title: CoAST: collaborative application-aware scheduling of last-mile cellular traffic

abstract: The explosive growth of mobile data traffic poses severe pressure on cellular providers to better manage their finite spectrum. Proposed solutions such as congestion-pricing exist, but they degrade users' ability to use the network when they want. In this paper, we propose a fundamentally different approach - rather than reducing the aggregate busy hour traffic, we seek to smooth the peaks that cause congestion. Our approach is based on two key insights obtained from traffic traces of a large cellular provider. First, mobile traffic demonstrates high short-term variation so that delaying traffic for very short periods of time can significantly reduce peaks. Second, by making collaborative decisions on which traffic gets delayed and by how much across all users of a cell, the delays need not result in any degradation of user experience. We design a system, CoAST, to implement this approach using three key mechanisms: a protocol to allow mobile applications and providers to exchange traffic information, an incentive mechanism to incentivize mobile applications to collaboratively delay traffic at the right time, and mechanisms to delay application traffic. We provide extensive evaluations that show that CoAST reduces traffic peaks by up to 50% even for applications that are not thought to be delay-tolerant, e.g., streaming and web browsing, but which together account for 70% of all cellular traffic.


title: Early Prediction of Students' Grade Point Averages at Graduation: A Data Mining Approach.

abstract: Problem Statement: There has recently been interest in educational databases containing a variety of valuable but sometimes hidden data that can be used to help less successful students to improve their academic performance. The extraction of hidden information from these databases often implements aspects of the educational data mining (EDM) theory, which aims to study available data in order to shed light on more valuable, hidden information. Data clustering, classification, and regression methods such as K-means clustering, neural networks (NN), extreme learning machine (ELM), and support vector machines (SVM) can be used for to predict aspects of the educational data. EDM outputs can ultimately identify which students will need additional help to improve their grade point averages (GPAs) at graduation. Purpose of Study: This study aims to implement several prediction techniques in data mining to assist educational institutions with predicting their students’ GPAs at graduation. If students are predicted to have low GPAs at graduation, then extra efforts can be made to improve their academic performance and, in turn, GPAs. Methods: NN, SVM, and ELM algorithms are applied to data of computer education and instructional technology students to predict their GPAs at graduation. Findings and Results: A comparative analysis of the results indicates that the SVM technique yielded more accurate predictions at a rate of 97.98%. By contrast, the ELM method yielded the second most accurate prediction rate (94.92%) evaluated based on the criterion of correlation coefficient. NN reported the least accurate prediction rate (93.76%). Conclusions and Recommendations: The use of data mining methodologies has recently expanded for a variety of educational purposes. The


Software and Hardware Implementation of Lattice-Cased Cryptography Schemes


title: Low-power Task Scheduling for GPU Energy Reduction

abstract: Graphics processing units (GPU) have been intensively used by high-performance computing applications. However, GPU’s large power consumption is a big issue coexisting with the high parallelism. Although Dynamic Voltage and Frequency Scaling (DVFS) [1] has been heavily studied and successfully applied to real products for saving CPU power consumption, DVFS is still relatively new for GPU energy studies. The lack of DVFS and other power management schemes on the GPU also makes its large power consumption significant in recent computer systems. In this Operating System course project, we propose a low-power scheme for GPU energy reduction. This project can be decomposed to GPU DVFS implementation and GPU linear regression power model. For enabling the DVFS on GPU, the settings of voltage and frequency in open-source Nouveau GPU driver [7] has been studied. For supporting the DVFS choosing voltage and frequency levels, a GPU linear regression power model has been built and evaluated.


title: The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

abstract: The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data-accuracy, mean response times, and response time distributions-into components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.


title: Pathologic alterations of cutaneous innervation and vasculature in affected limbs from patients with complex regional pain syndrome

abstract: Complex regional pain syndromes (CRPS, type I and type II) are devastating conditions that can occur following soft tissue (CRPS type I) or nerve (CRPS type II) injury. CRPS type I, also known as reflex sympathetic dystrophy, presents in patients lacking a well-defined nerve lesion, and has been questioned as to whether or not it is a true neuropathic condition with an organic basis. As described here, glabrous and hairy skin samples from the amputated upper and lower extremity from two CRPS type I diagnosed patients were processed for double-label immunofluorescence using a battery of antibodies directed against neural-related proteins and mediators of nociceptive sensory function. In CRPS affected skin, several neuropathologic alterations were detected, including: (1) the presence of numerous abnormal thin caliber NF-positive/MBP-negative axons innervating hair follicles; (2) a decrease in epidermal, sweat gland, and vascular innervation; (3) a loss of CGRP expression on remaining innervation to vasculature and sweat glands; (4) an inappropriate expression of NPY on innervation to superficial arterioles and sweat glands; and (5) a loss of vascular endothelial integrity and extraordinary vascular hypertrophy. The results are evidence of widespread cutaneous neuropathologic changes. Importantly, in these CRPS type I patients, the myriad of clinical symptoms observed had detectable neuropathologic correlates.


title: Using Lanchester Attrition Laws for Combat Prediction in StarCraft.

abstract: Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. Winning battles is crucial in RTS games, and while ahumans can decide when and how to attack based on their experience, it is challenging for AI agents to estimate combat outcomes accurately. A few existing models address this problem in the game of StarCraft but present many restrictions, such as not modeling injured units, supporting only a small number of unit types, or being able to predict the winner of a fight but not the remaining army. Prediction using simulations is a popular method, but generally slow and requires extensive coding to model the game engine accurately. This paper introduces a model based on Lanchester’s attrition laws which addresses the mentioned limitations while being faster than running simulations. Unit strength values are learned using maximum likelihood estimation from past recorded battles. We present experiments that use a StarCraft simulator for generating battles for both training and testing, and show that the model is capable of making accurate predictions. Furthermore, we implemented our method in a StarCraft bot that uses either this or traditional simulations to decide when to attack or to retreat. We present tournament results (against top bots from 2014 AIIDE competition) comparing the performances of the two versions, and show increased winning percentages for our method.


title: Consistent Temporal Variations in Many Outdoor Scenes

abstract: This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.


title: High-Intensity Interval Training , Solutions to the Programming Puzzle Part II : Anaerobic Energy , Neuromuscular Load and Practical Applications

abstract: High-intensity interval training (HIT) is a well-known, time-efficient training method for improving cardiorespiratory and metabolic function and, in turn, physical performance in athletes. HIT involves repeated short (<45 s) to long (2–4 min) bouts of rather high-intensity exercise interspersed with recovery periods (refer to the previously published first part of this review). While athletes have used ‘classical’ HIT formats for nearly a century (e.g. repetitions of 30 s of exercise interspersed with 30 s of rest, or 2–4-min interval repetitions ran at high but still submaximal intensities), there is today a surge of research interest focused on examining the effects of short sprints and all-out efforts, both in the field and in the laboratory. Prescription of HIT consists of the manipulation of at least nine variables (e.g. work interval intensity and duration, relief interval intensity and duration, exercise modality, number of repetitions, number of series, between-series recovery duration and intensity); any of which has a likely effect on the acute physiological response. Manipulating HIT appropriately is important, not only with respect to the expected middle- to long-term physiological and performance adaptations, but also to maximize daily and/or weekly training periodization. Cardiopulmonary responses are typically the first variables to consider when programming HIT (refer to Part I). However, anaerobic glycolytic energy contribution and neuromuscular load should also be considered to maximize the training outcome. Contrasting HIT formats that elicit similar (and maximal) cardiorespiratory responses have been associated with distinctly different anaerobic energy contributions. The high locomotor speed/power requirements of HIT (i.e. ≥95 % of the minimal velocity/power that elicits maximal oxygen uptake [v/p\( \dot{V} \)O2max] to 100 % of maximal sprinting speed or power) and the accumulation of high-training volumes at high-exercise intensity (runners can cover up to 6–8 km at v\( \dot{V} \)O2max per session) can cause significant strain on the neuromuscular/musculoskeletal system. For athletes training twice a day, and/or in team sport players training a number of metabolic and neuromuscular systems within a weekly microcycle, this added physiological strain should be considered in light of the other physical and technical/tactical sessions, so as to avoid overload and optimize adaptation (i.e. maximize a given training stimulus and minimize musculoskeletal pain and/or injury risk). In this part of the review, the different aspects of HIT programming are discussed, from work/relief interval manipulation to HIT periodization, using different examples of training cycles from different sports, with continued reference to the cardiorespiratory adaptations outlined in Part I, as well as to anaerobic glycolytic contribution and neuromuscular/musculoskeletal load.


