title: Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis

abstract: The proposed strap-down integration method exploits the cyclical nature of human gait: during the gait swing phase, the quaternion-based attitude representation is integrated using a gyroscope from initial conditions that are determined during stance by an accelerometer. Positioning requires double time integration of the gravity-compensated accelerometer signals during swing. An interpolation technique applied to attitude quaternions was developed to improve the accuracy of orientation and positioning estimates by accounting for the effect of sensor bias and scale factor drifts. A simulation environment was developed for the analysis and testing of the proposed algorithm on a synthetic movement trajectory. The aim was to define the true attitude and positioning used in the computation of estimation errors. By thermal modelling, the changes of bias and scale factor of the inertial sensors, calibrated at a single reference temperature, were analysed over a range of ±10°C, for measurement noise standard deviations up to σg = 2.5° s−1 (gyroscope) and σa = 0.05 m s−1 (accelerometer). The compensation technique reduced the maximum root mean square errors (RMSEs) to: RMSEθ=14.6° (orientation) and RMSEd=17.7 cm (positioning) for an integration interval of one gait cycle (an improvement of 3° and 7 cm); RMSEθ=14.8° and RMSEd=30.0 cm for an integration interval of two gait cycles (an improvement of 11° and 262 cm).


title: Participatory Visualization with Wordle

abstract: We discuss the design and usage of ldquoWordle,rdquo a Web-based tool for visualizing text. Wordle creates tag-cloud-like displays that give careful attention to typography, color, and composition. We describe the algorithms used to balance various aesthetic criteria and create the distinctive Wordle layouts. We then present the results of a study of Wordle usage, based both on spontaneous behaviour observed in the wild, and on a large-scale survey of Wordle users. The results suggest that Wordles have become a kind of medium of expression, and that a ldquoparticipatory culturerdquo has arisen around them.


title: The $m(n)$ out of $k(n)$ bootstrap for partial sums of St. Petersburg type games

abstract: This paper illustrates that the bootstrap of a partial sum given by i.i.d. copies of a random variable $X_1$ has to be dealt with care in general. It turns out that in various cases a whole spectrum of different limit laws of the $m(n)$ out of $k(n)$ bootstrap can be obtained for different choices of $m(n)/k(n) -> 0$ whenever $X_1$ does not lie in the domain of attraction of a stable law. As a concrete example we study bootstrap limit laws for the cumulated gain sequence of repeated St. Petersburg games. It is shown that here a continuum of different semi-stable bootstrap limit laws occurs.


title: Web-Based Assessment and Test Analyses (WATA) System: Development and Evaluation.

abstract: This study introduces the development of a Web-based assessment system, the Web-based Assessment and Test Analyses (WATA) system, and examines its impacts on teacher education. The WATA system is a follow-on system, which applies the Triple-A Model (assembling, administering, and appraising). Its functions include (1) an engine for teachers to administer and manage testing, (2) an engine for students to apply tests, and (3) an engine for generating test results and analyses for teachers. Two studies were undertaken to assess the usefulness and potential benefits of the WATA system for teacher education. In the first study, 47 in-service teachers were asked to assess the functions of the WATA system. The results indicated that they were satisfied with the Triple-A Model of the WATA system. In the second study, 30 pre-service teachers were required to use the WATA system during the teacher-training program. After 4 months of experience in using the WATA system, the pre-service teachers' perspectives of assessment have been changed significantly. The findings of these two studies might provide some guidance to help those who are interested in the development of Web-based assessment and intend to infuse information technology into teacher education.


title: Three dimensional Deep Learning approach for remote sensing image classification

abstract: Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today’s large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs.


title: The Ways of Coping Checklist: Revision and Psychometric Properties

abstract: This study examined the psychometric properties of the "original" seven factored scales derived by Aldwin et al. from Folkman and Lazarus' Ways of Coping Checklist (WCCL) versus a revised set of scales. Four psychometric properties were examined including the reproducibility of the factor structure of the original scales, the internal consistency reliabilities and intercorrelations of the original and the revised scales, the construct and concurrent validity of the scales, and their relationships to demographic factors. These properties were studied on three distressed samples: 83 psychiatric outpatients, 62 spouses of patients with Alzheimer's disease, and 425 medical students. The revised scales were consistently shown to be more reliable and to share substantially less variance than the original scales across all samples. In terms of construct validity, depression was positively related to the revised Wishful Thinking Scale and negatively related to the revised Problem-Focused Scale consistently across...


title: Design of a wideband dual-polarized stacked patch antenna with high isolation and low cross polarization for X-band applications

abstract: The design and realization of a low-profile wideband dual-polarized patch antenna with high isolation and low cross polarization has been presented. The aperture-coupled antenna element has four layers of substrates. Two microstrip lines terminated with circular patches are printed on the backside of the bottom substrate for feeding the radiating patches, while on the other side is a ground plane with two offset H-shaped slots. One port is excited for horizontally polarized wave and the other one is for vertical polarization. Two slots are arranged in a ‘T’ configuration to achieve a high isolation of over 35 dB. A square radiating patch is printed on the top layer of a Duroid substrate which is placed above the ground plane. A piece of Rohacell foam with a thickness of 3mm and a permittivity of 1.06 is located above the Duroid substrate. A top radiating patch is printed on the bottom side of a top Duroid substrate. Thus, a wideband performance can be obtained due to the multilayer structure and the impedance matching can be improved by adjusting the dimensions of the circular patches and the slots. Further to reduce the cross polarization, two identical elements with one rotated by 180° are combined and each pair of single linearly polarized elements are fed out of phase. In addition, a four-element array with two mirrored two-element arrays has been investigated. The obtained results can confirm that, the proposed dual-polarized patch array can provide a wideband performance across 8.5–11 GHz with VSWR < 2. The isolation between the two ports is over 35 dB and the cross polarization level is less than −35 dB over the operating frequency band. Therefore, the proposed design technique is promising for X-band SAR applications.


title: Stratification of ovarian tumor pathology by expression of programmed cell death-1 (PD-1) and PD-ligand- 1 (PD-L1) in ovarian cancer

abstract: Ovarian cancer is the major cause of death among gynecologic cancers with 75% of patients diagnosed with advanced disease, and only 20% of these patients having a survival duration of five years. Treatments blocking immune checkpoint molecules, programmed cell death (PD-1) or its ligand PD-ligand- I (PD-L1) have produced a beneficial and prolonged effect in a subgroup of these patients. However, there is debate in the literature concerning the prognostic value of the expression of these molecules in tumors, with immunotherapy responsiveness, and survival. We evaluated the immune landscape of the ovarian tumor microenvironment of patients, by measuring the impact of the expression of tumor PD-1, PD-L1 and infiltrating lymphocytes on stage and grade of tumors and survival, in a cohort of 55 patients with gynecologic malignancies. Most patients under study were diagnosed with advanced disease ovarian cancer. Our studies revealed that a low density of PD-1 and of PD-L1 expressing cells in tumor tissue were significantly associated with advanced disease (P = 0.028 and P = 0.033, respectively). Moreover, PD-L1 was expressed significantly more often in high grade tumors (41.5%) than in low grade tumors of patients (7.7%) (P = 0.040). The presence of CD3 or of FoxP3 infiltrating cells with PD-L1 in patient tumors did not impact the significance of the association of PD-L1 with high grade tumors (P = 0.040), and our analyses did not show an association between the presence of PD-1 or PD-L1 and survival. We conclude that a subgroup of advanced disease ovarian cancer patients with high grade tumors, expressing PD-L1, may be prime candidates for immunotherapy targeting PD-1 signaling.


title: Predictive Effects of Novelty Measured by Temporal Embeddings on the Growth of Scientific Literature

abstract: Novel scientiﬁc knowledge is constantly produced by the scientiﬁc community. Understanding the level of novelty characterized by scientiﬁc literature is key for modeling scientiﬁc dynamics and analyzing the growth mechanisms of scientiﬁc knowledge. Metrics derived from bibliometrics and citation analysis were effectively used to characterize the novelty in scientiﬁc development. However, time is required before we can observe links between documents such as citation links or patterns derived from the links, which makes these techniques more effective for retrospective analysis than predictive analysis. In this study, we present a new approach to measuring the novelty of a research topic in a scientiﬁc community over a speciﬁc period by tracking semantic changes of the terms and characterizing the research topic in their usage context. The semantic changes are derived from the text data of scientiﬁc literature by temporal embedding learning techniques. We validated the effects of the proposed novelty metric on predicting the future growth of scientiﬁc publications and investigated the relations between novelty and growth by panel data analysis applied in a large-scale publication dataset (MEDLINE/PubMed). Key ﬁndings based on the statistical investigation indicate that the novelty metric has signiﬁcant predictive effects on the growth of scientiﬁc literature and the predictive effects may last for more than ten years. We demonstrated the effectiveness and practical implications of the novelty metric in three case studies.


