title: Constrained Cohort Intelligence using Static and Dynamic Penalty Function Approach for Mechanical Components Design

abstract: AbstractMost of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.


title: Complex thermoelectric materials.

abstract: Thermoelectric materials, which can generate electricity from waste heat or be used as solid-state Peltier coolers, could play an important role in a global sustainable energy solution. Such a development is contingent on identifying materials with higher thermoelectric efficiency than available at present, which is a challenge owing to the conflicting combination of material traits that are required. Nevertheless, because of modern synthesis and characterization techniques, particularly for nanoscale materials, a new era of complex thermoelectric materials is approaching. We review recent advances in the field, highlighting the strategies used to improve the thermopower and reduce the thermal conductivity.


title: Inferring Strategies for Sentence Ordering in Multidocument News Summarization

abstract: The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.


title: Using recurrent neural networks for slot filling in spoken language understanding

abstract: Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark. In addition, we compared the approaches on two custom SLU data sets from the entertainment and movies domains. Our results show that the RNN-based models outperform the conditional random field (CRF) baseline by 2% in absolute error reduction on the ATIS benchmark. We improve the state-of-the-art by 0.5% in the Entertainment domain, and 6.7% for the movies domain.


title: Traffic sign recognition and analysis for intelligent vehicles

abstract: Abstract This paper deals with object recognition in outdoor environments. In this type of environments, lighting conditions cannot be controlled and predicted, objects can be partially occluded, and their position and orientation is not known a priori. The chosen type of objects is traffic or road signs, due to their usefulness for sign maintenance, inventory in highways and cities, Driver Support Systems and Intelligent Autonomous Vehicles. A genetic algorithm is used for the detection step, allowing an invariance localisation to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same colour. A neural network achieves the classification. The global system not only recognises the traffic sign but also provides information about its condition or state.


title: Inferior vena caval hemodynamics quantified in vivo at rest and during cycling exercise using magnetic resonance imaging

abstract: Compared with the abdominal aorta, the hemodynamic environment in the inferior vena cava (IVC) is not well described. With the use of cine phase-contrast magnetic resonance imaging (MRI) and a custom MRI-compatible cycle in an open magnet, we quantified mean blood flow rate, wall shear stress, and cross-sectional lumen area in 11 young normal subjects at the supraceliac and infrarenal levels of the aorta and IVC at rest and during dynamic cycling exercise. Similar to the aorta, the IVC experienced significant increases in blood flow and wall shear stress as a result of exercise, with greater increases in the infrarenal level compared with the supraceliac level. At the infrarenal level during resting conditions, the IVC experienced higher mean flow rate than the aorta (1.2 +/- 0.5 vs. 0.9 +/- 0.4 l/min, P < 0.01) and higher mean wall shear stress than the aorta (2.0 +/- 0.6 vs. 1.3 +/- 0.6 dyn/cm(2), P < 0.005). During exercise, wall shear stress remained higher in the IVC compared with the aorta, although not significantly. It was also observed that, whereas the aorta tapers inferiorly, the IVC tapers superiorly from the infrarenal to the supraceliac location. The hemodynamic and anatomic data of the IVC acquired in this study add to our understanding of the venous circulation and may be useful in a clinical setting.


title: Do Chinese and English speakers think about time differently? Failure of replicating Boroditsky (2001)

abstract: Abstract English uses the horizontal spatial metaphors to express time (e.g., the good days ahead of us). Chinese also uses the vertical metaphors (e.g., ‘the month above’ to mean last month). Do Chinese speakers, then, think about time in a different way than English speakers? Boroditsky [Boroditsky, L. (2001). Does language shape thought? Mandarin and English speakers’ conceptions of time. Cognitive Psychology, 43 (1), 1–22] claimed that they do, and went on to conclude that ‘language is a powerful tool in shaping habitual thought about abstract domains’ (such as time). By estimating the frequency of usage, we found that Chinese speakers actually use the horizontal spatial metaphors more often than the vertical metaphors. This offered no logical ground for Boroditsky’s claim. We were also unable to replicate her experiments in four different attempts. We conclude that Chinese speakers do not think about time in a different way than English speakers just because Chinese also uses the vertical spatial metaphors to express time.


title: A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls

abstract: In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.


title: Finding Plagiarisms among a Set of Programs with JPlag

abstract: JPlag is a web service that finds pairs of similar programs among a given set of programs. It has successfully been used in practice for detecting plagiarisms among student Java program submissions. Support for the languages C, C++ and Scheme is also available. We describe JPlag's architecture and its comparsion algorithm, which is based on a known one called Greedy String Tiling. Then, the contribution of this paper is threefold: First, an evaluation of JPlag's performance on several rather different sets of Java programs shows that JPlag is very hard to deceive. More than 90 percent of the 77 plagiarisms within our various benchmark program sets are reliably detected and a majority of the others at least raise suspicion. The run time is just a few seconds for submissions of 100 programs of several hundred lines each. Second, a parameter study shows that the approach is fairly robust with respect to its configuration parameters. Third, we study the kinds of attempts used for disguising plagiarisms, their frequency, and their success.


title: Flash Memory for Ubiquitous Hardware Security Functions: True Random Number Generation and Device Fingerprints

abstract: We demonstrate that unmodified commercial Flash memory can provide two important security functions: true random number generation and digital fingerprinting. Taking advantage of random telegraph noise (a type of quantum noise source in highly scaled Flash memory cells) enables high quality true random number generation at a rate up to 10Kbits / second. A scheme based on partial programming exploits process variation in threshold voltages to allow quick generation of many unique fingerprints that can be used for identification and authentication. Both schemes require no change to Flash chips or interfaces, and do not require additional hardware.


A Comparison of Lexicon-based Approaches for Sentiment Analysis of Microblog Posts


title: StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks

abstract: A widely studied non-deterministic polynomial time (NP) hard problem lies in finding a route between the two nodes of a graph. Often meta-heuristics algorithms such as $A^{*}$ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text translation. Particularly, we illustrate that utilising a context vector that has been learned from two different recurrent networks enables increased accuracies in learning the shortest route of a graph. Additionally, we show that one can boost the performance of the Seq2Seq network by smoothing the loss function using a homotopy continuation of the decoder's loss function.


title: Markov Localization using Correlation

abstract: Localization is one of the most important capabilities for autonomous mobile agents. Markov Localization (ML), applied to dense range images, has proven to be an effective technique. But its computational and storage requirements put a large burden on robot systems, and make it difficult to update the map dynamically. In this paper we introduce a new technique, based on correlation of a sensor scan with the map, that is several orders of magnitude more efficient than ML. CBML (correlation-based ML) permits video-rate localization using dense range scans, dynamic map updates, and a more precise error model than ML. In this paper we present the basic method of CBML, and validate its efficiency and correctness in a series of experiments on an implemented mobile robot base.


title: Time Synchronization Attack in Smart Grid: Impact and Analysis

abstract: Many operations in power grids, such as fault detection and event location estimation, depend on precise timing information. In this paper, a novel Time Synchronization Attack (TSA) is proposed to attack the timing information in smart grid. Since many applications in smart grid utilize synchronous measurements and most of the measurement devices are equipped with global positioning system (GPS) for precise timing, it is highly probable to attack the measurement system by spoofing the GPS. The effectiveness of TSA is demonstrated for three applications of phasor measurement unit (PMU) in smart grid, namely transmission line fault detection, voltage stability monitoring and event locationing. The validity of TSA is demonstrated by numerical simulations.


title: Understanding the Changing Roles of Scientific Publications via Citation Embeddings

abstract: Researchers may describe different aspects of past scientific publications in their publications and the descriptions may keep changing in the evolution of science. The diverse and changing descriptions (i.e., citation context) on a publication characterize the impact and contributions of the past publication. In this article, we aim to provide an approach to understanding the changing and complex roles of a publication characterized by its citation context. We described a method to represent the publications' dynamic roles in science community in different periods as a sequence of vectors by training temporal embedding models. The temporal representations can be used to quantify how much the roles of publications changed and interpret how they changed. Our study in the biomedical domain shows that our metric on the changes of publications' roles is stable over time at the population level but significantly distinguish individuals. We also show the interpretability of our methods by a concrete example.


