title: Precomputed Safety Shapes for Efficient and Accurate Height-Field Rendering

abstract: Height fields have become an important element of realistic real-time image synthesis to represent surface details. In this paper, we focus on the frequent case of static height-field data, for which we can precompute acceleration structures. While many rendering algorithms exist that impose tradeoffs between speed and accuracy, we show that even accurate rendering can be combined with high performance. A careful analysis of the surface defined by the height values, leads to an efficient and accurate precomputation method. As a result, each texel stores a safety shape inside which a ray cannot cross the surface twice. This property ensures that no intersections are missed during the efficient marching method. Our analysis is general and can even consider visibility constraints that are robustly integrated into the precomputation. Further, we propose a particular instance of safety shapes with little memory overhead, which results in a rendering algorithm that outperforms existing methods, both in terms of accuracy and performance.


title: What affects web credibility perception? An analysis of textual justifications

abstract: In this paper, we present the findings of a qualitative analysis of 15,750 com- ments left by 2,041 participants in a Reconcile web credibility evaluation study. While assessing the credibility of the presented pages, respondents of the Re- concile studies were also asked to justify their ratings in writing. This work attempts to give an insight into the factors that aected the credibility asses- sment. To the best of our knowledge, the presented study is the most-recent large-scale study of its kind carried out since 2003, when the Fogg et al. -How do users evaluate the credibility of Web sites? A study with over 2,500 parti- cipants' paper was published. The performed analysis shows that the findings made a decade ago are still mostly valid today despite the passage of time and the advancement of Internet technologies. However we report a weaker impact of webpage appearance. A much bigger dataset (as compared to Fogg's stu- dies) allowed respondents to reveal additional features, which influenced the credibility evaluations.


title: Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

abstract: Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework, the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.


title: The Partnership Between Organizational Learning and Knowledge Management

abstract: This chapter examines the interdependent relationship of learning and the emerging discipline of knowledge management (KM). Following a clarification of definitions, the common ground is explored in terms of structure, strategy, technology, leadership, and environment. Specific areas of intersection addressed include individual learning and KM; learning and communities of practice; learning and systems thinking, and learning and flow. A learning continuum is presented to illuminate the relationship between learning and the environment as change drives new knowledge needs.


title: Advanced analytics for harnessing the power of smart meter big data

abstract: Smart meters or advanced metering infrastructure (AMI) are being deployed in many countries around the world. Smart meters are the basic building block of the smart grid and governments have invested vast amounts in smart meter deployment targeting wide economic, social and environmental benefits. The key functionality of the smart meter is the capture and transfer of data relating to the consumption (electricity, gas) and events such as power quality and meter status. Such capability has also resulted in the generation of an unprecedented data volume, speed of collection and complexity, which has resulted in the so called big data challenge. To realize the hidden value and power in such data, it is important to use the appropriate tools and technology which are currently being called advanced analytics. In this paper we define a smart metering landscape and discuss different technologies available for harnessing the smart meter captured data. Main limitations and challenges with existing techniques with big data are also highlighted and several future directions in smart metering are presented.


title: Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems

abstract: It has been widely realized that Monte Carlo methods (approximation via a sample ensemble) may fail in large scale systems. This work offers some theoretical insight into this phenomenon in the context of the particle filter. We demonstrate that the maximum of the weights associated with the sample ensemble converges to one as both the sample size and the system dimension tends to infinity. Specifically, under fairly weak assumptions, if the ensemble size grows sub-exponentially in the cube root of the system dimension, the convergence holds for a single update step in state-space models with independent and identically distributed kernels. Further, in an important special case, more refined arguments show (and our simulations suggest) that the convergence to unity occurs unless the ensemble grows super-exponentially in the system dimension. The weight singularity is also established in models with more general multivariate likelihoods, e.g. Gaussian and Cauchy. Although presented in the context of atmospheric data assimilation for numerical weather prediction, our results are generally valid for high-dimensional particle filters.


title: An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

abstract: In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms. The ED essentially predict the equilibriums outcomes of the MAS where the agents use individual RL algorithms. More specifically, we show how the ED predict the learning trajectories of Q-Learners for iterated games. Moreover, we apply our results to (an extension of) the COllective INtelligence framework (COIN). COIN is a proved engineering approach for learning of cooperative tasks in MASs. The utilities of the agents are re-engineered to contribute to the global utility. We show how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED.


title: Designing Games to Discourage Sedentary Behaviour

abstract: Regular physical activity has many physical, cognitive and emotional benefits. Health researchers have shown that there are also risks to too much sedentary behaviour, regardless of a person’s level of physical activity, and there are now anti-sedentary guidelines alongside the guidelines for physical activity. Exergames (games that require physical exertion) have been successful at encouraging physical activity through fun and engaging gameplay; however, an individual can be both physically active (e.g. by going for a jog in the morning) and sedentary (e.g. by sitting at a computer for the rest of the day). In this chapter, we analyse existing exertion games through the lens of the anti-sedentary guidelines to determine which types of games also meet the requirements for anti-sedentary game design. We review our own game designs in this space and conclude with an identification of design opportunities and research challenges for the new area of anti-sedentary game design.


title: A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval

abstract: Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and then rank documents by the likelihood of the query according to the estimated language model. A core problem in language model estimation is smoothing , which adjusts the maximum likelihood estimator so as to correct the inaccuracy due to data sparseness. In this paper, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collections.


title: Static analysis of Android programs

abstract: Android is a programming language based on Java and an operating system for mobile or embedded devices. It features an extended event-based library and dynamic inflation of graphical views from declarative XML layout files. A static analyzer for Android programs must consider such features, for correctness and precision. This article is a description of how we extended the Julia system, based on abstract interpretation, to run formally correct analyses of Android programs. We have analyzed with Julia the Android sample applications by Google and a few larger open-source programs. Julia has found, automatically, bugs and flaws both in the Google samples and in the open-source programs.


1 Nanoelectronic Solutions for Hardware Security


Induced disgust, happiness and surprise: an addition to the mmi facial expression database


title: 1 Dynamic Price Forecasting In Simultaneous Online Art

abstract: In recent years, the simultaneous online auction (SOA) has become a popular mechanism for selling heterogeneous items such as antiques, art, furniture, and collectibles. These auctions sell multiple items concurrently to a selected group of bidders who often participate in multiple auctions simultaneously. Such bidder behavior creates a unique competitive environment where bidders compete against each other both within the same auction as well as across different auctions. In this chapter, we present a novel dynamic forecasting approach for predicting price in ongoing SOAs. Our proposed model generates a price forecast from the time of prediction until auction close. It updates its forecasts in real-time as the auction progresses based on newly arriving information, price dynamics and competition intensity. Applying this method to a dataset of contemporary Indian art SOAs, we find high predictive accuracy of the dynamic model in comparison to more traditional approaches. We further investigate the source of the predictive power of our model and find that price dynamics capture bidder competition within and across auctions. The importance of this finding is both conceptual and practical: price dynamics are simple to compute at high accuracy, as they require information only from the focal auction and are therefore a parsimonious representation of different forms of within-auction and between-auction competition.


