title: Algoriphagus boseongensis sp. nov., a member of the family Cyclobacteriaceae isolated from a tidal flat

abstract: A Gram-stain negative, aerobic, non-motile, non-spore-forming and rod-shaped bacterial strain, BS-R1(T), was isolated from a tidal flat at Boseong, South Korea. Strain BS-R1(T) was found to grow optimally at 30 °C, at pH 7.0-8.0 and in the presence of 2.0 % (w/v) NaCl. Phylogenetic analyses based on 16S rRNA gene sequences revealed that strain BS-R1(T) belongs to the genus Algoriphagus, clustering consistently with the type strain of Algoriphagus mannitolivorans, with which it exhibited 98.4 % sequence similarity. Sequence similarities between strain BS-R1(T) and the type strains of the other Algoriphagus species were between 92.7 and 97.0 %. Strain BS-R1(T) was found to contain MK-7 as the predominant menaquinone and iso-C15:0, iso-C17:0 3-OH and summed feature 3 (C16:1 ω7c and/or C16:1 ω6c) as the major fatty acids. The major polar lipids of strain BS-R1(T) were identified as phosphatidylcholine, phosphatidylethanolamine and one unidentified lipid. The DNA G+C content was determined to be 42.3 mol% and its mean DNA-DNA relatedness values with A. mannitolivorans KACC 11349(T) was 17 ± 5 %. The phylogenetic and genetic distinctiveness and differential phenotypic properties demonstrated that strain BS-R1(T) is distinguishable from the other Algoriphagus species as well as A. mannitolivorans. On the basis of the data presented, strain BS-R1(T) is considered to represent a novel species of the genus Algoriphagus, for which the name Algoriphagus boseongensis sp. nov. is proposed. The type strain is BS-R1(T) (=KCTC 32580(T) = CECT 8446(T)).


title: A 0.39mA 92dB modified-feedback ΣΔ modulator for digital RF hearing aid with 1.2V supply voltage in 0.35µm CMOS

abstract: A 0.39mA 92dB dynamic range switch-capacitor (SC) third-order ΣΔ modulator for a digital RF hearing aid in 0.35µm CMOS is presented here. A modified-feedback topology is used here that effectively achieves a high dynamic range and the low power consumption. In the implementation, the bootstrapping-switch and the low-voltage wide-swing OTA are essentially designed for 1.2V supply voltage with the 0.35um CMOS process. In the measurement result, this modulator achieves 90dB SNR in a 10-KHz bandwidth with an oversampling ratio of 128, and its power consumption is 0.47mW with a good FOM of 555e-6.


title: Will you spend more money and time on internet shopping when the product and situation are right?

abstract: Different from past online shopping research which focuses on exploring the cognitive and attitudinal aspects of online consumption behaviors, this study focuses on examining how online shopping motivation and product type affect the searching and spending behavior in an online website. In particular, the interactive effect between online shopping motivation and product type are explored in the study. A 2x2 between-subjects factorial experiment with two shopping motivations (i.e., goal-oriented and experiential) and two product types (i.e., hedonic and utilitarian product) was conducted. In addition to the main effects of online shopping motivation and product type, the results show that there is an interactive effect between online shopping motivation and product type on budget control. Participants conducted the loosest budget control when purchasing hedonic products in a goal-oriented online shopping trip because the shopping motivation provides a good justification for online shoppers to purchase hedonic goods without guilt or regret.


title: Assessing psychopathic attributes in a noninstitutionalized population.

abstract: The present study examined antisocial dispositions in 487 university students. Primary and secondary psychopathy scales were developed to assess a protopsychopathic interpersonal philosophy. An antisocial action scale also was developed for purposes of validation. The primary, secondary, and antisocial action scales were correlated with each other and with boredom susceptibility and disinhibition but not with experience seeking and thrill and adventure seeking. Secondary psychopathy was associated with trait anxiety. Multiple regression analysis revealed that the strongest predictors of antisocial action were disinhibition, primary psychopathy, secondary psychopathy, and sex, whereas thrill and adventure seeking was a negative predictor. This argues against a singular behavioral inhibition system mediating both antisocial and risk-taking behavior. These findings are also consistent with the view that psychopathy is a continuous dimension


Crushed Septal Cartilage Graft in Rhinoplasty and Nasal Septal Surgery : Clinical Outcomes and Complications


title: Faster parallel traversal of scale free graphs at extreme scale with vertex delegates

abstract: At extreme scale, irregularities in the structure of scale-free graphs such as social network graphs limit our ability to analyze these important and growing datasets. A key challenge is the presence of high-degree vertices (hubs), that leads to parallel workload and storage imbalances. The imbalances occur because existing partitioning techniques are not able to effectively partition high-degree vertices. We present techniques to distribute storage, computation, and communication of hubs for extreme scale graphs in distributed memory supercomputers. To balance the hub processing workload, we distribute hub data structures and related computation among a set of delegates. The delegates coordinate using highly optimized, yet portable, asynchronous broadcast and reduction operations. We demonstrate scalability of our new algorithmic technique using Breadth-First Search (BFS), Single Source Shortest Path (SSSP), K-Core Decomposition, and PageRank on synthetically generated scale-free graphs. Our results show excellent scalability on large scale-free graphs up to 131K cores of the IBM BG/P, and outperform the best known Graph500 performance on BG/P Intrepid by 15%.


title: Corpus-level Fine-grained Entity Typing Using Contextual Information

abstract: This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.


title: Event Identification in Social Media

abstract: Social media sites such as Flickr, YouTube, and Facebook host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events. These range from widely known events, such as the presidential inauguration, to smaller, community-specific events, such as annual conventions and local gatherings. By identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can greatly improve local event browsing and search in state-of-the-art search engines. To address our problem of focus, we exploit the rich “context” associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). We form a variety of representations of social media documents using different context dimensions, and combine these dimensions in a principled way into a single clustering solution—where each document cluster ideally corresponds to one event—using a weighted ensemble approach. We evaluate our approach on a large-scale, real-world dataset of event images, and report promising performance with respect to several baseline approaches. Our preliminary experiments suggest that our ensemble approach identifies events, and their associated images, more effectively than the state-of-the-art strategies on which we build.


title: Analysis of thick functionally graded plates by using higher-order shear and normal deformable plate theory and MLPG method with radial basis functions

abstract: Infinitesimal deformations of a functionally graded thick elastic plate are analyzed by using a meshless local Petrov–Galerkin (MLPG) method, and a higher-order shear and normal deformable plate theory (HOSNDPT). Two types of Radial basis functions RBFs, i.e. Multiquadrics and Thin Plate Splines, are employed for constructing the trial solutions, while a fourth-order Spline function is used as the weight/test function over a local subdomain. Effective material moduli of the plate, made of two isotropic constituents with volume contents varying only in the thickness direction, are computed using the Mori–Tanaka homogenization technique. Computed results for a simply supported aluminum/ceramic plate are found to agree well with those obtained analytically. Results for a plate with two opposite edges free and the other two simply supported agree very well with those obtained by analyzing three-dimensional deformations of the plate by the finite element method. The distributions of the deflection and stresses through the plate thickness are also presented for different boundary conditions. It is found that both types of basis functions give accurate values of plate deflection, but the multiquadrics give better values of stresses than the thin plate splines. � 2006 Elsevier Ltd. All rights reserved.


