title: A Level Set Approach to Image Segmentation With Intensity Inhomogeneity

abstract: It is often a difficult task to accurately segment images with intensity inhomogeneity, because most of representative algorithms are region-based that depend on intensity homogeneity of the interested object. In this paper, we present a novel level set method for image segmentation in the presence of intensity inhomogeneity. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances in which a sliding window is used to map the original image into another domain, where the intensity distribution of each object is still Gaussian but better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A maximum likelihood energy functional is then defined on the whole image region, which combines the bias field, the level set function, and the piecewise constant function approximating the true image signal. The proposed level set method can be directly applied to simultaneous segmentation and bias correction for 3 and 7T magnetic resonance images. Extensive evaluation on synthetic and real-images demonstrate the superiority of the proposed method over other representative algorithms.


title: Beyond Doctors: Future Health Prediction from Multimedia and Multimodal Observations

abstract: Although chronic diseases cannot be cured, they can be effectively controlled as long as we understand their progressions based on the current observational health records, which is often in the form of multimedia data. A large and growing body of literature has investigated the disease progression problem. However, far too little attention to date has been paid to jointly consider the following three observations of the chronic disease progression: 1) the health statuses at different time points are chronologically similar; 2) the future health statuses of each patient can be comprehensively revealed from the current multimedia and multimodal observations, such as visual scans, digital measurements and textual medical histories; and 3) the discriminative capabilities of different modalities vary significantly in accordance to specific diseases. In the light of these, we propose an adaptive multimodal multi-task learning model to co-regularize the modality agreement, temporal progression and discriminative capabilities of different modalities. We theoretically show that our proposed model is a linear system. Before training our model, we address the data missing problem via the matrix factorization approach. Extensive evaluations on a real-world Alzheimer's disease dataset well verify our proposed model. It should be noted that our model is also applicable to other chronic diseases.


KEGGgraph: Application Examples


title: A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism

abstract: This paper focuses on localization that serves as a smart service. Among the primary services provided by Internet of Things (IoT), localization offers automatically discoverable services. Knowledge relating to an object's position, especially when combined with other information collected from sensors and shared with other smart objects, allows us to develop intelligent systems to fast respond to changes in an environment. Today, wireless sensor networks (WSNs) have become a critical technology for various kinds of smart environments through which different kinds of devices can connect with each other coinciding with the principles of IoT. Among various WSN techniques designed for positioning an unknown node, the trilateration approach based on the received signal strength is the most suitable for localization due to its implementation simplicity and low hardware requirement. However, its performance is susceptible to external factors, such as the number of people present in a room, the shape and dimension of an environment, and the positions of objects and devices. To improve the localization accuracy of trilateration, we develop a novel distributed localization algorithm with a dynamic-circle-expanding mechanism capable of more accurately establishing the geometric relationship between an unknown node and reference nodes. The results of real world experiments and computer simulation show that the average error of position estimation is 0.67 and 0.225 m in the best cases, respectively. This suggests that the proposed localization algorithm outperforms other existing methods.


title: Variance filter for edge detection and edge-based image segmentation

abstract: In this paper problem of edge-based image segmentation is considered. Specifically, a new approach to edge detection is introduced. The proposed method utilizes variance filter to determine edge position. Results of edge detection using the new approach on synthetic and real images are presented and compared with results provided by the traditional i.e. image-derivative based approaches. Results of the comparison are analyzed and discussed.


title: Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video

abstract: This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate non-rigid structure from motion (nrsfm) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences. Unlike traditional factorization based approaches to nrsfm, which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.


Perception and Cognition in Immersive Virtual Reality,” Communication through Virtual


title: Mining frequent graph patterns with differential privacy

abstract: Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. Differential privacy has recently emerged as the de facto standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. We first show that previous techniques on differentially private discovery of frequent itemsets cannot apply in mining frequent graph patterns due to the inherent complexity of handling structural information in graphs. We then address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling based algorithm. Unlike previous work on frequent itemset mining, our techniques do not rely on the output of a non-private mining algorithm. Instead, we observe that both frequent graph pattern mining and the guarantee of differential privacy can be unified into an MCMC sampling framework. In addition, we establish the privacy and utility guarantee of our algorithm and propose an efficient neighboring pattern counting technique as well. Experimental results show that the proposed algorithm is able to output frequent patterns with good precision.


title: Linear Convergence of Stochastic Iterative Greedy Algorithms With Sparse Constraints

abstract: Motivated by recent work on stochastic gradient descent methods, we develop two stochastic variants of greedy algorithms for possibly non-convex optimization problems with sparsity constraints. We prove linear convergence in expectation to the solution within a specified tolerance. This generalized framework applies to problems such as sparse signal recovery in compressed sensing, low-rank matrix recovery, and covariance matrix estimation, giving methods with provable convergence guarantees that often outperform their deterministic counterparts. We also analyze the settings where gradients and projections can only be computed approximately, and prove the methods are robust to these approximations. We include many numerical experiments which align with the theoretical analysis and demonstrate these improvements in several different settings.


title: A Method for Analyzing a Linear Series-Fed Rectangular Microstrip Antenna Array

abstract: A novel method of analysis for a linear series-fed microstrip antenna array is developed, which is based on a set of canonical coefficients defined for the elements of the array. This method accounts for the mutual coupling between the elements, and allows for a design that has an arbitrary amplitude and phase of the radiating patch currents. This method has the simplicity of a CAD approach while maintaining an accuracy close to that of a full-wave method. The coefficients involved in the formulation are determined by full-wave simulation on either a single patch element or on two patch elements (for mutual coupling calculations).


title: The Spatial and Temporal Signatures of Word Production Components: A Critical Update

abstract: In the first decade of neurocognitive word production research the predominant approach was brain mapping, i.e. investigating the regional cerebral brain activation patterns correlated with word production tasks, such as picture naming and word generation. Indefrey and Levelt (2004) conducted a comprehensive meta-analysis of word production studies that used this approach and combined the resulting spatial information on neural correlates of component processes of word production with information on the time course of word production provided by behavioral and electromagnetic studies. In recent years, neurocognitive word production research has seen a major change towards a hypothesis testing approach. This approach is characterized by the design of experimental variables modulating single component processes of word production and testing for predicted effects on spatial or temporal neurocognitive signatures of these components. This change was accompanied by the development of a broader spectrum of measurement and analysis techniques. The article reviews the findings of recent studies using the new approach. The time course assumptions of Indefrey and Levelt (2004) have largely been confirmed requiring only minor adaptations. Adaptations of the brain structure/function relationships proposed by Indefrey and Levelt (2004) include the precise role of subregions of the left inferior frontal gyrus as well as a probable, yet to date unclear role of the inferior parietal cortex in word production.


Biologically inspired approaches to robotics: what can we learn from insects?


title: Beneficial effects of Trichoderma harzianum T-22 in tomato seedlings infected by Cucumber mosaic virus (CMV)

abstract: The study of the biochemical and molecular mechanisms deriving from the host-pathogen-antagonist interaction is essential to understand the dynamics of infectious processes and can be useful for the development of new strategies to control phytopathogens, particularly viruses, against which chemical treatments have no effect. In this work, we demonstrate the ability of the rhizospheric fungus Trichoderma harzianum strain T-22 (T22) to induce defense responses in tomato (Solanum lycopersicum var. cerasiforme) against Cucumber mosaic virus (CMV, family Bromoviridae, genus Cucumovirus) strain Fny. A granule formulation containing T22 was used for treating the plants before, simultaneously or after the CMV inoculation, in order to study the molecular and biochemical aspects of the interaction between T22 and tomato against the virus. Reactive oxygen species (ROS) and the genes encoding for ROS scavenging enzymes were investigated. Histochemical analysis revealed a different increase in the superoxide anion (\( {\text{O}}_{2}^{ \bullet - } \)) and hydrogen peroxide (H2O2) content in plants infected by CMV alone or in the presence of T22, confirming the involvement of ROS in plant defense responses. Gene expression analysis suggested a definite improvement in oxidative stress when plants were treated with T22 after inoculation with CMV. In conclusion, our data indicate that Trichoderma harzianum T-22 stimulates the induction of tomato defense responses against CMV, an action that implies the involvement of ROS, pointing towards its use as a treatment rather than as a preventive measure.


