title: Non-local Image Dehazing

abstract: Haze limits visibility and reduces image contrast in outdoor images. The degradation is different for every pixel and depends on the distance of the scene point from the camera. This dependency is expressed in the transmission coefficients, that control the scene attenuation and amount of haze in every pixel. Previous methods solve the single image dehazing problem using various patch-based priors. We, on the other hand, propose an algorithm based on a new, non-local prior. The algorithm relies on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors, that form tight clusters in RGB space. Our key observation is that pixels in a given cluster are often non-local, i.e., they are spread over the entire image plane and are located at different distances from the camera. In the presence of haze these varying distances translate to different transmission coefficients. Therefore, each color cluster in the clear image becomes a line in RGB space, that we term a haze-line. Using these haze-lines, our algorithm recovers both the distance map and the haze-free image. The algorithm is linear in the size of the image, deterministic and requires no training. It performs well on a wide variety of images and is competitive with other stateof-the-art methods.


title: Quality dimensions of wood products — perceptions of German organisational customers

abstract: This study examines quality dimensions of wood products, and compares wood and other construction materials with respect to those dimensions. Interviews conducted during 2000–2001 provide data from German companies or business units trading construction materials. Quality dimensions were analysed using factor analysis. Even though similarities can be found in the quality dimensions of various materials, product quality appears to take somewhat different dimensions regarding different products. Thus, it is difficult to define strictly uniform dimensions, or measurement tools for product quality. This is a challenge for marketing and product development: The quality measurement tools should be defined product by product, rather than using some general quality measurement instrument. However, research may contribute to develop a general frame for identifying quality dimensions that helps in constructing product specific quality measurement instruments.


title: Unsupervised Calibration for Multi-beam Lasers

abstract: Light Detection and Ranging (LIDAR) sensors have become increasingly common in both industrial and robotic applications. LIDAR sensors are particularly desirable for their direct distance measurements and high accuracy, but traditionally have been configured with only a single rotating beam. However, recent technological progress has spawned a new generation of LIDAR sensors equipped with many simultaneous rotating beams at varying angles, providing at least an order of magnitude more data than single-beam LIDARs and enabling new applications in mapping [6], object detection and recognition [15], scene understanding [16], and SLAM [9].


title: Dynamic Visualizations for Soccer Statistical Analysis

abstract: The analysis of large sums of soccer statistics can be extremely difficult if the data is not presented graphically. As a result, we developed an application, titled Soccer Scoop, which provides two separate visualizations that can aid a soccer team manager. With our application, a team manager can compare two players on different teams, analyze a particular player before signing them to a contract, measure the performance of a particular player at different positions, generate practice exercises, and determine if a particular player plays better on the road or at home. The visualizations used in our application apply information visualization techniques, such as glyphs, modified star plots, details on demand, color, and gestalt principles.


Deep Learning on a Raspberry Pi for Real Time Face Recognition


title: DEPTH IMAGE BASED VIEW SYNTHESIS WITH MULTIPLE REFERENCE VIEWS FOR VIRTUAL REALITY

abstract: This paper presents a method for view synthesis from multiple views and their depth maps for free navigation in Virtual Reality with six degrees of freedom (6DoF) and 360 video (3DoF+), including synthesizing views corresponding to stepping in or out of the scene. Such scenarios should support large baseline view synthesis, typically going beyond the view synthesis involved in light field displays [1]. Our method allows to input an unlimited number of reference views, instead of the usual left and right reference views. Increasing the number of reference views overcomes problems such as occlusions, tangential surfaces to the cameras axis and artifacts in low quality depth maps. We outperform MPEG’s reference software, VSRS [2], with a gain of up to 2.5 dB in PSNR when using four reference views.


title: Fast aircraft detection in satellite images based on convolutional neural networks

abstract: Aircraft detection in satellite images is generally difficult due to the variations of aircraft type, pose, size and complex background. In this paper, we propose a new aircraft detection framework based on objectiveness detection techniques (e.g., BING) and Convolutional Neural Networks (CNN). The advantages are two folds. On one hand, we first introduce the CNN for aircraft detection, as CNN can learn rich features from the raw data automatically and has yielded a state-of-the-art performance in many object detection tasks. On the other hand, the use of candidate object regions proposed by BING achieves a high object detection rate and saves time simultaneously. Experimental results show that the proposed method is fast and effective to detect aircrafts in complex airport scenes. We also construct a dataset for aircraft detection obtained from Google Earth.


title: Design and development of portable classroom attendance system based on Arduino and fingerprint biometric

abstract: In this paper, the design and development of a portable classroom attendance system based on fingerprint biometric is presented. Among the salient aims of implementing a biometric feature into a portable attendance system is security and portability. The circuit of this device is strategically constructed to have an independent source of energy to be operated, as well as its miniature design which made it more efficient in term of its portable capability. Rather than recording the attendance in writing or queuing in front of class equipped with fixed fingerprint or smart card reader. This paper introduces a portable fingerprint based biometric attendance system which addresses the weaknesses of the existing paper based attendance method or long time queuing. In addition, our biometric fingerprint based system is encrypted which preserves data integrity.


title: Research directions in software process improvement

abstract: Software process improvement (SPI) has become a driving force in the global software industry. However, it has not become a popular topic of rigorous research, especially at universities. This paper surveys ongoing research efforts around the world and identifies emerging or potential topics of research in this economically important area


title: Understanding performative interactions in public settings

abstract: Interactive digital technologies pervade our shared spaces in personal, mobile, infrastructural and other embedded forms. These changes challenge the ways we understand and investigate the relationships between people, computing and settings. Responding to this situation—where ubiquitous computing is not only personal but also public, and where digital interactions may happen anywhere — this special issue explores how HCI research can use the strengths of an intersection of theory, practice and innovation in order to best address this conjunction of interactive technologies, public spaces and people interacting with or within both. ::: ::: With this shift to interaction happening in public spaces, people can no longer be seen as just ‘users’ but have to be understood as acting assemblages of bodies and technologies in the space they inhabit. This shift highlights the need to understand users in every setting—including those at desks and at work—as socially and culturally situated humans with agency. While this is not a new research development, this special issue speculates on and discusses how the shift to ‘everywhere interactions’ makes it important for research in human–computer interaction to reach beyond existing conventions. With this work comes a need to understand the interacting human as a situated body that is at any moment also a possible spectacle: as a body, a human and a user who engages with technology in response to the situation while also being a ‘performance’ for others to witness. Understanding people as actors in their context allows us to regard every user as a ‘performing body’ and a ‘performing subject’, and it enables us to analyse the performative aspects of interaction in many other situations than those where we design for actually staged performances in a defined performance space or stage. In this sense, ‘performative interaction’ is both concerned with technologies for on-stage performers using technologies as part of artistic expression, and an analytical frame for understanding every situation in which people engage with technologies (in public space). The question becomes: where and in which situations is this analytical frame relevant and useful?


title: Statistical Physics for Natural Language Processing

abstract: This paper has been withdrawn by the author.


title: Computing political preference among twitter followers

abstract: There is great interest in understanding media bias and political information seeking preferences. As many media outlets create online personas, we seek to automatically estimate the political preferences of their audience, rather than of the outlet itself. In this paper, we present a novel method for computing preference among an organization's Twitter followers. We present an application of this technique to estimate political preference of the audiences of U.S. media outlets. We also discuss how these results may be used and extended.


title: Defining gamification: a service marketing perspective

abstract: During recent years "gamification" has gained significant attention among practitioners and game scholars. However, the current understanding of gamification has been solely based on the act of adding systemic game elements into services. In this paper, we propose a new definition for gamification, which emphases the experiential nature of games and gamification, instead of the systemic understanding. Furthermore, we tie this definition to theory from service marketing because majority of gamification implementations aim towards goals of marketing, which brings to the discussion the notion of how customer / user is always ultimately the creator of value. Since now, the main venue for academic discussion on gamification has mainly been the HCI community. We find it relevant both for industry practitioners as well as for academics to study how gamification can fit in the body of knowledge of existing service literature because the goals and the means of gamification and marketing have a significant overlap.


