title: Exploring the Users’ Preference Pattern of Application Services Between Different Mobile Phone Brands

abstract: User portrait analysis is one of the key points in human behavior analysis. It is important to describe or guess user’s characteristics through rational methods in business analysis. In this paper, we use the user details records data set from a mobile operator to analyze the preference of users with different brand phones for different APPs and propose the concept of mobile Internet life personas (MILP) and the latent MILP indexing (LMILPI) model for the analysis of users’ MILP. At the same time, we build user portrait analysis framework based on the latent semantic indexing (LSI) theme model, LMILPI model, and association rule mining. On the one hand, we analyze users’ preference for APP content when using different mobile brands. On the other hand, we analyze the relationship among mobile brands, user access time, and MILPs to describe users’ Internet behavior. Our research shows that there is a difference between users who use different brands of mobile phones: 1) users who use different brand phones have different preferences for different APPs. However, if mobile brand marketing methods or target users are same or similar, the APP preference of these brands will be similar; 2) MILPs are different between users who use different brands of mobile phones, but the MILPs displayed on Android platform are similar even though brands are not same, while the MILP displayed on iPhone is quite different from MILPs on Android; and 3) MILPs’ importance will be changed by mobile phone brands and time periods. The analytical framework which we propose can provide commercial solutions such as application recommendations, market strategy formulation, Internet access, and other fields.


title: Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets

abstract: Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior environmental knowledge. Previous RL approaches could handle high-dimensional feature space using a standard neural network, e.g., a convolutional neural network; however, to control traffic on a road network with multiple intersections, the geometric features between roads had to be created manually. Rather than using manually crafted geometric features, we developed an RL-based traffic signal control method that employs a graph convolutional neural network (GCNN). GCNNs can automatically extract features considering the traffic features between distant roads by stacking multiple neural network layers. We numerically evaluated the proposed method in a six-intersection environment. The results demonstrate that the proposed method can find comparable policies twice as fast as the conventional RL method with a neural network and can adapt to more extensive traffic demand changes.


title: Computational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data Points

abstract: Problem statement: Clustering is one of the most important research ar eas in the field of data mining. Clustering means creating groups of ob jects based on their features in such a way that th e objects belonging to the same groups are similar an d those belonging to different groups are dissimila r. Clustering is an unsupervised learning technique. T he main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Clustering algorithms can be applied in many domains. Approach: In this research, the most representative algorithms K-Mean s and K-Medoids were examined and analyzed based on their basic approach. The best algorithm i n each category was found out based on their performance. The input data points are generated by two ways, one by using normal distribution and another by applying uniform distribution. Results: The randomly distributed data points were taken as input to these algorithms and clusters are found ou t for each algorithm. The algorithms were implemented using JAVA language and the performance was analyzed based on their clustering quality. The execution time for the algorithms in each category was compar ed for different runs. The accuracy of the algorith m was investigated during different execution of the program on the input data points. Conclusion: The average time taken by K-Means algorithm is greater than the time taken by K-Medoids algorithm for both the case of normal and uniform distributions. The r esults proved to be satisfactory.


title: p53, the Cellular Gatekeeper for Growth and Division

abstract: The author would like to thank Maureen Murphy, Stuart Lutzker, and Deborah Freedman for critical input and advice. The author regrets the lack of citations for many important observations mentioned in the text, but their omission is made necessary by restrictions in the preparation of review manuscripts. The author thanks T. Barney for her help with the manuscript.


title: Physical modeling of spiral inductors on silicon

abstract: This paper presents a physical model for planar spiral inductors on silicon, which accounts for eddy current effect in the conductor, crossover capacitance between the spiral and center-tap, capacitance between the spiral and substrate, substrate ohmic loss, and substrate capacitance. The model has been confirmed with measured results of inductors having a wide range of layout and process parameters. This scalable inductor model enables the prediction and optimization of inductor performance.


title: Android botnets on the rise: Trends and characteristics

abstract: Smartphones are the latest technology trend of the 21st century. Today's social expectation of always staying connected and the need for an increase in productivity are the reasons for the increase in smartphone usage. One of the leaders of the smartphone evolution is Google's Android Operating System (OS). The openness of the design and the ease of customizing are the aspects that are placing Android ahead of the other smartphone OSs. Such popularity has not only led to an increase in Android usage but also to the rise of Android malware. Although such malware is not having a significant impact on the popularity of Android smartphones, it is however creating new possibilities for threats. One such threat is the impact of botnets on Android smartphones. Recently, malware has surfaced that revealed specific characteristics relating to traditional botnet activities. Malware such as Geinimi, Pjapps, DroidDream, and RootSmart all display traditional botnet functionalities. These malicious applications show that Android botnets is a reality. From a security perspective it is important to understand the underlying structure of an Android botnet. This paper evaluates Android malware with the purpose of identifying specific trends and characteristics relating to botnet behaviour. The botnet trends and characteristics are detected by a comprehensive literature study of well-known Android malware applications. The identified characteristics are then further explored in terms of the Android Botnet Development Model and the Android Botnet Discovery Process. The common identified trends and characteristics aid the understanding of Android botnet activities as well as the possible discovery of an Android bot.


title: Extreme Learning Machine for Multilayer Perceptron

abstract: Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l 1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.


elibrary@aisnet.org. Information Security Risk Management: In Which Security Solutions Is It Worth Investing?


title: Bringing Contextual Information to Google Speech Recognition

abstract: In automatic speech recognition on mobile devices, very often what a user says strongly depends on the particular context he or she is in. The n-grams relevant to the context are often not known in advance. The context can depend on, for example, particular dialog state, options presented to the user, conversation topic, location, etc. Speech recognition of sentences that include these n-grams can be challenging, as they are often not well represented in a language model (LM) or even include out-of-vocabulary (OOV) words. In this paper, we propose a solution for using contextual information to improve speech recognition accuracy. We utilize an on-the-fly rescoring mechanism to adjust the LM weights of a small set of n-grams relevant to the particular context during speech decoding. Our solution handles out of vocabulary words. It also addresses efficient combination of multiple sources of context and it even allows biasing class based language models. We show significant speech recognition accuracy improvements on several datasets, using various types of contexts, without negatively impacting the overall system. The improvements are obtained in both offline and live experiments.


title: Examining technology acceptance by school teachers: a longitudinal study

abstract: The role of information technology (IT) in education has significantly increased, but resistance to technology by public school teachers worldwide remains high. This study examined public school teachers' technology acceptance decision-making by using a research model that is based on key findings from relevant prior research and important characteristics of the targeted user acceptance phenomenon. The model was longitudinally tested using responses from more than 130 teachers attending an intensive 4-week training program on Microsoft PowerPointTM, a common but important classroom presentation technology. In addition to identifying key acceptance determinants, we examined plausible changes in acceptance drivers over the course of the training, including their influence patterns and magnitudes. Overall, our model showed a reasonably good fit with the data and exhibited satisfactory explanatory power, based on the responses collected from training commencement and completion. Our findings suggest a highly prominent and significant core influence path from job relevance to perceived usefulness and then technology acceptance. Analysis of data collected at the beginning and the end of the training supports most of our hypotheses and sheds light on plausible changes in their influences over time. Specifically, teachers appear to consider a rich set of factors in initial acceptance but concentrate on fundamental determinants (e.g. perceived usefulness and perceived ease of use) in their continued acceptance.


title: Gate drive for high speed, high power IGBTs

abstract: A new gate drive for high-voltage, high-power IGBT has been developed for the SLAC NLC (Next Linear Collider) Solid State Induction Modulator. This paper describes the design and implementation of a driver that allows an IGBT module rated at 800 A/3300 V to switch up to 3000 A at 2200 V in 3 /spl mu/s with a rate of current rise of more than 10000 A//spl mu/s, while still being short circuit protected. Issues regarding fast turn on, high de-saturation voltage detection, and low short circuit peak current are presented. A novel approach is also used to counter the effect of unequal current sharing between parallel chips inside most high-power IGBT modules. It effectively reduces the collector-emitter peak currents and thus protects the IGBT from being destroyed during soft short circuit conditions at high di/dt.


