title: Childhood Incident Asthma and Traffic-Related Air Pollution at Home and School

abstract: Background: Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma. Objectives: We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Methods: Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric. Results: Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO2 measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO2 and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO2 was attenuated (HR 1.37; 95% CI, 0.69-2.71). Conclusions: Traffic-related pollution exposure at school and homes may both contribute to the development of asthma. Editor's SummaryTraffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. McConnell et al. (p. 1021) evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Parent-reported physician diagnosis of new-onset asthma was identified during 3 years of follow-up of a cohort of kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. Traffic-related pollution exposure was assessed based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. The authors report an increase in asthma risk with modeled traffic-related pollution exposure from roadways near homes and schools. Ambient NO2 was also associated with increased risk. Models that included both NO2 and modeled traffic exposures suggested independent associations of asthma with traffic-related pollution at school and at home, whereas the estimate for NO2 was attenuated. The authors conclude that traffic-related pollution exposure at school and home may both contribute to the development of asthma


title: Ranking-Based Emotion Recognition for Music Organization and Retrieval

abstract: Determining the emotion of a song that best characterizes the affective content of the song is a challenging issue due to the difficulty of collecting reliable ground truth data and the semantic gap between human's perception and the music signal of the song. To address this issue, we represent an emotion as a point in the Cartesian space with valence and arousal as the dimensions and determine the coordinates of a song by the relative emotion of the song with respect to other songs. We also develop an RBF-ListNet algorithm to optimize the ranking-based objective function of our approach. The cognitive load of annotation, the accuracy of emotion recognition, and the subjective quality of the proposed approach are extensively evaluated. Experimental results show that this ranking-based approach simplifies emotion annotation and enhances the reliability of the ground truth. The performance of our algorithm for valence recognition reaches 0.326 in Gamma statistic.


title: Kernel ELM and CNN Based Facial Age Estimation

abstract: We propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pretrained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.


Derivatives of Regular Expressions


Chapter 4 Close Relationships and Virtual Reality


title: On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners

abstract: Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.


title: Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases

abstract: Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We use CBOW word embeddings to represent word meaning and learn a compositionality function that combines the individual constituents into a phrase representation, thus capturing the compositional attribute meaning. The resulting embedding model, while being fully interpretable, outperforms count- ::: based distributional vector space models that are tailored to attribute meaning in the two tasks of attribute selection and phrase similarity prediction. Moreover, as the model captures a generalized layer of attribute meaning, it bears the potential to be used for predictions over various attribute inventories without re-training.


title: Empirical validation of object-oriented metrics on open source software for fault prediction

abstract: Open source software systems are becoming increasingly important these days. Many companies are investing in open source projects and lots of them are also using such software in their own work. But, because open source software is often developed with a different management style than the industrial ones, the quality and reliability of the code needs to be studied. Hence, the characteristics of the source code of these projects need to be measured to obtain more information about it. This paper describes how we calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out. We checked the values obtained against the number of bugs found in its bug database - called Bugzilla - using regression and machine learning methods to validate the usefulness of these metrics for fault-proneness prediction. We also compared the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development cycle.


title: CPW fed slot antenna with reconfigurable rejection bands for UWB application

abstract: A coplanar waveguide (CPW) fed ultra wideband slot antenna with reconfigurable band rejection characteristics is presented. Ultra wide bandwidth of 3.01–10.6 GHz is achieved by exciting the rectangular slot antenna with C-shaped stub. Band notch characteristic is changed between WLAN and WiMAX band by the integrated switches placed across the half wavelength slot. The performance of the proposed antenna is investigated numerically and experimentally. Experimental results demonstrate that the antenna exhibits omni directional characteristics with the peak gain of 4.9 dB and a gain variation of less than 2 dB across the operating band.


title: Near-regular texture analysis and manipulation

abstract: A near-regular texture deviates geometrically and photometrically from a regular congruent tiling. Although near-regular textures are ubiquitous in the man-made and natural world, they present computational challenges for state of the art texture analysis and synthesis algorithms. Using regular tiling as our anchor point, and with user-assisted lattice extraction, we can explicitly model the deformation of a near-regular texture with respect to geometry, lighting and color. We treat a deformation field both as a function that acts on a texture and as a texture that is acted upon, and develop a multi-modal framework where each deformation field is subject to analysis, synthesis and manipulation. Using this formalization, we are able to construct simple parametric models to faithfully synthesize the appearance of a near-regular texture and purposefully control its regularity.


title: LLAMA: Leveraging Learning to Automatically Manage Algorithms

abstract: Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for researchers to explore different techniques for their specific problems. We present LLAMA, a modular and extensible toolkit implemented as an R package that facilitates the exploration of a range of different portfolio techniques on any problem domain. It implements the algorithm selection approaches most commonly used in the literature and leverages the extensive library of machine learning algorithms and techniques in R. We describe the current capabilities and limitations of the toolkit and illustrate its usage on a set of example SAT problems.


title: Cloud computing for industrial automation systems — A comprehensive overview

abstract: Cloud computing has recently emerged as a new computing paradigm in many application areas comprising office and enterprise systems. It offers various solutions to provide a dynamic and flexible infrastructure to host computing resources and deliver them as a service on-demand. Since industrial automation systems of the future have to be adaptable and agile, cloud computing can be considered as a promising solution for this area. However, the requirements of industrial automation systems differ significantly from the office and enterprise world. This paper provides an overview of the latest concepts of cloud computing technology for industrial automation. Existing works are categorized based on the levels of automation systems and research gaps are discussed based on these findings. A general cloud model for automation is derived from existing proposals by refining them further. This cloud model will offer automation functions as services from a dynamic infrastructure.


title: Defining key drivers of online impulse purchasing: A perspective of both impulse shoppers and system users

abstract: Online impulse purchasing is an important part of online shopping.Online impulse buyers are both the roles of shoppers and users in a virtual store.Three concerns arise for individual psychological state, technology use, and trust belief.This study proposes a model with the three issues, flow, website quality, and trust.The three issues well predict impulse buying behavior as flow also plays a key mediator. With the proliferation of e-commerce, a large part of online shopping is attributed to impulse buying. Hence, there is a particular necessity to understand impulse buying in the online context. Impulse shoppers incline to feel unable to control their tendencies and behaviors from various stimuli. Specifically, online consumers are both the impulse shoppers and the system users of websites in the purchase process. Impulse shoppers concern individual traits and system users cover the attributes of online stores. Online impulse buying therefore entails two key drivers, technology use and trust belief, and the mediator of flow experience. Grounding on flow experience, technology-use features, and trust belief, this study proposes a novel research model to examine impulse buying behavior in a complete manner. Data were collected from an online survey. Data analysis considers structural equation modeling technique with formative structure. Empirical results showed that flow experience is an important factor to affect impulse buying from its original drivers. Trust belief is also critical to impulse buying as perceived usefulness is not. Implications for managers and scholars are further discussed.


title: Comparison and Evaluation of Light Field Image Coding Approaches

abstract: The recent advances in light field imaging, supported among others by the introduction of commercially available cameras, e.g., Lytro or Raytrix, are changing the ways in which visual content is captured and processed. Efficient storage and delivery systems for light field images must rely on compression algorithms. Several methods to compress light field images have been proposed recently. However, in-depth evaluations of compression algorithms have rarely been reported. This paper aims at the evaluation of perceived visual quality of light field images and at comparing the performance of a few state-of-the-art algorithms for light field image compression. First, a processing chain for light field image compression and decompression is defined for two typical use cases, professional and consumer. Then, five light field compression algorithms are compared by means of a set of objective and subjective quality assessments. An interactive methodology recently introduced by authors, as well as a passive methodology is used to perform these evaluations. The results provide a useful benchmark for future development of compression solutions for light field images.


