title: Order and Chaos in some Trigonometric Series: Curious Adventures of a Statistical Mechanic

abstract: This paper tells the story how a MAPLE-assisted quest for an interesting undergraduate problem in trigonometric series led some “amateurs” to the discovery that the one-parameter family of deterministic trigonometric series Open image in new window , p>1, exhibits both order and apparent chaos, and how this has prompted some professionals to offer their expert insights. As to order, an elementary (undergraduate) proof is given that Open image in new window ∀t∈ℝ, with explicitly computed constant α p . As to chaos, the seemingly erratic fluctuations about this overall trend are discussed. Experts’ commentaries are reproduced as to why the fluctuations of Open image in new window are presumably not Gaussian. Inspired by a central limit type theorem of Marc Kac, a well-motivated conjecture is formulated to the effect that the fluctuations of the ⌈t 1/(p+1)⌉-th partial sum of Open image in new window , when properly scaled, do converge in distribution to a standard Gaussian when t→∞, though—provided that p is chosen so that the frequencies {n −p } n∈ℕ are rationally linear independent; no conjecture has been forthcoming for rationally dependent {n −p } n∈ℕ. Moreover, following other experts’ tip-offs, the interesting relationship of the asymptotics of Open image in new window to properties of the Riemann ζ function is exhibited using the Mellin transform.


title: In vitro drug permeation from chitosan pellets

abstract: a b s t r a c t The purpose of this study was to prepare and characterize coated pellets for controlled drug delivery. The influence of chitosan (CS) in pellets was evaluated by swelling, in vitro drug release and intestinal permeation assays. Pellets were coated with an enteric polymer, Kollicoat® MAE 30 DP, in a fluidized-bed apparatus and the coating formulations were based on a factorial design. Metronidazole (MT) released from coated and uncoated pellets were assessed by dissolution method using Apparatus I. Intestinal permeation was evaluated by everted intestinal sac model in rats, used to study the absorption of MT from coated pellets containing CS or not through the intestinal tissue. Although the film coating avoided drug dissolution in gastric medium, the overall drug release and intestinal permeation were dependent on the presence of CS. Thus, pellets containing CS show potential as a system for controlled drug delivery.


title: Stable reinforcement learning with autoencoders for tactile and visual data

abstract: For many tasks, tactile or visual feedback is helpful or even crucial. However, designing controllers that take such high-dimensional feedback into account is non-trivial. Therefore, robots should be able to learn tactile skills through trial and error by using reinforcement learning algorithms. The input domain for such tasks, however, might include strongly correlated or non-relevant dimensions, making it hard to specify a suitable metric on such domains. Auto-encoders specialize in finding compact representations, where defining such a metric is likely to be easier. Therefore, we propose a reinforcement learning algorithm that can learn non-linear policies in continuous state spaces, which leverages representations learned using auto-encoders. We first evaluate this method on a simulated toy-task with visual input. Then, we validate our approach on a real-robot tactile stabilization task.


title: Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

abstract: A software birthmark is a unique characteristic of a program. Thus, comparing the birthmarks between the plaintiff and defendant programs provides an effective approach for software plagiarism detection. However, software birthmark generation faces two main challenges: the absence of source code and various code obfuscation techniques that attempt to hide the characteristics of a program. In this paper, we propose a new type of software birthmark called DYnamic Key Instruction Sequence (DYKIS) that can be extracted from an executable without the need for source code. The plagiarism detection algorithm based on our new birthmarks is resilient to both weak obfuscation techniques such as compiler optimizations and strong obfuscation techniques implemented in tools such as SandMark , Allatori and Upx . We have developed a tool called DYKIS-PD (DYKIS Plagiarism Detection tool) and conducted extensive experiments on large number of binary programs. The tool, the benchmarks and the experimental results are all publicly available.


title: An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype

abstract: The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain. Thus, the objective of this work is to classify VoIP encrypted traffic, where Gtalk and Skype applications are taken as good representatives. To this end, three different machine learning based approaches, namely, C4.5, AdaBoost and Genetic Programming (GP), are evaluated under data sets common and independent from the training condition. In this case, flow based features are employed without using the IP addresses, source/destination ports and payload information. Results indicate that C4.5 based machine learning approach has the best performance.


title: Evaluation of lightweight wheelchairs using ANSI/RESNA testing standards

abstract: Lightweight wheelchairs are characterized by their low cost and limited range of adjustment. Our study evaluated three different folding lightweight wheelchair models using the American National Standards Institute/Rehabilitation Engi- neering Society of North America (ANSI/RESNA) standards to see whether quality had improved since the previous data were reported. On the basis of reports of increasing breakdown rates in the community, we hypothesized that the quality of these wheelchairs had declined. Seven of the nine wheelchairs tested failed to pass the multidrum test durability requirements. An average of 194,502 +/- 172,668 equivalent cycles was completed, which is similar to the previous test results and far below the 400,000 minimum required to pass the ANSI/ RESNA requirements. This was also significantly worse than the test results for aluminum ultralight folding wheelchairs. Overall, our results uncovered some disturbing issues with these wheelchairs and suggest that manufacturers should put more effort into this category to improve quality. To improve the durability of lightweight wheelchairs, we suggested that stronger regulations be developed that require wheelchairs to be tested by independent and certified test laboratories. We also proposed a wheelchair rating system based on the National Highway Transportation Safety Administration vehicle crash ratings to assist clinicians and end users when comparing the durability of different wheelchairs.


title: Syscoin: A Peer-to-Peer Electronic Cash System with Blockchain-Based Services for E-Business

abstract: While Bitcoin (Peer-to-Peer Electronic Cash) [Nak]solved the double spend problem and provided work withtimestamps on a public ledger, it has not to date extendedthe functionality of a blockchain beyond a transparent andpublic payment system. Satoshi Nakamoto's original referenceclient had a decentralized marketplace service which was latertaken out due to a lack of resources [Deva]. We continued withNakamoto's vision by creating a set of commercial-grade ser-vices supporting a wide variety of business use cases, includinga fully developed blockchain-based decentralized marketplace,secure data storage and transfer, and unique user aliases thatlink the owner to all services controlled by that alias.


Security Developer Studies with GitHub Users: Exploring a Convenience Sample


title: Explicit Modeling of Human-Object Interactions in Realistic Videos

abstract: We introduce an approach for learning human actions as interactions between persons and objects in realistic videos. Previous work typically represents actions with low-level features such as image gradients or optical flow. In contrast, we explicitly localize in space and track over time both the object and the person, and represent an action as the trajectory of the object w.r.t. to the person position. Our approach relies on state-of-the-art techniques for human detection [32], object detection [10], and tracking [39]. We show that this results in human and object tracks of sufficient quality to model and localize human-object interactions in realistic videos. Our human-object interaction features capture the relative trajectory of the object w.r.t. the human. Experimental results on the Coffee and Cigarettes dataset [25], the video dataset of [19], and the Rochester Daily Activities dataset [29] show that 1) our explicit human-object model is an informative cue for action recognition; 2) it is complementary to traditional low-level descriptors such as 3D--HOG [23] extracted over human tracks. We show that combining our human-object interaction features with 3D-HOG improves compared to their individual performance as well as over the state of the art [23], [29].


title: Unsupervised image segmentation using convolutional autoencoder with total variation regularization as preprocessing

abstract: Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation and enhance their performance. Our approach relies on a convolutional autoencoder (CAE) with the total variation loss (TVL) for unsupervised learning. We show that, after our CAE-TVL preprocessing step, the over-segmentation effect is significantly reduced using the same unsupervised image segmentation methods. We evaluate our approach using the BSDS500 image segmentation benchmark dataset and show the performance enhancement introduced by our approach in terms of both increased segmentation accuracy and reduced computation time. We examine the robustness of the trained CAE and show that it is directly applicable to other natural scene images.