title: Anatomy of the corrugator supercilii muscle: Part I. Corrugator topography

abstract: Background: Complete corrugator supercilii muscle resection is important for the surgical treatment of migraine headaches and may help prevent postoperative abnormalities in surgical forehead rejuvenation. Specific topographic analysis of corrugator supercilii muscle dimensions and its detailed association with the supraorbital nerve branching patterns has not been thoroughly delineated. Part I of this two-part study aims to define corrugator supercilii muscle topography with respect to external bony landmarks. Methods: Twenty-five fresh cadaver heads (50 corrugator supercilii muscles and 50 supraorbital nerves) were dissected to isolate the corrugator supercilii muscle from surrounding muscles. Standardized measurements of corrugator supercilii muscle dimensions were taken with respect to the nasion and lateral orbital rim. Results: Relative to the nasion, the most medial origin of the corrugator supercilii muscle was found at 2.9 ± 1.0 mm; the most lateral origin point, 14.0 ± 2.8 mm. The lateralmost insertion of the corrugator supercilii muscle measured 43.3 ± 2.9 mm from the nasion or 7.6 ± 2.7 mm medial to the lateral orbital rim. The most cephalic extent (apex) of the muscle was located 32.6 ± 3.1 mm cephalad to the nasion–lateral orbital rim plane and 18.0 ± 3.7 mm medial to the lateral orbital rim. There were no statistical differences noted between the right and left sides. Conclusions: The dimensions of the corrugator supercilii muscle are more extensive than previously described and can be easily delineated using fixed bony landmarks. These data may prove beneficial in performing safe, complete, and symmetric corrugator supercilii muscle resection for forehead rejuvenation and for effective decompression of the supraorbital nerve and supratrochlear nerve branches in the surgical treatment of migraine headaches.


title: Ill communication: technology, distraction & studentperformance

abstract: This paper investigates the impact of schools banning mobile phones on student test scores. By surveying schools in four English cities regarding their mobile phone policies and combining it with administrative data, we find that student performance in high stakes exams significantly increases post ban. We use a difference in differences (DID) strategy, exploiting variations in schools’ autonomous decisions to ban these devices, conditioning on a range of student characteristics and prior achievement. Our results indicate that these increases in performance are driven by the lowestachieving students. This suggests that restricting mobile phone use can be a low-cost policy to reduce educational inequalities.


title: Tandem connectionist feature extraction for conventional HMM systems

abstract: Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling. By training the network to generate the subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, we achieve relative error rate reductions of 35% or more on the multicondition Aurora noisy continuous digits task.


title: Approximate planning and verification for large markov decision processes

abstract: We focus on the planning and verification problems for very large probabilistic systems, such as Markov decision processes (MDPs), from a complexity point of view. More precisely, we deal with the problem of designing an efficient approximation method to compute a near-optimal policy for the planning problem in discounted MDPs and the satisfaction probabilities of interesting properties, like reachability or safety, over the Markov chain obtained by restricting the MDP to the near-optimal policy. In this paper, we present two different approaches. The first one is based on sparse sampling while the second uses a variant of the multiplicative weights update algorithm. The complexity of the first approximation method is independent of the size of the state space and uses only a probabilistic generator of the MDP. We give a complete analysis of this approach, for which the control parameter is mainly the targeted quality of the approximation. The second approach is more prospective and is different in the sense that the method can be controlled dynamically by observing its speed of convergence. Parts of this paper have already been presented in Lassaigne and Peyronnet (in Proceedings of the ACM Symposium on applied computing, SAC 2012, pp 1314---1319, ACM 2012), by the same authors.


title: Future e-Government Research: 13 Research Themes Identified in the eGovRTD2020 Project

abstract: E-government research has become a recognized research domain and many policies and strategies are formulated for e-government implementations. Most of these target the next few years and limited attention has been giving to the long term. The eGovRTD2020, a European Commission co-funded project, investigated the future research on e-government driven by changing circumstances and the evolution of technology. This project consists of an analysis of the state of play, a scenario-building, a gap analysis and a roadmapping activity. In this paper the roadmapping methodology fitting the unique characteristics of the e-government field is presented and the results are briefly discussed. The use of this methodology has resulted in the identification of a large number of e-government research themes. It was found that a roadmapping methodology should match the unique characteristics of e-government. The research shows the need of multidisciplinary research.


title: Two bitcoins at the price of one? double-spending attacks on fast payments in bitcoin

abstract: Bitcoin is a decentralized payment system that is based on Proof-of-Work. Bitcoin is currently gaining popularity as a digital currency; several businesses are starting to accept Bitcoin transactions. An example case of the growing use of Bitcoin was recently reported in the media; here, Bitcoins were used as a form of fast payment in a local fast-food restaurant. In this paper, we analyze the security of using Bitcoin for fast payments, where the time between the exchange of currency and goods is short (i.e., in the order of few seconds). We focus on doublespending attacks on fast payments and demonstrate that these attacks can be mounted at low cost on currently deployed versions of Bitcoin. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast transactions are not always effective in resisting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we leverage on our findings and propose a lightweight countermeasure that enables the detection of doublespending attacks in fast transactions.


title: A control architecture for quadruped locomotion over rough terrain

abstract: Legged robots have the potential to navigate a much larger variety of terrain than their wheeled counterparts. In this paper we present a hierarchical control architecture that enables a quadruped, the "LittleDog" robot, to walk over rough terrain. The controller consists of a high-level planner that plans a set of footsteps across the terrain, a low-level planner that plans trajectories for the robot's feet and center of gravity (COG), and a low-level controller that tracks these desired trajectories using a set of closed-loop mechanisms. We conduct extensive experiments to verify that the controller is able to robustly cross a wide variety of challenging terrains, climbing over obstacles nearly as tall as the robot's legs. In addition, we highlight several elements of the controller that we found to be particularly crucial for robust locomotion, and which are applicable to quadruped robots in general. In such cases we conduct empirical evaluations to test the usefulness of these elements.


title: The development of integrated career portal in university using agile methodology

abstract: A gap between university graduate's quality and industry sector demand is one of the challenge university has to tackle, which makes university graduates absorption in job market slows down. One of the tools that can be used to bridge between graduates and companies is an integrated career portal. This portal can be optimized to help industry find the employees they need from the alumni from university. On the other hand, this tool can help graduates to get job that suits their competency. By using the portal, alumni can hopefully minimize the number of unemployed university graduates and accelerate their absorption into job market. The development of this career portal will use agile methodology. Questionnaire was distributed to alumni and employers to collect information about what user need in the portal. The result of this research can create a career portal which integrated business process and social media.


title: Autoencoder-augmented neuroevolution for visual doom playing

abstract: Neuroevolution has proven effective at many reinforcement learning tasks, but does not seem to scale well to high-dimensional controller representations, which are needed for tasks where the input is raw pixel data. We propose a novel method where we train an autoencoder to create a comparatively low-dimensional representation of the environment observation, and then use CMA-ES to train neural network controllers acting on this input data. As the behavior of the agent changes the nature of the input data, the autoencoder training progresses throughout evolution. We test this method in the VizDoom environment built on the classic FPS Doom, where it performs well on a health-pack gathering task.