title: Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform

abstract: The rise in popularity of permissioned blockchain platforms in recent time is significant. Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation. The Fabric comprises various components such as smart-contracts, endorsers, committers, validators, and orderers. As the performance of blockchain platform is a major concern for enterprise applications, in this work, we perform a comprehensive empirical study to characterize the performance of Hyperledger Fabric and identify potential performance bottlenecks to gain a better understanding of the system. We follow a two-phased approach. In the first phase, our goal is to understand the impact of various configuration parameters such as block size, endorsement policy, channels, resource allocation, state database choice on the transaction throughput & latency to provide various guidelines on configuring these parameters. In addition, we also aim to identify performance bottlenecks and hotspots. We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks. In the second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our observations. We introduced and studied various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3x improvement in the performance) and parallelizing endorsement policy verification (7x improvement). Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5x improvement). By combining all three optimizations1, we improved the overall throughput by 16x (i.e., from 140 tps to 2250 tps).


title: A Theoretical Analysis of Pseudo-Relevance Feedback Models

abstract: Our goal in this study is to compare several widely used pseudo-relevance feedback (PRF) models and understand what explains their respective behavior. To do so, we first analyze how different PRF models behave through the characteristics of the terms they select and through their performance on two widely used test collections. This analysis reveals that several well-known models surprisingly tend to select very common terms, with low IDF (inverse document frequency). We then introduce several conditions PRF models should satisfy regarding both the terms they select and the way they weigh them, prior to study whether standard PRF models satisfy these conditions or not. This study reveals that most models are deficient with respect to at least one condition, and that this deficiency explains the results of our analysis of the behavior of the models, as well as some of the results reported on the respective performance of PRF models. Based on the PRF conditions, we finally propose possible corrections for the simple mixture model. The PRF models obtained after these corrections outperform their standard version and yield state-of-the-art PRF models which confirms the validity of our theoretical analysis.


title: Hydrologic Response Unit Routing in SWAT to Simulate Effects of Vegetated Filter Strip for South-Korean Conditions Based on VFSMOD

abstract: The Soil and Water Assessment Tool (SWAT) model has been used worldwide for many hydrologic and Non-Point Source (NPS) Pollution analyses on a watershed scale. However, it has many limitations in simulating the Vegetative Filter Strip (VFS) because it considers only ‘filter strip width’ when the model estimates sediment trapping efficiency and does not consider the routing of sediment with overland flow which is expected to maximize the sediment trapping efficiency from upper agricultural subwatersheds to lower spatially-explicit filter strips. Therefore, the SWAT overland flow option between landuse-subwatersheds with sediment routing capability was enhanced by modifying the SWAT watershed configuration and SWAT engine based on the numerical model VFSMOD applied to South-Korean conditions. The enhanced SWAT can simulate the VFS sediment trapping efficiency for South-Korean conditions in a manner similar to the desktop VFSMOD-w system. Due to this enhancement, SWAT is applicable to simulate the effects of overland flow from upper subwatersheds to reflect increased runoff volume at the lower subwatershed, which occurs in the field if no diversion channel is installed. In this study, the enhanced SWAT model was applied to small watersheds located at Jaun-ri in South-Korea to simulate a diversion channel and spatially-explicit VFS. Sediment can be reduced by 31%, 65%, and 68%, with a diversion channel, the VFS, and the VFS with diversion channel, respectively. The enhanced SWAT should be used in estimating site-specific effects on sediment reduction with diversion channels and VFS, instead of the currently available SWAT, which does not simulate sediment routing in overland flow and does not consider other sensitive factors affecting sediment reduction with VFS.


title: MagneBike: Compact magnetic wheeled robot for power plant inspection

abstract: The MagneBike robot is a magnetic wheeled robot designed for the inspection of ferromagnetic structures in power plants, especially steam chests. This video first presents the robot's locomotion concept, i.e. two aligned magnetic wheels integrating lateral lever arms, that allow the robot to pass over complex combinations of obstacles. Laboratory and field experiments show the high mobility of the robot. This video also describes the localization and mapping strategy that consists in combining 3D odometry with 3D scanning and scan registration. An animation of the 3D reconstruction of the environment shows that the localization procedure allows to provide the necessary 3D visual feedback for the remote user or for inspection mission planning.


title: Accelerometer-Based Hand Gesture Recognition by Neural Network and Similarity Matching

abstract: In this paper, we present an accelerometer-based pen-type sensing device and a user-independent hand gesture recognition algorithm. Users can hold the device to perform hand gestures with their preferred handheld styles. Gestures in our system are divided into two types: the basic gesture and the complex gesture, which can be represented as a basic gesture sequence. A dictionary of 24 gestures, including 8 basic gestures and 16 complex gestures, is defined. An effective segmentation algorithm is developed to identify individual basic gesture motion intervals automatically. Through segmentation, each complex gesture is segmented into several basic gestures. Based on the kinematics characteristics of the basic gesture, 25 features are extracted to train the feedforward neural network model. For basic gesture recognition, the input gestures are classified directly by the feedforward neural network classifier. Nevertheless, the input complex gestures go through an additional similarity matching procedure to identify the most similar sequences. The proposed recognition algorithm achieves almost perfect user-dependent and user-independent recognition accuracies for both basic and complex gestures. Experimental results based on 5 subjects, totaling 1600 trajectories, have successfully validated the effectiveness of the feedforward neural network and similarity matching-based gesture recognition algorithm.


From Automation to Autonomous Systems: A Legal Phenomenology with Problems of Accountability


title: Workload management: a technology perspective with respect to self-* characteristics

abstract: Rapid growth in data, maximum functionality requirements and changing behavior in the database workload tends the workload management to be more complex. Organizations have complex type of workloads that are very difficult to manage by humans and even in some cases this management becomes impossible. Human experts take long time to get sufficient experience so that they can manage the workload efficiently. The versatility in workload due to huge data size and user requirements leads us towards the new challenges. One of the challenges is the identification of the problematic queries and the decision about these, i.e. whether to continue their execution or stop. The other challenge is how to characterize the workload, as the tasks such as configuration, prediction and adoption are fully dependent on the workload characterization. Correct and timely characterization leads managing the workload in an efficient manner and vice versa. In this scenario, our objective is to produce a workload management strategy or framework that is fully adoptive. The paper provides a summary of the structure and achievements of the database tools that exhibit Autonomic Computing or self-* characteristics in workload management. We have categorized the database workload tools to these self-* characteristics and identified their limitations. Finally the paper presents the research done in the database workload management tools with respect to the workload type and Autonomic Computing.


title: Computationally Efficient M-Estimation of Log-Linear Structure Models

abstract: We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum (conditional) likelihood estimation of conditional random fields (Lafferty et al., 2001; an order of magnitude or more). We compare its performance and training time to an HMM, a CRF, an MEMM, and pseudolikelihood on a shallow parsing task. These experiments help tease apart the contributions of rich features and discriminative training, which are shown to be more than additive.


title: A Balanced Digital Phase Shifter by a Novel Switching-Mode Topology

abstract: A balanced digital phase shifter by a novel switching-mode topology is proposed. A 3-bit balanced digital phase shifter is given as an example, the working bandwidth of which is from 2.3 to 2.7 GHz. The proposed 3-bit balanced phase shifter comprises two balanced loaded-line phase shifters (90° and 45°) and six twist-line broadband 180° phase shifters. The balanced loaded-line digital phase shifter changes phases while the even- or odd-mode signal is excited. The 180° phase shifter controls the balanced signal to be even or odd mode. The commensurate transmission line filter is used as the biasing circuit.


title: The Market for Corporate Assets: Who Engages in Mergers and Asset Sales and Are There Efficiency Gains?

abstract: We analyze the market for corporate assets. There is an active market for corporate assets, with close to seven percent of plants changing ownership annually through mergers, acquisitions, and asset sales in peak expansion years. The probability of asset sales and whole-firm transactions is related to firm organization and ex ante efficiency of buyers and sellers. The timing of sales and the pattern of efficiency gains suggests that the transactions that occur, especially through asset sales of plants and divisions, tend to improve the allocation of resources and are consistent with a simple neoclassical model of profit maximizing by firms. Copyright The American Finance Association 2001.


title: A Mobile Vision System for Fast and Accurate Ellipse Detection

abstract: Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources, as in the case of mobile devices. This demo is based on a novel algorithm for fast and accurate ellipse detection. The proposed algorithm relies on a careful selection of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The demo will show it working on a commercial smart-phone.


