title: Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery

abstract: High-Throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally-related materials. A key problem in materials discovery, the phase map identification problem, involves the determination of the crystal phase diagram from the materials' composition and structural characterization data. We present Phase-Mapper, a novel AI platform to solve the phase map identification problem that allows humans to interact with both the data and products of AI algorithms, including the incorporation of human feedback to constrain or initialize solutions. Phase-Mapper affords incorporation of any spectral demixing algorithm, including our novel solver, AgileFD, which is based on a convolutive non-negative matrix factorization algorithm. AgileFD can incorporate constraints to capture the physics of the materials as well as human feedback. We compare three solver variants with previously proposed methods in a large-scale experiment involving 20 synthetic systems, demonstrating the efficacy of imposing physical constrains using AgileFD. Phase-Mapper has also been used by materials scientists to solve a wide variety of phase diagrams, including the previously unsolved Nb-Mn-V oxide system, which is provided here as an illustrative example.


title: An Empirical Evaluation of Machine Learning Approaches for Species Identification through Bioacoustics

abstract: In this paper, we investigate a complete system for identifying species from audio files. Audio data from both high quality and low quality sound files with varying degrees of background noise are collected and preprocessed for enhancing the learning capability of machine learning models. Then, fine-scale features are extracted to quantify acoustic properties of audio streams. Based on the proposed system, we evaluate a set of popular machine learning approaches on audio data from the cat and dog families. The experimental results show that the use of appropriate quality data and machine learning models yield compelling identification accuracy of species with limited user assistance.


title: Antimicrobial activity, cytotoxicity and chemical analysis of lemongrass essential oil (Cymbopogon flexuosus) and pure citral

abstract: The aim of this study was to determine the antimicrobial effects of lemongrass essential oil (C. flexuosus) and to determine cytotoxic effects of both test compounds on human dermal fibroblasts. Antimicrobial susceptibility screening was carried out using the disk diffusion method. Antimicrobial resistance was observed in four of five Acinetobacter baumannii strains with two strains confirmed as multi-drug-resistant (MDR). All the strains tested were susceptible to both lemongrass and citral with zones of inhibition varying between 17 to 80 mm. The mean minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of citral (mic—0.14 % and mbc—0.3 % v/v) was lower than that of Lemongrass (mic—0.65 % and mbc—1.1 % v/v) determined using the microtitre plate method. Cell viability using human dermal fibroblasts (HDF; 106-05a) was determined following exposure to both compounds and a control (Grapeseed oil) using the XTT assay and the IC50 determined at 0.095 % (v/v) for citral and 0.126 % (v/v) for lemongrass. Grapeseed oil had no effect on cell viability. Live cell imaging was performed using the LumaScope 500 imaging equipment and changes in HDF cell morphology such as necrotic features and shrinkage were observed. The ability of lemongrass essential oil (EO) and citral to inhibit and kill MDR A. baumannii highlights its potential for use in the management of drug-resistant infections; however, in vitro cytotoxicity does suggest further tests are needed before in vivo or ex vivo human exposure.


title: A survey and assessment of the capabilities of Cubesats for Earth observation

abstract: Abstract In less than a decade, Cubesats have evolved from purely educational tools to a standard platform for technology demonstration and scientific instrumentation. The use of COTS (Commercial-Off-The-Shelf) components and the ongoing miniaturization of several technologies have already led to scattered instances of missions with promising scientific value. Furthermore, advantages in terms of development cost and development time with respect to larger satellites, as well as the possibility of launching several dozens of Cubesats with a single rocket launch, have brought forth the potential for radically new mission architectures consisting of very large constellations or clusters of Cubesats. These architectures promise to combine the temporal resolution of GEO missions with the spatial resolution of LEO missions, thus breaking a traditional trade-off in Earth observation mission design. This paper assesses the current capabilities of Cubesats with respect to potential employment in Earth observation missions. A thorough review of Cubesat bus technology capabilities is performed, identifying potential limitations and their implications on 17 different Earth observation payload technologies. These results are matched to an exhaustive review of scientific requirements in the field of Earth observation, assessing the possibilities of Cubesats to cope with the requirements set for each one of 21 measurement categories. Based on this review, several Earth observation measurements are identified that can potentially be compatible with the current state-of-the-art of Cubesat technology although some of them have actually never been addressed by any Cubesat mission. Simultaneously, other measurements are identified which are unlikely to be performed by Cubesats in the next few years due to insuperable constraints. Ultimately, this paper is intended to supply a box of ideas for universities to design future Cubesat missions with high scientific payoff.


title: Text Mining Applied to SQL Queries: A Case Study for the SDSS SkyServer

abstract: SkyServer, the portal for the Sloan Digital Sky Survey (SDSS) catalog, provides data access tools for astronomers and scientific education. One of the interfaces allows users to enter ad hoc SQL statements to query the catalog, and has logged over 280 million queries since 2001. This paper describes text mining techniques and preliminary results on mining the logs of the SQL queries submitted to SkyServer, along with what other applications we foresee for such procedure.


title: Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation

abstract: Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.


title: Adaptive marginalized stacked denoising autoencoders and its application

abstract: In this paper, we propose a modified marginalized autoencoders. Here, the noise adding way at a fixed rate in marginalized autoencoders is replaced by the adaptive noise injection. Compared with the traditional marginalized autoencoders, the proposed method obviously enlarges the recognition performance. Furthermore, the proposed method is applied to identify high-speed train wheel wear conditions. Features of high speed train wheels wear vibration signals are abstracted by using the adaptive noise marginalized autoencoders, and the features is used to realize the wheel wear characteristics of vibration signal recognitions as the input of support vector machine (SVM). The experimental results show that the accuracy of the new method for identifying high-speed train wheel wear conditions is 99.8% on average.


title: Social Engineering in Social Networking Sites: The Art of Impersonation

abstract: Social networking sites (SNSs), with their large number of users and large information base, seem to be the perfect breeding ground for exploiting the vulnerabilities of people, who are considered the weakest link in security. Deceiving, persuading, or influencing people to provide information or to perform an action that will benefit the attacker is known as “social engineering.” Fraudulent and deceptive people use social engineering traps and tactics through SNSs to trick users into obeying them, accepting threats, and falling victim to various crimes such as phishing, sexual abuse, financial abuse, identity theft, and physical crime. Although organizations, researchers, and practitioners recognize the serious risks of social engineering, there is a severe lack of understanding and control of such threats. This may be partly due to the complexity of human behaviors in approaching, accepting, and failing to recognize social engineering tricks. This research aims to investigate the impact of source characteristics on users’ susceptibility to social engineering victimization in SNSs, particularly Facebook. Using grounded theory method, we develop a model that explains what and how source characteristics influence Facebook users to judge the attacker as credible.


title: BinClone: Detecting Code Clones in Malware

abstract: To gain an in-depth understanding of the behaviour of a malware, reverse engineers have to disassemble the malware, analyze the resulting assembly code, and then archive the commented assembly code in a malware repository for future reference. In this paper, we have developed an assembly code clone detection system called Bin Clone to identify the code clone fragments from a collection of malware binaries with the following major contributions. First, we introduce two deterministic clone detection methods with the goals of improving the recall rate and facilitating malware analysis. Second, our methods allow malware analysts to discover both exact and inexact clones at different token normalization levels. Third, we evaluate our proposed clone detection methods on real-life malware binaries. To the best of our knowledge, this is the first work that studies the problem of assembly code clone detection for malware analysis.


title: Integrated dc-dc based-GaN converter with screen printed capacitors allowing double side cooling

abstract: Converter topology, power devices, passives elements and technology are the keys elements to achieve highly efficient and integrated converters. This article discusses each of these items with a point of load converter application in mind. The potential advantages of series connection are discussed: increase of both the specific power and the efficiency, reduction of semiconductors voltage rating and amplitude of the dV/dt, increase of the apparent switching frequency. The aim of this paper is to investigate how the advantages of the versatile Flying Capacitor topology (DC/DC or DC/AC conversion) can be combined with those of gallium nitride (GaN) power transistors. Integrated screen-printed capacitors are also investigated to reduce parasitic elements and allow double side cooling. A 48V to 5V prototype with screen-printed flying capacitors is built to show the feasibility of such a solution.


