title: A Dynamic-Model-Based Wheel Slip Detector for Mobile Robots on Outdoor Terrain

abstract: This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, inertial measurement unit, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.


title: A survey of autonomic communications

abstract: Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.


title: Sonographers and Occupational Overuse Syndrome

abstract: Due to remarkable advances in technology, sonographers today are working more efficiently, serving considerably more patients per day. One would assume this to be a benefit, when in fact a new problem has surfaced directly related to these achievements. Ultrasound exams require a peculiar type of muscular effort on the part of the sonographer. Tiny muscle tears that are the result of repetitive manipulations of the transducer, without adequate rest between exams, progress to more extensive muscular damage. The muscular damage can lead to career-ending injury. The purpose of this article is to examine the causes of overuse injuries brought on by repetitive muscle stresses associated with the performance of ultrasound exams and to discuss the changes necessary to combat this ergonomic crisis. Change is difficult, but with the combined efforts of equipment designers, employers, and sonographers, change is possible and an auspicious future can be envisioned.


title: IndexFS: scaling file system metadata performance with stateless caching and bulk insertion

abstract: The growing size of modern storage systems is expected to exceed billions of objects, making metadata scalability critical to overall performance. Many existing distributed file systems only focus on providing highly parallel fast access to file data, and lack a scalable metadata service. In this paper, we introduce a middleware design called Index FS that adds support to existing file systems such as PVFS, Lustre, and HDFS for scalable high-performance operations on metadata and small files. Index FS uses a table-based architecture that incrementally partitions the namespace on a per-directory basis, preserving server and disk locality for small directories. An optimized log-structured layout is used to store metadata and small files efficiently. We also propose two client-based storm free caching techniques: bulk namespace insertion for creation intensive workloads such as N-N check pointing, and stateless consistent metadata caching for hot spot mitigation. By combining these techniques, we have demonstrated Index FS scaled to 128 metadata servers. Experiments show our out-of-core metadata throughput out-performing existing solutions such as PVFS, Lustre, and HDFS by 50% to two orders of magnitude.


title: Learning to Rank for Consumer Health Search: A Semantic Approach

abstract: For many internet users, searching for health advice online is the first step in seeking treatment. We present a Learning to Rank system that uses a novel set of syntactic and semantic features to improve consumer health search. Our approach was evaluated on the 2016 CLEF eHealth dataset, outperforming the best method by 26.6% in NDCG@10.


title: Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference

abstract: Transfer Learning is a paradigm in machine learning to solve a target problem by reusing the learning with minor modifications from a different but related source problem. In this paper we propose a novel feature transference approach, especially when the source and the target problems are drawn from different distributions. We use deep neural networks to transfer either low or middle or higher-layer features for a machine trained in either unsupervised or supervised way. Applying this feature transference approach on Convolutional Neural Network and Stacked Denoising Autoencoder on four different datasets, we achieve lower classification error rate with significant reduction in computation time with lower-layer features trained in supervised way and higher-layer features trained in unsupervised way for classifying images of uppercase and lowercase letters dataset.


title: Detecting Deceptive Opinion Spam using Linguistics, Behavioral and Statistical Modeling

abstract: With the advent of Web 2.0, consumer reviews have become an important resource for public opinion that influence our decisions over an extremely wide spectrum of daily and professional activities: e.g., where to eat, where to stay, which products to purchase, which doctors to see, which books to read, which universities to attend, and so on. Positive/negative reviews directly translate to financial gains/losses for companies. This unfortunately gives strong incentives for opinion spamming which refers to illegal human activities (e.g., writing fake reviews and giving false ratings) that try to mislead customers by promoting/demoting certain entities (e.g., products and businesses). The problem has been widely reported in the news. Despite the recent research efforts on detection, the problem is far from solved. What is worse is that opinion spamming is widespread. While credit card fraud is as rare as 0.2%, based on our research we estimated that up to 30% of the reviews on many Web sites could be fake. Thus, detecting fake reviews and opinions is a pressing and also profound issue as it is critical to ensure the trustworthiness of the information on the web. Without detecting them, the social media could become a place full of lies, fakes, and deceptions, and completely useless. Major review hosting sites and e-commerce vendors have already made some progress in detecting fake reviews. However, the task is still extremely challenging because it is very difficult to obtain large-scale ground truth samples of deceptive opinions for algorithm development and for evaluation, or to conduct large-scale domain expert evaluations. Further, in contrast to other kinds of spamming (e.g., Web and link spam, social/blog spam, email spam, etc.) opinion spam has a very unique flavor as it involves fluid sentiments of users and their evaluations. Thus, they require a very different treatment. Since our first paper in 2007 (Jindal and Liu, 2007) on the topic, our group and many other researchers have proposed several algorithms and bridged algorithmic methodologies from various scientific disciplines including computational linguistics (Ott et al., 2011), social and behavioral sciences (Jindal and Liu, 2008; Mukherjee et al., 2013a, b), machine learning, data mining and Bayesian statistics (Mukherjee et al., 2012; Fei et al., 2013; Mukherjee et al., 2013c; Li et al., 2014b; Li et al., 2014a) to solve the problem. The field of deceptive opinion spam has gained a lot of interest in communications (Hancock et al., 2008), psycholinguistics communities (Gokhman et al., 2012), and economic analysis (Wang, 2010) apart from mainstream NLP and Web mining as attested by publications in top tier venues in their respective communities. The problem has far reaching implications in various allied NLP topics including Lie Detection, Forensic Linguistics, Opinion Trust and Veracity Verification and Plagiarism Detection. However, owing to the inherent nature of the problem, a unique blend of NLP, data mining, machine learning, social, behavioral, and statistical techniques are required which many NLP researchers may not be familiar with. In this tutorial, we aim to cover the problem in its full depth and width, covering diverse algorithms that have been developed over the past 7 years. The most attractive quality of these techniques is that many of them can be adapted for cross-domain and unsupervised settings. Some of the methods are even in use by startups and established companies. Our focus is on insight and understanding, using illustrations and intuitive deductions. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable.


title: Nine level cascaded H-bridge multilevel DC-link inverter

abstract: This paper presents the nine level cascaded H-bridge multilevel inverter based on a multilevel DC link (MLDCL) and a bridge inverter to reduce the number of switches. An MLDCL can be a diode-clamped phase leg, a flying-capacitor phase leg, or cascaded half-bridge cells with each cell having its own DC source. Compared to diode clamped & flying capacitor type MLDCL inverters cascaded H-bridge multilevel inverter requires least no of components to achieve same no of voltage levels. Optimized circuit layout is possible because each level have same structure and there is no extra clamping diodes or capacitors. Soft switching techniques can also be used to reduce switching losses and device stresses. The MLDCL provides a DC voltage with the shape of a staircase approximating the rectified shape of a commanded sinusoidal wave to the bridge inverter, which in turn alternates the polarity to produce an AC voltage. Compared with the existing type of cascaded H-bridge multilevel inverter, the MLDCL inverters can significantly reduce the switch count as well as the number of gate drivers as the number of voltage levels increases. For a given number of voltage levels, the required number of active switches is 2 (m−1) for the existing multilevel inverters, but it is m+3 for the MLDCL inverters. Simulation results are presented to verify the performance of the cascaded H-bridge MLDCL inverter supplying induction motor load.


