title: Versatile low power media access for wireless sensor networks

abstract: We propose B-MAC, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, B-MAC employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. B-MAC supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing B-MAC's parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of B-MAC over a wide range of network conditions. We show that B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.


title: Language independent gender classification on Twitter

abstract: Online Social Networks (OSNs) generate a huge volume of user-originated texts. Gender classification can serve multiple purposes. For example, commercial organizations can use gender classification for advertising. Law enforcement may use gender classification as part of legal investigations. Others may use gender information for social reasons. Here we explore language independent gender classification. Our approach predicts gender using five color-based features extracted from Twitter profiles (e.g., the background color in a user's profile page). Most other methods for gender prediction are typically language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. Our approach is independent of the user's language, efficient, and scalable, while attaining a good level of accuracy. We prove the validity of our approach by examining different classifiers over a large dataset of Twitter profiles.


title: FindU: Privacy-preserving personal profile matching in mobile social networks

abstract: Making new connections according to personal preferences is a crucial service in mobile social networking, where the initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, the first privacy-preserving personal profile matching schemes for mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Several increasing levels of user privacy are defined, with decreasing amounts of exchanged profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize two of the user privacy levels, which can also be personalized by the users. We provide thorough security analysis and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes.


title: Thin, Transferred Layers of Silicon Dioxide and Silicon Nitride as Water and Ion Barriers for Implantable Flexible Electronic Systems

abstract: Thin, physically transferred layers of silicon dioxide (SiO2) thermally grown on the surfaces of silicon wafers offer excellent properties as long-lived, hermetic biofluid barriers in flexible electronic implants. This paper explores materials and physics aspects of the transport of ions through the SiO2 and the resultant effects on device performance and reliability. Accelerated soak tests of devices under electrical bias stress relative to a surrounding phosphate buffered saline (PBS) solution at a pH of 7.4 reveal the field dependence of these processes. Similar experimental protocols establish that coatings of SiNx on the SiO2 can block the passage of ions. Systematic experimental and theoretical investigations reveal the details associated with transport though this bilayer structure, and they serve as the basis for lifetime projections corresponding to more than a decade of immersion in PBS solution at 37 °C for the case of 100/200 nm of SiO2/SiNx. Temperature-dependent simulations offer further understanding of two competing failure mechanisms—dissolution and ion diffusion—on device lifetime. These findings establish a basic physical understanding of effects that are essential to the stable operation of flexible electronics as chronic implants.


title: Decentralized approaches to wastewater treatment and management: applicability in developing countries.

abstract: Providing reliable and affordable wastewater treatment in rural areas is a challenge in many parts of the world, particularly in developing countries. The problems and limitations of the centralized approaches for wastewater treatment are progressively surfacing. Centralized wastewater collection and treatment systems are costly to build and operate, especially in areas with low population densities and dispersed households. Developing countries lack both the funding to construct centralized facilities and the technical expertise to manage and operate them. Alternatively, the decentralized approach for wastewater treatment which employs a combination of onsite and/or cluster systems is gaining more attention. Such an approach allows for flexibility in management, and simple as well as complex technologies are available. The decentralized system is not only a long-term solution for small communities but is more reliable and cost effective. This paper presents a review of the various decentralized approaches to wastewater treatment and management. A discussion as to their applicability in developing countries, primarily in rural areas, and challenges faced is emphasized all through the paper. While there are many impediments and challenges towards wastewater management in developing countries, these can be overcome by suitable planning and policy implementation. Understanding the receiving environment is crucial for technology selection and should be accomplished by conducting a comprehensive site evaluation process. Centralized management of the decentralized wastewater treatment systems is essential to ensure they are inspected and maintained regularly. Management strategies should be site specific accounting for social, cultural, environmental and economic conditions in the target area.


title: Mobile advertising: evaluating the effects of animation, user and content relevance

abstract: The potential for user-relevant, context-appropriate, targeted advertising on mobile devices is enormous given device improvements and advances in personal and location-based data collection. However, little is known about how users experience display advertisements ('ads') on mobile devices, or what factors drive mobile ad effectiveness. In this paper, we investigate users' experiences of display advertising on mobile devices. We consider three factors that are often studied in desktop settings the ad's level of personal relevance to the user, its relevance to the page content, and within-ad properties, with a particular focus on the level of animation in the ad. Our findings reveal a few surprises. First, personal relevance to the user has little or no impact on ad efficacy measured by recall. Instead, content relevance boosts ad recall. Second, user relevance leads to a more pleasant and interesting experience, but content relevance has no effect. Third, contrary to the popular notion that animation often leads to more effective ads by garnering more user attention, we find that a simple type of animation, such as blinking animation, negatively affects user experience and reduces ad recall. Our findings, while focused on advertising, offer insights for design of mobile content presentation in general.


title: Echo State Networks for Self-Organizing Resource Allocation in LTE-U with Uplink-Downlink Decoupling

abstract: Uplink–downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink–downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band. Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as a noncooperative game that incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. This proposed algorithm allows the small base stations to autonomously choose their optimal resource allocation strategies given only limited information on the network’s and users’ states. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes a mixed strategy Nash equilibrium for their studied game. Simulation results show that the proposed approach yields significant gain, in terms of the sum-rate of the 50th percentile of users, that reaches up to 167% compared with a Q-learning algorithm. The results also show that the ESN significantly provides a considerable reduction of information exchange for the wireless network.


title: Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach

abstract: Push notification is a key component for E-commerce mobile applications, which has been extensively used for user growth and engagement. The effectiveness of the push notification is generally measured by message open rate. A push message can contain a recommended product, a shopping news and etc., but often only one or two items can be shown in the push message due to the limit of display space. This paper proposes a mixture model approach for predicting push message open rate for a post-purchase complementary product recommendation task. The mixture model is trained to learn latent prediction contexts, which are determined by user and item profiles, and then make open rate predictions accordingly. The item with the highest predicted open rate is then chosen to be included in the push notification message for each user. The parameters of the mixture model are optimized using an EM algorithm. A set of experiments are conducted to evaluate the proposed method live with a popular E-Commerce mobile app. The results show that the proposed method is superior than several existing solutions by a significant margin.


title: Is Maternal Smoking During Pregnancy a Risk Factor for Cigarette Smoking in Offspring? A Longitudinal Controlled Study of ADHD Children Grown Up

abstract: Objective: This study examined whether exposure to maternal smoking during pregnancy in children with and without ADHD is associated with smoking in offspring and whether this association is selective to ADHD children. Method: Ninety-six exposed and 400 unexposed participants were derived from two longitudinal studies of boys and girls with and without ADHD. Maternal smoking during pregnancy was defined by interviews with participants’ mothers. Results: A significant association was observed between exposure to maternal smoking in pregnancy and cigarette smoking in offspring (p = .02). Exposed offspring were also more likely to have higher rates of major depression (p = .04), bipolar disorder (p = .04), and conduct disorder (p = .04), and lower IQ (p = .01), lower Global Assessment of Functioning (GAF) score (p = .02), and more impaired Social Adjustment Inventory for Children and Adolescents (SAICA) scores versus unexposed offspring, adjusting for social class. Conclusion: Maternal smoking during pregnan...


title: An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

abstract: With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.


title: The 10 million follower fallacy: audience size does not prove domain-influence on Twitter

abstract: With the advent of social networks and micro-blogging systems, the way of communicating with other people and spreading information has changed substantially. Persons with different backgrounds, age and education exchange information and opinions, spanning various domains and topics, and have now the possibility to directly interact with popular users and authoritative information sources usually unreachable before the advent of these environments. As a result, the mechanism of information propagation changed deeply, the study of which is indispensable for the sake of understanding the evolution of information networks. To cope up with this intention, in this paper, we propose a novel model which enables to delve into the spread of information over a social network along with the change in the user relationships with respect to the domain of discussion. For this, considering Twitter as a case study, we aim at analyzing the multiple paths the information follows over the network with the goal of understanding the dynamics of the information contagion with respect to the change of the topic of discussion. We then provide a method for estimating the influence among users by evaluating the nature of the relationship among them with respect to the topic of discussion they share. Using a vast sample of the Twitter network, we then present various experiments that illustrate our proposal and show the efficacy of the proposed approach in modeling this information spread.


