title: Does Gender Play a Role in online Consumer Behavior

abstract: Electronic commerce (online shopping) is increasing in popularity nowadays due to the popularity of computer usage and the prevalence of internet. Electronic commerce can produce numerous benefits to both sellers and consumers. Hence, the study of online consumer behavior can generate a better online atmosphere to facilitate greater profit to sellers and better online purchasing experience to consumer. Gender has universal characteristics, regardless of culture and time period. In this study, the gender differences in different aspects of online consumer behavior is concerned and investigated.


title: Optimizable Object Reconstruction from a Single View

abstract: 3D shape reconstruction from a single image is a highly ill-posed problem. A number of current deep learning based systems aim to solve the shape reconstruction and shape pose problems by learning an end-to-end network to perform feed-forward inference. More traditional (non-deep learning) methods cast the problem in an iterative optimization framework. In this paper, inspired by these more traditional shape-prior-based approaches, which separate the 2D recognition and 3D reconstruction, we develop a system that leverages the power of both feed-forward and iterative approaches. Our framework uses the power of deep learning to capture 3D shape information from training data and provide high-quality initialization, while allowing both image evidence and shape priors to influence iterative refinement at inference time. Specifically, we employ an auto-encoder to learn a latent space of object shapes, a CNN that maps an image to the latent space, another CNN to predict 2D keypoints to recover object pose using PnP, and a segmentation network to predict an object's silhouette from an RGB image. At inference time these components provide high-quality initial estimates of the shape and pose, which are then further optimized based on the silhouette-shape constraint and a probabilistic shape prior learned on the latent space. Our experiments show that this optimizable inference framework achieves state-of-the-art results on a large benchmarking dataset with real images.


title: Combining predictions for accurate recommender systems

abstract: We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filtering (CF) algorithms, which include: SVD, Neighborhood Based Approaches, Restricted Boltzmann Machine, Asymmetric Factor Model and Global Effects. We show that linearly combining (blending) a set of CF algorithms increases the accuracy and outperforms any single CF algorithm. Furthermore, we show how to use ensemble methods for blending predictors in order to outperform a single blending algorithm. The dataset and the source code for the ensemble blending are available online.


title: To Walk or Not to Walk: Crowdsourced Assessment of External Vehicle-to-Pedestrian Displays

abstract: Researchers, technology reviewers, and governmental agencies have expressed concern that automation may necessitate the introduction of added displays to indicate vehicle intent in vehicle-to-pedestrian interactions. An automated online methodology for obtaining communication intent perceptions for 30 external vehicle-to-pedestrian display concepts was implemented and tested using Amazon Mechanic Turk. Data from 200 qualified participants was quickly obtained and processed. In addition to producing a useful early-stage evaluation of these specific design concepts, the test demonstrated that the methodology is scalable so that a large number of design elements or minor variations can be assessed through a series of runs even on much larger samples in a matter of hours. Using this approach, designers should be able to refine concepts both more quickly and in more depth than available development resources typically allow. Some concerns and questions about common assumptions related to the implementation of vehicle-to-pedestrian displays are posed.


BIM and Agent-Based Model Integration for Construction Management Optimization


title: The evolution of Crew Resource Management training in commercial aviation.

abstract: In this study, we describe changes in the nature of Crew Resource Management (CRM) training in commercial aviation, including its shift from cockpit to crew resource management. Validation of the impact of CRM is discussed. Limitations of CRM, including lack of cross-cultural generality are considered. An overarching framework that stresses error management to increase acceptance of CRM concepts is presented. The error management approach defines behavioral strategies taught in CRM as error countermeasures that are employed to avoid error, to trap errors committed, and to mitigate the consequences of error.


title: A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

abstract: We propose an automatic and dynamic trading rule based on flag pattern recognition.The strategy does not depend on the ability of the trader to guess the best configuration of the trading rule.We include several filters for the trades, one of them considering the EMA indicator in short and medium timeframes.The trading rule is applied on a large intraday database for the DJIA index.We can conclude that our proposal is far superior to the previous flag pattern strategies as regards both profitability and risk. In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating important innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pattern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are developed and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.


title: XML-Based NLP Tools For Analysing And Annotating Medical Language

abstract: We describe the use of a suite of highly flexible XML-based NLP tools in a project for processing and interpreting text in the medical domain. The main aim of the paper is to demonstrate the central role that XML mark-up and XML NLP tools have played in the analysis process and to describe the resultant annotated corpus of MEDLINE abstracts. In addition to the XML tools, we have succeeded in integrating a variety of non-XML 'off the shelf' NLP tools into our pipelines, so that their output is added into the mark-up. We demonstrate the utility of the annotations that result in two ways. First, we investigate how they can be used to improve parse coverage of a hand-crafted grammar that generates logical forms. And second, we investigate how they contribute to automatic lexical semantic acquisition processes.


title: An unusual case of suicidal asphyxia by smothering.

abstract: Abstract A case is presented of a 30-year-old man suffering from borderline personality disorder who committed suicide by means of smothering. He was hospitalized at the psychiatric department of a public hospital for several months and was found dead in the seclusion room. The death resulted from an obstruction of the upper aerodigestive tract after ingurgitation of pellets of toilet paper. A literature review revealed several cases of suicidal asphyxia by smothering of patients with a history of psychiatric illness. Our patient’s operating process had not been reported before to our knowledge.


title: hypercholesterolemia management

abstract: Suchy et al. [10] discuss the benefits of ezetimibe beyond lipid-lowering as well as its effects on atherosclerosis. Some comments may be of interest. Reducing low density lipoprotein cholesterol (LDL-C) is the main indication for ezetimibe [2, 7, 8]. This drug is useful in patients who cannot achieve LDL-C goals on statins or those who are statin intolerant [2, 4, 7, 8]. Meta-analyses suggested that adding ezetimibe decreases LDL-C levels more than statin monotherapy, after doubling the dose of a statin or selecting a more potent statin [2, 7, 8]. It was also suggested that ezetimibe can increase LDL particle size, especially in patients with high triglyceride levels [3, 11]. Ezetimibe may exert other beneficial actions which may or may not be related to its hypolipidemic capacity (e.g., for non-alcoholic fatty liver disease and renoprotection) [6]. Ezetimibe monotherapy reduced the plasma mass and activity of lipoprotein-associated phospholipase A2 (Lp-PLA2), which is considered risk predictor of cardiovascular (CV) events [9]. Furthermore, ezetimibe may lower the levels of oxidative stress markers [5]. In the end, the most important issue is reducing vascular events. In this context, the Study of Heart and Renal Protection (SHARP) [1] reported that the combination of ezetimibe with simvastatin was associated with reduced risk of CV events compared with placebo after 4.9 years in patients (n = 9,270) who are pre-dialysis or on dialysis. The clinical relevance of these points and those addressed by Suchy et al. [10] requires to be established by future studies.


title: Comparison between JSON and XML in Applications Based on AJAX

abstract: As the core technology of Web 2.0, Ajax has caught more and more attention. Xml, as the traditional data load format, needs to be resolved by DOM (Document Object Model ) both in client-side and server-side, which wastes system resource and makes a great reduction of user-friendliness. In this paper, a light-weightdata-interchanging format --JSON (Java Script Object Notation) will be introduced, which provides a higher level of flexibility and efficiency. We make a comparison between JSON and XML through expriment, then use JSON as data-transfering format in an actual project. Results show that JSON is more suitable as a data-loading tool for Ajax applications.


title: A Survey on Software Testing Techniques using Genetic Algorithm

abstract: Abstract The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issuesdemand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing. Keywords : Software testing, Genetic Algorithm 1. Introduction


title: Secure Data Provenance for the Internet of Things

abstract: The vision of smart environments, systems, and services is driven by the development of the Internet of Things (IoT). IoT devices produce large amounts of data and this data is used to make critical decisions in many systems. The data produced by these devices has to satisfy various security related requirements in order to be useful in practical scenarios. One of these requirements is data provenance which allows a user to trust the data regarding its origin and location. The low cost of many IoT devices and the fact that they may be deployed in unprotected spaces requires security protocols to be efficient and secure against physical attacks. This paper proposes a light-weight protocol for data provenance in the IoT. The proposed protocol uses physical unclonable functions (PUFs) to provide physical security and uniquely identify an IoT device. Moreover, wireless channel characteristics are used to uniquely identify a wireless link between an IoT device and a server/user. A brief security and performance analysis are presented to give a preliminary validation of the protocol.


