title: Cube Assessment Framework for B2C Websites Applied in a Longitudinal Study in the Luxury Fashion Industry

abstract: A literature review of existing assessment frameworks for B2C websites over more than ten years reveals that they are all built bottom-up through an essentially empirical approach of recording functionalities/features without any theoretical construct to guide the design of the framework. In an attempt to develop a theoretically consistent framework, this paper first identifies all relevant assessment categories and sub-categories for B2C websites in the luxury fashion industry. In total, eight categories and 29 sub-categories (actual assessment criteria) are identified. This framework is then applied to 15 luxury fashion brands in a longitudinal study in the years of 2006, 2008, 2010 and 2012. These four surveys document the substantial changes in website functionalities/features over the period, especially relating to how Web 2.0 and social media are diffused and adopted in the luxury fashion industry. A careful analysis of the eight categories and sub-categories reveals that the B2C website design can be characterized (and indeed defined) by virtue of three strategic choices regarding website design. The three strategic choices together with the eight categories form the basis for a theory for website design, which we name the CUBE model. We believe this model to be relevant beyond the luxury/fashion industry.


title: Area-efficient high-speed hybrid 1-bit full adder circuit using modified XNOR gate

abstract: A hybrid 1-bit full adder design is presented here using modified 3T-XNOR gate to improve the area and speed performance. The design is implemented for 1-bit full adder and then is scaled to 32-bit adder. Combination of CMOS and transmission gate logic is used to enhance the performance in terms of area, delay and power. The performance of the proposed design is evaluated through the simulation analysis in 90-nm technology with 1.2v supply voltage. The effect of scaling on the overall performance is also analyzed through the performance evaluation of 1-bit and 32-bit adder. The performance of proposed design is also compared with conventional design to verify the effectiveness in terms of area, power, delay.


title: Generic Black-Box End-to-End Attack against RNNs and Other API Calls Based Malware Classifiers

abstract: Deep neural networks (DNNs) are used to solve complex classification problems, for which other machine learning classifiers, such as SVM, fall short. Recurrent neural networks (RNNs) have been used for tasks that involves sequential inputs, such as speech to text. In the cyber security domain, RNNs based on API calls have been used effectively to classify previously un-encountered malware. In this paper, we present a black-box attack against RNNs, focusing on finding adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality. We also show that this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM. Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier, using the proposed attack, without access to the malware source code. We conclude by discussing possible defense mechanisms and countermeasures against the attack.


title: Practical Network Blocks Design with Q-Learning

abstract: Convolutional neural network provides an end-to-end solution to train many computer vision tasks and has gained great successes. However, the design of network architectures usually relies heavily on expert knowledge and is hand-crafted. In this paper, we provide a solution to automatically and efficiently design high performance network architectures. To reduce the search space of network design, we focus on constructing network blocks, which can be stacked to generate the whole network. Blocks are generated through an agent, which is trained with Q-learning to maximize the expected accuracy of the searching blocks on the learning task. Distributed asynchronous framework and early stop strategy are used to accelerate the training process. Our experimental results demonstrate that the network architectures designed by our approach perform competitively compared with hand-crafted state-of-the-art networks. We trained the Q-learning on CIFAR-100, and evaluated on CIFAR10 and ImageNet, the designed block structure achieved 3.60% error on CIFAR-10 and competitive result on ImageNet. The Q-learning process can be efficiently trained only on 32 GPUs in 3 days.


Supercomputer expands parallel processing options


title: 10,000 gold for 20 dollars: an exploratory study of World of Warcraft gold buyers

abstract: Buying virtual currencies with real money from a third-party often violates the terms of use of online games. This study quantitatively investigates players who buy in-game gold from a third-party in World of Warcraft. A cross-cultural survey dataset of 2865 players reveals that differences between Asian and Western players are negligible compared to differences across genders, job categories, and play motivations. Our findings have implications for the design and study of interactions between players and virtual currencies.


title: An augmented MapReduce framework for Building Information Modeling applications

abstract: Lifecycle and effective dada management has become and still remains a difficult task in architecture, engineering, and construction domain. Recently, the emerging Building Information Modeling (BIM) gains more and more momentum in construction data presenting, processing, and sharing. With precise 3D digital geometry graphs, BIM has the potential to integrate diverse data from construction phases (design, construction, maintenance) and therefore becomes a promising collaboration platform for involved personnel. High efficiency BIM model processing is one of the critical issues for construction industry to truly embrace BIM technology. However, this topic has been rarely studied compared with other issues in BIM research. This paper presents an augmented version of MapReduce (MR), the popular distributed parallel computing model, to solve this problem. The main contribution lies in two points: 1) BIM models are extracted into uneven partitions according to construction domain features, followed by a specifically designed pre-process mechanism, which solves the unsuitability of classical MR's even blocking in construction domain and further greatly improves performance; 2) process and thread level parallel computing techniques are introduced into MR in single node to form a two-tier hybrid parallel architecture, which is more adaptive to BIM's massive graphical data processing. The proposed framework has been successfully used in real business applications of project quantity computation and BIM model collision detection. Experiment result from lab environment proves its efficiency and usability.


title: Novel grid-connected non-isolated converters for photovoltaic systems with grounded generator

abstract: In the photovoltaic branch, transformerless inverters are nowadays the majority of the systems installed in Europe, mainly because of the higher level of efficiency and reduced cost and weight when compared with their counterparts with transformer, whether this concerns low- frequency or high-frequency models. Nevertheless, some limitations arise for the operation of such systems, mainly regarding the fact that normally the panel output cannot be grounded, what is, for example not allowed in USA. Non- grounded operation of panels composed of some new cells technologies can bring undesired effects like cell aging and efficiency reduction. In addition, security matters regarding leakage current due to the panel parasitic capacitance are also a common problem in such systems. In order to address such matters, this paper proposes a novel DC-DC converter for the application in transformerless photovoltaic systems. As main characteristic, it allows the grounding of the negative output of the photovoltaic array and provides a bipolar output voltage. Following the proposition of the circuit, the analysis and evaluation will be performed. A prototype was built and the experimental results are presented in the end.


title: A 3-Month Randomized Controlled Pilot Trial of a Patient-Centered, Computer-Based Self-Monitoring System for the Care of Type 2 Diabetes Mellitus and Hypertension

abstract: This study was performed to evaluate the effects of a patient-centered, tablet computer-based self-monitoring system for chronic disease care. A 3-month randomized controlled pilot trial was conducted to compare the use of a computer-based self-monitoring system in disease self-care (intervention group; n?=?33) with a conventional self-monitoring method (control group; n?=?30) in patients with type 2 diabetes mellitus and/or hypertension. The system was equipped with a 2-in-1 blood glucose and blood pressure monitor, a reminder feature, and video-based educational materials for the care of the two chronic diseases. The control patients were given only the 2-in-1 monitor for self-monitoring. The outcomes reported here included the glycated hemoglobin (HbA1c) level, fasting blood glucose level, systolic blood pressure, diastolic blood pressure, chronic disease knowledge, and frequency of self-monitoring. The data were collected at baseline and at 1-, 2-, and 3-month follow-up visits. The patients in the intervention group had a significant decrease in mean systolic blood pressure from baseline to 1 month (p?<?0.001) and from baseline to 3 months (p?=?0.043) compared with the control group. Significant improvements in the mean diastolic blood pressure were seen in the intervention group compared with the control group after 1 month (p?<?0.001) and after 2 months (p?=?0.028), but the change was not significant after 3 months. No significant differences were observed between the groups in the fasting blood glucose level, the HbA1c level, or chronic disease knowledge. The frequency of self-monitoring of blood glucose level and blood pressure was similar in both groups. The performances of the tablet computer-assisted and conventional disease self-monitoring appear to be useful to support/maintain blood pressure and diabetes control. The beneficial effects of the use of electronic self-care resources and support provided via mobile technologies require further confirmation in longer-term, larger trials.


