title: A Methodology for Direct and Indirect Discrimination Prevention in Data Mining

abstract: Data mining is an increasingly important technology for extracting useful knowledge hidden in large collections of data. There are, however, negative social perceptions about data mining, among which potential privacy invasion and potential discrimination. The latter consists of unfairly treating people on the basis of their belonging to a specific group. Automated data collection and data mining techniques such as classification rule mining have paved the way to making automated decisions, like loan granting/denial, insurance premium computation, etc. If the training data sets are biased in what regards discriminatory (sensitive) attributes like gender, race, religion, etc., discriminatory decisions may ensue. For this reason, anti-discrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination can be either direct or indirect. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made based on nonsensitive attributes which are strongly correlated with biased sensitive ones. In this paper, we tackle discrimination prevention in data mining and propose new techniques applicable for direct or indirect discrimination prevention individually or both at the same time. We discuss how to clean training data sets and outsourced data sets in such a way that direct and/or indirect discriminatory decision rules are converted to legitimate (nondiscriminatory) classification rules. We also propose new metrics to evaluate the utility of the proposed approaches and we compare these approaches. The experimental evaluations demonstrate that the proposed techniques are effective at removing direct and/or indirect discrimination biases in the original data set while preserving data quality.


Scene Graph Generation from Objects, Phrases and Caption Regions.


title: Action Detection From Skeletal Data Using Efficient Linear Search

abstract: Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency.


title: Deep Reinforcement Learning for Distributed Dynamic Power Allocation in Wireless Networks.

abstract: This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in emerging and future wireless networks. Various techniques have been proposed in the literature to find near-optimal power allocations, often by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a model-free distributed dynamic power allocation scheme is developed based on deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling (with weights that are changing over time). Both random variations and delays in the CSI are inherently addressed using deep Q-learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. This work indicates that deep reinforcement learning based radio resource management can be very fast and deliver highly competitive performance, especially in practical scenarios where the system model is inaccurate and CSI delay is non-negligible.


title: Compact Topside Millimeter-Wave Waveguide-to-Microstrip Transitions

abstract: This letter presents two different waveguide-to-microstrip transition designs for the 76-81 GHz frequency band. Both transitions are fabricated on a grounded single layer substrate using a standard printed circuit board (PCB) fabrication process. A coplanar patch antenna and a feed technique at the non-radiating edge are used for the impedance transformation. In the first design, a conventional WR-10 waveguide is connected. In the second design, a WR-10 waveguide flange with an additional inductive waveguide iris is employed to improve the bandwidth. Both designs were developed for the integration of multi-channel array systems allowing an element spacing of λ0/2 or less. Measurement results of the first transition without the iris show a bandwidth of 8.5 GHz (11%) for 10 dB return loss and a minimum insertion loss (IL) of 0.35 dB. The transition using the iris increases the bandwidth to 12 GHz (15%) for 10 dB return loss and shows a minimum insertion loss of 0.6 dB at 77 GHz.


title: Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

abstract: Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1h, 24h, 48h and 72h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.


title: Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation

abstract: We consider the problem of a robot learning the mechanical properties of objects through physical interaction with the object, and introduce a practical, data-efficient approach for identifying the motion models of these objects. The proposed method utilizes a physics engine, where the robot seeks to identify the inertial and friction parameters of the object by simulating its motion under different values of the parameters and identifying those that result in a simulation which matches the observed real motions. The problem is solved in a Bayesian optimization framework. The same framework is used for both identifying the model of an object online and searching for a policy that would minimize a given cost function according to the identified model. Experimental results both in simulation and using a real robot indicate that the proposed method outperforms state-of-the-art model-free reinforcement learning approaches.


title: Chorusing, synchrony, and the evolutionary functions of rhythm

abstract: A central goal of biomusicology is to understand the biological basis of human musicality. One approach to this problem has been to compare core components of human musicality (relative pitch perception, entrainment, etc.) with similar capacities in other animal species. Here we extend and clarify this comparative approach with respect to rhythm. First, whereas most comparisons between human music and animal acoustic behavior have focused on spectral properties (melody and harmony), we argue for the central importance of temporal properties, and propose that this domain is ripe for further comparative research. Second, whereas most rhythm research in non-human animals has examined animal timing in isolation, we consider how chorusing dynamics can shape individual timing, as in human music and dance, making group behavior key to understand the adaptive functions of rhythm. To illustrate the interdependence between individual and chorusing dynamics, we present a computational model of chorusing agents relating individual call timing with synchronous group behavior. Third, we distinguish and clarify mechanistic and functional explanations of rhythmic phenomena, often conflated in the literature, arguing that this distinction is key for understanding the evolution of musicality. Fourth, we expand biomusicological discussions beyond the species typically considered, providing an overview of chorusing and rhythmic behavior across a broad range of taxa (orthopterans, fireflies, frogs, birds, and primates). Finally, we propose an “Evolving Signal Timing” hypothesis, suggesting that similarities between timing abilities in biological species will be based on comparable chorusing behaviors. We conclude that the comparative study of chorusing species can provide important insights into the adaptive function(s) of rhythmic behavior in our “proto-musical” primate ancestors, and thus inform our understanding of the biology and evolution of rhythm in human music and language.


title: The Proper Place of Men and Machines in Language Translation

abstract: The only way in which the power of computers has been brought to bear on the problem of language translation is machine translation, that is, the automation of the entire process. Machine translation is an excellent research vehicle but stands no chance of filling actual needs for translation which are growing at a great rate. In the quarter century during which work on machine translation has been going on, there has been considerable progress in relevant areas of computer science. However, advances in linguistics, important though they may have been, have not touched the core of this problem. The proper thing to do is therefore to adopt the kinds of solution that have proved successful in other domains, namely to develop cooperative man–machine systems. This paper proposes a translator‘s amanuensis, incorporating into a word processor some simple facilities peculiar to translation. Gradual enhancements of such a system could eventually lead to the original goal of machine translation.


title: Rule-Base Structure Identification in an Adaptive-Network-Based Fuzzy Inference System

abstract: We summarize Jang's architecture of employing an adaptive network and the Kalman filtering algorithm to identify the system parameters. Given a surface structure, the adaptively adjusted inference system performs well on a number of interpolation problems. We generalize Jang's basic model so that it can be used to solve classification problems by employing parameterized t-norms. We also enhance the model to include weights of importance so that feature selection becomes a component of the modeling scheme. Next, we discuss two ways of identifying system structures based on Jang's architecture: the top-down approach, and the bottom-up approach. We introduce a data structure, called a fuzzy binary boxtree, to organize rules so that the rule base can be matched against input signals with logarithmic efficiency. To preserve the advantage of parallel processing assumed in fuzzy rule-based inference systems, we give a parallel algorithm for pattern matching with a linear speedup. Moreover, as we consider the communication and storage cost of an interpolation model. We propose a rule combination mechanism to build a simplified version of the original rule base according to a given focus set. This scheme can be used in various situations of pattern representation or data compression, such as in image coding or in hierarchical pattern recognition. >


title: Computational Television Advertising

abstract: Ever wonder why that Kia Ad ran during Iron Chef? Traditional advertising methodology on television is a fascinating mix of marketing, branding, measurement, and predictive modeling. While still a robust business, it is at risk with the recent growth of online and time-shifted (recorded) television. A particular issue is that traditional methods for television advertising are far less efficient than their counterparts in the online world which employ highly sophisticated computational techniques. This paper formalizes an approach to eliminate some of these inefficiencies by recasting the process of television advertising media campaign generation in a computational framework. We describe efficient mathematical approaches to solve for the task of finding optimal campaigns for specific target audiences. In two case studies, our campaigns report gains in key operational metrics of up to 56% compared to campaigns generated by traditional methods.


title: An empirical analysis of phishing blacklists

abstract: In this paper, we study the eectiveness of phishing blacklists. We used 191 fresh phish that were less than 30 minutes old to conduct two tests on eight anti-phishing toolbars. We found that 63% of the phishing campaigns in our dataset lasted less than two hours. Blacklists were ineective when protecting users initially, as most of them caught less than 20% of phish at hour zero. We also found that blacklists were updated at dierent speeds, and varied in coverage, as 47% - 83% of phish appeared on blacklists 12 hours from the initial test. We found that two tools using heuristics to complement blacklists caught signicantly more phish initially than those using only blacklists. However, it took a long time for phish detected by heuristics to appear on blacklists. Finally, we tested the toolbars on a set of 13,458 legitimate URLs for false positives, and did not nd any instance of mislabeling for either blacklists or heuristics. We present these ndings and discuss ways in which anti-phishing tools can be improved.


title: HadoopDB in action: building real world applications

abstract: HadoopDB is a hybrid of MapReduce and DBMS technologies, designed to meet the growing demand of analyzing massive datasets on very large clusters of machines. Our previous work has shown that HadoopDB approaches parallel databases in performance and still yields the scalability and fault tolerance of MapReduce-based systems. In this demonstration, we focus on HadoopDB's flexible architecture and versatility with two real world application scenarios: a semantic web data application for protein sequence analysis and a business data warehousing application based on TPC-H. The demonstration offers a thorough walk-through of how to easily build applications on top of HadoopDB.


