title: Customizing Travel Packages with Interactive Composite Items

abstract: We examine the applicability of Composite Items (CIs) for generating customized travel packages consisting of Points of Interest (POIs) in a given city. CIs have been shown to serve complex information needs such as selecting books for a reading club, identifying a set of products for a promotion, or planning a city tour. In the travel domain, a synthesized view of travel options in a city can be provided with a set of cohesive CIs, each of which is covering a different region in the city. In this paper, we attempt to understand the benefit of letting users customize travel packages, and examine the relationship between customization and personalization. For personalization, we gather user preferences on POI features when available or on latent topics extracted from POI tags. For customization, we develop a framework within which a user interacts with proposed travel packages and the system suggests new CIs according to refined user preferences. Our experiments reveal a tension between personalization and the cohesiveness of items forming each CI. As a result, customization is necessary to find a balance between POI personalization and CI cohesiveness. We also show that the refined user preferences obtained from customization in one city help build better travel packages in another city.


Parasitism, commensalism, and mutualism: exploring the many shades of symbioses


title: Reported Experiences with Medicaid Managed Care Models Among Parents of Children

abstract: The proportion of children enrolled in Medicaid managed care arrangements has grown significantly over the past decade. Yet, few studies have attempted to assess differences in parental reports and ratings of care for children enrolled in different types of Medicaid managed care. We examine parental reports and ratings of care to explore whether and how patient and parent experiences vary by child health status and managed care plan type, including provider-sponsored specialized plans serving only children. Parents of children in a Florida Medicaid demonstration project in two counties over 3 years were surveyed using Consumer Assessment of Health Providers and Systems surveys (n = 2,741-11,067). Ordered logistic regression models with interaction terms were used to assess relationships between plan type, presence of chronic condition, and measures of patient experience. Parents of children enrolled in provider-sponsored plans that focus on pediatrics were more likely to provide a positive rating for their doctor, health plan, and specialty care compared to parents of children in an health maintenance organization (HMO). Parents of children with a chronic condition were less likely than parents of children without a chronic condition to provide a favorable rating of overall health care, their doctor, or health plan. The interaction term that assessed whether patient experience by plan type was impacted by the child's health status was not statistically significant. Parents of Medicaid children may prefer provider-sponsored arrangements over HMOs. Findings can inform the future development of other integrated models of care involving provider-sponsored arrangements, such as pediatric Accountable Care Organizations and Patient-Centered Medical Homes.


CUSTOMER RELATIONSHIP MANAGEMENT AND CUSTOMER LOYALTY ; A SURVEY IN THE SECTOR OF BANKING Assist .


title: Synthesis and Implementation of UART Using VHDL Codes

abstract: The proposed paper describes the universal asynchronous receiver/transmitter i.e. UART which is the kind of serial communication protocol which allows the full duplex communication in serial link. This paper presents the hardware implementation of a high speed and efficient UART using FPGA. The UART consists of three main components namely transmitter, receiver and baud rate generator which is nothing but the frequency divider. This has been simulated on ModelSim SE 10.0a and has been implemented by using Verilog description language which has been synthesized on FPGA kits such as Virtex4 and Spartan3.


title: Novel Planar Compact Coupled-Line Single-Ended-to-Balanced Power Divider

abstract: In this paper, a novel planar compact coupled-line circuit is proposed to design a single-ended-to-balanced power divider (PD) that can be used in numerous balanced circuits and systems. By using the matrix transformation between the mixed-mode (MM) scattering parameters and the single-ended scattering parameters, the constraint rules are deduced to realize the performance of equal power division with in-phase, good input (output) port matching, high isolation between two balanced outputs, high suppression of the common-mode (CM) noise, as well as the mode-conversion suppression between differential-mode signals and CM noise. Then, the closed-form design equations are derived using the even- and odd-mode analysis. Finally, a microstrip PD, which works at 1 GHz, is designed and fabricated. The measured (simulated) operating relative bandwidth is 37% (38%) from 0.76 (0.83 GHz) to 1.13 GHz (1.21 GHz). The consistency between the simulated and measured MM scattering parameters verifies the correctness of the design theory.


title: Crystallize: An Immersive, Collaborative Game for Second Language Learning

abstract: Learning a second language is challenging. Becoming fluent requires learning contextual information about how language should be used as well as word meanings and grammar. The majority of existing language learning applications provide only thin context around content. In this paper, we present Crystallize, a collaborative 3D game that provides rich context along with scaffolded learning and engaging gameplay mechanics. Players collaborate through joint tasks, or quests. We present a user study with 42 participants that examined the impact of low and high levels of task interdependence on language learning experience and outcomes. We found that requiring players to help each other led to improved collaborative partner interactions, learning outcomes, and gameplay. A detailed analysis of the chat-logs further revealed that changes in task interdependence affected learning behaviors.


title: DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures

abstract: Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology. Interventional radiology, however, has not yet benefited substantially from the advent of deep learning, in particular because of two reasons: 1) Most images acquired during the procedure are never archived and are thus not available for learning, and 2) even if they were available, annotations would be a severe challenge due to the vast amounts of data. When considering fluoroscopy-guided procedures, an interesting alternative to true interventional fluoroscopy is in silico simulation of the procedure from 3D diagnostic CT. In this case, labeling is comparably easy and potentially readily available, yet, the appropriateness of resulting synthetic data is dependent on the forward model. In this work, we propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy and digital radiography from CT scans, tightly integrated with the software platforms native to deep learning. We use machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, combined with analytic forward projection and noise injection to achieve the required performance. On the example of anatomical landmark detection in X-ray images of the pelvis, we demonstrate that machine learning models trained on DeepDRRs generalize to unseen clinically acquired data without the need for re-training or domain adaptation. Our results are promising and promote the establishment of machine learning in fluoroscopy-guided procedures.


title: An L-Sleeve L-Monopole Antenna Fitting a Shark-Fin Module for Vehicular LTE, WLAN, and Car-to-Car Communications

abstract: An L-sleeve L-monopole (LSLM) antenna fitting a shark-fin module is proposed for vehicular long-term evolution (LTE) communication. Starting with classical sleeve monopole antennas, a low-profile evolution is introduced to explain the working principle of the LSLM. The influence of the ground plane, placement position, and bent of the LSLM is analyzed to fit different shark-fin modules. Designed prototypes (the LSLM and bended LSLM) cover the frequency range of 698–2700 MHz (VSWR < 3), which would serve major LTE standards. One tab monopole antenna covering the frequency range of 5–6 GHz can be integrated into the shark-fin module and provide services for wireless local area network and car-to-car communications. Low mutual coupling (lower than –23 dB) between the two antennas is achieved in the frequency range of 698–6000 MHz without any additional filters, and the design method is presented for better understanding the underlying physics. The proposed antenna is fabricated on an affordable printed circuit board, which is useful for vehicular applications.


title: A Basis Illumination Approach to BRDF Measurement

abstract: Realistic descriptions of surface reflectance have long been a topic of interest in both computer vision and computer graphics research. In this paper, we describe a novel high speed approach for the acquisition of bidirectional reflectance distribution functions (BRDFs). We develop a new theory for directly measuring BRDFs in a basis representation by projecting incident light as a sequence of basis functions from a spherical zone of directions. We derive an orthonormal basis over spherical zones that is ideally suited for this task. BRDF values outside the zonal directions are extrapolated by re-projecting the zonal measurements into a spherical harmonics basis, or by fitting analytical reflection models to the data. For specular materials, we experiment with alternative basis acquisition approaches such as compressive sensing with a random subset of the higher order orthonormal zonal basis functions, as well as measuring the response to basis defined by an analytical model as a way of optically fitting the BRDF to such a representation. We verify this approach with a compact optical setup that requires no moving parts and only a small number of image measurements. Using this approach, a BRDF can be measured in just a few minutes.


title: Modeling Users’ Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings

abstract: In this paper, we investigate the feasibility of identifying a small set of privacy profiles as a way of helping users manage their mobile app privacy preferences. Our analysis does not limit itself to looking at permissions people feel comfortable granting to an app. Instead it relies on static code analysis to determine the purpose for which an app requests each of its permissions, distinguishing for instance between apps relying on particular permissions to deliver their core functionality and apps requesting these permissions to share information with advertising networks or social networks. Using privacy preferences that reflect people’s comfort with the purpose for which different apps request their permissions, we use clustering techniques to identify privacy profiles. A major contribution of this work is to show that, while people’s mobile app privacy preferences are diverse, it is possible to identify a small number of privacy profiles that collectively do a good job at capturing these diverse preferences.


title: The Complexity of Graph Connectivity

abstract: In this paper we survey the major developments in understanding the complexity of the graph connectivity problem in several computational models, and highlight some challenging open problems.