title: Anxiety and cognitive performance: attentional control theory

abstract: Attentional control theory is an approach to anxiety and cognition representing a major development of Eysenck and Calvo's (1992) processing efficiency theory. It is assumed that anxiety impairs efficient functioning of the goal-directed attentional system and increases the extent to which processing is influenced by the stimulus-driven attentional system. In addition to decreasing attentional control, anxiety increases attention to threat-related stimuli. Adverse effects of anxiety on processing efficiency depend on two central executive functions involving attentional control: inhibition and shifting. However, anxiety may not impair performance effectiveness (quality of performance) when it leads to the use of compensatory strategies (e.g., enhanced effort; increased use of processing resources). Directions for future research are discussed.


title: Partially transferred convolution neural network with cross-layer inheriting for posture recognition from top-view depth camera

abstract: This paper proposes a new method for human posture recognition from top-view depth maps on small training datasets. There are two strategies developed to leverage the capability of convolution neural network (CNN) in mining the fundamental and generic features for recognition. First, the early layers of CNN should serve the function to extract feature without specific representation. By applying the concept of transfer learning, the first few layers from the pre-learned VGG model can be used directly without further fine-tuning. To alleviate the computational loading and to increase the accuracy of our partially transferred model, a cross-layer inheriting feature fusion (CLIFF) is proposed by using the information from the early layer in fully connected layer without further processing. The experimental result shows that combination of partial transferred model and CLIFF can provide better performance than VGG16 [1] model with re-trained FC layer and other hand-crafted features like RBPs [2].


title: t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification

abstract: The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certain shortcomings. First, the CM EER is not necessarily a reliable predic-tor of performance when ASV and CMs are combined. Second, the EER operating point is ill-suited to user authentication applications , e.g. telephone banking, characterised by a high target user prior but a low spoofing attack prior. We aim to migrate from CM-to ASV-centric assessment with the aid of a new tandem detection cost function (t-DCF) metric. It extends the conventional DCF used in ASV research to scenarios involving spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss costs for both systems, and (ii) prior probabilities of target and spoof trials (with an implied third, nontar-get prior). The study is intended to serve as a self-contained, tutorial-like presentation. We analyse with the t-DCF a selection of top-performing CM submissions to the 2015 and 2017 editions of ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to choose between countermeasure systems for lower priors, system rankings derived with the EER and t-DCF show differences for higher priors. We observe some ranking changes. Findings support the adoption of the DCF-based metric into the roadmap for future ASVspoof challenges, and possibly for other biometric anti-spoofing evaluations.


title: SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization

abstract: The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of dierent metamodeling approaches. This article exemplies how spot can be used for automatic and interactive tuning.


title: Style mining of electronic messages for multiple authorship discrimination: first results

abstract: This paper considers the use of computational stylistics for performing authorship attribution of electronic messages, addressing categorization problems with as many as 20 different classes (authors). Effective stylistic characterization of text is potentially useful for a variety of tasks, as language style contains cues regarding the authorship, purpose, and mood of the text, all of which would be useful adjuncts to information retrieval or knowledge-management tasks. We focus here on the problem of determining the author of an anonymous message, based only on the message text. Several multiclass variants of the Winnow algorithm were applied to a vector representation of the message texts to learn models for discriminating different authors. We present results comparing the classification accuracy of the different approaches. The results show that stylistic models can be accurately learned to determine an author's identity.


title: Information Research in Leisure: Implications from an Empirical Study of Backpackers

abstract: In the modern, increasingly flat world, many individuals have seen an increase in the amount of leisure time they have available. This leisure time is used for different purposes, often including travel and tourism. Among the many types of travel and tourism, backpack or budget travelling is becoming more and more popular. This increasingly common leisure activity presumably involves intensive information search activities. This study places the backpackers' search for travel information in an everyday life information seeking (ELIS) perspective. The search for information by backpackers can be seen as a three-stage information search process. In each stage, depending on the type of task, backpackers use various information resources for different purposes. Such sources may be used for more than one purpose and in more than one information search stage. However, their relative importance varies depending on the characteristics of the source of information and the information search stage in which the source is being used. In this article I suggest that studies of leisure information behaviors and leisure activities show theoretical and practical value for both the information seeking public and the information science community. We also suggest that library and information science scholars and practice communities direct attention and research resources to leisure research in general, and the concept of serious leisure and its structured information acquisition and sharing activities in particular.


title: Mapping Process of Digital Forensic Investigation Framework

abstract: Summary Digital forensics is essential for the successful prosecution of digital criminals which involve diverse digital devices such as computer system devices, network devices, mobile devices and storage devices. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court of law. Therefore, for digital forensic investigation to be performed successfully, there are a number of important steps that have to be taken into consideration. The aim of this paper is to produce the mapping process between the processes/activities and output for each phase in Digital Forensic Investigation Framework (DFIF). Existing digital forensic frameworks will be reviewed and then the mapping is constructed. The result from the mapping process will provide a new framework to optimize the whole investigation process.


title: A smart reminder system for complex human activities

abstract: A significant number of elders live with memory impairment issues, as a result of the normal aging process. Therefore various kinds of supporting systems have been developed to help the elders, who have mild memory problems. In this paper we propose a Smart Reminder System for reminding forgotten complex activities, in home environment. Subjected complex activities are the activities, which should be completed as originally intended, after they are initiated. Due to strong relationship among initiation and conclusion activities, those activities are called as “Coupling Activities” in this paper. Reminders for forgotten Coupling Activities are predicted according to the user's current behaviour, current location and past activity patterns. Therefore wearable sensors are used to gather required data for identifying user's context. A reason for forgetting also is predicted with the reminder. Reminders are predicted with minimum supervision of the user, as the system learns the user's dynamic behaviour by itself. Proposed Smart Reminder System could achieve 80% average accuracy rate for reminder prediction in a system evaluation, which was done using four subjects.


CONVERSION OF PROLIFERATIVE ENDOMETRIUM TO SECRETORY ENDOMETRIUM BY PROGESTERONE IN ORGAN CULTURE


title: Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction

abstract: This paper strives to find the sentence best describing the content of an image or video. Different from existing works, which rely on a joint subspace for image / video to sentence matching, we propose to do so in a visual space only. We contribute Word2VisualVec, a deep neural network architecture that learns to predict a deep visual encoding of textual input based on sentence vectorization and a multi-layer perceptron. We thoroughly analyze its architectural design, by varying the sentence vectorization strategy, network depth and the deep feature to predict for image to sentence matching. We also generalize Word2VisualVec for matching a video to a sentence, by extending the predictive abilities to 3-D ConvNet features as well as a visual-audio representation. Experiments on four challenging image and video benchmarks detail Word2VisualVec's properties, capabilities for image and video to sentence matching, and on all datasets its state-of-the-art results.


title: The Road to Everywhere: Evolution, Complexity and Progress in Natural and Artificial Systems

abstract: Evolution is notorious for its creative power, but also for giving rise to complex, unpredictable dynamics. As a result, practitioners of artificial evolution have encountered difficulties in predicting, analysing, or even understanding the outcome of their experiments. In particular, the concept of evolutionary "progress" (whether in the sense of performance increase or complexity growth) has given rise to much debate and confusion. After a careful description of the mechanisms of evolution and natural selection, we provide usable concepts of performance and progress in coevolution. In particular, we introduce a distinction between three types of progress: local, historical, and global, which we suggest underlies much of the confusion that surrounds coevolutionary dynamics. Similarly, we provide a comprehensive answer to the question of whether an "arrow of complexity" exists in evolution. We introduce several methods to detect and analyse performance and progress in coevolutionary experiments. We propose a statistical measure (Fitness Transmission) to detect the presence of adaptive Darwinian evolution in a reproducing population, based solely on genealogic records; we also point out the limitations of a popular method (the Bedau-Packard statistics of evolutionary activity) for this purpose. To test and illustrate our results, we implement a rich experimental system, inspired by the seminal work of Karl Sims, in which virtual creatures can evolve and interact under various conditions in a physically realistic three-dimensional (3D) environment. To our knowledge, this is the first complete reimplementation and extension of Sims' results. We later extend this system with the introduction of physical combat between creatures, also a first. Finally, we introduce Evosphere, an open, planet-like environment in which 3D artificial creatures interact, reproduce and evolve freely. We conclude our discussion by using Fitness Transmission to detect the onset of adaptive evolution in this system.


title: Demand-Side Management via Distributed Energy Generation and Storage Optimization

abstract: Demand-side management, together with the integration of distributed energy generation and storage, are considered increasingly essential elements for implementing the smart grid concept and balancing massive energy production from renewable sources. We focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy sources and/or energy storage devices. By means of a day-ahead optimization process regulated by an independent central unit, the latter users intend to reduce their monetary energy expense by producing or storing energy rather than just purchasing their energy needs from the grid. In this paper, we formulate the resulting grid optimization problem as a noncooperative game and analyze the existence of optimal strategies. Furthermore, we present a distributed algorithm to be run on the users' smart meters, which provides the optimal production and/or storage strategies, while preserving the privacy of the users and minimizing the required signaling with the central unit. Finally, the proposed day-ahead optimization is tested in a realistic situation.


title: Technostress Levels of Social Network Users Based on ICTs in Turkey

abstract: ICTs have become a rapidly renewing and important field. The use of these technologies, which constantly renew themselves in both software and hardware, is a source of pressure for both individual and social health and wellbeing. As a health concern, the pressure caused by intensive use of technology has been coined technostress. This study analyzes the technostress levels of social network users caused by the use of information and communication technologies through several variables. Social network users were chosen based on the assumption that they use and follow information and communication technologies intensively. This research, conducted on 287 participants, used data gathered through an online questionnaire. The study concluded that social networking users have “medium technostress level”, and that the significant contributing factors in technostress were social pressure regarding the use of technology, remembering large quantities of passwords and user names, anxiety regarding data loss, and technology giving shape to professional life. In addition, it was determined that technostress levels vary based on gender, profession and age.