Comparative Analysis of 28 T Full Adder with 14 T Full Adder using 180 nm


title: ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning,&Snapshot Ensembling

abstract: ViZDoom is a robust, first-person shooter reinforcement learning environment, characterized by a significant degree of latent state information. In this paper, double-Q learning and prioritized experience replay methods are tested under a certain ViZDoom combat scenario using a competitive deep recurrent Q-network (DRQN) architecture. In addition, an ensembling technique known as snapshot ensembling is employed using a specific annealed learning rate to observe differences in ensembling efficacy under these two methods. Annealed learning rates are important in general to the training of deep neural network models, as they shake up the status-quo and counter a model’s tending towards local optima. While both variants show performance exceeding those of built-in AI agents of the game, the known stabilizing effects of double-Q learning are illustrated, and priority experience replay is again validated in its usefulness by showing immediate results early on in agent development, with the caveat that value overestimation is accelerated in this case. In addition, some unique behaviors are observed to develop for priority experience replay (PER) and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves a valuable method for improving performance of the ViZDoom Marine.


title: Block-diagonal Hessian-free Optimization for Training Neural Networks

abstract: Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and the need for model-dependent algorithmic variations. We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes the curvature approximation matrix only for pairs of parameters from the same layer or block of the neural network and performs conjugate gradient updates independently for each block. Experiments on deep autoencoders, deep convolutional networks, and multilayer LSTMs demonstrate better convergence and generalization compared to the original Hessian-free approach and the Adam method.


title: Active Learning with Visualization for Text Data

abstract: Labeled datasets are always limited, and oftentimes the quantity of labeled data is a bottleneck for data analytics. This especially affects supervised machine learning methods, which require labels for models to learn from the labeled data. Active learning algorithms have been proposed to help achieve good analytic models with limited labeling efforts, by determining which additional instance labels will be most beneficial for learning for a given model. Active learning is consistent with interactive analytics as it proceeds in a cycle in which the unlabeled data is automatically explored. However, in active learning users have no control of the instances to be labeled, and for text data, the annotation interface is usually document only. Both of these constraints seem to affect the performance of an active learning model. We hypothesize that visualization techniques, particularly interactive ones, will help to address these constraints. In this paper, we implement a pilot study of visualization in active learning for text classification, with an interactive labeling interface. We compare the results of three experiments. Early results indicate that visualization improves high-performance machine learning model building with an active learning algorithm.


title: Visual servoing for dual arm motions on a humanoid robot

abstract: In this work we present a visual servoing approach that enables a humanoid robot to robustly execute dual arm grasping and manipulation tasks. Therefore the target object(s) and both hands are tracked alternately and a combined open-/ closed-loop controller is used for positioning the hands with respect to the target(s). We address the perception system and how the observable workspace can be increased by using an active vision system on a humanoid head. Furthermore a control framework for reactive positioning of both hands using position based visual servoing is presented, where the sensor data streams coming from the vision system, the joint encoders and the force/torque sensors are fused and joint velocity values are generated. This framework can be used for bimanual grasping as well as for two handed manipulations which is demonstrated with the humanoid robot Armar-III that executes grasping and manipulation tasks in a kitchen environment.


title: Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM

abstract: For an intelligent transportation system (ITS), traffic incident detection is one of the most important issues, especially for urban area which is full of signaled intersections. In this paper, we propose a novel traffic incident detection method based on the image signal processing and hidden Markov model (HMM) classifier. First, a traffic surveillance system was set up at a typical intersection of china, traffic videos were recorded and image sequences were extracted for image database forming. Second, compressed features were generated through several image processing steps, image difference with FFT was used to improve the recognition rate. Finally, HMM was used for classification of traffic signal logics (East-West, West-East, South-North, North-South) and accident of crash, the total correct rate is 74% and incident recognition rate is 84%. We believe, with more types of incident adding to the database, our detection algorithm could serve well for the traffic surveillance system.


title: Recognizing Groceries in situ Using in vitro Training Data

abstract: The problem of using pictures of objects captured under ideal imaging conditions (here referred to as in vitro) to recognize objects in natural environments (in situ) is an emerging area of interest in computer vision and pattern recognition. Examples of tasks in this vein include assistive vision systems for the blind and object recognition for mobile robots; the proliferation of image databases on the web is bound to lead to more examples in the near future. Despite its importance, there is still a need for a freely available database to facilitate study of this kind of training/testing dichotomy. In this work one of our contributions is a new multimedia database of 120 grocery products, GroZi-120. For every product, two different recordings are available: in vitro images extracted from the web, and in situ images extracted from camcorder video collected inside a grocery store. As an additional contribution, we present the results of applying three commonly used object recognition/detection algorithms (color histogram matching, SIFT matching, and boosted Haar-like features) to the dataset. Finally, we analyze the successes and failures of these algorithms against product type and imaging conditions, both in terms of recognition rate and localization accuracy, in order to suggest ways forward for further research in this domain.


title: Detection and reconstruction of clipped speech for speaker recognition

abstract: Abstract Clipping is often observed in speech acquisition, due to the limited numerical range or the non-linear compensation of recording devices. The clipping inevitably changes the spectrum of speech signals, and thus partially distorts the speaker information contained in the signal. This paper investigates the impact of signal clipping on speaker recognition, and proposes a simple yet effective clipping detection approach as well as a signal reconstruction approach based on deep neural networks (DNNs). The experiments are conducted on the core test of the NIST SRE2008 task by simulating clipped speech at various clipping rates. The results show that clipping does impact the performance of speaker recognition, but the impact is rather marginal unless the clipping rate is larger than 80%. We also find that the simple distribution-based detection method is capable of detecting clipped speech with a higher accuracy, and the DNN-based reconstruction can achieve promising performance gains for speaker recognition on clipped speech.


title: Addressing the epidemic of chronic kidney disease in Australia.

abstract: The Australia Diabetes, Obesity and Lifestyle Study (AUSDIAB) study provided, for the first time in Australia, a snapshot of the prevalence of kidney damage, reduced kidney function, hypertension and diabetes in the adult population. With this information, and the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) registry, that has recorded kidney failure statistics for many years, the extent of the chronic kidney disease burden in Australia is being better defined. This burden is even more pronounced in the Indigenous population where the incidence of kidney disease and kidney failure is increased several-fold. Diabetes is the second most common cause of kidney failure among Australians. The number of patients with diabetes accepted to dialysis has doubled in the last 7 years, the mean body weight of patients commencing dialysis has increased 7 kg in the past decade and the mean age at acceptance to dialysis is rising in a linear fashion (presently 60 years). These facts, together with a static transplant rate, all point to the prevalence of dialysis likely staying at or increasing beyond the present yearly growth rate of 6-7%. The evidence shows that a large proportion of chronic kidney disease patients are dying of cardiovascular risk factors before they reach dialysis or transplantation. There are many gaps in delivering appropriate preventative treatment to these patients. A relatively small reduction in the rise in dialysis numbers that might flow from an effective prevention of progression program, could make a significant impact on the spiralling numbers and associated cost of kidney failure treatment in Australia. We now need to develop and implement a national kidney disease strategy designed to address the whole continuum of chronic kidney disease from its earliest stage right through to dialysis and transplantation.


title: Toward a Physical Internet: meeting the global logistics sustainability grand challenge

abstract: This paper starts with the assertion that the way physical objects are currently transported, handled, stored, realized, supplied, and used throughout the world is unsustainable economically, environmentally, and socially. Evidence supporting this assertion is exposed through a set of key unsustainability symptoms. Then, the paper expresses the goal to revert this situation, thus meeting the global logistics sustainability grand challenge. It suggests exploiting the Digital Internet metaphor to develop a Physical Internet vision toward meeting this grand challenge. The paradigm breaking vision is introduced through a set of its key characteristics. The paper then proceeds with addressing the implications and requirements for implementing the Physical Internet vision as a means to meet the grand challenge. It concludes with a call for further research, innovation, and development to really shape and assess the vision and, much more important, to give it flesh through real initiatives and projects so as to really influence in a positive way the collective future. For this to happen, it emphasizes the requirement for multidisciplinary collaboration among and between academia, industry, and government across localities, countries, and continents.


title: An analysis of stochastic shortest path problems

abstract: We consider a stochastic version of the classical shortest path problem whereby for each node of a graph, we must choose a probability distribution over the set of successor nodes so as to reach a certain destination node with minimum expected cost. The costs of transition between successive nodes can be positive as well as negative. We prove natural generalizations of the standard results for the deterministic shortest path problem, and we extend the corresponding theory for undiscounted finite state Markovian decision problems by removing the usual restriction that costs are either all nonnegative or all nonpositive.


title: Subjective evaluation and objective measures for point clouds — State of the art

abstract: This paper presents a state of the art on subjective evaluation and existing objective quality measures for point cloud representations. A brief review on subjective evaluation protocols for 2D and 3D video quality assessment will be given, and its adaptation for point cloud evaluation described together with a description of existing point cloud objective quality measures. Afterwards, specific problems with subjective experiments and objective measures for point clouds will be discussed. Challenges and future research directions regarding point clouds quality assessment will be discussed.