title: DESIGN OF N-WAY POWER DIVIDER SIMILAR TO THE BAGLEY POLYGON DIVIDER WITH AN EVEN NUMBER OF OUTPUT PORTS

abstract: In this paper, a general design of an equal-split N-way power divider, similar to Bagley polygon power divider, but with an even number of output ports is proposed. A circular-shaped 4- way divider is designed and simulated using two difierent full-wave simulators. Very good matching at the input port is achieved, and good transmission parameters are obtained. After that, the same circular-shaped divider is redrawn in a rectangular form to save more circuit area, and to align the four output ports together. For veriflcation purposes, the 4-way rectangular-shaped Bagley power divider is simulated, fabricated and measured. Both simulation and measurement results prove the validity of the design.


title: Rate it again: increasing recommendation accuracy by user re-rating

abstract: A common approach to designing Recommender Systems (RS) consists of asking users to explicitly rate items in order to collect feedback about their preferences. However, users have been shown to be inconsistent and to introduce a non-negligible amount of natural noise in their ratings that affects the accuracy of the predictions. In this paper, we present a novel approach to improve RS accuracy by reducing the natural noise in the input data via a preprocessing step. In order to quantitatively understand the impact of natural noise, we first analyze the response of common recommendation algorithms to this noise. Next, we propose a novel algorithm to denoise existing datasets by means of re-rating: i.e. by asking users to rate previously rated items again. This denoising step yields very significant accuracy improvements. However, re-rating all items in the original dataset is unpractical. Therefore, we study the accuracy gains obtained when re-rating only some of the ratings.In particular, we propose two partial denoising strategies: data and user-dependent denoising. Finally, we compare the value of adding a rating of an unseen item vs. re-rating an item. We conclude with a proposal for RS to improve the quality of their user data and hence their accuracy: asking users to re-rate items might, in some circumstances, be more beneficial than asking users to rate unseen items.


title: Higher education scholars' participation and practices on Twitter

abstract: Scholars participate in online social networks for professional purposes. In such networks, learning takes the form of participation and identity formation through engagement in and contribution to networked practices. While current literature describes the possible benefits of online participation, empirical research on scholars' use of online social networks in the educational technology literature is negligible. The purpose of this paper is to understand scholars' naturalistic practices in social networks in general, and on Twitter in particular. Tweets from 45 scholars were analysed qualitatively to arrive at dominant themes describing online social network practice. Findings indicate that scholars participating on Twitter (1) shared information, resources, and media relating to their professional practice; (2) shared information about their classroom and their students; (3) requested assistance from and offered suggestions to others; (4) engaged in social commentary; (5) engaged in digital identity and impression management; (6) sought to network and make connections with others; and (7) highlighted their participation in online networks other than Twitter. These findings assist the field in understanding the emerging practice of scholarly participation in online networks. © 2012 Wiley Periodicals, Inc.


title: Rivulet: Exploring Participation in Live Events through Multi-Stream Experiences

abstract: Live streaming has recently emerged as a growing form of participatory social media. While current live streaming practice focuses on single stream experiences, there are increasing instances of events covered by multiple live streams. In order to explore how to support communication and participation in multi-stream experiences, we present the design and evaluation of Rivulet, an end-to-end mobile live streaming system designed to support participatory multi-stream experiences. Rivulet affords simultaneously watching multiple live streams and incorporates existing feedback mechanisms of text chat and hearts with a novel push-to-talk audio modality. By recruiting viewers through Mechanical Turk, we were able to conduct a study of Rivulet at scale. We found that Rivulet afforded new engaging experiences for participants and led to an impromptu sense of community.


title: Proposal for CSE 190 Spring 2011: Identifying the Cuisine of a Plate of Food

abstract: Food is an indispensible part of our lives. In today’s globalized market, food from different geographical regions show remarkable variations in the choice of ingredients and the ways to prepare them. Some cuisines have ingredient choices unimaginable to customers habituated to other cuisines, but still present surprisingly tasty dishes. Besides unique ingredients, even the same ingredients, depending on the preparation process, may end up preserving different fractions of the nutritional value and very different calories. If restaurant networks provide a mobile app that can aim at a photograph of a plate of food and report its cuisine, the composing ingredients, and list other similar dishes, it could be a good marketing strategy. In addition, restaurants of a specific cuisine can also refer to other cuisines’ use of ingredients to sparkle ideas for more creative dishes that utilize similar ingredients in new ways. One core component of such an application is to recognize the cuisine and ingredients on a plate of food. Then the information may later be used to compare cuisines and find a network of common ingredients in the dishes.


title: TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network

abstract: Illegal logging has been identified as a major problem in the world, which may be minimized through effective monitoring of forest covered areas. In this paper, we propose and describe the initial steps to build a new three-tier architecture for Forest Monitoring based on Wireless Sensor Network and Chainsaw Noise Identification using a Neural Network. In addition to detection of chainsaw noises, we also propose methodologies to localize the origin of the chainsaw noise.


title: A millimeter-wave FMCW radar system simulator for automotive applications including nonlinear component models

abstract: A fully differential millimeter wave frequency modulated continuous wave (FMCW) radar architecture is built up in Agilent's RF design and system simulation environment ADS® to investigate the influence of RF impairments on system level on the target signal. To take also the surroundings of the automotive scenario into account a 3D ray tracing channel simulator is coupled in a Matlab® co-simulation providing realistic data of the traffic situation. Therefore investigations on the performance of the radar frontend in an automotive long range radar (LRR) application are enabled, the functionality of the circuitry can be verified and components designed in the most beneficial way, so that the best performance is achieved with the available resources. For nonlinear behavior the frontend components are parameterized according to recently published values for 77GHz SiGe circuits. The functionality of the complete simulation environment is demonstrated by calculating a typical traffic situation.


title: Design Patterns and Cross-Domain Analogies in Biologically Inspired Sustainable Design

abstract: Sustainable design is as an important movement in design. Biologically inspired design is a major paradigm for sustainable design. In this paper, we analyze a corpus of biologically inspired design projects in terms of sustainability. We then describe a case study of analogical design of a fog harvesting net, and abstract from it the patterns of Hydrophobia and Hydrophilia. We indicate how these two function-mechanism design patterns occur in several design projects in our corpus. This analysis indicates how biologically inspired sustainable design can be analyzed in terms of cross-domain analogical transfer of design patterns.


title: A Learning Framework for Winner-Take-All Networks with Stochastic Synapses

abstract: Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this paper, we show that a biologically motivated model based on multi-layer winner-take-all (WTA) circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task, and in a semi-supervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.


title: Health literacy and public health: A systematic review and integration of definitions and models

abstract: Health literacy concerns the knowledge and competences of persons to meet the complex demands of health in modern society. Although its importance is increasingly recognised, there is no consensus about the definition of health literacy or about its conceptual dimensions, which limits the possibilities for measurement and comparison. The aim of the study is to review definitions and models on health literacy to develop an integrated definition and conceptual model capturing the most comprehensive evidence-based dimensions of health literacy. A systematic literature review was performed to identify definitions and conceptual frameworks of health literacy. A content analysis of the definitions and conceptual frameworks was carried out to identify the central dimensions of health literacy and develop an integrated model. The review resulted in 17 definitions of health literacy and 12 conceptual models. Based on the content analysis, an integrative conceptual model was developed containing 12 dimensions referring to the knowledge, motivation and competencies of accessing, understanding, appraising and applying health-related information within the healthcare, disease prevention and health promotion setting, respectively. Based upon this review, a model is proposed integrating medical and public health views of health literacy. The model can serve as a basis for developing health literacy enhancing interventions and provide a conceptual basis for the development and validation of measurement tools, capturing the different dimensions of health literacy within the healthcare, disease prevention and health promotion settings.


title: Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots

abstract: Ambiguity and noise in natural language instructions create a significant barrier towards adopting autonomous systems into safety critical workflows involving humans and machines. In this paper, we propose to build on recent advances in electrophysiological monitoring methods and augmented reality technologies, to develop alternative modes of communication between humans and robots involved in large-scale proximal collaborative tasks. We will first introduce augmented reality techniques for projecting a robot's intentions to its human teammate, who can interact with these cues to engage in real-time collaborative plan execution with the robot. We will then look at how electroencephalographic (EEG) feedback can be used to monitor human response to both discrete events, as well as longer term affective states while execution of a plan. These signals can be used by a learning agent, a.k.a an affective robot, to modify its policy. We will present an end-to-end system capable of demonstrating these modalities of interaction. We hope that the proposed system will inspire research in augmenting human-robot interactions with alternative forms of communications in the interests of safety, productivity, and fluency of teaming, particularly in engineered settings such as the factory floor or the assembly line in the manufacturing industry where the use of such wearables can be enforced.


