title: A simulation optimization framework for discrete event logistics systems (DELS)

abstract: For large-scale, complex systems, both simulation and optimization methods are needed to support system design and operational decision making. Integrating the two methodologies, however, presents a number of conceptual and technical problems. This paper argues that the required integration can be successfully achieved, within a specific domain, by using a formal domain specific language for specifying instance problems and for structuring the analysis models and their interfaces. The domain must include a large enough class of problems to justify the resulting specialization of analysis models.


title: Hip and Knee Strengthening Is More Effective Than Knee Strengthening Alone for Reducing Pain and Improving Activity in Individuals With Patellofemoral Pain: A Systematic Review With Meta‐analysis

abstract: Study Design Systematic review with meta-analysis. Background The addition of hip strengthening to knee strengthening for persons with patellofemoral pain has the potential to optimize treatment effects. There is a need to systematically review and pool the current evidence in this area. Objective To examine the efficacy of hip strengthening, associated or not with knee strengthening, to increase strength, reduce pain, and improve activity in individuals with patellofemoral pain. Methods A systematic review of randomized and/or controlled trials was performed. Participants in the reviewed studies were individuals with patellofemoral pain, and the experimental intervention was hip and knee strengthening. Outcome data related to muscle strength, pain, and activity were extracted from the eligible trials and combined in a meta-analysis. Results The review included 14 trials involving 673 participants. Random-effects meta-analyses revealed that hip and knee strengthening decreased pain (mean difference, -3.3; 95% confidence interval [CI]: -5.6, -1.1) and improved activity (standardized mean difference, 1.4; 95% CI: 0.03, 2.8) compared to no training/placebo. In addition, hip and knee strengthening was superior to knee strengthening alone for decreasing pain (mean difference, -1.5; 95% CI: -2.3, -0.8) and improving activity (standardized mean difference, 0.7; 95% CI: 0.2, 1.3). Results were maintained beyond the intervention period. Meta-analyses showed no significant changes in strength for any of the interventions. Conclusion Hip and knee strengthening is effective and superior to knee strengthening alone for decreasing pain and improving activity in persons with patellofemoral pain; however, these outcomes were achieved without a concurrent change in strength. Level of Evidence Therapy, level 1a-. J Orthop Sports Phys Ther 2018;48(1):19-31. Epub 15 Oct 2017. doi:10.2519/jospt.2018.7365.


title: Segmenting Bank Customers by Resistance to Mobile Banking

abstract: The objective of the study is to explore bank customers' varying reasons for resisting mobile banking services. Today mobile communications technologies offer vast additional value for consumers' banking transactions due to their always-on functionality and the option to bank virtually any time and anywhere. However, before being adopted, all innovations face various types of resistance that may paralyse customers' desire to adopt an innovation. Following Ram and Sheth [1], resistance was measured with five barriers namely usage barrier, value barrier, risk barrier, tradition barrier and image barrier. An Internet questionnaire was developed and 1,540 responses from the non-users of mobile banking were collected. The results of the cluster analysis showed that different bank customers do indeed have different reasons for resisting mobile banking. The results indicate that older and less educated people and those who are entrepreneurs, farmers or retired are significantly more opposed to banking innovations than the other members of a social system. Moreover, younger people who are working in a good, well-paid profession do not perceive mobile banking to be as risky as do other online customers. The study provides academics and bank managers with an enhanced understanding of the different reasons inhibiting mobile banking adoption and the consumer demographics that determine such resistance.


title: INFORMATION PRIVACY CONCERNS AMONG NOVICE AND EXPERT USERS OF SOLOMO

abstract: SoLoMo (Social-Local-Mobile) services are becoming dominant to the huge population of semiliterate users living in emerging economies due to low costs and ubiquity. However, usage of SoLoMo services is still susceptible by information privacy concerns. Studies typically addressed the ways to mitigate information privacy concerns for the literate users and not the semi-literate users. To fill the gap of semi-literate users and have a better understanding of the information privacy concerns among different communities, this study draws upon theories of privacy calculus, familiarity, intrinsic and extrinsic motivation and protection motivation to identify the precursors of information privacy concerns related to SoLoMo services and users’ expertise. The proposed research model is empirically tested in a laboratory experiment. The findings show that the two channels (covert and overt) of delivering SoLoMo services affect the degree of information privacy concerns between the literate and semi-literate users. Implications for improving usage intentions and mitigating information privacy concerns for SoLoMo services for different types of mobile applications are discussed.


title: Using positive tainting and syntax-aware evaluation to counter SQL injection attacks

abstract: SQL injection attacks pose a serious threat to the security of Web applications because they can give attackers unrestricted access to databases that contain sensitive information. In this paper, we propose a new, highly automated approach for protecting existing Web applications against SQL injection. Our approach has both conceptual and practical advantages over most existing techniques. From the conceptual standpoint, the approach is based on the novel idea of positive tainting and the concept of syntax-aware evaluation. From the practical standpoint, our technique is at the same time precise and efficient and has minimal deployment requirements. The paper also describes wasp , a tool that implements our technique, and a set of studies performed to evaluate our approach. In the studies, we used our tool to protect several Web applications and then subjected them to a large and varied set of attacks and legitimate accesses. The evaluation was a complete success: wasp successfully and efficiently stopped all of the attacks without generating any false positives.


Électronique et transmission de l ’ information LOW VOLTAGE CHARGE PUMP CIRCUIT USING 0 . 18 μ m CMOS TECHNOLOGY


title: Medial collateral ligament injuries of the knee: a rationale for treatment.

abstract: Isolated MCL I and MCL II lesions evidence good recovery of integrity and function under non-operative modes of treatment. Isolated MCL III lesions may also be treated by non-operative management, but with the expectation of a slightly less successful result. However, there is an 80% incidence of concomitant ligament injury with this grade of lesion. The prognosis following injury to the MCL is significantly influenced by the presence of concurrent compromise of other ligaments, particularly that of the anterior cruciate ligament. Resolution of knee instability and dysfunction following a mixed MCL injury of any grade severity is best accomplished through operative intervention. Therefore, it is the authors' opinion that isolated MCL III and all mixed MCL lesions are best managed with operative treatment. Because of the apparent influence compromise of other ligament structures have on the recovery of MCL function, an attempt must be made to repair all acutely injured structures at the time of surgery. The keystones of a satisfactory result are early and accurate diagnosis, prompt treatment, and when indicated, complete surgical repair.


title: Arbuscular mycorrhizal fungi improve the growth and nodulation of the annual legume messina (Melilotus siculus) under saline and non-saline conditions

abstract: Messina [Melilotus siculus (Turra) Vitman ex. B. D Jacks] is a salt- and waterlogging-tolerant annual legume that could be highly productive on saline land. Arbuscular mycorrhizal (AM) fungi form a symbiotic relationship with the majority of terrestrial plant species, and improved productivity of plants inoculated with AM fungi under saline conditions has been attributed to the increased uptake of nutrients such as phosphorus (P). However, the mycorrhizal status of M. siculus under saline or non-saline conditions is unknown, as is the role of AM in improved nutrition and nodulation. In this study, the role of AM fungi in growth improvement and nodulation of M. siculus was examined in saline and non-saline soil. The M. siculus plants were inoculated with either a single AM species or mixed AM species, or remained uninoculated, and were grown at three levels of sodium chloride (NaCl) (0, 80, and 250 mm NaCl). AM-inoculated plants had significantly greater nodulation than plants that did not receive AM inoculum, regardless of salinity level. Plants inoculated with mixed AM species at 250 mm NaCl showed improved survival (90%) compared with the plants inoculated with single AM species or uninoculated control plants (30%). Within each salinity level, plants inoculated with mixed AM species had significantly greater dry weight than all other treatments. In addition, plants inoculated with mixed AM species had increased total uptake of P. It is likely that the increased growth observed in AM-inoculated M. siculus plants is due to improved P nutrition, showing the potential of AM fungi to enhance the growth of M. siculus on saline land.


title: A Biologically Plausible Learning Rule for Deep Learning in the Brain

abstract: Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previous biologically plausible reinforcement learning rules, like AGREL and AuGMEnT, showed promising results but focused on shallow networks with three layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? Here, we demonstrate that these learning schemes indeed generalize to deep networks, if we include an attention network that propagates information about the selected action to lower network levels. The resulting learning rule, called Q-AGREL, is equivalent to a particular form of error-backpropagation that trains one output unit at any one time. To demonstrate the utility of the learning scheme for larger problems, we trained networks with two hidden layers on the MNIST dataset, a standard and interesting Machine Learning task. Our results demonstrate that the capability of Q-AGREL is comparable to that of error backpropagation, although the learning rate is 1.5-2 times slower because the network has to learn by trial-and-error and updates the action value of only one output unit at a time. Our results provide new insights into how deep learning can be implemented in the brain.


title: Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

abstract: Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.


