title: Extreme Learning Machine for Multi-Label Classification

abstract: Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers. ELM provides solutions for regression, clustering, binary classification, multiclass classifications and so on, but not for multi-label learning. Multi-label learning deals with objects having multiple labels simultaneously, which widely exist in real-world applications. Therefore, a thresholding method-based ELM is proposed in this paper to adapt ELM to multi-label classification, called extreme learning machine for multi-label classification (ELM-ML). ELM-ML outperforms other multi-label classification methods in several standard data sets in most cases, especially for applications which only have a small labeled data set.


title: An Animaloid Robot as a Cognitive Stimulator to support Elders with Cognitive Impairments: Preliminary Requirements

abstract: We focus on some prelimi~lcny, theoretical and practical, requirements for an agent-based Jystem for cognitive interaction with older people. CVe stress (a) the acceptability of an artiJicia1 agent by elders; (h) a user model, which includes user knowledge, e-epectations, goals; (c) a representation system, (0 an interaction Jysfem, including physical and verbal interaction. We describe in some detail a preliminary stuajv aimed at assessing the acceptabilif)? of interaction of cognitively impaired elders with an artiJicia1 companion by sfuajving their reactive behaviour during a simple experiinental session. First results show that &ective aspects of interaction with an artijicial companion are not affected b-v negative feelings towards technology, but that positive attitudes are required in order to achieve awareness of its usq5ilness for u cognitive interaction. IVe also give some suggestions about method for the development of a working cognitive interaction system. The agent should build its user model - including relevm~t knowledge, cepectations, and goals - by interactive learning, and operate jointly with a situation awareness engine


title: A Pattern-Based Password Authentication Scheme for Minimizing Shoulder Surfing Attack

abstract: The user usually uses password to avoid the attacks like a dictionary attack, brute force attack and shoulder surfing attack which is the famous attack nowadays. The shoulder surfing attack is a direct observation technique by watching over the user’s shoulder when they enter their password to get information. The most common authentication method used by the user is textual password. But, the textual password has many disadvantages because it is vulnerable to attack as it tends to shoulder surfing attack. In this project, a pattern-based password authentication will develop to overcome this problem. Using this scheme, the user needs to select the type of pattern that they like during registration. To login to their account, the user needs to enter the password in the form of the textual password in ordering manner based on a pattern that they choose during registration. The text password grid presented with a different style as it filled with random objects whether characters, numbers or images. This method is suitable to minimizing shoulder surfing attack as it can improve the security of user’s password and they can efficiently login to the system.


title: Robust Part-Based Hand Gesture Recognition Using Kinect Sensor

abstract: The recently developed depth sensors, e.g., the Kinect sensor, have provided new opportunities for human-computer interaction (HCI). Although great progress has been made by leveraging the Kinect sensor, e.g., in human body tracking, face recognition and human action recognition, robust hand gesture recognition remains an open problem. Compared to the entire human body, the hand is a smaller object with more complex articulations and more easily affected by segmentation errors. It is thus a very challenging problem to recognize hand gestures. This paper focuses on building a robust part-based hand gesture recognition system using Kinect sensor. To handle the noisy hand shapes obtained from the Kinect sensor, we propose a novel distance metric, Finger-Earth Mover's Distance (FEMD), to measure the dissimilarity between hand shapes. As it only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences. The extensive experiments demonstrate that our hand gesture recognition system is accurate (a 93.2% mean accuracy on a challenging 10-gesture dataset), efficient (average 0.0750 s per frame), robust to hand articulations, distortions and orientation or scale changes, and can work in uncontrolled environments (cluttered backgrounds and lighting conditions). The superiority of our system is further demonstrated in two real-life HCI applications.


title: A wearable health care system based on knitted integrated sensors

abstract: A comfortable health monitoring system named WEALTHY is presented. The system is based on a textile wearable interface implemented by integrating sensors, electrodes, and connections in fabric form, advanced signal processing techniques, and modern telecommunication systems. Sensors, electrodes and connections are realized with conductive and piezoresistive yarns. The sensorized knitted fabric is produced in a one step process. The purpose of this paper is to show the feasibility of a system based on fabric sensing elements. The capability of this system to acquire simultaneously several biomedical signals (i.e. electrocardiogram, respiration, activity) has been investigated and compared with a standard monitoring system. Furthermore, the paper presents two different methodologies for the acquisition of the respiratory signal with textile sensors. Results show that the information contained in the signals obtained by the integrated systems is comparable with that obtained by standard sensors. The proposed system is designed to monitor individuals affected by cardiovascular diseases, in particular during the rehabilitation phase. The system can also help professional workers who are subject to considerable physical and psychological stress and/or environmental and professional health risks.


Evaluating Argumentative and Narrative Essays using Graphs


title: Static scheduling algorithms for allocating directed task graphs to multiprocessors

abstract: Static scheduling of a program represented by a directed task graph on a multiprocessor system to minimize the program completion time is a well-known problem in parallel processing. Since finding an optimal schedule is an NP-complete problem in general, researchers have resorted to devising efficient heuristics. A plethora of heuristics have been proposed based on a wide spectrum of techniques, including branch-and-bound, integer-programming, searching, graph-theory, randomization, genetic algorithms, and evolutionary methods. The objective of this survey is to describe various scheduling algorithms and their functionalities in a contrasting fashion as well as examine their relative merits in terms of performance and time-complexity. Since these algorithms are based on diverse assumptions, they differ in their functionalities, and hence are difficult to describe in a unified context. We propose a taxonomy that classifies these algorithms into different categories. We consider 27 scheduling algorithms, with each algorithm explained through an easy-to-understand description followed by an illustrative example to demonstrate its operation. We also outline some of the novel and promising optimization approaches and current research trends in the area. Finally, we give an overview of the software tools that provide scheduling/mapping functionalities.


title: Hidden Conditional Random Fields

abstract: We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state conditional random field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.


title: Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data

abstract: Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that inflate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility.


Information Gain-based Exploration Using Rao-Blackwellized Particle Filters


title: Improving security level of LTE authentication and key agreement procedure

abstract: In recent years, LTE (Long Term Evolution) has been one of the mainstreams of current wireless communication systems. But when its HSS authenticates UEs, the random number RAND generated by HSS for creating other keys during its delivery from HSS to UE is unencrypted. Also, many parameters are generated by invoking a function with only one input key, thus very easily to be cracked. So in this paper, we propose an improved approach in which the Diffie-Hellman algorithm is employed to solve the exposure problem of RAND in the authentication process, and an Pair key mechanism is deployed when creating other parameters, i.e., parameters are generated by invoking a function with at least two input keys. The purpose is increasing the security levels of all generated parameters so as to make LTE more secure than before.


title: A Semantic Approach with Decision Support for Safety Service in Smart Home Management

abstract: Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.


Behind the Scenes of an Evolving Event Cloze Test


title: Two-factor authentication for the Bitcoin protocol

abstract: We show how to realize two-factor authentication for a Bitcoin wallet. To do so, we explain how to employ an ECDSA adaption of the two-party signature protocol by MacKenzie and Reiter (Int J Inf Secur 2(3---4):218---239, 2004. doi:10.1007/s10207-004-0041-0) in the context of Bitcoin and present a prototypic implementation of a Bitcoin wallet that offers both: two-factor authentication and verification over a separate channel. Since we use a smart phone as the second authentication factor, our solution can be used with hardware already available to most users and the user experience is quite similar to the existing online banking authentication methods.


abstract: A framework for evaluating security risks associated with technologies used at home.


title: A Raspberry Pi-Based Platform for Signal Processing Education [SP Education]

abstract: One of the most important application areas of signal processing (SP) is, without a doubt, the software-defined radio (SDR) field [1]-[3]. Although their introduction dates back to the 1980s, SDRs are now becoming the dominant technology in radio communications, thanks to the dramatic development of SP-optimized programmable hardware, such as field-programmable gate arrays (FPGAs) and digital signal processors (DSPs). Today, the computational throughput of these devices is such that sophisticated SP tasks can be efficiently handled, so that both the baseband and intermediate frequency (IF) sections of current communication systems are usually implemented, according to the SDR paradigm, by the FPGA's reconfigurable circuitry (e.g., [4]-[6]), or by the software running on DSPs.