title: Randomized, Placebo-Controlled Trial of Methyl B12 for Children with Autism

abstract: OBJECTIVE ::: Children with autism spectrum disorder (ASD) have been reported to have reduced ability to methylate DNA and elevated markers of oxidative stress. We sought to determine if methyl B12, a key metabolic cofactor for cellular methylation reactions and antioxidant defense, could improve symptoms of ASD. ::: ::: ::: METHODS ::: A total of 57 children with ASD were randomly assigned to 8 weeks of treatment with methyl B12 (75 μg/kg) or saline placebo every 3 days in a subcutaneous injection. The primary outcome measure was overall improvement in symptoms of ASD as measured by the Clinical Global Impressions-Improvement (CGI-I) score. Secondary outcome measures included changes in the Aberrant Behavior Checklist (ABC) and the Social Responsiveness Scale (SRS). Laboratory measures of methionine methylation and antioxidant glutathione metabolism were assessed at baseline and 8 weeks. ::: ::: ::: RESULTS ::: A total of 50 children (mean age 5.3 years, 79% male) completed the study. The primary outcome measure - the clinician rated CGI-I score - was statistically significantly better (lower) in the methyl B12 group (2.4) than in the placebo group (3.1) (0.7 greater improvement in the methyl B12 group, 95% CI 1.2-0.2, p = 0.005). Clinical improvement among children treated with methyl B12 was positively correlated with increases in plasma methionine (p = 0.05), decreases in S-adenosyl-l-homocysteine (SAH) (p = 0.007) and improvements in the ratio of S-adenosylmethionine (SAM) to SAH (p = 0.007), indicating an improvement in cellular methylation capacity. No improvements were observed in the parent-rated ABC or SRS. ::: ::: ::: CONCLUSIONS ::: Methyl B12 treatment improved clinician-rated symptoms of ASD that were correlated with improvements in measures of methionine metabolism and cellular methylation capacity. Clinical Trial Registry: Efficacy Study of Subcutaneous Methyl B12 in Children with Autism: NCT01039792 ( clinicaltrials.gov1 ).


title: A survey on anomaly and signature based intrusion detection system (IDS)

abstract: Security is considered as one of the most critical parameter for the acceptance of any networking technology. Information in transit must be protected from unauthorized release and modification, and the connection itself must be established and maintained securely malicious users have taken advantage of this to achieve financial gain or accomplish some corporate or personal agenda. Denial of Service (DoS) and distributed DoS (DDoS) attacks are evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money. Combination of Intrusion detection System and Firewall is used by Business Organizations to detect and p revent Organizations‟ network from these attacks. Signatures to detect them are not available. This paper presents a light-Weight mechanism to detect novel DoS/DDoS (Resource Consumption) attacks and automatic signature generation process to represent them in real time. Experimental results are provided to support the proposed mechanism.


title: A preprocessing method for printed Tamil documents: Skew correction and textual classification

abstract: An Optical character recognition (OCR) consists of the phases: preprocessing and segmentation, feature extraction, classification and post-processing. This paper focuses on preprocessing and segmentation tasks which plays a major role in the subsequent processes of an OCR. The objective of preprocessing and segmentation is to improve the quality of the input image. In addition this phase removes unnecessary portions of the input image that would otherwise complicate the subsequent steps of OCR and reduce the overall recognition rate. Preprocessing and segmentation step consists many sub processes namely, image binarisation, noise removal, skew detection and correction, page segmentation, text or non-text classification, line segmentation, word segmentation and character segmentation. This paper proposes a new method to calculate the skew angle for skew correction. In addition this paper proposes a more accurate method to segment the input image as blocks and classify the blocks as text or non-text. The skew angle is calculated on the scanned document using Wiener filter, smearing technique and Radon transform. Document image is segmented into blocks using run length smearing algorithm and connected component analysis. Features such as basic, density and HOG are extracted from each block for text and non-text classification. The proposed methods are tested on 54 documents. The testing results show a recognition rate of 96.30% for skew detection and correction whereas the recognition rate is 99.18% for text or non-text classification with binary SVMs using RBF kernel.


title: Implementation of a Grid-Integrated PV-Battery System for Residential and Electrical Vehicle Applications

abstract: A new control approach of integrating a solar photovoltaic (PV) with a battery storage is presented to a single-phase grid for residential and electric vehicle application. The main purpose of the proposed work is to feed a continuous power to the grid, thereby enhancing the viability of the battery energy storage support connected to the system. The charging and discharging of the battery achieve power leveling and load leveling along with increased reliability of the system. The multifunctional voltage-source converter acts as an active power filter and performs the harmonics mitigation along with reactive power compensation. In the proposed system, a unique control is developed for resynchronization of the grid during reconnection of the grid after the mitigation of a failure. The overall control of the system is adaptable under various practically occurring situations such as disconnection of the PV array, the battery, and the grid from the system. The detailed design and control of the proposed system are presented. The validity of the proposed system is performed through a laboratory prototype developed for a power rating of 2.2 kW connected to the utility grid. The performance of the system is found satisfactory under various disturbance, and the recorded results have been demonstrated.


title: Using sonification

abstract: The idea behind sonification is that synthetic nonverbal sounds can represent numerical data and provide support for information processing activities of many differentkinds. This article describes some of the ways that sonification has been used in assistive technologies, remote collaboration, engineering analyses, scientific visualisations, emergency services and aircraft cockpits. Approaches for designing sonifications are surveyed, and issues raised by the existing approaches and applications are outlined. Relationsare drawn to other areas of knowledge where similar issueshave also arisen, such as human-computer interaction, scientific visualisation, and computer music. At the end is a listof resources that will help you delve further into the topic.


title: RAMOBoost: Ranked Minority Oversampling in Boosting

abstract: In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imbalanced data. However, because of the complex characteristics of imbalanced data, many real-world solutions struggle to provide robust efficiency in learning-based applications. In an effort to address this problem, this paper presents Ranked Minority Oversampling in Boosting (RAMOBoost), which is a RAMO technique based on the idea of adaptive synthetic data generation in an ensemble learning system. Briefly, RAMOBoost adaptively ranks minority class instances at each learning iteration according to a sampling probability distribution that is based on the underlying data distribution, and can adaptively shift the decision boundary toward difficult-to-learn minority and majority class instances by using a hypothesis assessment procedure. Simulation analysis on 19 real-world datasets assessed over various metrics-including overall accuracy, precision, recall, F-measure, G-mean, and receiver operation characteristic analysis-is used to illustrate the effectiveness of this method.


title: Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

abstract: Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.


title: CloudPss-An Energy Internet Modeling and Simulation Tool in the Cloud

abstract: Energy internet is studied as a promising electricity-centered energy network, which revolves around reliable framework, widely interconnected, highly intelligent and open interaction. In this paper, we introduce a software tool named CloudPss. It bases on cloud computing, and focuses on solving the problem of modeling and simulation of the energy internet. Compared with other modeling and simulation tools, CloudPss is much more flexible and efficient.


title: Evaluation of the impact of Bus Rapid Transit on air pollution in Mexico City

abstract: Mexico City's bus rapid transit (BRT) network, Metrobus, was introduced in an attempt to reduce congestion, increase city transport efficiency and cut air polluting emissions. In June 2005, the first BRT line in the metropolitan area began service. We use the differences-in-differences technique to make the first quantitative assessment of the policy impact of a BRT system on air polluting emissions. The air pollutants considered are carbon monoxide (CO), nitrogen oxides (NOX), particulate matter of less than 10 μm (PM10), and sulfur dioxide (SO2). The ex-post analysis uses real field data from air quality monitoring stations for periods before and after BRT implementation. Results show that BRT constitutes an effective environmental policy, reducing emissions of CO, NOX, and PM10.


title: Subjective well-being. The science of happiness and a proposal for a national index.

abstract: One area of positive psychology analyzes subjective well-being (SWB), people's cognitive and affective evaluations of their lives. Progress has been made in understanding the components of SWB, the importance of adaptation and goals to feelings of well-being, the temperament underpinnings of SWB, and the cultural influences on well-being. Representative selection of respondents, naturalistic experience sampling measures, and other methodological refinements are now used to study SWB and could be used to produce national indicators of happiness.


title: A 50-70 GHz SIW-Based Linearly Tapered Slot Antenna with Low Cross Polarization

abstract: In this paper, a 50-70 GHz substrate integrated waveguide (SIW)-based linearly tapered slot antenna (LTSA) is proposed. An SIW-to-tapered-slot transition consists of a metallic plate of a rectangular trapezoid shape and two plated vias is used to reduce the cross-polarization level. The antenna shows a gain of 15.8±0.5 dB with return loss better than 18 dB. Over the entire band of interest, the antenna exhibits symmetric radiation pattern with cross-polarization and side lobe level (SLL) of -22 and -21 dB, respectively. The overall antenna efficiency is 94%. The design approach is validated by comparing CST MWS and Ansys HFSS simulation results which clearly exhibits good agreement.


