title: Female Gamers’ Experience of Online Harassment and Social Support in Online Gaming: A Qualitative Study

abstract: Female gaming is a relatively under-researched area, and female gamers often report experiencing harassment whilst playing online. The present study explored female experiences of social support while playing online video games, because of the previous research suggesting that females often experience harassment and negative interactions during game play. Data were collected from an online discussion forum, and comprised posts drawn from 271 female gamers. Thematic analysis of the discussions suggested that a lack of social support and harassment frequently led to female gamers playing alone, playing anonymously, and moving groups regularly. The female gamers reported experiencing anxiety and loneliness due to this lack of social support, and for many, this was mirrored in their experiences of social support outside of gaming. The female gamers frequently accepted the incorporation into their gaming of specific coping strategies to mitigate online harassment, including actively hiding their identity and avoiding all forms of verbal communication with other players. These themes are discussed in relation to relevant research in the area, along with recommendations for future research and consideration of possible explanations for the themes observed.


title: End-to-end driving simulation via angle branched network

abstract: Imitation learning for end-to-end autonomous driving has drawn attention from academic communities. Current methods either only use images as the input which is ambiguous when a car approaches an intersection, or use additional command information to navigate the vehicle but not automated enough. Focusing on making the vehicle drive along the given path, we propose a new navigation command that does not require human's participation and a novel model architecture called angle branched network. Both the new navigation command and the angle branched network are easy to understand and effective. Besides, we find that not only segmentation information but also depth information can boost the performance of the driving model. We conduct experiments in a 3D urban simulator and both qualitative and quantitative evaluation results show the effectiveness of our model.


ATM Location Problem and Cash Management in Automated Teller Machines


Mixed nominalizations, short verb movement and object shift in English


title: Using semi-supervised machine learning to address the Big Data problem in DNS networks

abstract: The problem of Big Data in cyber security (i.e., too much network data to analyze) compounds itself every day. Our approach is based on a fundamental characteristic of Big Data: an overwhelming majority of the network traffic in a traditionally secured enterprise (i.e., using defense-in-depth) is non-malicious. Therefore, one way of eliminating the Big Data problem in cyber security is to ignore the overwhelming majority of an enterprise's non-malicious network traffic and focus only on the smaller amounts of suspicious or malicious network traffic. Our approach uses simple clustering along with a dataset enriched with known malicious domains (i.e., anchors) to accurately and quickly filter out the non-suspicious network traffic. Our algorithm has demonstrated the predictive ability to accurately filter out approximately 97% (depending on the algorithm used) of the non-malicious data in millions of Domain Name Service (DNS) queries in minutes and identify the small percentage of unseen suspicious network traffic. We demonstrate that the resulting network traffic can be analyzed with traditional reputation systems, blacklists, or in-house threat tracking sources (we used virustotal.com) to identify harmful domains that are being accessed from within the enterprise network. Specifically, our results show that the method can reduce a dataset of 400k query-answer domains (with complete malicious domain ground truth) down to only 3% containing 99% of all malicious domains. Further, we demonstrate that this capability scales to 10 million query-answer pairs, which it can reduce by 97% in less than an hour.


title: Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading

abstract: Physical library collections are valuable and long standing resources for knowledge and learning. However, managing and finding books or other volumes on a large collection of bookshelves often leads to tedious manual work, especially for large collections where books or others might be missing or misplaced. Recently, deep neural-based models have been successful in detecting and recognizing text in images taken from natural scenes. Based on this, we investigate deep learning for facilitating book management. This task introduces further challenges including image distortion and varied lighting conditions. We present a library inventory building and retrieval system based on scene text reading. We specifically design our text recognition model using rich supervision to accelerate training and achieve state-of-the- art performance on several benchmark datasets. Our proposed system has the potential to greatly reduce the amount of manual labor required for managing book inventories.


title: Weapons of mass destruction, WMD

abstract: Abstract Purpose Since the invasion into Iraq in 2003, weapons of mass destruction (WMD), have come to general notice; they include today chemical, biological, and atomic/nuclear weapons, (CW, BW, and AW). Radiological findings shall be described. Material and methods X-ray findings of victims of WMD are described. From CW, own observations are reported. Examples of (possible) X-ray findings of victims of BW are described. AW may induce radiation disease. Results Exposure to sulfur-lost induces severe bronchitis; if the radiograph shows pulmonary infiltrations, the prognosis is bad; a late consequence maybe bronchiectasis. BW can be based on bacteria, virus or toxins. An approach of the X-ray findings for BW victims is based on the assumption that the disease induced by BW has the same (or a similar) clinic and radiology as that induced by the original microorganism or by the unchanged toxism. This approximation may have its limits, if the germ or toxin has been modified. In survivors of AW, the radiology is probably that of victims of thermal radiation and blast. Conclusion WMD seem to be a real or a possible threat. They can be used in war, in terrorist attacks, in crime, and in action of secret services. In case that WMD are employed, X-ray diagnostic will be used to evaluate the prognosis (triage) and the risk of infection.


title: The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity

abstract: Introduction The International Physical Activity Questionnaire (IPAQ) was developed to measure health-related physical activity (PA) in populations. The short version of the IPAQ has been tested extensively and is now used in many international studies. The present study aimed to explore the validity characteristics of the long-version IPAQ. Subjects and methods Forty-six voluntary healthy male and female subjects (age, mean±standard deviation: 40.7±10.3 years) participated in the study. PA indicators derived from the long, self-administered IPAQ were compared with data from an activity monitor and a PA log book for concurrent validity, and with aerobic fitness, body mass index (BMI) and percentage body fat for construct validity. Results Strong positive relationships were observed between the activity monitor data and the IPAQ data for total PA (ρ = 0.55, P P P = 0.051). Calculated MET-h day −1 from the PA log book was significantly correlated with MET-h day −1 from the IPAQ (ρ = 0.67, P P = 0.051) and BMI (ρ = 0.25, P = 0.009). No significant correlation was observed between percentage body fat and IPAQ variables. Bland–Altman analysis suggested that the inability of activity monitors to detect certain types of activities might introduce a source of error in criterion validation studies. Conclusions The long, self-administered IPAQ questionnaire has acceptable validity when assessing levels and patterns of PA in healthy adults.


title: A Wideband Variable Gain LNA With High OIP3 for 5G Using 40-nm Bulk CMOS

abstract: This letter presents a CMOS wideband variable gain LNA for 28-GHz 5G integrated phased-array transceivers preserving high third-order intercept point (OIP3) at all gain settings. The prototype LNA has three stages providing digitally controlled gain optimized for higher IIP3 at lower gain. The stages are coupled together using double-tuned transformers for maximum group delay flatness. Fabricated in a 40-nm CMOS process, it achieves 18–26 dB gain at 1-dB gain step with 12–14.5 dBm OIP3 and 3.3–4.3 dB noise figure, while consuming 21.5–31.4 mW across 26–33 GHz frequency range. The root-mean-square error of the gain steps is less than 0.38 dB.


Reinforcement Learning for Autonomous Maneuvering in Highway Scenarios


title: Broadband Substrate Integrated Waveguide 4$\,\times\,$4 Nolen Matrix Based on Coupler Delay Compensation

abstract: A broadband Nolen matrix in substrate integrated waveguide (SIW) technology is proposed and studied in this paper. Being serial feeding networks, Nolen matrices usually introduce significant phase dispersion in their standard form, thus limiting effective bandwidth. In this work, broadband frequency operation is achieved with adequate coupler delay compensation, resulting in a more "parallel" matrix topology. Consequently, H-plane couplers with continuous aperture are preferred in the proposed design as opposed to the cross-couplers previously used in the "serial" configuration. The SIW couplers and phase shifters of a balanced amplitude 4×4 Nolen matrix are designed at 77 GHz for radar applications. The resulting matrix that combines these elementary building blocks is fabricated and measured, and measured characteristics are in good agreement with theoretical and simulated predictions. This first design achieves excellent results over a 11.7% frequency bandwidth centered at 77 GHz. This modified Nolen matrix combines advantages of standard Nolen and Butler matrices and may be used as a building block for generalized orthogonal matrices.


title: Measure of Tessellation Quality of Voronoï Meshes

abstract: This article introduces a measure of the quality of Voronoi tessellations resulting from various mesh generators.Mathematical models of a number of mesh generators are given. A main result in this work is the identification ofthose mesh generators that produce the highest quality Voronoi tessellations. Examples illustrating the application ofthe quality measure are given in comparing Voronoi tessellations of digital images.


title: Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs

abstract: Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch. The proposed Attribute2Sketch2Face framework, which is based on a combination of deep Conditional Variational Autoencoder (CVAE) and Generative Adversarial Networks (GANs), consists of three stages: (1) Synthesis of facial sketch from attributes using a CVAE architecture, (2) Enhancement of coarse sketches to produce sharper sketches using a GAN-based framework, and (3) Synthesis of face from sketch using another GAN-based network. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed attribute-based three stage face synthesis method.


title: SnuCL: an OpenCL framework for heterogeneous CPU/GPU clusters

abstract: In this paper, we propose SnuCL, an OpenCL framework for heterogeneous CPU/GPU clusters. We show that the original OpenCL semantics naturally fits to the heterogeneous cluster programming environment, and the framework achieves high performance and ease of programming. The target cluster architecture consists of a designated, single host node and many compute nodes. They are connected by an interconnection network, such as Gigabit Ethernet and InfiniBand switches. Each compute node is equipped with multicore CPUs and multiple GPUs. A set of CPU cores or each GPU becomes an OpenCL compute device. The host node executes the host program in an OpenCL application. SnuCL provides a system image running a single operating system instance for heterogeneous CPU/GPU clusters to the user. It allows the application to utilize compute devices in a compute node as if they were in the host node. No communication API, such as the MPI library, is required in the application source. SnuCL also provides collective communication extensions to OpenCL to facilitate manipulating memory objects. With SnuCL, an OpenCL application becomes portable not only between heterogeneous devices in a single node, but also between compute devices in the cluster environment. We implement SnuCL and evaluate its performance using eleven OpenCL benchmark applications.


title: Steganalysis via a Convolutional Neural Network using Large Convolution Filters

abstract: Summary Introduction Steganography, the art to hide information inside host media like pictures and movies, and steganalysis, its countermeasure attempting to detect the presence of an hidden information within an innocent-looking document, are frequently reported as promising information security techniques for telemedicine. For the past few years, in the race between image steganography and steganalysis, deep learning has emerged as a very promising alternative to steganalyzer approaches based on rich image models combined with ensemble classifiers. A key knowledge of image steganalyzer, which combines relevant image features and innovative classification procedures, can be deduced by a deep learning approach called convolutional neural networks (CNN). This kind of deep learning networks is so well-suited for classification tasks based on the detection of variations in 2D shapes that it is the state-of-the-art in many image recognition problems. Materials and methods We design a CNN-based steganalyzer for images obtained by applying steganography with a unique embedding key. The proposed CNN has a quite different shape compared to the ones resulting from the earlier works, and it is able to provide high detection accuracy for several steganographics tools when the same stego key is reused during the embedding process. The convolutional part of our proposal starts by a global filtering, using a single filter, followed by a second convolutional layer that produces a reduced set of high-level features (256 features for 512 × 512 pixels input images) thanks to the use of large filters. Results The proposed architecture embeds less convolutions, with much larger filters in the final convolutional layer, and is more general: it is able to deal with larger images and lower payloads. For the “same embedding key” scenario, our proposal outperforms all other steganalyzers, in particular the existing CNN-based ones, and defeats many state-of-the-art image steganography schemes. The information encoded by the final vector of features is so discriminating that the classifier part can be reduced to only two output neurons. We finally evaluated the detection ability of the CNN against two spatial domain steganographic schemes and a frequency domain one. More precisely, we designed a perfect steganalyzer for embedding payloads of 0.4 bit per pixel, and for all the steganographic tools investigated in this article (working either in spatial or in frequency domains). Rather interesting results have been obtained too, albeit to a lesser extent, for a payload value of 0.1 bpp. Discussion and conclusions The obtained results are very encouraging, and they outperform all the previous deep learning proposals for steganalysis. A first step in the design of a universal detector has been achieved too, as we are able to detect HUGO based hidden messages even when a WOW steganographier has been used during the training stage. These results allow us to propose to add fragile watermarks on media like pictures or pdf medical documents, to guarantee the authenticity of the material: any attempt of modification of the support will alter the watermark, proving by doing so the modification. Another application is to add personal and medical information inside medical images.


title: Differentiable Functional Program Interpreters

abstract: Programming by Example (PBE) is the task of inducing computer programs from input-output examples. It can be seen as a type of machine learning where the hypothesis space is the set of legal programs in some programming language. Recent work on differentiable interpreters relaxes the discrete space of programs into a continuous space so that search over programs can be performed using gradient-based optimization. While conceptually powerful, so far differentiable interpreter-based program synthesis has only been capable of solving very simple problems. In this work, we study modeling choices that arise when constructing a differentiable programming language and their impact on the success of synthesis. The main motivation for the modeling choices comes from functional programming: we study the effect of memory allocation schemes, immutable data, type systems, and built-in control-flow structures. Empirically we show that incorporating functional programming ideas into differentiable programming languages allows us to learn much more complex programs than is possible with existing differentiable languages.


title: www.elsevier.com/locate/pr Image registration by local histogram matching

abstract: We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images.


title: A Multirobot Cooperation Framework for Sewing Personalized Stent Grafts

abstract: This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.


title: Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints

abstract: Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.


title: Assessing the effects of consumers' product evaluations and trust on repurchase intention in e-commerce environments

abstract: Abstract This study enhances the existing literature on online trust by integrating the consumers’ product evaluations model and technology adoption model in e-commerce environments. In this study, we investigate how perceived value influences the perceptions of online trust among online buyers and their willingness to repurchase from the same website. This study proposes a research model that compares the relative importance of perceived value and online trust to perceived usefulness in influencing consumers’ repurchase intention. The proposed model is tested using data collected from online consumers of e-commerce. The findings show that although trust and e-commerce adoption components are critical in influencing repurchase intention, product evaluation factors are also important in determining repurchase intention. Perceived quality is influenced by the perceptions of competitive price and website reputation, which in turn influences perceived value; and perceived value, website reputation, and perceived risk influence online trust, which in turn influence repurchase intention. The findings also indicate that the effect of perceived usefulness on repurchase intention is not significant whereas perceived value and online trust are the major determinants of repurchase intention. Major theoretical contributions and practical implications are discussed.


Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach


title: Using Relational Reasoning to Learn about Scientific Phenomena at Unfamiliar Scales.

abstract: Many scientific theories and discoveries involve reasoning about extreme scales, removed from human experience, such as time in geology and size in nanoscience. Thus, understanding scale is central to science, technology, engineering, and mathematics. Unfortunately, novices have trouble understanding and comparing sizes of unfamiliar large and small magnitudes. Relational reasoning is a promising tool to bridge the gap between direct experience and phenomena at extreme scales. However, instruction does not always improve understanding, and analogies can fail to bring about conceptual change, and even mislead students. Here, we review how people reason about phenomena across scales, in three sections: (a) we develop a framework for how relational reasoning supports understanding extreme scales; (b) we identify cognitive barriers to aligning human and extreme scales; and (c) we outline a theory-based approach to teaching scale information using relational reasoning, present two successful learning activities, and consider the role of a unified scale instruction across STEM education.


title: Building with Data: Architectural Models as Inspiration for Data Physicalization

abstract: In this paper we analyze the role of physical scale models in the architectural design process and apply insights from architecture for the creation and use of data physicalizations. Based on a survey of the architecture literature on model making and ten interviews with practicing architects, we describe the role of physical models as a tool for exploration and communication. From these observations, we identify trends in the use of physical models in architecture, which have the potential to inform the design of data physicalizations. We identify four functions of architectural modeling that can be directly adapted for use in the process of building rich data models. Finally, we discuss how the visualization community can apply observations from architecture to the design of new data physicalizations.


title: Direct sampling method for monostatic radar imaging

abstract: In this paper, we extend the use of conventional Direct sampling method (DSM) which is only applicable to the multistatic measurement data, to the monostatic measurement data for radar imaging applications. We define a novel testing function which can be used in the indicator function of DSM with monostatic data. Numerical examples demonstrate the feasibility of the proposed method.


title: Estimating the Natural Illumination Conditions from a Single Outdoor Image

abstract: Given a single outdoor image, we present a method for estimating the likely illumination conditions of the scene. In particular, we compute the probability distribution over the sun position and visibility. The method relies on a combination of weak cues that can be extracted from different portions of the image: the sky, the vertical surfaces, the ground, and the convex objects in the image. While no single cue can reliably estimate illumination by itself, each one can reinforce the others to yield a more robust estimate. This is combined with a data-driven prior computed over a dataset of 6 million photos. We present quantitative results on a webcam dataset with annotated sun positions, as well as quantitative and qualitative results on consumer-grade photographs downloaded from Internet. Based on the estimated illumination, we show how to realistically insert synthetic 3-D objects into the scene, and how to transfer appearance across images while keeping the illumination consistent.


title: Interaction Design for Mobile Visual Search

abstract: Mobile devices are becoming ubiquitous. People take pictures via their phone cameras to explore the world on the go. In many cases, they are concerned with the picture-related information. Understanding user intent conveyed by those pictures therefore becomes important. Existing mobile applications employ visual search to connect the captured picture with the physical world. However, they only achieve limited success due to the ambiguity nature of user intent in the picture-one picture usually contains multiple objects. By taking advantage of multitouch interactions on mobile devices, this paper presents a prototype of interactive mobile visual search, named TapTell, to help users formulate their visual intent more conveniently. This kind of search leverages limited yet natural user interactions on the phone to achieve more effective visual search while maintaining a satisfying user experience. We make three contributions in this work. First, we conduct a focus study on the usage patterns and concerned factors for mobile visual search, which in turn leads to the interactive design of expressing visual intent by gesture. Second, we introduce four modes of gesture-based interactions (crop, line, lasso, and tap) and develop a mobile prototype. Third, we perform an in-depth usability evaluation on these different modes, which demonstrates the advantage of interactions and shows that lasso is the most natural and effective interaction mode. We show that TapTell provides a natural user experience to use phone camera and gesture to explore the world. Based on the observation and conclusion, we also suggest some design principles for interactive mobile visual search in the future.


title: On physical-layer identification of wireless devices

abstract: Physical-layer device identification aims at identifying wireless devices during radio communication by exploiting unique characteristics of their analog (radio) circuitry. This work systematizes the existing knowledge on this topic in order to enable a better understanding of device identification, its implications on the analysis and design of security solutions in wireless networks and possible applications. We therefore present a systematic review of physical-layer identification systems and provide a summary of current state-of-the-art techniques. We further present a classification of attacks and discuss the feasibility, limitations, and implications in selected applications. We also highlight issues that are still open and need to be addressed in future work.


title: Adaptive automation, trust, and self-confidence in fault management of time-critical tasks

abstract: An experiment on adaptive automation is described. Reliability of automated fault diagnosis, mode of fault management (manual vs. automated), and fault dynamics affect variables including root mean square error, avoidance of accidents and false shutdowns, subjective trust in the system, and operator self-confidence. Results are discussed in relation to levels of automation, models of trust and self-confidence, and theories of human-machine function allocation. Trust in automation but not self-confidence was strongly affected by automation reliability. Operators controlled a continuous process with difficulty only while performing fault management but could prevent unnecessary shutdowns. Final authority for decisions and action must be allocated to automation in time-critical situations. Automation is any sensing, detection, informationprocessing, decision-making, or control action that could be performed by humans but is actually performed by machine. In supervisory control, an automated control system is monitored by human operators, who intervene only if they believe that the system is faulty or want to improve


A Survey on Search-Based Software Design


title: The determinants of helpfulness of online reviews

abstract: More and more people are gravitating to reading online product reviews prior to making purchasing decisions. Because a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate the various determinants of the helpfulness of reviews, and it also intends to examine the moderating effect of product type, that is, the experience or search goods in relation to the helpfulness of online reviews. The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review. The moderating effects of product type exist for these determinants on helpfulness. That is, the number of reviews for a product and the disclosure of reviewer identity have a greater influence on the helpfulness for experience goods, while reviewer reputation, review extremity, and review depth are more important for helpfulness in relation to search goods. The interaction effects exist for average review rating and average review depth for a product with review helpfulness on product sales. The results of the study will identify helpful online reviews and assist in designing review sites effectively.


title: Base-Delta-Immediate Compression: A Practical Data Compression Mechanism for On-Chip Caches

abstract: Cache compression is a promising technique to increase cache capacity and to decrease on-chip and off-chip bandwidth usage. Unfortunately, directly applying well-known compression algorithms (usually implemented in software) leads to high hardware complexity and unacceptable decompression/compression latencies, which in turn can negatively affect performance. Hence, there is a need for a simple yet efficient compression technique that can effectively compress common in-cache data patterns, and has minimal effect on cache access latency. In this paper, we propose a new compression algorithm called Base-Delta-Immediate (BDI) compression, a practical technique for compressing data in on-chip caches. The key idea of the algorithm is that, for many cache lines, the values within the cache line have a low dynamic range ‐ i.e., the differences between values stored within the cache line are small. As a result, a cache line can be represented using a base value and an array of differences whose combined size is much smaller than the original cache line (we call this the base+delta encoding). Moreover, many cache lines intersperse such base+delta values with small values ‐ our BDI technique efficiently incorporates such immediate values into its encoding. Compared to prior cache compression approaches, our studies show that BDI strikes a sweet-spot in the tradeoff between compression ratio, decompression/compression latencies, and hardware complexity. Our results show that BDI compression improves performance for both single-core (8.1% improvement) and multi-core workloads (9.5% / 11.2% improvement for two/four cores). For many applications, BDI provides the performance benefit of doubling the cache size of the baseline system, effectively increasing average cache capacity by 1.53X.


title: English as medium of instruction in the new global linguistic order: Global characteristics, local consequences

abstract: The English language is continuing to establish itself as a global lingua franca in a period of unprecedented globalisation. In the period 1995n2005 educational systems worldwide have shown interest in the adoption of English as a medium of instruction. Teaching through a second language has been successful in certain educational environments. A methodological approach, ‘Content and Language Integrated Learning’, emerged in Europe in 1995. The period 2000n 2006 has seen swift adoption of this educational approach across Europe, at all levels of education. This paper argues that failure to achieve satisfactory educational outcomes when teaching through English are commonplace in certain countries. This failure is compounded by stakeholders seeing barriers to learning in terms of language, as opposed to learning needs, cognition and methodologies. By integrating language and subject teaching, various forms of educational success can be achieved where classrooms comprise learners with diverse levels of linguistic competence.


title: Chunking in Soar: The anatomy of a general learning mechanism

abstract: In this article we describe an approach to the construction of a general learning mechanism based on chunking in Soar. Chunking is a learning mechanism that acquires rules from goal-based experience. Soar is a general problem-solving architecture with a rule-based memory. In previous work we have demonstrated how the combination of chunking and Soar could acquire search-control knowledge (strategy acquisition) and operator implementation rules in both search-based puzzle tasks and knowledge-based expert-systems tasks. In this work we examine the anatomy of chunking in Soar and provide a new demonstration of its learning capabilities involving the acquisition and use of macro-operators.


title: Utilizing Knowledge Bases in Text-centric Information Retrieval

abstract: General-purpose knowledge bases are increasingly growing in terms of depth (content) and width (coverage). Moreover, algorithms for entity linking and entity retrieval have improved tremendously in the past years. These developments give rise to a new line of research that exploits and combines these developments for the purposes of text-centric information retrieval applications. This tutorial focuses on a) how to retrieve a set of entities for an ad-hoc query, or more broadly, assessing relevance of KB elements for the information need, b) how to annotate text with such elements, and c) how to use this information to assess the relevance of text. We discuss different kinds of information available in a knowledge graph and how to leverage each most effectively. We start the tutorial with a brief overview of different types of knowledge bases, their structure and information contained in popular general-purpose and domain-specific knowledge bases. In particular, we focus on the representation of entity-centric information in the knowledge base through names, terms, relations, and type taxonomies. Next, we will provide a recap on ad-hoc object retrieval from knowledge graphs as well as entity linking and retrieval. This is essential technology, which the remainder of the tutorial builds on. Next we will cover essential components within successful entity linking systems, including the collection of entity name information and techniques for disambiguation with contextual entity mentions. We will present the details of four previously proposed systems that successfully leverage knowledge bases to improve ad-hoc document retrieval. These systems combine the notion of entity retrieval and semantic search on one hand, with text retrieval models and entity linking on the other. Finally, we also touch on entity aspects and links in the knowledge graph as it can help to understand the entities' context. This tutorial is the first to compile, summarize, and disseminate progress in this emerging area and we provide both an overview of state-of-the-art methods and outline open research problems to encourage new contributions.


title: A Data Mining Approach to Consumer Behavior

abstract: Various studies on consumer purchasing behaviors have been presented and used in real problems. Data mining techniques are expected to be a more effective tool for analyzing consumer behaviors. However, the data mining method has disadvantages as well as advantages. Therefore, it is important to select appropriate techniques to mine databases. The objective of this paper is to improve conventional data mining analysis by applying several methods including fuzzy clustering, principal component analysis, and discriminate analysis. Many defects included in the conventional methods are improved in the paper. Moreover, in an experiment, association rule is employed to mine rules for trusted customers using sales data in a fiber industry.


title: More Than a Tick Box: Medical Checklist Development, Design, and Use

abstract: Despite improving patient safety in some perioperative settings, some checklists are not living up to their potential and complaints of "checklist fatigue" and outright rejection of checklists are growing. Problems reported often concern human factors: poor design, inadequate introduction and training, duplication with other safety checks, poor integration with existing workflow, and cultural barriers. Each medical setting-such as an operating room or a critical care unit-and different clinical needs-such as a shift handover or critical event response-require a different checklist design. One size will not fit all, and checklists must be built around the structure of medical teams and the flow of their work in those settings. Useful guidance can be found in the literature; however, to date, no integrated and comprehensive framework exists to guide development and design of checklists to be effective and harmonious with the flow of medical and perioperative tasks. We propose such a framework organized around the 5 stages of the checklist life cycle: (1) conception, (2) determination of content and design, (3) testing and validation, (4) induction, training, and implementation, and (5) ongoing evaluation, revision, and possible retirement. We also illustrate one way in which the design of checklists can better match user needs in specific perioperative settings (in this case, the operating room during critical events). Medical checklists will only live up to their potential to improve the quality of patient care if their development is improved and their designs are tailored to the specific needs of the users and the environments in which they are used.


title: Feasibility of a Synthetic Aperture Radar with Rotating Antennas (ROSAR)

abstract: ROSAR (Rotor-SAR) is a synthetic aperture radar concept based on rotating antennas of a helicopter for pilot sight target detection and target localisation with high resolution. The ROSAR concept has potential benefits for civil and military helicopterborne imaging application, if the antennas are mounted at the tips of the rotor blades, for example (Fig. l). The concept has two main potential benefits, that are, the imaging field of view is 360° and there is no need for a forward velocity of the carrier platform. As opposed to SAR systems based on linear movement of the antenna, ROSAR imaging is based on synthetic apertures of a circular shape. Thus, the image formation process requires a polar format processing architecture. The ROSAR principle is also applicable for other radar mapping systems with rotating antennas, not only for helicopters.


title: Industry 4.0 and the current status as well as future prospects on logistics

abstract: Industry 4.0, referred to as the “Fourth Industrial Revolution”, also known as “smart manufacturing”, “industrial internet” or “integrated industry”, is currently a much-discussed topic that supposedly has the potential to affect entire industries by transforming the way goods are designed, manufactured, delivered and payed. This paper seeks to discuss the opportunities of Industry 4.0 in the context of logistics management, since implications are expected in this field. The authors pursue the goal of shedding light on the young and mostly undiscovered topic of Industry 4.0 in the context of logistics management, thus following a conceptual research approach. At first, a logistics-oriented Industry 4.0 application model as well as the core components of Industry 4.0 are presented. Different logistics scenarios illustrate potential implications in a practice-oriented manner and are discussed with industrial experts. The studies reveal opportunities in terms of decentralisation, self-regulation and efficiency. Moreover, it becomes apparent that the concept of Industry 4.0 still lacks a clear understanding and is not fully established in practice yet. The investigations demonstrate potential Industry 4.0 implications in the context of Just-in-Time/Just-in-Sequence and cross-company Kanban systems in a precise manner. Practitioners could use the described scenarios as a reference to foster their own Industry 4.0 initiatives, with respect to logistics management.


title: Deep Facial Expression Recognition: A Survey

abstract: With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.


title: Sparse Subspace Clustering: Algorithm, Theory, and Applications

abstract: Many real-world problems deal with collections of high-dimensional data, such as images, videos, text, and web documents, DNA microarray data, and more. Often, such high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories to which the data belong. In this paper, we propose and study an algorithm, called sparse subspace clustering, to cluster data points that lie in a union of low-dimensional subspaces. The key idea is that, among the infinitely many possible representations of a data point in terms of other points, a sparse representation corresponds to selecting a few points from the same subspace. This motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data into subspaces. Since solving the sparse optimization program is in general NP-hard, we consider a convex relaxation and show that, under appropriate conditions on the arrangement of the subspaces and the distribution of the data, the proposed minimization program succeeds in recovering the desired sparse representations. The proposed algorithm is efficient and can handle data points near the intersections of subspaces. Another key advantage of the proposed algorithm with respect to the state of the art is that it can deal directly with data nuisances, such as noise, sparse outlying entries, and missing entries, by incorporating the model of the data into the sparse optimization program. We demonstrate the effectiveness of the proposed algorithm through experiments on synthetic data as well as the two real-world problems of motion segmentation and face clustering.


title: Printed wideband antenna for LTE (long term evolution) application

abstract: This paper proposed a microstrip antenna with the operating bands of LTE700, LTE900 and LTE1800 for mobile device applications. This antenna was printed using FR-4 substrate material. The overall dimension of the antenna is 135 mm × 32 mm × 1.6 mm with 50 Ω impedance. The usage of ground plane can generate resonant modes to expand the bands at low frequency. Operating frequency bands of the antenna for the LTE and GSM/GPS/IMT-2000 meet the specification with 6 dB return loss. It is well suitable to be used for mobile device application.


title: A large-scale, longitudinal study of user profiles in world of warcraft

abstract: We present a survey of usage of the popular Massively Multiplayer Online Role Playing Game, World of Warcraft. Players within this game often self-organize into communities with similar interests and/or styles of play. By mining publicly available data, we collected a dataset consisting of the complete player history for approximately six million characters, with partial data for another six million characters. The paper provides a thorough description of the distributed approach used to collect this massive community data set, and then focuses on an analysis of player achievement data in particular, exposing trends in play from this highly successful game. From this data, we present several findings regarding player profiles. We correlate achievements with motivations based upon a previously-defined motivation model, and then classify players based on the categories of achievements that they pursued. Experiments show players who fall within each of these buckets can play differently, and that as players progress through game content, their play style evolves as well.


title: A resonant LED driver with capacitive power transfer

abstract: A quasi resonant topology suitable for LED drivers, featuring power factor correction and dimming in a single stage is presented. It contains a single active switch, and operates in zero-voltage-switching, zero-current-switching. This topology includes neither electrolytic capacitors nor transformer, and provides galvanic isolation between the AC mains and the DC side. Building on this new topology, a 15 W, 30 V, LED string driver with a typical operation frequency of 200 kHz was designed and experimented. Experimental results fully support the theoretical analysis and the simulation results. Measured efficiency exceeds 90% in the entire operation range and reached a peak efficiency of 96%. The driver is dimmable through the switching frequency. Dimming in the range of 4W-19W is demonstrated experimentally.


title: Acetylcodeine, an impurity of illicitly manufactured heroin, elicits convulsions, antinociception, and locomotor stimulation in mice.

abstract: Acetylcodeine is one of the major impurities present in illicitly manufactured heroin (diacetylmorphine). Data on its pharmacology and toxicology are limited and its ability to alter the toxic effects of diacetylmorphine is not known. The first objective of the present study was to compare the acute pharmacological and toxicological effects of acetylcodeine to those of codeine and diacetylmorphine in mice by assessing nociception in the tail-flick test, locomotor stimulation, and convulsive behavior. The second goal of this study was to determine whether acetylcodeine would alter the convulsant effects of diacetylmorphine. The antinociceptive potencies of acetylcodeine and codeine were similar, as reflected by their ED50 (95% confidence limits) values of 35 (29-44) and 51 (40-65) micromol/kg, respectively. Acetylcodeine was somewhat less potent than codeine in stimulating locomotor behavior, with ED50 values of 28 (22-37) and 12 (6-24) micromol/kg, respectively. Diacetylmorphine was considerably more potent than the other two drugs, producing antinociception and locomotor stimulation at ED50 values of 2.4 (1.4-4.1) and 0.65 (0.36-1.2) micromol/kg, respectively. On the other hand, the convulsant effects of acetylcodeine (ED50=138 (121-157) micromol/kg) and diacetylmorphine (ED50=115 (81-163) micromol/kg) were similar in potency and both were more potent than codeine (ED50=231 (188-283) micromol/kg). Finally, a subthreshold dose of acetylcodeine (72 micromol/kg) decreased the convulsant ED50 dose of diacetylmorphine to 40 (32-49). These findings suggest that the convulsant effects of acetylcodeine are more potent than predicted by its effects on locomotor activity and antinociception. The observation that acetylcodeine potentiated the convulsant effects of diacetylmorphine suggests a mechanism for some of the heroin-related deaths reported in human addicts.


Evaluation of Spectral Disease Index PMI to Detect Early Wheat Powdery Mildew using Hyperspectral Imagery Data


title: Multi-robot 6D graph SLAM connecting decoupled local reference filters

abstract: Teams of mobile robots can be deployed in search and rescue missions to explore previously unknown environments. Methods for joint localization and mapping constitute the basis for (semi-)autonomous cooperative action, in particular when navigating in GPS-denied areas. As communication losses may occur, a decentralized solution is required. With these challenges in mind, we designed a submap-based SLAM system that relies on inertial measurements and stereo-vision to create multi-robot dense 3D maps. For online pose and map estimation, we integrate the results of keyframe-based local reference filters through incremental graph SLAM. To the best of our knowledge, we are the first to combine these two methods to benefit from their particular advantages for 6D multi-robot localization and mapping: Local reference filters on each robot provide real-time, long-term stable state estimates that are required for stabilization, control and fast obstacle avoidance, whereas online graph optimization provides global multi-robot pose and map estimates needed for cooperative planning. We propose a novel graph topology for a decoupled integration of local filter estimates from multiple robots into a SLAM graph according to the filters' uncertainty estimates and independence assumptions and evaluated its benefits on two different robots in indoor, outdoor and mixed scenarios. Further, we performed two extended experiments in a multi-robot setup to evaluate the full SLAM system, including visual robot detections and submap matches as inter-robot loop closure constraints.


title: Synthesis of robust PID controllers for time delay systems

abstract: Tuning rules are frequently used to choose the parameters of PID controllers. However, they are often based on heuristics, are limited to systems of a certain class, use dead time approximations and ignore parameter uncertainties in the modeled system. This work develops a systematic, universal and transparent method to design a robust PID controller based on the parameter space approach, which is extended to cope with quasipolynomials.


title: A Systematic Literature Review of Crowdsourcing Research from a Human Resource Management Perspective

abstract: From a human resource management perspective I review the crowd sourcing literature included in top peer-reviewed journals and conferences, and build up a comprehensive picture. Based on this I identify empirical and design-oriented research needs.


title: Improved Calibration Algorithm for SDINS Considering Body -Frame Drift

abstract: A calibration algorithm for a strap -down inertial navig ation system ( SDINS ) is presented here . The b ody -frame drift induced by the variation of the accelerometer sensing ax e due to temperature change s is an important source of navigation error not considered in conventional calibration methods. In this paper, we propose an advanced calibration method that can compensate key SDINS error sources including not only bias, scale factor, and misalignm ent but also the body -frame drift by utilizing a 2 -ax is turntable . The body -frame drift is determined and compensated using horizontal accelerometer measurement s during a calibration procedure . Simulation results show that the body -frame drift behaves like an additional gyro bias error and that the proposed calibration technique can significantly improve pure navigation p erformance by removing the effect of body -frame drift .


Student Performance Prediction using Machine Learning


title: Effectiveness of Sensory Integration Interventions in Children With Autism Spectrum Disorders: A Pilot Study

abstract: The purpose of this pilot study was to establish a model for randomized controlled trial research, identify appropriate outcome measures, and address the effectiveness of sensory integration (SI) interventions in children with autism spectrum disorders (ASD). Children ages 6-12 with ASD were randomly assigned to a fine motor or SI treatment group. Pretests and posttests measured social responsiveness, sensory processing, functional motor skills, and social-emotional factors. Results identified significant positive changes in Goal Attainment Scaling scores for both groups; more significant changes occurred in the SI group, and a significant decrease in autistic mannerisms occurred in the SI group. No other results were significant. The study discusses considerations for designing future outcome studies for children with ASD.


title: Sketching Folds: Developable Surfaces from Non-Planar Silhouettes

abstract: We present the first sketch-based modeling method for developable surfaces with pre-designed folds, such as garments or leather products. The main challenge we address for building folded surfaces from sketches is that silhouette strokes on the sketch correspond to discontinuous sets of non-planar curves on the 3D model. We introduce a new zippering algorithm for progressively identifying silhouette edges on the model and tying them to silhouette strokes. Our solution ensures that the strokes are fully covered and optimally sampled by the model. This new method, interleaved with developability optimization steps, is implemented in a multiview sketching system where the user can sketch the contours of internal folds in addition to the usual silhouettes, borders, and seam lines. All strokes are interpreted as hard constraints, while developability is only optimized. The developability error map we provide then enables users to add local seams or darts where needed and progressively improve their design. This makes our method robust, even to coarse input for which no fully developable solution exists.


title: Android Malware Characterization using Metadata and Machine Learning Techniques

abstract: Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.


title: A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

abstract: The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.


title: Personalised News and Blog Recommendations based on User Location, Facebook and Twitter User Profiling

abstract: This demo presents a prototype mobile app that provides out-of-the-box personalised content recommendations to its users by leveraging and combining the user's location, their Facebook and/or Twitter feed and their in-app actions to automatically infer their interests. We build individual models for each user and each location. At retrieval time we construct the user's personalised feed by mixing different sources of content-based recommendations with content directly from their Facebook/Twitter feeds, locally trending articles and content propagated through their in-app social network. Both explicit and implicit feedback signals from the users' interactions with their recommendations are used to update their interests models and to learn their preferences over the different content sources.


title: A new 7.2kV medium voltage 3-Level-NPC inverter using 6.5kV-IGBTs

abstract: The SINAMICS GM150 IGBT, a new 3-level-neutral-point-clamped (3L-NPC) Voltage-Source- Converter (VSC) with series-connected 6.5 kV-IGBTs for industrial Medium Voltage Drives is presented. It covers the output voltage range from 6 kV up to 7.2 kV without the need for a step-up output transformer. The SINAMICS GM150 IGBT is based on the proven basic design of its predecessor SIMOVERT MV while the output voltage and current ratings have been extended. The maximum available output power of the water cooled version with a parallel connection of two inverters is 8.7 MVA at an output voltage of 7.2 kV. For the power section state of the art IGBT modules with AlSiC base plates are used. The dynamic voltage sharing for the IGBTs is done by the gate drive units without any additional passive snubber circuits. For the NPC-diodes small passive snubber circuits are used to ensure the dynamic voltage balancing. The mechanical design, the semiconductor ratings and the available converter performance of the SINAMICS GM150 (IGBT) is described. An additional focus is put on the semiconductor switching characteristics in series-connection as well as on the design and the performance of the sinusoidal output filter which is required in retrofit applications with conventional line motors.


title: Decentralized Reactive Power Sharing and Frequency Restoration in Islanded Microgrid

abstract: P-f and Q-V droop methods are the most common decentralized control methods in islanded microgrid. Although with the P-f droop method, an accurate active power sharing can be achieved among distributed energy resources (DERs), by Q-V droop, the reactive power sharing among DERs often deteriorates due to its highly dependence on the power line impedances and the local load. Variation of frequency and voltage by load changes is another challenge in droop control method. In this paper, a new autonomous control method is proposed to share reactive power among DERs accurately and restore frequency of a microgrid. The proposed method does not require any communication link and so maintains reliability and simplicity of the network. The synchronizing among DERs is obtained by the load change detection, which is accomplished by wavelet transform. The method operation principle is explained and analyzed. Simulation results are presented to validate the effectiveness of the proposed method.


title: Secure and Private Collaborative Linear Programming

abstract: The growth of the Internet has created tremendous opportunities for online collaborations. These often involve collaborative optimizations where the two parties are, for example, jointly minimizing costs without violating their own particular constraints (e.g., one party may have too much inventory, another too little inventory but too much production capacity, etc). Many of these optimizations can be formulated as linear programming problems, or, rather, as collaborative linear programming, in which two parties need to jointly optimize based on their own private inputs. It is often important to have online collaboration techniques and protocols that carry this out without either party revealing to the other anything about their own private inputs to the optimization (other than, unavoidably, what can be deduced from the collaboratively computed optimal solution). For example, two organizations who jointly invest in a project may want to minimize some linear objective function while satisfying both organizations' private and confidential constraints. Constraints are usually private when they reveal too much about the organizations' financial health, its future business strategy, etc. Linear programming problems have been widely studied in the literature. However, the existing solutions (e.g., the simplex method) do not extend to the above-mentioned framework in which the linear constraints are shared by the two parties, who do not want to disclose their own to the other party. In this paper, we give an efficient protocol for solving linear programming problems in the honest-but-curious model, such that neither party reveals anything about their private input to the other party (other than what can be inferred from the result). The amount of communication and computation done by our protocol is proportional to the time complexity of the simplex method, a widely used linear programming algorithm. We also provide a practical solution that prevents certain malicious behavior of the participants. The use of the known general circuit-simulation solutions to secure function evaluation is unacceptable for the simplex method, as it implies an exponential size circuit


Lifelong Robot Learning


title: JANUS, an on-line multiplier/divider for manipulating large numbers

abstract: The authors deal with the detailed VLSI implementation of a fast bit-serial operator designed to perform very high precision (600 decimal digits) additions, multiplications, and divisions, and some of the applications of the circuit are discussed. Online arithmetic needs carry-free redundant number systems. Frequently, the radix chosen is different from 2, since a carry-free addition algorithm can be used in radix r not=2. In radix 2, carry-free addition is possible, but with two inconveniences: the algorithm seems more complicated, and the delay is larger. The authors show that the first inconvenience vanishes if good binary representation of the digits in radix-2 signed digit notation is chosen. >


Particle swarm optimization


title: End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient

abstract: Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level annotations. However, one potential drawback of such approaches is that they myopically generate the next agent utterance without regard for dialog-level considerations. To resolve this concern, this paper describes an offline RL method for learning from unannotated corpora that can optimize a goal-oriented policy at both the utterance and dialog level. We introduce a novel reward function and use both on-policy and off-policy policy gradient to learn a policy offline without requiring online user interaction or an explicit state space definition.


title: Understanding current causes of women's underrepresentation in science.

abstract: Explanations for women's underrepresentation in math-intensive fields of science often focus on sex discrimination in grant and manuscript reviewing, interviewing, and hiring. Claims that women scientists suffer discrimination in these arenas rest on a set of studies undergirding policies and programs aimed at remediation. More recent and robust empiricism, however, fails to support assertions of discrimination in these domains. To better understand women's underrepresentation in math-intensive fields and its causes, we reprise claims of discrimination and their evidentiary bases. Based on a review of the past 20 y of data, we suggest that some of these claims are no longer valid and, if uncritically accepted as current causes of women's lack of progress, can delay or prevent understanding of contemporary determinants of women's underrepresentation. We conclude that differential gendered outcomes in the real world result from differences in resources attributable to choices, whether free or constrained, and that such choices could be influenced and better informed through education if resources were so directed. Thus, the ongoing focus on sex discrimination in reviewing, interviewing, and hiring represents costly, misplaced effort: Society is engaged in the present in solving problems of the past, rather than in addressing meaningful limitations deterring women's participation in science, technology, engineering, and mathematics careers today. Addressing today's causes of underrepresentation requires focusing on education and policy changes that will make institutions responsive to differing biological realities of the sexes. Finally, we suggest potential avenues of intervention to increase gender fairness that accord with current, as opposed to historical, findings.


title: Experimental Study of Bluetooth, ZigBee and IEEE 802.15.4 Technologies on Board High-Speed Trains

abstract: This paper studies the feasibility of using low-power wireless technologies such as Bluetooth, IEEE 802.15.4 and ZigBee in high-speed railway scenarios that involve bidirectional ground-to-train communication. The presented results have been obtained through experimental tests conducted at the Madrid-Barcelona high-speed rail line. A multiplatform communication system has been installed in a high-speed train, circulating at velocities up to 300 km/h, whereas autonomous devices have been disseminated along of the railway path to communicate with the onboard devices. The conclusions drawn from this work will be used as guidelines for the future implementation of autonomous communication platforms for high-speed rail connectivity.


title: DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

abstract: Clustering is one of the most useful methods of intelligent engineering domain, in which a set of similar objects are categorized into clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. This paper presents an efficient and effective clustering technique, named DBSCAN-GM that combines Gaussian-Means and DBSCAN algorithms. The idea of DBSCAN-GM is to cover the limitations of DBSCAN, by exploring the benefits of Gaussian-Means: it runs Gaussian-Means to generate small clusters with determined cluster centers, in purpose to estimate the values of DBSAN's parameters. The results of our method show that it is efficient even for large data sets especially data with large dimension and capable to handle noises, contrary to partitioning algorithms such as K-Means or Gaussian-Means. Additionally, DBSCAN-GM does not necessitate any priori information, in contrast to the density clustering DBSCAN obliging two input parameters which are hard to guess, namely Eps (the radius that bounds the neighborhood region of an object) and MinP ts (the minimum number of objects that must exist in the objects neighborhood region). Simulative experiments are carried out on a variety of datasets, which highlight the DBSCAN-GM's effectiveness and cluster validity to check the good quality of clustering results.


Topology Optimization for Additive Manufacturing


A Review of Experiences with Reliable Multicast


title: A 9.5–10.5GHz 60W AlGaN/GaN HEMT for X-band high power application

abstract: In this paper, we report our result of a 60W AlGaN/GaN HEMT with the operating frequency in X-band. The device technology is extension of well-established Eudyna commercial L-/S-band AlGaN/GaN HEMT technology. The device shows output power of over 60W and a high linear gain of 9.6dB in wide frequency range of 9.5-10.5GHz, operating at 40 V drain bias voltage with the pulsed conditions at a duty of 10% with a pulse width of 100 musec. The results show the developed 60W AlGaN/GaN HEMT has high power capability covering practical frequency range for X-band high power applications with proven device technology.


Advanced mine-to-target assignment algorithms and simulation


title: Approaching the Limit of Predictability in Human Mobility

abstract: In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.


title: Destruction behavior of power diodes beyond the SOA limit

abstract: Simulation results show how cathode-side filaments may trigger a thermal runaway at the end of a reverse-recovery period of diodes turned off with extremely high current rates. The mechanism is not essentially affected by the edge termination if an appropriate design is chosen. While multiple avalanche-induced filaments may appear during the reverse-recovery period, at the end of the turn-off phase a single “winning” filament carries the total current. This can result in a local melting of the diode. The appearance of a cathode-side filament by itself does not necessarily lead to the diode destruction. However, a high thermal carrier generation rate can result in an uncontrollable increase of the current density in a single filament connecting the anode and the cathode contact. It is shown t hat the reverse-recovery charge as a function of the dc-link voltage shows a characteristic super-linear increase below the critical value dc-link voltage at which the diode current increases uncontrollably.


title: Successful treatment of postburn flexion contracture in fingers of early childhood with dynamic splint therapy after operation: long-term follow-up

abstract: Background ::: Infants or early childhood individuals are prone to be burned on the palm and fingers, since they tend to extend their hands to the heat source. Wounds with more than 3-week-prolonged ulcers frequently result in flexion contracture. Despite successful surgery, the outcome might be unsatisfactory because of postoperative contracture. This study aims to investigate the effectiveness of our dynamic splint therapy following operation of postburn flexion contracture of fingers in infants or early childhood.


title: Interdependent Wearables (for Play): A Strong Concept for Design

abstract: Typically wearable devices are conceived of and constructed as stand-alone, individually based technologies. However, in practice wearables become part of the social context and ecology of overall device use. We present a strong concept for design: Interdependent Wearables (for play): wearables designed to require shared attention and mutual awareness, with interdependent functionality that encourages and rewards collocated interaction. The concept arose through design, development, and public exhibition of Hotaru, a collocated social game that uses wearables as game controllers. Hotaru has been shown in festivals and also formally playtested with 62 individuals. To more fully articulate the Interdependent Wearables strong concept, we compared this system's design with wearable and embodied systems for play and other purposes, and drew upon relevant HCI theory. The work is of benefit to those in the HCI/UX community focused on the design and development of social wearable technologies, especially those interested in supporting collocated interaction.


title: PEARL: Probing Entity Aggregation in Real Life

abstract: The classical approach for predicting tubercles bacillus (TB) prevalence falls in the partial differential equation (PDE) framework, which is essentially equal to the assumption of uniform random interaction network among people. The assumption, however, conflicts with common knowledge that some people interacts with many partners while others interact with limited partners. To accurately capture the interaction patterns among people, a mobile-based system called PEARL is proposed in this study. PEARL utilizes the characteristic that Bluetooth device has an effective range of ~10 meters, which is also the infectious distance of TB. Experimental results on several volunteers suggest that the interaction pattern roughly conforms to scale-free distribution, which helps improve prediction of prevalence of TB in China.


title: A General Identification Condition for Causal Effects

abstract: This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called "causal graph", in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion for the effects of a singleton variable on any set of variables.


title: Measuring Semantic Similarity for Bengali Tweets Using WordNet

abstract: Similarity between natural language texts, sentences in terms of meaning, known as textual entailment, is a generic problem in the area of computational linguistics. In the last few years researchers worked on various aspects of textual entailment problem, but mostly restricted to English language. Here in this paper we present a method for measuring the semantic similarity of Bengali tweets using WordNet. Moreover we defined partial textual entailment (PTE) as in real data partial entailment cases are equally prevalent with the complete/direct entailment. Although by definition entailment is a directional relationship, but here we consider entailment more as semantic similarity.


title: Benchmarking NFV Software Dataplanes

abstract: A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about its performance in practice. In this paper, we make a benchmark measurement study of NFV software dataplanes in terms of packet processing capability, one of the most fundamental and critical performance metrics. Specifically, we compare two state-of-the-art open-source NFV dataplanes, SoftNIC and ClickOS, using commodity 10GbE NICs under various typical workloads. Our key observations are that (1) both dataplanes have performance issues processing small (<=128B) packets; (2) it is not always best to put all VMs of a service chain on one server due to NUMA effect. We propose resource allocation strategies to remedy the problems, including carefully adding CPU cores and vNICs to VMs, and spreading VMs of a service chain to separate servers. To fundamentally address these problems and scale their performance, SoftNIC and ClickOS could improve the support for NIC queues and multiple cores.


title: Estimating the Flow Rule Installation Time of SDN Switches When Facing Control Plane Delay

abstract: The software defined networking (SDN) paradigm has numerous benefits for network operators, including cost aspects, flexibility, and programmability. Using the SDN approach of a separated control and data plane, the controller can order the installation of flow rules in the switches it manages, e.g., via FlowMod messages in case of the OpenFlow protocol. Since the processing time of these messages has a direct impact on the reaction time of the network, it is a key performance indicator for switches and quantifying it in a reliable manner is required for ensuring state consistency between the control and the data plane. Furthermore, real world deployments not only consist of different data plane hardware, but may feature varying control plane delays.


title: A silicon photonic add-drop filter for quantum emitters

abstract: Integration of single-photon sources and detectors to silicon-based photonics opens the possibility of complex circuits for quantum information processing. In this work, we demonstrate integration of quantum dots with a silicon photonic add-drop filter for on-chip filtering and routing of telecom photons. A silicon microdisk resonator acts as a narrow filter that transfers the quantum dot emission and filters the background over a wide wavelength range. Moreover, by tuning the quantum dot emission wavelength over the resonance of the microdisk, we can control the transmission of the quantum dot emission to the drop and through channels of the add-drop filter. This result is a step toward the on-chip control of single photons using silicon photonics for applications in quantum information processing, such as linear optical quantum computation and boson sampling.


title: Synaptic integrative mechanisms for spatial cognition

abstract: Synaptic integration is critical for determining how information in the brain is encoded, stored and retrieved. The authors review roles for synaptic integrative mechanisms in the selection, generation and plasticity of spatially modulated firing, and in related temporal codes for representation of space.


title: OSPF routing protocol performance in Software Defined Networks

abstract: Software Defined networks (SDNs) paradigm tries to improve the network performance, facilitating the network management and the scalability basing on open-source software and adding a new entity called controller that manages the entire network. One of the goals of the controller is taking decisions in regards to routing instead of distributing it among the network nodes, as usual. However, this field inside SDN needs more work because it is not clearly defined how the traditional routing protocols will work over a SDN. Thus, in this paper, we analyze how a dynamic routing protocol as Open Shortest Path First (OSPF) works in a SDN. The study analyzes the stability of network through parameters as convergence time and round trip time (RTT) and the quality of service (QoS) when a video is streamed between end devices. The results have been compared with a traditional network in order to evaluate the similarity in terms of stability and performance. Results show that, despite the proposal uses an OSPF implementation that does not have a performance as good as the traditional solution in terms of convergence time, with the SDN proposed we get better performance in terms of Quality of Service.


The NomBank Project: An Interim Report


title: GamIM: affecting chatting behavior by visualizing atmosphere of conversation

abstract: The frequent practice of instant messaging via mobile devices encourages rich social interaction in terms of text communication. Conventional visual attachments such as emoticons used to augment emotional cues from texts but such attachments fail to visualize the atmosphere of a conversation between two interlocutors. In this paper, we present GamIM, a gameful instant messenger prototype, allows to visualize emotional interaction animated by messages. We hypothesize that such gameful approach may potentially affect user's chatting behavior and encourage a more positive atmosphere of conversation.


title: C2TAM: A Cloud framework for cooperative tracking and mapping

abstract: The Simultaneous Localization And Mapping by an autonomous mobile robot-known by its acronym SLAM-is a computationally demanding process for medium and large-scale scenarios, in spite of the progress both in the algorithmic and hardware sides. As a consequence, a robot with SLAM capabilities has to be equipped with the latest computers whose weight and power consumption might limit its autonomy. This paper describes a visual SLAM system based on a distributed framework where the expensive map optimization and storage is allocated as a service in the Cloud, while a light camera tracking client runs on a local computer. The robot onboard computers are freed from most of the computation, the only extra requirement being an internet connection. The data flow from and to the Cloud is low enough to be supported by a standard wireless connection. The experimental section is focused on showing real-time performance for single-robot and cooperative SLAM using an RGBD camera. The system provides the interface to a map database where: (1) a map can be built and stored, (2) stored maps can be reused by other robots, (3) a robot can fuse its map online with a map already in the database, and (4) several robots can estimate individual maps and fuse them together if an overlap is detected.


title: An Energy Budget for Signaling in the Grey Matter of the Brain

abstract: Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%), and glutamate recycling using only 3%. Energy usage depends strongly on action potential rate--an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 mL/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with <or=15% of neurons simultaneously active, to reduce energy consumption and allow greater computing power from a fixed number of neurons. Functional magnetic resonance imaging signals are likely to be dominated by changes in energy usage associated with synaptic currents and action potential propagation.


