title: Public Goods and Ethnic Division

abstract: The authors present a model that links heterogeneity of preferences across ethnic groups in a city to the amount and type of public good the city supplies. Results show that the shares of spending on productive public goods - education, roads, sewers, and trash pickup _ in U.S. cities (metro areas/urban counties) are inversely related to the city's (metro area's/county's) ethnic fragmentation, even after controlling for other socioeconomic and demographic determinants. They conclude that the ethnic conflict is an important determinant of local public finances. In cities where ethnic groups are polarized, and where politicians have ethnic constituencies, the share of spending that goes to public goods is low. Their results are driven mainly by how white-majority cities react to varying minority-groups sizes. Voters choose lower public goods when a significant fraction of tax revenues collected from one ethnic group is used to provide public goods shared with other ethnic groups.


title: Genetic and Stress-Induced Loss of NG2 Glia Triggers Emergence of Depressive-like Behaviors through Reduced Secretion of FGF2

abstract: NG2-expressing glia (NG2 glia) are a uniformly distributed and mitotically active pool of cells in the central nervous system (CNS). In addition to serving as progenitors of myelinating oligodendrocytes, NG2 glia might also fulfill physiological roles in CNS homeostasis, although the mechanistic nature of such roles remains unclear. Here, we report that ablation of NG2 glia in the prefrontal cortex (PFC) of the adult brain causes deficits in excitatory glutamatergic neurotransmission and astrocytic extracellular glutamate uptake and induces depressive-like behaviors in mice. We show in parallel that chronic social stress causes NG2 glia density to decrease in areas critical to Major Depressive Disorder (MDD) pathophysiology at the time of symptom emergence in stress-susceptible mice. Finally, we demonstrate that loss of NG2 glial secretion of fibroblast growth factor 2 (FGF2) suffices to induce the same behavioral deficits. Our findings outline a pathway and role for NG2 glia in CNS homeostasis and mood disorders.


title: Machine learning based encrypted traffic classification: Identifying SSH and Skype

abstract: The objective of this work is to assess the robustness of machine learning based traffic classification for classifying encrypted traffic where SSH and Skype are taken as good representatives of encrypted traffic. Here what we mean by robustness is that the classifiers are trained on data from one network but tested on data from an entirely different network. To this end, five learning algorithms — AdaBoost, Support Vector Machine, Naie Bayesian, RIPPER and C4.5 — are evaluated using flow based features, where IP addresses, source/destination ports and payload information are not employed. Results indicate the C4.5 based approach performs much better than other algorithms on the identification of both SSH and Skype traffic on totally different networks.


title: Adaptive information extraction

abstract: The growing availability of online textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.


title: Accelerometer output and its association with energy expenditure in persons with multiple sclerosis.

abstract: Limited data support the strong association between rates of accelerometer activity counts and energy expenditure during dynamic activity in persons with multiple sclerosis (MS). This study examined the association between rates of activity counts and energy expenditure during walking by using two models of accelerometers and generated cut-points representing moderate-to-vigorous physical activity (MVPA) in persons with MS. Participants were 43 persons with MS and 43 controls who undertook 5 min of seated rest and up to five 6 min periods of walking at five different speeds on a treadmill. While walking, participants wore two models of accelerometers and a mouthpiece in-line with an open-circuit spirometry system for measuring energy expenditure (rate of oxygen consumption). Strong linear associations were found between accelerometer activity counts and energy expenditure, and the magnitude did not differ between MS and controls for both accelerometer models. The mean slopes of the linear relationships were steeper in persons with MS than controls and resulted in distinct cut-points for MVPA based on accelerometer counts for persons with MS and controls. The strong linear relationship between activity counts and energy expenditure and cut-points for quantifying time spent in MVPA should allow for better understanding of physical activity and examination of its predictors and consequences when using accelerometers in MS.


title: The role of inter-channel trust transfer in establishing mobile commerce trust

abstract: Mobile brokerage services represent an emerging application of mobile commerce in the brokerage industry. Compared with telephone-based trading services and online brokerage services, they have advantages such as ubiquity, convenience, and privacy. However, the number of investors using mobile brokerage services to conduct brokerage transactions is far smaller than those using other trading methods. A plausible reason for this is that investors lack initial trust in mobile brokerage services, which affects their acceptance of them. This research examines trust transfer as a means of establishing initial trust in mobile brokerage services. We analyze how an investor's trust in the online brokerage services of a brokerage firm affects her cognitive beliefs about the mobile brokerage services of the firm and what other key factors influence the formation of initial trust in mobile brokerage services. We develop and empirically test a theoretical model of trust transfer from the online to the mobile channels. Our results indicate that trust in online brokerage services not only has a direct effect on initial trust but also has an indirect effect through other variables. This study provides useful suggestions and implications for academics and practitioners.


title: A Narrative Literature Review and E-Commerce Website Research

abstract: In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show lessfocused research areas in addition to future directions.


title: Brightness preserving dynamic fuzzy histogram equalization

abstract: This paper proposes a novel modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. The modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing. Representation and processing of images in the fuzzy domain enables the technique to handle the inexactness of gray level values in a better way, resulting in improved performance. Execution time is dependent on image size and nature of the histogram, however experimental results show it to be faster as compared to the techniques compared here. The performance analysis of the BPDFHE along with that for BPDHE has been given for comparative evaluation.


title: Document Structure

abstract: We argue the case for abstract document structure as a separate descriptive level in the analysis and generation of written texts. The purpose of this representation is to mediate between the message of a text (i.e., its discourse structure) and its physical presentation (i.e., its organization into graphical constituents like sections, paragraphs, sentences, bulleted lists, figures, and footnotes). Abstract document structure can be seen as an extension of Nunberg's "text-grammar"; it is also closely related to "logical" markup in languages like HTML and LATEX. We show that by using this intermediate representation, several subtasks in language generation and language understanding can be defined more cleanly.


title: STEM education K-12: perspectives on integration

abstract: This commentary was stimulated by Yeping Li’s first editorial (2014) citing one of the journal’s goals as adding multidisciplinary perspectives to current studies of single disciplines comprising the focus of other journals. In this commentary, I argue for a greater focus on STEM integration, with a more equitable representation of the four disciplines in studies purporting to advance STEM learning.The STEM acronym is often used in reference to just one of the disciplines, commonly science. Although the integration of STEM disciplines is increasingly advocated in the literature, studies that address multiple disciplines appear scant with mixed findings and inadequate directions for STEM advancement. Perspectives on how discipline integration can be achieved are varied, with reference to multidisciplinary, interdisciplinary, and transdisciplinary approaches adding to the debates. Such approaches include core concepts and skills being taught separately in each discipline but housed within a common theme; the introduction of closely linked concepts and skills from two or more disciplines with the aim of deepening understanding and skills; and the adoption of a transdisciplinary approach, where knowledge and skills from two or more disciplines are applied to real-world problems and projects with the aim of shaping the total learning experience.Research that targets STEM integration is an embryonic field with respect to advancing curriculum development and various student outcomes. For example, we still need more studies on how student learning outcomes arise not only from different forms of STEM integration but also from the particular disciplines that are being integrated. As noted in this commentary, it seems that mathematics learning benefits less than the other disciplines in programs claiming to focus on STEM integration. Factors contributing to this finding warrant more scrutiny. Likewise, learning outcomes for engineering within K-12 integrated STEM programs appear under-researched. This commentary advocates a greater focus on these two disciplines within integrated STEM education research. Drawing on recommendations from the literature, suggestions are offered for addressing the challenges of integrating multiple disciplines faced by the STEM community.


title: Learning Kernels with Random Features

abstract: Randomized features provide a computationally efficient way to approximate kernel machines in machine learning tasks. However, such methods require a user-defined kernel as input. We extend the randomized-feature approach to the task of learning a kernel (via its associated random features). Specifically, we present an efficient optimization problem that learns a kernel in a supervised manner. We prove the consistency of the estimated kernel as well as generalization bounds for the class of estimators induced by the optimized kernel, and we experimentally evaluate our technique on several datasets. Our approach is efficient and highly scalable, and we attain competitive results with a fraction of the training cost of other techniques.


title: Deep clustering: Discriminative embeddings for segmentation and separation

abstract: We address the problem of "cocktail-party" source separation in a deep learning framework called deep clustering. Previous deep network approaches to separation have shown promising performance in scenarios with a fixed number of sources, each belonging to a distinct signal class, such as speech and noise. However, for arbitrary source classes and number, "class-based" methods are not suitable. Instead, we train a deep network to assign contrastive embedding vectors to each time-frequency region of the spectrogram in order to implicitly predict the segmentation labels of the target spectrogram from the input mixtures. This yields a deep network-based analogue to spectral clustering, in that the embeddings form a low-rank pair-wise affinity matrix that approximates the ideal affinity matrix, while enabling much faster performance. At test time, the clustering step "decodes" the segmentation implicit in the embeddings by optimizing K-means with respect to the unknown assignments. Preliminary experiments on single-channel mixtures from multiple speakers show that a speaker-independent model trained on two-speaker mixtures can improve signal quality for mixtures of held-out speakers by an average of 6dB. More dramatically, the same model does surprisingly well with three-speaker mixtures.