A News Summarization System using Fuzzy Graph Based Document Model


title: Perception of Social Media as a Source of Relevant Information

abstract: Social media today represent a global community of different nationalities - the size of China in terms of population, and social networking sites are online venues where users can create and post content. Social networks have also become one of the most popular ways for people to socialize, connect with friends and family, purchase items and gather relevant information about current and political topics and views. The most popular and biggest social network is Facebook and its influence in every pore of our society is evident, e.g. potential misuse of its user’s data in different purpose including manipulation in political purposes. This paper will also cover the findings of a survey conducted in Croatia and Serbia about the perception of social media and social networks as a source of gathering relevant information. JEL Classification: M38


title: Information Olfactation: Harnessing Scent to Convey Data

abstract: Olfactory feedback for analytical tasks is a virtually unexplored area in spite of the advantages it offers for information recall, feature identification, and location detection. Here we introduce the concept of information olfactation as the fragrant sibling of information visualization, and discuss how scent can be used to convey data. Building on a review of the human olfactory system and mirroring common visualization practice, we propose olfactory marks, the substrate in which they exist, and their olfactory channels that are available to designers. To exemplify this idea, we present vi S cent : A six-scent stereo olfactory display capable of conveying olfactory glyphs of varying temperature and direction, as well as a corresponding software system that integrates the display with a traditional visualization display. Finally, we present three applications that make use of the viScent system: A 2D graph visualization, a 2D line and point chart, and an immersive analytics graph visualization in 3D virtual reality. We close the paper with a review of possible extensions of viScent and applications of information olfactation for general visualization beyond the examples in this paper.


title: Neural Machine Translation and Sequence-to-sequence Models: A Tutorial

abstract: This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.


title: A precise embedded curvature sensor module for soft-bodied robots

abstract: Abstract As an emerging field, soft-bodied robots require profoundly different approaches to sensing and actuation compared to their rigid-bodied counterparts. Electro-mechanical design, fabrication, and operational challenges due to material elasticity significantly complicate embedded, modular and precise proprioceptive feedback. This work presents a novel curvature sensor module to address the unique soft robotic specifications. The proposed device utilizes a magnet and an electronic Hall effect sensing component to accurately measure curvatures on a soft-bodied bending segment on a flexible circuit board, ensuring contact-free sensing. We verify performance of sensor modules on static and dynamic bending deformations based on a single initial calibration step. To the best of our knowledge, the presented device is the first modular and integrated soft-bodied sensor design that is demonstrated to be accurate up to 7.5 Hz with a root mean square error of 0.023 cm −1 between measured and actual curvature without filtering out the intrinsic noise, and available for use with soft-bodied kinematic bending chains.


title: Neurodevelopmental Profiles of Children with Congenital Heart Disease at School Age

abstract: OBJECTIVES ::: To assess 6-year neurodevelopmental outcomes in a current cohort of children with congenital heart disease (CHD) who underwent cardiopulmonary bypass surgery (CPB), and to determine risk factors for adverse outcomes. ::: ::: ::: STUDY DESIGN ::: Outcomes were examined in 233 prospectively enrolled children with CHD (including 64 with a recognized genetic disorder) who underwent CPB between 2004 and 2009. Follow-up assessment included standardized neurologic, motor, and cognitive tests. Variables were collected prospectively, and multiple regression analysis was performed to determine independent risk factors for adverse outcome. ::: ::: ::: RESULTS ::: The mean patient age at assessment was 6.3 years (range, 5.1-6.8 years). IQ was lower in children with a genetic disorder (median, 55; range, 17-115) compared with children without a genetic disorder (median, 95; range, 47-135; P < .001). Cognitive and motor performance also were lower in children without a genetic disorder compared with the norm (P < .01 for both). The prevalence of children without a genetic disorder performing below -2 SD (IQ 70) was higher than the norm (5.3% vs 2.3%; P = .008), and the prevalence of poor motor performance (<10th percentile) ranged from 21.2% to 41.1% (P < .01 for all). Significant independent risk factors for poor neurodevelopmental outcome included a genetic disorder, longer length of intensive care stay, lower birth weight, postoperative seizures, and lower socioeconomic status. ::: ::: ::: CONCLUSIONS ::: Current cohorts of children with CHD undergoing CPB show favorable outcomes but remain at risk for long-term neurodevelopmental impairments, particularly those with a genetic disorder and a complicated postoperative course. Close neurodevelopmental surveillance is necessary to provide early therapeutic support.


title: Streaming Surface Reconstruction Using Wavelets

abstract: We present a streaming method for reconstructing surfaces from large data sets generated by a laser range scanner using wavelets. Wavelets provide a localized, multiresolution representation of functions and this makes them ideal candidates for streaming surface reconstruction algorithms. We show how wavelets can be used to reconstruct the indicator function of a shape from a cloud of points with associated normals. Our method proceeds in several steps. We first compute a low-resolution approximation of the indicator function using an octree followed by a second pass that incrementally adds fine resolution details. The indicator function is then smoothed using a modified octree convolution step and contoured to produce the final surface. Due to the local, multiresolution nature of wavelets, our approach results in an algorithm over 10 times faster than previous methods and can process extremely large data sets in the order of several hundred million points in only an hour.


title: Gene-Expression-Based Cancer Subtypes Prediction Through Feature Selection and Transductive SVM

abstract: With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. Microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type. Nevertheless, small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. A novel approach to combine feature (gene) selection and transductive support vector machine (TSVM) is proposed. We demonstrated that 1) potential gene markers could be identified and 2) TSVMs improved prediction accuracy as compared to the standard inductive SVMs (ISVMs). A forward greedy search algorithm based on consistency and a statistic called signal-to-noise ratio were employed to obtain the potential gene markers. The selected genes of the microarray data were then exploited to design the TSVM. Experimental results confirm the effectiveness of the proposed technique compared to the ISVM and low-density separation method in the area of semisupervised cancer classification as well as gene-marker identification.


title: An Improved PSFB PWM DC–DC Converter for High-Power and Frequency Applications

abstract: In the phase shifted full bridge (PSFB) pulse width modulation (PWM) converter, external snubber capacitors are connected in parallel to insulated gate bipolar transistors (IGBTs) in order to decrease turn-off losses. The zero voltage transition (ZVT) condition is not provided at light loads, thus the parallel capacitors discharge through IGBTs at turn on which causes switching losses and failure risk of the IGBTs. Capacitor discharge through IGBT restricts the use of high-value snubber capacitors, and turn-off loss of the IGBT increases at high currents. This problematic condition occurs especially at the lagging leg. In this study, a new technique enabling the use of high-value snubber capacitors with the lagging leg of the PSFB PWM converter is proposed. As advantages of the proposed technique, high-capacitive discharge current through IGBT is prevented at light loads, the turn-off switching losses of the IGBTs are decreased, and the performance of the converter is improved at high currents. The proposed PSFB PWM converter includes an auxiliary circuit, and it has a simple structure, low cost, and ease of control as well. The operation principle and detailed design procedure of the converter are presented. The theoretical analysis is verified exactly by a prototype of 75 kHz and 10 kW converter.