title: Systematic Review of Intestinal Microbiota Transplantation (Fecal Bacteriotherapy) for Recurrent Clostridium difficile Infection

abstract: Clostridium difficile infection (CDI) is a gastrointestinal disease believed to be causally related to perturbations to the intestinal microbiota. When standard treatment has failed, intestinal microbiota transplantation (IMT) is an alternative therapy for patients with CDI. IMT involves infusing intestinal microorganisms (in a suspension of healthy donor stool) into the intestine of a sick patient to restore the microbiota. However, protocols and reported efficacy for IMT vary. We conducted a systematic literature review of IMT treatment for recurrent CDI and pseudomembranous colitis. In 317 patients treated across 27 case series and reports, IMT was highly effective, showing disease resolution in 92% of cases. Effectiveness varied by route of instillation, relationship to stool donor, volume of IMT given, and treatment before infusion. Death and adverse events were uncommon. These findings can guide physicians interested in implementing the procedure until better designed studies are conducted to confirm best practices.


title: Cherry-picking: exploiting process variations in dark-silicon homogeneous chip multi-processors

abstract: It is projected that increasing on-chip integration with technology scaling will lead to the so-called dark silicon era in which more transistors are available on a chip than can be simultaneously powered on. It is conventionally assumed that the dark silicon will be provisioned with heterogeneous resources, for example dedicated hardware accelerators. In this paper we challenge the conventional assumption and build a case for homogeneous dark silicon CMPs that exploit the inherent variations in process parameters that exist in scaled technologies to offer increased performance. Since process variations result in core-to-core variations in power and frequency, the idea is to cherry pick the best subset of cores for an application so as to maximize performance within the power budget. To this end, we propose a polynomial time algorithm for optimal core selection, thread mapping and frequency assignment for a large class of multi-threaded applications. Our experimental results based on the Sniper multi-core simulator show that up to 22% and 30% performance improvement is observed for homogeneous CMPs with 33% and 50% dark silicon, respectively.


title: Learning Coexistence Discriminative Features for Multi-Class Object Detection

abstract: Existing methods on object detection have the ability to learn the discriminative features of local regions for object recognition; however, the coexistence relation between the multi-class objects could also benefit recognition. In this paper, we propose to learn the coexistence discriminative features for multi-class object detection. Given an image with multiple class objects, the strong supervision of the region-based annotations are first used as the image-level label to learn the independent discriminative features for each class. Then, the coexistence relation is fused as coexistence feature based on the attention mechanism. By combining the independent discriminative features and coexistence feature, the classification performance of multi-class object proposals can be consistently improved. Experimental results prove that the proposed end-to-end network outperforms the state-of-the-art object detection approaches, and the learned discriminative features can effectively capture the coexistence relations to improve classification performance of multi-class objects in the object detection task.


title: A Memo on the Proof-of-Stake Mechanism

abstract: We analyze the economic incentives generated by the proof-of-stake mechanism discussed in the Ethereum Casper upgrade proposal. Compared with proof-of-work, proof-of-stake has a different cost structure for attackers. In Budish (2018), three equations characterize the limits of Bitcoin, which has a proof-of-work mechanism. We investigate their counterparts and evaluate the risk of double-spending attack and sabotage attack. We argue that PoS is safer than PoW agaisnt double-spending attack because of the tractability of attackers, which implies a large "stock" cost for the attacker. Compared to a PoW system whose mining equipments are repurposable, PoS is also safer against a sabotage attack.


title: Experimental studies on the human gait using a tethered pelvic assist device (T-PAD)

abstract: This paper presents the prototype of a novel tethered pelvic assist device (T-PAD). This is a purely passive device, consisting of a set of elastic tethers with one end attached to a hip brace worn by a subject walking on a treadmill, and the other end attached to a fixed frame surrounding the subject. T-PAD offers the flexibility of varying the assistance required on the pelvis by changing the configuration of the tether attachment locations, number of tethers and tether elasticity. Experimental studies were conducted using a full and a partial pelvic constraint configuration of T-PAD, with varying tether elasticity. The studies were aimed at observing the effect of T-PAD on the human gait. Results show that T-PAD reduced the range-of-motion for the pelvic angles with increase of tether elasticity. However, it had mixed effects on the range-of-motion of the hip angles, but negligible effect on the knee and ankle joint angles. Overall, T-PAD shows potential as a low-cost pelvic support device with pelvic motion control capabilities, and can work in tandem with existing gait trainers.


title: A simple on-line bin-packing algorithm

abstract: The one-dimensional on-line bin-packing problem is considered, A simple <italic>O</italic>(1)-space and <italic>O</italic>(<italic>n</italic>)-time algorithm, called HARMONIC<italic><subscrpt>M</subscrpt></italic>, is presented. It is shown that this algorithm can achieve a worst-case performance ratio of less than 1.692, which is better than that of the <italic>O</italic>(<italic>n</italic>)-space and <italic>O</italic>(<italic>n</italic> log <italic>n</italic>)-time algorithm FIRST FIT. Also shown is that 1.691 … is a lower bound for <italic>all</italic> <italic>0</italic>(1)-space on-line bin-packing algorithms. Finally a revised version of HARMONIC<italic><subscrpt>M </subscrpt></italic>, an <italic>O</italic>(<italic>n</italic>)-space and <italic>O</italic>(<italic>n</italic>)- time algorithm, is presented and is shown to have a worst-case performance ratio of less than 1.636. ::: d


title: A Gradient-Based Comparison Measure for Visual analysis of Multifield Data

abstract: We introduce a multifield comparison measure for scalar fields that helps in studying relations between them. The comparison measure is insensitive to noise in the scalar fields and to noise in their gradients. Further, it can be computed robustly and efficiently. Results from the visual analysis of various data sets from climate science and combustion applications demonstrate the effective use of the measure.


title: A theory of goal-oriented communication

abstract: We put forward a general theory of goal-oriented communication, where communication is not an end in itself, but rather a means to achieving some goals of the communicating parties. Focusing on goals provides a framework for addressing the problem of potential “misunderstanding” during communication, where the misunderstanding arises from lack of initial agreement on what protocol and/or language is being used in communication. In this context, “reliable communication” means overcoming any initial misunderstanding between parties towards achieving a given goal. Despite the enormous diversity among the goals of communication, we propose a simple model that captures all goals. In the simplest form of communication we consider, two parties, a user and a server, attempt to communicate with each other in order to achieve some goal of the user. We show that any goal of communication can be modeled mathematically by introducing a third party, which we call the referee, who hypothetically monitors the conversation between the user and the server and determines whether or not the goal has been achieved. Potential misunderstanding between the players is captured by allowing each player (the user/server) to come from a (potentially infinite) class of players such that each player is unaware which instantiation of the other it is talking to. We identify a main concept, which we call sensing, that allows goals to be achieved even under misunderstanding. Informally, sensing captures the user's ability (potentially using help from the server) to simulate the referee's assessment on whether the communication is achieving the goal. We show that when the user can sense progress, the goal of communication can be achieved despite initial misunderstanding. We also show that in certain settings sensing is necessary for overcoming such initial misunderstanding. Our results significantly extend the scope of the investigation started by Juba and Sudan (STOC 2008) who studied the foregoing phenomenon in the case of a single specific goal. Our study shows that their main suggestion, that misunderstanding can be detected and possibly corrected by focusing on the goal, can be proved in full generality.


title: Fuzzy Random Walkers with Second Order Bounds: An Asymmetric Analysis

abstract: Edge-fuzzy graphs constitute an essential modeling paradigm across a broad spectrum of domains ranging from artificial intelligence to computational neuroscience and social network analysis. Under this model, fundamental graph properties such as edge length and graph diameter become stochastic and as such they are consequently expressed in probabilistic terms. Thus, algorithms for fuzzy graph analysis must rely on non-deterministic design principles. One such principle is Random Walker, which is based on a virtual entity and selects either edges or, like in this case, vertices of a fuzzy graph to visit. This allows the estimation of global graph properties through a long sequence of local decisions, making it a viable strategy candidate for graph processing software relying on native graph databases such as Neo4j. As a concrete example, Chebyshev Walktrap, a heuristic fuzzy community discovery algorithm relying on second order statistics and on the teleportation of the Random Walker, is proposed and its performance, expressed in terms of community coherence and number of vertex visits, is compared to the previously proposed algorithms of Markov Walktrap, Fuzzy Walktrap, and Fuzzy Newman–Girvan. In order to facilitate this comparison, a metric based on the asymmetric metrics of Tversky index and Kullback–Leibler divergence is used.