Pattern recognition with spiking neural networks: a simple training method


title: A planar probe double ladder waveguide power divider

abstract: The successful demonstration of a 1:4 power divider using microstrip probes and a WR-430 rectangular waveguide is presented. The 15-dB return loss bandwidth of the nonoptimized structure is demonstrated to be 22% and its 0.5-dB insertion loss bandwidth 26%. While realized through conventional machining, such a structure is assembled in a fashion consistent with proven millimeter and submillimeter-wave micromachining techniques. Thus, the structure presents a potential power dividing and power combining architecture, which through micromachining, may be used for applications well above 100GHz.


title: Energy harvesting from anti-corrosion power sources

abstract: This work presents energy harvesting techniques from low-voltage current used to prevent galvanic corrosion between a metallic structure and a permanent copper/copper sulfate (Cu/CuSO4) reference electrode. Supercapacitors are adopted to compensate for or overcome the limitations of batteries. Then, a boost converter is used to convert the low voltage levels of galvanic corrosion to that needed by the complementary metal oxide semiconductor (CMOS) technologies used for the wireless sensor systems. Experimental results show that our proposed harvesting schemes significantly reduce the overhead of the charging circuitry, which enables nearly full charging of supercapacitors of up to 350 F under the low power conditions of 3mW (i.e., 3mA at 1 V). More importantly, our system enables maintenance-free operation of remote-monitoring cathodic protection (RMCP) systems in harsh environments, where sunlight or wind power may be unavailable or unpredictable.


title: Discussions in the comments section: Factors influencing participation and interactivity in online newspapers’ reader comments

abstract: Posting comments on the news is one of the most popular forms of user participation in online newspapers, and there is great potential for public discourse that is associated with this form of user communication. However, this potential arises only when several users participate in commenting and when their communication becomes interactive. Based on an adaption of Galtung and Ruge’s theory of newsworthiness, we hypothesized that a news article’s news factors affect both participation levels and interactivity in a news item’s comments section. The data from an online content analysis of political news are consistent with the hypotheses. This article explores the ways in which news factors affect participation levels and interactivity, and it discusses the theoretical, normative, and practical implications of those findings.


title: Implications of Decision Making Research for Decision Support and Displays

abstract: To appear in J. A. Cannon-Bowers & E. Salas (Eds.), Decision Making Under Stress: ::: Implications for Training and Simulation.


title: EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

abstract: Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional Neural Networks (CNNs), which have been used in computer vision and speech recognition, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible. In this work we introduce EEGNet, a compact convolutional network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). We show that EEGNet generalizes across paradigms better than the reference algorithms when only limited training data is available. We demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features. Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks, suggesting that the observed performances were not due to artifact or noise sources in the data.


title: Aggregation and degradation in JetStream: streaming analytics in the wide area

abstract: We present JetStream, a system that allows real-time analysis of large, widely-distributed changing data sets. Traditional approaches to distributed analytics require users to specify in advance which data is to be backhauled to a central location for analysis. This is a poor match for domains where available bandwidth is scarce and it is infeasible to collect all potentially useful data. ::: ::: JetStream addresses bandwidth limits in two ways, both of which are explicit in the programming model. The system incorporates structured storage in the form of OLAP data cubes, so data can be stored for analysis near where it is generated. Using cubes, queries can aggregate data in ways and locations of their choosing. The system also includes adaptive filtering and other transformations that adjusts data quality to match available bandwidth. Many bandwidth-saving transformations are possible; we discuss which are appropriate for which data and how they can best be combined. ::: ::: We implemented a range of analytic queries on web request logs and image data. Queries could be expressed in a few lines of code. Using structured storage on source nodes conserved network bandwidth by allowing data to be collected only when needed to fulfill queries. Our adaptive control mechanisms are responsive enough to keep end-to-end latency within a few seconds, even when available bandwidth drops by a factor of two, and are flexible enough to express practical policies.


title: Effect of cooling and seminal plasma on the capacitation status of fresh boar sperm as determined using chlortetracycline assay.

abstract: Insemination of sows with frozen-thawed spermatozoa results in lower fertility, in part due to spermatozoa having undergone a capacitation-like reaction. The present study employed chlortetracycline (CTC) staining analysis to investigate the effect of adding 20% (v/v) boar seminal plasma (SP) to boar spermatozoa on the temporal progress of capacitation and the acrosome reaction in spermatozoa cooled to 5 degrees C or incubated at 39 degrees C. Based on CTC staining patterns, seminal plasma appeared to reverse capacitation in spermatozoa that had undergone capacitation while incubated at 39 degrees C in a capacitation-supporting medium from 59.7 to 36.6% capacitated (P<0.001). Similarly, the addition of SP to boar spermatozoa cooled to 5 degrees C resulted in both the prevention of the capacitation-like reaction, and the reversal of an established capacitation-like reaction from 63.3 to 34.2% capacitated (P<0.001). These observations indicated that some constituent(s) of boar SP both prevent spermatozoa from undergoing capacitation as well as reverse capacitation in spermatozoa that have already undergone the process.


title: Microcomputer Control for Sensorless Brushless Motor

abstract: The microcomputer control of a brushless motor without a shaft position sensor is studied. A method which permits the determination of the permanent magnet rotor position by the back electromotive force (EMF) induced in the stator windings and the starting technique which uses the motor as a synchronous motor at standstill are explained. The motor voltage is chopped by commutator transistors to change the motor speed. The control system consists of a 4-b single-chip microcomputer and two quad-comparators.


title: KARMA: A Secure Economic Framework for Peer-to-Peer Resource Sharing

abstract: Peer-to-peer systems are typically designed around the assumption that all peers will willingly contribute resources to a global pool. They thus suffer from freeloaders, that is, participants who consume many more resources than they contribute. In this paper, we propose a general economic framework for avoiding freeloaders in peer-to-peer systems. Our system works by keeping track of the resource consumption and resource contribution of each participant. The overall standing of each participant in the system is represented by a single scalar value, called their karma. A set of nodes, called a bankset, keeps track of each node’s karma, increasing it as resources are contributed, and decreasing it as they are consumed. Our framework is resistant to malicious attempts by the resource provider, consumer, and a fraction of the members of the bank set. We illustrate the application of this framework to a peer-to-peer filesharing


title: Evaluating appearance models for recognition, reacquisition, and tracking

abstract: The present invention relates to a method for forming microparticles of a material from microdroplets of a solution, wherein the solution comprises the material dissolved in a solvent. The method includes the steps of directing the microdroplets into a freezing zone, whereby the microdroplets freeze. The frozen microdroplets are then contacted with a liquid non-solvent to extract the solvent into the non-solvent, thereby forming the microparticles.


title: ANOVA model for network meta-analysis of diagnostic test accuracy data

abstract: Procedures combining and summarising direct and indirect evidence from independent studies assessing the diagnostic accuracy of different tests for the same disease are referred to network meta-analysis. Network meta-analysis provides a unified inference framework and uses the data more efficiently. Nonetheless, handling the inherent correlation between sensitivity and specificity continues to be a statistical challenge. We developed an arm-based hierarchical model which expresses the logit transformed sensitivity and specificity as the sum of fixed effects for test, correlated study-effects to model the inherent correlation between sensitivity and specificity and a random error associated with various tests evaluated in a given study. We present the accuracy of 11 tests used to triage women with minor cervical lesions to detect cervical precancer. Finally, we compare the results with those from a contrast-based model which expresses the linear predictor as a contrast to a comparator test. The proposed arm-based model is more appealing than the contrast-based model since the former permits more straightforward interpretation of the parameters, makes use of all available data yielding shorter credible intervals, and models more natural variance-covariance matrix structures.


title: Topic Segmentation with a Structured Topic Model

abstract: We present a new hierarchical Bayesian model for unsupervised topic segmentation. This new model integrates a point-wise boundary sampling algorithm used in Bayesian segmentation into a structured topic model that can capture a simple hierarchical topic structure latent in documents. We develop an MCMC inference algorithm to split/merge segment(s). Experimental results show that our model outperforms previous unsupervised segmentation methods using only lexical information on Choi’s datasets and two meeting transcripts and has performance comparable to those previous methods on two written datasets.