The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields


title: Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

abstract: We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with a wide range of expressions, poses, and illuminations conditioned by synthetic images sampled from a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with a large volume of paired data or train highly under-constrained two-way generative networks in an unsupervised fashion. We propose a semi-supervised adversarial learning framework to constrain the two-way networks by a small number of paired real and synthetic images, along with a large volume of unpaired data. A set-based loss is also proposed to preserve identity coherence of generated images. Qualitative results show that generated face images of new identities contain pose, lighting and expression diversity. They are also highly constrained by the synthetic input images while adding photorealism and retaining identity information. We combine face images generated by the proposed method with a real data set to train face recognition algorithms and evaluate the model quantitatively on two challenging data sets: LFW and IJB-A. The generated images by our framework consistently improve the performance of deep face recognition networks trained with the Oxford VGG Face dataset, and achieve comparable results to the state-of-the-art.


title: Trends of CPU, GPU and FPGA for high-performance computing

abstract: Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems.


title: Supervised Hierarchical Bayesian Model-Based Electomyographic Control and Analysis

abstract: This work suggests a supervised hierarchical Bayesian model for surface electromyography (sEMG)-based motion classification and its strategy analysis. The proposed model unifies the optimal feature extraction and classification through probabilistic inference and learning by identifying the latent neural states (LNSs) that govern a collection of sEMG signals. In addition, the inference step provides an approach to identify distinct muscle activation strategies according to sEMG patterns based on LNSs. To validate the model, nine-class classification using four sEMG sensors on the limb motions is tested. The model performance is evaluated with relatively high and low activation levels, generalized classification across subjects and online classification. The model, based on LNSs to capture various motions, is assessed with respect to activation levels, individual subjects and transition during online classification. Our approach cannot only classify sEMG patterns, but also provide the interpretation of sEMG strategic patterns. This work supports the potential of the proposed model for sEMG control-based applications.


title: Implementing the Sociocultural Theory While Teaching ESL

abstract: Previous literature presents there is a demand to teach adult learners English due to the amount of non-English speaking citizens. Therefore, educators have responded to the need for programs that teach adult learners English. English as a Second Language (ESL) classes help adults by providing a foundation of comprehension for the English language, this foundation helps adults navigate their lives. Previous educational research states that Vygotsky's (1978) Sociocultural Theory is a theory practiced in the ESL classroom because the Soctocultural Theory consists of cultural approach In addition to an educational approach. The Soctocultural Theory and has been beneficial for adult learners because It Involves social interaction, cultural involvement, and all components of the teaching environment. This article discusses a Service Learner's experience and reflection while incorporating the Sociocultural Theory into teaching English as a Second Language to adult learners.


title: LOW CORRELATION MIMO ANTENNAS WITH NEGATIVE GROUP DELAY

abstract: A key feature of upcoming 4G wireless communication systems is multiple-input-multiple-output (MIMO) technology. To make the best use of MIMO, the antenna correlation between adjacent antennas should be low (< 0:5). In this context, we propose a correlation reduction technique suitable for closely spaced antennas (distance, d < ‚=40). This technique reduces mutual coupling between antennas and concurrently uncorrelates antennas' radiation characteristics by inducing the negative group delay at the target frequency. The validity of the technique is demonstrated with a USB dongle MIMO antenna designed for LTE 700MHz band. Measurement results show that the antenna correlation is reduced more than 40% using the proposed technique.


title: Understanding the Energy Consumption of Dynamic Random Access Memories

abstract: Energy consumption has become a major constraint on the capabilities of computer systems. In large systems the energy consumed by Dynamic Random Access Memories (DRAM) is a significant part of the total energy consumption. It is possible to calculate the energy consumption of currently available DRAMs from their datasheets, but datasheets don’t allow extrapolation to future DRAM technologies and don’t show how other changes like increasing bandwidth requirements change DRAM energy consumption. This paper first presents a flexible DRAM power model which uses a description of DRAM architecture, technology and operation to calculate power usage and verifies it against datasheet values. Then the model is used together with assumptions about the DRAM roadmap to extrapolate DRAM energy consumption to future DRAM generations. Using this model we evaluate some of the proposed DRAM power reduction schemes.


title: Requirements of the Smart Factory System: A Survey and Perspective

abstract: With the development of Industry 4.0 and the emergence of the smart factory concept, the traditional philosophy of manufacturing systems will change. The smart factory introduces changes to the factors and elements of traditional manufacturing systems and incorporates the current requirements of smart systems so that it can compete in the future. An increasing amount of research in both academia and industry is dedicated to transitioning the concept of the smart factory from theory to practice. The purpose of the current research is to highlight the perspectives that shape the smart factory and to suggest approaches and technical support to enable the realization of those perspectives. This paper fills this gap by identifying and analyzing research on smart factories. We suggest a framework to analyze existing research and investigate the elements and features of smart factory systems.


A real-time logo detection system using data offloading on mobile devices


A DECISION SUPPORT SYSTEM DESIGN TO OVERCOME RESISTANCE TOWARDS SUSTAINABLE INNOVATIONS


title: Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model

abstract: The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thus can take advantage of reliable models that can accurately predict player behavior and scale to huge datasets. Churn prediction, a challenge common to a variety of sectors, is particularly relevant for the mobile game industry, as player retention is crucial for the successful monetization of a game. In this article, we present an approach to predicting game churn based on survival ensembles. Our method provides accurate predictions on both the level at which each player will leave the game and their accumulated playtime until that moment. Further, it is robust to different data distributions and applicable to a wide range of response variables, while also allowing for efficient parallelization of the algorithm. This makes our model well suited to perform real-time analyses of churners, even for games with millions of daily active users.


title: Admissible Heuristics for Automated Planning

abstract: The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities ...


title: Selection and pre-employment assessment in aviation security x-ray screening

abstract: This document concentrates on how aviation security can be largely improved by selecting the right people for the x-ray screening task. X-ray images dispose of a different colour range, single objects superimpose each other independently of whether they are in front or behind of each other, and the single objects look very different from what they look like under natural light. Therefore, individual visual abilities such as mental rotation, figure-ground segregation, or visual search of specific patterns are very important for the correct interpretation of x-ray images. In this paper we briefly discuss job and task analysis, as well as cognitive task analysis in x-ray screening. We then discuss tests used in pre-employment assessment, including a more detailed presentation of the X-Ray Object Recognition Test (X-Ray ORT). Finally, a new project is presented in which a cognitive test battery is developed to measure important visual abilities needed in x-ray screening and related tasks.


title: Too much, too little, or just right? Ways explanations impact end users' mental models

abstract: Research is emerging on how end users can correct mistakes their intelligent agents make, but before users can correctly “debug” an intelligent agent, they need some degree of understanding of how it works. In this paper we consider ways intelligent agents should explain themselves to end users, especially focusing on how the soundness and completeness of the explanations impacts the fidelity of end users' mental models. Our findings suggest that completeness is more important than soundness: increasing completeness via certain information types helped participants' mental models and, surprisingly, their perception of the cost/benefit tradeoff of attending to the explanations. We also found that oversimplification, as per many commercial agents, can be a problem: when soundness was very low, participants experienced more mental demand and lost trust in the explanations, thereby reducing the likelihood that users will pay attention to such explanations at all.