title: Curiosity driven reinforcement learning for motion planning on humanoids

abstract: Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.


title: Self-catheterization training of a child with myelomeningocele (toileting independence, incontinent children).

abstract: This paper describes how the occupational therapist, following a urologists' orders, teaches clean intermittent self-catheterization as a self-care skill to handicapped school children when no trained enterostomal therapist is available. It contains a review of the literature, a definition of neurogenic bladder types, a description of intermittent catheterization, and the steps involved in teaching the technique to children. A case study is given that shows the training of a child with myelomeningocele to catheterize herself, and the therapist's interaction with the school nurse and other members of the team.


title: Putting Gender Into Context: An Interactive Model of Gender-Related Behavior.

abstract: A model that describes conditions influencing the display of gender-related behavior is presented as a supplement to existent models of sex differences. Whereas many previous models stress the importance of distal factors, our model emphasizes the degree to which gender-related behavior is variable, proximally caused, and context dependent. More specifically, we propose that gender-related behaviore are influenced by the expectations of perceivers, self-systems of the target, and situational cues. This model of gender-related behavior builds on theory and data in the areas of (a) expectancy confirmation processes and (b) self-verification and self-presentation strategies. Support for the model is presented, and suggestions are offered for its future development.


title: 2D face liveness detection: An overview

abstract: Face recognition based on 2D images is a widely used biometric approach. This is mainly due to the simplicity and high usability of this approach. Nonetheless, those solutions are vulnerable to spoof attacks made by non-real faces. In order to identify malicious attacks on such biometric systems, 2D face liveness detection approaches are developed. In this work, face liveness detection approaches are categorized based on the type of liveness indicator used. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works dealing with face liveness detection works is presented. A discussion is made to link the state of the art solutions with the presented categorization along with the available and possible future datasets. All that aim to provide a clear path for the future development of innovative face liveness detection solutions.


title: Optimization via Reflection on Work Stealing in TBB

abstract: Intelreg Threading Building Blocks (Intelreg TBB) is a C++ library for parallel programming. Its templates for generic parallel loops are built upon nested parallelism and a work-stealing scheduler. This paper discusses optimizations where the high-level algorithm inspects or biases stealing. Two optimizations are discussed in detail. The first dynamically optimizes grain size based on observed stealing. The second improves prior work that exploits cache locality by biased stealing. This paper shows that in a task stealing environment, deferring task spawning can improve performance in some contexts. Performance results for simple kernels are presented.


title: A Survey of Automatic Event Detection in Multi-Camera Third Generation Surveillance Systems

abstract: Third generation surveillance systems are largely requested for intelligent surveillance of different scenarios such as public areas, urban traffic control, smart homes and so on. They are based on multiple cameras and processing modules that integrate data coming from a large surveillance space. The semantic interpretation of data from a multi-view context is a challenging task and requires the development of image processing methodologies that could support applications in extensive and real-time contexts. This paper presents a survey of automatic event detection functionalities that have been developed for third generation surveillance systems with a particular emphasis on open problems that limit the application of computer vision methodologies to commercial multi-camera systems.


forthcoming). The Economics and Psychology of Personality traits


title: Evolutionary computation and games

abstract: Games provide competitive, dynamic environments that make ideal test beds for computational intelligence theories, architectures, and algorithms. Natural evolution can be considered to be a game in which the rewards for an organism that plays a good game of life are the propagation of its genetic material to its successors and its continued survival. In natural evolution, the fitness of an individual is defined with respect to its competitors and collaborators, as well as to the environment. Within the evolutionary computation (EC) literature, this is known as co-evolution and within this paradigm, expert game-playing strategies have been evolved without the need for human expertise.


title: Artificial Life and Real Robots

abstract: The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behavior-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.


title: Problematic Internet use in British adolescents: An exploration of the addictive symptomatology

abstract: Research into online addictions has increased substantially over the last decade, particularly amongst youth. This study adapted the Problematic Internet Entertainment Use Scale for Adolescents [PIEUSA] for use with a British population. The adapted scale was used to (i) validate the instrument for English-speaking adolescent samples, (ii) estimate the prevalence of adolescent online problem users and describe their profile, and (iii) assess the accuracy of the scale’s classification of symptomatology. A survey was administered to 1097 adolescents aged between 11 and 18 years. The results indicated that (i) reliability of the adapted scale was excellent; factor validity showed unidimensionality, and construct validity was adequate. The findings also indicated that (ii) prevalence of online problem users was 5.2% and that they were more likely to younger males that engaged in online gaming for more than two hours most days. The majority of online problem users displayed negative addictive symptoms, especially ‘loss of control’ and ‘conflict’. The adapted scale showed (iii) very good sensitivity, specificity, and classification accuracy, and was able to clearly differentiate between problem and non-problem users. The results suggest certain differences between adolescent and adult online problem users based in the predominance of slightly different psychological components.


title: An improved algorithm to accelerate regular expression evaluation

abstract: Modern network intrusion detection systems need to perform regular expression matching at line rate in order to detect the occurrence of critical patterns in packet payloads. While deterministic finite automata (DFAs) allow this operation to be performed in linear time, they may exhibit prohibitive memory requirements. In [9], Kumar et al. propose Delayed Input DFAs (D2FAs), which provide a trade-off between the memory requirements of the compressed DFA and the number of states visited for each character processed, which corresponds directly to the memory bandwidth required to evaluate regular expressions. In this paper we introduce a general compression technique that results in at most 2N state traversals when processing a string of length N. In comparison to the D2FA approach, our technique achieves comparable levels of compression, with lower provable bounds on memory bandwidth (or greater compression for a given bandwidth bound). Moreover, our proposed algorithm has lower complexity, is suitable for scenarios where a compressed DFA needs to be dynamically built or updated, and fosters locality in the traversal process. Finally, we also describe a novel alphabet reduction scheme for DFA-based structures that can yield further dramatic reductions in data structure size.


title: E-Government Implementation: Lessons from South African Municipalities

abstract: The objective of the paper was to understand the challenges towards e-Government implementation in South Africa. The paper contributes to the ongoing discussion regarding the challenges facing e-government implementations in developing nations. It presents the outcomes of several discussions conducted with representatives from the municipal sector. These included semi structured interviews and a workshop with 40 attendees resulting in qualitative primary data. Through the application of an inductive thematic data analysis, the paper reflects on the critical role that municipalities hold in pursuing e-Government. It further discusses the different stakeholders that may influence the manifestation of e-Government for municipalities. It also highlights the barriers identified by respondents that require consideration from local government. The barriers include governance related issues, access to resources, leadership, ICT skills and funding


title: WarningBird: Detecting Suspicious URLs in Twitter Stream.

abstract: Twitter can suffer from malicious tweets containing suspicious URLs for spam, phishing, and malware distribution. Previous Twitter spam detection schemes have used account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. Malicious users, however, can easily fabricate account features. Moreover, extracting relation features from the Twitter graph is time and resource consuming. Previous suspicious URL detection schemes have classified URLs using several features including lexical features of URLs, URL redirection, HTML content, and dynamic behavior. However, evading techniques exist, such as time-based evasion and crawler evasion. In this paper, we propose WARNINGBIRD, a suspicious URL detection system for Twitter. Instead of focusing on the landing pages of individual URLs in each tweet, we consider correlated redirect chains of URLs in a number of tweets. Because attackers have limited resources and thus have to reuse them, a portion of their redirect chains will be shared. We focus on these shared resources to detect suspicious URLs. We have collected a large number of tweets from the Twitter public timeline and trained a statistical classifier with features derived from correlated URLs and tweet context information. Our classifier has high accuracy and low false-positive and falsenegative rates. We also present WARNINGBIRD as a realtime system for classifying suspicious URLs in the Twitter stream. ∗This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2011-C1090-1131-0009) and World Class University program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea(R31-10100).