title: Regression Trees from Data Streams with Drift Detection

abstract: The problem of extracting meaningful patterns from time changing data streams is of increasing importance for the machine learning and data mining communities. We present an algorithm which is able to learn regression trees from fast and unbounded data streams in the presence of concept drifts. To our best knowledge there is no other algorithm for incremental learning regression trees equipped with change detection abilities. The FIRT-DD algorithm has mechanisms for drift detection and model adaptation, which enable to maintain accurate and updated regression models at any time. The drift detection mechanism is based on sequential statistical tests that track the evolution of the local error, at each node of the tree, and inform the learning process for the detected changes. As a response to a local drift, the algorithm is able to adapt the model only locally, avoiding the necessity of a global model adaptation. The adaptation strategy consists of building a new tree whenever a change is suspected in the region and replacing the old ones when the new trees become more accurate. This enables smooth and granular adaptation of the global model. The results from the empirical evaluation performed over several different types of drift show that the algorithm has good capability of consistent detection and proper adaptation to concept drifts.


title: A Comparison of the Validity and Responsiveness of the EQ-5D-5L and SF-6D for Measuring Health Spillovers: A Study of the Family Impact of Meningitis

abstract: Background. The “health spillover” of patient illness on family members is important to capture in economic evaluation. This study compares the construct validity and responsiveness of 2 widely used health-related quality-of-life instruments, the EQ-5D-5L and SF-6D, in capturing health spillover effects for family members with and without an informal care role (carers and noncarers). Methods. Construct validity and responsiveness were assessed using data from a 2012 UK survey of the family impact of meningitis-related sequelae. Construct validity was assessed by testing associations between family members’ health status and variables anticipated to be associated with spillover effects (patient health status and informal care). Responsiveness was assessed by testing associations between the longitudinal change in family members’ health status and longitudinal change in patient health and caring hours. Results. Among noncarers, both the EQ-5D-5L and the SF-6D exhibited construct validity with 10 of the 11 associations that were hypothesized being statistically significant on both measures. There was less clear evidence of responsiveness of the measures for noncarers. Among carers, the EQ-5D-5L exhibited greater construct validity, as well as responsiveness, with respect to spillovers from patient health. This was evidenced by the EQ-5D-5L detecting 9 significant associations compared with 4 on the SF-6D. However, the SF-6D exhibited greater construct validity with respect to spillovers generated from informal care provision (5 associations significant compared with 2 on the EQ-5D-5L). Conclusion. Both the EQ-5D-5L and the SF-6D exhibited a degree of validity that could justify their use as measures of health-related quality-of-life spillovers on family members in economic evaluation.


title: An 868 MHz 7.5 µW wake-up receiver with −60 dBm sensitivity

abstract: Abstract. In wireless sensor networks (WSNs), batteries are unlikely to be replaced or recharged once they get depleted, because of costs and feasibility. In a typical application, sensor nodes should be accessible and able to respond within a defined period of time, especially in real-time applications. However, the idle listening of the radio wastes most of the energy since the radio transceiver is constantly active. On the other hand, putting it into sleep state disconnects the node from the network. To cope with such a challenge, an ultra-low-power radio receiver referred to as a wake-up receiver (WuRx) handles the idle listening while keeping the main radio completely off. A WuRx consumes much less power than the main transceiver and triggers an interrupt only when a packet with a user-defined address is received. Embedding such a device enables better event-triggered applications where real-time behavior is required and a longer lifetime is mandatory. The proposed WuRx features practical sensitivity and includes the minimum number of active components in order to remain within the power budget. In this paper, an ultra-low-power WuRx with a power of 7.5 µW and a sensitivity of −60 dBm is developed. The decoding process of 16 bit of a wake-up packet (WuPt) takes less than 15 ms.


title: Highly Efficient Analog Maximum Power Point Tracking (AMPPT) in a Photovoltaic System

abstract: A compact-size analog maximum power point tracking (AMPPT) technique is proposed in this paper for high power efficiency in the photovoltaic (PV) system. Combining existing MPPT approaches, we present a fast and accurate tracking performance. Here, a wide-range current multiplier, which tracks the maximum power point (MPP) in the solar power system, is implemented to detect the power slope condition of the solar panel. Experimental results show that the proposed technique can rapidly track the MPP with a high tracking accuracy of 97.3%. Furthermore, the proposed system can connect to the grid-connected inverter to supply ac power.


title: Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras

abstract: Understanding the surrounding environment of the vehicle is still one of the challenges for autonomous driving. This paper addresses 360-degree road scene semantic segmentation using surround view cameras, which are widely equipped in existing production cars. First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images. Experiments demonstrate the effectiveness of the RDC to handle images with large distortions, and that the proposed approach shows a good performance using surround view cameras with the help of the transformed images.


title: Resonant converter with coupling and load independent resonance for omnidirectional wireless power transfer application

abstract: Omnidirectional wireless power transfer (WPT) system has been studied recently due to its better user experience compared with directional WPT system. And this paper focus on resonant converter topologies study in omnidirectional WPT system. At first, three challenges of resonant converter design are identified: I. moving resonant frequency under variable coupling condition; II. voltage controllability under load change; III. complicated system control with coupling between multiple coils. CLCL-LC resonant converter is proposed to solve all the challenges and provide zero voltage switching (ZVS) operation for primary device at resonant frequency. A 6.78MHz CLCL-LC resonant converter for omnidirectional WPT system is built and tested.


title: Crowdsourcing Multi-Label Classification for Taxonomy Creation

abstract: Recent work has introduced CASCADE, an algorithm for creating a globally-consistent taxonomy by crowdsourcing microwork from many individuals, each of whom may see only a tiny fraction of the data (Chilton et al. 2013). While CASCADE needs only unskilled labor and produces taxonomies whose quality approaches that of human experts, it uses significantly more labor than experts. This paper presents DELUGE, an improved workflow that produces taxonomies with comparable quality using significantly less crowd labor. Specifically, our method for crowdsourcing multi-label classification optimizes CASCADE’s most costly step (categorization) using less than 10% of the labor required by the original approach. DELUGE’s savings come from the use of decision theory and machine learning, which allow it to pose microtasks that aim to maximize information gain.


title: ELT: Efficient Log-based Troubleshooting System for Cloud Computing Infrastructures

abstract: We present an Efficient Log-based Troubleshooting(ELT) system for cloud computing infrastructures. ELT adopts a novel hybrid log mining approach that combines coarse-grained and fine-grained log features to achieve both high accuracy and low overhead. Moreover, ELT can automatically extract key log messages and perform invariant checking to greatly simplify the troubleshooting task for the system administrator. We have implemented a prototype of the ELT system and conducted an extensive experimental study using real management console logs of a production cloud system and a Hadoop cluster. Our experimental results show that ELT can achieve more efficient and powerful troubleshooting support than existing schemes. More importantly, ELT can find software bugs that cannot be detected by current cloud system management practice.


title: Exceeding Nernst limit (59mV/pH): CMOS-based pH sensor for autonomous applications

abstract: A highly sensitive field-effect sensor immune to environmental potential fluctuation is proposed. The sensor circuit consists of two sensors each with a charge sensing field effect transistor (FET) and an extended sensing gate (SG). By enlarging the sensing gate of an extended gate ISFET, a remarkable sensitivity of 130mV/pH is achieved, exceeding the conventional Nernst limit of 59mV/pH. The proposed differential sensing circuit consists of a pair of matching n-channel and p-channel ion sensitive sensors connected in parallel and biased at a matched transconductance bias point. Potential fluctuations in the electrolyte appear as common mode signal to the differential pair and are cancelled by the matched transistors. This novel differential measurement technique eliminates the need for a true reference electrode such as the bulky Ag/AgCl reference electrode and enables the use of the sensor for autonomous and implantable applications.


title: Deep Metric Learning via Facility Location

abstract: Learning image similarity metrics in an end-to-end fashion with deep networks has demonstrated excellent results on tasks such as clustering and retrieval. However, current methods, all focus on a very local view of the data. In this paper, we propose a new metric learning scheme, based on structured prediction, that is aware of the global structure of the embedding space, and which is designed to optimize a clustering quality metric (NMI). We show state of the art performance on standard datasets, such as CUB200-2011 [37], Cars196 [18], and Stanford online products [30] on NMI and R@K evaluation metrics.