Semantic Word Clusters Using Signed Spectral Clustering.


title: Generating Predictive Movie Recommendations from Trust in Social Networks

abstract: Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types.


title: Simplified markov random fields for efficient semantic labeling of 3D point clouds

abstract: In this paper, we focus on 3D point cloud classification by assigning semantic labels to each point in the scene. We propose to use simplified Markov networks to model the contextual relations between points, where the node potentials are calculated from point-wise classification results using off-the-shelf classifiers, such as Random Forest and Support Vector Machines, and the edge potentials are set by physical distance between points. Our experimental results show that this approach yields comparable if not better results with improved speed compared with state-of-the-art methods. We also propose a novel robust neighborhood filtering method to exclude outliers in the neighborhood of points, in order to reduce noise in local geometric statistics when extracting features and also to reduce number of false edges when constructing Markov networks. We show that applying robust neighborhood filtering improves the results when classifying point clouds with more object categories.


title: Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

abstract: Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate.


title: Forensic discovery auditing of digital evidence containers

abstract: Current digital forensics methods capture, preserve, and analyze digital evidence in general-purpose electronic containers (typically, plain files) with no dedicated support to help establish that the evidence has been properly handled. Auditing of a digital investigation, from identification and seizure of evidence through duplication and investigation is, essentially, ad hoc, recorded in separate log files or in an investigator's case notebook. Auditing performed in this fashion is bound to be incomplete, because different tools provide widely disparate amounts of auditing information - including none at all - and there is ample room for human error. The latter is a particularly pressing concern given the fast growth of the size of forensic targets. Recently, there has been a serious community effort to develop an open standard for specialized digital evidence containers (DECs). A DEC differs from a general purpose container in that, in addition to the actual evidence, it bundles arbitrary metadata associated with it, such as logs and notes, and provides the basic means to detect evidence-tampering through digital signatures. Current approaches consist of defining a container format and providing a specialized library that can be used to manipulate it. While a big step in the right direction, this approach has some non-trivial shortcomings - it requires the retooling of existing forensic software and, thereby, limits the number of tools available to the investigator. More importantly, however, it does not provide a complete solution since it only records snapshots of the state of the DEC without being able to provide a trusted log of all data operations actually performed on the evidence. Without a trusted log the question of whether a tool worked exactly as advertised cannot be answered with certainty, which opens the door to challenges (both legitimate and frivolous) of the results. In this paper, we propose a complementary mechanism, called the Forensic Discovery Auditing Module (FDAM), aimed at closing this loophole in the discovery process. FDAM can be thought of as a 'clean-room' environment for the manipulation of digital evidence, where evidence from containers is placed for controlled manipulation. It functions as an operating system component, which monitors and logs all access to the evidence and enforces policy restrictions. This allows the immediate, safe, and verifiable use of any tool deemed necessary by the examiner. In addition, the module can provide transparent support for multiple DEC formats, thereby greatly simplifying the adoption of open standards.


title: Rethinking Visual Relationships for High-level Image Understanding

abstract: Relationships, as the bond of isolated entities in images, reflect the interaction between objects and lead to a semantic understanding of scenes. Suffering from visually-irrelevant relationships in current scene graph datasets, the utilization of relationships for semantic tasks is difficult. The datasets widely used in scene graph generation tasks are splitted from Visual Genome by label frequency, which even can be well solved by statistical counting. To encourage further development in relationships, we propose a novel method to mine more valuable relationships by automatically filtering out visually-irrelevant relationships. Then, we construct a new scene graph dataset named Visually-Relevant Relationships Dataset (VrR-VG) from Visual Genome. We evaluate several existing methods in scene graph generation in our dataset. The results show the performances degrade significantly compared to the previous dataset and the frequency analysis do not work on our dataset anymore. Moreover, we propose a method to learn feature representations of instances, attributes, and visual relationships jointly from images, then we apply the learned features to image captioning and visual question answering respectively. The improvements on the both tasks demonstrate the efficiency of the features with relation information and the richer semantic information provided in our dataset.


title: Measuring enjoyment of physical activity in older adults: invariance of the physical activity enjoyment scale (paces) across groups and time

abstract: The purpose of this study was to validate the Physical Activity Enjoyment Scale (PACES) in a sample of older adults. Participants within two different exercise groups were assessed at two time points, 6 months apart. Group and longitudinal invariance was established for a novel, 8-item version of the PACES. The shortened, psychometrically sound measure provides researchers and practitioners an expedited and reliable instrument for assessing the enjoyment of physical activity.


title: 3D puppetry: a kinect-based interface for 3D animation

abstract: We present a system for producing 3D animations using physical objects (i.e., puppets) as input. Puppeteers can load 3D models of familiar rigid objects, including toys, into our system and use them as puppets for an animation. During a performance, the puppeteer physically manipulates these puppets in front of a Kinect depth sensor. Our system uses a combination of image-feature matching and 3D shape matching to identify and track the physical puppets. It then renders the corresponding 3D models into a virtual set. Our system operates in real time so that the puppeteer can immediately see the resulting animation and make adjustments on the fly. It also provides 6D virtual camera \\rev{and lighting} controls, which the puppeteer can adjust before, during, or after a performance. Finally our system supports layered animations to help puppeteers produce animations in which several characters move at the same time. We demonstrate the accessibility of our system with a variety of animations created by puppeteers with no prior animation experience.


title: KenLM: Faster and Smaller Language Model Queries

abstract: We present KenLM, a library that implements two data structures for efficient language model queries, reducing both time and memory costs. The Probing data structure uses linear probing hash tables and is designed for speed. Compared with the widely-used SRILM, our Probing model is 2.4 times as fast while using 57% of the memory. The Trie data structure is a trie with bit-level packing, sorted records, interpolation search, and optional quantization aimed at lower memory consumption. Trie simultaneously uses less memory than the smallest lossless baseline and less CPU than the fastest baseline. Our code is open-source, thread-safe, and integrated into the Moses, cdec, and Joshua translation systems. This paper describes the several performance techniques used and presents benchmarks against alternative implementations.


title: Reactors: A Case for Predictable, Virtualized OLTP Actor Database Systems

abstract: The requirements for OLTP database systems are becoming ever more demanding. New OLTP applications require high degrees of scalability with controlled transaction latencies in in-memory databases. Deployments of these applications require low-level control of database system overhead and program-to-data affinity to maximize resource utilization in modern machines. Unfortunately, current solutions fail to meet these requirements. First, existing database solutions fail to expose a high-level programming abstraction in which latency of transactions can be influenced and reasoned about by application developers. Second, these solutions either inhibit or limit infrastructure engineers in exercising low-level control on the deployment of the system on a target infrastructure, further impacting performance. ::: In this paper, we propose a relational actor programming model for in-memory databases. Conceptually, relational actors, or reactors for short, are application-defined, isolated logical actors encapsulating relations that process function calls asynchronously. Reactors ease reasoning about correctness by guaranteeing serializability of application-level function calls. In contrast to classic transactional models, however, reactors allow developers to take advantage of intra-transaction parallelism to reduce latency and improve performance. Moreover, reactors enable a new degree of flexibility in database deployment. We present ReactDB, a novel system design exposing reactors that allows for flexible virtualization of database architecture between the extremes of shared-nothing and shared-everything without changes to application code. Our experiments with ReactDB illustrate performance predictability, multi-core scalability, and low overhead in OLTP benchmarks.


title: Revisiting Edge and Node Parallelism for Dynamic GPU Graph Analytics

abstract: Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, analyzing power grids, and studying protein interactions. However, its complexity makes its exact computation infeasible for large graphs of interest. Furthermore, networks tend to change over time, invalidating previously calculated results and encouraging new analyses regarding how centrality metrics vary with time. While GPUs have dominated regular, structured application domains, their high memory throughput and massive parallelism has made them a suitable target architecture for irregular, unstructured applications as well. In this paper we compare and contrast two GPU implementations of an algorithm for dynamic betweenness centrality. We show that typical network updates affect the centrality scores of a surprisingly small subset of the total number of vertices in the graph. By efficiently mapping threads to units of work we achieve up to a 110x speedup over a CPU implementation of the algorithm and can update the analytic 45x faster on average than a static recomputation on the GPU.


title: Cascade^CNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks

abstract: This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low-and high-precision unit in a cascade. A confidence evaluation unit is employed to identify misclassified cases from the excessively low-precision unit and forward them to the high-precision unit for re-processing. Experiments demonstrate that the proposed toolflow can achieve a performance boost up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy, without the need of retraining the model or accessing the training data.


title: Mashups: A Literature Review and Classification Framework

abstract: The evolution of the Web over the past few years has fostered the growth of a handful of new technologies (e.g. Blogs, Wiki’s, Web Services). Recently web mashups have emerged as the newest Web technology and have gained lots of momentum and attention from both academic and industry communities. Current mashup literature focuses on a wide array of issues, which can be partially explained by how new the topic is. However, to date, mashup literature lacks an articulation of the different subtopics of web mashup research. This study presents a broad review of mashup literature to help frame the 1subtopics in mashup research.