title: FROM 〈Bonehead〉 TO 〈cLoNehEAd〉: NICKNAMES, PLAY, AND IDENTITY ON INTERNET RELAY CHAT1

abstract: This article examines nicknames of IRC users. On IRC, a person's physical existence and identity must be condensed textually into a single line which states his or her nickname, the electronic address, and a slogan or the person's real name. IRC users attempt to make these representational elements as prominent as possible, by choosing an original nick which will tempt other participants to strike up a conversation. In this paper I demonstrate that although people play many kinds of games with their nicknames, the nicks they choose are very important to them. They are an inherent part of their Net- identity, and even of their “real-life” identity. Two hundred sixty nicknames were collected from IRC logs, and were analyzed and classified. Only rarely did the IRCers in this study use their real names. The largest category was that of nicks related to the self in some way, referring to character traits, physical appearance, the physiological or psychological state of the self, or the person's profession or hobbies. The list of nicknames and the relative frequency of the different categories illustrate prominent features of electronic culture, a culture in which the individual is placed at the center. Participants in this culture have a high awareness of technology and technological change. They value linguistic virtuosity, yet they show contempt for the rules of the language. Although there is freedom to engage in constantly changing identity games through the manipulation of nicks, most people tend to keep to one nick for a long period of time.


title: Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

abstract: In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor (k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.


title: Mining web query logs to analyze political issues

abstract: We present a novel approach to using anonymized web search query logs to analyze and visualize political issues. Our starting point is a list of politically annotated blogs (left vs. right). We use this list to assign a numerical political leaning to queries leading to clicks on these blogs. Furthermore, we map queries to Wikipedia articles and to fact-checked statements from politifact.com, as well as applying sentiment analysis to search results. With this rich, multi-faceted data set we obtain novel graphical visualizations of issues and discover connections between the different variables. Our findings include (i) an interest in "the other side" where queries about Democrat politicians have a right leaning and vice versa, (ii) evidence that "lies are catchy" and that queries pertaining to false statements are more likely to attract large volumes, and (iii) the observation that the more right-leaning a query it is, the more negative sentiments can be found in its search results.


title: Expressive search on encrypted data

abstract: Different from the traditional public key encryption, searchable public key encryption allows a data owner to encrypt his data under a user's public key in such a way that the user can generate search token keys using her secret key and then query an encryption storage server. On receiving such a search token key, the server filters all or related stored encryptions and returns matched ones as response. Searchable pubic key encryption has many promising applications. Unfortunately, existing schemes either only support simple query predicates, such as equality queries and conjunctive queries, or have a superpolynomial blowup in ciphertext size and search token key size. In this paper, based on the key-policy attribute-based encryption scheme proposed by Lewko et al. recently, we present a new construction of searchable public key encryption. Compared to previous works in this field, our construction is much more expressive and efficient and is proven secure in the standard model.


title: Metabolic syndrome and osteoporotic fracture: a population-based study in China

abstract: Associations between metabolic syndrome (MetS) and osteoporotic fracture have been reported. However, the epidemiological studies are not conclusive. The objective of the study was to determine whether metabolic syndrome associates with osteoporotic fracture. This was a cross-sectional study of 9930 Chinese adults aged 40 year or older in the Chongming District, Shanghai, China. A questionnaire, anthropometric measurements and laboratory tests were conducted. MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans. A history of fractures was collected with an interviewer-assisted questionnaire. Osteoporotic fractures were defined as fractures that occurred due to low-trauma in 2 years prior to the study. Among women, the prevalence of osteoporotic fractures was significantly higher in those with MetS (3.5 vs. 2.6 %, P =0.028). However, the difference was not found in men (2.6 vs. 2.4 %, P =0.737). The presence of Mets was significantly associated with increased odds of osteoporotic fracture among women (odds ratio 1.22; 95 % confidence interval 1.12–1.54; P = 0.039) after controlling for potential confounders. The significant associations were not detected in men. The presence of MetS was significantly associated with a recent history of osteoporotic fracture in middle-aged and elderly Chinese women.


title: A competition enzyme-linked immunosorbent assay (ELISA) for the measurement of pancreatic GP-2 glycoprotein.

abstract: A competition enzyme-linked immunosorbent assay has been developed for the quantitative detection of soluble and membrane-bound GP-2, a glycoprotein which is confined to the exocrine pancreas. Zymogen granule membranes fixed to microtiter plates with poly-L-lysine were used as the source of antigen. Detergent 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS] was added to the assay in order to reveal all the antigens, more particularly in membranous samples. Presence of detergent at concentrations as high as 0.5% did not interfere with any particular steps of the ELISA. This competition ELISA can detect 10 ng of GP-2 and will be useful for measuring soluble as well as membrane GP-2 in order to elucidate its role in the secretory process of the pancreas as well as in certain pathologies such as cystic fibrosis.


title: Wireless indoor surveillance robot

abstract: Self-propelled patrolling vehicle can patrol periodically in the designed area as a surveillance robot to ensure the safety like men do. The proposed robot based on the self-propelled vehicle not only can save manpower but also ensure the operation of surveillance being well performed. Due to the limitation of manpower and the fixed camera positions, using surveillance is different from the traditional patrolling system. The paper proposes a self-propelled patrolling vehicle which can move automatically to a wider range and record the monitored image within a predefined patrolling route to improve the performance of the traditional patrolling system. Besides, the surveillance robot can be connected to the mobile device or website on Internet at anytime and anywhere. Furthermore, the vehicle can be remote controlled by the instruction sent form the server or Smartphone to move to the position to get the indoor image we want. On the other hand, the position of self-propelled vehicles can be detected by the RFID readers mounted on the wall of the patrolling path as a feedback. The IP-CAM is also mounted on the proposed robot to record the images and transmit them back to the server via WiFi system for face tracking and discriminating analysis. As an alarm report, the proposed surveillance would use the build-in MSN module to notice users of the predefined events when happened. Experimental results are given in the paper to validate its performance.


Problematizing Agile in the Large: Alternative Assumptions for Large-Scale Agile Development


title: A Nonlinear Controller Based on a Discrete Energy Function for an AC/DC Boost PFC Converter

abstract: AC/DC converter systems generally have two stages: an input power factor correction (PFC) boost ac/dc stage that converts input ac voltage to an intermediate dc voltage while reducing the input current harmonics injected to the grid, followed by a dc/dc converter that steps up or down the intermediate dc-bus voltage as required by the output load and provides high-frequency galvanic isolation. Since a low-frequency ripple (second harmonic of the input ac line frequency) exists in the output voltage of the PFC ac/dc boost converter due to the power ripple, the voltage loop in the conventional control system must have a very low bandwidth in order to avoid distortions in the input current waveform. This results in the conventional PFC controller having a slow dynamic response against load variations with adverse overshoots and undershoots. This paper presents a new control approach that is based on a novel discrete energy function minimization control law that allows the front-end ac/dc boost PFC converter to operate with faster dynamic response than the conventional controllers and simultaneously maintain near unity input power factor. Experimental results from a 3-kW ac/dc converter built for charging traction battery of a pure electric vehicle are presented in this paper to validate the proposed control method and its superiority over conventional controllers.


title: Sensory and motor coduction in chronic carbon disulphide poisoning.

abstract: The greater decrease of conduction velocity in sensory than in motor fibres of the peroneal, median and ulnar nerves (particularly in the digital segments) found in patients with chronic carbon disulphide poisoning, permitted the diagnosis of polyneuropathy to be made in the subclinical stage, even while the conduction in motor fibres was still within normal limits. A process of axonal degeneration is presumed to underlie occurrence of neuropathy consequent to carbon disulphide poisoning.


title: Super-Resolution Using Convolutional Neural Networks Without Any Checkerboard Artifacts

abstract: It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts. A condition to avoid the checkerboard artifacts is proposed in this paper. So far, checkerboard artifacts have been mainly studied for linear multirate systems, but the condition to avoid checkerboard artifacts can not be applied to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for CNNs, and apply the proposed structure to some typical SR methods to confirm the effectiveness of the new scheme. Experiment results demonstrate that the proposed structure can perfectly avoid to generate checkerboard artifacts under two loss conditions: mean square error and perceptual loss, while keeping excellent properties that the SR methods have.