title: New Image Steganography Method Based on K-means Clustering

abstract: In this paper, a new image steganography method based on k-means clustering for embedding secret messages into a gray image is proposed. The proposed methodology is a combination of two techniques, image clustering and Least Significant Bits replacement. In the embedding process, a cover image is segmented into clusters using the K-means clustering algorithm. Each cluster is partitioned into two regions, smooth and complex, only smooth regions may contain the secret data. In this manner, degradation of the Stego image quality is imperceptible to the human eye. For better protection of secret message, a pseudo-random key mechanism is coupled to the implementation of the LSB method. The secret data can be recovered directly from the image Stego without reference to cover-image. The experimental results show that the proposed method has a high image quality and a good embedding capacity.


title: Using ID-Hopping to Defend Against Targeted DoS on CAN

abstract: With the exponential growth of automotive security research, new security vulnerabilities and attacks have been revealed and new challenges have emerged. In recent years, various attacks ranging from replay attacks, through false information injection, to Denial of Service (DoS), have shown how fragile automotive security is. As a result, a number of security solutions have been proposed that rely on techniques like encryption and firewalls. However, most proposals require performance and computational overheads that would become an additional burden rather than a solution. In this paper, we propose a new automotive network algorithm, called ID-Hopping, that aims to prevent targeted DoS attacks in which attackers target certain functions by injecting special frames that would prevent a car's normal operations. We aim to raise the bar for attackers by randomizing the expected patterns in the automotive network. Such randomization hinders the attacker's ability to launch targeted DoS attacks. We built a testing platform and implemented the randomization mechanism to evaluate the algorithm's effectiveness. Based on the evaluation, the algorithm holds a promising solution for targeted DoS, and even reverse engineering, which automotive networks are most vulnerable to.


title: Improving Barcode Detection with Combination of Simple Detectors

abstract: Barcode detection is required in a wide range of real-life applications. Imaging conditions and techniques vary considerably and each application has its own requirements for detection speed and accuracy. In our earlier works we built barcode detectors using morphological operations and uniform partitioning with several approaches and showed their behaviour on a set of test images. In this work, we examine ensemble efficiency of those simple detectors using various aggregation methods. Using a combination of several simple features localization performance improves significantly.


title: Optical flow based obstacle avoidance for the visually impaired

abstract: Vision is a vital cue for human navigation. Thus, visually impaired people encounter many challenges in day-today travelling. Identifying and avoiding obstacles in the environment is the most crucial among them. To empower blind navigation, numerous electronic travel aids were created in the past few decades, by using various obstacle sensing technologies such as sonar, infrared, and stereo vision. However, optical flow estimations based navigation, which is heavily used by insects and experimented in the field of robotics, has not been used in them. This project aimed to evaluate the potential optical flow estimation based techniques has in guiding a visually impaired person to avoid obstacles using auditory and tactile feedback. To demonstrate the researched core concepts, a prototype consisting of a virtual reality world was designed and developed. It also employs an existing optical flow algorithm for motion estimation, other image processing techniques, speech synthesis for auditory feedback and embedded programming for tactile feedback. The modular design of the prototype enables it to be used either in simulation mode, or as a standalone application in a real world environment. This work demonstrates the attractive possibilities of using optical flow estimations for visually impaired navigation.


title: A density-based cluster validity approach using multi-representatives

abstract: Although the goal of clustering is intuitively compelling and its notion arises in many fields, it is difficult to define a unified approach to address the clustering problem and thus diverse clustering algorithms abound in the research community. These algorithms, under different clustering assumptions, often lead to qualitatively different results. As a consequence the results of clustering algorithms (i.e., data set partitionings) need to be evaluated as regards their validity based on widely accepted criteria. In this paper a cluster validity index, CDbw, is proposed which assesses the compactness and separation of clusters defined by a clustering algorithm. The cluster validity index, given a data set and a set of clustering algorithms, enables (i) the selection of the input parameter values that lead an algorithm to the best possible partitioning of the data set, and (ii) the selection of the algorithm that provides the best partitioning of the data set. CDbw handles efficiently arbitrarily shaped clusters by representing each cluster with a number of points rather than by a single representative point. A full implementation and experimental results confirm the reliability of the validity index showing also that its performance compares favourably to that of several others.


title: Asymmetrical control with phase lock loop for induction cooking appliances

abstract: This paper presents an asymmetrical control with phase lock loop for series resonant inverters. This control strategy is used in full-bridge topologies for induction cookers. The operating frequency is automatically tracked to maintain a small constant lagging phase angle when load parameters change. The switching loss is minimized by operating the IGBT in the zero voltage resonance modes. The output power can be adjusted by using asymmetrical voltage cancellation control which is regulated with a PWM duty cycle control strategy.


title: The Emergence of Industrial Control Networks for Manufacturing Control, Diagnostics, and Safety Data

abstract: The most notable trend in manufacturing over the past five years is probably the move towards networks at all levels. At lower levels in the factory infrastructure, networks provide higher reliability, visibility, and diagnosability, and enable capabilities such as distributed control, diagnostics, safety, and device interoperability. At higher levels, networks can leverage internet services to enable factory-wide automat- ed scheduling, control, and diagnostics; improve data storage and visibility; and open the door to e-manufacturing. This paper explores current trends in the use of networks for distributed, multilevel control, diagnostics, and safety. Network performance characteristics such as delay, delay variability, and determinism are evaluated in the context of networked control applications. This paper also discusses future network- ing trends in each of these categories and describes the actual application of all three categories of networks on a reconfigur- able factory testbed (RFT) at the University of Michigan. Control, diagnostics, and safety systems are all enabled in the RFT utilizing multitier networked technology including Device- Net, PROFIBUS, OPC, wired and wireless Ethernet, and Safe- tyBUS p. This paper concludes with a discussion of trends in industrial networking, including the move to wireless for all categories, and the issues that must be addressed to realize these trends.


title: A prediction based approach for stock returns using autoregressive neural networks

abstract: This paper presents a prediction based neural networks approach for stock returns. An autoregressive neural network predictor is used to predict future stock returns. In this predictor, the differences between the values of the series of stock returns and a specified past value are the regression variables. Various error metrics have been used to evaluate the performance of the predictor. Experiments with real data from National stock exchange of India (NSE) were employed to examine the accuracy of this method.


title: Air muscle controller design in the distributed macro-mini (DM2) actuation approach

abstract: Recently, on the base of distributed macro-mini actuation approach (DM2), a new robotic manipulator with hybrid actuation, air muscles-DC motor, has been developed. Among existing actuators, the hybrid actuation employs air muscles because they represent an advantageous tradeoff of performance and safety, due to their power/weight ratio and inherent compliance. The air muscles, however, are limited in bandwidth and their behavior is highly nonlinear. In order to overcome these limitations, the paper presents a torque control strategy based on a pair of differentially connected force-controlled air muscles. This controller was implemented and evaluated on a single joint testbed, first by itself and then as macro component into the Macro-Mini control strategy.