CiteVis: Exploring Conference Paper Citation Data Visually


Embodied Interaction : Exploring the Foundations of a New Approach to HCI


title: Automatic RTL Test Generation from SystemC TLM Specifications

abstract: SystemC transaction-level modeling (TLM) is widely used to enable early exploration for both hardware and software designs. It can reduce the overall design and validation effort of complex system-on-chip (SOC) architectures. However, due to lack of automated techniques coupled with limited reuse of validation efforts between abstraction levels, SOC validation is becoming a major bottleneck. This article presents a novel top-down methodology for automatically generating register transfer-level (RTL) tests from SystemC TLM specifications. It makes two important contributions: i) it proposes a method that can automatically generate TLM tests using various coverage metrics, and (ii) it develops a test refinement specification for automatically converting TLM tests to RTL tests in order to reduce overall validation effort. We have developed a tool which incorporates these activities to enable automated RTL test generation from SystemC TLM specifications. Case studies using a router example and a 64-bit Alpha AXP pipelined processor demonstrate that our approach can achieve intended functional coverage of the RTL designs, as well as capture various functional errors and inconsistencies between specifications and implementations.


title: Dry eye: prevalence and attributable risk factors in a hospital-based population.

abstract: Purpose: To study the prevalence of dry eye in a hospital-based population and to evaluate the various risk factors attributable to dry eye. Materials and Methods: In this cross-sectional study, 500 patients above 20 years of age were screened randomly for dry eye. A 13-point questionnaire, Lissamine Green test, Tear film break-up time (TBUT), Schirmer's test and presence of strands/filaments were used to diagnose dry eye. The diagnosis was made when at least three of the tests were positive. The role of air pollution, sunlight, excessive winds, smoking, drugs and refractive status as dry eye risk factors was assessed. Results: Ninety-two (18.4%) patients had dry eye. Dry eye prevalence was maximum in those above 70 years of age (36.1%) followed by the age group 31-40 years (20%). It was significantly higher ( P = 0.024) in females (22.8%) than in males (14.9%), more common in rural residents (19.6%) than in urban (17.5%) and highest among farmers/labourers (25.3%). A 2.15 fold increase was found in the odds for dry eye in those exposed to excessive wind, 1.91 fold to sunlight exposure, 1.42 to smoking, 1.38 to air pollution and 2.04 for persons on drugs. Dry eye prevalence was 14% in emmetropes, 16.8% in myopes and 22.9% in hypermetropes. It was 15.6% in those with corrected and 25.3% in those with uncorrected refractive errors. Conclusion: Dry eye is an under-diagnosed ocular disorder. Reduction in the modifiable risk factors of dry eye is essential to reduce its prevalence


title: Branching Bisimulation and Concurrent Object Verification

abstract: Linearizability and progress properties are key correctness notions for concurrent objects. This paper presents novel verification techniques for both property classes. The key of our techniques is based on the branching bisimulation equivalence. We first show that it suffices to check linearizability on the quotient object program under branching bisimulation. This is appealing, as it does not rely on linearization points. Further, by exploiting divergence-sensitive branching bisimilarity, our approach proves progress properties (e.g., lock-, wait-freedom) by comparing the concurrent to-be-verified object program against an abstract program consisting of atomic blocks. Our work thus enables the usage of well-known proof techniques for branching bisimulation to check the correctness of concurrent objects. The potential of our approach is illustrated by verifying linearizability and lock-freedom of 14 benchmark algorithms from the literature. Our experiments confirm one known bug and reveals one new bug.


title: Health Care Workers Adherence to Infection Prevention Practices and Control Measures: A Case of a Level Four District Hospital in Kenya

abstract: Infection prevention practices and control are critical activities that influence the quality of health care services. This study was conducted to determine the health workers adherence to infection prevention and control policies and procedures at a Level 4 Hospital in Kenya. A descriptive cross-sectional survey design were used. The objectives of the study were to: -Identify existing infection prevention and control policy guidelines at the level four hospital; -Evaluate the implementation of infection prevention and control measures; 3) Identify the barriers to compliance with infection prevention practices and control measures. Data was collected by questionnaire, record review and a focus group discussion. Quantitative data was analyzed by the statistical Package for Social Sciences (SPSS) while information from focus group was analyzed thematically. The results revealed that Health Care Workers (HCWs) had good knowledge on infection prevention practices and control. There were written infection prevention practices and control (IPPC) policy guidelines and high awareness (98.7%) of the IPPC policy guidelines. The record review showed that there was approximately 6.7% nosocomial infections rate among hospitalized patients. Barriers to IPPC compliance among the health care workers (HCWs) included frequent shortage of water, inadequate updates on IPPC through continuing professional education and inactive IPPC committee. The study concluded that there was adequate compliance with IPPC, though there were challenges to implementation that needed to be addressed. The study was done in October 2010 to January 2011


title: Characterization of the Input Impedance of the Inset-Fed Rectangular Microstrip Antenna

abstract: The input impedance and radiation pattern of the inset-fed rectangular microstrip patch antenna are investigated. It is concluded that a shifted cosine-squared function describes well the variation of the resonant input resistance with the feed location (point of contact between the microstrip feed line and the notch) for a given patch and substrate geometry. The parameters of the shifted cosine-squared function depend on the notch width for a given patch and substrate geometry. The cross-polarization level is seen to increase as either the notch width or depth increases. However, for a fixed input resistance level, the notch depth decreases as the width increases, so that the cross-polarization level is not very sensitive to the notch width for a fixed input resistance.


title: Positive Cognitive Effects of Bilingualism and Multilingualism on Cerebral Function: a Review

abstract: A review of the current literature regarding bilingualism demonstrates that bilingualism is linked to higher levels of controlled attention and inhibition in executive control and can protect against the decline of executive control in aging by contributing to cognitive reserve. Bilinguals may also have smaller vocabulary size and slower lexical retrieval for each language. The joint activation theory is proposed to explain these results. Older trilingual adults experience more protection against cognitive decline and children and young adults showed similar cognitive advantages to bilinguals in inhibitory control. Second language learners do not yet show cognitive changes associated with multilingualism. The Specificity Principle states that the acquisition of multiple languages is moderated by multiple factors and varies between experiences. Bilingualism and multilingualism are both associated with immigration but different types of multilingualism can develop depending on the situation. Cultural cues and language similarity also play a role in language switching and multiple language acquisition.


title: Deadtime Effect on GaN-Based Synchronous Boost Converter and Analytical Model for Optimal Deadtime Selection

abstract: It is widely acknowledged that gallium nitride (GaN)-based power switching devices are superior to conventional silicon (Si) devices in terms of lower semiconductor loss and faster switching speed. However, the deadtime related losses in GaN HEMT-based converters can be detrimental if not optimized, especially when operating at very high switching frequencies. This paper proposes an original analytical model for deadtime optimization for the GaN converters. The proposed model is more accurate than conventional deadtime optimization methods used in Si converters. A GaN-based synchronous boost converter is used as a case study. Circuit simulation and experimental tests are successfully performed to verify the analysis and proposed model.


title: A multiport medium voltage isolated DC-DC converter

abstract: A multiport medium voltage (MV) isolated DC-DC converter with integrated energy storage, suited for connection of low voltage (LV) and medium voltage (MV) DC grids is presented. A multi-stage structure, adapted for MV ratings, is composed of multiple identical submodules connected in an Input-Series-Output-Parallel (ISOP) configuration. Each submodule has three fully bidirectional ports, that are galvanically isolated. This is achieved by means of a three-winding medium frequency transformer, where each leakage inductance combined with a resonant capacitor creates a resonant tank, benefiting from soft switching operation. Each submodule has two additional buck/boost stages, to actively control the power exchange with external DC circuits. The inner resonant stage is operated in open-loop and adapts its mode of operation based on the actual power flow. The remaining ports, dedicated to the storage, can be operated independently from the power flow between the MV and LV side, in order to charge or discharge the storage elements. Switched mode simulations are used to verify and demonstrate various operational modes and performance of the converter.