Analysis of LTE Radio Load and User Throughput


title: Submodular Hamming Metrics

abstract: We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tractable to optimize over. By exploiting submodularity, we are able to give hardness results and approximation algorithms for optimizing over such metrics. Additionally, we demonstrate empirically the effectiveness of these metrics and associated algorithms on both a metric minimization task (a form of clustering) and also a metric maximization task (generating diverse k-best lists).


title: Key factors for strategic investment projects in the agricultural sector in Iraq: Success and failure

abstract: This paper aims to find out the critical factors for Strategic Investment Projects In the agricultural Sector in Iraq It also aims to develop the strategies and means required to overcome the major difficulties that come in the course of failure in order to achieve futuristic project success. The paper adopts a quantitative analysis that applied to the data of the unsuccessful and slowing down projects that are issued by the Iraqi Ministry of Planning/ Ministry of Agriculture/ Federal Financial Control Bureau in 2017. depending on qualitative analysis the analysis shows that various fundamental factors have contributed to the increase in failure instances all over Iraq and these include financial reasons, financial allocations and discrepancy of funding sources, which are considered as the most important reasons behind the failure and success of projects.


title: Images of physicians in literature: from quacks to heroes

abstract: 2‐5 The practitioner most often studied has been the physician—usually a white man, although the spectrum of healers examined has broadened to include women (particularly nurses 6 ), people of colour, and non-traditional healers. 7 Writers have found the life of the physician a compelling subject “because they could not be indifferent to it. The physician is too intimately bound to hopes and fears of the ill in their struggle against disease and death”. 8 Readers, especially physician readers, have also found the doctor an intriguing subject of wide cultural appeal: “These images of phyicians pervade our culture; they reveal not only how the patient sees the doctor, but, even more significantly, how the doctor sees himself or herself and what behavior fits with that self-perception”. 9


title: A Neurolinguistic Theory of Bilingualism

abstract: This volume is the outcome of 25 years of research into the neurolinguistic aspects of bilingualism. In addition to reviewing the world literature and providing a state-of-the-art account, including a critical assessment of the bilingual neuroimaging studies, it proposes a set of hypotheses about the representation, organization and processing of two or more languages in one brain. It investigates the impact of the various manners of acquisition and use of each language on the extent of involvement of basic cerebral functional mechanisms. The effects of pathology as a means to understanding the normal functioning of verbal communication processes in the bilingual and multilingual brain are explored and compared with data from neuroimaging studies. In addition to its obvious research benefits, the clinical and social reasons for assessment of bilingual aphasia with a measuring instrument that is linguistically and culturally equivalent in each of a patient’s languages are stressed. The relationship between language and thought in bilinguals is examined in the light of evidence from pathology. The proposed linguistic theory of bilingualism integrates a neurofunctional model (the components of verbal communication and their relationships: implicit linguistic competence, metalinguistic knowledge, pragmatics, and motivation) and a set of hypotheses about language processing (neurofunctional modularity, the activation threshold, the language/cognition distinction, and the direct access hypothesis).


title: A Bayesian encourages dropout

abstract: Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.


title: A novel neural topic model and its supervised extension

abstract: Topic modeling techniques have the benefits of modeling words and documents uniformly under a probabilistic framework. However, they also suffer from the limitations of sensitivity to initialization and unigram topic distribution, which can be remedied by deep learning techniques. To explore the combination of topic modeling and deep learning techniques, we first explain the standard topic model from the perspective of a neural network. Based on this, we propose a novel neural topic model (NTM) where the representation of words and documents are efficiently and naturally combined into a uniform framework. Extending from NTM, we can easily add a label layer and propose the supervised neural topic model (sNTM) to tackle supervised tasks. Experiments show that our models are competitive in both topic discovery and classification/regression tasks.


title: MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices

abstract: Diabetes mellitus (DM) is reaching possibly epidemic proportions in India. The degree of disease and destruction due to diabetes and its potential complications are enormous, and originated a significant health care burden on both households and society. The concerning factor is that diabetes is now being proven to be linked with a number of complications and to be occurring at a comparatively younger age in the country. In India, the migration of people from rural to urban areas and corresponding modification in lifestyle are all moving the degree of diabetes. Deficiency of knowledge about diabetes causes untimely death among the population at large. Therefore, acquiring a proficiency that should spread awareness about diabetes may affect the people in India. In this work, a mobile/android application based solution to overcome the deficiency of awareness about diabetes has been shown. The application uses novel machine learning techniques to predict diabetes levels for the users. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes prediction using real world dataset collected from a reputed hospital in the Chhattisgarh state of India.


title: DepSky: Dependable and Secure Storage in a Cloud-of-Clouds

abstract: The increasing popularity of cloud storage services has lead companies that handle critical data to think about using these services for their storage needs. Medical record databases, large biomedical datasets, historical information about power systems and financial data are some examples of critical data that could be moved to the cloud. However, the reliability and security of data stored in the cloud still remain major concerns. In this work we present DepSky, a system that improves the availability, integrity, and confidentiality of information stored in the cloud through the encryption, encoding, and replication of the data on diverse clouds that form a cloud-of-clouds. We deployed our system using four commercial clouds and used PlanetLab to run clients accessing the service from different countries. We observed that our protocols improved the perceived availability, and in most cases, the access latency, when compared with cloud providers individually. Moreover, the monetary costs of using DepSky in this scenario is at most twice the cost of using a single cloud, which is optimal and seems to be a reasonable cost, given the benefits.


title: Consumer acceptance of virtual stores: a theoretical model and critical success factors for virtual stores

abstract: Virtual stores provide great efficiency in the retail value chain, and their existence has tremendously paved the way for the diffusion of electronic commerce. Understanding the determinants of consumer acceptance of virtual stores will provide important theoretical contributions to the area of business-to-consumer (B-to-C) electronic commerce and lead to the development of more effective and meaningful strategies for virtual stores. By expanding the Technology Acceptance Model and the Innovation Diffusion Theory, this study aims to provide an integral theoretical paradigm that can successfully support a wide array of technical, business, and consumer issues involved in online retailing. The results from a Web-based survey of 253 online consumers indicate that the proposed theoretical model is able to explain and predict consumer acceptance of virtual stores substantially well. The resulting theoretical model explains a large portion of the factors that lead to a user's behavioral intention to use and actual use of a virtual store. In addition to providing new theoretical grounds for studying the virtual store phenomenon, this article also supplies virtual stores with a number of operative critical success factors to remain competitive in the volatile electronic marketplace.


title: Variable structure control: a survey

abstract: A tutorial account of variable structure control with sliding mode is presented. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of this powerful control system design approach. This approach is particularly attractive for the control of nonlinear systems. Prominent characteristics such as invariance, robustness, order reduction, and control chattering are discussed in detail. Methods for coping with chattering are presented. Both linear and nonlinear systems are considered. Future research areas are suggested and an extensive list of references is included. >


title: Perspective rectification in vehicle number plate recognition using 2D-2D transformation of Planar Homography

abstract: Perspective rectification is an important part of Automatic Number Plate Recognition (ANPR) system. If the number plate is captured from non-orthogonal position, the recognition performance will be low due to perspective distortion on the number plate image. Planar Homography can be used to correct camera perspective to obtain clear and straight image of the number plate. Perspective rectification is performed by determining position of 4 corner points of the object in the image and finding the homography matrix that defines the movement of those corner points at new location. Experimental results show that the proposed method improves the recognition accuracy by 3% compared with conventional method without perspective rectification.


title: Oral squamous cell carcinoma overview

abstract: Most cancer in the head and neck is squamous cell carcinoma (HNSCC) and the majority is oral squamous cell carcinoma (OSCC). This is an overview of oral squamous cell carcinoma (OSCC) highlighting essential points from the contributors to this issue, to set the scene. It emphasises the crucial importance of prevention and the necessarily multidisciplinary approach to the diagnosis and management of patients with OSCC.