Real-time dense map fusion for stereo SLAM


title: Open-set face recognition across look-alike faces in real-world scenarios

abstract: The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios. This paper uses 3D reconstructed models to recognize look-alike faces.A feature is extracted from both facial reconstructed depth and texture images.This paper proposes likeness dictionary learning.Three open-set classification methods are proposed for real-world face recognition.


title: Implementation of LabVIEW®-based joint-linear motion blending on a lab-manufactured 6-axis articulated robot (RS2)

abstract: For fast and accurate motion of 6-axis articulated robot, more noble motion control strategy is needed. In general, the movement strategy of industrial robots can be divided into two kinds, PTP (Point to Point) and CP (Continuous Path). Recently, industrial robots which should be co-worked with machine tools are increasingly needed for performing various jobs, as well as simple handling or welding. Therefore, in order to cope with high-speed handling of the cooperation of industrial robots with machine tools or other devices, CP should be implemented so as to reduce vibration and noise, as well as decreasing operation time. This paper will realize CP motion (especially joint-linear) blending in 3-dimensional space for a 6-axis articulated (lab-manufactured) robot (called as "RS2") by using LabVIEW (6) programming, based on a parametric interpolation. Another small contribution of this paper is the proposal of motion blending simulation technique based on Recurdyn V7 and Solidworks, in order to figure out whether the joint-linear blending motion can generate the stable motion of robot in the sense of velocity magnitude at the end-effector of robot or not. In order to evaluate the performance of joint-linear motion blending, simple PTP (i.e., linear-linear) is also physically implemented on RS2. The implementation results of joint-linear motion blending and PTP are compared in terms of vibration magnitude and travel time by using the vibration testing equipment of Medallion of Zonic. It can be confirmed verified that the vibration peak of joint-linear motion blending has been reduced to 1/10, compared to that of PTP.


title: A 5.42nW/kB retention power logic-compatible embedded DRAM with 2T dual-Vt gain cell for low power sensing applications

abstract: A logic-compatible 2T dual-Vt embedded DRAM (eDRAM) is proposed for ultra-small sensing systems to achieve 8× longer retention time, 5× lower refresh power and 30% reduced area compared with the lowest power eDRAM previously reported. With an area-efficient single inverter sensing scheme designed for R/W speed compatibility with ultra-low power processors, 58% array efficiency is maintained for memories as small as 2kb and for as few as 32 bits per bitline.


title: Intrinsic network connectivity and own body perception in gender dysphoria

abstract: Gender dysphoria (GD) is characterized by incongruence between one’s identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception –default mode network (DMN) and salience network – and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher “self” ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.


title: Fraud Risk Modelling: Requirements Elicitation in the Case of Telecom Services

abstract: Telecom providers are losing tremendous amounts of money due to fraud risks posed to Telecom services and products. Currently, they are mainly focusing on fraud detection approaches to reduce the impact of fraud risks against their services. However, fraud prevention approaches should also be investigated in order to further reduce fraud risks and improve the revenue of Telecom providers. Fraud risk modelling is a fraud prevention approach aims at identifying the potential fraud risks, estimating the damage and setting up preventive mechanisms before the fraud risks lead to actual losses. In this paper, we highlight the important requirements for a usable and context-aware fraud risk modelling approach for Telecom services. To do so, we have conducted two workshops with experts from a Telecom provider and experts from multi-disciplinary areas. In order to show and document the requirements, we present two exemplary Telecom fraud scenarios, analyse and estimate the impacts of fraud risks qualitatively.


title: An Isolated Three-Port Bidirectional DC-DC Converter With Decoupled Power Flow Management

abstract: An isolated three-port bidirectional dc-dc converter composed of three full-bridge cells and a high-frequency transformer is proposed in this paper. Besides the phase shift control managing the power flow between the ports, utilization of the duty cycle control for optimizing the system behavior is discussed and the control laws ensuring the minimum overall system losses are studied. Furthermore, the dynamic analysis and associated control design are presented. A control-oriented converter model is developed and the Bode plots of the control-output transfer functions are given. A control strategy with the decoupled power flow management is implemented to obtain fast dynamic response. Finally, a 1.5 kW prototype has been built to verify all theoretical considerations. The proposed topology and control is particularly relevant to multiple voltage electrical systems in hybrid electric vehicles and renewable energy generation systems.


title: Breaking BAD: a data serving vision for big active data

abstract: Virtually all of today's Big Data systems are passive in nature. Here we describe a project to shift Big Data platforms from passive to active. We detail a vision for a scalable system that can continuously and reliably capture Big Data to enable timely and automatic delivery of new information to a large pool of interested users as well as supporting analyses of historical information. We are currently building a Big Active Data (BAD) system by extending an existing scalable open-source BDMS (AsterixDB) in this active direction. This first paper zooms in on the Data Serving piece of the BAD puzzle, including its key concepts and user model.


title: Flash memory cells—An overview

abstract: The aim of this paper is to give a thorough overview of flash memory cells. Basic operations and charge-injection mechanisms that are most commonly used in actual flash memory cells are reviewed to provide an understanding of the underlying physics and principles in order to appreciate the large number of device structures, processing technologies, and circuit designs presented in the literature. New cell structures and architectural solutions have been surveyed to highlight the evolution of the flash memory technology, oriented to both reducing cell size and upgrading product functions. The subject is of extreme interest: new concepts involving new materials, structures, principles, or applications are being continuously introduced. The worldwide semiconductor memory market seems ready to accept many new applications in fields that are not specific to traditional nonvolatile memories.


title: [Treatment of type 2 diabetes: revision of current therapeutical options and priorities].

abstract: The paradigm of type 2 diabetes mellitus treatment is based in an adequate meal plan and the regular practice of physical exercise. The benefits of these measures are of evident as it is the perseverance in their compliance. In the daily practice, this reality favours the early implementation of a pharmacological therapy that, in the opinion of different experts, would be simultaneous to the introduction of life style changes. In the last years, new drugs have been developed. On the one hand, there are agents with beneficial effects not only on blood glucose control but also on the components of metabolic syndrome. Other drugs, according to different experts, have doubtful therapeutical contribution. The appearance of new drugs to treat obesity, associated with diabetes or not, extends other possibilities of choice to take into account. The great variety of options makes the decision of the adequate therapy more difficult. In the last decade, many therapeutic guidelines have been developed, with convergences and divergences that may mislead the inexpert practitioners. In this review we propose some practical advise in diet and exercise, and an evaluation of the different drugs and rules of intervention proposed in the different therapeutic guidelines. The ultimate goal is to facilitate and direct type 2 diabetes management.


title: Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve

abstract: Cognitive reserve (CR) is a concept meant to account for the frequent discrepancy between an individual's measured level of brain pathology and her expected cognitive performance. It is particularly important within the context of aging and dementia, but has wider applicability to all forms of brain damage. As such, it has intimate links to related compensatory and neuroprotective concepts, as well as to the related notion of brain reserve. In this article, we introduce the concept of cognitive reserve and explicate its potential cognitive and neural implementation. We conclude that cognitive reserve is compatible and complementary with many related concepts, but that each much draw sharper conceptual boundaries in order to truly explain preserved cognitive function in the face of aging or brain damage.