title: File-level vs. module-level regression test selection for .NET

abstract: Regression testing is used to check the correctness of evolving software. With the adoption of Agile development methodology, the number of tests and software revisions has dramatically increased, and hence has the cost of regression testing. Researchers proposed regression test selection (RTS) techniques that optimize regression testing by skipping tests that are not impacted by recent program changes. Ekstazi is one such state-of-the art technique; Ekstazi is implemented for the Java programming language and has been adopted by several companies and open-source projects. We report on our experience implementing and evaluating Ekstazi#, an Ekstazi-like tool for .NET. We describe the key challenges of bringing the Ekstazi idea to the .NET platform. We evaluate Ekstazi# on 11 open-source projects, as well as an internal Microsoft project substantially larger than each of the open-source projects. Finally, we compare Ekstazi# to an incremental build system (also developed at Microsoft), which, out of the box, provides module-level dependency tracking and skipping tasks (including test execution) whenever dependencies of a task do not change between the current and the last successful build. Ekstazi# on average reduced regression testing time by 43.70% for the open-source projects and by 65.26% for the Microsoft project (the latter is in addition to the savings provided by incremental builds).


title: A non-linear switching controller for ball and plate system

abstract: The ball and plate system is a typical multi-variable non-linear plant. By extension of the traditional ball and beam system, it is used as a standard benchmark to inspect diverse control schemes. In this paper, a problem with trajectory-tracking of ball and plate systems is studied based on non-linear control theory. Firstly, we briefly summarise control problems of ball and plate systems and introduce a ball and plate system named BPVS-JLU developed in authors' laboratory. Then the system's mathematical model is derived using the Lagrange method. Lastly, a non-linear switching control scheme is put forward to deal with the trajectory-tracking problem after the non-linear control analysis of ball and plate system. Experimental results have shown that the proposed scheme can accomplish the task of trajectory-tracking effectively.


title: User experiences and expectations of vibrotactile, thermal and squeeze feedback in interpersonal communication

abstract: The haptic modality provides a new channel for interpersonal communication through technology by utilizing the sense of touch. In the development of novel haptic communication devices, it is essential to explore the potential users' perceptions of such a communication channel. To this end, we conducted two explorative user studies with two early prototypes that demonstrated three different haptic feedback types: vibrotactile, thermal and squeeze feedback. We arranged focus groups and interviews to study the participants' experiences, expectations and ideas of using these haptic technologies in interpersonal communication. The findings show, for example, that people prefer to use haptic communication mainly with people close to them. Haptics can be used for pragmatic purposes as well as in emotional communication, for example in mimicking touch between the communication partners. Squeezes were experienced as the most pleasant type of haptic feedback. Furthermore, the participants preferred receiving haptic stimuli to their hand area, through a mobile phone or a wristband-like device. We argue that using early prototypes in an early stage of research process in focus groups and interviews is especially useful for stimulating idea generation and discussions about expectations and experiences of haptic technologies.


title: Face Aging Simulation with Deep Convolutional Generative Adversarial Networks

abstract: Human face aging process simulated by computer has become a hot research issue of the computer vision field. In this paper we propose an improved face aging model based on Deep Convolutional Generative Adversarial Network (DCGAN). In this model, a given face is first mapped to a personal latent vector and age-conditional vector through two sub-encoders. Inputting these two vectors into generator, and then stable and photo-realistic face images are generated by preserving personalized face features and changing age condition. Perceptual similarity loss replace adversarial loss of Generative Adversarial Networks (GANs) as the objective function in this paper. Based on the existing face database, the experiment results demonstrate that face images synthesized by our method enjoys better authenticity and accuracy.


title: Behavioral intention formation in knowledge sharing: Examining the roles of KMS quality, KMS self-efficacy, and organizational climate

abstract: Firms can obtain competitive advantages from their employees' knowledge sharing behaviors. This paper presents a research model to examine the direct and indirect effects of knowledge management systems (KMS) quality, KMS self-efficacy, organizational climate and attitude on the intention to share knowledge in the new product development process. The hypotheses are tested on data collected from 134 major electronic manufacturing firms in Taiwan, using partial least squares regression. The results of the empirical study suggest that attitude is the key factor influencing intention to engage in knowledge sharing. The more a factor (such as KMS self-efficacy and organizational climate) positively contributes to attitude, the more the factor contributes to knowledge sharing. The findings provide useful insights into how organizations should encourage employees' collaborative behaviors or activities so as to reinforce KMS self-efficacy create a favorable organizational climate that will in turn enhance attitude and intention to engage in knowledge sharing leading to benefits for the organization as a whole.


title: User experience over time

abstract: The way we experience and evaluate interactive products develops over time. An exploratory study aimed at understanding how users form evaluative judgments during the first experiences with a product as well as after four weeks of use. Goodness, an evaluative judgment related to the overall satisfaction with the product, was largely formed on the basis of pragmatic aspects (i.e. utility and usability) during the first experiences; after four weeks of use identification (i.e. what the products expresses about its owner) became a dominant aspect of how good a product is. Surprisingly, beauty judgments were largely affected by stimulation (e.g. novelty) during the first experiences. Over time stimulation lost its power to make the product beautiful in the users' eyes.


title: Clustering based association rule mining on online stores for optimized cross product recommendation

abstract: The Online Shopping Experience has opened the new ways of business and shopping. Now the traditional terms of shopping have been changed and new terms to shop online emerge into customers' online shopping behaviors and preferences. Extracting interesting shopping patterns from ever increasing data is not a trivial task. We need intelligent association rule mining of the available data; that can be practically knowledgeable for the online retail stores, so that they can make viable business decisions. This paper will help to understand the importance of data mining techniques, i.e., association rules, clustering and concept hierarchy in order to provide business intelligence for improved sales, marketing and consumers' satisfaction.


title: Minimal Interaction Content Discovery in Recommender Systems

abstract: Many prior works in recommender systems focus on improving the accuracy of item rating predictions. In comparison, the areas of recommendation interfaces and user-recommender interaction remain underexplored. In this work, we look into the interaction of users with the recommendation list, aiming to devise a method that simplifies content discovery and minimizes the cost of reaching an item of interest. We quantify this cost by the number of user interactions (clicks and scrolls) with the recommendation list. To this end, we propose generalized linear search (GLS), an adaptive combination of the established linear and generalized search (GS) approaches. GLS leverages the advantages of these two approaches, and we prove formally that it performs at least as well as GS. We also conduct a thorough experimental evaluation of GLS and compare it to several baselines and heuristic approaches in both an offline and live evaluation. The results of the evaluation show that GLS consistently outperforms the baseline approaches and is also preferred by users. In summary, GLS offers an efficient and easy-to-use means for content discovery in recommender systems.


title: Hybrid $k$ -Nearest Neighbor Classifier

abstract: Conventional ${k}$ -nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches. Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space. Finally, the nonparametric tests are proposed to be adopted to compare the proposed method with other classification approaches over multiple datasets. The experiments on the real-world datasets from the Knowledge Extraction based on Evolutionary Learning dataset repository demonstrate that RS-HBKNN works well on real datasets, and outperforms most of the state-of-the-art classification approaches.


title: Long Range Obstacle Detection Using Laser Scanner and Stereovision

abstract: To be exploited for driving assistance purpose, a road obstacle detection system must have a good detection rate and an extremely low false detection rate. Moreover, the field of possible applications depends on the detection range of the system. With these ideas in mind, we propose in this paper a long range generic road obstacle detection system based on fusion between stereovision and laser scanner. The obstacles are detected and tracked by the laser sensor. Afterwards, stereovision is used to confirm the detections. An overview of the whole method is given. Then the confirmation process is detailed: three algorithms are proposed and compared on real road situations.


title: Energy-Efficient Resource Allocation for Multi-User Mobile Edge Computing

abstract: To increase mobile batteries' lifetime and improve quality of experience for computation-intensive and latency-sensitive applications, mobile edge computing has received significant interest. Designing energy-efficient mobile edge computing systems requires joint optimization of communication and computation resources. In this paper, we consider energy-efficient resource allocation for a multi-user mobile edge computing system. First, we establish on two computation-efficient models with negligible and non-negligible base station (BS) executing durations, respectively. Then, under each model, we formulate the overall weighted sum energy consumption minimization problem by optimally allocating communication and computation resources. The optimization problem for negligible BS executing duration is convex, and we obtain the optimal solution in closed-form to this problem. The optimization problem for non-negligible BS executing duration is NP-hard in general, and we obtain a sub-optimal solution with low-complexity to this problem, by connecting it to a three-stage flow-shop scheduling problem and wisely utilizing Johnson's algorithm. Finally, numerical results show that the proposed solutions outperform some baseline schemes.