title: Exploring sentiment analysis on twitter data

abstract: The growing popularity of microblogging websites has transformed these into rich resources for sentiment mining. Even though opinion mining has more than a decade of research to boost about, it is mostly confined to the exploration of formal text patterns like online reviews, news articles etc. Exploration of the challenges offered by informal and crisp microblogging have taken roots but there is scope for a large way ahead. The proposed work aims at developing a hybrid model for sentiment classification that explores the tweet specific features and uses domain independent and domain specific lexicons to offer a domain oriented approach and hence analyze and extract the consumer sentiment towards popular smart phone brands over the past few years. The experiments have proved that the results improve by around 2 points on an average over the unigram baseline.


title: Community Game Day: Using an End-of-Life Conversation Game to Encourage Advance Care Planning.

abstract: CONTEXT ::: Advance care planning (ACP) is an important process that involves discussing and documenting one's values and preferences for medical care, particularly end-of-life treatments. ::: ::: ::: OBJECTIVES ::: This convergent, mixed-methods study assessed whether an end-of-life conversation card game is an acceptable and effective means for performing ACP for patients with chronic illness and/or their caregivers when deployed in a community setting. ::: ::: ::: METHODS ::: Twenty-two games (n = 93 participants) were held in community settings surrounding Hershey, PA in 2016. Participants were recruited using random sampling from patient databases and also convenience sampling (i.e., flyers). Quantitative questionnaires and qualitative focus group interviews were administered to assess the game experience and subsequent performance of ACP behaviors. ::: ::: ::: RESULTS ::: Both quantitative and qualitative data found that Community Game Day was a well-received, positive experience for participants and 75% of participants performed ACP within three months post-intervention. ::: ::: ::: CONCLUSIONS ::: These findings suggest that using a conversation game during community outreach is a useful approach for engaging patients and caregivers in ACP. The convergence of quantitative and qualitative data strongly supports the continued investigation of the game in randomized controlled trials.


title: A 1-kW Contactless Energy Transfer System Based on a Rotary Transformer for Sealing Rollers

abstract: This paper introduces an innovative contactless energy transfer system for power supplying rotatable parts of automatic machineries as a robust alternative to sliding contacts. The design procedure of the key system devices, such as the dc/ac converter and the rotary transformer with its windings, is discussed. Different configurations in terms of switching network, number of turns, wire, and compensation are discussed with respect to their impact on the system efficiency and power factor. A thermal model to account for the core temperature is also introduced. Two setups, operating at 50 kHz and able to transfer up to 1.3 kW in a contactless fashion, are realized and tested to supply sealing roller resistors of automatic machineries. The system prototypes are then experimentally tested to validate the analytical models and the finite element simulations of the entire wireless power transfer link. Trade-offs emerge from the setups, in particular concerning efficiency, power factor, and electro-magnetic compatibility. As a consequence, we discuss the choice of the optimal configuration depending on the final application.


title: Computing persistent homology

abstract: We study the homology of a filtered d-dimensional simplicial complex K as a single algebraic entity and establish a correspondence that provides a simple description over fields. Our analysis enables us to derive a natural algorithm for computing persistent homology over an arbitrary field in any dimension. Our study also implies the lack of a simple classification over non-fields. Instead, we give an algorithm for computing individual persistent homology groups over an arbitrary PIDs in any dimension.


title: Beyond View Transformation: Cycle-Consistent Global and Partial Perception Gan for View-Invariant Gait Recognition

abstract: Cross-view gait recognition is a challenging problem when view-interval and pose variation are relatively large. In this paper, we propose Cycle-consistent Attentive Generative Adversarial Networks (CA-GAN) to map different views' gait images to view-consistent and photorealistic gait images for cross-view gait recognition. In CA-GAN, the generative network is composed of two branches, which simultaneously perceives human's global contexts and local body parts information respectively. Moreover, we design a novel Attentive Adversarial Network (AAN) to adaptively learn different weights for the discriminator's receptive fields with attention mechanism. Furthermore, as it is hard to collect the pose-aligned gait image pairs from different views for training CA-GAN’ we combine forward cycle-consistency loss and adver-sarial loss to learn the transformation relationship from source views to target view. The combined loss function can also preserve the discriminative gait structures of different identities at the training stage. Finally, we directly exploit the synthesized view-consistent gait images for cross-view gait recognition task. Experimental results on CASIA-B demonstrate that our method not only outperforms the state-of-the-art methods in cross-view gait recognition, but also presents compelling perceptual results even across the large view-interval.


title: Predicting SMEs' adoption of enterprise systems

abstract: Purpose – The purpose of this paper is to develop a model that can be used to predict which small to medium‐sized enterprises (SMEs) are more likely to become adopters of enterprise systems (ERP, CRM, SCM and e‐procurement).Design/methodology/approach – Direct interviews were used to collect data from a random sample of SMEs located in the Northwest of England. Using logistic regression, 102 responses were analysed.Findings – The results reveal that the factors influencing SMEs' adoption of enterprise systems are different from the factors influencing SMEs' adoption of other previously studied information systems (IS) innovations. SMEs were found to be more influenced by technological and organisational factors than environmental factors. Moreover, the results indicate that firms with a greater perceived relative advantage, a greater ability to experiment with these systems before adoption, greater top management support, greater organisational readiness and a larger size are predicted to become adopters ...


title: Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks

abstract: Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object. We present an approach that is able to learn part models in a completely unsupervised manner, without part annotations and even without given bounding boxes during learning. The key idea is to find constellations of neural activation patterns computed using convolutional neural networks. In our experiments, we outperform existing approaches for fine-grained recognition on the CUB200-2011, NA birds, Oxford PETS, and Oxford Flowers dataset in case no part or bounding box annotations are available and achieve state-of-the-art performance for the Stanford Dog dataset. We also show the benefits of neural constellation models as a data augmentation technique for fine-tuning. Furthermore, our paper unites the areas of generic and fine-grained classification, since our approach is suitable for both scenarios. The source code of our method is available online at this http URL


title: Named Entity Recognition Through Classifier Combination

abstract: This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based learning, and hidden Markov model) are combined under different conditions. When no gazetteer or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the F-measure error by a factor of 15 to 21% on the English data.


title: Online Community of Inquiry Review: Social, Cognitive, and Teaching Presence Issues

abstract: This paper explores four issues that have emerged from the research on social, cognitive and teaching presence in an online community of inquiry. The early research in the area of online communities of inquiry has raised several issues with regard to the creation and maintenance of social, cognitive and teaching presence that require further research and analysis. The other overarching issue is the methodological validity associated with the community of inquiry framework. The first issue is about shifting social presence from socio-emotional support to a focus on group cohesion (from personal to purposeful relationships). The second issue concerns the progressive development of cognitive presence (inquiry) from exploration to resolution. That is, moving discussion beyond the exploration phase. The third issue has to do with how we conceive of teaching presence (design, facilitation, direct instruction). More specifically, is there an important distinction between facilitation and direct instruction? Finally, the methodological issue concerns qualitative transcript analysis and the validity of the coding protocol.


title: Machine Learning-Based Runtime Scheduler for Mobile Offloading Framework

abstract: Remote offloading techniques have been proposed to overcome the limited resources of mobile platforms by leveraging external powerful resources such as personal work-stations or cloud servers. Prior studies have primarily focused on core mechanisms for offloading. Yet, adaptive scheduling in such systems is important because offloading effectiveness can be influenced by varying network conditions, workload requirements, and load at the target device. In this paper, we present a study on the feasibility of applying machine learning techniques to address the adaptive scheduling problem in mobile offloading framework. The study considers 19 different machine learning algorithms and four workloads, with a dataset obtained through the deployment of an Android-based remote offloading framework prototype on actual mobile and cloud resources. From this set, a subset of machine learning algorithms, which have relatively high scheduling accuracy, is selected to implement an offline offloading scheduler. Finally, by taking computational cost and the scheduling performance into account, we use Instance-Based Learning to evaluate an online adaptive scheduler for mobile offloading. In our evaluation, we observe that an Instance Learning-based online offloading scheduler selects the best scheduling decision in 87.5% instances, in an experiment setup in which an image processing workload is offloaded while subject to varying network bandwidth conditions and the amount of data transfer.