title: ROI analysis for remote photoplethysmography on facial video

abstract: As wide spreading of camera-equipped devices to the daily living environment, there are enormous opportunities to utilize the camera-based remote photoplethysmography (PPG) for daily physiological monitoring. In the camera-based remote PPG (rPPG) monitoring, the region of interest (ROI) is related to the signal quality and the computational load for the signal extraction processing. Designating the best ROI on the body while minimizing its size is essential for computationally efficient rPPG extraction. In this study, we densely analyzed the face region to find the computationally efficient ROI for facial rPPG extraction. We divided the face into seven regions and evaluated the quality of the signal of each region using the area ratio of high-SNR and high-correlation, and mean and standard deviation (SD) of SNR and correlation coefficient. The results show that a forehead and both cheeks especially have a potential to be a good candidates for computationally efficient ROI. On the other hand, the signal quality from a mouth and a chin was relatively low. A nasion and a nose have a limitation to be efficient ROI.


title: Evolving modular neural sequence architectures with genetic programming

abstract: Automated architecture search has demonstrated significant success for image data, where reinforcement learning and evolution approaches now outperform the best human designed networks ([12], [8]). These successes have not transferred over to models dealing with sequential data, such as in language modeling and translation tasks. While there have been several attempts to evolve improved recurrent cells for sequence data [7], none have achieved significant gains over the standard LSTM. Recent work has introduced high performing recurrent neural network alternatives, such as Transformer [11] and Wavenet [4], but these models are the result of manual human tuning.


title: A unified framework of density-based clustering for semi-supervised classification

abstract: Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we introduce a unified framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. Experimental results on a large collection of datasets show the advantages of the proposed framework.


title: CNTFET SPICE Model: Design of a Carbon Nanotube Field Effect Transistor

abstract: In this paper, we elucidate the development of SPICE model of Carbon Nanotube Field Effect Transistor (CNTFET) and analyze the performance of the proposed model. A set of key parameter can be obtained from this model analysis such as drain current variation as a function of the conductance and drain-source voltage. Furthermore, a SPICE small signal model nanotube transistor is developed. It is used for studying the performance of current gain as well as design of nanotube transistor circuits and phase angle with cut-off frequency. CNT diameter is responsible for the better performance of CNTFET. Therefore an optimum diameter of CNT is imposed here to develop CNTFET.


title: Oligarchic Control of Business-to-Business Blockchains

abstract: The innovative ideas behind blockchain offer exciting perspectives in research and development of electronic distributed ledger technologies. Bitcoin introduced an "eventual consistency" blockchain with the apparent intent of providing an open and inclusive system for secure, anonymous, yet cost-effective financial transactions based on peer-to-peer computing. It uses Proof ofWork as a cryptographic puzzle to control growth of a chain and to strengthen the resiliency of a chain against subsequent rewrite attempts. Commercial sectors and the public sector have realized the potential of this technical approach, and we now see a number of blockchains that may be called private, closed or permissioned – and that may eschew the values inherent in the design of Bitcoin type systems. From a value-neutral research perspective, there are no clear definitions of blockchain attributes such as "private" or "permissioned", nor is there typically a good understanding of the trust assumptions that clients must make when using such services – for example in systems that do not rely on Proof of Work but on a limited and controlled set of consensus-creating agents. We believe that more research in this direction could establish firm foundations for domain-specific or coalition-specific blockchains, and that such underpinnings would offer novel trust architectures beyond those provided by completely open/public blockchains and third-party operated, closed/private blockchains.


title: South African physicians’ acceptance of e-prescribing technology: an empirical test of a modified UTAUT model

abstract: E-prescribing systems hold promise for improving the quality and efficiency of the scripting process. Yet, the use of the technology has been associated with a number of challenges. The diffusion of e-prescribing into physician practices and the consequent realisation of its potential benefits will depend on whether physicians are willing to accept and engage with the technology. This study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) and recent literature on user trust in technology to develop and test a model of the factors influencing South African physicians’ acceptance of e-prescribing. Data was collected from a sample of 72 physicians. Results indicate a general acceptance of e-prescribing amongst physicians who on average reported strong intentions to use e-prescribing technologies if given the opportunity. PLS analysis revealed that physicians’ performance expectancies and perceptions of facilitating conditions had significant direct effects on acceptance whilst trust and effort expectancy had important indirect effects. Social influence and price value perceptions did not add additional explanatory power. The model explained 63% of the variation in physician acceptance.


Learning a MultiView Stereo Machine


title: A hand gesture recognition system based on canonical superpixel-graph

abstract: Abstract This paper presents a new hand gesture recognition system based on a novel canonical superpixel-graph earth mover’s distance (CSG-EMD) metric. It aims to improve the performance of the superpixel earth mover’s distance (SP-EMD), a recently proposed distance metric designed for depth-based hand gesture recognition. In real life, people have their own habits while performing certain hand gestures, which yields a variety of hand shapes with different finger poses. Such variety may affect the accuracy of SP-EMD and hence will degrade its performance. In this paper, we propose a new distance metric CSG-EMD to alleviate the problem. Scattered superpixels are organized in the form of canonical superpixel-graph which can factor out non-standard finger poses, resulting a well-structured finger-pose-neutral shape representation for hand gestures. Moreover, a structure stress based fusion scheme is applied to formulate the proposed distance metric, i.e. CSG-EMD, for gesture recognition. Experimental results on five public gesture datasets show that the proposed CSG-EMD-based system can achieve better recognition accuracy than other state-of-the-art algorithms compared. Its superiority is further demonstrated by two real-life applications.


title: Computer-Aided Teaching Using MATLAB/Simulink for Enhancing an IM Course With Laboratory Tests

abstract: This paper describes an automatic procedure using MATLAB software to plot the circle diagram for two induction motors (IMs), with wound and squirrel-cage rotors, from no-load and blocked-rotor tests. The advantage of this approach is that it avoids the need for a direct load test in predetermining the IM characteristics under reduced power. Additionally, to verify the validity of the equivalent circuit parameters deduced from experimental tests, these characteristics are used to simulate virtual machines implemented under Simulink/PSB models. Furthermore, a virtual load test is performed to validate the proposed model. Finally, the results are given of an assessment that reflects the positive impact the proposed methods have had on students' learning experience in electrical machinery courses in the Electrotechnics Department, Engineer Sciences Faculty, University Mentouri of Constantine (ED-ESF-UMC), Algeria.


title: Changes In Rhesus Macaque 'Coo' Vocalizations during Early Development

abstract: In order to test whether ‘coo’ calls of young rhesus macaques, Macaca mulatta, undergo some modifications during early development, and to explore which factors may influence these changes, we studied the ontogeny of their contact call, the ‘coo’ call. Vocalizations were recorded during brief periods of social separation. Infants were either raised with their mothers and other conspecifics, or separated from their mothers at birth and housed in a nursery with other infants. We recorded calls uttered in the separation context from 20 infants. We digitized the first 50 calls of a given series and subjected them to a Fourier transform. From each frequency–time spectrum, we extracted 65 acoustic parameters using a software program (LMA 5.9). We then used a cluster analysis to separate the ‘coo’ calls from other call types. With increasing age, the ‘coos’ dropped in pitch and became more even. The course of amplitude became more constant and the call duration increased slightly. Nevertheless, we found a high intra-individual variation throughout the 5 mo. Neither rearing condition nor sex had any apparent influence on age-related changes in ‘coo’ structure. With one exception, all parameters that correlated with age could be explained by variation in weight. Therefore, we conclude that growth is the main factor accounting for the observed changes.


title: Nonlinear causal discovery with additive noise models

abstract: The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models with additive noise are often used because these models are well understood and there are well-known methods to fit them to data. In reality, of course, many causal relationships are more or less nonlinear, raising some doubts as to the applicability and usefulness of purely linear methods. In this contribution we show that the basic linear framework can be generalized to nonlinear models. In this extended framework, nonlinearities in the data-generating process are in fact a blessing rather than a curse, as they typically provide information on the underlying causal system and allow more aspects of the true data-generating mechanisms to be identified. In addition to theoretical results we show simulations and some simple real data experiments illustrating the identification power provided by nonlinearities.