title: Neural networks performance in exchange rate prediction

abstract: Exploration of ANNs for the economic purposes is described and empirically examined with the foreign exchange market data. For the experiments, panel data of the exchange rates (USD/EUR, JPN/USD, USD/GBP) are examined and optimized to be used for time-series predictions with neural networks. In this stage the input selection, in which the processing steps to prepare the raw data to a suitable input for the models are investigated. The best neural network is found with the best forecasting abilities, based on a certain performance measure. A visual graphs on the experiments data set is presented after processing steps, to illustrate that particular results. The out-of-sample results are compared with training ones.


title: Brain plasticity and behaviour in the developing brain.

abstract: OBJECTIVE ::: To review general principles of brain development, identify basic principles of brain plasticity, and discuss factors that influence brain development and plasticity. ::: ::: ::: METHOD ::: A literature review of relevant English-language manuscripts on brain development and plasticity was conducted. ::: ::: ::: RESULTS ::: Brain development progresses through a series of stages beginning with neurogenesis and progressing to neural migration, maturation, synaptogenesis, pruning, and myelin formation. Eight basic principles of brain plasticity are identified. Evidence that brain development and function is influenced by different environmental events such as sensory stimuli, psychoactive drugs, gonadal hormones, parental-child relationships, peer relationships, early stress, intestinal flora, and diet. ::: ::: ::: CONCLUSIONS ::: The development of the brain reflects more than the simple unfolding of a genetic blueprint but rather reflects a complex dance of genetic and experiential factors that shape the emerging brain. Understanding the dance provides insight into both normal and abnormal development.


k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks


title: Automatic Modulation Classification Based on Kernel Density Estimation

abstract: In this paper, we propose an efficient automatic modulation classification (AMC) scheme for a group of narrowband and digitally modulated signals such as quadrature phase-shift keying (QPSK), 16-PSK, 64-PSK, 4-quadratic-amplitude modulation (QAM), 16-QAM, and 64-QAM. The classification was performed by analyzing the probability density distribution for the real and imaginary parts of the modulated signals. To simplify the complexity of the proposed approach, we performed the classification in two stages: first, we classified the modulation between QAM and PSK signaling, and then, we determined the $M$ -ary order of the modulation by developing kernel density estimation, which is typically used in nonparametric methods for the estimation of the probability density function of a random variable with finite data samples. Simulations were carried out to evaluate the performance of the proposed scheme for flat channels. It is observed that this simple efficient technique can find applications in blind AMC, as the performance comparison with the state of the art is promising.


title: SOCIAL-EMOTIONAL AND CHARACTER DEVELOPMENT SCALE Development and Initial Validation With Urban Elementary School Students

abstract: The Social-Emotional and Character Development Scale (SECDS) is intended as a measnre of social-emotional skills and character for elementary school-age children. This study investigated the mea sure's psychometric properties using data collected over 5 waves for a cohort of students followed from Grades 3 to 5 in 14 nrban elementary schools (NRange ~ 500 to 593). Confirmatory factor analysis indicated good fit to the measnre' s hypothesized first- and second-order factor structure at each wave. Scales corre sponding to the first-order factor structure demonstrated adequate internal consistency and test-retest reliabil ity. Consistent with previous research using similar measnres of social-emotional skills and character, a longitudinal analysis indicated that the means for these scales decreased over the 5 waves. The validity of the SECDS general and individual scales was supported via their patterns of association with measnres of expected correlates of social-emotional skills and character. Overall, findings suggest the SECDS is appropri ate for assessing the development of different facets of social-emotional skills and character dnring elemen tary grades and that it could be useful for investigating their role in the developmental pathways for a range of important youth outcomes.


title: Design and RFID signal analysis of a meander line UHF RFID tag antenna

abstract: A meander line UHF RFID tag antenna with an efficient matching network has been designed. Wideband matching and good radiation properties were achieved in parallel with small antenna size by increasing the structurepsilas electrical length by meander structure and thus lowering its self-resonant frequency. Antenna was modelled with a FEM-simulator and it showed good performance: matching for global RFID UHF frequency and simulated gain was higher than for an ideal half-wavelength dipole. Matching and backscattering properties were verified by measurements and the results were compared against two commercial Gen2 short dipole -type tags. Both, matching and backscattering properties of the designed tag were found to be competent in this respect.


title: A Comparison of Five Implementations of 3D

abstract: When implementing Delaunay tessellation in 3D, a number of engineering decisions must be made about update and location algorithms, arithmetics, perturbations, and representations. We compare five codes for computing 3D Delaunay tessellation: qhull, hull, CGAL, pyramid, and our own tess3, and explore experimentally how these decisions affect the correctness and speed of computation, particularly for input points that represent atoms coordinates in proteins.


title: A Sub-10 mV Power Converter With Fully Integrated Self-Start, MPPT, and ZCS Control for Thermoelectric Energy Harvesting

abstract: An inductive power converter for thermoelectric generator (TEG) is presented in this paper. A novel redundant inverter ring oscillator is proposed to self-oscillate in a very low supply voltage down to 45 mV. An additional self-start assist circuit with a differential clock booster and an exponential charge pump is implemented in the power converter to achieve lower self-start voltage. Boundary continuous-conduction mode, zero current switch (ZCS), and maximum power point tracking (MPPT) features have been incorporated into the power converter. Selective turn-ON dynamical-comparators have been used in ZCS and MPPT circuits to save power and achieve minimum operating supply voltage. Fabricated in 65-nm CMOS process, the chip can self-start at 210 mV. After self-start, only 7-mV input voltage is needed to sustain the power converter operation. With a TEG open circuit voltage of 80 mV and internal resistance of $5~\Omega $ , the chip can provide a maximum output power of $229~\mu \text{W}$ with end-to-end efficiency of 71.5%.


title: Human ovarian reserve from conception to the menopause

abstract: The human ovary contains a fixed number of non-growing follicles (NGFs) established before birth that decline with increasing age culminating in the menopause at 50-51 years. The objective of this study is to model the age-related population of NGFs in the human ovary from conception to menopause. Data were taken from eight separate quantitative histological studies (n = 325) in which NGF populations at known ages from seven weeks post conception to 51 years (median 32 years) were calculated. The data set was fitted to 20 peak function models, with the results ranked by obtained r2 correlation coefficient. The highest ranked model was chosen. Our model matches the log-adjusted NGF population from conception to menopause to a five-parameter asymmetric double Gaussian cumulative (ADC) curve (r2 = 0.81). When restricted to ages up to 25 years, the ADC curve has r2 = 0.95. We estimate that for 95% of women by the age of 30 years only 12% of their maximum pre-birth NGF population is present and by the age of 40 years only 3% remains. Furthermore, we found that the rate of NGF recruitment towards maturation for most women increases from birth until approximately age 14 years then decreases towards the menopause. To our knowledge, this is the first model of ovarian reserve from conception to menopause. This model allows us to estimate the number of NGFs present in the ovary at any given age, suggests that 81% of the variance in NGF populations is due to age alone, and shows for the first time, to our knowledge, that the rate of NGF recruitment increases from birth to age 14 years then declines with age until menopause. An increased understanding of the dynamics of human ovarian reserve will provide a more scientific basis for fertility counselling for both healthy women and those who have survived gonadotoxic cancer treatments.


title: Building Statistical Shape Spaces for 3D Human Modeling

abstract: Expressive 3D human shape models are proposed.The models are learned from the largest available dataset of laser scans.Various template fitting and posture normalization approaches are evaluated.High quality of the learned shape spaces is empirically demonstrated.Proposed models and code to data pre-processing and model fitting are released. Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems. Unfortunately, most publicly available models are of limited expressiveness as they were learned on very small databases that hardly reflect the true variety in human body shapes. In this paper, we contribute by rebuilding a widely used statistical body representation from the largest commercially available scan database, and making the resulting model available to the community (visit http://humanshape.mpi-inf.mpg.de). As preprocessing several thousand scans for learning the model is a challenge in itself, we contribute by developing robust best practice solutions for scan alignment that quantitatively lead to the best learned models. We make implementations of these preprocessing steps also publicly available. We extensively evaluate the improved accuracy and generality of our new model, and show its improved performance for human body reconstruction from sparse input data.


title: Novel design technique of address Decoder for SRAM

abstract: Address Decoder is an important digital block in SRAM which takes up to half of the total chip access time and significant part of the total SRAM power in normal read/write cycle. To design address decoder need to consider two objectives, first choosing the optimal circuit technique and second sizing of their transistors. Novel address decoder circuit is presented and analysed in this paper. Address decoder using NAND-NOR alternate stages with predecoder and replica inverter chain circuit is proposed and compared with traditional and universal block architecture, using 90nm CMOS technology. Delay and power dissipation in proposed decoder is 60.49% and 52.54% of traditional and 82.35% and 73.80% of universal block architecture respectively.


title: Fast and accurate map merging for multi-robot systems. Auton Robots 2008;25(3):305–16

abstract: We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.