Credit Scoring Methods


title: Alias analysis of executable code

abstract: Recent years have seen increasing interest in systems that reason about and manipulate executable code. Such systems can generally benefit from information about aliasing. Unfortunately, most existing alias analyses are formulated in terms of high-level language features, and are unable to cope with features, such as pointer arithmetic, that pervade executable programs. This paper describes a simple algorithm that can be used to obtain aliasing information for executabie code. In order to be practical, the algorithm is carefut to keep its memory requirements low, sacrificing precision where necessary to achieve this goal. Experimental results indicate that it is nevertheless able to provide a reasonable amount of information about memory references across a variety of benchmark programs.


title: Deep Pyramidal Residual Networks

abstract: Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. Generally, deep neural network architectures are stacks consisting of a large number of convolutional layers, and they perform downsampling along the spatial dimension via pooling to reduce memory usage. Concurrently, the feature map dimension (i.e., the number of channels) is sharply increased at downsampling locations, which is essential to ensure effective performance because it increases the diversity of high-level attributes. This also applies to residual networks and is very closely related to their performance. In this research, instead of sharply increasing the feature map dimension at units that perform downsampling, we gradually increase the feature map dimension at all units to involve as many locations as possible. This design, which is discussed in depth together with our new insights, has proven to be an effective means of improving generalization ability. Furthermore, we propose a novel residual unit capable of further improving the classification accuracy with our new network architecture. Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown that our network architecture has superior generalization ability compared to the original residual networks. Code is available at this https URL}


title: Detecting Permission Over-claim of Android Applications with Static and Semantic Analysis Approach

abstract: Android access control granularity based on its permission mechanism is relatively coarse, which cannot effectively protect the user privacy. Many Android applications do not strictly abide by the principle of least privilege (PLP). Both benign and malicious apps may request more permissions than those they really use. We rethink previous permission over-claim problem of Android applications, and extend it to three kinds of problems: Explicit Permission Over-claim, Implicit Permission Over-claim and Ad Library Permission Over-claim. The latter two problems are new that have not been raised by any previous work. Static analysis is to decompile the applications to generate intermediate code and then analyze the usage of permissions. Our static analysis on 10710 applications shows that 76.08% of them may have Explicit Permission Over-claim problem, among those there are 424 applications that have sensitive permissions, which are only used in the advertisement library’s code of the applications rather than developer’s own code. They have Ad Library Permission Over-claim problem. The main idea of our semantic analysis is to calculate the semantic similarity between apps’ descriptions and function phrases. If the similarity exceeds a certain threshold, the app is considered relevant to the corresponding function. We compare the results of the semantic analysis with those of manual reading of 102 Android application descriptions. The F-measures of the three chosen functions are 80.82%, 70.48% and 89.62%, respectively. The evaluation results show our method can efficiently detect the above three kinds of permission over claim problems which indicates that our method would be helpful for normal users to have a clear understanding of permission usage of Android applications.


title: Moving average reversion strategy for on-line portfolio selection

abstract: On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the datasets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.


title: Synergistic Effect of Silver Nanoparticles with Neomycin or Gentamicin Antibiotics on Mastitis-Causing <i>Staphylococcus aureus</i>

abstract: Objective: Mastitis is one of the most costly ::: diseases in dairy cows, which greatly decreases milk production. Use of antibiotics ::: in cattle leads to antibiotic-resistance of mastitis-causing bacteria. The present ::: study aimed to investigate synergistic effect of silver nanoparticles (AgNPs) with ::: neomycin or gentamicin antibiotic on mastitis-causing Staphylococcus aureus. Materials and Methods: In ::: this study, 46 samples of milk were taken from the cows with clinical and subclinical ::: mastitis during the august-October 2015 sampling period. In addition to biochemical ::: tests, nuc gene amplification by PCR was used to identify strains of Staphylococcus ::: aureus. Disk diffusion test and microdilution were performed to determine minimum ::: inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Fractional ::: Inhibitory Concentration (FIC) index was calculated to determine the interaction ::: between a combination of AgNPs and each one of the antibiotics. Results: Twenty strains of Staphylococcus ::: aureus were isolated from 46 milk samples and were confirmed by PCR. Based on ::: disk diffusion test, 35%, 10% and 55% of the strains were respectively susceptible, ::: moderately susceptible and resistant to gentamicin. In addition, 35%, 15% and 50% ::: of the strains were respectively susceptible, moderately susceptible and resistant ::: to neomycin. According to FIC index, gentamicin antibiotic and AgNPs had synergistic ::: effects in 50% of the strains. Furthermore, neomycin antibiotic and AgNPs had synergistic ::: effects in 45% of the strains. Conclusion: It could be concluded that a combination of AgNPs with either gentamicin ::: or neomycin showed synergistic antibacterial properties in Staphylococcus aureus isolates from mastitis. In addition, some hypotheses were proposed to explain antimicrobial ::: mechanism of the combination.


title: A Neural Autoregressive Approach to Collaborative Filtering

abstract: This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to improve the model by sharing parameters between different ratings. A factored version of CF-NADE is also proposed for better scalability. Furthermore, we take the ordinal nature of the preferences into consideration and propose an ordinal cost to optimize CF-NADE, which shows superior performance. Finally, CF-NADE can be extended to a deep model, with only moderately increased computational complexity. Experimental results show that CF-NADE with a single hidden layer beats all previous state-of-the-art methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more hidden layers can further improve the performance.


title: Trumpet: Timely and Precise Triggers in Data Centers

abstract: As data centers grow larger and strive to provide tight performance and availability SLAs, their monitoring infrastructure must move from passive systems that provide aggregated inputs to human operators, to active systems that enable programmed control. In this paper, we propose Trumpet, an event monitoring system that leverages CPU resources and end-host programmability, to monitor every packet and report events at millisecond timescales. Trumpet users can express many *network-wide events*, and the system efficiently detects these events using *triggers* at end-hosts. Using careful design, Trumpet can evaluate triggers by inspecting every packet at full line rate even on future generations of NICs, scale to thousands of triggers per end-host while bounding packet processing delay to a few microseconds, and report events to a controller within 10 milliseconds, even in the presence of attacks. We demonstrate these properties using an implementation of Trumpet, and also show that it allows operators to describe new network events such as detecting correlated bursts and loss, identifying the root cause of transient congestion, and detecting short-term anomalies at the scale of a data center tenant.


title: An improved 3-step contactless fingerprint image enhancement approach for minutiae detection

abstract: Fingerprint enhancement is a critical step in fingerprint recognition systems. There are many existing contact-based fingerprint image enhancement methods and they have their own strengths and weaknesses. However, image enhancement approaches that can be used for contactless fingerprints are rarely considered and the number of such approaches is limited. Furthermore, the performance of existing contactbased fingerprint enhancement methods on the contactless fingerprint samples are unsatisfactory. Therefore, in this paper we propose an improved 3-step fingerprint image quality enhancement approach, which can be used for enhancing contactless fingerprint samples. The evaluation results show that, the proposed enhancement method significantly increases the number of detected minutiae, and improves the performance of fingerprint recognition system by reducing 7% and 15% EER compared to existing methods, respectively.