title: Online Popularity under Promotion: Viral Potential, Forecasting, and the Economics of Time

abstract: Modeling the popularity dynamics of an online item is an important open problem in computational social science. This paper presents an in-depth study of popularity dynamics under external promotions, especially in predicting popularity jumps of online videos, and determining effective and efficient schedules to promote online content. The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reactions. Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion, and to quantify the time it takes for such effects to unfold. We make increasingly accurate forecasts of future popularity by including information about the intrinsic properties of the video, promotions it receives, and the non-linear effects of popularity ranking. We illustrate by simulation the interplay between the unfolding of popularity over time, and the time-sensitive value of resources. Lastly, our model lends a novel explanation of the commonly adopted periodic and constant promotion strategy in advertising, as increasing the perceived viral potential. This study provides quantitative guidelines about setting promotion schedules considering content virality, timing, and economics.


title: Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization

abstract: The healthcare industry's widespread digitization efforts are reshaping one of the largest sectors of the world's economy. This transformation is enabling systems that promise to use ever-improving data-driven evidence to help doctors make more precise diagnoses, institutions identify at risk patients for intervention, clinicians develop more personalized treatment plans, and researchers better understand medical outcomes within complex patient populations. Given the scale and complexity of the data required to achieve these goals, advanced data visualization tools have the potential to play a critical role. This article reviews a number of visualization challenges unique to the healthcare discipline.


title: A novel broadband dual-polarized low-profile antenna for 4G LTE applications

abstract: A novel broadband dual-polarized low-profile antenna is presented for the 4G Long Term Evolution (LTE) network. The antenna structure contains a metallic low-profile cone for vertical polarization (VP) and eight pairs planar dipole elements for horizontal polarization (HP). The profile of the dual-polarized omnidirectional antenna is approximately 36.8mm. The broad frequency bands with the S11 less than −10 dB from 0.93 GHz to 3.5 GHz for VP and with the S22 less than −9 dB from 2 GHz to 2.65 GHz for HP can be achieved with the compact volume. Owing to the noticeable low profile, broadband and omnidirectional radiation characteristics, the proposed antenna can be widely used in indoor roof antenna networking applications.


title: ©2006 Society for Design and Process Science THE BOEING 777: DEVELOPMENT LIFE CYCLE FOLLOWS ARTIFACT

abstract: Iterative life cycle models have become popular in software engineering, for example in agile development. In contrast, the waterfall model appears to prevail in manufacturing engineering disciplines. This includes aircraft engineering and Boeing’s project for developing its most recent passenger aircraft, the 777. The paper walks through the phases of the 777’s development and compares to iterative development. This suggests two observations:


title: Insights into User Behavior in Dealing with Internet Attacks

abstract: The Internet is a lucrative medium for criminals targeting Internet users. Most common Internet attacks require some form of user interaction such as clicking on an exploit link. Hence, the problem at hand is not only a technical one, but it also has a strong human aspect. Although the security community has proposed many technical solutions to common attacks, the behavior of users when they face current threats, and the way they evaluate the security implications of their actions remain largely unexplored. In this paper we describe an online experiment platform we built for testing the behavior of users when they are confronted with prevalent, concrete attack scenarios such as reflected cross-site scripting, session fixation, and file sharing scams. We conducted experiments with 164 Internet users with diverse backgrounds. Our findings suggest that many non-technical users can exhibit performance comparable to security experts at averting relatively simple threats that they are frequently exposed to in everyday life. They can do so solely by following their intuition, without actually perceiving the severity of the threat. However, when facing more sophisticated attacks, these non-technical users often rely on misleading cues such as the “size” and “length” of artifacts (e.g., URLs), and hence, fail to protect themselves. We also show that trick banners that are common in file sharing websites and shortened URLs have high success rates of deceiving non-technical users, thus posing a severe security risk.


title: Why and Where: A Characterization of Data Provenance

abstract: With the proliferation of database views and curated databases, the issue of data provenance - where a piece of data came from and the process by which it arrived in the database - is becoming increasingly important, especially in scientific databases where understanding provenance is crucial to the accuracy and currency of data. In this paper we describe an approach to computing provenance when the data of interest has been created by a database query. We adopt a syntactic approach and present results for a general data model that applies to relational databases as well as to hierarchical data such as XML. A novel aspect of our work is a distinction between why provenance (refers to the source data that had some influence on the existence of the data) and where provenance (refers to the location(s) in the source databases from which the data was extracted).


title: Personality, emotional intelligence and work preferences in software engineering: An empirical study

abstract: Context: There is an increasing awareness among Software Engineering (SE) researchers and practitioners that more focus is needed on understanding the engineers developing software. Previous studies show significant associations between the personalities of software engineers and their work preferences. Objective: Various studies on personality in SE have found large, small or no effects and there is no consensus on the importance of psychometric measurements in SE. There is also a lack of studies employing other psychometric instruments or using larger datasets. We aim to evaluate our results in a larger sample, with software engineers in an earlier state of their career, using advanced statistics. Method: An operational replication study where extensive psychometric data from 279 master level students have been collected in a SE program at a Swedish University. Personality data based on the Five-Factor Model, Trait Emotional Intelligence Questionnaire and Self-compassion have been collected. Statistical analysis investigated associations between psychometrics and work preferences and the results were compared to our previous findings from 47 SE professionals. Results: Analysis confirms existence of two main clusters of software engineers; one with more "intense" personalities than the other. This corroborates our earlier results on SE professionals. The student data also show similar associations between personalities and work preferences. However, for other associations there are differences due to the different population of subjects. We also found connections between the emotional intelligence and work preferences, while no associations were found for self-compassion. Conclusion: The associations can help managers to predict and adapt projects and tasks to available staff. The results also show that the Emotional Intelligence instrument can be predictive. The research methods and analytical tools we employ can detect subtle associations and reflect differences between different groups and populations and thus can be important tools for future research as well as industrial practice.


title: A G-Shaped Defected Isolation Wall for Mutual Coupling Reduction Between Patch Antenna and Microstrip Transmission Line

abstract: A simple defected isolation wall is proposed for giving an enhanced isolation between a closely installed patch antenna and a microstrip transmission line (TL). The distance between the patch antenna and TL is set to be 2mm. For obtaining a lower mutual coupling between the used antenna and the TL, a G-shaped defected isolation wall is designed and vertically placed between the used patch antenna and TL. The simulation results gave a conformation that the mutual coupling is decreased by 25 dB, and the performance of the patch antenna and microstrip transmission line are not affected.


title: Patch homologies and the integration of adenovirus DNA in mammalian cells.

abstract: Abstract ::: The hamster cell line HE5 has been derived from primary hamster embryo cells by transformation with human adenovirus type 2 (Ad2). Each cell contains 2-3 copies of Ad2 DNA inserted into host DNA at apparently identical sites. The site of the junction between the right terminus of Ad2 DNA and hamster cell DNA was cloned and sequenced. The eight [corrected] right terminal nucleotides of Ad2 DNA were deleted. The unoccupied cellular DNA sequence in cell line HE5 , corresponding to the site of the junction between Ad2 and hamster cell DNA, was also cloned; 120-130 nucleotides in the cellular DNA were found to be identical to the cellular DNA sequence in the cloned junction DNA fragment, up to the site of the junction. The unoccupied and the occupied cellular DNAs and the adjacent viral DNA exhibited a few short nucleotide homologies. Patch homologies ranging in length from dodeca - to octanucleotides were detected by computer analyses at locations more remote from the junction site. When the right terminal nucleotide sequence of Ad2 DNA was matched to randomly selected sequences of 401 nucleotides from vertebrate or prokaryotic DNA, similar homologies were observed. It is likely that foreign (viral) DNA can be inserted via short sequence homologies at many different sites of cellular DNA.