title: Live face video vs. spoof face video: Use of moiré patterns to detect replay video attacks

abstract: With the wide deployment of face recognition systems in applications from border control to mobile device unlocking, the combat of face spoofing attacks requires increased attention; such attacks can be easily launched via printed photos, video replays and 3D masks. We address the problem of facial spoofing detection against replay attacks based on the analysis of aliasing in spoof face videos. The application domain of interest is mobile phone unlock. We analyze the moire pattern aliasing that commonly appears during the recapture of video or photo replays on a screen in different channels (R, G, B and grayscale) and regions (the whole frame, detected face, and facial component between the nose and chin). Multi-scale LBP and DSIFT features are used to represent the characteristics of moire patterns that differentiate a replayed spoof face from a live face (face present). Experimental results on Idiap replay-attack and CASIA databases as well as a database collected in our laboratory (RAFS), which is based on the MSU-FSD database, shows that the proposed approach is very effective in face spoof detection for both cross-database, and intra-database testing scenarios.


title: Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms

abstract: Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An important challenge in the wide deployment of location-based services (LBSs) is the privacy-aware management of location information, providing safeguards for location privacy of mobile clients against vulnerabilities for abuse. This paper describes a scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs. This architecture includes the development of a personalized location anonymization model and a suite of location perturbation algorithms. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k-anonymity for a wide range of mobile clients with context-sensitive privacy requirements. This framework enables each mobile client to specify the minimum level of anonymity that it desires and the maximum temporal and spatial tolerances that it is willing to accept when requesting k-anonymity-preserving LBSs. We devise an efficient message perturbation engine to implement the proposed location privacy framework. The prototype that we develop is designed to be run by the anonymity server on a trusted platform and performs location anonymization on LBS request messages of mobile clients such as identity removal and spatio-temporal cloaking of the location information. We study the effectiveness of our location cloaking algorithms under various conditions by using realistic location data that is synthetically generated from real road maps and traffic volume data. Our experiments show that the personalized location k-anonymity model, together with our location perturbation engine, can achieve high resilience to location privacy threats without introducing any significant performance penalty.


title: A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks.

abstract: Empirical studies show that gradient based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. While a line of recent work explains in theory that gradient-based methods with proper random initialization can find the global minima of the training loss in over-parameterized DNNs, it does not explain the good generalization performance of the gradient-based methods for learning over-parameterized DNNs. In this work, we take a step further, and prove that under certain assumption on the data distribution that is milder than linear separability, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small expected error (i.e., population error). This leads to an algorithmic-dependent generalization error bound for deep learning. To the best of our knowledge, this is the first result of its kind that can explain the good generalization performance of over-parameterized deep neural networks learned by gradient descent.


title: HealthMate: Smart Wearable System for Health Monitoring (SWSHM)

abstract: For the recent few years, Smart Wearable Systems (SWS) have been one of the most productive areas that can be considered for clinical utilization. They are an end-to-end connected source and sink (aggregator) nodes implemented to be in communication together either locally or remotely. However, a number of these sensors can be integrated on the Wireless Body Area Networks (WBAN), which is a new method for health monitoring. In particular, technology has become a trend that affect the patients' needs for requesting an innovation, low cost and flexible system in order to be aware in the change of their physiological and non-physiological body parameters. To provide this demand, HealthMate system is proposed as a solution for real-time health monitoring system that is based on Smart Wearable System for Health Monitoring (SWSHM). This paper is aimed at providing a health monitoring system for patients to measure their body parameters through e-Health sensors via smart phone and enabling doctors to check the health status of their patients remotely. Our solution allows patients to measure their body physiological and non-physiological parameters using body temperature, electrocardiogram, pulse and oxygen in blood, airflow and body position sensors. In addition, by using internet technologies, XBee wireless (802.15.4), and SMS to transmit measured patients body parameters to either mobile application or database on a cloud server, where artificial intelligence is accomplished to examine the data in order to notify health professions in emergency case using SMS services or e-mail.


title: Periocular biometrics: When iris recognition fails

abstract: The performance of iris recognition is affected if iris is captured at a distance. Further, images captured in visible spectrum are more susceptible to noise than if captured in near infrared spectrum. This research proposes periocular biometrics as an alternative to iris recognition if the iris images are captured at a distance. We propose a novel algorithm to recognize periocular images in visible spectrum and study the effect of capture distance on the performance of periocular biometrics. The performance of the algorithm is evaluated on more than 11,000 images of the UBIRIS v2 database. The results show promise towards using periocular region for recognition when the information is not sufficient for iris recognition.


title: Data Missing Solution Using Rough Set theory and Swarm Intelligence

abstract: This paper presents a hybrid approach for solving null values problem, it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estimation through working with rough set theory. The results obtained from most code sets show that Bees algorithm better than ID3 in decreasing the number of extracted rules without affecting the accuracy and increasing the accuracy ratio of null values estimation, especially when the number of null values is increasing.


title: Computational modeling for the computer animation of legged figures

abstract: Modeling techniques for animating legged figures are described which are used in the PODA animation system. PODA utilizes pseudoinverse control in order to solve the problems associated with manipulating kinematically redundant limbs. PODA builds on this capability to synthesize a kinematic model of legged locomotion which allows animators to control the complex relationships between the motion of the body of a figure and the coordination of its legs. Finally, PODA provides for the integration of a simple model of legged locomotion dynamics which insures that the accelerations of a figure's body are synchronized with the timing of the forces applied by its legs.


title: Calysto: scalable and precise extended static checking

abstract: Automatically detecting bugs in programs has been a long-held goal in software engineering. Many techniques exist, trading-off varying levels of automation, thoroughness of coverage of program behavior, precision of analysis, and scalability to large code bases. This paper presents the Calysto static checker, which achieves an unprecedented combination of precision and scalability in a completely automatic extended static checker. Calysto is interprocedurally path-sensitive, fully context-sensitive, and bit-accurate in modeling data operations --- comparable coverage and precision to very expensive formal analyses --- yet scales comparably to the leading, less precise, static-analysis-based tool for similar properties. Using Calysto, we have discovered dozens of bugs, completely automatically, in hundreds of thousands of lines of production, open-source applications, with a very low rate of false error reports. This paper presents the design decisions, algorithms, and optimizations behind Calysto's performance.


The James-Lange theory of emotions: a critical examination and an alternative theory. By Walter B. Cannon, 1927.


title: Power transfer capability and bifurcation phenomena of loosely coupled inductive power transfer systems

abstract: Loosely coupled inductive power transfer (LCIPT) systems are designed to deliver power efficiently from a stationary primary source to one or more movable secondary loads over relatively large air gaps via magnetic coupling. In this paper, a general approach is presented to identify the power transfer capability and bifurcation phenomena (multiple operating modes) for such systems. This is achieved using a high order mathematical model consisting of both primary and secondary resonant circuits. The primary compensation is deliberately designed to make the primary zero phase angle frequency equal the secondary resonant frequency to achieve maximum power with minimum VA rating of the supply. A contactless electric vehicle battery charger was used to validate the theory by comparing the measured and calculated operational frequency and power transfer. For bifurcation-free operation, the power transfer capability and controllability are assured by following the proposed bifurcation criteria. Where controllable operation within the bifurcation region is achievable, a significant increase in power is possible.


title: Predicting the direction of stock market prices using random forest

abstract: Predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature. Intrinsic volatility in stock market across the globe makes the task of prediction challenging. Forecasting and diffusion modeling, although effective can't be the panacea to the diverse range of problems encountered in prediction, short-term or otherwise. Market risk, strongly correlated with forecasting errors, needs to be minimized to ensure minimal risk in investment. The authors propose to minimize forecasting error by treating the forecasting problem as a classification problem, a popular suite of algorithms in Machine learning. In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning. Some of the technical indicators such as Relative Strength Index (RSI), stochastic oscillator etc are used as inputs to train our model. The learning model used is an ensemble of multiple decision trees. The algorithm is shown to outperform existing algo- rithms found in the literature. Out of Bag (OOB) error estimates have been found to be encouraging. Key Words: Random Forest Classifier, stock price forecasting, Exponential smoothing, feature extraction, OOB error and convergence.


title: A review of associative classification mining

abstract: Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.