title: Voice conversion and spoofing attack on speaker verification systems

abstract: Speaker verification system automatically accepts or rejects the claimed identity of a speaker. Recently, we have made major progress in speaker verification which leads to mass market adoption, such as in smartphone and in online commerce for user authentication. A major concern when deploying speaker verification technology is whether a system is robust against spoofing attacks. Speaker verification studies provided us a better insight into speaker characterization, which has contributed to the progress of voice conversion technology. Unfortunately, voice conversion has become one of the most easily accessible techniques to carry out spoofing attack, therefore, presents a threat to speaker verification systems. In this paper, we will briefly introduce the fundamentals of voice conversion and speaker verification technology. We then give an overview of recent spoofing attack studies under different conditions with a focus on voice conversion spoofing attack. We will also discuss anti-spoofing attack measures for speaker verification.


title: A Wideband Circularly Polarized Rectangular Dielectric Resonator Antenna Excited by a Lumped Resistively Loaded Monofilar-Spiral-Slot

abstract: A wideband circularly polarized (CP) rectangular dielectric resonator antenna (DRA) based on the concept of traveling-wave excitation is presented. A lumped resistively loaded monofilar-spiral-slot is used to excite the rectangular DRA. The proposed DRA is theoretically and experimentally analyzed, including design concept, design guideline, parameter study, and experimental verification. It is found that by using such an excitation, a wide 3-dB axial-ratio (AR) bandwidth of 18.7% can be achieved.


title: Resource allocation in heterogeneous cloud radio access networks: advances and challenges

abstract: Base station densification is increasingly used by network operators to provide better throughput and coverage performance to mobile subscribers in dense data traffic areas. Such densification is progressively diffusing the move from traditional macrocell base stations toward heterogeneous networks with diverse cell sizes (e.g., microcell, picocell, femotcell) and diverse radio access technologies (e.g., GSM, CDMA), and LTE). The coexistence of the different network entities brings an additional set of challenges, particularly in terms of the provisioning of high-speed communications and the management of wireless interference. Resource sharing between different entities, largely incompatible in conventional systems due to the lack of interconnections, becomes a necessity. By connecting all the base stations from different tiers to a central processor (referred to as the cloud) through wire/wireline backhaul links, the heterogeneous cloud radio access network, H-CRAN, provides an open, simple, controllable, and flexible paradigm for resource allocation. This article discusses challenges and recent developments in H-CRAN design. It proposes promising resource allocation schemes in H-CRAN: coordinated scheduling, hybrid backhauling, and multicloud association. Simulations results show how the proposed strategies provide appreciable performance improvement compared to methods from recent literature.


title: A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

abstract: Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.


title: Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering

abstract: Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people's 'visual preferences,' as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by `global' structures in order to combat the sparsity (e.g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc.) and subtle (e.g. casualness) visual characteristics simultaneously.


title: Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classification

abstract: Accurate trajectory prediction of a lane changing vehicle is a key issue for risk assessment and early danger warning in advanced driver assistance systems (ADAS). This paper proposes a trajectory prediction approach for a lane changing vehicle considering high-level driver status. A driving behavior estimation and classification model is developed based on Hidden Markov Models (HMMs). The lane change behavior is estimated by observing the vehicle state emissions in the beginning stage of a lane change procedure, and then classified by the classifier before the vehicle crosses the lane mark. Furthermore, the future trajectory of the lane changing vehicle is predicted in a statistical way combining the driver status estimated by the classifier. The classifier is trained and tested using naturalistic driving data, which shows satisfactory performance in classifying driver status. The trajectory prediction method generates different trajectories based on the classification results, which is important for the design of both autonomous driving controller and early danger warning systems.


MEMS BISTABLE CLAMP WITH ELECTRICAL LOCKING AND RELEASE


title: A fuzzy logic based trust evaluation model for the selection of cloud services

abstract: Cloud computing enable users to avail the various services as per their wish. However, the quality about the service and its trust detail is not transparent to the user. In this paper, we propose a fuzzy logic based trust evaluation system that accepts the user's feedback in terms of fuzzy linguistic terms. The appropriate weight for the given feedback is calculated with the fuzzy inference system. Also, we have incorporated the fuzzy goal and constraint in our proposed model to predict the trust value with respect to the weight of feedback. The final trust value is derived by intersecting the goals and constraints of that service. A simulation on the MATLAB was performed and the results showed that, the proposed model can efficiently emulate the cloud user's feedback.


title: Smile Detection in the Wild Based on Transfer Learning

abstract: Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which lead to multiple applications, e.g., human behavior analysis and interactive controlling. Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. To leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection, an efficient transfer learning-based smile detection approach is proposed in this paper. Unlike previous works which use either hand-engineered features or train deep convolutional networks from scratch, a well-trained deep face recognition model is explored and fine-tuned for smile detection in the wild. Three different models are built as a result of fine-tuning the face recognition model with different inputs, including aligned, unaligned and grayscale images generated from the GENKI-4K dataset. Experiments show that the proposed approach achieves improved state-of-the-art performance. Robustness of the model to noise and blur artifacts is also evaluated in this paper.


title: Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation

abstract: The assessment of the discrimination ability of a survival analysis model is a problem of considerable theoretical interest and important practical applications. This issue is, however, more complex than evaluating the performance of a linear or logistic regression. Several different measures have been proposed in the biostatistical literature. In this paper we investigate the properties of the overall C index introduced by Harrell as a natural extension of the ROC curve area to survival analysis. We develop the overall C index as a parameter describing the performance of a given model applied to the population under consideration and discuss the statistic used as its sample estimate. We discover a relationship between the overall C and the modified Kendall's τand construct a confidence interval for our measure based on the asymptotic normality of its estimate. Then we investigate via simulations the length and coverage probability of this interval. Finally, we present a real life example evaluating the performance of a Framingham Heart Study model. Copyright © 2004 John Wiley & Sons, Ltd.


title: Automatic Building Extraction in Aerial Scenes Using Convolutional Networks

abstract: Automatic building extraction from aerial and satellite imagery is highly challenging due to extremely large variations of building appearances. To attack this problem, we design a convolutional network with a final stage that integrates activations from multiple preceding stages for pixel-wise prediction, and introduce the signed distance function of building boundaries as the output representation, which has an enhanced representation power. We leverage abundant building footprint data available from geographic information systems (GIS) to compile training data. The trained network achieves superior performance on datasets that are significantly larger and more complex than those used in prior work, demonstrating that the proposed method provides a promising and scalable solution for automating this labor-intensive task.


title: Developing a grounded theory approach: a comparison of Glaser and Strauss

abstract: Abstract Novice qualitative researchers are often unsure regarding the analysis of their data and, where grounded theory is chosen, they may be uncertain regarding the differences that now exist between the approaches of Glaser and Strauss, who together first described the method. These two approaches are compared in relation to roots and divergences, role of induction, deduction and verification, ways in which data are coded and the format of generated theory. Personal experience of developing as a ground theorist is used to illustrate some of the key differences. A conclusion is drawn that, rather than debate relative merits of the two approaches, suggests that novice researchers need to select the method that best suits their cognitive style and develop analytic skills through doing research.


title: The output hypothesis: just speaking and writing aren't enough

abstract: As a person who has been actively involved in research in French immersion education for approximately two decades, I am frequently told "immersion anecdotes" which often serve to provide a possibl...


title: Can"Hot Spots"in the Sciences Be Mapped Using the Dynamics of Aggregated Journal-Journal Citation Relations?

abstract: Using 3 years of the Journal Citation Reports 2011, 2012, and 2013, indicators of transitions in 2012 between 2011 and 2013 were studied using methodologies based on entropy statistics. Changes can be indicated at the level of journals using the margin totals of entropy production along the row or column vectors, but also at the level of links among journals by importing the transition matrices into network analysis and visualization programs and using community-finding algorithms. Seventy-four journals were flagged in terms of discontinuous changes in their citations, but 3,114 journals were involved in "hot" links. Most of these links are embedded in a main component; 78 clusters containing 172 journals were flagged as potential "hot spots" emerging at the network level. An additional finding was that PLoS ONE introduced a new communication dynamic into the database. The limitations of the methodology were elaborated using an example. The results of the study indicate where developments in the citation dynamics can be considered as significantly unexpected. This can be used as heuristic information, but what a "hot spot" in terms of the entropy statistics of aggregated citation relations means substantively can be expected to vary from case to case.


title: A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images

abstract: This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color-normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast, and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation.