WORKING PAPER SERIES Strategic Human Resource Management and Performance


title: On discussion of AC and DC side EMI filters design for conducted noise suppression in DC-fed three phase motor drive system

abstract: This paper discusses EMI filter design methodology for DC-fed three-phase motor drive system. An analysis of noise source and propagation path impedances of motor drive systems is conducted, a new filter design model for both AC side and DC side filter is proposed based on the impedance mismatching between the EMI filter impedances and noise source/load impedances. Using this model, the in circuit attenuation of EMI filter can be accurately predicted. The interaction between AC and DC filters is also studied in this analysis. By applying some constraint, EMI filters can be designed. This new design procedure is suitable for both DC and AC side EMI filters. Simulation and experimental results give a firm support to the proposed method.


title: A Comparison of Computational Color Constancy Algorithms - Part II: Experiments with Image Data

abstract: For pt.I see ibid., vol. 11, no.9, p.972-84 (2002). We test a number of the leading computational color constancy algorithms using a comprehensive set of images. These were of 33 different scenes under 11 different sources representative of common illumination conditions. The algorithms studied include two gray world methods, a version of the Retinex method, several variants of Forsyth's (1990) gamut-mapping method, Cardei et al.'s (2000) neural net method, and Finlayson et al.'s color by correlation method (Finlayson et al. 1997, 2001; Hubel and Finlayson 2000). We discuss a number of issues in applying color constancy ideas to image data, and study in depth the effect of different preprocessing strategies. We compare the performance of the algorithms on image data with their performance on synthesized data. All data used for this study are available online at http://www.cs.sfu.ca//spl sim/color/data, and implementations for most of the algorithms are also available (http://www.cs.sfu.ca//spl sim/color/code). Experiments with synthesized data (part one of this paper) suggested that the methods which emphasize the use of the input data statistics, specifically color by correlation and the neural net algorithm, are potentially the most effective at estimating the chromaticity of the scene illuminant. Unfortunately, we were unable to realize comparable performance on real images. Here exploiting pixel intensity proved to be more beneficial than exploiting the details of image chromaticity statistics, and the three-dimensional (3-D) gamut-mapping algorithms gave the best performance.


title: Fast depth image denoising and enhancement using a deep convolutional network

abstract: We propose a depth image denoising and enhancement framework using a light convolutional network. The network contains three layers for high dimension projection, missing data completion and image reconstruction. We jointly use both depth and visual images as inputs. For the gray image, we design a pre-processing procedure to enhance the edges and remove unnecessary detail. For the depth image, we propose a data augmentation strategy to regenerate and increase essential training data. Further, we propose a weighted loss function for network training to adaptively improve the learning efficiency. We tested our algorithm on benchmark data and obtained very promising visual and quantitative results at real-time speed.


Design and development of overhead detection and improvement system for AOP


title: Proving the TLS Handshake Secure (As It Is)

abstract: The TLS Internet Standard features a mixed bag of cryptographic algorithms and constructions, letting clients and servers negotiate their use for each run of the handshake. Although many ciphersuites are now well-understood in isolation, their composition remains problematic, and yet it is critical to obtain practical security guarantees for TLS, as all mainstream implementations support multiple related runs of the handshake and share keys between algorithms.


title: Knowledge and Utilization of Antenatal Care Services by Women of Child Bearing Age in Ilorin-East Local Government Area , North Central Nigeria .

abstract: Background: Antenatal care is an important determinant of maternal mortality rate of which live of the mothers and babies depend. The study examine the knowledge and utilization of antenatal care among the women of reproductive age group in Ilorin East Local Government, Kwara State, North Central Nigeria. Methodology: The study was carried out among women of reproductive age group (15-49) years in Ilorin East Local Area, using a descriptive cross-sectional study design. Data was collected using pretested, Interviewer administered, semi-structured questionnaire. A total of 405 respondents were selected using multistage sampling technique. Most recent pregnancy was used as reference to obtain all the necessary information. Data was analyzed using epi-info soft ware package and findings presented with appropriate tables. Results:The result shows 355 (87.7%) of the respondents were aware of antenatal care. Among the 355 respondents that were aware of antenatal care, 248 (69.9%) had good knowledge on the activities carried out in the provision of antenatal care services.There was high antenatal care attendance, 311 (76.8%) of the respondents attended antenatal care with high patronage of government health facilities by over three quater (86.5%) of the respondents that attended antenatal. Conclusion: There was a high level of awareness and attendance of antenatal care with high patronage of government health facilities. More than two third of the respondents that were aware of antenatal care had good knowledge on the activities carried out in the provision of antenatal care. Education should be made free for all girls up to secondary school level. Antenatal services should be made free for all pregnant women.


title: Chaos in Random Neural Networks

abstract: A continuous-time dynamic model of a network of $N$ nonlinear elements interacting via random asymmetric couplings is studied. A self-consistent mean-field theory, exact in the $N\ensuremath{\rightarrow}\ensuremath{\infty}$ limit, predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter. The autocorrelations of the chaotic flow as well as the maximal Lyapunov exponent are calculated.


title: A critique for macroecology

abstract: The numbers of published macroecological studies are increasing dramatically. Yet, progress within the field of macroecology, and a wider acceptance of its goals and rationale, do not seem to be developing at an equivalent rate. In this paper we examine the criticisms which have been, and can be, made. Attention is drawn to those which withstand scrutiny and are therefore of concern, and some solutions are proffered. We attempt to dispel those criticisms which are misplaced or ill-considered. We conclude that macroecology is likely to progress most effectively in the immediate future, and to make the greatest contribution to the broader study of ecology, if research follows three paths. It needs first to establish a more balanced and better documented body of published patterns, second to establish not simply the presence of patterns but also how in an 'anatomical' sense they come about, and third to expand the program of tests of multiple hypotheses that have been generated to explain observed patterns.


title: A fully integrated 75–83 GHz FMCW synthesizer for automotive radar applications with −97 dBc/Hz phase noise at 1 MHz offset and 100 GHz/mSec maximal chirp rate

abstract: We present a SiGe BiCMOS fully integrated 75–83 GHz FMCW synthesizer for automotive radar applications. Performance enhancements were achieved by utilizing the bulk-drain parasitic variable capacitance of P-channel transistors, embedded in a gm-boosted Colpitts VCO, for frequency control. This mechanism was incorporated in a dual path PLL, providing low loop bandwidth variation over the whole output frequency range, −97 dBc/Hz phase noise at 1 MHz offset and maximum chirp rate of 100 GHz/mSec.


title: Unsupervised, efficient and scalable key-frame selection for automatic summarization of surveillance videos

abstract: Recent years have witnessed a dramatical growth of the deployment of vision-based surveillance in public spaces. Automatic summarization of surveillance videos (ASOSV) is hence becoming more and more desirable in many real-world applications. For this purpose, a novel frame-selection framework is proposed in the present paper, which has three properties: 1) un-supervision: it can work without requirements of any supervised learning or training; 2) efficiency: it can work very fast, with experiments demonstrating efficiency faster than real-timeness and 3) scalability: it can achieve a hierarchical analysis/overview of video content. The performance of proposed framework is systematically evaluated and compared with various state-of-the-art frame selection techniques on some collected video sequences and publicly-available ViSOR dataset. The experimental results demonstrate promising performance and good applicability for real-world problems.