title: The Orbital Multiplexing C

abstract: Recently the orbital angular momentum (OAM) multiplexing scheme has been proposed to increase without limit the channel capacity between a transmitter and a receiver node communicating in line of sight. A controversy arose about the far-field effectiveness of this method and about it being or not a subset of MIMO communication schemes. In this contribution we first illustrate that OAM multiplexing can be completely understood as line of sight MIMO, and hence that the technique cannot bring additional advantages in far field communications. Secondly, we show that it is possible to build similar line of sight near field MIMO systems based on lenses with similar or better performance than their OAM counterparts.


title: Tailoring entity resolution for matching product offers

abstract: Product matching is a challenging variation of entity resolution to identify representations and offers referring to the same product. Product matching is highly difficult due to the broad spectrum of products, many similar but different products, frequently missing or wrong values, and the textual nature of product titles and descriptions. We propose the use of tailored approaches for product matching based on a preprocessing of product offers to extract and clean new attributes usable for matching. In particular, we propose a new approach to extract and use so-called product codes to identify products and distinguish them from similar product variations. We evaluate the effectiveness of the proposed approaches with challenging real-life datasets with product offers from online shops. We also show that the UPC information in product offers is often error-prone and can lead to insufficient match decisions.


title: A power-constrained MPU roadmap for the International Technology Roadmap for Semiconductors (ITRS)

abstract: Technology roadmaps help predict requirements for future technologies and guide ongoing technology research and development. The International Technology Roadmap for Semiconductors (ITRS) [1] has been published since 1999; it is fully revised in odd-numbered years and updated in even-numbered years. In this paper, we describe a modeling framework to predict future characteristics and implied technology requirements of the ITRS microprocessor (MPU) ‘system driver’, following Moore's Law scaling under power constraints. The outcomes of this modeling effort are the basis of ITRS updates to be published in the 2009 ITRS edition.


title: Digital Construction through BIM Systems will Drive the Re-engineering of Construction Business Practices

abstract: The construction industry has been facing a paradigm shift to (i) increase; productivity, efficiency, infrastructure value, quality and sustainability, (ii) reduce; lifecycle costs, lead times and duplications, via effective collaboration and communication of stakeholders in construction projects. Digital construction is a political initiative to address low productivity in the sector. This seeks to integrate processes throughout the entire lifecycle by utilising building information modelling (BIM) systems. The focus is to create and reuse consistent digital information by the stakeholders throughout the lifecycle. However, implementation and use of BIM systems requires dramatic changes in the current business practices, bring new challenges for stakeholders e.g., the emerging knowledge and skill gap. This paper reviews and discusses the status of implementation of the BIM systems around the globe and their implications to the industry. Moreover, based on the lessons learnt, it will provide a guide to tackle these challenges and to facilitate successful transition towards utilizing BIM systems in construction projects.


title: Unique fatality due to claw injuries in a tiger attack: A case report

abstract: This paper describes a unique case of a fatal tiger attack in the wild. In the present case, a tiger fatally mauled a 34-year-old female with its claws, instead of the usual mechanism of killing by the bite injury to the neck. The autopsy revealed multiple fatal and non-fatal injuries caused by the tiger claws. The characteristic injuries due to the tooth impacts were absent as the teeth of the offending tiger were either fallen or non-functional. To the best of our knowledge, probably this rare case would be the first reported human fatality due to the tiger claw injuries in the world. The purpose of the present article is to highlight the fatal injuries due to the tiger claws, as the claw-induced fatal injuries in a tiger attack are not reported in the medico-legal literature. Moreover, this report would be an illustrative one for differentiation between the fatal injuries due to the claws and tooth impacts in a tiger attack. Furthermore, the present report establishes the importance of the tiger claws as a source of fatal injuries in a tiger attack.


title: The interaction between sleep quality and academic performance

abstract: Abstract Sleep quality has significant effects on cognitive performance and is influenced by multiple factors such as stress. Contrary to the ideal, medical students and residents suffer from sleep deprivation and stress at times when they should achieve the greatest amount of learning. In order to examine the relationship between sleep quality and academic performance, 144 medical students undertaking the pre-clinical board exam answered a survey regarding their subjective sleep quality (Pittsburgh sleep quality index, PSQI), grades and subjective stress for three different time points: semester, pre- and post-exam. Academic performance correlated with stress and sleep quality pre-exam ( r = 0.276, p r = 0.158, p 5) during exam preparation compared to 29% during the semester and 8% post-exam. This study shows that in medical students it is not the generally poor sleepers, who perform worse in the medical board exams. Instead students who will perform worse on their exams seem to be more stressed and suffer from poor sleep quality. However, poor sleep quality may negatively impact test performance as well, creating a vicious circle. Furthermore, the rate of sleep disturbances in medical students should be cause for intervention.


title: Automated Generation of Road Marking Maps from Street-level Panoramic Images

abstract: Accurate maps of road markings are useful for many applications, such as road maintenance, improving navigation, and prediction of upcoming road situations within autonomously driving vehicles. This paper introduces a generic and learning-based system for the recognition of road markings from street-level panoramic images. This system starts with an Inverse Perspective Mapping, followed by segmentation to retrieve road marking candidates. The contours of all found segments are classified, after which a Markov Random Field is applied to adjust the resulting probabilities based on the surrounding context. Finally, the spatial placement of the found individual markings (e.g. shark teeth) is analyzed to retrieve the traffic situations (e.g. priority situations). This system is evaluated for priority, block, striped lines and pedestrian crossing markings, and is able to recognize 80-95% of the individual markings, and about 90% of the occurring situations (e.g. pedestrian crossings).


title: Wasserstein Barycenter Model Ensembling

abstract: In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models. We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation. These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.


title: Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

abstract: Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are even more accurate while maintaining 8x faster test time speeds.


title: Simple, real-time obstacle avoidance algorithm for mobile robots

abstract: This paper proposes a novel, reactive algorithm for real time obstacle avoidance, compatible with low cost sonar or infrared sensors, fast enough to be implemented on embedded microcontrollers. We called this algorithm "the bubble rebound algorithm". According to this algorithm, only the obstacles detected within an area called "sensitivity bubble" around the robot are considered. The shape and size of the sensitivity bubble are dynamically adjusted, depending on the kinematics of the robot. Upon detection of an obstacle, the robot "rebounds" in a direction having the lowest density of obstacles, and continues its motion in this direction until the goal becomes visible, or a new obstacle is encountered. The performances and drawbacks of the method are described, based on the experimental results with simulators and real robots.


title: Enhanced real-time head pose estimation system for mobile device

abstract: This article proposes a real-time Head Pose Estimation HPE technique designed to be used in mobile devices. The method enables the interaction between the user and mobile devices using the device's inbuilt camera. The proposed technique is composed of different computer vision methods, which were optimized to operate in a restricted environment. The method has three Degrees of Freedom DOF, roll, yaw and pitch. The HPE is obtained using these movements. Experiments were conducted using 363 videos of 27 different people taken in varied scenarios with changes in illumination and background. The results demonstrate the robustness and efficiency of the proposed method.


title: Technology in Support of Collaborative Learning

abstract: This paper reviews the research conducted in the last 20 years on the application of technology in support of collaborative learning in higher education. The review focuses primarily on studies that use Internet-based technologies and social interaction analysis. The review provides six sets of observations/recommendations regarding methodology, empirical evidence, and research gaps and issues that may help focus future research in this emerging field of study.


title: Service specific anomaly detection for network intrusion detection

abstract: The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.


title: Parallelizing SRAM Arrays with Customized Bit-Cell for Binary Neural Networks

abstract: Recent advances in deep neural networks (DNNs) have shown Binary Neural Networks (BNNs) are able to provide a reasonable accuracy on various image datasets with a significant reduction in computation and memory cost. In this paper, we explore two BNNs: hybrid BNN (HBNN) and XNOR-BNN, where the weights are binarized to +1/−1 while the neuron activations are binarized to 1/0 and +1/−1, respectively. Two SRAM bit cell designs are proposed, namely, 6T SRAM for HBNN and customized 8T SRAM for XNOR-BNN. In our design, the high-precision multiply-and-accumulate (MAC) is replaced by bitwise multiplication for HBNN or XNOR for XNOR-BNN plus bit-counting operations. To parallelize the weighted sum operation, we activate multiple word lines in the SRAM array simultaneously and digitize the analog voltage developed along the bit line by a multi-level sense amplifier (MLSA). In order to partition the large matrices in DNNs, we investigate the impact of sensing bit-levels of MLSA on the accuracy degradation for different sub-array sizes and propose using the nonlinear quantization technique to mitigate the accuracy degradation. With 64 × 64 sub-array size and 3-bit MLSA, HBNN and XNOR-BNN architectures can minimize the accuracy degradation to 2.37% and 0.88%, respectively, for an inspired VGG-16 network on the CIFAR-10 dataset. Design space exploration of SRAM based synaptic architectures with the conventional row-by-row access scheme and our proposed parallel access scheme are also performed, showing significant benefits in the area, latency and energy-efficiency. Finally, we have successfully taped-out and validated the proposed HBNN and XNOR-BNN designs in TSMC 65 nm process with measured silicon data, achieving energy-efficiency >100 TOPS/W for HBNN and >50 TOPS/W for XNOR-BNN.