title: Angle-based space partitioning for efficient parallel skyline computation

abstract: Recently, skyline queries have attracted much attention in the database research community. Space partitioning techniques, such as recursive division of the data space, have been used for skyline query processing in centralized, parallel and distributed settings. Unfortunately, such grid-based partitioning is not suitable in the case of a parallel skyline query, where allpartitions are examined at the same time, since many data partitions do not contribute to the overall skyline set, resulting in a lot of redundant processing. In this paper we propose a novel angle-based space partitioning scheme using the hyperspherical coordinates of the data points. We demonstrate both formally as well as through an exhaustive set of experiments that this new scheme is very suitable for skyline query processing in a parallel share-nothing architecture. The intuition of our partitioning technique is that the skyline points are equally spread to all partitions. We also show that partitioning the data according to the hyperspherical coordinates manages to increase the average pruning power of points within a partition. Our novel partitioning scheme alleviates most of the problems of traditional grid partitioning techniques, thus managing to reduce the response time and share the computational workload more fairly. As demonstrated by our experimental study, our technique outperforms grid partitioning in all cases, thus becoming an efficient and scalable solution for skyline query processing in parallel environments.


title: Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis

abstract: Exploratory factor analysis (EFA) is a complex, multi-step process. The goal of this paper is to collect, in one article, information that will allow researchers and practitioners to understand the various choices available through popular software packages, and to make decisions about ”best practices” in exploratory factor analysis. In particular, this paper provides practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size.


title: Unsupervised Feature Construction and Knowledge Extraction from Genome-Wide Assays of Breast Cancer with Denoising Autoencoders

abstract: Big data bring new opportunities for methods that efficiently summarize and automatically extract knowledge from such compendia. While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. Here we present denoising autoencoders (DAs), which employ a data-defined learning objective independent of known biology, as a method to identify and extract complex patterns from genomic data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. There are features that represent tumor or normal samples, estrogen receptor (ER) status, and molecular subtypes. Features constructed by the autoencoder generalize to an independent dataset collected using a distinct experimental platform. By integrating data from ENCODE for feature interpretation, we discover a feature representing ER status through association with key transcription factors in breast cancer. We also identify a feature highly predictive of patient survival and it is enriched by FOXM1 signaling pathway. The features constructed by DAs are often bimodally distributed with one peak near zero and another near one, which facilitates discretization. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.


title: Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

abstract: Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the ever-increasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges.


title: Attention to attributes and objects in working memory.

abstract: It has been debated on the basis of change-detection procedures whether visual working memory is limited by the number of objects, task-relevant attributes within those objects, or bindings between attributes. This debate, however, has been hampered by several limitations, including the use of conditions that vary between studies and the absence of appropriate mathematical models to estimate the number of items in working memory in different stimulus conditions. We re-examined working memory limits in two experiments with a wide array of conditions involving color and shape attributes, relying on a set of new models to fit various stimulus situations. In Experiment 2, a new procedure allowed identical retrieval conditions across different conditions of attention at encoding. The results show that multiple attributes compete for attention, but that retaining the binding between attributes is accomplished only by retaining the attributes themselves. We propose a theoretical account in which a fixed object capacity limit contains within it the possibility of the incomplete retention of object attributes, depending on the direction of attention.


title: Application and scope analysis of Augmented Reality in marketing using image processing technique

abstract: The main aim of the research paper is to understand the concept and applications of Augmented Reality (AR). It involves understanding the process of creation of an AR image, the hardware and software requirements for the process of making such an image. The paper also focuses on the ways in which the technology is used by firms and advertisers to give customers a better user experience. AR is a growing field with a lot of scope in the future, the paper also gives an insight on what is in store for AR in the near future.


title: Extracting information from textual documents in the electronic health record: a review of recent research.

abstract: Objectives: We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods: Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results: 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts. Cli


title: A theory on grasping objects using effectors with curved contact surfaces and its application to whole-arm grasping

abstract: This paper develops a theoretical framework for grasping objects using customized effectors that have curved contact surfaces, and presents its application to the problem of robotic whole-arm grasping. We present a collection of immobilizing grasps and cages that can effectively restrain the mobility of a wide range of object shapes including polyhedra. Each of the grasps or cages is formed by at most three effectors with appropriate contact surfaces in contrast to customary point fingertips. We also discuss the morphology of the curved contact surfaces that can realize the grasps and cages; the surfaces can simply be planar, cylindrical, or spherical. Stable grasps are obtained by simple motion planning and control. Our theory is based on a conservative assumption that all contacts are frictionless, rigid, unilateral. Finally, we present a robotic system, comprised of a software suite and a modular reconfigurable manipulator outfitted with exchangeable end-effectors and arm links, demonstrating the theory and our approach to whole-arm grasping.


title: Prediction of heart disease using a hybrid technique in data mining classification

abstract: Heart disease prediction is treated as most complicated task in the field of medical sciences. Thus there arises a need to develop a decision support system for detecting heart disease of a patient. In this paper, we propose efficient genetic algorithm hybrid with the back propagation technique approach for heart disease prediction. Today medical field have come a long way to treat patients with various kind of diseases. Among the most threatening one is the Heart disease which cannot be observed with a naked eye and comes instantly when its limitations are reached. Bad clinical decisions would cause death of a patient which cannot be afforded by any hospital. To achieve a correct and cost effective treatment computer-based and support Systems can be developed to make good decision. Many hospitals use hospital information systems to manage their healthcare or patient data. These systems produce huge amounts of data in the form of images, text, charts and numbers. Sadly, this data is rarely used to support the medical decision making. There is a bulk of hidden information in this data that is not yet explored which give rise to an important query of how to make useful information out of the data. So there is necessity of creating an excellent project which will help practitioners predict the heart disease before it occurs. The main objective of this paper is to develop a prototype which can determine and extract unknown knowledge (patterns and relations) related with heart disease from a past heart disease database record. It can solve complicated queries for detecting heart disease and thus assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. By providing efficient treatments, it can help to reduce costs of treatment.


title: Applying One-Class Classification Techniques to IP Flow Records for Intrusion Detection

abstract: Flow-basedintrusiondetectionsystemsanalyzeIPflowrecordstodetectattacksagainst computer networks. IP flow records contain aggregated packet header information; therefore, the amount of data processed by the intrusion detection system is reduced. In addition, since no pay- load is analyzed, the end-to-end encryption does not affect the deployment of intermediate intru- sion detection system. In this paper, we evaluate one-class classification techniques for detection of malicious flows at an initial stage of a multi-stage flow-based intrusion detection system. The initial stage uses minimal flow attributes and only decide if the IP flow is normal or malicious. Since there is only one class of interest (malicious) at the initial stage, we use one-class classifi- cation for detection of malicious flows. In this paper, we review available one-class classification techniques and evaluate them on a flow-based dataset to determine their performance for detec- tion of malicious flows. Our results show that one-class classification techniques using boundary methods give best results in detection of malicious IP flows.


title: Stealthy deception attacks on water SCADA systems

abstract: This article investigates the vulnerabilities of Supervisory Control and Data Acquisition (SCADA) systems which monitor and control the modern day irrigation canal systems. This type of monitoring and control infrastructure is also common for many other water distribution systems. We present a linearized shallow water partial differential equation (PDE) system that can model water flow in a network of canal pools which are equipped with lateral offtakes for water withdrawal and are connected by automated gates. The knowledge of the system dynamics enables us to develop a deception attack scheme based on switching the PDE parameters and proportional (P) boundary control actions, to withdraw water from the pools through offtakes. We briefly discuss the limits on detectability of such attacks. We use a known formulation based on low frequency approximation of the PDE model and an associated proportional integral (PI) controller, to create a stealthy deception scheme capable of compromising the performance of the closed-loop system. We test the proposed attack scheme in simulation, using a shallow water solver; and show that the attack is indeed realizable in practice by implementing it on a physical canal in Southern France: the Gignac canal. A successful field experiment shows that the attack scheme enables us to steal water stealthily from the canal until the end of the attack.