title: Efficient load Balancing in Cloud Computing using Fuzzy Logic

abstract: Cloud computing is an expanding area in research and industry today, which involves virtualization, distributed computing, internet, software and web services. A cloud consists of several elements such as clients, data centers and distributed servers, internet and it includes fault tolerance, high availability, effectiveness, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services and etc. The services of cloud computing are becoming ubiquitous, and serve as the primary source of computing power for different applications like enterprises and personal computing applications. In this paper we introduced the novel load balancing algorithm using fuzzy logic in cloud computing, in which load balancing is a core and challenging issue in Cloud Computing. The processor speed and assigned load of Virtual Machine (VM) are used to balance the load in cloud computing through fuzzy logic.


title: Adaptive frame length control for improving wireless link throughput, range, and energy efficiency

abstract: Wireless network links are characterized by rapidly time varying channel conditions and battery energy limitations at the wireless mobile user nodes. Therefore static link control techniques that make sense in comparatively well behaved wired links do not necessarily apply to wireless links. New adaptive link layer control techniques are needed to provide robust and energy efficient operation even in the presence of orders of magnitude variations in bit error rates and other radio channel conditions. For example, research has advocated adaptive link layer techniques such as adaptive error control, channel state dependent protocols, and variable spreading gain. We explore dynamic sizing of the MAC layer frame, the atomic unit that is sent through the radio channel. A trade-off exists between the desire to reduce the header and physical layer overhead by making frames large, and the need to reduce frame error rates in the noisy channel by using small frame lengths. Clearly the optimum depends on the channel conditions. Through analysis supported by physical measurements with Lucent's WaveLAN radio we show that adaptive sizing of the MAC layer frame in the presence of varying channel noise indeed has a large impact on the user seen throughput (goodput). In addition, we show how that adaptive frame length control can be exploited to improve the energy efficiency for a desired level of goodput, and to extend the usable radio range with graceful throughput degradation. We describe the implementation of the adaptive MAC frame length control mechanism in combination with adaptive hybrid FEC/ARQ error control in a reconfigurable wireless link layer packet processing architecture for a low-power adaptive wireless multimedia node.


title: A Feature Set Evaluation for Activity Recognition with Body-Worn Inertial Sensors

abstract: The automatic and unobtrusive identification of user activities is a challenging goal in human behavior analysis. The physical activity that a user exhibits can be used as contextual data, which can inform applications that reside in public spaces. In this paper, we focus on wearable inertial sensors to recognize physical activities. Feature set evaluation for 5 typical activities is performed by measuring accuracy for combinations of 6 often-used features on a set of 11 well-known classifiers. To verify significance of this analysis, a t-test evaluation was performed for every combination of these feature subsets. We identify an easy-to-compute feature set, which has given us significant results and at the same time utilizes a minimum of resources.


title: Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators

abstract: A composite indicator (CI) is a measuring and benchmark tool used to capture multi-dimensional concepts, such as Information and Communication Technology (ICT) usage. Individual indicators are selected and combined to reflect a phenomena being measured. Visualization of a composite indicator is recommended as a tool to enable interested stakeholders, as well as the public audience, to better understand the indicator components and evolution over time. However, existing CI visualizations introduce a variety of solutions and there is a lack in CI's visualization guidelines. Radial visualizations are popular among these solutions because of CI's inherent multi-dimensionality. Although in dispute, Radar-charts are often used for CI presentation. However, no empirical evidence on Radar's effectiveness and efficiency for common CI tasks is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares the Radar chart technique with two other radial visualization methods: Flower-charts as used in the well-known OECD Betterlife index, and Circle-charts which could be adopted for this purpose. Examples of these charts in the current context are shown in Figure 1 . We evaluated these charts, showing the same data with each of the mentioned techniques applying small multiple views for different dimensions of the data. We compared users' performance and preference empirically under a formal task-taxonomy. Results indicate that the Radar chart was the least effective and least liked, while performance of the two other options were mixed and dependent on the task. Results also showed strong preference of participants toward the Flower chart. Summarizing our results, we provide specific design guidelines for composite indicator visualization. Fig. 1: Three radial solutions for composite indicator visualizations compared empirically for users' performance and preferences.


title: Cake cutting: Not just child’s play

abstract: How to fairly allocate divisible resources, and why computer scientists should take notice.


title: The Impact of Brand Equity and Innovation on the Long-Term Effectiveness of Promotions

abstract: Abstract Although managers often hope to obtain long-term benefits with temporary marketing actions, academic studies imply that their chances are slim. Extant research has implicitly assumed that the brand itself carries no influence over whether marketing promotions have the power to lift sales permanently. Using panel data for seven years from 100 brands across seven product categories, the authors employ a two-stage approach in which long-term promotional effectiveness is first estimated with persistence modeling and then these effectiveness estimates are related to brand equity and new product introductions. By examining a broad range of brands in each category, the authors find that positive sales evolution from promotional efforts is fairly common, especially for small brands. Moreover, the authors find that both permanent and cumulative sales effects from marketing promotions are greater for brands with higher equity and more product introductions, whereas brands with low equity gain greater benef...


title: A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval

abstract: This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection . The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity.


title: Optimal Solutions for Multi-Unit Combinatorial Auctions: Branch and Bound Heuristics

abstract: Finding optimal solutions for multi-unit combinatorial auctions is a hard problem and finding approximations to the optimal solution is also hard. We investigate the use of Branch-and-Bound techniques: they require both a way to bound from above the value of the best allocation and a good criterion to decide which bids are to be tried first. Different methods for efficiently bounding from above the value of the best allocation are considered. Theoretical original results characterize the best approximation ratio and the ordering criterion that provides it. We suggest to use this criterion.


title: Analyzing Cyber-Physical Threats on Robotic Platforms †

abstract: Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBotTM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications.


The geography of diabetes mellitus.


title: Fully Integrated Digitally Assisted Low-Dropout Regulator for a NAND Flash Memory System

abstract: In this paper, a fully integrated digitally assisted low-dropout regulator (LDO) for a NAND flash memory system is proposed and verified using 500 nm I/O CMOS transistors. By combining an amplifier (AMP)-based LDO with a comparator (CMP)-based LDO, the proposed LDO achieves both fast load response in the transient state and accurate regulation in the steady state, which are advantages of the CMP-based LDO and AMP-based LDO, respectively. Moreover, loop frequency stability is satisfied in a wide range of load currents between 0 and 150 mA by using the simple structure of the $g_{m}$ -boost cell to insert an auxiliary path. For an input voltage range of 2.3–3 V and an output voltage of 2.1 V, the measured output droop is 225 mV for a 150 mA load step in the load transition time of 20 ns with the total bias current of $81\,\mu {\rm{A}}$ . The fabricated prototype chip occupies $160\,\times \,610\,\mu {\rm{m}}^{2}$ with an on-chip output capacitor of 2 nF.


title: An efficient method for evaluating alerts of Intrusion Detection Systems

abstract: With thousands of alerts identified by IDSs every day, the process of distinguishing which alerts are important (i.e., true positives) and which are is irrelevant (i.e., false positives) is become more complicated. The security administrator must analyze each single alert either a true of false alert. This paper proposes an alert prioritization model, which is based on risk assessment. The model uses indicators, such as priority, reliability, asset value, as decision factors to calculate alert's risk. The objective is to determine the impact of certain alerts generated by IDS on the security status of an information system, also improve the detection of intrusions using snort by classifying the most critical alerts by their levels of risk, thus, only the alerts that presents a real threat will be displayed to the security administrator, so, we reduce the number of false positives, also we minimize the analysis time of the alerts. The model was evaluated using KDD Cup 99 Dataset as test environment and a pattern matching algorithm.


title: Psychopathy as a Risk Factor for Violence

abstract: As a result of Kansas v Hendricks, many sex offenders in the U.S. are likely to be civilly committed to mental institutions for indefinite periods, and many others with histories of violent offenses may also be so committed. It therefore becomes critical for mental health professionals to understand the risk factors for re-offending that put the public in jeopardy. The most reliable of these factors is psychopathy, which will here be defined, along with its differentiation from the more commonly diagnosed antisocial personality disorder. The assessment of psychopathy, its relationship to crime—especially, to violent crime, its (non-) responsiveness to the usual treatment, and an outline of a potentially more effective one, are presented. Finally, and particularly in view of its widely accepted validity, the potential for abuse of the PCL-R and :SV are noted.


title: Search-evasion path planning for submarines using the Artificial Bee Colony algorithm

abstract: Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.