title: Engineering Calculations of Gas Condensate Well Productivity

abstract: The productivity of many gas condensate wells is reduced by the formation of a condensate bank in the near-well region. Forecasting of gas condensate well productivity usually requires fine-grid numerical simulation to model the formation of the condensate bank, and to account for high-velocity phenomena such as non-Darcy flow and changes in relative permeability at large capillary numbers. This paper presents a new technique for forecasting performance of gas condensate wells, using simpler techniques, which can be used in a spreadsheet. The calculation uses a material balance model for reservoir depletion and a two-phase pseudopressure integral for well inflow performance. The pseudopressure integral technique has been extended to include high-velocity effects and also to allow for the change in produced fluid composition due to the formation of the condensate bank. The new technique has been tested by comparison with the results of fine-grid compositional simulation, and the results are in good agreement for a wide range of cases covering vertical, horizontal and hydraulically fractured wells. The spreadsheet model provides a useful tool for rapid forecasts of condensate well performance, for examining the effects of condensate blockage in different well types or for studying sensitivities. It is also valuable where simple models of condensate reservoir performance are required for use in integrated studies involving issues such as surface facilities, drilling schedules and gas sales contracts.


title: Laboratory experiments for network security instruction

abstract: We describe a sequence of five experiments on network security that cast students successively in the roles of computer user, programmer, and system administrator. Unlike experiments described in several previous papers, these experiments avoid placing students in the role of attacker. Each experiment starts with an in-class demonstration of an attack by the instructor. Students then learn how to use open-source defense tools appropriate for the role they are playing and the attack at hand. Threats covered include eavesdropping, dictionary, man-in-the-middle, port scanning, and fingerprinting attacks. Defense skills gained by students include how to forward ports with OpenSSH, how to prevent weak passwords with CrackLib, how to salt passwords, how to set up a simple certifying authority, issue and verify certificates, and guarantee communication confidentiality and integrity using OpenSSL, and how to set up firewalls and IPsec-based virtual private networks. At two separate offerings, tests taken before and after each experiment showed that each has a statistically significant and large effect on students' learning. Moreover, surveys show that students finish the sequence of experiments with high interest in further studies and work in the area of security. These results suggest that the experiments are well-suited for introductory security or networking courses.


title: An organizational decision support system for effective R&D project selection

abstract: Research and development (R&D) project selection is an important task for organizations with R&D project management. It is a complicated multi-stage decision-making process, which involves groups of decision makers. Current research on R&D project selection mainly focuses on mathematical decision models and their applications, but ignores the organizational aspect of the decision-making process. This paper proposes an organizational decision support system (ODSS) for R&D project selection. Object-oriented method is used to design the architecture of the ODSS. An organizational decision support system has also been developed and used to facilitate the selection of project proposals in the National Natural Science Foundation of China (NSFC). The proposed system supports the R&D project selection process at the organizational level. It provides useful information for decision-making tasks in the R&D project selection process.


title: On the complexity of differentially private data release: efficient algorithms and hardness results

abstract: We consider private data analysis in the setting in which a trusted and trustworthy curator, having obtained a large data set containing private information, releases to the public a "sanitization" of the data set that simultaneously protects the privacy of the individual contributors of data and offers utility to the data analyst. The sanitization may be in the form of an arbitrary data structure, accompanied by a computational procedure for determining approximate answers to queries on the original data set, or it may be a "synthetic data set" consisting of data items drawn from the same universe as items in the original data set; queries are carried out as if the synthetic data set were the actual input. In either case the process is non-interactive; once the sanitization has been released the original data and the curator play no further role. For the task of sanitizing with a synthetic dataset output, we map the boundary between computational feasibility and infeasibility with respect to a variety of utility measures. For the (potentially easier) task of sanitizing with unrestricted output format, we show a tight qualitative and quantitative connection between hardness of sanitizing and the existence of traitor tracing schemes.


title: A Two-level Path Planning Method for On-road Autonomous Driving

abstract: We present a real-time path planning method for on-road autonomous driving. This method utilizes a two-level hierarchy consisting of on-road behavior planning and online path generating to achieve feasible paths for autonomous vehicle in dynamic on-road environment which is characterized by lots of structured information and flat terrain. An on-road behavior planning technique is used to transform the uncertain path planning problem into several determined sub-ones. Then, the online path planning algorithm is applied to solve these sub-problems. This method contains three major components: anytime behavioral replanning, online path planning and mitigation strategy for time lag due to an agent system with asynchronous multi-sensor. We evaluate the proposed method on the KUAFU-1, an autonomous vehicle prototype modified by ourselves. Experimental results show that the proposed method can successfully provide real-time feasible paths in various on-road traffic sceneries.


title: Clickbait Detection in Tweets Using Self-attentive Network

abstract: Clickbait detection in tweets remains an elusive challenge. In this paper, we describe the solution for the Zingel Clickbait Detector at the Clickbait Challenge 2017, which is capable of evaluating each tweet's level of click baiting. We first reformat the regression problem as a multi-classification problem, based on the annotation scheme. To perform multi-classification, we apply a token-level, self-attentive mechanism on the hidden states of bi-directional Gated Recurrent Units (biGRU), which enables the model to generate tweets' task-specific vector representations by attending to important tokens. The self-attentive neural network can be trained end-to-end, without involving any manual feature engineering. Our detector ranked first in the final evaluation of Clickbait Challenge 2017.


The dog as a "co-therapist".


title: Contextual Parameter Generation for Universal Neural Machine Translation

abstract: We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.


title: An empirical study of architecting for continuous delivery and deployment

abstract: Recently, many software organizations have been adopting Continuous Delivery and Continuous Deployment (CD) practices to develop and deliver quality software more frequently and reliably. Whilst an increasing amount of the literature covers different aspects of CD, little is known about the role of software architecture in CD and how an application should be (re-) architected to enable and support CD. We have conducted a mixed-methods empirical study that collected data through in-depth, semi-structured interviews with 21 industrial practitioners from 19 organizations, and a survey of 91 professional software practitioners. Based on a systematic and rigorous analysis of the gathered qualitative and quantitative data, we present a conceptual framework to support the process of (re-) architecting for CD. We provide evidence-based insights about practicing CD within monolithic systems and characterize the principle of “small and independent deployment units” as an alternative to the monoliths. Our framework supplements the architecting process in a CD context through introducing the quality attributes (e.g., resilience) that require more attention and demonstrating the strategies (e.g., prioritizing operations concerns) to design operations-friendly architectures. We discuss the key insights (e.g., monoliths and CD are not intrinsically oxymoronic) gained from our study and draw implications for research and practice.


title: Comparison of Hate Crime Rates Across Protected and Unprotected Groups – An Update

abstract: New research based on FBI data show that gay men face higher rates of hate-motivated physical violence than lesbians, bisexuals or other federally protected groups with high rates of hate crimes. This revelation is especially troubling given prior research has shown that sexual orientation-motivated hate crimes tend to be more violent. Among the research findings, 26 in 100,000 gay men reported being victims of hate-motivated crimes against persons, compared to 10 in 100,000 lesbians, 5 in 100,000 African Americans, and 5 in 100,000 Jewish Americans. Gay men also face the second highest risk of being victims of hate-motivated property crime (9 in 100,000 gay men). Further, reporting of such hate crimes is likely under represented since data reflect only those who report such crimes to local law enforcement, who then choose whether to report the data to the FBI. Although prior research has suggested that lesbians, gay men, and bisexuals, Jews, and Blacks experience similar levels of overall victimization, this study is the first to demonstrate that these groups do not experience the same types of hate crimes and that when lesbians, gay men, and bisexuals are considered separately, gay men experience more hate crimes.


title: Website Fingerprinting at Internet Scale

abstract: The website fingerprinting attack aims to identify the content (i.e., a webpage accessed by a client) of encrypted and anonymized connections by observing patterns of data flows such as packet size and direction. This attack can be performed by a local passive eavesdropper – one of the weakest adversaries in the attacker model of anonymization networks such as Tor. In this paper, we present a novel website fingerprinting attack. Based on a simple and comprehensible idea, our approach outperforms all state-of-the-art methods in terms of classification accuracy while being computationally dramatically more efficient. In order to evaluate the severity of the website fingerprinting attack in reality, we collected the most representative dataset that has ever been built, where we avoid simplified assumptions made in the related work regarding selection and type of webpages and the size of the universe. Using this data, we explore the practical limits of website fingerprinting at Internet scale. Although our novel approach is by orders of magnitude computationally more efficient and superior in terms of detection accuracy, for the first time we show that no existing method – including our own – scales when applied in realistic settings. With our analysis, we explore neglected aspects of the attack and investigate the realistic probability of success for different strategies a real-world adversary may follow.