title: Online Clustering of Trajectory Data Stream

abstract: Movement tracking becomes ubiquitous in many applications, which raises great interests in trajectory data analysis and mining. Most existing approaches cluster the whole trajectories offline. This allows characterizing the past movements of the objects but not current patterns. Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subsequently, they fail to capture moving objects' behavior over time. By continuously tracking moving objects' sub-trajectories at each time window, rather than just the last position, it becomes possible to gain insight on the current behavior, and potentially detect mobility patterns in real time. In this work, we tackle the problem of discovering and maintaining the density based clusters in trajectory data streams, despite the fact that most moving objects change their position over time. We propose CUTiS, an incremental algorithm to solve this problem, while tracking the evolution of the clusters as well as the membership of the moving objects to the clusters. Our experiments were conducted on real data sets, and it shows the efficiency and the effectiveness of our method.


title: Small-footprint keyword spotting using deep neural networks

abstract: Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.


title: Constructing Abstraction Hierarchies Using a Skill-Symbol Loop

abstract: We describe a framework for building abstraction hierarchies whereby an agent alternates skill- and representation-acquisition phases to construct a sequence of increasingly abstract Markov decision processes. Our formulation builds on recent results showing that the appropriate abstract representation of a problem is specified by the agent's skills. We describe how such a hierarchy can be used for fast planning, and illustrate the construction of an appropriate hierarchy for the Taxi domain.


title: Designing and deploying programming courses: Strategies, tools, difficulties and pedagogy

abstract: Designing and deploying programming courses is undoubtedly a challenging task. In this paper, an attempt to analyze important aspects of a sequence of two courses on imperative-procedural and object-oriented programming in a non-CS majors Department is made. This analysis is based on a questionnaire filled in by fifty students in a voluntary basis. The issues of the programming courses that are investigated refer to: the strategy selected for the introduction to programming; the sequence of the programming techniques and languages taught and the transition from the one to the other; students' difficulties with programming in general and with imperative-procedural and object-oriented programming in specific; the teaching and learning design of both courses; and the material that students rely on for learning programming. Based on the analysis of students' replies on the questionnaire, related work and the instructor's experience on teaching the courses, conclusions are drawn regarding all the aforementioned aspects of designing and deploying programming courses. The main contribution of the paper is the fact that all the important and interrelated aspects of a sequence of two programming courses are investigated in conjunction, providing realistic implications and guidelines for improving the quality and effectiveness of existing programming courses and designing and deploying new courses. The main results refer to the usage of a pseudo-language for an introduction to programming, the transition from procedural to object-oriented programming, the intrinsic difficulties of learning programming, and practices for a more successful teaching and learning design of programming courses.


Understanding starbursts through giant molecular clouds in high density environments


title: Multi-dimensional search result diversification

abstract: Most existing search result diversification algorithms diversify search results in terms of a specific dimension. In this paper, we argue that search results should be diversified in a multi-dimensional way, as queries are usually ambiguous at different levels and dimensions. We first explore mining subtopics from four types of data sources, including anchor texts, query logs, search result clusters, and web sites. Then we propose a general framework that explicitly diversifies search results based on multiple dimensions of subtopics. It balances the relevance of documents with respect to the query and the novelty of documents by measuring the coverage of subtopics. Experimental results on the TREC 2009 Web track dataset indicate that combining multiple types of subtopics do help better understand user intents. By incorporating multiple types of subtopics, our models improve the diversity of search results over the sole use of one of them, and outperform two state-of-the-art models.


Heredity Indexes for Estimating Heritability Using Known and Unknown Family Data Based on the Model of Polygenic Inheritance


title: Adversarial Texts with Gradient Methods

abstract: Adversarial samples for images have been extensively studied in the literature. Among many of the attacking methods, gradient-based methods are both effective and easy to compute. In this work, we propose a framework to adapt the gradient attacking methods on images to text domain. The main difficulties for generating adversarial texts with gradient methods are i) the input space is discrete, which makes it difficult to accumulate small noise directly in the inputs, and ii) the measurement of the quality of the adversarial texts is difficult. We tackle the first problem by searching for adversarials in the embedding space and then reconstruct the adversarial texts via nearest neighbor search. For the latter problem, we employ the Word Mover's Distance (WMD) to quantify the quality of adversarial texts. Through extensive experiments on three datasets, IMDB movie reviews, Reuters-2 and Reuters-5 newswires, we show that our framework can leverage gradient attacking methods to generate very high-quality adversarial texts that are only a few words different from the original texts. There are many cases where we can change one word to alter the label of the whole piece of text. We successfully incorporate FGM and DeepFool into our framework. In addition, we empirically show that WMD is closely related to the quality of adversarial texts.


title: External Remerge and linearization in ATB, RNR and PG constructions

abstract: We claim that across-the-board (ATB) and right node raising (RNR) constructions in English should be analyzed in a uniform way, but neither of them...


title: Improving Semantic Segmentation via Video Propagation and Label Relaxation

abstract: Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploit video prediction models' ability to predict future frames in order to also predict future labels. A joint propagation strategy is also proposed to alleviate mis-alignments in synthesized samples. We demonstrate that training segmentation models on datasets augmented by the synthesized samples leads to significant improvements in accuracy. Furthermore, we introduce a novel boundary label relaxation technique that makes training robust to annotation noise and propagation artifacts along object boundaries. Our proposed methods achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. Our single model, without model ensembles, achieves 72.8% mIoU on the KITTI semantic segmentation test set, which surpasses the winning entry of the ROB challenge 2018.


title: AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild

abstract: Automated affective computing in the wild setting is a challenging problem in computer vision. Existing annotated databases of facial expressions in the wild are small and mostly cover discrete emotions (aka the categorical model). There are very limited annotated facial databases for affective computing in the continuous dimensional model (e.g., valence and arousal). To meet this need, we collected, annotated, and prepared for public distribution a new database of facial emotions in the wild (called AffectNet). AffectNet contains more than 1,000,000 facial images from the Internet by querying three major search engines using 1,250 emotion related keywords in six different languages. About half of the retrieved images were manually annotated for the presence of seven discrete facial expressions and the intensity of valence and arousal. AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models. Two baseline deep neural networks are used to classify images in the categorical model and predict the intensity of valence and arousal. Various evaluation metrics show that our deep neural network baselines can perform better than conventional machine learning methods and off-the-shelf facial expression recognition systems.


title: Shape Quantization and Recognition with Randomized Trees

abstract: We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labeled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred L AT E X symbols. Stateof-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on L AT E X symbols is to analyze invariance, generalization error and related issues, and a comparison with artificial neural networks methods is presented in this context.


title: Parameter study and design of wide-band widescan dual-polarized tapered slot antenna arrays

abstract: A parameter study of dual-polarized tapered slot antenna (TSA) arrays shows the key features that affect the wide-band and widescan performance of these arrays. The overall performance can be optimized by judiciously choosing a combination of parameters. In particular, it is found that smaller circular slot cavities terminating the bilateral slotline improve the performance near the low end of the operating band, especially when scanning in the H-plane. The opening rate of the tapered slotline mainly determines the mid-band performance and it is possible to choose an opening rate to obtain balanced overall performance in the mid-band. A longer tapered slotline is shown to increase the bandwidth, especially in the lower end of the operating band. Finally, it is shown that the H-plane anomalies are affected by the array element spacing. A design example demonstrates that the results from the parameter study can be used to design a dual-polarized TSA array with about 4.5:1 bandwidth for a scan volume of not less than /spl theta/=45/spl deg/ from broadside in all planes.


title: Image steganography based on LSBMR using Sobel edge detection

abstract: Steganography is the science of embedding confidential data inside data so they can be sent to destination without suspicion. Image-steganography is the most popular type of carrier to hold information. Many algorithms have been proposed to hide information into digital images. The least significant bit algorithm (LSB) is one of these algorithms that is widely used in steganography. Several improvements of this algorithm have been proposed recently. In this paper we study and analyze the LSB matching revisited (LSBMR) algorithm and the edge adaptive image steganography based on LSBMR. We conduct several experiments on these algorithms on a set of 200 images and we show that an improvement can be made by using some image processing technique called Sobel edge detection to find edges of the images that can hold the secret information. We show that the proposed technique can improve the quality of the steganography images where sharper edges are used for low capacity rate.