A Sentence Interaction Network for Modeling Dependence between Sentences


title: Pedagogies of Engagement: Classroom-Based Practices

abstract: Educators, researchers, and policy makers have advocated student involvement for some time as an essential aspect of meaningful learning. In the past twenty years engineering educators have implemented several means of better engaging their undergraduate students, including active and cooperative learning, learning communities, service learning, cooperative education, inquiry and problem-based learning, and team projects. This paper focuses on classroom-based pedagogies of engagement, particularly cooperative and problem-based learning. It includes a brief history, theoretical roots, research support, summary of practices, and suggestions for redesigning engineering classes and programs to include more student engagement. The paper also lays out the research ahead for advancing pedagogies aimed at more fully enhancing students’ involvement in their learning.


title: A Compact Forward Coupler Using Coupled Composite Right/Left-Handed Transmission Lines

abstract: A novel forward coupler using coupled composite right/left-handed (CRLH) transmission lines (TLs) is presented. Forward coupling is enhanced by the CRLH TLs, which have a considerable difference between the effective phase constants in the even and odd modes. A 10-dB forward coupler using the coupled CRLH TLs is simulated and experimentally demonstrated in the S-band. Its coupled-line length is reduced to half that of the conventional right-handed forward coupler with the same coupling.


title: On the cognitive process of human problem solving

abstract: One of the fundamental human cognitive processes is problem solving. As a higher-layer cognitive process, problem solving interacts with many other cognitive processes such as abstraction, searching, learning, decision making, inference, analysis, and synthesis on the basis of internal knowledge representation by the object-attribute-relation (OAR) model. Problem solving is a cognitive process of the brain that searches a solution for a given problem or finds a path to reach a given goal. When a problem object is identified, problem solving can be perceived as a search process in the memory space for finding a relationship between a set of solution goals and a set of alternative paths. This paper presents both a cognitive model and a mathematical model of the problem solving process. The cognitive structures of the brain and the mechanisms of internal knowledge representation behind the cognitive process of problem solving are explained. The cognitive process is formally described using real-time process algebra (RTPA) and concept algebra. This work is a part of the cognitive computing project that designed to reveal and simulate the fundamental mechanisms and processes of the brain according to Wang's layered reference model of the brain (LRMB), which is expected to lead to the development of future generation methodologies for cognitive computing and novel cognitive computers that are capable of think, learn, and perceive.


title: Robust landmark selection for mobile robot navigation

abstract: Precise navigation is a key capability of autonomous mobile robots and required for many tasks including transportation or docking. To guarantee a robust and accurate localization and navigation performance, many practical approaches rely on observations of artificial landmarks. This raises the question of where to place the landmarks along the desired trajectory of the robot. In this paper, we present a novel approach to landmark selection, which aims at selecting the minimal set of landmarks that bounds the uncertainty about the deviation of the robot from its desired trajectory. At the same time the selected landmark sets are robust against the fact that a certain number of landmarks can be obscured from view during operation. Our algorithm is highly efficient due to a linearization of the whole navigation cycle and employs submodular optimization, for which strong formal bounds on the approximation quality are known. In extensive experiments, also carried out with a real robot, we demonstrate that our approach outperforms several other methods and that it enables robust autonomous robot navigation in practice.


title: The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation

abstract: For 3D walking control of a biped robot we analyze the dynamics of a 3D inverted pendulum in which motion is constrained to move along an arbitrarily defined plane. This analysis yields a simple linear dynamics, the 3D linear inverted pendulum mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM and a method for walking pattern generation are discussed. A simulation result of a walking control using a 12-DOF biped robot model is also shown.


title: Putting the ‘theory’ back into grounded theory: guidelines for grounded theory studies in information systems

abstract: Over the past decade, there has been increasing interest in the use of grounded theory in information systems research. Grounded theory is a qualitative research method that seeks to develop theory that is grounded in data systematically gathered and analysed. The purpose of this paper is to suggest guidelines for grounded theory studies in information systems. Our guidelines are based on a framework for theorizing in grounded theory studies that focuses on conceptualization and theory scope. Our hope is that the guidelines will help to raise the quality and aspirations of grounded theory studies in information systems.


title: Metaphoric structuring: understanding time through spatial metaphors

abstract: The present paper evaluates the claim that abstract conceptual domains are structured through metaphorical mappings from domains grounded directly in experience. In particular, the paper asks whether the abstract domain of time gets its relational structure from the more concrete domain of space. Relational similarities between space and time are outlined along with several explanations of how these similarities may have arisen. Three experiments designed to distinguish between these explanations are described. The results indicate that (1) the domains of space and time do share conceptual structure, (2) spatial relational information is just as useful for thinking about time as temporal information, and (3) with frequent use, mappings between space and time come to be stored in the domain of time and so thinking about time does not necessarily require access to spatial schemas. These findings provide some of the first empirical evidence for Metaphoric Structuring. It appears that abstract domains such as time are indeed shaped by metaphorical mappings from more concrete and experiential domains such as space.


title: Link Prediction via Matrix Completion

abstract: Inspired by the practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attention in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On the one hand, our algorithm is based on the sparse and low-rank property of the matrix, while, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whether it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved compared to many state-of-the-art algorithms.


title: Granger Causality: Basic Theory and Application to Neuroscience

abstract: Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has shown that Granger causality is a key technique to furnish this capability. The main goal of this article is to provide an expository introduction to the concept of Granger causality. Mathematical frameworks for both bivariate Granger causality and conditional Granger causality are developed in detail with particular emphasis on their spectral representations. The technique is demonstrated in numerical examples where the exact answers of causal influences are known. It is then applied to analyze multichannel local field potentials recorded from monkeys performing a visuomotor task. Our results are shown to be physiologically interpretable and yield new insights into the dynamical organization of large-scale oscillatory cortical networks.


title: Exploring generational cohort work satisfaction in hospital nurses.

abstract: Purpose Although extensive research exists regarding job satisfaction, many previous studies used a more restrictive, quantitative methodology. The purpose of this qualitative study is to capture the perceptions of hospital nurses within generational cohorts regarding their work satisfaction. Design/methodology/approach A preliminary qualitative, phenomenological study design explored hospital nurses' work satisfaction within generational cohorts - Baby Boomers (1946-1964), Generation X (1965-1980) and Millennials (1981-2000). A South Florida hospital provided the venue for the research. In all, 15 full-time staff nurses, segmented into generational cohorts, participated in personal interviews to determine themes related to seven established factors of work satisfaction: pay, autonomy, task requirements, administration, doctor-nurse relationship, interaction and professional status. Findings An analysis of the transcribed interviews confirmed the importance of the seven factors of job satisfaction. Similarities and differences between the generational cohorts related to a combination of stages of life and generational attributes. Practical implications The results of any qualitative research relate only to the specific venue studied and are not generalizable. However, the information gleaned from this study is transferable and other organizations are encouraged to conduct their own research and compare the results. Originality/value This study is unique, as the seven factors from an extensively used and highly respected quantitative research instrument were applied as the basis for this qualitative inquiry into generational cohort job satisfaction in a hospital setting.