Wireless Sensor Networks in Industrial Automation


DNA repair mechanisms in cancer development and therapy


Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls


title: Immersive Group-to-Group Telepresence

abstract: We present a novel immersive telepresence system that allows distributed groups of users to meet in a shared virtual 3D world. Our approach is based on two coupled projection-based multi-user setups, each providing multiple users with perspectively correct stereoscopic images. At each site the users and their local interaction space are continuously captured using a cluster of registered depth and color cameras. The captured 3D information is transferred to the respective other location, where the remote participants are virtually reconstructed. We explore the use of these virtual user representations in various interaction scenarios in which local and remote users are face-to-face, side-by-side or decoupled. Initial experiments with distributed user groups indicate the mutual understanding of pointing and tracing gestures independent of whether they were performed by local or remote participants. Our users were excited about the new possibilities of jointly exploring a virtual city, where they relied on a world-in-miniature metaphor for mutual awareness of their respective locations.


title: What is this, anyway: Automatic hypernym discovery

abstract: Can a system that “learns from reading” figure out on it’s own the semantic classes of arbitrary noun phrases? This is essential for text understanding, given the limited coverage of proper nouns in lexical resources such as WordNet. Previous methods that use lexical patterns to discover hypernyms suffer from limited precision and recall. We present methods based on lexical patterns that find hypernyms of arbitrary noun phrases with high precision. This more than doubles the recall of proper noun hypernyms provided by WordNet at a modest cost to precision. We also present a novel method using a Hidden Markov Model (HMM) to extend recall further. Introduction and Motivation A goal of “Machine Reading” is an automatic system that extracts information from text and supports a wide range of inferencing capabilities (Etzioni, Banko, and Cafarella 2006). To enable such inferencing, an Information Extraction (IE) system must go beyond representing extracted entities as text strings and ontologize the text strings (Pennacchiotti and Pantel 2006; Soderland and Mandhani 2007). This involves semantically typing the text strings, grounding the string in real world entities where possible, and mapping the string to a concept in a formal taxonomy. In many cases, particularly for proper nouns, the text string is not found in an existing taxonomy and must be added on the fly as a child of an existing concept. This paper focuses on just one step in this ontologizing process: finding hypernyms for an arbitrary noun phrase (NP). This is a necessary first step in semantically typing the NP and mapping it to a node in a taxonomy. The problem that we address here is as follows. Given an NP e, find a set of NPs ci such that each ci is a hypernym of e in some sense as judged by a human. hypernymOf(e) = {c1, c2, ...ck} Note that we define hypernymy as a relation between surface forms and not “synsets”, as is done in WordNet. Copyright c © 2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Consider the example of an NP extracted from the Web, “Borland Delphi”. This term is not found in WordNet, but lexical patterns in a large corpus suggest that Borland Delphi may have the following hypernyms: hypernymOf(Borland Delphi) = development environment = software development system = software tool = language = technology The rest of this paper is organized as follows. We discuss previous attempts at solving this problem. The manually engineered WordNet thesaurus (Miller et al. 1990) has good coverage of common nouns, but contains relatively few entries for Proper Nouns. Several researchers have used methods based on lexical patterns to discover hypernyms (Hearst 1992; Roark and Charniak 1998; Caraballo 1999; Snow, Jurafsky, and Ng 2005), although each of these methods suffers from limited precision and recall. We then present a series of methods for finding hypernyms of arbitrary NPs: HYPERNYMFINDERfreq which is based on the frequency of Hearst pattern matches and HYPERNYMFINDERsvm that uses a Support Vector Machine (SVM) classifier to incorporate additional features. Each of these systems learns to find hypernyms from lexical patterns and corpus statistics. Since this is an inherently error-prone process, each version of HYPERNYMFINDER assigns a probability to each ci it finds. Finally, we present HYPERNYMFINDERhmm, which uses an HMM language model to extend the recall of HYPERNYMFINDER to cover NPs that do not match any of the lexical patterns used by the previous versions. Previous Work on Hypernym Discovery The WordNet thesaurus (Miller et al. 1990) is a highprecision lexical resource that has served as a baseline for hypernym discovery since the 1990’s. On a test set of 953 Noun phrases randomly selected from our Web corpus, we found that only 17% of the proper nouns and 64% of the common nouns were covered by WordNet. Information is often spotty even for NPs that are found in WordNet. For example, WordNet has an entry for “Mel Gibson” as an instance of the class “actor”, a subclass of “human”, but does not indicate that he is a “man”. We would need to use other


title: Virtual Machine Warmup Blows Hot and Cold

abstract: Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: the initial warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling those parts into machine code; subsequently the program is said to be at a steady state of peak performance. Measurement methodologies almost always discard data collected during the warmup phase such that reported measurements focus entirely on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of pairs consistently reach a steady state of peak performance.


Sudden Infant Death Syndrome and Brainstem Research


title: Acceleration of AES Encryption with OpenCL

abstract: The occurrence of multi-core processors has made parallel techniques popular. OpenCL, enabling access to the computing power of multi-platforms, taking advantage of the parallel feature of computing devices, gradually obtains researchers' favor. However, when using parallel techniques, which computation granularity and memory allocation strategies to choose bother developers the most. To solve this problem, many researchers had implemented experiments on Nvidia GPUs and found out the best solution for using CUDA. When it comes to use OpenCL on AMD GPU, to the best of our knowledge, less solutions have been proposed in the literature. Therefore, we conduct several experiments to demonstrate the relation between computation granularity and memory allocation methods of the input data when using OpenCL on AES encoding. In granularity of 16 bytes/thread, the encryption throughput of our experiment can achieve 5 Gbps. Compared with previous works, the ratio between the price of GPU and performance from our experiment is promising.


title: Learning Topic Representation for SMT with Neural Networks

abstract: Statistical Machine Translation (SMT) usually utilizes contextual information to disambiguate translation candidates. However, it is often limited to contexts within sentence boundaries, hence broader topical information cannot be leveraged. In this paper, we propose a novel approach to learning topic representation for parallel data using a neural network architecture, where abundant topical contexts are embedded via topic relevant monolingual data. By associating each translation rule with the topic representation, topic relevant rules are selected according to the distributional similarity with the source text during SMT decoding. Experimental results show that our method significantly improves translation accuracy in the NIST Chinese-to-English translation task compared to a state-of-the-art baseline.


title: TOWARDS LEGGED AMPHIBIOUS MOBILE ROBOTICS

abstract: New areas of research focus on bridging the gap between mobile robotics on land and at sea. This paper describes the evolution of RHex, a power-autonomous legged land-based robot, into one capable of both sea and land-based locomotion. In working towards an amphibious robot, three versions of RHex with increasing levels of aquatic capability were created. While all three platforms share the same underlying software, electronic and mechanical architectures, varying emphasis on aspects of similar design criteria resulted in the development of varied platforms with increasing ability of amphibious navigation.


title: Driver Drowsiness Monitoring Based on Yawning Detection

abstract: Fatigue and drowsiness of drivers are amongst the significant causes of road accidents. In this paper, we discuss a method for detecting drivers' drowsiness and subsequently alerting them. The aim is to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety. Many special body and face gestures are used as sign of driver fatigue, including yawning, eye tiredness and eye movement, which indicate that the driver is no longer in a proper driving condition. Here, we propose a method of yawning detection based on the changes in the mouth geometric features.


title: Influence properties of partial least squares regression

abstract: Abstract In this paper, we compute the influence function (IF) for partial least squares (PLS) regression. Thereunto, we design two alternative algorithms, according to the PLS algorithm used. One algorithm for the computation of the influence function is based on the Helland PLS algorithm, whilst the other is compatible with SIMPLS. The calculation of the influence function leads to new influence diagnostic plots for PLS. An alternative to the well-known Cook's Distance (CD) plot is proposed, as well as a variant which is sample specific. Moreover, a novel estimate of prediction variance is deduced. The validity of the latter is corroborated by dint of a Monte Carlo simulation.


title: Aspect based opinion mining on restaurant reviews

abstract: Nowadays, expansion of social media and internet are driving to a whole another level. Most of the users critically review anything on the internet specially foods and services in restaurants to showcase their humble opinion. These opinions are very valuable in decision making process. Analyzing and extracting the actual opinion throughout these reviews manually is practically difficult since there are large numbers of reviews available in the various aspects. So, an automated methodology is needed to solve this problem. Opinion mining or sentiment analysis is such methodology to analysis these reviews and classify topics as positive, negative and neutral. There are three different levels of opinion mining; Document based, Sentence based and Aspect based. Document and Sentence based opinion mining focus on overall polarity of document and sentence respectively and do not describe the important aspects of each opinions which is more accurate. Hence Aspect based opining mining is the trending topic and this paper is specifically focused on it on reviews in the domain of restaurants.