Junior: The Stanford entry in the Urban Challenge


title: Operational capabilities development in mediated offshore software services models

abstract: The paper expands theoretical and empirical understanding of capabilities development in the mediated offshore outsourcing model whereby a small or a medium-sized firm delivers offshore software services to a larger information technology firm that in turn contracts and interfaces with the actual end-client onshore firms. Such a mediated model has received little prior research attention, although it is common particularly among Chinese firms exporting services to Japan, the largest export market for Chinese software services. We conducted case studies in four China-based software companies to understand the mechanisms used to develop their operational capabilities. We focused on client-specific, process, and human resources capabilities that have been previously associated with vendor success. We found a range of learning mechanisms to build the capabilities in offshore firms. Results show that the development of human resources capabilities was most challenging in the mediated model; yet foundational for the development of the other capabilities. This paper contributes to the information systems literature by improving our understanding of the development of operational capabilities in small- and medium-sized Chinese firms that deploy the mediated model of offshore software services.


title: Understanding Twitter Usage: What Drive People Continue to Tweet

abstract: In this study, we proposed a research model to investigate the factors influencing users’ continuance intention to use Twitter. Building on the uses and gratification framework, we have proposed four types of gratifications for Twitter usage, including content gratification, technology gratification, process gratification, and social gratification. We conducted an online survey and collected 124 responses. The data was analyzed using Partial Least Squares. Our results showed that content gratifications and new technology gratification are the two key types of gratifications affecting the continuance intention to use Twitter. We conclude with a discussion of theoretical and practical implications. We believe that this study will provide important insights for future research on Twitter.


title: Teaching spatial geometry in a virtual world: Using minecraft in mathematics in grade 5/6

abstract: Spatial geometry is one of the fundamental mathematical building blocks of any engineering education. However, it is overshadowed by planar geometry in the curriculum between playful early primary education and later analytical geometry, leaving a multi-year gap where spatial geometry is absent at large. Hence, we investigate the usage of Minecraft as tool to help bridge said gap, as the virtual worlds of Minecraft allow children to create three-dimensional objects in a constructive and algorithmic way. We study two learning scenarios in grade 5/6 with 103 students, reporting on our & the childrens' experiences. Based on our findings, we believe Minecraft to be a valuable mathematical tool that can be easily used to augment the current curriculum.


title: Procedural level generation using multi-layer level representations with MdMCs

abstract: The use of statistical and machine learning approaches, such as Markov chains, for procedural content generation (PCG) has been growing in recent years in the field of Game AI. However, many of these level generation approaches account for only the structural properties of the levels. We developed multi-layered representations of levels, where each layer is designed to capture distinct gameplay information. Specifically, we experiment with representing levels using three layers: a structural layer that captures the object placements in the level, a player path layer that captures the path of an agent through the level, and a height layer that captures sections of the level as various height groups. We test our approach by generating levels for Super Mario Bros. with a multi-dimensional Markov chain approach. We compare the levels sampled with our multi-layered representation with those sampled using a single- layer approach that only considers the structural layer.


title: On frame synchronization in aeronautical telemetry

abstract: Frame synchronizers, suitable for use with continuous phase modulation (CPM) in the presence of phase and frequency offsets, are derived for periodically inserted preamble bits in continuous-mode transmission. The resulting frame synchronizers involve single or double correlations of the received samples with a local copy of the CPM samples corresponding to the preamble bits. Of particular interest in aeronautical telemetry is shaped offset QPSK, telemetry group version (SOQPSK-TG), with the preamble defined in the integrated network enhanced telemetry (iNET) standard. Two low-complexity versions that leverage the special properties of the iNET preamble and SOQPSK-TG operating at 2 samples/bit are developed. Simulation results show that most frame synchronizers are capable of achieving acceptable performance for Eb/N0 ≥ 0 dB and the maximum frequency offset that can be tolerated increases with Eb/N0 to about 3%of the bit rate.


title: Parenting Styles and Overweight Status in First Grade

abstract: OBJECTIVE ::: The goal was to determine the relationship between the 4 parenting styles (authoritative, authoritarian, permissive, and neglectful) and overweight status in first grade. ::: ::: ::: METHODS ::: Data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development were analyzed. Children with complete data for parenting parameters at 54 months and measured weight and height in first grade were included in the analysis. Overweight was defined as BMI of > or =95th percentile. The 4 parenting styles were constructed with 2 scales, namely, maternal sensitivity and maternal expectations for child self-control. Multivariate logistic regression analysis was used to evaluate the relationship between parenting style and overweight in first grade, controlling for gender, race, maternal education, income/needs ratio, marital status, and child behavior problems. ::: ::: ::: RESULTS ::: A total of 872 children, 11.1% overweight and 82.8% white, were included in the analysis. Children of authoritarian mothers (n = 298) had an increased risk of being overweight, compared with children of authoritative mothers (n = 179). Children of permissive (n = 132) and neglectful (n = 263) mothers were twice as likely to be overweight, compared with children of authoritative mothers. Of the covariates, only income/needs ratio was significant and did not alter the relationship between parenting style and overweight risk. ::: ::: ::: CONCLUSIONS ::: Among the 4 parenting styles, authoritarian parenting was associated with the highest risk of overweight among young children. Understanding the mechanisms through which parenting styles are associated with overweight risk may lead to the development of more-comprehensive and better-targeted interventions.


title: Eosinophilic Esophagitis: Pathophysiology and Definition.

abstract: Eosinophilic esophagitis is an adaptive immune response to patient-specific antigens, mostly foods. Eosinophilic esophagitis is not solely IgE-mediated and is likely characterized by Th2 lymphocytes with an impaired esophageal barrier function. The key cytokines and chemokines are thymic stromal lymphopoeitin, interleukin-13, CCL26/eotaxin-3, and transforming growth factor-β, all involved in eosinophil recruitment and remodeling. Chronic food dysphagia and food impactions, the feared late complications, are related in part to dense subepithelial fibrosis, likely induced by interleukin-13 and transforming growth factor-β.


title: Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers

abstract: In line with the increasing use of sensors and health application, there are huge efforts on processing of collected data to extract valuable information such as accelerometer data. This study will propose activity recognition model aim to detect the activities by employing ensemble of classifiers techniques using the Wireless Sensor Data Mining (WISDM). The model will recognize six activities namely walking, jogging, upstairs, downstairs, sitting, and standing. Many experiments are conducted to determine the best classifier combination for activity recognition. An improvement is observed in the performance when the classifiers are combined than when used individually. An ensemble model is built using AdaBoost in combination with decision tree algorithm C4.5. The model effectively enhances the performance with an accuracy level of 94.04 %.


title: Ultra-wide bandwidth time-hopping spread spectrum impulse radio for wireless access communications

abstract: Attractive features of time-hopping spread-spectrum multiple-access systems employing impulse signal technology are outlined, and emerging design issues are described. Performance of such communications systems in terms of achievable transmission rate and multiple-access capability are estimated for both analog and digital data modulation formats under ideal multiple-access channel conditions.


title: Needle: Leveraging Program Analysis to Analyze and Extract Accelerators from Whole Programs

abstract: Technology constraints have increasingly led to the adoption of specialized coprocessors, i.e. hardware accelerators. The first challenge that computer architects encounter is identifying "what to specialize in the program". We demonstrate that this requires precise enumeration of program paths based on dynamic program behavior. We hypothesize that path-based [4] accelerator offloading leads to good coverage of dynamic instructions and improve energy efficiency. Unfortunately, hot paths across programs demonstrate diverse control flow behavior. Accelerators (typically based on dataflow execution), often lack an energy-efficient, complexity effective, and high performance (eg. branch prediction) support for control flow. We have developed NEEDLE, an LLVM based compiler framework that leverages dynamic profile information to identify, merge, and offload acceleratable paths from whole applications. NEEDLE derives insight into what code coverage (and consequently energy reduction) an accelerator can achieve. We also develop a novel program abstraction for offload calledBraid, that merges common code regions across different paths to improve coverage of the accelerator while trading off the increase in dataflow size. This enables coarse grained offloading, reducing interaction with the host CPU core. To prepare the Braids and paths for acceleration, NEEDLE generates software frames. Software frames enable energy efficient speculative execution on accelerators. They are accelerator microarchitecture independent support speculative execution including memory operations. NEEDLE is automated and has been used to analyze 225K paths across 29 workloads. It filtered and ranked 154K paths for acceleration across unmodified SPEC, PARSEC and PERFECT workload suites. We target NEEDLE's offload regions toward a CGRA and demonstrate 34% performance and 20% energy improvement.


title: Experience report on the FIDES reliability prediction method

abstract: This paper examines predicted failure rates for three circuit card assemblies (CCA's) using the new FIDES prediction methodology. These results are compared to observed field failure rates, as well as predicted failure rates derived from MIL-HDBK 217, Reliability Prediction of Electronic Equipment, and the PRISM/spl reg/ prediction software tool that become satisfactory. The FIDES prediction methodology, implemented in Excel workbooks, is easy to use and according to our preliminary study, appears to be an accurate prediction tool.