Spoken Language Identification Using LSTM-Based Angular Proximity


title: Parallel k-Means Image Segmentation Using Sort, Scan and Connected Components on a GPU

abstract: Image segmentation is required to run fast and without supervision to speed up subsequent processes such as object recognition or other high level tasks. General purpose computing on the GPU is a powerful tool to perform efficient image processing and has been applied to the image segmentation problem. However, state-of-the-art approaches still perform parts of the computations on the CPU requiring costly data exchange with the main memory. In this paper we suggest a fully unsupervised color image segmentation that runs completely on the GPU including the calculation of region features. We compare our results to a popular CPU-based and a recent GPU-based method and report a computation time advantage.


Metric Learning and Manifolds Metric Learning and Manifolds : Preserving the Intrinsic Geometry


title: Learning a Deep Embedding Model for Zero-Shot Learning

abstract: Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be projected to for nearest neighbour search. Despite the success of deep neural networks that learn an end-to-end model between text and images in other vision problems such as image captioning, very few deep ZSL model exists and they show little advantage over ZSL models that utilise deep feature representations but do not learn an end-to-end embedding. In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space. Instead of embedding into a semantic space or an intermediate space, we propose to use the visual space as the embedding space. This is because that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. This model design also provides a natural mechanism for multiple semantic modalities (e.g.,~attributes and sentence descriptions) to be fused and optimised jointly in an end-to-end manner. Extensive experiments on four benchmarks show that our model significantly outperforms the existing models.


title: Deep Cross-Modal Hashing

abstract: Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, most existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a novel CMH method, called deep cross-modal hashing (DCMH), by integrating feature learning and hash-code learning intothe same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.


title: UniBench: A Benchmark for Multi-model Database Management Systems

abstract: Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.


title: CMOS low-dropout regulator with improved time response

abstract: A new configuration of CMOS low-dropout (LDO) regulator is presented. In the proposed regulator a low-output-resistance stage with improved time response is used as an output stage. The analysis of basic parameters together with simulation results are provided to characterize properties of the circuit. The comparison of the new configuration and a know structure using flipped voltage follower (FVF) shows over 100% improvement in suppression of the output voltage undershoot.


title: High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks

abstract: The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73–100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.


title: Abstract Cork: Dynamic Memory Leak Detection for Java£

abstract: Despite all the benefits of garbage collection, memory leaks remain a problem for Java programs. A memory leak in Java occurs when a program inadvertently maintains references to objects that it no longer needs, preventing the garbage collector from reclaiming space. At best, leaks degrade performance. At worst, they cause programs to run out of memory and crash. Small continuous leaks in long-running programs are notoriously hard to find and can crash the program only after days or weeks of execution. We introduce Cork, a low-overhead, accurate technique for detecting memory leaks in Java programs. Cork identifies overa ll monotonic heap growth by piggybacking on the garbage collector. On each full-heap collection, Cork builds a summary type pointsto graph annotated with type volumes. Cork identifies potentia lly leaking types that grow over multiple collections. Cork reports the slice in the type points-to graph that is growing (i.e., the data st ructure that points to the leaking type). We implement Cork in MMTk for Jikes RVM, where it adds an average overhead of 2.4% for moderate heap sizes and 1.7% for large heap sizes to SPECjvm and DaCapo benchmarks using a generational mark-sweep collector. Cork exactly identifies a single growing data structure in ea ch of three popular benchmarks (fop, 202 jess, and SPECjbb2000). Due to the precision of Cork’s report, we eliminated these le aks in 202 jess and SPECjbb2000, whereas their developers had not previously done so. Cork is the first tool to find leaks in Java w ith low enough overhead to consider using online.


title: Anticipated benefits of cloud computing adoption in Australian regional municipal governments: an exploratory study

abstract: It is expected that computing services will increasingly be accessed as another utility a similar way to other services such as water, electricity, telephone or gas. Many organizations including government deliver their services through information and communication technology (ICT) tools including e-government. Cloud computing is a relatively new way of providing services over the internet. In this research in-depth interviews of Australian councils’ IT managers were conducted with the aim of providing insights into the perceived benefits of cloud computing adoption. The research indicated that cloud computing is seen to offer the many benefits that are identified in the research literature; additional anticipated benefits emerged which related to reduced level of risk, remote access, reduce staff, and saving time.


title: Basics in nutrition and wound healing

abstract: Wound healing is a process that can be divided into three different phases (inflammatory, proliferative, and maturation). Each is characterized by certain events that require specific components. However, wound healing is not always a linear process; it can progress forward and backward through the phases depending on various intrinsic and extrinsic factors. If the wound-healing process is affected negatively, this can result in chronic wounds. Chronic wounds demand many resources in the clinical daily routine. Therefore, local wound management and good documentation of the wound is essential for non-delayed wound healing and prevention of the development of chronic wounds. During the wound-healing process much energy is needed. The energy for the building of new cells is usually released from body energy stores and protein reserves. This can be very challenging for undernourished and malnourished patients. Malnutrition is very common in geriatric patients and patients in catabolic phases of stress such as after injury or surgery. For that reason a close survey of the nutritional status of patients is necessary to start supplementation quickly, if applicable. Wound healing is indeed a very complex process that deserves special notice. There are some approaches to develop guidelines but thus far no golden standard has evolved. Because wounds, especially chronic wounds, cause also an increasing economic burden, the development of guidelines should be advanced.


title: Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information

abstract: Modeling, understanding, and predicting the spatio-temporal dynamics of online memes are important tasks, with ramifications on location-based services, social media search, targeted advertising and content delivery networks. However, the raw data revealing these dynamics are often incomplete and error-prone; for example, API limitations and data sampling policies can lead to an incomplete (and often biased) perspective on these dynamics. Hence, in this paper, we investigate new methods for uncovering the full (underlying) distribution through a novel spatio-temporal dynamics recovery framework which models the latent relationships among locations, memes, and times. By integrating these hidden relationships into a tensor-based recovery framework -- called AirCP -- we find that high-quality models of meme spread can be built with access to only a fraction of the full data. Experimental results on both synthetic and real-world Twitter hashtag data demonstrate the promising performance of the proposed framework: an average improvement of over 27% in recovering the spatio-temporal dynamics of hashtags versus five state-of-the-art alternatives.


title: Emotion-based music recommendation by affinity discovery from film music

abstract: With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user's preference on music. However, sometimes, it might better meet users' requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average.


title: Ontology-based information content computation

abstract: The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concept's semantics. As a result, IC has been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of appearance of concepts in corpora. However, the applicability and scalability of this method are hampered due to corpora dependency and data sparseness. More recently, some authors proposed IC-based measures using taxonomical features extracted from an ontology for a particular concept, obtaining promising results. In this paper, we analyse these ontology-based approaches for IC computation and propose several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept. Our approach has been evaluated and compared with related works (both corpora and ontology-based ones) when applied to the task of semantic similarity estimation. Results obtained for a widely used benchmark show that our method enables similarity estimations which are better correlated with human judgements than related works.