The Dendritic Cell Algorithm


Bit-serial Reed - Solomon encoders


abstract: The new Duval Pentagons available for DGA diagnosis in transformers filled with mineral and ester oils are presented in this paper. More than 300 data points were used to define the different fault “zones” within the Pentagons, either coming from inspected faulty transformers (mineral oils) or laboratory tests (ester oils).


title: Effective Implementation of DGEMM on modern multicore CPU

abstract: In this paper we will present a detailed study on tuning double-precision matrix-matrix multiplication (DGEMM) on the Intel Xeon E5-2680 CPU. We selected an optimal algorithm from the instruction set perspective as well software tools optimized for Intel Advance Vector Extensions (AVX). Our optimizations included the use of vector memory operations, and AVX instructions. Our proposed algorithm achieves a performance improvement of 33% compared to the latest results achieved using the Intel Math Kernel Library DGEMM subroutine.


title: Automatic detection and interpretation of nominal metaphor based on the theory of meaning

abstract: Automatic processing of metaphors can be explicitly divided into two subtasks: recognition and interpretation. This paper presents an approach to recognize nominal metaphorical references and to interpret metaphors by exploiting distributional semantics word embedding techniques and calculating semantic relatedness. In terms of detection, our idea is that nominal metaphors consist of source and target domains and that domains present in metaphors will be less related than domains present in non-metaphors. We represent the meaning of the concept as a vector in high-dimensional conceptual space derived from the corpus and compute the relatedness between the vectors to complete the task of detection. Relatedness here is based on the semantics of concepts. Thus, the model we present deals with metaphors where target and source have the same direct ancestors, such as "A surgeon is a butcher".Then, using the relatedness between target and source domain, based on the properties of source domain and dynamic transfer of properties, we present an approach to interpret metaphors with dynamic transfer. Based on the view that metaphor interpretation is the cooperation of source and target domains, we divide metaphor interpretation into two subtasks: properties extraction and properties transfer. Creatively, we use annotations to express a non-binary evaluation, and we take the degree of the annotators' acceptability to evaluate our interpretation of metaphors. HighlightsWe exploit distributional semantics word embedding techniques and semantic relatedness in the metaphor detection and interpretation fields.Our method is based on the theory of meaning. We consider that the difference between source and target domains is in the semantic level, rather than that the domains belong to two different categories.Our method can be flexibly applied to Chinese and English languages. In the Chinese language, we achieve detection accuracy of 85% and interpretation accuracy of 87%. In the English language, we achieve detection accuracy of 85.2% and interpretation accuracy of 85%.


title: Cosmetic Oral Nutritional Supplementation Accelerates Skin Wound Healing: A Randomized, Placebo- Controlled, Double-Arm, Crossover Study

abstract: Nutritional therapy is critical for wound healing in people with severe malnutrition or specific metabolic deficiencies. Medical claims from manufacturers of many oral supplements are marketed to surgical patients for decreasing edema, bruising, and discomfort. The effect of supplementing nutrients on soft-tissue wound healing in otherwise normal, healthy adults is an area of clinical importance, but little information is available. Proteolytic enzymes have been reported to moderate the inflammatory cycle and may up-regulate the healing process. The goal of this study was to perform a clinical trial in normal, healthy adults that examined the effects of an oral nutritional supplement (InflammEnz, Enzymes, Inc., Parkville, Mo.) on soft-tissue healing times. Twenty-six normal, healthy volunteers were recruited into a randomized, crossover, placebo-controlled, clinical trial consisting of two phases, each lasting 21 days. In phase I, subjects were subjected to a 3-mm forearm skin biopsy and randomly received a placebo or oral supplement (four capsules per day for 7 days). After a 2-week washout period, a second biopsy was performed to start phase II, with each subject receiving the respective placebo or supplement capsules. Digital photographs were taken during wound healing in both phases and analyzed for wound areas (in square millimeters) and perimeters (in millimeters). Twenty-two subjects completed the clinical trial. On the basis of wound surface areas, 17 subjects had improved wound healing and five subjects did not respond or responded only slightly to the supplement treatment. The mean +/- SD healing time of the subjects responding to supplement-treated wounds was 15 +/- 2.2 days, compared with 18 +/- 2.5 days for the placebo group. The 17 percent acceleration of wound-healing time was significant (p < 0.005). In subjects responding to oral supplements, less redness in the wounds was observed that may have been associated with less inflammation. The authors' results demonstrate that InflammEnz oral supplementation accelerated soft-tissue wound healing in 77 percent of normal, healthy subjects studied. The authors' study validates observations made that this supplement modulates the wound-healing process and suggests that many patients with minor soft-tissue wounds may benefit from treatment.


title: Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction

abstract: Mixed Reality (MR) is a powerful interactive technology that yields new types of user experience. We present a semantic based interactive MR framework that exceeds the current geometry level approaches, a step change in generating high-level context-aware interactions. Our key insight is to build semantic understanding in MR that not only can greatly enhance user experience through object-specific behaviours, but also pave the way for solving complex interaction design challenges. The framework generates semantic properties of the real world environment through dense scene reconstruction and deep image understanding. We demonstrate our approach with a material-aware prototype system for generating context-aware physical interactions between the real and the virtual objects. Quantitative and qualitative evaluations are carried out and the results show that the framework delivers accurate and fast semantic information in interactive MR environment, providing effective semantic level interactions.


title: How deep neural networks can improve emotion recognition on video data

abstract: We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.


title: Music Genre Classification via Compressive Sampling

abstract: Compressive sampling (CS) is a new research topic in signal processing that has piqued the interest of a wide range of researchers in different fields recently. In this paper, we present a CS-based classifier for music genre classification, with two sets of features, including short-time and long-time features of audio music. The proposed classifier generates a compact signature to achieve a significant reduction in the dimensionality of the audio music signals. The experimental results demonstrate that the computation time of the CS-based classifier is only about 20% of SVM on GTZAN dataset, with an accuracy of 92.7%. Several experiments were conducted in this study to illustrate the feasibility and robustness of the proposed methods as compared to other approaches.


title: The Compassionate Sexist? How Benevolent Sexism Promotes and Undermines Gender Equality in the Workplace.

abstract: Although sexist attitudes are generally thought to undermine support for employment equity (EE) policies supporting women, we argue that the effects of benevolent sexism are more complex. Across 4 studies, we extend the ambivalent sexism literature by examining both the positive and the negative effects benevolent sexism has for the support of gender-based EE policies. On the positive side, we show that individuals who endorse benevolent sexist attitudes on trait measures of sexism (Study 1) and individuals primed with benevolent sexist attitudes (Study 2) are more likely to support an EE policy, and that this effect is mediated by feelings of compassion. On the negative side, we find that this support extends only to EE policies that promote the hiring of women in feminine, and not in masculine, positions (Study 3 and 4). Thus, while benevolent sexism may appear to promote gender equality, it subtly undermines it by contributing to occupational gender segregation and leading to inaction in promoting women in positions in which they are underrepresented (i.e., masculine positions). (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: A Temporal Decorrelation Model for Polarimetric Radar Interferometers

abstract: This paper describes a physical model of the temporal changes that occur in vegetated land surfaces observed by a repeat-pass radar interferometer. We assume the temporal changes to be caused by a Gaussian-statistic motion of the vegetation elements, with motion variance changing along the vertical direction. We show that the temporal correlation between two interferometric radar signals is affected by the structural parameters of the vegetation, such as canopy height, and varies with the wave polarization. We validate the model using L-band data acquired by the Jet Propulsion Laboratory with the Uninhabited Aerial Vehicle Synthetic Aperture Radar airborne radar. This work provides new insights into the role of temporal decorrelation in interferometric radar applications.


title: Running with a load increases leg stiffness

abstract: Spring-mass models have been used to characterize running mechanics and leg stiffness in a variety of conditions, yet it remains unknown how running while carrying a load affects running mechanics and leg stiffness. The purpose of this study was to test the hypothesis that running with a load increases leg stiffness. Twenty-seven subjects ran at a constant speed on a force-measuring treadmill while carrying no load, and while wearing weight vests loaded with 10%, 20%, and 30% of body weight. We measured lower extremity motion and created a scaled musculoskeletal model of each subject, which we used to estimate lower extremity joint angles and leg length. We estimated dimensionless leg stiffness as the ratio of the peak vertical ground reaction force (normalized to body weight) and the change in stance phase leg length (normalized to leg length at initial foot contact). Leg length was calculated as the distance from the center of the pelvis to the center-of-pressure under the foot. We found that dimensionless leg stiffness increased when running with load (p ¼0.001); this resulted from an increase in the peak vertical ground reaction force (po0.001) and a smaller change in stance phase leg length (p¼0.025). When running with load, subjects had longer ground contact times (po0.020), greater hip (po0.001) and knee flexion (p ¼0.048) at the time of initial foot contact, and greater peak stance phase hip, knee, and ankle flexion (po0.05). Our results reveal that subjects run in a more crouched posture and with higher leg stiffness to accommodate an added load.


title: Communication-Efficient Learning of Deep Networks from Decentralized Data

abstract: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.