title: Application of Antenna Arrays to Mobile Communications, Part II: Beam-Forming and Direction-of-Arrival Considerations

abstract: Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.


title: Construction of a Highly Dependable Operating System

abstract: It has been well established that most operating system crashes are due to bugs in device drivers. Because drivers are normally linked into the kernel address space, a buggy driver can wipe out kernel tables and bring the system crashing to a grinding halt. We have greatly mitigated this problem by reducing the kernel to an absolute minimum and running each driver as a separate, unprivileged user-mode process. In addition, we implemented a POSIX-conformant operating system, MINIX 3, as multiple user-mode servers. In this design, a server or driver failure no longer is fatal and does not require rebooting the computer. This paper discusses how we designed and implemented the system, which problems we encountered, and how we solved these problems. We also discuss the performance effects of our changes and evaluate how our multiserver design improves operating system dependability over monolithic designs.


title: Shape sensing of miniature snake-like robots using optical fibers

abstract: Snake like continuum robots are increasingly used for minimally invasive surgery. Most robotic devices of this sort that have been reported to date are controlled in an open loop manner. Using shape sensing to provide closed loop feedback would allow for more accurate control of the robot's position and, hence, more precise surgery. Fiber Bragg Gratings, magnetic sensors and optical reflectance sensors have all been reported for this purpose but are often limited by their cost, size, stiffness or complexity of fabrication. To address this issue, we designed, manufactured and tested a prototype two-link robot with a built-in fiber-optic shape sensor that can deliver and control the position of a CO 2 -laser fiber for soft tissue ablation. The shape sensing is based on optical reflectance, and the device (which has a 4 mm outer diameter) is fabricated using 3D printing. Here we present proof-of-concept results demonstrating successful shape sensing — i.e. measurement of the angular displacement of the upper link of the robot relative to the lower link — in real time with a mean measurement error of only 0.7°.


title: Single-Layer Cavity-Backed Slot Array Fed by Groove Gap Waveguide

abstract: An effective feeding procedure for slotted waveguide arrays in the context of so-called groove gap waveguides (GGWs) is presented. Slots are fed through a corporate feed network with E-plane splitters and backed by coaxial cavities, which are more compact than conventional cavities. The coaxial cavity is integrated within the same bed of nails used to host the groove in the gap waveguide. The cavity resonance is generated by merely lowering the height of the desired nail with respect to the surrounding ones. Therefore, no increase in complexity is introduced. The feeding network is built in the same layer as the cavities and the slots, considerably reducing array volume compared to multilayer topologies. A $4 \times 4$ slot array has been designed using this concept. The goal of this letter is to demonstrate the viability of groove gap waveguides and coaxial cavities for the design and fabrication of high-frequency single-layer slot-array antennas. Simulation results as well as measurements are shown.


Implementation of Automated Door Accessing System with Face Design and Recognition


title: Gray-box optimization and factorized distribution algorithms: where two worlds collide

abstract: The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves about the idea of exploiting the problem structure to implement more efficient evolutionary algorithms (EAs). Work on factorized distribution algorithms (FDAs), whose factorizations are directly derived from the problem structure, has also contributed to show how exploiting the problem structure produces important gains in the efficiency of EAs. In this paper we analyze the general question of using problem structure in EAs focusing on confronting work done in gray-box optimization with related research accomplished in FDAs. This contrasted analysis helps us to identify, in current studies on the use problem structure in EAs, two distinct analytical characterizations of how these algorithms work. Moreover, we claim that these two characterizations collide and compete at the time of providing a coherent framework to investigate this type of algorithms. To illustrate this claim, we present a contrasted analysis of formalisms, questions, and results produced in FDAs and gray-box optimization. Common underlying principles in the two approaches, which are usually overlooked, are identified and discussed. Besides, an extensive review of previous research related to different uses of the problem structure in EAs is presented. The paper also elaborates on some of the questions that arise when extending the use of problem structure in EAs, such as the question of evolvability, high cardinality of the variables and large definition sets, constrained and multi-objective problems, etc. Finally, emergent approaches that exploit neural models to capture the problem structure are covered.


title: High Phase Accuracy QVCO with Current Reuse and Bulk-Coupled Structure

abstract: In this paper, a novel QVCO applied in 2.4 GHz IoT receiver is proposed. With the current reuse structure, MOSFETs in both VCO open and close at the same period, which saves the power. This QVCO uses bulk-coupled technology, which leads to low phase noise and high phase accuracy. In case this QVCO works in voltage-limited mode, extra NMOS is added as the current source. Additionally, this asymmetrical current reuse VCO core is aided by a symmetrical class-B core. This symmetrical circuit can guarantee a stable start-up and good amplitude balance. The proposed QVCO is simulated in TSMC 0.18 μm CMOS technology. The total area cost is 1.1 mm×0.87 mm. The post-simulation shows that the tuning range is 12.1%, and can cover the whole 2.4 GHz band. The phase noise at 1 MHz offset is −116.81 dBc/Hz and the phase error is 1.03°. It consumes 5.2 mW from 1.8 V supply voltage. And the FoM is −177.254.


title: Website Fingerprinting Attack on Anonymity Networks Based on Profile Hidden Markov Model

abstract: Website fingerprinting attacks can reveal the receiver in anonymous networks and cause a potential threat to users' privacy. Previous studies focus more on identifying individual webpages. They also neglect the hyperlink transition information, because it induces extra “noise” to classify the original webpage. However, it is a common scenario that the users surf a website by clicking hyperlinks on the webpage. In this paper, we propose a website modeling method based on profile hidden Markov model (PHMM) which is widely used in bioinformatics for DNA sequencing analysis. Our technique explicitly accounts for possible hyperlink transitions made by users when fingerprinting a target website, and therefore can work in a more realistic environment than existing methods. Using SSH and Shadowsocks, we collect various data sets and conduct extensive evaluations. We also show that our approach could work both in webpage and website identification in a closed world setting. The experimental results demonstrate that our website fingerprinting is more accurate and robust than existing methods.


title: IDF Diabetes Atlas: Global estimates of the prevalence of diabetes for 2011 and 2030

abstract: INTRODUCTION ::: Diabetes is an increasingly important condition globally and robust estimates of its prevalence are required for allocating resources. ::: ::: ::: METHODS ::: Data sources from 1980 to April 2011 were sought and characterised. The Analytic Hierarchy Process (AHP) was used to select the most appropriate study or studies for each country, and estimates for countries without data were modelled. A logistic regression model was used to generate smoothed age-specific estimates which were applied to UN population estimates for 2011. ::: ::: ::: RESULTS ::: A total of 565 data sources were reviewed, of which 170 sources from 110 countries were selected. In 2011 there are 366 million people with diabetes, and this is expected to rise to 552 million by 2030. Most people with diabetes live in low- and middle-income countries, and these countries will also see the greatest increase over the next 19 years. ::: ::: ::: DISCUSSION ::: This paper builds on previous IDF estimates and shows that the global diabetes epidemic continues to grow. Recent studies show that previous estimates have been very conservative. The new IDF estimates use a simple and transparent approach and are consistent with recent estimates from the Global Burden of Disease study. IDF estimates will be updated annually.


title: The sustainability balanced scorecard. Linking sustainability management to business strategy. Business Strategy and the Environment 11

abstract: The Balanced Scorecard of Kaplan and Norton is a management tool that supports the successful implementation of corporate strategies. It has been discussed and considered widely in both practice and research. By linking operational and non-financial corporate activities with causal chains to the firm's long-term strategy, the Balanced Scorecard supports the alignment and management of all corporate activities according to their strategic relevance. The Balanced Scorecard makes it possible to take into account non-monetary strategic success factors that significantly impact the economic success of a business. The Balanced Scorecard is thus a promising starting-point to also incorporate environmental and social aspects into the main management system of a firm. Sustainability management with the Balanced Scorecard helps to overcome the shortcomings of conventional approaches to environmental and social management systems by integrating the three pillars of sustainability into a single and overarching strategic management tool. After a brief discussion of the different possible forms of a Sustainability Balanced Scorecard the article takes a closer look at the process and steps of formulating a Sustainability Balanced Scorecard for a business unit. Before doing so, the basic conventional approach of the Balanced Scorecard and its suitability for sustainability management will be outlined in brief. Copyright © 2002 John Wiley & Sons, Ltd and ERP Environment.