title: Attribute based Multifactor Authentication for Cloud Applications

abstract: Online information maintenance through cloud applications allows users to store, manage, control and share their information with other users as well as Cloud service providers. There have been serious privacy concerns about outsourcing user information to cloud servers. But also due to an increasing number of cloud data security incidents happened in recent years. Proposed system is a privacypreserving system using Attribute based Multifactor Authentication. Proposed system provides privacy to users data with efficient authentication and store them on cloud servers such that servers do not have access to sensitive user information. Meanwhile users can maintain full control over access to their uploaded files and data, by assigning finegrained, attribute-based access privileges to selected files and data, while different users can have access to different parts of the System. This application allows clients to set privileges to different users to access their data.


title: Toward understanding characteristics of dedicated short range communications (DSRC) from a perspective of vehicular network engineers

abstract: IEEE 802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. Here, using a large set of empirical measurement data taken in a rich variety of realistic driving environments, we attempt to characterize communication properties of DSRC as well as to analyze the causes of communication loss. Specifically, from a perspective of vehicular network engineers, the fundamental characteristic of DSRC communications is Packet Delivery Ratio (PDR). We investigate the impact of both uncontrollable environmental factors and controllable radio parameters on DSRC characteristics. Moreover, we also examine temporal correlation, spatial correlation and symmetric correlation of DSRC characteristics under realistic vehicular environments.


title: Fuzzy Logic Control of Wind Turbine System Connection to PM Synchronous Generator for Maximum Power Point Tracking

abstract: in this paper, a fuzzy logic control (FLC) is proposed for maximum power point tracking (MPPT) in wind turbine connection to Permanent Magnet Synchronous Generator (PMSG). The proposed fuzzy logic controller tracks the maximum power point (MPP) by measurements the load voltage and current. This controller calculates the load power and sent through the fuzzy logic system. The main goal of this paper is design of the fuzzy logic controller in the model of DC- DC converter (boost converter). This method allows the MPPT controller output (duty cycle) adjusts the voltage input to the converter to track the maximum power point of the wind generator.


title: Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks

abstract: Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex; standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms. This paper provides the first convergence guarantee applicable to modern convnets, which furthermore matches a lower bound for convex nonsmooth functions. The key technical tool is the neural Taylor approximation -- a straightforward application of Taylor expansions to neural networks -- and the associated Taylor loss. Experiments on a range of optimizers, layers, and tasks provide evidence that the analysis accurately captures the dynamics of neural optimization. The second half of the paper applies the Taylor approximation to isolate the main difficulty in training rectifier nets -- that gradients are shattered -- and investigates the hypothesis that, by exploring the space of activation configurations more thoroughly, adaptive optimizers such as RMSProp and Adam are able to converge to better solutions.


title: IR evaluation methods for retrieving highly relevant documents

abstract: In a process for low particle sterilization of temperature resistant containers (glass bottles), the containers are continuously sterilized by radiant heat in a continuous heating furnace and then cooled by highly purified filtered air. One essential factor for obtaining the freedom from particles is the provision of a non-turbulent stream of highly purified air maintained in the continuous heating furnace in a direction parallel and opposite to the direction of movement of the containers.


title: A weighted adaptive least-squares finite element method for the Poisson-Boltzmann equation

abstract: Abstract The finite element methodology has become a standard framework for approximating the solution to the Poisson–Boltzmann equation in many biological applications. In this article, we examine the numerical efficacy of least-squares finite element methods for the linearized form of the equations. In particular, we highlight the utility of a first-order form, noting optimality, control of the flux variables, and flexibility in the formulation, including the choice of elements. We explore the impact of weighting and the choice of elements on conditioning and adaptive refinement. In a series of numerical experiments, we compare the finite element methods when applied to the problem of computing the solvation free energy for realistic molecules of varying size.


title: Bounding the effect of partition camping in GPU kernels

abstract: Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge and complement these performance models and tools, by modeling and analyzing a lesser known, but very severe performance pitfall, called Partition Camping, in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of GPU kernels by up to seven-fold. There is no existing tool that can detect the partition camping effect in GPU kernels. Unlike the traditional performance modeling approaches, we predict a performance range that bounds the partition camping effect in the GPU kernel. Our idea of predicting a performance range, instead of the exact performance, is more realistic due to the large performance variations induced by partition camping. We design and develop the prediction model by first characterizing the effects of partition camping with an indigenous suite of micro-benchmarks. We then apply rigorous statistical regression techniques over the micro-benchmark data to predict the performance bounds of real GPU kernels, with and without the partition camping effect. We test the accuracy of our performance model by analyzing three real applications with known memory access patterns and partition camping effects. Our results show that the geometric mean of errors in our performance range prediction model is within 12% of the actual execution times. We also develop and present a very easy-to-use spreadsheet based tool called CampProf, which is a visual front-end to our performance range prediction model and can be used to gain insights into the degree of partition camping in GPU kernels. Lastly, we demonstrate how CampProf can be used to visually monitor the performance improvements in the kernels, as the partition camping effect is being removed.


title: Data Driven Evaluation of Crowds

abstract: There are various techniques for simulating crowds, however, in most cases the quality of the simulation is measured by examining its "look-and-feel". Even if the aggregate movement of the crowd looks natural from afar, the behaviors of individuals might look odd when examined more closely. In this paper we present a data-driven approach for evaluating the behaviors of individuals within a simulated crowd. Each decision of an individual agent is expressed as a state-action pair, which stores a representation of the characteristics being evaluated and the factors that influence it. Based on video footage of a real crowd, a database of state-action examples is generated. Using a similarity measure, the queries are matched with the database of examples. The degree of similarity can be interpreted as the level of "naturalness" of the behavior. Essentially, this sort of evaluation offers an objective answer to the question of how similar are the simulated behaviors compared to real ones. By changing the input video we can change the context of evaluation.


title: Challenges in enterprise resource planning implementation: state‐of‐the‐art

abstract: Purpose – The purpose of this paper is to present an integrative and detailed review of the critical factors that cause enterprise resource planning (ERP) implementation failures, based on an in‐depth literature review (1997‐2009).Design/methodology/approach – Keywords relating to the subject of this paper were defined, and used to search web engines and journal databases for papers on ERP implementation failures. These papers were further analysed and classified into various categories.Findings – Nine factors are found to be critical in the failure of ERP implementations: excessive customisation, dilemma of internal integration, poor understanding of business implications and requirements, lack of change management, poor data quality, misalignment of IT with business, hidden costs, limited training and lack of top management support.Research limitations/implications – The paper proposes the development of a framework to address each ERP implementation failure factor in its entirety based on the frequency...


title: A Truthful Incentive Mechanism for Emergency Demand Response in Geo-Distributed Colocation Data Centers

abstract: Data centers are key participants in demand response programs, including emergency demand response (EDR), in which the grid coordinates consumers of large amounts of electricity for demand reduction in emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers, this work studies EDR in geo-distributed multitenant colocation data centers in which servers are owned and managed by individual tenants. EDR in colocation data centers is significantly more challenging due to lack of incentives to reduce energy consumption by tenants who control their servers and are typically on fixed power contracts with the colocation operator. Consequently, to achieve demand reduction goals set by the EDR program, the operator has to rely on the highly expensive and/or environmentally unfriendly on-site energy backup/generation. To reduce cost and environmental impact, an efficient incentive mechanism is therefore needed, motivating tenants’ voluntary energy reduction in the case of EDR. This work proposes a novel incentive mechanism, Truth-DR, which leverages a reverse auction to provide monetary remuneration to tenants according to their agreed energy reduction. Truth-DR is computationally efficient, truthful, and achieves 2-approximation in colocation-wide social cost. Trace-driven simulations verify the efficacy of the proposed auction mechanism.