title: Combining ConceptNet and WordNet for Word Sense Disambiguation

abstract: Knowledge-based Word sense Disambiguation (WSD) methods heavily depend on knowledge. Therefore enriching knowledge is one of the most important issues in WSD. This paper proposes a novel idea of combining WordNet and ConceptNet for WSD. First, we present a novel method to automatically disambiguate the concepts in ConceptNet; and then we enrich WordNet with large amounts of semantic relations from the disambiguated ConceptNet for WSD. The evaluation experiments on the Semeval-2007 coarse-grained all-words disambiguation task show that the enriched WordNet can significantly improve the performance of knowledge-based WSD methods.


title: A spectral graph wiener filter in graph fourier domain for improved image denoising

abstract: A Wiener filtering scheme in graph Fourier domain is proposed for improving image denoising performance achieved by various spectral graph based denoising methods. The proposed Wiener filter is estimated by using graph Fourier coefficients of the noisy image after they are processed for denoising, to further improve the already achieved denoising accuracy as a post-processing step. It can be estimated from and applied to the entire image, or can be used patchwise in a locally adaptive manner. Our results indicate that the proposed step yields consistent accuracy improvement for different choices of weighted adjacency and graph Laplacian matrices used in computing the graph Fourier transform and for different processing methods used to denoise obtained transform coefficients. We obtain higher peak signal-to-noise ratio values than the BM3D method for some images.


title: Design Structure Matrix Extensions and Innovations: A Survey and New Opportunities

abstract: The design structure matrix (DSM), also called the dependency structure matrix, has become a widely used modeling framework across many areas of research and practice. The DSM brings advantages of simplicity and conciseness in representation, and, supported by appropriate analysis, can also highlight important patterns in system architectures (design structures), such as modules and cycles. A literature review in 2001 cited about 100 DSM papers; there have been over 1000 since. Thus, it is useful to survey the latest DSM extensions and innovations to help consolidate progress and identify promising opportunities for further research. This paper surveys the DSM literature, primarily from archival journals, and organizes the developments pertaining to building, displaying, analyzing, and applying product, process, and organization DSMs. It then addresses DSM applications in other domains, as well as recent developments with domain mapping matrices (DMMs) and multidomain matrices (MDMs). Overall, DSM methods are becoming more mainstream, especially in the areas of engineering design, engineering management, management/organization science, and systems engineering. Despite significant research contributions, however, DSM awareness seems to be spreading more slowly in the realm of project management.


title: EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection

abstract: In this paper, we propose a deep learning based approach for facial action unit (AU) detection by enhancing and cropping regions of interest of face images. The approach is implemented by adding two novel nets (a.k.a. layers): the enhancing layers and the cropping layers, to a pretrained convolutional neural network (CNN) model. For the enhancing layers (noted as E-Net), we have designed an attention map based on facial landmark features and apply it to a pretrained neural network to conduct enhanced learning. For the cropping layers (noted as C-Net ), we crop facial regions around the detected landmarks and design individual convolutional layers to learn deeper features for each facial region. We then combine the E-Net and the C-Net to construct a so-called Enhancing and Cropping Net (EAC-Net), which can learn both features enhancing and region cropping functions effectively. The EAC-Net integrates three important elements, i.e., learning transfer, attention coding, and regions of interest processing, making our AU detection approach more efficient and more robust to facial position and orientation changes. Our approach shows a significant performance improvement over the state-of-the-art methods when tested on the BP4D and DISFA AU datasets. The EAC-Net with a slight modification also shows its potentials in estimating accurate AU intensities. We have also studied the performance of the proposed EAC-Net under two very challenging conditions: (1) faces with partial occlusion and (2) faces with large head pose variations. Experimental results show that (1) the EAC-Net learns facial AUs correlation effectively and predicts AUs reliably even with only half of a face being visible, especially for the lower half; (2) Our EAC-Net model also works well under very large head poses, which outperforms significantly a compared baseline approach. It further shows that the EAC-Net works much better without a face frontalization than with face frontalization through image warping as pre-processing, in terms of computational efficiency and AU detection accuracy.


title: Effects of Weightlifting vs. Kettlebell Training on Vertical Jump, Strength, and Body Composition

abstract: The present study compared the effects of 6 weeks of weightlifting plus traditional heavy resistance training exercises vs. kettlebell training on strength, power, and anthropometric measures. Thirty healthy men were randomly assigned to 1 of 2 groups: (a) weightlifting (n = 13; mean ± SD: age, 22.92 ± 1.98 years; body mass, 80.57 ± 12.99 kg; height, 174.56 ± 5.80 cm) or (b) kettlebell (n = 17; mean ± SD: age, 22.76 ± 1.86 years; body mass, 78.99 ± 10.68 kg; height, 176.79 ± 5.08 cm) and trained 2 times a week for 6 weeks. A linear periodization model was used for training; at weeks 1�3 volume was 3 × 6 (kettlebell swings or high pull), 4 × 4 (accelerated swings or power clean), and 4 × 6 (goblet squats or back squats), respectively, and the volume increased during weeks 4�6 to 4 × 6, 6 × 4, and 4 × 6, respectively. Participants were assessed for height (in centimeters), body mass (in kilograms), and body composition (skinfolds). Strength was assessed by the back squat 1 repetition maximum (1RM), whereas power was assessed by the vertical jump and power clean 1RM. The results of this study indicated that short-term weightlifting and kettlebell training were effective in increasing strength and power. However, the gain in strength using weightlifting movements was greater than that during kettlebell training. Neither method of training led to significant changes in any of the anthropometric measures. In conclusion, 6 weeks of weightlifting induced significantly greater improvements in strength compared with kettlebell training. No between-group differences existed for the vertical jump or body composition.


title: A Survey on Joint Object Detection and Pose Estimation using Monocular Vision

abstract: In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.


title: High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration

abstract: The integration of Global Positioning System (GPS) real-time kinematics (RTK) and an inertial navigation system (INS) has been widely used in many applications, such as mobile mapping and autonomous vehicle control. Such applications require high-accuracy position information. However, continuous and reliable high-accuracy positioning is still challenging for GPS/INS integration in urban environments because of the limited satellite visibility, increasing multipath, and frequent signal blockages. Recently, with the rapid deployment of multi-constellation Global Navigation Satellite System (multi-GNSS) and the great advances in low-cost micro-electro-mechanical-system (MEMS) inertial measurement units (IMUs), it is expected that the positioning performance could be improved significantly. In this contribution, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration is developed to provide precise and continuous positioning solutions in urban environments. The innovation-based outlier-resistant ambiguity resolution (AR) and Kalman filtering strategy are proposed specifically for the integrated system to resist the measurement outliers or poor-quality observations. A field vehicular experiment was conducted in Wuhan City to evaluate the performance of the proposed algorithm. Results indicate that it is feasible for the proposed algorithm to obtain high-accuracy positioning solutions in the presence of measurement outliers. Moreover, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration even outperforms the dual-frequency multi-GNSS RTK in terms of AR and positioning performance for short baselines in urban environments.


title: Single Image Haze Removal Algorithm using Color Attenuation Prior and Multi-Scale Fusion

abstract: This paper compares the Fast Single Image Haze Removal (FSIHR) using Color Attenuation Prior (CAP) and MultiScale Fusion (MSF) methods. Single image haze removal has been a challenging problem due to its ill-posed environment. FSIHR works as simple but powerful color attenuation earlier, for removal of haze from a single input hazy image. MSF method is a fusion-based approach that results from two original hazy image inputs by applying a white balance and a contrast enhancing process. To merge the information of the derived inputs successfully, to maintain the regions with good visibility, it filters their important features by computing three measures (weight maps): luminance (Y), chromaticity (C), and saliency (S). The other FSIHR using CAP creates a linear model for modeling the picture depth of the hazy image with a supervised learning method; the depth information can be well recovered. With the depth map of the hazy image, the transmission and the scene radiance restoration via the atmospheric scattering model, and thus efficiently remove the haze from a single image. While the MSF method is faster than existing single image dehazing strategies and yields precise results.