title: Compliance and patching and atropine amblyopia treatments

abstract: Abstract In the past 20 years, there has been a great advancement in knowledge pertaining to compliance with amblyopia treatments. The occlusion dose monitor introduced quantitative monitoring methods in patching, which sparked our initial understanding of the dose–response relationship for patching amblyopia treatment. This review focuses on current compliance knowledge and the impact it has on patching and atropine amblyopia treatment.


title: Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for nonlinear hybrid systems

abstract: The computation load remains the main challenge facing the control techniques of hybrid systems with discrete and continuous control signals. In this paper, a new hybrid controller based on Analytical Nonlinear Model Predictive Control (ANMPC) and Particle Swarm Optimization (PSO) for nonlinear hybrid systems is presented. The proposed controller offer sub-optimal solution in reasonable time while respecting the given constraints. The new developed technique is not considered as a computation burden, thus real-time implementation is possible for many hybrid systems. Besides, it can be applied directly to the nonlinear models, avoiding linearization which may lead to inaccurate model and unexpected behaviour. An application of the proposed controller to a three tanks example is presented.


title: Active Rectifier Using DQ Vector Control for Aircraft Power System

abstract: There is a general move in the aerospace industry to increase the amount of electrically powered equipment on future aircraft. This is generally referred to as the "more electric aircraft" and brings on a number of technical challenges that need to be addressed and overcome. Recent advancements in power electronics technology are enabling new systems to be developed and applied to aerospace applications. The growing trend is to connect the AC generator to the aircraft engine via a direct connection or a fixed ratio transmission thus, resulting in the generator providing a variable frequency supply. This move offers benefits to the aircraft such as reducing the weight and improving the reliability. Many aircraft power systems are now operating with a variable frequency over a typical range of 350 Hz to 800 Hz which varies with the engine speed[1,2]. This paper presents the results from a simple scheme for an adaptive control algorithm which could be suitable for use with an electric actuator (or other) aircraft load. The design of this system poses significant challenges due to the nature of the load range and supply frequency variation and requires many features such as: 1) Small input current harmonics to minimize losses., 2) Minimum size and weight to maximize portability and power density. Details will be given on the design methodology and simulation results obtained.


title: Regular Expression Learning for Information Extraction

abstract: Regular expressions have served as the dominant workhorse of practical information extraction for several years. However, there has been little work on reducing the manual effort involved in building high-quality, complex regular expressions for information extraction tasks. In this paper, we propose ReLIE, a novel transformation-based algorithm for learning such complex regular expressions. We evaluate the performance of our algorithm on multiple datasets and compare it against the CRF algorithm. We show that ReLIE, in addition to being an order of magnitude faster, outperforms CRF under conditions of limited training data and cross-domain data. Finally, we show how the accuracy of CRF can be improved by using features extracted by ReLIE.


title: Compact Half Diamond Dual-Band Textile HMSIW On-Body Antenna

abstract: A novel wearable dual-band textile antenna, designed for optimal on-body performance in the 2.4 and 5.8 GHz Industrial, Scientific and Medical bands, is proposed. By using brass eye-lets and a combination of conducting and non-conductive textile materials, a half-mode substrate integrated waveguide cavity with ground plane is realized that is very compact and flexible, while still directing radiation away from the wearer. Additional miniaturization is achieved by adding a row of shorting vias and slots. Beside excellent free space performance in the 2.4 and 5.8 GHz bands, respectively, with measured impedance bandwidth of 4.9% and 5.1%, maximal measured free-space gain of 4.1 and 5.8 dBi, and efficiency of 72.8% and 85.6%, very stable on-body performance is obtained, with minimal frequency detuning when deploying the antenna on the human body and when bent around cylinders with radii of 75 and 40 mm. At 2.45 and 5.8 GHz, respectively, the measured on-body gain is 4.4 and 5.7 dBi, with sufficiently small calculated SAR values of 0.55 and 0.90 W/kg. These properties make the proposed antenna excellently suited for wearable on-body systems.


title: Salivary gland scintigraphy in gastro-esophageal reflux disease

abstract: Gastro-esophageal reflux disease (GERD) is associated with a decreased salivary flow as well as gastric acid production. This study therefore aimed to investigate functional disorders of salivary glands in patients with GERD.


title: Application of the Aqueous Porous Pathway Model to Quantify the Effect of Sodium Lauryl Sulfate on Ultrasound-Induced Skin Structural Perturbation

abstract: ABSTRACT This study investigated the effect of sodium lauryl sulfate (SLS) on skin structural perturbation when utilized simultaneously with low-frequency sonophoresis (LFS). Pig full-thickness skin (FTS) and pig split-thickness skin (STS) treated with LFS/SLS and LFS were analyzed in the context of the aqueous porous pathway model to quantify skin perturbation through changes in skin pore radius and porosity-to-tortuosity ratio ( e / τ ). In addition, skin treatment times required to attain specific levels of skin electrical resistivity were analyzed to draw conclusions about the effect of SLS on reproducibility and predictability of skin perturbation. We found that LFS/SLS-treated FTS, LFS/SLS-treated STS, and LFS-treated FTS exhibited similar skin perturbation. However, LFS-treated STS exhibited significantly higher skin perturbation, suggesting greater structural changes to the less robust STS induced by the purely physical enhancement mechanism of LFS. Evaluation of e / τ values revealed that LFS/SLS-treated FTS and STS have similar transport pathways, whereas LFS-treated FTS and STS have lower e / τ values. In addition, LFS/SLS treatment times were much shorter than LFS treatment times for both FTS and STS. Moreover, the simultaneous use of SLS and LFS not only results in synergistic enhancement, as reflected in the shorter skin treatment times, but also in more predictable and reproducible skin perturbation.


title: Optimal force distribution for the legs of an hexapod robot

abstract: In this paper we present direct and inverse kinematic models and a methodology for optimal force distribution calculation for the legs of an hexapod robot. This approach is used in the case of real time hexapod force control. The force distribution problem is formulated in terms of a nonlinear programming problem under equality and inequality constraints. Then, according to, the friction constraints are transformed from nonlinear inequalities into a combination of linear equalities and linear inequalities. The original nonlinear constrained programming problem is then transformed into a quadratic optimization problem. Some simulation results are given and perspectives on hexapod control are discussed.


title: Vulnerability to Social Stress: Coping as a Mediator or Moderator of Sociotropy and Symptoms of Anxiety and Depression

abstract: Although stressful events clearly play an important role in the development of symptoms of depression and anxiety, individuals are not equally sensitive to stress. Attempts to explain differences in adjustment have focused both on the coping strategies employed in response to stress, and on personality-related vulnerabilities to specific stressors. However, little is known about the interplay between coping and personality traits such as sociotropy, which is associated with increased sensitivity to negative social events. Measures of sociotropy and symptoms of depression and anxiety were obtained in a sample of undergraduates, along with reports of coping with interpersonal stress. Regressions controlling for recent stressful events indicated that coping does not directly mediate the relationship between sociotropy and distress, but does moderate the relationship. Both primary and secondary control engagement coping buffer the link between sociotropy and anxiety/depression, whereas disengagement coping augments the relationship. Implications for social cognitive models of vulnerability to stress are highlighted.


title: Designed oligomers of cyanovirin-N show enhanced HIV neutralization.

abstract: Cyanovirin-N (CV-N) is a small, cyanobacterial lectin that neutralizes many enveloped viruses, including human immunodeficiency virus type I (HIV-1). This antiviral activity is attributed to two homologous carbohydrate binding sites that specifically bind high mannose glycosylation present on envelope glycoproteins such as HIV-1 gp120. We created obligate CV-N oligomers to determine whether increasing the number of binding sites has an effect on viral neutralization. A tandem repeat of two CV-N molecules (CVN(2)) increased HIV-1 neutralization activity by up to 18-fold compared to wild-type CV-N. In addition, the CVN(2) variants showed extensive cross-clade reactivity and were often more potent than broadly neutralizing anti-HIV antibodies. The improvement in activity and broad cross-strain HIV neutralization exhibited by these molecules holds promise for the future therapeutic utility of these and other engineered CV-N variants.