Medical diagnosis of stroke using inductive machine learning


title: Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction

abstract: Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data. Materials and Methods: The clinically meaningful representations of medical concepts and patients are the key for health analytic applications. Most of existing approaches directly construct features mapped to raw data (e.g., ICD or CPT codes), or utilize some ontology mapping such as SNOMED codes. However, none of the existing approaches leverage EHR data directly for learning such concept representation. We propose a new way to represent heterogeneous medical concepts (e.g., diagnoses, medications and procedures) based on co-occurrence patterns in longitudinal electronic health records. The intuition behind the method is to map medical concepts that are co-occuring closely in time to similar concept vectors so that their distance will be small. We also derive a simple method to construct patient vectors from the related medical concept vectors. Results: We evaluate similar medical concepts across diagnosis, medication and procedure. The results show xx% relevancy between similar pairs of medical concepts. Our proposed representation significantly improves the predictive modeling performance for onset of heart failure (HF), where classification methods (e.g. logistic regression, neural network, support vector machine and K-nearest neighbors) achieve up to 23% improvement in area under the ROC curve (AUC) using this proposed representation. Conclusion: We proposed an effective method for patient and medical concept representation learning. The resulting representation can map relevant concepts together and also improves predictive modeling performance.


title: Towards a “canonical” agranular cortical microcircuit

abstract: Based on regularities in the intrinsic microcircuitry of cortical areas, variants of a 'canonical' cortical microcircuit have been proposed and widely adopted, particularly in computational neuroscience and neuroinformatics. However, this circuit is founded on striate cortex, which manifests perhaps the most extreme instance of cortical organization, in terms of a very high density of cells in highly differentiated cortical layers. Most other cortical regions have a less well differentiated architecture, stretching in gradients from the very dense eulaminate primary cortical areas to the other extreme of dysgranular and agranular areas of low density and poor laminar differentiation. It is unlikely for the patterns of inter- and intra-laminar connections to be uniform in spite of strong variations of their structural substrate. This assumption is corroborated by reports of divergence in intrinsic circuitry across the cortex. Consequently, it remains an important goal to define local microcircuits for a variety of cortical types, in particular, agranular cortical regions. As a counterpoint to the striate microcircuit, which may be anchored in an exceptional cytoarchitecture, we here outline a tentative microcircuit for agranular cortex. The circuit is based on a synthesis of the available literature on the local microcircuitry in agranular cortical areas of the rodent brain, investigated by anatomical and electrophysiological approaches. A central observation of these investigations is a weakening of interlaminar inhibition as cortical cytoarchitecture becomes less distinctive. Thus, our study of agranular microcircuitry revealed deviations from the well-known 'canonical' microcircuit established for striate cortex, suggesting variations in the intrinsic circuitry across the cortex that may be functionally relevant.


Extracting Structured Scholarly Information from the Machine Translation Literature.


title: Toward Autonomous Rotation-Aware Unmanned Aerial Grasping

abstract: Autonomous Unmanned Aerial Manipulators (UAMs) have shown promising potentials to transform passive sensing missions into active 3-dimension interactive missions, but they still suffer from some difficulties impeding their wide applications, such as target detection and stabilization. This letter presents a vision-based autonomous UAM with a 3DoF robotic arm for rotational grasping, with a compensation on displacement for center of gravity. First, the hardware, software architecture and state estimation methods are detailed. All the mechanical designs are fully provided as open-source hardware for the reuse by the community. Then, we analyze the flow distribution generated by rotors and plan the robotic arm's motion based on this analysis. Next, a novel detection approach called Rotation-SqueezeDet is proposed to enable rotation-aware grasping, which can give the target position and rotation angle in near real-time on Jetson TX2. Finally, the effectiveness of the proposed scheme is validated in multiple experimental trials, highlighting it's applicability of autonomous aerial grasping in GPS-denied environments.


title: A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition

abstract: Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.


title: Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data

abstract: Predicting traffic accidents is a crucial problem to improving transportation and public safety as well as safe routing. The problem is also challenging due to the rareness of accidents in space and time and spatial heterogeneity of the environment (e.g., urban vs. rural). Most previous research on traffic accident prediction conducted by domain researchers simply applied classical prediction models on limited data without addressing the above challenges properly, thus leading to unsatisfactory performance. A small number of recent works have attempted to use deep learning for traffic accident prediction. However, they either ignore time information or use only data from a small and homogeneous study area (a city), without handling spatial heterogeneity and temporal auto-correlation properly at the same time. In this paper we perform a comprehensive study on the traffic accident prediction problem using the Convolutional Long Short-Term Memory (ConvLSTM) neural network model. A number of detailed features such as weather, environment, road condition, and traffic volume are extracted from big datasets over the state of Iowa across 8 years. To address the spatial heterogeneity challenge in the data, we propose a Hetero-ConvLSTM framework, where a few novel ideas are implemented on top of the basic ConvLSTM model, such as incorporating spatial graph features and spatial model ensemble. Extensive experiments on the 8-year data over the entire state of Iowa show that the proposed framework makes reasonably accurate predictions and significantly improves the prediction accuracy over baseline approaches.


title: Interpolation technique for encoder resolution improvement in permanent magnet synchronous motor drives

abstract: This paper presents an interpolation scheme for high-resolution rotor position estimation using data from a low-resolution encoder. The estimator is produced by incorporating a geometric polynomial equation into the rotor movement profile. The low and transient speed characteristics are examined and it is shown that the technique can produce effective resolution improvements by a factor of 16


title: Performance Evaluation of the CPU Scheduler in XEN

abstract: Virtual machines with advances in software and architectural support have emerged as the basis for enterprises to allocate resources recently. One main benefit of virtual machine is server consolidation. However, flexible and complex consolidation results in some unpredictable performance problems and introduces new requirements, such as proper configurations for scheduler and reasonable arrangements for services. In this paper, we present a comparative performance evaluation of several different typical application consolidations in different configurations of scheduler parameters (under Credit scheduler) in Xen. We analyze the impact of the configurations of scheduler and mutual impact between VMs which run different types of applications, present proposals for users to adopt an efficient scheduler configuration in applying virtualization, and offer insight into the relationship of performance and scheduler parameters to motivate future innovation in virtualization.


title: Scalable tracking system for public buses using IoT technologies

abstract: Reliability in public transport is of great importance today. Millions of people travelling by public buses waste a lot of time waiting at bus stops. This paper focuses on presenting a solution to tackle the said problem by harnessing IoT technology stack. If the people travelling get accurate real time location of the buses along with estimate time for arrival at bus stop based on the real time traffic conditions, it will facilitate an overall increase in reliability on the public buses. The solution proposed in this paper involves using the existing internet enabled devices on the bus (like the e-ticketing system) or a simple android tablet to capture the real time location and send to the servers. Accessing this location data from servers will be facilitated by Representational State Transfer (REST) APIs which users can access through android application, SMS or web-portals. The system proposed will have distributed architecture in order to tackle high number of requests from users. Although there are existing solutions which harness the use of Global Positioning System (GPS) for bus tracking, they aren't ready to handle high demand on the backend which will exist in the near future. We have addressed this problem. The primary contribution of this paper is that it shows that a backend based on Message Queue Telemetry Transport (MQTT) instead of the traditionally used Hypertext transfer protocol (HTTP) based REST will be light weight, data efficient and scalable. We have proposed and implemented the backend as well as the front end required for the tracking system and presented the improvements.


title: CAA of Short Non-MCQ Answers

abstract: This paper presents a new approach for the computer-assisted assessment (CAA) ::: of non- multiple choice questions (Non-MCQ) type and short answers given by ::: students. The technique is developed for the assessment of text contents of free text ::: answers to questions of factual disciplines. ::: The Automated Text Marker (ATM) prototype automatically breaks down an expertly ::: written model answer, to a closed-ended question, into the smallest viable unit of ::: concepts with their dependencies accounted for by automatically tagging the ::: resultant concepts and their dependencies with numbers. The same process is ::: applied to each student’s answer and the resultant concepts and their ::: dependencies are then pattern-matched with those of the model examiner’s answer. ::: Two main components of ATM are the syntax and semantics analysers. In a ::: prototype test, ATM provides for one score for the grammars and the other for the ::: text contents. ::: The focus of this paper is on semantic analysis of text contents since the syntactic ::: analysis of sentences has been generally and successfully automated. ::: Various examples of sentences of different factual disciplines such as those of ::: Prolog programming, psychology and biology-related fields are analysed. ::: Justifications for these analyses of sentences are provided and the corresponding ::: prototype tests are conducted. The expected results from prototyping using ATM are ::: obtained, indicating the reliability and feasibility of this new approach for the detailed ::: assessment of text contents incorporating word order. Work is currently underway for building a larger and more comprehensive ATM ::: system for analysing and assessing text components larger than sentences such as ::: paragraphs and whole text passages. Unlike existing computerised assessment ::: systems, ATM is not a predictive system, although, like a human assessor, it is not ::: perfect.


title: Dynamic modeling of elastic tendon actuators with tendon slackening

abstract: This paper presents a new, detailed dynamics model of a novel type of actuators based on tendons with integrated springs that allows for offline adjustment of the stiffness characteristics. Like other cable or belt actuators, the elastic tendon actuator allows to radically reduce the link inertia by placing the motors near the robot base. But by additionally integrating springs in the tendons, the motor and the joint are elastically decoupled, which increases the lifespan of the tendons and the safety of the actuator. A detailed mathematical model of the actuator is derived taking tendon slackening effects into consideration. The result is a degressive stiffness curve that depends on the pretension force of the integrated tendon springs. The derived model is validated against static and dynamic experimental measurement data of a robot arm equipped with elastic tendon actuators.