title: An Integrated Method for Anomaly Detection From Massive System Logs

abstract: Logs are generated by systems to record the detailed runtime information about system operations, and log analysis plays an important role in anomaly detection at the host or network level. Most existing detection methods require a priori knowledge, which cannot be used to detect the new or unknown anomalies. Moreover, the growing volume of logs poses new challenges to anomaly detection. In this paper, we propose an integrated method using K-prototype clustering and k-NN classification algorithms, which uses a novel clustering-filtering-refinement framework to perform anomaly detection from massive logs. First, we analyze the characteristics of system logs and extract 10 features based on the session information to characterize user behaviors effectively. Second, based on these extracted features, the K-prototype clustering algorithm is applied to partition the data set into different clusters. Then, the obvious normal events which usually present as highly coherent clusters are filtered out, and the others are regarded as anomaly candidates for further analysis. Finally, we design two new distance-based features to measure the local and global anomaly degrees for these anomaly candidates. Based on these two new features, we apply the k-NN classifier to generate accurate detection results. To verify the integrated method, we constructed a log collection and anomaly detection platform in the campus network center of Xi’an Jiaotong University. The experimental results based on the data sets collected from the platform show our method has high detection accuracy and low computational complexity.


title: Sparse Cepstral, Phase Codes for Guitar Playing Technique Classification

abstract: Automatic recognition of guitar playing techniques is challenging as it is concerned with subtle nuances of guitar timbres. In this paper, we investigate this research problem by a comparative study on the performance of features extracted from the magnitude spectrum, cepstrum and phase derivatives such as group-delay function (GDF) and instantaneous frequency deviation (IFD) for classifying the playing techniques of electric guitar recordings. We consider up to 7 distinct playing techniques of electric guitar and create a new individual-note dataset comprising of 7 types of guitar tones for each playing technique. The dataset contains 6,580 clips and 11,928 notes. Our evaluation shows that sparse coding is an effective means of mining useful patterns from the primitive time-frequency representations and that combining the sparse representations of logarithm cepstrum, GDF and IFD leads to the highest average F-score of 71.7%. Moreover, from analyzing the confusion matrices we find that cepstral and phase features are particularly important in discriminating highly similar techniques such as pull-off, hammer-on and bending. We also report a preliminary study that demonstrates the potential of the proposed methods in automatic transcription of real-world electric guitar solos.


title: Learning for semantic parsing

abstract: Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning representation. Over the past decade, we have developed a number of machine learning methods for inducing semantic parsers by training on a corpus of sentences paired with their meaning representations in a specified formal language. We have demonstrated these methods on the automated construction of natural-language interfaces to databases and robot command languages. This paper reviews our prior work on this topic and discusses directions for future research.


title: Recognition based segmentation of connected characters in text based CAPTCHAs

abstract: Text based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is the most widely used mechanism adopted by numerous popular web sites in order to differentiate between machines and humans, however due to extensive research carried out by computer vision researchers, it is now a days vulnerable against automated attacks. Segmentation is the most difficult task in automatic recognition of CAPTCHAs, therefore contemporary Text based CAPTCHAs try to combine the characters together in order to make them as segmentation resistant against these attacks as possible. In this research, we have found vulnerabilities in such CAPTCHAs, a novel mechanism, i.e. the recognition based segmentation is applied to crop such connected characters, a sliding window based neural network classifier is used to recognize and segment the connected characters. Experimental results have proved 95.5% recognition success rate and 58.25% segmentation success rate on our dataset of tmall CAPTCHAs, this algorithm is further tested on two other datasets of slightly different implementations and promising results were achieved.


title: A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry

abstract: Voxel-based morphometry (VBM) has already been applied to MRI scans of patients with Alzheimer's disease (AD). The results of these studies demonstrated atrophy of the hippocampus, temporal pole, and insula, but did not describe any global brain changes or atrophy of deep cerebral structures. We propose an optimized VBM method, which accounts for these shortcomings. Additional processing steps are incorporated in the method, to ensure that the whole spectrum of brain atrophy is visualized. A local group template was created to avoid registration bias, morphological opening was performed to eliminate cerebrospinal fluid voxel misclassifications, and volume preserving modulation was used to correct for local volume changes. Group differences were assessed and thresholded at P < 0.05 (corrected). Our results confirm earlier findings, but additionally we demonstrate global cortical atrophy with sparing of the sensorimotor cortex, occipital poles, and cerebellum. Moreover, we show atrophy of the caudate head nuclei and medial thalami. Our findings are in full agreement with the established neuropathological descriptions, offering a comprehensive view of atrophy patterns in AD.


title: Instance based human physical activity(hpa) recognition using shimmer2 wearable sensor data sets

abstract: Human physical activity (HPA) recognition is one of the a large amount emerging fields of research in pervasive summing. In wearable computing scenarios, human physical activities such as standing still, sitting and relaxing, lying down, walking, climbing stairs, waist bends forward, front elevation of arms, knee bending, cycling, jogging, running and jump front and back can be implicit from sensor data provided by shimmer2 acceleration sensors. In such scenarios, most methods use a one or two dimensional features, nevertheless of which activity to be identified. This paper we can identified how to predict human physical activity using tri-accelerometer three dimensional data generated by shimmer2 wearable sensor device. We represent a efficient sensor data analysis of features computed from a realistic accelerometer sensor data set and different classifiers are studied on instances based data sets. This shows that the choice of time domain feature and the window dimension accomplished on which the computed features that transform the activity accuracy rates for different huamn physical activities.


title: Snooze: A Scalable and Autonomic Virtual Machine Management Framework for Private Clouds

abstract: With the advent of cloud computing and the need to satisfy growing customers resource demands, cloud providers now operate increasing amounts of large data centers. In order to ease the creation of private clouds, several open-source Infrastructure-as-a-Service (IaaS) cloud management frameworks (e.g. Open Nebula, Nimbus, Eucalyptus, Open Stack) have been proposed. However, all these systems are either highly centralized or have limited fault tolerance support. Consequently, they all share common drawbacks: scalability is limited by a single master node and Single Point of Failure (SPOF). In this paper, we present the design, implementation and evaluation of a novel scalable and autonomic (i.e. self-organizing and healing) virtual machine (VM) management framework called Snooze. For scalability the system utilizes a self-organizing hierarchical architecture and performs distributed VM management. Moreover, fault tolerance is provided at all levels of the hierarchy, thus allowing the system to self-heal in case of failures. Our evaluation conducted on 144 physical machines of the Grid'5000 experimental test bed shows that the fault tolerance features of the framework do not impact application performance. Moreover, negligible cost is involved in performing distributed VM management and the system remains highly scalable with increasing amounts of resources.


title: Security vs. privacy: How integrity attacks can be masked by the noise of differential privacy

abstract: Privacy concerns have increased in the last years due to the unprecedented scale of data collected regarding human activity. Differential privacy has emerged in the last decade as an important mechanism to ensure privacy by adding random noise with a specific distribution to the information being collected (e.g., adding noise to smart meters, and sensor readings). Differential privacy has been mainly used in private databases, but lately it has also been extended to consider applications like estimation, consensus algorithms, and control of dynamical systems.


title: Back to the future: leveraging Belady's algorithm for improved cache replacement

abstract: Belady's algorithm is optimal but infeasible because it requires knowledge of the future. This paper explains how a cache replacement algorithm can nonetheless learn from Belady's algorithm by applying it to past cache accesses to inform future cache replacement decisions. We show that the implementation is surprisingly efficient, as we introduce a new method of efficiently simulating Belady's behavior, and we use known sampling techniques to compactly represent the long history information that is needed for high accuracy. For a 2MB LLC, our solution uses a 16KB hardware budget (excluding replacement state in the tag array). When applied to a memory-intensive subset of the SPEC 2006 CPU benchmarks, our solution improves performance over LRU by 8.4%, as opposed to 6.2% for the previous state-of-the-art. For a 4-core system with a shared 8MB LLC, our solution improves performance by 15.0%, compared to 12.0% for the previous state-of-the-art.


title: Compression of inverted indexes For fast query evaluation

abstract: Compression reduces both the size of indexes and the time needed to evaluate queries. In this paper, we revisit the compression of inverted lists of document postings that store the position and frequency of indexed terms, considering two approaches to improving retrieval efficiency: better implementation and better choice of integer compression schemes. First, we propose several simple optimisations to well-known integer compression schemes, and show experimentally that these lead to significant reductions in time. Second, we explore the impact of choice of compression scheme on retrieval efficiency.In experiments on large collections of data, we show two surprising results: use of simple byte-aligned codes halves the query evaluation time compared to the most compact Golomb-Rice bitwise compression schemes; and, even when an index fits entirely in memory, byte-aligned codes result in faster query evaluation than does an uncompressed index, emphasising that the cost of transferring data from memory to the CPU cache is less for an appropriately compressed index than for an uncompressed index. Moreover, byte-aligned schemes have only a modest space overhead: the most compact schemes result in indexes that are around 10% of the size of the collection, while a byte-aligned scheme is around 13%. We conclude that fast byte-aligned codes should be used to store integers in inverted lists.