title: Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

abstract: Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.


title: Local elevation mapping based on low mounted lidar sensors with narrow vertical field of view

abstract: Environment mapping is a key component of autonomous navigation. In order to distinguish low obstacles from uneven ground, the shape of the ground surface has to be represented and estimated within the environment model. Elevation mapping is a computationally efficient approach that represents ground heights as well as obstacle heights without classification in a single model. This paper presents a novel approach for lidar-based elevation mapping that does not only use reflection points but combines reflections and three-dimensional ray geometry from different measurements directly in the height estimation process. Especially vehicles using low mounted lidar sensors with narrow vertical field of view benefit from an increased reliability and environment coverage. The approach is described from a theoretical concept to a scalable realization and first real test case results are shown.


title: The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics

abstract: This paper reviews the evolution of video quality measurement techniques and their current state of the art. We start with subjective experiments and then discuss the various types of objective metrics and their uses. We also introduce V-Factor, a "hybrid" metric using both transport- and bitstream information. Finally, we summarize the main standardization activities, such as the work of the video quality experts group (VQEG), and we take a look at emerging trends in quality measurement, including image preference, visual attention, and audiovisual quality.


Multi-objective Optimization for Computer Security and Privacy


title: Self-Localization Based on Visual Lane Marking Maps: An Accurate Low-Cost Approach for Autonomous Driving

abstract: Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.


title: A Bottom-Source Single-Gate Vertical Channel (BS-SGVC) 3D NAND Flash Architecture and Studies of Bottom Source Engineering

abstract: Vertical channel (VC) 3D NAND Flash may be categorized into two types of channel formation: (1) "U-turn" string, where both BL and source are connected at top thus channel current flows in a U-turn way; (2) "Bottom source", where source is connected at the bottom thus channel current flows only in one way. For the single-gate vertical channel (SGVC) 3D NAND architecture [1], it is also possible to develop a bottom source structure. The detailed array decoding method is illustrated. In this work, the challenges of bottom source processing and thin poly channel formation are extensively studied. It is found that the two-step poly formation and the bottom recess control are two key factors governing the device initial performance. In general, the two-step poly formation with additional poly spacer etching technique seems to cause degradation of both the poly mobility and device subthreshold slope. Sufficient thermal annealing is needed to recover the damage. Moreover, the bottom connection needs an elegant recess control for better read current as well as bottom ground-select transistor (GSL) device optimizations.


title: New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

abstract: A new content-based image retrieval (CBIR) scheme is proposed based on the optimised combination of the colour and texture features to enhance the image retrieval precision. This work focuses on a uniform partitioning scheme which is applied in the Hue, Saturation and Value (HSV) colour space to extract dominant colour descriptor (DCD) features. In the proposed CBIR scheme, the DCD features are initially extracted as the colour features, and then an appropriate similarity measure is applied. Also, several wavelet and curvelet features are defined as texture features to overcome the noise and the problem of image translation. Finally, the colour and texture features are optimally combined by using the particle swarm optimisation algorithm. The findings show that not only the proposed colour, wavelet and curvelet features outperform the existing ones but also their optimum combination has a better accuracy in comparison with several contemporary CBIR systems. The performance analysis shows that the proposed method improves the average precision metric from 67.85 to 71.05% for DCD, 58.90 to 65.43% for wavelet and 53.18 to 56.00% for curvelet using Corel dataset. In addition, the optimum combination presents the average precision of %76.50 which is significantly higher than the other state-of-the-art methods.


title: Neuromorphic sensory systems

abstract: Biology provides examples of efficient machines which greatly outperform conventional technology. Designers in neuromorphic engineering aim to construct electronic systems with the same efficient style of computation. This task requires a melding of novel engineering principles with knowledge gleaned from neuroscience. We discuss recent progress in realizing neuromorphic sensory systems which mimic the biological retina and cochlea, and subsequent sensor processing. The main trends are the increasing number of sensors and sensory systems that communicate through asynchronous digital signals analogous to neural spikes; the improved performance and usability of these sensors; and novel sensory processing methods which capitalize on the timing of spikes from these sensors. Experiments using these sensors can impact how we think the brain processes sensory information.


title: Consumer health information seeking on the Internet: the state of the art.

abstract: Increasingly, consumers engage in health information seeking via the Internet. Taking a communication perspective, this review argues why public health professionals should be concerned about the topic, considers potential benefits, synthesizes quality concerns, identifies criteria for evaluating online health information and critiques the literature. More than 70 000 websites disseminate health information; in excess of 50 million people seek health information online, with likely consequences for the health care system. The Internet offers widespread access to health information, and the advantages of interactivity, information tailoring and anonymity. However, access is inequitable and use is hindered further by navigational challenges due to numerous design features (e.g. disorganization, technical language and lack of permanence). Increasingly, critics question the quality of online health information; limited research indicates that much is inaccurate. Meager information-evaluation skills add to consumers' vulnerability, and reinforce the need for quality standards and widespread criteria for evaluating health information. Extant literature can be characterized as speculative, comprised of basic 'how to' presentations, with little empirical research. Future research needs to address the Internet as part of the larger health communication system and take advantage of incorporating extant communication concepts. Not only should research focus on the 'net-gap' and information quality, it also should address the inherently communicative and transactional quality of Internet use. Both interpersonal and mass communication concepts open avenues for investigation and understanding the influence of the Internet on health beliefs and behaviors, health care, medical outcomes, and the health care system.


title: Capsule Network Performance on Complex Data

abstract: In recent years, convolutional neural networks (CNN) have played an important role in the field of deep learning. Variants of CNN's have proven to be very successful in classification tasks across different domains. However, there are two big drawbacks to CNN's: their failure to take into account of important spatial hierarchies between features, and their lack of rotational invariance. As long as certain key features of an object are present in the test data, CNN's classify the test data as the object, disregarding features' relative spatial orientation to each other. This causes false positives. The lack of rotational invariance in CNN's would cause the network to incorrectly assign the object another label, causing false negatives. To address this concern, Hinton et al. propose a novel type of neural network using the concept of capsules in a recent paper. With the use of dynamic routing and reconstruction regularization, the capsule network model would be both rotation invariant and spatially aware. The capsule network has shown its potential by achieving a state-of-the-art result of 0.25% test error on MNIST without data augmentation such as rotation and scaling, better than the previous baseline of 0.39%. To further test out the application of capsule networks on data with higher dimensionality, we attempt to find the best set of configurations that yield the optimal test error on CIFAR10 dataset.


title: Knowledge base completion via search-based question answering

abstract: Over the past few years, massive amounts of world knowledge have been accumulated in publicly available knowledge bases, such as Freebase, NELL, and YAGO. Yet despite their seemingly huge size, these knowledge bases are greatly incomplete. For example, over 70% of people included in Freebase have no known place of birth, and 99% have no known ethnicity. In this paper, we propose a way to leverage existing Web-search-based question-answering technology to fill in the gaps in knowledge bases in a targeted way. In particular, for each entity attribute, we learn the best set of queries to ask, such that the answer snippets returned by the search engine are most likely to contain the correct value for that attribute. For example, if we want to find Frank Zappa's mother, we could ask the query `who is the mother of Frank Zappa'. However, this is likely to return `The Mothers of Invention', which was the name of his band. Our system learns that it should (in this case) add disambiguating terms, such as Zappa's place of birth, in order to make it more likely that the search results contain snippets mentioning his mother. Our system also learns how many different queries to ask for each attribute, since in some cases, asking too many can hurt accuracy (by introducing false positives). We discuss how to aggregate candidate answers across multiple queries, ultimately returning probabilistic predictions for possible values for each attribute. Finally, we evaluate our system and show that it is able to extract a large number of facts with high confidence.