title: Efficient random graph matching via degree profiles

abstract: Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erd\H{o}s-R\'{e}nyi graphs $G(n,\frac{d}{n})$. This can be viewed as an average-case and noisy version of the graph isomorphism problem. Under this model, the maximum likelihood estimator is equivalent to solving the intractable quadratic assignment problem. This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges. For dense graphs and sparse graphs, this can be improved to $\delta = O( \log^{-2/3}(n) )$ and $\delta = O( \log^{-2}(d) )$ respectively, both in polynomial time. The methodology is based on appropriately chosen distance statistics of the degree profiles (empirical distribution of the degrees of neighbors). Before this work, the best known result achieves $\delta=O(1)$ and $n^{o(1)} \leq d \leq n^c$ for some constant $c$ with an $n^{O(\log n)}$-time algorithm \cite{barak2018nearly} and $\delta=\tilde O((d/n)^4)$ and $d = \tilde{\Omega}(n^{4/5})$ with a polynomial-time algorithm \cite{dai2018performance}.


title: Medial gastrocnemius muscle fascicle active torque-length and Achilles tendon properties in young adults with spastic cerebral palsy.

abstract: Individuals with spastic cerebral palsy (CP) typically experience muscle weakness. The mechanisms responsible for muscle weakness in spastic CP are complex and may be influenced by the intrinsic mechanical properties of the muscle and tendon. The purpose of this study was to investigate the medial gastrocnemius (MG) muscle fascicle active torque-length and Achilles tendon properties in young adults with spastic CP. Nine relatively high functioning young adults with spastic CP (GMFCS I, 17±2 years) and 10 typically developing individuals (18±2 years) participated in the study. Active MG torque-length and Achilles tendon properties were assessed under controlled conditions on a dynamometer. EMG was recorded from leg muscles and ultrasound was used to measure MG fascicle length and Achilles tendon length during maximal isometric contractions at five ankle angles throughout the available range of motion and during passive rotations imposed by the dynamometer. Compared to the typically developing group, the spastic CP group had 33% lower active ankle plantarflexion torque across the available range of ankle joint motion, partially explained by 37% smaller MG muscle and 4% greater antagonistic co-contraction. The Achilles tendon slack length was also 10% longer in the spastic CP group. This study confirms young adults with mild spastic CP have altered muscle-tendon mechanical properties. The adaptation of a longer Achilles tendon may facilitate a greater storage and recovery of elastic energy and partially compensate for decreased force and work production by the small muscles of the triceps surae during activities such as locomotion.


title: Closed-form Jensen-Renyi Divergence for Mixture of Gaussians & Applications to Group-wise Shape Registration ⋆

abstract: In this paper, we propose a generalized group-wise non-rigid registration strategy for multiple unlabeled point-sets of unequal cardinality, with no bias toward any of the given point-sets. To quantify the divergence between the probability distributions--specifically Mixture of Gaussians--estimated from the given point sets, we use a recently developed information-theoretic measure called Jensen-Renyi (JR) divergence. We evaluate a closed-form JR divergence between multiple probabilistic representations for the general case where the mixture models differ in variance and the number of components. We derive the analytic gradient of the divergence measure with respect to the non-rigid registration parameters, and apply it to numerical optimization of the group-wise registration, leading to a computationally efficient and accurate algorithm. We validate our approach on synthetic data, and evaluate it on 3D cardiac shapes.


title: Tor: The Second-Generation Onion Router

abstract: We present Tor, a circuit-based low-latency anonymous communication service. This second-generation Onion Routing system addresses limitations in the original design by adding perfect forward secrecy, congestion control, directory servers, integrity checking, configurable exit policies, and a practical design for location-hidden services via rendezvous points. Tor works on the real-world Internet, requires no special privileges or kernel modifications, requires little synchronization or coordination between nodes, and provides a reasonable tradeoff between anonymity, usability, and efficiency. We briefly describe our experiences with an international network of more than 30 nodes. We close with a list of open problems in anonymous communication.


An Expanded Taxonomy of Semiotic Classes for Text Normalization


title: ALOS/PALSAR Data Evaluation for Land Use and Land Cover Mapping in the Amazon Region

abstract: In tropical biomes such as the Amazon, the cloud cover is frequent. The use of synthetic aperture radar (SAR) sensor systems is important to monitor and study these biomes because they can acquire data under cloud coverage. In this paper, an advanced land observing system phased array L-band synthetic aperture radar fine-beam dual (ALOS/PALSAR-FBD) image was evaluated for land use and land cover (LULC) classification of an Amazon test site. The features extracted from this image were also evaluated. To perform this task, a method for feature selection, considering the desired classes, was proposed. In order to better understand the applicability of this type of data in Brazilian Government projects (such as DETER-B and TerraClass), the results obtained with SAR images were compared with those from LANDSAT5/TM. The results show that the PALSAR-FBD image and the features selected are not suitable for the discrimination of densely forested classes. They presented, however, a good discrimination among the group of forested and agropastoral classes, as well as among nondensely forested classes (i.e., pastures, bare soil, and new regeneration). Therefore, these data present good applicability for mapping and monitoring of both deforestation and LULC and they can be used in the above mentioned projects. The classification of selected features, with eight classes of interest, achieved an increase of about 133% and 69% in the Kappa index (0.32) and an Overall Accuracy (0.54) regarding the PALSAR-FBD classification (0.136 and 0.322, respectively). This result shows the applicability of the proposed method. It is also expected that the features selected in this paper will improve the classification of similar study sites.


title: Algebraic feature extraction for image recognition based on an optimal discriminant criterion

abstract: Abstract A novel algebraic feature extraction method for image recognition is presented. The main difference between the present method and any other existing algebraic feature extraction method is that the present method uses an optimal discriminant criterion to extract algebraic features. For the given training image samples, a set of optimal discriminant projection vectors is calculated according to a generalized Fisher criterion function. On the basis of the optimal discriminant projection vector, the projective feature vectors of an image (i.e. algebraic features) are extracted by projecting the image onto all optimal discriminant projection vectors. The method of calculating the set of optimal discriminant projection vectors is discussed. A minimum distance classifier is also proposed for classifying projective feature vectors. Experimental results showed that the present method has good recognition performance. An important conclusion about the present method is that the Foley-Sammon optimal set of discriminant vectors is a special case of the set of optimal discriminant projection vectors.


title: Solar powered smart wearable health monitoring and tracking device based on GPS and GSM technology for children with autism

abstract: In this paper, a smart wearable health monitoring technology is presented with the GPS tracking ability for the children with autism. The main problem faced by the parents of an autistic child is to regular monitor the health condition of their children. An autistic child may have several health issues and requires constant monitoring of heart rate, pulse and body temperature but it is quite tough for parents and caretaker to measure health parameters regulary. Another acute problem is to keep tracking of the location of an autistic child whenever he or she is outside of the home without parents or caretakers. Conventional tracking devices have the limitation of small battery life. To overcome these problems, a wearable device is proposed which will be able to measure the temperature and pulse of the child and send notification with detailed information to the parents. The device will be able to detect the coordinates of it's user. Parents will have the ability to set parameters for a specific region of area. The device will send the coordinates of the child with regular notification and emergency alerts will be immediately notified whenever the pulse rate or temperature varies more or less than the standard value and whenever the child will be detected out of predefined coordinates. A wearable solar charging unit is proposed to overcome the limitation of small battery life so that the device will be always charged up whenever the child is traveling or outside of home.


title: Security lapses and the omission of information security measures: A threat control model and empirical test

abstract: Organizations and individuals are increasingly impacted by misuses of information that result from security lapses. Most of the cumulative research on information security has investigated the technical side of this critical issue, but securing organizational systems has its grounding in personal behavior. The fact remains that even with implementing mandatory controls, the application of computing defenses has not kept pace with abusers' attempts to undermine them. Studies of information security contravention behaviors have focused on some aspects of security lapses and have provided some behavioral recommendations such as punishment of offenders or ethics training. While this research has provided some insight on information security contravention, they leave incomplete our understanding of the omission of information security measures among people who know how to protect their systems but fail to do so. Yet carelessness with information and failure to take available precautions contributes to significant civil losses and even to crimes. Explanatory theory to guide research that might help to answer important questions about how to treat this omission problem lacks empirical testing. This empirical study uses protection motivation theory to articulate and test a threat control model to validate assumptions and better understand the ''knowing-doing'' gap, so that more effective interventions can be developed.