Dermatofibroma mimicking melanoma dermoscopically


title: Standalone Frequency Based Automated Trash Bin and Segregator of Plastic Bottles and Tin Cans

abstract: Segregation is the first step to total waste management. Studies show that majority of the population do not segregate wastes because they find it inconvenient. A previous study entitled “Resonant Frequency Based Automated Waste Material Segregator using MATLAB” was done to address this inconvenience. The previous study presented a device that isolates plastic bottles and tin cans through the use of the object's resonant frequency. However, it was computer-dependent and had limitations with regard to the position of drop of the object. Thus, this paper presents a standalone system, an automated trash bin that distinguishes plastic bottles and tin cans regardless of the object's position of drop. The system used a piezoelectric microphone for input signal acquisition and a comparator for noise elimination. Instead of resonant frequency, the system relied on the average frequency response of the object as it hits the platform. The decision process is done by the microcontroller Arduino. The platform used is made of galvanized iron connected to a DC motor that physically segregates the objects. This study will help the community on waste recovery and aims to make recycling more convenient.


title: Functional MRI Analysis of Body and Body Part Representations in the Extrastriate and Fusiform Body Areas

abstract: This study examined the contributions of two previously identified brain regions—the extrastriate and fusiform body areas (EBA and FBA)—to the visual representation of the human form. Specifically we measured in these two areas the magnitude of fMRI response as a function of the amount of the human figure that is visible in the image, in the range from a single finger to the entire body. A second experiment determined the selectivity of these regions for body and body part stimuli relative to closely matched control images. We found a gradual increase in the selectivity of the EBA as a function of the amount of body shown. In contrast, the FBA shows a steplike function, with no significant selectivity for individual fingers or hands. In a third experiment we demonstrate that the response pattern seen in EBA does not extend to adjacent motion-selective human midtemporal area. We propose an interpretation of these results by analogy to nearby face-selective regions occipital face area (OFA) and fusiform face area (FFA). Specifically, we hypothesize that the EBA analyzes bodies at the level of parts (as has been proposed for faces in the OFA), whereas FBA (by analogy to FFA) may have a role in processing the configuration of body parts into wholes.


title: Detection of traffic light using structural information

abstract: Driver support systems using images are paid attention and various researches on recognizing and understanding the road environment have been done. If it is possible to detect and recognize traffic lights, it will give useful information to a driver to understand the road environment. In this paper, a method of detecting a traffic light in a scene image is proposed. Considering the structure of a traffic light, we propose a method for detecting a traffic light based on the Hough transform. Experimental results using images including a traffic light taken by a digital camera through a windshield verifies the effectiveness of the proposed method.


title: Interval-valued fuzzy planar graphs

abstract: In this paper, interval-valued fuzzy planar graphs are defined and several properties are studied. The interval-valued fuzzy graphs are more efficient than fuzzy graphs, since the degree of membership of vertices and edges lie within the interval [0, 1] instead at a point in fuzzy graphs. We also use the term ‘degree of planarity’ to measures the nature of planarity of an interval-valued fuzzy graph. The other relevant terms such as strong edges, interval-valued fuzzy faces, strong interval-valued fuzzy faces are defined here. The interval-valued fuzzy dual graph which is closely associated to the interval-valued fuzzy planar graph is defined. Several properties of interval-valued fuzzy dual graph are also studied. An example of interval-valued fuzzy planar graph is given.


title: Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks

abstract: Traffic sign recognition (TSR) is an important and challenging task for intelligent transportation systems. The authors describe the details of a model's architecture for TSR and suggest a hinge loss stochastic gradient descent (HLSGD) method to train convolutional neural networks (CNNs). The CNN consists of three stages (70–110–180) with 1,162,284 trainable parameters. The HLSGD is evaluated on the German Traffic Sign Recognition Benchmark, which offers a faster and more stable convergence and a state-of-the-art recognition rate of 99.65%. The authors write a graphics processing unit package to train several CNNs and establish the final classifier in an ensemble way.


title: Development of slotless type brushless DC motors and planetary gear heads for robot

abstract: This paper presents the development results for slotless type brushless DC motors and planetary gear heads for service and special purpose robot applications. The designed slotless BLDC motor has a hexagonal winding shape, and the developed motor sizes are 22, 32, and 40mm with the outer diameters. The developed gear heads are planetary type, and the gears are manufactured by the powder injection molding (PIM) method to increase the gear strength and to decrease the backlash. The output performances of the motors and gear heads are tested in various conditions.


title: Regular bipolar fuzzy graphs

abstract: We introduce the concepts of regular and totally regular bipolar fuzzy graphs. We prove necessary and sufficient condition under which regular bipolar fuzzy graph and totally bipolar fuzzy graph are equivalent. We introduce the notion of bipolar fuzzy line graphs and present some of their properties. We state a necessary and sufficient condition for a bipolar fuzzy graph to be isomorphic to its corresponding bipolar fuzzy line graph. We examine when an isomorphism between two bipolar fuzzy graphs follows from an isomorphism of their corresponding bipolar fuzzy line graphs.


title: Adaptive Recommendation-based Modeling Support for Data Analysis Workflows

abstract: RapidMiner is a software framework for the development and execution of data analysis workflows. Like many modern software development environments, the tool comprises a visual editor which allows the user to design processes on a conceptual level, thereby abstracts technical details, and thus helps the user focus on the core modeling task. The large set of pre-implemented data analysis operations available in the framework, as well as their logical dependencies, can, however, be overwhelming in particular for novice users. In this work we present an intelligent add-on to the RapidMiner framework that supports the user during the modeling phase by recommending additional operations to insert into the currently developed data analysis workflow. In the paper, we first propose different recommendation techniques and evaluate them in an offline setting using a pool of several thousand existing workflows. Second, we present the results of a laboratory study, which show that our tool helps users to significantly increase the efficiency of the modeling process.


title: Resonator-based analysis of the combination of mobile handset antenna and chassis

abstract: The performance of the mobile phone handset antenna-chassis combination is analyzed based on an approximate decomposition of the waves on the structure into two resonant wavemodes: the antenna-element wavemode and the chassis wavemode. A double resonator equivalent circuit model is presented and used to estimate the impedance bandwidth and the respective distributions of radiation losses with typical parameter values at 900 and 1800 MHz. It is noticed that at 900 MHz, the radiation losses of the antenna element wavemode represent typically less than 10% of the total power. Thus, the antenna element works mainly as a matching element, which couples to the low-Q resonant wavemode of the chassis. At 1800 MHz, the contribution of the antenna element wavemode is larger. By enhancing the coupling and by tuning the chassis resonance, it is possible to obtain an impedance bandwidth of over 50% (6 dB return loss) at both at 900 and 1800 MHz. The results given by the equivalent circuit study are fully supported by those of three-dimensional phone-model simulations, including calculation of the SAR and efficiency values. In prototyping, the 6 dB bandwidth of 5.5% was obtained at 980 MHz with a nonradiating coupling element with a volume of 1.6 cm/sup 3/ on a 120 mm long chassis.


title: Using LSTM Encoder-Decoder Algorithm for Detecting Anomalous ADS-B Messages

abstract: Although the ADS-B system is going to play a major role in the safe navigation of airplanes and air traffic control (ATC) management, it is also well known for its lack of security mechanisms. Previous research has proposed various methods for improving the security of the ADS-B system and mitigating associated risks. However, these solutions typically require the use of additional participating nodes (or sensors) (e.g., to verify the location of the airplane by analyzing the physical signal) or modification of the current protocol architecture (e.g., adding encryption or authentication mechanisms.) Due to the regulation process regarding avionic systems and the fact that the ADS-B system is already deployed in most airplanes, applying such modifications to the current protocol at this stage is impractical. In this paper we propose an alternative security solution for detecting anomalous ADS-B messages aimed at the detection of spoofed or manipulated ADS- B messages sent by an attacker or compromised airplane. The proposed approach utilizes an LSTM encoder-decoder algorithm for modeling flight routes by analyzing sequences of legitimate ADS-B messages. Using these models, aircraft can autonomously evaluate received ADS-B messages and identify deviations from the legitimate flight path (i.e., anomalies). We examined our approach on six different flight route datasets to which we injected different types of anomalies. Using our approach we were able to detect all of the injected attacks with an average false alarm rate of 4.3% for all of datasets.


title: An expert system working upon an ensemble PSO-based approach for diagnosis of coronary artery disease

abstract: It is evident that usage of data mining methods in disease diagnosis has been increasing gradually. In this paper, diagnosis of Coronary Artery Disease, which is one of the most well-known diseases that cause heart failure, was conducted with such a data mining system. Many researchers have attempted to develop a medical expert system to increase the ability of physicians in detecting this disease. This paper proposes a new ensemble PSO-based approach to extract a set of rules for diagnosis of coronary artery disease. The new presented boosting mechanism considers the cooperation between generated fuzzy if-then rules using the PSO meta-heuristic. We have evaluated our new classification approach using the well-known Cleveland data set. Results indicate that the proposed learning method can detect the coronary artery disease with an acceptable accuracy. In addition, the extracted fuzzy rules have significant interpretability either.