title: A lane change detection approach using feature ranking with maximized predictive power

abstract: Risk estimation for the current traffic situation is crucial for safe autonomous driving systems. One part of the uncertainty in risk estimation is the behavior of the surrounding traffic participants. In this paper we focus on highway scenarios, where possible behaviors consist of a change in acceleration and lane change maneuvers. We present a novel approach for the recognition of lane change intentions of traffic participants. Our novel approach is an extension of the Naive Bayesian approach and results in a generative model. It builds on the relations to the directly surrounding vehicles and to the static traffic environment. We obtain the conditional probabilities of all relevant features using Gaussian mixtures with a flexible number of components. We systematically reduce the number of features by selecting the most powerful ones. Furthermore we investigate the predictive power of each feature with respect to the time before a lane change event. In a large scale experiment on real world data with over 160.781 samples collected on a test drive of 1100km we trained and validated our intention prediction model and achieved a significant improvement in the recognition performance of lane change intentions compared to current state of the art methods.


title: Benchmarking GPUs to tune dense linear algebra

abstract: We present performance results for dense linear algebra using recent NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs up to 60% faster than the vendor's implementation and approaches the peak of hardware capabilities. Our LU, QR and Cholesky factorizations achieve up to 80-90% of the peak GEMM rate. Our parallel LU running on two GPUs achieves up to ~540 Gflop/s. These results are accomplished by challenging the accepted view of the GPU architecture and programming guidelines. We argue that modern GPUs should be viewed as multithreaded multicore vector units. We exploit blocking similarly to vector computers and heterogeneity of the system by computing both on GPU and CPU. This study includes detailed benchmarking of the GPU memory system that reveals sizes and latencies of caches and TLB. We present a couple of algorithmic optimizations aimed at increasing parallelism and regularity in the problem that provide us with slightly higher performance.


title: What are the high-level concepts with small semantic gaps?

abstract: Concept-based multimedia search has become more and more popular in multimedia information retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. , there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we propose a novel framework to develop a lexicon of high-level concepts with small semantic gaps (LCSS) from a large-scale Web image dataset. By defining a confidence map and content-context similarity matrix, images with small semantic gaps are selected and clustered. The final concept lexicon is mined from the surrounding descriptions (titles, categories and comments) of these images. This lexicon offers a set of high-level concepts with small semantic gaps, which is very helpful for people to focus for data collection, annotation and modeling. It also shows a promising application potential for image annotation refinement and rejection. The experimental results demonstrate the validity of the developed concepts lexicon.


title: Trainer’s Resource Handout The Traumatic Impact of Child Sexual Abuse: A Conceptualization The Traumatic Impact of Child Sexual Abuse: A Conceptualization 1

abstract: A framework is proposed for a more systematic understanding of the effects of child sexual abuse. Four traumagenic dynamics--traumatic sexualization betrayal stigmatization and powerlessness--are identified as the core of the psychological injury inflicted by abuse. These dynamics can be used to make assessments of victimized children and to anticipate problems to which these children may be vulnerable subsequently. Implications for research are also considered. (authors)


title: Raw light field image compression of sliced lenslet array

abstract: A light field image is a sampling of the intensity and direction of the light rays crossing the optical lens of a digital light field camera. Processing of the light field information captured by a camera is necessary for rendering images of the scene to the final user at a given focal plane or viewpoint. In the area of light field imaging, several compression techniques are currently under research in order to foster the development of novel applications. An analysis of raw light field image compression of sliced lenslet array is presented in this paper. The distortion introduced by compression is measured in terms of peak signal-to-noise ratio and the structural similarity index. The obtained results are finally discussed.


title: Attribute-based proxy re-encryption method for revocation in cloud storage: Reduction of communication cost at re-encryption

abstract: In recent years, many users have uploaded data to the cloud for easy storage and sharing with other users. At the same time, security and privacy concerns for the data are growing. Attribute-based encryption (ABE) enables both data security and access control by defining users with attributes so that only those users who have matching attributes can decrypt them. For real-world applications of ABE, revocation of users or their attributes is necessary so that revoked users can no longer decrypt the data. In actual implementations, ABE is used in hybrid with a symmetric encryption scheme such as the advanced encryption standard (AES) where data is encrypted with AES and the AES key is encrypted with ABE. The hybrid encryption scheme requires re-encryption of the data upon revocation to ensure that the revoked users can no longer decrypt that data. To re-encrypt the data, the data owner (DO) must download the data from the cloud, then decrypt, encrypt, and upload the data back to the cloud, resulting in both huge communication costs and computational burden on the DO depending on the size of the data to be re-encrypted. In this paper, we propose an attribute-based proxy re-encryption method in which data can be re-encrypted in the cloud without downloading any data by adopting both ABE and Syalim's encryption scheme. Our proposed scheme reduces the communication cost between the DO and cloud storage. Experimental results show that the proposed method reduces the communication cost by as much as one quarter compared to that of the trivial solution.


title: Sliding-switching power supply circuit for class-D audio amplifier

abstract: The paper presents a robust power supply circuit for class-D audio amplifier especially for multilevel converter. Based on pulse-width-modulation (PWM) buck converter, we develop a sliding-switching technique comprises a loading monitor and a voltage level controller. The loading monitor continuously checks the internal control signal of class-D amplifier to identify whether the loading is light or heavy. The voltage level controller then feedbacks corresponding voltage level to modulate pulse width of control signal in buck converter and adjust supplying current and power delivery. The proposed sliding-switching technique stabilizes the voltage variation of power supply under the situation of alternating light and heavy loading and consequently enhances the performance of total-harmonic-distortion (THD). To demonstrate the practicability, an integrated multilevel class-D amplifier with proposed power supply circuit is implemented under TSMC 0.35µm process. The simulation results show that the total-harmonic-distortion is improved more than 11dB without increasing power consumption of circuit.


Increasing Deep Learning Melanoma Classification by Classical And Expert Knowledge Based Image Transforms


title: Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

abstract: Data mining is the extraction of knowledge from large databases. One of the popular data mining techniques is Classification in which different objects are classified into different classes depending on the common properties among them. Decision Trees are widely used in Classification. This paper proposes a tool which applies an enhanced Decision Tree Algorithm to detect the suspicious e-mails about the criminal activities. An improved ID3 Algorithm with enhanced feature selection method and attribute- importance factor is applied to generate a better and faster Decision Tree. The objective is to detect the suspicious criminal activities and minimize them. That's why the tool is named as “Z-Crime” depicting the “Zero Crime” in the society. This paper aims at highlighting the importance of data mining technology to design proactive application to detect the suspicious criminal activities.


title: A hardware CABAC encoder for HEVC

abstract: This paper presents a hardware design of context-based adaptive binary arithmetic coding (CABAC) for the emerging High efficiency video coding (HEVC) standard. While aiming at higher compression efficiency, the CABAC in HEVC also invests a lot of effort in the pursuit of parallelism and reducing hardware cost. Simulation results show that our design processes 1.18 bins per cycle on average. It can work at 357 MHz with 48.940K gates targeting 0.13 μm CMOS process. This processing rate can support real-time encoding for all sequences under common test conditions of HEVC standard conforming to the main profile level 6.1 of main tier or main profile level 5.1 of high tier.