title: Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images

abstract: Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.


title: Applied Risk Analysis: Moving Beyond Uncertainty in Business

abstract: Introduction PART ONE: Risk Identification CHAPTER 1: Beyond Uncertainty A Brief History of Risk: What Exactly Is Risk? Uncertainty Versus Risk Why Is Risk Important in Making Decisions? Dealing with Risk the Old Fashioned Way The Look and Feel of Risk and Uncertainty Five-Minute Industry Spotlight: ExperCorp Questions PART TWO: Risk Evaluation CHAPTER 2: From Risk to Riches Taming the Beast The Basics of Risk The Nature of Risk and Returns The Statistics of Risk The Measurements of Risk Five-Minute Industry Spotlight: Farmland Five-Minute Industry Spotlight: Environmental Protection Agency Questions CHAPTER 3: A Gentleman's Guide to Model Building Document the Model Separate Inputs, Calculations, and Results Protect the Models Make the Model User-Friendly: Data Validation and Alerts Track the Model Automate the Model with VBA Appendix A Primer on VBA Modeling and Writing Macros Exercises PART THREE: Risk Quantification CHAPTER 4: On the Shores of Monaco What Is Monte Carlo Simulation? Why Are Simulations Important? Comparing Simulation with Traditional Analyses Using Crystal Ball and Excel to Perform Simulations Five-Minute Industry Spotlight: Colorado School of Mines Five-Minute Industry Spotlight: Hewlett-Packard Appendix Simulation Questions CHAPTER 5: Peering into the Crystal Ball The Basics of Crystal Ball Software Getting Started with Crystal Ball Software The Simulation Environment Creating a Simulation Interpreting the Simulation Results Five-Minute Industry Spotlight: TRW Five-Minute Industry Spotlight: DuPont Merck Questions CHAPTER 6: Pandora's Tool Box Tornado and Sensitivity Tools in Simulation Correlating and Fitting a Distribution Plus Precision Control Precision Control Bootstrap Simulation Two-Dimensional Simulation Decision Tables Five-Minute Industry Spotlight: 3M Five-Minute Industry Spotlight: Deloitte & Touche Consulting Appendix Goodness-of-Fit Tests Questions PART FOUR: Industry Applications CHAPTER 7: Extended Business Cases I: From Pharma to Black Gold Case Study: Pharmaceutical and Biotech High Precision Quantitative Deal Structuring in the Biotechnology and Pharmaceutical Industries Case Study: Oil and Gas Production and Exploration Five-Minute Industry Spotlight: Sierra Systems Five-Minute Industry Spotlight: Motorola PART FIVE: Risk Prediction CHAPTER 8: Tomorrow's Forecast Today What Is Forecasting? The Nature and View of Forecasting Five-Minute Industry Spotlight: Hewlett-Packard Five-Minute Industry Spotlight: SunTrust Bank Questions CHAPTER 9: Using the Past to Predict the Future Time-Series Forecasting Methodology No Trend and No Seasonality With Trend But No Seasonality No Trend But With Seasonality With Seasonality and With Trend Regression Analysis The Pitfalls of Forecasting: Outliers, Nonlinearity, Multicollinearity, Heteroskedasticity, Autocorrelation, and Structural Breaks Other Technical Issues in Regression Analysis Introdu


title: Time domain simulation of dispersive materials from microwave to optical frequencies

abstract: Accurate material modeling is an important factor in simulation of dispersive materials. We present different dispersive models for the simulation of dispersive materials, and can be used from microwave to optical frequencies depending on the nature of application. Most of available dispersive models are in frequency domain, to make them consistent with time domain methods; different approaches are also discussed. The alternating differential implicit finite difference time domain method (ADI-FDTD) is implemented for few dispersive models and results are compared with the FDTD method.


title: The challenge of realistic music generation: modelling raw audio at scale

abstract: Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds.


title: Decentralized Blacklistable Anonymous Credentials with Reputation

abstract: Blacklistable anonymous credential systems provide service providers with a way to authenticate users according to their historical behaviors, while guaranteeing that all users can access services in an anonymous and unlinkable manner, thus are potentially useful in practice. Traditionally, to protect services from illegal access, the credential issuer, which completes the registration with users, must be trusted by the service provider. However, in practice, this trust assumption is usually unsatisfied.


title: Adiabatic Resonant Microrings (ARMs) with directly integrated thermal microphotonics

abstract: A new class of microphotonic-resonators, Adiabatic Resonant Microrings (ARMs), is introduced. The ARM resonator geometry enables heater elements to be formed within the resonator, simultaneously enabling record low-power (4.4 W/GHz) and record high-speed (1µs) thermal tuning.


title: A Python Framework for Exhaustive Machine Learning Algorithms and Features Evaluations

abstract: Machine learning domain has grown quickly the last few years, in particular in the mobile eHealth domain. In the context of the D1NAMO project, we aimed to detect hypoglycemia on Type 1 diabetes patients by using their ECG, recorded with a sport-like chest belt. In order to know if the data contain enough information for this classification task, we needed to apply and evaluate machine learning algorithms on several kinds of features. We have built a Python toolbox for this reason. It is built on top of the scikit-learn toolbox and it allows evaluating a defined set of machine learning algorithms on a defined set of features extractors, taking care of applying good machine learning techniques such as cross-validation or parameters grid-search. The resulting framework can be used as a first analysis toolbox to investigate the potential of the data. It can also be used to fine-tune parameters of machine learning algorithms or parameters of features extractors. In this paper we explain the motivation of such a framework, we present its structure and we show a case study presenting negative results that we could quickly spot using our toolbox.


title: Semantic classifications for detection of verb metaphors

abstract: We investigate the effectiveness of semantic generalizations/classifications for capturing the regularities of the behavior of verbs in terms of their metaphoricity. Starting from orthographic word unigrams, we experiment with various ways of defining semantic classes for verbs (grammatical, resource-based, distributional) and measure the effectiveness of these classes for classifying all verbs in a running text as metaphor or non metaphor.


title: Legal goal-oriented requirement language (legal GRL) for modeling regulations

abstract: Every year, governments introduce new or revised regulations that are imposing new types of requirements on software development. Analyzing and modeling these legal requirements is time consuming, challenging and cumbersome for software and requirements engineers. Having regulation models can help understand regulations and converge toward better compliance levels for software and systems. This paper introduces a systematic method to extract legal requirements from regulations by mapping the latter to the Legal Profile for Goal-oriented Requirements Language (GRL) (Legal GRL). This profile provides a conceptual meta-model for the anatomy of regulations and maps its elements to standard GRL with specialized annotations and links, with analysis techniques that exploit this additional information. The paper also illustrates examples of Legal GRL models for The Privacy and Electronic Communications Regulations. Existing tool support (jUCMNav) is also extended to support Legal GRL modeling.


title: Remediating Organizational Functioning in Children with ADHD: Immediate and Long-Term Effects from a Randomized Controlled Trial.