Artificial Neural Network based Web Application Firewall for SQL Injection


title: Shape dependent Laplace vortices in deformed liquid-liquid slug flow

abstract: Understanding the hydrodynamics of liquid-liquid slug flow is important in the emerging field of plug-based microfluidics; however, the subtle aspects of the vortex geometry are still not comprehensively understood. This paper discusses the hydrodynamics of deformation dependent vortices that develop inside a water-in-oil slug as it flows through a channel. In contrast to prior studies, our simulations and experiments on slug flow reveal multiple vortices inside the moving slug, caused by the deformation of the hemispherical caps by Laplace pressures. These vortices appear in the front and rear of the plug at capillary number between 10(-4) and 10(-2). A theoretical and simulation model shows the cause of asymmetry in slug deformation and the resulting vortices. Understanding the relevant parameters helps in optimizing slug flow for mixing and particle manipulation, which is important for plug-based microreactors and bead based assays.


title: Sleep problems and their effect in ADHD

abstract: Sleep problems are common in attention-deficit/hyperactivity disorder (ADHD) to the extent that they mimic or exacerbate daytime symptoms expression. In this review, we advocate the need for a better understanding of sleep alterations in youths with ADHD and their impact on neurobehavioral functions including learning, memory and emotional regulation. An in-depth exploration of existing data showed that although extensively studied, the actual nature of sleep problems in ADHD and their effects on daytime behavior are still less well understood. Important issues, among which developmental changes in sleep architecture and role of subtle sleep electroencephalogram signatures, are generally neglected. Future research of sleep effects on behavior in ADHD would benefit from considering developmental aspects and links between brain activation patterns during sleep and wake.


title: Structure-aware synthesis for predictive woven fabric appearance

abstract: Woven fabrics have a wide range of appearance determined by their small-scale 3D structure. Accurately modeling this structural detail can produce highly realistic renderings of fabrics and is critical for predictive rendering of fabric appearance. But building these yarn-level volumetric models is challenging. Procedural techniques are manually intensive, and fail to capture the naturally arising irregularities which contribute significantly to the overall appearance of cloth. Techniques that acquire the detailed 3D structure of real fabric samples are constrained only to model the scanned samples and cannot represent different fabric designs. This paper presents a new approach to creating volumetric models of woven cloth, which starts with user-specified fabric designs and produces models that correctly capture the yarn-level structural details of cloth. We create a small database of volumetric exemplars by scanning fabric samples with simple weave structures. To build an output model, our method synthesizes a new volume by copying data from the exemplars at each yarn crossing to match a weave pattern that specifies the desired output structure. Our results demonstrate that our approach generalizes well to complex designs and can produce highly realistic results at both large and small scales.


title: A 9-pJ/Pulse 1.42-Vpp OOK CMOS UWB Pulse Generator for the 3.1–10.6-GHz FCC Band

abstract: This paper presents the design of a fully integrated ultra-wideband (UWB) pulse generator for the Federal Communications Commission (FCC) 3.1-10.6-GHz band. This generator is reserved for medium rate applications and achieves pulses for an on-off keying (OOK) modulation, pulse position modulation, or pulse interval modulation. This UWB transmitter is based on the impulse response filter method, which uses an edge combiner in order to excite an integrated bandpass filter. The circuit has been integrated in an ST-Microelectronics CMOS 0.13-?m technology with 1.2-V supply voltage and the die size is 0.54 mm2. The pulse generator power consumption is 9 pJ per pulse and achieves a peak to peak magnitude of 1.42 V. The pulse is FCC compliant and the generator can be used with a rate up to 38 Mbs-1 with an OOK modulation. Based on the FCC power spectral density limitation, a sizing method is also presented.


title: Three-dimensional analysis of shape rolling using a generalized upper bound approach

abstract: Abstract A generalized analytical method has been proposed to study external shape and calculate pressure and torque for the process of rolling shaped sections. The proposed method is based on the upper bound analysis. To determine kinematically admissible velocity fields, flow lines have been mathematically formulated using the parameterization of curves. Based on the calculated velocity fields, an upper bound has been found on rolling power. The calculated power has been minimized with respect to unknown variables to yield the value closest to the actual power required. The proposed general method has been applied to certain rolling passes including square-to-oval and round-to-oval. Velocity fields and power relations have been obtained for each process. Computer analysis has been carried out for all the mentioned passes using dimensions and conditions from experiments conducted by other workers. External shape, average roll torque and average rolling pressure results from the analysis have been compared with analytical solutions, numerical analyses and experimental data presented by other workers. It was concluded that the present method gave very good results and was quicker and easier to use.


title: Palliative management of malodorous squamous cell carcinoma of the oral cavity with Manuka honey.

abstract: BACKGROUND ::: The management of malignant malodorous wounds within the oral cavity can be challenging due to limited availability of dressings that are safe, efficacious, and ingestible. ::: ::: ::: CASE DESCRIPTION ::: An 80-year-old woman with squamous cell carcinoma of the oral cavity was admitted to home care with complaints and distress related to extreme malodor. ::: ::: ::: CONCLUSION ::: Manuka honey proved a safe, effective, palliative treatment to reduce odor and inflammation in wounds secondary to squamous cell carcinoma of the oral cavity in this patient.


title: Untapped mutualistic paradigms linking host plant and endophytic fungal production of similar bioactive secondary metabolites

abstract: The biosynthetic potential of endophytic fungi has gained impetus in recent times owing to the continual discovery of fungal endophytes capable of synthesizing plant compounds. However, the sustained production of the desired plant compounds has not yet been achieved using endophytes. It is thus imperative to investigate the diverse interactions that endophytes have with coexisting endophytes, host plants, insect pests, and other specific herbivores. The precise role of these associations on the endophytic production of host plant compounds is mostly overlooked and open to future discoveries. Here, highlighted are the implications of the poorly investigated links and molecular mechanisms that might trigger similar chemical responses in both plants and endophytes. Elucidating such connections can not only enhance the understanding of evolution of complex defense mechanisms in plants and associated organisms, but also help in the sustained production of plant compounds using endophytes harbored within them.


title: Myofascial release therapy in the treatment of occupational mechanical neck pain: a randomized parallel group study

abstract: As myofascial release therapy is currently under development, the objective of this study was to compare the effectiveness of myofascial release therapy with manual therapy for treating occupational mechanical neck pain.A randomized, single-blind parallel group study was developed. The sample (n = 59) was divided into GI, treated with manual therapy, and GII, treated with myofascial release therapy. Variables studied were intensity of neck pain, cervical disability, quality of life, craniovertebral angle, and ranges of cervical motion.At five sessions, clinical significance was observed in both groups for all the variables studied, except for flexion in GI. At this time point, an intergroup statistical difference was observed, which showed that GII had better craniovertebral angle (P = 0.014), flexion (P = 0.021), extension (P = 0.003), right side bending (P = 0.001), and right rotation (P = 0.031). A comparative analysis between therapies after intervention showed statistical differences indicating that GII had better craniovertebral angle (P = 0.000), right (P = 0.000) and left (P = 0.009) side bending, right (P = 0.024) and left (P = 0.046) rotations, and quality of life.The treatment of occupational mechanical neck pain by myofascial release therapy seems to be more effective than manual therapy for correcting the advanced position of the head, recovering range of motion in side bending and rotation, and improving quality of life.


title: An In-Depth Analysis of Disassembly on Full-Scale x86/x64 Binaries

abstract: It is well-known that static disassembly is an unsolved problem, but how much of a problem is it in real software—for instance, for binary protection schemes? This work studies the accuracy of nine state-of-the-art disassemblers on 981 real-world compiler-generated binaries with a wide variety of properties. In contrast, prior work focuses on isolated corner cases; we show that this has led to a widespread and overly pessimistic view on the prevalence of complex constructs like inline data and overlapping code, leading reviewers and researchers to underestimate the potential of binary-based research. On the other hand, some constructs, such as function boundaries, are much harder to recover accurately than is reflected in the literature, which rarely discusses much needed error handling for these primitives. We study 30 papers recently published in six major security venues, and reveal a mismatch between expectations in the literature, and the actual capabilities of modern disassemblers. Our findings help improve future research by eliminating this mismatch.


title: Why Are Autism Spectrum Conditions More Prevalent in Males?

abstract: Autism Spectrum Conditions (ASC) are much more common in males, a bias that may offer clues to the etiology of this condition. Although the cause of this bias remains a mystery, we argue that it occurs because ASC is an extreme manifestation of the male brain. The extreme male brain (EMB) theory, first proposed in 1997, is an extension of the Empathizing-Systemizing (E-S) theory of typical sex differences that proposes that females on average have a stronger drive to empathize while males on average have a stronger drive to systemize. In this first major update since 2005, we describe some of the evidence relating to the EMB theory of ASC and consider how typical sex differences in brain structure may be relevant to ASC. One possible biological mechanism to account for the male bias is the effect of fetal testosterone (fT). We also consider alternative biological theories, the X and Y chromosome theories, and the reduced autosomal penetrance theory. None of these theories has yet been fully confirmed or refuted, though the weight of evidence in favor of the fT theory is growing from converging sources (longitudinal amniocentesis studies from pregnancy to age 10 years old, current hormone studies, and genetic association studies of SNPs in the sex steroid pathways). Ultimately, as these theories are not mutually exclusive and ASC is multi-factorial, they may help explain the male prevalence of ASC.