title: Exponential moving average Q-learning algorithm

abstract: A multi-agent policy iteration learning algorithm is proposed in this work. The Exponential Moving Average (EMA) mechanism is used to update the policy for a Q-learning agent so that it converges to an optimal policy against the policies of the other agents. The proposed EMA Q-learning algorithm is examined on a variety of matrix and stochastic games. Simulation results show that the proposed algorithm converges in a wider variety of situations than state-of-the-art multi-agent reinforcement learning (MARL) algorithms.


title: A 77-GHz Phased-Array Transceiver With On-Chip Antennas in Silicon: Transmitter and Local LO-Path Phase Shifting

abstract: Integration of mm-wave multiple-antenna systems on silicon-based processes enables complex, low-cost systems for highfrequency communication and sensing applications. In this paper, the transmitter and LO-path phase-shifting sections of the first fully integrated 77-GHz phased-array transceiver are presented. The SiGe transceiver utilizes a local LO-path phase-shifting architecture to achieve beam steering and includes four transmit and receive elements, along with the LO frequency generation and distribution circuitry. The local LO-path phase-shifting scheme enables a robust distribution network that scales well with increasing frequency and/or number of elements while providing high-resolution phase shifts. Each element of the heterodyne transmitter generates +12.5 dBm of output power at 77 GHz with a bandwidth of 2.5 GHz leading to a 4-element effective isotropic radiated power (EIRP) of 24.5 dBm. Each on-chip PA has a maximum saturated power of +17.5 dBm at 77 GHz. The phased-array performance is measured using an internal test option and achieves 12-dB peak-to-null ratio with two transmit and receive elements active.


title: Language, mind and brain

abstract: Language serves as a cornerstone of human cognition. However, our knowledge about its neural basis is still a matter of debate, partly because ‘language’ is often ill-defined. Rather than equating language with ‘speech’ or ‘communication’, we propose that language is best described as a biologically determined computational cognitive mechanism that yields an unbounded array of hierarchically structured expressions. The results of recent brain imaging studies are consistent with this view of language as an autonomous cognitive mechanism, leading to a view of its neural organization, whereby language involves dynamic interactions of syntactic and semantic aspects represented in neural networks that connect the inferior frontal and superior temporal cortices functionally and structurally. Friederici et al. outline a view of the neural organization of language that is compatible with a description of language as a biologically determined computational mechanism that yields an infinite number of hierarchically structured expressions.


title: Visual exploration of Location-Based Social Networks data in urban planning

abstract: The increasing amount of data generated by Location Based Social Networks (LBSN) such as Twitter, Flickr, or Foursquare, is currently drawing the attention of urban planners, as it is a new source of data that contains valuable information about the behavior of the inhabitants of a city. Making this data accessible to the urban planning domain can add value to the decision making processes. However, the analysis of the spatial and temporal characteristics of this data in the context of urban planning is an ongoing research problem. This paper describes ongoing work in the design and development of a visual exploration tool to facilitate this task. The proposed design provides an approach towards the integration of a visual exploration tool and the capabilities of a visual query system from a multilevel perspective (e.g., multiple spatial scales and temporal resolutions implicit in LBSN data). A preliminary discussion about the design and the potential insights that can be gained from the exploration and analysis of this data with the proposed tool is presented, along with the conclusions and future work for the continuation of this work.


title: Clustering-based selection for evolutionary multi-objective optimization

abstract: In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity of the new strategy, we apply it into one state of the art multi-objective evolutionary algorithm. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.


title: Quantifying the influence of geography and environment on the northeast Atlantic mackerel spawning distribution

abstract: Mackerel (Scomber scombrus) in the northeast Atlantic have shown changes in distribution at certain times of the year, which have impacted their exploitation and management. In this study, mackerel spawning habitat over 21 recent years was characterised using generalised additive modelling, based on spatial egg density data collected every third year during targeted ichthyoplankton surveys. Mackerel spawning distribution was found to depend primarily on geographical variables (coordinates and bottom depth), with preferred spawning locations on the shelf-edge from the north of the Iberian peninsula to the west of Scotland, with a maximum west of Ireland. Environmental drivers had a lesser influence on egg distribution. Dome shaped relationships were found with temperature and mixed layer depth, with respective optimum at 13°C and around –300 m. The model was used to reconstruct maps of the potential habitat (areas where conditions were suitable, but not necessarily used, for spawning). Little changes were observed over the years in the potential habitat, suggesting that the expansion of the egg distribution (realised habitat) was not triggered by changes in the environmental variables investigated. Little evidence was found for density-dependent habitat selection. There was a tendency for mackerel to make more use of areas of lesser suitability in years with large stock size (1992 and 2010). This pattern, however, broke down in 2013, when stock size was the highest, as spawning occurred very south and concentrated in the most suitable habitat.


title: A people counting method based on head detection and tracking

abstract: This paper proposes a novel people counting method based on head detection and tracking to evaluate the number of people who move under an over-head camera. There are four main parts in the proposed method: foreground extraction, head detection, head tracking, and crossing-line judgment. The proposed method first utilizes an effective foreground extraction method to obtain foreground regions of moving people, and some morphological operations are employed to optimize the foreground regions. Then it exploits a LBP feature based Adaboost classifier for head detection in the optimized foreground regions. After head detection is performed, the candidate head object is tracked by a local head tracking method based on Meanshift algorithm. Based on head tracking, the method finally uses crossing-line judgment to determine whether the candidate head object will be counted or not. Experiments show that our method can obtain promising people counting accuracy about 96% and acceptable computation speed under different circumstances.


title: Educational data mining applications and tasks: A survey of the last 10 years

abstract: Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.


title: Routing Protocols for Unmanned Aerial Vehicles

abstract: Nowadays, mini-drones, officially called unmanned aerial vehicles, are widely used in many military and civilian fields. Compared to traditional ad hoc networks, the mobile ad hoc networks established by UAVs are more efficient in completing complex tasks in harsh environments. However, due to the unique characteristics of UAVs (e.g., high mobility and sparse deployment), existing protocols or algorithms cannot be directly used for UAVs. In this article, we focus on the routes designed for UAVs, and aim to present a somewhat complete survey of the routing protocols. Moreover, the performance of existing routing protocols is compared in detail, which naturally leads to a great number of open research problems that are outlined afterward.


title: How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?

abstract: A key hypothesis in sensory system neuroscience is that sensory representations are adapted to the statistical regularities in sensory signals and thereby incorporate knowledge about the outside world. Supporting this hypothesis, several probabilistic models of local natural image regularities have been proposed that reproduce neural response properties. Although many such physiological links have been made, these models have not been linked directly to visual sensitivity. Previous psychophysical studies of sensitivity to natural image regularities focus on global perception of large images, but much less is known about sensitivity to local natural image regularities. We present a new paradigm for controlled psychophysical studies of local natural image regularities and compare how well such models capture perceptually relevant image content. To produce stimuli with precise statistics, we start with a set of patches cut from natural images and alter their content to generate a matched set whose joint statistics are equally likely under a probabilistic natural image model. The task is forced choice to discriminate natural patches from model patches. The results show that human observers can learn to discriminate the higher-order regularities in natural images from those of model samples after very few exposures and that no current model is perfect for patches as small as 5 by 5 pixels or larger. Discrimination performance was accurately predicted by model likelihood, an information theoretic measure of model efficacy, indicating that the visual system possesses a surprisingly detailed knowledge of natural image higher-order correlations, much more so than current image models. We also perform three cue identification experiments to interpret how model features correspond to perceptually relevant image features.