title: “Release Early, Release Often”? An Empirical Analysis of Release Strategy in Open Source Software Co-Creation

abstract: “Release early, release often” is becoming a popular new product introduction strategy in open source software development. We study the influence of release strategies on the download market share of open source projects. Using a panel data set collected from Sourceforge.net, we find that while more frequent releases are associated with better subsequent download market share, the relationship is curvilinear. Too frequent releases could backfire due to the subtle effects on the demand and supply sides of open source software production. From the demand side, we find that releasing frequently may work less effectively in projects with higher adoption costs. From the supply side, fast releases may work less effectively in projects with weak community contributions. Even when the community contributions are strong, the restrictiveness of open source license moderates the effectiveness of releasing early and often. These results have implications for managing open source projects and research on open source software, open innovation, and software adoption.


title: Brain tumor types and grades classification based on statistical feature set using support vector machine

abstract: In this paper, Support Vector Machine (SVM) method has been employed to perform classification of brain tumor images into their types and grades. Mainly the focus is on four brain tumor types-Normal, Glioma, Meningioma, Metastasis and the four grades of Astrocytomas, which is a common type of Glioma. This work experimented the SVM classifier using different statistical feature set such as first order features, second order features and both together and the final analysis shows that for brain tumor types and grades classification, second order features alone achieve an accuracy of 85% and 78.26% respectively, whereas first order features alone achieve 65.517% and 62.31% respectively and when both are used together 84.48% and 68.1% accuracy is achieved respectively. The experiments are repeated for different SVM types, kernel types and gamma values of kernel type. Analysis on the misclassification is performed for each feature set using specificity and sensitivity measures. At the end of this work, we inferred that the SVM classification method is classifying the brain tumor types satisfactorily but comparatively lacks in tumor grade classification.


title: Geographical Routing With Location Service in Intermittently Connected MANETs

abstract: Combining mobile platforms such as manned or unmanned vehicles and peer-assisted wireless communication is an enabler for a vast number of applications. A key enabler for the applications is the routing protocol that directs the packets in the network. Routing packets in fully connected mobile ad hoc networks (MANETs) has been studied to a great extent, but the assumption on full connectivity is generally not valid in a real system. This case means that a practical routing protocol must handle intermittent connectivity and the absence of end-to-end connections. In this paper, we propose a geographical routing algorithm called location-aware routing for delay-tolerant networks (LAROD), enhanced with a location service, location dissemination service (LoDiS), which together are shown to suit an intermittently connected MANET (IC-MANET). Because location dissemination takes time in IC-MANETs, LAROD is designed to route packets with only partial knowledge of geographic position. To achieve low overhead, LAROD uses a beaconless strategy combined with a position-based resolution of bids when forwarding packets. LoDiS maintains a local database of node locations, which is updated using broadcast gossip combined with routing overhearing. The algorithms are evaluated under a realistic application, i.e., unmanned aerial vehicles deployed in a reconnaissance scenario, using the low-level packet simulator ns-2. The novelty of this paper is the illustration of sound design choices in a realistic application, with holistic choices in routing, location management, and the mobility model. This holistic approach justifies that the choice of maintaining a local database of node locations is both essential and feasible. The LAROD-LoDiS scheme is compared with a leading delay-tolerant routing algorithm (spray and wait) and is shown to have a competitive edge, both in terms of delivery ratio and overhead. For spray and wait, this case involved a new packet-level implementation in ns-2 as opposed to the original connection-level custom simulator.


title: A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

abstract: To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.


title: Evaluation of Flygt Mixers for Application in Savannah River Site Tank Summary of Test Results from Phase A, B, and C Testing

abstract: Staff from the Savannah River Site (SRS), Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), and ITT Flygt Corporation in Trumbull, Connecticut, are conducting a joint mixer testing program to evaluate the applicability of Flygt mixers to SRS Tank 19 waste retrieval and waste retrieval in other U.S. Department of Energy (DOE) tanks. This report provides the results of the Phase C Flygt mixer testing and summarizes the key findings from the Phase A and B tests. Phase C Flygt mixer testing used full-scale, Model 4680 Flygt mixers (37 kW, 51-cm propeller) installed in a fall-scale tank (25.9-m diameter) at SRS. Phase A testing used a 0.45-m tank and Flygt mixers with 7.8-cm diameter propellers. Phase B testing used Model 4640 Flygt mixers (3 kW, 37-cm propeller) installed in 1.8-m and 5.7-m tanks. Powell et al. (1999z4 1999b) provide detailed descriptions of the Phase A and B tests. In Phase C, stationary submerged jet mixers manufactured by ITT Flygt Corporation were tested in the 25.9-m diameter tank at the SRS TNX facility. The Model 4680 mixers used in Phase C have 37-kW (50-hp) electric motors that drive 51-cm (20-in.) diameter propellers at 860 rpm. Fluid velocity was measured at selected locations with as many as four Model 4680 mixers operating simultaneously in the 25.9-m tank, which was filled with water to selected levels. Phase C involved no solids suspension or sludge mobilization tests.


title: Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction from a Single Image

abstract: An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image. Hitherto, the problem has been addressed through the application of canonical deep learning methods to regress from the image directly to the 3D shape and pose labels. These approaches, however, are problematic from two perspectives. First, they are minimizing the error between 3D shapes and pose labels - with little thought about the nature of this label error when reprojecting the shape back onto the image. Second, they rely on the onerous and ill-posed task of hand labeling natural images with respect to 3D shape and pose. In this paper we define the new task of pose-aware shape reconstruction from a single image, and we advocate that cheaper 2D annotations of objects silhouettes in natural images can be utilized. We design architectures of pose-aware shape reconstruction which re-project the predicted shape back on to the image using the predicted pose. Our evaluation on several object categories demonstrates the superiority of our method for predicting pose-aware 3D shapes from natural images.


title: Internet Denial of Service Attacks and Defense Mechanisms

abstract: developments in this area in recent years. In this article, we present an in-depth study of the denial of service problem in the Internet, and provide a comprehensive survey of attacks and their countermeasures. We investigate various DoS attack mechanisms, derive a more practical taxonomy of attack mechanisms, and summarize the challenges in DoS defense. We critically review the state of the art in DoS defense, analyze the strengths and weaknesses of dierent proposals, and conclude a comprehensive taxonomy of various defense mechanisms.


title: Comparison of Embedded System Design for Industrial Applications

abstract: This paper presents a survey on embedded systems design and applications. Several platforms for embedded systems, including microcontrollers, microprocessors, field-programmable gate arrays, digital signal processors, and application-specific integrated circuits are discussed and compared. A survey of embedded system-based industrial applications is presented. Examples of real-life design decisions specific to development of such systems are also presented. The carefully selected three design case study examples include industrial control of wind tunnel with emphasis on actuator control, a mobile robot navigation system with emphasis on integration and synchronization of several subsystems, and optimized implementation of computationally intensive control system on a small microcontroller system.


Learning user tastes: a first step to generating healthy meal plans?


title: Considerations for the design of exergames

abstract: Exergaming is the use of video games in an exercise activity. In this paper we consider game design for successful exergames. To do this, we review the history of exergaming and the current state of research in this field. We find that there exists some research aimed at evaluating the physical and health characteristics of exergames, but research on how to design exercise games is still in the early stages. From an analysis of this information, and drawing on established principles from sports science for the prescription of exercise programs, we then attempt to identify success factors to guide designers of exergaming systems.


title: Assessing the threat of web worker distributed attacks

abstract: In this paper, we identify and evaluate the potential of new distributed attacks launched through web browsers using the HTML5 Web Workers API. Web worker attacks rely on the new multi-threading capabilities of Web Workers, which can allow malicious JavaScript code to run in the background of a web page without impacting foreground JavaScript performance or user experience. These background computing tasks can be used to launch application-layer DDoS attacks or offload computationally intensive attack tasks, such as password cracking, to the browsers of users visiting a compromised website. These attacks do not harm the compromised users directly but offer a potential path for attackers to gain control of large pools of computing resources, similar to botnets. We evaluate the feasibility of using online advertisement services to gain access to such computing pool and quantitatively evaluate the economics of these attacks and point out the key factors affecting the cost effectiveness of launching attacks through Web Workers in comparison with cloud computing or rented botnets.


title: Mashup Service Recommendation Based on User Interest and Social Network

abstract: With the rapid development of Web2.0 and its related technologies, Mashup services (i.e., Web applications created by combining two or more Web APIs) are becoming a hot research topic. The explosion of Mashup services, especially the functionally similar or equivalent services, however, make services discovery more difficult than ever. In this paper, we present an approach to recommend Mashup services to users based on user interest and social network of services. This approach firstly extracts users' interests from their Mashup service usage history and builds a social network based on social relationships information among Mashup services, Web APIs and their tags. The approach then leverages the target user's interest and the social network to perform Mashup service recommendation. Large-scale experiments based on a real-world Mashup service dataset show that our proposed approach can effectively recommend Mashup services to users with excellent performance. Moreover, a Mashup service recommendation prototype system is developed.


