD is the least successfully treated of the anxiety disorders, and there are often residuals symptoms in patients who are successfully treated. Mindfulness-based therapies may provide an alternative and/or adjunctive approach to CBT. This chapter discusses CBT and mindfulness for GAD as well as the recent literature on the neurobiology of GAD. A conceptual framework for a mindfulness-based approach to GAD is developed. Specific case examples are provided to illustrate key concepts. The author proposes an 8-week protocol specific to GAD based on mindfulness-based cognitive therapy (MBCT) developed by Segal et al. (Mindfulness-based cognitive therapy for depression: a new approach to preventing relapse. Guilford, 2002). Challenges implementing MBCT are reviewed as well as recommendations for professionals considering training in MBCT.


title: An efficient flow-based botnet detection using supervised machine learning

abstract: Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs to be observed per flow in order to capture the patterns of malicious traffic. The proposed system has been tested through the series of experiments using traffic traces originating from two well-known P2P botnets and diverse non-malicious applications. The results of experiments indicate that the system is able to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates a strong potential of using the proposed approach within a future on-line detection framework.


title: Weekly Calendar Planning Activity (WCPA): a performance-based assessment of executive function piloted with at-risk adolescents.

abstract: OBJECTIVE. We piloted the Weekly Calendar Planning Activity (WCPA), a performance-based measure of executive function (EF), to establish a baseline for at-risk adolescents. ::: ::: METHOD. Participants were 113 youths ages 16–21 who were enrolled at a charter school for youth returning to high school after dropping out. We administered the WCPA and collected demographic information. ::: ::: RESULTS. On average, participants spent 15.9 min on the WCPA, made 7.9 errors, and followed 4.0 of 5 possible rules. No ceiling effect was observed in overall accuracy. Participants used a mean of 3.1 strategies (standard deviation = 1.9) while completing the WCPA. Participants who used more strategies spent more time planning and completing the task and were more accurate. ::: ::: CONCLUSION. The WCPA may be useful to occupational therapists as a performance measure of EF. This assessment allows evaluation of complex task performance, strategy use, self-evaluation of performance, and error patterns, which can be used in developing intervention strategies


title: Web-based Demonstration of Semantic Similarity Detection Using Citation Pattern Visualization for a Cross Language Plagiarism Case

abstract: In a previous paper, we showed that analyzing citation patterns in the well-known plagiarized thesis by K. T. zu Guttenberg clearly outperformed current detection methods in identifying cross-language plagiarism. However, the experiment was a proof of concept and we did not provide a prototype. This paper presents a fully functional, web-based visualization of citation patterns for this verified cross-language plagiarism case, allowing the user to interactively experience the benefits of citation pattern analysis for plagiarism detection. Using examples from the Guttenberg plagiarism case, we demonstrate that the citation pattern visualization reduces the required examiner effort to verify the extent of plagiarism.


title: Matlab/Pspice hybrid simulation modeling of solar PV cell/module

abstract: The power generated by solar photovoltaic (PV) module depends on surrounding irradiance, temperature and shading conditions. Under partial shading conditions (PSC) the power from the PV module can be dramatically reduced and maximum power point tracking (MPPT) control will be affected. This paper presents a hybrid simulation model of PV cell/module and system using Matlab®/Simulink® and Pspice®. The hybrid simulation model includes the solar PV cells and the converter power stage and can be expanded to add MPPT control and other functions. The model is able to simulate both the I-V characteristics curves and the P-V characteristics curves of PV modules under uniform shading conditions (USC) and PSC. The model is used to study different parameters variations effects on the PV array. The developed model is suitable to simulate several homogeneous or/and heterogeneous PV cells or PV panels connected in series and/or in parallel.