abstract: Organization, time management and planning (OTMP) difficulties, features of Attention-Deficit/Hyperactivity Disorder (ADHD), adversely affect children’s functioning and persist through adulthood (Barkley & Fischer, 2011). During childhood, poor organizational skills, manifested by misplacing or losing materials, forgetting materials, failing to record assignments and due dates, can compromise school performance and scholastic attainment (Power, Werba, Watkins, Angelucci, & Eiraldi, 2006). Impairments in time management and inefficient planning lead to difficulties starting and completing daily and long-term assignments (Barkley, Koplowitz, Anderson, & McMurray, 1997), which can contribute to conflicts with parents and teachers around school performance. Family conflicts also occur when children are disorganized with their belongings at home. When OTMP difficulties result in problems such as misplacing or forgetting materials for games and sports, peer relations can also be adversely affected (Dimantopoulou, Rydell, Thorell & Bohlin, 2007). ::: ::: Despite the negative impact of OTMP problems, few systematic efforts have targeted these problems in elementary-school aged children with ADHD, the age when youth first encounter expectations for many independent, organized behaviors at home and school. Two studies, which did not require quantified OTMP impairment for inclusion, reported gains in organizational functioning with behavioral treatment. An eight-week after-school intervention with 4th–7th graders (N = 37) “identified by school personnel as having ADHD-related problems and in need of academic intervention” (p.416), improved children’s organization of school materials and parent-rated homework problems. However, teacher ratings of academic performance did not differ for intervention and control (n = 13) students (Langberg, Epstein, Urbanowicz, Simon, & Graham, 2008). A randomized controlled trial evaluated a 12-week home-school intervention for youngsters with ADHD-Inattentive type (N = 69) consisting of teacher consultation, parent training, child training in social competence and independent living skills including organization skills (Pfiffner et al., 2007). Children’s organizational skills improved significantly with treatment and gains were maintained at a five month follow up; however, the groups did not differ in organizational skills at follow-up because of improvements in the control group. In a small placebo-controlled trial in children with ADHD and OTMP difficulties (N = 19), organizational functioning improved with stimulants, presumably because of improvements in ADHD symptoms; however, OTMP functioning for the majority of children remained in the clinical range (Abikoff et al., 2008). ::: ::: OTMP problems may represent behavioral manifestations of poor executive function (EF) (Barkley, 2006; Pennington & Ozonoff, 1996). Impairments in arousal, inhibitory control, delay tolerance, working memory, and time perception likely impede self-regulatory behaviors and interfere with organizing actions and planning (Barkley, Koplowitz, Anderson, & McMurray, 1997; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Thus, one approach for ameliorating OTMP difficulties may be through interventions that address such EF deficits. However, to our knowledge, there is no evidence from controlled studies that efforts to improve executive processes in children with ADHD ameliorate their OTMP difficulties. ::: ::: It is also conceivable that OTMP difficulties result primarily from specific skills deficits. Our clinical work with children with ADHD and OTMP problems suggested that many of these youngsters lack the knowledge and proficiency to effectively organize materials, track assignments, manage time and plan their work. If a skills deficit is operative, behavioral treatment that emphasizes skills instruction, practice, and breaking skills into sub-steps would be expected to be useful. Support for this notion was obtained in a pilot study of an Organizational Skills Training (OST) intervention in 3rd–5th graders with ADHD and OTMP difficulties (N = 20). OST significantly improved organizational behaviors at home and school and reduced homework problems and family conflicts (Abikoff & Gallagher, 2008). ::: ::: Finally, OTMP difficulties may result principally from a performance deficit. Namely, children with ADHD and OTMP problems may have the organizational knowledge and skills, but fail to apply them due to insufficient motivation, lack of carry through, and/or task avoidance. Underlying this performance deficit are two factors: 1) a motivational or “delay aversion” deficit (Sonuga-Barke, 2003), which makes it difficult for children with ADHD to wait for desired outcomes and work effectively over extended periods of time without considerable structure and reinforcement; and 2) deficits in executive function that adversely affect the ability to connect knowledge with performance in a manner that results in efficient self-directed adaptive behavior. Barkley (2006), a proponent of the performance deficit model, has described ADHD as primarily “not a disorder of knowing what to do, but of doing what one knows” (p. 324), due to faulty executive functions. If OTMP problems reflect a performance deficit, then interventions should enhance motivation by rewarding goal (i.e., “end-point”) behaviors, which should increase the occurrence of these behaviors and ostensibly reinforce the behavior chain linked to goal attainment as well. ::: ::: Uncertainty regarding whether children’s organizational difficulties result predominantly from skills or performance deficits is not unique to this functional domain; it reflects longstanding speculations about the presence and impact of skills and/or performance deficits on the functioning of children with ADHD (Douglas, 1983; Green & Ablon, 2001; Gresham, Sugai, & Horner, 2001; Stein, Szumowski, Blondis, & Roizen, 1995). ::: ::: The current study was informed by several issues noted above; namely, clinical observations of knowledge gaps and organizational skills deficits in children with OTMP difficulties, pilot results suggesting utility of a skills-based treatment, and a dearth of controlled studies evaluating the impact of performance-based interventions on the OTMP functioning of elementary-school children with ADHD. The study was designed to test the specific and relative efficacy of a skills-based intervention in ameliorating the organizational difficulties of children with ADHD, and to evaluate long-term maintenance effects. We hypothesized that skills-based treatment (OST) would be superior to: 1) a wait-list control (specific efficacy); 2) a performance-based intervention (relative efficacy); and that 3) treatment gains would be maintained significantly better for skills-based than for performance-based treatment. The study also compared the performance-based intervention and controls, although no a priori hypotheses were made. ::: ::: To test these hypotheses, we conducted a dual-site randomized controlled trial evaluating OST’s efficacy compared to (1) a wait-list control (WL) and (2) a performance-based, contingency management intervention (“Parents and Teachers Helping Kids Organize”, PATHKO), which targets OTMP “end-point” goals and precludes skill training, allowing for evaluation of interventions derived from different underlying deficit models on children’s OTMP functioning and key functional domains adversely affected by OTMP difficulties. ::: ::: Difficulty achieving generalization across settings and time has been a long-standing issue in ADHD psychosocial research (Hinshaw, Klein & Abikoff, 2007) attributed to a range of factors such as insufficient learning of target behaviors and skills, a focus on inappropriate treatment targets, restricted settings where treatment procedures are implemented, and focusing on broad rather than circumscribed aspects of functioning (Abikoff, 2009; Abikoff & Gallagher, 2008). Maintenance problems are especially common in contingency management interventions. Once children are no longer systematically reinforced for achieving targeted goals, reductions in behavioral gains typically occur (e.g., Barkley et al., 2000; Carlson, Pelham, Milich, & Dixon, 1992). Because OST ostensibly helps children learn specific skills whereas children in PATHKO are contingently reinforced for meeting OTMP end-point behaviors, we hypothesized that OST would result in significantly better maintenance effects compared to PATHKO when children were in the next grade, confronted with new and more complex OTMP demands.


title: Weight Uncertainty in Neural Networks

abstract: We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning.


title: Tetracyanoquinodimethane-Mediated Flow Injection Analysis Electrochemical Sensor for NADH Coupled with Dehydrogenase Enzymes

abstract: Abstract A flow injection analysis (FIA) sensor for the oxidation of NADH is reported. The system utilizes a graphite paste electrode modified with the electroactive material tetracyanoquinodimethane (TCNQ). TCNQ acts as an efficient mediator for the oxidation of NADH to biologically active NAD + . Alcohol dehydrogenase/lactate dehydrogenase and NAD + were coimmobilized in TCNQ-modified graphite paste using polyethylenimine to develop a FIA sensor for ethanol/lactate. The system responded rapidly with wide linearity. Response curves for ethanol/lactate and NADH are reported,


title: A comparison of feature extraction techniques for malware analysis

abstract: The manifold growth of malware in recent years has resulted in extensive research being conducted in the domain of malware analysis and detection, and theories from a wide variety of scientific knowledge domains have been applied to solve this problem. The algorithms from the machine learning paradigm have been particularly explored, and many feature extraction methods have been proposed in the literature for representing malware as feature vectors to be used in machine learning algorithms. In this paper we present a comparison of several feature extraction techniques by first applying them on system call logs of real malware, and then evaluating them using a random forest classifier. In our experiment the HMM-based feature extraction method outperformed the other methods by obtaining an F-measure of 0.87. We also explored the possibility of using ensembles of feature extraction methods, and discovered that combination of HMM-based features with bigram frequency features improved the F-measure by 1.7%.


title: Neural Models for Key Phrase Detection and Question Generation

abstract: A method, system, and storage device storing a computer program, for generating questions based on provided content, such as, for example, a document having words. The method comprises automatically estimating the probability of interesting phrases in the provided content, and generating a question in natural language based on the estimating. In one example embodiment herein, the estimating includes predicting the interesting phrases as answers, and the estimating is performed by a neural model. The method further comprises conditioning a question generation model based on the interesting phrases predicted in the predicting, the question generation model generating the question. The method also can include training the neural model. In one example, the method further comprises identifying start and end locations of the phrases in the provided content, and the identifying includes performing a dot product attention mechanism parameterizing a probability distribution.