title: A review of abstract concept learning in embodied agents and robots

abstract: This paper reviews computational modelling approaches to the learning of abstract concepts and words in embodied agents such as humanoid robots. This will include a discussion of the learning of abstract words such as 'use' and 'make' in humanoid robot experiments, and the acquisition of numerical concepts via gesture and finger counting strategies. The current approaches share a strong emphasis on embodied cognition aspects for the grounding of abstract concepts, and a continuum, rather than dichotomy, view of concrete/abstract concepts differences.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.


title: Tracing Information Flows Between Ad Exchanges Using Retargeted Ads

abstract: Numerous surveys have shown that Web users are concerned about the loss of privacy associated with online tracking. Alarmingly, these surveys also reveal that people are also unaware of the amount of data sharing that occurs between ad exchanges, and thus underestimate the privacy risks associated with online tracking. ::: In reality, the modern ad ecosystem is fueled by a flow of user data between trackers and ad exchanges. Although recent work has shown that ad exchanges routinely perform cookie matching with other exchanges, these studies are based on brittle heuristics that cannot detect all forms of information sharing, especially under adversarial conditions. ::: In this study, we develop a methodology that is able to detect client- and server-side flows of information between arbitrary ad exchanges. Our key insight is to leverage retargeted ads as a tool for identifying information flows. Intuitively, our methodology works because it relies on the semantics of how exchanges serve ads, rather than focusing on specific cookie matching mechanisms. Using crawled data on 35,448 ad impressions, we show that our methodology can successfully categorize four different kinds of information sharing behavior between ad exchanges, including cases where existing heuristic methods fail. ::: We conclude with a discussion of how our findings and methodologies can be leveraged to give users more control over what kind of ads they see and how their information is shared between ad exchanges.


title: of LaborBrain Drain and Inequality Across Nations

abstract: Is the brain drain a curse or a boon for developing countries? This paper reviews what is known to date about the magnitude of the brain drain from developing to developed countries, its determinants and the way it affects the well-being of those left behind. First, I present alternative measures of the brain drain and characterize its evolution over the last 25 years. Then, I review the theoretical and empirical literature. Although the brain drain is a major source of concern for origin countries, it also induces positive effects through various channels such as remittances, return migration, diaspora externalities, quality of governance and increasing return to education. Whilst many scientists and international institutions praise the unambiguous benefits of unskilled migration for developing countries, my analysis suggests that a limited but positive skilled emigration rate (say between 5 and 10 percent) can also be good for development. Nevertheless, the current spatial distribution of the brain drain is such that many poor countries are well above this level, such as sub-Saharan African and Central American countries.


title: New design of Zero Bias Power Limiter Based on Schottky and PIN Diodes

abstract: In this paper, a new broadband microwave microstrip power limiter is designed and validated. The power limiter is based on microstrip technology integrating Schottky diodes and PIN diodes. Schottky diodes are used as detector diodes in order to generate DC current needed to bias PIN diode. The power limiter is optimized and validated using ADS software from Agilent technologies. The first circuit uses two single Schottky diodes and one PIN diode. In order to improve and simplify the first circuit, the second circuit is obtained by using a common anode Schottky diode. The third circuit is obtained by cascading two limiter stages of the second circuit. Simulation of circuits shows a limiting rate between 18 and 24 dB with a threshold input power level of 0 dBm until a maximum input power level of 30 dBm.


title: Mining Opinion Leaders in Big Social Network

abstract: Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.


title: Social media responses to heat waves

abstract: Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.


title: Nanosecond Pulse Generator with Variable Pulse Duration for the Study of Pulse Induced Biological Effects

abstract: We have developed a Blumlein line pulse generator which utilizes the superposition of electrical pulses launched from two individually switched pulse forming lines. By using a fast power MOSFET as a switch on each end of the Blumlein line, we were able to generate pulses with amplitudes of 1 kV across a 100-Omega load. Pulse duration and polarity can be controlled by the temporal delay in the triggering of the two switches. In addition, the use of identical switches allows us to overcome pulse distortions arising from the use of non-ideal switches in the traditional Blumlein configuration. With this pulse generator, pulses with durations between 8 and 300 ns were applied to Jurkat cells (a leukemia cell line) to investigate the pulse dependent increase in calcium levels. The development of the calcium levels in individual cells was studied by spinning-disc confocal fluorescent microscopy with the calcium indicator, fluo-4. With this fast imaging system, fluorescence changes, representing calcium mobilization, could be resolved with an exposure of 5 ms every 18 ms. For a 60-ns pulse duration, each rise in intracellular calcium was greater as the electric field strength was increased from 25 kV/cm to 100 kV/cm. Only for the highest electric field strength is the response dependent on the presence of extracellular calcium. The results complement ion-exchange mechanisms previously observed during the charging of cellular membranes, which were suggested by observations of membrane potential changes during exposure.


title: Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis

abstract: Background ::: High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.


title: A Comparative Review of Footwear-Based Wearable Systems

abstract: Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.


title: Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification

abstract: Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.


title: Knowledge and heuristic-based modeling of laser-scanned trees

abstract: We present a semi-automatic and efficient method for producing full polygonal models of range scanned trees, which are initially represented as sparse point clouds. First, a skeleton of the trunk and main branches of the tree is produced based on the scanned point clouds. Due to the unavoidable incompleteness of the point clouds produced by range scans of trees, steps are taken to synthesize additional branches to produce plausible support for the tree crown. Appropriate dimensions for each branch section are estimated using allometric theory. Using this information, a mesh is produced around the full skeleton. Finally, leaves are positioned, oriented and connected to nearby branches. Our process requires only minimal user interaction, and the full process including scanning and modeling can be completed within minutes.


title: Efficacy of desloratadine in the treatment of allergic rhinitis : a meta-analysis of randomized, double-blind, controlled trials

abstract: The objective of the study is to assess the efficacy of the nonsedating antihistamine, desloratadine, in the treatment of allergic rhinitis (AR). A search of MEDLINE, EMBASE, LILACS, and CINAHL databases was undertaken from January, 1966 to May, 2006. Double-blind, randomized, controlled studies of desloratadine in the treatment of AR in adult patients were carried out. The measured outcomes included the total symptoms score, the total nasal symptoms score, nasal airflow, and inflammatory markers (nasal eosinophils, nasal interleukin-4). The analysis included the calculation of standardized mean difference (SMD). A total of 57 studies were analyzed, and 13 randomized, double-blind, controlled trials were included in the meta-analysis. The trials included 3108 subjects who had completed studies involving desloratadine. There was significant heterogeneity among the study results, because of differing study methodologies. Desloratadine was associated with significant reductions in total symptoms scores (SMD -1.63; 95% Cl -2.75 to -0.51; P = 0.004) and total nasal symptoms score (SMD -0.66; 95% CT -0.91 to -0.42; P < 0.001), when compared with placebo. Analysis of objective data on nasal blockage demonstrated a significant improvement in nasal airflow with desloratadine, when compared with placebo (SMD 0.32; 95% Cl 0.10 to 0.55; P = 0.005). A benefit favoring desloratadine over placebo in terms of nasal eosinophil levels was also noted in the analysis. This meta-analysis confirms the reduction of AR symptoms and improvement in nasal airflow seen in individual studies of desloratadine. Objective improvements in nasal airflow, total symptoms, and total nasal symptoms seen with desloratadine are supported by la evidence.


title: Cyberstalking, personal privacy, and moral responsibility

abstract: This essay examines some ethical aspects of stalking incidents in cyberspace. Particular attention is focused on the Amy Boyer/Liam Youens case of cyberstalking, which has raised a number of controversial ethical questions. We limit our analysis to three issues involving this particular case. First, we suggest that the privacy of stalking victims is threatened because of the unrestricted access to on-line personal information, including on-line public records, currently available to stalkers. Second, we consider issues involving moral responsibility and legal liability for Internet service providers (ISPs) when stalking crimes occur in their `space' on the Internet. Finally, we examine issues of moral responsibility for ordinary Internet users to determine whether they are obligated to inform persons whom they discover to be the targets of cyberstalkers.