Management of Car Parking System Using Wireless Sensor Network


title: BachProp: Learning to Compose Music in Multiple Styles

abstract: Hand in hand with deep learning advancements, algorithms of music composition increase in performance. However, most of the successful models are designed for specific musical structures. Here, we present BachProp, an algorithmic composer that can generate music scores in any style given sufficient training data. To adapt BachProp to a broad range of musical styles, we propose a novel normalized representation of music and train a deep network to predict the note transition probabilities of a given music corpus. In this paper, new music scores sampled by BachProp are compared with the original corpora via crowdsourcing. This evaluation indicates that the music scores generated by BachProp are not less preferred than the original music corpus the algorithm was provided with.


title: Different Watermarking Techniques & its Applications: A Review

abstract: It is the expansion of internet that has increased the availability & uses of digital data like video, audio, image to the world. Therefore copyright protection of digital data is an important research issue of information security. This paper presents a review of some influential work in the area of digital watermarking technique and main contribution in this field such as categories of digital watermarking process by which one can understand easily that which watermarking method can be used.


title: ECCN: an extended CCN architecture to improve data access in vehicular content-centric network

abstract: Content-centric network (CCN) has been considered for VANETs, due to its scalability, flexibility and security. In this paper, an extended CCN architecture is proposed to reduce critical data access time in vehicular content centric networks by storing them in the number of vehicles around the source. The critical data include sensitive data such as accidents, traffic that require quick and reliable access. The aim is to reduce access time for this type of data in the discovery phase. Each vehicle stores or updates critical data in cooperation with other vehicles in the preparation phase. During the preparation phase, the critical data spread around the source by a combination of physical movement and radio propagation. This gives the requesting vehicle a better chance to find the data in a few hops, with minimal network overhead and lower delay, during the discovery phase. The efficiency of this architecture is evaluated by simulation and shown that interest packet delay and number of duplicated data packets are generally lower than the existing methods.


title: Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions

abstract: We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product databases. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include product recommendations, product comparison, and demand forecasting. We formulate the extraction as a classification problem and use a semi-supervised algorithm (co-EM) along with (Naive Bayes). The extraction system requires very little initial user supervision: using unlabeled data, we automatically extract an initial seed list that serves as training data for the supervised and semi-supervised classification algorithms. Finally, the extracted attributes and values are linked to form pairs using dependency information and co-location scores. We present promising results on product descriptions in two categories of sporting goods.


title: Classification of common partial discharge types in oil-paper insulation system using acoustic signals

abstract: This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class classification problem, namely; PD from a sharp point to ground plane, surface discharge, PD from a void in the insulation, and PD from semi parallel planes. The collected AE signals are processed using pattern classification techniques to identify their corresponding PD types. The measurement conditions include the influences of various PD locations, oil temperatures, and having a barrier in the line-of-sight between the PD source and the AE sensor. A recognition rate of 94% is achieved when classifying the different PD types measured at the same conditions. In addition, it has been found that the different PD source locations, oil temperatures, and barrier insertion have an impact on the recognition rate. However, by including AE samples at these different conditions in the training process, a recognition rate around 90% for all cases is achieved.


title: A First Look at Quality of Mobile Live Streaming Experience: the Case of Periscope

abstract: Live multimedia streaming from mobile devices is rapidly gaining popularity but little is known about the QoE they provide. In this paper, we examine the Periscope service. We first crawl the service in order to understand its usage patterns. Then, we study the protocols used, the typical quality of experience indicators, such as playback smoothness and latency, video quality, and the energy consumption of the Android application.


title: ReMESH: An Interactive Environment to Edit and Repair Triangle Meshes

abstract: Polygonal meshes obtained from acquisition of real-world objects may easily exhibit topological or geometrical defects, which often prevent subsequent processing and analysis to provide satisfactory results. This paper describes the foundations of ReMESH, a user-friendly graphical tool which incorporates several mesh-repairing features, and allows to perform a kind of low-level editing which is often missing in most existing software packages. We show how state-of-the-art techniques have been adapted and extended to form an intuitive and integrated environment, and introduce some optimizations and novel ideas that make ReMESH particularly efficient. The main application in which the tool proves to be extremely useful is the post-processing of scanned surface models. In this context, ReMESH represents a valid support for the production of certified quality meshes


title: Snakes , Shapes , and Gradient Vector Flow

abstract: Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.


title: Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer

abstract: Optimal allocation of distributed generation units is essential to ensure power loss minimization, while meeting the real and reactive power demands in a distribution network. This paper proposes a solution to this non-convex, discrete problem by using the hybrid grey wolf optimizer, a new metaheuristic algorithm. This algorithm is applied to IEEE 33-, IEEE 69-, and Indian 85-bus radial distribution systems to minimize the power loss. The results show that there is a considerable reduction in the power loss and an enhancement of the voltage profile of the buses across the network. Comparisons show that the proposed method outperforms all other metaheuristic methods, and matches the best results by other methods, including exhaustive search, suggesting that the solution obtained is a global optimum. Furthermore, unlike for most other metaheuristic methods, this is achieved with no tuning of the algorithm on the part of the user, except for the specification of the population size.


title: The Impact of Perceived Stress and Coping Adequacy on the Health of Nurses: A Pilot Investigation

abstract: Stress and coping abilities influence the health and work performance of nurses. However, little is known about the combined influence of stress perception and perceived coping adequacy and its impact on the health of nurses. This study examined the relationship between stress, coping, and the combined influences of perceived stress and coping abilities on health and work performance. A valid and reliable questionnaire was completed by 120 nurses in a Midwestern hospital in the USA. In general, the nurses were not healthy: 92% had moderate-to-very high stress levels; 78% slept less than 8 hours of sleep per night; 69% did not exercise regularly; 63% consumed less than 5 servings of fruits and vegetables per day; and 22% were classified as binge drinkers. When confronted with workplace stress, 70% of nurses reported that they consumed more junk food and 63% reported that they consumed more food than usual as a way of coping. Nurses in the "high stress/poor coping" group had the poorest health outcomes and highest health risk behaviors compared to those in other groups. The combined variables of perceived stress and perceived coping adequacy influenced the health of nurses. Therefore, worksite health promotion programs for nurses should focus equally on stress reduction, stress management, and the development of healthy coping skills.


title: Human Detection Using HOG-SVM, Mixture of Gaussian and Background Contours Subtraction

abstract: Automatic moving object/Human detection in a video sequence is one of the most difficult problems in the field of image processing and computer vision. The HOG-SVM provides a detection windows that is not perfectly adjusted to the silhouette of the Human detected. It is possible to apply a postprocess based on background subtraction to improve the segmentation of the detection. In this paper, we present thus a detection method that improves results provided by HOG-SVM with a combination of mixture of Gaussian and background contours subtraction.


Virtual garments: A Fully Geometric Approach for Clothing Design


18 Critical Discourse Analysis


title: ERICO: Effective Removal of Inline Caching Overhead in Dynamic Typed Languages

abstract: Dynamically typed programming languages have become very popular in recent years. These languages ease the task of programmers but introduce significant runtime overheads since variables are neither declared nor bound to a particular type. In particular, every time an object property is accessed, we have to obtain the type of the object to which it belongs just before the access, in order to properly compute the address of the property. This adds an important overhead to each of these accesses. In this paper, we present ERICO, a hybrid HW/SW mechanism that removes most of this overhead. This technique is based on a runtime-built structure that provides the information required to identify the addresses of object properties in a very efficient manner. Besides, a hardware cache of this structure stores its most frequently used elements to speedup its access. To demonstrate the effectiveness of this new mechanism, we implement it in a JavaScript engine and show that on average it produces 11% speedup (up to 31% for some applications) and 10.6% energy reduction.


title: In search of no-loss strategies for the game of tic-tac-toe using a customized genetic algorithm

abstract: The game of Tic-tac-toe is one of the most commonly known games. This game does not allow one to win all the time and a significant proportion of games played results in a draw. Thus, the best a player can hope is to not lose the game. This study is aimed at evolving a number of no-loss strategies using genetic algorithms and comparing them with existing methodologies. To efficiently evolve no-loss strategies, we have developed innovative ways of representing and evaluating a solution, initializing the GA population, developing GA operators including an elite preserving scheme. Interestingly, our GA implementation is able to find more than 72 thousands no-loss strategies for playing the game. Moreover, an analysis of these solutions has given us insights about how to play the game to not lose it. Based on this experience, we have developed specialized efficient strategies having a high win-to-draw ratio. The study and its results are interesting and can be encouraging for the techniques to be applied to other board games for finding efficient strategies.