Clustering-based Approach for Anomaly Detection in XACML Policies


title: Next-generation sequencing data interpretation: enhancing reproducibility and accessibility

abstract: Areas of life sciences research that were previously distant from each other in ideology, analysis practices and toolkits, such as microbial ecology and personalized medicine, have all embraced techniques that rely on next-generation sequencing instruments. Yet the capacity to generate the data greatly outpaces our ability to analyse it. Existing sequencing technologies are more mature and accessible than the methodologies that are available for individual researchers to move, store, analyse and present data in a fashion that is transparent and reproducible. Here we discuss currently pressing issues with analysis, interpretation, reproducibility and accessibility of these data, and we present promising solutions and venture into potential future developments.


title: Value of Bulk Energy Storage for Managing Wind Power Fluctuations

abstract: This paper considers the impact of uncertain wind forecasts on the value of stored energy (such as pumped hydro) in a future U.K. system, where wind supplies over 20% of the energy. Providing more of the increased requirement for reserves from standing reserve sources could increase system operation efficiency, enhance wind power absorption, achieve fuel cost savings, and reduce CO2 emissions. Generally, storage-based standing reserve's value is driven by the amount of installed wind and by generation system flexibility. Benefits are more significant in systems with low generation flexibility and with large installed wind capacity. Storage is uniquely able to stock up generated excesses during high-wind/low-demand periods, and subsequently discharge this energy as needed. When storage is combined with standing reserve provided from conventional generation (e.g., open-cycle gas turbines), it is valuable in servicing the highly frequent smaller imbalances


title: Securing digital identities in the cloud by selecting an apposite Federated Identity Management from SAML, OAuth and OpenID Connect

abstract: Access to computer systems and the information held on them, be it commercially or personally sensitive, is naturally, strictly controlled by both legal and technical security measures. One such method is digital identity, which is used to authenticate and authorize users to provide access to IT infrastructure to perform official, financial or sensitive operations within organisations. However, transmitting and sharing this sensitive information with other organisations over insecure channels always poses a significant security and privacy risk. An example of an effective solution to this problem is the Federated Identity Management (FIdM) standard adopted in the cloud environment. The FIdM standard is used to authenticate and authorize users across multiple organisations to obtain access to their networks and resources without transmitting sensitive information to other organisations. Using the same authentication and authorization details among multiple organisations in one federated group, it protects the identities and credentials of users in the group. This protection is a balance, mitigating security risk whilst maintaining a positive experience for users. Three of the most popular FIdM standards are Security Assertion Markup Language (SAML), Open Authentication (OAuth), and OpenID Connect (OIDC). This paper presents an assessment of these standards considering their architectural design, working, security strength and security vulnerability, to cognise and ascertain effective usages to protect digital identities and credentials. Firstly, it explains the architectural design and working of these standards. Secondly, it proposes several assessment criteria and compares functionalities of these standards based on the proposed criteria. Finally, it presents a comprehensive analysis of their security vulnerabilities to aid in selecting an apposite FIdM. This analysis of security vulnerabilities is of great significance because their improper or erroneous deployment may be exploited for attacks.


title: Transformational leadership and teacher commitment to organizational values: The mediating effects of collective teacher efficacy

abstract: Transformational leadership researchers have given little attention to teacher expectations that mediate between goals and actions. The most important of these expectations, teacher efficacy, refers to teacher beliefs that they will be able to bring about student learning. This study examined the mediating effects of teacher efficacy by comparing two models derived from Bandura's social-cognitive theory. Model A hypothesized that transformational leadership would contribute to teacher commitment to organizational values exclusively through collective teacher efficacy. Model B hypothesized that leadership would have direct effects on teacher commitment and indirect effects through teacher efficacy. Data from 3,074 teachers in 218 elementary schools in a cross-validation sample design provided greater support for Model B than Model A. Transformational leadership had an impact on the collective teacher efficacy of the school; teacher efficacy alone predicted teacher commitment to community partnerships; and ...


title: Heterogeneous transfer learning for image classification

abstract: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset.


title: Victim Willingness to Report Crime to Police: Does Procedural Justice or Outcome Matter Most?

abstract: Research has shown that procedural justice is an important predictor of victims' satisfaction with the criminal justice system. What remains relatively unclear, however, is whether procedural justice is more important to victims than other instrumental factors, such as the outcome favorability of their encounters with police. Some studies find that victims are more satisfied with the criminal justice system when they have received a favorable outcome, while others show that procedural justice elements dominate their concerns. To date, only three studies have investigated this issue in the context of victims' willingness to cooperate with the police. Again, however, the results have been inconclusive. The present study utilizes survey data collected from a representative sample of 1,204 Australians to show that the effect of procedural justice on victims' willingness to report crime to police is context specific. For some victim types, procedural justice is more important, while for other victim types, instrumental factors dominate their decision to report crime.


title: Fast and Robust Neural Network Joint Models for Statistical Machine Translation

abstract: Recent work has shown success in using neural network language models (NNLMs) as features in MT systems. Here, we present a novel formulation for a neural network joint model (NNJM), which augments the NNLM with a source context window. Our model is purely lexicalized and can be integrated into any MT decoder. We also present several variations of the NNJM which provide significant additive improvements.


title: Using Data Mining Techniques for Detecting Terror-Related Activities on the Web

abstract: An innovative knowledge-based methodology for terrorist detection by using Web traffic content as the audit information is presented. The proposed methodology learns the typical behavior (‘profile’) of terrorists by applying a data mining algorithm to the textual content of terror-related Web sites. The resulting profile is used by the system to perform real-time detection of users suspected of being engaged in terrorist activities. The Receiver-Operator Characteristic (ROC) analysis shows that this methodology can outperform a commandbased intrusion detection system.


title: A Data-Driven Approach to Predict the Success of Bank Telemarketing

abstract: Abstract We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DTs), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation set, using the most recent data (after July 2012) and a rolling window scheme. The NN presented the best results (AUC = 0.8 and ALIFT = 0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.


title: Rogowski coil with an active integrator for measurement of switching impulse current

abstract: This paper presents design and construction of an impulse current measuring system based on a Rogowski coil with various types of integrators, a conventional RC integrator, a compensated RC integrator, and an active integrator. The developed measuring systems were designed for measurement of switching impulse current waveform used in a residual voltage test of arresters. The comparative measurement of the Rogowski coil with those integrators and non-inductive shunt resistor were carried out in an HV Laboratory. From the experiment results measured by the same Rogowski coil with various integrators show that all current measuring systems provide accurate front time. The Rogowski coil with the conventional RC and compensated integrators provide inaccurate measurement of inverse current peak and tail time. The Rogowski coil with the active integrator provides all fairly accurate waveform parameters when it was compared to the measured results by the non-inductive shunt resistor.


title: Location based services using android (LBSOID)

abstract: Initially mobile phones were developed only for voice communication but now days the scenario has changed, voice communication is just one aspect of a mobile phone. There are other aspects which are major focus of interest. Two such major factors are web browser and GPS services. Both of these functionalities are already implemented but are only in the hands of manufacturers not in the hands of users because of proprietary issues, the system does not allow the user to access the mobile hardware directly. But now, after the release of android based open source mobile phone a user can access the hardware directly and design customized native applications to develop Web and GPS enabled services and can program the other hardware components like camera etc. In this paper we will discuss the facilities available in android platform for implementing LBS services (geo-services).


title: Smart Water Distribution Management System Architecture Based on Internet of Things and Cloud Computing

abstract: The fast population growth needs to provide clean and affordable water that meet the human requirements. The water faces a problem in the future because of global climate change. An efficient water management and treatment is necessary to keep water quality and availability. Information and Communications Technologies (ICT) is combined with the Internet of thing (IoT) to facilitate water management and provide efficient operation of water resources, water distribution, and water quality. This paper presents an architecture for Smart Water Distribution System (SWDS) that corporate the IoT and Cloud Computing technologies with ICT. This architecture is designed for intermittent water supply while the previous works supposed a continuous water supply. No practical step is taken towards any phase in this paper, it contains all the theoretical requirements necessary to implement such system, especially in third world countries where the water supply is intermittent. A review of ICT-based water distribution management is provided and an overview of the most promising technologies related to them are thus explained.