Label Embedding for Zero-shot Fine-grained Named Entity Typing


title: Round-robin Arbiter Design and Generation

abstract: In this paper, we introduce a Round-robin Arbiter Generator (RAG) tool. The RAG tool can generate a design for a Bus Arbiter (BA). The BA is able to handle the exact number of bus masters for both on-chip and off-chip buses. RAG can also generate a distributed and parallel hierarchical Switch Arbiter (SA). The first contribution of this paper is the automated generation of a round-robin token passing BA to reduce time spent on arbiter design. The generated arbiter is fair, fast, and has a low and predictable worst-case wait time. The second contribution of this paper is the design and integration of a distributed fast arbiter, e.g., for a terabit switch, based on 2/spl times/2 and 4/spl times/4 switch arbiters (SAs). Using a .25/spl mu/ TSMC standard cell library from LEDA Systems [10, 14], we show the arbitration time of a 256/spl times/256 SA for a terabit switch and demonstrate that the SA generated by RAG meets the time constraint to achieve approximately six terabits of throughput in a typical network switch design. Furthermore, our generated SA performs better than the Ping-Pong Arbiter and Programmable Priority Encoder by a factor of 1.9/spl times/ and 2.4/spl times/, respectively.


title: Stakeholder Analysis for a Cross-Country Pipeline Project in India

abstract: INTRODUCTIONInfrastructure facilities in India are inadequate for our huge population and for our growing economy. Economic growth calls for development of infrastructural facilities, be it roads, railways, ports, electric power or oil and gas. Pipelines are an efficient means for transporting oil and gas. In the year 2013, India had a cumulative crude oil, gas and petroleum product pipeline network of 35,647 km as compared to 2,225,032 km in the US at the same time (CIA, 2014). According to a report prepared by the Planning Commission (2011), based on a midterm appraisal of the 12th Five Year Plan, the investment in infrastructure in India should be almost 10% of GDP during the period 2012-2017. According to Deloitte (2014), this investment in infrastructure is projected to be one trillion Dollars. There is a strong need to set up oil and gas pipelines in India to make the oil and gas supply chain more efficient. An investment of about 1,489 billion has been earmarked for setting up oil and gas pipelines in the 12th Five Year Plan (Kumar, 2013).Cross-country pipeline projects, like other infrastructure projects, are complex involving many stakeholders. Such projects entail huge costs and take a long time to be completed. They require multiple approvals from various agencies. Even land acquisition can be a major challenge as often agricultural land has to be acquired and adequate compensation has to be paid to landowners. Stakeholder management is important for every project. For infrastructure projects this becomes even more critical, firstly, due to multiplicity of stakeholders and secondly, because many of them wield lot of power. However, cross - country pipeline projects have a certain uniqueness compared to other infrastructure projects, too. The cross-country pipelines are laid in the Right of Way (ROW), acquired under Petroleum and Mineral Pipelines Act, 1962. Under the said Act, the cross-country pipeline laying agency is granted only with power of Right of User (ROU) over the land and the ownership of the land vests with the original land owner. The land owners are paid at 10% of the value of the land as user fee. In most other projects, land is acquired by the project implementing agency and once acquired, the ownership rights are transferred to the implementing agency. The new owner is fully vested with power in utilizing the acquired land and the hindrances from previous land owners are not common.STAKEHOLDER THEORYA stakeholder can be defined as any individual or group of individuals who are directly or indirectly impacted by an entity or a task (Sutterfield, Friday and Shivers, 2006). Alternatively, any individual, group or organization that can affect a project or gets affected by the achievement of an organization's objectives can be terme d as a stakeholder (Freeman, 1984). Stakeholders can be both internal and external to an organization. Irrespective of whether they are internal or external, each stakeholder may have certain expectations from a project. There may be stakeholders who support a project or those who may oppose it or some who may be just be neutral or indifferent. Project success, among other things, depends to a large extent on how stakeholders are managed. The purpose of stakeholder management is to reduce opposition from the negative stakeholders and enhance the support of positive stakeholders. A project manager needs to understand the interest each stakeholder has in the project. The project manager also needs to understand the power of the stakeholder to influence the outcome of the project. Based on the interest of each stakeholder and the power to influence the project, a stakeholder management plan can be developed which can guide the project manager to handle stakeholders more effectively.REVIEW OF LITERATUREIn a study of strategic decisions, Nutt (2002) found that half of the decisions taken by firms were either not implemented or partially implemented or did not have good results. …


A Novel Filling Disocclusion Method Based on Background Extraction in Depth-Image-Based-Rendering


title: Enabling Practical Backscatter Communication for On-body Sensors

abstract: In this paper, we look at making backscatter practical for ultra-low power on-body sensors by leveraging radios on existing smartphones and wearables (e.g. WiFi and Bluetooth). The difficulty lies in the fact that in order to extract the weak backscattered signal, the system needs to deal with self-interference from the wireless carrier (WiFi or Bluetooth) without relying on built-in capability to cancel or reject the carrier interference. Frequency-shifted backscatter (or FS-Backscatter) is based on a novel idea --- the backscatter tag shifts the carrier signal to an adjacent non-overlapping frequency band (i.e. adjacent WiFi or Bluetooth band) and isolates the spectrum of the backscattered signal from the spectrum of the primary signal to enable more robust decoding. We show that this enables communication of up to 4.8 meters using commercial WiFi and Bluetooth radios as the carrier generator and receiver. We also show that we can support a range of bitrates using packet-level and bit-level decoding methods. We build on this idea and show that we can also leverage multiple radios typically present on mobile and wearable devices to construct multi-carrier or multi-receiver scenarios to improve robustness. Finally, we also address the problem of designing an ultra-low power tag that can frequency shift by 20MHz while consuming tens of micro-watts. Our results show that FS-Backscatter is practical in typical mobile and static on-body sensing scenarios while only using commodity radios and antennas.


title: The work‐family dyad: multi‐level perspectives

abstract: Purpose – The purpose of this paper is to introduce a selection of papers within the issue that discuss the work‐family interface.Design/methodology/approach – The themes of the papers in the issue are outlinedFindings – The papers address the following: conflict, facilitation and individual coping styles across the work and family domains; generational differences in work‐family conflict and work‐family synergy for Generation X, baby boomers and matures; the development and transmission of work‐related attitudes; a cross cultural comparison of female managers attitudes, experiences and career choices; the impact of individual and organisational characteristics on work‐family conflict and career outcomes, and the variation of work life integration experiences of mid‐career professional women.Originality/value – The paper introduces the special issue which provides a varied mix of theoretical approaches and multi‐level perspectives to scope out and explain the links between work and family life.


title: Warmth and competence in your face! Visual encoding of stereotype content

abstract: Previous research suggests that stereotypes about a group's warmth bias our visual representation of group members. Based on the stereotype content model (SCM) the current research explored whether the second big dimension of social perception, competence, is also reflected in visual stereotypes. To test this, participants created typical faces for groups either high in warmth and low in competence (male nursery teachers) or vice versa (managers) in a reverse correlation image classification task, which allows for the visualization of stereotypes without any a priori assumptions about relevant dimensions. In support of the independent encoding of both SCM dimensions hypotheses-blind raters judged the resulting visualizations of nursery teachers as warmer but less competent than the resulting image for managers, even when statistically controlling for judgments on one dimension. People thus seem to use facial cues indicating both relevant dimensions to make sense of social groups in a parsimonious, non-verbal and spontaneous manner.


title: Multimodal speech recognition: increasing accuracy using high speed video data

abstract: To date, multimodal speech recognition systems based on the processing of audio and video signals show significantly better results than their unimodal counterparts. In general, researchers divide the solution of the audio–visual speech recognition problem into two parts. First, in extracting the most informative features from each modality and second, in the most successful way of fusion both modalities. Ultimately, this leads to an improvement in the accuracy of speech recognition. Almost all modern studies use this approach with video data of a standard recording speed of 25 frames per second. The choice of such a recording speed is easily explained, since the vast majority of existing audio–visual databases are recorded with this rate. However, it should be noticed that the number of 25 frames per second is a world standard for many areas and has never been specifically calculated for speech recognition tasks. The main purpose of this study is to investigate the effect brought by the high-speed video data (up to 200 frames per second) on the speech recognition accuracy. And also to find out whether the use of a high-speed video camera makes the speech recognition systems more robust to acoustical noise. To this end, we recorded a database of audio–visual Russian speech with high-speed video recordings, which consists of records of 20 speakers, each of them pronouncing 200 phrases of continuous Russian speech. Experiments performed on this database showed an improvement in the absolute speech recognition rate up to 3.10%. We also proved that the use of the high-speed camera with 200 fps allows achieving better recognition results under different acoustically noisy conditions (signal-to-noise ratio varied between 40 and 0 dB) with different types of noise (e.g. white noise, babble noise).


title: Dermoscopy for the family physician.

abstract: Noninvasive in vivo imaging techniques have become an important diagnostic aid for skin cancer detection. Dermoscopy, also known as dermatoscopy, epiluminescence microscopy, incident light microscopy, or skin surface microscopy, has been shown to increase the clinician's diagnostic accuracy when evaluating cutaneous neoplasms. A handheld instrument called a dermatoscope or dermoscope, which has a transilluminating light source and standard magnifying optics, is used to perform dermoscopy. The dermatoscope facilitates the visualization of subsurface skin structures that are not visible to the unaided eye. The main purpose for using dermoscopy is to help correctly identify lesions that have a high likelihood of being malignant (i.e., melanoma or basal cell carcinoma) and to assist in differentiating them from benign lesions clinically mimicking these cancers. Colors and structures visible with dermoscopy are required for generating a correct diagnosis. Routinely using dermoscopy and recognizing the presence of atypical pigment network, blue-white color, and dermoscopic asymmetry will likely improve the observer's sensitivity for detecting pigmented basal cell carcinoma and melanoma. A two-step algorithm based on a seven-level criterion ladder is the foundation for dermoscopic evaluation of skin lesions. The first step of the algorithm is intended to help physicians differentiate melanocytic lesions from the following nonmelanocytic lesions: dermatofibroma, basal cell carcinoma, seborrheic keratosis, and hemangioma. The second step is intended to help physicians differentiate nevi from melanoma using one of several scoring systems. From a management perspective, the two-step algorithm is intended to guide the decision-making process on whether to perform a biopsy, or to refer or reassure the patient.