title: Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons Learned

abstract: We present a smartphone-based system for real-time tele-monitoring of physical activity in patients with chronic heart-failure (CHF). We recently completed a pilot study with 15 subjects to evaluate the feasibility of the proposed monitoring in the real world and examine its requirements, privacy implications, usability, and other challenges encountered by the participants and healthcare providers. Our tele-monitoring system was designed to assess patient activity via minute-by-minute energy expenditure (EE) estimated from accelerometry. In addition, we tracked relative user location via global positioning system (GPS) to track outdoors activity and measure walking distance. The system also administered daily surveys to inquire about vital signs and general cardiovascular symptoms. The collected data were securely transmitted to a central server where they were analyzed in real time and were accessible to the study medical staff to monitor patient health status and provide medical intervention if needed. Although the system was designed for tele-monitoring individuals with CHF, the challenges, privacy considerations, and lessons learned from this pilot study apply to other chronic health conditions, such as diabetes and hypertension, that would benefit from continuous monitoring through mobile-health (mHealth) technologies.


title: O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images

abstract: Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years. Despite this growing interest, the scientific community is still lacking a reference dataset to evaluate objectively and quantitatively the performance of proposed dehazing methods. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. In practice, hazy images have been captured in presence of real haze, generated by professional haze machines, and OHAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. To illustrate its usefulness, O-HAZE is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current techniques, and questions some of their underlying assumptions.


title: Working Memory: Theories, Models, and Controversies

abstract: I present an account of the origins and development of the multicomponent approach to working memory, making a distinction between the overall theoretical framework, which has remained relatively stable, and the attempts to build more specific models within this framework. I follow this with a brief discussion of alternative models and their relationship to the framework. I conclude with speculations on further developments and a comment on the value of attempting to apply models and theories beyond the laboratory studies on which they are typically based.


title: A High Performance Switchable Multiband Inductor Structure for LC-VCOs

abstract: A switchable number of turns, symmetric, parallel stacked, variable width and spacing inductor structure with performance optimized for multi-band operation and an area efficient multi-band voltage controlled oscillator circuit (VCO) are presented. The design of the proposed multi-band inductor structure and the impact of transistor switch parasitics on its quality factor performance are thoroughly discussed. The VCO circuit presented in this work is targeted towards wireless RF communication applications and operates across three frequency bands of 900 MHz, 1800 MHz, and 2.4 GHz, respectively. The VCO circuit is designed and simulated in a 0.18 um silicon on insulator process with a nominal supply voltage of 1.5 V. The presented VCO circuit achieves a phase noise performance of -104.7 dBc/Hz, -107.7 dBc/Hz, and -101.7 dBc/Hz at 1 MHz offsets from carrier frequencies 900 MHz, 1800MHz and 2.4 GHz, consuming currents of 3.8 mA, 4.5 mA, and 5.3 mA, respectively, from a 1.5 V supply.


title: A gravitational search algorithm for flow shop scheduling

abstract: An improved gravitational search algorithm(IGSA) was proposed to solve the flow shop scheduling problem with the objective of minimizing production time.First,to make a GSA suitable for permutation of the flow shop scheduling problem(PFSSP),a new largest-rank-rule based on a random key was introduced to convert the continuous position of the GSA into the discrete job permutation so that the GSA could be used for solving PFSSP.Second,a new boundary mutation was proposed.This operation stopped the agents which have mutations as a result of using the above method from gathering at the border.They were distributed at a feasible distance from the boundary.This improvement also improved the population diversity.Third,by combining the communicating operator and inserting operator,the new local search was designed to help the algorithm escape from the local minimum.Finally,the convergence of the iterative algorithm and its complexities in time and space were proven.Additionally,simulations and comparisons based on PFSSP benchmarks were carried out,which show that the proposed algorithm is both effective and efficient.


title: Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

abstract: Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues. Consequently, there is still a need for the development of robust ANN model development approaches.


title: Unconstrained Salient Object Detection via Proposal Subset Optimization

abstract: We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that directly outputs a compact set of detection windows, if any, for an input image. Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects. Location proposals tend to be highly overlapping and noisy. Based on the Maximum a Posteriori principle, we propose a novel subset optimization framework to generate a compact set of detection windows out of noisy proposals. In experiments, we show that our subset optimization formulation greatly enhances the performance of our system, and our system attains 16-34% relative improvement in Average Precision compared with the state-of-the-art on three challenging salient object datasets.


title: The impact of cross-platform development approaches for mobile applications from the user's perspective

abstract: Mobile app developers today have a hard decision to make: to independently develop native apps for different operating systems or to develop an app that is cross-platform compatible. The availability of different tools and approaches to support cross-platform app development only makes the decision harder. In this study, we used user reviews of apps to empirically understand the relationship (if any) between the approach used in the development of an app and its perceived quality. We used Natural Language Processing (NLP) models to classify 787,228 user reviews of the Android version and iOS version of 50 apps as complaints in one of four quality concerns: performance, usability, security, and reliability. We found that hybrid apps (on both Android and iOS platforms) tend to be more prone to user complaints than interpreted/generated apps. In a study of Facebook, an app that underwent a change in development approach from hybrid to native, we found that change in the development approach was accompanied by a reduction in user complaints about performance and reliability.


title: Sima de los Huesos (Sierra de Atapuerca, Spain). The site.

abstract: In this article a topographical description of the Cueva Mayor Cueva de Silo cave system is provided, including a more detailed topography of the Sala de los Ciclopes Sala de las Oseras-Sima de los Huesos sector. The history of the excavations and discoveries of human and carnivore fossils in Sima de los Huesos and adjacent passages is briefly reported, as well as the increase, throughout the succeeding field seasons, of the human collection and changes in the relative representation of the different skeletal elements and major biases. The carnivore assemblage structure is also considered. Examining the characteristics of the bone breccia, and the current and ancient karst topography, different alternative accesses are discussed for the accumulation of carnivores and humans in the Sima de los Huesos. Taking into account all the available information, an anthropic origin for the accumulation of human fossils seems to us to be the most likely explanation.


title: Incumbent User Active Area Detection for Licensed Shared Access

abstract: Licensed Shared Access is a European standardisation effort which promotes repository based quasi-static hierarchical spectrum sharing. In this scheme the sharing time base is in the order of months if not years. For widespread use of Licensed Shared Access, shrinking the sharing time base is crucial. In this paper we propose a scheme to reduce the sharing time base to seconds or minutes scale. We present a new technique named lightweight Radio Environment Map based on a Kalman Filter derived from geo-location aware spectrum measurements, which can be run at the shared access licensee end. Our objective is to determine the active area of a static or slowly moving incumbent. We consider a challenging scenario where a large fraction of measurements is missing and the available measurements are highly distorted. Performance of our incumbent active area detection approach is evaluated by simulating a low power incumbent in an urban cellular environment. Simulation results show a substantial improvement of missed detection area in comparison to the counterpart that does not use our lightweight Radio Environment Map.


title: ARA*: Anytime A* with Provable Bounds on Sub-Optimality

abstract: In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasible solution quickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA*, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. While improving its bound, ARA* reuses previous search efforts and, as a result, is significantly more efficient than other anytime search methods. In addition to our theoretical analysis, we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and a dynamic path planning problem for an outdoor rover.


title: Environmental sound classification based on time-frequency representation

abstract: This paper proposes a feature extraction method for environmental sound event classification based on time-frequency representation such as spectrogram. There are three portions to perform environmental classification. Firstly, the input signal is converted into spectrogram image with time-frequency representation using short time Fourier transforms. Secondly, this spectrogram is used to extract features with local binary pattern of three different radius and neighborhood sizes. The three distinct features resulted from local binary pattern based on spectrogram are concatenated and used as one feature vector. Finally, multi support vector machine is used for classification of environmental sound event. Evaluation is tested on ESC-10 dataset.


title: Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning

abstract: We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.