title: Improving Legal Document Summarization Using Graphical Models

abstract: In this paper, we propose a novel idea for applying probabilistic graphical models for automatic text summarization task related to a legal domain. Identification of rhetorical roles present in the sentences of a legal document is the important text mining process involved in this task. A Conditional Random Field (CRF) is applied to segment a given legal document into seven labeled components and each label represents the appropriate rhetorical roles. Feature sets with varying characteristics are employed in order to provide significant improvements in CRFs performance. Our system is then enriched by the application of a term distribution model with structured domain knowledge to extract key sentences related to rhetorical categories. The final structured summary has been observed to be closest to 80% accuracy level to the ideal summary generated by experts in the area.


title: Design of low-density parity-check codes using linear programming for modulation and detection

abstract: LDPC-MIMO system provides great capacity increment in wireless communications. Based on the powerful analysis tool of the extrinsic information transfer chart, we are able to design optimal LDPC code via curve fitting. Degree profile of the code can be optimized using linear programming and mutual information transfer function. Via simulation, our profile provides a better curve match and performance in MIMO fading channels. This fact manifests the significance to design good LDPC codes to fit different channels.


title: Dynamic neuromuscular stabilization & sports rehabilitation.

abstract: UNLABELLED ::: Dynamic neuromuscular (core) stability is necessary for optimal athletic performance and is not achieved purely by adequate strength of abdominals, spinal extensors, gluteals or any other musculature; rather, core stabilization is accomplished through precise coordination of these muscles and intra-abdominal pressure regulation by the central nervous system. Understanding developmental kinesiology provides a framework to appreciate the regional interdependence and the inter-linking of the skeleton, joints, musculature during movement and the importance of training both the dynamic and stabilizing function of muscles in the kinetic chain. The Dynamic Neuromuscular Stabilization (DNS) approach provides functional tools to assess and activate the intrinsic spinal stabilizers in order to optimize the movement system for both pre-habilitation and rehabilitation of athletic injuries and performance. ::: ::: ::: LEVEL OF EVIDENCE ::: 5.


title: Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques

abstract: Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.


title: Tri-training: exploiting unlabeled data using three classifiers

abstract: In many practical data mining applications, such as Web page classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attracted much attention. In this paper, a new co-training style semi-supervised learning algorithm, named tri-training, is proposed. This algorithm generates three classifiers from the original labeled example set. These classifiers are then refined using unlabeled examples in the tri-training process. In detail, in each round of tri-training, an unlabeled example is labeled for a classifier if the other two classifiers agree on the labeling, under certain conditions. Since tri-training neither requires the instance space to be described with sufficient and redundant views nor does it put any constraints on the supervised learning algorithm, its applicability is broader than that of previous co-training style algorithms. Experiments on UCI data sets and application to the Web page classification task indicate that tri-training can effectively exploit unlabeled data to enhance the learning performance.


title: Target Identity-aware Network Flow for online multiple target tracking

abstract: In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously. Our method allows us to overcome the confinements of data association based MOT approaches; where the performance is dependent on the object detection results provided at input level. At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip. The inference of our structured learning is done through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The proposed Lagrangian relaxation optimization finds the high quality solution to the network. During optimization a soft spatial constraint is enforced between the nodes of the graph which helps reducing the ambiguity caused by nearby targets with similar appearance in crowded scenarios. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. Our experiments involve challenging yet distinct datasets and show that our method can achieve results better than the state-of-art.


title: A biclustering approach for crowd judgment analysis

abstract: Collection of multiple annotations from the crowd workers is useful for diverse applications. In this paper, the problem of obtaining the final judgment from such crowd-based annotations has been addressed in an unsupervised way using a biclustering-based approach. Results on multiple datasets show that the proposed approach is competitively better than others, without even using the entire dataset.


title: Review of agriculture robotics: Practicality and feasibility

abstract: Concerns over food security have risen sharply in recent years. The growing human population, coupled with the shrinking agriculture resources, caused many governments and international conglomerates around the world to seek new ways to improve agriculture efficiency. This has lead to increased interest, and spending, in Agriculture Robotics. In Part 1 of this work, research activities on agriculture robotics were reviewed, with many showing promising results. However, agriculture robots remain experimental and far from being implemented on large operational scales. This paper investigates the possible reasons for this phenomena, by continuing the review of agriculture robots, only this time focusing on practicality and feasibility. Upon extensive review and analysis, the authors concluded that practical agriculture robots rely not only on advances in robotics, but also on the presence of a support infrastructure. This infrastructure encompasses all services and technologies needed by agriculture robots while in operation, this include a reliable wireless connection, an effective framework for Human Robot Interaction (HRI) between robots and agriculture workers, and a framework for software sharing and re-use. Without such infrastructure being in place, agriculture robots, no matter how advanced in design they could be, would remain impractical and infeasible. However, for many organizations, the technological and monitory costs of establishing such infrastructure could be very prohibitive, which renders agriculture robots uneconomical and enviable. Therefore, the paper concludes that the key to practical agriculture robotics is to find a novel, cost-effective, and a reliable approach to develop the support infrastructure needed for agriculture robots.


title: Power Converter EMI Analysis Including IGBT Nonlinear Switching Transient Model

abstract: It is well known that very high dv/dt and di/dt during the switching instant is the major high-frequency electromagnetic interference (EMI) source. This paper proposes an improved and simplified EMI-modeling method considering the insulated gate bipolar transistor switching-behavior model. The device turn-on and turn-off dynamics are investigated by dividing the nonlinear transition by several stages. The real device switching voltage and current are approximated by piecewise linear lines and expressed using multiple dv/dt and di/dt superposition. The derived EMI spectra suggest that the high-frequency noise is modeled with an acceptable accuracy. The proposed methodology is verified by experimental results using a dc-dc buck converter


title: Mylar: a degree-of-interest model for IDEs

abstract: Even when working on a well-modularized software system, programmers tend to spend more time navigating the code than working with it. This phenomenon arises because it is impossible to modularize the code for all tasks that occur over the lifetime of a system. We describe the use of a degree-of-interest (DOI) model to capture the task context of program elements scattered across a code base. The Mylar tool that we built encodes the DOI of program elements by monitoring the programmer's activity, and displays the encoded DOI model in views of Java and AspectJ programs. We also present the results of a preliminary diary study in which professional programmers used Mylar for their daily work on enterprise-scale Java systems.


title: A conceptual framework for incorporating cognitive principles into geographical database representation

abstract: The advancement of GIS data models to allow the effective utilization of very large heterogeneous geographic databases requires a new approach that incorporates models of human cognition. The ultimate goal is to provide a cooperative human-computer environment for spatial analysis. We describe the pyramid framework as an example of this new approach within the context of some important aspects of how humans conceptually store spatial information. The proposed framework provides the means to create multiple structural interpretations of observed geographic data and the ability to build knowledge hierarchies through the application of data mining and other statistical techniques.


title: Towards Open-Set Identity Preserving Face Synthesis

abstract: We propose a framework based on Generative Adversarial Networks to disentangle the identity and attributes of faces, such that we can conveniently recombine different identities and attributes for identity preserving face synthesis in open domains. Previous identity preserving face synthesis processes are largely confined to synthesizing faces with known identities that are already in the training dataset. To synthesize a face with identity outside the training dataset, our framework requires one input image of that subject to produce an identity vector, and any other input face image to extract an attribute vector capturing, e.g., pose, emotion, illumination, and even the background. We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute. Our proposed framework does not need to annotate the attributes of faces in any way. It is trained with an asymmetric loss function to better preserve the identity and stabilize the training process. It can also effectively leverage large amounts of unlabeled training face images to further improve the fidelity of the synthesized faces for subjects that are not presented in the labeled training face dataset. Our experiments demonstrate the efficacy of the proposed framework. We also present its usage in a much broader set of applications including face frontalization, face attribute morphing, and face adversarial example detection.


title: SimaticScan: towards a specialised vulnerability scanner for industrial control systems

abstract: Over the years, modern Industrial Control Systems (ICS) have become widely computerised and connected via the Internet and are, therefore, potentially vulnerable to cyber attacks. Currently there is a lack of vulnerability scanners specialised to ICS settings. Systems such as PLCScan and ModScan output pertinent information from a Programmable Logic Controller (PLC). However, they do not offer any information as to how vulnerable a PLC is to an attack. In this paper, we address these limitations and propose SimaticScan, a vulnerability acanner specialised to Siemens SIMATIC PLCs. Through experimentation in a comprehensive water treatment testbed, we demonstrate SimaticScan's effectiveness in determining a range of vulnerabilities across three distinct PLCs, including a previously unknown vulnerability in one of the PLCs. Our experiments also show that SimaticScan outperforms the widely used Nessus vulnerability scanner (with relevant ICS-specific plugins deployed).


title: A comparison of inclusive and restrictive strategies in modern missing data procedures.

abstract: Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.