title: A Study of Android Application Security

abstract: The fluidity of application markets complicate smartphone security. Although recent efforts have shed light on particular security issues, there remains little insight into broader security characteristics of smartphone applications. This paper seeks to better understand smartphone application security by studying 1,100 popular free Android applications. We introduce the ded decompiler, which recovers Android application source code directly from its installation image. We design and execute a horizontal study of smartphone applications based on static analysis of 21 million lines of recovered code. Our analysis uncovered pervasive use/misuse of personal/ phone identifiers, and deep penetration of advertising and analytics networks. However, we did not find evidence of malware or exploitable vulnerabilities in the studied applications. We conclude by considering the implications of these preliminary findings and offer directions for future analysis.


title: Neighborhood Repulsed Metric Learning for Kinship Verification

abstract: Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without a kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers.


title: Conformal UHF RFID tag antenna mountable on winebottle neck

abstract: Wine industry is hungry for item-level UHF RFID solutions. In this paper, a conformal UHF tag antenna directly mountable on the winebottle neck is proposed. 360-degree readability is achieved by taking advantage of the antenna curving, and good impedance matching is realized by following a co-design approach. Simulation and measurement results demonstrate that the tag antenna provides a 10-dB bandwidth of 96 MHz, and a read range of 3∼7 m around a full winebottle.


title: Workflows and e-Science: An overview of workflow system features and capabilities

abstract: Scientific workflow systems have become a necessary tool for many applications, enabling the composition and execution of complex analysis on distributed resources. Today there are many workflow systems, often with overlapping functionality. A key issue for potential users of workflow systems is the need to be able to compare the capabilities of the various available tools. There can be confusion about system functionality and the tools are often selected without a proper functional analysis. In this paper we extract a taxonomy of features from the way scientists make use of existing workflow systems and we illustrate this feature set by providing some examples taken from existing workflow systems. The taxonomy provides end users with a mechanism by which they can assess the suitability of workflow in general and how they might use these features to make an informed choice about which workflow system would be a good choice for their particular application.


title: Authentic leadership, performance, and job satisfaction: the mediating role of empowerment

abstract: AIM ::: To report a study conducted to test a model linking authentic leadership of managers with nurses' perceptions of structural empowerment, performance, and job satisfaction. ::: ::: ::: BACKGROUND ::: Authentic leadership has been proposed as the root element of effective leadership needed to build healthier work environments because there is special attention to the development of empowering leader-follower relationships. Although the influence of leadership style and empowerment on job satisfaction is well documented, there are few studies examining the influence of authentic leadership on nurses' empowerment and work outcomes. ::: ::: ::: DESIGN ::: A non-experimental, predictive survey. ::: ::: ::: METHOD ::: In 2008, a random sample of 600 Registered Nurses working in acute care hospitals across Ontario in Canada was surveyed. The final sample consisted of 280 (48% response rate) nurses. Variables were measured using the Authentic Leadership Questionnaire, Conditions of Work Effectiveness Questionnaire, Global Job Satisfaction Survey, and General Performance scale. The theoretical model was tested using structural equation modelling. ::: ::: ::: RESULTS/OUTCOMES ::: The final model fit the data acceptably. Authentic leadership significantly and positively influenced staff nurses' structural empowerment, which in turn increased job satisfaction and self-rated performance. ::: ::: ::: CONCLUSION ::: The results suggest that the more managers are seen as authentic, by emphasizing transparency, balanced processing, self-awareness and high ethical standards, the more nurses perceive they have access to workplace empowerment structures, are satisfied with their work, and report higher performance.


title: Superposter behavior in MOOC forums

abstract: Discussion forums, employed by MOOC providers as the primary mode of interaction among instructors and students, have emerged as one of the important components of online courses. We empirically study contribution behavior in these online collaborative learning forums using data from 44 MOOCs hosted on Coursera, focusing primarily on the highest-volume contributors---"superposters"---in a forum. We explore who these superposters are and study their engagement patterns across the MOOC platform, with a focus on the following question---to what extent is superposting a positive phenomenon for the forum? Specifically, while superposters clearly contribute heavily to the forum in terms of quantity, how do these contributions rate in terms of quality, and does this prolific posting behavior negatively impact contribution from the large remainder of students in the class? We analyze these questions across the courses in our dataset, and find that superposters display above-average engagement across Coursera, enrolling in more courses and obtaining better grades than the average forum participant; additionally, students who are superposters in one course are significantly more likely to be superposters in other courses they take. In terms of utility, our analysis indicates that while being neither the fastest nor the most upvoted, superposters' responses are speedier and receive more upvotes than the average forum user's posts; a manual assessment of quality on a subset of this content supports this conclusion that a large fraction of superposter contributions indeed constitute useful content. Finally, we find that superposters' prolific contribution behavior does not `drown out the silent majority'---high superposter activity correlates positively and significantly with higher overall activity and forum health, as measured by total contribution volume, higher average perceived utility in terms of received votes, and a smaller fraction of orphaned threads.


X-Ray Mineralogy StudiesLeg 43


BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings.


title: Design of Highly Efficient Broadband Class-E Power Amplifier Using Synthesized Low-Pass Matching Networks

abstract: A new methodology for designing and implementing high-efficiency broadband Class-E power amplifiers (PAs) using high-order low-pass filter-prototype is proposed in this paper. A GaN transistor is used in this work, which is carefully modeled and characterized to prescribe the optimal output impedance for the broadband Class-E operation. A sixth-order low-pass filter-matching network is designed and implemented for the output matching, which provides optimized fundamental and harmonic impedances within an octave bandwidth (L-band). Simulation and experimental results show that an optimal Class-E PA is realized from 1.2 to 2 GHz (50%) with a measured efficiency of 80%-89%, which is the highest reported today for such a bandwidth. An overall PA bandwidth of 0.9-2.2 GHz (84%) is measured with 10-20-W output power, 10-13-dB gain, and 63%-89% efficiency throughout the band. Furthermore, the Class-E PA is characterized through measurements using constant-envelop global system for mobile communications signals, indicating a favorable adjacent channel power ratio from -40 to -50 dBc within the entire bandwidth.


title: A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation

abstract: In this paper, a multilevel thresholding method which allows the determination of the appropriate number of thresholds as well as the adequate threshold values is proposed. This method combines a genetic algorithm with a wavelet transform. First, the length of the original histogram is reduced by using the wavelet transform. Based on this lower resolution version of the histogram, the number of thresholds and the threshold values are determined by using a genetic algorithm. The thresholds are then projected onto the original space. In this step, a refinement procedure may be added to detect accurate threshold values. Experiments and comparative results with multilevel thresholding methods over a synthetic histogram and real images show the efficiency of the proposed method.


title: A micropower dry-electrode ECG preamplifier

abstract: This paper describes the development of a very low-power preamplifier intended for use in pasteless-electrode recording of the human electrocardiogram. The expected input signal range is 100 /spl mu/V-10 mV from a lead-II electrode configuration. The amplifier provides a gain of 43 dB in a 3-dB bandwidth of 0.05 Hz-2 kHz with a defined high input impedance of 75 M/spl Omega/. It uses a driven common electrode to enhance rejection of common-mode interfering signals, including low-frequency motion artifact, achieving a common-mode rejection ratio (CMRR) of better than 80 dB over its entire bandwidth. The gain and phase characteristics meet the recommendations of the American Heart Association, ensuring low distortion of the output ECG signal and making it suitable for clinical monitoring. The amplifier has a power consumption of 30 /spl mu/W operating from a 3.3-V battery and is intended for use in small, lightweight, portable electrocardiographic equipment and heart-rate monitoring instrumentation.


title: Developing Web Applications from Conceptual Models

abstract: This paper presents a strategy for the systematic development of dynamic web applications. The proposal extends an existing OO software production method introducing navigation and presentation expressiveness for applications of this kind. This extensions allow the method going from the problem space to the solution space in a systematic way.


title: A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework

abstract: Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage of sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter selection to TSC can be avoided, meanwhile with a shallow sRNN, the reconstruction coefficients can be inferred within a forward pass, which reduces the computational cost for learning sparse coefficients. The contributions of this paper are two-fold: i) We propose a TSC, which can be mapped to a sRNN which facilitates the parameter optimization and accelerates the anomaly prediction. ii) We build a very large dataset which is even larger than the summation of all existing dataset for anomaly detection in terms of both the volume of data and the diversity of scenes. Extensive experiments on both a toy dataset and real datasets demonstrate that our TSC based and sRNN based method consistently outperform existing methods, which validates the effectiveness of our method.