title: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

abstract: Digital circuits such as the flip-flop use feedback to achieve multi-stability and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multistable and graded1,2,3,4. We propose that the neocortex combines digital selection of an active set of neurons with analogue response by dynamically varying the positive feedback inherent in its recurrent connections. Strong positive feedback causes differential instabilities that drive the selection of a set of active neurons under the constraints embedded in the synaptic weights. Once selected, the active neurons generate weaker, stable feedback that provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimulus selection, gain modulation and spatiotemporal pattern generation in the neocortex.


abstract: Installation Theory: The Societal Construction and Regulation of Behaviour provides researchers and practitioners with a simple and powerful framework to analyse and change behaviour. Informed by a wide range of empirical evidence, it includes an accessible synthesis of former theories (ecological psychology, activity theory, situated action, distributed cognition, social constructionism, actor-network theory and social representations). 'Installations' are the familiar, socially constructed, apparatuses which elicit, enable, scaffold and control - and make predictable most of our 'normal' behaviour; from shower-cabins or airport check-ins to family dinners, classes or hospitals. The book describes their threefold structure with a new model enabling systematic and practical analysis of their components. It details the mechanisms of their construction, resilience and evolution, illustrated with dozens of examples, from restaurants to nuclear plant operation. The book also provides a detailed analysis of the processes of creation and selection of innovations, proposing a model for the maintenance and evolution of social systems.


title: Which source should we use to measure quality of life in children with asthma: The children themselves or their parents? Coinparing the psychomnetric properties of a child - and a parent - report scale

abstract: This study compares the reproducibility, construct validity and responsiveness of self-report and parentreport quality of life questionnaires How Are You (HAY) for 8-12-year-old children with asthma. A total of 228 Dutch children with asthma and their parents completed the HAY and daily recorded the child's asthma symptoms in a diary. Additionally 296 age- and -gender matched healthy children and their parents completed the generic part of the HAY. Reproducibility and responsiveness were examined in a sub-group of 80 children with asthma. In this group, three measurements were carried out, at baseline, after one week and once during the following 6 month when the clinical asthma status had changed. The within-subject standard deviations (SD) of three dimensions (physical activities, social activities, self-management) differed significantly (p < 0.05) in favour of the parent-version, indicating that the reproducibility of the parent version was better than that of the child version. The mean score-differences between children with asthma and healthy children as reported by parents did not significantly differ from those reported by children, except for cognitive activities (e.g. be able to concentrate on school work). The mean differences with regard to children with a different actual asthma status (symptom analysis), as reported by both informants, did not differ. Compared to the child-version, the parent-version showed greater ability to detect changes in children's quality of life over time for all but one dimension, indicating better responsiveness. The results indicate that in discriminative studies child and parents reports can be substituted on a group-level. In longitudinal studies data have to be obtained from parents. Consequently, caregivers collecting quality of life data for longitudinal purposes in daily practice should collect these data simply from parents.


title: Workload Modeling for Computer Systems Performance Evaluation, version 0.34

abstract: Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.


title: POMDPs.jl: a framework for sequential decision making under uncertainty

abstract: POMDPs.jl is an open-source framework for solving Markov decision processes (MDPs) and partially observable MDPs (POMDPs). POMDPs.jl allows users to specify sequential decision making problems with minimal effort without sacrificing the expressive nature of POMDPs, making this framework viable for both educational and research purposes. It is written in the Julia language to allow flexible prototyping and large-scale computation that leverages the high-performance nature of the language. The associated JuliaPOMDP community also provides a number of state-of-the-art MDP and POMDP solvers and a rich library of support tools to help with implementing new solvers and evaluating the solution results. The most recent version of POMDPs.jl, the related packages, and documentation can be found at https://github.com/JuliaPOMDP/POMDPs.jl.


title: Continuous 3D Label Stereo Matching using Local Expansion Moves

abstract: We present an accurate stereo matching method using local expansion moves based on graph cuts. This new move-making scheme is used to efficiently infer per-pixel 3D plane labels on a pairwise Markov random field (MRF) that effectively combines recently proposed slanted patch matching and curvature regularization terms. The local expansion moves are presented as many $\alpha$ -expansions defined for small grid regions. The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation. By localization, we use different candidate $\alpha$ -labels according to the locations of local $\alpha$ -expansions. By spatial propagation, we design our local $\alpha$ -expansions to propagate currently assigned labels for nearby regions. With this localization and spatial propagation, our method can efficiently infer MRF models with a continuous label space using randomized search. Our method has several advantages over previous approaches that are based on fusion moves or belief propagation; it produces submodular moves deriving a subproblem optimality ; it helps find good, smooth, piecewise linear disparity maps; it is suitable for parallelization; it can use cost-volume filtering techniques for accelerating the matching cost computations. Even using a simple pairwise MRF, our method is shown to have best performance in the Middlebury stereo benchmark V2 and V3.


title: Surface Electromyography Signal Processing and Classification Techniques

abstract: Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.


title: Posterior Capsular Contracture of the Shoulder

abstract: Posterior capsular contracture is a common cause of shoulder pain in which the patient presents with restricted internal rotation and reproduction of pain. Increased anterosuperior translation of the humeral head occurs with forward flexion and can mimic the pain reported with impingement syndrome; however, the patient with impingement syndrome presents with normal range of motion. Initial management of posterior capsular contracture should be nonsurgical, emphasizing range-of-motion stretching with the goal of restoring normal motion. For patients who fail nonsurgical management, arthroscopic posterior capsule release can result in improved motion and pain relief. In the throwing athlete, repetitive forces on the posteroinferior capsule may cause posteroinferior capsular hypertrophy and limited internal rotation. This may be the initial pathologic event in the so-called dead arm syndrome, leading to a superior labrum anteroposterior lesion and, possibly, rotator cuff tear. Management involves regaining internal rotation such that the loss of internal rotation is not greater than the increase in external rotation. In the athlete who fails nonsurgical management, a selective posteroinferior capsulotomy can improve motion, reduce pain, and prevent further shoulder injury.


title: The quest for Ubiquity: A roadmap for software and systems traceability research

abstract: Traceability underlies many important software and systems engineering activities, such as change impact analysis and regression testing. Despite important research advances, as in the automated creation and maintenance of trace links, traceability implementation and use is still not pervasive in industry. A community of traceability researchers and practitioners has been collaborating to understand the hurdles to making traceability ubiquitous. Over a series of years, workshops have been held to elicit and enhance research challenges and related tasks to address these shortcomings. A continuing discussion of the community has resulted in the research roadmap of this paper. We present a brief view of the state of the art in traceability, the grand challenge for traceability and future directions for the field.


title: Validation of a new coma scale: The FOUR score

abstract: The Glasgow Coma Scale (GCS) has been widely adopted. Failure to assess the verbal score in intubated patients and the inability to test brainstem reflexes are shortcomings. We devised a new coma score, the FOUR (Full Outline of UnResponsiveness) score. It consists of four components (eye, motor, brainstem, and respiration), and each component has a maximal score of 4. We prospectively studied the FOUR score in 120 intensive care unit patients and compared it with the GCS score using neuroscience nurses, neurology residents, and neurointensivists. We found that the interrater reliability was excellent with the FOUR score (w 0.82) and good to excellent for physician rater pairs. The agreement among raters was similar with the GCS (w 0.82). Patients with the lowest GCS score could be further distinguished using the FOUR score. We conclude that the agreement among raters was good to excellent. The FOUR score provides greater neurological detail than the GCS, recognizes a locked-in syndrome, and is superior to the GCS due to the availability of brainstem reflexes, breathing patterns, and the ability to recognize different stages of herniation. The probability of in-hospital mortality was higher for the lowest total FOUR score when compared with the lowest total GCS score. Ann Neurol 2005;58:585–593 Assessment of coma is a core clinical skill for physicians. Scales have been constructed to improve communication among health care personnel and also to standardize examination of the unconscious patient. The most commonly used scale is the Glasgow Coma Scale (GCS). 1 Although the originators of the GCS have reported data on practical reliability of the scale, 2 other studies have showed some difficulties in application by untrained nursing staff. 3 Trained personnel tend to apply the GCS better, although interpretation of intermediate scores on the GCS remains difficult for emergency physician. 4 Other shortcomings of the GCS have been recognized. First, because many comatose patients are intubated, the verbal component cannot be tested. Some clinicians use the lowest possible score; others extrapolate the verbal response based on other neurological findings. Second, abnormal brainstem reflexes, changing breathing patterns, and the need for mechanical ventilation could reflect severity of coma, but the GCS does not include those clinical indicators. Third, the GCS may not detect subtle changes in neurological examination. More recently, a study in traumatic head injury found lack of correlation between outcome and GCS score. 5 Attempts have been made to modify the GCS; however, most of these scales were more complicated, and were seldom used outside the country of origin. 6,8 Others have suggested simplification of the GCS score after documenting poor interobserver reliability in traumatic brain injury. 9 These concerns and prior attempts to design new scales strongly suggest a new scale is needed that could provide further neurological detail in coma that is easy to use and that could predict outcome. We sought to validate a new coma scale, the FOUR (Full Outline of UnResponsiveness) score, and thus compared it with the GCS.