title: Lightweight and human-size dual arm aerial manipulator

abstract: This paper presents the design of a dual-arm aerial manipulator consisting of a multi-rotor platform with an ultra-lightweight (1.8 Kg) human-size dual arm prototype and its control system. Each arm provides three degrees of freedom (DOF) for positioning the end- effector, and two DOF for orientation. As most model-based controllers assume that joint torque feedback is available, a torque estimator for the arms is developed. Note that low cost servos used for building low weight manipulators do not provide any torque feedback or control capability. The redundant DOFs in the dual arm prototype are exploited for generating coordinated motions during contact-less phases in such a way that reaction torques can be partially canceled. Preliminary flight tests have been conducted in outdoors, evaluating the torque compensation capability in test-bench. The influence of the reaction torques exerted by the arms over the UAV controller is also analyzed in simulation.


title: A Comparative Study on English and Chinese Word Uses with LIWC

abstract: This paper compared the linguistic and psychological word uses in English and Chinese languages with LIWC (Linguistic Inquiry and Word Count) programs. A Principal Component Analysis uncovered six linguistic and psychological components, among which five components were significantly correlated. The correlated components were ranked as Negative Valence (r=.92), Embodiment (r=.88), Narrative (r=.68), Achievement (r=.65), and Social Relation (r=.64). However, the results showed the order of the representative features differs in two languages and certain word categories co-occurred with different components in English and Chinese. The differences were interpreted from the perspective of distinctive eastern and western cultures.


Sponsorships in ESports


title: Concurrently Inhibitory and Allelopathic Effects of Allelochemicals Secreted by Myriophyllum Spicatum on Growth of Blue-green Algae.

abstract: The objectives of this study were to determine (i) the concurrent action of four polyphenols ; pyrogallic acid, gallic acid, (+)-catechin and ellagic acid secreted by Myriophyllum spicatum on their inhibitory effects on growth of blue-green algae (Microcyctis aeruginosa) and (ii) their contribution to allelopathy between M. spicatum and M. aeruginosa.Comparing the inhibitory effects of a mixture of the four polyphenols with those expected by Colby's equation suggested that the mixture of the four polyphenols caused synergistic growth inhibition of M. aeruginosa. There was much difference between the EC50 (the concentration inhibited normal growth of M. aeruginosa by 50 %) of each polyphenol and an apparent concentration detected in the culture solution of M. spicatum, because the four polyphenols were easily autoxidized in the culture solution of M. spicatum. Total amounts of the four polyphenols secreted from M. spicatum were estimated from their apparent concentrations and their autoxidation rates. The artificial solution was prepared based on the total amounts of the four secreted polyphenols. A comparison of the inhibitory effects of the culture solution of M. spicatum with those of the artificial culture solution prepared by using the four polyphenols was made on the basis of the equivalent M. spicatum concentrations of both culture solutions. The results showed that the four polyphenols could contribute to the allelopathic effects of M. spicatum at 10-100%.


title: Face Attention Network: An Effective Face Detector for the Occluded Faces

abstract: The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign strategy and data augmentation techniques, we obtain state-of-art results on public face detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.


title: What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model

abstract: This study presents an extended technology acceptance model (TAM) that integrates innovation diffusion theory, perceived risk and cost into the TAM to investigate what determines user mobile commerce (MC) acceptance. The proposed model was empirically tested using data collected from a survey of MC consumers. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. Our findings indicated that all variables except perceived ease of use significantly affected users' behavioral intent. Among them, the compatibility had the most significant influence. Furthermore, a striking, and somewhat puzzling finding was the positive influence of perceived risk on behavioral intention to use. The implication of this work to both researchers and practitioners is discussed.


title: Orofacial granulomatosis associated with Crohn's disease.

abstract: Orofacial granulomatosis (OFG) is a term used to describe swelling of the orofacial area, mainly in the lips, secondary to an underlying granulomatous inflammatory process. OFG has been reported in association with systemic conditions such as sarcoidosis and Crohn's disease (CD). OFG may precede gastrointestinal disease, such as CD, by several years and may be the only obvious focus of the disease. Herein, we report a patient with OFG and non-symptomatic ulcerations of the ileocecal valve. The patient received intralesional triamcinolone injections every 2 weeks. After 6 weeks, all oral lesions showed marked improvement. The favourable treatment response of this patient suggests that intralesional triamcinolone can be used as a treatment option for patients with CD that have oral lesions. In addition, patients presenting with OFG should be carefully evaluated for gastrointestinal signs and symptoms.


title: Failover mechanisms for distributed SDN controllers

abstract: Distributed SDN controllers have been proposed to address performance and resilience issues. While approaches for datacenters are built on strongly-consistent state sharing among controllers, others for WAN and constrained networks rely on a loosely-consistent distributed state. In this paper, we address the problem of failover for distributed SDN controllers by proposing two strategies for neighbor active controllers to take over the control of orphan OpenFlow switches: (1) a greedy incorporation and (2) a pre-partitioning among controllers. We built a prototype with distributed Floodlight controllers to evaluate these strategies. The results show that the failover duration with the greedy approach is proportional to the quantity of orphan switches while the pre-partitioning approach, introducing a very small additional control traffic, enables to react quicker in less than 200ms.


title: Progressive Duplicate Detection

abstract: Duplicate detection is the process of identifying multiple representations of same real world entities. Today, duplicate detection methods need to process ever larger datasets in ever shorter time: maintaining the quality of a dataset becomes increasingly difficult. We present two novel, progressive duplicate detection algorithms that significantly increase the efficiency of finding duplicates if the execution time is limited: They maximize the gain of the overall process within the time available by reporting most results much earlier than traditional approaches. Comprehensive experiments show that our progressive algorithms can double the efficiency over time of traditional duplicate detection and significantly improve upon related work.


title: Composing Relationships with Translations

abstract: Performing link prediction in Knowledge Bases (KBs) with embedding-based models, like with the model TransE (Bordes et al., 2013) which represents relationships as translations in the embedding space, have shown promising results in recent years. Most of these works are focused on modeling single relationships and hence do not take full advantage of the graph structure of KBs. In this paper, we propose an extension of TransE that learns to explicitly model composition of relationships via the addition of their corresponding translation vectors. We show empirically that this allows to improve performance for predicting single relationships as well as compositions of pairs of them.


Best Practices for the Design of RESTful Web Services


title: Oysters and rye bread: Polarising living standards in Flanders, 1800 1860

abstract: This article presents the results of an extensive inquiry into urban consumption of foodstuffs in Flanders between 1800 and 1860. The octroi tax, an indirect tax levied on various consumer items, formed the principal source. During the initial phase of Belgian industrialisation the inhabitants of the eight cities under study (Antwerp, Bruges, Brussels, Courtrai, Ghent, Lokeren, Leuven and Mechelen) experienced a sharp decrease in the consumption of bread grains, meat, fish, beer, wine and gin. A number of other indicators suggest at the same time an accelerated widening of the gap between rich and poor. This process of impoverishment and pauperisation, which occurred in all cities (small or large, industrial or merchant centre), is explained as a result of Malthusian tensions. The rapid population growth, which started around 1750, generated the division of arable land and a sharp increase in land prices. The growing demand for food, stagnating agricultural output, and the trade and agricultural policy of the government led to a significant increase in the price level of foodstuffs. The capitalism hypothesis of Lis and Soly, elaborated for Antwerp, is rejected.


title: Alternative Consent Models for Biobanks: The New Spanish Law on Biomedical Research

abstract: This article provides an overview of recent contributions to the debate on the ethical use of previously collected biobank samples, as well as a country report about how this issue has been regulated in Spain by means of the new Biomedical Research Act, enacted in the summer of 2007. By contrasting the Spanish legal situation with the wider discourse of international bioethics, we identify and discuss a general trend moving from the traditional requirements of informed consent towards new models more favourable to research in a post-genomic context.


title: Automatic localization of EEG electrode markers within 3D MR data

abstract: The electrical activity of the brain can be monitored using ElectroEncephaloGraphy (EEG). From the positions of the EEG electrodes, it is possible to localize focal brain activity. Thereby, the accuracy of the localization strongly depends on the accuracy with which the positions of the electrodes can be determined. In this work, we present an automatic, simple, and accurate scheme that detects EEG electrode markers from 3D MR data of the human head.