title: SemEval-2013 Task 4: Free Paraphrases of Noun Compounds

abstract: In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via paraphrasing. Given a two-word noun compound, the participating system is asked to produce an explicitly ranked list of its free-form paraphrases. The list is automatically compared and evaluated against a similarly ranked list of paraphrases proposed by human annotators, recruited and managed through Amazon’s Mechanical Turk. The comparison of raw paraphrases is sensitive to syntactic and morphological variation. The “gold” ranking is based on the relative popularity of paraphrases among annotators. To make the ranking more reliable, highly similar paraphrases are grouped, so as to downplay superficial differences in syntax and morphology. Three systems participated in the task. They all beat a simple baseline on one of the two evaluation measures, but not on both measures. This shows that the task is difficult.


title: Toward Modular Soft Robotics: Proprioceptive Curvature Sensing and Sliding-Mode Control of Soft Bidirectional Bending Modules

abstract: Real-world environments are complex, unstructured, and often fragile. Soft robotics offers a solution for robots to safely interact with the environment and human coworkers, but suffers from a host of challenges in sensing and control of continuously deformable bodies. To overcome these challenges, this article considers a modular soft robotic architecture that offers proprioceptive sensing of pressure-operated bending actuation modules. We present integrated custom magnetic curvature sensors embedded in the neutral axis of bidirectional bending actuators. We describe our recent advances in the design and fabrication of these modules to improve the reliability of proprioceptive curvature feedback over our prior work. In particular, we study the effect of dimensional parameters on improving the linearity of curvature measurements. In addition, we present a sliding-mode controller formulation that drives the binary solenoid valve states directly, giving the control system the ability to hold the actuator steady without continuous pressurization and depressurization. In comparison to other methods, this control approach does not rely on pulse width modulation and hence offers superior dynamic performance (i.e., faster response rates). Our experimental results indicate that the proposed soft robotic modules offer a large range of bending angles with monotonic and more linear embedded curvature measurements, and that the direct sliding-mode control system exhibits improved bandwidth and a notable reduction in binary valve actuation operations compared to our earlier iterative sliding-mode controller.


title: A comprehensive and robust procedure for obtaining the nofit polygon using Minkowski sums

abstract: The nofit polygon is a powerful and effective tool for handling the geometric requirements of solution approaches to irregular cutting and packing problems. Although the concept was first described in 1966, it was not until the early 90s that the general trend of research moved away from direct trigonometry to favour the nofit polygon. Since then, the ability to calculate the nofit polygon has practically become a pre-requisite for researching irregular packing problems. However, realization of this concept in the form of a robust algorithm is a highly challenging task with few instructive approaches published. In this paper, a procedure using the mathematical concept of Minkowski sums for the calculation of the nofit polygon is presented. The described procedure is more robust than other approaches using Minkowski sum knowledge and includes details of the removal of internal edges to find holes, slits and lock and key positions. The procedure is tested on benchmark data sets and gives examples of complicated cases. Scope and purpose: Cutting and packing problems involving irregular shapes feature in a wide variety of manufacturing processes. Automated solution techniques that can generate packing arrangements more efficiently than current technology that employs user intervention, must be able to handle the complex geometry that arises from these problems. The nofit polygon has been demonstrated to be an effective tool in providing efficient handling of the geometric characteristics of these problems. The paper presents a new algorithmic procedure for deriving this tool.


title: Gender and creativity: an overview of psychological and neuroscientific literature

abstract: The topic of gender differences in creativity is one that generates substantial scientific and public interest, but also courts considerable controversy. Owing to the heterogeneous nature of the findings associated with this line of research, the general picture often appears puzzling or obscure. This article presents a selective overview of psychological and neuroscientific literature that has a relevant bearing on the theme of gender and creativity. Topics that are explored include the definition and methods of assessing creativity, a summary of behavioral investigations on gender in relation to creativity, postulations that have been put forward to understand gender differences in creative achievement, gender-based differences in the structure and function of the brain, gender-related differences in behavioral performance on tasks of normative cognition, and neuroscientific studies of gender and creativity. The article ends with a detailed discussion of the idea that differences between men and women in creative cognition are best explained with reference to the gender-dependent adopted strategies or cognitive style when faced with generative tasks.


title: Timed-Elastic-Bands for time-optimal point-to-point nonlinear model predictive control

abstract: This contribution presents a novel approach for nonlinear time-optimal model predictive control (MPC) based on Timed-Elastic-Bands (TEB). The TEB merges the states, control inputs and time intervals into a joint trajectory representation which enables planning of time-optimal trajectories in the context of model predictive control. Model predictive control integrates the planning of the optimal trajectory with state feedback in the control loop. The TEB approach formulates the fixed horizon optimal control problem for point-to-point transitions as a nonlinear program. The comparative analysis of the TEB approach with state-of-the-art approaches demonstrates its computational efficiency. The TEB approach generates a trajectory that approximates the analytical time-optimal trajectory in few iterations. This efficiency enables the refinement of the planned state and control sequence within the underlying closed-loop control during runtime.


title: Implementing Neural Turing Machines

abstract: Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an external memory unit. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of NTMs exist but are unstable during training and/or fail to replicate the reported performance of NTMs. This paper presents the details of our successful implementation of a NTM. Our implementation learns to solve three sequential learning tasks from the original NTM paper. We find that the choice of memory contents initialization scheme is crucial in successfully implementing a NTM. Networks with memory contents initialized to small constant values converge on average 2 times faster than the next best memory contents initialization scheme.


title: LIGHTING DESIGN FOR AUTOMATED PAVEMENT SURFACE DISTRESS EVALUATION

abstract: Automated pavement surface distress evaluation would be a useful component of computerized pavement management systems. Methods are described for applying computer simulations of pavement distress appearance to the design of those evaluation systems. By generalizing the results of crack visibility calculations under varying conditions of lighting, a number of conclusions about automated evaluation systems can be drawn, including the fact that there will always be some distress that escapes detection but that the detection probability is calculable. Lighting is important in determining which types of distress and what fraction of distress will evade detection. Differences between controlled and natural lighting, between ambient or omnidirectional lighting and directed lighting, and between single versus multiple light sources are discussed. Finally, there is consideration of how detailed examination of modeling results can be used to optimize the design of lighting, data acquisition hardware, and image-processing software. Because the conclusions depend strongly on the assumptions made in the work, a detailed listing of the assumptions is presented along with suggestions for further testing.


title: Outtweeting the Twitterers- Predicting Information Cascades in Microblogs

abstract: Microblogging sites are a unique and dynamic Web 2.0 communication medium. Understanding the information flow in these systems can not only provide better insights into the underlying sociology, but is also crucial for applications such as content ranking, recommendation and filtering, spam detection and viral marketing. In this paper, we characterize the propagation of URLs in the social network of Twitter, a popular microblogging site. We track 15 million URLs exchanged among 2.7 million users over a 300 hour period. Data analysis uncovers several statistical regularities in the user activity, the social graph, the structure of the URL cascades and the communication dynamics. Based on these results we propose a propagation model that predicts which users are likely to mention which URLs. The model correctly accounts for more than half of the URL mentions in our data set, while maintaining a false positive rate lower than 15%.


Visualization Techniques for Digital forensics: A Survey


title: Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation

abstract: We describe a general method of transforming arbitrary programming languages into probabilistic programming languages with straightforward MCMC inference engines. Random choices in the program are “named” with information about their position in an execution trace; these names are used in conjunction with a database holding values of random variables to implement MCMC inference in the space of execution traces. We encode naming information using lightweight source-to-source compilers. Our method enables us to reuse existing infrastructure (compilers, profilers, etc.) with minimal additional code, implying fast models with low development overhead. We illustrate the technique on two languages, one functional and one imperative: Bher, a compiled version of the Church language which eliminates interpretive overhead of the original MIT-Church implementation, and Stochastic Matlab, a new open-source language.


title: A tutorial on the dynamics and control of wind turbines and wind farms

abstract: Wind energy is currently the fastest-growing energy source in the world, with a concurrent growth in demand for the expertise of engineers and researchers in the wind energy field. There are still many unsolved challenges in expanding wind power, and there are numerous problems of interest to systems and control researchers. In this paper, we first review the basic structure of wind turbines and then describe wind turbine control systems and control loops. Of great interest are the generator torque and blade pitch control systems, where significant performance improvements are achievable with more advanced systems and control research. We describe recent developments in advanced controllers for wind turbines and wind farms, and we also outline many open problems in the areas of modeling and control of wind turbines.