title: A Robust Spoken Language Architecture to Control a 2D Game

abstract: Speech has been increasingly used as input and/or output modality in commercial systems and academic prototypes. Its capability to complement and enhance applications makes it possible to consider spoken language as a new means to support human interaction with computers in interactive domains: computer games being one of them. This paper presents an architecture that we developed to play a 2D graphical version of a board game using spoken natural language. In our system, the sensed player’s speech is syntactically parsed with a robust weighted finite state transducer algorithm that creates a simplified representation of the input sentence. In a pipelined process, a semantic parser is then responsible of splitting the input representation generated during syntactical analysis into a series of data structures each representing the semantics of the underlying text chunks within the original sentence. These data structures are then passed on to the game logic module which generates game commands, resolves possible ambiguities or incompleteness within the data structures, and checks preconditions related to the validity of the commands. If the preconditions are met, the instructions are carried out and the game state is updated as direct effect of the commands issued. In a series of preliminary tests to assess the robustness of our approach, we obtained a correct classification of input sentences for up to 95.8% of the instructions issued by the user. Interaction with our systems occurs in real-time.


Vehicle Accident Detection and Reporting System Using GPS and GSM


title: PSIM and MATLAB Co-Simulation of Photovoltaic System using “P and O” and “Incremental Conductance” MPPT

abstract: The photovoltaic (PV) generator shows a nonlinear current-voltage (I-V) characteristic that its maximum power point (MPP) differs with irradiance and temperature. by employing simple maximum power point tracking algorithms, we can track this MPP and increase the efficiency of our photovoltaic system. Two methods for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and insolation conditions are discussed in this work: Incremental Conductance compared to conventional tracking algorithm (P&O). ::: In this paper, a new modeling solution is presented, using co-simulation between a specialist modeling tool called PSIM and the popular Matlab software using simcoupler module. Co-simulation is carried out by implementing the MPPT command circuits in PSIM and PV panel, boost DC-DC converter and battery in MATLAB/Simulink.


title: Do we need hundreds of classifiers to solve real world classification problems

abstract: We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).


title: Written-domain language modeling for automatic speech recognition.

abstract: Language modeling for automatic speech recognition (ASR) systems has been traditionally in the verbal domain. In this paper, we present finite-state modeling techniques that we developed for language modeling in the written domain. The first technique we describe is for the verbalization of written-domain vocabulary items, which include lexical and non-lexical entities. The second technique is the decomposition–recomposition approach to address the out-of-vocabulary (OOV) and the data sparsity problems with non-lexical entities such as URLs, email addresses, phone numbers, and dollar amounts. We evaluate the proposed written-domain language modeling approaches on a very large vocabulary speech recognition system for English. We show that the written-domain language modeling improves the speech recognition and the ASR transcript rendering accuracy in the written domain over a baseline system using a verbal-domain language model. In addition, the writtendomain system is much simpler since it does not require complex and error-prone text normalization and denormalization rules, which are generally required for verbal-domain language modeling.


Data Provenance at Internet Scale: Architecture, Experiences, and the Road Ahead


title: Study of the optimal and simplified Kalman filters for echo cancellation

abstract: In this paper, we study the time-domain Kalman filter in the context of echo cancellation. We explain the fundamental differences between the Kalman filter and the recursive least-squares (RLS) algorithm. Also, we show that the normalized least-mean-square (NLMS) algorithm has a clear relationship with the Kalman filter. Furthermore, a simplified Kalman filter is derived and by a judicious choice of its parameters, this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the optimal and simplified Kalman filtering algorithms.


title: Mining text and social streams: a review

abstract: The large amount of text data which are continuously produced over time in a variety of large scale applications such as social networks results in massive streams of data. Typically massive text streams are created by very large scale interactions of individuals, or by structured creations of particular kinds of content by dedicated organizations. An example in the latter category would be the massive text streams created by news-wire services. Such text streams provide unprecedented challenges to data mining algorithms from an efficiency perspective. In this paper, we review text stream mining algorithms for a wide variety of problems in data mining such as clustering, classification and topic modeling. A recent challenge arises in the context of social streams, which are generated by large social networks such as Twitter. We also discuss a number of future challenges in this area of research.


Synergies Between Big Data and Theory


title: Teachers' expectations from computer technology and interactive whiteboard: A survey

abstract: This study investigated the level of technological tools and interactive whiteboards (IWB) usage in teaching, and the teachers' expectations from these IWB technologies. Furthermore, the most challenging problems with IWB systems that the teachers face were also investigated. A total of 527 primary and high schools teachers were selected randomly from 22 different schools and 5 different cities in Turkey, and a survey was administered to all subjects and 486 (n=486) valid questionnaires were returned for analyzing. The results showed that while 75.3% (366 of 486) of the participants use technological tools such as projection devices, educational software and computers, only 36.6% (178 of 486) use IWB systems in their lectures, and only 27.4% (133/486) of the teachers were active IWB users. In addition, some other useful results were gathered, such as the lack of significant gender difference found in the level of technological tool usage, however, significant difference was found according to number of years of teaching experience.


The Anxiety of Irrelevance: Digital Humanities and Contemporary Critical Theory


title: Software test automation practices in agile development environment: an industry experience report

abstract: The increased importance of Test Automation in software engineering is very evident considering the number of companies investing in automated testing tools nowadays, with the main aim of preventing defects during the development process. Test Automation is considered an essential activity for agile methodologies being the key to speed up the quality assurance process. This paper presents empirical observations and the challenges of a test team new to agile practices and Test Automation using open source testing tools integrated in software projects that use the Scrum methodology. The results obtained showed some important issues to be discussed and the Test Automation practices collected based on the experiences and lessons learned.


title: CELIA: A Device and Architecture Co-Design Framework for STT-MRAM-Based Deep Learning Acceleration

abstract: A large variety of applications rely on deep learning to process big data, learn sophisticated features, and perform complicated tasks. Utilizing emerging non-volatile memory (NVM)'s unique characteristics, including the crossbar array structure and gray-scale cell resistances, to perform neural network (NN) computation is a well-studied approach in accelerating deep learning tasks. Compared to other NVM technologies, STT-MRAM has its unique advantages in performing NN computation. However, the state-of-the-art research have not utilized STT-MRAM for deep learning acceleration due to its device- and architecture-level challenges. Consequently, this paper enables STT-MRAM, for the firs time, as an effective and practical deep learning accelerator. In particular, it proposes a full-stack solution across multiple design layers, including device-level fabrication, circuit-level enhancement, architecture-level data quantization, and system-level accelerator design. The proposed framework significantly mitigates the model accuracy loss due to reduced data precision in a cohesive manner, constructing a comprehensive STT-MRAM accelerator system for fast NN computation with high energy efficiency and low cost.