title: Detection of contour boundary for sublicensing in spectrum access systems

abstract: Associated to database and a local centralized system, the short-term spectrum sublicensing is one of the potential approaches to improve the spectrum efficiency in the 5G networks. If the sublicences are assigned to the heterogeneous base stations that are from different operators, the interference issue becomes challenging to tackle. In this paper, we propose a solution of contour boundary detection for sublicensing to coordinate the interference among heterogeneous base stations in the space domain. As the direct access points to sever the end users, the base stations know and even could predict the traffic requirements over their coverage areas. Therefore, we enable individual base station as a sublicensee based on self-reported protection contour and optimize the best allocation for the system to guarantee the interference-free operations among those who spatially use the same sublicense simultaneously. Considering the selfish behaviours of base stations, we employ the sensor networks to monitor and measure their contours. Then we estimate the contour boundary by detecting the inner and outer contour boundary. Finally we compare the estimation accuracies of different sensor networks and demonstrate an interference-free allocation for the sublicensees.


title: SIFT: Snort Intrusion Filter for TCP

abstract: Intrusion rule processing in reconfigurable hardware enables intrusion detection and prevention services to run at multiGigabit/second rates. High-level intrusion rules mapped directly into hardware separate malicious content from benign content in network traffic. Hardware parallelism allows intrusion systems to scale to support fast network links, such as OC-192 and 10 Gbps Ethernet. In this paper, a snort intrusion filter for TCP (SIFT) is presented that operates as a preprocessor to prevent benign traffic from being inspected by an intrusion monitor running Snort. Snort is a popular open-source rule-processing intrusion system. SIFT selectively forwards IP packets that contain questionable headers or defined signatures to a PC where complete rule processing is performed. SIFT alleviates the need for most network traffic from being inspected by software. Statistics, like how many packets match rules, are used to optimize rule processing systems. SIFT has been implemented and tested in FPGA hardware and used to process Internet traffic from a campus Internet backbone with live data.


title: Benchmarking the Extraction and Disambiguation of Named Entities on the Semantic Web

abstract: Named entity recognition and disambiguation are of primary importance for extracting information and for populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as those in DBpedia, has been tackled by the Semantic Web community. As these tasks are treated in different communities, there is as yet no oversight on the performance of these tasks combined. We present an approach that combines the state-of-the art from named entity recognition in the natural language processing domain and named entity linking from the semantic web community. We report on experiments and results to gain more insights into the strengths and limitations of current approaches on these tasks. Our approach relies on the numerous web extractors supported by the NERD framework, which we combine with a machine learning algorithm to optimize recognition and linking of named entities. We test our approach on four standard data sets that are composed of two diverse text types, namely newswire and microposts.


title: Cooperative adaptive cruise control with communication constraints

abstract: This paper investigates Cooperative Adaptive Cruise Control (CACC) for a group of vehicles with communication constraints such as communication delays, packet dropouts and disorders. In order to address the problem of the communication constraint, a novel data processing mechanism is proposed by structuring a Smart Data Processor (SDP). Under this mechanism each vehicle in the CACC system can successfully select the latest data packets of its former vehicle by using a logic Data Packet Processor and the matched ones of its own from its own from its Buffer. Based on these data packets, a new control law for each vehicle is given such that the control problem of the CACC system can be formulated as the stability problem of a class of systems with distributed time-varying delays. By employing Lyapunov-Krasoviskii functional method, a stability criterion guaranteeing the asymptotic and string stability of the CACC system can be obtained. Then, a sufficient condition for the controller design is de rived. A numerous simulation results show the efficiency of the proposed methods.


Current Trends in Occupational Therapy Treatment for People with Stroke


title: Analytics-driven data ingestion and derivation in the AWESOME polystore

abstract: Polystores, i.e., data management systems that use multiple stores for different data models, are gaining popularity. We are developing a polystore-based system called AWESOME to support social data analytics. The AWESOME polystore can support relational, semistructured, graph and text data and houses a Spark computation engine to produce derived data during ingestion. ADIL, the data ingestion language of AWESOME allows a user to flexibly specify the placement of original and derived data into and across component stores and the computation engine. The paper also outlines a number of optimization strategies for managing data placement in AWESOME.


title: Automatic user identification method across heterogeneous mobility data sources

abstract: With the ubiquity of location based services and applications, large volume of mobility data has been generated routinely, usually from heterogeneous data sources, such as different GPS-embedded devices, mobile apps or location based service providers. In this paper, we investigate efficient ways of identifying users across such heterogeneous data sources. We present a MapReduce-based framework called Automatic User Identification (AUI) which is easy to deploy and can scale to very large data set. Our framework is based on a novel similarity measure called the signal based similarity (SIG) which measures the similarity of users' trajectories gathered from different data sources, typically with very different sampling rates and noise patterns. We conduct extensive experimental evaluations, which show that our framework outperforms the existing methods significantly. Our study on one hand provides an effective approach for the mobility data integration problem on large scale data sets, i.e., combining the mobility data sets from different sources in order to enhance the data quality. On the other hand, our study provides an in-depth investigation for the widely studied human mobility uniqueness problem under heterogeneous data sources.


title: Searching for specific health-related information in MedlinePlus: Behavioral patterns and user experience

abstract: Searches for specific factual health information constitute a significant part of consumer health information requests, but little is known about how users search for such information. This study attempts to fill this gap by observing users' behavior while using MedlinePlus to search for specific health information. Nineteen students participated in the study, and each performed 12 specific tasks. During the search process, they submitted short queries or complete questions, and they examined less than 1 result per search. Participants rarely reformulated queries; when they did, they tended to make a query more specific or more general, or iterate in different ways. Participants also browsed, primarily relying on the alphabetical list and the anatomical classification, to navigate to specific health topics. Participants overall had a positive experience with MedlinePlus, and the experience was significantly correlated with task difficulty and participants' spatial abilities. The results suggest that, to better support specific item search in the health domain, systems could provide a more "natural" interface to encourage users to ask questions; effective conceptual hierarchies could be implemented to help users reformulate queries; and the search results page should be reconceptualized as a place for accessing answers rather than documents. Moreover, multiple schemas should be provided to help users navigate to a health topic. The results also suggest that users' experience with information systems in general and health-related systems in particular should be evaluated in relation to contextual factors, such as task features and individual differences.


title: IoT enabled intelligent bus transportation system

abstract: With the increasing size of urban population, cities are facing a diverse range of issues such as severe traffic congestion, deteriorating air quality, increasing road accidents, and exploding growth in number of private vehicles. At the same time, the share of public transport is decreasing. The reason is lack of dependable public transport facilities. With the development of Information technology, Internet of Things (IoT) is becoming a reality. IoT can assist in the integration of communication, control and information processing present in the public transportation system. This paper, deals with implementation of IoT in public transport system by providing information such as bus number, arrival time and number of persons on board to the end user. The cost effective prototype model developed collects data at the vehicle terminal, uploads the updated data to the server through the internet and makes data visible to the awaiting user. Key technologies used in the proposed work are RFID, GPS and controller with in-built Wi-Fi module. A prototype model of the proposed system has been developed and demonstrated.


title: ACE: A Colour Palette Design Tool for Balancing Aesthetics and Accessibility

abstract: Colour can convey a mood or elicit a particular emotion and, in terms of web design, colour can influence attitudes, perceptions, and behaviours. However, many websites demonstrate inaccessible colour choices. Numerous online colour palette design tools only focus on assisting designers with either the aesthetics or accessibility of colours. With a user-centered design approach, we developed the Accessible Colour Evaluator (ACE, daprlab.com/ace) which enhances web developers’ and designers’ ability to balance aesthetic and accessibility constraints. We distributed an online questionnaire to 28 web developers and designers to understand their attitudes and utilisation of accessibility guidelines, as well as to gather initial design requirements for ACE. With this information, we created three low-fidelity paper prototypes that were used to create two high-fidelity prototypes. The high-fidelity prototypes were discussed with 4 web developers and designers during a design workshop, and their feedback was used to develop the final version of ACE. A comparative evaluation of ACE and three existing alternative tools was conducted with 10 new web developers and designers. All participants were able to complete a colour palette design task when using ACE and identified ACE as their most preferred tool. The mean scores for the six TLX measures show ACE as providing the best performance and causing the lowest frustration. Finally, we conducted a small focus group with 3 web developers and designers to gather qualitative feedback about ACE. Participants identified a number of ACE’s strengths and made suggestions for future extensions and improvements.