title: A dynamic inventory model with supplier selection in a serial supply chain structure

abstract: Considering the inherent connection between supplier selection and inventory management in supply chain networks, this article presents a multi-period inventory lot-sizing model for a single product in a serial supply chain, where raw materials are purchased from multiple suppliers at the first stage and external demand occurs at the last stage. The demand is known and may change from period to period. The stages of this production–distribution serial structure correspond to inventory locations. The first two stages stand for storage areas for raw materials and finished products in a manufacturing facility, and the remaining stages symbolize distribution centers or warehouses that take the product closer to customers. The problem is modeled as a time-expanded transshipment network, which is defined by the nodes and arcs that can be reached by feasible material flows. A mixed integer nonlinear programming model is developed to determine an optimal inventory policy that coordinates the transfer of materials between consecutive stages of the supply chain from period to period while properly placing purchasing orders to selected suppliers and satisfying customer demand on time. The proposed model minimizes the total variable cost, including purchasing, production, inventory, and transportation costs. The model can be linearized for certain types of cost structures. In addition, two continuous and concave approximations of the transportation cost function are provided to simplify the model and reduce its computational time.


title: Local Item-Item Models For Top-N Recommendation

abstract: Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way -- instead there exist subsets of like-minded users. By using different item-item models for these user subsets, we can capture differences in their preferences and this can lead to improved performance for top-N recommendations. In this work, we extend SLIM by combining global and local SLIM models. We present a method that computes the prediction scores as a user-specific combination of the predictions derived by a global and local item-item models. We present an approach in which the global model, the local models, their user-specific combination, and the assignment of users to the local models are jointly optimized to improve the top-N recommendation performance. Our experiments show that the proposed method improves upon the standard SLIM model and outperforms competing top-N recommendation approaches.


title: A Max-Min Allocation Problem: Its Solutions and Applications

abstract: This paper describes a max-min allocation problem that has M constraints and N nonnegative integer variables. By exploring the structure of the optimal solution to this max-min allocation problem, we develop a nonsimplex-based algorithm that finds an optimal solution within OMN2 operations. In addition, we show that a space allocation problem for a flexible component insertion machine can be formulated as a special case of this max-min allocation problem with one constraint. Furthermore, we present some manufacturing and production problems that can he formulated as max-min allocation problems, and hence, can be solved efficiently.


title: Deep Learning with Differential Privacy

abstract: Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.


title: Long-term Forecasting using Tensor-Train RNNs

abstract: We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation. Our proposed tensor recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher order moments and high-order state transition functions. Furthermore, we decompose the higher-order structure using the tensor-train (TT) decomposition to reduce the number of parameters while preserving the model performance. We theoretically establish the approximation properties of Tensor-Train RNNs for general sequence inputs, and such guarantees are not available for usual RNNs. We also demonstrate significant long-term prediction improvements over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world climate and traffic data.


title: Characterizations of Online Harassment: Comparing Policies Across Social Media Platforms

abstract: Harassment in online spaces is increasingly part of public debate and concern. Pervasive problems like cyberbullying, hate speech, and the glorification of self-harm have highlighted the breadth and depth of harassment taking place online. In this study we conduct a content analysis of the governing policies for fifteen social media platforms as they relate to harassment (of oneself and/or of community members) and other associated behaviors. We find that there is a striking inconsistency in how platform-specific policies depict harassment. Additionally, how these policies prescribe responses to harassment vary from mild censuring to the involvement of law enforcement. Finally, based on our analysis and findings, we discuss the potential for harnessing the power of the online communities to create norms around problematic behaviors.


title: Analysis of Human Grasping Behavior: Object Characteristics and Grasp Type

abstract: This paper is the first of a two-part series analyzing human grasping behavior during a wide range of unstructured tasks. The results help clarify overall characteristics of human hand to inform many domains, such as the design of robotic manipulators, targeting rehabilitation toward important hand functionality, and designing haptic devices for use by the hand. It investigates the properties of objects grasped by two housekeepers and two machinists during the course of almost 10,000 grasp instances and correlates the grasp types used to the properties of the object. We establish an object classification that assigns each object properties from a set of seven classes, including mass, shape and size of the grasp location, grasped dimension, rigidity, and roundness. The results showed that 55 percent of grasped objects had at least one dimension larger than 15 cm, suggesting that more than half of objects cannot physically be grasped using their largest axis. Ninety-two percent of objects had a mass of 500 g or less, implying that a high payload capacity may be unnecessary to accomplish a large subset of human grasping behavior. In terms of grasps, 96 percent of grasp locations were 7 cm or less in width, which can help to define requirements for hand rehabilitation and defines a reasonable grasp aperture size for a robotic hand. Subjects grasped the smallest overall major dimension of the object in 94 percent of the instances. This suggests that grasping the smallest axis of an object could be a reliable default behavior to implement in grasp planners.