title: Inlined Information Flow Monitoring for JavaScript

abstract: Extant security mechanisms for web apps, notably the "same-origin policy", are not sufficient to achieve confidentiality and integrity goals for the many apps that manipulate sensitive information. The trend in web apps is "mashups" which integrate JavaScript code from multiple providers in ways that can undercut existing security mechanisms. Researchers are exploring dynamic information flow controls (IFC) for JavaScript, but there are many challenges to achieving strong IFC without excessive performance cost or impractical browser modifications. This paper presents an inlined IFC monitor for ECMAScript 5 with web support, using the no-sensitive-upgrade (NSU) technique, together with experimental evaluation using synthetic mashups and performance benchmarks. On this basis it should be possible to conduct experiments at scale to evaluate feasibility of both NSU and inlined monitoring.


title: A Biomechanical Comparison of Back and Front Squats in Healthy Trained Individuals

abstract: The strength and stability of the knee plays an integral role in athletics and activities of daily living. A better understanding of knee joint biomechanics while performing variations of the squat would be useful in rehabilitation and exercise prescription. We quantified and compared tibiofemoral joint kinetics as well as muscle activity while executing front and back squats. Because of the inherent change in the position of the center of mass of the bar between the front and back squat lifts, we hypothesized that the back squat would result in increased loads on the knee joint and that the front squat would result in increased knee extensor and decreased back extensor muscle activity. A crossover study design was used. To assess the net force and torque placed on the knee and muscle activation levels, a combination of video and force data, as well as surface electromyographic data, were collected from 15 healthy trained individuals. The back squat resulted in significantly higher compressive forces and knee extensor moments than the front squat. Shear forces at the knee were small in magnitude, posteriorly directed, and did not vary between the squat variations. Although bar position did not influence muscle activity, muscle activation during the ascending phase was significantly greater than during the descending phase. The front squat was as effective as the back squat in terms of overall muscle recruitment, with significantly less compressive forces and extensor moments. The results suggest that front squats may be advantageous compared with back squats for individuals with knee problems such as meniscus tears, and for long-term joint health.


title: Advanced traffic light control system using barrier gate and GSM

abstract: The aim of the project is to design a density based traffic light control system interfaced with a barrier gate and a GSM technology. The signal timing changes automatically with the density of the traffic and delay is provided with the help of microcontroller. When the signal is red the interfaced barrier gate closes and a buzzer notifies the closing of gate, thereby blocking the traffic but when the signal is green the same barrier opens and allows a proper flow of vehicles to avoid traffic jam. The density of traffic is detected using IR sensor and the output is given to the microcontroller for timing change of the signal, and buzzer action. In front of the barrier gate a stop line is drawn and with the help of another IR sensor, the vehicle is tracked whenever it crosses the stop line. If the vehicle crosses the stop line, the intimation is given to the nearby control room, carried out by GSM technology. PIC microcontroller is used for signal timing change based on density of traffic.


title: Customer Loyalty Explained by Electronic Recovery Service Quality: Implications of the Customer Relationship Re-Establishment for Consumer Electronics E-Tailers

abstract: This non-experimental, causal study related to examine and explore the relationships among electronic service quality, customer satisfaction, electronics recovery service quality, and customer loyalty for consumer electronics e-tailers. This study adopted quota and snowball sampling. A total of 121 participants completed the online survey. Out of seven hypotheses in this study, five were supported, whereas two were not supported. Findings indicated that electronic recovery service quality had positive effect on customer loyalty. However, findings indicated that electronic recovery service quality had no effect on perceived value and customer satisfaction. Findings also indicated that perceived value and customer satisfaction were two significant variables that mediated the relationships between electronic service quality and customer loyalty. Moreover, this study found that electronic service quality had no direct effect on customer satisfaction, but had indirect positive effects on customer satisfaction for consumer electronics e-tailers. In terms of practical implications, consumer electronics e-tailers' managers could formulate a competitive strategy based on the modified Electronic Customer Relationship Re-Establishment model to retain current customers and to enhance customer relationship management (CRM). The limitations and recommendations for future research were also included in this study. ::: Keywords: Service Recovery, Customer Loyalty, E-S-QUAL, E-Tailer


title: Accentuated Molecular Detection Technique to Segregate and Identify Helminths of Fish through High Resolution Melting (HRM) Analysis

abstract: Real-time PCR coupled with high-resolution molecular (HRM) analysis was conducted to target the second internal transcribed spacer (ITS-2) of nuclear ribosomal DNA. The latter acted as a genetic marker to identify and distinguish two anisakid and one cucullanid species parasitizing marine and freshwater fish. Unique and distinct characteristics of HRM patterns were produced for each of the three roundworms investigated. The melt profiles and threshold of the cycles (Ct values), at which amplification commenced, for Anisakis simplex (Rudolphi), Contrcaecum osculatum (Rudolphi) Dujardin, Contrcaecum sp. and Dacnitoides cotylophora (Ward and Magath) were diagnostic for species. The molecular analyses by sequencing and comparing the internal transcribed spacer (ITS) of the ribosomal DNA of Dacnitoides, Anisakis and Contrcaecum assays established their distinct identity. The present investigation propagates molecular-phylogenetic and morpho-molecular analysis to characterize diagnostic constituents of cucullanid and anisakid roundworms.


title: NONLINEAR FIBER ELEMENT ANALYSIS OF A REINFORCED CONCRETE SHEAR WALL SUBJECTED TO EARTHQUAKE RECORDS

abstract: Verifying the behavior of shear walls in a tall building requires reliable response results. This paper examined nonlinear fiber element modeling of a slender reinforced concrete shear wall during large-scale shaking table testing. The goal was to understand and validate the inelastic responses given by fiber models using time history analysis. Reasonable agreement was found between the numerical and experimental responses. It was demonstrated that the spread of the second plastic hinge into the upper level of a shear wall can be adequately captured using fiber modeling in response to the effect of higher modes. The parameters of damping, shear stiffness, axial load, concrete strength, longitudinal reinforcement ratio and mass were examined. The shear and moment demand distribution were sensitive to axial loading, mass and reinforcement ratio. The drift distribution along the height, rotation, and top horizontal displacement were also investigated and it was found that the sole use of Rayleigh damping did not produce accurate responses. Increasing longitudinal reinforcement did not prevent nonlinear flexural behavior in the upper levels.


title: Measurements of ligamentum flavum thickening at lumbar spine using MRI

abstract: Introduction ::: As the ligamentum flavum (LF) covers most of the posterolateral part of the lumbar spinal canal, its thickening can be attributed to the development of lumbar canal encroachment. Nevertheless, there have been few reports describing the natural history of the LF.


title: Supercloud: A Library Cloud for Exploiting Cloud Diversity

abstract: Infrastructure-as-a-Service (IaaS) cloud providers hide available interfaces for virtual machine (VM) placement and migration, CPU capping, memory ballooning, page sharing, and I/O throttling, limiting the ways in which applications can optimally configure resources or respond to dynamically shifting workloads. Given these interfaces, applications could migrate VMs in response to diurnal workloads or changing prices, adjust resources in response to load changes, and so on. This article proposes a new abstraction that we call a Library Cloud and that allows users to customize the diverse available cloud resources to best serve their applications. We built a prototype of a Library Cloud that we call the Supercloud. The Supercloud encapsulates applications in a virtual cloud under users’ full control and can incorporate one or more availability zones within a cloud provider or across different providers. The Supercloud provides virtual machine, storage, and networking complete with a full set of management operations, allowing applications to optimize performance. In this article, we demonstrate various innovations enabled by the Library Cloud.


title: A generic and configurable source-code instrumentation component

abstract: A common prerequisite for a number of debugging and performance-analysis techniques is the injection of auxiliary program code into the application under investigation, a process called instrumentation . To accomplish this task, source-code preprocessors are often used. Unfortunately, existing preprocessing tools either focus only on a very specific aspect or use hard-coded commands for instrumentation. In this paper, we examine which basic constructs are required to specify a user-defined routine entry/exit instrumentation. This analysis serves as a basis for a generic instrumentation component working on the source-code level where the instructions to be inserted can be flexibly configured. We evaluate the identified constructs with our prototypical implementation and show that these are sufficient to fulfill the needs of a number of todays' performance-analysis tools.