title: A Study of Attribute-based Proxy Re-encryption Scheme in Cloud Environments

abstract: Attribute-based proxy re-encryption (ABPRE) scheme is one of the proxy cryptography, which can delegate the re-encryption capability to the proxy and re-encrypt the encrypted data by using the re-encryption key. ABPRE ex-tending the traditional proxy cryptography and attributes plays an important role. In ABPRE, users are identified by attributes, and the access policy is designed to control the user's access. Using ABPRE can have these advantages: (i) The proxy can be delegated to execute the re-encryption operation, which reduces the computation overhead of the data owner;(ii) The authorized user just uses his own secret key to decrypt the encrypted data, and he doesn't need to store an additional decryption key for deciphering;(iii) The sensitive information cannot be revealed to the proxy in re-encryption, and the proxy only complies to the data owner's command. In this paper, we survey two various access policy attribute-based proxy re-encryption schemes and analyze these schemes. Thereafter, we list the comparisons of them by some criteria.


title: A study of Thread Level Parallelism on mobile devices

abstract: Mobile devices continue to increase the number of cores in an attempt to meet the needs of performance-demanding applications. However, the increasing number of cores does not necessarily translate into performance gain and/or power reduction. In this paper we investigate how multi-core mobile devices are utilized by applications. Our results demonstrate that mobile applications are utilizing less than 2 cores on average, which shows that multi-cores are generally underutilized by today's mobile applications. Unless application developers can significantly improve core utilization, further increasing core counts will result in little gain.


title: EnAli: entity alignment across multiple heterogeneous data sources

abstract: Entity alignment is the problem of identifying which entities in a data source refer to the same real-world entity in the others. Identifying entities across heterogeneous data sources is paramount to many research fields, such as data cleaning, data integration, information retrieval and machine learning. The aligning process is not only overwhelmingly expensive for large data sources since it involves all tuples from two or more data sources, but also need to handle heterogeneous entity attributes. In this paper, we propose an unsupervised approach, called EnAli, to match entities across two or more heterogeneous data sources. EnAli employs a generative probabilistic model to incorporate the heterogeneous entity attributes via employing exponential family, handle missing values, and also utilize the locality sensitive hashing schema to reduce the candidate tuples and speed up the aligning process. EnAli is highly accurate and efficient even without any ground-truth tuples. We illustrate the performance of EnAli on re-identifying entities from the same data source, as well as aligning entities across three real data sources. Our experimental results manifest that our proposed approach outperforms the comparable baseline.


title: Multiplayer online games as educational tools: Facing new challenges in learning

abstract: This paper outlines a proposal for the development of educational multiplayer online games based on the activity theory, as an alternative to the current trend in multiplayer gaming and a means of promoting collaboration among students. In order to examine whether online games are engaging for learners, we consider multiple factors regarding game play - such as frequency of game use, gender differences, identification with the characters, and game preferences - as well as some psychosocial factors that may influence learning - such as academic performance, self-esteem, and computer self-efficacy. This paper suggests that online multiplayer educational games should be approached as a complex learning system, based on the principles of activity theory, where the Subjects would interact with other Subjects, Objects and Tools of the game, under specified Rules and create Communities through division of labor, leading to the expected learning outcome. Thus, we suggest taking into account some important issues concerning the Subjects that the activity theory refers to, such as gender differences in playing games, academic performance, self-esteem and computer self-efficacy.


title: Information Fusion in the Immune System

abstract: Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial Immune Systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has show how AISs which use multi-level information sources as input data can be used to build effective algorithms for real time computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs


title: Endocrine system on chip for a diabetes treatment model

abstract: The endocrine system is a collection of glands producing hormones which, among others, regulates metabolism, growth and development. One important group of endocrine diseases is diabetes, which is caused by a deficiency or diminished effectiveness of endogenous insulin. By using a microfluidic perfused 3D cell-culture chip, we developed an 'endocrine system on chip' to potentially be able to screen drugs for the treatment of diabetes by measuring insulin release over time. Insulin-secreting β-cells are located in the pancreas, while L-cells, located in the small intestines, stimulate insulin secretion. Thus, we constructed a co-culture of intestinal-pancreatic cells to measure the effect of glucose on the production of glucagon-like peptide-1 (GLP-1) from the L-cell line (GLUTag) and insulin from the pancreatic β-cell line (INS-1). After three days of culture, both cell lines formed aggregates, exhibited 3D cell morphology, and showed good viability (>95%). We separately measured the dynamic profile of GLP-1 and insulin release at glucose concentrations of 0.5 and 20 mM, as well as the combined effect of GLP-1 on insulin production at these glucose concentrations. In response to glucose stimuli, GLUTag and INS-1 cells produced higher amounts of GLP-1 and insulin, respectively, compared to a static 2D cell culture. INS-1 combined with GLUTag cells exhibited an even higher insulin production in response to glucose stimulation. At higher glucose concentrations, the diabetes model on chip showed faster saturation of the insulin level. Our results suggest that the endocrine system developed in this study is a useful tool for observing dynamical changes in endocrine hormones (GLP-1 and insulin) in a glucose-dependent environment. Moreover, it can potentially be used to screen GLP-1 analogues and natural insulin and GLP-1 stimulants for diabetes treatment.


title: Robust non-local denoising of colored depth data

abstract: We give a brief discussion of denoising algorithms for depth data and introduce a novel technique based on the NL-means filter. A unified approach is presented that removes outliers from depth data and accordingly achieves an unbiased smoothing result. This robust denoising algorithm takes intra-patch similarity and optional color information into account in order to handle strong discontinuities and to preserve fine detail structure in the data. We achieve fast computation times with a GPU-based implementation. Results using data from a time-of-flight camera system show a significant gain in visual quality.


title: Using genetic algorithm for advanced municipal waste collection in Smart City

abstract: The Internet of Things (IoT), as expected infrastructure for envisioned concept of Smart City, brings new possibilities for the city management. IoT vision introduces promising and economical solutions for massive data collection and its analysis which can be applied in many domains and so make them operating more efficiently. In this paper, we are discussing one of the most challenging issues — municipal waste-collection within the Smart City. To optimize the logistic procedure of waste collection, we use own genetic algorithm implementation. The presented solution provides calculation of more efficient garbage-truck routes. As an output, we provide a set of simulations focused on mentioned area. All our algorithms are implemented within the integrated simulation framework which is developed as an open source solution with respect to future modifications.


title: Circularly Polarized Stacked Annular-Ring Microstrip Antenna With Integrated Feeding Network for UHF RFID Readers

abstract: A novel circularly polarized (CP) annular-ring microstrip antenna (ARMSA) is proposed for American ultrahigh frequency (UHF) radio frequency identification (RFID) readers. The annular ring is fed by an equal-split power divider with 90° phase difference. For the compactness of the entire antenna, the divider with the shape of Archimedes gradual-change lines is designed at the inside of the annular ring. A circular parasitic patch is suspended above the ring to improve the impedance matching and bandwidth. It also enhances the performances of axial ratio (AR). The impedance bandwidth for S11 <; -10 dB and 3-dB AR bandwidth are 870-967 MHz (10.6% at 915 MHz) and 893-948 MHz (6%), respectively. The CP gain is 8.9 dBi at 915 MHz with the front-to-back ratio (F/B) of 16.5 dB. The proposed antenna is low profile as the total height is 0.043 λ0 (λ0 is the free-space wavelength).


title: Improved Inference for Unlexicalized Parsing

abstract: We present several improvements to unlexicalized parsing with hierarchically state-split PCFGs. First, we present a novel coarse-to-fine method in which a grammar’s own hierarchical projections are used for incremental pruning, including a method for efficiently computing projections of a grammar without a treebank. In our experiments, hierarchical pruning greatly accelerates parsing with no loss in empirical accuracy. Second, we compare various inference procedures for state-split PCFGs from the standpoint of risk minimization, paying particular attention to their practical tradeoffs. Finally, we present multilingual experiments which show that parsing with hierarchical state-splitting is fast and accurate in multiple languages and domains, even without any language-specific tuning.


title: Improvement of Eclat Algorithm Based on Support in Frequent Itemset Mining

abstract: Finding frequent itemsets is computationally the most expensive step in association rules mining, and most of the research attention has been focused on it. With the observation that support plays an important role in frequent item mining, in this paper, a conjecture on support count is proved and improvements of traditional Eclat algorithm are presented. The new Bi-Eclat algorithm sorted on support: Items sort in descending order according to the frequencies in transaction cache while itemsets use ascending order of support during support count. Compared with traditional Eclat algorithm, the results of experiments show that the Bi-Eclat algorithm gains better performance on several public databases given. Furthermore, the Bi-Eclat algorithm is applied in analyzing combination principles of prescriptions for Hepatitis B in Traditional Chinese Medicine, which shows its efficiency and effectiveness in practical usefulness.