title: Multi-armed bandits in metric spaces

abstract: In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of $n$ trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a complete solution for the multi-armed problem in this setting. That is, for every metric space (L,X) we define an isometry invariant Max Min COV(X) which bounds from below the performance of Lipschitz MAB algorithms for $X$, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions.


title: Sequenced event set pattern matching

abstract: Event pattern matching is a query technique where a sequence of input events is matched against a complex pattern that specifies constraints on extent, order, values, and quantification of matching events. The increasing importance of such query techniques is underpinned by a significant amount of research work, the availability of commercial products, and by a recent proposal to extend SQL for event pattern matching. The proposed SQL extension includes an operator PERMUTE, which allows to express patterns that match any permutation of a set of events. No implementation of this operator is known to the authors. In this paper, we study the sequenced event set pattern matching problem, which is the problem of matching a sequence of input events against a complex pattern that specifies a sequence of sets of events rather than a sequence of single events. Similar to the PERMUTE operator, events that match with a set specified in the pattern can occur in any permutation, whereas events that match with different sets have to be strictly consecutive, following the order of the sets in the pattern specification. We formally define the problem of sequenced event set pattern matching, propose an automaton-based evaluation algorithm, and provide a detailed analysis of its runtime complexity. An empirical evaluation with real-world data shows that our algorithm outperforms a brute force approach that uses existing techniques to solve the sequenced event set pattern matching problem, and it validates the results from our complexity analysis.


title: Using Educational Tools for Teaching Object Oriented Design and Programming

abstract: The development of software systems is a complex process which requires a diverse set of skills and expertise. The Object Oriented programming paradigm has been proven to better organize the inherent complexity of software systems, than the traditional procedural paradigm. Hence, Object Oriented (OO) is becoming the dominant paradigm in the recent years. The software industry is placing increasing emphasis on newer, object-oriented programming languages and tools, such as Java. It is highly interested for software engineers capable to analyze and develop systems using the OO programming paradigm. The early exposure of students to object oriented programming has become increasingly important in both academia and industry. As an effect, after IEEE and ACM recommendations, the object oriented programming approach has established its place in the Computing Science (CS) Curricula of the Universities. Furthermore, a lot of research is being conducted with regards to the effective instructional methods for the Object Oriented design and programming. Such methods propose the utilization of educational tools for helping students overcome learning difficulties. The scope of this paper is to present some of the well known educational tools which can support the instructional process of Object Oriented design and programming. It also aims to show how one of the most widely known educational tool, called BlueJ, has been utilised and evaluated by students within an educational setting. Finally it discusses trends in the development of this tool (and other related ones) which could influence the teaching of Object Oriented design and programming .


title: Predicting eye fixations using convolutional neural networks

abstract: It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors. In this paper, we propose a novel computational framework to simultaneously learn these two types of visual features from raw image data using a multiresolution convolutional neural network (Mr-CNN) for predicting eye fixations. The Mr-CNN is directly trained from image regions centered on fixation and non-fixation locations over multiple resolutions, using raw image pixels as inputs and eye fixation attributes as labels. Diverse top-down visual features can be learned in higher layers. Meanwhile bottom-up visual saliency can also be inferred via combining information over multiple resolutions. Finally, optimal integration of bottom-up and top-down cues can be learned in the last logistic regression layer to predict eye fixations. The proposed approach achieves state-of-the-art results over four publically available benchmark datasets, demonstrating the superiority of our work.


title: Emotion Recognition in Speech Using Neural Networks

abstract: Emotion recognition in speech is a topic on which little research has been done to date. In this paper, we discuss why emotion recognition in speech is an interesting and applicable research topic and present a system for emotion recognition using one-class-in-one neural networks. By using a large database of phoneme balanced words, our system is speaker and context independent. We achieve a recognition rate of approximately 50% when testing eight emotions.


title: Soil Carbon Dynamics and Net Carbon Dioxide Fluxes in Tropical Forest and Corn Plantation System

abstract: Deforestation and changes of land use from natural forest to agriculture are the important contributors to increasing concentrations of atmospheric CO2. This also leads to a reduction in soil carbon storage due to the rapid decomposition of organic carbon when exposing the soil to agricultural practices. However, the agricultural ecosystems have the potential to mitigate CO2 emission by sequestrating C in the soil. This study aims to investigate the changes of soil carbon stocks and net CO2 emissions when tropical forest is converted to corn plantation. The study period was during 2004-2005.There were two study sites in the east of Thailand. The first site was located within the Khao Hin Sorn Development and Study Centre. The CO2 emissions from soil were 7.97 and 8.96 ton C ha -1 y -1 for native forest and corn plantation soil, respectively. On the other hand, inputs through cultivation practices also emitted certain about of CO2 into the atmosphere. The amounts of soil organic carbon (1 m-depth) were 28.45 and 32.59 ton C ha -1 for forest and corn planting soil, respectively. These data indicate that there was no significant difference in CO2 emission from forest and corn cultivation soils and the amount of soil carbon stock in corn cultivation soil was higher than that in forest soil. Thus, the main carbon stock in case forest is likely in the standing biomass (which was not determined in the current study) while at the corn plantation site is in the soil. At the corn site, farmyard manure has been applied continuously for more than 25 years, and this is likely the reason why soil carbon stock at this site is higher than at forest site. The second study site was located in Tha Ta Kieb (TTK) District, about 20 km east of KHS site. The amounts of soil C were 212.1, 167.2, 137.0, 108.8, 85.4 and 91.6 ton C ha -1 in forest soil, corn planting soil for 3, 4, 7, 8 and 10 years, respectively. More than 80% of this organic carbon was found in the upper 50 cm of the soil profile. When plotted the values of years after deforestation against time, was found that soil carbon was reduced at the rate of 22.87 ton C ha -1 yr -1 which has the turnover time of 9.28 years. From these results, it can be concluded that deforestation results in a rapid reduction of soil organic carbon. However, certain cultivation practices after deforestation could help maintain or increase soil carbon content and thus reduce CO2 emission to the atmosphere.


title: Non-carboxylic analogues of naproxen: design, synthesis, and pharmacological evaluation of some 1,3,4-oxadiazole/thiadiazole and 1,2,4-triazole derivatives.

abstract: A series of substituted 1,3,4-oxadiazole (2-7 and 14-19), 1,2,4-triazole (20-25), and 1,3,4-thiadiazole (26-31) derivatives of naproxen have been synthesized by cyclization of 2-(6-methoxy-2-naphthyl)propanoic acid hydrazide 1 and N(1)[2-(6-methoxy-2-naphthyl) propanoyl]-N(4)-alkyl/aryl-thiosemicarbazides (8-13) under various reaction conditions. All the compounds were screened for their anti-inflammatory activity by carrageenan-induced rat paw edema test method. Compounds showing high anti-inflammatory activity were also tested for their analgesic, ulcerogenic, and lipid peroxidation. Few of the synthesized compounds showed significant anti-inflammatory and analgesic activities along with reduced ulcerogenic effect and lipid peroxidation.


title: 3D-Aware Scene Manipulation via Inverse Graphics

abstract: We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene representations learned by neural networks are often uninterpretable, limited to a single object, or lacking 3D knowledge. In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model. Our scene encoder performs inverse graphics, translating a scene into a structured object-wise representation. Our decoder has two components: a differentiable shape renderer and a neural texture generator. The disentanglement of semantics, geometry, and appearance supports 3D-aware scene manipulation, e.g., rotating and moving objects freely while keeping the consistent shape and texture, and changing the object appearance without affecting its shape. Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart.