title: Using Runtime Measured Workload Characteristics in Parallel Processor Scheduling

abstract: We consider the use of runtime measured workload characteristics in parallel processor scheduling. Although many researchers have considered the use of application characteristics in this domain, most of this work has assumed that such information is available a priori. In contrast, we propose and evaluate experimentally dynamic processor allocation policies that rely on determining job characteristics at runtime; in particular, we focus on measuring and using job efficiency and speedup.


title: Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

abstract: Facial Key Points (FKPs) Detection is an important and challenging problem in the fields of computer vision and machine learning. It involves predicting the co-ordinates of the FKPs, e.g. nose tip, center of eyes, etc, for a given face. In this paper, we propose a LeNet adapted Deep CNN model - NaimishNet, to operate on facial key points data and compare our model's performance against existing state of the art approaches.


title: Parallel k-truss decomposition on multicore systems

abstract: We discuss our submission to the HPEC 2017 Static Graph Challenge on k-truss decomposition and triangle counting. Our results use an algorithm called PKT (Parallel k-truss) designed for multicore systems. We are able to process almost all Graph Challenge datasets in under a minute on a 24-core server with 128 GB memory. For a synthetic Graph500 graph with 17 million vertices and 523 million edges, triangle counting takes 16 seconds and truss decomposition takes 29 minutes on the 24-core server.


title: LPS pretreatment ameliorates peritonitis-induced myocardial inflammation and dysfunction: role of myocytes

abstract: Peritonitis induced by cecal ligation and puncture (CLP) produces a systemic inflammatory response that can be largely mitigated by pretreatment of the animals with lipopolysaccharide (LPS tolerance). Although cells of myeloid origin and endothelial cells have been shown to contribute to the development of LPS tolerance, little is known regarding the potential role of parenchymal cells in this phenomenon. The major aim of the present study was to assess whether cardiac parenchymal cells (myocytes) contribute to the development of LPS tolerance. Six hours after induction of CLP rats were neutropenic and acidotic, the myocardium contained a leukocyte infiltrate [myeloperoxidase (MPO) activity was increased], and myocardial contractile function was impaired (left ventricular developed pressure was decreased). In animals that were pretreated with LPS these manifestations of sepsis were largely reversed. Further studies focused on the responses of cardiac myocytes to CLP and whether myocytes contributed to the development of LPS tolerance. Myocytes were isolated from rat hearts 6 h after induction of CLP. These myocytes 1) exhibited an impaired ability to shorten in response to pacing, 2) contained the nuclear transcription factor NF-κB in their nuclei, 3) increased their surface levels of intercellular adhesion molecule-1 (ICAM-1), and 4) were hyperadhesive for neutrophils. All of these events did not occur in myocytes obtained from animals that were pretreated with LPS before induction of CLP. These findings indicate that LPS tolerance can be induced in myocytes with respect to polymorphonuclear leukocyte adhesion, presumably by an inability of CLP to mobilize NF-κB to the myocyte nuclei and, thereby, preventing an increase in surface levels of ICAM-1.


title: A dynamic energy management scheme for multi-tier data centers

abstract: Multi-tier data centers have become a norm for hosting modern Internet applications because they provide a flexible, modular, scalable and high performance environment. However, these benefits come at a price of the economic dent incurred in powering and cooling these large hosting centers. Thus, energy efficiency has become a critical consideration in designing Internet data centers. In this paper, we propose a multifaceted approach, Hybrid, consisting of dynamic provisioning, frequency scaling and dynamic power management (DPM) schemes to reduce the energy consumption of multi-tier data centers, while meeting the Service Level Agreements (SLAs). We formulate a mathematical model of the energy and performance/SLA optimization problem followed by a queueing theory based approach to develop two heuristics for solving the optimization problem. The first heuristic dynamically provisions the optimal number of servers required in each tier. The second heuristic proactively decides the CPU speed and the duration of sleep states of a server to achieve further energy savings. We evaluate our heuristics using a simulator that was validated with real measurements on a prototype three-tier data center consisting of 25 servers with two multi-tier application benchmarks. Our experimental results indicate that the proposed scheme, Hybrid, can reduce the energy consumption by 50% relative to static provisioning without CPU frequency scaling and DPM. We demonstrate that Hybrid satisfies the SLAs for dynamically varying workloads. In addition, the proposed multifaceted approach is more energy efficient than the other methods such as dynamic provisioning with exploiting deep sleep states.


title: Psycho-physiological measures for assessing cognitive load

abstract: With a focus on presenting information at the right time, the ubicomp community can benefit greatly from learning the most salient human measures of cognitive load. Cognitive load can be used as a metric to determine when or whether to interrupt a user. In this paper, we collected data from multiple sensors and compared their ability to assess cognitive load. Our focus is on visual perception and cognitive speed-focused tasks that leverage cognitive abilities common in ubicomp applications. We found that across all participants, the electrocardiogram median absolute deviation and median heat flux measurements were the most accurate at distinguishing between low and high levels of cognitive load, providing a classification accuracy of over 80% when used together. Our contribution is a real-time, objective, and generalizable method for assessing cognitive load in cognitive tasks commonly found in ubicomp systems and situations of divided attention.


title: Ontology-Based Interpretation of Natural Language

abstract: For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web.


title: Resting EEG Discrimination of Early Stage Alzheimer’s Disease from Normal Aging Using Inter-Channel Coherence Network Graphs

abstract: Amnestic mild cognitive impairment (MCI) is a degenerative neurological disorder at the early stage of Alzheimer's disease (AD). This work is a pilot study aimed at developing a simple scalp-EEG-based method for screening and monitoring MCI and AD. Specifically, the use of graphical analysis of inter-channel coherence of resting EEG for the detection of MCI and AD at early stages is explored. Resting EEG records from 48 age-matched subjects (mean age 75.7 years)--15 normal controls (NC), 16 with early-stage MCI, and 17 with early-stage AD--are examined. Network graphs are constructed using pairwise inter-channel coherence measures for delta-theta, alpha, beta, and gamma band frequencies. Network features are computed and used in a support vector machine model to discriminate among the three groups. Leave-one-out cross-validation discrimination accuracies of 93.6% for MCI vs. NC (p < 0.0003), 93.8% for AD vs. NC (p < 0.0003), and 97.0% for MCI vs. AD (p < 0.0003) are achieved. These results suggest the potential for graphical analysis of resting EEG inter-channel coherence as an efficacious method for noninvasive screening for MCI and early AD.


title: Positive Psychology Interventions Addressing Pleasure, Engagement, Meaning, Positive Relationships, and Accomplishment Increase Well-Being and Ameliorate Depressive Symptoms: A Randomized, Placebo-Controlled Online Study

abstract: Objective: Seligman (2002) suggested three paths to well-being, the pursuit of pleasure, the pursuit of meaning, and the pursuit of engagement, later adding two more, positive relationships and accomplishment, in his 2011 version. The contribution of these new components to well-being has yet to be addressed. Method: In an online positive psychology intervention study, we randomly assigned 1,624 adults aged 18 to 78 (M = 46.13; 79.2% women) to seven conditions. Participants wrote down three things they related to either one of the five components of Seligman’s Well-Being theory (Conditions 1-5), all of the five components (Condition 6) or early childhood memories (placebo control condition). We assessed happiness (AHI) and depression (CES-D) before and after the intervention, and 1-, 3-, and 6 months afterwards. Additionally, we considered moderation effects of well-being levels at baseline. Results: Results confirmed that all interventions were effective in increasing happiness and most ameliorated depressive symptoms. The interventions worked best for those in the middle-range of the well-being continuum. Conclusion: We conclude that interventions based on pleasure, engagement, meaning, positive relationships, and accomplishment are effective strategies for increasing well-being and ameliorating depressive symptoms and that positive psychology interventions are most effective for those people in the middle range of the well-being continuum.