title: A 24-GHz Receiver Frontend With an LO Signal Generator in 0.18-$\mu$m CMOS

abstract: Utilizing a standard 0.18-mum CMOS process, a receiver frontend and a local oscillator (LO) module are implemented for RF applications at the 24-GHz industrial, scientific, medical band. The proposed frontend is composed of a three-stage low-noise amplifier, a down-conversion mixer, and IF amplifiers. With an IF frequency of 4.82 GHz, the fabricated circuit demonstrates a conversion gain of 28.4 dB and a noise figure of 6.0 dB while maintaining an input return loss better than 14 dB. The measured P in - 1dB and IIP3 of the receiver frontend are -23.2 and -13.0 dBm, respectively. In addition, a circuit module, which generates the required dual down-conversion LO signals, is also included in this study. The proposed LO generator consists of a 19-GHz low-phase-noise voltage-controlled oscillator (VCO), a 4 : 1 frequency divider, and a quadrature phase-tuning circuit. From the measurement results, the VCO exhibits a tuning range of 850 MHz and a phase noise of -110 dBc/Hz at 1-MHz offset frequency. Operated at a supply voltage of 1.8 V, the current consumptions for the receiver frontend and the LO generator are both 30 mA.


title: Aphasia Therapy in the Age of Globalization: Cross-Linguistic Therapy Effects in Bilingual Aphasia

abstract: Introduction. Globalization imposes challenges to the field of behavioural neurology, among which is an increase in the prevalence of bilingual aphasia. Thus, aphasiologists have increasingly focused on bilingual aphasia therapy and, more recently, on the identification of the most efficient procedures for triggering language recovery in bilinguals with aphasia. Therapy in both languages is often not available, and, thus, researchers have focused on the transfer of therapy effects from the treated language to the untreated one. Aim. This paper discusses the literature on bilingual aphasia therapy, with a focus on cross-linguistic therapy effects from the language in which therapy is provided to the untreated language. Methods. Fifteen articles including two systematic reviews, providing details on pre- and posttherapy in the adult bilingual population with poststroke aphasia and anomia are discussed with regard to variables that can influence the presence or absence of cross-linguistic transfer of therapy effects. Results and Discussion. The potential for CLT of therapy effects from the treated to the untreated language depends on the word type, the degree of structural overlap between languages, the type of therapy approach, the pre- and postmorbid language proficiency profiles, and the status of the cognitive control circuit.


Business Model Generation


title: A Quasi-Experimental Evaluation of a Breastfeeding Support Program for Low Income Women in Michigan

abstract: We examined the effectiveness of a peer counseling breastfeeding support program for low income women in Michigan who participate in WIC. Because there was more demand for services than could be met by the program, many women who requested services were not subsequently contacted by a peer counselor. We used a quasi-experimental methodology that utilized this excess demand for services to estimate the causal effect of the support program on several breastfeeding outcomes. We relied on data derived from administrative and survey-based sources. After providing affirmative evidence that our key assumption is consistent with the data, we estimated that the program caused the breastfeeding initiation to increase by about 27 percentage points and the mean duration of breastfeeding to increase by more than 3 weeks. The support program we evaluated was very effective at increasing breastfeeding among low income women who participate in WIC, a population that nationally breastfeeds at rates well below the national average and below what is recommended by public health professionals. Given the substantial evidence that breastfeeding is beneficial for both the child and mother, the peer counseling breastfeeding support program should be subjected to a cost/benefit analysis and evaluated at other locales.


