title: A 28-GHz Symmetrical Doherty Power Amplifier Using Stacked-FET Cells

abstract: A two-stage Ka-band Doherty power amplifier (DPA) using a stacked field-effect transistor (FET) cell is presented. We demonstrate, for the first time, the two critical parameters in a stacked-FET topology that must be designed specifically for a Doherty operation: the gate capacitance and the bias voltage of the stacked transistor. The two parameters strongly determine the DPA output power, power-added efficiency (PAE), and power gain flatness. Furthermore, we also demonstrate a dual-driver topology to boost the amplifier gain without scarifying PAE, and a proposed bias circuitry to deal with the high gate leakage current of an enhancement mode (E-mode) process. The stacked-FET DPA prototype is fabricated in an E-mode 0.15- $\mu \text{m}$ gallium arsenide (GaAs) process to verify the concept. At 28 GHz, the 2.9 mm $\times1.7$ mm integrated chip exhibits a measured gain of 14.4 dB, an output power of 28.7 dBm, with an associated 37% peak PAE and 27% PAE at 6-dB power back-off. To the best of the authors’ knowledge, the DPA achieves among the highest gain and highest power density of all reported DPAs at millimeter-wave frequencies.


title: On the Semantic Patterns of Passwords and their Security Impact

abstract: We present the first framework for segmentation, semantic classification, and semantic generalization of passwords and a model that captures the semantic essence of password samples. Researchers have only touched the surface of patterns in password creation, with the semantics of passwords remaining largely unexplored, leaving a gap in our understanding of their characteristics and, consequently, their security. In this paper, we begin to fill this gap by employing Natural Language Processing techniques to extract and leverage understanding of semantic patterns in passwords. The results of our investigation demonstrate that the knowledge captured by our model can be used to crack more passwords than the state-of-the-art approach. In experiments limited to 3 billion guesses, our approach can guess approximately 67% more passwords from the LinkedIn leak and 32% more passwords from the MySpace leak.


title: The people-prototype problem: understanding the interaction between prototype format and user group

abstract: When gathering feedback about an envisioned system, prototypes communicate design ideas to user groups. However, it is unclear how user responses are affected by prototype format. We conducted a 2x2 quasi-experiment (video /storyboard format x older and younger user groups) to test for an interaction between prototype format and user group. We found a significant interaction between prototype format and responses across user groups. Our results indicate that differences in user responses can be misinterpreted as the result of user group characteristics. We advise using multiple prototype formats to counteract a 'media effect'. Alternatively, we advise using storyboards for a smaller 'media effect'.


title: Neocognitron trained by winner-kill-loser with triple threshold

abstract: The neocognitron is a hierarchical, multi-layered neural network capable of robust visual pattern recognition. The neocognitron acquires the ability to recognize visual patterns through learning. The winner-kill-loser is a competitive learning rule recently shown to outperform standard winner-take-all learning when used in the neocognitron to perform a character recognition task. In this paper, we improve over the winner-kill-loser rule by introducing an additional threshold to the already existing two thresholds used in the original version. It is shown theoretically, and also by computer simulation, that the use of a triple threshold makes the learning process more stable. In particular, a high recognition rate can be obtained with a smaller network.


title: Emilya: Emotional body expression in daily actions database

abstract: The studies of bodily expression of emotion have been so far mostly focused on body movement patterns associated with emotional expression. Recently, there is an increasing interest on the expression of emotion in daily actions, called also non-emblematic movements (such as walking or knocking at the door). Previous studies were based on database limited to a small range of movement tasks or emotional states. In this paper, we describe our new database of emotional body expression in daily actions, where 11 actors express 8 emotions in 7 actions. We use motion capture technology to record body movements, but we recorded as well synchronized audio-visual data to enlarge the use of the database for different research purposes. We investigate also the matching between the expressed emotions and the perceived ones through a perceptive study. The first results of this study are discussed in this paper.


title: The Research of Data Security Mechanism Based on Cloud Computing

abstract: With the wide use of cloud computing services, users require higher and higher security. So the safety of cloud computing is the first consideration of users to choose. In the development of cloud computing, the application proportion of virtualization gradually increase, the scope and depth of the safety gradually expand. The related concepts of cloud computing and development situation are introduced in this paper. Not only the key technologies of cloud computing security are analyzed, but also a cloud security framework is put forward combined with the current security problems needed to resolve in cloud computing. We analyze and compare the present research results of security model and mechanism in the cloud. At last, we propose a security mechanism based on cloud computing.


title: Itrustpage: a user-assisted anti-phishing tool

abstract: Despite the many solutions proposed by industry and the research community to address phishing attacks, this problem continues to cause enormous damage. Because of our inability to deter phishing attacks, the research community needs to develop new approaches to anti-phishing solutions. Most of today's anti-phishing technologies focus on automatically detecting and preventing phishing attacks. While automation makes anti-phishing tools user-friendly, automation also makes them suffer from false positives, false negatives, and various practical hurdles. As a result, attackers often find simple ways to escape automatic detection. This paper presents iTrustPage - an anti-phishing tool that does not rely completely on automation to detect phishing. Instead, iTrustPage relies on user input and external repositories of information to prevent users from filling out phishing Web forms. With iTrustPage, users help to decide whether or not a Web page is legitimate. Because iTrustPage is user-assisted, iTrustPage avoids the false positives and the false negatives associated with automatic phishing detection. We implemented iTrustPage as a downloadable extension to FireFox. After being featured on the Mozilla website for FireFox extensions, iTrustPage was downloaded by more than 5,000 users in a two week period. We present an analysis of our tool's effectiveness and ease of use based on our examination of usage logs collected from the 2,050 users who used iTrustPage for more than two weeks. Based on these logs, we find that iTrustPage disrupts users on fewer than 2% of the pages they visit, and the number of disruptions decreases over time.


title: Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms

abstract: Fingerprint minutiae extraction is a critical issue in fingerprint recognition. Both missing and spurious minutiae hinder the posterior matching process. Spurious minutiae are more frequent than missing ones, but they can be removed by post-processing. In this work, we study the usage of a state-of-the-art minutiae extractor, MINDTCT, and we analyze its major drawback: the presence of spurious minutiae lying on the borders of the fingerprint and out its area. In order to overcome this problem, we use two different filtering approaches based on the convex hull of the minutiae and the segmentation of the fingerprint. We will analyze, supported by an exhaustive experimental study, the efficacy of these methods to remove spurious minutiae. We will evaluate both the effect on different state-of-the-art matchers and the goodness of the minutiae, by comparing the extracted minutiae with the ground-truth ones. For this purpose, the experiments have been performed on several databases of both real and synthetic fingerprints. The filters used allow us to remove spurious minutiae, resulting in more accurate results even in the case of robust matchers. The EER is improved up to 2% for good quality databases, and up to 25% for FVC databases. Additionally, the matching time is accelerated, since less minutiae are processed, attaining up to a 60% runtime reduction for the tested database.


title: Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration

abstract: We investigate in this article the rigid registration of large sets of points, generally sampled from surfaces. We formulate this problem as a general Maximum-Likelihood (ML) estimation of the transformation and the matches. We show that, in the specific case of a Gaussian noise, it corresponds to the Iterative Closest Point algorithm(ICP) with the Mahalanobis distance.Then, considering matches as a hidden variable, we obtain a slightly more complex criterion that can be efficiently solved using Expectation-Maximization (EM) principles. In the case of a Gaussian noise, this new methods corresponds to an ICP with multiple matches weighted by normalized Gaussian weights, giving birth to the EM-ICP acronym of the method.The variance of the Gaussian noise is a new parameter that can be viewed as a "scale or blurring factor" on our point clouds. We show that EMICP robustly aligns the barycenters and inertia moments with a high variance, while it tends toward the accurate ICP for a small variance. Thus, the idea is to use a multi-scale approach using an annealing scheme on this parameter to combine robustness and accuracy. Moreover, we show that at each "scale", the criterion can be efficiently approximated using a simple decimation of one point set, which drastically speeds up the algorithm.Experiments on real data demonstrate a spectacular improvement of the performances of EM-ICP w.r.t. the standard ICP algorithm in terms of robustness (a factor of 3 to 4) and speed (a factor 10 to 20), with similar performances in precision. Though the multiscale scheme is only justified with EM, it can also be used to improve ICP, in which case the performances reaches then the one of EM when the data are not too noisy.


title: Comparing classification methods for link context based focused crawlers

abstract: Focused crawlers aim to fetch pages only related to a specific subject area from millions of web pages on the Internet. The essential task in a focused crawler is to predict whether a page is related to the target subject area or not without actually fetching the page content itself. Link context based focused crawlers focus on the surrounding text around each link to classify the page pointed by the URL. In this paper, we aim to compare three different classification methods (naive bayes, decision tree, and support vector machines) for the task of link context based focused crawling.


title: Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model

abstract: This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatically select the most salient facial landmarks. By introducing 3D face shape model, we use procrustes analysis to achieve pose-free facial landmark initialization. For deformation, the first step uses mean-shift local search with constrained local model to rapidly approach the global optimum. The second step uses component-wise active contours to discriminatively refine the subtle shape variation. Our framework can simultaneously handle face detection, pose-free landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental face databases and face-in-the-wild databases. All results demonstrate that our approach has certain advantages over state-of-the-art methods in handling pose variations.


title: Real-time accumulation of occlusion-based snow

abstract: This paper describes a technique to allow the real-time simulation of snowfall accumulation in a dynamic 3D environment. The implementation maps surface-bound accumulation buffers to each object in the scene, forming height-maps of accumulated snow cover. The environment is rendered from above similar to the way shadow mapping is typically produced. However, unique buffer IDs and texture co-ordinates are output within each pixel of the render. A new technique for performing the mapping between buffers is proposed where a series of quads are rendered, one per pixel of the occlusion projection, to map directly visible surfaces to their corresponding accumulation height-map. Blurring is performed on the maps and per-pixel detail is given by procedurally generated normal maps of each surface updated frame by frame. Additional detail and shaping is performed using view-dependent tessellation and the snow surface is created by re-colouring and offsetting the existing scene geometry. This is the first implementation of snow simulation which allows persistent accumulation on a dynamic, moving scene in real-time.


title: Out, Damned Spot: Can the “Macbeth Effect” Be Replicated?

abstract: Zhong and Liljenquist (2006) reported evidence of a “Macbeth Effect” in social psychology: a threat to people's moral purity leads them to seek, literally, to cleanse themselves. In an attempt to build upon these findings, we conducted a series of direct replications of Study 2 from Z&L's seminal report. We used Z&L's original materials and methods, investigated samples that were more representative of the general population, investigated samples from different countries and cultures, and substantially increased the power of our statistical tests. Despite multiple good-faith efforts, however, we were unable to detect a “Macbeth Effect” in any of our experiments. We discuss these findings in the context of recent concerns about replicability in the field of experimental social psychology.


title: A study on the black start capability of VSC-HVDC using soft-starting mode

abstract: A good Black-Start scheme is important and necessary to resume power supply fast and efficiently after black out, and how to choose black-start power is the first thing to consider in the scheme. In this paper, the advantages of VSC-HVDC as black start power are analyzed. The ability of VSC-HVDC to soft energize major power system equipment is proved through simulation by software PSCAD/EMTDC. Simulation waveforms during soft energization are illustrated. These plots have described the good voltage and frequency characteristics of VSC-HVDC. Furthermore, the transition from black start control to normal power flow control is discussed. Simulation results indicate that by using soft energization, VSC-HVDC is an ideal black start power.


title: Multidimensional Routing Protocol in Human-Associated Delay-Tolerant Networks

abstract: Human-associated delay-tolerant networks (HDTNs) are new networks where mobile devices are associated with humans and can be viewed from multiple dimensions including geographic and social aspects. The combination of these different dimensions enables us to comprehend delay-tolerant networks and consequently use this multidimensional information to improve overall network efficiency. Alongside the geographic dimension of the network, which is concerned with geographic topology of routing, social dimensions such as social characters can be used to guide the routing message to improve not only the routing efficiency for individual nodes, but also efficiency for the entire network. We propose a multidimensional routing protocol (M-Dimension) for the human-associated delay-tolerant networks which uses local information derived from multiple dimensions to identify a mobile node more accurately. The importance of each dimension has been measured by the weight function and it is used to calculate the best route. The greedy routing strategy is applied to select an intermediary node to forward message. We compare M-Dimension to the existing benchmark routing protocols via MIT reality Data Set and INFOCOM 2006 Data Set, which are real human-associated mobile network trace files. The results of our simulations show that M-Dimension significantly increases the average success ratio with a competitive end-to-end delay when compared with other multicast DTNs routing protocols.


title: Entanglement Property and Monogamy Relation of Gerneralized Mixed W

abstract: We introduce a new class of multipartite entangled mixed states with pure state decompositions of generalized W states, similar to Schmidt-correlated states having generalized GHZ states in the pure state decomposition. The entanglement and separability properties are studied according to PPT operations. Monogamy relations linked to these states are also investigated.


A credobaptist defense for including children in corporate worship through a biblically appropriate application of developmental psychology


PubViz: Lightweight Visual Presentation of Publication Data.


Frame Semantics across Languages: Towards a Multilingual FrameNet.


title: An Auto-tuning Framework for Autonomous Vehicles

abstract: Many autonomous driving motion planners generate trajectories by optimizing a reward/cost functional. Designing and tuning a high-performance reward/cost functional for Level-4 autonomous driving vehicles with exposure to different driving conditions is challenging. Traditionally, reward/cost functional tuning involves substantial human effort and time spent on both simulations and road tests. As the scenario becomes more complicated, tuning to improve the motion planner performance becomes increasingly difficult. To systematically solve this issue, we develop a data-driven auto-tuning framework based on the Apollo autonomous driving framework. The framework includes a novel rank-based conditional inverse reinforcement learning algorithm, an offline training strategy and an automatic method of collecting and labeling data. Our auto-tuning framework has the following advantages that make it suitable for tuning an autonomous driving motion planner. First, compared to that of most inverse reinforcement learning algorithms, our algorithm training is efficient and capable of being applied to different scenarios. Second, the offline training strategy offers a safe way to adjust the parameters before public road testing. Third, the expert driving data and information about the surrounding environment are collected and automatically labeled, which considerably reduces the manual effort. Finally, the motion planner tuned by the framework is examined via both simulation and public road testing and is shown to achieve good performance.


title: Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings

abstract: The tasks in fine-grained opinion mining can be regarded as either a token-level sequence labeling problem or as a semantic compositional task. We propose a general class of discriminative models based on recurrent neural networks (RNNs) and word embeddings that can be successfully applied to such tasks without any taskspecific feature engineering effort. Our experimental results on the task of opinion target identification show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based models. Our framework is flexible, allows us to incorporate other linguistic features, and achieves results that rival the top performing systems in SemEval-2014.


title: OSS/BSS Framework Based on NGOSS

abstract: As the key supporting system of telecommunication enterprises, OSS/BSS needs to support the service steadily in the long-term running and maintenance process. The system architecture must remain steady and consistent in order to accomplish its goal, which is quite difficult when both the technique and business requirements are changing so rapidly. The framework method raised in this article can guarantee the system architecture’s steadiness and business processing’s consistence by means of describing business requirements, application and information abstractly, becoming more specific and formalized in the planning, developing and maintaining process, and getting the results needed. This article introduces firstly the concepts of framework method, then recommends its applications and superiority in OSS/BSS systems, and lastly gives the prospect of its application.


title: Epidemic information dissemination in distributed systems

abstract: Easy to deploy, robust, and highly resilient to failures, epidemic algorithms are a potentially effective mechanism for propagating information in large peer-to-peer systems deployed on Internet or ad hoc networks. It is possible to adjust the parameters of epidemic algorithm to achieve high reliability despite process crashes and disconnections, packet losses, and a dynamic network topology. Although researchers have used epidemic algorithms in applications such as failure detection, data aggregation, resource discovery and monitoring, and database replication, their general applicability to practical, Internet-wide systems remains open to question. We describe four key problems: membership maintenance, network awareness, buffer management, and message filtering, and suggest some preliminary approaches to address them.


BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES


title: Dynamic Resource Discovery Based on Preference and Movement Pattern Similarity for Large-Scale Social Internet of Things

abstract: Given the wide range deployment of disconnected delay-tolerant social Internet of Things (SIoT), efficient resource discovery remains a fundamental challenge for large-scale SIoT. The existing search mechanisms over the SIoT do not consider preference similarity and are designed in Cartesian coordinates without sufficient consideration of real-world network deployment environments. In this paper, we propose a novel resource discovery mechanism in a 3-D Cartesian coordinate system with the aim of enhancing the search efficiency over the SIoT. Our scheme is based on both of preference and movement pattern similarity to achieve higher search efficiency and to reduce the system overheads of SIoT. Simulation experiments have been conducted to evaluate this new scheme in a large-scale SIoT environment. The simulation results show that our proposed scheme outperforms the state-of-the-art resource discovery schemes in terms of search efficiency and average delay.


title: Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images

abstract: Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).


Co-Designing Software and Hardware for Declarative Network Performance Measurement


title: Automated text document categorization

abstract: During the last two decades the number of text documents in digital form has grown enormously. It is necessary to categorize documents into topics and sub topics for easy retrieval. Manual categorization of text documents can be done only by experts and it is a time consuming task. As a consequence, it is of great practical importance to be able to automatically organize and classify documents. There are two approaches, rule-based and machine learning-based, that are used to automate classification task. Both have some limitations. Rules may conflict each other and have to be reconstructed when a target domain changes, are such two limitations in the rule based approaches. Machine learning approaches require proper training data and they do not accountable with the classification results. Motivated by such limitations, this paper proposes a Latent Dirichlet Allocation (LDA) based approach to automatically classify text documents. In order to develop and test the proposed approach on a realistic set up, ACM (Association for Computing Machinery) Computing Classification System (CCS) is selected as the target platform and 9100 computer science related articles categorized under ACMCCS were selected. The experimental results show that the proposed approach is effective for classifying text documents and is applicable to a domain with large number of categories in multiple levels.


title: Graph Topic Scan Statistic for Spatial Event Detection

abstract: Spatial event detection is an important and challenging problem. Unlike traditional event detection that focuses on the timing of global urgent event, the task of spatial event detection is to detect the spatial regions (e.g. clusters of neighboring cities) where urgent events occur. In this paper, we focus on the problem of spatial event detection using textual information in social media. We observe that, when a spatial event occurs, the topics relevant to the event are often discussed more coherently in cities near the event location than those far away. In order to capture this pattern, we propose a new method called Graph Topic Scan Statistic (Graph-TSS) that corresponds to a generalized log-likelihood ratio test based on topic modeling. We first demonstrate that the detection of spatial event regions under Graph-TSS is NP-hard due to a reduction from classical node-weighted prize-collecting Steiner tree problem (NW-PCST). We then design an efficient algorithm that approximately maximizes the graph topic scan statistic over spatial regions of arbitrary form. As a case study, we consider three applications using Twitter data, including Argentina civil unrest event detection, Chile earthquake detection, and United States influenza disease outbreak detection. Empirical evidence demonstrates that the proposed Graph-TSS performs superior over state-of-the-art methods on both running time and accuracy.


title: Inertia-based ICR kinematic model for tracked skid-steer robots

abstract: Kinematic models for skid-steer vehicles based on the locations of instantaneous centers of rotation (ICRs) of treads have been useful for motion control and dead reckoning. These models have typically assumed constant track ICRs because actual ICRs remain in bounded regions. However, this assumption neglects tread ICR variations during motion caused by dynamics. This paper analyzes the effect of inertial forces on track ICR positions. The study is based on dynamic simulations of a tracked mobile robot moving on hard horizontal terrain. Furthermore, a new kinematic model is defined in terms of three indices for sliding, eccentricity and steering efficiency. The proposed model allows to estimate actual track ICR positions as a function of inertia measurements and track speeds.


title: Physiological Responses to Near-Miss Outcomes and Personal Control During Simulated Gambling

abstract: Near-miss outcomes during gambling are non-win outcomes that fall close to a pay-out. While objectively equivalent to an outright miss, near-misses motivate ongoing play and may therefore be implicated in the development of disordered gambling. Given naturalistic data showing increases in heart rate (HR) and electrodermal activity (EDA) during periods of real gambling play, we sought to explore the phasic impact of win, near-miss and full-miss outcomes on physiological arousal in a controlled laboratory environment. EDA and HR were monitored as healthy, student participants (n = 33) played a simulated slot-machine task involving unpredictable monetary wins. A second gambling distortion, perceived personal control, was manipulated within the same task by allowing the participant to select the play icon on some trials, and having the computer automatically select the play icon on other trials. Near-misses were rated as less pleasant than full-misses. However, on trials that involved personal choice, near-misses produced higher ratings of ‘continue to play’ than full-misses. Winning outcomes were associated with phasic EDA responses that did not vary with personal choice. Compared to full-misses, near-miss outcomes also elicited an EDA increase, which was greater on personal choice trials. Near-misses were also associated with greater HR acceleration than other outcomes. Near-miss outcomes are capable of eliciting phasic changes in physiological arousal consistent with a state of subjective excitement, despite their objective non-win status.


title: Epidemiology of Multiple Sclerosis: From Risk Factors to Prevention—An Update

abstract: Although genetic susceptibility explains the clustering of multiple sclerosis (MS) within families and the sharp decline in risk with increasing genetic distance, it cannot fully explain the geographical variations in MS frequency and the changes in risk that occur with migration, which support the action of strong environmental factors. Among these, vitamin D status, obesity in early life, infection with the Epstein-Barr virus, and cigarette smoking are the most consistent environmental predictors of MS risk. The authors review the epidemiological data, critically discuss the evidence for causality of these and other associations, and briefly review the possibility of interventions to reduce MS risk.


Chapter 33 Backwards analysis


title: Exploring the Relationship Between Experiential Avoidance, Alcohol Use Disorders and Alcohol-Related Problems Among First-Year College Students

abstract: Abstract Objective: This study explored the relationship of experiential avoidance (eg, the tendency to avoid, suppress, or otherwise control internal experiences even when doing so causes behavioral harm) to alcohol use disorders and alcohol-related problems. Participants: Cross-sectional data were collected from 240 undergraduate college students in their first year in college between December 2009 and April 2010. Methods: Participants completed a diagnostic interview and online self-report survey. Results: Students with a history of alcohol abuse or dependence had significantly higher levels of experiential avoidance relative to students with no alcohol use disorder diagnosis. A hierarchical linear regression analysis found that experiential avoidance significantly predicted alcohol-related problems, even after controlling for gender and psychological distress. Furthermore, experiential avoidance mediated the relationship of psychological distress to alcohol-related problems. Conclusions: These finding...


DUORC: TOWARDS COMPLEX LANGUAGE UNDER-


title: Array Data Transformation for Source Code Obfuscation

abstract: Obfuscation is a low cost software protection methodology to avoid reverse engineering and re engineering of applications. Source code obfuscation aims in obscuring the source code to hide the functionality of the codes. This paper proposes an Array data transformation in order to obfuscate the source code which uses arrays. The applications using the proposed data structures force the programmer to obscure the logic manually. It makes the developed obscured codes hard to reverse engineer and also protects the functionality of the codes. HE first part of the translation is from Java source to Java Virtual Machine (JVM) machine code. Followed by, translation to real machine instruction in the browser on the user's machine. Since byte code retains almost all information of the source file, there are possibilities for reverse engineering (10) and reengineering. Reverse Engineering or Reengineering is a form of intellectual property theft which is illegal. The code obfuscation was a novel move for software protection and the intention is to hide the functionality of the codes, to limit the possibilities of reverse engineering or reengineering. The possibility of the execution of the obfuscated object code, has led to the popularity of obfuscation. The popular code obfuscation transformation techniques are (i) Layout transformation (making the code unreadable) (ii) Data transformation (obscuring data and data structures) (iii) Control transformation (obscuring the flow of execution) (2) (7) (8). Source code obfuscation is achieved through source code transformations, Java bytecode obfuscation through bytecode transformations and binary obfuscation through binary rewriting (6). Our focus is on source code obfuscation, aiming at obscuring the source code manually. It consists of techniques to target at making source code less comprehensible and automatically transform the programmer's source code into more complex, functionally equivalent source code.


title: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation

abstract: Optical flow estimation is classically marked by the requirement of dense sampling in time. While coarse-to-fine warping schemes have somehow relaxed this constraint, there is an inherent dependency between the scale of structures and the velocity that can be estimated. This particularly renders the estimation of detailed human motion problematic, as small body parts can move very fast. In this paper, we present a way to approach this problem by integrating rich descriptors into the variational optical flow setting. This way we can estimate a dense optical flow field with almost the same high accuracy as known from variational optical flow, while reaching out to new domains of motion analysis where the requirement of dense sampling in time is no longer satisfied.


title: On modeling and predicting individual paper citation count over time

abstract: Evaluating a scientist's past and future potential impact is key in decision making concerning with recruitment and funding, and is increasingly linked to publication citation count. Meanwhile, timely identifying those valuable work with great potential before they receive wide recognition and become highly cited papers is both useful for readers and authors in many regards. We propose a method for predicting the citation counts of individual publications, over an arbitrary time period. Our approach explores paper-specific covariates, and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively. Empirical results on the Microsoft Academic Graph data suggests that our model can be useful for both prediction and interpretability.


Automatic Speech Recognition System for Isolated & Connected Words of Hindi Language By Using Hidden Markov Model Toolkit ( HTK )


title: Evaluation of TV commercials using neurophysiological responses

abstract: Background ::: In recent years, neuroscientific knowledge has been applied to marketing as a novel and efficient means to comprehend the cognitive and behavioral aspects of consumers. A number of studies have attempted to evaluate media contents, especially TV commercials using various neuroimaging techniques such as electroencephalography (EEG). Yet neurophysiological examination of detailed cognitive and affective responses in viewers is still required to provide practical information to marketers. Here, this study develops a method to analyze temporal patterns of EEG data and extract affective and cognitive indices such as happiness, surprise, and attention for TV commercial evaluation.


title: A CMOS 77 GHz radar receiver front-end

abstract: A receiver (Rx) front-end for automotive radar application operating at 76-77 GHz is presented. The Rx frontend employs double conversion architecture, which consists of a 5-stage low noise amplifier (LNA), a subharmonic mixer (SHM), and a double-balanced passive mixer (PSM). In the layout of the LNA, the output of each stage is placed adjoining to the input of the follow stage creating a LC resonance load. Hence complex interstage matching networks is avoided. SHM driven by 38 GHz LO is adopted to avoid push/pull effect and power hunger of VCO. PSM is utilized for second conversion since it consumes no DC current and has low flickering noise. With the proposed architecture, complex PLL design for radar system can be relaxed. The proposed Rx front-end is implemented in a 65 nm CMOS technology and the measurement results show 16 dB voltage gain, and 13 dB calculated noise figure (NF) while dissipating 23.5 mA from a 1.2 V supply.


title: Something in the Way We Move: Motion Dynamics, not Perceived Sex, Influence Head Movements in Conversation

abstract: During conversation, women tend to nod their heads more frequently and more vigorously than men. An individual speaking with a woman tends to nod his or her head more than when speaking with a man. Is this due to social expectation or due to coupled motion dynamics between the speakers? We present a novel methodology that allows us to randomly assign apparent identity during free conversation in a videoconference, thereby dissociating apparent sex from motion dynamics. The method uses motion-tracked synthesized avatars that are accepted by naive participants as being live video. We find that 1) motion dynamics affect head movements but that apparent sex does not; 2) judgments of sex are driven almost entirely by appearance; and 3) ratings of masculinity and femininity rely on a combination of both appearance and dynamics. Together, these findings are consistent with the hypothesis of separate perceptual streams for appearance and biological motion. In addition, our results are consistent with a view that head movements in conversation form a low level perception and action system that can operate independently from top-down social expectations.


title: Predicting Early Academic Failure in High School From Prior Academic Achievement, Psychosocial Characteristics, and Behavior

abstract: The authors examined the differential effects of prior academic achievement, psychosocial, behavioral, demographic, and school context factors on early high school grade point average (GPA) using a prospective study of 4,660 middle-school students from 24 schools. The findings suggest that (a) prior grades and standardized achievement are the strongest predictors of high school GPA and (b) psychosocial and behavioral factors (e.g., motivation, self-regulation, and social control) add incremental validity to the prediction of GPA. When comparing the relative importance of each set of predictors (the dominance analysis technique), the variance accounted for by psychosocial and behavioral factors is comparable to that accounted for by prior grades. These findings highlight the importance of effective risk assessment based on multiple measures (i.e., academic, psychosocial, and behavioral) for the purpose of identifying risk, referring students to intervention, and improving academic success.


title: Self-detection of optical contamination or occlusion in vehicle vision systems

abstract: We present a novel and practical algorithm for the self-detection problem of contamination or occlusions on the lens of a camera mounted on a vehicle. First, we analyze the intrinsic characteristics of such contamination on the video image. Based on this, cumulative differences are used to segment the static region in the image. A blurred edge detection algorithm based on wavelet decomposition is introduced to confirm if the static region belongs to contamination or an occlusion. Through the combination of these algorithms, contamination or occlusions can be detected. Experimental data are analyzed to show the detection performance of our algorithm and the effect of different contamination or occlusion material.


title: The agile supply chain: competing in volatile markets

abstract: A dry peeler is provided for food products such as vegetables, potatoes or the like which have had their skins softened and loosened such as by a caustic treatment bath. The dry peeler is formed as a rotary cylindrical cage having an abradant surface provided by an envelope of axially extending, circumferentially spaced, parallel, braided stainless steel wire cables, stretched between axially spaced rings, with a screw conveyor fitting within the envelope of cables. Both the cage of cables and the screw are rotated and the flexible cables abrade off and undercut a high percentage of the skins of the pre-treated products without the addition of water during the peeling process. Alternate cable supporting rings are rotatably adjustable to vary the tension on the cables.


title: Wideband Compact CPW-Fed Circularly Polarized Antenna for Universal UHF RFID Reader

abstract: A compact circularly polarized slot antenna is proposed this in communication, which has a much wider operation bandwidth compared to the antennas with similar sizes. This antenna is designed for universal ultrahigh frequency (UHF) radio-frequency identification (RFID) reader applications. The antenna is coplanar waveguide (CPW) fed by an L-shaped feeding line. To achieve good impedance matching and broadband CP operation, two L-shaped strip lines are inserted into the circular slot in the ground plane. The measured 10-dB return loss bandwidth is 380 MHz (618–998 MHz, 47.0% centered at 808 MHz). The measured 3-dB axial ratio bandwidth is about 332 MHz (791–1123 MHz, 34.7% centered at 957 MHz). The overall size of the antenna is ${120} \times {120} \times {0}.{8}\mathrm{m}{\mathrm{m}^{3}}$ .


title: Performance and Power Management for Cloud Infrastructures

abstract: A key issue for Cloud Computing data-centers is to maximize their profits by minimizing power consumption and SLA violations of hosted applications. In this paper, we propose a resource management framework combining a utility-based dynamic Virtual Machine provisioning manager and a dynamic VM placement manager. Both problems are modeled as constraint satisfaction problems. The VM provisioning process aims at maximizing a global utility capturing both the performance of the hosted applications with regard to their SLAs and the energy-related operational cost of the cloud computing infrastructure. We show several experiments how our system can be controlled through high level handles to make different trade-off between application performance and energy consumption or to arbitrate resource allocations in case of contention.


1 WIND TURBINE DRIVETRAIN CONDITION MONITORING – AN OVERVIEW


title: Stories of the Smartphone in everyday discourse: conflict, tension & instability

abstract: As the smartphone proliferates in American society, so too do stories about its value and impact. In this paper we draw on advertisements and news articles to analyze cultural discourse about the smartphone. We highlight two common tropes: one calling for increased technological integration, the other urging individuals to dis-integrate the smartphone from daily life. We examine the idealized subject positions of these two stories and show how both simplistic tropes call on the same overarching values to compel individuals to take opposing actions. We then reflect on the conflicts individuals experience in trying to align and account for their actions in relation to multiple contradictory narratives. Finally, we call for CHI researchers to tell and provoke more complicated stories of technologies and their relationships with values in conversations, publications, and future designs.


title: Neural basis of anxiolytic effects of cannabidiol (CBD) in generalized social anxiety disorder: a preliminary report

abstract: Animal and human studies indicate that cannabidiol (CBD), a major constituent of cannabis, has anxiolytic properties. However, no study to date has investigated the effects of this compound on human pathological anxiety and its underlying brain mechanisms. The aim of the present study was to investigate this in patients with generalized social anxiety disorder (SAD) using functional neuroimaging. Regional cerebral blood flow (rCBF) at rest was measured twice using (99m)Tc-ECD SPECT in 10 treatment-naive patients with SAD. In the first session, subjects were given an oral dose of CBD (400 mg) or placebo, in a double-blind procedure. In the second session, the same procedure was performed using the drug that had not been administered in the previous session. Within-subject between-condition rCBF comparisons were performed using statistical parametric mapping. Relative to placebo, CBD was associated with significantly decreased subjective anxiety (p < 0.001), reduced ECD uptake in the left parahippocampal gy...


Three-Dimensional Shape Searching: State-of-the-Art Review and Future Trends


title: The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings

abstract: The increasing use of web 2.0 applications has generated numerous online user reviews. Prior studies have revealed the influence of user-generated reviews on the sales of products such as CDs, books, and movies. However, the influence of online user-generated reviews in the tourism industry is still largely unknown both to tourism researchers and practitioners. To bridge this knowledge gap in tourism management, we conducted an empirical study to identify the impact of online user-generated reviews on business performance using data extracted from a major online travel agency in China. The empirical findings show that traveler reviews have a significant impact on online sales, with a 10 percent increase in traveler review ratings boosting online bookings by more than five percent. Our results highlight the importance of online user-generated reviews to business performance in tourism.


title: The true self online: Personality correlates of preference for self-expression online, and observer ratings of personality online and offline

abstract: We may have a sense of 'true self', easier to present online than in daily life.Conscientiousness and Extraversion (mediated by Shyness) predict offline selves.Shyness and Neuroticism (partly mediated by Shyness) predict online selves.Self, on- and off-line Extraversion, Agreeableness, Conscientiousness scores covary.There is no evidence for more authentic self-expression online. Theorists have suggested some people find it easier to express their "true selves" online than in person. Among 523 participants in an online study, Shyness was positively associated with online 'Real Me' self location, while Conscientiousness was negatively associated with an online self. Extraversion was indirectly negatively associated with an online self, mediated by Shyness. Neuroticism was positively associated with an online self, partly mediated by Shyness. 107 online and offline friends of participants provided ratings of them. Overall, both primary participants and their observers indicated that offline relationships were closer. However, participants who located their Real Me online reported feeling closer to their online friends than did those locating their real selves offline. To test whether personality is better expressed in online or offline interactions, observers' ratings of participants' personalities were compared. Both online and offline observers' ratings of Extraversion, Agreeableness and Conscientiousness correlated with participants' self-reports. However, only offline observers' ratings of Neuroticism correlated with participants' own. Except for Neuroticism, the similarity of online and offline observers' personality ratings to participants' self-reports did not differ significantly. The study provides no evidence that online self-presentations are more authentic; indeed Neuroticism may be more visibly expressed offline.


title: Edge and Curve Detection for Visual Scene Analysis

abstract: Simple sets of parallel operations are described which can be used to detect texture edges, "spots," and "streaks" in digitized pictures. It is shown that, by comparing the outputs of the operations corresponding to (e.g.,) edges of different sizes, one can construct a composite output in which edges between differently textured regions are detected, and isolated objects are also detected, but the objects composing the textures are ignored. Relationships between this class of picture processing operations and the Gestalt psychologists' laws of pictorial pattern organization are also discussed.


title: Face Recognition at a Distance

abstract: Face recognition at a distance is generally motivated by the desire to automatically recognize noncooperative subjects over a wide area. This remote biometric collection and identification problem has been addressed with high-resolution stationary cameras and active camera systems. Key challenges include optical system design, pan-tilt-zoom camera targeting and control, and face recognition with low-resolution images and no pose or illumination control. We discuss major applications, challenges and approaches in this field, and review research literature on this and closely related topics. We further describe a specific face recognition at a distance system that uses the active camera approach, algorithms for facial image modeling and alignment for low-resolution images, and a multi-frame super-resolution process for facial images.


title: Compressed Coded Distributed Computing

abstract: Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme.


Nasal Reshaping with Hyaluronic Acid: An Alternative or Complement to Surgery


title: Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

abstract: Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.


title: Near-optimal navigation of high speed mobile robots on uneven terrain

abstract: This paper proposes a method for near-optimal navigation of high speed mobile robots on uneven terrain. The method relies on a layered control strategy. A high-level planning layer generates an optimal desired trajectory through uneven terrain. A low-level navigation layer guides a robot along the desired trajectory via a potential field-based control algorithm. The high-level planner is guaranteed to yield optimal trajectories but is computationally intensive. The low-level navigation layer is sub-optimal but computationally efficient. To guard against failures at the navigation layer, a model-based lookahead approach is employed that utilizes a reduced form of the optimal trajectory generation algorithm. Simulation results show that the proposed method can successfully navigate a mobile robot over uneven terrain while avoiding hazards. A comparison of the methodpsilas performance to a similar algorithm is also presented.


title: Advancing wireless link signatures for location distinction

abstract: Location distinction is the ability to determine when a device has changed its position. We explore the opportunity to use sophisticated PHY-layer measurements in wireless networking systems for location distinction. We first compare two existing location distinction methods - one based on channel gains of multi-tonal probes, and another on channel impulse response. Next, we combine the benefits of these two methods to develop a new link measurement that we call the complex temporal signature. We use a 2.4 GHz link measurement data set, obtained from CRAWDAD [10], to evaluate the three location distinction methods. We find that the complex temporal signature method performs significantly better compared to the existing methods. We also perform new measurements to understand and model the temporal behavior of link signatures over time. We integrate our model in our location distinction mechanism and significantly reduce the probability of false alarms due to temporal variations of link signatures.


title: Routing Questions to the Right Users in Online Communities

abstract: Online forums contain huge amounts of valuable user-generated content. In current forum systems, users have to passively wait for other users to visit the forum systems and read/answer their questions. The user experience for question answering suffers from this arrangement. In this paper, we address the problem of "pushing" the right questions to the right persons, the objective being to obtain quick, high-quality answers, thus improving user satisfaction. We propose a framework for the efficient and effective routing of a given question to the top-k potential experts (users) in a forum, by utilizing both the content and structures of the forum system. First, we compute the expertise of users according to the content of the forum system—-this is to estimate the probability of a user being an expert for a given question based on the previous question answering of the user. Specifically, we design three models for this task, including a profile-based model, a thread-based model, and a cluster-based model. Second, we re-rank the user expertise measured in probability by utilizing the structural relations among users in a forum system. The results of the two steps can be integrated naturally in a probabilistic model that computes a final ranking score for each user. Experimental results show that the proposals are very promising.


title: Decision Support for e-Governance: A Text Mining Approach

abstract: Information and communication technology has the capability to improve the process by which governments involve citizens in formulating public policy and public projects. Even though much of government regulations may now be in digital form (and often available online), due to their complexity and diversity, identifying the ones relevant to a particular context is a non-trivial task. Similarly, with the advent of a number of electronic online forums, social networking sites and blogs, the opportunity of gathering citizens’ petitions and stakeholders’ views on government policy and proposals has increased greatly, but the volume and the complexity of analyzing unstructured data makes this difficult. On the other hand, text mining has come a long way from simple keyword search, and matured into a discipline capable of dealing with much more complex tasks. In this paper we discuss how text-mining techniques can help in retrieval of information and relationships from textual data sources, thereby assisting policy makers in discovering associations between policies and citizens’ opinions expressed in electronic public forums and blogs etc. We also present here, an integrated text mining based architecture for e-governance decision support along with a discussion on the Indian scenario.


title: Optimizing coflow completion times with utility max-min fairness

abstract: In data parallel frameworks such as MapReduce and Spark, a coflow represents a set of network flows used to transfer intermediate data between successive computation stages for a job. The completion time of a job is then determined by the collective behavior of such a coflow, rather than any individual flow within, and influenced by the amount of network bandwidth allocated to it. Different jobs in a shared cluster have different degrees of sensitivity to their completion times, modeled by their respective utility functions. In this paper, we focus on the design and implementation of a new utility optimal scheduler across competing coflows, in order to provide differential treatment to coflows with different degrees of sensitivity, yet still satisfying max-min fairness across these coflows. Though this objective can be formulated as a lexicographical maximization problem, it is challenging to solve in practice due to its inherent multi-objective and discrete nature. To address this challenge, we first divide the problem into iterative steps of single-objective subproblems; and in each of these steps, we then perform a series of transformations to obtain an equivalent linear programming (LP) problem, which can be efficiently solved in practice. To demonstrate that our solutions are practically feasible, we have implemented it as a real-world coflow scheduler based on the Varys open-source framework to evaluate its effectiveness.


title: Architecture and DSP Implementation of a DVB-S2 Baseband Demodulator

abstract: This paper presents the design and implementation of a baseband demodulator for DVB-S2 satellite receivers. In order to meet the requirements of different complex and multi- domain signal processing stages of the DVB-S2 baseband signal- flow, the presented architecture is based on efficient fixed-point implementation of the various demodulation algorithms and on the use of a dynamic time-sharing scheduler for the various DSP software tasks. The prototyping of the demodulator and its verification in the design of a complete digital DVB-S2 satellite receiver using a versatile testbed is also presented.


title: Life Cycle of Smart Contracts in Blockchain Ecosystems

abstract: This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.


title: Machine Learning for Real Time Poses Classification Using Kinect Skeleton Data

abstract: Poses recognition is an important research topic because some situations require silent communication (sign language, surgeon poses to the nurse for assistance etc.). Traditionally, poses recognition requires high quality expensive cameras and complicated computer vision algorithms. This is not the case thanks to the Microsoft Kinect sensor which provides an inexpensive and easy way for real time user interaction. In this paper, we proposed a real time human poses classification technique, by using skeleton data provided by the Kinect sensor. Different users performed a set of tasks from a vocabulary of eighteen poses. From skeleton data of each pose, twenty features are extracted so that they are invariant with respect to the user's size and its position in the scene. We then compared the generalization performances of four machine learning algorithms, support vectors machines (SVM), artificial neural networks (ANN), k-nearest neighbors (KNN) and Bayes classifier (BC). The method used in this work shows that SVM outperforms the other algorithms.


title: Learning Parametric Models for Context-Aware Query Auto-Completion via Hawkes Processes

abstract: Query auto completion (QAC) is a prominent feature in modern search engines. High quality QAC substantially improves search experiences by helping users in typing less while submitting the queries. Many studies have been proposed to improve quality and relevance of the QAC methods from different perspectives, including leveraging contexts in long term and short term query histories, investigating the temporal information for time-sensitive QAC, and analyzing user behaviors. Although these studies have shown the context, temporal, and user behavior data carry valuable information, most existing QAC approaches do not fully exploit or even completely ignore these information. We propose a novel Hawkes process based QAC algorithm, comprehensively taking into account the context, temporal, and position of the clicked recommended query completions (a type of user behavior data), for reliable query completion prediction. Our understanding of ranking query completions is consistent with the mathematical rationale of Hawke process; such a coincidence in turn validates our motivation of using Hawkes process for QAC. We also develop an efficient inference algorithm to compute the optimal solutions of the proposed QAC algorithm. The proposed method is evaluated on two real-world benchmark data in comparison with state-of-art methods, and the obtained experiments clearly demonstrate their effectiveness.


title: Expectation-maximization algorithm for multi-pitch estimation and separation of overlapping harmonic spectra

abstract: This paper addresses the problem of multi-pitch estimation, which consists in estimating the fundamental frequencies of multiple harmonic sources, with possibly overlapping partials, from their mixture. The proposed approach is based on the expectation-maximization algorithm, which aims at maximizing the likelihood of the observed spectrum, by performing successive single-pitch and spectral envelope estimations. This algorithm is illustrated in the context of musical chord identification.


title: Applications of solar water heating system with phase change material

abstract: The aim of the paper is to provide the review towards the solar water heating system with phase change material. Research questions relating to the solar water heating system using phase change material were analyzed in two sides, i.e., structural characterization and research methodology, followed with the brief introduction of phase change material. The opportunities for further works were identified in technical and economical aspects. The technical opportunities involved in improving characteristics of phase change material for application in solar water heating system, developing novel solar water heating system for integration of system components with phase change material, performing long-term, real-weather and in-situ testing for optimizing operational and structural parameters of novel system, and proposing standardization of novel system for design, manufacture, utilization and marketing. The economical opportunities were presented as in conducting economical analysis to propose cost-effective system and evaluate the feasibility in different locations of the world, and providing subsidies to promote cost-effective system. This study will help identify the potential research gaps for the improvement of the energy efficiency of the solar water heating system and reduce the conventional fossil fuel consumption and carbon emission in the world. This research will also provide the guidance for the domestic and global researchers and engineers working on this area.


title: Cytosm: Declarative Property Graph Queries Without Data Migration

abstract: The property graph model has recently gained significant popularity, combining great expressiveness with powerful declarative graph query languages. However, in order to take advantage of these features, data must be loaded into a specialised graph database. Additionally, property graphs are often schema-free, complicating efficient query execution. In this paper we present Cytosm, a middleware application which enables the execution of property graph queries, on non-graph databases, without data migration. Cytosm relies on gTop, a schema containing an abstract property graph topology, and its mapping to specific database backends. Cytosm uses gTop to efficiently execute OpenCypher queries, exploiting schema information to optimise the query plan, and mapping query concepts to the relational backend. Our experiments show that Cytosm achieves competitive query execution times on relational backends, when compared to leading graph databases.


title: Recent developments and future trends in phased arrays

abstract: An update is here given of the amazing advances in phased arrays from small low cost arrays to large arrays. Now >500 A/C AESAs have been deployed with > 400,000 operational flight hours; >1.8 million T/Rs manufactured; over a million blind spot car radars manufactured; Patriot upgraded to 2012 state-of-the-art; 8 AN/TPY-2s delivered and 5 more to be built; JLENS, CJR, AN/SPY-3 and Dual Band Radar about to be deployed; DBF advancing with Australian S-band CEAFAR, which has A/D at each element, having undergone sea tests; AMDR and Space Fence have undergone their initial development phase; GaN allowing 5x power for same footprint as GaAs; 3D integrated circuit digital chips in production - Moore's Law marches on; DARPA looking at developing a low cost 94 GHz array with a cost goal of $1/element; DARPA COSMOS program taking monolithic microwave integrated circuits (MMIC) to the next level allowing mixed signal integration.


title: Comparison of EEG signal features and ensemble learning methods for motor imagery classification

abstract: Classifying electroencephalogram (EEG) signal in Brain Computer Interface (BCI) is a useful methods to analysis different organs of human body and it can be used for communicate with the outside world and controlling external device. Accuracy classification of extracted features from EEG signals is a problem which many researcher try to improve it. Although many methods for extracting feature and classifying EEG signal have been proposed and developed, many of them suffer from extracting less accurate data from EEG signals. In this work, four signal feature extraction and three ensemble learning method have been reviewed and performances of classification techniques are compared for motor imagery task.


A Comparison of Two Learning Algorithms for Text Categorization


title: Depth Estimation with Occlusion Modeling Using Light-Field Cameras

abstract: Light-field cameras have become widely available in both consumer and industrial applications. However, most previous approaches do not model occlusions explicitly, and therefore fail to capture sharp object boundaries. A common assumption is that for a Lambertian scene, a pixel will exhibit photo-consistency, which means all viewpoints converge to a single point when focused to its depth. However, in the presence of occlusions this assumption fails to hold, making most current approaches unreliable precisely where accurate depth information is most important – at depth discontinuities. In this paper, an occlusion-aware depth estimation algorithm is developed; the method also enables identification of occlusion edges, which may be useful in other applications. It can be shown that although photo-consistency is not preserved for pixels at occlusions, it still holds in approximately half the viewpoints. Moreover, the line separating the two view regions (occluded object versus occluder) has the same orientation as that of the occlusion edge in the spatial domain. By ensuring photo-consistency in only the occluded view region, depth estimation can be improved. Occlusion predictions can also be computed and used for regularization. Experimental results show that our method outperforms current state-of-the-art light-field depth estimation algorithms, especially near occlusion boundaries.


title: Physical layer security in wireless networks: a tutorial

abstract: Wireless networking plays an extremely important role in civil and military applications. However, security of information transfer via wireless networks remains a challenging issue. It is critical to ensure that confidential data are accessible only to the intended users rather than intruders. Jamming and eavesdropping are two primary attacks at the physical layer of a wireless network. This article offers a tutorial on several prevalent methods to enhance security at the physical layer in wireless networks. We classify these methods based on their characteristic features into five categories, each of which is discussed in terms of two metrics. First, we compare their secret channel capacities, and then we show their computational complexities in exhaustive key search. Finally, we illustrate their security requirements via some examples with respect to these two metrics.


title: Predicting human interruptibility with sensors: a Wizard of Oz feasibility study

abstract: A person seeking someone else's attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today's computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be.The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90% of its predictions, while retaining 75% overall accuracy.


title: Connective prediction using machine learning for implicit discourse relation classification

abstract: Implicit discourse relation classification is a challenge task due to missing discourse connective. Some work directly adopted machine learning algorithms and linguistically informed features to address this task. However, one interesting solution is to automatically predict implicit discourse connective. In this paper, we present a novel two-step machine learning-based approach to implicit discourse relation classification. We first use machine learning method to automatically predict the discourse connective that can best express the implicit discourse relation. Then the predicted implicit discourse connective is used to classify the implicit discourse relation. Experiments on Penn Discourse Treebank 2.0 (PDTB) and Biomedical Discourse Relation Bank (BioDRB) show that our method performs better than the baseline system and previous work.