title: A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

abstract: Detection of damages caused by natural disasters is a delicate and difficult task due to the time constraints imposed by emergency situations. Therefore, an automatic Change Detection (CD) algorithm, with less user interaction, is always very interesting and helpful. So far, there is no existing CD approach that is optimal and applicable in the case of (a) labeled samples not existing in the study area; (b) multi-temporal images being corrupted by either noise or non-normalized radiometric differences; (c) difference images having overlapped change and no-change classes that are non-linearly separable from each other. Also, a low degree of automation is not optimal for real-time CD applications and also one-dimensional representations of classical CD methods hide the useful information in multi-temporal images. In order to resolve these problems, two automatic kernel-based CD algorithms (KCD) were proposed based on kernel clustering and support vector data description (SVDD) algorithms in high dimensional Hilbert space. In this paper (a( a new similarity space was proposed in order to increase the separation between change and no-change classes, and also to decrease the processing time, (b) three kernel-based approaches were proposed for transferring the multi-temporal images from spectral space into high dimensional Hilbert space, (c) automatic approach was proposed to extract the precise labeled samples; (d) kernel parameter was selected automatically by optimizing an improved cost function and (e) initial value of the kernel parameter was estimated by a statistical method based on the L2-norm distance. Two different datasets including Quickbird and Landsat TM/ETM+ imageries were used for the accuracy of analysis of proposed methods. The comparative analysis showed the accuracy improvements of kernel clustering based CD and SVDD based CD methods with respect to the conventional CD techniques such as Minimum Noise Fraction, Independent Component Analysis, Spectral Angle Mapper, Simple Image differencing and Image Rationing, and also the computational cost analysis showed that implementation of the proposed CD method in similarity space decreases the processing runtime.


title: A support vector machine-based context-ranking model for question answering

abstract: Modern information technologies and Internet services are suffering from the problem of selecting and managing a growing amount of textual information, to which access is often critical. Machine learning techniques have recently shown excellent performance and flexibility in many applications, such as artificial intelligence and pattern recognition. Question answering (QA) is a method of locating exact answer sentences from vast document collections. This paper presents a machine learning-based question-answering framework, which integrates a question classifier, simple document/passage retrievers, and the proposed context-ranking models. The question classifier is trained to categorize the answer type of the given question and instructs the context-ranking model to re-rank the passages retrieved from the initial retrievers. This method provides flexible features to learners, such as word forms, syntactic features, and semantic word features. The proposed context-ranking model, which is based on the sequential labeling of tasks, combines rich features to predict whether the input passage is relevant to the question type. We employ TREC-QA tracks and question classification benchmarks to evaluate the proposed method. The experimental results show that the question classifier achieves 85.60% accuracy without any additional semantic or syntactic taggers, and reached 88.60% after we employed the proposed term expansion techniques and a predefined related-word set. In the TREC-10 QA task, by using the gold TREC-provided relevant document set, the QA model achieves a 0.563 mean reciprocal rank (MRR) score, and a 0.342 MRR score is achieved after using the simple document and passage retrieval algorithms.


title: A practical algorithm for the determination of phase from image and diffraction plane pictures

abstract: An algorithm is presented for the rapid solution of the phase of the complete wave function whose intensity in the diffraction and imaging planes of an imaging system are known. A proof is given showing that a defined error between the estimated function and the correct function must decrease as the algorithm iterates. The problem of uniqueness is discussed and results are presented demonstrating the power of the method.


title: The Politics of Denial: Israel and the Palestinian Refugee Problem

abstract: The 1948 war ended in the expulsion of hundreds of thousands of Palestinians from their villages and homes. Israeli settlers moved in to occupy their land and the Palestinian refugees found themselves in refugee camps, or in neighbouring Arab countries. Today there are nearly four million Palestinian refugees -- and they want the right to go home. Their problem is the greatest and most enduring refugee problem in the world. Since 1948 Israeli refugee policy has become a classic case of denial: the denial that Zionist "transfer committees" had operated between 1937 and 1948; denial of any wrong-doing or any historical injustice; denial of the "right of return"; denial of restitution of property and compensation; and indeed denial of any moral responsibility or culpability for the creation of the refugee problem. The aim of this book is to analyse Israeli policies towards the Palestinian refugees as they evolved from the 1948 catastrophe (or nakba) to the present. It is the first volume to look in detail at Israeli law and policy surrounding the refugee question. Drawing on extensive primary sources and previously classified archive material, Masalha discusses the 1948 exodus; Israeli resettlement schemes since 1948; Israeli approaches to compensation and restitution of property; Israeli refugee policies towards the internally displaced ('present absentees'); and Israeli refugee policies during the Madrid and Oslo negotiations. Masalha asks what rights Palestinians possess under international law? How can a refugee population be compensated, and will they ever be able to return to their homes? Masalha questions the official Israeli position that the only solution to the problem is resettlement of the refugees in Arab states or elsewhere. This book is a valuable resource for anyone interested in the subject that lies at the heart of the ongoing conflict in the Middle East.


title: Channel Dynamics and SNR Tracking in Millimeter Wave Cellular Systems

abstract: The millimeter wave (mmWave) frequencies are likely to play a significant role in fifth-generation (5G) cellular systems. A key challenge in developing systems in these bands is the potential for rapid channel dynamics: since mmWave signals are blocked by many materials, small changes in the position or orientation of the handset relative to objects in the environment can cause large swings in the channel quality. This paper addresses the issue of tracking the signal to noise ratio (SNR), which is an essential procedure for rate prediction, handover and radio link failure detection. A simple method for estimating the SNR from periodic synchronization signals is considered. The method is then evaluated using real experiments in common blockage scenarios combined with outdoor statistical models.


A Real Time Traffic Analysis System using Computer Vision


title: Autism and Mind-Body Therapies: A Systematic Review.

abstract: BACKGROUND ::: Mind-body therapies are often used by people with autism spectrum disorders (ASD). However, there has been little examination into which types of mind-body therapies have been investigated for people with ASD and for what purposes. A systematic review was conducted to evaluate the existing evidence for mind-body therapies for people with ASD, particularly to determine the types of mind-body therapies used and the outcomes that are targeted. ::: ::: ::: METHODS ::: PubMed, PsychInfo, and Scopus were searched using terms for ASD and mind-body therapies. Sixteen studies were selected for review; these studies tested interventions using mindfulness, meditation, yoga, Nei Yang Gong, and acceptance commitment therapy. Most study outcomes targeted behavior, psychological symptoms, and quality of life for children and adults with ASD as well as their parents. ::: ::: ::: RESULTS ::: There was little overlap between studies on the types of mind-body therapies used and associated outcomes, and only three of the studies were randomized controlled trials. Most studies were small and uncontrolled. Some studies modified the mind-body therapies to increase accessibility for people with ASD. ::: ::: ::: CONCLUSION ::: The evidence for mind-body therapies for people with ASD is limited and would benefit from larger randomized controlled trials.


title: Unsupervised alignment of actions in video with text descriptions

abstract: Advances in video technology and data storage have made large scale video data collections of complex activities readily accessible. An increasingly popular approach for automatically inferring the details of a video is to associate the spatio-temporal segments in a video with its natural language descriptions. Most algorithms for connecting natural language with video rely on pre-aligned supervised training data. Recently, several models have been shown to be effective for unsupervised alignment of objects in video with language. However, it remains difficult to generate good spatio-temporal video segments for actions that align well with language. This paper presents a framework that extracts higher level representations of low-level action features through hyperfeature coding from video and aligns them with language. We propose a two-step process that creates a high-level action feature codebook with temporally consistent motions, and then applies an unsupervised alignment algorithm over the action codewords and verbs in the language to identify individual activities. We show an improvement over previous alignment models of objects and nouns on videos of biological experiments, and also evaluate our system on a larger scale collection of videos involving kitchen activities.