title: Dysconnection in Schizophrenia: From Abnormal Synaptic Plasticity to Failures of Self-monitoring

abstract: Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.


title: Comparison of a standard CO2 pressure pneumoperitoneum insufflator versus AirSeal™: study protocol of a randomized controlled trial

abstract: AirSeal™ is a novel class of valve-free insufflation system that enables a stable pneumoperitoneum with continuous smoke evacuation and carbon dioxide (CO2) recirculation during laparoscopic surgery. Comparison data to standard CO2 pressure pneumoperitoneum insufflators is scarce. The aim of this study is to evaluate the potential advantages of AirSeal™ compared to a standard CO2 insufflator. This is a single center randomized controlled trial comparing elective laparoscopic cholecystectomy, colorectal surgery and hernia repair with AirSeal™ (group A) versus a standard CO2 pressure insufflator (group S). Patients are randomized using a web-based central randomization and registration system. Primary outcome measures will be operative time and level of postoperative shoulder pain by using the visual analog score (VAS). Secondary outcomes include the evaluation of immunological values through blood tests, anesthesiological parameters, surgical side effects and length of hospital stay. Taking into account an expected dropout rate of 5%, the total number of patients is 182 (n = 91 per group). All tests will be two-sided with a confidence level of 95% (P <0.05). The duration of an operation is an important factor in reducing the patient’s exposure to CO2 pneumoperitoneum and its adverse consequences. This trial will help to evaluate if the announced advantages of AirSeal™, such as clear sight of the operative site and an exceptionally stable working environment, will facilitate the course of selected procedures and influence operation time and patients clinical outcome. ClinicalTrials.gov NCT01740011, registered 23 November 2012.


title: A door lock system with augmented reality technology

abstract: This paper presents a door lock system combine with augmented reality technology (AR) and mobile phone remote control system. The system consists four parts: augmented reality application, wireless network module, microcontroller, and electronic door lock. General access control has a keyhole or numeric keypad. Interested people can easily start from here that can open the door. The system only has a similar Quick Response Code (QR Code) sign above the doorknob. Therefore, those people who have interested will not know how to start to open the door. The owner who only needs to active the AR application and press the correct password that will be able to open the door.


title: LEAPS: Detecting Camouflaged Attacks with Statistical Learning Guided by Program Analysis

abstract: Currently cyber infrastructures are facing increasingly stealthy attacks that implant malicious payloads under the cover of benign programs. Existing attack detection approaches based on statistical learning methods may generate misleading decision boundaries when processing noisy data with such a mixture of benign and malicious behaviors. On the other hand, attack detection based on formal program analysis may lack completeness or adaptivity when modelling attack behaviors. In light of these limitations, we have developed LEAPS, an attack detection system based on supervised statistical learning to classify benign and malicious system events. Furthermore, we leverage control flow graphs inferred from the system event logs to enable automatic pruning of the training data, which leads to a more accurate classification model when applied to the testing data. Our extensive evaluation shows that, compared with pure statistical learning models, LEAPS achieves consistently higher accuracy when detecting real-world camouflaged attacks with benign program cover-up.


title: Very Small Footprint 60 GHz Stacked Yagi Antenna Array

abstract: Millimeter wave applications such as short-range high-speed wireless links require modular, compact-size and high-directivity antennas. In this paper, high-gain compact stacked multilayered Yagi designs are proposed and demonstrated in the V-band. This novel design shows for the first time an antenna array of Yagi elements in millimeter wave stacked structure. To demonstrate the proposed concepts and design features, a 4 × 4 antenna array is created having excellent gain performance as well as very small footprint. A single element stacked Yagi antenna fed with microstrip is studied in order to obtain the desired performance. An analysis is performed to define the structure limitations. Measured results of the fabricated antenna prototypes are in good agreement with simulated results The measured Yagi antenna attains 11 dBi gain over 4.2% bandwidth with a size of 6.5 × 6.5 × 3.4 mm3. A 4 × 4 array of Yagi antenna using an SIW (Substrate Integrated Waveguide) feeding technique is conceived. Both simulated and measured results match with each other very well. The 4 × 4 array has a size of 28 × 24 × 2.4 mm3 , and reaches a measured gain of 18 dBi over 7% bandwidth. An alternate configuration of the array using angled Yagi antenna elements allows a significant improvement of the side lobe level (SLL) with a low impact on the gain performances. The proposed antennas are excellent candidates for integrated low-cost millimeter-wave and even terahertz systems. The small foot print, the antenna design flexibility as well as its easy adaptation to automatic fabrication processes are good assets for making short range portable imaging systems.


title: Power control for a doubly fed induction generator

abstract: This paper proposes a direct power control scheme of a doubly-fed induction generator under wind speed variations. The PI controllers are used to control active and reactive power. A control scheme is developed that uses stator flux orientated vector control. Power transfers are carried out in hyper synchronous and hypo synchronous mode, depending on the generator speed. Back to back converters with PWM control are used for both grid side and rotor side converter.


title: Classification of software behaviors for failure detection: a discriminative pattern mining approach

abstract: Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs and anomalies are prevalent. Bugs have caused billions of dollars loss, in addition to privacy and security threats. In this work, we address software reliability issues by proposing a novel method to classify software behaviors based on past history or runs. With the technique, it is possible to generalize past known errors and mistakes to capture failures and anomalies. Our technique first mines a set of discriminative features capturing repetitive series of events from program execution traces. It then performs feature selection to select the best features for classification. These features are then used to train a classifier to detect failures. Experiments and case studies on traces of several benchmark software systems and a real-life concurrency bug from MySQL server show the utility of the technique in capturing failures and anomalies. On average, our pattern-based classification technique outperforms the baseline approach by 24.68% in accuracy.


title: Restoring lost ecological function: ecological surrogates facilitate maintenance of coastal turf communities

abstract: ABSTRACTLost or extinct ecosystem function presents challenges for managers of natural ecosystems for conservation. One solution is to use ecological surrogates to replace these processes but the settings under which this often controversial approach succeeds are unclear. We tested whether mammalian grazing could be used as a surrogate for grazing by an extinct avian herbivore guild in two threatened coastal turf communities that are structured by an exposure and salt gradient, and vulnerable to non-native encroachment and invasion. Prostrate species had higher cover and species density relative to erect taxa when grazed, with the effect greater for native than non-native taxa. Prostrate species, dominated by native taxa, were most abundant closest to the ocean, with a secondary peak in abundance 25–35 m inland. A positive nutrient feedback mechanism consistent with a grazing lawn was not observed suggesting a primary role for the salinity gradient, augmented by opportunistic grazing of taller species, in...


title: Real-time autonomous ground vehicle navigation in heterogeneous environments using a 3D LiDAR

abstract: The ability to drive autonomously in heterogeneous environments without GPS, pattern identification (e.g. road following), or artificial landmarks is key to field robotics. To address this challenge, we present a complete waypoint navigation framework for unmanned ground vehicles. A Velodyne PUCK VLP-16 LiDAR and an IMU are mounted on an autonomous, full size utility vehicle and used for localization within a previously created base map. We redesign a six degrees of freedom LiDAR SLAM algorithm to achieve 3D localization on the base map, as well as real-time vehicle navigation. We fuse the low-frequency, high precision SLAM updates with high-frequency, odometric local state estimates from the vehicle. The navigation costmap consists of a 2D occupancy grid which is computed from the 3D base map. Relying on this setup, the vehicle is capable of navigating through a complex site completely autonomously. The test site has densely and sparsely built areas, bushland, industrial activities, pedestrians, and other manned or unmanned vehicles. Extensive testing was done using both current and outdated base maps for comparisons, and a high precision RTK-GPS was used for ground truth. So far, more than 60 km of completely autonomous driving has been performed without a single system or navigation failure.


title: The Intel Pentium M Processor: Microarchitecture and Performance

abstract: A thermal-sensitive transparency is disclosed. A transparent film substrate is coated with a mixture of a binder and sulfonic acid. A second coating of a binder and a chromogenic compound is applied over the first coating. The binder of the first coating is not soluble in the solvent used to apply the second coating. The thus coated transparency can be imaged in conventional thermal copy machines and used in conventional projection equipment.