title: A Capacitive-Type Novel Six-Axis Force/Torque Sensor for Robotic Applications

abstract: This paper presents an innovative design of a six-axis force/torque sensor, which enables ease of manufacturing, cost reduction, and ruggedness in operation. The advantages are realized by simplifying the structure of the sensor and reducing manual labor during the manufacturing process. In order to achieve a simple sensor structure, we propose a capacitive-type force sensing scheme in which the sensing elements are aligned in-plane. By using this method, all the sensing elements can be fabricated on single printed circuit board, and thus, manual tasks, such as bonding and coating the sensing elements, can be eliminated. In order to verify the feasibility of the idea, a prototype six-axis force/torque sensor was manufactured, and its performances were evaluated. The characteristics of the prototype are analyzed in terms of condition number, static response, time domain response, hysteresis, repeatability, and time drift.


title: Cognitive Architectures for Sensory Processing

abstract: This paper describes our efforts to design a cognitive architecture for object recognition in video. Unlike most efforts in computer vision, our work proposes a Bayesian approach to object recognition in video, using a hierarchical, distributed architecture of dynamic processing elements that learns in a self-organizing way to cluster objects in the video input. A biologically inspired innovation is to implement a top-down pathway across layers in the form of causes, creating effectively a bidirectional processing architecture with feedback. To simplify discrimination, overcomplete representations are utilized. Both inference and parameter learning are performed using empirical priors, while imposing appropriate sparseness constraints. Preliminary results show that the cognitive architecture has features that resemble the functional organization of the early visual cortex. One example showing the use of top-down connections is given to disambiguate a synthetic video from correlated noise.


title: NEUTRAMS: Neural network transformation and co-design under neuromorphic hardware constraints

abstract: With the recent reincarnations of neuromorphic computing comes the promise of a new computing paradigm, with a focus on the design and fabrication of neuromorphic chips. A key challenge in design, however, is that programming such chips is difficult. This paper proposes a systematic methodology with a set of tools to address this challenge. The proposed toolset is called NEUTRAMS ( Neu ral network Tra nsformation, M apping and S imulation), and includes three key components: a neural network (NN) transformation algorithm, a configurable clock-driven simulator of neuromorphic chips and an optimized runtime tool that maps NNs onto the target hardware for better resource utilization. To address the challenges of hardware constraints on implementing NN models (such as the maximum fan-in/fan-out of a single neuron, limited precision, and various neuron models), the transformation algorithm divides an existing NN into a set of simple network units and retrains each unit iteratively, to transform the original one into its counterpart under such constraints. It can support both spiking neural networks (SNNs) and traditional artificial neural networks (ANNs), including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). With the combination of these tools, we have explored the hardware/software co-design space of the correlation between network error-rates and hardware constraints and consumptions. Doing so provides insights which can support the design of future neuromorphic architectures. The usefulness of such a toolset has been demonstrated with two different designs: a real Complementary Metal-Oxide-Semiconductor (CMOS) neuromorphic chip for both SNNs and ANNs and a processing-in-memory architecture design for ANNs.


title: Optimization by Self-Organized Criticality

abstract: Self-organized criticality (SOC) is a phenomenon observed in certain complex systems of multiple interacting components, e.g., neural networks, forest fires, and power grids, that produce power-law distributed avalanche sizes. Here, we report the surprising result that the avalanches from an SOC process can be used to solve non-convex optimization problems. To generate avalanches, we use the Abelian sandpile model on a graph that mirrors the graph of the optimization problem. For optimization, we map the avalanche areas onto search patterns for optimization, while the SOC process receives no feedback from the optimization itself. The resulting method can be applied without parameter tuning to a wide range of optimization problems, as demonstrated on three problems: finding the ground-state of an Ising spin glass, graph coloring, and image segmentation. We find that SOC search is more efficient compared to other random search methods, including simulated annealing, and unlike annealing, it is parameter free, thereby eliminating the time-consuming requirement to tune an annealing temperature schedule.


title: The 10 Laws of Fogonomics

abstract: The economics of the cloud—Cloudonomics—can be extended, adapted, and contrasted with the economics of the fog and edge—Fogonomics. In some cases clouds have the upper hand, thanks to the statistics of workload aggregation and proximity to cloud-resident data. In other cases, the fog has the advantage, such as latency reduction and backhaul traffic reduction for interactive tasks supporting user devices and things.


title: Book spine segmentation for various book orientations

abstract: This work proposes a novel approach to separate books which are placed disorderly on a bookshelf. This work applies grayscale morphological reconstruction and Lo gradient minimization to erase texts on books while preserving book boundaries. Afterwards, Hough transform and the proposed SVM-based recovery scheme are applied to find book boundaries accurately. The proposed SVM-based recovery scheme is able to recover the book boundaries missed by Hough transform without incurring noticeable false alarm detections. Experiments demonstrate that the proposed method is able to segment books which are placed in various orientations.


title: Supply chain network design under uncertainty: A comprehensive review and future research directions

abstract: Abstract Supply chain network design (SCND) is one of the most crucial planning problems in supply chain management (SCM). Nowadays, design decisions should be viable enough to function well under complex and uncertain business environments for many years or decades. Therefore, it is essential to make these decisions in the presence of uncertainty, as over the last two decades, a large number of relevant publications have emphasized its importance. The aim of this paper is to provide a comprehensive review of studies in the fields of SCND and reverse logistics network design under uncertainty. The paper is organized in two main parts to investigate the basic features of these studies. In the first part, planning decisions, network structure, paradigms and aspects related to SCM are discussed. In the second part, existing optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, risk-averse stochastic programming, robust optimization, and fuzzy mathematical programming are explored in terms of mathematical modeling and solution approaches. Finally, the drawbacks and missing aspects of the related literature are highlighted and a list of potential issues for future research directions is recommended.


title: Cognitive Radars: On the Road to Reality: Progress Thus Far and Possibilities for the Future

abstract: This article describes some key ideas and applications of cognitive radars, highlighting the limits and the path forward. Cognitive radars are systems based on the perception-action cycle of cognition that senses the environment, learns relevant information from it about the target and the background, and then adapts the radar sensor to optimally satisfy the needs of the mission according to a desired goal. The concept of cognitive radar was originally introduced only for active radar. In this article, we explain how this paradigm can also be applied to passive radar (PR).


title: Medical students’ attitudes towards early clinical exposure in Iran

abstract: OBJECTIVE ::: This study was carried out to investigate the medical students' attitudes towards early clinical exposure at Tehran University of Medical Sciences. ::: ::: ::: METHODS ::: A cross-sectional study was conducted during 2012-2015. A convenience sample of 298 first- and second-year students, enrolled in the undergraduate medical curriculum, participated in an early clinical exposure program. To collect data from medical students, a questionnaire consisting of open-ended questions and structured questions, rated on a five-point Likert scale, was used to investigate students' attitudes toward early clinical exposure. ::: ::: ::: RESULTS ::: Of the 298 medical students, 216 (72%) completed the questionnaires. The results demonstrated that medical students had a positive attitude toward early clinical exposure. Most students (80.1%) stated that early clinical exposure could familiarize them with the role of basic sciences knowledge in medicine and how to apply this knowledge in clinical settings. Moreover, 84.5% of them believed that early clinical exposure increased their interest in medicine and encouraged them to read more. Furthermore, content analysis of the students' responses uncovered three main themes of early clinical exposure, were considered helpful to improve learning: "integration of theory and practice", "interaction with others and professional development" and "desire and motivation for learning medicine". ::: ::: ::: CONCLUSIONS ::: Medical students found their first experience with clinical setting valuable. Providing clinical exposure in the initial years of medical curricula and teaching the application of basic sciences knowledge in clinical practice can enhance students' understanding of the role they will play in the future as a physician.


title: A generalized rule based tracker for dialogue state tracking

abstract: Dialogue state tracking plays an important role in statistical dialogue management. Domain-independent rule-based approaches are attractive due to their efficiency, portability and interpretability. However, recent rule-based models are still not quite competitive to statistical tracking approaches. In this paper, a novel framework is proposed to formulate rule-based models in a general way. In the framework, a rule is considered as a special kind of polynomial function satisfying certain linear constraints. Under some particular definitions and assumptions, rule-based models can be seen as feasible solutions of an integer linear programming problem. Experiments showed that the proposed approach can not only achieve competitive performance compared to statistical approaches, but also have good generalisation ability. It is one of the only two entries that outperformed all the four baselines in the third Dialog State Tracking Challenge.