title: Academic Self-Efficacy and First-Year College Student Performance and Adjustment.

abstract: A longitudinal study of 1st-year university student adjustment examined the effects of academic self-efficacy and optimism on students' academic performance, stress, health, and commitment to remain in school. Predictor variables (high school grade-point average, academic self-efficacy, and optimism) and moderator variables (academic expectations and self-perceived coping ability) were measured at the end of the first academic quarter and were related to classroom performance, personal adjustment, stress, and health, measured at the end of the school year. Academic self-efficacy and optimism were strongly related to performance and adjustment, both directly on academic performance and indirectly through expectations and coping perceptions (challenge-threat evaluations) on classroom performance, stress, health, and overall satisfaction and commitment to remain in school. Observed relationships corresponded closely to the hypothesized model.


title: Price Prediction of Share Market using Artificial Neural Network (ANN)

abstract: Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network. General Terms Artificial Neural Network, Machine Learning, Back propagation Algorithms, Share Market.


title: Interruptions in the workplace: A case study to reduce their effects

abstract: Collaboration is an important aspect for virtually all workplace environments. Workplaces often encourage and foster collaboration in a variety of ways with the purpose to collectively focus the group's attention on a specific problem and solve it as quickly and as efficiently as possible. While collaboration is generally viewed as a positive aspect of the workplace, the negative aspect-interruption-cannot be ignored. Interruptions are an important research area of human-computer interaction and with the growth of pervasive or ubiquitous computing on the rise, the number of interruptions we experience on a daily basis is also growing. It is for these reasons that interruption is and will continue to be a key issue in workplaces. This report presents the findings of a qualitative research project which explored interruptions in a mid-size software development company based in Ontario, Canada. The purpose of this research was to identify the types of interruptions (both on- and off-task) that occur during typical office software related activities, explore the contextual characteristics surrounding these interruptions, and identify methodologies that could be used to reduce the cost of interruptions and increase employee effectiveness and satisfaction.


title: Forward and inverse kinematics model for robotic welding process using KR-16KS KUKA robot

abstract: This paper aims to model the forward and inverse kinematics of a KUKA KR-16KS robotic arm in the application of a simple welding process. A simple welding task to weld a block onto a metal sheet is carried out in order to investigate the forward and inverse kinematics models of KR-16KS. A movement flow planning is designed and further developed into the KR-16KS programming. Eleven points of movement are studied for the forward kinematic modeling. A summary of calculation is obtained. A general D-H representation of forward and inverse matrix is obtained. This can be used in each of the welding operation movement based on KUKA KR-16KS robotic arm. A forward kinematic and an inverse kinematic aspect of KUKA KR-16KS is successfully modeled based on a simple welding task.


title: [Imperforate hymen: Can it be treated without damaging the hymenal structure?].

abstract: UNLABELLED ::: Imperforate hymen is a rare congenital anomaly, with an incidence of about 1 in 2000 female births. It is generally diagnosed during puberty. Treatment generally consists of a hymenotomy or a hymenectomy. Because the hymen is a symbol of virginity in some communities, its destruction can be source of social problems for some girls. ::: ::: ::: OBJECTIVES ::: We discuss the diagnostic but especially therapeutic aspects of imperforate hymens and possible surgical techniques, in particular those that preserve the hymen. ::: ::: ::: MATERIAL AND METHODS ::: We describe the cases of 5 girls treated in our department for imperforate hymen between 2001 and 2007. Two of them required the safeguarding of the normal architecture of their hymen to preserve the appearance of virginity. We analysed diagnostic features and surgical techniques. ::: ::: ::: RESULTS ::: The average age of our patients was 14.8 years (range: 11 and 17 years). The most frequent reason for consultation was pelvic pain with primary amenorrhea. Inspection of the vulva revealed in all cases a dome-shaped purplish-red hymeneal membrane. Hymeneal incision allowed drainage of old previously blocked menstrual blood. Three patients were treated by radial incisions of the hymen. The parents of 2 patients demanded that their hymens be preserved. Accordingly, one had a simple excision of a central flange of the hymen and the other was treated by a similar technique that also used a Foley catheter . All five patients did well after surgical treatment. The techniques used to preserve the hymen resulted in an apparently intact annular hymen. ::: ::: ::: CONCLUSION ::: Imperforate hymen is a rare anomaly. Its diagnosis is simple. The traditional technique of radial incisions is a simple procedure that yields good results. The technique using the Foley catheter is an adequate alternative when preservation of the hymen is required.


Comparative Hypolipidemic Properties between the Lyophilized Fruit Juice of Morinda citrifolia L . ( Rubiaceae ) and Lyophilized Commercial Noni Juice in Triton and Atherogenic Diet-Induced Dyslipidemic Rats


Opening ILT blackbox: Exploring recognition-based leadership perceptions with conjoint analysis


title: Real-Time Anomaly Detection for Streaming Analytics

abstract: Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in real-time. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.


title: Data mining with differential privacy

abstract: We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework. Differential privacy requires that computations be insensitive to changes in any particular individual's record, thereby restricting data leaks through the results. The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy. However, as we show in the paper, a naive utilization of the interface to construct privacy preserving data mining algorithms could lead to inferior data mining results. We address this problem by considering the privacy and the algorithmic requirements simultaneously, focusing on decision tree induction as a sample application. The privacy mechanism has a profound effect on the performance of the methods chosen by the data miner. We demonstrate that this choice could make the difference between an accurate classifier and a completely useless one. Moreover, an improved algorithm can achieve the same level of accuracy and privacy as the naive implementation but with an order of magnitude fewer learning samples.


title: Type classes as objects and implicits

abstract: Type classes were originally developed in Haskell as a disciplined alternative to ad-hoc polymorphism. Type classes have been shown to provide a type-safe solution to important challenges in software engineering and programming languages such as, for example, retroactive extension of programs. They are also recognized as a good mechanism for concept-based generic programming and, more recently, have evolved into a mechanism for type-level computation. This paper presents a lightweight approach to type classes in object-oriented (OO) languages with generics using the CONCEPT pattern and implicits (a type-directed implicit parameter passing mechanism). This paper also shows how Scala's type system conspires with implicits to enable, and even surpass, many common extensions of the Haskell type class system, making Scala ideally suited for generic programming in the large.


title: Serverless Data Analytics with Flint

abstract: Serverless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. In this paper, we explore a completely different part of the application space and examine the feasibility of analytical processing on big data using a serverless architecture. We present Flint, a prototype Spark execution engine that takes advantage of AWS Lambda to provide a pure pay-as-you-go cost model. With Flint, a developer uses PySpark exactly as before, but without needing an actual Spark cluster. We describe the design, implementation, and performance of Flint, along with the challenges associated with serverless analytics.


title: Thunderella: Blockchains with Optimistic Instant Confirmation

abstract: State machine replication, or “consensus”, is a central abstraction for distributed systems where a set of nodes seek to agree on an ever-growing, linearly-ordered log. In this paper, we propose a practical new paradigm called Thunderella for achieving state machine replication by combining a fast, asynchronous path with a (slow) synchronous “fall-back” path (which only gets executed if something goes wrong); as a consequence, we get simple state machine replications that essentially are as robust as the best synchronous protocols, yet “optimistically” (if a super majority of the players are honest), the protocol “instantly” confirms transactions.


title: A bibliometric approach to Systematic Mapping Studies: The case of the evolution and perspectives of community detection in complex networks

abstract: Critical analysis of the state of the art is a necessary task when identifying new research lines worthwhile to pursue. To such an end, all the available work related to the field of interest must be taken into account. The key point is how to organize, analyze, and make sense of the huge amount of scientific literature available today on any topic. To tackle this problem, we present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus, a modify SMS protocol is used relying on the scientific references metadata to extract, process and interpret the wealth of information contained in nowadays research literature. As a test case, the procedure is applied to determine the current state and perspectives of community detection in complex networks. Our results show that community detection is a still active, far from exhausted, in development, field. In addition, we find that, by far, the most exploited methods are those related to determining hierarchical community structures. On the other hand, the results show that fuzzy clustering techniques, despite their interest, are underdeveloped as well as the adaptation of existing algorithms to parallel or, more specifically, distributed, computational systems.