title: Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation.

abstract: Motivation: The simulation of biochemical kinetic systems is a powerful approach that can be used for : (i) checking the consistency ofa postulated model with a set ofexperimental measurements, (ii) answering 'what if?' questions and (iii) exploring possible behaviours ofa model. Here we describe a generic approach to combine numerical optimization methods with biochemical kinetic simulations, which is suitable for use in the rational design of improved metabolic pathways with industrial significance (metabolic engineering) and for solving the inverse problem of metabolic pathways, i.e. the estimation of parameters from measured variables. Results: We discuss the suitability of various optimization methods, focusing especially on their ability or otherwise to find global optima. We recommend that a suite of diverse optimization methods should be available in simulation software as no single one performs best for all problems. We describe how we have implemented such a simulation-optimization strategy in the biochemical kinetics simulator Gepasi and present examples of its application. Availability: The new version of Gepasi (3.20), incorporating the methodology described here, is available on the Internet at http://gepasi.dbs.aber. ac. uk/softw/Gepasi.html.


title: VL2: a scalable and flexible data center network

abstract: To be agile and cost effective, data centers should allow dynamic resource allocation across large server pools. In particular, the data center network should enable any server to be assigned to any service. To meet these goals, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end-system based address resolution to scale to large server pools, without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 seconds - sustaining a rate that is 94% of the maximum possible.


title: Stability and generalization

abstract: We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.


title: JavaRAG: a Java library for reference attribute grammars

abstract: Reference attribute grammars (RAGs) is a powerful formalism for developing modular extensible compilers and program analysis tools. This paper presents JavaRAG, an implementation of RAGs as a Java library that is independent of the abstract syntax tree structure. This makes it possible to extend legacy compilers implemented in Java with RAG computations. We have evaluated the approach by integrating with EMF, ANTLR, and hand-built abstract syntax trees, and we compare performance and specification size with JastAdd and Kiama which are other RAG-based tools. Our JavaRAG library is open source and is used in a compiler for the dataflow language CAL.


Quantification of 16S rRNAs in complex bacterial communities by multiple competitive RT-PCR in TGGE fingerprints.


title: DESIGN UNDER UNCERTAINTY EMPLOYING STOCHASTIC EXPANSION METHODS

abstract: Non-intrusive polynomial chaos expansion (PCE) and stochastic collocation (SC) methods are attractive techniques for uncertainty quantification (UQ) due to their strong mathematical basis and ability to produce functional representations of stochastic variability. PCE estimates coefficients for known orthogonal polynomial basis functions based on a set of response function evaluations, using sampling, linear regression, tensor-product quadrature, or Smolyak sparse grid approaches. SC, on the other hand, forms interpolation functions for known coefficients, and requires the use of structured collocation point sets derived from tensor product or sparse grids. When tailoring the basis functions or interpolation grids to match the forms of the input uncertainties, exponential convergence rates can be achieved with both techniques for general probabilistic analysis problems. Once PCE or SC representations have been obtained for a response metric of interest, analytic expressions can be derived for the moments of the expansion and for the design derivatives of these moments, allowing for efficient design under uncertainty formulations involving moment control (e.g., robust design). This paper presents two approaches for moment design sensitivities, one involving response function expansions over both design and uncertain variables and one involving response derivative expansions over only the uncertain variables. These approaches present a trade-off between increased dimensionality in the expansions (and therefore increased simulation runs required to construct them) with global expansion validity versus increased data requirements per simulation with local expansion validity. Given this capability for analytic moments and their sensitivities, we explore bilevel, sequential, and multifidelity formulations for OUU. Initial experiences with these approaches is presented for a number of benchmark test problems.


title: Geos: Geodesic image segmentation

abstract: This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data. ::: ::: The segmentation problem is cast as approximate energy minimization in a conditional random field. A new, parallel filtering operator built upon efficient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. An economical search algorithm finds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings. ::: ::: Advantages include: i) computational efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. Comparison with max-flow indicates up to 60 times greater computational efficiency as well as greater memory efficiency. ::: ::: GeoS is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. Numerous results on interactive andautomatic segmentation of photographs, video and volumetric medical image data are presented.


title: Nonlinear Network-Induced Time Delay Systems With Stochastic Learning

abstract: This paper presents a new control approach for nonlinear network-induced time delay systems by combining online reset control, neural networks, and dynamic Bayesian networks. We use feedback linearization to construct a nominal control for the system then use reset control and a neural network to compensate for errors due to the time delay. Finally, we obtain a stochastic model of the Networked Control System (NCS) using a Dynamic Bayesian Network (DBN) and use it to design a predictive control. We apply our control methodology to a nonlinear inverted pendulum and evaluate its performance through numerical simulations. We also test our approach with real-time experiments on a dc motor-load NCS with wireless communication implemented using a Ubiquitous Sensor Network (USN). Both the simulation and experimental results demonstrate the efficacy of our control methodology.


title: Flash memories: Successes and

abstract: Flash memory grew from a simple concept in the early 1980s to a technology that generated close to $23 billion in worldwide revenue in 2007, and this represents one of the many success stories in the semiconductor industry. This success was made possible by the continuous innovation of the industry along many different fronts. In this paper, the history, the basic science, and the successes of fash memories are briefly presented. Flash memories have followed the Moore's Law scaling trend for which finer line widths, achieved by improved lithographic resolution, enable more memory bits to be produced for the same silicon area, reducing cost per bit. When looking toward the future, significant challenges exist to the continued scaling of flash memories. In this paper, I discuss possible areas that need development in order to overcome some of the size-scaling challenges. Innovations are expected to continue in the industry, and flash memories will continue to follow the historical trend in cost reduction of semiconductor memories through the rest of this decade.


title: Integration of visual motion and locomotion in mouse visual cortex

abstract: Successful navigation through the world requires accurate estimation of one's own speed. To derive this estimate, animals integrate visual speed gauged from optic flow and run speed gauged from proprioceptive and locomotor systems. The primary visual cortex (V1) carries signals related to visual speed, and its responses are also affected by run speed. To study how V1 combines these signals during navigation, we recorded from mice that traversed a virtual environment. Nearly half of the V1 neurons were reliably driven by combinations of visual speed and run speed. These neurons performed a weighted sum of the two speeds. The weights were diverse across neurons, and typically positive. As a population, V1 neurons predicted a linear combination of visual and run speeds better than either visual or run speeds alone. These data indicate that V1 in the mouse participates in a multimodal processing system that integrates visual motion and locomotion during navigation.


title: Current practice in measuring usability: Challenges to usability studies and research

abstract: How to measure usability is an important question in HCI research and user interface evaluation. We review current practice in measuring usability by categorizing and discussing usability measures from 180 studies published in core HCI journals and proceedings. The discussion distinguish several problems with the measures, including whether they actually measure usability, if they cover usability broadly, how they are reasoned about, and if they meet recommendations on how to measure usability. In many studies, the choice of and reasoning about usability measures fall short of a valid and reliable account of usability as quality-in-use of the user interface being studied. Based on the review, we discuss challenges for studies of usability and for research into how to measure usability. The challenges are to distinguish and empirically compare subjective and objective measures of usability; to focus on developing and employing measures of learning and retention; to study long-term use and usability; to extend measures of satisfaction beyond post-use questionnaires; to validate and standardize the host of subjective satisfaction questionnaires used; to study correlations between usability measures as a means for validation; and to use both micro and macro tasks and corresponding measures of usability. In conclusion, we argue that increased attention to the problems identified and challenges discussed may strengthen studies of usability and usability research.


title: Automatic Identification of Character Types from Film Dialogs

abstract: We study the detection of character types from fictional dialog texts such as screenplays. As approaches based on the analysis of utterances’ linguistic properties are not sufficient to identify all fictional character types, we develop an integrative approach that complements linguistic analysis with interactive and communication characteristics, and show that it can improve the identification performance. The interactive characteristics of fictional characters are captured by the descriptive analysis of semantic graphs weighted by linguistic markers of expressivity and social role. For this approach, we introduce a new data set of action movie character types with their corresponding sequences of dialogs. The evaluation results demonstrate that the integrated approach outperforms baseline approaches on the presented data set. Comparative in-depth analysis of a single screenplay leads on to the discussion of possible limitations of this approach and to directions for future research.


title: Opinion Dynamics and the Evolution of Social Power in Influence Networks

abstract: This paper studies the evolution of self-appraisal, social power, and interpersonal influences for a group of individuals who discuss and form opinions about a sequence of issues. Our empirical model combines the averaging rule of DeGroot to describe opinion formation processes and the reflected appraisal mechanism of Friedkin to describe the dynamics of individuals' self-appraisal and social power. Given a set of relative interpersonal weights, the DeGroot--Friedkin model predicts the evolution of the influence network governing the opinion formation process. We provide a rigorous mathematical formulation of the influence network dynamics, characterize its equilibria, and establish its convergence properties for all possible structures of the relative interpersonal weights and corresponding eigenvector centrality scores. The model predicts that the social power ranking among individuals is asymptotically equal to their centrality ranking, that social power tends to accumulate at the top of the hierarchy,...


title: The cascaded Hough transform as an aid in aerial image interpretation

abstract: Cartography and other applications of remote sensing have led to an increased interest in the (semi-)automatic interpretation of structures in aerial images of urban and suburban areas. Although these areas are particularly challenging because of their complexity, the degree of regularity in such man-made structures also helps to tackle the problems. The paper presents the iterated application of the Hough transform as a means to exploit such regularities. It shows how such 'Cascaded Hough Transform' (or CHT for short) yields straight lines, vanishing points, and vanishing lines. It also illustrates how the latter assist in improving the precision of the former. The examples are based on real aerial photographs.


title: 17-Italian Foot Function Index with numerical rating scale: development, reliability, and validity of a modified version of the original Foot Function Index.

abstract: BACKGROUND ::: Clinical research quantifies symptoms and signs of pain. ::: ::: ::: OBJECTIVE ::: To develop a brief outcome measure to assess foot and ankle conditions, the psychometric properties of a modified version of the original Foot Function Index (FFI) were examined. ::: ::: ::: METHODS ::: Eighty-six subjects with musculoskeletal foot and ankle disorders were enrolled. The internal consistency and test-retest reliability were evaluated by using Cronbach's α and intraclass correlation coefficient (ICC). Criterion validity was tested by Pearson's correlation coefficient between 17 items of the Italian FFI (17-IFFI) and the Lower Extremity Functional Scale (LEFS). The responsiveness was calculated using the receiver operating characteristic curve (ROC). ::: ::: ::: RESULTS ::: Cronbach's Alpha was 0.95 (95% CI: 0.92, 0.99). The intra-interviewer and inter-interviewer ICC values were, respectively, 0.92 (95% CI: 0.88-10 0.96) and 0.90 (95% CI: 0.89-0.94). Correlations between the 17-IFFI scores and the LEFS scores were -0.564 and -0.456 at the initial and at the end of the treatment, respectively. The ROC analysis revealed an area under the curve of 0.732 (95% CI: 0.61-0.82) for the 17-IFFI and 0.633 (95% CI: 0.52-0.71) for the LEFS score. ::: ::: ::: CONCLUSIONS ::: The 17-IFFI is a reliable and valid scale and we recommend its application to evaluate the effectiveness of a treatment in patients with musculoskeletal foot and ankle disorders.


title: A 16–18.8-GHz Sub-Integer-N Frequency Synthesizer for 60-GHz Transceivers

abstract: An 18-GHz range frequency synthesizer is implemented in 0.13-mum SiGe BiCMOS technology as part of a 60-GHz superheterodyne transceiver chipset. It provides for RF channels of 56.5-64 GHz in 500-MHz steps, and features a phase-rotating multi-modulus divider capable of sub-integer division. Output frequency range from the synthesizer is 16.0 to 18.8 GHz, while the enabled RF frequency range is 3.5 times this, or 55.8 to 65.8 GHz. The measured RMS phase noise of the synthesizer is 0.8deg (1 MHz to 1 GHz integration), while phase noise at 100-kHz and 10-MHz offsets are -90 and -124 dBc/Hz, respectively. Reference spurs are 69 dBc; sub-integer spurs are -65 dBc; and combined power consumption from 1.2 and 2.7 V is 144 mW.


title: Learning to Learn for Global Optimization of Black Box Functions

abstract: We present a learning to learn approach for training recurrent neural networks to perform black-box global optimization. In the meta-learning phase we use a large set of smooth target functions to learn a recurrent neural network (RNN) optimizer, which is either a long-short term memory network or a differentiable neural computer. After learning, the RNN can be applied to learn policies in reinforcement learning, as well as other black-box learning tasks, including continuous correlated bandits and experimental design. We compare this approach to Bayesian optimization, with emphasis on the issues of computation speed, horizon length, and exploration-exploitation trade-offs.


title: Energy efficiency of 2-step charging power-clock for adiabatic logic

abstract: The generation of power-clocks in adiabatic integrated circuits is investigated. Specifically, we consider the energy efficiency of a 2-step charging strategy based on a single tank-capacitor circuit. We have investigated the impact of various parameters such as tank-capacitance to load capacitance ratio, ramping time, transistors sizing and power supply voltage scaling on energy recovery achievable in the 2-step charging circuit. We show that energy recovery achievable in the 2-step charging circuit depends on the tank-capacitor and load capacitor size concluding that tank-capacitance (CT) versus load capacitance (CL) is the significant parameter. We also show that the energy performance depends on the ramping time and improves for higher ramping times (lower frequencies). Energy recovery also improves if the transistors sizes in the step charging circuit are sized at their minimum dimensions. Lastly, we show that energy recovery decreases as the power supply voltage is scaled down. Specifically, the decrease in the energy recovery with decreasing power supply is significant for lower ramping times (higher frequencies). We propose that a Ct/Cl ratio of 10, keeping the width of the transistors in the step charging circuit minimum, can be chosen as a convenient `rule-of-thumb' in practical designs.


title: Compact FPGA-based true and pseudo random number generators

abstract: Two FPGA-based (field programmable gate array) implementations of random number generators intended for embedded cryptographic applications are presented. The first is a true random number generator (TRNG) which employs oscillator phase noise, and the second is a bit serial implementation of a Blum Blum Shub (BBS) pseudorandom number generator (PRNG). Both designs are extremely compact and can be implemented on any FPGA of PLD device. They were designed specifically for use as FPGA-based cryptographic hardware cores. The TRNG and PRNG were tested using the NIST and Diehard random number test suites.


title: How Linguistic Metaphor Scaffolds Reasoning

abstract: Language helps people communicate and think. Precise and accurate language would seem best suited to achieve these goals. But a close look at the way people actually talk reveals an abundance of apparent imprecision in the form of metaphor: ideas are ‘light bulbs', crime is a ‘virus', and cancer is an ‘enemy' in a ‘war'. In this article, we review recent evidence that metaphoric language can facilitate communication and shape thinking even though it is literally false. We first discuss recent experiments showing that linguistic metaphor can guide thought and behavior. Then we explore the conditions under which metaphors are most influential. Throughout, we highlight theoretical and practical implications, as well as key challenges and opportunities for future research.


title: A Sober Look at Clustering Stability

abstract: Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties. Quite surprisingly, the conclusion of our analysis is that for large sample size, stability is fully determined by the behavior of the objective function which the clustering algorithm is aiming to minimize. If the objective function has a unique global minimizer, the algorithm is stable, otherwise it is unstable. In particular we conclude that stability is not a well-suited tool to determine the number of clusters – it is determined by the symmetries of the data which may be unrelated to clustering parameters. We prove our results for center-based clusterings and for spectral clustering, and support our conclusions by many examples in which the behavior of stability is counter-intuitive.


title: Siamese Capsule Networks

abstract: Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable. Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points. In doing so we introduce \textit{Siamese Capsule Networks}, a new variant that can be used for pairwise learning tasks. The model is trained using contrastive loss with $\ell_2$-normalized capsule encoded pose features. We find that \textit{Siamese Capsule Networks} perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.


title: A survey of pre-retrieval query performance predictors

abstract: The focus of research on query performance prediction is to predict the effectiveness of a query given a search system and a collection of documents. If the performance of queries can be estimated in advance of, or during the retrieval stage, specific measures can be taken to improve the overall performance of the system. In particular, pre-retrieval predictors predict the query performance before the retrieval step and are thus independent of the ranked list of results; such predictors base their predictions solely on query terms, the collection statistics and possibly external sources such as WordNet. In this poster, 22 pre-retrieval predictors are categorized and assessed on three different TREC test collections.


Assessment of Assembly Processes in European Industry