title: A practical micropower programmable bandpass filter for use in bionic ears

abstract: Subthreshold Gm-C filters offer the low power and wide tunable range required for use in fully implantable bionic ears. The major design challenge that must be met is increasing the linear range. A capacitive-attenuation technique is presented and refined to allow the construction of wide-linear-range bandpass filters with greater than 1 V/sub pp/ swings. For a 100-200 Hz fully differential filter with second-order roll off slopes and greater than 60 dB dynamic range, experimental results from a 1.5-/spl mu/m, 2.8-V BiCMOS chip yield only 0.23 /spl mu/W power consumption; for a 5-10 kHz filter with the same specifications the power only increased to 6.36 /spl mu/W. Fully differential filters with first-order slopes had a dynamic range of 66 dB and power consumptions of 0.12 and 3.36 /spl mu/W in the 100-200 Hz and 5-10 kHz cases, respectively. We show that our experimental results of noise and linear range are in good accord with theoretical estimates of these quantities.


title: When saliency meets sentiment: Understanding how image content invokes emotion and sentiment

abstract: Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. On the other hand, psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several meta-level scene-type dimensions that are relevant to most images, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Facilitated by state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and shed light on the mechanism of visual sentiment evocation.


title: Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine

abstract: Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.


title: Scaling Similarity Joins over Tree-Structured Data

abstract: Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-of-the-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.


title: High Dimensional Cluster Analysis Using Path Lengths

abstract: A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimension ($N_{_{D}}>3$). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering, however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-Of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. Finally, a robust clustering technique is discussed based on reaching a consensus among the multiple approaches, overcoming the weaknesses found individually.


title: Deep $k$-Means: Jointly clustering with $k$-Means and learning representations

abstract: We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for $k$-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.


title: Self-Compassion and Body Dissatisfaction in Women: A Randomized Controlled Trial of a Brief Meditation Intervention

abstract: Body dissatisfaction is a major source of suffering among women of all ages. One potential factor that could mitigate body dissatisfaction is self-compassion, a construct that is garnering increasing research attention due to its strong association with psychological health. This study investigated whether a brief 3-week period of self-compassion meditation training would improve body satisfaction in a multigenerational group of women. Participants were randomized either to the meditation intervention group (N = 98; M age = 38.42) or to a waitlist control group (N = 130; M age = 36.42). Results suggested that compared to the control group, intervention participants experienced significantly greater reductions in body dissatisfaction, body shame, and contingent self-worth based on appearance, as well as greater gains in self-compassion and body appreciation. All improvements were maintained when assessed 3 months later. Self-compassion meditation may be a useful and cost-effective means of improving body image in adult women.


Physical Therapist Interventions for Parkinson Disease


title: Noisy evolutionary optimization algorithms – A comprehensive survey

abstract: Abstract Noisy optimization is currently receiving increasing popularity for its widespread applications in engineering optimization problems, where the objective functions are often found to be contaminated with noisy sensory measurements. In absence of knowledge of the noise-statistics, discriminating better trial solutions from the rest becomes difficult in the “selection” step of an evolutionary optimization algorithm with noisy objective/s. This paper provides a thorough survey of the present state-of-the-art research on noisy evolutionary algorithms for both single and multi-objective optimization problems. This is undertaken by incorporating one or more of the five strategies in traditional evolutionary algorithms. The strategies include (i) fitness sampling of individual trial solution, (ii) fitness estimation of noisy samples, (iii) dynamic population sizing over the generations, (iv) adaptation of the evolutionary search strategy, and (v) modification in the selection strategy.


title: Comparative Study on Voice Activity Detection Algorithm

abstract: This paper discussed several algorithms of the voice activity detection (VAD) in detail based on the feature coefficients extraction and analysis between voice signal and noise, including short-time energy, short-time average magnitude function (AM), short-time average zero-crossing rate (ZCR), short-time Auto Correlation, short-time average magnitude difference function (AMDF) in time domain, and Fourier analytics in frequency domain. Moreover the paper illustrated experiment simulation of these algorithms and comparative analysis of simulation results. The results show that each algorithm has its own advantages, and voice detection has disadvantage in certain areas by using an algorithm individually. It can maximize the advantages of each algorithm and improve signal detection accuracy by combining several algorithms learn from each other to build a multi-level VAD system.


title: Convolutional Neural Networks for object recognition on mobile devices: A case study

abstract: Deep Learning (DL), especially Convolutional Neural Networks (CNN), has become the state-of-the-art for a variety of pattern recognition issues. Technological developments have allowed the use of high-end General Purpose Graphic Processor Units (GPGPU) for accelerating numerical problem solving. They resort no only to lower computational time, but also allow considering much larger networks. Hence, nowadays computers are able to drive deeper, wider and more powerful models. State of the art CNNs have achieved human-like performance in several recognition tasks such as: handwritten character recognition, face recognition, scene labelling, object detection and image classification among others. Meanwhile, mobile devices have become powerful enough to handle the computations required for deploying CNNs models in near real-time. Here, we investigate the implementation of light-weight CNN schemes on mobile devices for domain-specific objection recognition tasks.


title: Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

abstract: The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition. Two popular approaches for solving the problem are nuclear-norm-regularized matrix approximation (Candes and Tao, 2009, Mazumder, Hastie and Tibshirani, 2010), and maximum-margin matrix factorization (Srebro, Rennie and Jaakkola, 2005). These two procedures are in some cases solving equivalent problems, but with quite different algorithms. In this article we bring the two approaches together, leading to an efficient algorithm for large matrix factorization and completion that outperforms both of these. We develop a software package"softImpute"in R for implementing our approaches, and a distributed version for very large matrices using the"Spark"cluster programming environment.


title: An Unsupervised Bayesian Modelling Approach for Storyline Detection on News Articles

abstract: Storyline detection from news articles aims at summarizing events described under a certain news topic and revealing how those events evolve over time. It is a difficult task because it requires first the detection of events from news articles published in different time periods and then the construction of storylines by linking events into coherent news stories. Moreover, each storyline has different hierarchical structures which are dependent across epochs. Existing approaches often ignore the dependency of hierarchical structures in storyline generation. In this paper, we propose an unsupervised Bayesian model, called dynamic storyline detection model, to extract structured representations and evolution patterns of storylines. The proposed model is evaluated on a large scale news corpus. Experimental results show that our proposed model outperforms several baseline approaches.


Efficient Dependency Graph Matching with the IMS Open Corpus Workbench


title: Rehabilitation robot in primary walking pattern training for SCI patient: Training robot for home-use based on the experiments of the hospital-use type

abstract: Recently, attention has been focused on incomplete-type spinal cord injury (SCI) to the central spine caused by pressure on parts of the white matter conducting pathway such as the pyramidal tract. In this paper, we focus on a training robot designed to assist with primary walking pattern training. The target patient of this training robot is one who is relearning the basic functions of the usual walking pattern, especially for patients with the incomplete type SCI to the central spine, who are capable of standing by themselves but are not capable of performing walking motions. We investigated the movement of joints of the lower extremities, the circumference of the lower extremities and the exercise intensity with the machine. From the experimental results the training robot for home-use based on the experiments on the hospital-use type is proposed. The operating panel, accessibility from wheel chair to the robot and the mechanism were modified for the home-use.


title: A tutorial on support vector regression

abstract: In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.


title: A smuggling approach to the passive in english

abstract: Abstract. I propose a theory of the passive that combines aspects of the principles and parameters analysis (no specific rules, no downward movement) and Chomsky's (1957) Syntactic Structures analysis (the arguments in the passive are generated in the same positions as they are in the active).