Nonlinear Dynamics of Chaotic Attractor of Chua Circuit and Its Application for Secure Communication


Fragmented Sleep and Cortical Thinning in Old Adults: Time to Wake Up?


title: Coactive Learning

abstract: We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. Interactions in the Coactive Learning model take the following form: at each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking); the user responds by correcting the system if necessary, providing a slightly improved - but not necessarily optimal - object as feedback. We argue that such preference feedback can be inferred in large quantity from observable user behavior (e.g., clicks in web search), unlike the optimal feedback required in the expert model or the cardinal valuations required for bandit learning. Despite the relaxed requirements for the feedback, we show that it is possible to adapt many existing online learning algorithms to the coactive framework. In particular, we provide algorithms that achieve O(1/√T) average regret in terms of cardinal utility, even though the learning algorithm never observes cardinal utility values directly. We also provide an algorithm with O(log(T)/T) average regret in the case of λ-strongly convex loss functions. An extensive empirical study demonstrates the applicability of our model and algorithms on a movie recommendation task, as well as ranking for web search.


title: Dataless Text Classification: A Topic Modeling Approach with Document Manifold

abstract: Recently, dataless text classification has attracted increasing attention. It trains a classifier using seed words of categories, rather than labeled documents that are expensive to obtain. However, a small set of seed words may provide very limited and noisy supervision information, because many documents contain no seed words or only irrelevant seed words. In this paper, we address these issues using document manifold, assuming that neighboring documents tend to be assigned to a same category label. Following this idea, we propose a novel Laplacian seed word topic model (LapSWTM). In LapSWTM, we model each document as a mixture of hidden category topics, each of which corresponds to a distinctive category. Also, we assume that neighboring documents tend to have similar category topic distributions. This is achieved by incorporating a manifold regularizer into the log-likelihood function of the model, and then maximizing this regularized objective. Experimental results show that our LapSWTM significantly outperforms the existing dataless text classification algorithms and is even competitive with supervised algorithms to some extent. More importantly, it performs extremely well when the seed words are scarce.


title: Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models

abstract: Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.


title: When Deep Fool Meets Deep Prior: Adversarial Attack on Super-Resolution Network

abstract: This paper investigates the vulnerability of the deep prior used in deep learning based image restoration. In particular, the image super-resolution, which relies on the strong prior information to regularize the solution space and plays important roles in the image pre-processing for future viewing and analysis, is shown to be vulnerable to the well-designed adversarial examples. We formulate the adversarial example generation process as an optimization problem, and given super-resolution model three different types of attack are designed based on the subsequent tasks: (i) style transfer attack; (ii) classification attack; (iii) caption attack. Another interesting property of our design is that the attack is hidden behind the super-resolution process, such that the utilization of low resolution images is not significantly influenced. We show that the vulnerability to adversarial examples could bring risks to the pre-processing modules such as super-resolution deep neural network, which is also of paramount significance for the security of the whole system. Our results also shed light on the potential security issues of the pre-processing modules, and raise concerns regarding the corresponding countermeasures for adversarial examples.


title: Taiwan IT Engineers' Perceptions toward Organizational Justice and Individual Work Performance

abstract: The purpose of this study was to investigate IT engineers' perceptions toward organizational justice and individual work performance. The study conducted a quantitative study, and the participants were IT engineers. Random sampling was used. The questionnaires were conducted online. The cover of the questionnaire explained the purposes of the study. 161 copies of questionnaires were sent and viewed, and 84 copies were returned. One participant indicated that she was in charge of the financial consultant, and therefore was excluded from analysis, resulting in a final sample of 83 engineers. All respondents completed the questionnaire on their personal computers, and returned their responses via the online survey system. The response rate was 51.5%. The data was analyzed by descriptive statistics, One-way ANOVA and Pearson's Correlation Coefficient in IBM SPSS 22. The findings of the study show that (1) position grade and profession have a significant influence on individual work performance, (2) distributive justice is positively associated with individual work performance, (3) procedural justice is positively associated with individual work performance, and (4) informational justice is positively associated with individual work performance.


title: Deep Understanding of Cooking Procedure for Cross-modal Recipe Retrieval

abstract: Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the \em cause of dish presentation rather than the visual \em effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its possible consequence on visual appearance. In this paper, we approach this problem from the perspective of attention modeling. Specifically, we model the attention of words and sentences in a recipe and align them with its image feature such that both text and visual features share high similarity in multi-dimensional space. Through a large food dataset, Recipe1M, we empirically demonstrate that understanding the cooking procedure can lead to improvement in a large margin compared to the existing methods which mostly consider only ingredient information. Furthermore, with attention modeling, we show that language-specific named-entity extraction based on domain knowledge becomes optional. The result gives light to the feasibility of performing cross-lingual cross-modal recipe retrieval with off-the-shelf machine translation engines.


title: Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method

abstract: Given a set of texts discussing a particular entity (e.g., customer reviews of a smartphone), aspect based sentiment analysis (ABSA) identifies prominent aspects of the entity (e.g., battery, screen) and an average sentiment score per aspect. We focus on aspect term extraction (ATE), one of the core processing stages of ABSA that extracts terms naming aspects. We make publicly available three new ATE datasets, arguing that they are better than previously available ones. We also introduce new evaluation measures for ATE, again arguing that they are better than previously used ones. Finally, we show how a popular unsupervised ATE method can be improved by using continuous space vector representations of words and phrases.


title: Automatic photo pop-up

abstract: This paper presents a fully automatic method for creating a 3D model from a single photograph. The model is made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration. Our main insight is that instead of attempting to recover precise geometry, we statistically model geometric classes defined by their orientations in the scene. Our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. Because of the inherent ambiguity of the problem and the statistical nature of the approach, the algorithm is not expected to work on every image. However. it performs surprisingly well for a wide range of scenes taken from a typical person's photo album.


NEW DESIGN METHODOLOGIES FOR HIGH-SPEED MIXED-MODE CMOS FULL ADDER CIRCUITS ABSTRACT


title: CloudDB: One Size Fits All Revived

abstract: We present a data management platform in the cloud, CloudDB. The guiding principle of CloudDB’s design is establishing data independence for the applications that need to use diverse underlying data stores that are optimized for varying workload needs and characteristics. The applications should not have to be aware of the physical organization of the data and how the data is accessed. Ideally, an application only needs a logical specification of the data access layer and the data access requests are handled in a declarative way. CloudDB hosts variety of specialized databases that deliver high performance, scalability, and cost efficiency for varying application needs. CloudDB’s API layer is designed in such a way to give data independence to the higher level applications. The goal is to let the clients use just a simple, standard, and uniform language API to access data management functions as a service.


title: Beam-steerable patch antenna array using parasitic coupling and reactive loading

abstract: A parasitically-coupled and reactively-loaded patch antenna array is demonstrated to achieve beam steering in an analog manner. The strong parasitic coupling between closely-spaced patch antennas is used to form a phased array with a single driven element. The phase shift between the array elements is adjustable by changing the reactive loading on the parasitic patch antenna. A prototype of this antenna array is demonstrated at 2.3 GHz.


title: Improving object proposals with multi-thresholding straddling expansion

abstract: Recent advances in object detection have exploited object proposals to speed up object searching. However, many of existing object proposal generators have strong localization bias or require computationally expensive diversification strategies. In this paper, we present an effective approach to address these issues. We first propose a simple and useful localization bias measure, called superpixel tightness. Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification. Our method is essentially a box refinement process, which is intuitive and beneficial, but seldom exploited before. The greatest benefit of our method is that it can be integrated into any existing model to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC dataset demonstrates that our approach improves numerous existing models significantly with little computational overhead.