title: When Cellular Networks Met IPv6: Security Problems of Middleboxes in IPv6 Cellular Networks

abstract: Recently, cellular operators have started migrating to IPv6 in response to the increasing demand for IP addresses. With the introduction of IPv6, cellular middleboxes, such as firewalls for preventing malicious traffic from the Internet and stateful NAT64 boxes for providing backward compatibility with legacy IPv4 services, have become crucial to maintain stability of cellular networks. This paper presents security problems of the currently deployed IPv6 middleboxes of five major operators. To this end, we first investigate several key features of the current IPv6 deployment that can harm the safety of a cellular network as well as its customers. These features combined with the currently deployed IPv6 middlebox allow an adversary to launch six different attacks. First, firewalls in IPv6 cellular networks fail to block incoming packets properly. Thus, an adversary could fingerprint cellular devices with scanning, and further, she could launch denial-of-service or over-billing attacks. Second, vulnerabilities in the stateful NAT64 box, a middlebox that maps an IPv6 address to an IPv4 address (and vice versa), allow an adversary to launch three different attacks: 1) NAT overflow attack that allows an adversary to overflow the NAT resources, 2) NAT wiping attack that removes active NAT mappings by exploiting the lack of TCP sequence number verification of firewalls, and 3) NAT bricking attack that targets services adopting IP-based blacklisting by preventing the shared external IPv4 address from accessing the service. We confirmed the feasibility of these attacks with an empirical analysis. We also propose effective countermeasures for each attack.


title: Searching the Web of Things: State of the Art, Challenges, and Solutions

abstract: Technological advances allow more physical objects to connect to the Internet and provide their services on the Web as resources. Search engines are the key to fully utilize this emerging Web of Things, as they bridge users and applications with resources needed for their operation. Developing these systems is a challenging and diverse endeavor due to the diversity of Web of Things resources that they work with. Each combination of resources in query resolution process requires a different type of search engine with its own technical challenges and usage scenarios. This diversity complicates both the development of new systems and assessment of the state of the art. In this article, we present a systematic survey on Web of Things Search Engines (WoTSE), focusing on the diversity in forms of these systems. We collect and analyze over 200 related academic works to build a flexible conceptual model for WoTSE. We develop an analytical framework on this model to review the development of the field and its current status, reflected by 30 representative works in the area. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and an ideal WoTSE.


title: Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning

abstract: This paper describes a Twitter sentiment analysis system that classifies a tweet as positive or negative based on its overall tweet-level polarity. Supervised learning classifiers often misclassify tweets containing conjunctions such as “but” and conditionals such as “if”, due to their special linguistic characteristics. These classifiers also assign a decision score very close to the decision boundary for a large number tweets, which suggests that they are simply unsure instead of being completely wrong about these tweets. To counter these two challenges, this paper proposes a system that enhances supervised learning for polarity classification by leveraging on linguistic rules and sentic computing resources. The proposed method is evaluated on two publicly available Twitter corpora to illustrate its effectiveness.


title: Oil and gas properties and correlations

abstract: Crude oil and gas are naturally occurring mixtures composed of mainly hydrocarbons and small amounts of nonhydrocarbon compounds such as sulfur, oxygen, and nitrogen. Crude oil and gas samples are characterized in petroleum engineering by their different physical properties. The composition of reservoir fluid is known as the most significant factor, which affects its PVT behavior. The phase behavior of the reservoir fluid and reservoir temperature are two important factors; the type of reservoir fluid is determined based on them. Crude oil and gas properties are used in various steps of petroleum engineering in order to evaluate oil and gas reserves, recovery efficiency, production optimization, etc. More particularly, the phase behavior of natural gas should be addressed precisely not only for gas reservoirs, but also for oil reservoirs, due to its substantial role in oil production mechanisms of saturated oil reservoirs. Therefore, an accurate evaluation of reservoir fluid properties is required for prediction of oil and gas production during the life time of a hydrocarbon reservoir. Obviously, the best source for description of properties is laboratory experiments of actual reservoir fluid sample. However, there are many correlations that can be used in lack of experimental data for prediction of oil and gas properties. In this chapter, the most physical and thermodynamic properties of crude oil (oil density, oil gravity, compressibility, bubble point pressure, solution gas ratio, oil formation volume factor, and viscosity) and gas (gas density, gas compressibility, gas formation volume factor, and viscosity) are defined, and the corresponding correlations are presented.


title: Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I)

abstract: An extreme learning machine (ELM) can be regarded as a two stage feed-forward neural network (FNN) learning system which randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons in the second stage. Therefore, ELM training is essentially a linear learning problem, which significantly reduces the computational burden. Numerous applications show that such a computation burden reduction does not degrade the generalization capability. It has, however, been open that whether this is true in theory. The aim of our work is to study the theoretical feasibility of ELM by analyzing the pros and cons of ELM. In the previous part on this topic, we pointed out that via appropriate selection of the activation function, ELM does not degrade the generalization capability in the expectation sense. In this paper, we launch the study in a different direction and show that the randomness of ELM also leads to certain negative consequences. On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning. On the other hand, we theoretically justify that there also exists an activation function such that the corresponding ELM degrades the generalization capability. In particular, we prove that the generalization capability of ELM with Gaussian kernel is essentially worse than that of FNN with Gaussian kernel. To facilitate the use of ELM, we also provide a remedy to such a degradation. We find that the well-developed coefficient regularization technique can essentially improve the generalization capability. The obtained results reveal the essential characteristic of ELM and give theoretical guidance concerning how to use ELM.


title: Chemical Defense in Larval Tortoise Beetles: Essential Oil Composition of Fecal Shields of Eurypedus nigrosignata and Foliage of its Host Plant, Cordia curassavica

abstract: Larvae of the tortoise beetle Eurypedus nigrosignata construct fecal shields using cast skins and fecal strands. Survival of larvae with intact shields was higher in the field than for larvae with shields removed. In the laboratory, E. nigrosignata feculae had a deterrent effect on feeding in the ant Myrmica rubra as did an extract of the host plant, Cordia curassavica. Three chemical types were identified in the host-plant foliage and were named β-terpinene, α-pinene, and sabinene, depending on their mono- and sesquiterpene composition. This is the first report of lower terpenes (essential oils) in foliage of Cordia. Fecal shields of E. nigrosignata displayed the same terpene pattern as larval host-plant leaves. The absolute concentration of mono- and sesquiterpenes in the dorsal fecal shield depended on the plant chemical type and tended to decrease with larval age. No oxidation or detoxification products of ingested terpenes were detected in the larval fecula, indicating that the chemical composition of the larval fecal shield is influenced primarily by the host-plant secondary chemistry.


title: A Compact Microstrip Slot Triple-Band Antenna for WLAN/WiMAX Applications

abstract: A compact triple-band microstrip slot antenna applied to WLAN/WiMAX applications is proposed in this letter. This antenna has a simpler structure than other antennas designed for realizing triple-band characteristics. It is just composed of a microstrip feed line, a substrate, and a ground plane on which some simple slots are etched. Then, to prove the validation of the design, a prototype is fabricated and measured. The experimental data show that the antenna can provide three impedance bandwidths of 600 MHz centered at 2.7 GHz, 430 MHz centered at 3.5 GHz, and 1300 MHz centered at 5.6 GHz.


title: Dynamic sliding modes control of uncertain nonlinear MIMO three tank system

abstract: In this paper controller synthesis based on standard and dynamic sliding modes for an uncertain nonlinear MIMO Three tank System is presented. Two types of sliding mode controllers are synthesized; first controller is based on standard first order sliding modes while second controller uses dynamic sliding modes. Sliding manifolds for both controllers are designed in-order to ensure finite time convergence of sliding variable for tracking the desired system trajectories. Simulation results are presented showing the performance analysis of both sliding mode controllers. Simulations are also carried out to assess the performance of dynamic sliding mode controller against parametric uncertainties / disturbances. A comparison of designed sliding mode controllers with LMI based robust H ∞ controller is also discussed. The performance of dynamic sliding mode control in terms of response time, control effort and robustness of dynamic sliding mode controller is shown to be better than standard sliding mode controller and H ∞ controllers.