An Update on Business-IT Alignment: "A Line" Has Been Drawn


title: The Confidence in Diabetes Self-Care Scale: Psychometric properties of a new measure of diabetes-specific self-efficacy in Dutch and U.S. patients with type 1 diabetes

abstract: OBJECTIVE —To examine psychometric properties of the Confidence in Diabetes Self-Care (CIDS) scale, a newly developed instrument assessing diabetes-specific self-efficacy in Dutch and U.S. patients with type 1 diabetes. RESEARCH DESIGN AND METHODS —Reliability and validity of the CIDS scale were evaluated in Dutch ( n = 151) and U.S. ( n = 190) outpatients with type 1 diabetes. In addition to the CIDS scale, assessment included HbA 1c , emotional distress, fear of hypoglycemia, self-esteem, anxiety, depression, and self-care behavior. The Dutch sample completed additional measures on perceived burden and importance of self-care. Test-retest reliability was established in a second Dutch sample ( n = 62). RESULTS —Internal consistency (Cronbach’s α = 0.86 for Dutch patients and 0.90 U.S. patients) and test-retest reliability (Spearman’s r = 0.85, P 1c levels were associated with low CIDS scores in the U.S. sample only. Low CIDS scores were positively associated with self-care but not with glycemic control in the original samples. CIDS scores in the U.S. and Dutch samples did not show any statistically significant differences. U.S. men had higher CIDS scores than U.S. women. CONCLUSIONS —The CIDS scale is a reliable and valid measure of diabetes-specific self-efficacy for use in patients with type 1 diabetes. High psychometric similarity allows for cross-cultural comparisons.


Finding Rough Set Reducts with Ant Colony Optimization


title: Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm

abstract: Machine learning is being implemented in bioinformatics and computational biology to solve challenging problems emerged in the analysis and modeling of biological data such as DNA, RNA, and protein. The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. The accurate classification of protein sequence would be helpful in determining the structure and function of novel protein sequences. In this article, we have proposed a distance-based sequence encoding algorithm that captures the sequence's statistical characteristics along with amino acids sequence order information. A statistical metric-based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. The performance of the proposed technique is validated using some of the best performing classifiers implemented previously for protein sequence classification. An average classification accuracy of 92% was achieved on the yeast protein sequence data set downloaded from the benchmark UniProtKB database.


title: Double Dipole Antenna For Wideband Phased Array Applications

abstract: In this paper, the regular microstrip dipole antenna shape is modified by adding upper and lower triangular patches, which makes it look like a rhombus. The rhombus shape is chosen because of its wideband characteristics over the rectangular one. The presented antenna provides a wide bandwidth of more than 100%. The measured and computed VSWR and radiation patterns for one element and the mutual coupling for two elements are presented in the paper. The proposed antenna provides endfire radiation patterns with wide beamwidth and low cross polarization level which make it a good candidate for phased arrays and radar systems. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 2461–2464, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21978


Passive Frontal Plane Stabilization in 3 D Walking


title: E-tribalized Marketing?: The Strategic Implications of Virtual Communities of Consumption

abstract: On the Internet, electronic tribes structured around consumer interests have been growing rapidly. To be effective in this new environment, managers must consider the strategic implications of the existence of different types of both virtual community and community participation. Contrasted with database-driven relationship marketing, marketers seeking success with consumers in virtual communities should consider that they: (1) are more active and discerning; (2) are less accessible to one-on-one processes, and (3) provide a wealth of valuable cultural information. Strategies for effectively targeting more desirable types of virtual communities and types of community members include: interaction-based segmentation, fragmentation-based segmentation, co-opting communities, paying-for-attention, and building networks by giving product away.


title: Protothreads: simplifying event-driven programming of memory-constrained embedded systems

abstract: Event-driven programming is a popular model for writing programs for tiny embedded systems and sensor network nodes. While event-driven programming can keep the memory overhead down, it enforces a state machine programming style which makes many programs difficult to write, maintain, and debug. We present a novel programming abstraction called protothreads that makes it possible to write event-driven programs in a thread-like style, with a memory overhead of only two bytes per protothread. We show that protothreads significantly reduce the complexity of a number of widely used programs previously written with event-driven state machines. For the examined programs the majority of the state machines could be entirely removed. In the other cases the number of states and transitions was drastically decreased. With protothreads the number of lines of code was reduced by one third. The execution time overhead of protothreads is on the order of a few processor cycles.


title: Learning real-time MRF inference for image denoising

abstract: Many computer vision problems can be formulated in a Bayesian framework with Markov Random Field (MRF) or Conditional Random Field (CRF) priors. Usually, the model assumes that a full Maximum A Posteriori (MAP) estimation will be performed for inference, which can be really slow in practice. In this paper, we argue that through appropriate training, a MRF/CRF model can be trained to perform very well on a suboptimal inference algorithm. The model is trained together with a fast inference algorithm through an optimization of a loss function on a training set containing pairs of input images and desired outputs. A validation set can be used in this approach to estimate the generalization performance of the trained system. We apply the proposed method to an image denoising application, training a Fields of Experts MRF together with a 1-4 iteration gradient descent inference algorithm. Experimental validation on unseen data shows that the proposed training approach obtains an improved benchmark performance as well as a 1000-3000 times speedup compared to the Fields of Experts MRF trained with contrastive divergence. Using the new approach, image denoising can be performed in real-time, at 8 fps on a single CPU for a 256 × 256 image sequence, with close to state-of-the-art accuracy.


title: Nonlinear random matrix theory for deep learning

abstract: Neural network configurations with random weights play an important role in the analysis of deep learning. They define the initial loss landscape and are closely related to kernel and random feature methods. Despite the fact that these networks are built out of random matrices, the vast and powerful machinery of random matrix theory has so far found limited success in studying them. A main obstacle in this direction is that neural networks are nonlinear, which prevents the straightforward utilization of many of the existing mathematical results. In this work, we open the door for direct applications of random matrix theory to deep learning by demonstrating that the pointwise nonlinearities typically applied in neural networks can be incorporated into a standard method of proof in random matrix theory known as the moments method. The test case for our study is the Gram matrix $Y^TY$, $Y=f(WX)$, where $W$ is a random weight matrix, $X$ is a random data matrix, and $f$ is a pointwise nonlinear activation function. We derive an explicit representation for the trace of the resolvent of this matrix, which defines its limiting spectral distribution. We apply these results to the computation of the asymptotic performance of single-layer random feature methods on a memorization task and to the analysis of the eigenvalues of the data covariance matrix as it propagates through a neural network. As a byproduct of our analysis, we identify an intriguing new class of activation functions with favorable properties.


title: Friending your way up the ladder: Connecting massive multiplayer online game behaviors with offline leadership

abstract: Abstract In what ways do the online behaviors of wizards and ogres map to players’ actual leadership status in the offline world? What can we learn from players’ experience in Massively Multiplayer Online games (MMOGs) to advance our understanding of leadership, especially leadership in online settings (E-leadership)? As part of a larger agenda in the emerging field of empirically testing the “mapping” between the online and offline worlds, this study aims to tackle a central issue in the E-leadership literature: how have technology and technology mediated communications transformed leadership-diagnostic traits and behaviors? To answer this question, we surveyed over 18,000 players of a popular MMOG and also collected behavioral data of a subset of survey respondents over a four-month period. Motivated by leadership theories, we examined the connection between respondents’ offline leadership status and their in-game relationship-oriented and task-related-behaviors. Our results indicate that individuals’ relationship-oriented behaviors in the virtual world are particularly relevant to players’ leadership status in voluntary organizations, while their task-oriented behaviors are marginally linked to offline leadership status in voluntary organizations, but not in companies.