title: Context-Aware Data Loss Prevention for Cloud Storage Services

abstract: With the wide adoption of hybrid cloud, there are many potential risks that need to be mitigated to ensure that the utilizations of services are at their optimal levels. One of the major risks that has garnered much attention is maintaining maximum security and confidentiality for sensitive information. Detecting sensitive content at near real-time and at cloud scale has become a critical first step for organizations to prevent data loss and comply with data privacy laws and regulations. Proactive detection raises security awareness at the early stage and thus can be used to govern how the information should be managed, protected, and utilized in the hybrid cloud. In contrast to traditional dictionary or policy-based approaches, we introduce a system that detects sensitive content by leveraging its semantic contextual information through various machine learning and deep learning techniques at different levels of granularity within the document, and is the first of its kind.


title: A Human Capital Model for Mitigating Security Analyst Burnout

abstract: Security Operation Centers (SOCs) are being operated by universities, government agencies, and corporations to defend their enterprise networks in general and in particular to identify malicious behaviors in both networks and hosts. The success of a SOC depends on having the right tools, processes and, most importantly, efficient and effective analysts. One of the worrying issues in recent times has been the consistently high burnout rates of security analysts in SOCs. Burnout results in analysts making poor judgments when analyzing security events as well as frequent personnel turnovers. In spite of high awareness of this problem, little has been known so far about the factors leading to burnout. Various coping strategies employed by SOC management such as career progression do not seem to address the problem but rather deal only with the symptoms. In short, burnout is a manifestation of one or more underlying issues in SOCs that are as of yet unknown. In this work we performed an anthropological study of a corporate SOC over a period of six months and identified concrete factors contributing to the burnout phenomenon. We use Grounded Theory to analyze our fieldwork data and propose a model that explains the burnout phenomenon. Our model indicates that burnout is a human capital management problem resulting from the cyclic interaction of a number of human, technical, and managerial factors. Specifically, we identified multiple vicious cycles connecting the factors affecting the morale of the analysts. In this paper we provide detailed descriptions of the various vicious cycles and suggest ways to turn these cycles into virtuous ones. We further validated our results on the fieldnotes from a SOC at a higher education institution. The proposed model is able to successfully capture and explain the burnout symptoms in this other SOC as well.


title: Corpus-Based Translation Induction in Indian Languages Using Auxiliary Language Corpora from Wikipedia

abstract: Identifying translations from comparable corpora is a well-known problem with several applications. Existing methods rely on linguistic tools or high-quality corpora. Absence of such resources, especially in Indian languages, makes this problem hard; for example, state-of-the-art techniques achieve a mean reciprocal rank of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. In this work, we address the problem of comparable corpora-based translation correspondence induction (CC-TCI) when the only resources available are small noisy comparable corpora extracted from Wikipedia. We observe that translations in the source and target languages have many topically related words in common in other “auxiliary” languages. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for CC-TCI. Extensive experiments on 35 comparable corpora showed dramatic improvements in performance. We extend these ideas to propose a method for measuring cross-lingual semantic relatedness (CLSR) between words. To stimulate further research in this area, we make publicly available two new high-quality human-annotated datasets for CLSR. Experiments on the CLSR datasets show more than 200% improvement in correlation on the CLSR task. We apply the method to the real-world problem of cross-lingual Wikipedia title suggestion and build the WikiTSu system. A user study on WikiTSu shows a 20% improvement in the quality of titles suggested.


Nonnative and second-language speech perception : Commonalities and complementarities 1


title: The category theoretic understanding of universal algebra: Lawvere theories and monads

abstract: Lawvere theories and monads have been the two main category theoretic formulations of universal algebra, Lawvere theories arising in 1963 and the connection with monads being established a few years later. Monads, although mathematically the less direct and less malleable formulation, rapidly gained precedence. A generation later, the definition of monad began to appear extensively in theoretical computer science in order to model computational effects, without reference to universal algebra. But since then, the relevance of universal algebra to computational effects has been recognised, leading to renewed prominence of the notion of Lawvere theory, now in a computational setting. This development has formed a major part of Gordon Plotkin's mature work, and we study its history here, in particular asking why Lawvere theories were eclipsed by monads in the 1960's, and how the renewed interest in them in a computer science setting might develop in future.


title: Targetable Named Entity Recognition in Social Media

abstract: We present a novel approach for recognizing what we call targetable named entities; that is, named entities in a targeted set (e.g, movies, books, TV shows). Unlike many other NER systems that need to retrain their statistical models as new entities arrive, our approach does not require such retraining, which makes it more adaptable for types of entities that are frequently updated. For this preliminary study, we focus on one entity type, movie title, using data collected from Twitter. Our system is tested on two evaluation sets, one including only entities corresponding to movies in our training set, and the other excluding any of those entities. Our final model shows F1-scores of 76.19% and 78.70% on these evaluation sets, which gives strong evidence that our approach is completely unbiased to any particular set of entities found during training.


title: MOBMAS: A methodology for ontology-based multi-agent systems development

abstract: Ontologies offer significant benefits to multi-agent systems: interoperability, reusability, support for multi-agent system (MAS) development activities (such as system analysis and agent knowledge modeling) and support for MAS operation (such as agent communication and reasoning). This paper presents an ontology-based methodology, MOBMAS, for the analysis and design of multi-agent systems. MOBMAS is the first methodology that explicitly identifies and implements the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In this paper, we present comprehensive documentation and validation of MOBMAS.


title: Sports highlights generation based on acoustic events detection: A rugby case study

abstract: We approach the challenging problem of generating highlights from sports broadcasts utilizing audio information only. A language-independent, multi-stage classification approach is employed for detection of key acoustic events which then act as a platform for summarization of highlight scenes. Objective results and human experience indicate that our system is highly efficient.


title: © 2011 INFORMS Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator

abstract: Using the well-known product-limit form of the Kaplan-Meier estimator from statistics, we propose a new class of nonparametric adaptive data-driven policies for stochastic inventory control problems. We focus on the distribution-free newsvendor model with censored demands. The assumption is that the demand distribution is not known and there are only sales data available. We study the theoretical performance of the new policies and show that for discrete demand distributions they converge almost surely to the set of optimal solutions. Computational experiments suggest that the new policies converge for general demand distributions, not necessarily discrete, and demonstrate that they are significantly more robust than previously known policies. As a by-product of the theoretical analysis, we obtain new results on the asymptotic consistency of the Kaplan-Meier estimator for discrete random variables that extend existing work in statistics. To the best of our knowledge, this is the first application of the Kaplan-Meier estimator within an adaptive optimization algorithm, in particular, the first application to stochastic inventory control models. We believe that this work will lead to additional applications in other domains.


title: View-based interpretation of real-time optical flow for gesture recognition

abstract: The authors have developed a real-time, view-based gesture recognition system. Optical flow is estimated and segmented into motion blobs. Gestures are recognized using a rule-based technique based on characteristics of the motion blobs such as relative motion and size. Parameters of the gesture (e.g., frequency) are then estimated using context specific techniques. The system has been applied to create an interactive environment for children.


title: Generating Image Descriptions Using Dependency Relational Patterns

abstract: This paper presents a novel approach to automatic captioning of geo-tagged images by summarizing multiple web-documents that contain information related to an image's location. The summarizer is biased by dependency pattern models towards sentences which contain features typically provided for different scene types such as those of churches, bridges, etc. Our results show that summaries biased by dependency pattern models lead to significantly higher ROUGE scores than both n-gram language models reported in previous work and also Wikipedia baseline summaries. Summaries generated using dependency patterns also lead to more readable summaries than those generated without dependency patterns.


title: Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

abstract: Nowadays, we see more and more cyber-attacks on major Internet sites and enterprise networks. Intrusion Detection System (IDS) is a critical component of such infrastructure defense mechanism. IDS monitors and analyzes networks' activities for potential intrusions and security attacks. Machine-learning (ML) models have been well accepted for signature-based IDSs due to their learn ability and flexibility. However, the performance of existing IDSs does not seem to be satisfactory due to the rapid evolution of sophisticated cyber threats in recent decades. Moreover, the volumes of data to be analyzed are beyond the ability of commonly used computer software and hardware tools. They are not only large in scale but fast in/out in terms of velocity. In big data IDS, the one must find an efficient way to reduce the size of data dimensions and volumes. In this paper, we propose novel feature selection methods, namely, RF-FSR (Random Forest-Forward Selection Ranking) and RF-BER (Random Forest-Backward Elimination Ranking). The features selected by the proposed methods were tested and compared with three of the most well-known feature sets in the IDS literature. The experimental results showed that the selected features by the proposed methods effectively improved their detection rate and false-positive rate, achieving 99.8% and 0.001% on well-known KDD-99 dataset, respectively.