title: Morphology and life history of an ant parasitoid, Psilocharis afra (Hymenoptera: Eucharitidae)

abstract: Eucharitidae (Hymenoptera) are specialized ant (Formicidae) parasitoids. As we begin to develop a better understanding of their phylogenetic relationships, it is critical to establish baselines for morphological and biological data. A morphological review and the first report of life history data for Psilocharis afra Heraty is provided based on new material from the Mpumalanga Province of South Africa. Psilocharis Heraty is included in Eucharitinae, but it is unclear whether it is the sister group of all other members of the subfamily, or sister group to Neolosbanus Girault in a monophyletic Psilocharitini, which would in turn be sister group to Eucharitini. The oviposition habits of P. afra differ from those of other Eucharitidae in that eggs are placed among trichomes under bracts at flower bases, instead of either being inserted into cavities formed in plant tissue by an enlarged ovipositor (as in Oraseminae and some Neolosbanus ) or inserted into cavities in plant tissue, as in most Eucharitini. The egg and first-instar planidia larva are described, and adult morphology is discussed with reference to Eucharitidae and other parasitoid Hymenoptera.


title: Lichen planopilaris: Epidemiology and prevalence of subtypes - a retrospective analysis in 104 patients

abstract: BACKGROUND ::: Management of patients with lichen planopilaris (LPP) and frontal fibrosing alopecia (FFA) is rendered difficult as robust epidemiologic data, insights on pathogenesis, associated diseases, possible relevance of concomitant medications or environmental factors are lacking. ::: ::: ::: PATIENTS AND METHODS ::: Retrospective analysis of demography, skin status, concomitant medication and diagnostic procedures were performed on 104 medical records (71 classic LPP, 32 FFA, and one Graham-Little-Piccardi-Lassueur syndrome). ::: ::: ::: RESULTS ::: Women were more often affected (distribution F: M classic LPP 4.9: 1; FFA: 31: 1). Compared to LPP patients, patients with FFA were significantly older (p < 0.001), more often postmenopausal, and more frequently on hormone replacement therapy. No other specific associations were identified. An association with lichen planus, other autoimmune diseases, or hepatitis virus infection was found only in individual patients. Clinically, FFA patients were significantly more often reported to have reduced eyebrows (p < 0.005), axillary, and/or pubic hair (p = 0.050). ::: ::: ::: CONCLUSIONS ::: The findings obtained from this study, with currently largest LPP/FFA patient cohort in Germany, encouraged us to set up a national FFA patient registry. Prospective data collected from larger numbers of patients with standardized questionnaires will help to assess assumed associations and influencing factors and to develop, in the long-term, recommendations for diagnosis and treatment.


title: Ultra-Wideband Bandpass Filter With Improved Upper Stopband Performance Using Defected Ground Structure

abstract: A novel ultra-wideband (UWB) bandpass filter (BPF) with improved upper stopband performance using a defected ground structure (DGS) is presented in this letter. The proposed BPF is composed of seven DGSs that are positioned under the input and output microstrip line and coupled double step impedance resonator (CDSIR). By using CDSIR and open loop defected ground structure (OLDGS), we can achieve UWB BPF characteristics, and by using the conventional CDGSs under the input and output microstrip line, we can improve the upper stopband performance. Simulated and measured results are found in good agreement with each other, showing a wide passband from 3.4 to 10.9 GHz, minimum insertion loss of 0.61 dB at 7.02 GHz, a group delay variation of less than 0.4 ns in the operating band, and a wide upper stopband with more than 30 dB attenuation up to 20 GHz. In addition, the proposed UWB BPF has a compact size (0.27?g ~ 0.29?g , ?g : guided wavelength at the central frequency of 6.85 GHz).


title: Analysis of sampling techniques for imbalanced data: An n=648 ADNI study

abstract: Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results.


title: DART: dense articulated real-time tracking with consumer depth cameras

abstract: This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies connected through a kinematic tree. DART covers a broad set of objects encountered in indoor environments, including furniture and tools, and human and robot bodies, hands and manipulators. To achieve efficient and robust tracking, DART extends the signed distance function representation to articulated objects and takes full advantage of highly parallel GPU algorithms for data association and pose optimization. We demonstrate the capabilities of DART on different types of objects that have each required dedicated tracking techniques in the past.


title: Bridging the divide: Using UTAUT to predict multigenerational tablet adoption practices

abstract: We surveyed a large crosssectional sample diverse in age and technology experience.Generational differences exist across UTAUT determinants.The most salient gaps are between the oldest and youngest generations.We predicted intention with age and two user experience variables as moderators.Only effort expectancy and facilitating conditions predict tablet use intention. This study examined the "Unified Theory of Acceptance and Use of Technology" (UTAUT) in the context of tablet devices across multiple generations. We tested the four UTAUT determinants, performance expectancy, effort expectancy, social influence, and facilitating conditions, to determine their contributions for predicting behavioral intention to use tablets with age, gender, and user experience as moderators. 899 respondents aged 19-99 completed the survey. We found consistent generational differences in UTAUT determinants, most frequently between the oldest and youngest generations. Effort expectancy and facilitating conditions were the only determinants that positively predicted tablet use intentions after controlling for age, gender, and tablet use. We also discuss the implications of ageism and gender discrimination of technology adoption. Finally, we argue that our findings can be extended to create effective training programs for the teaching, learning, and adoption of new technologies in a variety of organizational settings.


title: Mind the gap: large-scale frequent sequence mining

abstract: Frequent sequence mining is one of the fundamental building blocks in data mining. While the problem has been extensively studied, few of the available techniques are sufficiently scalable to handle datasets with billions of sequences; such large-scale datasets arise, for instance, in text mining and session analysis. In this paper, we propose MG-FSM, a scalable algorithm for frequent sequence mining on MapReduce. MG-FSM can handle so-called "gap constraints", which can be used to limit the output to a controlled set of frequent sequences. At its heart, MG-FSM partitions the input database in a way that allows us to mine each partition independently using any existing frequent sequence mining algorithm. We introduce the notion of w-equivalency, which is a generalization of the notion of a "projected database" used by many frequent pattern mining algorithms. We also present a number of optimization techniques that minimize partition size, and therefore computational and communication costs, while still maintaining correctness. Our experimental study in the context of text mining suggests that MG-FSM is significantly more efficient and scalable than alternative approaches.


title: A technology platform for automatic high-level tennis game analysis

abstract: Abstract Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours.


title: Educational Data Mining & Students’ Performance Prediction

abstract: It is important to study and analyse educational data especially students’ performance. Educational Data Mining (EDM) is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations. This study is equally concerned with this subject, specifically, the students’ performance. This study explores multiple factors theoretically assumed to affect students’ performance in higher education, and finds a qualitative model which best classifies and predicts the students’ performance based on related personal and social factors.


title: A Safeguard against ATM Fraud

abstract: Electronic transaction security is becoming more and more important in all aspects of Automated Teller Machine (ATM). As the number of persons using an ATM, are becoming more desirable targets for attacks. These attacks could be counted as a security risk in the form of card cloning or PIN release, etc. It has been observed that the secure electronic transaction has become a top priority to avoid ATM fraud. In this research article, the tools and techniques of ATM fraud are contemplated. A secure layer electronic transaction mechanism of ATM is developed to prevent ATM frauds. Through this mechanism a cardholder identification, authentication, authorization and security clearances are boosted. Against shoulder suffering (a fraud technique) two technical security tools are proposed to enhance ATM transaction security.