title: A Stage Model of Intranet Technology Implementation and Management

abstract: In the seventies, Nolan was the first to address the need for a descriptive stage theory concerning the planning, organizing, and controlling activities associated with managing the organizational computer resource. The arrival of newer technologies such as those based on the Internet calls for fresh approaches in terms of their implementation and management. Intranet technology – which is based on Internet technology – differs from other types of IT in terms of its use and implementation. We propose a four-stage model for intranet implementation and management. Each stage is portrayed by seven general characteristics. We propose that in order to ensure intranet institutionalization, three existential crises must be overcome. Firstly, if the intranet is not nurtured by a sponsor it cannot evolve beyond its experimental beginnings. Secondly, if a critical mass of both users and content cannot be reached simultaneously, the intranet will not progress. Finally, if the intranet remains uncontrolled, it will be perceived to be useless and therefore users will abandon it.


title: Modeling relationship strength in online social networks

abstract: Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy.


title: Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

abstract: Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.


title: Scientific workflow systems: Pipeline Pilot and KNIME

abstract: There are many examples of scientific workflow systems [1, 2]; in this short article I will concentrate only on cheminformatics applications and the workflow tools most commonly used in cheminformatics, namely Pipeline Pilot [3] and KNIME [4]. Workflow solutions have been used for years in bioinformatics and other sciences, and some also have applications in so-called “business intelligence” and “predictive analytics”. Readers can find details of Discovery Net, Galaxy, Kepler, Triana, SOMA, SMILA, VisTrails, and others on the Web. Kappler has compared Competitive Workflow, Taverna and Pipeline Pilot [5]. Taverna has been widely used in bioinformatics but is also used with the Chemistry Development Kit (CDK) [6, 7]. CDK-Taverna workflows are made freely available at myExperiment.org [8]. (myExperiment.org also includes KNIME workflows.) ::: ::: DiscoveryNet was one of the earliest examples of a scientific workflow system; its concepts were later commercialized in InforSense Knowledge Discovery Environment (KDE). My 2007 review [1] centered on Pipeline Pilot and InforSense KDE; KNIME was then a relative newcomer. In 2009 the loss-making InforSense organization was acquired by IDBS and KDE has made progress in translational medicine [9]. InforSense’s ChemSense [10] used ChemAxon’s JChem Cartridge, and ChemAxon chemical structure, property prediction, and enumeration tools. ChemSense’s three major pharmaceutical customers have turned to other solutions. The InforSense Suite lives on but it not seen as a “personal productivity tool”; rather it is integrated into the IDBS ELN platform. KNIME and Pipeline Pilot are now the market leaders in personal productivity in cheminformatics.


On the semantics of temporal prepositions and prepositionphrasesIan Pratt and


title: The Feature Selection and Intrusion Detection Problems

abstract: Cyber security is a serious global concern. The potential of cyber terrorism has posed a threat to national security; meanwhile the increasing prevalence of malware and incidents of cyber attacks hinder the utilization of the Internet to its greatest benefit and incur significant economic losses to individuals, enterprises, and public organizations. This paper presents some recent advances in intrusion detection, feature selection, and malware detection. ::: ::: In intrusion detection, stealthy and low profile attacks that include only few carefully crafted packets over an extended period of time to delude firewalls and the intrusion detection system (IDS) have been difficult to detect. In protection against malware (trojans, worms, viruses, etc.), how to detect polymorphic and metamorphic versions of recognized malware using static scanners is a great challenge. ::: ::: We present in this paper an agent based IDS architecture that is capable of detecting probe attacks at the originating host and denial of service (DoS) attacks at the boundary controllers. We investigate and compare the performance of different classifiers implemented for intrusion detection purposes. Further, we study the performance of the classifiers in real-time detection of probes and DoS attacks, with respect to intrusion data collected on a real operating network that includes a variety of simulated attacks. ::: ::: Feature selection is as important for IDS as it is for many other modeling problems. We present several techniques for feature selection and compare their performance in the IDS application. It is demonstrated that, with appropriately chosen features, both probes and DoS attacks can be detected in real time or near real time at the originating host or at the boundary controllers. ::: ::: We also briefly present some encouraging recent results in detecting polymorphic and metamorphic malware with advanced static, signature-based scanning techniques.


title: Optimization and stabilization of trajectories for constrained dynamical systems

abstract: Contact constraints, such as those between a foot and the ground or a hand and an object, are inherent in many robotic tasks. These constraints define a manifold of feasible states; while well understood mathematically, they pose numerical challenges to many algorithms for planning and controlling whole-body dynamic motions. In this paper, we present an approach to the synthesis and stabilization of complex trajectories for both fully-actuated and underactuated robots subject to contact constraints. We introduce a trajectory optimization algorithm (DIRCON) that extends the direct collocation method, naturally incorporating manifold constraints to produce a nominal trajectory with third-order integration accuracy—a critical feature for achieving reliable tracking control. We adapt the classical time-varying linear quadratic regulator to produce a local cost-to-go in the manifold tangent plane. Finally, we descend the cost-to-go using a quadratic program that incorporates unilateral friction and torque constraints. This approach is demonstrated on three complex walking and climbing locomotion examples in simulation.


COMPARING CROSS-PLATFORM DEVELOPMENT APPROACHES FOR MOBILE APPLICATIONS


title: Supervised Learning Based Algorithm Selection for Deep Neural Networks

abstract: Many recent deep learning platforms rely on thirdparty libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observe that they may achieve suboptimal performance because the library functions are not used appropriately. In this paper, we target at optimizing the operations of multiplying a matrix with the transpose of another matrix (referred to as NT operation hereafter), which contribute half of the training time of fully connected deep neural networks. Rather than directly calling the library function, we propose a supervised learning based algorithm selection approach named MTNN, which uses a gradient boosted decision tree to select one from two alternative NT implementations intelligently: (1) calling the cuBLAS library function; (2) calling our proposed algorithm TNN that uses an efficient out-of-place matrix transpose. We evaluate the performance of MTNN on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve 96% of prediction accuracy with very low computational overhead, which results in an average of 54% performance improvement on a range of NT operations. To further evaluate the impact of MTNN on the training process of deep neural networks, we have integrated MTNN into a popular deep learning platform Caffe. Our experimental results show that the revised Caffe can outperform the original one by an average of 28%. Both MTNN and the revised Caffe are open-source.


title: Visual Memory for Robust Path Following

abstract: Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.


Educational Data mining for Prediction of Student Performance Using Clustering Algorithms


title: Text Mining the History of Medicine

abstract: Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform.


title: GaAs based diffusion welded high voltage diode stacks

abstract: The determination of technical requirements for GaAs epistructures intended for high voltage diode stacks has been made. The suitable doping level of p+ substrate was estimated by the contact resistance measurements. Analysis has shown that for the p+ substrates with the current densities about 0.5-1 A/cm2 the specific contact resistance depends weakly on doping concentration (at least in the range from 5x1018 to 1x1019cm–3). The I-V measurements showed that Al/p+−pin contacts for n-layer concentration 1x1015cm–3 have lock-type barrier causing very high voltage drops in diode stacks. For p+−pin−n+ structures the forward voltage drop depends on doping level as well as on epilayer thickness. The reverse voltage depends on pin-layer thickness only. It was found that for diode stacks the suitable doping for p+ substrate is about 5x1018cm–3 and n+ layer doping in epitaxial p+−pin−n+ GaAs structures concentration must be higher than 1x1018 cm3.


title: Hidden Markov model-based speech emotion recognition

abstract: In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines and results achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English language is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. As a basis of comparison the similar judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.


Abstract Supporting data quality management in decision-making


title: Information technology governance in information technology investment decision processes: the impact of investment characteristics, external environment, and internal context

abstract: This study identifies governance patterns for information technology investment decision processes and explores the impact of organizations' investment characteristics, external environment, and internal context on the shaping of those patterns. By identifying the lead actors of the initiation, development, and approval stages in IT governance, the patterns of 57 IT investment decisions at 6 hospitals are analyzed. The results reveal seven IT governance archetypes: (1) top management monarchy, (2) top management-IT duopoly, (3) IT monarchy, (4) administration monarchy, (5) administration- IT duopoly, (6) professional monarchy, and (7) professional- IT duopoly. Each archetype is analyzed by taking into account four specific factors: IT investment level, external influence, organizational centralization, and IT function power. This study makes several contributions to IT governance theory and practice. First, IT governance is reframed to include pre-decision stages, highlighting the importance of participants other than the final decision maker. Second, the variation of IT governance archetypes suggests that even when top management approval is required, the IT department may not play a key role in the IT investment decision process. Third, governance of the pre-decision initiation and development stages is found to be jointly affected by several contextual factors, suggesting that the allocation of final decision rights is only a part of IT governance. While decision rights may be allocated by the organization a priori, the actual patterns of IT governance are contingent on contextual factors. It is important to understand how IT governance archetypes are shaped because they may affect desired outcomes of IT investments.


title: Questionnaires in Second Language Research: Construction, Administration, and Processing

abstract: Contents: Introduction. Questionnaires in Second Language Research. Constructing the Questionnaire. Administering the Questionnaire. Processing Questionnaire Data. Conclusion and Checklist. Appendix: Selected List of Published L2 Questionnaires.


title: Woman Entrepreneurs of Small and Medium Enterprises in Rajshahi Area: Opportunities and Challenges

abstract: Woman entrepreneurship development, especially among women largely focuses on the empowerment of women through developing skills in small to medium-sized enterprise (SME) and business ventures by taking risk of making investment decisions. This study reveals various aspects of woman entrepreneurs of SMEs. The opportunities and challenges faced by the woman entrepreneurs of SMEs in Rajshahi area is the main focus of the article. The study finds out the role of woman entrepreneurs of SMEs, various SME industries for woman entrepreneurs, financial and technical aids for the women, problems faced by entrepreneurial activities and opportunities to increase entrepreneurial skills among the woman entrepreneurs as well as it recommends suggestions as well. The study has some sort of limitations.


title: DC-DC boost converter design for solar electric system

abstract: The main focus of this project is to design and construct a ::: DC to DC converter (boost type) which is one of the main ::: parts in solar electric system. Besides, to ensure that the ::: output voltage will be step up from 12 V to 24 V. The 12 V ::: input voltage is from the battery storage equipment and the ::: 24 V output voltage will be the input of the inverter in solar ::: electric system. In designing process, the switching ::: frequency,/is set at 20 kHz and the duty cycle, D is 50%. ::: The tool that been used for circuits simulation and ::: validation are National Instrument Multisim software and ::: OrCAD software. Then, all the parameter values that ::: obtained from the hardware measurement are compared ::: with the calculation estimation and the circuit simulation ::: for validation purposes. Output of the project, 24 V ::: regulated DC voltage is successfully met the requirement.


title: CMOS Amperometric Instrumentation and Packaging for Biosensor Array Applications

abstract: An integrated CMOS amperometric instrument with on-chip electrodes and packaging for biosensor arrays is presented. The mixed-signal integrated circuit supports a variety of electrochemical measurement techniques including linear sweep, constant potential, cyclic and pulse voltammetry. Implemented in 0.5 μm CMOS, the 3 × mm2 chip dissipates 22.5 mW for a 200 kHz clock. The highly programmable chip provides a wide range of user-controlled stimulus rate and amplitude settings with a maximum scan range of 2 V and scan rates between 1 mV/sec and 400 V/sec. The amperometric readout circuit provides ±500 fA linear resolution and supports inputs up to ±47 μA. A 2 × 2 gold electrode array was fabricated on the surface of the CMOS instrumentation chip. An all-parylene packaging scheme was developed for compatibility with liquid test environments as well as a harsh piranha electrode cleaning process. The chip was tested using cyclic voltammetry of different concentrations of potassium ferricyanide at 100 mV/s and 200 mV/s, and results were identical to measurements using commercial instruments.


title: Fast Multi-frame Stereo Scene Flow with Motion Segmentation

abstract: We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo camera rig. Our technique also segments out moving objects from the rigid scene. In our method, we first estimate the disparity map and the 6-DOF camera motion using stereo matching and visual odometry. We then identify regions inconsistent with the estimated camera motion and compute per-pixel optical flow only at these regions. This flow proposal is fused with the camera motion-based flow proposal using fusion moves to obtain the final optical flow and motion segmentation. This unified framework benefits all four tasks – stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency. Our method is currently ranked third on the KITTI 2015 scene flow benchmark. Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3 orders of magnitude faster than the top six methods. We also report a thorough evaluation on challenging Sintel sequences with fast camera and object motion, where our method consistently outperforms OSF [30], which is currently ranked second on the KITTI benchmark.


The psychology of prejudice: In-group love or out-group hate


title: Fast top-k search in knowledge graphs

abstract: Given a graph query Q posed on a knowledge graph G, top-k graph querying is to find k matches in G with the highest ranking score according to a ranking function. Fast top-k search in knowledge graphs is challenging as both graph traversal and similarity search are expensive. Conventional top-k graph search is typically based on threshold algorithm (TA), which can no long fit the demand in the new setting. This work proposes STAR, a top-k knowledge graph search framework. It has two components: (a) a fast top-k algorithm for star queries, and (b) an assembling algorithm for general graph queries. The assembling algorithm uses star query as a building block and iteratively sweeps the star match lists with a dynamically adjusted bound. For top-k star graph query where an edge can be matched to a path with bounded length d, we develop a message passing algorithm, achieving time complexity O(d2|E| + md) and space complexity linear to d|V| (assuming the size of Q and k is bounded by a constant), where m is the maximum node degree in G. STAR can further be leveraged to answer general graph queries by decomposing a query to multiple star queries and joining their results later. Learning-based techniques to optimize query decomposition are also developed. We experimentally verify that STAR is 5–10 times faster than the state-of-the-art TA-style graph search algorithm, and 10–100 times faster than a belief propagation approach.


title: Modeling in OWL 2 without Restrictions

abstract: The Semantic Web ontology language OWL 2 DL comes with a variety of language features that enable sophisticated and practically useful modeling. However, the use of these features has been severely restricted in order to retain decidability of the language. For example, OWL 2 DL does not allow a property to be both transitive and asymmetric, which would be desirable, e.g., for representing an ancestor relation. In this paper, we argue that the so-called global restrictions of OWL 2 DL preclude many useful forms of modeling, by providing a catalog of basic modeling patterns that would be available in OWL 2 DL if the global restrictions were discarded. We then report on the results of evaluating several state-of-the-art OWL 2 DL reasoners on problems that use combinations of features in a way that the global restrictions are violated. The systems turn out to rely heavily on the global restrictions and are thus largely incapable of coping with the modeling patterns. Next we show how off-the-shelf first-order logic theorem proving technology can be used to perform reasoning in the OWL 2 direct semantics, the semantics that underlies OWL 2 DL, but without requiring the global restrictions. Applying a naive proof-of-concept implementation of this approach to the test problems was successful in all cases. Based on our observations, we make suggestions for future lines of research on expressive description logic-style OWL reasoning.


title: Event structure influences language production : Evidence from structural priming in motion event description

abstract: This priming study investigates the role of conceptual structure during language production, probing whether English speakers are sensitive to the structure of the event encoded by a prime sentence. In two experiments, participants read prime sentences aloud before describing motion events. Primes differed in (1) syntactic frame, (2) degree of lexical and conceptual overlap with target events, and (3) distribution of event components within frames. Results demonstrate that conceptual overlap between primes and targets led to priming of (a) the information that speakers chose to include in their descriptions of target events, (b) the way that information was mapped to linguistic elements, and (c) the syntactic structures that were built to communicate that information. When there was no conceptual overlap between primes and targets, priming was not successful. We conclude that conceptual structure is a level of representation activated during priming, and that it has implications for both Message Planning and Linguistic Formulation.


title: Quasi-cyclic LDPC codes for fast encoding

abstract: In this correspondence we present a special class of quasi-cyclic low-density parity-check (QC-LDPC) codes, called block-type LDPC (B-LDPC) codes, which have an efficient encoding algorithm due to the simple structure of their parity-check matrices. Since the parity-check matrix of a QC-LDPC code consists of circulant permutation matrices or the zero matrix, the required memory for storing it can be significantly reduced, as compared with randomly constructed LDPC codes. We show that the girth of a QC-LDPC code is upper-bounded by a certain number which is determined by the positions of circulant permutation matrices. The B-LDPC codes are constructed as irregular QC-LDPC codes with parity-check matrices of an almost lower triangular form so that they have an efficient encoding algorithm, good noise threshold, and low error floor. Their encoding complexity is linearly scaled regardless of the size of circulant permutation matrices.


title: Sensory reflex control for humanoid walking

abstract: Since a biped humanoid inherently suffers from instability and always risks tipping itself over, ensuring high stability and reliability of walk is one of the most important goals. This paper proposes a walk control consisting of a feedforward dynamic pattern and a feedback sensory reflex. The dynamic pattern is a rhythmic and periodic motion, which satisfies the constraints of dynamic stability and ground conditions, and is generated assuming that the models of the humanoid and the environment are known. The sensory reflex is a simple, but rapid motion programmed in respect to sensory information. The sensory reflex we propose in this paper consists of the zero moment point reflex, the landing-phase reflex, and the body-posture reflex. With the dynamic pattern and the sensory reflex, it is possible for the humanoid to walk rhythmically and to adapt itself to the environmental uncertainties. The effectiveness of our proposed method was confirmed by dynamic simulation and walk experiments on an actual 26-degree-of-freedom humanoid.


title: Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

abstract: Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring).


title: Understanding the Participant Behaviors in the Online English Learning Mobile App: A Case Study among 106,912 Learners via Google Play App Store

abstract: In this paper, we examined users' behavior and perception in a mobile English learning application called "English Practice" (EP app). EP app has been placed on Google Play store over four years and has more than 800.000 installed. The study was conducted over two months from October 2015 to December 2015 through Google Analytic by drawing on random sample of 106,912 active users from 208 countries, especially 48,176 active users of ten countries included India, Philippine, Sudan, Turkey, US, Vietnam, Thailand, Pakistan, Malaysia and Egypt were analyzed. The results showed among top ten countries, India, Philippines and Malaysia seem the countries where users spent the longer duration per session as well as more screen views per session. Further understanding about English language status among top ten countries, English popularity inside domestic was likely one of the reasons to explain the difference. In addition, the similar trend found among ten countries in which Quiz function seems been visited mostly, followed by Chat-room and Flashcard. Grammar lessons were the less popular function. We suggested that simplify learning task and reducing the content presenting in each session might look more apparel to users of EP app.


title: Learning to Understand Goal Specifications by Modelling Reward

abstract: Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.


Occupational Therapy for Children with Autism


title: Introduction: The Study of Contemporary Spanish Cinema

abstract: The past two decades have seen a rise to greater prominence of Spanish film studies as part of a wider upsurge of interest in foreign-language film that goes beyond the canonical cinematic movements such as Italian neo-realism and the French New Wave, and more recently has begun to look beyond canonical and arthouse films to study popular cinema, genre and commercial cinema and the industry that goes with these, and in addition how the general public — as opposed to the film scholar — experiences Spanish film. This chapter traces the developments in the field and highlights the implications for Spanish film study, before presenting the papers that are to follow, suggesting how they exemplify some of the current trends in the cinema, and how they also draw attention to the aspects of Spanish cinema that today’s students and scholars need to be aware of, opening up these specificities to the student and scholar for further interrogation.


Video Watermarking Using RST Resilient Technique


Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models


Polysemy: Theoretical and Computational Approaches


title: Template Based Affix Stemmer for a Morphologically Rich Language

abstract: Word stemming is one of the most significant factors that affect the performance of a Natural Language Processing (NLP) application such as Information Retrieval (IR) system, part of speech tagging, machine translation system and syntactic parsing. Urdu language raises several challenges to NLP largely due to its rich morphology. In Urdu language, stemming process is different as compared to that for other languages, as it not only depends on removing prefixes and suffixes but also on removing infixes. In this paper, we introduce a template based stemmer that eliminates all kinds of affixes i.e., prefixes, infixes and suffixes, depending on the morphological pattern of the word. The presented results are excellent and this stemmer can prove to be very affective for a morphologically rich language.


title: Hash functions: Theory, attacks, and applications

abstract: We survey theory and applications of cryptographic hash functions, such as MD5 and SHA-1, especially their resistance to collision-finding attacks. We review definitions, design principles, trace genealogy of standard hash functions, discuss generic attacks, attacks on iterative hash functions, and recent attacks on specific functions.


title: Closed-Form Training of Mahalanobis Distance for Supervised Clustering

abstract: Clustering is the task of grouping a set of objects so that objects in the same cluster are more similar to each other than to those in other clusters. The crucial step in most clustering algorithms is to find an appropriate similarity metric, which is both challenging and problem-dependent. Supervised clustering approaches, which can exploit labeled clustered training data that share a common metric with the test set, have thus been proposed. Unfortunately, current metric learning approaches for supervised clustering do not scale to large or even medium-sized datasets. In this paper, we propose a new structured Mahalanobis Distance Metric Learning method for supervised clustering. We formulate our problem as an instance of large margin structured prediction and prove that it can be solved very efficiently in closed-form. The complexity of our method is (in most cases) linear in the size of the training dataset. We further reveal a striking similarity between our approach and multivariate linear regression. Experiments on both synthetic and real datasets confirm several orders of magnitude speedup while still achieving state-of-the-art performance.


title: Semidirect Composite Onlay With Cavity Sealing: A Review of Clinical Procedures

abstract: UNLABELLED ::: The evolution in adhesive dentistry has broadened the indication of esthetic restorative procedures especially with the use of resin composite material. Depending on the clinical situation, some restorative techniques are best indicated. As an example, indirect adhesive restorations offer many advantages over direct techniques in extended cavities. In general, the indirect technique requires two appointments and a laboratory involvement, or it can be prepared chairside in a single visit either conventionally or by the use of computer-aided design/computer-aided manufacturing systems. In both cases, there will be an extra cost as well as the need of specific materials. This paper describes the clinical procedures for the chairside semidirect technique for composite onlay fabrication without the use of special equipments. The use of this technique combines the advantages of the direct and the indirect restoration. ::: ::: ::: CLINICAL SIGNIFICANCE ::: The semidirect technique for composite onlays offers the advantages of an indirect restoration and low cost, and can be the ideal treatment option for extended cavities in case of financial limitations.


title: Large Display User Experience

abstract: As large displays become more affordable, researchers are investigating the effects on productivity, and techniques for making the large display user experience more effective. Recent work has demonstrated significant productivity benefits, but has also identified numerous usability issues that inhibit productivity. Studies show that larger displays enable users to create and manage many more windows, as well as to engage in more complex multitasking behavior. In this paper, we describe various usability issues, including losing track of the cursor, accessing windows and icons at a distance, dealing with bezels in multimonitor displays, window management, and task management. We also present several novel techniques that address these issues and make users more productive on large display systems. Author


title: Flow Wars: Systemizing the Attack Surface and Defenses in Software-Defined Networks

abstract: Emerging software defined network (SDN) stacks have introduced an entirely new attack surface that is exploitable from a wide range of launch points. Through an analysis of the various attack strategies reported in prior work, and through our own efforts to enumerate new and variant attack strategies, we have gained two insights. First, we observe that different SDN controller implementations, developed independently by different groups, seem to manifest common sets of pitfalls and design weakness that enable the extensive set of attacks compiled in this paper. Second, through a principled exploration of the underlying design and implementation weaknesses that enables these attacks, we introduce a taxonomy to offer insight into the common pitfalls that enable SDN stacks to be broken or destabilized when fielded within hostile computing environments. This paper first captures our understanding of the SDN attack surface through a comprehensive survey of existing SDN attack studies, which we extend by enumerating 12 new vectors for SDN abuse. We then organize these vulnerabilities within the well-known confidentiality, integrity, and availability model, assess the severity of these attacks by replicating them in a physical SDN testbed, and evaluate them against three popular SDN controllers. We also evaluate the impact of these attacks against published SDN defense solutions. Finally, we abstract our findings to offer the research and development communities with a deeper understanding of the common design and implementation pitfalls that are enabling the abuse of SDN networks.