title: Becoming What We Love: autogynephilic transsexualism conceptualized as an expression of romantic love

abstract: The increasing prevalence of male-to-female (MtF) transsexualism in Western countries is largely due to the growing number of MtF transsexuals who have a history of sexual arousal with cross-dressing or cross-gender fantasy. Ray Blanchard proposed that these transsexuals have a paraphilia he called autogynephilia, which is the propensity to be sexually aroused by the thought or image of oneself as female. Autogynephilia defines a transsexual typology and provides a theory of transsexual motivation, in that Blanchard proposed that MtF transsexuals are either sexually attracted exclusively to men (homosexual) or are sexually attracted primarily to the thought or image of themselves as female (autogynephilic), and that autogynephilic transsexuals seek sex reassignment to actualize their autogynephilic desires. Despite growing professional acceptance, Blanchard's formulation is rejected by some MtF transsexuals as inconsistent with their experience. This rejection, I argue, results largely from the misconception that autogynephilia is a purely erotic phenomenon. Autogynephilia can more accurately be conceptualized as a type of sexual orientation and as a variety of romantic love, involving both erotic and affectional or attachment-based elements. This broader conception of autogynephilia addresses many of the objections to Blanchard's theory and is consistent with a variety of clinical observations concerning autogynephilic MtF transsexualism.


title: An Empirically-Derived Question Taxonomy for Task-Oriented Tutorial Dialogue

abstract: Devising an expressive question taxonomy is a central problem in question generation. Through examination of a corpus of human-human task- oriented tutoring, we have found that existing question taxonomies do not capture all of the tutorial questions present in this form of tutoring. We propose a hierarchical question classification scheme for tutorial questions in which the top level corresponds to the tutor's goal and the second level corresponds to the question type. The application of this hierarchical classification scheme to a corpus of keyboard-to-keyboard tutoring of introductory computer science yielded high inter-rater reliability, suggesting that such a scheme is appropriate for classifying tutor questions in design-oriented tutoring. We discuss numerous open issues that are highlighted by the current analysis.


title: Safe Screening of Non-Support Vectors in Pathwise SVM Computation

abstract: In this paper, we claim that some of the nonsupport vectors (non-SVs) that have no influence on the SVM classifier can be screened out prior to the training phase in pathwise SVM computation scenario, in which one is asked to train a sequence (or path) of SVM classifiers for different regularization parameters. Based on a recently proposed framework so-called safe screening rule, we derive a rule for screening out non-SVs in advance, and discuss how we can exploit the advantage of the rule in pathwise SVM computation scenario. Experiments indicate that our approach often substantially reduce the total pathwise computation cost.


title: Models in information behaviour research

abstract: This paper presents an outline of models of information seeking and other aspects of information behaviour, showing the relationship between communication and information behaviour in general with information seeking and information searching in information retrieval systems. It is suggested that these models address issues at various levels of information behaviour and that they can be related by envisaging a ‘nesting’ of models. It is also suggested that, within both information seeking research and information searching research, alternative models address similar issues in related ways and that the models are complementary rather than conflicting. Finally, an alternative, problem‐solving model is presented, which, it is suggested, provides a basis for relating the models in appropriate research strategies.


title: Bagging ensemble selection for regression

abstract: Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classification, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non-negative least squares algorithm is a viable approach for pruning an ensemble of ensembles.


title: Self-automated parking lots for autonomous vehicles based on vehicular ad hoc networking

abstract: Parking is a major problem of car transportation, with important implications in traffic congestion and urban landscape. Reducing the space needed to park cars has led to the development of fully automated and mechanical parking systems. These systems are, however, limitedly deployed because of their construction and maintenance costs. Leveraging on semi and fully-autonomous vehicular technology, as well as on the electric propulsion paradigm and in vehicular ad hoc networking, we propose a new parking concept where the mobility of parked vehicles is managed by a parking lot controller to create space for cars entering or exiting the parking lot, in a collaborative manner. We show that the space needed to park such vehicles can be reduced to half the space needed with conventional parking lot designs. We also show that the total travelled distance of vehicles in this new parking lot paradigm can be 30% less than in conventional parking lots. Our proposal can have important consequences in parking costs and in urban landscape.


title: A novel optimum operating point tracker of the solar cell power supply system

abstract: When the solar array is used as an input power source, the optimum operating point tracker is often employed to exploit more effectively the solar array as an electric power source and to obtain the maximum electric power at all times even when the light intensity and environmental temperature of the solar array are varied. Usually, the optimum operating point is determined by computing the electric power from the solar array power supply with a microcomputer, DSP and so forth. However, such a method has the problems of the complex control circuit configuration, high cost and low control speed. From this viewpoint, this paper proposes a new optimum operating point tracker of the solar cell power supply system, in which inexpensive pn-junction diodes are used to generate the reference voltage of the operating point of the solar array. Using the proposed method, the high degree of the solar array optimum point tracking performance can be obtained, even when the light intensity and environmental temperature of the solar array are varied. Furthermore, the proceeding paper provides the operation principle, design-oriented analysis and so forth, of the proposed solar cell power supply system.


title: Current Status of the Use of Single-Nucleotide Polymorphisms in Forensic Practices

abstract: Forensic geneticists often use short tandem repeats (STRs) to solve cases. However, STRs can be insufficient when DNA samples are degraded due to environmental exposure and mass disasters, alleged and real relatives are genetically related in paternity or kinship analyses, or a suspect is lacking. In such cases, single-nucleotide polymorphisms (SNPs) can provide valuable information and thus should be seriously considered as a tool to help resolve challenging cases. In this review, the current status of SNP analyses in forensic applications and the comparative advantages and disadvantages of SNPs with other biomarkers are discussed.


title: Analysis of " One Village One Product" (OVOP) or "Satu Daerah Satu Industri (SDSI)" in Kelantan.

abstract: The purpose of the study was to determine the effectiveness of One Village, One Product or in Bahasa Malaysia, "Satu Daerah, Sati Industri (SDSI) policies. The study investigates the success factors of Small and Medium Enterprises (SMEs) start-up and evaluation of SDSI policies, to clarify the business behavior and operational problems of rural base SDSI firms, and study on the effectiveness of SDSI and its relevancy in the state of Kelantan. In additional, this study analyzed the background of SDSI firms and entrepreneurs, their business condition and prospects, the evaluation of SDSI policy, and its future direction.


title: Single-phase grid-tie inverter control using DQ transform for active and reactive load power compensation

abstract: This paper presents a current control for single phase grid connected inverters. The method allows for inverter active and reactive power control. The method uses the Direct-Quadrature (DQ) synchronous reference frame transformation for single-phase converters. This method transforms an orthogonal pair consisting of the inverter output current and a time shifted version of this current from a stationary frame to a rotating frame synchronous to the fundamental output frequency. Alternatives to using the time shifted current are discussed. The steady state current components in this rotating DQ frame are DC values and thus PI control methods can be used with zero error. A household scale grid-tie inverter is used as an example application of this method. A Simulink simulation model and results where the inverter output is controlled to match a local load's active and reactive power demand is presented.


title: An Overview of Digital Video Watermarking

abstract: The illegal distribution of a digital movie is a common and significant threat to the film industry. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can now be easily distributed to a global audience. A possible means of limiting this type of digital theft is digital video watermarking whereby additional information, called a watermark, is embedded in the host video. This watermark can be extracted at the decoder and used to determine whether the video content is watermarked. This paper presents a review of the digital video watermarking techniques in which their applications, challenges, and important properties are discussed, and categorizes them based on the domain in which they embed the watermark. It then provides an overview of a few emerging innovative solutions using watermarks. Protecting a 3D video by watermarking is an emerging area of research. The relevant 3D video watermarking techniques in the literature are classified based on the image-based representations of a 3D video in stereoscopic, depth-image-based rendering, and multi-view video watermarking. We discuss each technique, and then present a survey of the literature. Finally, we provide a summary of this paper and propose some future research directions.


title: Chaperone-Like Activity of a Bacterioferritin Comigratory Protein from Thermococcus kodakaraensis KOD1.