title: Fast Global Illumination Approximations on Deep G-Buffers

abstract: Deep Geometry Buffers (G-buffers) combine the fine-scale and efficiency of screen-space data with much of the robustness of voxels. We introduce a new hardware-aware method for computing two-layer deep G-buffers and show how to produce dynamic indirect radiosity, ambient occlusion (AO), and mirror reflection from them in real-time. Our illumination computation approaches the performance of today’s screen-space AO-only rendering passes on current GPUs and far exceeds their quality. Our G-buffer generation method is order-independent, guarantees a minimum separation between layers, operates in a (small) bounded memory footprint, and avoids any sorting. Moreover, to address the increasingly expensive cost of pre-rasterization computations, our approach requires only a single pass over the scene geometry. We show how to apply Monte Carlo sampling and reconstruction to these to efficiently compute global illumination terms from the deep G-buffers. The resulting illumination captures small-scale detail and dynamic illumination effects and is substantially more robust than screen space estimates. It is necessarily still view-dependent and lower-quality than offline rendering. However, it is real-time, temporally coherent, and plausible based on visible geometry. Furthermore, the lighting algorithms automatically identify undersampled areas to fill from broad-scale or precomputed illumination. All techniques described are both practical today for real-time rendering and designed to scale with near-future hardware architecture and content trends. We include pseudocode for deep G-buffer generation, and source code and a demo for the global illumination sampling and filtering.


title: Experimental Investigation of 193-nm Laser Breakdown in Air

abstract: We present the measurements and analysis of laser-induced breakdown processes in dry air at a wavelength of 193 nm by focusing 180-mJ 10-MW high-power 193-nm UV ArF laser radiation onto a 30-mum-radius spot size. We examine pressures ranging from 40 torr to 5 atm, for laser power densities of 1 TW/cm2, well above the threshold power flux for air ionization. The breakdown threshold electric field is measured and compared with classical and quantum theoretical ionization models at this short wavelength. A universal scaling analysis of these results allows one to predict aspects of high-power microwave breakdown based on measured laser breakdown observations. Comparison of 193-nm laser-induced effective field intensities for air breakdown data calculated based on the collisional cascade and multiphoton breakdown theories is used successfully to determine the collisional microwave scaled portion with good agreement regarding both pressure dependence and breakdown threshold electric fields. Using a laser shadowgraphy diagnostic technique, the plasma and shock-wave dynamics are analyzed. Blast shock-wave expansion of the plasma and laser-heated neutral gas is observed with average velocities of 47 km/s, and the temporal shock-wave velocity variation is used to determine electron temperature evolution just behind the shock wave.


title: Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran

abstract: Rapid urban growth is becoming a serious problem in most developing countries. Tehran, the capital of Iran, stands out as a vibrant metropolitan area, facing uncontrolled urban expansion. Public authorities and decision makers require planning criteria regarding possible spatial developments. To monitor past developmental trends and to simulate emerging spatiotemporal patterns of urban growth, this research applies a geosimulation approach that couples agent-based modeling with multicriteria analysis (MCA) for the period between 1986 and 2006. To model the major determinants controlling urban development, three agent groups are defined, namely developer agents, government agents, and resident agents. The behaviors of each agent group are identified by qualitative surveys and are considered separately using multi-criteria analysis. The interactions of the agents are then combined through overlay functions within a Geographic Information System (GIS). This analysis results in the creation of a propensity surface of growth that is able to identify the most probable sites for urban development. Subsequently, a Markov Chain Model (MCM) and a concise statistical extrapolation are used to determine the amount of probable future expansion in Tehran. For validation purposes, the model is estimated using 2011 data and then validated based on actual urban expansion. Given the accurate predictions of the Markov Chain Model, further predictions were carried out for 2016 and 2026. This simulation provides strong evidence that during the next decade planning authorities will have to cope with continuous as well as heterogeneously distributed urban growth. Both the monitoring of growth and simulation revealed significant developments in the northwestern part of Tehran, continuing toward the south along the interchange networks.


title: Fast Ellipse Detection Algorithm Using Hough Transform on the GPU

abstract: GPUs (Graphics Processing Units) are specialized microprocessors that accelerate 3D or 2D graphics operations. Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. To utilize the powerful computing ability, GPUs are widely used for general purpose computing. The main purpose of this paper is an ellipse detection algorithm with Hough transform. The feature of our algorithm is that to reduce computational time and space, the parameter spaces in the Hough transform are decomposed for each parameter and each parameter is computed in series. Also, we implemented our algorithm on a modern GPU system. The experimental results show that, for an input image with size of 2040$\times$2040, our GPU implementation can achieve a speedup factor of approximately 64 times over the sequential implementation without the GPU support.


title: Predictive Control of a Complex District Heating Network

abstract: The short term optimization and control of district heating networks is of great interest for Energy Industries because of the technical, economical and environmental benefits which could be earned from an appropriate management. However, models of such complicated systems are strongly non linear and suffer from important uncertainties. In this article, models well suited to industrial issues are first designed. The whole technological string "production - distribution - consumption" is taken into account. The aim of this study is then to compute an optimal and robust control law for the network. Because of the errors in consumers' demand prediction and modelling uncertainties, a closed loop strategy has to be used to compute a robust control law for the district heating network. In this paper, a robust predictive control strategy of the network is thus developed. The method has been successfully tested on a benchmark network created by EDF ('Electricite de France') and some results are presented here.


title: Psychopathy and Pride: Testing Lykken’s Hypothesis Regarding the Implications of Fearlessness for Prosocial and Antisocial Behavior

abstract: Despite widespread assumptions that psychopathy is associated with serious and repeated law-breaking, individuals with psychopathic personality traits do not invariably become chronic criminal offenders. As a partial explanation for this finding, Lykken (1995) ventured that a fearless temperament underlies both psychopathic traits and heroic behavior, and that heroic individuals' early exposure to effective socializing forces such as warm parenting or healthy self-esteem often fosters a characteristic adaption that tends to beget “successful” behaviors, thereby differentiating heroes from convicts. In this study, we investigate relations between psychopathy, principally its Fearless dominance dimension, pride, and prosocial and antisocial behavior in a community sample (N = 339). Fearless dominance and Self-centered Impulsivity components of psychopathy yielded differential relations with authentic and hubristic pride (Tracy & Robins, 2004), such that Fearless dominance was significantly positively correlated with both facets of pride while Self-centered Impulsivity was significantly negatively correlated with authentic pride and significantly positively correlated with hubristic pride. Further, authentic pride moderated (potentiated) the relation between Fearless dominance and transformational leadership, one of the two outcome measures for prosocial behavior employed in our investigation. Authentic pride did not moderate the relations between Fearless dominance and either our other measure of prosocial behavior (heroism) or antisocial behavior, nor did positive parenting moderate the relations between psychopathy components and social behavior. Unexpectedly, hubristic pride significantly moderated the relation between impulsive-antisocial features and antisocial behavior in a protective manner.


title: Long-Range Low-Power Wireless Networks and Sampling Strategies in Electricity Metering

abstract: This paper studies a specific low-power wireless technology capable of reaching a long range, namely LoRa. Such a technology can be used by different applications in cities involving many transmitting devices while requiring loose communication constrains. We focus on electricity grids, where LoRa end-devices are smart-meters that send the average power demanded by their respective households during a given period. The successfully decoded data by the LoRa gateway are used by an aggregator to reconstruct the daily households' profiles. We show how the interference from concurrent transmissions from both LoRa and non-LoRa devices negatively affect the communication outage probability and the link effective bit-rate. Besides, we use actual electricity consumption data to compare time-based and event-based sampling strategies, showing the advantages of the latter. We then employ this analysis to assess the gateway range that achieves an average outage probability that leads to a signal reconstruction with a given requirement. We also discuss that, although the proposed analysis focuses on electricity metering, it can be easily extended to any other smart city application with similar requirements, like water metering or traffic monitoring.


title: Direct Multisearch for Multiobjective Optimization

abstract: ¶ Abstract. In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, prevent- ing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational ex- perience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a com- parison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.