title: Tutorial Tutorial on maximum likelihood estimation

abstract: In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable tool for many statistical modeling techniques, in particular in non-linear modeling with non-normal data. The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles.


Towards Lower Bounds on Number of Dimensions for Word Embeddings


title: Misconceptions about real-time computing: a serious problem for next-generation systems

abstract: The author defines real-time computing and states and dispels the most common misconceptions about it. He discusses the fundamental technical issues of real-time computing. He examines specification and verification, scheduling theory, operating systems, programming languages and design methodology, distributed databases, artificial intelligence, fault tolerance, architectures, and communication. >


title: A Scale-Out Enterprise Storage Architecture

abstract: A robust enterprise SSD design should provide scalable throughput and storage capacity by integrating (up to thousands) flash chips in a scale-out fashion. However, the current "channel-based" SSD architecture is not a scalable design choice to allow such a dense integration. Motivated by the inherent architectural scalability of PCIe, we propose UT-SSD, a novel enterprise-scale scale-out SSD architecture, which enables the connection of a large number of (1000s) flash chips using the native PCIe buses instead of the conventional channels. We also propose an architectural enhancement that further improves the performance of our base UT-SSD by maximizing flash utilization. Our experimental analysis of UT-SSD with workloads drawn from various domains shows that the throughput of UT-SSD can reach up to 110 GB/s by successfully aggregating the bandwidth of 4096 flash chips. In addition, our proposed enhancement over this base UT-SSD increases the flash utilization by 50.7%, which in turn results in 116% additional throughput improvement.


title: Finding Similar Clothes Based on Semantic Description for the Purpose of Fashion Recommender System

abstract: The fashion domain has been one of the most growing areas of e-commerce, hence the issue of facilitating cloth searching in fashion-related websites becomes an important topic of research. The paper deals with measuring the similarity between items of clothing and between complete outfits, based on the semantic description prepared by users and experts according to a previously developed fashion ontology. Proposed approach deals with different types of attributes describing clothes and allows for calculating similarity between the whole outfits in a domain-aware manner. Exemplary results of experiments performed on real clothing datasets are presented.


title: Is multiagent deep reinforcement learning the answer or the question? A brief survey

abstract: Deep reinforcement learning (DRL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. In this context, first, this article provides a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Second, it provides guidelines to complement this emerging area by (i) showcasing examples on how methods and algorithms from DRL and multiagent learning (MAL) have helped solve problems in MDRL and (ii) providing general lessons learned from these works. We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists in both areas (DRL and MAL) in a joint effort to promote fruitful research in the multiagent community.


title: Meta-Learning Related Tasks with Recurrent Networks: Optimization and Generalization

abstract: There have been recent interest in meta-learning systems: i.e., networks that are trained to learn across multiple tasks. This paper focuses on optimization and generalization of a meta-learning system based on recurrent networks. The optimization investigates the influence of diverse structures and parameters on its performance. We demonstrate the generalization (robustness) of our meta-learning system to learn across multiple tasks including tasks unseen during the metatraining phase. We introduce a meta-cost function (Mean Squared Fair Error) that enhances the performance of the system by not penalizing it during transitions to learning a new task. Evaluation results are presented for Boolean and quadratic functions datasets. The best performance is obtained using a Long Short-Term Memory (LSTM) topology without a forget gate and with a clipped memory cell. The results demonstrate i) the impact of different LSTM architectures, parameters, and error functions on the meta-learning process; ii) that the mean squared fair error function does improve performance for best learning; and iii) the robustness of our meta-learning framework as it generalizes well when tested on tasks unseen during meta-training. Comparison between No-Forget-Gate LSTM and Gated Recurrent Unit also suggest that absence of a memory cell tends to degrade performance.


title: Gandiva: introspective cluster scheduling for deep learning

abstract: We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific knowledge to improve latency and efficiency of training deep learning models in a GPU cluster. ::: ::: One key characteristic of deep learning is feedback-driven exploration, where a user often runs a set of jobs (or a multi-job) to achieve the best result for a specific mission and uses early feedback on accuracy to dynamically prioritize or kill a subset of jobs; simultaneous early feedback on the entire multi-job is critical. A second characteristic is the heterogeneity of deep learning jobs in terms of resource usage, making it hard to achieve best-fit a priori. Gandiva addresses these two challenges by exploiting a third key characteristic of deep learning: intra-job predictability, as they perform numerous repetitive iterations called mini-batch iterations. Gandiva exploits intra-job predictability to time-slice GPUs efficiently across multiple jobs, thereby delivering low-latency. This predictability is also used for introspecting job performance and dynamically migrating jobs to better-fit GPUs, thereby improving cluster efficiency. ::: ::: We show via a prototype implementation and micro-benchmarks that Gandiva can speed up hyper-parameter searches during deep learning by up to an order of magnitude, and achieves better utilization by transparently migrating and time-slicing jobs to achieve better job-to-resource fit. We also show that, in a real workload of jobs running in a 180-GPU cluster, Gandiva improves aggregate cluster utilization by 26%, pointing to a new way of managing large GPU clusters for deep learning.


title: Measuring the web crawler ethics

abstract: Web crawlers are highly automated and seldom regulated manually. The diversity of crawler activities often leads to ethical problems such as spam and service attacks. In this research, quantitative models are proposed to measure the web crawler ethics based on their behaviors on web servers. We investigate and define rules to measure crawler ethics, referring to the extent to which web crawlers respect the regulations set forth in robots.txt configuration files. We propose a vector space model to represent crawler behavior and measure the ethics of web crawlers based on the behavior vectors. The results show that ethicality scores vary significantly among crawlers. Most commercial web crawlers' behaviors are ethical. However, many commercial crawlers still consistently violate or misinterpret certain robots.txt rules. We also measure the ethics of big search engine crawlers in terms of return on investment. The results show that Google has a higher score than other search engines for a US website but has a lower score than Baidu for Chinese websites.


title: A 79 GHz microstrip grid array antenna using a laminated waveguide feed in LTCC

abstract: This paper presents a novel planar microstrip grid array antenna on low temperature co-fired ceramics (LTCC) with a feeding structure in laminated waveguide technique. The antenna is designed to operate in the 79 GHz frequency range for automotive radar applications. Measured results of a first antenna design show that the antenna is a good candidate for 79 GHz applications and particularly suitable for LTCC integration.