title: MTAF: An Adaptive Design for Keyword-Based Content Dissemination on DHT Networks

abstract: Beyond offering the widely used keyword search function, many peer-to-peer systems nowadays support the subscription function. For example, Vuze allows users to create subscription filters based on the keyword search. Given the subscription, episodic or related content will be delivered to the users whenever new episodes are available. Unfortunately, these applications suffer from the downsides, for example, high network traffic in the nodes maintaining popular terms. In this paper, we propose the MTAF mechanism to overcome the issues. The key of MTAF is to carefully select a subset of terms without incurring false negatives and to forward the content item toward the home nodes of such selected terms for low content forwarding cost. Experimental results based on real datasets indicate that the proposed solutions are efficient compared to existing approaches. In particular, the similarity-based replication of filters is shown to mitigate the effect of hot spots that arise due to the fact that some document terms are substantially more popular than the others.


www.informatik2011.de Why Citizens Engage in Open Government Platforms?


title: A time-series compression technique and its application to the smart grid

abstract: Time-series data is increasingly collected in many domains. One example is the smart electricity infrastructure, which generates huge volumes of such data from sources such as smart electricity meters. Although today these data are used for visualization and billing in mostly 15-min resolution, its original temporal resolution frequently is more fine-grained, e.g., seconds. This is useful for various analytical applications such as short-term forecasting, disaggregation and visualization. However, transmitting and storing huge amounts of such fine-grained data are prohibitively expensive in terms of storage space in many cases. In this article, we present a compression technique based on piecewise regression and two methods which describe the performance of the compression. Although our technique is a general approach for time-series compression, smart grids serve as our running example and as our evaluation scenario. Depending on the data and the use-case scenario, the technique compresses data by ratios of up to factor 5,000 while maintaining its usefulness for analytics. The proposed technique has outperformed related work and has been applied to three real-world energy datasets in different scenarios. Finally, we show that the proposed compression technique can be implemented in a state-of-the-art database management system.


title: Conversational Contextual Cues: The Case of Personalization and History for Response Ranking

abstract: We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history. Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset. In particular, we exploit the structure of Reddit comments and posts to extract 2.1 billion messages and 133 million conversations. We evaluate our models on the task of predicting the next response in a conversation, and we find that modeling both context and participants improves prediction accuracy.


title: Satisfiability-based Set Membership Filters

abstract: Introduced here is a novel application of Satisfiability (SAT) to the set membership problem with specific focus on efficiently testing whether large sets contain a given element. Such tests can be greatly enhanced via the use of filters, probabilistic algorithms that can quickly decide whether or not a given element is in a given set. This article proposes SAT filters (i.e., filters based on SAT) and their use in the set membership problem. Both the theoretical advantages of SAT filters and experimental results show that this technique yields significant performance improvements over previous techniques. Specifically, a SAT filter is a filter construction that is simple yet efficient in terms of both query time and filter size; i.e., SAT filters asymptotically achieve the information-theoretic limit while providing fast querying. As well, this is the first application that makes use of the random k-SAT phase transition results and may drive research into efficient solvers for this and similar applications.


title: Frequency and anatomical features of the mandibular lingual foramina: systematic review and meta-analysis

abstract: Lingual mandibular foramina are peculiar anatomical variations often present on the bone surface of the inferior jaw. These structures host vascular and nervous anastomosis deriving from the branches of the submental artery, lingual artery, and the mylohyoid nerve. Reports pointed out intra-operative massive bleeding accidents during implant interventions occurred in this area. The aim of this systematic review and meta-analysis was to summarize quantitatively the features of the lingual foramina on the midline of the inferior jaw. We performed a systematic review and meta-analysis complying the PRISMA statement and registering it in PROSPERO database. The frequency rate of the foramina resulted to have a point estimate of 0.965. The means of the diameters showed to have a point estimate of 0.840 with a standard error of 0.06. The results showed a significant high frequency reported in the literature of this variation, with a quite important diameter, sign of significant caliber of the related vessels. Even if this variation is underreported in modern textbooks of oral anatomy, we suggest an accurate screening via CT scan pre-operatively. In addition, a proper risk management is necessary to minimize injure hazard and properly managing the vascular plexus of the anterior floor of oral cavity.


Wavelet Based Pseudo Quantum Steganography within Pseudo Color Barcode


Automatically Correcting Typing Errors for People with Motor Impairments


title: Discover breaking events with popular hashtags in twitter

abstract: In this paper, we utilize tags in Twitter (the hashtags) as an indicator of events. We first study the properties of hashtags for event detection. Based on several observations, we proposed three attributes of hashtags, including (1) instability for temporal analysis, (2) Twitter meme possibility to distinguish social events from virtual topics or memes, and (3) authorship entropy for mining the most contributed authors. Based on these attributes, breaking events are discovered with hashtags, which cover a wide range of social events among different languages in the real world.


title: Off-policy temporal-difference learning with function approximation

abstract: We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms the basis for popular reinforcement learning methods such as Q-learning, which has been known to diverge with linear function approximation, and because it is critical to the practical utility of multi-scale, multi-goal, learning frameworks such as options, HAMs, and MAXQ. Our new algorithm combines TD(λ) over state–action pairs with importance sampling ideas from our previous work. We prove that, given training under any -soft policy, the algorithm converges w.p.1 to a close approximation (as in Tsitsiklis and Van Roy, 1997; Tadic, 2001) to the action-value function for an arbitrary target policy. Variations of the algorithm designed to reduce variance introduce additional bias but are also guaranteed convergent. We also illustrate our method empirically on a small policy evaluation problem. Our current results are limited to episodic tasks with episodes of bounded length. 1Although Q-learning remains the most popular of all reinforcement learning algorithms, it has been known since about 1996 that it is unsound with linear function approximation (see Gordon, 1995; Bertsekas and Tsitsiklis, 1996). The most telling counterexample, due to Baird (1995) is a seven-state Markov decision process with linearly independent feature vectors, for which an exact solution exists, yet This is a re-typeset version of an article published in the Proceedings of the 18th International Conference on Machine Learning (2001). It differs from the original in line and page breaks, is crisper for electronic viewing, and has this funny footnote, but otherwise it is identical to the published article. for which the approximate values found by Q-learning diverge to infinity. This problem prompted the development of residual gradient methods (Baird, 1995), which are stable but much slower than Q-learning, and fitted value iteration (Gordon, 1995, 1999), which is also stable but limited to restricted, weaker-than-linear function approximators. Of course, Q-learning has been used with linear function approximation since its invention (Watkins, 1989), often with good results, but the soundness of this approach is no longer an open question. There exist non-pathological Markov decision processes for which it diverges; it is absolutely unsound in this sense. A sensible response is to turn to some of the other reinforcement learning methods, such as Sarsa, that are also efficient and for which soundness remains a possibility. An important distinction here is between methods that must follow the policy they are learning about, called on-policy methods, and those that can learn from behavior generated by a different policy, called off-policy methods. Q-learning is an off-policy method in that it learns the optimal policy even when actions are selected according to a more exploratory or even random policy. Q-learning requires only that all actions be tried in all states, whereas on-policy methods like Sarsa require that they be selected with specific probabilities. Although the off-policy capability of Q-learning is appealing, it is also the source of at least part of its instability problems. For example, in one version of Baird’s counterexample, the TD(λ) algorithm, which underlies both Qlearning and Sarsa, is applied with linear function approximation to learn the action-value function Q for a given policy π. Operating in an on-policy mode, updating state– action pairs according to the same distribution they would be experienced under π, this method is stable and convergent near the best possible solution (Tsitsiklis and Van Roy, 1997; Tadic, 2001). However, if state-action pairs are updated according to a different distribution, say that generated by following the greedy policy, then the estimated values again diverge to infinity. This and related counterexamples suggest that at least some of the reason for the instability of Q-learning is that it is an off-policy method; they also make it clear that this part of the problem can be studied in a purely policy-evaluation context. Despite these problems, there remains substantial reason for interest in off-policy learning methods. Several researchers have argued for an ambitious extension of reinforcement learning ideas into modular, multi-scale, and hierarchical architectures (Sutton, Precup & Singh, 1999; Parr, 1998; Parr & Russell, 1998; Dietterich, 2000). These architectures rely on off-policy learning to learn about multiple subgoals and multiple ways of behaving from the singular stream of experience. For these approaches to be feasible, some efficient way of combining off-policy learning and function approximation must be found. Because the problems with current off-policy methods become apparent in a policy evaluation setting, it is there that we focus in this paper. In previous work we considered multi-step off-policy policy evaluation in the tabular case. In this paper we introduce the first off-policy policy evaluation method consistent with linear function approximation. Our mathematical development focuses on the episodic case, and in fact on a single episode. Given a starting state and action, we show that the expected offpolicy update under our algorithm is the same as the expected on-policy update under conventional TD(λ). This, together with some variance conditions, allows us to prove convergence and bounds on the error in the asymptotic approximation identical to those obtained by Tsitsiklis and Van Roy (1997; Bertsekas and Tsitsiklis, 1996). 1. Notation and Main Result We consider the standard episodic reinforcement learning framework (see, e.g., Sutton & Barto, 1998) in which a learning agent interacts with a Markov decision process (MDP). Our notation focuses on a single episode of T time steps, s0, a0, r1, s1, a1, r2, . . . , rT , sT , with states st ∈ S, actions at ∈ A, and rewards rt ∈ <. We take the initial state and action, s0 and a0, to be given arbitrarily. Given a state and action, st and at, the next reward, rt+1, is a random variable with mean rt st and the next state, st+1, is chosen with probabilities pt stst+1 . The final state is a special terminal state that may not occur on any preceding time step. Given a state, st, 0 < t < T , the action at is selected according to probability π(st, at) or b(st, at) depending on whether policy π or policy b is in force. We always use π to denote the target policy, the policy that we are learning about. In the on-policy case, π is also used to generate the actions of the episode. In the off-policy case, the actions are instead generated by b, which we call the behavior policy. In either case, we seek an approximation to the action-value function Q : S ×A 7→ < for the target policy π: Q(s, a) = Eπ { rt+1 + · · ·+ γrT | st = s, at = a } , where 0 ≤ γ ≤ 1 is a discount-rate parameter. We consider approximations that are linear in a set of feature vectors {φsa}, s ∈ S, a ∈ A: Q(s, a) ≈ θφsa = n ∑