title: Towards a Tool for the Subjective Assessment of Speech System Interfaces (SASSI)

abstract: Applications of speech recognition are now widespread, but user-centred evaluation methods are necessary to ensure their success. Objective evaluation techniques are fairly well established, but previous subjective techniques have been unstructured and unproven. This paper reports on the first stage in the development of a questionnaire measure for the Subjective Assessment of Speech System Interfaces (SASSI). The aim of the research programme is to produce a valid, reliable and sensitive measure of users' subjective experiences with speech recognition systems. Such a technique could make an important contribution to theory and practice in the design and evaluation of speech recognition systems according to best human factors practice. A prototype questionnaire was designed, based on established measures for evaluating the usability of other kinds of user interface, and on a review of the research literature into speech system design. This consisted of 50 statements with which respondents rated their level of agreement. The questionnaire was given to users of four different speech applications, and Exploratory Factor Analysis of 214 completed questionnaires was conducted. This suggested the presence of six main factors in users' perceptions of speech systems: System Response Accuracy, Likeability, Cognitive Demand, Annoyance, Habitability and Speed. The six factors have face validity, and a reasonable level of statistical reliability. The findings form a useful theoretical and practical basis for the subjective evaluation of any speech recognition interface. However, further work is recommended, to establish the validity and sensitivity of the approach, before a final tool can be produced which warrants general use.


title: The Impact of Big Data on Chronic Disease Management

abstract: Population health management and specifically chronic disease management depend on the ability of providers to prevent development of high-cost and high-risk conditions such as diabetes, heart failure, and chronic respiratory diseases and to control them. The advent of big data analytics has potential to empower health care providers to make timely and truly evidence-based informed decisions to provide more effective and personalized treatment while reducing the costs of this care to patients. The goal of this study was to identify real-world health care applications of big data analytics to determine its effectiveness in both patient outcomes and the relief of financial burdens. The methodology for this study was a literature review utilizing 49 articles. Evidence of big data analytics being largely beneficial in the areas of risk prediction, diagnostic accuracy and patient outcome improvement, hospital readmission reduction, treatment guidance, and cost reduction was noted. Initial applications of big data analytics have proved useful in various phases of chronic disease management and could help reduce the chronic disease burden.


title: Intelligent algorithm design by using fuzzy inference on two-wheeled self-balancing vehicle

abstract: Based on the merits of handy and flexibility for two-wheeled self-balancing vehicles (TWSBV), some of the practicable applications have been enhanced and extended. In this research, we implement an empirical design method by utilizing distinct fuzzy controllers for a TWSBV, which can be implemented by Arduino Leonardo development platform, with performing the standing balance, moving balance and carrying balance. By the experiment with TWSBV platform, we demonstrate that the proposed approach outperforms the baselines.


title: GWCNN: A Metric Alignment Layer for Deep Shape Analysis

abstract: Deep neural networks provide a promising tool for incorporating semantic information in geometry processing applications. Unlike image and video processing, however, geometry processing requires handling unstructured geometric data, and thus data representation becomes an important challenge in this framework. Existing approaches tackle this challenge by converting point clouds, meshes, or polygon soups into regular representations using, e.g., multi-view images, volumetric grids or planar parameterizations. In each of these cases, geometric data representation is treated as a fixed pre-process that is largely disconnected from the machine learning tool. In contrast, we propose to optimize for the geometric representation during the network learning process using a novel metric alignment layer. Our approach maps unstructured geometric data to a regular domain by minimizing the metric distortion of the map using the regularized Gromov-Wasserstein objective. This objective is parameterized by the metric of the target domain and is differentiable; thus, it can be easily incorporated into a deep network framework. Furthermore, the objective aims to align the metrics of the input and output domains, promoting consistent output for similar shapes. We show the effectiveness of our layer within a deep network trained for shape classification, demonstrating state-of-the-art performance for nonrigid shapes.


title: Mapping relational database into OWL Structure with data semantic preservation

abstract: This paper proposes a solution for migrating an RDB into Web semantic. The solution takes an existing RDB as input, and extracts its metadata representation (MTRDB). Based on the MTRDB, a Canonical Data Model (CDM) is generated. Finally, the structure of the classification scheme in the CDM model is converted into OWL ontology and the recordsets of database are stored in owl document. A prototype has been implemented, which migrates a RDB into OWL structure, for demonstrate the practical applicability of our approach by showing how the results of reasoning of this technique can help improve the Web systems.


title: Learning Natural Language Inference with LSTM

abstract: Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution uses a match-LSTM to perform word-by-word matching of the hypothesis with the premise. This LSTM is able to place more emphasis on important word-level matching results. In particular, we observe that this LSTM remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label. On the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the state of the art.


title: A Probabilistic Calculus of Actions

abstract: We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which represents the observation X = x, and causal conditioning, P(y|do(X = x)), read the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as Bayesian networks in which some conditional probabilities may not be available.


title: Basal metabolic rate studies in humans: measurement and development of new equations

abstract: OBJECTIVE ::: To facilitate the Food and Agriculture Organization/World Health Organization/United Nations University Joint (FAO/WHO/UNU) Expert Consultation on Energy and Protein Requirements which met in Rome in 1981, Schofield et al. reviewed the literature and produced predictive equations for both sexes for the following ages: 0-3, 3-10, 10-18, 18-30, 30-60 and >60 years. These formed the basis for the equations used in 1985 FAO/WHO/UNU document, Energy and Protein Requirements. While Schofield's analysis has served a significant role in re-establishing the importance of using basal metabolic rate (BMR) to predict human energy requirements, recent workers have subsequently queried the universal validity and application of these equations. A survey of the most recent studies (1980-2000) in BMR suggests that in most cases the current FAO/WHO/UNU predictive equations overestimate BMR in many communities. The FAO/WHO/UNU equations to predict BMR were developed using a database that contained a disproportionate number--3388 out of 7173 (47%)--of Italian subjects. The Schofield database contained relatively few subjects from the tropical region. The objective here is to review the historical development in the measurement and application of BMR and to critically review the Schofield et al. BMR database presenting a series of new equations to predict BMR. ::: ::: ::: DESIGN ::: This division, while arbitrary, will enable readers who wish to omit the historical review of BMR to concentrate on the evolution of the new BMR equations. ::: ::: ::: SETTING ::: BMR data collected from published and measured values. ::: ::: ::: SUBJECTS ::: A series of new equations (Oxford equations) have been developed using a data set of 10,552 BMR values that (1) excluded all the Italian subjects and (2) included a much larger number (4018) of people from the tropics. ::: ::: ::: RESULTS ::: In general, the Oxford equations tend to produce lower BMR values than the current FAO/WHO/UNU equations in 18-30 and 30-60 year old males and in all females over 18 years of age. ::: ::: ::: CONCLUSIONS ::: This is an opportune moment to re-examine the role and place of BMR measurements in estimating total energy requirements today. The Oxford equations' future use and application will surely depend on their ability to predict more accurately the BMR in contemporary populations.


title: Design Optimization under Parameter Uncertainty for General Black-Box Models

abstract: Accurate knowledge of the effect of parameter uncertainty on process design and operation is essential for optimal and feasible operation of a process plant. Existing approaches for dealing with uncertainty at the design and process operations level assume the existence of a well-defined model to represent process behavior and, in almost all cases, convexity of the involved equations. However, most of the realistic case studies are described by nonconvex and, more importantly, poorly characterized models. Thus, a new approach is presented in this paper that is based on the development of input−output mapping with respect to a system's flexibility and a design's optimality. High-dimensional model reduction is used as our basic mapping methodology, although different mapping techniques can be employed. The result of the proposed approach is a parametric expression of the optimal objective with respect to uncertain parameters. The proposed approach is general to accommodate black-box models as it does not re...