XSD 2 OWL 2 : AUTOMATIC MAPPING FROM XML SCHEMA INTO OWL 2 ONTOLOGY


The prerepresentational self and its affective core.


title: Experimental characteristics of semiconductor opening switch diode

abstract: This paper describes experimental characteristics of a semiconductor opening switch (SOS) diode. After minority carriers were accumulated in the SOS by forward pumping current, reverse current flows while minority carriers are turned back. The reverse current interruption is caused due to the decrease of minority carriers in p-n area. An influence of the forward pumping current on interruption time of the reverse current was investigated. The result shows that if the peak of reverse current is not varied, a few accumulated carriers lead to fast current interruption. This is because the amount of carriers turned back is few. Further, the peak voltage and the efficiency increased with a decrease of interruption time of the reverse current. We expect that low and short forward pumping current and high reverse current lead to fast current interruption.


title: The Survey on Near Field Communication

abstract: Near Field Communication (NFC) is an emerging short-range wireless communication technology that offers great and varied promise in services such as payment, ticketing, gaming, crowd sourcing, voting, navigation, and many others. NFC technology enables the integration of services from a wide range of applications into one single smartphone. NFC technology has emerged recently, and consequently not much academic data are available yet, although the number of academic research studies carried out in the past two years has already surpassed the total number of the prior works combined. This paper presents the concept of NFC technology in a holistic approach from different perspectives, including hardware improvement and optimization, communication essentials and standards, applications, secure elements, privacy and security, usability analysis, and ecosystem and business issues. Further research opportunities in terms of the academic and business points of view are also explored and discussed at the end of each section. This comprehensive survey will be a valuable guide for researchers and academicians, as well as for business in the NFC technology and ecosystem.


title: A systematic review of shockwave therapies in soft tissue conditions: focusing on the evidence.

abstract: BACKGROUND ::: 'Shock wave' therapies are now extensively used in the treatment of musculoskeletal injuries. This systematic review summarises the evidence base for the use of these modalities. ::: ::: ::: METHODS ::: A thorough search of the literature was performed to identify studies of adequate quality to assess the evidence base for shockwave therapies on pain in specific soft tissue injuries. Both focused extracorporeal shockwave therapy (F-ESWT) and radial pulse therapy (RPT) were examined. ::: ::: ::: RESULTS ::: 23 appropriate studies were identified. There is evidence for the benefit of F-ESWT and of RPT in a number of soft tissue musculoskeletal conditions, and evidence that both treatment modalities are safe. There is evidence that F-ESWT is effective in the treatment of plantar fasciitis, calcific tendinitis, and that RPT is effective in plantar fasciitis. Where benefit is seen in F-ESWT, it appears to be dose dependent, with greater success seen with higher dose regimes. There is low level evidence for lack of benefit of low-dose F-ESWT and RPT in non-calcific rotator cuff disease and mixed evidence in lateral epicondylitis.


title: Abstract Analysis and Evolution of Journaling File Systems

abstract: We develop and apply two new methods for analyzing file system behavior and evaluating file system changes. First, semantic block-level analysis (SBA) combines knowledge of on-disk data structures with a trace of disk traffic to infer file system behavior; in contrast to standard benchmarking approaches, SBA enables users to understand why the file system behaves as it does. Second, semantic trace playback (STP) enables traces of disk traffic to be easily modified to represent changes in the file system implementation; in contrast to directly modifying the file system, STP enables users to rapidly gauge the benefits of new policies. We use SBA to analyze Linux ext3, ReiserFS, JFS, and Windows NTFS; in the process, we uncover many strengths and weaknesses of these journaling file systems. We also apply STP to evaluate several modifications to ext3, demonstrating the benefits of various optimizations without incurring the costs of a real implementation.


title: A Mixed-Initiative Approach to Interactive Robot Tutoring

abstract: Integrating the components described in the previous articles of this chapter, we introduce the Bielefeld “Curious Robot”, which is able to acquire new knowledge and skills in direct human-robot interaction. This paper focuses on the cognitive architecture of the overall system. We propose to combine (i) a communication layer based on a generic, human-accessible XML data format, (ii) multiple low-level sensor and control processes publishing their sensor information into the system and receiving commands or parameterizations from higher-level deliberative processes, and (iii) high-level coordination processes based on hierarchical state machines. The efficiency of the proposed approach is shown in an interactive tutoring scenario, where the Bielefeld “Curious Robot”, a bimanual robot system, should learn to identify, grasp, and clean various everyday objects from a table. The capability of the system to interact with lay persons is proven in a user study.


COMPARISON OF MAXIMUM POWER POINT TRACKING ALGORITHMS FOR PHOTOVOLTAIC SYSTEM


title: AST-Based Deep Learning for Detecting Malicious PowerShell

abstract: With the celebrated success of deep learning, some attempts to develop effective methods for detecting malicious PowerShell programs employ neural nets in a traditional natural language processing setup while others employ convolutional neural nets to detect obfuscated malicious commands at a character level. While these representations may express salient PowerShell properties, our hypothesis is that tools from static program analysis will be more effective. We propose a hybrid approach combining traditional program analysis (in the form of abstract syntax trees) and deep learning. This poster presents preliminary results of a fundamental step in our approach: learning embeddings for nodes of PowerShell ASTs. We classify malicious scripts by family type and explore embedded program vector representations.


title: Temporal Orientation of Tweets for Predicting Income of Users

abstract: Automatically estimating a user’s socioeconomic profile from their language use ::: in social media can significantly help social science research and various downstream applications ranging from business ::: to politics. The current paper presents the ::: first study where user cognitive structure ::: is used to build a predictive model of income. In particular, we first develop a ::: classifier using a weakly supervised learning framework to automatically time-tag ::: tweets as past, present, or future. We ::: quantify a user’s overall temporal orientation based on their distribution of tweets, ::: and use it to build a predictive model of ::: income. Our analysis uncovers a correlation between future temporal orientation ::: and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.


title: Crowd Scene Understanding from Video: A Survey

abstract: Crowd video analysis has applications in crowd management, public space design, and visual surveillance. Example tasks potentially aided by automated analysis include anomaly detection (such as a person walking against the grain of traffic or rapid assembly/dispersion of groups of people), population and density measurements, and interactions between groups of people. This survey explores crowd analysis as it relates to two primary research areas: crowd statistics and behavior understanding. First, we survey methods for counting individuals and approximating the density of the crowd. Second, we showcase research efforts on behavior understanding as related to crowds. These works focus on identifying groups, interactions within small groups, and abnormal activity detection such as riots and bottlenecks in large crowds. Works presented in this section also focus on tracking groups of individuals, either as a single entity or a subset of individuals within the frame of reference. Finally, a summary of datasets available for crowd activity video research is provided.


title: Neural associative memories and sparse coding

abstract: The theoretical, practical and technical development of neural associative memories during the last 40 years is described. The importance of sparse coding of associative memory patterns is pointed out. The use of associative memory networks for large scale brain modeling is also mentioned.


title: Brief mindfulness induction reduces inattentional blindness

abstract: Abstract Prior research has linked mindfulness to improvements in attention, and suggested that the effects of mindfulness are particularly pronounced when individuals are cognitively depleted or stressed. Yet, no studies have tested whether mindfulness improves declarative awareness of unexpected stimuli in goal-directed tasks. Participants (N = 794) were either depleted (or not) and subsequently underwent a brief mindfulness induction (or not). They then completed an inattentional blindness task during which an unexpected distractor appeared on the computer monitor. This task was used to assess declarative conscious awareness of the unexpected distractor’s presence and the extent to which its perceptual properties were encoded. Mindfulness increased awareness of the unexpected distractor (i.e., reduced rates of inattentional blindness). Contrary to predictions, no mindfulness × depletion interaction emerged. Depletion however, increased perceptual encoding of the distractor. These results suggest that mindfulness may foster awareness of unexpected stimuli (i.e., reduce inattentional blindness).


title: DualCAD: Integrating Augmented Reality with a Desktop GUI and Smartphone Interaction

abstract: Head-Mounted Displays (HMDs) combined with 3-or-more Degree-of-Freedom (DoF) input enable rapid manipulation of stereoscopic 3D content. However, such input is typically performed with hands in midair and therefore lacks precision and stability. Also, recent consumer-grade HMDs suffer from limited angular resolution and/or limited field-of-view as compared to a desktop monitor. We present the DualCAD system that implements two solutions to these problems. First, the user may freely switch at runtime between an augmented reality HMD mode, and a traditional desktop mode with precise 2D mouse input and an external desktop monitor. Second, while in the augmented reality HMD mode, the user holds a smartphone in their non-dominant hand that is tracked with 6 DoF, allowing it to be used as a complementary high-resolution display as well as an alternative input device for stylus or multitouch input. Two novel bimanual interaction techniques that leverage the properties of the smartphone are presented. We also report initial user feedback.


title: DARI: Distance metric And Representation Integration for Person Verification

abstract: The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i.e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches.


title: A survey on clustering algorithms for wireless sensor networks

abstract: The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs; highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.