Learning Context-Aware Convolutional Filters for Text Processing


title: Lexical and Discourse Analysis of Online Chat Dialog

abstract: One of the ultimate goals of natural language processing (NLP) systems is understanding the meaning of what is being transmitted, irrespective of the medium (e.g., written versus spoken) or the form (e.g., static documents versus dynamic dialogues). Although much work has been done in traditional language domains such as speech and static written text, little has yet been done in the newer communication domains enabled by the Internet, e.g., online chat and instant messaging. This is in part due to the fact that there are no annotated chat corpora available to the broader research community. The purpose of this research is to build a chat corpus, tagged with lexical (token part-of-speech labels), syntactic (post parse tree), and discourse (post classification) information. Such a corpus can then be used to develop more complex, statistical-based NLP applications that perform tasks such as author profiling, entity identification, and social network analysis.


title: GCaR: Garbage Collection aware Cache Management with Improved Performance for Flash-based SSDs

abstract: Garbage Collection (GC) is an important performance concern for flash-based SSDs, because it tends to disrupt the normal operations of an SSD. This problem continues to plague flash-based storage systems, particularly in the high performance computing and enterprise environment. An important root cause for this problem, as revealed by previous studies, is the serious contention for the flash resources and the severe mutually adversary interference between the user I/O requests and GC-induced I/O requests. The on-board buffer cache within SSDs serves to play an essential role in smoothing the gap between the upper-level applications and the lower-level flash chips and alleviating this problem to some extend. Nevertheless, the existing cache replacement algorithms are well optimized to reduce the miss rate of the buffer cache by reducing the I/O traffic to the flash chips as much as possible, but without considering the GC operations within the flash chips. Consequently, they fail to address the root cause of the problem and thus are far from being sufficient and effective in reducing the expensive I/O traffic to the flash chips that are in the GC state. To address this important performance issue in flash-based storage systems, particularly in the HPC and enterprise environment, we propose a Garbage Collection aware Replacement policy, called GCaR, to improve the performance of flash-based SSDs. The basic idea is to give higher priority to caching the data blocks belonging to the flash chips that are in the GC state. This substantially lessens the contentions between the user I/O operations and the GC-induced I/O operations. To verify the effectiveness of GCaR, we have integrated it into the SSD extended Disksim simulator. The simulation results show that GCaR can significantly improve the storage performance by reducing the average response time by up to 40.7%.


title: Integrating agent based modeling into a discrete event simulation

abstract: Movement of entities in discrete event simulation typically requires predefined paths with decision points that dictate entity movement. Human-like travel is difficult to model correctly with these constraints because that is not how people move and large individual differences exist in capabilities and strategies. Agent based modeling is considered a better way to simulate the real-time interaction of people with their environment. In this paper we propose to integrate agent based modeling with discrete event simulation to simulate the movement of people in a discrete event system. An agent based module was constructed within the AutoMod simulation package, and a test case was modeled in which people (agents) at a theme-park interact with objects and people in their environment to get directions and then walk or take a tram to their final destination. We explain the details of model implementation and describe the verification and initial validation of the model.


title: The elastic biaxial modulus of Ag–Pd multilayered thin films measured using the bulge test

abstract: Analysis and sample preparation techniques for the bulge test have been improved to the point where the test can provide reliable and accurate measurements of the mechanical properties of thin films. Ag--Pd multilayer films of variable bilayer period were prepared for this study and characterized by cross-section transmission electron microscopy and by x-ray methods. The films were tested in the bulge test to determine their biaxial moduli. The data show no peak in biaxial modulus at a critical composition wavelength and no nonlinear elastic behavior. They do show a slight trend toward increasing elastic modulus with increasing strength of (111) crystallographic texture. These findings refute a previous report of the supermodulus'' effect in this system and add to the evidence that the effect is caused by artifacts of the mechanical testing technique. Methods for eliminating such artifacts are discussed.


title: The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection

abstract: Wagstaff (2012) draws attention to the pervasiveness of abstract evaluation metrics that explicitly ignore or remove problem specifics. While such metrics allow practitioners to compare numbers across application domains, they offer limited insight into the impact of algorithmic decisions on humans and their perception of the algorithm's correctness. Even for problems that are mathematically the same, both the real-cost of (mathematically) identical errors, as well as their perceived-cost by users, may significantly vary according to the specifics of each problem domain, as well as of the user perceiving the result. While the real-cost of errors has been considered previously, little attention has been paid to the perceived-cost issue. We advocate for the inclusion of human-centered metrics that elicit error costs from humans from two perspectives: the nature of the error, and the user context. Focusing on hate speech detection on social media, we demonstrate that even when fixing the performance as measured by an abstract metric such as precision, user perception of correctness varies greatly depending on the nature of errors and user characteristics.


title: How To Solve Moral Conundrums with Computability Theory

abstract: Various moral conundrums plague population ethics: The Non-Identity Problem, The Procreation Asymmetry, The Repugnant Conclusion, and more. I argue that the aforementioned moral conundrums have a structure neatly accounted for, and solved by, some ideas in computability theory. I introduce a mathematical model based on computability theory and show how previous arguments pertaining to these conundrums fit into the model. This paper proceeds as follows. First, I do a very brief survey of the history of computability theory in moral philosophy. Second, I follow various papers, and show how their arguments fit into, or don't fit into, our model. Third, I discuss the implications of our model to the question why the human race should or should not continue to exist. Finally, I show that our model ineluctably leads us to a Confucian moral principle.


title: Predicting India Volatility Index: An Application of Artificial Neural Network

abstract: has always been an area of interest for the researchers in various realms of finance especially in the stock market e.g. stock index, return on a stock, etc. Stock market volatility is one such area. Since the inception of implied volatility index (VIX) by the Chicago Board of Options Exchange (CBOE) in 1993, VIX index has generated a lot of interest. This study examines the predicting ability of several technical indicators related to VIX index to forecast the next trading day's volatility. There is a wide set of methods available for forecasting in finance. In this study, Artificial neural network (ANN) modeling technique has been employed to forecast the upwards or downwards movement in next trading day's volatility using India VIX (a volatility index based on the NIFTY Index Option prices) based indicators. The results of the study reveal that ANN models can be real handy in forecasting the downwards movement in VIX. The knowledge about a more probable downwards movement in volatility might be significant value add for the investors and help them in making decisions related to trading.


title: A systematic review of the survival and complication rates of fixed partial dentures (FPDs) after an observation period of at least 5 years. III. Conventional FPDs

abstract: OBJECTIVES ::: The objective of this systematic review was to assess the 5- and 10-year survival of implant supported fixed partial dentures (FPDs) and to describe the incidence of biological and technical complications. ::: ::: ::: METHODS ::: An electronic MEDLINE search complemented by manual searching was conducted to identify prospective and retrospective cohort studies on FPDs with a mean follow-up time of at least 5 years. Patients had to have been examined clinically at the follow-up visit. Assessment of the identified studies and data abstraction was performed independently by two reviewers. Failure and complication rates were analyzed using random-effects Poisson regression models to obtain summary estimates of 5- and 10-year survival proportions. ::: ::: ::: RESULTS ::: The search provided 3844 titles and 560 abstracts. Full-text analysis was performed for 176 articles resulting in 21 studies that met the inclusion criteria. Meta-analysis of these studies indicated an estimated survival of implants in implant-supported FPDs of 95.4% (95 percent confidence interval (95% CI): 93.9-96.5%) after 5 and 92.8% (95% CI: 90-94.8%) after 10 years. The survival rate of FPDs supported by implants was 95% (95% CI: 92.2-96.8%) after 5 and 86.7% (95% CI: 82.8-89.8%) after 10 years of function. Only 61.3% (95% CI: 55.3-66.8%) of the patients were free of any complications after 5 years. Peri-implantitis and soft tissue complications occurred in 8.6% (95% CI: 5.1-14.1%) of FPDs after 5 years. Technical complications included implant fractures, connection-related and suprastructure-related complications. The cumulative incidence of implant fractures after 5 years was 0.4% (95% CI: 0.1-1.2%). After 5 years, the cumulative incidence of connection-related complications (screw loosening or fracture) was 7.3% and 14% for suprastructure-related complications (veneer and framework fracture). ::: ::: ::: CONCLUSION ::: Despite a high survival of FPDs, biological and technical complications are frequent. This, in turn, means that substantial amounts of chair time have to be accepted by the clinician following the incorporation of implant-supported FPDs. More studies with follow-up times of 10 and more years are needed as only few studies have described the long-term outcomes.


title: Progressive Refinement: A Method of Coarse-to-Fine Image Parsing Using Stacked Network

abstract: To parse images into fine-grained semantic parts, the complex fine-grained elements will put it in trouble when using off-the-shelf semantic segmentation networks. In this paper, for image parsing task, we propose to parse images from coarse to fine with progressively refined semantic classes. It is achieved by stacking the segmentation layers in a segmentation network several times. The former segmentation module parses images at a coarser-grained level, and the result will be feed to the following one to provide effective contextual clues for the finer-grained parsing. To recover the details of small structures, we add skip connections from shallow layers of the network to fine-grained parsing modules. As for the network training, we merge classes in groundtruth to get coarse-to-fine label maps, and train the stacked network with these hierarchical supervision end-to-end. Our coarse-to-fine stacked framework can be injected into many advanced neural networks to improve the parsing results. Extensive evaluations on several public datasets including face parsing and human parsing well demonstrate the superiority of our method.