title: Adaptive Lasso for sparse highdimensional regression

abstract: We study the asymptotic properties of the adaptive Lasso estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase with the sample size. We consider variable selection using the adap- tive Lasso, where the L1 norms in the penalty are re-weighted by data-dependent weights. We show that, if a reasonable initial estimator is available, under ap- propriate conditions, the adaptive Lasso correctly selects covariates with nonzero coefficients with probability converging to one, and that theestimators of nonzero coefficients have the same asymptotic distribution they would have if the zero co- efficients were known in advance. Thus, the adaptive Lasso hasan oracle property in the sense of Fan and Li (2001) and Fan and Peng (2004). In addition, under a partial orthogonality condition in which the covariates with zero coefficients are weakly correlated with the covariates with nonzero coefficients, marginal regression can be used to obtain the initial estimator. With this initial estimator, the adaptive Lasso has the oracle property even when the number of covariates is much larger than the sample size.


title: BOAT adaptive credit card fraud detection system

abstract: Fraud is increasing with the extensive use of internet and the increase of online transactions. More advanced solutions are desired to protect financial service companies and credit card holders from constantly evolving online fraud attacks. The main objective of this paper is to construct an efficient fraud detection system which is adaptive to the behavior changes by combining classification and clustering techniques. This is a two stage fraud detection system which compares the incoming transaction against the transaction history to identify the anomaly using BOAT algorithm in the first stage. In second stage to reduce the false alarm rate suspected anomalies are checked with the fraud history database and make sure that the detected anomalies are due to fraudulent transaction or any short term change in spending profile. In this work BOAT supports incremental update of transactional database and it handles maximum fraud coverage with high speed and less cost. Proposed model is evaluated on both synthetically generated and real life data and shows very good accuracy in detecting fraud transaction.


title: The effect of flame retardants on the hot‐plate welding of talc‐filled polypropylene

abstract: The effect of a flame retardant on the weldability of polypropylene with two different talc loadings was studied by microscopy and mechanical tests on hot-plate welded injection molded tensile test bars. Welding changes the orientation of the talc particles, which align parallel to the weld interface, and can cause voiding of the material with a consequent decrease of weld strength. Welds of the material containing a flame retardant, which melts and volatizes at the temperatures used in welding, exhibited higher voiding and lower relative weld strengths on welding than the grade without a flame retardant. Voiding may be reduced using lower hot plate temperatures and higher welding displacements, but that results in an increase in the transverse orientation of the talc particles in the weld zone. The relative weld strength, which is affected by the composition of the material, was about 50% for the 20% talc-filled polypropylene and about 45% for the 30% talc-filled grade containing a flame retardant.


title: A dynamic model of cultural reproduction

abstract: The authors draw on Pierre Bourdieu’s theory of cultural reproduction to develop a formal model of the pathways through which cultural capital acts to enhance children’s educational and socioeconomic success. The authors’ approach brings conceptual and empirical clarity to an important area of study. Their model describes how parents transmit cultural capital to their children and how children convert cultural capital into educational success. It also provides a behavioral framework for interpreting parental investments in cultural capital. The authors review results from existing empirical research on the role of cultural capital in education to demonstrate the usefulness of their model for interpretative purposes, and they use National Longitudinal Survey of Youth 1979—Children and Young Adults survey data to test some of its implications.


title: Performance of coconut oil as an alternative transformer liquid insulation

abstract: Transformer mineral oil has been replaced by alternative oils such as synthetic oil and natural esters due to their biodegradability and environmentally friendly nature. This paper presents performance of coconut oil as such an alternative. Generally, as the other alternative oils, coconut oil has high conductivity due to the presence of free fatty acids. In this study, three different types of coconut oil samples consisting of virgin, copra and RBD (refined, bleached and deodorized) were initially tested by frequency dielectric spectroscopy (FDS) measurements to see how the conductivity was improved by dehydration and neutralization. The FDS results were fitted by inverse power dependence and Cole-Cole models to estimate the conductivity and response functions. Afterwards, a set of new coconut oil samples extracted from copra were thermally aged at 120°C under sealed conditions and compared with that of mineral oil. The performances of oil samples were evaluated using the test results of breakdown voltage, acidity, interfacial tension and FDS measurements under different aging periods such as 2, 5 and 7 weeks. Another 4 sets of new coconut and mineral oil samples were subjected to simulated thermal faults and electrical faults which include aging for 12 hours at 160°C, one hour at 200°C, exposing to partial discharges for four hours and subjecting to 20 low energy breakdowns. The performance comparisons were done by FDS measurements and dissolved gas analysis. In parallel, field-aged coconut oil samples collected from a sealed distribution transformer with 11 years of service were also tested by FDS measurements. In general, coconut oil shows its suitability as an alternative to the mineral oils for transformers, despite limitations found in some of their physical properties. It was found that the FDS results were in good agreement with chemical test results and with the estimated conductivity values.


title: Frequent item set mining

abstract: Frequent item set mining is one of the best known and most popular data mining methods. Originally developed for market basket analysis, it is used nowadays for almost any task that requires discovering regularities between (nominal) variables. This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task. It continues by discussing how the search space is structured to avoid redundant search, how it is pruned with the a priori property, and how the output is reduced by confining it to closed or maximal item sets or generators. In addition, it reviews some of the most important algorithmic techniques and data structures that were developed to make the search for frequent item sets as efficient as possible. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.


Fast , Interactive Origami Simulation using GPU Computation


title: Serotonin alters decisions to withdraw in fighting crayfish, Astacus astacus: the motivational concept revisited

abstract: The biogenic amine serotonin is thought to play an important role in aggression in many species, including man. This paper summarizes experimental approaches which attempt to link this neuromodulator with fighting in a crayfish model for which the complex agonistic behavior is well characterized. Based on a quantitative analysis of fighting we demonstrate that the infusion of small amounts of serotonin into freely-moving crayfish alters fighting behavior by specifically interfering with the timing of a treated animal's decision to withdraw from an encounter. In the presence of added serotonin, fights last considerably longer compared to controls, but no changes were detected in the rules of escalation, the likelihood of initiating an interaction, or its eventual outcome. Attempts to dissect the underlying neuronal mechanisms pharmacologically hinged on fluoxetine as a potent inhibitor of serotonin re-uptake. Although no behavioral changes were associated with acute infusion of fluoxetine alone, in combination with serotonin it effectively prevented the previously observed fight-enhancing effects. Our data strongly support the significance of functional amine re-uptake mechanisms for behavior and continued use of this invertebrate model should prove a promising route to unravel further the complex bases of aggression.


title: Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review

abstract: Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.


title: Differences in health care utilisation between elderly from ethnic minorities and ethnic Dutch elderly

abstract: In the Netherlands, as in other Western countries, ethnic minority elderly are more often in poorer health than the indigenous population. The expectation is that this health disadvantage results in more frequent use of health care services. We studied registered data on the proportion of health care receivers, frequency of use, and health care costs collected by a major Dutch health insurance company in 2010. Data from 10,316 Turkish, 14,490 Moroccan, 8,619 Surinamese, and 1,064 Moluccan adults aged 55 years and older were compared with data from a sample of 33,725 ethnic Dutch older adults. Unadjusted and adjusted (for age and gender) analyses showed the following. Moluccans had lower usage levels for all types of health care services. Use of primary health care facilities was higher for Turks, Moroccans, and Surinamese compared with the ethnic Dutch, with the exception that physical therapy was less frequently used among the Turks and Moroccans. Use of hospital care was lower, except for the Surinamese, who had a similar level of usage to that of the ethnic Dutch. The health disadvantage previously observed within most ethnic minority elderly populations does not result in an overall more frequent use of health care services. Further research is needed for the interpretation of the ethnic variations in health care use as potentially inequitable, by taking medical need, patient treatment preferences, and treatment adherence into account.