title: GossipTrust for Fast Reputation Aggregation in Peer-to-Peer Networks

abstract: In peer-to-peer (P2P) networks, reputation aggregation and ranking are the most time-consuming and space-demanding operations. This paper proposes a new gossip protocol for fast score aggregation. We developed a Bloom filter architecture for efficient score ranking. These techniques do not require any secure hashing or fast lookup mechanism, thus are applicable to both unstructured and structured P2P networks. We report the design principles and performance results of a simulated GossipTrust reputation system. Randomized gossiping with effective use of power nodes enables light-weight aggregation and fast dissemination of global scores in O(log2 n) time steps, where n is the P2P network size. The Gossip-based protocol is designed to tolerate dynamic peer joining and departure, as well as to avoid possible peer collusions. The scheme has a considerably low gossiping message overhead, i.e. O(n log2 n) messages for n nodes. Bloom filters demand at most 512 KB memory per node for a 10,000-node network. We evaluate the performance of GossipTrust with distributed P2P file-sharing and parameter-sweeping applications. The simulation results demonstrate that GossipTrust has small aggregation time, low memory demand, and high ranking accuracy. These results suggest promising advantages of using the GossipTrust system for trusted P2P applications.


Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey


title: Development of a robotic finger with an active dual-mode twisting actuation and a miniature tendon tension sensor

abstract: A robot finger is developed with enhanced grasping force and speed using active dual-mode twisting actuation, which is a type of twisted string actuation. This actuation system has two twisting modes (Speed Mode and Force Mode) with different radii of the twisted string, and the twisting mode can be changed automatically. Therefore, the actuator operates like an automatic transmission having two transmission ratios, and the robot finger can generate a large grasping force or fast motion depending on the twisting mode. In addition, a miniature tendon tension sensor is developed and embedded in the fingertip for automatic mode change and grasping force control. A relation between the fingertip force and the tension of the tendon is derived by kinematic and kinetic analyses of the robot finger. The performance of the robotic finger and the tension sensor are verified by experimental results.


title: Structural analysis of cooking preparation steps in Japanese

abstract: We propose a method to create process flow graphs automatically from textbooks for cooking programs. This is realized by understanding context by narrowing down the domain to cooking, and making use of domain specific constraints and knowledge. Since it is relatively easy to extract significant keywords from cooking procedures, we create a domain specific dictionary by statistical methods, and propose a structural analysis method using the dictionary. In order to evaluate the ability of the proposed method, we applied the method to actual procedures as an experiment, which showed effective results. The same experiment was also performed on a different program, which showed lower accuracy but also showed realistic results.


title: Oxygen activation by photoexcited protoberberinium alkaloids from Mahonia aquifolium

abstract: Protoberberinium salts, i.e. berberine (I), palmatine (II) and jatrorrhizine (III) prepared from Mahonia aquifolium (Pursh) Nutt. belong to isoquinoline alkaloids possessing interesting biological activity (e.g. antibacterial, antimalarial, antitumor). The characteristic UV/Vis absorption band maxima of I–III iodide salts were found in regions 350 and 425 nm in dimethylsulfoxide (DMSO) and ethanol solvents, and were only negligibly inﬂuenced by substitution changes on the C-2 and C-3 positions. The ﬂuorescence intensity of protoberberinium salts monitored in ethanol solutions was signiﬁcantly lowered by iodide counter-ions, and decreased in the order berberine > palmatine > jatrorrhizine. EPR spectroscopy supplied evidence of the formation of super-oxide anion radicals and singlet oxygen upon irradiation of berberine in oxygenated DMSO solvent. The photochemical generation of O2·− and 1O2 in DMSO solutions of palmatine and jatrorrhizine was substantially lower, and probably reﬂected the replacement of a photolabile methylenedioxy group at C-2 and C-3 positions in the berberine molecule by two methoxy groups in palmatine, and methoxyl (C-2) and hydroxyl (C-3) substitution in jatrorrhizine. Additionally, the powder EPR spectra of protoberberinium iodides I–III measured at 290 K revealed the presence of single-line EPR signals (geff = 2.0044), which were attributed to hydroperoxidic structures produced by the autoxidation process. The photochemical reactions of protoberbenium salts producing reactive oxygen species after UVA excitation should be integrated in biological activity investigations, as well as in their applications in skin disorder treatment. Copyright © 2004 John Wiley & Sons, Ltd.


title: Information Diffusion in Social Networks in Two Phases

abstract: The problem of maximizing information diffusion, given a certain budget expressed in terms of the number of seed nodes, is an important topic in social networks research. Existing literature focuses on single phase diffusion where all seed nodes are selected at the beginning of diffusion and all the selected nodes are activated simultaneously. This paper undertakes a detailed investigation of the effect of selecting and activating seed nodes in multiple phases. Specifically, we study diffusion in two phases assuming the well-studied independent cascade model. First, we formulate an objective function for two-phase diffusion, investigate its properties, and propose efficient algorithms for finding seed nodes in the two phases. Next, we study two associated problems: (1) budget splitting which seeks to optimally split the total budget between the two phases and (2) scheduling which seeks to determine an optimal delay after which to commence the second phase. Our main conclusions include: (a) under strict temporal constraints, use single phase diffusion, (b) under moderate temporal constraints, use two-phase diffusion with a short delay while allocating most of the budget to the first phase, and (c) when there are no temporal constraints, use two-phase diffusion with a long delay while allocating roughly one-third of the budget to the first phase.


PHONOLOGY OF GENDER IN ENGLISH AND FRENCH GIVEN NAMES


title: Modulated model predictive control of three level flying capacitor buck converter

abstract: In this paper a new control method for a three level flying capacitor buck converter has been developed using a modulated model predictive control. The proposed method takes into account all the possible switching configurations of the converter and selects two switching configurations to be applied to the converter in the next sampling time, using a cost function. The cost function has been defined such the two selected consecutive switching configurations minimize the error of output voltage and the flying capacitor voltage simultaneously. The effectiveness of the proposed method has been evaluated using the experimental results.


An energy-ef fi cient coarse grained spatial architecture for convolutional neural networks


title: Application of Virtual Instrumentation LabVIEW for Power Electronic System Analysis

abstract: Virtual instrumentation is defined as the combination of measurement and control hardware and application software with industry-standard computer technology to create user-defined instrumentation systems. Computer-aided laboratory exercises based on the LabVIEW system are efficiently used on the topic of Power electronic systems. The paper presents the application mentioned virtual instrument system in the education of boost type DC/DC converter in the continuous and discontinuous conduction mode. Its main application is in regulated dc power supplies and the regenerative braking of dc motors. Time-resolved measurements provided by NI ELVIS suite enable the students to explore and explain a behavior of circuits in the many different stages. A presented education form provides using different pedagogical tools and approaches. It is suitable for self-study purposes and in the field of e-learning platform education.


title: Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning

abstract: On-road vehicle detection is a critical operation in automotive active safety systems such as collision avoidance, merge assist, lane change assistance, etc. In this paper, we present VeDAS—Vehicle Detection using Active learning and Symmetry. VeDAS is a multipart-based vehicle detection algorithm that employs Haar-like features and Adaboost classifiers for the detection of fully and partially visible rear views of vehicles. In order to train the classifiers, a modified active learning framework is proposed that selects positive and negative samples of multiple parts in an automated manner. Furthermore, the detected parts from the classifiers are associated by using a novel iterative window search algorithm and a symmetry-based regression model to extract fully visible vehicles. The proposed method is evaluated on seven different datasets that capture varying road, traffic, and weather conditions. Detailed evaluations show that the proposed method gives high true positive rates of over 95% and performs better than existing state-of-the-art rear-view-based vehicle detection methods. Additionally, VeDAS also detects partially visible rear views of vehicles using the residues left behind after detecting the fully visible vehicles. VeDAS is able to detect partial rear views with a detection rate of 87% on a new partially visible rear-view vehicle dataset that we release as part of this paper.


1 Topic Segmentation


Female Gender Differences in Peer Review Scrutiny