title: 3TAAV: A three-tier architecture for pseudonym-based anonymous authentication in VANETs

abstract: Vehicular Ad-hoc Network (VANET) is an emerging communication framework to abet in day-to-day management of vehicular traffic and safety of vehicles. Anonymous authentication is one of the key requirements in VANET ensures the privacy of the origin of the message. Existing security protocols in VANETs, that provide anonymous authentication based on the two-tier architecture, consists of two VANET elements, namely vehicles and road side units (RSUs) working as the key generating server (KGS). These protocols rely heavily on RSU to provide anonymous identity to vehicles. In these protocols, with the movement of the vehicle from one RSU to another RSU, a fresh anonymous identity needs to be generated for every vehicle with additional handing over and taking over procedures. In this paper, we propose a novel security protocol for anonymous authentication based on three-tier architecture involving three VANET elements, viz., vehicle, RSU and pseudonym server (PSS) for achieving multiple levels of anonymity in VANETs. The proposed protocol reduces the overhead incurred by the RSU for certificate management and eliminates the requirement of handing over and taking over of vehicles while changing from one RSU to another. Considering the overhead of RSU in certificate management, the number of pseudonyms cached by each vehicle and blackout-time (duration for which vehicle is not part of VANET due to non-acquisition of pseudo-identity) as performance metrics for simulation, the simulation results demonstrate the efficacy of the proposed approach.


title: Modeling attacks on physical unclonable functions

abstract: We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF. The PUFs we attacked successfully include standard Arbited PUFs and Ring Oscillator PUFs of arbitrary sizes, and XO Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike.


title: Cospectrality and similarity for a pair of matrices under multiplicative and additive composition with diagonal matrices

abstract: Given n×n complex matrices A and B of equal nonzero determinant (of equal trace), we discuss when there exists an invertible diagonal matrix D (a diagonal matrix D) such that AD and BD (A+D and B+D) have equal spectrum and, also, when D may be chosen so that they are similar. In case D is relaxed to be a general nonsingular (general) matrix, then similarity may always be achieved.


title: Humanlike Behavior Generation in Urban Environment Based on Learning-Based Potentials With a Low-Cost Lane Graph

abstract: It is crucial to understand the surrounding cars with respect to the road context and interact with them harmoniously for the success of autonomous cars used in the mixed urban traffic. In this paper, a vision-based approach is proposed to implement the humanlike autonomous driving function along a predefined lane-level route in the complex urban environment with daily traffic. At first, the surrounding cars are located in the lane level by a deep neural network based detector with a low-cost lane graph. Subsequently, a Bayesian network is employed to classify the detected cars into six categories based on their states of operation, i.e., leader car , parked car , tail-end car , exiting car , merging car, and other car . Finally, a hybrid potential map, consisting of a trajectory-induction potential and a risk-prevention potential, is constructed for each of the cars according to their categories, which will be combined to be used for generating an appropriate behavior. Particularly, both of the trajectory-induction potentials and the risk-prevention potentials are learned from naturalistic driving data of the same situations in the daily urban traffic to encode the human driving skills and experiences. Therefore, the behavior generated based on the proposed learning-based potentials is close to a humanlike performance, which is important to achieve harmony in the mixed traffic. Experimental results in various typical but challenging urban traffic scenes have substantiated the effectiveness of the proposed system.


title: Miniaturized Low-Cost Phased-Array Antenna Using SIW Slot Elements

abstract: In this letter, a miniaturized C-band phased-array antenna based on a novel series-fed network is introduced. The series-fed network, radiating elements, and phase shifters are the main parts of the proposed phased-array antenna. In order to use the identical phase shifters, quarter-wavelength transformers are used in the design of the series-fed network. Furthermore, by choosing the proper place of the quarter-wavelength transformers, the distance between adjacent radiating elements is significantly reduced to 0.33λ0. To reduce mutual couplings between radiating elements, miniaturized negative-order substrate integrated waveguide (SIW) slot antennas are chosen as radiating elements. A low-cost and compact 4-bit composite right/left-handed (CRLH) phase shifter is used due to the capability of being placed in a series-fed network. The proposed low-cost phased-array antenna is fabricated on a single-layer printed circuit board (PCB), and good agreement between the simulation and measurement results is obtained. The beam scanning range is 38°, and the impedance bandwidth of the phased-array antenna is 25 MHz at the center frequency of 5 GHz.


title: The sexual differentiation of Cannabis sativa L.: A morphological and molecular study

abstract: Cannabis sativa L. is a dioecious species with sexual dimorphism occurring in a late stage of plant development. Sex is determined by heteromorphic chromosomes (X and Y): male is the heterogametic sex (XY) and female is the homogametic one (XX). The sexual phenotype of Cannabis often shows some flexibility leading to the differentiation of hermaphrodite flowers or bisexual inflorescences (monoecious phenotype). Sex is considered an important trait for hemp genetic improvement; therefore, the study of the mechanism of sexual differentiation is of paramount interest in hemp research. A morphological and molecular study of Cannabis sativa sexual differentiation has been carried out in the Italian dioecious cultivar Fibranova.


title: Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification

abstract: Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi-label classification can be divided into two kinds: binary relevance neural network (BRNN) and threshold dependent neural network (TDNN). However, the former needs to train a set of isolate binary networks which ignore dependencies between labels and have heavy computational load, while the latter needs an additional threshold function mechanism to transform the multi-class probabilities to multi-label outputs. In this paper, we propose a joint binary neural network (JBNN), to address these shortcomings. In JBNN, the representation of the text is fed to a set of logistic functions instead of a softmax function, and the multiple binary classifications are carried out synchronously in one neural network framework. Moreover, the relations between labels are captured via training on a joint binary cross entropy (JBCE) loss. To better meet multi-label emotion classification, we further proposed to incorporate the prior label relations into the JBCE loss. The experimental results on the benchmark dataset show that our model performs significantly better than the state-of-the-art multi-label emotion classification methods, in both classification performance and computational efficiency.


title: Development of a Microwave Proximity Sensor for Industrial Applications

abstract: The paper presents the fundamentals, design, realization and characterization of a state-of-the-art microwave (muW) based proximity sensor (PS) for position detection of stationary or moving targets in industrial applications. PS using popular sensing technologies (inductive, capacitive, magnetic, optical, ultrasonic, etc.) have a large utilization range in rugged industrial environments and are able to fulfill the tough specific requirements. On the other hand every version has specific weaknesses, which dramatically limit the application fields. Microwave technology is established in many sectors except for factory automation. In this study we describe an initial implementation of muW technology in an industrial PS for this field. The result represents an upgrade from the sensors based on the classical operating modes. The degree of novelty consists in the determination of the specifically addressed difficulties, new ideas and the description of the original solutions to adapt the muW technology to PS demands.


title: Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach

abstract: We construct a “universe” of over 18,000 fundamental signals from financial statements and use a bootstrap approach to evaluate the impact of data mining on fundamental-based anomalies. We find that many fundamental signals are significant predictors of cross-sectional stock returns even after accounting for data mining. This predictive ability is more pronounced following high-sentiment periods and among stocks with greater limits to arbitrage. Our evidence suggests that fundamental-based anomalies, including those newly discovered in this study, cannot be attributed to random chance, and they are better explained by mispricing. Our approach is general and we also apply it to past return–based anomalies.


title: Insider Threats to Cloud Computing: Directions for New Research Challenges

abstract: Cloud computing related insider threats are often listed as a serious concern by security researchers, but to date this threat has not been thoroughly explored. We believe the fundamental nature of current insider threats will remain relatively unchanged in a cloud environment, but the paradigm does reveal new exploit possibilities. The common notion of a cloud insider as a rogue administrator of a service provider is discussed, but we also present two additional cloudrelated insider risks: the insider who exploits a cloud-related vulnerability to steal information from a cloud system, and the insider who uses cloud systems to carry out an attack on an employer's local resources. We also characterize a hierarchy of administrators within cloud service providers, give examples of attacks from real insider threat cases, and show how the nature of cloud systems architectures enables attacks to succeed. Finally, we discuss our position on future cloud research.


title: Unsupervised Learning of Models for Object Recognition

abstract: A method is presented to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. The variability across a class of objects is modeled in a principled way, treating objects as flexible constellations of rigid parts (features). Variability is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. Corresponding “constellation models” can be learned in a completely unsupervised fashion. In a first stage, the learning method automatically identifies distinctive parts in the training set by applying a clustering algorithm to patterns selected by an interest operator. It then learns the statistical shape model using expectation maximization. Mixtures of constellation models can be defined and applied to “discover” object categories in an unsupervised manner. The method achieves very good classification results on human faces, cars, leaves, handwritten letters, and cartoon characters.