title: Non-intrusive Load Monitoring for Home Energy Usage with Multiple Power States Recognition

abstract: Active study has recently been conducted on Non-intrusive Load Monitoring (NILM) system to construct a cost-efficient household energy management framework. The conventional method, however, is not easily applicable to appliances that exhibit a complex operating mode or multiple states of power consumption. In this paper, we propose a NILM system that considers especially multiple power states of appliance. The proposed system profiles the power state of appliances based on the distribution of the real power and apparent power of the device. We utilize the state transition probability between power states, and use Hidden Markov model (HMM) for power state recognition.


title: The time course of visual word recognition as revealed by linear regression analysis of ERP data

abstract: Abstract EEG correlates of a range of psycholinguistic word properties were used to investigate the time course of access to psycholinguistic information during visual word recognition. Neurophysiological responses recorded in a visual lexical decision task were submitted to linear regression analysis. First, 10 psycholinguistic features of each of 300 stimulus words were submitted to a principal component analysis, which yielded four orthogonal variables likely to reflect separable processes in visual word recognition: Word length, Letter n-gram frequency, Lexical frequency and Semantic coherence of a word's morphological family. Since the lexical decision task required subjects to distinguish between words and pseudowords, the binary variable Lexicality was also investigated using a factorial design. Word–pseudoword differences in the event-related potential first appeared at 160 ms after word onset. However, regression analysis of EEG data documented a much earlier effect of both Word length and Letter n-gram frequency around 90 ms. Lexical frequency showed its earliest effect slightly later, at 110 ms, and Semantic coherence significantly correlated with neurophysiological measures around 160 ms, simultaneously with the lexicality effect. Source estimates indicated parieto-temporo-occipital generators for the factors Length, Letter n-gram frequency and Word frequency, but widespread activation with foci in left anterior temporal lobe and inferior frontal cortex related to Semantic coherence. At later stages (>200 ms), all variables exhibited simultaneous EEG correlates. These results indicate that information about surface form and meaning of a lexical item is first accessed at different times in different brain systems and then processed simultaneously, thus supporting cascaded interactive processing models.


title: Velocity-Based Stability Margins for Fast Bipedal Walking

abstract: We present velocity-based stability margins for fast bipedal walking that are sufficient conditions for stability, allow comparison between different walking algorithms, are measurable and computable, and are meaningful. While not completely necessary conditions, they are tighter necessary conditions than several previously proposed stability margins. The stability margins we present take into consideration a biped's Center of Mass position and velocity, the reachable region of its swing leg, the time required to swing its swing leg, and the amount of internal angular momentum available for capturing balance. They predict the opportunity for the biped to place its swing leg in such a way that it can continue walking without falling down. We present methods for estimating these stability margins by using simple models of walking such as an inverted pendulum model and the Linear Inverted Pendulum model. We show that by considering the Center of Mass location with respect to the Center of Pressure on the foot, these estimates are easily computable. Finally, we show through simulation experiments on a 12 degree-of-freedom distributed-mass lower-body biped that these estimates are useful for analyzing and controlling bipedal walking.


title: Strategic Versus Tactical Design

abstract: We seek to distinguish Strategic design decisions (e.g., to adopt a programming paradigm, architectural style, CBSE standard or application framework) from tactical design decisions (e.g. to use a design pattern, refactoring or programming idiom). This distinction is important since strategic statements carry far-reaching implications over the implementation and therefore must be made early in the development process, whereas tactical statements have localized effect and must be deferred to a latter stage in the process. We formulate the Locality criterion, a well-defined and language-independent criterion which divides all design statements in two abstraction classes. We apply our criterion to a broad range of statements and demonstrate that strategic statements are non-local and that tactical design statements are local. We also demonstrate that assumptions leading to architectural mismatch are non-local.


title: Reishi or Ling Zhi (Ganoderma lucidum)

abstract: In Latin, lucidum means shiny or brilliant and aptly describes this mushroom’s fruiting body, which has a modeled, sculptured, varnished appearance. The Chinese and Koreans know it as Ling Zhi (mushroom of herb and immortality), whereas the Japanese call this mushroom reishi or mannentake (10,000 year mushroom). The virtues of G. lucidum extracts, handed down from generation to generation, include it as a ‘‘cancer cure’’ and a symbol of happy augury, good fortune, good health, longevity, and even immortality. Beginning with the Yuan Dynasty (1280–1368 A.D.), G. lucidum has been endlessly represented in art—in paintings, carvings of jade and deer’s antlers, furniture and carpet designs, balustrades, jewelry, women’s hair combs, perfume bottles—in short, wherever an artistic urge found an outlet. The earliest mention of Ling Zhi was in the era of the first emperor of China, Shinghuang of the Ch’in Dynasty (221–207 B.C.). Subsequently, depictions of this fungus proliferated through Chinese literature and art. The mushroom is known by many in North America and Europe as one of the ‘‘artist’s conk’’ fungi (the true artist conk is Ganoderma applanatum). A detailed description of the reishi mushroom and its taxonomy can be found in Refs. (Fig. 1).


title: Conducting Feasibility Studies for Knowledge Based Systems

abstract: This paper describes how to carry out a feasibility study for a potential knowledge based system application. It discusses factors to be considered under three headings: the business case, the technical feasibility, and stakeholder issues. It concludes with a case study of a feasibility study for a KBS to guide surgeons in diagnosis and treatment of thyroid conditions.


title: Multimodal Classification of Events in Social Media

abstract: A large amount of social media hosted on platforms like Flickr and Instagram is related to social events. The task of social event classification refers to the distinction of event and non-event-related content as well as the classification of event types (e.g. sports events, concerts, etc.). In this paper, we provide an extensive study of textual, visual, as well as multimodal representations for social event classification. We investigate strengths and weaknesses of the modalities and study synergy effects between the modalities. Experimental results obtained with our multimodal representation outperform state-of-the-art methods and provide a new baseline for future research.


title: SHARE Interface in Flash Storage for Relational and NoSQL Databases

abstract: Database consistency and recoverability require guaranteeing write atomicity for one or more pages. However, contemporary database systems consider write operations non-atomic. Thus, many database storage engines have traditionally relied on either journaling or copy-on-write approaches for atomic propagation of updated pages to the storage. This reliance achieves write atomicity at the cost of various write amplifications such as redundant writes, tree-wandering, and compaction. This write amplification results in reduced performance and, for flash storage, accelerates device wear-out. In this paper, we propose a flash storage interface, SHARE. Being able to explicitly remap the address mapping inside flash storage using SHARE interface enables host-side database storage engines to achieve write atomicity without causing write amplification. We have implemented SHARE on a real SSD board, OpenSSD, and modified MySQL/InnoDB and Couchbase NoSQL storage engines to make them compatible with the extended SHARE interface. Our experimental results show that this SHARE-based MySQL/InnoDB and Couchbase configurations can significantly boost database performance. In particular, the inevitable and costly Couchbase compaction process can complete without copying any data pages.


title: An Energy-Aware Trust Derivation Scheme With Game Theoretic Approach in Wireless Sensor Networks for IoT Applications

abstract: Trust evaluation plays an important role in securing wireless sensor networks (WSNs), which is one of the most popular network technologies for the Internet of Things (IoT). The efficiency of the trust evaluation process is largely governed by the trust derivation, as it dominates the overhead in the process, and performance of WSNs is particularly sensitive to overhead due to the limited bandwidth and power. This paper proposes an energy-aware trust derivation scheme using game theoretic approach, which manages overhead while maintaining adequate security of WSNs. A risk strategy model is first presented to stimulate WSN nodes’ cooperation. Then, a game theoretic approach is applied to the trust derivation process to reduce the overhead of the process. We show with the help of simulations that our trust derivation scheme can achieve both intended security and high efficiency suitable for WSN-based IoT networks.


title: Research of the FP-Growth Algorithm Based on Cloud Environments

abstract: The emergence of cloud computing solves the problems that traditional data mining algorithms encounter when dealing with large data. This paper studies the FP-Growth algorithm and proposes a parallel linked list-based FPG algorithm based on MapReduce programming model, named as the PLFPG algorithm. And then it describes the main idea of algorithm. Finally, by using different data sets to test the algorithm, the experimental result shows that PLFPG algorithm has higher efficiency and better flexibility and scalability.


title: Hedonic and ergonomic quality aspects determine a software's appeal

abstract: The present study examines the role of subjectively perceived ergonomic quality (e.g. simplicity, controllability) and hedonic quality (e.g. novelty, originality) of a software system in forming a judgement of appeal. A hypothesised research model is presented. The two main research question are: (1) Are ergonomic and hedonic quality subjectively different quality aspects that can be independently perceived by the users? and (2) Is the judgement of appeal formed by combining and weighting ergonomic and hedonic quality and which weights are assigned? The results suggest that both quality aspects can be independently perceived by users. Moreover, they almost equally contributed to the appeal of the tested software prototypes. A simple averaging model implies that both quality aspects will compensate each other. Limitations and practical implication of the results are discussed.


abstract: Imitation learning is an efficient method for many robots to acquire complex skills. Some recent approaches to imitation learning provide strong theoretical performance guarantees. However, there remain crucial practical issues, especially during the training phase, where the training strategy may require execution of control policies that are possibly harmful to the robot or its environment. Moreover, these algorithms often require more demonstrations than necessary to achieve good performance in practice. This paper introduces a new approach called Maximum Mean Discrepancy Imitation Learning that uses fewer demonstrations and safer exploration policy than existing methods, while preserving strong theoretical guarantees on performance. We demonstrate empirical performance of this method for effective navigation control of a social robot in a populated environment, where safety and efficiency during learning are primary considerations.


title: PNUTS: Yahoo!'s hosted data serving platform

abstract: We describe PNUTS, a massively parallel and geographically distributed database system for Yahoo!'s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to reduce operational complexity. The first version of the system is currently serving in production. We describe the motivation for PNUTS and the design and implementation of its table storage and replication layers, and then present experimental results.


title: Tolerance synthesis of fastened metal-composite joints based on probabilistic and worst-case approaches

abstract: Abstract Variabilities in geometrical and material properties occur systematically after manufacturing and joining operations on different parts of an assembly. The behaviour and structural performance of composite/composite or composite/metal fastened joints are particularly sensitive to some of these variabilities. Controlling the effect of variabilities by tolerancing uncertain parameters is therefore crucial to avoid the failure of the structure. However, performing a variability study is generally costly because a large number of configurations need to be evaluated, especially when the behaviour model of the system is a numerical one. This paper presents an approach for tolerance synthesis of uncertain parameters in fastened metal-composite joints. The low time-cost of the approach is ensured by using a reduced finite element model of the joints and a strategy to reduce the number of calculations. Both probabilistic and worst-case approaches for the propagation of uncertainties can be applied through the proposed tolerancing synthesis. An allowable tolerance value for an uncertain parameter can then be easily calculated by identifying an analytical law which links tolerance to a structural performance criterion. The robustness of the proposed approach is illustrated by its application to a 4-bolt metal-composite single-lap joint where several sources of variability are introduced (i.e. hole-location error, pin/hole clearance, fastener preload).


Image Caption with Global-Local Attention


title: Learning Domain-Specific, L1-Specific Measures of Word Readability

abstract: Improved readability ratings for second-language readers could have a huge impact in areas such as education, advertising, and information retrieval. We propose ways to adapt readability measures for users who (a) are proficient in a particular domain, and (b) have a particular native language (L1). Specifically, we predict the readability of individual words. Our learned models use a range of creative features based on diverse statistical, etymological, lexical, and morphological information. We evaluate on a corpus of computational linguistics articles divided according to seven L1s; we show that we can accurately predict the target readability scores in this domain. Our technique improves over several reasonable baselines. We provide an in-depth analysis showing which kinds of information are most predictive of word difficulty in different L1s, and show how this differs for style and content words.


Heart Disease Prediction Using Data Mining preprocessing and Hierarchical Clustering


title: Supporting Process Mining Workflows with RapidProM

abstract: Process mining is gaining more and more attention both in industry and practice. As such, the number of process mining products is steadily increasing. However, none of these products allow for composing and executing analysis work flows consisting of multiple process mining algorithms. As a result, the analyst needs to perform repetitive process mining tasks manually and scientific process experiments are extremely labor intensive. To this end, we have RapidMiner 5, which allows for the definition and execution of analysis work flows, connected with the process mining framework ProM 6. As such any discovery, conformance, or extension algorithm of ProM can be used within a RapidMiner analysis process thus supporting process mining work flows.


title: A hybrid approach of interpolations and CNN to obtain super-resolution

abstract: We propose a novel architecture that learns an end-to-end mapping function to improve the spatial resolution of the input natural images. The model is unique in forming a nonlinear combination of three traditional interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest neighbor interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high-resolution images, thereby preserving the high-frequency components with fine details. We have compared with the state of the art and recent deep learning based natural image super-resolution techniques and found that our methods are able to preserve the sharp details in the image, while also obtaining comparable or better PSNR than them. Since our methods use only traditional interpolations and a shallow CNN with less number of smaller filters, the computational cost is kept low. We have reported the results of two proposed architectures on five standard datasets, for an upscale factor of 2. Our methods generalize well in most cases, which is evident from the better results obtained with increasingly complex datasets. For 4-times upscaling, we have designed similar architectures for comparing with other methods.


title: Towards deep learning with segregated dendrites

abstract: Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.


title: Hierarchical Reinforcement Learning and Hidden Markov Models for Task-Oriented Natural Language Generation

abstract: Surface realisation decisions in language generation can be sensitive to a language model, but also to decisions of content selection. We therefore propose the joint optimisation of content selection and surface realisation using Hierarchical Reinforcement Learning (HRL). To this end, we suggest a novel reward function that is induced from human data and is especially suited for surface realisation. It is based on a generation space in the form of a Hidden Markov Model (HMM). Results in terms of task success and human-likeness suggest that our unified approach performs better than greedy or random baselines.


title: Effects of elastic therapeutic taping on motor function in children with motor impairments: a systematic review.

abstract: BACKGROUND ::: The elastic therapeutic taping has been considered a promising resource for disabled children. ::: ::: ::: OBJECTIVE ::: To systematically review the evidence of the effects of elastic therapeutic taping on motor function in children with motor impairments. ::: ::: ::: METHOD ::: Three independent evaluators conducted searches in electronic databases (MEDLINE/PubMed, Scopus, LILACS, BIREME/BVS, Science Direct, SciELO, and PEDro). Clinical studies design, published until 2016, involving elastic therapeutic taping and children aged 0-12 years with motor impairments were included. The variables considered were the methodological aspects (study design, participants, outcome measurements, and experimental conditions); results presented in the studies, and also the methodological quality of studies. ::: ::: ::: RESULTS ::: Final selection was composed by 12 manuscripts (five randomized controlled trials), published in the last 10 years. Among them, cerebral palsy (CP) was the most recurrent disorder (n = 7), followed by congenital muscular torticollis (n = 2) and brachial plexus palsy (n = 2). Positive results were associated with taping application: improvement in the upper limb function, gross motor skills, postural control, muscular balance, and performance in the dynamics functional and daily activities. ::: ::: ::: LIMITATIONS ::: Lower quality of the studies, clinical and population heterogeneity existed across studies. ::: ::: ::: CONCLUSIONS ::: The elastic therapeutic taping has been shown to be a promising adjunct resource to the conventional rehabilitation in children with motor impairments. However, high methodological studies about its efficacy in this population are already scarce. Implications for Rehabilitation Elastic therapeutic taping has been shown to be a promising adjunct resource to the conventional rehabilitation in disabled children. Clinical trials have indicated improvement in the postural control and functional activities with both, upper and lower limbs, and increase in the functional independency resulting from the taping use. Randomized control trials and well-established protocols are needed to increase the confidence in applying elastic therapeutic taping to specific clinical conditions.