title: Optimal investment, growth options, and security returns

abstract: As a consequence of optimal investment choices, firms' assets and growth options change in predictable ways. Using a dynamic model, we show that this imparts predictability to changes in a firm's systematic risk, and its expected return. Simulations show that the model simultaneously reproduces: (i) the time series relation between the book-to-market ratio and asset returns, (ii) the cross-sectional relation between book to market, market value and return, (iii) contrarian effects at short horizons, (iv) momentum effects at longer horizons, and (v) the inverse relation between interest rates and the market risk premium.


title: AMOEBA-I: A Shape-Shifting Modular Robot for Urban Search and Rescue

abstract: This work intends to enhance the mobility and flexibility of a tracked mobile robot through changing its shape in unstructured environments. A shape-shifting mobile robot, AMOEBA-I, has been developed. With three tracked modules, AMOEBA-I has nine locomotion configurations and three of them are symmetrical configurations. The key advantage of this design over other mobile robots is its adaptability and flexibility because of its various configurations. It can change its configuration fluently and automatically to adapt to different environments or missions. A modularized structure of the control system is proposed and designed for AMOEBA-I to improve the fault tolerance and substitutability of the system. The strategies of cooperative control, including cooperative shape shifting, cooperative turning and cooperative obstacle negotiation, have been proposed to improve the capability of shape shifting, locomotion and obstacle negotiation for AMOEBA-I. A series of experiments have been carried out, and demon...


title: High flow nasal cannula (HFNC) versus nasal continuous positive airway pressure (nCPAP) for the initial respiratory management of acute viral bronchiolitis in young infants: a multicenter randomized controlled trial (TRAMONTANE study)

abstract: Purpose ::: Nasal continuous positive airway pressure (nCPAP) is currently the gold standard for respiratory support for moderate to severe acute viral bronchiolitis (AVB). Although oxygen delivery via high flow nasal cannula (HFNC) is increasingly used, evidence of its efficacy and safety is lacking in infants.


Towards Understanding the Geometry of Knowledge Graph Embeddings


title: Drivers of change in global agriculture

abstract: As a result of agricultural intensification, more food is produced today than needed to feed the entire world population and at prices that have never been so low. Yet despite this success and the impact of globalization and increasing world trade in agriculture, there remain large, persistent and, in some cases, worsening spatial differences in the ability of societies to both feed themselves and protect the long-term productive capacity of their natural resources. This paper explores these differences and develops a country×farming systems typology for exploring the linkages between human needs, agriculture and the environment, and for assessing options for addressing future food security, land use and ecosystem service challenges facing different societies around the world.


title: International Journal of Computing Algorithm IMPACT OF SOCIAL MEDIA ON YOUTH ACTIVISM AND NATION BUILDING IN PERVASIVE SOCIAL COMPUTING USING NEUTROSOPHIC COGNITIVE MAPS (NCMS)

abstract: Youth is the major assets of a nation, we need to channel their energy accordingly and dissipate it appropriately for the benefits of a nation and humanity as a whole. Social media has how become indispensable in our societies. Most of the major social media are predominated by the youth, exploiting it for one purpose or the other. In this paper we analyzed how youth could constructively, the role of social media and it’s how it build a nation and achieve a promising future not only for themselves but equality for the upcoming generations using Neutrosophic cognitive maps. This paper has four sections. In section one, we give an introduction about Pervasive social Media, Section two we recall the definition of Neutrosophic Cognitive Maps (NCMs) Section three is deals with the methods of finding the hidden pattern in NCMs and analysis of Features or Characters of Youth and Youth Activism. In final section we give the conclusion based on our study.


title: A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering

abstract: While deep convolutional neural networks frequently approach or exceed human-level performance in benchmark tasks involving static images, extending this success to moving images is not straightforward. Video understanding is of interest for many applications, including content recommendation, prediction, summarization, event/object detection, and understanding human visual perception. However, many domains lack sufficient data to explore and perfect video models. In order to address the need for a simple, quantitative benchmark for developing and understanding video, we present MovieFIB, a fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models predictions, and compare these with human performance. We investigate the relative importance of language, static (2D) visual features, and moving (3D) visual features, the effects of increasing dataset size, the number of frames sampled, and of vocabulary size. We illustrate that: this task is not solvable by a language model alone, our model combining 2D and 3D visual information indeed provides the best result, all models perform significantly worse than human-level. We provide human evaluation for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgment. We suggest avenues for improving video models, and hope that the MovieFIB challenge can be useful for measuring and encouraging progress in this very interesting field.


title: Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs

abstract: Conversational participants tend to immediately and unconsciously adapt to each other's language styles: a speaker will even adjust the number of articles and other function words in their next utterance in response to the number in their partner's immediately preceding utterance. This striking level of coordination is thought to have arisen as a way to achieve social goals, such as gaining approval or emphasizing difference in status. But has the adaptation mechanism become so deeply embedded in the language-generation process as to become a reflex? We argue that fictional dialogs offer a way to study this question, since authors create the conversations but don't receive the social benefits (rather, the imagined characters do). Indeed, we find significant coordination across many families of function words in our large movie-script corpus. We also report suggestive preliminary findings on the effects of gender and other features; e.g., surprisingly, for articles, on average, characters adapt more to females than to males.