title: Learning Textual Graph Patterns to Detect Causal Event Relations

abstract: This paper presents a novel method for discovering causal relations between events encoded in text. In order to determine if two events from the same sentence are in a causal relation or not, we first build a graph representation of the sentence that encodes lexical, syntactic, and semantic information. In a second step, we automatically extract multiple graph patterns (or subgraphs) from such graph representations and sort them according to their relevance in determining the causality between two events from the same sentence. Finally, in order to decide if these events are causal or not, we train a binary classifier based on what graph patterns can be mapped to the graph representation associated with the two events. Our experimental results show that capturing the feature dependencies of causal event relations using a graph representation significantly outperforms an existing method that uses a flat representation of features.


title: A Cyber Kill Chain Based Taxonomy of Banking Trojans for Evolutionary Computational Intelligence

abstract: Malware such as banking Trojans are popular with financially-motivated cybercriminals. Detection of banking Trojans remains a challenging task, due to the constant evolution of techniques used to obfuscate and circumvent existing detection and security solutions. Having a malware taxonomy can facilitate the design of mitigation strategies such as those based on evolutionary computational intelligence. Specifically, in this paper, we propose a cyber kill chain based taxonomy of banking Trojans features. This threat intelligence based taxonomy provides a stage-by-stage operational understanding of a cyber-attack, and can be highly beneficial to security practitioners and inform the design of evolutionary computational intelligence on Trojans detection and mitigation strategy. The proposed taxonomy is built upon our analysis of a real-world dataset of 127 banking Trojans collected from December 2014 to January 2016 by a major UK-based financial organization.


title: Nanoparticle therapeutics: an emerging treatment modality for cancer

abstract: Nanoparticles — particles in the size range 1–100 nm — are emerging as a class of therapeutics for cancer. Early clinical results suggest that nanoparticle therapeutics can ::: show enhanced efficacy, while simultaneously reducing side effects, owing to properties such as more targeted localization in tumours and active cellular uptake. Here, we ::: highlight the features of nanoparticle therapeutics that distinguish them from previous anticancer therapies, and describe how these features provide the potential for ::: therapeutic effects that are not achievable with other modalities. While large numbers of preclinical studies have been published, the emphasis here is placed on preclinical and clinical studies that are likely to affect clinical investigations and their implications for advancing the treatment of patients with cancer.


title: Writer identification using machine learning approaches: a comprehensive review

abstract: Handwriting is one of the most common types of questioned writing encountered and frequently attracts the attention in litigation. Contrary to the physiological characteristics, handwriting is a behavioral characteristic thus no two individuals with mature handwriting are exactly alike or an individual cannot produce the others writing exactly. Writing behavior and individualities are examined for similarities for both specimen and questioned document, thus, it is very efficient and effective strategy for biometrics. In this paper, we present a comprehensive review of writer identification methods and intend to provide taxonomy of dataset, feature extraction methods, as well as classification (conventional and deep learning based) for writer identification. For ease of reader, we grouped the discussion into English, Arabic, Western and Other languages from script prospective, whereas, from algorithm and methods perspective, we grouped the discussion with respect to implementation steps sequence. In the end, we highlighted the challenges and open research issues in the field of writer identification. Finally, we also suggest future direction.


Distributed Capability-based Access Control for the Internet of Things


Power System Stability and Optimization Techniques : An


title: Profiles in Driver Distraction: Effects of Cell Phone Conversations on Younger and Older Drivers

abstract: Our research examined the effects of hands-free cell phone conversations on simulated driving. We found that driving performance of both younger and older adults was influenced by cell phone conversations. Compared with single-task (i.e., drivingonly) conditions, when drivers used cell phones their reactions were 18% slower, their following distance was 12% greater, and they took 17% longer to recover the speed that was lost following braking. There was also a twofold increase in the number of rear-end collisions when drivers were conversing on a cell phone. These cellphone-induced effects were equivalent for younger and older adults, suggesting that older adults do not suffer a significantly greater penalty for talking on a cell phone while driving than compared with their younger counterparts. Interestingly, the net effect of having younger drivers converse on a cell phone was to make their average reactions equivalent to those of older drivers who were not using a cell phone. Actual or potential applications of this research include providing guidance for recommendations and regulations concerning the use of mobile technology while driving.


title: Analysis of LSB based image steganography techniques

abstract: There have been many techniques for hiding messages in images in such a manner that the alterations made to the image are perceptually indiscernible. However, the question whether they result in images that are statistically indistinguishable from untampered images has not been adequately explored. We look at some specific image based steganography techniques and show that an observer can indeed distinguish between images carrying a hidden message and images which do not carry a message. We derive a closed form expression of the probability of detection and false alarm in terms of the number of bits that are hidden. This leads us to the notion of steganographic capacity, that is, how many bits can we hide in a message without causing statistically significant modifications? Our results are able to provide an upper bound on the this capacity. Our ongoing work relates to adaptive steganographic techniques that take explicit steps to foil the detection mechanisms. In this case we hope to show that the number of bits that can be embedded increases significantly.


title: How Speakers Refer: The Role of Accessibility

abstract: One of the core components of language is referring, which requires the speaker to choose between expressions that are highly explicit (e.g., the UNC professor ,o rPeter), and reduced lexical forms (e.g., he). This paper reviews claims that this process is largely driven by the accessibility or salience of the referent, and the psychological processes that underlie these effects. Two classes of constraint are examined: (1) Discourse status, which has traditionally been identified as the determinant of referential choices and (2) Non-linguistic processing constraints that increase the use of explicit forms. These effects together support a modified version of the traditional claim that speakers choose referential explicitness so that the listener can identify the referent, and underscore the need for accessibility to be mediated by a non-linguistic representation.


title: VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations

abstract: VCells, the proposed Edge-Weighted Centroidal Voronoi Tessellations (EWCVTs)-based algorithm, is used to generate superpixels, i.e., an oversegmentation of an image. For a wide range of images, the new algorithm is capable of generating roughly uniform subregions and nicely preserving local image boundaries. The undersegmentation error is effectively limited in a controllable manner. Moreover, VCells is very efficient with core computational cost at O(K√nc·N) in which K, nc, and N are the number of iterations, superpixels, and pixels, respectively. Extensive qualitative discussions are provided, together with the high-quality segmentation results of VCells on a wide range of complex images. The simplicity and efficiency of our model are demonstrated by complexity analysis, time, and accuracy evaluations.


A segmental rash in a young male: a quiz. Naevoid urticaria pigmentosa.


title: Realizing Compositional Scheduling through Virtualization

abstract: We present a co-designed scheduling framework and platform architecture that together support compositional scheduling of real-time systems. The architecture is built on the Xen virtualization platform, and relies on compositional scheduling theory that uses periodic resource models as component interfaces. We implement resource models as periodic servers and consider enhancements to periodic server design that significantly improve response times of tasks and resource utilization in the system while preserving theoretical schedulability results. We present an extensive evaluation of our implementation using workloads from an avionics case study as well as synthetic ones.


title: Deconstructing the Energy Consumption of the Mobile Page Load

abstract: Modeling the energy consumption of applications on mobile devices is an important topic that has received much attention in recent years. However, there has been very little research on modeling the energy consumption of the mobile Web. This is primarily due to the short-lived yet complex page load process that makes it infeasible to rely on coarse-grained resource monitoring for accurate power estimation. ::: We present RECON, a modeling approach that accurately estimates the energy consumption of any Web page load and deconstructs it into the energy contributions of individual page load activities. Our key intuition is to leverage low-level application semantics in addition to coarse-grained resource utilizations for modeling the page load energy consumption. By exploiting fine-grained information about the individual activities that make up the page load, RECON enables fast and accurate energy estimations without requiring complex models. Experiments across 80 Web pages and under four different optimizations show that RECON can estimate the energy consumption for a Web page load with an average error of less than 7%. Importantly, RECON helps to analyze and explain the energy effects of an optimization on the individual components of Web page loads.


title: E-Lithe: A Lightweight Secure DTLS for IoT

abstract: Internet of Things (IoT) has emerged as a new paradigm which aims to connect millions of devices that communicate and exchange data amongst each other. However, this communication between devices makes IoT vulnerable to security threats. Different security solutions have been proposed for constrained IoT devices such as Constrained Application Protocol (CoAP) integrated with Datagram Transport Layer Security (DTLS). However, DTLS is computationally expensive for constrained devices. Furthermore, it is vulnerable to the Denial of Service (DoS) attacks. This research aims to propose an enhanced and lightweight DTLS for constraint IoT devices named as Enhanced Lightweight DTLS for IoT (E-Lithe). The enhancement to the proposed E-Lithe is made by adding the concept of a Trusted Third Party (TTP) for the pre-exchange of secret keys. This TTP makes the DTLS resilient against DoS attacks. Furthermore, to make the proposed DTLS lightweight, a compression scheme called Next Header Compression (NHC) is used. To implement the proposed E-Lithe, Contiki OS with Cooja simulator is used. We evaluate the performance of E-Lithe and the results reveal that the proposed E-Lithe outperforms the existing approaches in terms of three parameters, i.e., power consumption, packet size and running time.