ChemGrab : Identification of Chemical Names Using a Combined Negative-Dictionary and Rule-Based Approach


title: Building Neural Representations of Habits

abstract: Memories for habits and skills (“implicit or procedural memory”) and memories for facts (“explicit or episodic memory”) are built up in different brain systems and are vulnerable to different neurodegenerative disorders in humans. So that the striatum-based mechanisms underlying habit formation could be studied, chronic recordings from ensembles of striatal neurons were made with multiple tetrodes as rats learned a T-maze procedural task. Large and widely distributed changes in the neuronal activity patterns occurred in the sensorimotor striatum during behavioral acquisition, culminating in task-related activity emphasizing the beginning and end of the automatized procedure. The new ensemble patterns remained stable during weeks of subsequent performance of the same task. These results suggest that the encoding of action in the sensorimotor striatum undergoes dynamic reorganization as habit learning proceeds.


title: A reliable Internet of Things based architecture for oil and gas industry

abstract: Anomaly detection systems deployed for monitoring in oil and gas industries are mostly WSN based systems or SCADA systems which all suffer from noteworthy limitations. WSN based systems are not homogenous or incompatible systems. They lack coordinated communication and transparency among regions and processes. On the other hand, SCADA systems are expensive, inflexible, not scalable, and provide data with long delay. In this paper, a novel IoT based architecture is proposed for Oil and gas industries to make data collection from connected objects as simple, secure, robust, reliable and quick. Moreover, it is suggested that how this architecture can be applied to any of the three categories of operations, upstream, midstream and downstream. This can be achieved by deploying a set of IoT based smart objects (devices) and cloud based technologies in order to reduce complex configurations and device programming. Our proposed IoT architecture supports the functional and business requirements of upstream, midstream and downstream oil and gas value chain of geologists, drilling contractors, operators, and other oil field services. Using our proposed IoT architecture, inefficiencies and problems can be picked and sorted out sooner ultimately saving time and money and increasing business productivity.


title: PSO based memetic algorithm for face recognition Gabor filters selection

abstract: A Gabor filters based face recognition algorithm named POMA-Gabor is proposed in this paper. The algorithm uses particular Gabor wavelets in the feature extraction on specific areas of the face image and a particle swarm optimization (PSO) based memetic algorithm (POMA), which combines comprehensive learning particle swarm optimizer (CLPSO) global search and self-adaptive intelligent single particle optimizer (AdpISPO) local search, is introduced to select the Gabor filter parameters. The experimental results demonstrate that POMA obtains better performance than other comparative PSO algorithms. Employing POMA for Gabor filter design, POMA-Gabor is capable of obtaining more representative information and higher recognition rate with less computational time.


title: Textile sensors to measure sweat pH and sweat-rate during exercise

abstract: Sweat analysis can provide a valuable insight into a persons well-being. Here we present wearable textile-based sensors that can provide real-time information regarding sweat activity. A pH sensitive dye incorporated into a fabric fluidic system is used to determine sweat pH. To detect the onset of sweat activity a sweat rate sensor is incorporated into a textile substrate. The sensors are integrated into a waistband and controlled by a central unit with wireless connectivity. The use of such sensors for sweat analysis may provide valuable physiological information for applications in sports performance and also in healthcare.


title: Silver nitrate influences in vitro root formation in Decalepis hamiltonii Wight and Arn.

abstract: Effects of AgNO3 on root formation were examined ::: in vitro using axillary bud cultures of Decalepis hamiltonii. ::: In the case of a medium comprising of MS ::: salts with indole 3-acetic acid (IAA) (0.5 mg l–1), poor ::: rooting was observed and root emergence did not ::: occur until after 25 days. The resultant roots were ::: stunted. Addition of 40 μM AgNO3 improved root ::: initiation and elongation. The promotive effects of ::: AgNO3 on rooting may result from inhibition of ethylene ::: action. Upon addition of ethephon to the rooting ::: medium, excessive callusing was observed in ::: explants in all the treatments. Addition of 40 μM ::: AgNO3 to ethephon-containing medium resulted in ::: improvement in root initiation and elongation. Ethylene ::: production was monitored in all the treatments ::: with IAA/AgNO3/ethephon and it was observed that ::: the treatment with IAA (0.5 mg l–1) alone showed a ::: greater increase in ethylene production when compared ::: with AgNO3, ethephon and their combinations.


X.(2004) Principal Component Analysis and Effective K-Means Clustering


title: Virtual View Generation for 3D Digital Video

abstract: Virtual reality systems use digital models to provide interactive viewing. We present a 3D digital video system that attempts to provide the same capabilities for actual performances such as dancing. Recreating the original dynamic scene in 3D, the system allows photorealistic interactive playback from arbitrary viewpoints using video streams of a given scene from multiple perspectives.


title: A 6-$\mu$ W Chip-Area-Efficient Output-Capacitorless LDO in 90-nm CMOS Technology

abstract: An output-capacitorless low-dropout regulator (LDO) compensated by a single Miller capacitor is implemented in a commercial 90-nm CMOS technology. The proposed LDO makes use of the small transistors realized in nano-scale technology to achieve high stability, fast transient performance and small voltage spikes under rapid load-current changes without the need of an off-chip capacitor connected at the LDO output. Experimental result verifies that the proposed LDO is stable for a capacitive load from 0 to 50 pF (estimated equivalent parasitic capacitance from load circuits) and with load capability of 100 mA. Moreover, the gain-enhanced structure provides sufficient loop gain to improve line regulation to 3.78 mV/V and load regulation to 0.1 mV/mA, respectively. The embedded voltage-spike detection circuit enables pseudo Class-AB operation to drive the embedded power transistor promptly. The measured power consumption is only 6 μW under a 0.75-V supply. The maximum overshoot and undershoot under a 1.2-V supply are less than 66 mV for full load current changes within 100-ns edge time, and the recovery time is less than 5 μs.


A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts.


title: Orthodontic Treatment of a Mandibular Incisor Extraction Case with Invisalign

abstract: Mandibular incisor extraction for orthodontic treatment is considered an unusual treatment option because of the limited number of patients that meet the criteria for such treatment. Accurate diagnosis and treatment planning is essential to achieve the desired results. Adult orthodontic patients are increasingly motivated by esthetic considerations and reject the idea of conventional fixed appliances. In recent years, Invisalign appliances have gained tremendous attention for orthodontic treatment of adult patients to meet their esthetic demands. In this case report, a case of Class I malocclusion was treated with mandibular incisor extraction using the Invisalign appliance system. Successful tooth alignment of both arches was achieved. The use of Invisalign appliance is an effective treatment option in adult patients with Class I malocclusion that requires incisor extraction due to moderate to severe mandibular anterior crowding.


title: Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

abstract: We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISSGP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.


title: Repairing Ontologies via Axiom Weakening

abstract: Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators. We introduce the theoretical framework for weakening DL ontologies, propose algorithms to repair ontologies based on the framework, and provide an analysis of the computational complexity. Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms.


title: Improved Deep Learning of Object Category Using Pose Information

abstract: Despite significant recent progress, the best available computer vision algorithms still lag far behind human capabilities, even for recognizing individual discrete objects under various poses, illuminations, and backgrounds. Here we present a new approach to using object pose information to improve deep network learning. While existing large-scale datasets, e.g. ImageNet, do not have pose information, we leverage the newly published turntable dataset, iLab-20M, which has ~22M images of 704 object instances shot under different lightings, camera viewpoints and turntable rotations, to do more controlled object recognition experiments. We introduce a new convolutional neural network architecture, what/where CNN (2W-CNN), built on a linear-chain feedforward CNN (e.g., AlexNet), augmented by hierarchical layers regularized by object poses. Pose information is only used as feedback signal during training, in addition to category information; during test, the feedforward network only predicts category. To validate the approach, we train both 2W-CNN and AlexNet using a fraction of the dataset, and 2W-CNN achieves 6% performance improvement in category prediction. We show mathematically that 2W-CNN has inherent advantages over AlexNet under the stochastic gradient descent (SGD) optimization procedure. Further more, we fine-tune object recognition on ImageNet by using the pretrained 2W-CNN and AlexNet features on iLab-20M, results show that significant improvements have been achieved, compared with training AlexNet from scratch. Moreover, fine-tuning 2W-CNN features performs even better than fine-tuning the pretrained AlexNet features. These results show pretrained features on iLab- 20M generalizes well to natural image datasets, and 2WCNN learns even better features for object recognition than AlexNet.


title: An integrated approach to characterize genetic interaction networks in yeast metabolism

abstract: Balazs Papp and colleagues construct a genetic interaction map of yeast metabolism and use a genome-scale systems biology model to examine the structure of the metabolic network. They use an automated machine-learning method to reconcile differences between the experimental and computational genetic interaction maps. In contrast to previous studies, they do not find evidence for prevalent positive interactions in essential metabolic genes.


title: 3D Semantic Mapping Based on Convolutional Neural Networks

abstract: As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation, localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the objects, which lacks the semantic information. A method is proposed in the paper, this method combines convolutional neural networks (CNNs) and Simultaneous Localization and Mapping (SLAM) to create global dense 3D semantic maps for indoor scenes. The deep neural network that includes convolution and deconvolution is designed to predict semantic category of every pixel. RGB-D camera is used to obtain scene information, accomplish localization and build 3D maps simultaneously. the semantic information is integrated into the 3D scene, we present an octree map method to replace traditional point clouds method, which can reduce the error from pose estimation and single frame labeling. By this method, the accuracy of semantic information is greatly improved.


title: Alpha estimation in natural images

abstract: Many boundaries between objects in the world project onto curves in an image. However, boundaries involving natural objects (e.g., trees, hair, water, smoke) are often unworkable under this model because many pixels receive light from more than one object. We propose a technique for estimating alpha, the proportion in which two colors mix to produce a color at the boundary. The technique extends blue screen matting to backgrounds that have almost arbitrary color distributions, though coarse knowledge of the boundary's location is required. Results show a number of different objects moved from one image to another while maintaining naturalism.


An End-to-End Neural Architecture for Reading Comprehension


Informal Leadership in the Clinical Setting: Occupational Therapist Perspectives


title: Deterministic replay of Java multithreaded applications

abstract: A multithreaded program includes sequences of events wherein each sequence is associated with one of a plurality of execution threads. In a record mode, the software tool of the present invention records a run-time representation of the program by distinguishing critical events from non-critical events of the program and identifying the execution order of such critical events. Groups of critical events are generated wherein, for each group Gi, critical events belonging to the group Gi belong to a common execution thread, critical events belonging to the group Gi are consecutive, and only non-critical events occur between any two consecutive critical events in the group Gi. In addition, the groups are ordered and no two adjacent groups include critical events that belong to a common execution thread. For each execution thread, a logical thread schedule is generated that identifies a sequence of said groups associated with the execution thread. The logical thread schedules are stored in persistent storage for subsequent reuse. In a replay mode, for each execution thread, the logical thread schedule associated with the execution thread is loaded from persistent storage and the critical events identified by the logical thread schedule are executed.


title: An evaluation of lightweight classification methods for identifying malicious URLs

abstract: Recent research has shown that it is possible to identify malicious URLs through lexical analysis of their URL structures alone. This paper intends to explore the effectiveness of these lightweight classification algorithms when working with large real world datasets including lists of malicious URLs obtained from Phishtank as well as largely filtered benign URLs obtained from proxy traffic logs. Lightweight algorithms are defined as methods by which URLs are analysed that do not use external sources of information such as WHOIS lookups, blacklist lookups and content analysis. These parameters include URL length, number of delimiters as well as the number of traversals through the directory structure and are used throughout much of the research in the paradigm of lightweight classification. Methods which include external sources of information are often called fully featured classifications and have been shown to be only slightly more effective than a purely lexical analysis when considering both false-positives and false-negatives. This distinction allows these algorithms to be run client side without the introduction of additional latency, but still providing a high level of accuracy through the use of modern techniques in training classifiers. Analysis of this type will also be useful in an incident response analysis where large numbers of URLs need to be filtered for potentially malicious URLs as an initial step in information gathering as well as end user implementations such as browser extensions which could help protect the user from following potentially malicious links. Both AROW and CW classifier update methods will be used as prototype implementations and their effectiveness will be compared to fully featured analysis results. These methods are interesting because they are able to train on any labelled data, including instances in which their prediction is correct, allowing them to build a confidence in specific lexical features. This makes it possible for them to be trained using noisy input data, making them ideal for real world applications such as link filtering and information gathering.


title: A Miniaturized, Dual-Band, Circularly Polarized Microstrip Antenna for Installation Into Satellite Mobile Phones

abstract: A miniaturized, dual-band, circularly polarized microstrip antenna (CPMA) for satellite mobile phones was designed. Two miniaturized radiation elements using folded structures are stacked vertically to achieve dual bands. Each radiation element shows left-handed circular polarization. The total volume of this dual-band, circularly polarized, microstrip antenna is 46 mm (length) x 24 mm (width) x 13 mm (height). The overall antenna is mounted on a phone-shaped ground plane to fit inside satellite mobile phones.


title: ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications

abstract: In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes. We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity. Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement. Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme.


title: An energy-efficient charge recycling approach for a SAR converter with capacitive DAC

abstract: A new method for switching the capacitors in the DAC capacitor array of a successive approximation register (SAR) ADC is presented. By splitting the MSB capacitor into b - 1 binary scaled sub-capacitors, the average switching energy of the array can be reduced by 37% compared to a conventional switching method. A formal solution of the switching energy in four different switching methods is included, and the equations are verified using HSPICE simulations of a 10b capacitor array in a 0.18 /spl mu/m CMOS process.


title: Perpendicular Magnetic Anisotropy and Easy Cone State in Ta/Co60Fe20B20 /MgO

abstract: We used broadband ferromagnetic resonance (FMR) spectroscopy to measure the second- and fourth-order perpendicular magnetic anisotropies in Ta/(t) Co ::: 60 ::: Fe ::: 20 ::: B ::: 20 ::: /MgO layers over a Co ::: 60 ::: Fe ::: 20 ::: B ::: 20 ::: thickness range of 5.0 nm ≥ t ≥ 0.8 nm. Fort > 1.0 nm, the easy axis is in the plane of the film, but when t <; 1.0 nm, the easy axis is directed perpendicular to the surface. However, the presence of a substantial higher order perpendicular anisotropy results in an easy cone state when t = 1.0 nm. Angular-dependent FMR measurements verify the presence of the easy cone state. Measurement of the spectroscopic g-factor via FMR for both the in-plane and out-of-plane geometries suggests a significant change in electronic and/or physical structure at t ≈ 1.0 nm thickness.


title: Is momentum really momentum

abstract: Momentum is primarily driven by firms' performance 12 to seven months prior to portfolio formation, not by a tendency of rising and falling stocks to keep rising and falling. Strategies based on recent past performance generate positive returns but are less profitable than those based on intermediate horizon past performance, especially among the largest, most liquid stocks. These facts are not particular to the momentum observed in the cross section of US equities. Similar results hold for momentum strategies trading international equity indices, commodities, and currencies.


title: Sentence Ranking with the Semantic Link Network in Scientific Paper

abstract: Sentence ranking is one of the most important research issues in text analysis. It can be used in text summarization and information retrieval. Graph-based methods are a common way of ranking and extracting sentences. In graph based methods, sentences are nodes of graph and edges are built based on the sentence similarities or on sentence co-occurrence relationship. PageRank style algorithms can be applied to get sentence ranks. In this paper, we focus on how to rank sentences in a single scientific paper. A scientific literature has more structural information than general texts and this structural information has not been fully explored yet in graph based ranking models. We investigated several different methods that used the is-part-of link on paragraph and section and similar link and co-occurrence link to construct a heterogeneous graph for ranking sentences. We conducted experiments on these methods to compare the results on sentence ranking. It shows that structural information can help identify more representative sentences.


title: Delayed feedback effects on rule-based and information-integration category learning

abstract: The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.


title: The Philosophical Underpinnings of Educational Research

abstract: This article traces the underlying theoretical framework of educational research. It outlines the definitions of epistemology, ontology and paradigm and the origins, main tenets, and key thinkers of the 3 paradigms; positivist, interpetivist and critical. By closely analyzing each paradigm, the literature review focuses on the ontological and epistemological assumptions of each paradigm. Finally the author analyzes not only the paradigm’s weakness but also the author’s own construct of reality and knowledge which align with the critical paradigm. Key terms: Paradigm, Ontology, Epistemology, Positivism, Interpretivism The English Language Teaching (ELT) field has moved from an ad hoc field with amateurish research to a much more serious enterprise of professionalism. More teachers are conducting research to not only inform their teaching in the classroom but also to bridge the gap between the external researcher dictating policy and the teacher negotiating that policy with the practical demands of their classroom. I was a layperson, not an educational researcher. Determined to emancipate myself from my layperson identity, I began to analyze the different philosophical underpinnings of each paradigm, reading about the great thinkers’ theories and the evolution of social science research. Through this process I began to examine how I view the world, thus realizing my own construction of knowledge and social reality, which is actually quite loose and chaotic. Most importantly, I realized that I identify most with the critical paradigm assumptions and that my future desired role as an educational researcher is to affect change and challenge dominant social and political discourses in ELT. The following literature review is the product of my transformation from teacher to educational researcher. I will begin by defining the operational definitions of ontology, epistemology and paradigm. Then, I trace the origins, main tenets, and key thinkers of the 3 paradigms; positivist, interpetivist and critical, focusing on the ontological and epistemological assumptions of each paradigm. Through this analysis of different paradigms, I will expose not only each paradigm’s weakness but also my own construct of reality and knowledge. 1. ONTOLOGY, EPISTEMOLOGY AND PARADIGMS Simply put, one’s view of reality and being is called ontology and the view of how one acquires knowledge is termed epistemology. Ontology is the starting point which will likely lead to your own theoretical framework. For this paper, I employ Blaikie’s (as cited in Grix, 2004, p. 59) definition of ontology as the study of “claims and assumptions that are made about the nature of social reality, claims about what exists, what it looks like, what units make it up and how these units interact with each other.” In other words, if someone studies ontology they study what we mean when we say something exists. If ontologists study what we mean when we say something exists then an epistemologist studies what we mean when we say we know something. For this paper, I utilize Crotty’s definition of epistemology, defined as “the theory of knowledge embedded in the theoretical perspective and thereby in the methodology” (1998, p. 3). Together, ontological and epistemological assumptions make up a paradigm. The term paradigm, first termed by Thomas Kuhn in his 1972 book, titled “The structure of Scientific Revolutions”, refers to an overall theoretical research framework. In this paper, I employ Bodgan & Biklen’s (as cited in Mackenzie and Knipe, 2001, p. 2) Polyglossia Volume 19, October 2010 6 definition of a paradigm as “a loose collection of logically related assumptions, concepts or propositions that orient thinking and research.” Why does one’s view of knowledge and social reality relate to educational research? One view is that the researcher’s intentions, goals and philosophical assumptions are inextricably linked with the research they do. Grix, (2004) warns that people who want to conduct clear, precise research and evaluate other’s research need to understand the philosophical underpinnings that inform their choice of research questions, methodology, methods and intentions (p. 57). Therefore, how one views the constructs of social reality and knowledge affects how they will go about uncovering knowledge of relationships among phenomena and social behavior and how they evaluate their own and other’s research. Crotty (1998) argues that researchers can choose which stage to begin at, ontological, epistemological, methods or methodology. Other authors stress that research is best conducted by identifying your ontological assumptions first. According to Grix (2004) research is best done by: setting out clearly the relationship between what a researcher thinks can be researched (her ontological position) linking it to what we can know about it (her epistemological position) and how to go about acquiring it (her methodological approach), you can begin to comprehend the impact your ontological position can have on what and how you decide to study (Grix, 2004, p. 68). Moreover your ontological assumptions inform your epistemological assumptions which inform your methodology and these all give rise to your methods employed to collect data. 2. THE POSITIVIST PARADIGM The positivist paradigm is also called the scientific paradigm. The purpose of research in this paradigm is to prove or disprove a hypothesis. Other characteristics of positivist research include an emphasis on the scientific method, statistical analysis, and generalizable findings. Furthermore, positivist research usually has a control and experimental group and a pre/test post method. The term positivism was first coined by the founder of positivism, Auguste Comte, the French philosopher who believed that reality can be observed. Cohen, Manion, and Morrison (2007) claim that “Comte’s position was to lead to a general doctrine of positivism which held that all genuine knowledge is based on sense experience and can be advanced only by means of observation and experiment” (p. 9). Positivism maintains that the scientist is the observer of an objective reality. From this understanding of ontology, the methodology for observation in natural science was adopted for social science research. Main Thinkers Philosophy Aristotle Descartes Galileo Auguste Comte Vienna Circle Francis Bacon Karl Popper Deductive reasoning Realism Scientific method Positivism Logical positivism Inductive reasoning


title: Effect of the number of portal area on modified histological activity index of viral hepatitis and histological findings

abstract: AIM ::: Viral hepatitis is one of the most important causes of chronic hepatitis. Liver biopsy is used to verify clinical diagnosis and to evaluate necroinflammation and fibrosis. Biopsy is the guide for therapy and can be performed also after treatment to assess the effect of therapy on liver. This paper aimed to explore histopathological characteristics of biopsy samples, which had been referred to our department with the clinical diagnosis of chronic viral hepatitis, in reference to Ishak Modified Hepatic Activity Index (IMHAI), as well as to compare inflammatory scores and stages in the groups created according to the number of portal area (PA). ::: ::: ::: MATERIALS AND METHODS ::: The study included 107 patients that underwent liver biopsy in 2011 being diagnosed with chronic viral hepatitis. Age, gender and type of viral hepatitis were retrospectively reviewed and histological findings such as IMHAI inflammatory score and stage, hepatosteatosis and ground glass hepatocytes were re-assessed by two pathologists. ::: ::: ::: RESULTS ::: Of the present cases, 97 had chronic hepatitis B, 5 had chronic hepatitis C, and 5 had chronic hepatitis BDThe group with PA number of 2-4 consisted of 8 cases and the group with PA number of 11 and over consisted of 37 cases. ::: ::: ::: CONCLUSION ::: Statistical analysis performed by comparing IMHAI inflammatory score and stage with PA revealed that score and stage were significantly higher in PA ≥ 11 groups as compared to PA 2-4 group.


title: UAV Swarm Mission Planning and Routing using Multi-Objective Evolutionary Algorithms

abstract: The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles (UAV). The system integrates several problem domains including path planning, vehicle routing, and swarm behavior as based upon a hierarchical architecture. The developed system consists of a parallel, multi-objective evolutionary algorithm-based terrain-following parallel path planner and an evolutionary algorithm-based vehicle router. Objectives include minimizing cost and risk generally associated with a three dimensional vehicle routing problem (VRP). The culmination of this effort is the development of an extensible developmental path planning model integrated with swarm behavior and tested with a parallel UAV simulation. Discussions on the system's capabilities are presented along with recommendations for further development.


title: Detecting Skype flows in Web traffic

abstract: Network managers face nowadays a challenging problem to detect traffic from Skype, a very popular application for VoIP communications. If no restrictive firewalls are adopted, Skype uses UDP as its preferred transport protocol, but it is known that due to its high capacity of adaptation, Skype can operate behind many firewalls and network proxies without user configuration. Behind restrictive firewalls, Skype uses Web TCP ports (80 or 443) as a fallback mechanism to delude firewalls and other network elements. This strategy renders Skype traffic disguised as Web traffic quite difficult to detect by network operators. In this paper, we propose a method to efficiently detect Skype flows hidden among Web traffic. We validate our proposal using real-world experimental data gathered at a commercial Internet service provider (ISP) and an academic institution. Our experimental results show a performance of around 90% detection rate of disguised Skype flows with a false positive rate of only 2%, whereas a 100% detection rate of Skype flows in Web traffic is achieved with a false positive rate limited to only 5%. We also evaluate the feasibility of our proposal in a real-time Skype detection scenario.