title: Using artificial intelligence in wireless sensor routing protocols

abstract: This paper represents a dissertation about how an artificial intelligence technique can be applied to wireless sensor networks. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded in these kind of networks. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore the performance of two very well known routing paradigms, directed diffusion and Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed.


title: The Smart Wheelchair Component System

abstract: While the needs of many individuals with disabilities can be satisfied with power wheelchairs, some members of the disabled community find it difficult or impossible to operate a standard power wheelchair. To accommodate this population, several researchers have used technologies originally developed for mobile robots to create "smart wheelchairs" that reduce the physical, perceptual, and cognitive skills necessary to operate a power wheelchair. We are developing a Smart Wheelchair Component System (SWCS) that can be added to a variety of commercial power wheelchairs with minimal modification. This paper describes the design of a prototype of the SWCS, which has been evaluated on wheelchairs from four different manufacturers.


title: List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table

abstract: Efficient application scheduling algorithms are important for obtaining high performance in heterogeneous computing systems. In this paper, we present a novel list-based scheduling algorithm called Predict Earliest Finish Time (PEFT) for heterogeneous computing systems. The algorithm has the same time complexity as the state-of-the-art algorithm for the same purpose, that is, O(v2.p) for v tasks and p processors, but offers significant makespan improvements by introducing a look-ahead feature without increasing the time complexity associated with computation of an optimistic cost table (OCT). The calculated value is an optimistic cost because processor availability is not considered in the computation. Our algorithm is only based on an OCT that is used to rank tasks and for processor selection. The analysis and experiments based on randomly generated graphs with various characteristics and graphs of real-world applications show that the PEFT algorithm outperforms the state-of-the-art list-based algorithms for heterogeneous systems in terms of schedule length ratio, efficiency, and frequency of best results.


title: Kite Generator System Modeling and Grid Integration

abstract: This paper deals principally with the grid connection problem of a kite-based system, named the “Kite Generator System (KGS).” It presents a control scheme of a closed-orbit KGS, which is a wind power system with a relaxation cycle. Such a system consists of a kite with its orientation mechanism and a power transformation system that connects the previous part to the electric grid. Starting from a given closed orbit, the optimal tether's length rate variation (the kite's tether radial velocity) and the optimal orbit's period are found. The trajectory-tracking problem is not considered in this paper; only the kite's tether radial velocity is controlled via the electric machine rotation velocity. The power transformation system transforms the mechanical energy generated by the kite into electrical energy that can be transferred to the grid. A Matlab/simulink model of the KGS is employed to observe its behavior, and to insure the control of its mechanical and electrical variables. In order to improve the KGS's efficiency in case of slow changes of wind speed, a maximum power point tracking (MPPT) algorithm is proposed.


title: To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression

abstract: We have witnessed the tremendous growth of videos over the Internet, where most of these videos are typically paired with abundant sentence descriptions, such as video titles, captions and comments. Therefore, it has been increasingly crucial to associate specific video segments with the corresponding informative text descriptions, for a deeper understanding of video content. This motivates us to explore an overlooked problem in the research community — temporal sentence localization in video, which aims to automatically determine the start and end points of a given sentence within a paired video. For solving this problem, we face three critical challenges: (1) preserving the intrinsic temporal structure and global context of video to locate accurate positions over the entire video sequence; (2) fully exploring the sentence semantics to give clear guidance for localization; (3) ensuring the efficiency of the localization method to adapt to long videos. To address these issues, we propose a novel Attention Based Location Regression (ABLR) approach to localize sentence descriptions in videos in an efficient end-to-end manner. Specifically, to preserve the context information, ABLR first encodes both video and sentence via Bi-directional LSTM networks. Then, a multi-modal co-attention mechanism is presented to generate both video and sentence attentions. The former reflects the global video structure, while the latter highlights the sentence details for temporal localization. Finally, a novel attention based location prediction network is designed to regress the temporal coordinates of sentence from the previous attentions. We evaluate the proposed ABLR approach on two public datasets ActivityNet Captions and TACoS. Experimental results show that ABLR significantly outperforms the existing approaches in both effectiveness and efficiency.


title: A Secure Registration Scheme for Femtocell Embedded Networks

abstract: Recently, femtocell received a signification interest to improve the indoor coverage and provide better voice and data services. Lots of work has been done to improve the femtocell security, but still there are some issues which need to be addressed. Our contribution to the femtocell security is to protect secure zone (femtocell coverage area within macrocell) from unauthorized (non-CSG) users. In this paper, we propose a secure registration scheme for femtocell embedded network. In this scheme, only Closed Subscriber Group (CSG) users are allowed to access both the femtocell and macrocell services within the secure zone. By prioritizing the femtocell over macrocell within the secure zone, every user will try to camp on femtocell and invoke location registration to the femtocell as the user enters to the femtocell coverage area. If the user is within the allowed users list, the femtocell will allow the user otherwise femtocell will send a reject message to the user and also send the user information to the core network.


title: Automotive battery management systems

abstract: Battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, controlling the charge, determining the state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL) of the battery, cell balancing, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates three critical characteristics of the battery (SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on an equivalent circuit battery model consisting of resistors, capacitor, and Warburg impedance. The resistors usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. We investigate the use of support vector machines to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. The circuit parameters are estimated from electrochemical impedance spectroscopy (EIS) test data using nonlinear least squares estimation techniques. Predictions of remaining useful life (RUL) of the battery are obtained by support vector regression of the power fade and capacity fade estimates.


title: Discovering authorities in question answer communities by using link analysis

abstract: Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.


title: A Honeypot Architecture for Detecting and Analyzing Unknown Network Attacks

abstract: In this paper, we propose a honeypot architecture for detecting and analyzing unknown network attacks. The main focus of our approach lies in improving the “significance” of recorded events and network traffic that need to be analyzed by a human network security operator in order to identify a new attacking pattern. Our architecture aims to achieve this goal by combining three main components: 1. a packet filter that suppresses all known attacking packets, 2. a proxy host that performs session-individual logging of network traffic, and 3. a honeypot host that executes actual network services to be potentially attacked from the Internet in a carefully supervised environment and that reports back to the proxy host upon the detection of suspicious behavior. Experiences with our first prototype of this concept show that it is relatively easy to specify suspicious behavior and that traffic belonging to an attack can be successfully identified and marked.


title: SIW Hybrid Feeding Network-Integrated 2-D DRA Array: Simulations and Experiments

abstract: Simulation and experimental results of substrate integrated waveguide (SIW)-integrated 2-D dielectric resonator antenna (DRA) array operating at millimeter-wave frequency band are presented. The antenna array consists of 64 elements arranged in 8 ×8. Hybrid feeding network combining series-fed and corporate-fed is used to provide a uniform power distribution over the entire aperture of the array. The antenna array is fabricated and tested. The measured reflection coefficient shows an impedance bandwidth ( S11 ≤ - 10 dB) of 2 GHz and a measured gain of 21.60 dB over the operating frequency band 35-37 GHz. A very good correlation between the measured results and simulated results is observed.


title: The peculiarities of knowledge management processes in SMEs: the case of Singapore

abstract: Purpose – The objectives of this study are two-fold. The first is to examine the peculiarities of KM processes that are unique in SMEs from three perspectives, namely knowledge creation, knowledge sharing and knowledge reuse. Secondly, to identify enablers and impediments of these KM processes that influence the competitiveness of SMEs. Design/methodology/approach – The study adopted a case study approach involving 21 participants comprising management staff and front-line employees from four Singaporean SMEs. Findings – The SME owner, rather than the employees, was found to be the key source and creator of knowledge and the sole driver of the KM processes. In SMEs, knowledge creation takes the form of innovative customized solutions to meet customers' needs; knowledge sharing occurs through cross functionality, overlapping roles, and facilitated by close physical proximity in open workspaces; and knowledge reuse is often made tacitly, where common knowledge is prevalently embedded within the KM processes...


title: A powerful and efficient algorithm for numerical function optimization : artificial bee colony ( ABC ) algorithm

abstract: Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.


Top-K High Utility Episode Mining from a Complex Event Sequence