title: Kinematics of robot manipulators in the motor algebra

abstract: In the literature we find a variety of mathematical approaches for solving problems in robotics which we will review now briefly. Denavit and Hartenberg [60] introduced the mostly used kinematic notation for lower pair mechanisms based on matrix algebra, Walker [236] used the epsilon algebra for the treatment of the manipulator kinematics, Gu and Luh [95] utilized dualmatrices for computing the Jacobians useful for kinematics and robot dynamics and Pennock and Yang [183] derived closed-form solutions for the inverse kinematics problem for various types of robot manipulators employing dual-matrices. McCarthy [166] used the dual form of the Jacobian for the analysis of multi-links similarly. Funda and Paul [85] gave a detailed computational analysis of the use of screw transformations in robotics. These authors explained that since the dual quaternion can represent the rotation and translation transformations simultaneously it is more effective than the unit quaternion formalism for dealing with the kinematics of robot chains. Kim and Kumar [128] computed a closed-form solution of the inverse kinematics of a 6 degree of freedom robot manipulator in terms of line transformations using dual quaternions. Aspragathos and Dimitros [9] confirmed once again that the use of dual quaternion and Lie algebra in robotics were overseen so far and that their use helps to reduce the number of representation parameters.


title: Unsupervised learning of depth and motion

abstract: We present a model for the joint estimation of disparity and motion. The model is based on learning about the interrelations between images from multiple cameras, multiple frames in a video, or the combination of both. We show that learning depth and motion cues, as well as their combinations, from data is possible within a single type of architecture and a single type of learning algorithm, by using biologically inspired "complex cell" like units, which encode correlations between the pixels across image pairs. Our experimental results show that the learning of depth and motion makes it possible to achieve state-of-the-art performance in 3-D activity analysis, and to outperform existing hand-engineered 3-D motion features by a very large margin.


title: Simple English Wikipedia: A New Text Simplification Task

abstract: In this paper we examine the task of sentence simplification which aims to reduce the reading complexity of a sentence by incorporating more accessible vocabulary and sentence structure. We introduce a new data set that pairs English Wikipedia with Simple English Wikipedia and is orders of magnitude larger than any previously examined for sentence simplification. The data contains the full range of simplification operations including rewording, reordering, insertion and deletion. We provide an analysis of this corpus as well as preliminary results using a phrase-based translation approach for simplification.


title: scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells

abstract: Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n = 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at bioinfo.uth.edu/scrnaseqdb/.


title: Test coverage: what does it mean when a board test passes?

abstract: Characterizing board test coverage as a percentage of devices or nodes having tests does not accurately portray coverage, especially in a limited access testing environment that today includes a variety of diverse testing approaches from visual and penetrative inspection to classical in-circuit test. A better depiction of test coverage is achieved by developing a list of potential defects referred to as the defect universe, where the capabilities of the chosen test strategy are not considered in development of this defect list. Coverage is measured by grading the capabilities of each test process against the defect universe. The defect universe is defined to be meaningful to the bulk of the electronics industry and to provide a consistent framework for coverage metrics and comparisons.


title: Perceived risk of information security and privacy in online shopping: A study of environmentally sustainable products

abstract: Consumers’ information security and privacy for online shopping is a fundamental concern of decision-making for purchase. In this study, the technology acceptance model was used as a framework to explore website characteristics related to perceived risk of information security and purchase intention. A survey was conducted with a sample size of 387 online shoppers, methodology was done using LISREL 8.54 and SPSS 12.0 to perform factor analysis in order to obtain the factors of the construct and to measure their validity and reliability, the study revealed that website characteristics positively influence perceived risk of information security and privacy and further, have an impact on purchase intention. This study showed that perceived risk of information security and privacy on a website is strongly related to purchase intention. Website management must therefore be strengthened- first, by upgrading the network security of e-commerce technology, and second, by developing e-commerce security management systems. Moreover, a website platform should be constructed to improve awareness of consumer information security and to ensure a secure environment for online shopping. Finally, it is important to strengthen integrity management in order to enable consumers to securely buy goods online. ::: ::: ::: ::: Key words: TAM, perceived risk of information security and privacy, purchase intention, environmentally sustainable products.


title: Quality Inspection and Grading Of Mangoes by Computer Vision & Image Analysis

abstract: The paper presents the recent development and application of image analysis and computer vision system in quality evaluation of products in the field of agricultural and food. It is very much essential to through light on basic concepts and technologies associated with computer vision system, a tool used in image analysis and automated sorting and grading is highlighted. In agricultural industry the efficiency and the proper grading process is very important to increase the productivity. Currently, the agriculture industry has a better improvement, particularly in terms of grading of fruits, but the process is needed to be upgraded. This is because the grading of the fruit is vital to improve the quality of fruits. Indirectly, high quality fruits can be exported to other countries and generates a good income. Mango is the third most important fruit product next to pineapple and banana in term of value and volume of production. There are demands for this fresh fruit from both local and foreign market. However, mangoes grading by humans in agricultural setting are inefficient, labor intensive and prone to errors. Automated grading system not only speeds up the time of the process but also minimize error. Therefore, there is a need for an efficient mango grading method to be developed. In this study, we proposed and implement methodologies and algorithms that utilize digital image processing, content predicated analysis, and statistical analysis to determine the grade of local mango production. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry.


title: dbrec — Music Recommendations Using DBpedia

abstract: This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while building it, providing relevant insights for people developing applications consuming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm.


title: Graph-wavelet filterbanks for edge-aware image processing

abstract: In our recent work, we proposed the construction of critically-sampled wavelet filterbanks for analyzing functions defined on the vertices of arbitrary undirected graphs. These graph based functions, referred to as graph-signals, provide a flexible model for representing many datasets with arbitrary location and connectivity. An application area considered in that work is image-processing, where pixels can be connected with their neighbors to form undirected graphs. In this paper, we propose various graph-formulations of images, which capture both directionality and intrinsic edge-information. The proposed graph-wavelet filterbanks provide a sparse, edge-aware representation of image-signals. Our preliminary results in non-linear approximation and denoising using graphs show promising gains over standard separable wavelet filterbank designs.


title: BranchHull: Convex bilinear inversion from the entrywise product of signals with known signs

abstract: Abstract We consider the bilinear inverse problem of recovering two vectors, x and w, in R L from their entrywise product. For the case where the vectors have known signs and belong to known subspaces, we introduce the convex program BranchHull, which is posed in the natural parameter space that does not require an approximate solution or initialization in order to be stated or solved. Under the structural assumptions that x and w are members of known K and N dimensional random subspaces, we present a recovery guarantee for the noiseless case and a noisy case. In the noiseless case, we prove that the BranchHull recovers the vectors up to the inherent scaling ambiguity with high probability when L ≫ 2 ( K + N ) . The analysis provides a precise upper bound on the coefficient for the sample complexity. In a noisy case, we show that with high probability the BranchHull is robust to small dense noise when L = Ω ( K + N ) .


title: Propagated image filtering

abstract: We propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions. In particular, our filter does not to utilize explicit spatial kernel functions as bilateral and guided filters do. We will show that our propagation filter can be viewed as a robust estimator, which minimizes the expected difference between the filtered and desirable image outputs. We will also relate propagation filtering to belief propagation, and suggest techniques if further speedup of the filtering process is necessary. In our experiments, we apply our propagation filter to a variety of applications such as image denoising, smoothing, fusion, and high-dynamic-range (HDR) compression. We will show that improved performance over existing image filters can be achieved.


title: An experimental card game for teaching software engineering processes

abstract: The typical software engineering course consists of lectures in which concepts and theories are conveyed, along with a small "toy" software engineering project which attempts to give students the opportunity to put this knowledge into practice. Although both of these components are essential, neither one provides students with adequate practical knowledge regarding the process of software engineering. Namely, lectures allow only passive learning and projects are so constrained by the time and scope requirements of the academic environment that they cannot be large enough to exhibit many of the phenomena occurring in real-world software engineering processes. To address this problem, we have developed Problems and Programmers, an educational card game that simulates the software engineering process and is designed to teach those process issues that are not sufficiently highlighted by lectures and projects. We describe how the game is designed, the mechanics of its game play, and the results of an experiment we conducted involving students playing the game.


title: The subspace Gaussian mixture model - A structured model for speech recognition

abstract: We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.


title: Using Sampling and Queries to Extract Rules from Trained Neural Networks

abstract: Abstract Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of- N rules, and present experiments that show that our method is more efficient than conventional search-based approaches.