title: Visual odometry and mapping for autonomous flight using an RGB-D camera

abstract: RGB-D cameras provide both a color image and per-pixel depth estimates. The richness of their data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent state-of-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on unreliable wireless links. We evaluate the effectiveness of our system for stabilizing and controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.


title: Time-series analysis for rapid event-related skin conductance responses

abstract: Event-related skin conductance responses (SCRs) are traditionally analysed by comparing the amplitude of individual peaks against a pre-stimulus baseline. Many experimental manipulations in cognitive neuroscience dictate paradigms with short inter trial intervals, precluding accurate baseline estimation for SCR measurements. Here, we present a novel and general approach to SCR analysis, derived from methods used in neuroimaging that estimate responses using a linear convolution model. In effect, the method obviates peak-scoring and makes use of the full SCR. We demonstrate, across three experiments, that the method has face validity in analysing reactions to a loud white noise and emotional pictures, can be generalised to paradigms where the shape of the response function is unknown and can account for parametric trial-by-trial effects. We suggest our approach provides greater flexibility in analysing SCRs than existing methods.


title: Drift-Step-Recovery Diode Characterization by a Bipolar Pulsed Power Circuit

abstract: A stack of drift- step-recovery diodes (DSRDs) can produce high-voltage pulses with a rise rate of the order of 1 kV/ns. Their building blocks, i.e., the DSRD dies, are designed and manufactured at Soreq Nuclear Research Center based on silicon epitaxial layers. A characterization circuit for DSRDs is presented. The circuit features a power MOSFET, which serves to pump the DSRD in the forward direction and then to pulse it in the reverse direction, and a bias voltage source to balance the forward pumping current with respect to the reverse discharge. Placing the DSRD in series between the MOSFET and the load results in temporal and polarity separation of the MOSFET and DSRD pulses at the load, thus allowing viewing of the net DSRD signal. High-voltage probes are employed to measure the MOSFET and load voltages. Based on this measurement, we formulate the voltage and current extraction of the circuit signals. A 1-ns 190-V epi-Si DSRD die was characterized by this circuit. We show that the DSRD pulses when its reverse discharge is equal to the forward charge, as expected. The DSRD switching loss was measured. An accurate 98% energy balance, which includes the input and output energies with respect to the MOSFET and DSRD switching losses, was obtained. The relationship between the bias voltage, the pumping current, and the main supply voltage is provided. The experimental results are supported by a numerical simulation of the DSRD in the circuit using the Synopsys technological computer-aided design incorporated with a SPICE solver.


title: A bootstrap transimpedance amplifier for high speed optical transcutaneous wireless links

abstract: We report on a prototype of an experimental bootstrap transimpedance preamplifier (BTA) for high speed transcutaneous data transfer. Due to the strong scattering effect of the human skin and the corresponding widely spread optical power distribution, a large size photodiode is required at the external receiver in order to increase the transmission efficiency as well as the misalignment tolerance in the transcutaneous optical telemetric link (TOTL). However, the increased junction capacitance associated with a larger size photodiodes makes the design of high speed transimpedance amplifiers (TIA) challenging. To mitigate this tradeoff, in this paper, we use bootstrapping technique to achieve a large TIA bandwidth even in the presence of large-size photodiodes. Thereby, an emitter follower is employed as the bootstrapping buffer to minimize the junction capacitance seen by the transimpedance stage. The proposed circuit is tested on circuit board level in combination with a 10 mm2 photodiode with a parasitic capacitance as large as 350 pF and achieves a 61.5 dBΩ transimpedance gain over a bandwidth of 61 MHz with an input referred current noise density of 158 pA/√Hz. Therewith, a transcutaneous optical link with 75 MBps is achieved.


title: Zero defections: quality comes to services.

abstract: Companies that want to improve their service quality should take a cue from manufacturing and focus on their own kind of scrap heap: customers who won't come back. Because that scrap heap can be every bit as costly as broken parts and misfit components, service company managers should strive to reduce it. They should aim for "zero defections"--keeping every customer they can profitably serve. As companies reduce customer defection rates, amazing things happen to their financials. Although the magnitude of the change varies by company and industry, the pattern holds: profits rise sharply. Reducing the defection rate just 5% generates 85% more profits in one bank's branch system, 50% more in an insurance brokerage, and 30% more in an auto-service chain. And when MBNA America, a Delaware-based credit card company, cut its 10% defection rate in half, profits rose a whopping 125%. But defection rates are not just a measure of service quality; they are also a guide for achieving it. By listening to the reasons why customers defect, managers learn exactly where the company is falling short and where to direct their resources. Staples, the stationery supplies retailer, uses feedback from customers to pinpoint products that are priced too high. That way, the company avoids expensive broad-brush promotions that pitch everything to everyone. Like any important change, managing for zero defections requires training and reinforcement. Great-West Life Assurance Company pays a 50% premium to group health-insurance brokers that hit customer-retention targets, and MBNA America gives bonuses to departments that hit theirs.


title: Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals

abstract: The electromyographic (EMG) signal observed at the surface of the skin is the sum of thousands of small potentials generated in the muscle fiber. There are many approaches to analyzing EMG signals with spectral techniques. In this study, the short time Fourier Transform (STFT) and wavelet transform (WT) were applied to EMG signals and coefficients were obtained. In these studies, MATLAB 7.01 program was used. According to obtained results, it was determined that WT is more useful than STFT in the fields of eliminating of resolution problem and providing of changeable resolution during analyze.


title: Dynamic modelling of a 2 MW DFIG wind turbine for converter issues: Part 1

abstract: A comprehensive approach to the dynamic modelling of a 2 MW DFIG wind turbine is shown. In this first part mechanical issues are addressed. In a following second part the electrical system and generator will be analysed. Six cp curves from literature are considered. Demonstrating two simple control strategies, the need for a deeper analysis of the dependance of the cp curves on the gearbox ratio and radius is shown. This dependence is analysed then. A suitable speed control strategy is developed. The matching cp curve, radius and gearbox ratio is chosen. A realistic rotor inertia is chosen. The generator inertia is computed.


title: An Index and Test of Linear Moderated Mediation

abstract: I describe a test of linear moderated mediation in path analysis based on an interval estimate of the parameter of a function linking the indirect effect to values of a moderator—a parameter that I call the index of moderated mediation. This test can be used for models that integrate moderation and mediation in which the relationship between the indirect effect and the moderator is estimated as linear, including many of the models described by Edwards and Lambert (2007) and Preacher, Rucker, and Hayes (2007) as well as extensions of these models to processes involving multiple mediators operating in parallel or in serial. Generalization of the method to latent variable models is straightforward. Three empirical examples describe the computation of the index and the test, and its implementation is illustrated using Mplus and the PROCESS macro for SPSS and SAS.


title: Parallelism in Randomized Incremental Algorithms

abstract: In this paper we show that most sequential randomized incremental algorithms are in fact parallel. We consider several random incremental algorithms including algorithms for comparison sorting and Delaunay triangulation; linear programming, closest pair, and smallest enclosing disk in constant dimensions; as well as least-element lists and strongly connected components on graphs. We analyze the dependence between iterations in an algorithm, and show that the dependence structure is shallow for all of the algorithms, implying high parallelism. We identify three types of dependences found in the algorithms studied and present a framework for analyzing each type of algorithm. Using the framework gives work-efficient polylogarithmic-depth parallel algorithms for most of the problems that we study. Some of these algorithms are straightforward (e.g., sorting and linear programming), while others are more novel and require more effort to obtain the desired bounds (e.g., Delaunay triangulation and strongly connected components). The most surprising of these results is for planar Delaunay triangulation for which the incremental approach is by far the most commonly used in practice, but for which it was not previously known whether it is theoretically efficient in parallel.


title: Seismic response of bridge pier on rigid caisson foundation in soil stratum

abstract: An analytical method to study the seismic response of a bridge pier supported on a rigid caisson foundation embedded in a deep soil stratum underlain by a homogeneous half space is developed. The method reproduces the kinematic and inertial responses, using translational and rotational distributed Winkler springs and dashpots to simulate the soil-caisson interaction. Closed-form solutions are given in the frequency domain for vertical harmonic S-wave excitation. Comparison with results from finite element (FE) analysis and other available solutions demonstrates the reliability of the model. Results from parametric studies are given for the kinematic and inertial responses. The modification of the fundamental period and damping ratio of the bridge due to soil-structure interaction is graphically illustrated.