title: Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations

abstract: Much attention is currently being paid in both the academic and practitioner literatures to the value that organisations could create through the use of big data and business analytics (Gillon et al, 2012; Mithas et al, 2013). For instance, Chen et al (2012, p. 1166–1168) suggest that business analytics and related technologies can help organisations to ‘better understand its business and markets’ and ‘leverage opportunities presented by abundant data and domain-specific analytics’. Similarly, LaValle et al (2011, p. 22) report that top-performing organisations ‘make decisions based on rigorous analysis at more than double the rate of lower performing organisations’ and that in such organisations analytic insight is being used to ‘guide both future strategies and day-to-day operations’. We argue here that while there is some evidence that investments in business analytics can create value, the thesis that ‘business analytics leads to value’ needs deeper analysis. In particular, we argue here that the roles of organisational decision-making processes, including resource allocation processes and resource orchestration processes (Helfat et al, 2007; Teece, 2009), need to be better understood in order to understand how organisations can create value from the use of business analytics. Specifically, we propose that the first-order effects of business analytics are likely to be on decision-making processes and that improvements in organisational performance are likely to be an outcome of superior decision-making processes enabled by business analytics.


title: Extrinsic wrist ligaments: prevalence of injury by magnetic resonance imaging and association with intrinsic ligament tears.

abstract: OBJECTIVE ::: The objective of this study was to determine the prevalence of extrinsic wrist ligament injury by magnetic resonance imaging and its association with intrinsic ligament tears. ::: ::: ::: METHODS ::: We reviewed conventional magnetic resonance images performed over a 5-year period from adult patients in the setting of wrist trauma. Two musculoskeletal radiologists examined the integrity of wrist ligaments and presence of bone abnormalities. ::: ::: ::: RESULTS ::: In a cohort of 75 subjects, extrinsic ligament injury was present in 75%, with radiolunotriquetral being most frequently affected (45%). Intrinsic ligament injury was present in 60%. Almost half of subjects had combined intrinsic and extrinsic ligament injury. Bone abnormalities were seen in 69%. The rate of extrinsic injury was higher in subjects with bone injury (P = 0.008). ::: ::: ::: CONCLUSIONS ::: There is high prevalence of extrinsic ligament injury in the setting of wrist trauma, especially in the presence of bone abnormalities, with combined injury of intrinsic and extrinsic ligaments in about half of cases.


title: Survalytics: An Open-Source Cloud-Integrated Experience Sampling, Survey, and Analytics and Metadata Collection Module for Android Operating System Apps

abstract: Background: We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment. Objective: Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps. Methods: Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information. Results: The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects. Conclusions: The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes. [JMIR Mhealth Uhealth 2016;4(2):e46]


title: Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques

abstract: Conventional electric energy can be easily adopted to a large scale by providing high quality electricity for wide-area transmissions. However, these energies are usually generated from exhaustible sources such as oil, natural gas, and coal, which are highly expensive in the long run and are the main causes of global warming. Meanwhile a large centralized energy system is more fragile and highly risky in countries like Japan where natural disasters occur frequently. A decentralized renewable energy system containing photovoltaic energy and wind power has been proposed as an alternative energy supply method. Within this system, the photovoltaic energy and wind power are well suited for the “local production and local consumption” with domestic energy transmission and are resilient to the unexpected disasters. The challenge of forming an optimal decentralized renewable energy system is to overcome its intrinsic disadvantages such as the instability and the limit of the power output. The research in this regard has drawn a lot of attention for the past twenty years. A decentralized renewable energy optimization problem is in principle categorized as nonlinear mixed integer programing problem(NMIP). Several challenging issues still remained in finding effective solution to NMIP through mathematical optimization. For instance, there is lack of reliable method to predict the energy generation and consumption; the weak scalability to large scale system is also existed due to the limited computing resource and the algorithm which are intrinsically not suitable for high speed computing. In this work, we report on employing the deep learning artificial intelligence techniques to predict the energy consumption and power generation together with the weather forecasting numerical simulation. The prediction and optimization are further examined by a small scale decentralized verification system (i-REMS) constructed inside the University campus. a novel optimization tool platform using Boltzmann machine algorithm for NMIP problem is also proposed for better computing scalable decentralized renewable energy system.


title: Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits

abstract: Background ::: Over the last several years, it has become apparent that there are critical problems with the hypothesis that brain dopamine (DA) systems, particularly in the nucleus accumbens, directly mediate the rewarding or primary motivational characteristics of natural stimuli such as food. Hypotheses related to DA function are undergoing a substantial restructuring, such that the classic emphasis on hedonia and primary reward is giving way to diverse lines of research that focus on aspects of instrumental learning, reward prediction, incentive motivation, and behavioral activation.


Visual Sliding Window SLAM with Application to Planetary Landers


title: Hostile Behaviors in Preschoolers’ Physical Play: Gender Effects

abstract: Abstract Through physical play children enjoy and learn in a holistic manner. Preschools can provide many opportunities to enhance children's social skills, via social interaction and cooperation in physical play. Deficits in social skills can have short-term detrimental effects on children's development as well as long-term impacts later in their life. Although research has emphasized the beneficial effects of physical play on children's social competence, studies on problem behaviors of preschool children in physical play, are limited. The present study aimed at identifying hostile behaviors of preschool children during physical play and possible gender differences in this regard. It is part of a broader research which aims to evaluate the social skills of preschool children in different settings, after testing the psychometric properties of MESSY-II in Greek population. One hundred preschool children, 2.5-3.5 years of age participated. Hostile behaviors were assessed by children's teachers with MESSY-II (Matson, Neal, Worley, Kozlowski, &Fodstad, 2012). T-test for independent samples indicated that boys were rated as presenting significantly more hostile behaviors than girls during physical play. These differences should be taken into account in developing treatment as well as preventive strategies to facilitate preschool children's social development.


title: Internet of Things for Enterprise Systems of Modern Manufacturing

abstract: Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.


title: Low-Complexity SSOR-Based Precoding for Massive MIMO Systems

abstract: With the increase of the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems, linear precoding schemes are able to achieve the near-optimal performance, and thus are more attractive than nonlinear precoding techniques. However, conventional linear precoding schemes such as zero-forcing (ZF) precoding involve the matrix inversion of large size with high computational complexity, especially in massive MIMO systems. To reduce the complexity, in this letter, we propose a low-complexity linear precoding scheme based on the symmetric successive over relaxation (SSOR) method. Moreover, we propose a simple way to approximate the optimal relaxation parameter of the SSOR-based precoding by exploiting the channel property of asymptotical orthogonality in massive MIMO systems. We show that the proposed SSOR-based precoding can reduce the complexity of the classical ZF precoding by about one order of magnitude without performance loss, and it also outperforms the recently proposed linear approximate precoding schemes in typical fading channels.


title: The Application of Convolution Neural Networks in Handwritten Numeral Recognition

abstract: Convolutional neural networks are a technology that combines artificial neural networks and recent deep learning methods. They have been applied to many image recognition tasks and have attracted the attention of the researchers of many countries in recent years. This paper summarizes the latest development of convolutional neural networks and expounds the relative research of image recognition technology and elaborates on the application of convolutional neural networks in handwritten numeral recognition.


title: Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

abstract: We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset. Alice will "propose" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.


A language for bitmap manipulation


A Review of Different Text Categorization Techniques


title: Real-time computer-generated integral imaging and 3D image calibration for augmented reality surgical navigation.

abstract: Abstract Autostereoscopic 3D image overlay for augmented reality (AR) based surgical navigation has been studied and reported many times. For the purpose of surgical overlay, the 3D image is expected to have the same geometric shape as the original organ, and can be transformed to a specified location for image overlay. However, how to generate a 3D image with high geometric fidelity and quantitative evaluation of 3D image's geometric accuracy have not been addressed. This paper proposes a graphics processing unit (GPU) based computer-generated integral imaging pipeline for real-time autostereoscopic 3D display, and an automatic closed-loop 3D image calibration paradigm for displaying undistorted 3D images. Based on the proposed methods, a novel AR device for 3D image surgical overlay is presented, which mainly consists of a 3D display, an AR window, a stereo camera for 3D measurement, and a workstation for information processing. The evaluation on the 3D image rendering performance with 2560 × 1600 elemental image resolution shows the rendering speeds of 50–60 frames per second (fps) for surface models, and 5–8 fps for large medical volumes. The evaluation of the undistorted 3D image after the calibration yields sub-millimeter geometric accuracy. A phantom experiment simulating oral and maxillofacial surgery was also performed to evaluate the proposed AR overlay device in terms of the image registration accuracy, 3D image overlay accuracy, and the visual effects of the overlay. The experimental results show satisfactory image registration and image overlay accuracy, and confirm the system usability.