3 D Segmentation Techniques for Medical Volumes


title: Smart industry pollution monitoring and controlling using LabVIEW based IoT

abstract: With the advent of IoT based technologies; the overall industrial sector is amenable to undergo a fundamental and essential change alike to the industrial revolution. Online Monitoring solutions of environmental polluting parameter using Internet Of Things (IoT) techniques help us to gather the parameter values such as pH, temperature, humidity and concentration of carbon monoxide gas, etc. Using sensors and enables to have a keen control on the environmental pollution caused by the industries. This paper introduces a LabVIEW based online pollution monitoring of industries for the control over pollution caused by untreated disposal of waste. This paper proposes the use of an AT-mega 2560 Arduino board which collects the temperature and humidity parameter from the DHT-11 sensor, carbon dioxide concentration using MG-811 and update it into the online database using MYSQL. For monitoring and controlling, a website is designed and hosted which will give a real essence of IoT. To increase the reliability and flexibility an android application is also developed.


title: Treatment Outcomes and Long-term Survival in Patients with Extensively Drug-resistant Tuberculosis

abstract: RATIONALE ::: The increasing worldwide incidence of extensively drug-resistant tuberculosis (XDR-TB) has emerged as a threat to public health and tuberculosis (TB) control. Treatment outcomes have varied among studies, and data on long-term survival are still scarce. ::: ::: ::: OBJECTIVES ::: To retrospectively assess the burden, clinical characteristics, treatment outcomes, and long-term survival rate of patients with XDR-TB in a cohort of patients with HIV-negative multidrug-resistant tuberculosis (MDR-TB) in South Korea. ::: ::: ::: METHODS ::: Medical records were reviewed of patients newly diagnosed with or retreated for MDR-TB from 2000 to 2002. The cohort was monitored for 3 to 7 years after the initiation of treatment. Initial treatment outcomes and cumulative survival rates were analyzed, and predictors of treatment success and survival were defined. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Of 1,407 patients with MDR-TB 75 (5.3%) had XDR-TB at treatment initiation. The default rate was high (453/1,407; 32%), and patients with XDR-TB had lower treatment success (29.3 vs. 46.2%; P = 0.004) and higher all-cause (49.3 vs. 19.4%; P < 0.001) and TB-related disease mortality (41.3 vs. 11.8%; P < 0.001) than other patients with MDR-TB. The presence of XDR-TB significantly affected treatment success (odds ratio, 0.23; 95% confidence interval [CI], 0.08-0.64; P = 0.005), all-cause mortality (hazards ratio, 3.25; 95% CI, 1.91-5.53; P < 0.001), and TB-related mortality (hazards ratio, 4.45; 95% CI, 2.48-8.00; P < 0.001) on multivariate analyses. ::: ::: ::: CONCLUSIONS ::: XDR-TB occurred in a substantial proportion of patients with MDR-TB in South Korea, and was the strongest predictor of treatment outcomes and long-term survival in patients with MDR-TB. Adequate TB control policies should be implemented to prevent the further development and spread of drug resistance.


title: A Scalable Architecture for Modular Multiplication Based on Montgomery’s Algorithm

abstract: This paper presents a scalable architecture for the computation of modular multiplication, based on the Montgomery multiplication (MM) algorithm. A word-based version of MM is presented and used to explain the main concepts in the hardware design. The proposed multiplier is able to work with any precision of the input operands, limited only by memory or control constraints. Its architecture gives enough freedom to select the word size and the degree of parallelism to be used, according to the available area and/or desired performance. Design trade offs are analyzed in order to identify adequate hardware configurations for a given area or bandwidth requirement.


title: Monotonicity of effect sizes: Questioning kappa-squared as mediation effect size measure.

abstract: Mediation analysis is important for research in psychology and other social and behavioral sciences. Great progress has been made in testing mediation effects and in constructing their confidence intervals. Mediation effect sizes have also been considered. Preacher and Kelley (2011) proposed and recommended κ² as an effect size measure for a mediation effect. In this article, we argue that κ² is not an appropriate effect size measure for mediation models, because of its lack of the property of rank preservation (e.g., the magnitude of κ² may decrease when the mediation effect that κ² represents increases). Furthermore, κ² can lead to paradoxical results in multiple mediation models. We show that the problem of κ² is due to (a) the improper calculation of the maximum possible value of the indirect effect, and (b) mathematically, the maximum possible indirect effect is infinity, implying that the definition of κ² is mathematically incorrect. At this time, it appears that the traditional mediation effect size measure PM (the ratio of the indirect effect to the total effect), together with some other statistical information, should be preferred for basic mediation models. But for inconsistent mediation models where the indirect effect and the direct effect have opposite signs, the situation is less clear. Other considerations and suggestions for future research are also discussed.


title: Trust, privacy and security in e-business: requirements and solutions

abstract: An important aspect of e-business is the area of e-commerce. One of the most severe restraining factors for the proliferation of e-commerce, is the lack of trust between customers and sellers, consumer privacy concerns and the lack of security measures required to assure both businesses and customers that their business relationship and transactions will be carried out in privacy, correctly, and timely. This paper considers trust privacy and security issues in e-commerce applications and discusses methods and technologies that can be used to fulfil the pertinent requirements.


title: Men at risk for paradoxical adipose hyperplasia after cryolipolysis

abstract: Summary ::: ::: Cryolipolysis, an aesthetic procedure that reduces adipose tissue by exposure to cold temperature, is generally well tolerated with mild side effects including temporary numbness, erythema, and tenderness. However, as cryolipolysis is gaining popularity and more treatments are being performed, reports of rare adverse events including delayed onset pain and paradoxical adipose hyperplasia (PAH) have been described. Recent studies have suggested that PAH can be more common than expected and have a predilection for males, as a disproportionate number of the cases reported in the literature have occurred in men despite the fact that fewer men are likely to be treated with cryolipolysis. Sexual dimorphism in adipose anatomy may provide insight into the increased susceptibility of men to PAH. Careful patient selection avoiding men with visceral abdominal adipose and firm, nondistensible, fibrous fat may be important to minimize the risk of PAH.


title: MPLUS: A Probabilistic Medical Language Understanding System

abstract: This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.


title: Hyaluronic acid fillers: a comprehensive review.

abstract: Over the past decade, the popularity of nonsurgical cosmetic procedures has increased exponentially. Last year, according to the American Society of Aesthetic Plastic Surgery, more than 5 million procedures were performed using cosmetic injectables such as botulinum toxin and dermal filling agents. According to the society's recent statistics, more than 85% of all dermal filler procedures occurred with a hyaluronic acid derivative.These numbers are expected to rise in the future as there is currently no other class of filling agent that rivals the popularity of hyaluronic acid. The popularity of hyaluronic acid specifically stems from its effectiveness, ease of administration, and safety profile.


title: MicroThings: A Generic IoT Architecture for Flexible Data Aggregation and Scalable Service Cooperation

abstract: The Internet of Things has been widely deployed in various areas of daily life through heterogeneous communications protocols. Each unstandardized protocol focuses on a specific IoT communication pattern. Inspired by the Internet architecture over which divergent devices can easily be accessed and a considerable number of applications can be run, we propose a generic architecture for IoT. This architecture supports two DIY areas: network DIY for data aggregation and application DIY for service cooperation. To connect these two DIYs, a centralized controller has been designed to provide standardized interfaces for data acquisition, organization, and storage, and to support elastic and supportive computing. With these properties, divergent devices can coexist in a uniform microworld, and rich services can be developed and provided on demand to interoperate with physical devices. This article discusses the background, design principles, and advantages of the proposed architecture, as well as open problems and our initial solution, which substantiates a novel IoT architecture and new research ground.