abstract: Peroxiredoxins (Prxs) are ubiquitous and conserved proteins that can catalyze the reduction of inorganic and organic hydroperoxides to protect against damage by reactive oxygen species. In this study, a Prx subfamily member, and specifically a bacterioferritin comigratory protein from hyperthermophilic Thermococcus kodakaraensis KOD1 (TkBcp), was overexpressed, purified and characterized. Based on the conserved cysteine (Cys) residues in its amino acids sequence, TkBcp can be grouped into 1-Cys Prx family. Size exclusion chromatography analysis showed that TkBcp exists in three oligomeric forms: 700 kDa, 70 kDa, and 20 kDa. The peroxidase function was found to predominate in the lowmolecular- weight (MW) form, whereas the high-MW complex has the chaperone function. Oxidative reagents caused the protein structure of TkBcp to shift from low-MW form to high-MW complexes, whereas reducing reagents caused a shift in the reverse direction. Furthermore, the high-MW form of TkBcp preferred to tightly bind DNA. The relationship of TkBcp with other homologs was also examined.


title: Mining topics in documents: standing on the shoulders of big data

abstract: Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning/modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines.


title: Belief-state Monte-Carlo tree search for Phantom games

abstract: Playing games with imperfect information is a very challenging issue in AI field due to its high complexity. Phantom game is a kind of such games, which usually has a large game-tree complexity and has little research achievements until now. In Phantom games, rational human players commonly select actions according to their beliefs in the game, which can be represented as a concept of belie f-state. To the best of our knowledge, our paper is the first article to incorporate belief-states in the Monte-Carlo Tree Search, and the proposed algorithm is named BS-MCTS (Belief-state Monte-Carlo Tree Search). In BS-MCTS, a belief-state tree, in which each node is a belief-state, is constructed and the search procedure is in accordance with beliefs updated by heuristic search information. We also present two novel implementations in the belief learning, that are Opponent Guessing and Opponent Predicting, concerning the probability on the possible states and on future actions of the opponent respectively. To prove the effectiveness of our algorithm, BS-MCTS is applied to Phantom Tic-Tac-Toe and Phantom Go against other Monte-Carlo methods. The experimental results demonstrate that our method is outstanding and advanced. Moreover, based on BS-MCTS, our Phantom Go program had consecutively won three championships in Chinese National Tournaments.


title: Repulsive reaction vector generator for whole-arm collision avoidance of 7-DoF redundant robot manipulator

abstract: In this paper, we propose a repulsive reaction vector generator (RVG) for whole-arm collision avoidance of 7-DoF redundant robot manipulator, and elucidate the case of spherical-revolute-spherical (S-R-S) type manipulator. Furthermore, this article discusses the obstacle-avoiding function of RVG, in which the generated reaction vector can aid the manipulator to achieve collision avoidance in an intuitive way by taking arm angle as the redundant parameter. First of all, we use six representative points to describe the manipulator, and generate the repulsive vectors according to the relationship between obstacle and the tip, wrist, and arm body of the manipulator, respectively. Next, after the pose of the manipulator, including position, orientation, and arm angle, is rectified by the reaction vector, the command would be sent to proposed vOTG to complete the reactive motion. As a result, the manipulator can achieve intuitive repulsive reaction, active whole-arm collision avoidance, and meanwhile avoiding the minor-risk obstacle by rotating the arm plane. Experimental results with NTU-iCeiRA arm developed in our lab and Microsoft Kinect depth sensor are presented.


title: A Novel Distributed Fog-Based Networked Architecture to Preserve Energy in Fog Data Centers

abstract: The distinguishing feature of the Fog Computing (FC) paradigm is that FC spreads communication and computing resources over the wireless access network, so as to provide resource augmentation to resource and energy-limited wireless (possibly mobile) devices. Since FC would lead to substantial reductions in energy consumption and access latency, it will play a key role in the realization of the Fog of Everything (FoE) paradigm. The core challenge of the resulting FoE paradigm is tomaterialize the seamless convergence of three distinct disciplines, namely, broadband mobile communication, cloud computing, and Internet of Everything (IoE). In this paper, we present a new IoE architecture for FC in order to implement the resulting FoE technological platform. Then, we elaborate the related Quality of Service (QoS) requirements to be satisfied by the underlying FoE technological platform. Furthermore, in order to corroborate the conclusion that advancements in the envisioned architecture description, we present: (i) the proposed energy-aware algorithm adopt Fog data center; and, (ii) the obtained numerical performance, for a real-world case study that shows that our approach saves energy consumption impressively in theFog data Center compared with the existing methods and could be of practical interest in the incoming Fog of Everything (FoE) realm.


title: Principles and clinical implications of the brain–gut–enteric microbiota axis

abstract: In this article, Rhee and colleagues review the ample preclinical evidence suggesting that the commensal bacterial flora physiologically present in the gut modulates and influences bidirectional communication between the host's gut and central nervous system. The authors propose possible mechanisms by which these three-way mutual interactions may occur and may affect the host's healthy and diseased states.


title: Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects

abstract: In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.


title: Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation

abstract: Discriminative methods often generate hand poses kinematically implausible, then generative methods are used to correct (or verify) these results in a hybrid method. Estimating 3D hand pose in a hierarchy, where the high-dimensional output space is decomposed into smaller ones, has been shown effective. Existing hierarchical methods mainly focus on the decomposition of the output space while the input space remains almost the same along the hierarchy. In this paper, a hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO). The spatial attention mechanism integrates cascaded and hierarchical regression into a CNN framework by transforming both the input (and feature space) and the output space, which greatly reduces the viewpoint and articulation variations. Between the levels in the hierarchy, the hierarchical PSO forces the kinematic constraints to the results of the CNNs. The experimental results show that our method significantly outperforms four state-of-the-art methods and three baselines on three public benchmarks.


title: Link level performance evaluation of higher order modulation in Small Cells

abstract: In Heterogeneous networks (HetNet), low-power Small Cells (SCs) can serve a good portion of the mobile users. These users are likely to have good Signal-to-Interference-plus-Noise-Ratio (SINR) values, which can be exploited to satisfy the expected demand of huge data rates. In this paper, we investigate the effect of introducing higher order modulation, mainly 256QAM, from a link-level perspective as a possible solution to this data explosion. 256 QAM in ideal channel conditions and high SINR range, achieves higher spectral efficiency compared to 64 QAM. However, due to transmitter and receiver impairments the gain of 256 QAM is questionable. we developed a physical-layer abstraction for the link-level performance of the 256QAM modulation order. Then, we evaluated the performance of 256 QAM in realistic environment including the impairments at the transmitter and receiver. These impairments can diminish the gains of 256 QAM depending on their magnitude.


abstract: A 1.8V CMOS chopper four-quadrant analog multiplier, intending to serve as an autonomous IC block for low-frequency signal processing, is presented. Particular emphasis is laid upon achieving low output noise by means of chopper stabilization, while the multiplier's operation is based on the MOS Translinear Principle. The proposed design has been implemented and simulated in TSMC 0.18 µm CMOS process.


title: Wearable tactile keypad with stretchable artificial skin

abstract: A hyperelastic, thin, transparent pressure sensitive keypad is fabricated by embedding a silicone rubber film with conductive liquid-filled microchannels. Applying pressure to the surface of the elastomer deforms the cross-section of underlying microchannels and changes the electrical resistance across the affected channels. Perpendicular conductive channels form a quasi-planar network within an elastomeric matrix that registers the location, intensity and duration of applied pressure. Pressing channel intersections of the keypad triggers one of twelve keys, allowing the user to write any combination of alphabetic letters. A 5% change in channel output voltage must be achieved to trigger a key. It is found that approximately 100 kPa of pressure is necessary to produce a 5% change in voltage across a conductive microchannel that is 20 microns in height and 200 microns in width. Sensitivity of the keypad is tunable via channel geometry and choice of elastomeric material.


title: Encoding Geometric Information in Road Networks Extracted from Binary Images

abstract: The authors discuss recent progress in extracting road networks from digital imagery. We briefly review recent developments in methods for the automatic extraction of road centre line networks and propose a related algorithm aimed at encoding the geometry of road networks with line segments. Our algorithm is inspired by 'axial lines', which have been defined as lines of uninterrupted movement within urban streetscapes or buildings. We show that axial lines appear as ridges in isovist fields. These are formed from the maximum diametric lengths of the individual isovists, sometimes called viewsheds, which make up these fields. We present an image-processing technique for the identification of lines from ridges, discuss current strengths and weaknesses of the method, and show how it can be implemented easily and effectively.