title: Air Traffic Security: Aircraft Classification Using ADS-B Message’s Phase-Pattern

abstract: Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance system used in Air Traffic Control. With this system, the aircraft transmits their own information (identity, position, velocity, etc.) to any equipped listener for surveillance scope. The ADS-B is based on a very simple protocol and does not provide any kind of authentication and encryption, making it vulnerable to many types of cyber-attacks. In the paper, the use of the airplane/transmitter carrier phase is proposed as a feature to perform a classification of the aircraft and, therefore, distinguish legitimate messages from fake ones. The feature extraction process is described and a classification method is selected. Finally, a complete intruder detection algorithm is proposed and evaluated with real data.


title: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

abstract: A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it is often thought that a main source of difficulty for these local methods to find the global minimum is the proliferation of local minima with much higher error than the global minimum. Here we argue, based on results from statistical physics, random matrix theory, neural network theory, and empirical evidence, that a deeper and more profound difficulty originates from the proliferation of saddle points, not local minima, especially in high dimensional problems of practical interest. Such saddle points are surrounded by high error plateaus that can dramatically slow down learning, and give the illusory impression of the existence of a local minimum. Motivated by these arguments, we propose a new approach to second-order optimization, the saddle-free Newton method, that can rapidly escape high dimensional saddle points, unlike gradient descent and quasi-Newton methods. We apply this algorithm to deep or recurrent neural network training, and provide numerical evidence for its superior optimization performance.


title: Towards High Performance Video Object Detection for Mobiles

abstract: Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).


title: Real-Time Wavelet Analysis of a Vibration Signal Based on Arduino-UNO and LabVIEW

abstract: This paper deals with on-line (Real-time) multi- resolution signal analysis using wavelet transform by means of graphical programming using LabVIEW. A system for wavelet analysis has been designed based on Arduino-Uno board interfaced to LabVIEW. To achieve the interfacing LIFA has been used. Firstly, the noise from the signal was remove using wavelet transform, and then the de-noised signal analyzed to multi-level frequency bands. A Matlab code to do wavelet analysis was written in a Matlab script node in LabVIEW. To test this system, a vibration signal form robotic arm was captured and analyzed using this system, and the result utilized to establish if there is fault in the robot. The result showed that multi-resolution analysis can be achieved efficiently using this system and can be applied in many applications. 


title: Consortium Blockchain-Based Malware Detection in Mobile Devices

abstract: To address the problem of detecting malicious codes in malware and extracting the corresponding evidences in mobile devices, we construct a consortium blockchain framework, which is composed of a detecting consortium chain shared by test members and a public chain shared by users. Specifically, in view of different malware families in Android-based system, we perform feature modeling by utilizing statistical analysis method, so as to extract malware family features, including software package feature, permission and application feature, and function call feature. Moreover, for reducing false-positive rate and improving the detecting ability of malware variants, we design a multi-feature detection method of Android-based system for detecting and classifying malware. In addition, we establish a fact-base of distributed Android malicious codes by blockchain technology. The experimental results show that, compared with the previously published algorithms, the new proposed method can achieve higher detection accuracy in limited time with lower false-positive and false-negative rates.


title: What maintains parental support for vaccination when challenged by anti-vaccination messages? A qualitative study

abstract: This study sought to explore how parents respond to competing media messages about vaccine safety. Six focus groups with mothers of infants were shown television vignettes of typical pro- and anti-vaccination claims. Thematic analysis of transcripts was undertaken. Mothers expressed surprise and concern about alleged vaccine risks but quickly reinstated their support for vaccination by deference to authority figures; type-casting immunisation opponents; and notions of anticipatory regret, good parenting and social responsibility. We conclude that personal experiences, value systems and level of trust in health professionals are fundamental to parental decision making about vaccination. Vaccination advocacy should increase the focus on matters of process such as maintaining trust and public confidence, particularly in health professionals. Stories about people affected by vaccine-preventable diseases need to re-enter the public discourse.


title: Cardiac Dysrhythmias, Cardiomyopathy and Muscular Dystrophy in Patients with Emery-Dreifuss Muscular Dystrophy and Limb-Girdle Muscular Dystrophy Type 1B

abstract: Emery-Dreifuss muscular dystrophy (EDMD) and limb-girdle muscular dystrophy type 1B (LGMD1B) are characterized by cardiac dysrhythmias, late-onset cardiomyopathy, slowly progressive skeletal myopathy and contractures of the neck, elbows and ankles. The causative mutation is either in the emerin gene (X-linked recessive EDMD) or lamin A/C gene (autosomal dominant EDMD2 or LGMD1B). We report three cases of EDMD, EDMD2 and LGMD1B. A 14-yr-old boy showed limitation of cervical flexion and contractures of both elbows and ankles. Sinus arrest with junctional escape beats was noted. He was diagnosed as X-linked recessive EDMD (MIM 310300). A 28-yr-old female showed severe wasting and weakness of humeroperoneal muscles. Marked limitation of cervical flexion and contractures of both elbows and ankles were noted. Varying degrees of AV block were noted. She was diagnosed as autosomal dominant EDMD2 (MIM 181350). A 41-yr-old female had contractures of both ankles and limb-girdle type muscular dystrophy. ECG revealed atrial tachycardia with high grade AV block. She was diagnosed as autosomal dominant LGMD1B (MIM 159001). Cardiac dysrhythmias in EDMD and LGMD1B include AV block, bradycardia, atrial tachycardia, atrial fibrillation, and atrial standstill, causing sudden death necessitating pacemaker implantation. Cardiologists should know about these unusual genetic diseases with conduction defects, especially in young adults.


title: Air-Stable Surface Charge Transfer Doping of MoS2by Benzyl Viologen

abstract: Air-stable doping of transition metal dichalcogenides is of fundamental importance to enable a wide range of optoelectronic and electronic devices while exploring their basic material properties. Here we demonstrate the use of benzyl viologen (BV), which has one of the highest reduction potentials of all electron-donor organic compounds, as a surface charge transfer donor for MoS2 flakes. The n-doped samples exhibit excellent stability in both ambient air and vacuum. Notably, we obtained a high electron sheet density of ∼1.2 × 1013 cm–2, which corresponds to the degenerate doping limit for MoS2. The BV dopant molecules can be reversibly removed by immersion in toluene, providing the ability to control the carrier sheet density as well as selective removal of surface dopants on demand. By BV doping of MoS2 at the metal junctions, the contact resistances are shown to be reduced by a factor of >3. As a proof of concept, top-gated field-effect transistors were fabricated with BV-doped n+ source/drain contacts...


title: DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization

abstract: Answering questions with data is a difficult and time-consuming process. Visual dashboards and templates make it easy to get started, but asking more sophisticated questions often requires learning a tool designed for expert analysts. Natural language interaction allows users to ask questions directly in complex programs without having to learn how to use an interface. However, natural language is often ambiguous. In this work we propose a mixed-initiative approach to managing ambiguity in natural language interfaces for data visualization. We model ambiguity throughout the process of turning a natural language query into a visualization and use algorithmic disambiguation coupled with interactive ambiguity widgets. These widgets allow the user to resolve ambiguities by surfacing system decisions at the point where the ambiguity matters. Corrections are stored as constraints and influence subsequent queries. We have implemented these ideas in a system, DataTone. In a comparative study, we find that DataTone is easy to learn and lets users ask questions without worrying about syntax and proper question form.


title: *SEM 2013 shared task: Semantic Textual Similarity

abstract: In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core task (CORE), and (ii) a typed-similarity task (TYPED). CORE is similar in set up to SemEval STS 2012 task with pairs of sentences from sources related to those of 2012, yet different in genre from the 2012 set, namely, this year we included newswire headlines, machine translation evaluation datasets and multiple lexical resource glossed sets. TYPED, on the other hand, is novel and tries to characterize why two items are deemed similar, using cultural heritage items which are described with metadata such as title, author or description. Several types of similarity have been defined, including similar author, similar time period or similar location. The annotation for both tasks leverages crowdsourcing, with relative high interannotator correlation, ranging from 62% to 87%. The CORE task attracted 34 participants with 89 runs, and the TYPED task attracted 6 teams with 14 runs.