An artificial neural network model for river flow forecasting a comparison between ANN and ARIMA


title: Test case generation from BPMN models for automated testing of Web-based BPM applications

abstract: This article proposes an approach to generate test cases from BPMN models, for automated testing of Web applications implemented with the support of BPM suites. The work is primarily focused on functional testing and has the following objectives: (i) identify execution paths from the flow analysis in the BPMN model and (ii) generate the initial code of test scripts to be run on a given Web application testing tool. Throughout the article, we describe the design and implementation of a solution to achieve these goals, targeting automated tests using Selenium and Cucumber as tools. The approach was applied to processes from a public repository and was able to generate test scenarios from different BPMN models.


Big Data Application in Biomedical Research and Health Care: A Literature Review


title: Deploying social robots as teaching aid in pre-school K2 classes: A proof-of-concept study

abstract: This paper describes the outcome of a pilot study of deploying humanoid social robots as a teaching aid in preschool classroom. Sixteen K2 students each from two pre-schools were recruited to test commercially available robots, Pepper and Nao. The robots were assigned to different schools and were programmed to deliver 6 lessons over a span of 3 months. To assess the deployment and the performance of the children, we used Likert-scale based survey to record our observations. One of the survey forms used was TEPI, which measures the performance of the children's behavior based on three major criteria. The TEPI data gathered suggests that during the lessons children display desirable behavior such as critical thinking, imagination and creativity, and social interaction and independence. We also observed classroom atmosphere, classroom management, and class behavior during the lessons. Data from the survey highlights the potential benefits and the challenges of deploying robots as a teaching aid in a real classroom setting.


title: A Circularly Polarized Cylindrical Dielectric Resonator Antenna Using a Helical Exciter

abstract: A novel circularly polarized cylindrical dielectric resonator antenna excited by an external tape helix is presented. The helix is fed by a coaxial line through a small hole on a finite size ground plane. The configuration offers a compact and easy to fabricate feeding network providing a 3 dB axial-ratio bandwidth of 6.4%. A prototype of the proposed configuration is fabricated and measured. Measured and simulated return loss, axial-ratio, radiation pattern, and realized gain are presented and discussed together with design guidelines.


title: RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks

abstract: Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D light-weight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed light-weight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+D-NTU (NTU) dataset, we achieve 93.2% and 97.6% for cross-subject and cross-view measurement, and on the Northwestern-UCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view.


title: Human health effects of air pollution.

abstract: Over the past three or four decades, there have been important advances in the understanding of the actions, exposure-response characteristics, and mechanisms of action of many common air pollutant...


title: Cognition-Inspired 5G Cellular Networks: A Review and the Road Ahead

abstract: Despite the evolution of cellular networks, spectrum scarcity and the lack of intelligent and autonomous capabilities remain a cause for concern. These problems have resulted in low network capacity, high signaling overhead, inefficient data forwarding, and low scalability, which are expected to persist as the stumbling blocks to deploy, support and scale next-generation applications, including smart city and virtual reality. Fifth-generation (5G) cellular networking, along with its salient operational characteristics—including the cognitive and cooperative capabilities, network virtualization, and traffic offload—can address these limitations to cater to future scenarios characterized by highly heterogeneous, ultra-dense, and highly variable environments. Cognitive radio (CR) and cognition cycle (CC) are key enabling technologies for 5G. CR enables nodes to explore and use underutilized licensed channels; while CC has been embedded in CR nodes to learn new knowledge and adapt to network dynamics. CR and CC have brought advantages to a cognition-inspired 5G cellular network, including addressing the spectrum scarcity problem, promoting interoperation among heterogeneous entities, and providing intelligence and autonomous capabilities to support 5G core operations, such as smart beamforming. In this paper, we present the attributes of 5G and existing state of the art focusing on how CR and CC have been adopted in 5G to provide spectral efficiency, energy efficiency, improved quality of service and experience, and cost efficiency. This main contribution of this paper is to complement recent work by focusing on the networking aspect of CR and CC applied to 5G due to the urgent need to investigate, as well as to further enhance, CR and CC as core mechanisms to support 5G. This paper is aspired to establish a foundation and to spark new research interest in this topic. Open research opportunities and platform implementation are also presented to stimulate new research initiatives in this exciting area.


Virtual Identity: Communities of Broadcast, Communities of Interactivity


title: Communication paradigms for mobile computing

abstract: The widespread deployment and use of wireless data communications causes the need for middleware to interconnect the components that comprise a mobile application. Middleware for mobile computing must deal with the increased complexity that comes with a dynamically changing population of application components and the resulting dynamic reconfiguration of the connections between these components. This paper presents an overview of two communication paradigms that are well suited as the basis for middleware for mobile computing, namely the event-based communication model and proximity-based group communication.


title: Human Drivers Based Active-Passive Model for Automated Lane Change

abstract: Lane change maneuver is a complicated maneuver, and incorrect maneuvering is an important reason for expressway accidents and fatalities. In this scenario, automated lane change has great potential to reduce the number of accidents. Previous research in this area, typically, focuses on the generation of an optimal lane change trajectory, while ignoring the human behavior model. To understand the human lane change behavior model, we carried out experiments on Japanese expressways. By analyzing the human-driver lane change data, we propose a two-segment lane change model that mimics the human-driver. We categorize the driving environment based on the observation grid and propose different lane change behaviors to handle the different scenarios. We develop an intuitive method to select the suitable lane change behavior, for a given scenario, using active (accelerate/decelerate) and passive (wait) information derived from the distance and related velocity (dx/dv) graph. Additionally, we also identify the most desirable and safe conditions for doing lane change based on the human driver preference data. We evaluated the proposed model by performing lane change simulations in the PreScan environment, while considering the vehicle motion/control model. The simulation results show the proposed model is able to handle complicated lane change scenarios with human driver-like performance.


title: Early detection of plant faults by using machine learning

abstract: To detect fault early is very important for safety. This paper proposes a method to detect plant faults early. For the early detections, to classify whether the signals is an early sign of faults or not in subtle signal difference is necessary. To the classification, this paper uses SVM (Support Vector Machine), which is one of a powerful classification technique of machine learning. Data in abnormal state of plants is obtained a few moments later after a fault occurs, hence in early stage of faults, the data of abnormal state is not available. Only measured data at normal state of plants is available in learning stage of the method. For a classification of such cases where only one side of data, which is from normal state, available, one class SVM is useful. In addition, to classify a very subtle signal as a signal of abnormal state, a high generalization ability is necessary for SVM. To get the high ability, a new kernel function, the generalized Gaussian function is used. To show the effectiveness of the method given in this paper, a simulation example of a water level control experimental plant is given.


title: Dual-Space Vector Control of Open-End Winding Permanent Magnet Synchronous Motor Drive Fed by Dual Inverter

abstract: This paper proposes a dual-space vector control scheme for the open-end winding permanent magnet synchronous motor (OEW-PMSM) drive fed by the dual inverter with a single dc supply. Potential zero-sequence current in the open-end winding drive system has to be considered since it causes circulating current in the winding and leads to high current stress of power semiconductor devices and high losses. Zero-sequence current in open-end winding ac motor drives is usually caused by the zero-sequence voltage, and therefore switching combinations which do not produce zero-sequence voltage are used to synthesize the reference voltage in existing methods. But even so, the zero-sequence voltage can also be produced by the dead time of the inverter. In order to suppress zero-sequence current in the OEW-PMSM drive, a dual-space vector control scheme is proposed and a novel dual-inverter space vector pulse width modulation (PWM) with the zero-sequence voltage reference is employed to regulate system zero-sequence voltage in this paper. Compared with existing dual inverter PWM strategies, the novel algorithm build a regulation mechanism for the zero-sequence voltage. The proposed method is compared with the conventional vector control by simulations and experiments, and the results shown that the proposed scheme can suppress zero-sequence current effectively.


title: Identifying diverse usage behaviors of smartphone apps

abstract: Smartphone users are increasingly shifting to using apps as "gateways" to Internet services rather than traditional web browsers. App marketplaces for iOS, Android, and Windows Phone platforms have made it attractive for developers to deploy apps and easy for users to discover and start using many network-enabled apps quickly. For example, it was recently reported that the iOS AppStore has more than 350K apps and more than 10 billion downloads. Furthermore, the appearance of tablets and mobile devices with other form factors, which also use these marketplaces, has increased the diversity in apps and their user population. Despite the increasing importance of apps as gateways to network services, we have a much sparser understanding of how, where, and when they are used compared to traditional web services, particularly at scale. This paper takes a first step in addressing this knowledge gap by presenting results on app usage at a national level using anonymized network measurements from a tier-1 cellular carrier in the U.S. We identify traffic from distinct marketplace apps based on HTTP signatures and present aggregate results on their spatial and temporal prevalence, locality, and correlation.