title: A Wireless and Batteryless 10-Bit Implantable Blood Pressure Sensing Microsystem With Adaptive RF Powering for Real-Time Laboratory Mice Monitoring

abstract: An implantable real-time blood pressure monitoring microsystem for laboratory mice has been demonstrated. The system achieves a 10-bit blood pressure sensing resolution and can wirelessly transmit the pressure information to an external unit. The implantable device is operated in a batteryless manner, powered by an external RF power source. The received RF power level can be sensed and wirelessly transmitted along with blood pressure signal for feedback control of the external RF power. The microsystem employs an instrumented silicone cuff, wrapped around a blood vessel with a diameter of approximately 200 ?m, for blood pressure monitoring. The cuff is filled by low-viscosity silicone oil with an immersed MEMS capacitive pressure sensor and integrated electronic system to detect a down-scaled vessel blood pressure waveform with a scaling factor of approximately 0.1. The integrated electronic system, consisting of a capacitance-to-voltage converter, an 11-bit ADC, an adaptive RF powering system, an oscillator-based 433 MHz FSK transmitter and digital control circuitry, is fabricated in a 1.5 ?m CMOS process and dissipates a power of 300 ?W. The packaged microsystem weighs 130 milligram and achieves a capacitive sensing resolution of 75 aF over 1 kHz bandwidth, equivalent to a pressure sensing resolution of 1 mmHg inside an animal vessel, with a dynamic range of 60 dB. Untethered laboratory animal in vivo evaluation demonstrates that the microsystem can capture real-time blood pressure information with a high fidelity under an adaptive RF powering and wireless data telemetry condition.


RAPID DEPLOYABLE ANTENNA CONCEPT SELECTION FOR CUBESATS


title: Wristwash: towards automatic handwashing assessment using a wrist-worn device

abstract: Washing hands is one of the easiest yet most effective ways to prevent spreading illnesses and diseases. However, not adhering to thorough handwashing routines is a substantial problem worldwide. For example, in hospital operations lack of hygiene leads to healthcare associated infections. We present WristWash, a wrist-worn sensing platform that integrates an inertial measurement unit and a Hidden Markov Model-based analysis method that enables automated assessments of handwashing routines according to recommendations provided by the World Health Organization (WHO). We evaluated Wrist-Wash in a case study with 12 participants. WristWash is able to successfully recognize the 13 steps of the WHO handwashing procedure with an average accuracy of 92% with user-dependent models, and with 85% for user-independent modeling. We further explored the system's robustness by conducting another case study with six participants, this time in an unconstrained environment, to test variations in the hand-washing routine and to show the potential for real-world deployments.


title: A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities

abstract: Advanced driver assistance systems are advancing at a rapid pace and all major companies started investing in developing the autonomous vehicles. But the security and reliability is still uncertain and debatable. Imagine that a vehicle is compromised by the attackers and then what they can do. An attacker can control brake, accelerate and even steering which can lead to catastrophic consequences. This paper gives a very short and brief overview of most of the possible attacks on autonomous vehicle software and hardware and their potential implications.


title: Detection and identification algorithm of the S1 and S2 heart sounds

abstract: This article attempts to contribute to heart abnormalities detection by suggesting a segmentation algorithm of S1 and S2 heart sounds. It describes the steps followed and the results achieved during the realization of the first PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) Classifying Heart Sounds Challenge. The performed work leads to improved results, compared to the results found by the three finalists of this challenge, using the envelope of Shannon energy and other methods, to detect and identify first and second heart sound of phonocardiogram signals.


title: Robust representations for face recognition: The power of averages

abstract: We are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple image-averaging technique to derive abstract representations of known faces. Using Principal Components Analysis, we show that computational systems based on these averages consistently outperform systems based on collections of instances. Furthermore, the quality of the average improves as more images are used to derive it. These simulations are carried out with famous faces, over which we had no control of superficial image characteristics. We then present data from three experiments demonstrating that image averaging can also improve recognition by human observers. Finally, we describe how PCA on image averages appears to preserve identity-specific face information, while eliminating non-diagnostic pictorial information. We therefore suggest that this is a good candidate for a robust face representation.


title: A shelf antenna using near-field without dead zones in UHF RFID

abstract: This paper presents a shelf antenna for near-field RFID systems which has an electromagnetic field distribution characteristic without any dead zones presented widely in the vicinity of reader antennas. These dead zones are caused by the electric-magnetic field cancelation since an incident wave is summed with the reflected wave caused by the terminals on the feed line of an antenna. The proposed antenna is made up of a feed line on the bottom layer and a meander line on the top layer in a rectangular wave guide. The feed line acts as a principle of Standing wave having periodical dead zones and the meander line acts as a radiator coupled with the feed line of bottom layer. Therefore, these reactions between the feed line and the meander line make the proposed shelf antenna eliminate the effect of dead zones on the surface of the antenna properly.


title: An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation

abstract: Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec (Mikolov et al., 2013a) to learn document-level embeddings. Despite promising results in the original paper, others have struggled to reproduce those results. This paper presents a rigorous empirical evaluation of doc2vec over two tasks. We compare doc2vec to two baselines and two state-of-the-art document embedding methodologies. We found that doc2vec performs robustly when using models trained on large external corpora, and can be further improved by using pre-trained word embeddings. We also provide recommendations on hyper-parameter settings for general purpose applications, and release source code to induce document embeddings using our trained doc2vec models.


title: Development of a Hand-Assist Robot With Multi-Degrees-of-Freedom for Rehabilitation Therapy

abstract: This paper presents a virtual reality (VR)-enhanced new hand rehabilitation support system that enables patients to exercise alone. This system features a multi-degrees-of-freedom (DOF) motion assistance robot, a VR interface for patients, and a symmetrical master-slave motion assistance training strategy called "self-motion control," in which the stroke patient's healthy hand on the master side creates the assistance motion for the impaired hand on the slave side. To assist in performing the fine exercise motions needed for functional recovery of the impaired hand, the robot was constructed in an exoskeleton with 18 DOFs, to assist finger and thumb independent motions such as flexion/extension and abduction/adduction, thumb opposability, and hand-wrist co- ordinated motions. To enhance the effectiveness of the exercises, audio-visual instructions of each training motion using VR technology were designed with the input of clinician researchers. Experimental results from healthy subjects and patients show sufficient performance in the range of motion of the robot as well as sufficient assistance forces.


title: Design of memristor-based up-down counter using material implication logic

abstract: ‘Memristor’ is a new emerging nanodevice that is gaining a lot of appreciation from the researchers these days. They possess dual properties of resistor, memory and find immense application in the fields of nanoelectronic circuit and memory designs. Material implication logic is applied in memristor-based circuit designs as it can be performed easily using two memristors and one resistor. In this paper a memristor-based T (toggle) flip-flop is implemented using material implication logic. Thereby this T flip-flop is employed in designing an Up-Down counter, based on the implication operations using memristors. The designs thus presented for the T flip-flop and counter need 6, 16 memristors respectively. Memristor technology being highly dense, our counter design will occupy lesser area as compared to its conventional CMOS-based design. Also the proposed T flip flop takes 11 computation steps to generate its outputs and 52 steps are needed by the Up-Down counter to perform its operation. Moreover in our memristor-based counter circuit, counting can be started, stopped and resumed at any desired logic states by simply controlling the externally applied voltages.


title: Spelling Correction as a Foreign Language

abstract: In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer recurrent neural networks as an encoder and a decoder. We demonstrate the effectiveness of this model using an internal dataset, where the training data is automatically obtained from user logs. The model offers competitive performance as compared to the state of the art methods but does not require any feature engineering nor hand tuning between models.


title: A gripper based on a compliant bitable mechanism for gripping and active release of objects

abstract: A gripper with an embedded compliant bistable mechanism for gripping and autonomous release of objects is developed. Due to adhesion forces, objects might stick to the end-effector of a gripper upon release, where a shaking operation is necessary to release the objects. A bar adjacent to the shuttle mass of the bistable mechanism may achieve autonomous release of objects adhered to the end-effectors. Gripping and autonomous release of objects are accomplished when the bistable mechanism moves backward and forward, respectively. With the embedded bistable mechanism, the gripper requires no power input during gripping of objects. The compliant structure of the gripper renders no backlash, friction and wear for its operation.