title: Decentralizing Privacy: Using Blockchain to Protect Personal Data

abstract: The recent increase in reported incidents of surveillance and security breaches compromising users' privacy call into question the current model, in which third-parties collect and control massive amounts of personal data. Bit coin has demonstrated in the financial space that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we describe a decentralized personal data management system that ensures users own and control their data. We implement a protocol that turns a block chain into an automated access-control manager that does not require trust in a third party. Unlike Bit coin, transactions in our system are not strictly financial -- they are used to carry instructions, such as storing, querying and sharing data. Finally, we discuss possible future extensions to block chains that could harness them into a well-rounded solution for trusted computing problems in society.


title: Feeding State Modulates Behavioral Choice and Processing of Prey Stimuli in the Zebrafish Tectum

abstract: Summary Animals use the sense of vision to scan their environment, respond to threats, and locate food sources. The neural computations underlying the selection of a particular behavior, such as escape or approach, require flexibility to balance potential costs and benefits for survival. For example, avoiding novel visual objects reduces predation risk but negatively affects foraging success. Zebrafish larvae approach small, moving objects ("prey") and avoid large, looming objects ("predators"). We found that this binary classification of objects by size is strongly influenced by feeding state. Hunger shifts behavioral decisions from avoidance to approach and recruits additional prey-responsive neurons in the tectum, the main visual processing center. Both behavior and tectal function are modulated by signals from the hypothalamic-pituitary-interrenal axis and the serotonergic system. Our study has revealed a neuroendocrine mechanism that modulates the perception of food and the willingness to take risks in foraging decisions.


title: Joint discriminative dimensionality reduction and dictionary learning for face recognition

abstract: In linear representation based face recognition (FR), it is expected that a discriminative dictionary can be learned from the training samples so that the query sample can be better represented for classification. On the other hand, dimensionality reduction is also an important issue for FR. It cannot only reduce significantly the storage space of face images, but also enhance the discrimination of face feature. Existing methods mostly perform dimensionality reduction and dictionary learning separately, which may not fully exploit the discriminative information in the training samples. In this paper, we propose to learn jointly the projection matrix for dimensionality reduction and the discriminative dictionary for face representation. The joint learning makes the learned projection and dictionary better fit with each other so that a more effective face classification can be obtained. The proposed algorithm is evaluated on benchmark face databases in comparison with existing linear representation based methods, and the results show that the joint learning improves the FR rate, particularly when the number of training samples per class is small.


title: Anomalous Hysteresis in Perovskite Solar Cells

abstract: Perovskite solar cells have rapidly risen to the forefront of emerging photovoltaic technologies, exhibiting rapidly rising efficiencies. This is likely to continue to rise, but in the development of these solar cells there are unusual characteristics that have arisen, specifically an anomalous hysteresis in the current–voltage curves. We identify this phenomenon and show some examples of factors that make the hysteresis more or less extreme. We also demonstrate stabilized power output under working conditions and suggest that this is a useful parameter to present, alongside the current-voltage scan derived power conversion efficiency. We hypothesize three possible origins of the effect and discuss its implications on device efficiency and future research directions. Understanding and resolving the hysteresis is essential for further progress and is likely to lead to a further step improvement in performance.


title: A General Projection Property for Distribution Families

abstract: Surjectivity of linear projections between distribution families with fixed mean and covariance (regardless of dimension) is re-derived by a new proof. We further extend this property to distribution families that respect additional constraints, such as symmetry, unimodality and log-concavity. By combining our results with classic univariate inequalities, we provide new worst-case analyses for natural risk criteria arising in classification, optimization, portfolio selection and Markov decision processes.


title: CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions

abstract: By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and accurate private Web search solution. CYCLOSA improves security by relying on trusted execution environments (TEEs) as provided by Intel SGX. Further, CYCLOSA proposes a novel adaptive privacy protection solution that reduces the risk of user re- identification. CYCLOSA sends fake queries to the search engine and dynamically adapts their count according to the sensitivity of the user query. In addition, CYCLOSA meets scalability as it is fully decentralized, spreading the load for distributing fake queries among other nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real query and the fake queries separately, in contrast to other existing solutions that mix fake and real query results.


How to compare movement? A review of physical movement similarity measures in geographic information science and beyond


title: Evaluation of Extremely Small Sound Source Signals Used in Speaking-Aid System with Statistical Voice Conversion

abstract: We have so far proposed a speaking-aid system for laryngectomees using a statistical voice conversion technique. In the proposed system, artificial speech articulated with extremely small sound source signals is detected with a Non-Audible Murmur (NAM) microphone, and then, the detected artificial speech is converted into more natural voice in a probabilistic manner. Although this system basically allows laryngectomees to speak while keeping the external source signals silent, it is still questionable how much these new sound source signals affect the converted speech quality. In this paper, we investigate the impact of various sound source signals on voice conversion accuracy. Various small sound source signals are designed by changing the spectral envelope and the waveform power independently. We conduct objective and subjective evaluations. The results of these experimental evaluations demonstrate that voice conversion accepts 1) various sound source signals with different spectral envelopes and 2) large degree of power of the sound source signals unless the power of speaking parts is almost equal to that of silence parts. Moreover, we also investigate the effectiveness of enhancing auditory feedback during speaking with the extremely small sound source signals.


title: High-Quality Face Image SR Using Conditional Generative Adversarial Networks

abstract: We propose a novel single face image super-resolution method, which named Face Conditional Generative Adversarial Network(FCGAN), based on boundary equilibrium generative adversarial networks. Without taking any facial prior information, our method can generate a high-resolution face image from a low-resolution one. Compared with existing studies, both our training and testing phases are end-to-end pipeline with little pre/post-processing. To enhance the convergence speed and strengthen feature propagation, skip-layer connection is further employed in the generative and discriminative networks. Extensive experiments demonstrate that our model achieves competitive performance compared with state-of-the-art models.


title: Matrix-Addressable Micropixellated InGaN Light-Emitting Diodes With Uniform Emission and Increased Light Output

abstract: Micropixellated InGaN light-emitting diodes (micro- LEDs) have a wide number of potential applications in areas including microdisplays, fluorescence-based assays and microscopy, and cell micromanipulation. Here, we present fabrication and performance details of matrix-addressable micro-LED devices which show significant improvements over their earlier counterparts. Devices with 64 x 64 micropixel elements, each of them having a 16-mum-diameter emission aperture on a 50-mum pitch, have been fabricated at blue (470 nm), green (510 nm), and UV (370 nm) wavelengths, respectively. Importantly, we have adopted a scheme of running n-metal tracks adjacent to each n-GaN mesa, so that resistance variation between the devices is reduced to below 8%, in contrast to the earlier fivefold resistance variation encountered. We have also made improvements to the spreading-layer formation scheme, resulting in significant increases in output power per element, improved current handling, and reduced turn-on voltages. These devices have been combined with a computer- driven programmable driver interface operating in constant- current mode, and representative microdisplay outputs are presented.


title: Feature detection and tracking with constrained local models

abstract: We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the Active Appearance Model due to Cootes et al. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our constrained local model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.


title: Spectrogram Enhancement Using Multiple Window Savitzky-Golay MWSG Filter for Robust Bird Sound Detection

abstract: Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay MWSG filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency T-F plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score $10.24\%$ relative compared to the best of the six baseline schemes considered in this work.