title: The skin whitening industry in the Philippines

abstract: Skin whitening is a big and booming industry in many developing countries. Its popularity owes mainly to post-colonial, internalized racism. This study examines whether government intervention is necessary and more efficient than market-driven approaches in addressing the health risks and harms associated with skin whitening. We gathered empirical data on the quality and quantity of health-related information about skin whiteners with a multi-stage probability sample of consumers in two cities in the Philippines (n=110; α=0.05). Regardless of their socio-demographic characteristics, we find that cognitive biases and information asymmetries build and sustain consumers' trust in manufacturers and distributors of skin whiteners while, paradoxically, breeding uncertainties over the integrity of these products. The results are product adulteration and misbranding, leading to pricing advantages for toxic whiteners over safer products. This has impeded regulatory efficacy. We recommend anchoring government intervention in transaction cost-reduction, containing the externalities of skin bleaching, and institutionalizing third party partnerships. Failure to do so would leave consumers extremely vulnerable to the forces of supply and demand that favor toxic whiteners, particularly in a market where voluntary collective action is difficult to organize.


title: A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population

abstract: Background ::: To improve population health it is crucial to understand the different care needs within a population. Traditional population groups are often based on characteristics such as age or morbidities. However, this does not take into account specific care needs across care settings and tends to focus on high-needs patients only. This paper explores the potential of using utilization-based cluster analysis to segment a general patient population into homogenous groups.


title: Export of Plastic Debris by Rivers into the Sea

abstract: A substantial fraction of marine plastic debris originates from land-based sources and rivers potentially act as a major transport pathway for all sizes of plastic debris. We analyzed a global compilation of data on plastic debris in the water column across a wide range of river sizes. Plastic debris loads, both microplastic (particles 5 mm) are positively related to the mismanaged plastic waste (MMPW) generated in the river catchments. This relationship is nonlinear where large rivers with population-rich catchments delivering a disproportionately higher fraction of MMPW into the sea. The 10 top-ranked rivers transport 88–95% of the global load into the sea. Using MMPW as a predictor we calculate the global plastic debris inputs form rivers into the sea to range between 0.41 and 4 × 106 t/y. Due to the limited amount of data high uncertainties were expected and ultimately confirmed. The empirical analysis to quantify plastic loads in rivers can be extended easily by a...


title: Segmentation of edges into lines and arcs

abstract: Abstract A long standing problem in computer vision is the extraction of meaningful features from images. This paper describes a method of segmenting curves in images into a combination of circular arcs and straight lines. This uses a recursive algorithm that first analyses lists of connected edge points and finds a polygonal description, and then analyses this description fitting arcs to groups of connected lines. The result is a description of image edges consisting of circular arcs and lines. The algorithm uses no thresholding. Instead the best option is chosen at each decision stage.


title: Permanent magnet volume minimization of spoke type fractional slot synchronous motors

abstract: The combination of the spoke type interior permanent magnet synchronous motor together with the NdFeB is an effective solution when high torque density is required. On the other hand, in the last years, the instability and the high price of the rare earth make the magnet minimization and the optimization of the motor design critical and mandatory. This paper deals with the design criteria of a spoke type, fractional slot concentrated winding, interior permanent magnet motor, especially as regards the minimization of the magnet volume, maximization of the airgap flux density and optimization of the split ratio. An analytical procedure is presented and validated by means of finite element analysis. At last, the advantages and the differences among the solutions are highlighted.


title: The analysis and prediction of customer review rating using opinion mining

abstract: The customer review is important to improve service for company, which have both close opinion and open opinion. The open opinion means the comment as text which shows emotion and comment directly from customer. However, the company has many contents or group to evaluation themselves by rating and total rating for a type of services which there are many customer who needs to review. The problem is some customers given rating contrast with their comments. The other reviewers must read many comments and comprehensive the comments that are different from the rating. Therefore, this paper proposes the analysis and prediction rating from customer reviews who commented as open opinion using probability's classifier model. The classifier models are used case study of customer review's hotel in open comments for training data to classify comments as positive or negative called opinion mining. In addition, this classifier model has calculated probability that shows value of trend to give the rating using naive bayes techniques, which gives correctly classifier to 94.37% compared with decision tree Techniques.


title: Fast SIFT Design for Real-Time Visual Feature Extraction

abstract: Visual feature extraction with scale invariant feature transform (SIFT) is widely used for object recognition. However, its real-time implementation suffers from long latency, heavy computation, and high memory storage because of its frame level computation with iterated Gaussian blur operations. Thus, this paper proposes a layer parallel SIFT (LPSIFT) with integral image, and its parallel hardware design with an on-the-fly feature extraction flow for real-time application needs. Compared with the original SIFT algorithm, the proposed approach reduces the computational amount by 90% and memory usage by 95%. The final implementation uses 580-K gate count with 90-nm CMOS technology, and offers 6000 feature points/frame for VGA images at 30 frames/s and ~ 2000 feature points/frame for 1920 × 1080 images at 30 frames/s at the clock rate of 100 MHz.


title: Of Nests and Trees: A Unified Approach to Processing Queries That Contain Nested Subqueries, Aggregates, and Quantifiers

abstract: Existing query optimizers focus on Restrict-Project-Join queries. In practice, however, query languages such as SQL and DAPLEX have many powerful features (eg., control over duplicates, nested subqueries, grouping, aggregates, and quantifiers) that are not expressible as sequences of Restrict, Project, and Join operations. Existing optimizers are severely limited in their strategies for processing such queries; typically they use only tuple substitution, and process nested subquery blocks top down. Tuple substitution, however, is generally inefficient and especially so when the database is distributed. Hence, it is imperative to develop alternative strategies. This paper introduces new operations for these difficult features, and describes implementation methods for them. From the algebraic properties of these operations, new query processing tactics are derived. It is shown how these new tactics can be deployed to greatly increase the space of interesting strategies for optimisa tion, without seriously altering the architecture of existing optimisers. The contribution of the paper is in demonstrating the feasibility and desirability of developing an integrated framework for optimising all of SQL or other query languages that have similiar features.


title: Inductive invariant generation via abductive inference

abstract: This paper presents a new method for generating inductive loop invariants that are expressible as boolean combinations of linear integer constraints. The key idea underlying our technique is to perform a backtracking search that combines Hoare-style verification condition generation with a logical abduction procedure based on quantifier elimination to speculate candidate invariants. Starting with true, our method iteratively strengthens loop invariants until they are inductive and strong enough to verify the program. A key feature of our technique is that it is lazy: It only infers those invariants that are necessary for verifying program correctness. Furthermore, our technique can infer arbitrary boolean combinations (including disjunctions) of linear invariants. We have implemented the proposed approach in a tool called HOLA. Our experiments demonstrate that HOLA can infer interesting invariants that are beyond the reach of existing state-of-the-art invariant generation tools.


title: TVAE: Triplet-Based Variational Autoencoder using Metric Learning

abstract: Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning. At the same time, variational autoencoder (VAE) has widely been used to approximate inference and proved to have a good performance for directed probabilistic models. However, for traditional VAE, the data label or feature information are intractable. Similarly, traditional representation learning approaches fail to represent many salient aspects of the data. In this project, we propose a novel integrated framework to learn latent embedding in VAE by incorporating deep metric learning. The features are learned by optimizing a triplet loss on the mean vectors of VAE in conjunction with standard evidence lower bound (ELBO) of VAE. This approach, which we call Triplet based Variational Autoencoder (TVAE), allows us to capture more fine-grained information in the latent embedding. Our model is tested on MNIST data set and achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma&Welling, 2013) achieves triplet accuracy of 75.08%.