Small Perturbations of C *-Dynamical Systems


title: Making sense of another mind: The role of the right temporo-parietal junction

abstract: Human adults conceive of one another as beings with minds, and attribute to one another mental states like perceptions, desires and beliefs. That is, we understand other people using a ‘Theory of Mind’. The current study investigated the contributions of four brain regions to Theory of Mind reasoning. The right temporo-parietal junction (RTPJ) was recruited selectively for the attribution of mental states, and not for other socially relevant facts about a person, and the response of the RTPJ was modulated by the congruence or incongruence of multiple relevant facts about the target’s mind. None of the other three brain regions commonly implicated in Theory of Mind reasoning – the left temporo-parietal junction (LTPJ), posterior cingulate (PC) and medial prefrontal cortex (MPFC) – showed an equally selective profile of response. The implications of these results for an alternative theory of reasoning about other minds – Simulation Theory – are discussed. © 2005 Elsevier Ltd. All rights reserved.


title: Supervised Learning of Semantic Classes for Image Annotation and Retrieval

abstract: A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning


title: Design and Simulation of Smart Prepaid-Postpaid Energy Meter with Alarm and Theft Control

abstract: This paper proposes a mathematical model of multi-featured prepaid-postpaid energy meter. These features are (a) automatic billing, (b) prepaid collection, (c) choice of prepaid or postpaid, (d) detection of abnormal operational condition, (e) protection against electricity theft, (f) data sharing between consumer and utility end, (g) improved accuracy, (h) user friendly as well as economical and (i) motivating towards electric energy conservation. The model uses RS-232 and GSM module for two-way communication. The proposed model is simulated and tested over MATLAB/ Simulink®platform. The results obtained are promising and complying with the existing features of the energy meter.


title: Deep Spatial Regression Model for Image Crowd Counting

abstract: Computer vision techniques have been used to produce accurate and generic crowd count estimators in recent years. Due to severe occlusions, appearance variations, perspective distortions and illumination conditions, crowd counting is a very challenging task. To this end, we propose a deep spatial regression model(DSRM) for counting the number of individuals present in a still image with arbitrary perspective and arbitrary resolution. Our proposed model is based on Convolutional Neural Network (CNN) and long short term memory (LSTM). First, we put the images into a pretrained CNN to extract a set of high-level features. Then the features in adjacent regions are used to regress the local counts with a LSTM structure which takes the spatial information into consideration. The final global count is obtained by a sum of the local patches. We apply our framework on several challenging crowd counting datasets, and the experiment results illustrate that our method on the crowd counting and density estimation problem outperforms state-of-the-art methods in terms of reliability and effectiveness.


title: Blob Enhancement and Visualization for Improved Intracranial Aneurysm Detection

abstract: Several researches have established that the sensitivity of visual assessment of smaller intracranial aneurysms is not satisfactory. Computer-aided diagnosis based on volume rendering of the response of blob enhancement filters may shorten visual inspection and increase detection sensitivity by directing a diagnostician to suspicious locations in cerebral vasculature. We proposed a novel blob enhancement filter based on a modified volume ratio of Hessian eigenvalues that has a more uniform response inside the blob-like structures compared to state-of-the-art filters. Because the response of proposed filter is independent of the size and intensity of structures, it is especially sensitive for detecting small blob-like structures such as aneuryms. We proposed a novel volume rendering method, which is sensitive to signal energy along the viewing ray and which visually enhances the visualization of true positives and suppresses usually sharp false positive responses. The proposed and state-of-the-art methods were quantitatively evaluated on a synthetic dataset and 42 clinical datasets of patients with aneurysms. Because of the capability to accurately enhance the aneurysm’s boundary and due to a low number of visualized false positive responses, the combined use of the proposed filter and visualization method ensures a reliable detection of (small) intracranial aneurysms.


title: SegICP-DSR: Dense Semantic Scene Reconstruction and Registration

abstract: To enable autonomous robotic manipulation in unstructured environments, we present SegICP-DSR, a real- time, dense, semantic scene reconstruction and pose estimation algorithm that achieves mm-level pose accuracy and standard deviation (7.9 mm, {\sigma}=7.6 mm and 1.7 deg, {\sigma}=0.7 deg) and suc- cessfully identified the object pose in 97% of test cases. This represents a 29% increase in accuracy, and a 14% increase in success rate compared to SegICP in cluttered, unstruc- tured environments. The performance increase of SegICP-DSR arises from (1) improved deep semantic segmentation under adversarial training, (2) precise automated calibration of the camera intrinsic and extrinsic parameters, (3) viewpoint specific ray-casting of the model geometry, and (4) dense semantic ElasticFusion point clouds for registration. We benchmark the performance of SegICP-DSR on thousands of pose-annotated video frames and demonstrate its accuracy and efficacy on two tight tolerance grasping and insertion tasks using a KUKA LBR iiwa robotic arm.


title: Research on field-weakening control of induction motor based on torque current component of the voltage closed-loop

abstract: The paper is based on vector control research on field-weakening control strategy of induction motor. In field-weakening induction motor torque output declines, in order to improve dynamic performance, on the basis of the existing voltage closed-loop field-weakening control strategy, the torque current tracking error is handled on the calculation of the reference excitation current in field-weakening region, so as to improve the tracking ability of current and torque of the motor output properties. Use MATLAB/SIMULINK simulation model of induction motor field-weakening control system. System simulation and experimental results will be proved the feasibility and effectiveness of the method.


title: Understanding Blind Deconvolution Algorithms

abstract: Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. We show that, using reasonable image priors, a naive simulations MAP estimation of both latent image and blur kernel is guaranteed to fail even with infinitely large images sampled from the prior. On the other hand, we show that since the kernel size is often smaller than the image size, a MAP estimation of the kernel alone is well constrained and is guaranteed to succeed to recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. As a first step toward this experimental evaluation, we have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrate that the shift-invariant blur assumption made by most algorithms is often violated.


title: Grounded Situation Models for Robots: Where words and percepts meet

abstract: Our long-term objective is to develop robots that engage in natural language-mediated cooperative tasks with humans. To support this goal, we are developing an amodal representation and associated processes which is called a grounded situation model (GSM). We are also developing a modular architecture in which the GSM resides in a centrally located module, around which there are language, perception, and action-related modules. The GSM acts as a sensor-updated "structured blackboard", that serves as a workspace with contents similar to a "theatrical stage" in the robot's "mind", which might be filled in with present, past or imagined situations. Two main desiderata drive the design of the GSM: first, "parsing" situations into ontological types and relations that reflect human language semantics, and second, allowing bidirectional translation between sensory-derived data/expectations and linguistic descriptions. We present an implemented system that allows of a range of conversational and assistive behavior by a manipulator robot. The robot updates beliefs (held in the GSM) about its physical environment, the human user, and itself, based on a mixture of linguistic, visual and proprioceptive evidence. It can answer basic questions about the present or past and also perform actions through verbal interaction. Most importantly, a novel contribution of our approach is the robot's ability for seamless integration of both language- and sensor-derived information about the situation: for example, the system can acquire parts of situations either by seeing them or by "imagining" them through descriptions given by the user: "There is a red ball at the left". These situations can later be used to create mental imagery and sensory expectations, thus enabling the aforementioned bidirectionality


title: Neural Conditional Gradients

abstract: The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a learning problem based on Frank-Wolfe Networks: recurrent networks implementing the Frank-Wolfe algorithm aka. conditional gradients. This allows them to learn to exploit structure when, e.g., optimizing over rank-1 matrices. Our LSTM-learned optimizers outperform hand-designed as well learned but unconstrained ones. We demonstrate this for training support vector machines and softmax classifiers.


Handbook for Networked Local Area Networks in Aircraft


Towards Mechanical Level of Detail for Knitwear Simulation


title: A 1.6-mm2 38-mW 1.5-Gb/s LDPC decoder enabled by refresh-free embedded DRAM

abstract: Memory dominates the power consumption of high-throughput LDPC decoders. A 700 MHz refresh-free embedded DRAM (eDRAM) is designed as a low-power memory to retain data for the required access window. 32 1-kb eDRAM arrays are integrated in a 1.6 mm2, 65nm LDPC decoder suitable for IEEE 802.11ad. The LDPC decoder consumes 38 mW for a 1.5 Gb/s throughput at 90 MHz and 10 decoding iterations, and it achieves up to 9 Gb/s at 540 MHz.