title: Abstract Introductory Invited Paper Electronic circuit reliability modeling

abstract: The intrinsic failure mechanisms and reliability models of state-of-the-art MOSFETs are reviewed. The simulation tools and failure equivalent circuits are described. The review includes historical background as well as a new approach for accurately predicting circuit reliability and failure rate from the system point of view.


title: iVAT and aVAT: enhanced visual analysis for cluster tendency assessment

abstract: Given a pairwise dissimilarity matrix D of a set of n objects, visual methods (such as VAT) for cluster tendency assessment generally represent D as an n×n image $\mathrm{I}(\tilde{\bf D})$ where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is the inability to highlight cluster structure in $\mathrm{I}(\tilde{\bf D})$ when D contains highly complex clusters. To address this problem, this paper proposes an improved VAT (iVAT) method by combining a path-based distance transform with VAT. In addition, an automated VAT (aVAT) method is also proposed to automatically determine the number of clusters from $\mathrm{I}(\tilde{\bf D})$. Experimental results on several synthetic and real-world data sets have demonstrated the effectiveness of our methods.


title: Factors effecting service oriented architecture implementation

abstract: With the advancement of computing the arena of software development life cycle and its traditional software architectural models are moving towards more flexible and agile techniques. Service oriented architecture is one of the modern architecture techniques which are composed of loosely coupled and flexible software components called services. Service oriented architecture supports usability, scalability and flexibility and each of these have their own implementation complexities. In this paper the focus is on those factors that affect service oriented architecture design and implementation. Authors aim to do a literature review for factors effecting service oriented architecture. The factors that have been identified earlier in service oriented architecture implementation are accumulated in this paper. The factors effecting service oriented architecture are categorized with a help of a proposed model. The proposed model can be used as a guideline for the SOA projects according to size of organization.


title: Belief, awareness, and limited reasoning

abstract: Several new logics for belief and knowledge are introduced and studied, all of which have the property that agents are not logically omniscient. In particular, in these logics, the set of beliefs of an agent does not necessarily contain all valid formulas. Thus, these logics are more suitable than traditional logics for modelling beliefs of humans (or machines) with limited reasoning capabilities. Our first logic is essentially an extension of Levesque's logic of implicit and explicit belief, where we extend to allow multiple agents and higher-level belief (i.e., beliefs about beliefs). Our second logic deals explicitly with "awareness," where, roughly speaking, it is necessary to be aware of a concept before one can have beliefs about it. Our third logic gives a model of "local reasoning," where an agent is viewed as a "society of minds," each with its own cluster of beliefs, which may contradict each other.


title: Learning Efficient Algorithms with Hierarchical Attentive Memory

abstract: In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM). It is based on a binary tree with leaves corresponding to memory cells. This allows HAM to perform memory access in O(log n) complexity, which is a significant improvement over the standard attention mechanism that requires O(n) operations, where n is the size of the memory. ::: ::: We show that an LSTM network augmented with HAM can learn algorithms for problems like merging, sorting or binary searching from pure input-output examples. In particular, it learns to sort n numbers in time O(n log n) and generalizes well to input sequences much longer than the ones seen during the training. We also show that HAM can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue.


title: A hybrid neural model for type classification of entity mentions

abstract: The semantic class (i.e., type) of an entity plays a vital role in many natural language processing tasks, such as question answering. However, most of existing type classification systems extensively rely on hand-crafted features. This paper introduces a hybrid neural model which classifies entity mentions to a wide-coverage set of 22 types derived from DBpedia. It consists of two parts. The mention model uses recurrent neural networks to recursively obtain the vector representation of an entity mention from the words it contains. The context model, on the other hand, employs multilayer perceptrons to obtain the hidden representation for contextual information of a mention. Representations obtained by the two parts are used together to predict the type distribution. Using automatically generated data, these two parts are jointly learned. Experimental studies illustrate that the proposed approach outperforms baseline methods. Moreover, when type information provided by our method is used in a question answering system, we observe a 14.7% relative improvement for the top-1 accuracy of answers.


title: A Bayesian Perspective on Locality Sensitive Hashing with Extensions for Kernel Methods

abstract: Given a collection of objects and an associated similarity measure, the all-pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user-specified threshold. In order to reduce the number of candidates to search, locality-sensitive hashing (LSH) based indexing methods are very effective. However, most such methods only use LSH for the first phase of similarity search—that is, efficient indexing for candidate generation. In this article, we present BayesLSH, a principled Bayesian algorithm for the subsequent phase of similarity search—performing candidate pruning and similarity estimation using LSH. A simpler variant, BayesLSH-Lite, which calculates similarities exactly, is also presented. Our algorithms are able to quickly prune away a large majority of the false positive candidate pairs, leading to significant speedups over baseline approaches. For BayesLSH, we also provide probabilistic guarantees on the quality of the output, both in terms of accuracy and recall. Finally, the quality of BayesLSH’s output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation, unlike standard approaches. For two state-of-the-art candidate generation algorithms, AllPairs and LSH, BayesLSH enables significant speedups, typically in the range 2 × --20 × for a wide variety of datasets. We also extend the BayesLSH algorithm for kernel methods—in which the similarity between two data objects is defined by a kernel function. Since the embedding of data points in the transformed kernel space is unknown, algorithms such as AllPairs which rely on building inverted index structure for fast similarity search do not work with kernel functions. Exhaustive search across all possible pairs is also not an option since the dataset can be huge and computing the kernel values for each pair can be prohibitive. We propose K-BayesLSH an all-pairs similarity search problem for kernel functions. K-BayesLSH leverages a recently proposed idea—kernelized locality sensitive hashing (KLSH)—for hash bit computation and candidate generation, and uses the aforementioned BayesLSH idea for candidate pruning and similarity estimation. We ran a broad spectrum of experiments on a variety of datasets drawn from different domains and with distinct kernels and find a speedup of 2 × --7 × over vanilla KLSH.


title: An Empirical Study of the Applications of Data Mining Techniques in Higher Education

abstract: Few years ago, the information flow in education field was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and to use this data to improve the quality of managerial decisions. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper addresses the applications of data mining in educational institution to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples. In modern world a huge amount of data is available which can be used effectively to produce vital information. The information achieved can be used in the field of Medical science, Education, Business, Agriculture and so on. As huge amount of data is being collected and stored in the databases, traditional statistical techniques and database management tools are no longer adequate for analyzing this huge amount of data. Data Mining (sometimes called data or knowledge discovery) has become the area of growing significance because it helps in analyzing data from different perspectives and summarizing it into useful information. (1) There are increasing research interests in using data mining in education. This new emerging field, called Educational Data Mining, concerns with developing methods that discover knowledge from data originating from educational environments (1). The data can be collected from various educational institutes that reside in their databases. The data can be personal or academic which can be used to understand students' behavior, to assist instructors, to improve teaching, to evaluate and improve e-learning systems , to improve curriculums and many other benefits.(1)(2) Educational data mining uses many techniques such as decision trees, neural networks, k-nearest neighbor, naive bayes, support vector machines and many others.(3) Using these techniques many kinds of knowledge can be discovered such as association rules, classifications and clustering. The discovered knowledge can be used for organization of syllabus, prediction regarding enrolment of students in a particular programme, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students and so on. This paper is organized as follows: Section II describes the related work. Section III describes the research question. Section IV describes data mining techniques adopted. Section V discusses the application areas of these techniques in an educational institute. Section VI concludes the paper.