title: The Open-Access Journal for the Basic Principles of Diffusion Theory, Experiment and Application Methods for Droplet Size Distribution Determination of Water-inoil Emulsions using Low-Field NMR

abstract: A method using Pulsed Field Gradient Nuclear Magnetic Resonance PFG-NMR for water-incrude oil emulsion droplet size determination has been optimized and compared with optical microscope for validation. The method applies a combination of Pulsed-Field Gradient (PFG) NMR, Stimulated Echo (STE), and Carr-Purcell-Meimboom-Gill (CPMG) sequences for measuring diffusion, resolving oil and water signal and for measuring the attenuation due to a distribution in T2 values. This returns the droplet size distributions of water-in-oil emulsions within a minute. No prior assumption is made on the shape of the droplet size distribution, which enables the method to resolve for instance bimodal distributions. To validate this method, three different crude oils were used in the experiment. The emulsions prepared had water cuts from 10 to 40 %. The correlation between PFG-NMR and optical microscopy was good for the emulsions. Any potential discrepancies between the two techniques are discussed, so are the limitations and advantages of the methods.


title: Metabolic pathways promoting cancer cell survival and growth

abstract: Activation of oncogenes and loss of tumour suppressors promote metabolic reprogramming in cancer, resulting in enhanced nutrient uptake to supply energetic and biosynthetic pathways. However, nutrient limitations within solid tumours may require that malignant cells exhibit metabolic flexibility to sustain growth and survival. Here, we highlight these adaptive mechanisms and also discuss emerging approaches to probe tumour metabolism in vivo and their potential to expand the metabolic repertoire of malignant cells even further.


title: Folded Rotman lens multibeam antenna in SIW technology at 24 GHz

abstract: We introduce here a new dual-layer multibeam antenna with a folded Rotman lens used as a compact beam forming network in SIW technology. The objective is to reduce the overall size of the antenna system by folding the Rotman lens on two layers along the array port contour and using a transition based on an exotic reflector and several coupling vias holes. To validate the proposed concepts, an antenna system has been designed at 24.15 GHz. The radiating structure is a SIW slotted waveguide array made of fifteen resonant waveguides. The simulated results show very good scanning performances over ±47°. It is also demonstrated that the proposed transition can lead to a size reduction of about 50% for the lens, and more than 33% for the overall size of the antenna.


title: Factors Influencing Adoption of Information Technology Based Banking Services: A Case Study of Pakistan

abstract: The purpose of this study is to examine factors that positively influence users to adopt latest banking services which are based on information technology (IT). The study focus on three main banking services, internet banking (IB), phone banking (PB) and ATM because latest statistics show that Pakistani population has rapidly accepted these three services, compared to other IT services provided by banks. The basic constructs of Technology Acceptance Model (TAM) along with other constructs are employed to study the cause of rapid acceptance of these banking technologies. A survey is conducted in twin cities of Islamabad and Rawalpindi by distributing a questionnaire to customers of four different banks. The study shows that the intention to use the service is mainly due to trust in the service provided by banks. Results of this empirical study also reveal that factors affecting customers' intention to use the technology are not same for the three banking technologies.


title: Adolescent Behavioral, Affective, and Cognitive Engagement in School: Relationship to Dropout

abstract: BACKGROUND: High school dropout represents an important public health issue. This study assessed the 3 distinct dimensions of student engagement in high school and examined the relationships between the nature and course of such experiences and later dropout. ::: ::: METHODS: We administered questionnaires to 13,330 students (44.7% boys) from 69 high schools in the province of Quebec (Canada). During 3 consecutive high school years, students reported their behavioral, emotional, and cognitive engagement to school. Information on later dropout status was obtained through official records. ::: ::: RESULTS: Although many adolescents remained highly engaged in high school, one third reported changes, especially decreases in rule compliance, interest in school, and willingness to learn. Students reporting low engagement or important decrements in behavioral investment from the beginning of high school presented higher risks of later dropout. ::: ::: CONCLUSION: School-based interventions should address the multiple facets of high school experiences to help adolescents successfully complete their basic schooling. Creating a positive social-emotional learning environment promises better adolescent achievement and, in turn, will contribute to a healthier lifestyle.


title: A 3D Printer for Interactive Electromagnetic Devices

abstract: We introduce a new form of low-cost 3D printer to print interactive electromechanical objects with wound in place coils. At the heart of this printer is a mechanism for depositing wire within a five degree of freedom (5DOF) fused deposition modeling (FDM) 3D printer. Copper wire can be used with this mechanism to form coils which induce magnetic fields as a current is passed through them. Soft iron wire can additionally be used to form components with high magnetic permeability which are thus able to shape and direct these magnetic fields to where they are needed. When fabricated with structural plastic elements, this allows simple but complete custom electromagnetic devices to be 3D printed. As examples, we demonstrate the fabrication of a solenoid actuator for the arm of a Lucky Cat figurine, a 6-pole motor stepper stator, a reluctance motor rotor and a Ferrofluid display. In addition, we show how printed coils which generate small currents in response to user actions can be used as input sensors in interactive devices.


title: Deploying information technologies for organizational innovation: Lessons from case studies

abstract: Organizations must innovate if they are to survive in today's fiercely competitive marketplace. In this paper, we explore how leading organizations are using emerging technologies to enable novel forms of ideation that can radically increase the sheer volume of ideas they explore. In addition, we outline how organizations use technologies to cost effectively manage this increased volume of ideas by optimizing generation, mobilization, advocacy and screening, experimentation, commercialization, and even the diffusion and implementation of ideas. Critical to this is the management of knowledge during the innovation process.


title: How Social are we in the NT? An exploratory study of the use of Social Media in the Northern Territory

abstract: This paper provides an analysis of the use of social media in the Northern Territory of Australia. The data was collected through an on line Territory wide survey of 419 self-identified social media users and five focus groups. An analysis of the respondent’s demographic characteristics and social media behaviour determined there were significant age and gender differences in social media usage. Statistically significant results were found for the frequency of applications accessed; the purpose of social media use; the manner of social media use and: attitudes and behavior towards social media use.1


title: SANA: Secure and Scalable Aggregate Network Attestation

abstract: Large numbers of smart connected devices, also named as the Internet of Things (IoT), are permeating our environments (homes, factories, cars, and also our body - with wearable devices) to collect data and act on the insight derived. Ensuring software integrity (including OS, apps, and configurations) on such smart devices is then essential to guarantee both privacy and safety. A key mechanism to protect the software integrity of these devices is remote attestation: A process that allows a remote verifier to validate the integrity of the software of a device. This process usually makes use of a signed hash value of the actual device's software, generated by dedicated hardware. While individual device attestation is a well-established technique, to date integrity verification of a very large number of devices remains an open problem, due to scalability issues. In this paper, we present SANA, the first secure and scalable protocol for efficient attestation of large sets of devices that works under realistic assumptions. SANA relies on a novel signature scheme to allow anyone to publicly verify a collective attestation in constant time and space, for virtually an unlimited number of devices. We substantially improve existing swarm attestation schemes by supporting a realistic trust model where: (1) only the targeted devices are required to implement attestation; (2) compromising any device does not harm others; and (3) all aggregators can be untrusted. We implemented SANA and demonstrated its efficiency on tiny sensor devices. Furthermore, we simulated SANA at large scale, to assess its scalability. Our results show that SANA can provide efficient attestation of networks of 1,000,000 devices, in only 2.5 seconds.


title: Genetic Algorithm for VRP with Constraints Based on Feasible Insertion

abstract: In the paper we propose a genetic algorithm based on insertion heuristics for the vehicle routing problem with constraints. A random insertion heuristic is used to construct initial solutions and to reconstruct the existing ones. The location where a randomly chosen node will be inserted is selected by calculating an objective function. The process of random insertion preserves stochas- tic characteristics of the genetic algorithm and preserves feasibility of generated individuals. The defined crossover and mutation operators incorporate random insertion heuristics, analyse individ- uals and select which parts should be reinserted. Additionally, the second population is used in the mutation process. The second population increases the probability that the solution, obtained in the mutation process, will survive in the first population and increase the probability to find the global optimum. The result comparison shows that the solutions, found by the proposed algorithm, are similar to the optimal solutions obtained by other genetic algorithms. However, in most cases the proposed algorithm finds the solution in a shorter time and it makes this algorithm competitive with others.