title: Keyword Search in Spatial Databases: Towards Searching by Document

abstract: This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords. Given a set of keywords from a document, mCK query can be very useful in geotagging the document by comparing the keywords to other geotagged documents in a database. To answer mCK queries efficiently, we introduce a new index called the bR*-tree, which is an extension of the R*-tree. Based on bR*-tree, we exploit a priori-based search strategies to effectively reduce the search space. We also propose two monotone constraints, namely the distance mutex and keyword mutex, as our a priori properties to facilitate effective pruning. Our performance study demonstrates that our search strategy is indeed efficient in reducing query response time and demonstrates remarkable scalability in terms of the number of query keywords which is essential for our main application of searching by document.


title: An approach based on visual text mining to support categorization and classification in the systematic mapping

abstract: Context: Systematic mapping provides an overview of a research area to assess the quantity of evidence existing on a topic of interest. In spite of its relevance, the establishment of consistent categories and classification of primary studies in these categories are manually conducted. ::: ::: Objective: We propose an approach, named SM-VTM (Systematic Mapping based on Visual Text Mining), to support categorization and classification stages in the systematic mapping using Visual Text Mining (VTM), aiming at reducing time and effort required in this process. ::: ::: Method: We established SM-VTM, selected a VTM tool and conducted a case study comparing results of two systematic mappings: one performed manually and another using our approach. ::: ::: Results: The results of both systematic mappings were very similar, showing the viability of SM-VTM. Furthermore, since our approach was applied using a tool, reduction of time and effort can be achieved. Conclusions: The application of VTM seems to be very relevant in the context of systematic mapping.


Multiobjective Optimization of Trusses using Genetic Algorithms


title: An Extended Fixed Priority Scheme for Mixed Criticality Systems

abstract: This paper considers a novel implementation scheme for fixed priority (FP) uniprocessor scheduling of mixed criticality systems. The scheme requires that jobs have their execution times monitored. If system behavior inconsistent with lower criticality levels is detected during run-time via such monitoring, (i) tasks of lower criticalities are discarded (this is already done by current FP mixed-criticality scheduling algorithms); and (ii) the priorities of the remaining tasks may be re-ordered. Evaluations illustrate the benefits of this scheme.


title: Indoor climate, psychosocial work environment and symptoms in open-plan offices

abstract: To study the indoor climate, the psychosocial work environment and occupants' symptoms in offices a cross-sectional questionnaire survey was made in 11 naturally and 11 mechanically ventilated office buildings. Nine of the buildings had mainly cellular offices; five of the buildings had mainly open-plan offices, whereas eight buildings had a mixture of cellular, multi-person and open-plan offices. A total of 2301 occupants, corresponding to a response rate of 72%, completed a retrospective questionnaire. The questionnaire comprised questions concerning environmental perceptions, mucous membrane irritation, skin irritation, central nervous system (CNS) symptoms and psychosocial factors. Occupants in open-plan offices are more likely to perceive thermal discomfort, poor air quality and noise and they more frequently complain about CNS and mucous membrane symptoms than occupants in multi-person and cellular offices. The association between psychosocial factors and office size was weak. Open-plan offices may not be suited for all job types. PRACTICAL IMPLICATION: Open-plan offices may be a risk factor for adverse environmental perceptions and symptoms.


title: Detecting Repeating Objects Using Patch Correlation Analysis

abstract: In this paper we describe a new method for detecting and counting a repeating object in an image. While the method relies on a fairly sophisticated deformable part model, unlike existing techniques it estimates the model parameters in an unsupervised fashion thus alleviating the need for a user-annotated training data and avoiding the associated specificity. This automatic fitting process is carried out by exploiting the recurrence of small image patches associated with the repeating object and analyzing their spatial correlation. The analysis allows us to reject outlier patches, recover the visual and shape parameters of the part model, and detect the object instances efficiently. In order to achieve a practical system which is able to cope with diverse images, we describe a simple and intuitive active-learning procedure that updates the object classification by querying the user on very few carefully chosen marginal classifications. Evaluation of the new method against the state-of-the-art techniques demonstrates its ability to achieve higher accuracy through a better user experience.