Block Chain based Searchable Symmetric Encryption.


title: Index Financial Time Series Based on Zigzag-Perceptually Important Points

abstract: Problem statement: Financial time series were usually large in size, unstructured and of ::: high dimensionality. Since, the illustration of financial time series shape was typically characterized by ::: a few number of important points. These important points moved in zigzag directions which could ::: form technical patterns. However, these important points exhibited in different resolutions and difficult ::: to determine. Approach: In this study, we proposed novel methods of financial time series indexing ::: by considering their zigzag movement. The methods consist of two major algorithms: first, the ::: identification of important points, namely the Zigzag-Perceptually Important Points (ZIPs) ::: identification method and next, the indexing method namely Zigzag based M-ary Tree (ZM-Tree) to ::: structure and organize the important points. Results: The errors of the tree building and retrieving ::: compared to the original time series increased when the important points increased. The dimensionality ::: reduction using ZM-Tree based on tree pruning and number of retrieved points techniques performed ::: better when the number of important points increased. Conclusion: Our proposed techniques ::: illustrated mostly acceptable performance in tree operations and dimensionality reduction comparing ::: to existing similar technique like Specialize Binary Tree (SB-Tree).


title: Systematic Toxicological Analysis Revealing a Rare Case of Captan Ingestion

abstract: This article presents a case of suicide by intoxication with various pharmaceuticals, particularly anticonvulsants, combined with the fungicide captan. A cause of death could not be ascertained at autopsy. However, systematic toxicological analysis (STA) including a screening via solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS) for (semi) volatile organic compounds revealed results suggesting a possible cause of death. The effects of captan on the human organism, its metabolism, and distribution will be discussed. Macroscopically, the cause of death was unascertained. STA revealed clonazepam, citalopram, and its metabolites, lamotrigine, levetiracetam, lacosamide, clonazepam, captan, and its metabolite tetrahydrophthalimide (THPI). For the first time, it was detected in human viscera. A quantification of THPI was performed to obtain distribution in the organs. The significance of a complete STA must be emphasized. The presence of THPI would have been missed without previous detection of captan. Consequently, this fatality would not have been investigated satisfactorily. Language: en


title: Robot vision for the visually impaired

abstract: We present a head-mounted, stereo-vision based navigational assistance device for the visually impaired. The head-mounted design enables our subjects to stand and scan the scene for integrating wide-field information, compared to shoulder or waist-mounted designs in literature which require body rotations. In order to extract and maintain orientation information for creating a sense of egocentricity in blind users, we incorporate visual odometry and feature based metric-topological SLAM into our system. Using camera pose estimates with dense 3D data obtained from stereo triangulation, we build a vicinity map of the user's environment. On this map, we perform 3D traversability analysis to steer subjects away from obstacles in the path. A tactile interface consisting of microvibration motors provides cues for taking evasive action, as determined by our vision processing algorithms. We report experimental results of our system (running at 10 Hz) and conduct mobility tests with blindfolded subjects to demonstrate the usefulness of our approach over conventional navigational aids like the white cane.


title: The biological effects of tocotrienol on bone: a review on evidence from rodent models

abstract: Osteoporosis causes significant health care and economic burden to society, leading to a relentless search for effective preventive agents. Tocotrienol, a member of the vitamin E family, has demonstrated promising potential as an osteoporosis-preventing agent. This review summarizes evidence on the effects of tocotrienol on bone in animal models. Techniques used to examine the effects of tocotrienol on bone in animals included bone histomorphometry, X-ray microtomography, dual-energy X-ray absorptiometry, bone turnover markers, bone calcium content, and biomechanical strength. Tocotrienol was shown to improve osteoblast number, bone formation, mineral deposition, and bone microarchitecture in osteopenic rats. It also decreased osteoclast number and bone erosion in the rats. Tocotrienol supplementation resulted in an improvement in bone mineral density, although biomechanical strength was not significantly altered in the rats. The beneficial effects of tocotrienol on bone can be attributed to its role as an antioxidant, anti-inflammatory agent, suppressor of the mevalonate pathway, and modulator of genes favorable to bone formation.


title: Viewer Experience of Obscuring Scene Elements in Photos to Enhance Privacy

abstract: With the rise of digital photography and social networking, people are sharing personal photos online at an unprecedented rate. In addition to their main subject matter, photographs often capture various incidental information that could harm people's privacy. While blurring and other image filters may help obscure private content, they also often affect the utility and aesthetics of the photos, which is important since images shared in social media are mainly for human consumption. Existing studies of privacy-enhancing image filters either primarily focus on obscuring faces, or do not systematically study how filters affect image utility. To understand the trade-offs when obscuring various sensitive aspects of images, we study eleven filters applied to obfuscate twenty different objects and attributes, and evaluate how effectively they protect privacy and preserve image quality for human viewers.


title: ST-DBSCAN: An algorithm for clustering spatial-temporal data

abstract: This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial-temporal data warehouse system designed for storing and clustering a wide range of spatial-temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results.


title: Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model

abstract: Low-light image enhancement methods based on classic Retinex model attempt to manipulate the estimated illumination and to project it back to the corresponding reflectance. However, the model does not consider the noise, which inevitably exists in images captured in low-light conditions. In this paper, we propose the robust Retinex model, which additionally considers a noise map compared with the conventional Retinex model, to improve the performance of enhancing low-light images accompanied by intensive noise. Based on the robust Retinex model, we present an optimization function that includes novel regularization terms for the illumination and reflectance. Specifically, we use $\ell _{1}$ norm to constrain the piece-wise smoothness of the illumination, adopt a fidelity term for gradients of the reflectance to reveal the structure details in low-light images, and make the first attempt to estimate a noise map out of the robust Retinex model. To effectively solve the optimization problem, we provide an augmented Lagrange multiplier based alternating direction minimization algorithm without logarithmic transformation. Experimental results demonstrate the effectiveness of the proposed method in low-light image enhancement. In addition, the proposed method can be generalized to handle a series of similar problems, such as the image enhancement for underwater or remote sensing and in hazy or dusty conditions.


title: An Ultralow-Voltage Energy-Efficient Level Shifter

abstract: This brief presents an energy-efficient level shifter (LS) able to convert extremely low level input voltages to the nominal voltage domain. To obtain low static power consumption, the proposed architecture is based on the single-stage differential-cascode-voltage-switch scheme. Moreover, it exploits self-adapting pull-up networks to increase the switching speed and to reduce the dynamic energy consumption, while a split input inverting buffer is used as the output stage to further improve energy efficiency. When implemented in a commercial 180-nm CMOS process, the proposed design can up-convert from the deep subthreshold regime (sub-100 mV) to the nominal supply voltage (1.8 V). For the target voltage level conversion from 0.4 to 1.8 V, our LS exhibits an average propagation delay of 31.7 ns, an average static power of less than 60 pW, and an energy per transition of 173 fJ, as experimentally measured across the test chips.


Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning.


title: Multi-task Learning Using Multi-modal Encoder-Decoder Networks with Shared Skip Connections

abstract: Multi-task learning is a promising approach for efficiently and effectively addressing multiple mutually related recognition tasks. Many scene understanding tasks such as semantic segmentation and depth prediction can be framed as cross-modal encoding/decoding, and hence most of the prior work used multi-modal datasets for multi-task learning. However, the inter-modal commonalities, such as one across image, depth, and semantic labels, have not been fully exploited. We propose a multi-modal encoder-decoder networks to harness the multi-modal nature of multi-task scene recognition. In addition to the shared latent representation among encoder-decoder pairs, our model also has shared skip connections from different encoders. By combining these two representation sharing mechanisms, the proposed method efficiently learns a shared feature representation among all modalities in the training data. Experiments using two public datasets shows the advantage of our method over baseline methods that are based on encoder-decoder networks and multi-modal auto-encoders.


title: Landmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks

abstract: Precise localization in dense urban areas is a challenging task for both mobile mapping and driver assistance systems. This paper proposes a strategy to use road markings as localization landmarks for vision based systems. First step consists in reconstructing a map of road marks. A mobile mapping system equipped with precise georeferencing devices is applied to scan the scene in 3D and to generate an ortho-image of the road surface. A RJMCMC sampler that is coupled with a simulated annealing method is applied to detect occurrences of road marking templates instanced from an extensible database of road mark patterns. The detected objects are reconstructed in 3D using the height information obtained from 3D points. A calibrated camera and a low cost GPS receiver are embedded on a vehicle and used as localization devices. Local bundle adjustment (LBA) is applied to estimate the trajectory of the vehicle. In order to reduce the drift of the trajectory, images are matched with the reconstructed road marks frequently. The matching is initialized by the initial poses that are estimated by LBA and optimized by a MCMC algorithm. The matching provides ground control points that are integrated in the LBA in order to refine the pose parameters. The method is evaluated on a set of images acquired in a real urban area and is compared with a precise ground-truth.