Possibilistic interest discovery from uncertain information in social networks


title: Compact 60-GHz IPD-Based Branch-Line Coupler for System-on-Package V-Band Radios

abstract: A compact 60-GHz band branch-line coupler using cpacitively loaded lower-ground coplanar-waveguide (LG-CPW) lines has been successfully demonstrated in a glass-substrate integrated passive device technology. The fabricated coupler has a size reduction of more than 83% compared to that of a conventional CPW branch-line coupler. The capacitive loading is achieved by utilizing the signal layer and the LG of the LG-CPW structure to form microstrip open-circuited stubs. The measured results show a phase error of less than 0.5° between the coupler's output ports and an amplitude imbalance of less than 1.2 dB over the frequency band 57-64 GHz. The measurements also show that both the return loss and isolation are better than 25 dB at 60 GHz and better than 15 dB over the 57-64-GHz band. The proposed coupler is well suited for low-cost high-performance system-on-package V-band radio front ends for high data rate applications.


title: DC-Link Capacitance Estimation in AC/DC/AC PWM Converters Using Voltage Injection

abstract: In this paper, a new online capacitance estimation method for dc-link capacitors in a three-phase ac/dc/ac pulsewidth-modulation converter is proposed. A controlled ac voltage with a lower frequency than the line frequency is injected into the dc-link voltage, which then causes ac power ripples at the dc output side. By extracting the ac power component on the dc output side using digital filters, the capacitance can then be calculated using a support vector regression method. A function that defines the relation between a given capacitor power and its corresponding capacitance is determined using a set of training data. This function is then used to predict the output for the given input which is not included in the training set. The proposed method can be simply implemented with only software and no additional hardware. Experimental results confirm that the estimation error is less than 0.15%.


Secure multiparty computation for privacy preserving data mining


title: Visual vs. compact: a comparison of privacy policy interfaces

abstract: In this paper, we compare the impact of two different privacy policy representations -- AudienceView and Expandable Grids -- on users modifying privacy policies for a social network site. Despite the very different interfaces, there were very few differences in user performance. However, users had clear, and different, preferences and acknowledged the tradeoffs between the two representations. Our results imply that while either interface would be a usable option for policy settings, a combination may appeal to a wider audience and offer the best of both worlds.


Agent-Based Distributed Learning Applied to Fraud Detection


title: Proof of Concept for Robot-Aided Upper Limb Rehabilitation Using Disturbance Observers

abstract: This paper presents a wearable upper body exoskeleton system with a model-based compensation control framework to support robot-aided shoulder–elbow rehabilitation and power assistance tasks. To eliminate the need for EMG and force sensors, we exploit off-the-shelf compensation techniques developed for robot manipulators. Thus, target rehabilitation tasks are addressed by using only encoder readings. A proof-of-concept evaluation was conducted with five able-bodied participants. The patient-active rehabilitation task was realized via observer-based user torque estimation, in which resistive forces were adjusted using virtual impedance. In the patient-passive rehabilitation task, the proposed controller enabled precise joint tracking with a maximum positioning error of 0.25 $^\circ$ . In the power assistance task, the users’ muscular activities were reduced up to 85% while exercising with a 5 kg dumbbell. Therefore, the exoskeleton system was regarded as being useful for the target tasks, indicating that it has a potential to promote robot-aided therapy protocols.


title: Axial-flux PM starter/alternator machine with a novel mechanical device for extended flux weakening capabilities

abstract: In modern vehicles, the demand for on-board electric energy generation is rapidly growing in order to satisfy new requirements for passengers comfort and safety. This growth, which is predicted to reach 6 kW in the very next years, is forcing manufacturers to explore new solutions concerning the whole electrical system inside the vehicle. In particular, novel topologies of alternators are under research since the currently used Lundell type (claw-pole) alternator is reaching its power limits. In this paper an original torque dependant mechanical device is proposed in order to extend the speed range of an Axial Flux PM machine. This device displaces the rotors realizing a mechanical flux weakening with constant output power in generation. Simulations and experimental tests on a prototype realized in the lab are fully discussed.


title: Hybrid PS full bridge and LLC half bridge DC-DC converter for low-voltage and high-current output applications

abstract: A hybrid phase shifted full bridge (PSFB) and LLC half bridge (HB) dc-dc converter for low-voltage and high-current output applications is proposed in this paper. The PSFB shares its lagging leg with the LLC-HB and their outputs are parallel connected. When the output current is small, the energy of LLC circuit in combination with the energy stored in the leakage inductance of PSFB's transformer can help the lagging leg switches to realize ZVS turn on, which can reduce voltage stress and avoid annoying voltage spikes over switches. For the power distribution at rated load, the PSFB converter undergoes most of the power while the LLC-HB converter working as an auxiliary part converts only a small portion of the total power. To improve the conversion efficiency, synchronous rectification technique for the PSFB dc-dc converter is implemented. The design principle is given in view of ZVS for lagging leg switches and low transconductance of LLC converter. The validity of the proposed converter has been verified by experimental results of a 2.5kW prototype.


title: Shadow Enhancement in Synthetic Aperture Sonar Using Fixed Focusing

abstract: A shadow cast by an object on the seafloor is important information for target recognition in synthetic aperture sonar (SAS) images. Synthetic aperture imaging causes a fundamental limitation to shadow clarity because the illuminator is moved during the data collection. This leads to a blend of echo and shadow, or geometrical fill-in in the shadow region. The fill-in is most dominant for widebeam synthetic aperture imaging systems. By treating the shadow as a moving target and compensating for the motion during the synthetic aperture imagery, we avoid the geometrical shadow fill-in. We show this to be equivalent to fixing the focus at the range of the shadow caster. This novel technique, referred to as fixed focus shadow enhancement (FFSE) can be used directly as an imaging method on hydrophone data or as a postprocessing technique on the complex SAS image. We demonstrate the FFSE technique on simulated data and on real data from a rail-based SAS, and on two different SAS systems operated on a HUGIN autonomous underwater vehicle.


title: SO(2) and SO(3), omni-directional personal mobility with link-driven spherical wheels

abstract: There are many researches about mobile robot with a varied wheel. Mobile robots or mobilities with wheels such like a car or bike have non-holonomic constraint and then it is difficult to move toward wheel's axis direction. Namely, it is impossible to move toward omni-direction due to the constraints by wheel's mechanical structure. Omni-directional movement means the mobility can move toward any direction at anytime. This paper adapted link-driven spherical wheel as an omnidirectional wheel and solved singularity problem caused by mechanical structure of it. We developed omni-directional mobilities( SO(2) and SO(3)) installing link-driven spherical wheels and experimented omni-directional movement with designed controllers.


title: A V-band front-end with 3-D integrated cavity filters/duplexers and antenna in LTCC technologies

abstract: This paper presents a compact system-on-package-based front-end solution for 60-GHz-band wireless communication/sensor applications that consists of fully integrated three-dimensional (3-D) cavity filters/duplexers and antenna. The presented concept is applied to the design, fabrication, and testing of V-band (receiver (Rx): 59-61.5 GHz, transmitter (Tx): 61.5-64 GHz) transceiver front-end module using multilayer low-temperature co-fired ceramic technology. Vertically stacked 3-D low-loss cavity bandpass filters are developed for Rx and Tx channels to realize a fully integrated compact duplexer. Each filter exhibits excellent performance (Rx: IL < 2.37 dB, 3-dB bandwidth (BW)∼ 3.5%, Tx:IL < 2.39dB,3-dBBW ∼ 3.33%). The fabrication tolerances contributing to the resonant frequency experimental downshift were investigated and taken into account in the simulations of the rest devices. The developed cavity filters are utilized to realize the compact duplexers by using microstrip T-junctions. This integrated duplexer shows Rx/Tx BW of 4.20% and 2.66% and insertion loss of 2.22 and 2.48 dB, respectively. The different experimental results of the duplexer compared to the individual filters above are attributed to the fabrication tolerance, especially on microstrip T-junctions. The measured channel-to-channel isolation is better than 35.2 dB across the Rx band (56-58.4 GHz) and better than 38.4 dB across the Tx band (59.3-60.9 GHz). The reported fully integrated Rx and Tx filters and the dual-polarized cross-shaped patch antenna functions demonstrate a novel 3-D deployment of embedded components equipped with an air cavity on the top. The excellent overall performance of the full integrated module is verified through the 10-dB BW of2.4GHz ( ∼ 4.18%) at 57.45 and 2.3 GHz (∼ 3.84%) at 59.85 GHz and the measured isolation better than 49 dB across the Rx band and better than 51.9 dB across the Tx band.