title: Progress in supervised neural networks

abstract: Theoretical results concerning the capabilities and limitations of various neural network models are summarized, and some of their extensions are discussed. The network models considered are divided into two basic categories: static networks and dynamic networks. Unlike static networks, dynamic networks have memory. They fall into three groups: networks with feedforward dynamics, networks with output feedback, and networks with state feedback, which are emphasized in this work. Most of the networks discussed are trained using supervised learning. >


title: An Energy Efficient Routing Protocol for Wireless Internet-of-Things Sensor Networks

abstract: Internet of Things (IoT) are increasingly being adopted into practical applications such as security systems, smart infrastructure, traffic management, weather systems, among others. While the scale of these applications is enormous, device capabilities, particularly in terms of battery life and energy efficiency are limited. Despite research being done to ameliorate these shortcomings, wireless IoT networks still cannot guarantee satisfactory network lifetimes and prolonged sensing coverage. Moreover, proposed schemes in literature are convoluted and cannot be easily implemented in real-world scenarios. This necessitates the development of a simple yet energy efficient routing scheme for wireless IoT sensor networks. This paper models the energy constraint problem of devices in IoT applications as an optimization problem. To conserve the energy of device nodes, the routing protocol first aggregates devices into clusters based on a number of different features such as distance from base station, data/message length and data sensed from the environment in the current epoch. Then, a cluster head is elected for each cluster and a directed acyclic graph (DAG) is generated with all the cluster heads as nodes. Edges represent communication intent from transmitter to receiver and the edge weights are computed using a formulated equation. The minimum cost path to the base station is computed to allow for efficient real-time routing. Sleep scheduling is also optionally used to further boost network energy efficiency. The proposed routing protocol has been simulated and outperforms existing routing protocols in terms of metrics such as number of active nodes, energy dynamics and network coverage.


title: Query expansion with ConceptNet and WordNet: An intrinsic comparison

abstract: This paper compares the utilization of ConceptNet and WordNet in query expansion. Spreading activation selects candidate terms for query expansion from these two resources. Three measures including discrimination ability, concept diversity, and retrieval performance are used for comparisons. The topics and document collections in the ad hoc track of TREC-6, TREC-7 and TREC-8 are adopted in the experiments. The results show that ConceptNet and WordNet are complementary. Queries expanded with WordNet have higher discrimination ability. In contrast, queries expanded with ConceptNet have higher concept diversity. The performance of queries expanded by selecting the candidate terms from ConceptNet and WordNet outperforms that of queries without expansion, and queries expanded with a single resource.


title: A highly-linear modified pseudo-differential current starved delay element with wide tuning range

abstract: This paper describes an efficient structure of a pseudo-differential current starved delay element that is used in a four stages delay line targeted for analog/mixed Delay-Locked-Loops. The designed circuit has been simulated in ADS software, using TSMC 0.18 um CMOS process at 1.5 V supply voltage. Body feed technique is used to widen applicable range of control voltage. The linearity of circuit is, also, improved compared to the conventional current starved delay elements. Moreover, improving the noise performance is achieved by taking advantage of differential structure. The simulation results indicate that tunable delay range of proposed delay cell is within 0.26-1.6 ns. Sweeping the control voltage from 0 to 1.2 V at 350 MHz, the calculated gain is almost 1.11 ns/V. The operation frequency range of the four stages delay line is 180 to 500 MHz. While operating at 350 MHz, the peak-to-peak and rms jitters are 9.5 and 32 ps, respectively, and the maximum power consumption in this frequency is 0.4 mW.