title: Concrete Problems in AI Safety

abstract: Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function ("avoiding side effects" and "avoiding reward hacking"), an objective function that is too expensive to evaluate frequently ("scalable supervision"), or undesirable behavior during the learning process ("safe exploration" and "distributional shift"). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems. Finally, we consider the high-level question of how to think most productively about the safety of forward-looking applications of AI.


title: No reference image quality classification for JPEG-distorted images

abstract: Abstract In this paper, we address the Image Quality Assessment (IQA) of JPEG-distorted images. We approach the IQA field by focusing on a classification problem that maps different objective metrics into different categorical quality classes. To this end, we adopt a machine learning classification approach, where No Reference (NR) metrics are considered as features, while the assigned classes come from psycho-visual experiments. Eleven NR metrics have been considered: seven specific for blockiness and four general purpose. We evaluate the performance of single metrics and investigate if a pool of metrics can reach better performances than each of the single ones. Five as well as three quality classes are considered, and the corresponding classifiers are tested on two well known databases available in the literature (LIVE and MICT), and on a new database (IVL) presented in this paper.


title: Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

abstract: Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. ::: ::: We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). ::: ::: The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. ::: ::: With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.


title: Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow

abstract: We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach to predict the changes of pressure fields over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. We demonstrate for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and we arrive at a neural-network based simulation algorithm with significant practical speed-ups. We highlight the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.


title: A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining

abstract: Mining frequent subgraphs from a collection of input graphs is an important task for exploratory data analysis on graph data. However, if the input graphs contain sensitive information, releasing discovered frequent subgraphs may pose considerable threats to individual privacy. In this paper, we study the problem of frequent subgraph mining (FSM) under the rigorous differential privacy model. We present a two-phase differentially private FSM algorithm, which is referred to as DFG . In DFG , frequent subgraphs are privately identified in the first phase, and the noisy support of each identified frequent subgraph is calculated in the second phase. In particular, to privately identity frequent subgraphs, we propose a frequent subgraph identification approach, which can improve the accuracy of discovered frequent subgraphs through candidate pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support computation approach, which leverages the inclusion relations between the discovered frequent subgraphs to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that DFG satisfies $\epsilon$ -differential privacy. Extensive experimental results on real datasets show that DFG can privately find frequent subgraphs while achieving high data utility.


title: Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

abstract: This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.


title: Formal methods for semi-autonomous driving

abstract: We give an overview of the main challenges in the specification, design, and verification of human cyber-physical systems, with a special focus on semi-autonomous vehicles. We identify unique characteristics of formal modeling, specification, verification and synthesis in this domain. Some initial results and design principles are presented along with directions for future work.


title: A Novel UWB Monopole Antenna With Tunable Notched Behavior Using Varactor Diode

abstract: This letter presents a novel ultrawideband (UWB) antenna with tunable notched band. The antenna is assembled on an FR4 substrate with thickness of 0.8 mm and er = 4.4. By inserting a π-shaped slot on the radiating patch, band-notch function is achieved. By loading the slot with lumped varactor, tunability of the created notch would be possible. Based on this technique, an electronically controlled notched-band antenna is designed and fabricated including a single varactor diode with a varying capacitance value of 0.63-2.67 pF. Using the cited varactor, notched band tunability of 2.7-7.2 GHz is obtained.


title: Phase Noise Reduction in an MEMS Oscillator Using a Nonlinearly Enhanced Synchronization Domain

abstract: We investigate the phase dynamics of a closed-loop MEMS-based oscillator and demonstrate how one can exploit nonlinear behavior to improve oscillator phase noise characteristics by synchronizing the oscillator with a weak harmonic drive. Analytical predictions are based on an oscillator model that incorporates a resonator element with a weak cubic (i.e., Duffing type) nonlinearity, weak coupling to a clean external harmonic drive, and both thermal and frequency noise terms. The method of stochastic averaging is used to derive an expression for the rate of phase diffusion induced by the noises, and the results predict a remarkable phase noise reduction of three order of magnitude when the oscillator is synchronized to the weak external field. The predictions are experimentally demonstrated using a closed-loop oscillator with a double-anchored double-ended-tuning-fork MEMS resonator with coupling to a small external sinusoidal signal from a signal generator, demonstrating a 30 dB/Hz drop in phase noise. The results show how one can generate a clean high-power signal from a relative noisy oscillator by synchronizing it with a low-power external signal. The results also confirm recent studies showing that the parameter range of synchronization is significantly expanded when the resonator operates in its nonlinear regime. [2016-0067]


title: Editorial: evidence-based guidelines for avoiding the most prevalent and serious APA error in journal article submissions - The citation error

abstract: In a previous editorial, Onwuegbuzie, Combs, Slate, and Frels (2010) discussed the findings of Combs, Onwuegbuzie, and Frels (2010), who identified the 60 most common American Psychological Association (APA) errors among authors who submitted manuscripts to Research in the Schools over a 6-year period. This editorial led to representatives of APA inviting us to write a blog post (http://blog.apastyle.org/apastyle/#tp), which also was included on APA's facebook (http://www.facebook.com/#!/APAStyle) and Twitter (http://twitter.com/APA_Style) accounts. According to these APA representatives, our editorial will motivate 60 sets of discussion posts (already several posts have been made; see, for e.g., http://blog.apastyle.org/apastyle/2011/01/numbersanyone.html)—one for each of the 60 most common APA errors that we found, which will involve at least 135,000 users from 177 countries. Of the 60 APA errors that were identified, the most prevalent error was the incorrect use of numbers, which was committed by 57.3% of the authors. As noted by Onwuegbuzie et al. (2010), this prevalence rate represents an extremely large effect size. However, it should be noted that there was one APA error that Combs et al. did not examine, namely, that pertaining to citing references in text and presenting references in the reference list. This APA error was omitted deliberately by Combs et al. because, as we will demonstrate, this error represents the most serious APA error and thus deserves sole examination.


title: Real-time image-based chinese ink painting rendering

abstract: Chinese ink painting, also known as ink and wash painting, is a technically demanding art form. Creating Chinese ink paintings usually requires great skill, concentration, and years of training. This paper presents a novel real-time, automatic framework to convert images into Chinese ink painting style. Given an input image, we first construct its saliency map which captures the visual contents in perceptually salient regions. Next, the image is abstracted and its salient edges are calculated with the help of the saliency map. Then, the abstracted image is diffused by a non-physical ink diffusion process. After that, we combine the diffused image and the salient edges to obtain a composition image. Finally, the composition image is decolorized and texture advected to synthesize the resulting image with Chinese ink painting style. The whole pipeline is implemented on the GPU, enabling a real-time performance. We also propose some optional steps (foreground segmentation and image inversion) to improve the rendering quality. Experimental results show that our model is two to three orders of magnitude faster, while producing results comparable the ones obtained with the current image-based Chinese ink painting rendering method.


title: A Provably-Secure ECC-Based Authentication Scheme for Wireless Sensor Networks

abstract: A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.


Modeling Multibody Dynamic Systems With Uncertainties . Part I : Theoretical and Computational Aspects


title: Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility

abstract: Purpose – The paper aims to examine ways to reduce privacy risk and its effects so that adoption of e‐services can be enhanced.Design/methodology/approach – Consumers that form a viable target market for an e‐service are presented with the task of experiencing the e‐service and expressing their attitudes and intentions toward it. Structural equation modeling is used to analyze the responses.Findings – The paper finds that consumer beliefs that the e‐service will be easy to use and that the e‐service provider is credible and capable reduce privacy risk and its effects, thus enhancing adoption likelihood.Research limitations/implications – The focus on a financial services product (online bill paying) suggests that similar research should be conducted with other high‐risk e‐services (such as those dealing with healthcare) and lower‐risk e‐services (such as subscription services and social networks).Practical implications – In addition to addressing consumers' privacy risk directly, e‐service providers can a...