The Rhetorical Parsing Of Unrestricted Natural Language Texts


title: An Efficient Real Time Method of Fingertip Detection

abstract: Fingertips detection has been used in many applications, and it is very popular and commonly used in the area of Human Computer Interaction these days. This paper presents a novel time efficient method that will lead to fingertip detection after cropping the irrelevant parts of input image. Binary silhouette of the input image is generated using HSV color space based skin filter and hand cropping done based on histogram of the hand image. The cropped image will be used to figure out the fingertips.


title: Stereo visual odometry for pipe mapping

abstract: Pipe inspection is a critical activity in gas production facilities and many other industries. In this paper, we contribute a stereo visual odometry system for creating high resolution, sub-millimeter maps of pipe surfaces. Such maps provide both 3D structure and appearance information that can be used for visualization, cross registration with other sensor data, inspection and corrosion detection tasks. We present a range of optical configuration and visual odometry techniques that we use to achieve high accuracy while minimizing specular reflections. We show empirical results from a range of datasets to demonstrate the performance of our approach.


title: Pregnancy Outcome in Hyperthyroidism: A Case Control Study

abstract: BACKGROUND ::: Data comparing pregnancy outcome in hyperthyroid women with euthyroid women are scarce. Hence, this study was carried out to assess the maternal and fetal outcome in pregnant women with hyperthyroidism to ascertain the effect of disease on pregnancy. ::: ::: ::: METHODOLOGY ::: This retrospective study was conducted over a period of 28 years. We compared the maternal and fetal outcomes of 208 hyperthyroid women with 403 healthy controls, between women with well-controlled and uncontrolled disease and amongst women diagnosed with hyperthyroidism before and during pregnancy. ::: ::: ::: RESULTS ::: Maternal outcome: women with hyperthyroidism were at increased risk for preeclampsia (OR = 3.94), intrauterine growth restriction (OR = 2.16), spontaneous preterm labor (OR = 1.73), preterm birth (OR = 1.7), gestational diabetes mellitus (OR = 1.8), and cesarean delivery (OR = 1.47). Hyperthyroid women required induction of labor more frequently (OR = 3.61). Fetal outcome: newborns of hyperthyroid mothers had lower birth weight than normal ones (p = 0.0001). Women with uncontrolled disease had higher odds for still birth (OR = 8.42; 95% CI: 2.01-35.2) and lower birth weight (p = 0.0001). ::: ::: ::: CONCLUSIONS ::: Obstetrical complications were higher in women with hyperthyroidism than normal women. Outcome was worsened by uncontrolled disease. Women with pregestational hyperthyroidism had better outcomes than those diagnosed with it during pregnancy.


title: For the Law , Neuroscience Changes Nothing and Everything

abstract: The rapidly growing field of cognitive neuroscience holds the promise of explaining the operations of the mind in terms of the physical operations of the brain. Some suggest that our emerging understanding of the physical causes of human (mis)behaviour will have a transformative effect on the law. Others argue that new neuroscience will provide only new details and that existing legal doctrine can accommodate whatever new information neuroscience will provide. We argue that neuroscience will probably have a transformative effect on the law, despite the fact that existing legal doctrine can, in principle, accommodate whatever neuroscience will tell us. New neuroscience will change the law, not by undermining its current assumptions, but by transforming people's moral intuitions about free will and responsibility. This change in moral outlook will result not from the discovery of crucial new facts or clever new arguments, but from a new appreciation of old arguments, bolstered by vivid new illustrations provided by cognitive neuroscience. We foresee, and recommend, a shift away from punishment aimed at retribution in favour of a more progressive, consequentialist approach to the criminal law.


title: Knowledge Questions from Knowledge Graphs

abstract: We address the problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose a novel end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options as distractors. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach.


title: Decision support capabilities of enterprise content management systems

abstract: Enterprise content management (ECM) systems help organizations cope with the increasing complexity and volume of data and information. Despite the growing popularity of ECM, published literature indicates that organizations primarily use ECM for operational benefits, while the strategic decision making capabilities are rarely considered. Thus, the most significant rewards of ECM implementation may be largely forgone. This study investigates the potential of ECM technology for decision support. A research model is proposed and validated via an empirical investigation. The results show that ECM positively influences problem identification and definition, decision making speed and analysis, decision quality, and decision makers' satisfaction. Investigate decision support capabilities of enterprise content management systemsDevelop conceptual model linking content stewardship with decision support activitiesHypotheses on impact of enterprise content management system use on decision makingOn-line survey of enterprise content management system users at large academic institutionECM systems help with problem identification and decision making speed and improve decision quality


title: How to Attract and Retain Readers in Enterprise Blogging?

abstract: We investigate the dynamics of blog reading behavior of employees in an enterprise blogosphere. A dynamic model is developed and calibrated using longitudinal data from a Fortune 1,000 IT services firm. Our modeling framework allows us to segregate the impact of textual characteristics sentiment and quality of a post on attracting readers from retaining them. We find that the textual characteristics that appeal to the sentiment of the reader affect both reader attraction and retention. However, textual characteristics that reflect only the quality of the posts affect only reader retention. We identify a variety-seeking behavior of blog readers where they dynamically switch from reading on one set of topics to another. The modeling framework and findings of this study highlight opportunities for the firm to influence blog-reading behavior of its employees to align it with its goals. Overall, this study contributes to improved understanding of reading behavior of individuals in communities formed around user generated content.


Automatic Road Extraction from High Resolution Imagery Based On Statistical Region Merging and Skeletonization


Commercial Motion Sensor Based Low-Cost and Convenient Interactive Treadmill †


title: Formalisation and analysis of Dalvik bytecode

abstract: Abstract With the large, and rapidly increasing, number of smartphones based on the Android platform, combined with the open nature of the platform that allows “apps” to be downloaded and executed on the smartphone, misbehaving and malicious (malware) apps are set to become a serious problem. To counter this problem, automated tools for analysing and verifying apps are essential. Furthermore, to ensure high-fidelity of such tools, it is essential to formally specify both semantics and analyses. In this paper we present, to the best of our knowledge, the first formalisation of the complete Dalvik bytecode language including reflection features and the first formally specified control flow analysis for the language, including advanced control flow features such as dynamic dispatch, exceptions, and reflection. To determine which features to include in the formalisation and analysis, 1700 Android apps from the Google Play app market (formerly known as Android Market) were downloaded and examined.


title: Data Collection and Analysis

abstract: The focus of this chapter is on methods of data collection and analysis for the assessment of learning processes and complex performance, the last part of the empirical cycle after theory development and experimental design. In the introduction (van Gog and Paas), the general background and the relation between the chapter sections are briefly described. The section by Savenye, Robinson, Niemczyk, and Atkinson focuses on methods of data collection and analysis for assessment of individual learning processes, whereas the section by Johnson and O’Connor is concerned with methods for assessment of group learning processes. The chapter section by van Gog, Rikers, and Ayres discusses the assessment of complex performance, and the final chapter section by Duley, Ward, Szalma, and Hancock is concerned with setting up laboratories to measure learning and complex performance.


title: The Design and Architecture of Microservices

abstract: Microservices are sweeping through cloud design architectures, at once embodying new trends and making use of previous paradigms. This column explores the basis for these trends in both modern and historical standards, and sets out a direction for the future of microservices development.


title: A 0.55V 100MHz ADPLL with ΔΣ LDO and Relaxation DCO in 65nm CMOS

abstract: A 0.55V 100MHz LDO-embedded ADPLL is implemented in 65nm CMOS. A digitally-controlled relaxation oscillator (DCRXO) with a variable-threshold inverter and a digital calibration circuit is designed for robust start up under ultra-low supply voltage. A ΔΣ modulated pseudo-digital LDO provides a PVT-insensitive dithered internal voltage for the DCRXO. The 100MHz ADPLL including the LDO consumes a 67μW from a 0.55V supply and achieves the phase noise of −82.9dBc/Hz at 1MHz offset frequency. The LDO achieves >20dB PSRR when sinusoidal noise is injected to the supply.


title: Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring

abstract: Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.