A contractor muscle based continuum trunk robot


title: Constrained Multilegged Robot System Modeling and Fuzzy Control With Uncertain Kinematics and Dynamics Incorporating Foot Force Optimization

abstract: This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.


title: Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems

abstract: Millimeter-wave (mm-wave) signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramer-Rao bound (CRB) on position and rotation angle estimation uncertainty from mm-wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of-sight, non-line-of-sight, or obstructed-line-of-sight conditions.


title: Single-Switch Single Power-Conversion PFC Converter Using Regenerative Snubber

abstract: This paper presents a single-switch single power-conversion (S $^\text{3}$ PC) power factor correction (PFC) converter. Using the series-resonant circuit, the S $^\text{3}$ PC converter provides bidirectional core excitation for the transformer and obtains higher power capability compared with the conventional single-switch PFC converters. Also, the series-resonant circuit provides zero-current switching turn-off for the output diodes, so that the reverse-recovery loss is alleviated. The regenerative snubber not only avoids the voltage spike of the switch but also recycles the absorbed energy. Because these features are obtained with only a single switch and through single power-conversion, the S $^\text{3}$ PC converter has high efficiency and simple structure. The control algorithm derived from feedback linearization enables the S $^\text{3}$ PC converter to obtain good controllability. Also, by using this control algorithm, the proposed converter performs both PFC control and output power control through single power-conversion. With these advantages, the S $^\text{3}$ PC converter provides maximum efficiency of 96.4% and high power factor in excess of 0.994. The proposed converter is theoretically analyzed in detail, and experimental results are applied to a 1-kW prototype to show its validity.


title: Dynamic Filters in Graph Convolutional Networks

abstract: Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. While CNNs naturally extend to other domains, such as audio and video, where data is also organized in rectangular grids, they do not easily generalize to other types of data such as 3D shape meshes, social network graphs or molecular graphs. To handle such data, we propose a novel graph-convolutional network architecture that builds on a generic formulation that relaxes the 1-to-1 correspondence between filter weights and data elements around the center of the convolution. The main novelty of our architecture is that the shape of the filter is a function of the features in the previous network layer, which is learned as an integral part of the neural network. Experimental evaluations on digit recognition, semi-supervised document classification, and 3D shape correspondence yield state-of-the-art results, significantly improving over previous work for shape correspondence.


title: Query-Adaptive R-CNN for Open-Vocabulary Object Detection and Retrieval.

abstract: We address the problem of open-vocabulary object retrieval and localization, which is to retrieve and localize objects from a very large-scale image database immediately by a textual query (e.g., a word or phrase). We first propose Query-Adaptive R-CNN, a simple yet strong framework for open-vocabulary object detection. Query-Adaptive R-CNN is a simple extension of Faster R-CNN from closed-vocabulary to open-vocabulary object detection: instead of learning a class-specific classifier and regressor, we learn a detector generator that transforms a text into classifier and regressor weights. All of its components can be learned in an end-to-end manner. Even with its simple architecture, it outperforms all state-of-the-art methods in the Flickr30k Entities phrase localization task. In addition, we propose negative phrase augmentation, a generic approach for exploiting hard negatives in the training of open-vocabulary object detection that significantly improves the discriminative ability of the generated classifier. We show that our system can retrieve and localize objects specified by a textual query from one million images in only 0.5 seconds.


title: Avatar reshaping and automatic rigging using a deformable model

abstract: 3D scans of human figures have become widely available through online marketplaces and have become relatively easy to acquire using commodity scanning hardware. In addition to static uses of such 3D models, such as 3D printed figurines or rendered 3D still imagery, there are numerous uses for an animated 3D character that uses such 3D scan data. In order to effectively use such models as dynamic 3D characters, the models must be properly rigged before they are animated. In this work, we demonstrate a method to automatically rig a 3D mesh by matching a set of morphable models against the 3D scan. Once the morphable model has been matched against the 3D scan, the skeleton position and skinning attributes are then copied, resulting in a skinning and rigging that is similar in quality to the original hand-rigged model. In addition, the use of a morphable model allows us to reshape and resize the 3D scan according to approximate human proportions. Thus, a human 3D scan can be modified to be taller, shorter, fatter or skinnier. Such manipulations of the 3D scan are useful both for social science research, as well as for visualization for applications such as fitness, body image, plastic surgery and the like.


title: Testing Web-based applications: The state of the art and future trends

abstract: Software testing is a difficult task and testing Web-based applications may be even more difficult, due to the peculiarities of such applications. In the last years, several problems in the field of Web-based applications testing have been addressed by research work, and several methods and techniques have been defined and used to test Web-based applications effectively. This paper will present the main differences between Web-based applications and traditional ones, how these differences impact the testing of the former ones, and some relevant contributions in the field of Web application testing developed in recent years. The focus is mainly on testing the functionality of a Web-based application, even if some discussion about the testing of non-functional requirements is provided too. Some indications about future trends in Web application testing are also outlined in the paper.


title: Design and testing of a low-cost robotic wheelchair prototype

abstract: Many people who are mobility impaired are, for a variety of reasons, incapable of using an ordinary wheelchair. In some instances, a power wheelchair also cannot be used, usually because of the difficulty the person has in controlling it (often due to additional disabilities). This paper describes two low-cost robotic wheelchair prototypes that assist the operator of the chair in avoiding obstacles, going to pre-designated places, and maneuvering through doorways and other narrow or crowded areas. These systems can be interfaced to a variety of input devices, and can give the operator as much or as little moment by moment control of the chair as they wish. This paper describes both systems, the evolution from one system to another, and the lessons learned.


title: Design and implementation of rotor aerial manipulator system

abstract: With the development of rotor aerial robot, it has been widely used as a stabilized flying platform to achieve observation, communication relay and so on by equipping corresponding equipment, which are always called passive tasks. However, many applications need rotor aerial robots to possess more active capability, such as grasp, carry and assemblage. Thus, the demand for an aerial manipulation system gradually appears, and becomes a hot research issue in recent years. Aerial manipulator system, usually composed of rotor aerial robot and the manipulator, is a new type of robot system with active operating capability. Compared with the mobile manipulator based on the ground and underwater mobile robot which is now widely applied, the aerial manipulation has lager working range and better flexibility obviously. Unfortunately, the new aerial manipulation system also brings new challenges, such as strong dynamics coupling, the modeling and stabilization problem, etc. which may affect its further application. Thus, in our work, an actual physical aerial manipulator system is designed and implemented for its system and application research. Based on the system, the preliminary modeling and control was researched and also an experiment for grasping and carry application is conducted. The conclusions of these researches will help us to realize and improve the system in the future.


title: A full GPU virtualization solution with mediated pass-through

abstract: Graphics Processing Unit (GPU) virtualization is an enabling technology in emerging virtualization scenarios. Unfortunately, existing GPU virtualization approaches are still suboptimal in performance and full feature support. ::: ::: This paper introduces gVirt, a product level GPU virtualization implementation with: 1) full GPU virtualization running native graphics driver in guest, and 2) mediated pass-through that achieves both good performance and scalability, and also secure isolation among guests. gVirt presents a virtual full-fledged GPU to each VM. VMs can directly access performance-critical resources, without intervention from the hypervisor in most cases, while privileged operations from guest are trap-and-emulated at minimal cost. Experiments demonstrate that gVirt can achieve up to 95% native performance for GPU intensive workloads, and scale well up to 7 VMs.


Towards Explaining the Willingness to Disclose Personal Self-Tracking Data to Service Providers


title: Perturbation Theory Based on a Nodal Model

abstract: The standard point kinetics equations and formally exact expressions for reactivity, prompt neutron lifetime, and effective delayed neutron fractions are derived from the matrix form of the nodal code QUANDRY. Perturbation theory expressions for reactivity based both on the standard quadratic-transverse-leakage form of QUANDRY and on the coarse-mesh finite difference (CMFD) form, made accurate by the use of discontinuity factors, are derived. With three-dimensional CMFD QUANDRY transient calculations taken as numerical standards, the accuracy of several standard point kinetics methods as well as the improved quasi-static method is tested. Results suggest that point kinetics methods are poor for rod ejection calculations, even if a precalculated table of rod worth versus position is used to infer the reactivity contribution of the moving rods. For transients not involving rod motion, the point kinetics equations are more accurate. Use of core-averaged (rather than node-dependent) temperature coefficients, however, can produce significant errors. The quasi-static scheme appears to yield acceptably accurate results but, for the tests run, consistently required more computing time than needed for the full three-dimensional solutions.


title: Motion Control of Passive Intelligent Walker Using Servo Brakes

abstract: We propose a new intelligent walker based on passive robotics that assists the elderly, handicapped people, and the blind who have difficulty in walking. We developed a prototype of the robot technology walker (RT walker), a passive intelligent walker that uses servo brakes. The RT walker consists of a support frame, two casters, two wheels equipped with servo brakes, and it has passive dynamics that change with respect to applied force/moment. This system is intrinsically safe for humans, as it cannot move unintentionally, i.e., it has no driving actuators. In addition, the RT walker provides a number of navigational features, including good maneuverability, by appropriately controlling the torque of servo brakes based on RT. We propose a human adaptive motion control algorithm that changes the apparent dynamics to adapt to user difficulties, and an environmentally adaptive motion control algorithm, which incorporates environmental information to provide obstacle/step avoidance and gravity compensation functions. The proposed control algorithms are experimentally applied to the RT walker to test their validity.


title: Nonlinear learning using local coordinate coding

abstract: This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning. The learned bases provide a set of anchor points to form a local coordinate system, such that each data point x on the manifold can be locally approximated by a linear combination of its nearby anchor points, and the linear weights become its local coordinate coding. We show that a high dimensional nonlinear function can be approximated by a global linear function with respect to this coding scheme, and the approximation quality is ensured by the locality of such coding. The method turns a difficult nonlinear learning problem into a simple global linear learning problem, which overcomes some drawbacks of traditional local learning methods.


Media consumption and perceptions of social reality: effects and underlying processes


title: Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing

abstract: This paper provides the first comparison of performance and energy efficiency of high productivity computing systems based on FPGA (Field-Programmable Gate Array) and GPU (Graphics Processing Unit) technologies. The search for higher performance compute solutions has recently led to great interest in heterogeneous systems containing FPGA and GPU accelerators. While these accelerators can provide significant performance improvements, they can also require much more design effort than a pure software solution, reducing programmer productivity. The CUDA system has provided a high productivity approach for programming GPUs. This paper evaluates the High-Productivity Reconfigurable Computer (HPRC) approach to FPGA programming, where a commodity CPU instruction set architecture is augmented with instructions which execute on a specialised FPGA co-processor, allowing the CPU and FPGA to co-operate closely while providing a programming model similar to that of traditional software. To compare the GPU and FPGA approaches, we select a set of established benchmarks with different memory access characteristics, and compare their performance and energy efficiency on an FPGA-based Hybrid-Core system with a GPU-based system. Our results show that while GPUs excel at streaming applications, high-productivity reconfigurable computing systems outperform GPUs in applications with poor locality characteristics and low memory bandwidth requirements.


title: Towards resilience and wellbeing in nurses

abstract: Nursing is a physically and emotionally demanding profession. High role expectations and difficult working conditions place some nurses at risk of burnout and stress-related illness. In spite of the challenges in the current healthcare system, nurses continue to deliver high-quality patient care, retain resilience and progress professionally in the face of adversity. This article provides an overview, with discussion of the evidence in the literature, of some of the suggested methods to improve resilience and enhance staff wellbeing in the nursing profession at an individual and organisational level.


title: Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition

abstract: A neural network model for visual pattern recognition, called the "neocognitron, "' was previously proposed by the author In this paper, we discuss the mechanism of the model in detail. In order to demonstrate the ability of the neocognitron, we also discuss a pattern-recognition system which works with the mechanism of the neocognitron. The system has been implemented on a minicomputer and has been trained to recognize handwritten numerals. The neocognitron is a hierarchical network consisting of many layers of cells, and has variable connections between the cells in adjoining layers. It can acquire the ability to recognize patterns by learning, and can be trained to recognize any set of patterns. After finishing the process of learning, pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the input patterns. In the hierarchical network of the neocognitron, local features of the input pattern are extracted by the cells of a lower stage, and they are gradually integrated into more global features. Finally, each cell of the highest stage integrates all the information of the input pattern, and responds only to one specific pattern. Thus, the response of the cells of the highest stage shows the final result of the pattern-recognition of the network. During this process of extracting and integrating features, errors in the relative position of local features are gradually tolerated. The operation of tolerating positional error a little at a time at each stage, rather than all in one step, plays an important role in endowing the network with an ability to recognize even distorted patterns.


title: The Cricket location-support system

abstract: This paper presents the design, implementation, and evaluation of Cricket , a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.


title: Current methods and advances in forecasting of wind power generation

abstract: Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.


title: Prevention of Relay Attack Using NFC

abstract: Near Field Communication (NFC) is one of the emerging and promising technological developments for mobile phones and other contactless devices. NFC technologies allow two active devices embedded with chip transmit small pieces of data between each other via short range wireless connection and at low speed depending on the configurations. It offers low friction process because of the close range that the two NFC enabled devices can setup a connection. The combination of NFC with smart devices has led to development and range of NFC that includes data exchange, service discovery, connection, e-payment, and ticketing. With the help of a NFC enabled phone and card reader device, contactless card transaction can be performed. Security problems related to relay attack were analyzed and identified a proper solution to prevent the attack. In the proposed system, a Frame wait integer is used to check and verify data manipulation, by attaching the transacted data with a signed integer.


Patient’s Satisfaction with Removable Partial Dentures: A Retrospective Case Series


title: Web Accessibility Testing: When the Method is the Culprit

abstract: Testing accessibility of a web site is still an art. Lack of appropriate definitions of accessibility and of standard testing methods are some of the reasons why Web accessibility is so difficult to achieve ::: ::: The paper describes a heuristic walkthrough method based on barriers; it then discusses how methods like this can be evaluated, and it shows experimental data about validity and usefulness of the method when compared to standards review


title: IMPROVING THE PERFORMANCE OF MEMS IMU/GPS POS SYSTEMS FOR LAND BASED MMS UTILIZING TIGHTLY COUPLED INTEGRATION AND ODOMETER

abstract: Integrated GPS/INS systems provide an enhanced navigation system that has superior performance in comparison with either system operating in stand-alone mode as it can overcome each of their limitations. The high cost and government regulations prevent the wider inclusion of high quality IMUs to augment GPS as a commercialized navigation system in many navigation applications. The progress in MEMS technology enables complete inertial units on a chip, composed of multiple integrated MEMS accelerometers and gyroscopes. In addition to their compact and portable size, the price of MEMS based is far less than those high quality IMUs as well, however, due to the lightweight and fabrication process, MEMS sensors have large bias instability and noise, which consequently affect the obtained accuracy from MEMS-based IMUs. Many research works have been conducted to improve the performance of low cost MEMS-based INS/GPS integrated systems. In this study, LC and TC integration are provided to analyse the performance of these two integration schemes for land based MMS applications.


title: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

abstract: Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.


title: Information Technology Literacy: Implications on Teaching and Learning

abstract: This paper aims to discuss the role and impact that information technology (IT) has on the future and existing style of learning and teaching. It highlights the importance of acquiring computer skills and being literate in IT. The focus is put on certain areas related to IT and education which include pedagogy and training to build IT literacy among both educators and learners. Particularly, it covers the current trends in IT development and how it has started to change and will further influence the way learning and teaching will take place in the future. This paper also discusses various theoretical frameworks and methodologies designed to cope with progress in IT. In summary, this paper delivers a message that IT literacy is the key to today’s empowerment and that education is the best foundation for it.


title: PSiOS: bring your own privacy & security to iOS devices

abstract: Apple iOS is one of the most popular mobile operating systems. As its core security technology, iOS provides application sandboxing but assigns a generic sandboxing profile to every third-party application. However, recent attacks and incidents with benign applications demonstrate that this design decision is vulnerable to crucial privacy and security breaches, allowing applications (either benign or malicious) to access contacts, photos, and device IDs. Moreover, the dynamic character of iOS apps written in Objective-C renders the currently proposed static analysis tools less useful. In this paper, we aim to address the open problem of preventing (not only detecting) privacy leaks and simultaneously strengthening security against runtime attacks on iOS. Compared to similar research work on the open Android, realizing such a system for the closed-source iOS is highly involved. We present the design and implementation of PSiOS, a tool that features a novel policy enforcement framework for iOS. It provides fine-grained, application-specific, and user/administrator defined sandboxing for each third-party application without requiring access to the application source code. Our reference implementation deploys control-flow integrity based on the recently proposed MoCFI (Mobile CFI) framework that only protects applications against runtime attacks. We evaluated several popular iOS applications (e.g., Facebook, WhatsApp) to demonstrate the efficiency and effectiveness of PSiOS.


title: Spectral Ranking

abstract: This note tries to attempt a sketch of the history of spectral ranking, a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities. Albeit recently made famous by the ample press coverage of Google's PageRank algorithm, spectral ranking was devised more than sixty years ago, almost exactly in the same terms, and has been studied in psychology and social sciences. I will try to describe it in precise and modern mathematical terms, highlighting along the way the contributions given by previous scholars.


title: The choking game: a cause of unintentional strangulation.

abstract: A 12-year-old girl was brought to the pediatric emergency department by ambulance after her mother found her hanging from her bunk bed. The patient was resuscitated initially but died 5 days later after support was withdrawn. A sexual assault examination was performed, and the finding was negative. The case was investigated as a possible homicide or suicide. Upon questioning relatives, it was disclosed that the deceased had played the choking game. No one knew she had been playing the game alone. The choking game is popular with adolescents and is particularly dangerous when played alone. Emergency physicians should be aware of the characteristic warning signs that include frequent severe headaches, altered mental status after spending time alone, neck markings, and bloodshot eyes and counsel adolescents about the real risks associated with the activity. Accident, suicide, homicide, autoerotic behavior, and the "choking game" should be considered in the differential when an adolescent presents with evidence of strangulation.


title: Simultaneously detecting fake reviews and review spammers using factor graph model

abstract: Review spamming is quite common on many online shopping platforms like Amazon. Previous attempts for fake review and spammer detection use features of reviewer behavior, rating, and review content. However, to the best of our knowledge, there is no work capable of detecting fake reviews and review spammers at the same time. In this paper, we propose an algorithm to achieve the two goals simultaneously. By defining features to describe each review and reviewer, a Review Factor Graph model is proposed to incorporate all the features and to leverage belief propagation between reviews and reviewers. Experimental results show that our algorithm outperforms all of the other baseline methods significantly with respect to both efficiency and accuracy.


title: Interior Point Decoding for Linear Vector Channels

abstract: In this paper, a novel decoding algorithm for low-density parity-check (LDPC) codes based on convex optimization is presented. The decoding algorithm, called interior point decoding, is designed for linear vector channels. The linear vector channels include many practically important channels such as inter symbol interference channels and partial response channels. It is shown that the maximum likelihood decoding (MLD) rule for a linear vector channel can be relaxed to a convex optimization problem, which is called a relaxed MLD problem. The proposed decoding algorithm is based on a numerical optimization technique so called interior point method with barrier function. Approximate variations of the gradient descent and the Newton methods are used to solve the convex optimization problem. In a decoding process of the proposed algorithm, a search point always lies in the fundamental polytope defined based on a low-density parity-check matrix. Compared with a convectional joint message passing decoder, the proposed decoding algorithm achieves better BER performance with less complexity in the case of partial response channels in many cases.


Machine Learning Methods for Climbing Route Classification


HORMONE SECRETION BY HUMAN PLACENTA GROWN IN TISSUE CULTURE


title: Multilabel classification via calibrated label ranking

abstract: Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. The key idea of the approach is to introduce an artificial calibration label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed as a combination of pairwise preference learning and the conventional relevance classification technique, where a separate classifier is trained to predict whether a label is relevant or not. Empirical results in the area of text categorization, image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.


title: Large-scale high-precision topic modeling on twitter

abstract: We are interested in organizing a continuous stream of sparse and noisy texts, known as "tweets", in real time into an ontology of hundreds of topics with measurable and stringently high precision. This inference is performed over a full-scale stream of Twitter data, whose statistical distribution evolves rapidly over time. The implementation in an industrial setting with the potential of affecting and being visible to real users made it necessary to overcome a host of practical challenges. We present a spectrum of topic modeling techniques that contribute to a deployed system. These include non-topical tweet detection, automatic labeled data acquisition, evaluation with human computation, diagnostic and corrective learning and, most importantly, high-precision topic inference. The latter represents a novel two-stage training algorithm for tweet text classification and a close-loop inference mechanism for combining texts with additional sources of information. The resulting system achieves 93% precision at substantial overall coverage.


title: Improving Content-based and Hybrid Music Recommendation using Deep Learning

abstract: Existing content-based music recommendation systems typically employ a \textit{two-stage} approach. They first extract traditional audio content features such as Mel-frequency cepstral coefficients and then predict user preferences. However, these traditional features, originally not created for music recommendation, cannot capture all relevant information in the audio and thus put a cap on recommendation performance. Using a novel model based on deep belief network and probabilistic graphical model, we unify the two stages into an automated process that simultaneously learns features from audio content and makes personalized recommendations. Compared with existing deep learning based models, our model outperforms them in both the warm-start and cold-start stages without relying on collaborative filtering (CF). We then present an efficient hybrid method to seamlessly integrate the automatically learnt features and CF. Our hybrid method not only significantly improves the performance of CF but also outperforms the traditional feature mbased hybrid method.


title: An algorithm image Enhancement for Segmentation Palm-Leaf Manuscript

abstract: This paper presents Enhancement of Palm-Leaf Manuscript as noise occurred in images. It's suitable for Segmentation. The proposed method consists of image adjustment, contrast stretching and histogram equalization technique. All these techniques improve the quality of images for human viewing. The method we proposed, it made more effective images of palm-leaf manuscripts. The color image of palm-leaf manuscripts became pale and the visible images are not beautiful and hardly colorless because of image enhancement in image processing. In the other hand, the proposed method enhanced clarity of black alphabets. Thus, the color images were transformed into grayscale images affects the clearest alphabets on palm-leaf manuscripts. It also reduces noise. Next, the output images are suitable for carrying out recognition.


title: Physically-based real-time lens flare rendering

abstract: Lens flare is caused by light passing through a photographic lens system in an unintended way. Often considered a degrading artifact, it has become a crucial component for realistic imagery and an artistic means that can even lead to an increased perceived brightness. So far, only costly offline processes allowed for convincing simulations of the complex light interactions. In this paper, we present a novel method to interactively compute physically-plausible flare renderings for photographic lenses. The underlying model covers many components that are important for realism, such as imperfections, chromatic and geometric lens aberrations, and antireflective lens coatings. Various acceleration strategies allow for a performance/quality tradeoff, making our technique applicable both in real-time applications and in high-quality production rendering. We further outline artistic extensions to our system.


Complications of Toxins and Fillers and Evidence Based Protocols for Their Immediate, Short-Term and Long-Term Management


title: ChaD: Chat-Oriented Dialog Systems

abstract: Historically, conversational systems have focused on goal-directed interaction and this focus defined much of the work in the field of spoken dialog systems. More recently researchers have started to focus on non- goal-oriented dialog systems often referred to as “chat” systems. We refer to these as Chat-oriented Dialog (ChaD) systems. ChaD systems are not task-oriented and focus on what could be described as social conversation where the goal is to interact with a human interlocutor while maintaining an appropriate level of engagement. Research to date has identified a number of techniques that can be used to implement working ChaDs but it has also highlighted important limitations. This note describes key ChaD characteristics and proposes a research agenda.


title: Election result prediction using Twitter sentiment analysis

abstract: The proliferation of social media in the recent past has provided end users a powerful platform to voice their opinions. Businesses (or similar entities) need to identify the polarity of these opinions in order to understand user orientation and thereby make smarter decisions. One such application is in the field of politics, where political entities need to understand public opinion and thus determine their campaigning strategy. Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Popular text classification algorithms like Naive Bayes and SVM are Supervised Learning Algorithms which require a training data set to perform Sentiment analysis. The accuracy of these algorithms is contingent upon the quantity as well as the quality (features and contextual relevance) of the labeled training data. Since most applications suffer from lack of training data, they resort to cross domain sentiment analysis which misses out on features relevant to the target data. This, in turn, takes a toll on the overall accuracy of text classification. In this paper, we propose a two stage framework which can be used to create a training data from the mined Twitter data without compromising on features and contextual relevance. Finally, we propose a scalable machine learning model to predict the election results using our two stage framework.


title: OSA: An Optical Switching Architecture for Data Center Networks With Unprecedented Flexibility

abstract: A detailed examination of evolving traffic characteristics, operator requirements, and network technology trends suggests a move away from nonblocking interconnects in data center networks (DCNs). As a result, recent efforts have advocated oversubscribed networks with the capability to adapt to traffic requirements on-demand. In this paper, we present the design, implementation, and evaluation of OSA, a novel Optical Switching Architecture for DCNs. Leveraging runtime reconfigurable optical devices, OSA dynamically changes its topology and link capacities, thereby achieving unprecedented flexibility to adapt to dynamic traffic patterns. Extensive analytical simulations using both real and synthetic traffic patterns demonstrate that OSA can deliver high bisection bandwidth (60%-100% of the nonblocking architecture). Implementation and evaluation of a small-scale functional prototype further demonstrate the feasibility of OSA.


Designing Vivaldi Antenna with various sizes using CST Software


title: Segmentation of volumetric MRA images by using Capillary Active Contour

abstract: Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) images can be a very useful computer aided diagnosis (CAD) tool for clinical routines. Level sets based evolution schemes, which have been shown to be effective and easy to implement for many segmentation applications, are being applied to MRA data sets. In this paper, we present a segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm models capillary action and derives a capillary active contour for segmentation of thin vessels. The algorithm is implemented using the level set method and has been applied successfully on real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, experiments show that our method facilitates more accurate segmentation of thin blood vessels.


title: Augmented reality in laparoscopic surgical oncology

abstract: Minimally invasive surgery represents one of the main evolutions of surgical techniques aimed at providing a greater benefit to the patient. However, minimally invasive surgery increases the operative difficulty since the depth perception is usually dramatically reduced, the field of view is limited and the sense of touch is transmitted by an instrument. However, these drawbacks can currently be reduced by computer technology guiding the surgical gesture. Indeed, from a patient's medical image (US, CT or MRI), Augmented Reality (AR) can increase the surgeon's intra-operative vision by providing a virtual transparency of the patient. AR is based on two main processes: the 3D visualization of the anatomical or pathological structures appearing in the medical image, and the registration of this visualization on the real patient. 3D visualization can be performed directly from the medical image without the need for a pre-processing step thanks to volume rendering. But better results are obtained with surface rendering after organ and pathology delineations and 3D modelling. Registration can be performed interactively or automatically. Several interactive systems have been developed and applied to humans, demonstrating the benefit of AR in surgical oncology. It also shows the current limited interactivity due to soft organ movements and interaction between surgeon instruments and organs. If the current automatic AR systems show the feasibility of such system, it is still relying on specific and expensive equipment which is not available in clinical routine. Moreover, they are not robust enough due to the high complexity of developing a real-time registration taking organ deformation and human movement into account. However, the latest results of automatic AR systems are extremely encouraging and show that it will become a standard requirement for future computer-assisted surgical oncology. In this article, we will explain the concept of AR and its principles. Then, we will review the existing interactive and automatic AR systems in digestive surgical oncology, highlighting their benefits and limitations. Finally, we will discuss the future evolutions and the issues that still have to be tackled so that this technology can be seamlessly integrated in the operating room.


title: A new spatial multi-criteria decision support tool for site selection for implementation of managed aquifer recharge.

abstract: This study reports the development of a new spatial multi-criteria decision analysis (SMCDA) software tool for selecting suitable sites for Managed Aquifer Recharge (MAR) systems. The new SMCDA software tool functions based on the combination of existing multi-criteria evaluation methods with modern decision analysis techniques. More specifically, non-compensatory screening, criteria standardization and weighting, and Analytical Hierarchy Process (AHP) have been combined with Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA). This SMCDA tool may be implemented with a wide range of decision maker's preferences. The tool's user-friendly interface helps guide the decision maker through the sequential steps for site selection, those steps namely being constraint mapping, criteria hierarchy, criteria standardization and weighting, and criteria overlay. The tool offers some predetermined default criteria and standard methods to increase the trade-off between ease-of-use and efficiency. Integrated into ArcGIS, the tool has the advantage of using GIS tools for spatial analysis, and herein data may be processed and displayed. The tool is non-site specific, adaptive, and comprehensive, and may be applied to any type of site-selection problem. For demonstrating the robustness of the new tool, a case study was planned and executed at Algarve Region, Portugal. The efficiency of the SMCDA tool in the decision making process for selecting suitable sites for MAR was also demonstrated. Specific aspects of the tool such as built-in default criteria, explicit decision steps, and flexibility in choosing different options were key features, which benefited the study. The new SMCDA tool can be augmented by groundwater flow and transport modeling so as to achieve a more comprehensive approach to the selection process for the best locations of the MAR infiltration basins, as well as the locations of recovery wells and areas of groundwater protection. The new spatial multicriteria analysis tool has already been implemented within the GIS based Gabardine decision support system as an innovative MAR planning tool.


Designing Telepresence Robot Systems for Use by People with Special Needs


title: Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs

abstract: Convolutional neural networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity: 1) we exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category and 2) we utilize the knowledge of extra networks to produce a soft label for each image. Then, the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7%) and SUN397 (72.0%). We release the code and models at https://github.com/wanglimin/MRCNN-Scene-Recognition .


title: COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints

abstract: Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not? Active clustering methods aim to minimize the number of queries needed to obtain a good clustering by querying the most informative pairs first. Ideally, a user should be able to answer a couple of these queries, inspect the resulting clustering, and repeat these two steps until a satisfactory result is obtained. We present COBRAS, an approach to active clustering with pairwise constraints that is suited for such an interactive clustering process. A core concept in COBRAS is that of a super-instance: a local region in the data in which all instances are assumed to belong to the same cluster. COBRAS constructs such super-instances in a top-down manner to produce high-quality results early on in the clustering process, and keeps refining these super-instances as more pairwise queries are given to get more detailed clusterings later on. We experimentally demonstrate that COBRAS produces good clusterings at fast run times, making it an excellent candidate for the iterative clustering scenario outlined above.