title: Empath: A framework for evaluating entity-level sentiment analysis

abstract: Sentiment analysis is the fundamental component in text-driven monitoring or forecasting systems, where the general sentiment towards real-world entities (e.g., people, products, organizations) are analyzed based on the sentiment signals embedded in a myriad of web text available today. Building such systems involves several practically important problems, from data cleansing (e.g., boilerplate removal, web-spam detection), and sentiment analysis at individual mention-level (e.g., phrase, sentence-, document-level) to the aggregation of sentiment for each entity-level (e.g., person, company) analysis. Most previous research in sentiment analysis however, has focused only on individual mention-level analysis, and there has been relatively less work that copes with other practically important problems for enabling a large-scale sentiment monitoring system. In this paper, we propose Empath, a new framework for evaluating entity-level sentiment analysis. Empath leverages objective measurements of entities in various domains such as people, companies, countries, movies, and sports, to facilitate entity-level sentiment analysis and tracking. We demonstrate the utility of Empath for the evaluation of a large-scale sentiment system by applying it to various lexicons using Lydia, our own large scale text-analytics tool, over a corpus consisting of more than a terabyte of newspaper data. We expect that Empath will encourage research that encompasses end-to-end pipelines to enable a large-scale text-driven monitoring and forecasting systems.


title: The Application of Queuing Theory in Multi-Stage Production Lines

abstract: The purpose of this paper is to carry out queuing analysis to examine multi-stage production line performance to facilitate more realistic resource planning. It is one of a few such studies to improve the performance of multiproduct multi-stage production lines. This work aims to help managers in improving the efficiency, effectiveness and selecting the most suitable policy for assembly systems. The paper adopts an analytical approach based on real life data from an international battery company producing battery covers for camera model EC-196. The battery production line consists of six independent workstations namely injection molding, first color spray, second color spray, ultra-violet (UV) station, assembly station and a packing workstation. The relevant data for each workstation was collected and the chi-squared goodness test was applied to determine the arriving and leaving distributions data of processing parts. Queuing analysis reported in this work provides a basis for estimating and analyzing production systems by measures such as utilization, percentage of idle workstation, number of batches in system, number of batches in queue, expected time spent in queue, and expected time spent in system. The comparison between results and standard data in the company showed an accuracy of 93.80%.


title: The 500MHz band low power rectenna for DTV in the Tokyo area

abstract: In this paper, the 500MHz band low power rectenna with high sensitivity characteristic is described. The rectenna is consisting of the folded dipole antenna with a 1.6kΩ impedance and the Cockcroft-Walton type rectifier for improvement of the rectification efficiency and the output dc voltage. Furthermore an L-Type LPF is added to reduce the negative influence due to the junction capacitance of the SBD. The developed rectennas (stage number: 1, 2) achieve the rectification efficiency of 48.9 % (stage number: 1) and 43.1 % (stage number: 2) at −15dBm of the input power. In addition to above, the top level efficiency of 8.7 % (stage number:1) at the input power of −30dBm can be achieved. In the Tokyo are, the output dc voltage of 0.22 V can be measured at the 25 km point from the antenna tower.


title: Sleep and Athletic Performance: The Effects of Sleep Loss on Exercise Performance, and Physiological and Cognitive Responses to Exercise

abstract: Although its true function remains unclear, sleep is considered critical to human physiological and cognitive function. Equally, since sleep loss is a common occurrence prior to competition in athletes, this could significantly impact upon their athletic performance. Much of the previous research has reported that exercise performance is negatively affected following sleep loss; however, conflicting findings mean that the extent, influence, and mechanisms of sleep loss affecting exercise performance remain uncertain. For instance, research indicates some maximal physical efforts and gross motor performances can be maintained. In comparison, the few published studies investigating the effect of sleep loss on performance in athletes report a reduction in sport-specific performance. The effects of sleep loss on physiological responses to exercise also remain equivocal; however, it appears a reduction in sleep quality and quantity could result in an autonomic nervous system imbalance, simulating symptoms of the overtraining syndrome. Additionally, increases in pro-inflammatory cytokines following sleep loss could promote immune system dysfunction. Of further concern, numerous studies investigating the effects of sleep loss on cognitive function report slower and less accurate cognitive performance. Based on this context, this review aims to evaluate the importance and prevalence of sleep in athletes and summarises the effects of sleep loss (restriction and deprivation) on exercise performance, and physiological and cognitive responses to exercise. Given the equivocal understanding of sleep and athletic performance outcomes, further research and consideration is required to obtain a greater knowledge of the interaction between sleep and performance.


title: Biomedical applications of biodegradable polymers

abstract: Utilization of polymers as biomaterials has greatly impacted the advancement of modern medicine. Specifically, polymeric biomaterials that are biodegradable provide the significant advantage of being able to be broken down and removed after they have served their function. Applications are wide ranging with degradable polymers being used clinically as surgical sutures and implants. To fit functional demand, materials with desired physical, chemical, biological, biomechanical, and degradation properties must be selected. Fortunately, a wide range of natural and synthetic degradable polymers has been investigated for biomedical applications with novel materials constantly being developed to meet new challenges. This review summarizes the most recent advances in the field over the past 4 years, specifically highlighting new and interesting discoveries in tissue engineering and drug delivery applications.


title: Immunohistochemical Detection of Coxiella Burnetii in Formalin-Fixed Placenta

abstract: Coxiella burnetii is a zoonotically transmitted agent in small ruminants that can cause placentitis and subsequent abortion. In humans, Zoonotic transmission results in an acute febrile and sometimes fatal illness. Because domestic animals are the most important agent reservoir, accurate diagnosis is essential for identification of infectious tissue and infected animals. Detection of Coxiella-specific antibodies in complement fixation (CF) or agglutination assays can confirm prior exposure in sheep and goats. However, this does not offer a rapid diagnosis and must be correlated with typical clinical history and microscopic changes to fulfill the criteria for definitive diagnosis. Isolation of Coxiella from milk or placental tissues offers direct detection of the agent but necessitates working with fresh tissue infected with a human pathogen. Historically, the time-consuming method of indirect detection of specific antibodies after 14 or 28 days has been given diagnostic preference to prevent laboratory infections. Although the placental lesions associated with Coxiella infection have been adequately described, the diagnostician would benefit from the ability to definitively identify the organisms in fixed tissue. Immunohistochemical techniques are routinely utilized to diagnose abortion caused by Chlamydia psittaci, another Zoonotic cause of placentitis in small ruminants. To expand capabilities for definitive diagnosis of Coxiella abortion, we developed an avidin-biotin-peroxidase complex immunohistochemistry staining method to demonstrate Coxiella in formalin-fixed paraffin-processed placental tissues. This procedure provides the advantage of a rapid definitive diagnosis without the need to work with tissues containing viable organisms, thus eliminating the source of laboratory infections. Human Coxiella burnetii-negative (A. Louis, 190-91) and Coxiella burnetii-positive (JW261M1-2) antisera, the only known commercially available antisera, were negative for hepatitis B surface antigen, hepatitis C, human immunodeficiency virus (HIV)-1 and HIV-2 (by enzyme-linked immunosorbent assay). Titer against Campylobacter (Vibrio) by microtiter agglutination was < 25 and for Toxoplasma by indirect hemagglutination was < 64. The positive antisera had an indirect immunofluorescence titer against Coxiella burnetii of 1:60-320 for phase 1 and was negative for phase 11.


title: A Single-Stage PV Module Integrated Converter Based on a Low-Power Current-Source Inverter

abstract: This paper presents a transformerless three-phase inverter designed for the integration into a special type of a photovoltaic (PV) module, which is capable of providing an output voltage of several hundred volts. The chosen topology, a current-source inverter, features a single-stage power conversion system that directly feeds into the grid. The principle operation and control is described, and a modified modulation strategy is proposed to attenuate common-mode currents. A robust and highly efficient laboratory prototype of a 250-W module integrated converter has been implemented and tested. Its compact and flat design allows the direct attachment to the PV module.


title: Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa

abstract: This paper synthesises the research literature on teachers‟ use of Information and Communication Technology (ICT) in primary and secondary schools in sub-Saharan Africa, with a particular emphasis on improving the quality of subject teaching and learning. We focus on the internal factors of influence on teachers‟ use, or lack of use, of technology in the classroom. Our discussion attends to perceptions and beliefs about ICT and their motivating effects, technological literacy and confidence levels, pedagogical expertise related to technology use, and the role of teacher education. These factors are discussed in light of significant infrastructure and other external issues. We conclude by drawing out a number of pedagogical implications for initial teacher education and professional development to bring schooling within developing contexts into the 21 st century.