The Transtheoretical Model and Exercise Behavior: A Comparison of Five Staging Methods


title: Assessing Cross Laminated Timber (CLT) as an Alternative Material for Mid-Rise Residential Buildings in Cold Regions in China—A Life-Cycle Assessment Approach

abstract: Timber building has gained more and more attention worldwide due to it being a generic renewable material and having low environmental impact. It is widely accepted that the use of timber may be able to reduce the embodied energy of a building. However, the development of timber buildings in China is not as rapid as in some other countries. This may be because of the limitations of building regulations and technological development. Several new policies have been or are being implemented in China in order to encourage the use of timber in building construction and this could lead to a revolutionary change in the building industry in China. This paper is the first one to examine the feasibility of using Cross Laminated Timber (CLT) as an alternative solution to concrete by means of a cradle-to-grave life-cycle assessment in China. A seven-storey reference concrete building in Xi’an was selected as a case study in comparison with a redesigned CLT building. Two cities in China, in cold and severe cold regions (Xi’an and Harbin), were selected for this research. The assessment includes three different stages of the life span of a building: materialisation, operation, and end-of-life. The inventory data used in the materialisation stage was mostly local, in order to ensure that the assessment appropriately reflects the situation in China. Energy consumption in the operation stage was obtained from simulation by commercialised software IESTM, and different scenarios for recycling of timber material in the end-of-life are discussed in this paper. The results from this paper show that using CLT to replace conventional carbon intensive material would reduce energy consumption by more than 30% and reduce CO2 emission by more than 40% in both cities. This paper supports, and has shown the potential of, CLT being used in cold regions with proper detailing to minimise environmental impact.


title: Optimal design of formation tracking control for a tractor-trailer robotic system with omni-directional wheels

abstract: This paper addresses the formation tracking control and optimal selection of control design parameters for a tractor-trailer omni-directional type robotic system with on-axle hitching. We design a sliding mode controller incorporating the dynamics of the system to drive the tractor robot to track a desired trajectory and the trailer robot to maintain a desired orientation with respect to the tractor. We also define an optimization problem to find the control design parameters that maximize the control performance and solve it using meta-heuristics optimization techniques. The stability of the developed controller is studied using Lyapunov theorem. We demonstrate the effectiveness of our proposed methodology using simulation results. It is concluded that the proposed control methodology can be adopted in real-time experiments and is applicable in real-world applications for good formation tracking performance under large disturbances.


title: Dynamic modelling and control of variable stiffness actuators

abstract: After briefly summarizing the mechanical design of the two joint prototypes for the new DLR variable compliance arm, the paper exemplifies the dynamic modelling of one of the prototypes and proposes a generic variable stiffness joint model for nonlinear control design. Based on this model, the design of a simple, gain scheduled state feedback controller for active vibration damping of the mechanically very weakly damped joint is presented. Moreover, the computation of the motor reference values out of the desired stiffness and position is addressed. Finally, simulation and experimental results validate the proposed methods.


title: Aerothermoelastic Reduced-Order Model of a Hypersonic Vehicle

abstract: Model reduction techniques are applied to a hypersonic vehicle on terminal trajectories to capture the aerodynamic, thermodynamic, and structural dynamic system evolution and couplings. The General Pseudospectral Optimization Software (GPOPS-II) was used to determine a set of terminal trajectories which maximized impact velocity. Shock, Prandtl-Meyer expansion, and piston theory were combined to create an approximate flow solution over the outer mold line which was then compared to Fully Unstructured Navier-Stokes 3-Dimensional (FUN3D) computational fluid dynamics solutions. Proper orthogonal decomposition of the thermal state of the vehicle was conducted leading to 33 thermal degrees of freedom rather than approximately 28,000 contained by a representative finite element model, while sacrificing negligible system energy. Free vibration mode shapes are derived and used to generalize the structural dynamics equations of motion reducing the number of structural degrees of freedom to 8 from the original 130,000. Finally, the combination of these reduced order models is discussed in the context of future work toward a full vehicle simulation.


title: Low-pass filter approximation with evolutionary techniques

abstract: In this work, two evolutionary techniques, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms are used to optimize the denominator coefficients of the low-pass filter transfer function. Optimum selection of the coefficients will approximate the transfer function to ideal characteristic. Two different order of transfer functions are taken into consideration. Compared to conventional methods, both PSO and ABC minimize the approximation error in a short computation time.


title: Designing Reusable Classes Abstract Designing Reusable Classes

abstract: Object-oriented programming is as much a different way of designing programs as it is a different way of designing programming languages. This paper describes what it is like to design systems in Smalltalk. In particular, since a major motivation for object-oriented programming is software reuse, this paper describes how classes are developed so that they will be reusable.


title: Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

abstract: The random forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and return measures of variable importance. This paper synthesizes 10 years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is paid to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.


title: Hierarchical Probabilistic Neural Network Language Model

abstract: In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbols) in a continuous space that helps to smooth the language model and provide good generalization even when the number of training examples is insufficient. However, these models are extremely slow in comparison to the more commonly used n-gram models, both for training and recognition. As an alternative to an importance sampling method proposed to speed-up training, we introduce a hierarchical decomposition of the conditional probabilities that yields a speed-up of about 200 both during training and recognition. The hierarchical decomposition is a binary hierarchical clustering constrained by the prior knowledge extracted from the WordNet semantic hierarchy.


title: Extended Co-occurrence HOG with Dense Trajectories for Fine-Grained Activity Recognition

abstract: In this paper we propose a novel feature descriptor Extended Co-occurrence HOG (ECoHOG) and integrate it with dense point trajectories demonstrating its usefulness in fine grained activity recognition. This feature is inspired by original Co-occurrence HOG (CoHOG) that is based on histograms of occurrences of pairs of image gradients in the image. Instead relying only on pure histograms we introduce a sum of gradient magnitudes of co-occurring pairs of image gradients in the image. This results in giving the importance to the object boundaries and straightening the difference between the moving foreground and static background. We also couple ECoHOG with dense point trajectories extracted using optical flow from video sequences and demonstrate that they are extremely well suited for fine grained activity recognition. Using our feature we outperform state of the art methods in this task and provide extensive quantitative evaluation.


Generating Stylistically Consistent Dialog Responses with Transfer Learning


title: Modeling and Calibration of Inertial and Vision Sensors

abstract: This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The method is based on a physical model which can also be used in solving, for example, sensor fusion problems. The experimental results show that the method works well in practice, both for perspective and spherical cameras.


title: Particle Swarm CMA Evolution Strategy for the optimization of multi-funnel landscapes

abstract: We extend the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) by collaborative concepts from Particle Swarm Optimization (PSO). The proposed Particle Swarm CMA-ES (PS-CMA-ES) algorithm is a hybrid real-parameter algorithm that combines the robust local search performance of CMA-ES with the global exploration power of PSO using multiple CMA-ES instances to explore different parts of the search space in parallel. Swarm intelligence is introduced by considering individual CMA-ES instances as lumped particles that communicate with each other. This includes non-local information in CMA-ES, which improves the search direction and the sampling distribution. We evaluate the performance of PS-CMA-ES on the IEEE CEC 2005 benchmark test suite. The new PS-CMA-ES algorithm shows superior performance on noisy problems and multi-funnel problems with non-convex underlying topology.


title: Routing protocol design for secure WSN: Review and open research issues

abstract: Wireless sensor networks (WSNs) have gained a substantial attention in wireless research community as these networks are envisioned to support a large number of practical applications. Due to salient features of sensor networks, the security design for WSN is significantly challenging. Despite a good number of available surveys on this particular topic, we feel that there is a gap in the existing literature in terms of timeliness, emphasis, and comprehensiveness. This paper reviews the state-of-the-art for secure WSN routing protocols that illustrates the issues and challenges in the context design matters. Further, we propose the schematic taxonomy of key design issues for WSN routing protocols. We also define design factors categorization relevant to secure routing: basic, essential, and optional. The similarities and differences of secure routing approaches are summarized on the basis of key design attributes, security objectives, and attacks prevention. Finally, we outline possible future research trends on secure routing design in WSN.


title: Mammalian circadian clock and metabolism - the epigenetic link

abstract: Circadian rhythms regulate a wide variety of physiological and metabolic processes. The clock machinery comprises complex transcriptional–translational feedback loops that, through the action of specific transcription factors, modulate the expression of as many as 10% of cellular transcripts. This marked change in gene expression necessarily implicates a global regulation of chromatin remodeling. Indeed, various descriptive studies have indicated that histone modifications occur at promoters of clock-controlled genes (CCGs) in a circadian manner. The finding that CLOCK, a transcription factor crucial for circadian function, has intrinsic histone acetyl transferase (HAT) activity has paved the way to unraveling the molecular mechanisms that govern circadian chromatin remodeling. A search for the histone deacetylase (HDAC) that counterbalances CLOCK activity revealed that SIRT1, a nicotinamide adenin dinucleotide (NAD+)-dependent HDAC, functions in a circadian manner. Importantly, SIRT1 is a regulator of aging, inflammation and metabolism. As many transcripts that oscillate in mammalian peripheral tissues encode proteins that have central roles in metabolic processes, these findings establish a functional and molecular link between energy balance, chromatin remodeling and circadian physiology. Here we review recent studies that support the existence of this link and discuss their implications for understanding mammalian physiology and pathology.