title: Coreference annotation and resolution in the Colorado Richly Annotated Full Text (CRAFT) corpus of biomedical journal articles

abstract: Coreference resolution is the task of finding strings in text that have the same referent as other strings. Failures of coreference resolution are a common cause of false negatives in information extraction from the scientific literature. In order to better understand the nature of the phenomenon of coreference in biomedical publications and to increase performance on the task, we annotated the Colorado Richly Annotated Full Text (CRAFT) corpus with coreference relations. The corpus was manually annotated with coreference relations, including identity and appositives for all coreferring base noun phrases. The OntoNotes annotation guidelines, with minor adaptations, were used. Interannotator agreement ranges from 0.480 (entity-based CEAF) to 0.858 (Class-B3), depending on the metric that is used to assess it. The resulting corpus adds nearly 30,000 annotations to the previous release of the CRAFT corpus. Differences from related projects include a much broader definition of markables, connection to extensive annotation of several domain-relevant semantic classes, and connection to complete syntactic annotation. Tool performance was benchmarked on the data. A publicly available out-of-the-box, general-domain coreference resolution system achieved an F-measure of 0.14 (B3), while a simple domain-adapted rule-based system achieved an F-measure of 0.42. An ensemble of the two reached F of 0.46. Following the IDENTITY chains in the data would add 106,263 additional named entities in the full 97-paper corpus, for an increase of 76% percent in the semantic classes of the eight ontologies that have been annotated in earlier versions of the CRAFT corpus. The project produced a large data set for further investigation of coreference and coreference resolution in the scientific literature. The work raised issues in the phenomenon of reference in this domain and genre, and the paper proposes that many mentions that would be considered generic in the general domain are not generic in the biomedical domain due to their referents to specific classes in domain-specific ontologies. The comparison of the performance of a publicly available and well-understood coreference resolution system with a domain-adapted system produced results that are consistent with the notion that the requirements for successful coreference resolution in this genre are quite different from those of the general domain, and also suggest that the baseline performance difference is quite large.


title: Quantitative Authorship Attribution: An Evaluation of Techniques

abstract: The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author's writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of these measurements been tested on the same dataset. A large-scale comparison of textual measurements is crucial if current techniques are to be used effectively and if new and more powerful techniques are to be developed. This article presents the results of a comparison of thirty-nine different types of textual measurements commonly used in attribution studies, in order to determine which are the best indicators of authorship. Based on the results of these tests, a more accurate approach to quantitative authorship attribution is proposed, which involves the analysis of many different textual measurements.


title: Surgical Treatment of Erectile Dysfunction

abstract: Abstract Introduction Currently, oral pharmacotherapy is the dominant treatment for erectile dysfunction. Aim The aim of this article is to give an up-to-date summary of the possibilities of surgical therapy. Methods A search of the literature over the last decade was performed. Relevant papers and guidelines on the topic were reviewed and included. Main Outcome Measure We evaluate therapy options, such as penile vein surgery, arterial revascularization, and penile prosthetics. Results Vein surgery has faded into total insignificance due to the efficiency of oral and intracavernous therapeutics, and because of the known pathophysiology of corporal veno-occlusive dysfunction and the disappointing long-term results published in the literature. Penile revascularization surgery is performed today only in very limited number of patients with strict selection criteria such as age and exclusion of vascular risk factors. Conclusions Penile prosthetics is the only surgical therapy option maintaining its significance as a cure for erectile dysfunction. There are convincing long-term results with a high degree of patient and partner satisfaction, high patient acceptance, and a good functional durability of the mostly three-piece inflatable devices. Bertero EB and Antunes DL. Surgical treatment of erectile dysfunction.


title: On the applicability of inverse perspective mapping for the forward distance estimation based on the HSV colormap

abstract: Advanced Driver Assistance System (ADAS) functionalities present in modern vehicles provide traffic improvement, comfort and safety for drivers, pedestrians, and the environment through the application of electronic, control and software. A digital camera is one of the technologies used in ADAS functions that can be installed in front of the vehicle. The camera acquires video and image in real time to measure the distance between the car and a point or object ahead. However, the problem of perspective effects can be seen in this domain, once the image acquired don't represents the real image due to distortions and errors in the processing stage. Some systems based on video processing require the ego vehicle to the target vehicle distance, as well as traffic signs and venerable obstacles such as pedestrians' distances. The method of Inverse Perspective Mapping (IPM) supports this demand by removing image distortions caused by perspective effect, generating new image coordinates in real-time. This paper presents the IPM and the implementation of a function to identify the distance from an object removing the perspective effect in the HSV colormap. The IPM method has been implemented in a real platform and tested in a vehicle under the road for the validation of its applicability.


Context-Aware Neural Model for Temporal Information Extraction


title: Big data analytics in power distribution systems

abstract: Penetration of advanced sensor systems such as advanced metering infrastructure (AMI), high-frequency overhead and underground current and voltage sensors have been increasing significantly in power distribution systems over the past few years. According to U.S. energy information administration (EIA), the aggregated AMI installation experienced a 17 times increase from 2007 to 2012. The AMI usually collects electricity usage data every 15 minute, instead of once a month. This is a 3,000 fold increase in the amount of data utilities would have processed in the past. It is estimated that the electricity usage data collected through AMI in the U.S. amount to well above 100 terabytes in 2012. To unleash full value of the complex data sets, innovative big data algorithms need to be developed to transform the way we operate and plan for the distribution system. This paper not only proposes promising applications but also provides an in-depth discussion of technical and regulatory challenges and risks of big data analytics in power distribution systems. In addition, a flexible system architecture design is proposed to handle heterogeneous big data analysis workloads.


title: Analysis of modified ROCOF relays for islanding detection of distribution systems with DFIG

abstract: Wind power has evolved as a primary source of sustainable energy for bulk generation as well as small scale units. Power distribution systems with wind generation units like Doubly Fed Induction Generator (DFIG) are bound by the standards like IEEE 1547 to detect formation of power islands and disconnect the DFIG from the system. This approach is not an economically viable option as the DFIG system with appropriate control may successfully continue to supply power to the local loads and hence operate as an islanded Microgrid. However, this decreases the time frame within which islanding detection relays (IDR) must operate. This paper presents a comparative study of advanced techniques based on the principle of a classic Rate of Change of Frequency (ROCOF) relay that has been proposed in recent research works for utilization in distribution systems with DFIG. This work evaluates these IDRs in terms of their operating characteristics. Simulations have been carried out in PSCAD to obtain the operating curves and to assess the vulnerability of these IDRs to non-islanding transient. The effect of these techniques on power quality is also highlighted.


title: VR Serious Game Design Based on Embodied Cognition Theory

abstract: Embodied Cognition (EC) theory states that systems for sensing, acting and thinking are intrinsically interdependent and that human cognition is made of modality-specific representations. This approach is based on evolutionary principles of how human beings relate to their environment and for this reason, it is very suitable to be applied to improve design of VR systems, which are clearly based on physical interactions. We can find examples of this theory in common human behaviours as counting with own fingers or walking around an empty room to think about how furniture are going to be placed.


title: Lithium effects on circadian rhythms in fibroblasts and suprachiasmatic nucleus slices from Cry knockout mice

abstract: Abstract Lithium is widely used as a treatment of bipolar disorder, a neuropsychiatric disorder associated with disrupted circadian rhythms. Lithium is known to lengthen period and increase amplitude of circadian rhythms. One possible pathway for these effects involves inhibition of glycogen synthase kinase-3β (GSK-3β), which regulates degradation of CRY2, a canonical clock protein determining circadian period. CRY1 is also known to play important roles in regulating circadian period and phase, although there is no evidence that it is similarly phosphorylated by GSK-3β. In this paper, we tested the hypothesis that lithium affects circadian rhythms through CRYs. We cultured fibroblasts and slices of the suprachiasmatic nucleus (SCN), the master circadian pacemaker of the brain, from Cry1 -/-, Cry2 -/-, or wild-type (WT) mice bearing the PER2:LUC circadian reporter. Lithium was applied in the culture medium, and circadian rhythms of PER2 expression were measured. In WT and Cry2-/- fibroblasts, 10 mM lithium increased PER2 expression and rhythm amplitude but not period, and 1 mM lithium did not affect either period or amplitude. In non-rhythmic Cry1 -/- fibroblasts, 10 mM lithium increased PER2 expression. In SCN slices, 1 mM lithium lengthened period ∼1 h in all genotypes, but did not affect amplitude except in Cry2-/- SCN. Thus, the amplitude-enhancing effect of lithium in WT fibroblasts was unaffected by Cry2 knockout and occurred in the absence of period-lengthening, whereas the period-lengthening effect of lithium in WT SCN was unaffected by Cry1 or Cry2 knockout and occurred in the absence of rhythm amplification, suggesting that these two effects of lithium on circadian rhythms are independent of CRYs and of each other.


title: Improvisation facilitates divergent thinking and creativity: Realizing a benefit of primary school arts education

abstract: The benefit of arts education for cultural engagement, wider academic achievement, and as a contributor to the creative economy is a subject of significant debate. In the present work, we focus on the potential for simple, arts-based improvisation activities to enhance divergent thinking skills and creativity in primary school-age children. In the first experiment, we compare the effect of children taking part in an improvised versus nonimprovised dance class on their subsequent performance on the Instances Task (Wallach & Kogan, 1965) and on a creative “toy” design task. In a second experiment, children took part in verbal and acting improvisation games or in matched control games before completing figural activity 1 of the Torrance Tests of Creative Thinking (TTCT; Torrance, 1974). In both experiments, we found that children who took part in the improvisation interventions showed better divergent thinking and creativity after the intervention. Our findings suggest that simple, arts-based improvisation interventions could have domain-general benefits for creative cognition processes. Furthermore, they indicate one way in which simply making better use of existing arts education provision could provide a cost-effective way to increase creativity-relevant skills in primary schoolchildren. We consider putative mechanisms for the improvisation effects and specify directions for future work.


title: Parakeet: a continuous speech recognition system for mobile touch-screen devices

abstract: We present Parakeet, a system for continuous speech recognition on mobile touch-screen devices. The design of Parakeet was guided by computational experiments and validated by a user study. Participants had an average text entry rate of 18 words-per-minute (WPM) while seated indoors and 13 WPM while walking outdoors. In an expert pilot study, we found that speech recognition has the potential to be a highly competitive mobile text entry method, particularly in an actual mobile setting where users are walking around while entering text.


title: WRAP: Wearable, restricted-aperture pneumatics for haptic guidance

abstract: Wearable haptic feedback devices for virtual reality, human-robot interaction, and motion guidance require lightweight actuators that display clearly discernible cues to the user. These goals motivate the design of WRAP, a wearable, pneumatically actuated haptic feedback device. WRAP displays a variety of tactile sensations to the user by inflating a thermoplastic pneumatic actuator in direct contact with the skin. This paper describes the design and construction of WRAP, shows its effectiveness in indicating direction cues to users, and demonstrates two other appUcations for WRAP in image-guided medical interventions and human-computer interaction. Users were able to identify translation and rotation cues from WRAP with 99.4% accuracy. Our results suggest that WRAP is suitable for a variety of wearable and portable applications in which direction cues are beneficial.


title: Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services

abstract: Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.


title: BEYOND SIMPLE EXPOSURE Media Orientations and Their Impact on Political Processes

abstract: This study adopts a broad approach to problems associated with confusion in the literature on media orientations and political processes. Three different dependent variables representing cognition, attitude and behavior are examined in relation to multiple measures of media dependence: time spent, reliance, exposure to content, level of attention and gratifications sought. Results suggest complex relationships between media use and the dependent variables and again confirm an integrative role for television that is often hidden in analyses based on simple exposure time.


title: The Meanings Jamaicans Associate with Corporal Punishment

abstract: This study examined the meanings Jamaicans associate with corporal punishment and its perceived effects on children, caregiversâ€™/parentsâ€™ relationship with children and in a broader sense, Jamaican culture. A total of 183 readersâ€™ comments on 13 articles that focused on corporal punishmentâ€”published in a prominent Jamaican online newspaperâ€”were qualitatively analysed. The results indicated that not all Jamaicans engage in or embrace the use of corporal punishment. There is a clear divide between Jamaican individuals who practise corporal punishment and those who believe it is socially unacceptable. Those in favour of it reported that it was religiously justified and effective as a method of discipline. Opponents of this view highlighted the negative physical and mental effects of corporal punishment on children and provided alternative discipline methods that were less punitive and more responsive to the needs of children and the parentâ€“child relationship. Implications of the results are discussed.


title: Diagnostic accuracy of tubomanometry R value in detecting the Eustachian tube pressure equalizing function

abstract: Tubomanometry is a relatively novel Eustachian tube (ET) function testing method. A number of recent studies have utilized the R value of Tubomanometry as the main objective measure in reporting their outcome in balloon dilation of ET. There is, however, a lack of evidence concerning the reliability or validity of Tubomanometry measurements. The objectives of this study are to determine the accuracy of Tubomanometry for detecting ET opening as compared to tympanometry and determine its usefulness as a measure of ET function. Healthy subjects between ages 8 and 76 years with an intact tympanic membrane and no middle ear (ME) effusion were prospectively tested. Primary outcomes were the ET opening as determined by the Tubomanometry R value and the increase in ME pressure with tympanometry-measured in relation to a swallow at a nasopharyngeal pressure of 300 daPa. The accuracy of the tubomanometry R value for identifying a successful ET opening was made in reference to the change of ME pressure. A total of 280 measurements were available from 258 ears in 137 subjects. The presence of tubomanometry R value showed high sensitivity of detecting ET opening for the criteria of >5 daPa ME pressure increase (187/202) but low specificity for detecting ET non-opening (34/78). The R value criterion described in the original manuscripts on Tubomanometry is sensitive but not specific for ET opening. The need for validation of the tubomanometry test obviates its use as the main objective outcome measure for the balloon dilation of ET procedure.


Ramblr: Making Reassembly Great Again


title: Compact Surface-Wave Assisted Beam-Steerable Antenna Based on HIS

abstract: The concept of taking surface wave as an assistant role to obtain wide beams with main directions tilting to endfire is introduced in this paper. Planar Yagi-Uda-like antennas support TE ::: 0 ::: surface wave propagation and exhibit endfire radiation patterns. However, when such antennas are printed on a thin grounded substrate, there is no propagation of TE mode and beams tilting to broadside. Benefiting from the advantage that the high impedance surface (HIS) could support TE and/or TM modes propagation, the idea of placing a planar Yagi-Uda-like antenna in close proximity to a HIS to excite unidirectional predominately TE surface wave in HIS is proposed. Power radiated by the feed antenna, in combination with power diffracted by the surface wave determines the total radiation pattern, resulting in the desired pattern. For verification, a compact, low-profile, pattern-reconfigurable parasitic array (having an interstrip spacing of 0.048 λ ::: 0 ::: ) with an integrated DC biasing circuit was fabricated and tested. Good agreement was obtained between measured and simulated results.


title: Kinematics and Dynamics Modelling of a Mecanum Wheeled Mobile Platform

abstract: Omni-directional mobile platforms have the ability to move instantaneously in any direction from any configuration. As such, it is important to have a mathematical model of the platform, especially if the platform is to be used as an autonomous vehicle. Autonomous behaviour requires that the mobile robot choose the optimum vehicle motion in different situations for object/collision avoidance and task achievement. This paper develops and verifies a mathematical model of a mobile robot platform that implements mecanum wheels to achieve omni-directionality. The mathematical model will be used to achieve optimum autonomous control of the developed mobile robot as an office service robot. Omni-directional mobile platforms have improved performance in congested environments and narrow aisles, such as those found in factory workshops, offices, warehouses, hospitals, etc.


title: Character-based Neural Machine Translation

abstract: We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.


title: Attention deficit hyperactivity symptoms and Internet addiction

abstract: The objective of this study was to evaluate the relationship between attention deficit-hyperactivity/ impulsivity symptoms and Internet addiction. In total, 535 elementary school students (264 boys, 271 girls; mean age, 11.0 ± 1.0 years) were recruited. The presence or severity of Internet addiction was assessed by the Young's Internet Addiction test. Parents and teachers of the children com- pleted the DuPaul's attention deficit hyperactivity disorder (ADHD) rating scale (ARS; Korean version, K-ARS) and Child Behavior Checklists. Children with the highest and lowest quartiles in K-ARS scores were defined to be in ADHD and non-ADHD groups, respectively. Five children (0.9%) met criteria for a definite Internet addiction and 75 children (14.0%) met criteria for a probable Internet addiction. K-ARS scores had significant positive correlations with Young's Inter- net Addiction test scores. The Internet addiction group had higher total scores of K-ARS and ADHD-related subcategories in the Child Behavior Checklists than the non-addiction group. The ADHD group had higher Internet addiction scores compared with the non-ADHD group. There- fore, significant associations have been found between the level of ADHD symptoms and the severity of Internet addiction in children. In addition, current findings suggest that the presence of ADHD symptoms, both in inattention and hyperactivity-impulsivity domains, may be one of the important risk factors for Internet addiction.


Process Knowledge Extraction