ANN Based Currency Recognition System using Compressed Gray Scale and Application for Sri Lankan Currency Notes- SLCRec


Image Retrieval with Deep Local Features and Attention-based Keypoints


title: Accuracy Enhancement of the Viola-Jones Algorithm for Thermal Face Detection

abstract: Face detection is the first step for many facial analysis applications and has been extensively researched in the visible spectrum. While significant progress has been made in the field of face detection in the visible spectrum, the performance of current face detection methods in the thermal infrared spectrum is far from perfect and unable to cope with real-time applications. As the Viola-Jones algorithm has become a common method of face detection, this paper aims to improve the performance of the Viola-Jones algorithm in the thermal spectrum for detecting faces with or without eyeglasses. A performance comparison has been made of three different features, HOG, LBP, and Haar-like, to find the most suitable one for face detection from thermal images. Additionally, to accelerate the detection speed, a pre-processing stage is added in both training and detecting phases. Two pre-processing methods have been tested and compared, together with the three features. It is found that the proposed process for performance enhancement gave higher detection accuracy (95%) than the Viola-Jones method (90%) and doubled the detection speed as well.


title: Cholesteryl ester transfer protein and lecithin:cholesterol acyltransferase activities in hispanic and anglo postmenopausal women: associations with total and regional body fat.

abstract: Reverse cholesterol transport is one process by which high-density lipoprotein (HDL) cholesterol has been hypothesized to play a role in reducing the risk of coronary heart disease. This study was designed to examine cholesteryl ester transfer protein (CETP) and lecithin:cholesterol acyltransferase (LCAT) activities, 2 modulators of reverse cholesterol transport, in Hispanic and Anglo postmenopausal women. The associations between plasma CETP and LCAT activities and body composition were also examined. Of the 199 subjects, 33% were of Hispanic origin and 47% were undergoing hormone replacement therapy (HRT). Body composition was measured by dual-energy x-ray absorptiometry (DXA) and anthropometry. Plasma CETP activity was higher in Hispanic compared to Anglo women, although the difference was eliminated when data were adjusted for abdominal fat. Hispanic women had lower plasma HDL cholesterol concentrations, higher total cholesterol:HDL cholesterol ratios and triglyceride concentrations, and greater susceptibility of low-density lipoprotein (LDL) particles to oxidation. Hispanic women also had a significantly greater relative deposition of body fat on the trunk and intra-abdominally than did Anglo women, even after adjusting for total body fat. There were no significant ethnic differences in LCAT activity. Plasma CETP and LCAT activities were negatively correlated with HDL cholesterol and positively correlated with total cholesterol, LDL cholesterol, and triglycerides, as well as total and regional body composition variables. In conclusion, results suggest a greater risk for coronary heart disease in Hispanic women compared to Anglo women.


title: Viscero-visceral hyperalgesia: Characterization in different clinical models

abstract: Co-existing algogenic conditions in two internal organs in the same patient may mutually enhance pain symptoms (viscero-visceral hyperalgesia). The present study assessed this phenomenon in different models of visceral interaction. In a prospective evaluation, patients with: (a) coronary artery disease (CAD) + gallstone (Gs) (common sensory projection: T5); (b) irritable bowel syndrome (IBS) + dysmenorrhea (Dys) (T10-L1); (c) dysmenorrhea/endometriosis + urinary calculosis (Cal)(T10-L1); and (d) gallstone + left urinary calculosis (Gs + LCal) (unknown common projection) were compared with patients with CAD, Gs, IBS, Dys or Cal only, for spontaneous symptoms (number/intensity of pain episodes) over comparable time periods and for referred symptoms (muscle hyperalgesia; pressure/electrical pain thresholds) from each visceral location. In patients' subgroups, symptoms were also re-assessed after treatment of each condition or after no treatment. (a) CAD + Gs presented more numerous/intense angina/biliary episodes and more referred muscle chest/abdominal hyperalgesia than CAD or Gs; cardiac revascularization or cholecystectomy also reduced biliary or cardiac symptoms, respectively (0.001 < p < 0.05). (b) IBS + Dys had more intestinal/menstrual pain and abdomino/pelvic muscle hyperalgesia than IBS or Dys; hormonal dysmenorrhea treatment also reduced IBS symptoms; IBS dietary treatment also improved dysmenorrhea (0.001 < p < 0.05) while no treatment of either conditions resulted in no improvement in time of symptoms from both. (c) Cal + Dys had more urinary/menstrual pain and referred lumbar/abdominal hyperalgesia than Cal or Dys; hormonal dysmenorrhea treatment/laser treatment for endometriosis also improved urinary symptoms; lithotripsy for urinary stone also reduced menstrual symptoms (0.001 < p < 0.05). (d) In Gs + LCal, cholecystectomy or urinary lithotripsy did not improve urinary or biliary symptoms, respectively. Mechanisms of viscero-visceral hyperalgesia between organs with documented partially common sensory projection probably involve sensitization of viscero-viscero-somatic convergent neurons.


title: Plutus: Scalable Secure File Sharing on Untrusted Storage

abstract: Plutus is a cryptographic storage system that enables secure file sharing without placing much trust on the file servers. In particular, it makes novel use of cryptographic primitives to protect and share files. Plutus features highly scalable key management while allowing individual users to retain direct control over who gets access to their files. We explain the mechanisms in Plutus to reduce the number of cryptographic keys exchanged between users by using filegroups, distinguish file read and write access, handle user revocation efficiently, and allow an untrusted server to authorize file writes. We have built a prototype of Plutus on OpenAFS. Measurements of this prototype show that Plutus achieves strong security with overhead comparable to systems that encrypt all network traffic.


title: Threat Sensitivity in Bipolar Disorder

abstract: Life stress is a major predictor of the course of bipolar disorder. Few studies have used laboratory paradigms to examine stress reactivity in bipolar disorder, and none have assessed autonomic reactivity to laboratory stressors. In the present investigation we sought to address this gap in the literature. Participants, 27 diagnosed with bipolar I disorder and 24 controls with no history of mood disorder, were asked to complete a complex working memory task presented as “a test of general intelligence.” Self-reported emotions were assessed at baseline and after participants were given task instructions; autonomic physiology was assessed at baseline and continuously during the stressor task. Compared to controls, individuals with bipolar disorder reported greater increases in pretask anxiety from baseline and showed greater cardiovascular threat reactivity during the task. Group differences in cardiovascular threat reactivity were significantly correlated with comorbid anxiety in the bipolar group. Our results suggest that a multimethod approach to assessing stress reactivity—including the use of physiological parameters that differentiate between maladaptive and adaptive profiles of stress responding— can yield valuable information regarding stress sensitivity and its associations with negative affectivity in bipolar disorder.


title: Primate Ventromedial Prefrontal Cortex Neurons Continuously Encode the Willingness to Engage in Reward-Directed Behavior

abstract: To survive in their complex environment, primates must integrate information over time and adjust their actions beyond immediate events. The underlying neurobiological processes, however, remain unclear. Here, we assessed the contribution of the ventromedial prefrontal cortex (VMPFC), a brain region important for value-based decision making. We recorded single VMPFC neurons in monkeys performing a task where obtaining fluid rewards required squeezing a grip. The willingness to perform the action was modulated not only by visual information about Effort and Reward levels, but also by contextual factors such as Trial Number (i.e fatigue and/or satiety) or behavior in recent trials. A greater fraction of VMPFC neurons encoded contextual information, compared to visual stimuli. Moreover, the dynamics of VMPFC firing was more closely related to slow changes in motivational states driven by these contextual factors rather than rapid responses to individual task events. Thus, the firing of VMPFC neurons continuously integrated contextual information and reliably predicted the monkeys’s willingness to perform the task. This function might be critical when animals forage in a complex environment and need to integrate information over time. Its relation with motivational states also resonates with the VMPFC’s implication in the ‘default mode’ or in mood disorders. !2 peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/062315 doi: bioRxiv preprint first posted online Jul. 6, 2016;


title: IDSense: A Human Object Interaction Detection System Based on Passive UHF RFID

abstract: In order to enable unobtrusive human object interaction detection, we propose a minimalistic approach to instrumenting everyday objects with passive (i.e. battery-free) UHF RFID tags. By measuring the changes in the physical layer of the communication channel between the RFID tag and reader (such as RSSI, RF phase, and read rate) we are able to classify, in real time, tag/object motion events along with two types of touch events. Through a user study, we demonstrate that our real-time classification engine is able to simultaneously track 20 objects and identify four movement classes with 93% accuracy. To demonstrate how robust this general-purpose interaction mechanism is, we investigate three usage scenarios 1) interactive storytelling with toys 2) inference of daily activities in the home 3) identification of customer browsing habits in a retail setting.


Life Insurance Customers segmentation using fuzzy clustering