Principles and Models for Organizing the IT Function


title: Classification of plant leaf images with complicated background

abstract: Classifying plant leaves has so far been an important and difficult task, especially for leaves with complicated background where some interferents and overlapping phenomena may exist. In this paper, an efficient classification framework for leaf images with complicated background is proposed. First, a so-called automatic marker-controlled watershed segmentation method combined with pre-segmentation and morphological operation is introduced to segment leaf images with complicated background based on the prior shape information. Then, seven Hu geometric moments and sixteen Zernike moments are extracted as shape features from segmented binary images after leafstalk removal. In addition, a moving center hypersphere (MCH) classifier which can efficiently compress feature data is designed to address obtained mass high-dimensional shape features. Finally, experimental results on some practical plant leaves show that proposed classification framework works well while classifying leaf images with complicated background. There are twenty classes of practical plant leaves successfully classified and the average correct classification rate is up to 92.6%.


title: Social commerce: an e-commerce perspective

abstract: Social commerce is spreading rapidly around the globe creating new and improved business models for conducting e-commerce. As an integration of social media marketing, Web 2.0 and e-commerce it is being viewed differently by different people and companies. In this paper we look at social commerce through e-commerce lens: namely as the delivery of e-commerce activities in social networks and by social software tools. Specifically, we identify those areas of e-commerce which are impacted by social commerce. The papers also identify the major issues related to adoption and use of the technology. Finally, we suggest some research issues related to social commerce.


title: Modulation of the response to a somatosensory stimulation of the hand during the observation of manual actions

abstract: Observation of hand movements has been repeatedly demonstrated to increase the excitability of the motor cortical representation of the hand. Little attention, however, has been devoted to its effect on somatosensory processing. Movement execution is well known to decrease somatosensory cortical excitability, a phenomenon termed ‘gating’. As executed and observed actions share common cortical representations, we hypothesized that action observation (hand movements) should also modulate the cortical response to sensory stimulation of the hand. Seventeen healthy subjects participated in these experiments in which electroencephalographic (EEG) recordings of the somatosensory steady-state response (SSSR) were obtained. The SSSR provides a continuous measure of somatosensory processing. Recordings were made during a baseline condition and five observation conditions in which videos showed either a: (1) hand action; (2) passive stimulation of a hand; (3) static hand; (4) foot action; or (5) static object. The method employed consisted of applying a continuous 25 Hz vibratory stimulation to the index finger during the six conditions and measuring potential gating effects in the SSSR within the 25 Hz band (corresponding to the stimulation frequency). A significant effect of condition was found over the contralateral parietal cortex. Observation of hand actions resulted in a significant gating effect when compared to baseline (average gating of 22%). Observation of passive touch of the hand also gated the response (17% decrease). In conclusion, the results show that viewing a hand performing an action or being touched interferes with the processing of somatosensory information arising from the hand.


title: Time-efficient architecture for face detection from images using support vector machine

abstract: One of the most prolific topics of research in the field of computer vision is pattern detection in images. A large number of practical applications for face detection exist. Contemporary work even suggests that any of the results from specialized detectors can be approximated by using fast detection classifiers. In this project, we developed an algorithm which detected faces from the input image with a lower false detection rate and lower computation cost using the ensemble effects of computer vision concepts. This algorithm utilized the concepts of recognizing skin color, filtering the binary image, detecting blobs and extracting different features from the face. The result is supported by the statistics obtained from calculating the parameters defining the parts of the face. The project also implements the highly powerful concept of Support Vector Machine that is used for the classification of images into face and non-face class. This classification is based on the training data set and indicators of luminance value, chrominance value, saturation value, elliptical value and eye and mouth map values.


title: Video summarization based on user interaction

abstract: It is frequently the case that viewers want to watch sports in less time than a game takes to play. Computing a summarization of a game depends upon information about each play. In contrast with previous video summarization work this paper describes how the interactive behavior of prior viewers can be used to compute which plays are most interesting. This passive feedback is examined as a possible source for the degree of interest in a play. In addition, a mechanism is described for briefly showing the game action that was removed to create the summary. This helps to preserve the continuity of the game in the viewer's mind.


title: Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering

abstract: A range of Natural Language Processing tasks involve making judgments about the semantic relatedness of a pair of sentences, such as Recognizing Textual Entailment (RTE) and answer selection for Question Answering (QA). A key challenge that these tasks face in common is the lack of explicit alignment annotation between a sentence pair. We capture the alignment by using a novel probabilistic model that models tree-edit operations on dependency parse trees. Unlike previous tree-edit models which require a separate alignment-finding phase and resort to ad-hoc distance metrics, our method treats alignments as structured latent variables, and offers a principled framework for incorporating complex linguistic features. We demonstrate the robustness of our model by conducting experiments for RTE and QA, and show that our model performs competitively on both tasks with the same set of general features.


title: Underwater image enhancement via extended multi-scale Retinex

abstract: Underwater exploration has become an active research area over the past few decades. The image enhancement is one of the challenges for those computer vision based underwater researches because of the degradation of the images in the underwater environment. The scattering and absorption are the main causes in the underwater environment to make the images decrease their visibility, for example, blurry, low contrast, and reducing visual ranges. To tackle aforementioned problems, this paper presents a novel method for underwater image enhancement inspired by the Retinex framework, which simulates the human visual system. The term Retinex is created by the combinations of Retina and Cortex. The proposed method, namely LAB-MSR, is achieved by modifying the original Retinex algorithm. It utilizes the combination of the bilateral filter and trilateral filter on the three channels of the image in CIELAB color space according to the characteristics of each channel. With real world data, experiments are carried out to demonstrate both the degradation characteristics of the underwater images in different turbidities, and the competitive performance of the proposed method.


title: Transfer learning between texture classification tasks using Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have set the state-of-the-art in many computer vision tasks in recent years. For this type of model, it is common to have millions of parameters to train, commonly requiring large datasets. We investigate a method to transfer learning across different texture classification problems, using CNNs, in order to take advantage of this type of architecture to problems with smaller datasets. We use a Convolutional Neural Network trained on a source dataset (with lots of data) to project the data of a target dataset (with limited data) onto another feature space, and then train a classifier on top of this new representation. Our experiments show that this technique can achieve good results in tasks with small datasets, by leveraging knowledge learned from tasks with larger datasets. Testing the method on the the Brodatz-32 dataset, we achieved an accuracy of 97.04% - superior to models trained with popular texture descriptors, such as Local Binary Patterns and Gabor Filters, and increasing the accuracy by 6 percentage points compared to a CNN trained directly on the Brodatz-32 dataset. We also present a visual analysis of the projected dataset, showing that the data is projected to a space where samples from the same class are clustered together - suggesting that the features learned by the CNN in the source task are relevant for the target task.


title: Real-Time Analysis of Students’ Activities on an E-Learning Platform based on Apache Spark

abstract: Real time analytics is the capacity to extract valuables insights from data that comes continuously from activities on the web or network sensors. It is largely used in web based business to drive decisions based on user’s experiences, such dynamic pricing and personalized advertising. Many universities have adopted web based learning in their learning process. They use data-mining techniques to better understand students’ behavior, and most of the tools developed are based on historical and stored data, and do not allow real time reactivity. Online activities of learners generate at high speed a huge amount of data in form of users’ interactions which have all characteristics to be considered as Big data. Deal with volume and velocity of these data in order to inform and enable decisions-makers to act at right time lead us to use new methods to capture E-Learning data, and process it in real time. ::: This paper focuses on the design and implementation of modern and hybrid real time data pipeline architecture using Apache Flume to collect data, Apache Spark as an unified engine computation for performing analytics on students’ activities data and Apache Hive as a data warehouse for storing the processed data and for use by various reporting tools. To conceive this platform we conduct an experiment on Moodle database source.


title: Multi-label classification by exploiting label correlations

abstract: Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called multi-label classification using rough sets (MLRS) and MLRS using local correlation (MLRS-LC). The proposed algorithms consider two important factors that affect the accuracy of prediction, namely the correlation among the labels and the uncertainty that exists within the mapping between the feature space and the label space. MLRS provides a global view at the label correlation while MLRS-LC deals with the label correlation at the local level. Given a new instance, MLRS determines its location and then computes the probabilities of labels according to its location. The MLRS-LC first finds out its topic and then the probabilities of new instance belonging to each class is calculated in related topic. A series of experiments reported for seven multi-label datasets show that MLRS and MLRS-LC achieve promising performance when compared with some well-known multi-label learning algorithms.


title: Online McCoy Ad et al. Intrusiveness An Experimental Study of Antecedents and Consequences of Online Ad Intrusiveness

abstract: Internet advertising has shown signs of continued healthy growth in spite of the burst Internet bubble. Several types of ads have been used, and there are important generic characteristics that can be gleaned from these ads: whether they obscure content and whether users have the control to remove them. These factors were tested in a laboratory study with 258 student participants. It was hypothesized that the factors would predict intrusiveness, which would predict perceived irritation. This, in turn, would predict attitudes about the site and, finally, intentions to return. Intrusiveness was also predicted to directly relate to recognition of the ads. All hypotheses were supported at high levels of statistical significance using analysis of variance and structural equation modeling. Explained variance was very high for intrusiveness (42%) and irritation (63%), but very low explained variance for ad recognition (11%) resulted in an alternative model that doubled explained variance by removing intrusivenes...


title: Data augmentation using synthetic data for time series classification with deep residual networks

abstract: Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warping distance, for a deep learning model for TSC. To evaluate the potential of augmenting the training set, we performed extensive experiments using the UCR TSC benchmark. Our preliminary experiments reveal that data augmentation can drastically increase deep CNN's accuracy on some datasets and significantly improve the deep model's accuracy when the method is used in an ensemble approach.


Selection Indices for Yield Improvement in Bread Wheat (Triticum aestivum L.)


title: Credit card fraud detection through parenclitic network analysis

abstract: The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.


title: Optical coherence tomography.

abstract: A technique called optical coherence tomography (OCT) has been developed for noninvasive cross-sectional imaging in biological systems. OCT uses low-coherence interferometry to produce a two-dimensional image of optical scattering from internal tissue microstructures in a way that is analogous to ultrasonic pulse-echo imaging. OCT has longitudinal and lateral spatial resolutions of a few micrometers and can detect reflected signals as small as approximately 10(-10) of the incident optical power. Tomographic imaging is demonstrated in vitro in the peripapillary area of the retina and in the coronary artery, two clinically relevant examples that are representative of transparent and turbid media, respectively.


title: The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones

abstract: Fall detection receives significant attention in the field of preventive medicine, wellness provision and assisted living, especially for the elderly. As a result, numerous commercial fall detection systems exist to date and most of them use accelerometers and/ or gyroscopes attached on a person's body as primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency if necessary, when someone has fallen. Apparently, automatic fall detection will continue to evolve in the following years. The aim of this work is to introduce a human activity dataset that will be helpful in testing new methods, as well as performing objective comparisons between different algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different falls and nine different activities of daily living. Using this dataset, the results of an initial evaluation of three fall detection algorithms are finally presented.


title: Automating Data warehouse design using ontology

abstract: Requirement Analysis is a crucial and arduous task in data warehouse design process. We provide a solution to support data warehouse design, which combines need driven and data driven design approaches. Our solution is based on ontology representation language and work results on ontology alignment. Our design method occurs in two steps. The first one, which is interactive and semi-automatic, transforms user requirements into ontologies, the second step that is fully automatic, aligns the resulting ontologies from the first step and produces an ontology associated to the Multidimensional Data model. The waste management field will be our guideline to illustrate our approach.


title: Loss Functions for Multiset Prediction

abstract: We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging. In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making. The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions. The experiments reveal the effectiveness of the proposed loss function over the others.


title: Approximating checkers for simulation acceleration

abstract: Simulation-based functional verification is the key validation methodology the industry. The performance of logic simulators, however, is not sufficient to attain acceptable verification coverage on large industrial designs within the time-frame available. Acceleration platforms are a valuable addition to the verification effort in that they can provide much higher coverage in less time. Unfortunately, these platforms do not provide the rich checking capability of software-based simulation. We propose a novel solution to deploy those complex checkers, typical of simulation-based environments, onto acceleration platforms. To this end, checkers must be transformed into synthesizable, compact logic blocks with bug-detection capabilities similar to that of their software counterparts. Our "approximate checkers" trade off logic complexity with bug detection accuracy by leveraging novel techniques to approximate complex software checkers into small synthesizable hardware blocks, which can be simulated along with the design on an acceleration platform. We present a general checker taxonomy, propose a range of approximation techniques based on a checker's characteristic and provide metrics for evaluating its bug detection capabilities.


UNSUPERVISED TRANSFORMATION OF PROCEDURAL PROGRAMS TO OBJECT-ORIENTED DESIGN


Modeling and Estimating Airspace Movements Using Air Traffic Control Transcription Data A Data-Driven Approach


title: Leaving the Beaten Tracks in Creative Work – A Design Theory for Systems that Support Convergent and Divergent Thinking

abstract: Existing knowledge is a vital prerequisite for creativity. I t provides a c entral source of inspiration for new ideas and determines the p athways a vailable f or c reative p roblem solving. N otwithstanding i ts indisputable r ole, knowledge may also compromise creativity. The human mind is prone to reproduce what it is used to, and the provision of e xplicit knowledge constitutes a potential inhibitor of imagination. Hence, IT s ystems supporting creative work have to support creative individuals by extending their personal knowledge while, at the same time, preventing them f rom merely walking down beaten tracks. In this article, grounded in theory on human cognition a nd literature on creativity s upport, we p ropose a design theory for I T s ystems that support b oth convergent and divergent thinking, that is, the central cognitive processes in creative work. We provide details on a p rototypical implementation, d iscuss an i llustrative c ase from the c reative i ndustries in or der to demonstrate the design's applicability, and outline plans for an empirical evaluation of the proposed design theory.


title: Trajectory Mining Using Uncertain Sensor Data

abstract: Trajectory mining is an interesting data mining problem. Traditionally, it is either assumed that the time-ordered location data recorded as trajectories are either deterministic or that the uncertainty, e.g., due to equipment or technological limitations, is removed by incorporating some pre-processing routines. Thus, the trajectories are processed as deterministic paths of mobile object location data. However, it is important to understand that the transformation from uncertain to deterministic trajectory data may result in the loss of information about the level of confidence in the recorded events. Probabilistic databases offer ways to model uncertainties using possible world semantics. In this paper, we consider uncertain sensor data and transform this to probabilistic trajectory data using pre-processing routines. Next, we model this data as tuple level uncertain data and propose dynamic programming-based algorithms to mine interesting trajectories. A comprehensive empirical study is performed to evaluate the effectiveness of the approach. The results show that the trajectories could be modeled and worked as probabilistic data and that the results could be computed efficiently using dynamic programming.


title: Single-Phase Hybrid Switched-Capacitor Voltage-Doubler SEPIC PFC Rectifiers

abstract: In this paper, the switched-capacitor concept is extended to the voltage-doubler discontinuous conduction mode SEPIC rectifier. As a result, a set of single-phase hybrid SEPIC power factor correction rectifiers able to provide lower voltage stress on the semiconductors and/or higher static gain, which can be easily increased with additional switched-capacitor cells, is proposed. Hence, these rectifiers could be employed in applications that require higher output voltage. In addition, the converters provide a high power factor and a reduced total harmonic distortion in the input current. The topology employs a three-state switch, and three different implementations are described, two being bridgeless versions, which can provide gains in relation to efficiency. The structures and the topological states, a theoretical analysis in steady state, a dynamic model for control, and a design example are reported herein. Furthermore, a prototype with specifications of 1000-W output power, 220-V input voltage, 800-V output voltage, and 50-kHz switching frequency was designed in order to verify the theoretical analysis.


title: Contextual Bag-of-Words for Visual Categorization

abstract: Bag-of-words (BOW), which represents an image by the histogram of local patches on the basis of a visual vocabulary, has attracted intensive attention in visual categorization due to its good performance and flexibility. Conventional BOW neglects the contextual relations between local patches due to its Naive Bayesian assumption. However, it is well known that contextual relations play an important role for human beings to recognize visual categories from their local appearance. This paper proposes a novel contextual bag-of-words (CBOW) representation to model two kinds of typical contextual relations between local patches, i.e., a semantic conceptual relation and a spatial neighboring relation. To model the semantic conceptual relation, visual words are grouped on multiple semantic levels according to the similarity of class distribution induced by them, accordingly local patches are encoded and images are represented. To explore the spatial neighboring relation, an automatic term extraction technique is adopted to measure the confidence that neighboring visual words are relevant. Word groups with high relevance are used and their statistics are incorporated into the BOW representation. Classification is taken using the support vector machine with an efficient kernel to incorporate the relational information. The proposed approach is extensively evaluated on two kinds of visual categorization tasks, i.e., video event and scene categorization. Experimental results demonstrate the importance of contextual relations of local patches and the CBOW shows superior performance to conventional BOW.


title: Circuit for Shor’s algorithm using 2n+3 qubits

abstract: We try to minimize the number of qubits needed to factor an integer of n bits using Shor's algorithm on a quantum computer. We introduce a circuit which uses 2n+3 qubits and O(n^3 lg(n)) elementary quantum gates in a depth of O(n^3) to implement the factorization algorithm. The circuit is computable in polynomial time on a classical computer and is completely general as it does not rely on any property of the number to be factored. ::: Keywords: Factorization, quantum circuits, modular arithmetics


title: A Genetic Based Improved Load Balanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing

abstract: Cloud computing is evolving as a new model of big-scale distributed computing. It provides own services to online on-demand and pay-as-you-go basis. In cloud computing environment load balancing is a key issue which is required to distributing the dynamic workload over multiple machines to make certain that no single machine is overloaded. In order it helps in ideal use of resource and as a consequence enhancing the performance of the system, we need an efficient task scheduling algorithm. The Min-Min algorithm is simple that produce a schedule that minimize the makespan but this algorithm does not make use of resource effectively. In this paper, we proposed an Improved Load Balanced Min-Min (ILBMM) algorithm using genetic algorithm (GA) in order to minimize the makespan and increase the utilization of resource. The implementation of proposed algorithm has been completed using CloudSim simulator and simulation outcomes demonstration that the proposed algorithm outperforms to current algorithm on same objectives.


title: SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model

abstract: We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees’ individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route.


title: An industrial study on the risk of software changes

abstract: Modelling and understanding bugs has been the focus of much of the Software Engineering research today. However, organizations are interested in more than just bugs. In particular, they are more concerned about managing risk, i.e., the likelihood that a code or design change will cause a negative impact on their products and processes, regardless of whether or not it introduces a bug. In this paper, we conduct a year-long study involving more than 450 developers of a large enterprise, spanning more than 60 teams, to better understand risky changes, i.e., changes for which developers believe that additional attention is needed in the form of careful code or design reviewing and/or more testing. Our findings show that different developers and different teams have their own criteria for determining risky changes. Using factors extracted from the changes and the history of the files modified by the changes, we are able to accurately identify risky changes with a recall of more than 67%, and a precision improvement of 87% (using developer specific models) and 37% (using team specific models), over a random model. We find that the number of lines and chunks of code added by the change, the bugginess of the files being changed, the number of bug reports linked to a change and the developer experience are the best indicators of change risk. In addition, we find that when a change has many related changes, the reliability of developers in marking risky changes is negatively affected. Our findings and models are being used today in practice to manage the risk of software projects.


Analysis of Credit Card Fraud Detection Techniques


title: Application of Quantum Annealing to Training of Deep Neural Networks

abstract: In Deep Learning, a well-known approach for training a Deep Neural Network starts by training a generative Deep Belief Network model, typically using Contrastive Divergence (CD), then fine-tuning the weights using backpropagation or other discriminative techniques. However, the generative training can be time-consuming due to the slow mixing of Gibbs sampling. We investigated an alternative approach that estimates model expectations of Restricted Boltzmann Machines using samples from a D-Wave quantum annealing machine. We tested this method on a coarse-grained version of the MNIST data set. In our tests we found that the quantum sampling-based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional CD-based training. Further investigation is needed to determine whether similar improvements can be achieved for other data sets, and to what extent these improvements can be attributed to quantum effects.


title: Two improvements to detect duplicates in Stack Overflow

abstract: Stack Overflow is one of the most popular question-and-answer sites for programmers. However, there are a great number of duplicate questions that are expected to be detected automatically in a short time. In this paper, we introduce two approaches to improve the detection accuracy: splitting body into different types of data and using word-embedding to treat word ambiguities that are not contained in the general corpuses. The evaluation shows that these approaches improve the accuracy compared with the traditional method.


title: Accelerating Adobe Photoshop with reconfigurable logic

abstract: This paper presents the results of a project designed to produce a commercial application for reconfigurable logic. We describe how we took the popular image processing application Adobe Photoshop and used its plug-in technology to devise a set of FPGA-based filters to accelerate colour space conversion and image convolution operations. Some of the barriers that make it difficult to produce portable FPGA-based filters are explored.


title: Dynamic Capabilities and the Role of Managers in Business Strategy and Economic Performance

abstract: This paper discusses some developments in the theory of the organizational capabilities of the business enterprise. Antecedents are recognized, and some promising new developments and areas for future research are identified. The role of managers in the economic system is highlighted and discussed within the context of economic and organizational research. Suggestions for future developments of dynamic capability research involve employment of evolutionary and behavioral theories.


title: Fornix—Thalamus fibers, motivational states, and contextual retrieval

abstract: Abstract The effect of selectively transecting fornix fibers bound for the anterior thalamic nuclei upon the means by which motivational states influence selection of learned responses was assessed. Rats were deprived of food or water on alternate days. During short sessions they had an opportunity to learn to turn one direction to obtain food and the opposite direction to obtain water in the same T-maze. The control group was a combination of animals which had received sham operations and those in which the attempted transection was off target. Experimental animals behaved much less differentially than controls. The performance levels of the experimental animals in obtaining food and water were more negatively correlated than those of controls. During early stages of the learning procedure the experimental animals were more likely to develop strong individual response preferences or biases than controls. Experimental animals also required more trials to reach criterion than controls. The results are interpreted as indicating that after transection of connections between the subiculum and the anterior thalamic nuclei, motivational states no longer act conditionally upon the selection of learned behavior for performance. Rather they are pooled together with other stimuli. Such an interpretation is derived from the theory that the hippocampus is part of a system mediating contextual retrieval of information from memory.


title: Applying Software Patterns to Address Interoperability in Blockchain-based Healthcare Apps

abstract: Since the inception of the Bitcoin technology, its underlying data structure--the blockchain--has garnered much attention due to properties such as decentralization, transparency, and immutability. These properties make blockchains suitable for apps that require disintermediation through trustless exchange, consistent and incorruptible transaction records, and operational models beyond cryptocurrency. In particular, blockchain and its smart contract capabilities have the potential to address healthcare interoperability issues, such as enabling effective interactions between users and medical applications, delivering patient data securely to a variety of organizations and devices, and improving the overall efficiency of medical practice workflow. Despite the interest in using blockchain for healthcare interoperability, however, little information is available on the concrete architectural styles and patterns for applying blockchain to healthcare apps. This paper provides an initial step in filling this gap by showing: (1) the features and implementation challenges in healthcare interoperability, (2) an end-to-end case study of a blockchain-based healthcare app we are developing, and (3) how applying foundational software patterns can help address common interoperability challenges faced by blockchain-based healthcare apps.


Multiple-Instance Video Segmentation with Sequence-Specific Object Proposals


title: Clinical decision-support systems

abstract: If you ask people what the phrase “computers in medicine” means, they often describe a computer program that helps physicians to make diagnoses. Although computers play numerous important medical roles, from the earliest days of computing people have recognized that computers might support physicians by helping these people to sift through the vast collection of possible diseases and symptoms. This idea has been echoed in futuristic works of science fiction. In Star Trek, for example, medical workers routinely point devices at injured crew members to determine instantly what is the problem and how serious is the damage. The prevalence of such expectations, coupled with a general societal concern about the influence of computers on interpersonal relationships and on job security, has naturally raised questions among health workers. Just what can computers do today to support clinical decision-making? How soon will diagnostic tools be generally available? How good will they be? What will their effects be on the practice of medicine, on medical education, and on relationships among colleagues or between physicians and patients?


title: Gender, Math Confidence, and Grit: Relationships with Quantitative Skills and Performance in an Undergraduate Biology Course

abstract: In a world filled with big data, mathematical models, and statistics, the development of strong quantitative skills is becoming increasingly critical for modern biologists. Teachers in this field must understand how students acquire quantitative skills and explore barriers experienced by students when developing these skills. In this study, we examine the interrelationships among gender, grit, and math confidence for student performance on a pre-post quantitative skills assessment and overall performance in an undergraduate biology course. Here, we show that females significantly underperformed relative to males on a quantitative skills assessment at the start of term. However, females showed significantly higher gains over the semester, such that the gender gap in performance was nearly eliminated by the end of the semester. Math confidence plays an important role in the performance on both the pre and post quantitative skills assessments and overall performance in the course. The effect of grit on student performance, however, is mediated by a student's math confidence; as math confidence increases, the positive effect of grit decreases. Consequently, the positive impact of a student's grittiness is observed most strongly for those students with low math confidence. We also found grit to be positively associated with the midterm score and the final grade in the course. Given the relationships established in this study among gender, grit, and math confidence, we provide "instructor actions" from the literature that can be applied in the classroom to promote the development of quantitative skills in light of our findings.


Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Maize Expert System


title: The Impact of Pretend Play on Children's Development: A Review of the Evidence

abstract: Pretend play has been claimed to be crucial to children’s healthy development. Here we examine evidence for this position versus 2 alternatives: Pretend play is 1 of many routes to positive developments (equifinality), and pretend play is an epiphenomenon of other factors that drive development. Evidence from several domains is considered. For language, narrative, and emotion regulation, the research conducted to date is consistent with all 3 positions but insufficient to draw conclusions. For executive function and social skills, existing research leans against the crucial causal position but is insufficient to differentiate the other 2. For reasoning, equifinality is definitely supported, ruling out a crucially causal position but still leaving open the possibility that pretend play is epiphenomenal. For problem solving, there is no compelling evidence that pretend play helps or is even a correlate. For creativity, intelligence, conservation, and theory of mind, inconsistent correlational results from sound studies and nonreplication with masked experimenters are problematic for a causal position, and some good studies favor an epiphenomenon position in which child, adult, and environment characteristics that go along with play are the true causal agents. We end by considering epiphenomenalism more deeply and discussing implications for preschool settings and further research in this domain. Our take-away message is that existing evidence does not support strong causal claims about the unique importance of pretend play for development and that much more and better research is essential for clarifying its possible role.


title: Narcissism and academic dishonesty: The exhibitionism dimension and the lack of guilt

abstract: Abstract Narcissism is associated with morally questionable behavior in the workplace, but little is known about the role of specific dimensions of narcissism or the mechanism behind these effects. The current study assessed academic dishonesty among college students. One hundred and ninety-nine participants either self-reported or reported others’ cheating behavior and completed the Narcissistic Personality Inventory (NPI; Raskin & Terry, 1988 ). The exhibitionism dimension of the NPI predicted greater cheating; this effect was explained by the lack of guilt. The effects of exhibitionism held for the self but not other-report conditions, highlighting the key role of the self in narcissism. Findings held when controlling for relevant demographic variables and other narcissism factors. Thus the narcissists’ ambitions for their own academic achievement lead to cheating in school, facilitated by a lack of guilt for their immoral behavior.


Adaptive Appearance Rendering


title: Overlapping Community Detection for Multimedia Social Networks

abstract: Finding overlapping communities from multimedia social networks is an interesting and important problem in data mining and recommender systems. However, extant overlapping community discovery with swarm intelligence often generates overlapping community structures with superfluous small communities. To deal with the problem, in this paper, an efficient algorithm (LEPSO) is proposed for overlapping communities discovery, which is based on line graph theory, ensemble learning, and particle swarm optimization (PSO). Specifically, a discrete PSO, consisting of an encoding scheme with ordered neighbors and a particle updating strategy with ensemble clustering, is devised for improving the optimization ability to search communities hidden in social networks. Then, a postprocessing strategy is presented for merging the finer-grained and suboptimal overlapping communities. Experiments on some real-world and synthetic datasets show that our approach is superior in terms of robustness, effectiveness, and automatically determination of the number of clusters, which can discover overlapping communities that have better quality than those computed by state-of-the-art algorithms for overlapping communities detection.


title: Achievement Goals and Intrinsic Motivation: A Meta-Analytic Review

abstract: This article presents a meta-analysis of the experimental literature that has examined the effect of performance and mastery achievement goals on intrinsic motivation. Summary analyses provided supportfor the hypothesis that the pursuit ofperformance goals has an undermining effect on intrinsic motivation relative to the pursuit of mastery goals. Moderator analyses were conducted in an attempt to explain significant variation in the magnitude and direction of this effect across studies. Results indicated that the undermining effect ofperformance goals relative to mastery goals was contingent on whether participants received confirming or nonconfirming competence feedback, and on whether the experimental procedures induced a performance-approach or performance-avoidance orientation. These findings provide conceptual clarity to the literature on achievement goals and intrinsic motivation and suggest numerous avenues for subsequent empirical work.


title: From Shapeshifter to Lava Monster: Gender Stereotypes in Disney’s Moana

abstract: Moana (2016) continues a tradition of Disney princess movies that perpetuate gender stereotypes. The movie contains the usual Electral undercurrent, with Moana seeking to prove her independence to her overprotective father. Moana’s partner in her adventures, Maui, is overtly hypermasculine, a trait epitomized by a phallic fishhook that is critical to his identity. Maui’s struggles with shapeshifting also reflect male anxieties about performing masculinity. Maui violates the Mother Island, first by entering her cave and then by using his fishhook to rob her of her fertility. The repercussions of this act are the basis of the plot: the Mother Island abandons her form as a nurturing, youthful female (Te Fiti) focused on creation to become a vengeful lava monster (Te Kā). At the end, Moana successfully urges Te Kā to get in touch with her true self, a brave but simple act that is sufficient to bring back Te Fiti, a passive, smiling green goddess. The association of youthful, fertile females with good and witch-like infertile females with evil implies that women’s worth and well-being are dependent upon their procreative function. Stereotypical gender tropes that also include female abuse of power and a narrow conception of masculinity merit analysis in order to further progress in recognizing and addressing patterns of gender hegemony in popular Disney films.


title: Alert Correlation through Triggering Events and Common Resources

abstract: Complementary security systems are widely deployed in networks to protect digital assets. Alert correlation is essential to understanding the security threats and taking appropriate actions. This paper proposes a novel correlation approach based on triggering events and common resources. One of the key concepts in our approach is triggering events, which are the (low-level) events that trigger alerts. By grouping alerts that share "similar" triggering events, a set of alerts can be partitioned into different clusters such that the alerts in the same cluster may correspond to the same attack. Our approach further examines whether the alerts in each cluster are consistent with relevant network and host configurations, which help analysts to partially identify the severity of alerts and clusters. The other key concept in our approach is input and output resources. Intuitively, input resources are the necessary resources for an attack to succeed, and output resources are the resources that an attack supplies if successful. This paper proposes to model each attack through specifying input and output resources. By identifying the "common" resources between output resources of one attack and input resources of another, it discovers causal relationships between alert clusters and builds attack scenarios. The experimental results demonstrate the usefulness of the proposed techniques.


title: SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity

abstract: Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000 sentence pairs from previously existing paraphrase datasets and machine translation evaluation resources. The test data also comprised 2000 sentences pairs for those datasets, plus two surprise datasets with 400 pairs from a different machine translation evaluation corpus and 750 pairs from a lexical resource mapping exercise. The similarity of pairs of sentences was rated on a 0-5 scale (low to high similarity) by human judges using Amazon Mechanical Turk, with high Pearson correlation scores, around 90%. 35 teams participated in the task, submitting 88 runs. The best results scored a Pearson correlation >80%, well above a simple lexical baseline that only scored a 31% correlation. This pilot task opens an exciting way ahead, although there are still open issues, specially the evaluation metric.


title: Racing towards academic social networks

abstract: Purpose – The purpose of this paper is to explore the driving forces moving Sakai to join the new era of social applications by adopting a content‐focused methodology.Design/methodology/approach – The exploration is performed by looking at the way in which the role of content has developed through various phases of the internet, and how educational computing has leveraged those developments. The paper then goes on to relate these developments to the way in which initiatives like OpenSocial are changing the nature of application development and hosting, discussing the impact of the new world of cloud‐aware and cloud‐based applications on the development of Sakai.Findings – Shifting to a modern web development paradigm that includes heavy use of client‐side programming methodologies such as AJAX, a content‐centric architecture, and an implementation of social networking capabilities will increase student satisfaction, while reducing development time and cost for systems like Sakai.Originality/value – The pa...