title: Partial AUC Estimation and Regression

abstract: Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer.


title: Super-Resolution via Deep Learning

abstract: The recent phenomenal interest in convolutional neural networks (CNNs) must have made it inevitable for the super-resolution (SR) community to explore its potential. The response has been immense and in the last three years, since the advent of the pioneering work, there appeared too many works not to warrant a comprehensive survey. This paper surveys the SR literature in the context of deep learning. We focus on the three important aspects of multimedia - namely image, video and multi-dimensions, especially depth maps. In each case, first relevant benchmarks are introduced in the form of datasets and state of the art SR methods, excluding deep learning. Next is a detailed analysis of the individual works, each including a short description of the method and a critique of the results with special reference to the benchmarking done. This is followed by minimum overall benchmarking in the form of comparison on some common dataset, while relying on the results reported in various works.


title: Understanding the fundamentals of Kanban and CONWIP pull systems using simulation

abstract: This paper presents an introductory overview and tutorial in simulation modeling and control of serial Kanban and CONWIP (Constant Work In Process) pull systems using ARENA/SIMAN 3.5/4.0. Card level estimation is discussed for both types of pull systems, and a heuristic method to adjust card levels controlling system WIP (work in process) is provided. The objective is to present a tutorial for students and practicing engineers familiar with the basics of simulation, but unfamiliar with pull system fundamentals.


title: Cultural Heritage: Studying cartographic heritage: Analysis and visualization of geometric distortions

abstract: Old maps are increasingly used as a source for historical research. This is a consequence of the increased availability of old maps in digital form, of the emergence of user-friendly Geographical Information Systems, and of a heightened awareness of the unique information stored in old maps. As with every source for historical studies, when old maps are georeferenced and information is extracted for historical research, the accuracy and reliability of the geometric and semantic information must be assessed. In this paper, a method based on a series of geometric transformations is presented, which transforms control points of a modern reference map to the coordinate system of an old map. Based on these transformed points, the planimetric and geodetic accuracy of the old map can be computationally analyzed and various visualizations of space deformation can be generated. The results are graphical representations of map distortion, such as distortion grids or displacement vectors, as well as statistical and geodetic measures describing the map geometry (e.g., map scale, rotation angle, and map projection). The visualizations help to assess the geometric accuracy of historical geographical information before using the data for geo-historical studies. The visualizations can also provide valuable information to the map historian about the history of a particular map and its creation.


Motivations and Methods for Text Simplification


title: Partially observable markov decision processes for spoken dialog systems

abstract: In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed on three largely disparate techniques: parallel dialog state hypotheses, local use of confidence scores, and automated planning. While in isolation each of these approaches can improve action selection, taken together they currently lack a unified statistical framework that admits global optimization. In this paper we cast a spoken dialog system as a partially observable Markov decision process (POMDP). We show how this formulation unifies and extends existing techniques to form a single principled framework. A number of illustrations are used to show qualitatively the potential benefits of POMDPs compared to existing techniques, and empirical results from dialog simulations are presented which demonstrate significant quantitative gains. Finally, some of the key challenges to advancing this method - in particular scalability - are briefly outlined.


title: Disruption in the Balance Between Goal-Directed Behavior and Habit Learning in Obsessive-Compulsive Disorder

abstract: Objective:Obsessive-compulsive disorder (OCD) is characterized by repetitive, ritualistic behaviors and thought patterns. Although patients with OCD report that these compulsive behaviors are unproductive and often senseless, they are unable to desist. This study investigated whether the urge to perform compulsive acts is mediated by a disruption in the balance between flexible, goal-directed action control and habitual behavior. Method:A total of 21 patients with OCD and 30 healthy comparison subjects participated in a set of tasks designed to assess relative goal-directed versus habitual behavioral control. In the training stage, participants were asked to respond to different pictured stimuli in order to gain rewarding outcomes. In the subsequent (instructed) outcome devaluation test and in a novel “slips-of-action” test, the authors assessed whether participants were able to flexibly adjust their behavior to changes in the desirability of the outcomes. The authors also used a questionnaire to test exp...


title: Investigating the functions of subregions within anterior hippocampus

abstract: Previous functional MRI (fMRI) studies have associated anterior hippocampus with imagining and recalling scenes, imagining the future, recalling autobiographical memories and visual scene perception. We have observed that this typically involves the medial rather than the lateral portion of the anterior hippocampus. Here, we investigated which specific structures of the hippocampus underpin this observation. We had participants imagine novel scenes during fMRI scanning, as well as recall previously learned scenes from two different time periods (one week and 30 min prior to scanning), with analogous single object conditions as baselines. Using an extended segmentation protocol focussing on anterior hippocampus, we first investigated which substructures of the hippocampus respond to scenes, and found both imagination and recall of scenes to be associated with activity in presubiculum/parasubiculum, a region associated with spatial representation in rodents. Next, we compared imagining novel scenes to recall from one week or 30 min before scanning. We expected a strong response to imagining novel scenes and 1-week recall, as both involve constructing scene representations from elements stored across cortex. By contrast, we expected a weaker response to 30-min recall, as representations of these scenes had already been constructed but not yet consolidated. Both imagination and 1-week recall of scenes engaged anterior hippocampal structures (anterior subiculum and uncus respectively), indicating possible roles in scene construction. By contrast, 30-min recall of scenes elicited significantly less activation of anterior hippocampus but did engage posterior CA3. Together, these results elucidate the functions of different parts of the anterior hippocampus, a key brain area about which little is definitely known.


title: A consensus model for hesitant fuzzy preference relations and its application in water allocation management

abstract: Abstract This paper investigates a consensus model for hesitant fuzzy preference relations (HFPRs). First, we present a revised definition of HFPRs, in which the values are not ordered for the hesitant fuzzy element. Second, we propose an additive consistency based estimation measure to normalize the HFPRs, based on which, a consensus model is developed. Here, two feedback mechanisms are proposed, namely, interactive mechanism and automatic mechanism, to obtain a solution with desired consistency and consensus levels. In the interactive mechanism, the experts are suggested to give their new preference values in a specific range. If the experts are unwilling to offer their updated preferences, the automatic mechanism could be adopted to carry out the consensus process. Induced ordered weighted averaging (IOWA) operator is used to aggregate the individual HFPRs into a collective one. A score HFPR is proposed for collective HFPR, and then the quantifier-guided dominance degrees of alternatives by using an OWA operator are obtained to rank the alternatives. Finally, both a case of study for water allocation management in Jiangxi Province of China and a comparison with the existing approaches are carried out to show the advantages of the proposed method.


title: Mathematical model and control strategy of a two-wheeled self-balancing robot

abstract: In this paper a control strategy and sensor concept for a two-wheeled self-balancing robot is proposed. First a mathematical model of the robot is derived using Lagrangian mechanics. Based on the model a full state feedback controller, in combination with two higher-level controls are deployed for stabilization and drive control. A gyroscope, an accelerometer and rotational encoders are used for state determination, introducing a new method of measurement data fusion for the accelerometer and the gyro by using a drift compensation controller. Furthermore measurement proceedings for the model parameters of a real prototype robot are suggested and the control for this robot is designed. The proposed mathematical model, as well as the control strategy are then verified by comparing the behavior of the constructed robot with model simulations.


title: Sentiment Analysis and Opinion Mining: A Survey

abstract: In this age, in this nation, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions (Abraham Lincoln, 1858 ) [1]. It is apparent from President Lincoln's well known quote that legislators understood the force of open assumption quite a while prior. In today world, the Internet is the main source of information. An enormous amount of information and opinion online is scattered and unstructured with no machine to arrange it. Because of demand the public to know opinions about exact product and services, political issues, or social scientists. That’s led us to study of field Opining Mining and Sentiment Analysis. Opining Mining and Sentiment Analysis have recently played a significant role for researchers because analysis of online text is beneficial for the market research political issue, business intelligence, online shopping, and scientific survey from psychological. Sentiment Analysis identifies the polarity of extracted public opinions. This paper presents a survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification.