title: Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages

abstract: We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y for some resource-rich language X2 that is closely related to X1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X1 and X2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian → English using Malay and for Spanish → English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real" training data by a factor of 2-5.


title: How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study

abstract: Competitive technical intelligence addresses the landscape of both opportunities and competition for emerging technologies, as the boom of newly emerging science & technology (NEST)--characterized by a challenging combination of great uncertainty and great potential--has become a significant feature of the globalized world. We have been focusing on the construction of a "NEST Competitive Intelligence" methodology that blends bibliometric and text mining methods to explore key technological system components, current R&D emphases, and key players for a particular NEST. This paper emphasizes the semantic TRIZ approach as a useful tool to process "Term Clumping" results to retrieve "problem & solution (P&S)" patterns, and apply them to technology roadmapping. We attempt to extend our approach into NEST Competitive Intelligence studies by using both inductive and purposive bibliometric approaches. Finally, an empirical study for dye-sensitized solar cells is used to demonstrate these analyses.


title: Nighttime Turn Signal Detection by Scatter Modeling and Reflectance-Based Direction Recognition

abstract: The rapid expansion of car ownership worldwide has further raised the importance of vehicle safety. The reduced cost of cameras and optical devices has made it economically feasible to deploy front-mounted intelligent systems for visual-based event detection for forward collision avoidance and mitigation. While driving at night, vehicles in front are generally visible by their tail lights. The turn signals are particularly important because they signal lane change and potential collision. Therefore, this paper proposes a novel visual-based approach, based on the Nakagami- $m$ distribution, for detecting turn signals at night by scatter modeling of tail lights. In addition, to recognize the direction of turn signals, reflectance is decomposed from the original image. Rather than using knowledge of heuristic features, such as the symmetry, position, and size of the rear-facing vehicle, we focus on finding the invariant features to model turn signal scattering by Nakagami imaging and therefore, conduct the detection process in a part-based manner. Experiments on an extensive data set show that our proposed system can effectively detect vehicle braking under different lighting and traffic conditions, and thus, demonstrates its feasibility in real-world environments.


title: Innovations in Special C

abstract: In this paper, we have presented the need for Middle of Line (MOL) Local Interconnects in a fixed layout shape called “Special Constructs” in order to make 20nm a very compelling technology for product migration from 28nm. These constructs are used to demonstrate considerable area, cost, and power/performance benefits by enabling efficient and manufacturable high density standard cell libraries. Also, these constructs have become an essential elements of optical lithography based advanced CMOS nodes for cost effective technology scaling even beyond 20nm.


title: AutoPPG: Towards Automatic Generation of Privacy Policy for Android Applications

abstract: A privacy policy is a statement informing users how their information will be collected, used, and disclosed. Failing to provide a correct privacy policy may result in a fine. However, writing privacy policy is tedious and error-prone, because the author may not well understand the source code, which could be written by others (e.g., outsourcing), or does not know the internals of third-party libraries without source codes. In this paper, we propose and develop a novel system named AutoPPG to automatically construct correct and readable descriptions to facilitate the generation of privacy policy for Android applications (i.e., apps). Given an app, AutoPPG first conducts various static code analyses to characterize its behaviors related to users' private information and then applies natural language processing techniques to generating correct and accessible sentences for describing these behaviors. The experimental results using real apps and crowdsourcing indicate that: (1) AutoPPG creates correct and easy-to-understand descriptions for privacy policies; and (2) the privacy policies constructed by AutoPPG usually reveal more operations related to users' private information than existing privacy policies.


title: Fields of Experts: a framework for learning image priors

abstract: We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov random field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits nonlinear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field of Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with and even outperform specialized techniques.


title: AutoLabel: labeling places from pictures and websites

abstract: Most location based services require semantic place names such as Staples, rather than physical coordinates. Past work has mostly focussed on achieving localization accuracy, while assuming that the translation of physical coordinates to semantic names will be done manually. This paper makes an effort to automate this step, by leveraging the presence of a website corresponding to each store and the availability of a repository of WiFi-tagged pictures from different stores. By correlating the text inside the pictures, against the text extracted from store websites, our proposed system, called AutoLabel, can automatically label clusters of pictures, and the corresponding WiFi APs, with store names. Later, when a user enters a store, her mobile device scans the WiFi APs and consults a lookup table to recognize the store she is in. Experiment results from 40 different stores show recognition accuracy upwards of 87%, even with as few as 10 pictures from a store, offering hope that automatic large-scale semantic labeling may indeed be possible from pictures and websites of stores.


title: VabCut: A video extension of GrabCut for unsupervised video foreground object segmentation

abstract: This paper introduces VabCut, a video extension of GrabCut, an original unsupervised solution to tackle the video foreground object segmentation task. Vabcut works on an extension of the RGB colour domain to RGBM, where M is the motion. It requires a prior step: the computation of the motion layer (M-layer) of the frame to segment. In order to compute this layer we propose to intersect the frame to segment with N temporally close aligned frames. This paper also introduces a new iterative and collaborative method for an optimal frame alignment, based on points of interest and RANSAC, which automatically discards outliers and refines the homographies in turns. The whole method is fully automatic and can handle standard video, i.e. not professional, shaky, blurry or else. We tested VabCut on the SegTrack 2011 benchmark, and demonstrated its effectiveness, it especially outperforms the state of the art methods while being faster.


title: Phrase-based extraction of user opinions in mobile app reviews

abstract: Mobile app reviews often contain useful user opinions like bug reports or suggestions. However, looking for those opinions manually in thousands of reviews is inefective and time- consuming. In this paper, we propose PUMA, an automated, phrase-based approach to extract user opinions in app reviews. Our approach includes a technique to extract phrases in reviews using part-of-speech (PoS) templates; a technique to cluster phrases having similar meanings (each cluster is considered as a major user opinion); and a technique to monitor phrase clusters with negative sentiments for their outbreaks over time. We used PUMA to study two popular apps and found that it can reveal severe problems of those apps reported in their user reviews.


title: Stress That Doesn't Pay: The Commuting Paradox

abstract: People spend a lot of time commuting and often find it a burden. According to economics, the burden of commuting is chosen when compensated either on the labor or on the housing market so that individuals’ utility is equalized. However, in a direct test of this strong notion of equilibrium, we find that people with longer commuting time report systematically lower subjective well-being. Additional empirical analyses do not find institutional explanations of the empirical results that commuters systematically incur losses. We discuss several possibilities of an extended model of human behavior able to explain this ‘commuting paradox’.


title: Fast GPU-based locality sensitive hashing for k-nearest neighbor computation

abstract: We present an efficient GPU-based parallel LSH algorithm to perform approximate k-nearest neighbor computation in high-dimensional spaces. We use the Bi-level LSH algorithm, which can compute k-nearest neighbors with higher accuracy and is amenable to parallelization. During the first level, we use the parallel RP-tree algorithm to partition datasets into several groups so that items similar to each other are clustered together. The second level involves computing the Bi-Level LSH code for each item and constructing a hierarchical hash table. The hash table is based on parallel cuckoo hashing and Morton curves. In the query step, we use GPU-based work queues to accelerate short-list search, which is one of the main bottlenecks in LSH-based algorithms. We demonstrate the results on large image datasets with 200,000 images which are represented as 512 dimensional vectors. In practice, our GPU implementation can obtain more than 40X acceleration over a single-core CPU-based LSH implementation.


title: Asynchronous Distributed Semi-Stochastic Gradient Optimization

abstract: With the recent proliferation of large-scale learning problems,there have been a lot of interest on distributed machine learning algorithms, particularly those that are based on stochastic gradient descent (SGD) and its variants. However, existing algorithms either suffer from slow convergence due to the inherent variance of stochastic gradients, or have a fast linear convergence rate but at the expense of poorer solution quality. In this paper, we combine their merits by proposing a fast distributed asynchronous SGD-based algorithm with variance reduction. A constant learning rate can be used, and it is also guaranteed to converge linearly to the optimal solution. Experiments on the Google Cloud Computing Platform demonstrate that the proposed algorithm outperforms state-of-the-art distributed asynchronous algorithms in terms of both wall clock time and solution quality.


title: A web personalization system based on web usage mining techniques

abstract: In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. In this work we present a Web mining strategy for Web personalization based on a novel pattern recognition strategy which analyzes and classifies both static and dynamic features. The results of experiments on the data from a large commercial web site are presented to show the effectiveness of the proposed system.


title: Interactive assembly algorithms for molecular cloning

abstract: Molecular biologists routinely clone genetic constructs from DNA segments and formulate plans to assemble them. However, manual assembly planning is complex, error prone and not scalable. We address this problem with an algorithm-driven DNA assembly planning software tool suite called Raven (http://www.ravencad.org/) that produces optimized assembly plans and allows users to apply experimental outcomes to redesign assembly plans interactively. We used Raven to calculate assembly plans for thousands of variants of five types of genetic constructs, as well as hundreds of constructs of variable size and complexity from the literature. Finally, we experimentally validated a subset of these assembly plans by reconstructing four recombinase-based 'genetic counter' constructs and two 'repressilator' constructs. We demonstrate that Raven's solutions are significantly better than unoptimized solutions at small and large scales and that Raven's assembly instructions are experimentally valid.


title: MyoVibe: vibration based wearable muscle activation detection in high mobility exercises

abstract: Skeletal muscles are activated to generate the force needed for movement in most high motion sports and exercises. However, incorrect skeletal muscle activation during these sports and exercises, can lead to sub-optimal performance and injury. Existing techniques are susceptible to motion artifacts, particularly when used in high motion sports (e.g. jumping, cycling, etc.). They require limited body movement, or experts to manually interpret results, making them unsuitable in sports scenarios. This paper presents MyoVibe, a wearable system for determining muscle activation in high motion exercise scenarios. MyoVibe senses muscle vibration signals obtained from a wearable network of accelerometers to determine muscle activation. By modeling the characteristics of muscles and high motion noise using extreme value analysis, MyoVibe can reduce noise due to high mobility exercises. Our system can predict muscle activation with greater than 97% accuracy in isometric low motion exercise cases, up to 90% accuracy in high motion exercises.


title: Evaluation of pupil center-eye corner vector for gaze estimation using a web cam

abstract: Low cost eye tracking is an actual challenging research topic for the eye tracking community. Gaze tracking based on a web cam and without infrared light is a searched goal to broaden the applications of eye tracking systems. Web cam based eye tracking results in new challenges to solve such as a wider field of view and a lower image quality. In addition, no infrared light implies that glints cannot be used anymore as a tracking feature. In this paper, a thorough study has been carried out to evaluate pupil (iris) center-eye corner (PC-EC) vector as feature for gaze estimation based on interpolation methods in low cost eye tracking, as it is considered to be partially equivalent to the pupil center-corneal reflection (PC-CR) vector. The analysis is carried out both based on simulated and real data. The experiments show that eye corner positions in the image move slightly when the user is looking at different points of the screen, even with a static head position. This lowers the possible accuracy of the gaze estimation, significantly reducing the accuracy of the system under standard working conditions to 2--3 degrees.


title: The Effects of Color on Memory

abstract: This study examined whether color has an effect on memory. Participants were 115 college undergraduates enrolled in an introductory psychology course. They were each given a packet consisting of a list of 20 words, a mathematical task, a blank recall sheet, and a demographic questionnaire. All sheets within each packet were the same color, but different participants received warm colored (red and yellow), cool colored (green and blue), or white packets. The participants were given one minute to memorize the list of words. They then completed the mathematical task. Lastly, they tried to recall as many words as possible on the blank recall sheet. The percentage of words recalled correctly was the dependent variable. It was hypothesized that participants who received warm-colored packets would recall more words than participants who received either the cool-colored packets or the white packets. A one-way ANOVA was conducted and the results indicated that there was not a statistically significant difference in percentage of words recalled based on packet color. Possible explanations are examined.


title: Enterprise Resource Planning (ERP): a review of the literature

abstract: This article is a review of work published in various journals on the topics of Enterprise Resource Planning (ERP) between January 2000 and May 2006. A total of 313 articles from 79 journals are reviewed. The article intends to serve three goals. First, it will be useful to researchers who are interested in understanding what kinds of questions have been addressed in the area of ERP. Second, the article will be a useful resource for searching for research topics. Third, it will serve as a comprehensive bibliography of the articles published during the period. The literature is analysed under six major themes and nine sub-themes.


title: How Software Developers Mitigate Collaboration Friction with Chatbots

abstract: Modern software developers rely on an extensive set of social media tools and communication channels. The adoption of team communication platforms has led to the emergence of conversation-based tools and integrations, many of which are chatbots. Understanding how software developers manage their complex constellation of collaborators in conjunction with the practices and tools they use can bring valuable insights into socio-technical collaborative work in software development and other knowledge work domains. ::: In this paper, we explore how chatbots can help reduce the friction points software developers face when working collaboratively. Using a socio-technical model for collaborative work, we identify three main areas for conflict: friction stemming from team interactions with each other, an individual's interactions with technology, and team interactions with technology. Finally, we provide a set of open questions for discussion within the research community.


title: A Survey on Quality Assurance Techniques for Big Data Applications

abstract: With the rapid advance of big data and cloud computing, building high quality big data systems in different application fields has gradually became a popular research topic in academia and industry as well as government agencies. However, more quality problems lead to application errors. Although the current research work has discussed how to ensure the quality of big data applications from several aspects, there is no systematic discussion on how to ensure the quality of large data applications. Therefore, a systematic study on big data application quality assurance is very necessary and critical. This paper focuses on the survey of quality assurance techniques of big data applications, and it introduces big data properties and quality attributes. It mainly discusses the key approaches to ensure the quality of big data applications and they are testing, model-driven architecture (MDA), monitoring, fault tolerance, verification and also prediction techniques. In addition, this paper also discusses the impact of big data characteristics on big data applications.


title: Intelligent visual surveillance — A survey

abstract: Detection, tracking, and understanding of moving objects of interest in dynamic scenes have been active research areas in computer vision over the past decades. Intelligent visual surveillance (IVS) refers to an automated visual monitoring process that involves analysis and interpretation of object behaviors, as well as object detection and tracking, to understand the visual events of the scene. Main tasks of IVS include scene interpretation and wide area surveillance control. Scene interpretation aims at detecting and tracking moving objects in an image sequence and understanding their behaviors. In wide area surveillance control task, multiple cameras or agents are controlled in a cooperative manner to monitor tagged objects in motion. This paper reviews recent advances and future research directions of these tasks. This article consists of two parts: The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding. The second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.


title: Comparison of Training Methods for Deep Neural Networks

abstract: This report describes the difficulties of training neural networks and in particular deep neural networks. It then provides a literature review of training methods for deep neural networks, with a focus on pre-training. It focuses on Deep Belief Networks composed of Restricted Boltzmann Machines and Stacked Autoencoders and provides an outreach on further and alternative approaches. It also includes related practical recommendations from the literature on training them. In the second part, initial experiments using some of the covered methods are performed on two databases. In particular, experiments are performed on the MNIST hand-written digit dataset and on facial emotion data from a Kaggle competition. The results are discussed in the context of results reported in other research papers. An error rate lower than the best contribution to the Kaggle competition is achieved using an optimized Stacked Autoencoder.


title: Greed is good: Approximating independent sets in sparse and bounded-degree graphs

abstract: Theminimum-degree greedy algorithm, or Greedy for short, is a simple and well-studied method for finding independent sets in graphs. We show that it achieves a performance ratio of (Δ+2)/3 for approximating independent sets in graphs with degree bounded by Δ. The analysis yields a precise characterization of the size of the independent sets found by the algorithm as a function of the independence number, as well as a generalization of Turan's bound. We also analyze the algorithm when run in combination with a known preprocessing technique, and obtain an improved $$(2\bar d + 3)/5$$ performance ratio on graphs with average degree $$\bar d$$ , improving on the previous best $$(\bar d + 1)/2$$ of Hochbaum. Finally, we present an efficient parallel and distributed algorithm attaining the performance guarantees of Greedy.


title: How the Influence of Unethical Leaders on Followers Is Affected by Their Implicit Followership Theories

abstract: Our research examines the role of followers in unethical leadership. Drawing on a social–cognitive approach to leadership and recent research in the field of behavioral ethics, we focus on how leader behavior and follower information processing interact to produce unethical outcomes. In two experimental studies simulating a personnel selection context, we examine to what extent individual implicit assumptions regarding the follower role (i.e., implicit followership theories, IFTs) relate to employees’ tendency to comply with leader unethical suggestions. In Study 1, controlling for possible alternative explanations such as personal need for structure, romance of leadership, and moral disengagement, we found that the IFT Good Citizen increased and the IFT Insubordination decreased followers’ tendencies to contribute to unethical leadership. In Study 2, we varied the leader’s unethical suggestions to further investigate the conditions under which these effects occur and included authoritarianism as an addit...


title: A constant output current three-phase diode bridge rectifier employing a novel "Electronic Smoothing Inductor"

abstract: This paper presents an improvement of the well-known conventional three-phase diode bridge rectifier with dc output capacitor. The proposed circuit increases the power factor (PF) at the ac input and reduces the ripple current stress on the smoothing capacitor. The basic concept is the arrangement of an active voltage source between the output of the diode bridge and the smoothing capacitor which is controlled in a way that it emulates an ideal smoothing inductor. With this the input currents of the diode bridge which usually show high peak amplitudes are converted into a 120/spl deg/ rectangular shape which ideally results in a total PF of 0.955. The active voltage source mentioned before is realized by a low-voltage switch-mode converter stage of small power rating as compared to the output power of the rectifier. Starting with a brief discussion of basic three-phase rectifier techniques and of the drawbacks of three-phase diode bridge rectifiers with capacitive smoothing, the concept of the proposed active smoothing is described and the stationary operation is analyzed. Furthermore, control concepts as well as design considerations and analyses of the dynamic systems behavior are given. Finally, measurements taken from a laboratory model are presented.


title: Special section: land use options in dry tropical woodland ecosystems in Zimbabwe: introduction, overview and synthesis

abstract: The articles included in this special section focus on the ecological and economic interactions of woodland use in Western Zimbabwe. One of the aims was to investigate the use of modelling to achieve integration among disciplines. The integrated model draws on the models in the different papers comprising the special section. The model has five ecological sectors, five sectors covering woodland use by local people and the state forestry organisation, two sectors to cover agriculture, one sector for population growth and land use, a sector to cover carbon sequestration, and a sector to calculate net present values of the various uses. The state has usually attempted to keep people and their livestock out of the state forest. We show that the private benefits of cropland may be greater than those related to state or local use of the woodland, but further work is required to incorporate the public costs of subsidies to cropland, and the public benefits of woodland services. Livestock production in the woodlands is compatible with woodland management, both from economic and ecological perspectives. Expulsion of forest 1 dwellers from the state forest makes little ecological impact on the woodland, and does not improve the economic value of the woodland to the state. However, if the Forestry Commission relaxes the current control on in-migration, it is likely that the woodland will be rapidly depleted in the face of massive in-migration. Modelling is seen as a framework for integration of ecological and economic issues, but further work is required to incorporate institutional perspectives from the sociological and anthropological disciplines. © 2000 Elsevier Science B.V. All rights reserved.


title: Variational Inference using Implicit Distributions

abstract: Generative adversarial networks (GANs) have given us a great tool to fit implicit generative models to data. Implicit distributions are ones we can sample from easily, and take derivatives of samples with respect to model parameters. These models are highly expressive and we argue they can prove just as useful for variational inference (VI) as they are for generative modelling. Several papers have proposed GAN-like algorithms for inference, however, connections to the theory of VI are not always well understood. This paper provides a unifying review of existing algorithms establishing connections between variational autoencoders, adversarially learned inference, operator VI, GAN-based image reconstruction, and more. Secondly, the paper provides a framework for building new algorithms: depending on the way the variational bound is expressed we introduce prior-contrastive and joint-contrastive methods, and show practical inference algorithms based on either density ratio estimation or denoising.


title: Channel Equalization Using Neural Networks: A Review

abstract: Equalization refers to any signal processing technique used at the receiver to combat intersymbol interference in dispersive channels. This paper reviews the applications of artificial neural networks (ANNs) in modeling nonlinear phenomenon of channel equalization. The literature associated with different feedforward neural network (NN) based equalizers like multilayer perceptron, functional-link ANN, radial basis function, and its variants are reviewed. Feedback-based NN architectures like recurrent NN equalizers are described. Training algorithms are compared in terms of convergence time and computational complexity for nonlinear channel models. Finally, some limitation of current research activities and further research direction is provided.


title: The Scientific Research Potential of Virtual Worlds

abstract: Online virtual worlds, electronic environments where people can work and interact in a somewhat realistic manner, have great potential as sites for research in the social, behavioral, and economic sciences, as well as in human-centered computer science. This article uses Second Life and World of Warcraft as two very different examples of current virtual worlds that foreshadow future developments, introducing a number of research methodologies that scientists are now exploring, including formal experimentation, observational ethnography, and quantitative analysis of economic markets or social networks.


title: Foundations of Control and Estimation Over Lossy Networks

abstract: This paper considers control and estimation problems where the sensor signals and the actuator signals are transmitted to various subsystems over a network. In contrast to traditional control and estimation problems, here the observation and control packets may be lost or delayed. The unreliability of the underlying communication network is modeled stochastically by assigning probabilities to the successful transmission of packets. This requires a novel theory which generalizes classical control/estimation para- digms. The paper offers the foundations of such a novel theory. The central contribution is to characterize the impact of the network reliability on the performance of the feedback loop. Specifically, it is shown that for network protocols where successful transmissions of packets is acknowledged at the receiver (e.g., TCP-like protocols), there exists a critical threshold of network reliability (i.e., critical probabilities for the successful delivery of packets), below which the optimal controller fails to stabilize the system. Further, for these protocols, the separation principle holds and the optimal LQG controller is a linear function of the estimated state. In stark contrast, it is shown that when there is no acknowledgement of successful delivery of control packets (e.g., UDP-like protocols), the LQG optimal controller is in general nonlinear. Conse- quently, the separation principle does not hold in this circumstance.


title: Direct flux and torque control of induction motor drive for speed regulator using PI and fuzzy logic controllers

abstract: This paper presents a direct flux and torque control (DTC) of induction motor drive (IMD) for speed regulator (SR) using fuzzy logic controller (FLC). This control method is based on DTC operating principles. The DTC is one of the most excellent strategies of torque and flux ripples control of IMD. The DTC control method has been optimized by using conventional PI controller in the SR loop of IMD. The main drawback of the DTC of IMD using conventional PI controller based SR is high torque, stator flux ripples and speed of IMD is decreasing under transient and steady state operating conditions. This drawback was eliminated using the FLC based SR loop. The FLC based SR control scheme combines the benefits of DTC technique along with FLC technique. Also Comparing with the conventional PI based SR, the FLC based SR is achieved high quality of torque, stator flux, and rated speed of IMD. The stability of IMD during transient and steady state operations is assured through the application of FLC based SR along with DTC. Finally the effectiveness, validity, and performance of the DTC of IMD using both conventional PI and FL controller based SR has been analyzed, studied, compared, and confirmed by simulating results, from the simulation results found that the low torque, stator flux ripples, and rated speed with the FLC technique using MATLAB/SIMULINK.


title: The Human Organization

abstract: The Human Organization is a response to the network morphology. As networks supersede hierarchy as the predominant form of organization, fluid processes and flexible teams need to replace fixed reporting lines and familiar functions. The barriers to achieving this are more often cultural and emotional than they are commercial and technological. This paper proposes that effective knowledge‐based businesses will be built on human network connections. This requires much greater investment in social processes of integration and in our individual ability to connect with each other. Without this human agenda, the openness and learning on which the generative knowledge‐based environment depends will remain beyond our reach, together with our ability to work and transfer knowledge across complex and shifting organizational boundaries.


Forecasting Wheat Price Using Backpropagation And NARX Neural Network


title: A Study of Cyberbullying Detection and Mitigation on Instagram

abstract: My dissertation addresses developing applications to mit-igate anxiety and depression resulting from cyberbullying. Through the “Continuum of Harm” framework, the techno-logical solutions resulting from participatory design ses-sions I conducted with adolescents can be categorized through a three-pronged approach: 1) Primary Preven-tion, in which the cyberbullying incident is prevented before it starts; 2) Secondary Prevention, where the goal is to decrease the problem after it has been identified, and 3) Tertiary Prevention, when intervention occurs after a prob-lem has already caused harm. I investigate the design and effectiveness of technological mechanisms to mit-igate cyberbullying through Tertiary prevention on the popular social networking platform Instagram.


title: Histopathological image classification with bilinear convolutional neural networks

abstract: The computer-aided quantitative analysis for histopathological images has attracted considerable attention. The stain decomposition on histopathological images is usually recommended to address the issue of co-localization or aliasing of tissue substances. Although the convolutional neural networks (CNN) is a popular deep learning algorithm for various tasks on histopathological image analysis, it is only directly performed on histopathological images without considering stain decomposition. The bilinear CNN (BCNN) is a new CNN model for fine-grained classification. BCNN consists of two CNNs, whose convolutional-layer outputs are multiplied with outer product at each spatial location. In this work, we propose a novel BCNN-based method for classification of histopathological images, which first decomposes histopathological images into hematoxylin and eosin stain components, and then perform BCNN on the decomposed images to fuse and improve the feature representation performance. The experimental results on the colorectal cancer histopathological image dataset with eight classes indicate that the proposed BCNN-based algorithm is superior to the traditional CNN.


title: Accurate, scalable in-network identification of p2p traffic using application signatures

abstract: The ability to accurately identify the network trafc associated with different P2P applications is important to a broad range of network operations including application-specic trafc engineering, capacity planning, provisioning, service differentiation, etc. However, traditional trafc to higher-level application mapping techniques such as default server TCP or UDP network-port based disambiguation is highly inaccurate for some P2P applications. In this paper, we provide an efcient approach for identifying the P2P application trafc through application level signatures. We rst identify the application level signatures by examining some available documentations, and packet-level traces. We then utilize the identied signatures to develop online lters that can efciently and accurately track the P2P trafc even on high-speed network links. We examine the performance of our application-level identication approach using ve popular P2P protocols. Our measurements show that our technique achieves less than false positive and false negative ratios in most cases. We also show that our approach only requires the examination of the very rst few packets (less than packets) to identify a P2P connection, which makes our approach highly scalable. Our technique can signicantly improve the P2P trafc volume estimates over what pure network port based approaches provide. For instance, we were able to identify times as much trafc for the popular Kazaa P2P protocol, compared to the traditional port-based approach.


title: Predicting ICT integration into classroom teaching in Chinese primary schools: exploring the complex interplay of teacher-related variables

abstract: Available research has explored a wide variety of factors influencing information and communication technologies (ICT) adoption and integration in classroom teaching; however, existing research seldom centre on the combined impact of these variables. In addition, the little research available is set up in the Chinese context. The latter is important given that the different cultural context in which the interplay between teacher beliefs and educational practices has only been documented quiet little. The present study centres on the complex interplay of a number of internal teacher variables to explain ICT classroom integration. These variables comprise 'teachers' constructivist teaching beliefs', 'teacher attitudes towards computers in education', 'teachers' computer motivation', 'teacher perception of ICT-related policy'. A survey was set up, involving 820 Chinese primary school teachers. Path modeling was used to explore the direct and indirect effects of the teacher-related variables on their level of ICT classroom integration. Firstly, two distinctive types of ICT use can be distinguished in the Chinese context: (a) teacher supportive use of ICT that refers to the use of ICT for e.g. student administration, preparing worksheets, developing evaluation activities; and (b) classroom use of ICT to support and enhance the actual teaching and learning process. The results show that classroom use of ICT directly depends on teachers' computer motivation and the supportive use of ICT. Teachers' constructivist beliefs, their attitudes towards computers in education and perceptions about the ICT-related school policy influence ICT integration in an indirect way. The results demonstrate how the complex interplay between teacher-related variables and ICT integration in the classroom is partly in line with findings in non-Asian contexts. A number of differences can be explained by the particular Chinese context. In particular an indirect relationship was found between teachers' constructivist beliefs and their level of ICT integration.


title: A convolutional neural network for robotic arm guidance using sEMG based frequency-features

abstract: Recently, robotics has been seen as a key solution to improve the quality of life of amputees. In order to create smarter robotic prosthetic devices to be used in an everyday context, one must be able to interface them seamlessly with the end-user in an inexpensive, yet reliable way. In this paper, we are looking at guiding a robotic device by detecting gestures through measurement of the electrical activity of muscles captured by surface electromyography (sEMG). Reliable sEMG-based gesture classifiers for end-users are challenging to design, as they must be extremely robust to signal drift, muscle fatigue and small electrode displacement without the need for constant recalibration. In spite of extensive research, sophisticated sEMG classifiers for prostheses guidance are not yet widely used, as systems often fail to solve these issues simultaneously. We propose to address these problems by employing Convolutional Neural Networks. Specifically as a first step, we demonstrate their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband (Thalmic Labs) on able-bodied subjects. To this effect, we assessed the robustness of this machine learning oriented approach by classifying a combination of 7 hand/wrist gestures with an accuracy of ∼97.9% in real-time, over a period of 6 consecutive days with no recalibration. In addition, we used the classifier (in conjunction with orientation data) to guide a 6DoF robotic arm, using the armband with the same speed and precision as with a joystick. We also show that the classifier is able to generalize to different users by testing it on 18 participants.


title: Photonic ADC: overcoming the bottleneck of electronic jitter

abstract: Accurate conversion of wideband multi-GHz analog signals into the digital domain has long been a target of analog-to-digital converter (ADC) developers, driven by applications in radar systems, software radio, medical imaging, and communication systems. Aperture jitter has been a major bottleneck on the way towards higher speeds and better accuracy. Photonic ADCs, which perform sampling using ultra-stable optical pulse trains generated by mode-locked lasers, have been investigated for many years as a promising approach to overcome the jitter problem and bring ADC performance to new levels. This work demonstrates that the photonic approach can deliver on its promise by digitizing a 41 GHz signal with 7.0 effective bits using a photonic ADC built from discrete components. This accuracy corresponds to a timing jitter of 15 fs - a 4-5 times improvement over the performance of the best electronic ADCs which exist today. On the way towards an integrated photonic ADC, a silicon photonic chip with core photonic components was fabricated and used to digitize a 10 GHz signal with 3.5 effective bits. In these experiments, two wavelength channels were implemented, providing the overall sampling rate of 2.1 GSa/s. To show that photonic ADCs with larger channel counts are possible, a dual 20-channel silicon filter bank has been demonstrated.


title: Current state of research on cross-site scripting (XSS) – A systematic literature review

abstract: Context: Cross-site scripting (XSS) is a security vulnerability that affects web applications. It occurs due to improper or lack of sanitization of user inputs. The security vulnerability caused many problems for users and server applications. Objective: To conduct a systematic literature review on the studies done on XSS vulnerabilities and attacks. Method: We followed the standard guidelines for systematic literature review as documented by Barbara Kitchenham and reviewed a total of 115 studies related to cross-site scripting from various journals and conference proceedings. Results: Research on XSS is still very active with publications across many conference proceedings and journals. Attack prevention and vulnerability detection are the areas focused on by most of the studies. Dynamic analysis techniques form the majority among the solutions proposed by the various studies. The type of XSS addressed the most is reflected XSS. Conclusion: XSS still remains a big problem for web applications, despite the bulk of solutions provided so far. There is no single solution that can effectively mitigate XSS attacks. More research is needed in the area of vulnerability removal from the source code of the applications before deployment.


title: Proteomic analysis of post-translational modifications

abstract: Post-translational modifications modulate the activity of most eukaryote proteins. Analysis of these modifications presents formidable challenges but their determination generates indispensable insight into biological function. Strategies developed to characterize individual proteins are now systematically applied to protein populations. The combination of function- or structure-based purification of modified 'subproteomes', such as phosphorylated proteins or modified membrane proteins, with mass spectrometry is proving particularly successful. To map modification sites in molecular detail, novel mass spectrometric peptide sequencing and analysis technologies hold tremendous potential. Finally, stable isotope labeling strategies in combination with mass spectrometry have been applied successfully to study the dynamics of modifications.


title: Collaborative stereo

abstract: In this paper, we propose a method to recover the relative pose of two robots in absolute scale and in real-time using one monocular camera on each robot. We achieve this by fusing measurements from the onboard inertial sensors on each platform with information obtained from feature correspondences between the two cameras using an Extended Kalman Filter (EKF). This forms a flexible stereo rig, providing the ability to treat the two robots as one single dynamic sensor, which can adapt to the environment and thus improve environmental mapping, obstacle avoidance and navigation. We demonstrate the power of this approach on both simulation and real datasets, employing two micro aerial vehicles (MAVs) to illustrate successful operation over general 3D motion.


title: A Survey on Multi-view Learning

abstract: In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In trying to organize and highlight similarities and differences between the variety of multi-view learning approaches, we review a number of representative multi-view learning algorithms in different areas and classify them into three groups: 1) co-training, 2) multiple kernel learning, and 3) subspace learning. Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace. Though there is significant variance in the approaches to integrating multiple views to improve learning performance, they mainly exploit either the consensus principle or the complementary principle to ensure the success of multi-view learning. Since accessing multiple views is the fundament of multi-view learning, with the exception of study on learning a model from multiple views, it is also valuable to study how to construct multiple views and how to evaluate these views. Overall, by exploring the consistency and complementary properties of different views, multi-view learning is rendered more effective, more promising, and has better generalization ability than single-view learning.


title: JSAI: a static analysis platform for JavaScript

abstract: JavaScript is used everywhere from the browser to the server, including desktops and mobile devices. However, the current state of the art in JavaScript static analysis lags far behind that of other languages such as C and Java. Our goal is to help remedy this lack. We describe JSAI, a formally specified, robust abstract interpreter for JavaScript. JSAI uses novel abstract domains to compute a reduced product of type inference, pointer analysis, control-flow analysis, string analysis, and integer and boolean constant propagation. Part of JSAI's novelty is user-configurable analysis sensitivity, i.e., context-, path-, and heap-sensitivity. JSAI is designed to be provably sound with respect to a specific concrete semantics for JavaScript, which has been extensively tested against a commercial JavaScript implementation. We provide a comprehensive evaluation of JSAI's performance and precision using an extensive benchmark suite, including real-world JavaScript applications, machine generated JavaScript code via Emscripten, and browser addons. We use JSAI's configurability to evaluate a large number of analysis sensitivities (some well-known, some novel) and observe some surprising results that go against common wisdom. These results highlight the usefulness of a configurable analysis platform such as JSAI.


title: Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

abstract: Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt to the changes in its radio environment. In this work, the cycle frequency domain profile (CDP) is used for signal detection and preprocessing for signal classification. Signal features are extracted from CDP using a threshold-test method. For classification, a Hidden Markov Model (HMM) has been used to process extracted signal features due to its robust pattern-matching capability. We also investigate the effects of varied observation length on signal detection and classification. It is found that the CDP-based detector and the HMM-based classifier can detect and classify incoming signals at a range of low SNRs.


title: A Dual-Band Dual-Sleeve Monopole Antenna

abstract: This letter proposes a dual-band monopole antenna with two sleeves. The lower sleeve is short-circuited to the monopole at its bottom end, while its top end is an open circuit since the length of the coaxial line formed by the sleeve and the monopole is one quarter-wavelength at the higher operating frequency. Meanwhile, the upper sleeve is so designed that a short circuit is produced across the gap between these two sleeves at the lower operating frequency, and therefore, these sleeves are virtually connected. As a result, this antenna is able to operate like a quarter-wavelength monopole at both frequency bands. A prototype of this dual-sleeve monopole is fabricated, and its measured results are in good agreement with simulated ones. It is found that this dual-sleeve monopole not only has good impedance matching, but also can radiate like a quarter-wavelength monopole at both bands.


title: Lightweight compression with encryption based on Asymmetric Numeral Systems

abstract: Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce power usage. This requirement is crucial for battery-powered devices with limited resources, such as autonomous remote sensors or implants. Well-known and popular encryption techniques are frequently too expensive. This problem is on the increase as machine-to-machine communication and the Internet of Things are becoming a reality. Therefore, there is growing demand for finding trade-offs between security, cost and performance in lightweight cryptography. This article discusses Asymmetric Numeral Systems -- an innovative approach to entropy coding which can be used for compression with encryption. It provides compression ratio comparable with arithmetic coding at similar speed as Huffman coding, hence, this coding is starting to replace them in new compressors. Additionally, by perturbing its coding tables, the Asymmetric Numeral System makes it possible to simultaneously encrypt the encoded message at nearly no additional cost. The article introduces this approach and analyzes its security level. The basic application is reducing the number of rounds of some cipher used on ANS-compressed data, or completely removing additional encryption layer if reaching a satisfactory protection level.


title: Process for improving GPS acquisition assistance data and server-side location determination for cellular networks

abstract: This paper introduces a process for analyzing and improving the quality of the acquisition assistance data produced for Assisted GPS (A-GPS) positioning in cellular networks. An experimental test bed is introduced and a series of experiments and results are provided. The experiments validate the GPS acquisition assistance data in open-sky conditions. Accuracy results for initial testing of our server location determination engine in a range of different environments are also given with results of a long-term run. Acquisition assistance data provides the GPS handset with information that allows it to detect the GPS signals more quickly and allows detection of much weaker signals. It does this by providing information to the handset about where to look for the signals. The AGPS server is a mobile location server determining the location of devices within a cellular network. In order to measure the quality of the acquisition assistance data produced by the A-GPS server a piece of hardware has been developed called the “A-GPS Trainer”. This takes assistance data and uses it to lock on to the satellites. It then provides code phase measurements back to the server for it to do a location calculation. The trainer also reports on the amount of time it takes to lock on to the signals of the individual satellites. It can run multiple calculations over a period of time and report the results.


title: PTAS for MAP Assignment on Pairwise Markov Random Fields in Planar Graphs

abstract: We present a PTAS for computing the maximum a posteriori assignment on Pairwise Markov Random Fields with non-negative weights in planar graphs. This algorithm is practical and not far behind state-of-the-art techniques in image processing. MAP on Pairwise Markov Random Fields with (possibly) negative weights cannot be approximated unless P = NP, even on planar graphs. We also show via reduction that this yields a PTAS for one scoring function of Correlation Clustering in planar graphs.


title: Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers

abstract: Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server consolidation supported by virtualization. However, server consolidation may incur significant degradation to workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various workloads. Inside this framework, we first profile the performance losses of various workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) consolidation planning module which, given a set of workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the workload performance profiles, both modules can guarantee the performance losses of various workloads below configurable thresholds. Our experiments for workload profiling are conducted with real data center workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.


A Bermuda Triangle ?-A Review of Method Application and Triangulation in User Experience Evaluation


title: Surface Extraction from Multi-Material Components for Metrology using Dual Energy CT

abstract: This paper describes a novel method for creating surface models of multi-material components using dual energy computed tomography (DECT). The application scenario is metrology and dimensional measurement in industrial high resolution 3D X-ray computed tomography (3DCT). Based on the dual source / dual exposure technology this method employs 3DCT scans of a high precision micro-focus and a high energy macro-focus X-ray source. The presented work makes use of the advantages of dual X-ray exposure technology in order to facilitate dimensional measurements of multi-material components with high density material within low density material. We propose a workflow which uses image fusion and local surface extraction techniques: a prefiltering step reduces noise inherent in the data. For image fusion the datasets have to be registered. In the fusion step the benefits of both scans are combined. The structure of the specimen is taken from the low precision, blurry, high energy dataset while the sharp edges are adopted and fused into the resulting image from the high precision, crisp, low energy dataset. In the final step a reliable surface model is extracted from the fused dataset using a local adaptive technique. The major contribution of this paper is the development of a specific workflow for dimensional measurements of multi-material industrial components, which takes two X-ray CT datasets with complementary strengths and weaknesses into account. The performance of the workflow is discussed using a test specimen as well as two real world industrial parts. As result, a significant improvement in overall measurement precision, surface geometry and mean deviation to reference measurement compared to single exposure scans was facilitated.


title: Spectrum Decision in Cognitive Radio Networks: A Survey

abstract: Spectrum decision is the ability of a cognitive radio (CR) to select the best available spectrum band to satisfy secondary users' (SUs') quality of service (QoS) requirements, without causing harmful interference to licensed or primary users (PUs). Each CR performs spectrum sensing to identify the available spectrum bands and the spectrum decision process selects from these available bands for opportunistic use. Spectrum decision constitutes an important topic which has not been adequately explored in CR research. Spectrum decision involves spectrum characterization, spectrum selection and CR reconfiguration functions. After the available spectrum has been identified, the first step is to characterize it based not only on the current radio environment conditions, but also on the PU activities. The second step involves spectrum selection, whereby the most appropriate spectrum band is selected to satisfy SUs' QoS requirements. Finally, the CR should be able to reconfigure its transmission parameters to allow communication on the selected band. Key to spectrum characterization is PU activity modelling, which is commonly based on historical data to provide the means for predicting future traffic patterns in a given spectrum band. This paper provides an up-to-date survey of spectrum decision in CR networks (CRNs) and addresses issues of spectrum characterization (including PU activity modelling), spectrum selection and CR reconfiguration. For each of these issues, we highlight key open research challenges. We also review practical implementations of spectrum decision in several CR platforms.


title: Unpacking the privacy paradox: Irrational decision-making within the privacy calculus

abstract: Understanding privacy is important to individuals and organizations. This research in progress develops a theoretical model that incorporates both actual disclosure behavior and the potential for irrational behavior induced by overriding situational factors presented at the time of the privacy disclosure decision. Actual disclosure behavior is seldom measured in privacy research, and most prior work has assumed the disclosure decision to be a fully rational one. This research challenges that assumption, and the eventual completion of the project will contribute uniquely to our understanding of the privacy calculus in online transactions.


title: Robust recognition of physical team behaviors using spatio-temporal models

abstract: This paper presents a framework for robustly recognizing physical team behaviors by exploiting spatio-temporal patterns. Agent team behaviors in athletic and military domains typically exhibit an observable structure characterized by the relative positions of teammates and external landmarks, such as a team of soldiers ambushing an opponent or a soccer player moving to receive a pass. We demonstrate how complex team relationships that are not easily expressed by region-based heuristics can be modeled from data and domain knowledge in a way that is robust to noise and spatial variation. To represent team behaviors in our domain of MOUT (Military Operations in Urban Terrain) planning, we employ two classes of spatial models: 1) team templates that encode static relationships between team members and external landmarks; and 2) spatially-invariant Hidden Markov Models (HMMs) to represent evolving agent team configurations over time. These two classes of models can be combined to improve recognition accuracy, particularly for behaviors that appear similar in static snapshots. We evaluate our modeling techniques on large urban maps and position traces of two-person human teams performing MOUT behaviors in a customized version of Unreal Tournament (a commercially available first-person shooter game).


title: A preliminary test of Google Scholar as a source for citation data: a longitudinal study of Nobel prize winners

abstract: Most governmental research assessment exercises do not use citation data for the Social Sciences and Humanities as Web of Science or Scopus coverage in these disciplines is considered to be insufficient. We therefore assess to what extent Google Scholar can be used as an alternative source of citation data. In order to provide a credible alternative, Google Scholar needs to be stable over time, display comprehensive coverage, and provide non-biased comparisons across disciplines. This article assesses these conditions through a longitudinal study of 20 Nobel Prize winners in Chemistry, Economics, Medicine and Physics. Our results indicate that Google Scholar displays considerable stability over time. However, coverage for disciplines that have traditionally been poorly represented in Google Scholar (Chemistry and Physics) is increasing rapidly. Google Scholar's coverage is also comprehensive; all of the 800 most cited publications by our Nobelists can be located in Google Scholar, although in four cases there are some problems with the results. Finally, we argue that Google Scholar might provide a less biased comparison across disciplines than the Web of Science. The use of Google Scholar might therefore redress the traditionally disadvantaged position of the Social Sciences in citation analysis.


title: Non-interactive verifiable computing: Outsourcing computation to untrusted workers

abstract: We introduce and formalize the notion of Verifiable Computation, which enables a computationally weak client to "outsource" the computation of a function F on various dynamically-chosen inputs x1, ...,xk to one or more workers. The workers return the result of the function evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out correctly on the given value xi. The primary constraint is that the verification of the proof should require substantially less computational effort than computing F(i) from scratch. ::: ::: We present a protocol that allows the worker to return a computationally-sound, non-interactive proof that can be verified in O(mċpoly(λ)) time, where m is the bit-length of the output of F, and λ is a security parameter. The protocol requires a one-time pre-processing stage by the client which takes O(|C|ċpoly(λ)) time, where C is the smallest known Boolean circuit computing F. Unlike previous work in this area, our scheme also provides (at no additional cost) input and output privacy for the client, meaning that the workers do not learn any information about the xi or yi values.


Honey dressing on a leg ulcer with tendon exposure in a patient with type 2 diabetes


Irradiation increases the immunogenicity of lung cancer cells and irradiation-based tumor cell vaccine elicits tumor-specific T cell responses in vivo


title: Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks

abstract: This paper introduces a new semi-automated methodology combining a level set method with a top-down segmentation produced by a deep belief network for the problem of left ventricle segmentation from cardiac magnetic resonance images (MRI). Our approach combines the level set advantages that uses several a priori facts about the object to be segmented (e.g., smooth contour, strong edges, etc.) with the knowledge automatically learned from a manually annotated database (e.g., shape and appearance of the object to be segmented). The use of deep belief networks is justified because of its ability to learn robust models with few annotated images and its flexibility that allowed us to adapt it to a top-down segmentation problem. We demonstrate that our method produces competitive results using the database of the MICCAI grand challenge on left ventricle segmentation from cardiac MRI images, where our methodology produces results on par with the best in the field in each one of the measures used in that challenge (perpendicular distance, Dice metric, and percentage of good detections). Therefore, we conclude that our proposed methodology is one of the most competitive approaches in the field.


title: Personalized mobile advertising: Its key attributes, trends, and social impact

abstract: Advertising media are a means of communication that creates different marketing and communication results among consumers. Over the years, newspaper, magazine, TV, and radio have provided a one-way media where information is broadcast and communicated. Due to the widespread application of the Internet, advertising has entered into an interactive communications mode. In the advent of 3G broadband mobile communication systems and smartphone devices, consumers' preferences can be pre-identified and advertising messages can therefore be delivered to consumers in a multimedia format at the right time and at the right place with the right message. In light of this new advertisement possibility, designing personalized mobile advertising to meet consumers' needs becomes an important issue. This research uses the fuzzy Delphi method to identify the key personalized attributes in a personalized mobile advertising message for different products. Results of the study identify six important design attributes for personalized advertisements: price, preference, promotion, interest, brand, and type of mobile device. As personalized mobile advertising becomes more integrated in people's daily activities, its pros and cons and social impact are also discussed. The research result can serve as a guideline for the key parties in mobile marketing industry to facilitate the development of the industry and ensure that advertising resources are properly used.


title: SubwayPS: towards smartphone positioning in underground public transportation systems

abstract: Thanks to rapid advances in technologies like GPS and Wi-Fi positioning, smartphone users are able to determine their location almost everywhere they go. This is not true, however, of people who are traveling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this paper, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch app that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.


title: Rules of Play: Game Design Fundamentals

abstract: This text offers an introduction to game design and a unified model for looking at all kinds of games, from board games and sports to computer and video games. Also included are concepts, strategies, and methodologies for creating and understanding games.


title: A 0.5 volt rail-to-rail CMOS pseudo-differential OTA using simple feed-forward technique

abstract: This paper presents a low voltage CMOS pseudo differential OTA using simple feed-forward technique. The circuit employs feed-forward technique to suppress the common-mode gain, and positive feedback to enhance the output impedance. The circuit is designed using 0.18 µm CMOS technology under 0.5 V supply. The simulation results show rail-to-rail input/output swing, achieved with low common-mode gain (−35 dB). The output swing of the circuit is 0.3 Vpp. The power dissipation of the circuit is 50 µW.


title: Efficacy of the Incredible Years Basic parent training programme as an early intervention for children with conduct problems and ADHD

abstract: BACKGROUND ::: The efficacy of the Incredible Years (IY) Basic parent training (PT) programme for a community-based sample of families with pre-school children at risk of developing both conduct problems and attention deficit hyperactivity disorder (ADHD) was examined. ::: ::: ::: METHODS ::: Pre-school children displaying signs of both early-onset conduct problems and ADHD were randomly allocated to either IY PT intervention, or to a waiting list (WL) control group. Child symptoms were assessed before and after the intervention. ::: ::: ::: RESULTS ::: Post intervention, the intervention group was associated with significantly lower levels of parent-reported inattention and hyperactive/impulsive difficulties, even after controlling for post-intervention changes in child deviance. In addition, 52% of those in the intervention condition, compared with 21% in the control condition, displayed clinically reliable improvements post intervention, giving an absolute risk reduction of 31% and a number needed to treat of 3.23. ::: ::: ::: CONCLUSIONS ::: Findings from this study indicate that the IY PT programme is a valuable intervention for many pre-school children displaying early signs of ADHD.


title: A Genetic Algorithm for Shortest Path Routing Problem and the Sizing of Populations

abstract: This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. This paper also develops a population-sizing equation that facilitates a solution with desired quality. It is based on the gambler ruin model; the equation has been further enhanced and generalized. The equation relates the size of the population, quality of solution, cardinality of the alphabet, and other parameters of the proposed algorithm. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types for almost all source-destination pairs. Furthermore, simulation studies emphasize the usefulness of the population-sizing equation. The equation scales to larger networks. It is felt that it can be used for determining an adequate population size in the SP routing problem.


Reply to “In vivo polypropylene mesh degradation is hardly a myth”


title: Predicting Instructor Performance Using Data Mining Techniques in Higher Education

abstract: Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this paper, four different classification techniques—decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis—are used to build classifier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The findings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve the measurement instruments.


title: A two-stage gene selection scheme utilizing MRMR filter and GA wrapper

abstract: Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminates biological samples of different types. In this paper, we propose a two-stage selection algorithm for genomic data by combining MRMR (Minimum Redundancy–Maximum Relevance) and GA (Genetic Algorithm). In the first stage, MRMR is used to filter noisy and redundant genes in high-dimensional microarray data. In the second stage, the GA uses the classifier accuracy as a fitness function to select the highly discriminating genes. The proposed method is tested for tumor classification on five open datasets: NCI, Lymphoma, Lung, Leukemia and Colon using Support Vector Machine (SVM) and Naive Bayes (NB) classifiers. The comparison of the MRMR-GA with MRMR filter and GA wrapper shows that our method is able to find the smallest gene subset that gives the most classification accuracy in leave-one-out cross-validation (LOOCV).


title: Change Detection under Global Viewpoint Uncertainty

abstract: This paper addresses the problem of change detection from a novel perspective of long-term map learning. We are particularly interested in designing an approach that can scale to large maps and that can function under global uncertainty in the viewpoint (i.e., GPS-denied situations). Our approach, which utilizes a compact bag-of-words (BoW) scene model, makes several contributions to the problem: 1) Two kinds of prior information are extracted from the view sequence map and used for change detection. Further, we propose a novel type of prior, called motion prior, to predict the relative motions of stationary objects and anomaly ego-motion detection. The proposed prior is also useful for distinguishing stationary from non-stationary objects. 2) A small set of good reference images (e.g., 10) are efficiently retrieved from the view sequence map by employing the recently developed Bag-of-Local-Convolutional-Features (BoLCF) scene model. 3) Change detection is reformulated as a scene retrieval over these reference images to find changed objects using a novel spatial Bag-of-Words (SBoW) scene model. Evaluations conducted of individual techniques and also their combinations on a challenging dataset of highly dynamic scenes in the publicly available Malaga dataset verify their efficacy.


Trust and Distrust in Two-sided Markets: An Example in the Sharing Economy


title: Probabilistic Deep Learning using Random Sum-Product Networks

abstract: The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods. However, most current approaches have severe limitations when it comes to inference, since many of these models do not even permit to evaluate exact data likelihoods. Sum-product networks (SPNs), on the other hand, are an excellent architecture in that regard, as they allow to efficiently evaluate likelihoods, as well as arbitrary marginalization and conditioning tasks. Nevertheless, SPNs have not been fully explored as serious deep learning models, likely due to their special structural requirements, which complicate learning. In this paper, we make a drastic simplification and use random SPN structures which are trained in a"classical deep learning manner", i.e. employing automatic differentiation, SGD, and GPU support. The resulting models, called RAT-SPNs, yield prediction results comparable to deep neural networks, while still being interpretable as generative model and maintaining well-calibrated uncertainties. This property makes them highly robust under missing input features and enables them to naturally detect outliers and peculiar samples.


title: An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

abstract: BackgroundMelanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency.MethodsIn this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier.ResultsThe proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively.ConclusionThe base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.


title: Haptic Logos: Insight into the Feasibility of Digital Haptic Branding

abstract: Companies design brands to invoke distinct perceptions in customers about their products. Currently digital consumers experience the identity of a brand subconsciously through only visual and aural logos. However, the sense of touch is a valuable conduit for establishing strong connections. Although haptic properties (e.g. engrossing textures) are part of a brand’s identity in the physical world, the digital world lacks this presence. In this paper we present the concept of creating haptic logos for brands that can be digitally distributed to consumers. To achieve this task, we utilize vibrotactile haptic feedback. Haptic logos for brands were created by varying frequency, waveform and temporal properties resulting in distinct vibration patterns. We conducted two user studies: (1) assignment of appropriate haptic logos to brands by participants, and (2) judgment of the logos based on user experience and emotional response. Based on these studies, the applicability of haptic logos has been discussed.


title: Compact Tri-Band Bandpass Filter Using Novel Eight-Mode Resonator for 5G WiFi Application

abstract: This letter presents a novel compact tri-band bandpass filter (BPF) with wide third passband to satisfy the requirement of 5G WiFi. The proposed filter utilizes a novel multi-stub-loaded resonator with eight resonance modes. By utilizing these modes, a tri-band BPF with controllable operating frequencies is designed. Among the three bands, the highest one is formed by four modes to cover the 5G WiFi band. Each of the other two passbands centered at 2.4 and 3.5 GHz is realized by two modes. To validate the proposed idea, an experimental filter is implemented. Good agreement between the predicated and measured results demonstrates the proposed idea.


title: Effects of comparative feedback from a Socially Assistive Robot on self-efficacy in post-stroke rehabilitation

abstract: We present a study with an autonomous Socially Assistive Robot (SAR) coach that investigates the effect of comparative feedback given by a SAR on the self-efficacy of individuals post-stroke in a seated reaching task. We compare two types of feedback, self-comparative and other-comparative, against a control of no comparative feedback, with 23 participants post-stroke. We find that participants receiving other-comparative feedback have significantly more delay time on the task than participants receiving self or no comparative feedback. In addition, we demonstrate that participants show task performance improvement over time, and provide responses to self-efficacy probes that vary along several dimensions.


Light Field Rendering


title: Ka-Band High Power GaN SPDT Switch MMIC

abstract: A Ka-Band high power GaN/SiC reflective SPDT Switch MMIC is demonstrated with unprecedented 49dBm P1dB and at least 58dBm survival threshold. The key enabling semiconductor technology is Raytheon's mm-Wave GaN HEMT with 1.36 ohm-mm Ron and 0.131 pF/mm Coff. Measured insertion loss is better than 1.3 dB over 27-31 GHz, and the minimum insertion loss of 0.8 dB has achieved at higher frequencies. Measured isolation is greater than 25 dB over 27-31 GHz, with the highest isolation of 30 dB at higher frequencies.


title: A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching

abstract: Many vision applications require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most real-time stereo applications rely on local winner-takes-all optimization in the disparity computation process. These local approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that, through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate whether these cost aggregation approaches can be adopted in real-time stereo applications and, if so, how well they perform under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing power of programmable graphics hardware. Six recent cost aggregation approaches are implemented and optimized for graphics hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing speed and result quality are reported.


title: A Co-Simulation Framework for Design of Time-Triggered Automotive Cyber Physical Systems

abstract: Abstract Designing cyber-physical systems (CPS) is challenging due to the tight interactions between software, network/platform, and physical components. Automotive control system is a typical CPS example and often designed based on a time-triggered paradigm. In this paper, a co-simulation framework that considers interacting CPS components for assisting time-triggered automotive CPS design is proposed. Virtual prototyping of automotive vehicles is the core of this framework, which uses SystemC to model the cyber components and integrates CarSim to model the vehicle dynamics. A network/platform model in SystemC forms the backbone of the virtual prototyping. The network/platform model consists of processing elements abstracted by real-time operating systems, communication systems, sensors, and actuators. The framework is also integrated with a model-based design tool to enable rapid prototyping. The framework is validated by comparing simulation results with the results from a hardware-in-the-loop automotive simulator. The framework is also used for design space exploration (DSE).


title: The sharing economy: Why people participate in collaborative consumption

abstract: Information and communications technologies ICTs have enabled the rise of so-called "Collaborative Consumption" CC: the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services. CC has been expected to alleviate societal problems such as hyper-consumption, pollution, and poverty by lowering the cost of economic coordination within communities. However, beyond anecdotal evidence, there is a dearth of understanding why people participate in CC. Therefore, in this article we investigate people's motivations to participate in CC. The study employs survey data N=168 gathered from people registered onto a CC site. The results show that participation in CC is motivated by many factors such as its sustainability, enjoyment of the activity as well as economic gains. An interesting detail in the result is that sustainability is not directly associated with participation unless it is at the same time also associated with positive attitudes towards CC. This suggests that sustainability might only be an important factor for those people for whom ecological consumption is important. Furthermore, the results suggest that in CC an attitude-behavior gap might exist; people perceive the activity positively and say good things about it, but this good attitude does not necessary translate into action.


Nanobiomedicine Aminosilane Functionalization and Cytotoxicity Effects of Upconversion Nanoparticles Y 2 O 3 and Gd 2 O 3 Co-doped with Yb 3 + and Er 3 +


title: A mobile robot for inspection of overhead transmission lines

abstract: A new mobile robot prototype for inspection of overhead transmission lines is proposed. The mobile platform is composed of 3 arms. And there is a motorized rubber wheel on the end of each arm. On the two end arms, a gripper is designed to clamp firmly onto the conductors from below to secure the robot. Each arm has a motor to achieve 2 degrees of freedom which is realized by moving along a curve. It could roll over some obstacles (compression splices, vibration dampers, etc). And the robot could clear other types of obstacles (spacers, suspension clamps, etc).


title: Differential diagnosis and comorbidity of attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) in adults

abstract: ■ Abstract Attention-deficit/hyperactivity disorder (ADHD) in adults and borderline personality Disorder (BPD) share some similar clinical features (e. g. impulsivity, emotional dysregulation, cognitive impairment). ADHD in childhood has been reported to be highly associated with the diagnosis of BPD in adulthood and adult ADHD often co-occurs with BPD. Treatment studies revealed an efficacy of dialectical behavioral therapy (DBT) and DBT-based psychotherapy, respectively, in BPD and adult ADHD as well as neuroimaging and psychopharmacological studies showed some evidence for a potential common neurobiological dysfunction suggesting the hypothesis that ADHD and BPD may not be two distinct disorders, but represent at least in a subgroup of patients two dimensions of one disorder.


title: 2D Shape Decomposition Based on Combined Skeleton-Boundary Features

abstract: Decomposing a shape into meaningful components plays a strong role in shape-related applications. In this paper, we combine properties of skeleton and boundary to implement a general shape decomposition approach. It is motivated by recent studies in visual human perception discussing the importance of certain shape boundary features as well as features of the shape area; it utilizes certain properties of the shape skeleton combined with boundary features to determine protrusion strength. Experiments yield results similar to those from human subjects on abstract shape data. Also, experiments of different data sets prove the robustness of the combined skeleton-boundary approach.


title: Interactions between Symmetric Cone and Information Geometries: Bruhat-Tits and Siegel Spaces Models for High Resolution Autoregressive Doppler Imagery

abstract: Main issue of High Resolution Doppler Imagery is related to robust statistical estimation of Toeplitz Hermitian positive definite covariance matrices of sensor data time series (e.g. in Doppler Echography, in Underwater acoustic, in Electromagnetic Radar, in Pulsed Lidar...). We consider this problem jointly in the framework of Riemannian symmetric spaces and the framework of Information Geometry. Both approaches lead to the same metric, that has been initially considered in other mathematical domains (study of Bruhat-Tits complete metric Space and Upper-half Siegel Space in Symplectic Geometry). Based on Frechet-Karcher barycenter definition and geodesics in Bruhat-Tits space, we address problem of N Covariance matrices Mean estimation. Our main contribution lies in the development of this theory for Complex Autoregressive models (maximum entropy solution of Doppler Spectral Analysis). Specific Blocks structure of the Toeplitz Hermitian covariance matrix is used to define an iterative and parallel algorithm for Siegel metric computation. Based on Affine Information Geometry theory, we introduce for Complex Autoregressive Model, Kahler metric on reflection coefficients based on Kahler potential function given by Doppler signal Entropy. The metric is closely related to Kahler-Einstein manifold and complex Monge-Ampere Equation. Finally, we study geodesics in space of Kahler potentials and action of Calabi and Kahler-Ricci Geometric Flows for this Complex Autoregressive Metric. We conclude with different results obtained on real Doppler Radar Data in HF and X bands : X-band radar monitoring of wake vortex turbulences, detection for Coastal X-band and HF Surface Wave Radars.


title: An Evaluation Framework for Mobile User Interfaces

abstract: We present the extensions to a software framework which supports the construction and evaluation of mixed-fidelity prototypes for mobile devices. The framework is available for desktop and mobile devices and allows designers and users to test the prototypes on actual devices. Additionally, the extensions aim at allowing designers to gather usage information, both passively and actively, tailoring the used techniques to users or project's goals. It supports contextual and ubiquitous evaluation also including in-situ prototyping and participatory design on-the-go. We address the evaluation's features and their contribution to the field of mobile interaction design, presenting real-life case studies and achieved results.


Compositionality and Systematicity


title: Cyber Disorders: The Mental Health Concern for the New Millennium

abstract: Anecdotal evidence has suggested that mental health practitioners' report increased caseloads of clients whose primary complaint involves the Internet. However, little is known about the incidence, associated behaviors, attitudes of practitioners, and interventions involved related to this relatively new phenomenon. Therefore, this study surveyed therapists who have treated clients suffering from cyber-related problems to gather such outcome information. Respondents reported an average caseload of nine clients who they classified as Internet-addicted, with a range between 2 and 50 clients treated within the past year. Five general subtypes of Internet addiction were categorized based on the most problematic types of online applications, and they include addictions to Cybersex, Cyber-relationships, online stock trading or gambling, information surfing, and computer games. Treatment strategies included cognitivebehavioral approaches, sexual offender therapy, marital and family therapy, social skills trainin...


title: Automatic turn segmentation for Movie & TV subtitles

abstract: Movie and TV subtitles contain large amounts of conversational material, but lack an explicit turn structure. This paper present a data-driven approach to the segmentation of subtitles into dialogue turns. Training data is first extracted by aligning subtitles with transcripts in order to obtain speaker labels. This data is then used to build a classifier whose task is to determine whether two consecutive sentences are part of the same dialogue turn. The approach relies on linguistic, visual and timing features extracted from the subtitles themselves and does not require access to the audiovisual material - although speaker diarization can be exploited when audio data is available. The approach also exploits alignments with related subtitles in other languages to further improve the classification performance. The classifier achieves an accuracy of 78 % on a held-out test set. A follow-up annotation experiment demonstrates that this task is also difficult for human annotators.


title: Computer-assisted pronunciation training: From pronunciation scoring towards spoken language learning

abstract: This paper reviews the research approaches used in computer-assisted pronunciation training (CAPT), addresses the existing challenges, and discusses emerging trends and opportunities. To complement existing work, our analysis places more emphasis on pronunciation teaching and learning (as opposed to pronunciation assessment), prosodic error detection (as opposed to phonetic error detection), and research work from the past five years given the recent rapid development in spoken language technology.


title: Indoor Pedestrian Navigation System Using a Modern Smartphone

abstract: In this work we present a pedestrian navigation system for indoor environments based on the dead reckoning positioning method, 2D barcodes, and data from accelerometers and magnetometers. All the sensing and computing technologies of our solution are available in common smart phones. The need to create indoor navigation systems arises from the inaccessibility of the classic navigation systems, such as GPS, in indoor environments.


title: Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks

abstract: We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources from different domains, i.e. raw point-sets and images. The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection. An image background patch is used as constraint to bias environmental texturing. A global approximation function within the generator is directly applied on the point-cloud (Point-Net). Hence, the representative learning model incorporates global 3D characteristics directly at the latent feature space. Conditions are used to bias the background and the viewpoint of the generated image. This opens up new ways in augmenting or texturing 3D data to aim the generation of fully individual images. We successfully evaluated our method on the Kitti and SunRGBD dataset with an outstanding object detection inception score.


title: Idest: Learning a Distributed Representation for Event Patterns

abstract: This paper describes IDEST, a new method for learning paraphrases of event patterns. It is based on a new neural network architecture that only relies on the weak supervision signal that comes from the news published on the same day and mention the same real-world entities. It can generalize across extractions from different dates to produce a robust paraphrase model for event patterns that can also capture meaningful representations for rare patterns. We compare it with two state-of-the-art systems and show that it can attain comparable quality when trained on a small dataset. Its generalization capabilities also allow it to leverage much more data, leading to substantial quality improvements.