title: A 330nA energy-harvesting charger with battery management for solar and thermoelectric energy harvesting

abstract: Harvesting energy from ambient energy sources such as solar and thermal gradients is one solution to address the dramatic increase in energy consumption of personal electronics. In this paper, an ultra low quiescent current charger and battery management IC that can efficiently extract energy from solar panels and thermoelectric generators to charge batteries and super capacitors is presented. While previous works on energy harvesting [1–5] report efficient DC-DC converters to extract the energy from the harvester, not all of them provide complete battery management functionality for various chemistries. Also, maximum power point tracking (MPPT) which is critical in energy harvesting applications is not provided in some of the published work. In this paper, a charger and battery management IC with 330nA quiescent current is presented. The IC can cold start from 330mV and 5μW of input power. The charger achieves efficiency greater than 80% at single cell solar voltages of 0.5V. A low quiescent current battery management architecture involving sampled circuits, sub regulated rails and clock gating is demonstrated.


title: Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

abstract: As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. In particular, automated machine learning (AutoML) systems seek to automate the process of designing and optimizing machine learning pipelines. In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem. Further, we analyze a large database of pipelines that were previously used to solve various supervised classification problems and identify 100 short series of machine learning operations that appear the most frequently, which we call the building blocks of machine learning pipelines. We harness these building blocks to initialize TPOT with promising solutions, and find that this sensible initialization method significantly improves TPOT's performance on one benchmark at no cost of significantly degrading performance on the others. Thus, sensible initialization with machine learning pipeline building blocks shows promise for GP-based AutoML systems, and should be further refined in future work.


title: A SLAM Based Semantic Indoor Navigation System for Visually Impaired Users

abstract: The main objective of our work is to demonstrate how recommender systems can be deployed to enhance access to museum collections. Visitors of museums are often overwhelmed by the information available in the space they are exploring. Therefore, finding relevant artworks to see in a limited amount of time is a crucial task. In this paper, we present a recommender system for mobile devices that (1) adapts to the users profiles and (2) is sensitive to their context (location, time, expertise, etc.). Our system aims to improve the visitors' experience and help them build their tours on-site according to their preferences and constraints. We first describe our recommendation framework, which consists in a hybrid recommendation system. It combines a semantic approach for the representation of museum domain using ontologies and thesauruses with a semantically-enhanced collaborative filtering method. Then, we present a contextual post filtering that enables the generation of a highly personalized tour based on the physical environment, the location of the visitors and the time they want to spend in the museum.


title: Using the Similarity of Main Melodies to Identify Cover Versions of Popular Songs for Music Document Retrieval *

abstract: Automatic extraction of information from music data is an important and challenging issue in the field of content-based music retrieval. As part of the research effort, this study presents a technique that automatically identifies cover versions of songs specified by users. The technique enables users to search for songs with an identical tune, but performed by different singers, in different languages, genres, and so on. The proposed system takes an excerpt of the song specified by the user as input, and returns a ranked list of songs similar to the input excerpt in terms of the main melody. To handle likely discrepancies, e.g., in tempo, transposition, and accompaniment, between cover versions and the original song, methods are presented to remove the non-vocal portions of the song, extract the sung notes from the accompanied vocals, and compare the similarities between the sung note sequences. Our experiments on a database of 594 cross-lingual popular songs show the feasibility of identifying cover versions of songs for music retrieval.


title: Communication in automotive systems: Principles, limits and new trends for vehicles, airplanes and vessels

abstract: This paper deals mainly with state of the art and next-decade technologies for fiber-optic data buses in automotive applications. Nowadays, fiber-optic data buses in automotive applications are exclusively used in the infotainment domain, MOST (Media Oriented System Transport). Current data rates are in the order of 150 MBit/s. Consequently, the use of LEDs and polymer optical fibers (POF) is sufficient. For higher data rates, also alternative solutions are discussed: The LED as transmitter can be replaced by a vertical surface emitting laser (VCSEL), and the plastic fiber (POF) by a polymer-cladded silica (PCS) fiber. Due to the inherent fact that, as a result, the fiber diameter is reduced, the detector area of the well-known Silicon photo diodes can also be reduced greatly. As a consequence, data rates can be extended into the Gbit/s-region. Furthermore there are different physical layers like optical wireless and radio frequency systems (Radar) to be taken into account.