title: Integrating Sample-based Planning and Model-based Reinforcement Learning

abstract: Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, despite their exponentially large state spaces. Unfortunately, these algorithms all require access to a planner that computes a near optimal policy, and while many traditional MDP algorithms make this guarantee, their computation time grows with the number of states. We show how to replace these over-matched planners with a class of sample-based planners—whose computation time is independent of the number of states—without sacrificing the sample-efficiency guarantees of the overall learning algorithms. To do so, we define sufficient criteria for a sample-based planner to be used in such a learning system and analyze two popular sample-based approaches from the literature. We also introduce our own sample-based planner, which combines the strategies from these algorithms and still meets the criteria for integration into our learning system. In doing so, we define the first complete RL solution for compactly represented (exponentially sized) state spaces with efficiently learnable dynamics that is both sample efficient and whose computation time does not grow rapidly with the number of states.


title: Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

abstract: Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.


title: On the Importance of Distinguishing Hedonia and Eudaimonia when Contemplating the Hedonic Treadmill.

abstract: and nurture of joy and contentment. New York: St. Martin’s Griffin. McGue, M., Bacon, S., & Lykken, D. T. (1992). Personality stability and change in early adulthood: A behavioral genetic analysis. Developmental Psychology, 29, 96–109. Tellegen, A., Lykken, D. T., Bouchard, T. J., Jr., Wilcox, K., Segal, N., & Rich, S. (1988). Personality similarity in twins reared apart and together. Journal of Personality and Social Psychology, 54, 1031–1039. Tellegen, A., & Waller, N. (1994). Exploring personality through test construction: Development of the Multidimensional Personality Questionnaire. In S. R. Briggs & J. M. Cheek (Eds.), Personality measures: Development and evaluation (Vol. 1, pp. 133–161). Greenwich, CT: JAI Press. Wong, A. H. C., Gottesman, I. I., & Petronis, A. (2005). Phenotypic differences in genetically identical organisms: The epigenetic perspective. Human Molecular Genetics, 14, 11–18.


title: Effect of silver diamine fluoride and potassium iodide on residual bacteria in dentinal tubules

abstract: Background ::: This study evaluated the antimicrobial effect of a silver diamine fluoride (SDF)/potassium iodide (KI) product (Riva Star) on the viability of intratubular bacteria. ::: ::: Methods ::: Forty-five dentine discs prepared from caries-free maxillary premolars were randomly divided into nine groups. Group 1 (negative control) contained non-infected sound dentine discs. The remaining discs were infected with Streptococcus mutans suspension and received dentine treatments as follows: Group 2 (positive control), discs were left untreated; Group 3 SDF/KI (Riva Star); Group 4 chlorhexidine (CHX); Group 5 CHX + SDF/KI; Group 6 Carisolv; Group 7 Carisolv + SDF/KI; Group 8 Papacarie, and Group 9 Papacarie + SDF/KI. The discs were then fractured into two halves, stained with fluorescent LIVE/DEAD stain and observed using confocal laser scanning microscopy. ::: ::: Results ::: SDF/KI exhibited a potent antibacterial effect, as represented by a significantly higher percentage of dead bacteria, in comparison with Carisolv and Papacarie (p < 0.05). The application of SDF/KI following Carisolv and Papacarie chemomechanical caries removel gels significantly reduced the viability of intra-tubular bacteria in these groups. ::: ::: Conclusions ::: The use of the silver diamine fluoride/potassium iodide product is effective in reducing the numbers of S. mutans in dentinal tubules infected with this organism.


EasyGD : Gaze Detection on Wireless Mobile Systems CS 386 W Wireless Networking : Course Project Final Report


title: A low-voltage level shifter based on double-gate MOSFET

abstract: Energy consumption is a major concern in nanoscale integrated circuits. Aggressive low-power design to maximize the battery life is a significant challenge. Multi-VDD design is an effective approach for decreasing the power consumption especially in battery-powered digital VLSI designs. Hence, in these designs, voltage level shifters (LSs) as an interface between these different voltage islands are required. This paper presents a new single-supply level shifter (SSLS) based on independent double-gate (IDG) SOI MOSFET. In the proposed technique, the back gate of transistors is utilized for tuning the threshold voltage (Vt) of transistors in order to convert low-voltage input signals and decrease static power consumption. Simulation results conducted based on Berkeley Independent Multi-Gate model at 32nm technology node demonstrate a qualified performance of the proposed multi-Vt design compared to the other common and most efficient LS circuits.


title: Scalar Calibration of Vector Magnetometers

abstract: The calibration parameters of a vector magnetometer are estimated only by the use of a scalar reference magnetometer. The method presented in this paper differs from those previously reported in its linearized parametrization. This allows the determination of three offsets or signals in the absence of a magnetic field, three scale factors for normalization of the axes and three non-orthogonality angles which build up an orthogonal system intrinsically in the sensor. The advantage of this method compared with others lies in its linear least squares estimator, which finds independently and uniquely the parameters for a given data set. Therefore, a magnetometer may be characterized inexpensively in the Earth's magnetic-field environment. This procedure has been used successfully in the pre-flight calibration of the state-of-the-art magnetometers on board the magnetic mapping satellites ?rsted, Astrid-2, CHAMP and SAC-C. By using this method, full-Earth-field-range magnetometers (??65536.0 nT) can be characterized down to an absolute precision of 0.5 nT, non-orthogonality of only 2 arcsec and a resolution of 0.2 nT.


title: Women outperform men in remembering to remember

abstract: The study of gender differences in prospective memory (i.e., remembering to remember) has received modest attention in the literature. The few reported studies investigating either subjective or objective evaluations of prospective memory have shown inconsistent data. In this study, we aimed to verify the presence of gender differences during the performance of an objective prospective memory test by considering the weight of specific variables such as length of delay, type of response, and type of cue. We submitted a sample of 100 healthy Italian participants (50 men and 50 women) to a test expressly developed to assess prospective memory: The Memory for Intentions Screening Test. Women performed better than men in remembering to do an event-based task (i.e., prompted by an external event) and when the task required a physical response modality. We discuss the behavioural differences that emerged by considering the possible role of sociological, biological, neuroanatomical, and methodological variables.


A Framework for Human Factors in Information Security


title: Lessons from the journey: a query log analysis of within-session learning

abstract: The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.


title: Blood glucose-insulin regulation and management system using MATLAB/SIMULINK

abstract: Biomedical signal processing has exponentially been expanding and demanding area for research and subsequent applications in its respective quarters. Exploration on diabetes is one of the main segments of this area which is being addressed for the decades. One of the main functions of the pancreas is to regulate glucose concentration in blood through release of the enzyme insulin. Some theoretical analysis of the control of blood glucose level in diabetic individuals is undertaken using a simple mathematical model of the dynamics of glucose and insulin interaction in the blood system developed by Stolwijk and Hardy dynamic model. This paper is focused to envisage the regulation and management of the concentration of glucose and insulin in the blood of a diabetic. The model was modified by adding a term for exogenous insulin infusion. Using this model, a closed-loop feed back system which regulates and manages the blood glucose-insulin has been designed, implemented and analyzed using Matlab/Simulink. This system imitates as an artificial pancreas on day-to-day basis. A time-consuming and tedious series of diabetes experiments was carried out and results were analyzed on the basis of medical parameters. The obtained results showed significant regulation and management of the glucose-insulin delivery in the blood. Hence, we are reasonably confident of having contribution in the subject area.


title: Optimizing P2P Streaming Throughput Under Peer Churning

abstract: High-throughput P2P streaming relies on peer selection, the strategy a peer uses to select other peer(s) as its parent(s) of streaming. Although this problem has been thoroughly investigated in the classical optimization framework, it still remains unaddressed as how to sustain throughput competitive to the optimum under highly dynamic peer churning. We propose an online distributed peer selection algorithm based on "shortest- path" routing where the overlay edge length is an asymptotic function of its traffic load. This basic algorithm is further extended under practical settings such as multi-parent streaming, admission control, delay constraint, and simplified topology. In all above settings, we prove approximation bound of our algorithm to the optimal throughput. Through evaluation under different topological setups and peer churning sequences, we show our solution to consistently deliver competitive throughput, which greatly outperform its theoretical bound.


title: Hat-Delta — One Right Does Make a Wrong

abstract: We outline two methods for locating bugs in a program. This is done by comparing computations of the same program with different input. At least one of these computations must produce a correct result, while exactly one must exhibit some erroneous behaviour. Firstly reductions that are thought highly likely to be correct are eliminated from the search for the bug. Secondly, a program slicing technique is used to identify areas of code that are likely to be correct. Both methods have been implemented. In combination with algorithmic debugging they provide a system that quickly and accurately identifies bugs.