title: Structure and motion estimation from rolling shutter video

abstract: The majority of consumer quality cameras sold today have CMOS sensors with rolling shutters. In a rolling-shutter camera, images are read out row by row, and thus each row is exposed during a different time interval. A rolling-shutter exposure causes geometric image distortions when either the camera or the scene is moving, and this causes state-of-the-art structure and motion algorithms to fail. We demonstrate a novel method for solving the structure and motion problem for rolling-shutter video. The method relies on exploiting the continuity of the camera motion, both between frames, and across a frame. We demonstrate the effectiveness of our method by controlled experiments on real video sequences. We show, both visually and quantitatively, that our method outperforms standard structure and motion, and is more accurate and efficient than a two-step approach, doing image rectification and structure and motion.


title: Development of a hybrid methodology for ERP system selection

abstract: Enterprise resource planning (ERP) systems that aim to integrate, synchronize and centralize organizational data are generally regarded as a vital tool for companies to be successful in the rapidly changing global marketplace. Due to its high acquisition-purchasing, installation and implementation-cost and the wide range of offerings, the selection of ERP systems is a strategically important and difficult decision. Since there is a wide range of tangible and intangible criteria to be considered, it is often defined as a multi-criteria decision making problem. To overcome the challenges imposed by the multifaceted nature of the problem, herein a three-stage hybrid methodology is proposed. The process starts with the identification of most prevailing criteria through a series of brainstorming sessions that include people from different organizational units. Then, due to the varying importance of the criteria, a fuzzy Analytic Hierarchy Process, which handles the vagueness inherent in the decision making process, is used to obtain the relative importance/weights of the criteria. These weighted criteria are then used as input to the Technique for Order Preference by Similarity to Ideal Solution method to rank the decision alternatives. As a real-world illustrative case, the proposed methodology is applied to the ERP selection problem at Turkish Airlines. Because of the collaborative and systematic nature of the methodology, the results obtained from the process were found to be highly satisfactory and trustworthy by the decision makers. ERP system selection is a complex multi-criteria decision making process.Imprecision and large-number of criteria are among the most important challenges.Use of a hybrid methodology improves the decision outcomes.Fuzzy logic, AHP and TOPSIS are collectively and synergistically used in this study.The proposed methodology produced encouraging results to the ERP selection problem.


title: Vertical bagging decision trees model for credit scoring

abstract: In recent years, more and more people, especially young people, begin to use credit card with the changing of consumption concept in China so that the business on credit cards is growing fast. Therefore, it is significative that some effective tools such as credit-scoring models are created to help those decision makers engaged in credit cards. A novel credit-scoring model, called vertical bagging decision trees model (abbreviated to VBDTM), is proposed for the purpose in this paper. The model is a new bagging method that is different from the traditional bagging. The VBDTM model gets an aggregation of classifiers by means of the combination of predictive attributes. In the VBDTM model, all train samples and just parts of attributes take part in learning of every classifier. By contrast, classifiers are trained with the sample subsets in the traditional bagging method and every classifier has the same attributes. The VBDTM has been tested by two credit databases from the UCI Machine Learning Repository, and the analysis results show that the performance of the method proposed by us is outstanding on the prediction accuracy.


title: Ultra-Wideband Microstrip Line-to-Microstrip Line Transition in Multilayer LCP Substrate at Millimeter-Wave Frequencies

abstract: In this letter, an ultrawideband microstrip line (MSL)-to-MSL transition in multilayer liquid crystal polymer (LCP) substrate is presented for millimeter-wave applications. The proposed transition consists of three circuit layers and three LCP layers. The MSLs’ signal lines are in the top circuit layer, while their grounds are distributed in the inner and bottom layers, respectively, and are connected using through vias. Full-wave simulation reveals an insertion loss of less than 1.1 dB for the proposed transition from dc to 110 GHz. To experimentally demonstrate this design, its back-to-back transition was fabricated and characterized. The measured insertion loss and reflection of the back-to-back transition are less than 2.5 and −13 dB below 110 GHz, respectively, showing a good agreement with the simulated data.


title: A Supervised Learning Approach to Automatic Synonym Identification Based on Distributional Features

abstract: Distributional similarity has been widely used to capture the semantic relatedness of words in many NLP tasks. However, various parameters such as similarity measures must be hand-tuned to make it work effectively. Instead, we propose a novel approach to synonym identification based on supervised learning and distributional features, which correspond to the commonality of individual context types shared by word pairs. Considering the integration with pattern-based features, we have built and compared five synonym classifiers. The evaluation experiment has shown a dramatic performance increase of over 120% on the F-1 measure basis, compared to the conventional similarity-based classification. On the other hand, the pattern-based features have appeared almost redundant.


title: Graph similarity search with edit distance constraint in large graph databases

abstract: Due to many real applications of graph databases, it has become increasingly important to retrieve graphs g (in graph database D) that approximately match with query graph q, rather than exact subgraph matches. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of the minimum edit distance. Specifically, we derive a lower bound, branch-based bound, which can greatly reduce the search space of the graph similarity search. We also propose a tree index structure, namely b-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches by orders of magnitude, in terms of both pruning power and query response time.


title: A Novel Broadband Even-Mode Matching Network for Marchand Baluns

abstract: In this paper, we present the development of an even-mode matching network for a Marchand balun to achieve 50-? match and high isolation at the balanced ports. A very wide matching bandwidth can be accomplished with the novel addition of an open-circuit stub to the network. Experimental results demonstrate that the even-mode matched balun achieves return losses that are greater than 15 dB at the balanced ports from 6 to 17 GHz. The measured isolation between the balanced ports is greater than 15 dB from 4 to 19 GHz. For the unbalanced port, the return loss is greater than 15 dB from 4 to 19 GHz. The measured insertion loss is less than 1 dB, and amplitude and phase imbalances are ±0.5 dB and ±5° from 4 to 18 GHz, respectively.


title: A Preliminary Study on the Supporting Strategies for Caring and Teaching Young Children from Multi-cultural Families

abstract: The purpose of this study was to establish strategies that support multi-cultural children' development and adaptation in day care centers. The subjects were 61 young children, 49 parents from multi-cultural family and 550 Korean young children. The instruments utilized were the Sukemune-Hiew Resilience Test for parents and the Danaka Deveopment Test for children. The statistical instruments used were the t-test and the Pearson product correlation coefficients. Our results indicated that : (1) In the case five or six years old children from multi-cultural families, language development lagged behind that of Korean children. (2) Social support systems were insufficient for parents of multi-cultural families. Our cnclusions suggest that establishing strategies is necessary for not only supporting the linguistic development of children from multi-cultural families, but strategies aimed at supporting their parents in day care centers need to be developed.


title: MoNet: Deep Motion Exploitation for Video Object Segmentation

abstract: In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i.e., frame representation learning and segmentation refinement. Concretely, MoNet exploits computed motion cue (i.e., optical flow) to reinforce the representation of the target frame by aligning and integrating representations from its neighbors. The new representation provides valuable temporal contexts for segmentation and improves robustness to various common contaminating factors, e.g., motion blur, appearance variation and deformation of video objects. Moreover, MoNet exploits motion inconsistency and transforms such motion cue into foreground/background prior to eliminate distraction from confusing instances and noisy regions. By introducing a distance transform layer, MoNet can effectively separate motion-inconstant instances/regions and thoroughly refine segmentation results. Integrating the proposed two motion exploitation components with a standard segmentation network, MoNet provides new state-of-the-art performance on three competitive benchmark datasets.


Compendium of Recent Single Event Effects Results for Candidate Spacecraft Electronics for NASA