Interactive Theorem Proving and Program Development


title: Stable whole-body motion generation for humanoid robots to imitate human motions

abstract: This work presents a methodology to generate dynamically stable whole-body motions for a humanoid robot, which are converted from human motion capture data. The methodology consists of the kinematic and dynamical mappings for human-likeness and stability, respectively. The kinematic mapping includes the scaling of human foot and Zero Moment Point (ZMP) trajectories considering the geometric differences between a humanoid robot and a human. It also provides the conversion of human upper body motions using the method in [1]. The dynamic mapping modifies the humanoid pelvis motion to ensure the movement stability of humanoid whole-body motions, which are converted from the kinematic mapping. In addition, we propose a simplified human model to obtain a human ZMP trajectory, which is used as a reference ZMP trajectory for the humanoid robot to imitate during the kinematic mapping. A human whole-body dancing motion is converted by the methodology and performed by a humanoid robot with online balancing controllers.


title: Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

abstract: Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains about 30000 facial images from thousands of individuals. Each image has been individually labeled about 40 times, then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study shows that the action units of basic emotions in RAF-DB are much more diverse than, or even deviate from, those of lab-controlled ones. To address this problem, we propose a new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters. The benchmark experiments on the 7-class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+ databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning based methods for the expression recognition in the wild.


title: The Economics of Microservices

abstract: Microservices address the problem of efficiently building and managing complex software systems. For medium-sized systems, they can deliver cost reduction, quality improvement, agility, and decreased time to market. For large cloud systems, they fundamentally change the rules of the game. However, although microservices approaches offer substantial benefits, a microservices architecture requires extra machinery, which can impose substantial costs. This column explores both the costs and benefits of a microservices architecture.


title: A Force Control Approach to a Robot-assisted Cell Microinjection System

abstract: Robotic cell microinjection is a technique that utilizes automation technology to insert substances into a single living cell with a fine needle. Compared with manual microinjection, the main benefits of the robotic cell injection are quality, productivity and repeatability. In this paper we aim to control the penetration force during robotic cell injection to quantify the influence of the penetration force on cells. A force-control-based cell injection approach that is capable of regulating the penetration force in a desired force trajectory is developed. The proposed force control framework includes two control loops. The inner loop is an impedance control used to specify the interaction between the needle and the cell. The outer loop is a force tracking non-linear controller using a feedback linearization technique. The cell model is identified online with a least-squares parameter estimator. With the proposed force control approach, the penetration force can be regulated explicitly to follow the desired force trajectory during the cell injection process. Experiments performed on fish embryos verify the effectiveness of the proposed approach.


title: Weighted dynamic time warping for time series classification

abstract: Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. This may lead to misclassification especially in applications where the shape similarity between two sequences is a major consideration for an accurate recognition. Therefore, we propose a novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW. Our approach penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers. The rationale underlying the proposed distance measure is demonstrated with some illustrative examples. A new weight function, called the modified logistic weight function (MLWF), is also proposed to systematically assign weights as a function of the phase difference between a reference point and a testing point. By applying different weights to adjacent points, the proposed algorithm can enhance the detection of similarity between two time series. We show that some popular distance measures such as DTW and Euclidean distance are special cases of our proposed WDTW measure. We extend the proposed idea to other variants of DTW such as derivative dynamic time warping (DDTW) and propose the weighted version of DDTW. We have compared the performances of our proposed procedures with other popular approaches using public data sets available through the UCR Time Series Data Mining Archive for both time series classification and clustering problems. The experimental results indicate that the proposed approaches can achieve improved accuracy for time series classification and clustering problems.


title: Some practical Guidelines for Effective Sample Size Determination Page 1

abstract: Sample size determination is often an important step in planning a statistical study—and it is usually a difficult one. Among the important hurdles to be surpassed, one must obtain an estimate of one or more error variances and specify an effect size of importance. There is the temptation to take some shortcuts. This article offers some suggestions for successful and meaningful sample size determination. Also discussed is the possibility that sample size may not be the main issue, that the real goal is to design a high-quality study. Finally, criticism is made of some ill-advised shortcuts relating to power and sample size.


title: Syntheses and characterisation of methyltrioxorhenium adducts of low-valence organometallic Lewis bases

abstract: Methyltrioxorhenium(VII) forms air and moisture stable trigonal-bipyramidal adducts with several organometallic Lewis bases having a pyridine nitrogen atom as the donor moiety. These complexes have been isolated and fully characterised. A single-crystal structure of one of the products has been determined. Whilst the Re–N interaction is weaker than in some simple organic Lewis base adducts of Re(CH3)O3, e.g. with pyridine or 4-tert-butylpyridine, the more pronounced stability of the complexes described in this work is probably due to steric crowding and the moderate donor capability of the organometallic ligands. The donor capability of the ligands applied increases in the order Re(CO)3Br(4,4′-bpy)2 < 1,1′-bis(4-pyridylethynyl)ferrocene < ferrocenyl-4-pyridylacetylene.


title: The Ko-PER intersection laserscanner and video dataset

abstract: Public intersections are, due to their complexity, challenging locations for drivers. Therefore the German joint project Ko-PER, which is part of the project initiative Ko-FAS, has equipped a public intersection with several laserscanners and video cameras to generate a comprehensive dynamic model of the ongoing traffic. Results of the intersection perception can be communicated to equipped vehicles by wireless communication. This contribution presents a dataset of the Ko-PER intersection to the research community for further research in the field of multi-object detection and tracking. Therefor the dataset consists of sensor data from the laserscanners network and cameras as well as reference data and object labels. With that dataset, the authors aim to stimulate further research in this area.


title: A balanced thinking-feelings model of information systems continuance

abstract: Most studies on technology adoption and usage continuance examine cognitive factors, leaving affective factors or the feelings of users relatively unexplored. In contrast, researchers in the diverse fields of human-computer interaction, medicine, psychology and marketing have begun to note the importance of feelings in understanding and predicting human behavior. Feelings are anticipated to be essential particularly in the context of modern applications, such as mobile Internet (M-Internet) services. Users of modern technology are not simply technology users but also service consumers and may consider both cognitive and emotional benefits. Drawing upon multidisciplinary findings, this study proposes a balanced thinking-feelings model of IS continuance. In the process of developing this model, the concepts of attitude, thinking and feelings are further articulated, defined and distinguished. The balanced thinking-feelings model is validated in a survey of M-Internet service users. To encourage continuance, companies should consider ways to enhance both cognitive and emotional benefits for users. The model could be also useful for balanced understanding of other behaviors.


title: Evaluation of SIP Call Setup Delay for VoIP in IMS

abstract: IP Mulimedia Subsystem (IMS) is an architecture that can provide innovative solutions for multimedia services deployment regardless the type and network topology. New delivered value added services require significant changes to the multi-services IP network design, including the activation of multiple functions such as quality of service (QoS) capabilities, security mechanisms, multicast routing, etc. To ensure that services delivery meets the high expectations of end users, factors affecting QoS or user’s quality of experience (QoE) must be properly considered. This paper will give a study of a the ’SIP Call Setup Delay’ metric which will serve us to accomplish the QoE measurement in the case of VoIP sessions carried over IPv4 and IPv6 IMS networks.


title: Electrical Dimension of the Nuclear Envelope

abstract: Eukaryotic chromosomes are confined to the nucleus, which is separated from the rest of the cell by two concentric membranes known as the nuclear envelope (NE). The NE is punctuated by holes known as nuclear pore complexes (NPCs), which provide the main pathway for transport of cellular material across the nuclear-cytoplasmic boundary. The single NPC is a complicated octameric structure containing more than 100 proteins called nucleoporins. NPCs function as transport machineries for inorganic ions and macromolecules. The most prominent feature of an individual NPC is a large central channel, ~7 nm in width and 50 nm in length. NPCs exhibit high morphological and functional plasticity, adjusting shape to function. Macromolecules ranging from 1 to >100 kDa travel through the central channel into (and out of) the nucleoplasm. Inorganic ions have additional pathways for communication between cytosol and nucleus. NE can turn from a simple sieve that separates two compartments by a given pore size to a smart barrier that adjusts its permeabiltiy to the metabolic demands of the cell. Early microelectrode work characterizes the NE as a membrane barrier of highly variable permeability, indicating that NPCs are under regulatory control. Electrical voltage across the NE is explained as the result of electrical charge separation due to selective barrier permeability and unequal distribution of charged macromolecules across the NE. Patch-clamp work discovers NE ion channel activity associated with NPC function. From comparison of early microelectrode work with patch-clamp data and late results obtained by the nuclear hourglass technique, it is concluded that NPCs are well-controlled supramolecular structures that mediate transport of macromolecules and small ions by separate physical pathways, the large central channel and the small peripheral channels, respectively. Electrical properties of the two pathways are still unclear but could have great impact on the understanding of signal transfer across NE and gene expression.