title: Detecting Transient Bottlenecks in n-Tier Applications through Fine-Grained Analysis

abstract: Identifying the location of performance bottlenecks is a non-trivial challenge when scaling n-tier applications in computing clouds. Specifically, we observed that an n-tier application may experience significant performance loss when there are transient bottlenecks in component servers. Such transient bottlenecks arise frequently at high resource utilization and often result from transient events (e.g., JVM garbage collection) in an n-tier system and bursty workloads. Because of their short lifespan (e.g., milliseconds), these transient bottlenecks are difficult to detect using current system monitoring tools with sampling at intervals of seconds or minutes. We describe a novel transient bottleneck detection method that correlates throughput (i.e., request service rate) and load (i.e., number of concurrent requests) of each server in an n-tier system at fine time granularity. Both throughput and load can be measured through passive network tracing at millisecond-level time granularity. Using correlation analysis, we can identify the transient bottlenecks at time granularities as short as 50ms. We validate our method experimentally through two case studies on transient bottlenecks caused by factors at the system software layer (e.g., JVM garbage collection) and architecture layer (e.g., Intel SpeedStep).


title: Supporting social presence through lightweight photo sharing on and off the desktop

abstract: Lightweight photo sharing, particularly via mobile devices, is fast becoming a common communication medium used for maintaining a presence in the lives of friends and family. How should such systems be designed to maximize this social presence while maintaining simplicity? An experimental photo sharing system was developed and tested that, compared to current systems, offers highly simplified, group-centric sharing, automatic and persistent people-centric organization, and tightly integrated desktop and mobile sharing and viewing. In an experimental field study, the photo sharing behaviors of groups of family or friends were studied using their normal photo sharing methods and with the prototype sharing system. Results showed that users found photo sharing easier and more fun, shared more photos, and had an enhanced sense of social presence when sharing with the experimental system. Results are discussed in the context of design principles for the rapidly increasing number of lightweight photo sharing systems.


title: Use of an Electronic Medical Record “Dotphrase” Data Template for a Prospective Head Injury Study

abstract: Introduction: The adoption of electronic medical records (EMRs) in emergency departments (EDs) has changed the way that healthcare information is collected, charted, and stored. A challenge for researchers is to determine how EMRs may be leveraged to facilitate study data collection efforts. Our objective is to describe the use of a unique data collection system leveraging EMR technology and to compare data entry error rates to traditional paper data collection. Methods: This was a retrospective review of data collection methods during a multicenter study of ED, anti-coagulated, head injury patients. On-shift physicians at 4 centers enrolled patients and prospectively completed data forms. These physicians had the option of completing a paper data form or an electronic “dotphrase” (DP) data form. A feature of our Epic®-based EMR is the ability to use DPs to assist in medical information entry. A DP is a preset template that may be inserted into the EMR when the physician types a period followed by a code phrase (in this case “.ichstudy”). Once the study DP was inserted at the bottom of the electronic ED note, it prompted enrolling physicians to answer study questions. Investigators then extracted data directly from the EMR. Results: From July 2009 through December 2010, we enrolled 883 patients. DP data forms were used in 288 (32.6%; 95% confidence interval [CI] 29.5, 35.7%) cases and paper data forms in 595 (67.4%; 95% CI 64.3, 70.5%). Sixty-six (43.7%; 95% CI 35.8, 51.6%) of 151 physicians enrolling patients used DP data entry at least once. Using multivariate analysis, we found no association between physician age, gender, or tenure and DP use. Data entry errors were more likely on paper forms (234/595, 39.3%; 95% CI 35.4, 43.3%) than DP forms (19/288, 6.6%; 95% CI 3.7, 9.5%), difference in error rates 32.7% (95% CI 27.9, 37.6%, P < 0.001). Conclusion: DP data collection is a feasible means of data collection. DP data forms maintain all study data within the secure EMR environment, obviating the need to maintain and collect paper data forms. This innovation was embraced by many of our emergency physicians and resulted in lower data entry error rates. [West J Emerg Med 2013;14(2):109-113.]


title: A memetic algorithm for bi-objective integrated forward/reverse logistics network design

abstract: Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics networks. First, a bi-objective mixed integer programming formulation is developed to minimize the total costs and maximize the responsiveness of a logistics network. To find the set of non-dominated solutions, an efficient multi-objective memetic algorithm is developed. The proposed solution algorithm uses a new dynamic search strategy by employing three different local searches. To assess the quality of the novel solution approach, the quality of its Pareto-optimal solutions is compared to those generated by an existing powerful multi-objective genetic algorithm from the recent literature and to exact solutions obtained by a commercial solver.


title: Adding Standards Based Job Submission to a Commodity Grid Broker

abstract: The Condor matchmaker provides a powerful mechanism for matching together both users job requirements and resource providers requirements in such a manner that not only is a paring selected which satisfies both requirements but is optimal for both. This has made the Condor system a good choice for use as a meta-scheduler within the Grid. Integrating Condor within a wider Grid context is possible but only through modification to the Condor source code. Here we describe how the standards for job submission and resource descriptions can be integrated into the Condor system to allow arbitrary Grid resources which support these standards to be brokered through Condor.


title: A Real Time Facial Expression Classification System Using Local Binary Patterns

abstract: Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns(LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.


title: A First Look at Online Reputation on Airbnb, Where Every Stay is Above Average

abstract: Judging by the millions of reviews left by guests on the Airbnb platform, this "trusted community marketplace" is fulfilling its mission of matching travelers seeking accommodation with hosts who have room to spare remarkably well. Based on our analysis of ratings we collected for over 600,000 properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average user-generated rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star rating. We contrast this with the ratings of approximately half a million hotels worldwide that we collected on TripAdvisor, where there is a much lower average rating of 3.8 stars, and more variance across reviews. Considering properties by accommodation type and by location, we find considerable variability in ratings, and observe that vacation rental properties on TripAdvisor have ratings most similar to ratings of Airbnb properties. Last, we consider several thousand properties that are listed on both platforms. For these cross-listed properties, we find that even though the average ratings on Airbnb and TripAdvisor are similar, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only weak correlation in the ratings of individual cross-listed properties across the two platforms. Our work is a first step towards understanding and interpreting nuances of user-generated ratings in the context of the sharing economy.


title: Creating the spectacle: Designing interactional trajectories through spectator interfaces

abstract: An ethnographic study reveals how professional artists created a spectator interface for the interactive game Day of the Figurines, designing the size, shape, height and materials of two tabletop interfaces before carefully arranging them in a local setting. We also show how participants experienced this interface. We consider how the artists worked with a multi-scale notion of interactional trajectory that combined trajectories through individual displays, trajectories through a local ecology of displays, and trajectories through an entire experience. Our findings shed light on discussions within HCI concerning interaction with tangible and tabletop displays, spectator interfaces, ecologies of displays, and trajectories through cultural experiences.


title: Categorization and Conversions for Indexing Methods of Discrete Global Grid Systems

abstract: Digital Earth frameworks provide a tool to receive, send and interact with large location-based datasets, organized usually according to Discrete Global Grid Systems (DGGS). In DGGS, an indexing method is used to assign a unique index to each cell of a global grid, and the datasets corresponding to these cells are retrieved or allocated using this unique index. There exist many methods to index cells of DGGS. Toward facility, interoperability and also defining a “standard” for DGGS, a conversion is needed to translate a dataset from one DGGS to another. In this paper, we first propose a categorization of indexing methods of DGGS and then define a general conversion method from one indexing to another. Several examples are presented to describe the method.