title: Spider mites associated to soybean in Rio Grande do Sul, Brazil

abstract: The objective of this work was to study the occurrence and geographic distribution of phytophagous mites associated to soybean in Rio Grande do Sul, Brazil. Samplings were performed from January to May 2005 on genetically modified soybean (glyphosate-tolerant) crops, in 27 municipalities of six regions - Alto Vale do Uruguai, Campanha, Depressao Central, Planalto Medio, Missoes and Serra do Sudeste. Five phytophagous mite species belonging to the family Tetranychidae - Mononychellus planki, Tetranychus desertorum, T. gigas, T. ludeni and T. urticae - occurred in 21, 12, 5, 3 and 14 municipalities, respectively. A map of Rio Grande do Sul with the geographic distribution of each species is presented, as well as an illustrated dichotomous key to help the identification of the spider mites found.


title: Speed Daemon: Experience-Based Mobile Robot Speed Scheduling

abstract: A time-optimal speed schedule results in a mobile robot driving along a planned path at or near the limits of the robot's capability. However, deriving models to predict the effect of increased speed can be very difficult. In this paper, we present a speed scheduler that uses previous experience, instead of complex models, to generate time-optimal speed schedules. The algorithm is designed for a vision-based, path-repeating mobile robot and uses experience to ensure reliable localization, low path-tracking errors, and realizable control inputs while maximizing the speed along the path. To our knowledge, this is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints. The proposed speed scheduler was tested in over 4 km of path traversals in outdoor terrain using a large Ackermann-steered robot travelling between 0.5 m/s and 2.0 m/s. The approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robot's capability.


中图法分类号 TP 391 A MultiView Face Detection Based on Real Adaboost Algorithm


Secure Communications with D 2 D Cooperation


title: Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

abstract: We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e., requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol.


title: Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision

abstract: The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision.


title: A PWM DC-DC boost converter with peak current-mode control for TFT LCD power supply

abstract: This paper presents an asynchronous 1 MHz DC-DC boost converter for TFT LCD power supply, using a pulse-width-modulated peak current mode controller. Both complete small signal analysis and overall efficiency investigation of the boost converter are performed, as well as the discussion of compensation method. An error amplifier with a compensation network and a sum-comparator with three input channels are proposed. A maximum power conversion efficiency of higher than 90% is achieved.


title: Implementation of a three phase inverter for BLDC motor drive

abstract: In this paper a three phase inverter is designed for operating a brushless dc motor. Following the operating principle of a brushless dc motor, required switching is done by a microcontroller (arduino uno). Necessary gate driver circuit is also designed for proper operation of the inverter. The practical result matches with the simulation result and the motor runs successfully.


title: Fast nonlinear Model Predictive Control for unified trajectory optimization and tracking

abstract: This paper presents a framework for real-time, full-state feedback, unconstrained, nonlinear model predictive control that combines trajectory optimization and tracking control in a single, unified approach. The proposed method uses an iterative optimal control algorithm, namely Sequential Linear Quadratic (SLQ), in a Model Predictive Control (MPC) setting to solve the underlying nonlinear control problem and simultaneously derive the optimal feedforward and feedback terms. Our customized solver can generate trajectories of multiple seconds within only a few milliseconds. The performance of the approach is validated on two different hardware platforms, an AscTec Firefly hexacopter and the ball balancing robot Rezero. In contrast to similar approaches, we perform experiments that require leveraging the full system dynamics.


Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning


Intuitionistic fuzzy-based model for failure detection


Short-Term Administration of Serelaxin Produces Predominantly Vascular Benefits in the Angiotensin II/L-NAME Chronic Heart Failure Model


title: Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

abstract: In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.


title: Open Knowledge Accessing Method in IoT-Based Hospital Information System for Medical Record Enrichment

abstract: For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based hospital information system (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer, and other medical devices, they are raw, simple, weak-content, and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in linked open data. A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing.


title: The audio notebook: paper and pen interaction with structured speech

abstract: This paper addresses the problem that a listener experiences when attempting to capture information presented during a lecture, meeting, or interview. Listeners must divide their attention between the talker and their notetaking activity. We propose a new device-the Audio Notebook-for taking notes and interacting with a speech recording. The Audio Notebook is a combination of a digital audio recorder and paper notebook, all in one device. Audio recordings are structured using two techniques: user structuring based on notetaking activity, and acoustic structuring based on a talker's changes in pitch, pausing, and energy. A field study showed that the interaction techniques enabled a range of usage styles, from detailed review to high speed skimming. The study motivated the addition of phrase detection and topic suggestions to improve access to the audio recordings. Through these audio interaction techniques, the Audio Notebook defines a new approach for navigation in the audio domain.


title: Modeling of 10-nm-scale ballistic MOSFET's

abstract: We have performed numerical modeling of nanoscale dual-gate ballistic n-MOSFET's with ultrathin undoped channel, taking into account the effects of quantum tunneling along the channel and through the gate oxide. The results show that transistors with channel length as small as 8 nm can exhibit either a transconductance up to 4000 mS/mm or gate modulation of current by more than 8 orders of magnitude, depending on the gate oxide thickness. These characteristics make the sub-10-nm devices potentially suitable for logic and memory applications, though their parameters are rather sensitive to size variations.


title: Exploring Trajectories of Pornography Use Through Adolescence and Emerging Adulthood

abstract: While the scholarly literature on pornography use is growing, much of this literature has examined pornography use as a static feature that does not change. Despite this trend, pornography use, like most sexual behaviors, is likely best viewed as a dynamic feature that may shift across the developmental life span. Using a sample of 908 adults from the United States, retrospective data on pornography use through adolescence and emerging adulthood were gathered to explore trajectories of pornography use across these developmental periods. Latent mixture models suggested the presence of common patterns of use across both developmental periods. Adolescence patterns appeared to largely be distinguished by those who either engaged or did not engage with pornography, while emerging adulthood data revealed the presence of a group of experimenters who engaged in pornography through adolescence but then decreased use through their 20s. Men were found to be more likely to have consistent profiles of pornography use,...


title: Robust 3D Object Tracking from Monocular Images Using Stable Parts

abstract: We present an algorithm for estimating the pose of a rigid object in real-time under challenging conditions. Our method effectively handles poorly textured objects in cluttered, changing environments, even when their appearance is corrupted by large occlusions, and it relies on grayscale images to handle metallic environments on which depth cameras would fail. As a result, our method is suitable for practical Augmented Reality applications including industrial environments. At the core of our approach is a novel representation for the 3D pose of object parts: We predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object; the 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN.


title: Steps toward artificial intelligence

abstract: The problems of heuristic programming-of making computers solve really difficult problems-are divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction. A computer can do, in a sense, only what it is told to do. But even when we do not know how to solve a certain problem, we may program a machine (computer) to Search through some large space of solution attempts. Unfortunately, this usually leads to an enormously inefficient process. With Pattern-Recognition techniques, efficiency can often be improved, by restricting the application of the machine's methods to appropriate problems. Pattern-Recognition, together with Learning, can be used to exploit generalizations based on accumulated experience, further reducing search. By analyzing the situation, using Planning methods, we may obtain a fundamental improvement by replacing the given search with a much smaller, more appropriate exploration. To manage broad classes of problems, machines will need to construct models of their environments, using some scheme for Induction. Wherever appropriate, the discussion is supported by extensive citation of the literature and by descriptions of a few of the most successful heuristic (problem-solving) programs constructed to date.


title: Main Content Extraction from Detailed Web Pages

abstract: As we know internet detailed web pages contains information which are not considered as primary content such as advertisements, headers, footers, navigation links and copyright information. Also information on web pages such as comments and reviews are not preferred by search engines to index as informative content, thereby having an algorithm to extracts only main content could help better quality on web page indexing. Almost all algorithms have been proposed are tag dependent means they could only look for primary content among specific tags such as or . The algorithm in this paper simulates a web page user visit and how the user finds the main content block position in the page. The proposed method is tag independent and has two phases to accomplish the extraction job. First it transforms input DOM tree obtained from input HTML detailed web page into a block tree based on their visual representation and DOM structure in a way that on every node it will have specification vector, then it traverses the obtained small block tree to find main block having dominant computed value in comparison with other block nodes based on its specification vector values. The introduced method doesn’t have any learning phases and could find informative content on any random input detailed web page. This method has been tested in large variety of websites and as we will show, it gains better precision and recall based on other compared method K-FE.


Leader traits and attributes.


title: High performance SOI RF switches for wireless applications

abstract: This paper describes 0.18um CMOS silicon-on-insulator (SOI) technology and design techniques for SOI RF switch designs for wireless applications. The measured results of SP4T (single pole four throw) and SP8T (single pole eight throw) switch reference designs are presented. It has been demonstrated that SOI RF switch performance, in terms of power handling, linearity, insertion loss and isolation, is very competitive with those utilizing GaAs pHEMT and silicon-on-sapphire (SOS) technologies, while maintaining a cost and manufacturing advantage.


title: Photocatalytic Water Treatment by Titanium Dioxide: Recent Updates

abstract: Photocatalytic water treatment using nanocrystalline titanium dioxide (NTO) is a well-known advanced oxidation process (AOP) for environmental remediation. With the in situ generation of electron-hole pairs upon irradiation with light, NTO can mineralize a wide range of organic compounds into harmless end products such as carbon dioxide, water, and inorganic ions. Photocatalytic degradation kinetics of pollutants by NTO is a topic of debate and the mostly reporting Langmuir-Hinshelwood kinetics must accompanied with proper experimental evidences. Different NTO morphologies or surface treatments on NTO can increase the photocatalytic efficiency in degradation reactions. Wisely designed photocatalytic reactors can decrease energy consumption or can avoid post-separation stages in photocatalytic water treatment processes. Doping NTO with metals or non-metals can reduce the band gap of the doped catalyst, enabling light absorption in the visible region. Coupling NTO photocatalysis with other water-treatment technologies can be more beneficial, especially in large-scale treatments. This review describes recent developments in the field of photocatalytic water treatment using NTO.


title: Using Graphic Organizers to Attain Relational Knowledge From Expository Text

abstract: Many students with learning disabilities (LD) experience difficulties with extracting relationships from expository text, especially if they are implicit. Results from studies with K—12 participants have been inconclusive regarding the potency of the graphic organizer (GO) as a comprehension tool. This study attempted to address some of the concerns with GO research by examining the effects of using GOs with middle school students with LD to convey and cue relational knowledge, using a longer intervention and using written essays to assess the students' attainment of relational knowledge. The results lend support for using GOs with students with LD to gain relational knowledge from expository textbooks. When factual knowledge was assessed via multiple choice tests and quizzes, no differences were found between treatment and control conditions. As in other GO studies, both groups demonstrated attainment of facts and concepts. But when relational knowledge was assessed, the two groups responded differential...


title: Simplifying datacenter network debugging with pathdump

abstract: Datacenter networks continue to grow complex due to larger scales, higher speeds and higher link utilization. Existing tools to manage and debug these networks are even more complex, requiring in-network techniques like collecting per-packet per-switch logs, dynamic switch rule updates, periodically collecting data plane snapshots, packet mirroring, packet sampling, traffic replay, etc. ::: ::: This paper calls for a radically different approach to network management and debugging: in contrast to implementing the functionality entirely in-network, we should carefully partition the debugging tasks between the edge devices and the network elements. We present the design, implementation and evaluation of PathDump, a minimalistic tool that utilizes resources at edge devices for network debugging. PathDump currently runs over a real network comprising only of commodity hardware, and yet, can support a surprisingly large class of network debugging problems. Evaluation results show that Path-Dump requires minimal switch and edge resources, while enabling network debugging at fine-grained time scales.


title: Ontology-based models in pervasive computing systems

abstract: Pervasive computing is by its nature open and extensible, and must integrate the information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognized within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply the ontological techniques successfully to next-generation pervasive systems.


title: A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization

abstract: The Teaching-Learning-Based Optimization (TLBO) algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO) algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.


title: Implementation of an FPGA based accelerator for virtual private networks

abstract: Virtual Private Networks (VPN) are becoming increasingly popular network architectures for corporate networks. As VPNs are built on the Internet infrastructure, the data exchange among different local area networks will be passed through the Internet and thus can be easily eavesdropped, masqueraded, etc. Therefore, certain security measures must be used to deal with these privacy issues. The Internet Protocol Security (IPSec) by the Internet Engineering Task Force (IETF) addresses the abovementioned security issues and the Free Secure Wide Area Network (FreeS/WAN) is an open source software implementation of IPSec for Linux which uses triple-DES as the default encryption mode. As shown in this paper, the performance of FreeS/WAN with IPSec is 50% of that without encryption. In order to improve its performance, a field programmable gate array (FPGA) based triple-DES accelerator was built on a reconfigurable computing development platform called Pilchard and achieved a throughput of more than 120 Mb/sec for triple-DES in cipher-block chaining mode, a speedup of 3 over a software implementation, Measurements show that an FPGA-accelerated FreeS/WAN offers a 30% speedup for the TCP protocol over the original software library.


title: From Data Mining to Knowledge Discovery in Databases

abstract: The data that most organizations possess is considered a valuable asset. To really benefit from it, organizations must be able to analyze these data efficiently and effectively. This activity is nowadays referred to as business intelligence and one class of algorithms used within this activity are called data mining methods. There are some famous success stories about knowledge discovered via data mining but there is also a lot of disappointment so far. The paper argues that one of the reasons for some failures to produce better results with data mining is the reliance on transactional and master data only. It points to other types of data that can enrich transactional data and help in this way to produce more interesting and more rewarding data patterns.


title: Assessing brain activations associated with emotional regulation during virtual reality mood induction procedures

abstract: We analyze two emotional regulation strategies through of Emotiv EPOC.We use a virtual reality environment to induce a sadness emotion.Assessed activations are consistent with those obtained in other studies.Emotiv Epoc can be used in emotional regulation studies like an objective tool. Emotional regulation strategies are used by people to influence their emotional responses to external or internal emotional stimuli. The aim of this study is to evaluate the brain activations that are associated with the application of two different emotional regulation strategies (cognitive reappraisal and expressive suppression) during virtual reality mood induction procedures. We used Emotiv EPOC to measure the brain electrical activity of participants while sadness is induced using a virtual reality environment. We monitored 24 participants, who were distributed among three experimental groups: a control group, a cognitive reappraisal group and an expressive suppression group. In the control group, we found significant activations in several right frontal regions that are related to the induction of negative emotions'. We also found significant activations in the limbic, occipital, and parietal regions in the emotional regulation groups. These regions are related to the application of emotional regulation strategies. The results are consistent with those shown in the literature, which were obtained through clinical neuroimaging systems.


title: Combination of muscle tension and crust-freeze-air-chilling improved efficacy of air chilling and quality of broiler fillets

abstract: The objective of this study was to evaluate the effects of water immersion chilling (WIC), air chilling (AC), and crust-freeze-air-chilling (CFAC) on the efficacy of broiler chilling and quality of breast fillets with and without muscle tension (MT), restraining both wings behind the carcass. In each of 3 replications, 66 birds were purchased locally and processed at the Michigan State University meat laboratory. After evisceration, one-half of the birds were subjected to MT and the remaining birds received no MT. Twenty-two birds (11 with and 11 without MT) per chilling were randomly assigned to WIC (ice/water slurry at 0.2°C), AC (air at 1°C/1.5 m/s), or CFAC (air at -12°C/1.5 m/s). After chilling and aging for 3 h postmortem (PM), all breast fillets were deboned, one-half of which were immediately quick-frozen or cooked, whereas the remaining half were further aged on ice for 24 h PM. All left fillets were used for pH, R-value, and sarcomere measurements, whereas all right fillets were used for shear force. During chilling, breast temperature was reduced from 40 to 4°C in an average of 62, 68, and 140 min for WIC, CFAC, and AC, respectively. The birds at 3 h PM had higher pH and shear force than those of birds at 24 h PM except the CFAC with no MT (P < 0.05). Breast fillets with MT showed lower shear force than the fillets without MT at 24 h PM (P < 0.05) in AC and CFAC. Based on these findings, the combination of CFAC and MT appears to improve both air chilling efficacy and breast fillet quality.


title: Data mining for shopping centres – customer knowledge‐management framework

abstract: Relying on complex interdependencies between shoppers, retailers and owners, shopping centres are ideal for knowledge at management study. Retailers have been the forefront of data mining, but shopping centres have received little attention. Aspects of customer knowledge management for shopping centres are considered using analogies drawn from an exploratory questionnaire survey.


title: Climatic effects on ice-jam flooding of the Peace-Athabasca Delta

abstract: The Peace-Athabasca Delta (PAD) in northern Alberta is one of the world's largest inland freshwater deltas, home to large populations of waterfowl, muskrat, beaver, and free-ranging wood bison. In recent decades, a paucity of ice-jam flooding in the lower Peace River has resulted in prolonged dry periods and considerable reduction in the area covered by lakes and ponds that provide habitat for aquatic life in the PAD region. Building on previous work that has identified the salient hydro-climatic factors, the frequency of ice-jam floods is considered under ‘present’ (1961–1990) and ‘future’ (2070–2099) climatic conditions. The latter are determined using temperature and precipitation output from the Canadian Climate Centre's second-generation Global Climate Model (CGCM2) for two different greenhouse-gas/sulphate emission scenarios. The analysis indicates that the ice season is likely to be reduced by 2–4 weeks, while future ice covers would be slightly thinner than they are at present. More importantly, a large part of the Peace River basin is expected to experience frequent and sustained mid-winter thaws, leading to significant melt and depleted snowpacks in the spring. Using an empirical relationship between ice-jam flood occurrence and size of the spring snowpack, a severe reduction in the frequency of ice-jam flooding is predicted under both future-climate scenarios that were considered. In turn, this trend is likely to accelerate the loss of aquatic habitat in the PAD region. Implications for potential mitigation and adaptation strategies are discussed. Copyright © 2006 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.


Complex Declarative Learning


title: The Editor's Eye: Curation and Comment Relevance on the New York Times

abstract: The journalistic curation of social media content from platforms like Facebook and YouTube or from commenting systems is underscored by an imperative for publishing accurate and quality content. This work explores the manifestation of editorial quality criteria in comments that have been curated and selected on the New York Times website as "NYT Picks." The relationship between comment selection and comment relevance is examined through the analysis of 331,785 comments, including 12,542 editor's selections. A robust association between editorial selection and article relevance or conversational relevance was found. The results are discussed in terms of their implications for reducing journalistic curatorial work load, or scaling the ability to examine more comments for editorial selection, as well as how end-user commenting experiences might be improved.


title: Chaotic block ciphers: from theory to practical algorithms

abstract: Digital chaotic ciphers have been investigated for more than a decade. However, their overall performance in terms of the tradeoff between security and speed, as well as the connection between chaos and cryptography, has not been sufficiently addressed. We propose a chaotic Feistel cipher and a chaotic uniform cipher. Our plan is to examine crypto components from both dynamical-system and cryptographical points of view, thus to explore connection between these two fields. In the due course, we also apply dynamical system theory to create cryptographically secure transformations and evaluate cryptographical security measures


title: A Survey on Energy-Efficient Routing Techniques with QoS Assurances for Wireless Multimedia Sensor Networks

abstract: The recent technological advances in micro electro-mechanical systems have promoted the development of a powerful class of sensor-based distributed intelligent systems capable of ubiquitously retrieving multimedia information, namely Wireless Multimedia Sensor Networks (WMSNs). WMSNs are gaining more popularity day by day as they are envisioned to support a large number of both non-real time and real-time multimedia applications. However, satisfying the stringent quality of service (QoS) requirements of multimedia transmission in a resource-constrained sensor network environment places new challenges to routing. As an outcome, optimal energy and application-specific QoS aware routing for WMSNs has gained considerable research attention recently. In this paper, current state-of-the-art in energy-efficient routing techniques for WMSNs is surveyed together with the highlights of the performance issues of each strategy. We outline the design challenges of routing protocols for WMSNs followed by the limitations of current techniques designed for non-multimedia data transmission. Further, a classification of recent routing protocols for WMSNs and a discussion of possible future research trends are presented.


title: A Closed Loop Speed Control of a Switched Reluctance Motor with a New Converter Topology

abstract: Switched Reluctance Motor (SRM) has become a competitive selection for many applications of electric machine drive systems recently due to its relative simple construction and its robustness. . This paper describes the design of new converter, consisting of "half- bridge" IGBT modules and SCRs, for closed loop control of switched reluctance motor drives are proposed. The proposed converter topology is a variation of the conventional asymmetric bridge converter for switched reluctance motor drives. However, utilization of switch modules is enhanced considerably. The requirements of converters for switched reluctance motor drives and the operation of the proposed new converter are analyzed and discussed. In this paper a new converter topology for speed control of a switched reluctance motor (SRM) is proposed. The topology is verified through MATLAB simulation.


title: Learning Multilingual Subjective Language via Cross-Lingual Projections

abstract: This paper discusses learning multilingual subjective language via cross-lingual projections.


title: Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

abstract: Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires a considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals.


title: Smartphones as Practical and Secure Location Verification Tokens for Payments.

abstract: We propose a novel location-based second-factor authentication solution for modern smartphones. We demonstrate our solution in the context of point of sale transactions and show how it can be effectively used for the detection of fraudulent transactions caused by card theft or counterfeiting. Our scheme makes use of Trusted Execution Environments (TEEs), such as ARM TrustZone, commonly available on modern smartphones, and resists strong attackers, even those capable of compromising the victim phone applications and OS. It does not require any changes in the user behavior at the point of sale or to the deployed terminals. In particular, we show that practical deployment of smartphone-based second-factor authentication requires a secure enrollment phase that binds the user to his smartphone TEE and allows convenient device migration. We then propose two novel enrollment schemes that resist targeted attacks and provide easy migration. We implement our solution within available platforms and show that it is indeed realizable, can be deployed with small software changes, and does not hinder user experience.


title: CoAdapt P300 speller: optimized flashing sequences and online learning

abstract: This paper presents a series of recent improvements made on the P300 speller paradigm in the context of the CoAdapt project. The flashing sequence is elicited by a new design called RIPRAND, in which the flashing rate of elements can be controlled independently of grid cardinality. Element-based evidence accumulation allows early-stopping of the flashes as soon as the symbol has been detected with confidence. No calibration session is nec-essary, thanks to a mixture-of-experts method which makes the initial predictions. When sufficient data can be buffered, subject-specific spatial and temporal filters are learned, with which the interface seamlessly makes its predictions, and the classifiers are adapted online. This paper, which presents results of three online sessions totalling 26 subjects, is the first to report online performance of a P300 speller with no calibration. 1 Material and Methods The P300 speller presented in this work was implemented in C++ with OpenViBE [7], and a dedicated stimulating software controlled the keyboard display. The software is opensource and part of OpenViBE release 0.18 We used a single Windows laptop to run all software components. The P300 speller keyboard was displayed on a separate LCD screen. A TMSi Refa8 amplifier, synchronized via hardware to the laptop, was used to record from 12 actively shielded electrodes. The visual stimulations consisted of briefly flashing "smiley" pictures. The P300 wave was detected via 3 channels of an xDAWN spatial filter [8], combined with a Regularized LDA classifier hereforth called RDA, which incorporates a regularisation of the common covariance matrix. The output of the classifier at each flashing time t is denote y(t). To save time, elements are always flashed in groups. Initial design of P300 speller groups involved rows and columns of a square matrix [2] or their randomizations [1]. The target element is then found at the intersection of the groups eliciting a P300 response. But repetitively flashing the same groups causes elements within the target groups to be wrongly selected, because of visual attention effects, and because of the contamination of all group elements by classification errors. Element-wise evidence accumulation avoids these two effects. A different random per-mutation can then be performed at each repetition of the flashes, effectively changing elements' group membership across repetitions. At each flash t, let the binary vector a(t) represent the set of n flashed elements within the grid of cardinality N . The score α(t) of each element (initialized to 0 at time 0) is updated with the following scheme, in which both target and non-target flashes contribute to the accumulation: α(t) = α(t − 1) + log


title: DSH: data sensitive hashing for high-dimensional k-nnsearch

abstract: The need to locate the k-nearest data points with respect to a given query point in a multi- and high-dimensional space is common in many applications. Therefore, it is essential to provide efficient support for such a search. Locality Sensitive Hashing (LSH) has been widely accepted as an effective hash method for high-dimensional similarity search. However, data sets are typically not distributed uniformly over the space, and as a result, the buckets of LSH are unbalanced, causing the performance of LSH to degrade. In this paper, we propose a new and efficient method called Data Sensitive Hashing (DSH) to address this drawback. DSH improves the hashing functions and hashing family, and is orthogonal to most of the recent state-of-the-art approaches which mainly focus on indexing and querying strategies. DSH leverages data distributions and is capable of directly preserving the nearest neighbor relations. We show the theoretical guarantee of DSH, and demonstrate its efficiency experimentally.


title: EOS: A multilingual text archive of international newspaper & blog articles

abstract: The Expandable Open Source (EOS) database maintains an archive of Internet accessible newspaper and blog articles from across the world. Today, the archive contains over 700 million articles and is growing by approximately 100,000 articles each day. In this work, we describe the components of EOS, our community of data users and data providers, some real world use cases, challenges associated with maintaining this archive, and a vision for its future as a text analytic portal.


title: Disco clothing, female sexual motivation, and relationship status: Is she dressed to impress?

abstract: The relationship between a female's clothing choice, sexual motivation, hormone levels, and partnership status (single or not single, partner present or not present) was analyzed in 351 females attending Austrian discotheques. We digitally analyzed clothing choice to determine the amount of skin display, sheerness, and clothing tightness. Participants self‐reported sexual motivation, and we assessed estradiol and testosterone levels through saliva sampling. Results show that females are aware of the social signal function of their clothing and that they in some cases alter their clothing style to match their courtship motivation. In particular, sheer clothing—although rare in the study—positively correlated with the motivation for sex. Hormone levels influenced clothing choice in many groups, with testosterone levels correlating positively with physique display. Infernales who had a partner but were at the disco unaccompanied by the partner, estradiol levels correlated positively with skin display and clo...


title: Social identification and acculturation in the Basque Autonomous Community

abstract: Abstract Basque origin undergraduates in four universities of the Basque Autonomous Community (BAC) completed the Host Community Acculturation Scale (HCAS) in three life domains: marriage, culture, and work. Undergraduates with a strong Basque identity ( N = 308) and those with a strong dual Basque-Spanish identity ( N = 219) completed the HCAS towards the co-national Spanish minority and “devalued” Moroccan immigrants residing in the BAC. Results indicate that (1) undergraduates have a preference for individualism and integrationism towards Spanish co-nationals and Moroccans; (2) Strong Dual identifiers endorse individualism and integrationism more than Strong Basque identifiers towards Spanish co-nationals; (3) Strong Dual identifiers favored Spanish co-nationals over Moroccan immigrants in their acculturation orientations, whereas Strong Basque identifiers endorsed more welcoming acculturation orientations towards Moroccans than towards Spanish co-nationals; and (4) undergraduates endorsed more individualism, integrationism, and assimilationism in the work domain, while they endorsed more segregationism and exclusionism in the culture domain. Results are discussed using the Interactive Acculturation Model.


Consensus on Changing Trends, Attitudes, and Concepts of Asian Beauty


title: Structure and Validity of Self-Concept Clarity Measures

abstract: We examine the structure and validity of existing measures of self-concept clarity (SCC). We document six different measurement strategies that have been employed in the self-concept clarity literature, review existing research on their relationships with each other and with self-esteem, and present in-progress research designed to examine their structure and validity. We conclude that these measures largely reflect different constructs and that they demonstrate distinct patterns of relationships with criteria previously examined in the self-concept clarity literature. Further, we examine incremental validity over self-esteem, noting that measures of self-concept clarity demonstrate considerably weaker relationships with criteria once self-esteem is controlled for in the analyses. We discuss measurement of self-concept clarity, placing special emphasis on understanding potentially diverse measures of SCC-related constructs, the role of self-esteem in self-concept clarity research, and potential cultural boundedness of extant assessment strategies.


On the Security of an Mutual Verifiable Provable Data Auditing in Public Cloud Storage


title: Learning cross-view binary identities for fast person re-identification

abstract: In this paper, we propose to learn cross-view binary identities (CBI) for fast person re-identification. To achieve this, two sets of discriminative hash functions for two different views are learned by simultaneously minimising their distance in the Hamming space, and maximising the cross-covariance and margin. Thus, similar binary codes can be found for images of a same person captured at different views by embedding the images into the Hamming space. Therefore, person re-identification can be solved by efficiently computing and ranking the Hamming distances between the images. Extensive experiments are conducted on two public datasets and CBI produces comparable results as state-of-the-art re-identification approaches but is at least 2200 times faster.


title: Histological assessment of the palatal mucosa and greater palatine artery with reference to subepithelial connective tissue grafting

abstract: This study aimed to measure the thickness of the epithelium and lamina propria of the palatal mucosa and to elucidate the location of the greater palatine artery to provide the anatomical basis for subepithelial connective tissue grafting. Thirty-two maxillary specimens, taken from the canine distal area to the first molar distal area, were embedded in paraffin and stained with hematoxylin-eosin. The thickness of the epithelium and lamina propria of the palatal mucosa was measured at three positions on these specimens, starting from 3 mm below the alveolar crest and in 3-mm intervals. The location of the greater palatine artery was evaluated by using image-processing software. The mean epithelial thickness decreased significantly in the posterior teeth; it was 0.41, 0.36, 0.32, and 0.30 mm in the canine, first premolar, second premolar, and first molar distal areas, respectively. The lamina propria was significantly thicker in the canine distal; it was 1.36, 1.08, 1.09, and 1.05 mm, respectively. The mean length from the alveolar crest to the greater palatine artery increased toward the posterior molar; it was 7.76, 9.21, 10.93, and 11.28 mm, respectively. The mean depth from the surface of the palatal mucosa to the greater palatine artery decreased from the canine distal to the first premolar distal but increased again toward the posterior molar; it was 3.97, 3.09, 3.58, and 5.50 mm, respectively. Detailed histological assessments of the lamina propria of the palatal mucosa and the greater palatine artery are expected to provide useful anatomical guidelines for subepithelial connective tissue grafting.


title: Urban tree species mapping using hyperspectral and lidar data fusion

abstract: article i nfo Article history: In this study we fused high-spatial resolution (3.7 m) hyperspectral imagery with 22 pulse/m 2 lidar data at the individual crown object scale to map 29 common tree species in Santa Barbara, California, USA. We first adapted andparallelizedawatershedsegmentationalgorithmtodelineateindividualcrownsfromagriddedcanopymax- ima model. From each segment, we extracted all spectra exceeding a Normalized Difference Vegetation Index (NDVI) threshold and a suite of crown structural metrics computed directly from the three-dimensional lidar point cloud. The variables were fused and crowns were classified using canonical discriminant analysis. The full complement of spectral bands along with 7 lidar-derived structural metrics were reduced to 28 canonical variates and classified. Species-level and leaf-type level maps were produced with respective overall accuracies of 83.4% (kappa = 82.6) and 93.5%. The addition of lidar data resulted in an increase in classification accuracy of 4.2 percentage points over spectral data alone. The value of the lidar structural metrics for urban species dis- crimination became particularly evident when mapping crowns that were either small or morphologically unique. For instance, the accuracy with which we mapped the tall palm species Washingtonia robusta increased from 29% using spectral bands to 71% with the fused dataset. Additionally, we evaluated the role that automated segmentation plays in classification error and the prospects for mapping urban forest species not included in a training sample. The ability to accurately map urban forest species is an important step towards spatially explicit urban forest ecosystem assessment.


title: A miniaturized wideband dual-polarized linear array with balanced antipodal Vivaldi antenna

abstract: Previous studies on Vivaldi wideband phased arrays are mainly focused on Two-dimensional scanning phased arrays. A miniaturized balanced antipodal Vivaldi antenna (BAVA) is presented in this paper. A novel vertical parasitic metal strip loading is employed in the dual-polarized linear phased arrays, it to make a compact structure. With the arc-shaped slots and metal strip loads, the radiation performance in lower operating band can be greatly enhanced. The proposed antenna is simulated in the infinite condition through periodic boundary with the size of 100mm (length) *100mm (width) *125mm (depth). The antenna achieves an impedance bandwidth when scanning to ±50° for VSWR≤3 achieves 4∶1 (0.5GHz–2GHz) bandwidth and 5∶1 (0.4GHz–2GHz) bandwidth in the vertical polarization and horizontal polarization, respectively, and the isolation is less than −18dB over the operating frequency range.


title: A Mach-Zehnder Modulator Bias Controller Based on OMA and Average Power Monitoring

abstract: We demonstrate an integrated circuit (IC) implemented in 28-nm CMOS technology for Mach-Zehnder modulator (MZM) bias control. Our circuit determines the optimal MZM bias voltage that provides the largest optical modulation amplitude and maintains this bias voltage by monitoring the average modulated power. The controller IC consists of digital-to-analog converter, analog-to-digital converter, trans-impedance amplifier, power detector, track-and-hold circuit, comparator, and digital controller, all of which are integrated in a single chip. With small power consumption and chip area, our approach should be applicable for future photonic electronic integrated circuits based on the Si platform.


title: A wideband stacked patch-ring antenna with corner perturbations for circular polarization

abstract: This paper presents a method of wideband circular polarization (CP) generation using a stacked patch-ring microstrip antenna with a single probe feed. An enhanced axial ratio (AR) bandwidth is obtained when a combination of a square ring and a square patch with negative perturbations is used as parasitic and driven elements, respectively. The axial ratio bandwidth obtained is significantly larger compared to the case when square rings are used as parasitic and driven rings with a single feed. A simulated impedance bandwidth (Sn< −10dB) of 16%, 3dB AR bandwidth of 8%and a peak gain of 8.65 dBic are obtained from this antenna.


title: Steering angle estimation for autonomous vehicle navigation using hough and Euclidean transform

abstract: The recent trend of proliferation of road accidents and fatalities, calls for a methodology that can appease the burden on the drivers during certain crucial moments, requiring fast response and decision making. This paper expatiates a novel and effective procedure for determining the steering angle required to keep the vehicle in the middle of the lane. The procedure used is divided in multiple stages. The first stage includes determination of the road's midpoint using Robert edge detection and morphological operations. This midpoint is then used to determine the angle by which a vehicle must steer to maintain its position at the center of the road. It is done using Euclidean distance transform and trigonometric equations. The input data suffers non-linearity, irregular intensity, non-uniform contrast difference which makes edge extraction ineffective. To tackle such situations, Hough transform and virtual road edge was used to determine the road and lane edges. The algorithm was tested rigorously using video samples, acquired from camera mounted on hood of a car, characterizing various road conditions. The results underscored the robustness and effectiveness of the same.


Service Based Brand Equity, Measure of Purchase Intention, Mediating Role of Brand Performance


title: : Introducing educational games in the learning process

abstract: Within the last years educational games have attracted some attention from the academic community. Multiple enhancements of the learning experience are usually attributed to educational games, although the most cited is their potential to improve students' motivation. In spite of these expected advantages, how to introduce video games in the learning process is an issue that is not completely clear yet, which reduces the potential impact of educational video games. Our goal at the research group is to identify the barriers that are limiting the integration of games in the learning process and propose approaches to tackle them. The result of this work is the platform, an educational game authoring tool that aims to make of video games just another educational tool at the disposal of the instructors. In this paper we describe how contributes to the integration of games in the learning process through three main focuses: reduction of the high development costs of educational games, involvement of instructors in the development process to enhance the educational value, and the production of the games using a white-box model. In addition we describe the current research that we are conducting using the platform as a test-bed.


title: Conducting a meta-ethnography of qualitative literature: Lessons learnt

abstract: BackgroundQualitative synthesis has become more commonplace in recent years. Meta-ethnography is one of several methods for synthesising qualitative research and is being used increasingly within health care research. However, many aspects of the steps in the process remain ill-defined.DiscussionWe utilized the seven stages of the synthesis process to synthesise qualitative research on adherence to tuberculosis treatment. In this paper we discuss the methodological and practical challenges faced; of particular note are the methods used in our synthesis, the additional steps that we found useful in clarifying the process, and the key methodological challenges encountered in implementing the meta-ethnographic approach.The challenges included shaping an appropriate question for the synthesis; identifying relevant studies; assessing the quality of the studies; and synthesising findings across a very large number of primary studies from different contexts and research traditions. We offer suggestions that may assist in undertaking meta-ethnographies in the future.SummaryMeta-ethnography is a useful method for synthesising qualitative research and for developing models that interpret findings across multiple studies. Despite its growing use in health research, further research is needed to address the wide range of methodological and epistemological questions raised by the approach.


title: A genome triplication associated with early diversification of the core eudicots

abstract: BACKGROUND ::: Although it is agreed that a major polyploidy event, gamma, occurred within the eudicots, the phylogenetic placement of the event remains unclear. ::: ::: ::: RESULTS ::: To determine when this polyploidization occurred relative to speciation events in angiosperm history, we employed a phylogenomic approach to investigate the timing of gene set duplications located on syntenic gamma blocks. We populated 769 putative gene families with large sets of homologs obtained from public transcriptomes of basal angiosperms, magnoliids, asterids, and more than 91.8 gigabases of new next-generation transcriptome sequences of non-grass monocots and basal eudicots. The overwhelming majority (95%) of well-resolved gamma duplications was placed before the separation of rosids and asterids and after the split of monocots and eudicots, providing strong evidence that the gamma polyploidy event occurred early in eudicot evolution. Further, the majority of gene duplications was placed after the divergence of the Ranunculales and core eudicots, indicating that the gamma appears to be restricted to core eudicots. Molecular dating estimates indicate that the duplication events were intensely concentrated around 117 million years ago. ::: ::: ::: CONCLUSIONS ::: The rapid radiation of core eudicot lineages that gave rise to nearly 75% of angiosperm species appears to have occurred coincidentally or shortly following the gamma triplication event. Reconciliation of gene trees with a species phylogeny can elucidate the timing of major events in genome evolution, even when genome sequences are only available for a subset of species represented in the gene trees. Comprehensive transcriptome datasets are valuable complements to genome sequences for high-resolution phylogenomic analysis.


title: Declarative Terrain Modeling for Military Training Games

abstract: Military training instructors increasingly often employ computer games to train soldiers in all sorts of skills and tactics. One of the difficulties instructors face when using games as a training tool is the creation of suitable content, including scenarios, entities, and corresponding terrain models. Terrain plays a key role in many military training games, as for example, in our case game Tactical Air Defense. However, current manual terrain editors are both too complex and too time-consuming to be useful for instructors; automatic terrain generation methods show a lot of potential, but still lack user control and intuitive editing capabilities. We present a novel way for instructors to model terrain for their training games: instead of constructing a terrain model using complex modeling tools, instructors can declare the required properties of their terrain using an advanced sketching interface. Our framework integrates terrain generation methods and manages dependencies between terrain features in order to automatically create a complete 3D terrain model that matches the sketch. With our framework, instructors can easily design a large variety of terrain models that meet their training requirements.


title: The zebrafish world of colors and shapes: preference and discrimination.

abstract: Natural environment imposes many challenges to animals, which have to use cognitive abilities to cope with and exploit it to enhance their fitness. Since zebrafish is a well-established model for cognitive studies and high-throughput screening for drugs and diseases that affect cognition, we tested their ability for ambient color preference and 3D objects discrimination to establish a protocol for memory evaluation. For the color preference test, zebrafish were observed in a multiple-chamber tank with different environmental color options. Zebrafish showed preference for blue and green, and avoided yellow and red. For the 3D objects discrimination, zebrafish were allowed to explore two equal objects and then observed in a one-trial test in which a new color, size, or shape of the object was presented. Zebrafish showed discrimination for color, shape, and color+shape combined, but not size. These results imply that zebrafish seem to use some categorical system to discriminate items, and distracters affect their ability for discrimination. The type of variables available (color and shape) may favor zebrafish objects perception and facilitate discrimination processing. We suggest that this easy and simple memory test could serve as a useful screening tool for cognitive dysfunction and neurotoxicological studies.


title: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

abstract: Most literature on short-term traffic flow forecasting focused mainly on normal, or non-incident, conditions and, hence, limited their applicability when traffic flow forecasting is most needed, i.e., incident and atypical conditions. Accurate prediction of short-term traffic flow under atypical conditions, such as vehicular crashes, inclement weather, work zone, and holidays, is crucial to effective and proactive traffic management systems in the context of intelligent transportation systems (ITS) and, more specifically, dynamic traffic assignment (DTA). To this end, this paper presents an application of a supervised statistical learning technique called Online Support Vector machine for Regression, or OL-SVR, for the prediction of short-term freeway traffic flow under both typical and atypical conditions. The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models. The resultant performance comparisons suggest that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies. Yet OL-SVR is the best performer under non-recurring atypical traffic conditions. It appears that for deployed ITS systems that gear toward timely response to real-world atypical and incident situations, OL-SVR may be a better tool than GML.


title: Reality mining based on Social Network Analysis

abstract: Data Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.


title: Adversarial Decomposition of Text Representation

abstract: In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, each of them responsible for a specific aspect of the input sentence. We evaluate the proposed method on two case studies: the conversion between different social registers and diachronic language change. We show that the proposed method is capable of fine-grained controlled change of these aspects of the input sentence. It is also learning a continuous (rather than categorical) representation of the style of the sentence, which is more linguistically realistic. The model uses adversarial-motivational training and includes a special motivational loss, which acts opposite to the discriminator and encourages a better decomposition. Furthermore, we evaluate the obtained meaning embeddings on a downstream task of paraphrase detection and show that they significantly outperform the embeddings of a regular autoencoder.


title: Planar shape recognition by shape morphing

abstract: Abstract A novel method based on shape morphing is proposed for 2D shape recognition. In this framework, the shape of objects is described by using their contour. Shape recognition involves a morph between the contours of the objects being compared. The morph is quantified by using a physics-based formulation. This quantification is used as a dissimilarity measure to find the reference shape most similar to the input. The dissimilarity measure is shown to have the properties of a metric as well as invariance to Euclidean transformations. The recognition paradigm is applicable to both convex and non-convex shapes. Moreover, the applicability of the method is not constrained to closed shapes. Based on the metric properties of the dissimilarity method, a search strategy is described that obviates an exhaustive search of the template database during recognition experiments. Experimental results on the recognition of various types of shapes are presented.


title: A Fully Digital, Energy-Efficient, Adaptive Power-Supply Regulator

abstract: A voltage scaling technique for energy-efficient operation requires an adaptive power-supply regulator to significantly reduce dynamic power consumption in synchronous digital circuits. A digitally controlled power converter that dynamically tracks circuit performance with a ring oscillator and regulates the supply voltage to the minimum required to operate at a desired frequency is presented. This paper investigates the issues involved in designing a fully digital power converter and describes a design fabricated in a MOSIS 0.8-/spl mu/m process. A variable-frequency digital controller design takes advantage of the power savings available through adaptive supply-voltage scaling and demonstrates converter efficiency greater than 90% over a dynamic range of regulated voltage levels.


title: A Study on Music Genre Recognition and Classification Techniques

abstract: Automatic classification of music genre is widely studied topic in music information retrieval (MIR) as it is an efficient method to structure and organize the large numbers of music files available on the Internet. Generally, the genre classification process of music has two main steps: feature extraction and classification. The first step obtains audio signal information, while the second one classifies the music into various genres according to extracted features. In this paper, we present a study on techniques for automatic music genre recognition and classification. We first describe machine learning based chord recognition methods, such as hidden Markov models, neural networks, dynamic Bayesian network and rule-based methods, and template matching methods. We then explain supervised, unsupervised and semi-supervised classification methods classifying music genres. Finally, we briefly describe the proposed method for automatic classification of music genres, which consists of three steps: chord labeling, genre matching and classification.


title: Domain specific query generation from natural language text

abstract: This paper presents an approach to automate the generation of Structure Query Language from Natural Language Text. Software requirements specifications are most important part of Natural Language Processing, as little mistake in this phase results in absurd software design. Software Specifications are used in software industry. When we automatic translate these Natural Language Text into Structured Query Text we find Many issues because Software Specification is not an independent sentence they have many module related to each other. so when we translate these English text we have found many issues such as discourse, semantic and negation problem. The evaluation method for Natural Language texts is to test against a list of sentences, each of which is paired with yes or no. For this case I have study Natural Language text and their problems in my MS thesis. What Natural Language Texts are and what issues are found when we automatic translate these Texts into SQL. We used Stanford dependency parser for text translation.


title: Cones of matrices and set-functions and 0-1 optimization

abstract: It has been recognized recently that to represent a polyhedron as the projection of a higher dimensional, but simpler, polyhedron, is a powerful tool in poly- hedral combinatorics. We develop a general method to construct higher-dimensional polyhedra (or, in some cases, convex sets) whose projection approximates the convex hull of 0-1 valued solutions of a system of linear inequalities. An important feature of these approximations is that one can optimize any linear objective function over them in polynomial time. In the special case of the vertex packing polytope, we obtain a sequence of systems of inequalities, such that already the first system includes clique, odd hole, odd anti- hole, wheel, and orthogonality constraints. In particular, for perfect (and many other) graphs, this first system gives the vertex packing polytope. For various classes of graphs, including t-perfect graphs, it follows that the stable set polytope is the projection of a polytope with a polynomial number of facets. We also discuss an extension of the method, which establishes a connection with certain submodular functions and the Mobius function of a lattice.


title: A 10 nm Si-based bulk FinFETs 6T SRAM with multiple fin heights technology for 25% better static noise margin

abstract: For the first time, 10nm Si-based bulk FinFETs 6T SRAM (beta ratio = 2) with novel multiple fin heights technology is successfully demonstrated with 25% better static noise margin at 0.6 V than single fin-height baseline. Meanwhile, presented technology also provides advantage in SRAM cell size by 20% scaling down. It can furthermore offer potential of beyond 10nm Si-based CMOS computing circuit technology.


title: Universal discriminative quantum neural networks

abstract: Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science. In this work, we train near-term quantum circuits to classify data represented by non-orthogonal quantum probability distributions using the Adam stochastic optimization algorithm. This is achieved by iterative interactions of a classical device with a quantum processor to discover the parameters of an unknown non-unitary quantum circuit. This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs. Notably we use universal circuit topologies, with a theoretically motivated circuit design, which guarantees that our circuits can in principle learn to perform arbitrary input-output mappings. Our numerical simulations show that shallow quantum circuits could be trained to discriminate among various pure and mixed quantum states exhibiting a trade-off between minimizing erroneous and inconclusive outcomes with comparable performance to theoretically optimal POVMs. We train the circuit on different classes of quantum data and evaluate the generalization error on unseen mixed quantum states. This generalization power hence distinguishes our work from standard circuit optimization and provides an example of quantum machine learning for a task that has inherently no classical analogue.


title: The Training of Physician's Assistants

abstract: Abstract A large-scale audit of patient records was used to educate and evaluate physician's assistants. Clinical algorithms (sets of step-by-step instructions for solving a medical problem) for 11...


title: Energy-performance design exploration of a low-power microprogrammed deep-learning accelerator

abstract: This paper presents the design space exploration of a novel microprogrammable accelerator in which PEs are connected with a Network-on-Chip and benefit from low-power features enabled through a practical implementation of a Dual-V dd assignment scheme. An analytical model, fitted with postlayout data obtained with a 28nm FDSOI design kit, returns implementations with optimal energy-performance tradeoff by taking into consideration all the key design-space variables. The obtained Pareto analysis helps us infer optimization rules aimed at improving quality of design.