title: A framework for supply chain performance measurement

abstract: Abstract Supply chain management (SCM) has been a major component of competitive strategy to enhance organizational productivity and profitability. The literature on SCM that deals with strategies and technologies for effectively managing a supply chain is quite vast. In recent years, organizational performance measurement and metrics have received much attention from researchers and practitioners. The role of these measures and metrics in the success of an organization cannot be overstated because they affect strategic, tactical and operational planning and control. Performance measurement and metrics have an important role to play in setting objectives, evaluating performance, and determining future courses of actions. Performance measurement and metrics pertaining to SCM have not received adequate attention from researchers or practitioners. We developed a framework to promote a better understanding of the importance of SCM performance measurement and metrics. Using the current literature and the results of an empirical study of selected British companies, we developed the framework presented herein, in hopes that it would stimulate more interest in this important area.


title: An operationalized model for defining computational thinking

abstract: The Computational Thinking (CT) conceptual framework is entering its second decade of research yet still lacks a cohesive definition by which the field can coalesce. The lack of clear definition makes assessment tool challenging to formulate, pedagogical efforts difficult to compare, and research difficult to synthesize. This paper looks to operationalize differing definitions of CT enhancing the ability to teach then assess the presence of CT. Expanding upon CT definitions, industry practices and processes, and educational theory, we link existing concepts and propose a new element to model an active definition of CT as a theoretical framework to guide future research. Our model updates existing CT definition by formally including Modeling, introducing Socio-Technical processes, separating Information Gathering from Data Collection and adding emphasis to Testing as a vital CT concept. We feel these elements and interconnections make CT is easier to describe and measure.


title: Beam Switching Conformal Antenna Array for mm-Wave Communications

abstract: In this letter, the design and measurement results of a millimeter-wave beam switching antenna are presented. Several antenna prototypes have been fabricated and measured in planar and conformal installations. The developed prototype consists of three antenna arrays of 16 patch elements in each array and it is operating at 61 GHz with linear polarization. A single-pole three-throw semiconductor switch is attached to the manufactured prototype using copper pillar attachment. The main beam direction of the convex antenna can be changed by switching between the antenna arrays in the prototype. Half sphere 3-D radiation pattern measurements have been performed. For the convex prototype, bent on the cylinder with radius 25 mm, beam switching with ${-{\hbox {32}}^\circ / + {\hbox {34}}^\circ} $ degrees is achieved.


title: Detail-Enhanced Multi-Scale Exposure Fusion

abstract: Multi-scale exposure fusion is an effective image enhancement technique for a high dynamic range (HDR) scene. In this paper, a new multi-scale exposure fusion algorithm is proposed to merge differently exposed low dynamic range (LDR) images by using the weighted guided image filter to smooth the Gaussian pyramids of weight maps for all the LDR images. Details in the brightest and darkest regions of the HDR scene are preserved better by the proposed algorithm without relative brightness change in the fused image. In addition, a new weighted structure tensor is introduced to the differently exposed images and it is adopted to design a detail extraction component for the proposed fusion algorithm, such that users are allowed to manipulate fine details in the enhanced image according to their preference. The proposed multi-scale exposure fusion algorithm is also applied to design a simple single image brightening algorithm for both low-light imaging and back-light imaging.


title: Galvanic Skin Response: A Physiological Sensor System for Affective Computing

abstract: Electro-dermal response of any bio-medical system is the change in electrical properties of skin due to variation in physiological and psychological conditions. The change is caused by the degree to which a person's sweat glands are active. Psychological status of a person tends to make the glands active and this change the skin resistance. Drier the skin is, higher will be the skin resistance. This variation in skin resistance ranges from 5kΩ to 25kΩ. In the current work, a subject whose Galvanic skin response (GSR) is to be measured, has been shown/played a movie clipping, images or recorded audio signals. Depending upon the theme, emotion will be evoked in the subject. Due to the change in emotion, GSR varies. This variation in GSR is recorded for a fixed time interval. In the present work, a total of 75 subjects are selected. People from different age groups and social background, both male and female have been carefully considered. Data acquisition and analysis is carried out by using LabVIEW. The results obtained are convincing and follows the ground reality with a high level of accuracy. As the subject also gives the feedback about the emotions he/she experienced, the results obtained are validated.


title: Optimized Distributed Work-Stealing

abstract: Work-stealing is a popular approach for dynamic load balancing of task-parallel programs. However, as has been widely studied, the use of classical work-stealing algorithms on massively parallel and distributed supercomputers introduces several performance issues. One such issue is the overhead of failed steals (communicating with a victim that has no work), which is far more severe in the distributed context than within a single SMP node. Due to the cost of inter-node communication, it is critical to reduce the number of failed steals in a distributed context. Prior work has demonstrated that load-aware victim processor selection can reduce the number of failed steals, but it cannot eliminate the failed steals completely.In this paper, we present two different load-aware implementations of distributed work-stealing algorithm in HabaneroUPC++ PGAS library — BaselineWS and SuccessOnlyWS. BaselineWS follows prior work in implementing a distributed work-stealing strategy. SuccessOnlyWS implements a novel distributed work-stealing strategy that completely eliminate inter-node failed attempts by introducing a new policy for moving work from busy to idle processors. This strategy also avoids querying the same processor multiple times with failed steals. We evaluate both BaselineWS and SuccessOnlyWS by using up to 12288 cores of Edison, a CRAY-XC30 supercomputer and by using dynamic irregular applications, as exemplified by the UTS and NQueens benchmarks. We demonstrate that SuccessOnlyWS provides performance improvements up to 7% over BaselineWS.


title: A Nested Two Stage Game-Based Optimization Framework in Mobile Cloud Computing System

abstract: The rapidly developing cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute applications remotely. A mobile device should judiciously decide whether to offload computation and which portion of application should be offloaded to the cloud. In this paper, we consider a mobile cloud computing (MCC) interaction system consisting of multiple mobile devices and the cloud computing facilities. We provide a nested two stage game formulation for the MCC interaction system. In the first stage, each mobile device determines the portion of its service requests for remote processing in the cloud. In the second stage, the cloud computing facilities allocate a portion of its total resources for service request processing depending on the request arrival rate from all the mobile devices. The objective of each mobile device is to minimize its power consumption as well as the service request response time. The objective of the cloud computing controller is to maximize its own profit. Based on the backward induction principle, we derive the optimal or near-optimal strategy for all the mobile devices as well as the cloud computing controller in the nested two stage game using convex optimization technique. Experimental results demonstrate the effectiveness of the proposed nested two stage game-based optimization framework on the MCC interaction system. The mobile devices can achieve simultaneous reduction in average power consumption and average service request response time, by 21.8% and 31.9%, respectively, compared with baseline methods.


title: Visualization of Graphs and Trees for Software Analysis

abstract: A software architecture is an abstraction of a software system, which is indispensable for many software engineering tasks. Unfortunately, in many cases information pertaining to the software architecture is not available, outdated, or inappropriate for the task at hand. The RECONSTRUCTOR project focuses on software architecture reconstruction, i.e., obtaining architectural information from an existing system. Our research, which is part of RECONSTRUCTOR, focuses on interactive visualization and tries to answer the following question: How can users be enabled to understand the large amounts of information relevant for program understanding using visual representations? To answer this question, we have iteratively developed a number of techniques for visualizing software systems. A large number of these cases consists of hierarchically organized data, combined with adjacency relations. Examples are function calls within a hierarchically organized software system and correspondence relations between two different versions of a hierarchically organized software system. Hierarchical Edge Bundles (HEBs) are used to visualize adjacency relations in hierarchically organized data, such as the aforementioned function calls within a software system. HEBs significantly reduce visual clutter by visually bundling relations together. Massive Sequence Views (MSVs) are used in conjunction with HEBs to enable analysis of sequences of relations, such as function-call traces. HEBs are furthermore used to visually compare hierarchically organized data, e.g., two different versions of a software system. HEBs visually emphasize splits, joins, and relocations of subhierarchies and provide for interactive selection of sets of relations. Since HEBs require a hierarchy to perform the bundling, we present Force-Directed Edge Bundles (FDEBs) as an alternative to visually bundle relations together in the absence of a hierarchical component. FDEBs use a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. As a result, visual clutter is reduced and high-level edge patterns are better visible. Finally, in all these methods, a clear depiction of the direction of edges is important. We have therefore performed a separate study in which we evaluated ten representations (including the standard arrow) for depicting directed edges in a controlled user study.


title: Causal inference and the data-fusion problem

abstract: We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion—piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks.