title: A Shooting Training and Instructing System Based on Image Analysis

abstract: The main shortcoming of the traditional way to train shooting athletes is that the effect is hard to be evaluated without live firing. We have designed and implemented a computer-aided shooting training and instructing system that can help coaches and athletes do more quality than quantity work. The minimum system consists of 6 components: a gun, a laser aiming device, a target, a camera, an image card and a computer. It works as follows: the camera snaps a target image, then the aiming point which is marked by the laser spot is extracted from the image and the corresponding score is calculated and printed in the screen in real time. The trace of the aiming point in a training session is also drawn in the screen. The system can prompt the best trigger moment. Based on the recorded performance data in the whole training session, the coach can make objective evaluation on the training effect and give pointed instructions to the athlete. The evaluation and instructions can be made by the expert system instead. Up to 16 targets can be processed simultaneously by one system.


title: Understanding symmetries in deep networks

abstract: Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that a commonly used deep network, which uses convolution, batch normalization, reLU, max-pooling, and sub-sampling pipeline, possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights. We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold. Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold. Our empirical evidence based on the MNIST dataset shows that the proposed updates improve the test performance beyond what is achieved with batch normalization and without sacrificing the computational efficiency of the weight updates.


title: “When the going gets tough, who keeps going?” Depletion sensitivity moderates the ego-depletion effect

abstract: Self-control relies on a limited resource that can get depleted, a phenomenon that has been labeled ego-depletion. We argue that individuals may differ in their sensitivity to depleting tasks, and that consequently some people deplete their self-control resource at a faster rate than others. In three studies, we assessed individual differences in depletion sensitivity, and demonstrate that depletion sensitivity moderates ego-depletion effects. The Depletion Sensitivity Scale (DSS) was employed to assess depletion sensitivity. Study 1 employs the DSS to demonstrate that individual differences in sensitivity to ego-depletion exist. Study 2 shows moderate correlations of depletion sensitivity with related self-control concepts, indicating that these scales measure conceptually distinct constructs. Study 3 demonstrates that depletion sensitivity moderates the ego-depletion effect. Specifically, participants who are sensitive to depletion performed worse on a second self-control task, indicating a stronger ego-depletion effect, compared to participants less sensitive to depletion.


title: Wearable RGBD Indoor Navigation System for the Blind

abstract: In this paper, we present a novel wearable RGBD camera based navigation system for the visually impaired. The system is composed of a smartphone user interface, a glass-mounted RGBD camera device, a real-time navigation algorithm, and haptic feedback system. A smartphone interface provides an effective way to communicate to the system using audio and haptic feedback. In order to extract orientational information of the blind users, the navigation algorithm performs real-time 6-DOF feature based visual odometry using a glass-mounted RGBD camera as an input device. The navigation algorithm also builds a 3D voxel map of the environment and analyzes 3D traversability. A path planner of the navigation algorithm integrates information from the egomotion estimation and mapping and generates a safe and an efficient path to a waypoint delivered to the haptic feedback system. The haptic feedback system consisting of four micro-vibration motors is designed to guide the visually impaired user along the computed path and to minimize cognitive loads. The proposed system achieves real-time performance at \(28.4\)Hz in average on a laptop, and helps the visually impaired extends the range of their activities and improve the mobility performance in a cluttered environment. The experiment results show that navigation in indoor environments with the proposed system avoids collisions successfully and improves mobility performance of the user compared to conventional and state-of-the-art mobility aid devices.


title: Categorical matrix completion

abstract: We consider the problem of completing a matrix with categorical-valued entries from partial observations. This is achieved by extending the formulation and theory of one-bit matrix completion. We recover a low-rank matrix $X$ by maximizing the likelihood ratio with a constraint on the nuclear norm of $X$, and the observations are mapped from entries of $X$ through multiple link functions. We establish theoretical upper and lower bounds on the recovery error, which meet up to a constant factor $\mathcal{O}(K^{3/2})$ where $K$ is the fixed number of categories. The upper bound in our case depends on the number of categories implicitly through a maximization of terms that involve the smoothness of the link functions. In contrast to one-bit matrix completion, our bounds for categorical matrix completion are optimal up to a factor on the order of the square root of the number of categories, which is consistent with an intuition that the problem becomes harder when the number of categories increases. By comparing the performance of our method with the conventional matrix completion method on the MovieLens dataset, we demonstrate the advantage of our method.


title: Bayesian multi-camera surveillance

abstract: The task of multicamera surveillance is to reconstruct the paths taken by all moving objects that are temporally visible from multiple non-overlapping cameras. We present a Bayesian formalization of this task, where the optimal solution is the set of object paths with the highest posterior probability given the observed data. We show how to efficiently approximate the maximum a posteriori solution by linear programming and present initial experimental results.


title: Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From Sum–Difference Coarray Viewpoint

abstract: This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral search approach [25] , which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum–difference coarray (SDCA) is derived for larger degrees of freedom (DOFs). Besides, to optimize the selection of regularization parameter, we also construct a new sparse representation algorithm by estimating the errors between the signal and noise parts. Further, an iterative scheme is presented to transform the 2-D grids searching to several times of 1-D searching, where the initial values are obtained by extracting one difference coarray from SDCA. So the proposed method can achieve aperture extension, high estimation performance, and low computational complexity. Besides, the sparse array extension model for multiple-input multiple-output radars is discussed and the Cramer–Rao bound for 2-D DOA estimation with CPPA is also derived in detail. Finally, simulation results demonstrate the effectiveness of proposed method compared to the state-of-the-art methods.


title: Signal Processing Applications of Oblique Projection Operators

abstract: Oblique projection operators are used to project measurements onto a low-rank subspace along a direction that is oblique to the subspace. They may be used to enhance signals while nulling interferences. In the paper, the authors give several basic results for oblique projections, including formulas for constructing oblique projections with desired range and null space. They analyze the algebra and geometry of oblique projections in order to understand their properties. They then show how oblique projections can be used to separate signals from structured noise (such as impulse noise), damped or undamped interfering sinusoids (such as power line interference), and narrow-band noise. In some of the problems addressed, the oblique projection provides an alternative way to implement an already known solution. Expressing these solutions as oblique projections brings geometrical insight to the study of the solution. The geometry of oblique projections enables one to compute performance in terms of angles between signal and noise subspaces. As a special case of removing impulse noise, the authors can use oblique projections to interpolate missing data samples. In array processing, oblique projections can be used to simultaneously steer beams and nulls. In communications, oblique projections can be used to remove intersymbol interference. >


title: Data Mining Based Product Marketing Technique for Banking Products

abstract: In direct marketing, in order to increase the return rate of a marketing campaign, the massive customer dataset is needed to be analyzed, to make best product offers to the customers through the most proper channels. However, this problem is very challenging, since, usually only for very small portions of the whole dataset, some positive returns are received. This paper studies the similar problem for bank product marketing. The proposed approach is a two layer system, which first clusters the customers and then, constructs a classification model for product and communication channel offers. Experimental analysis on real life banking campaign dataset shows promising results.


title: Follow me and like my beautiful selfies: Singapore teenage girls' engagement in self-presentation and peer comparison on social media

abstract: This study explores teenage girls' narrations of the relationship between self-presentation and peer comparison on social media in the context of beauty. Social media provide new platforms that manifest media and peer influences on teenage girls' understanding of beauty towards an idealized notion. Through 24 in-depth interviews, this study examines secondary school girls' self-presentation and peer comparison behaviors on social network sites where the girls posted self-portrait photographs or "selfies" and collected peer feedback in the forms of "likes," "followers," and comments. Results of thematic analysis reveal a gap between teenage girls' self-beliefs and perceived peer standards of beauty. Feelings of low self-esteem and insecurity underpinned their efforts in edited self-presentation and quest for peer recognition. Peers played multiple roles that included imaginary audiences, judges, vicarious learning sources, and comparison targets in shaping teenage girls' perceptions and presentation of beauty. Findings from this study reveal the struggles that teenage girls face today and provide insights for future investigations and interventions pertinent to teenage girls' presentation and evaluation of self on social media. This study examines teenage girls' self-presentation and peer comparison on SNSs.Teenage girls conform to peer norms when presenting and making sense of beauty.Edited self-presentation on SNSs is a means of seeking peer recognition.Peer comparison is considered unhealthy but unavoidable.Likes and followers are important measures of peer attention and validation.


title: Personality and leader effectiveness: A moderated mediation model of leadership self-efficacy, job demands, and job autonomy.

abstract: Abstract The trait theory of leadership is advanced by a joint investigation of the mediating role of (a) leadership self-efficacy (LSE = leader's perceived capabilities to perform leader roles) in linking neuroticism, extraversion, and conscientiousness with leader effectiveness and (b) the moderating role of job demands and job autonomy in influencing the mediation. Using K. J. Preacher, D. D. Rucker, and A. F. Hayes' (2007) moderated mediation framework, the authors tested the model (over a 2-year period) with matched data from 394 military leaders and their supervisors. Results showed that LSE mediated the relationships for neuroticism, extraversion, and conscientiousness with leader effectiveness. Moderated mediation analyses further revealed that LSE mediated the relationships for (a) all 3 personality variables for only those leaders with low job demands; (b) neuroticism and conscientiousness for only those leaders with high job autonomy; and (c) extraversion, regardless of a leader's level of job autonomy. Results underscore the importance of accounting for leaders' situational contexts when examining the relationships between personality, LSE, and effectiveness.


title: Asthma in pregnancy.

abstract: Worldwide the prevalence of asthma among pregnant women is on the rise, and pregnancy leads to a worsening of asthma for many women. This article examines the changes in asthma that may occur during pregnancy, with particular reference to asthma exacerbations. Asthma affects not only the mother but the baby as well, with potential complications including low birth weight, preterm delivery, perinatal mortality, and preeclampsia. Barriers to effective asthma management and opportunities for optimized care and treatment are discussed, and a summary of the clinical guidelines for the management of asthma during pregnancy is presented.


title: Entering the Digital Era – The Impact of Digital Technology-related M&As on Business Model Innovations of Automobile OEMs

abstract: Digital technologies have reached the sphere of industrial-age, primarily physical industries, thus forcing incumbent firms to digitally innovate their business models. Employing a longitudinal dataset of the world’s largest automobile manufacturers from 2000 to 2013, we found empirical evidence of a positive effect of digital technology– related mergers and acquisitions (M&As) on digital business model innovativeness. Moreover, this effect is enhanced by previous non-digital M&A experience, a diversified M&A history, as well as early experience with digital technology–related M&As. Consequently, our findings reveal that OEMs acquiring complementary and heterogeneous external knowledge on digital technologies and possessing the absorptive capacity to integrate as well as commercialize this type of knowledge are better prepared to master the digital transformation of their business. Furthermore, we find indications of a positive influence of digital business model innovations on the expected future firm performance of automobile manufacturers, thus substantiating the importance of digital transformation.


title: A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China

abstract: Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data segments from 219 subjects. The dataset covers an age range of 20–89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes. Machine-accessible metadata file describing the reported data (ISA-Tab format)


title: PCG-Based Game Design Patterns

abstract: People enjoy encounters with generative software, but rarely are they encouraged to interact with, understand or engage with it. In this paper we define the term 'PCG-based game', and explain how this concept follows on from the idea of an AI-based game. We look at existing examples of games which foreground their AI, put forward a methodology for designing PCG-based games, describe some example case study designs for PCG-based games, and describe lessons learned during this process of sketching and developing ideas.


title: AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS

abstract: We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.


title: Exploring social-historical ties on location-based social networks

abstract: Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to “check-in” at geographical locations and share such experiences with their friends. Millions of “check-in” records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user’s social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user’s check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user’s check-in behavior. In particular, our model captures the property of user’s check-in history in forms of power-law distribution and short-term effect, and helps in explaining user’s check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user’s checkins and shows how social and historical ties can help location prediction.


title: Real time action recognition using histograms of depth gradients and random decision forests

abstract: We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second.


title: Perceived correlates of illegal behavior in organizations

abstract: A survey was conducted of the perceived correlates of illegal abuses in the electronics industry. Human resource directors of thirty-one firms responded to a questionnaire which assessed their perceptions of the degree to which illegal behavior was caused by (1) deficiencies in the moral character of employees (2) the clarity of expectations and standards describing illegal behavior and (3) the presence of reinforcements and punishments contingent on these behaviors. All three variables were related to the frequency of abuses in three areas of organizational crime (e.g. administrative, labor, environment) and three areas of personal crime (theft, falsifying records kickbacks) as reported by the directors and/or indicated by archival records. The implications of these findings are discussed in terms of how organizations may reduce illegal activity.


title: Emerging Dynamics in Crowdfunding Campaigns

abstract: Crowdfunding platforms are becoming more and more popular for fund-raising of entrepreneurial ventures, but the success rate of crowdfunding campaigns is found to be less than 50%. Recent research has shown that, in addition to the quality and representations of project ideas, dynamics of investment during a crowdfunding campaign also play an important role in determining its success. To further understand the role of investment dynamics, we did an exploratory analysis of the time series of money pledges to campaigns in Kickstarter to investigate the extent to which simple inflows and first-order derivatives can predict the eventual success of campaigns. Using decision tree models, we found that there were discrete stages in money pledges that predicted the success of crowdfunding campaigns. Specifically, we found that, for the majority of projects that had the default campaign duration of one month in Kickstarter, money pledges inflow occurring in the initial 10% and 40-60%, and the first order derivative of inflow at 95-100% of the duration of the campaigns had the strongest impact on the success of campaigns. In addition, merely utilizing the initial 15% money inflows, which could be regarded as “seed money”, to build a predictor can correctly predict 84% of the success of campaigns. Implication of current results to crowdfunding campaigns is also discussed.


Machine Learning for Ontology Learning


title: Stability-Based Validation of Clustering Solutions

abstract: Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract "natural" group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question. We introduce a measure of cluster stability to assess the validity of a cluster model. This stability measure quantifies the reproducibility of clustering solutions on a second sample, and it can be interpreted as a classification risk with regard to class labels produced by a clustering algorithm. The preferred number of clusters is determined by minimizing this classification risk as a function of the number of clusters. Convincing results are achieved on simulated as well as gene expression data sets. Comparisons to other methods demonstrate the competitive performance of our method and its suitability as a general validation tool for clustering solutions in real-world problems.


title: The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: a supplement to the NIMH multimodal treatment study of AD/HD

abstract: Impulsivity is a primary symptom of the combined type of Attention Deficit/Hyperactivity Disorder (AD/HD). The Stop Signal Paradigm is premised upon a primary deficit in inhibitory control in AD/HD, whereas the Delay Aversion Hypothesis, by contrast, conceptualizes impulsivity in AD/HD, not as an inability to inhibit a response, but rather as a choice to avoid delay. This study compared the ecological validity of the Stop Signal Task (SST) and Choice-Delay Task (C-DT) measure of delay aversion, with respect to their relative utility in discriminating AD/HD children from normal control participants, and their correlations with classroom observations and with ratings of impulsivity and other core AD/HD symptoms on the Conners and SNAP-IV checklists. The tasks exhibited modest discriminant validity when used individually and excellent discriminant validity when used in combination. The C-DT correlated with teacher ratings of impulsivity, hyperactivity, and conduct problems, and with observations of gross motor activity, physical aggression, and an AD/HD composite score. The SST correlated with the observations only. These results suggest that delay aversion is associated with a broad range of AD/HD characteristics whereas inhibitory failure seems to tap a more discrete dimension of executive control


title: PID controlled synchronous motor for power factor correction

abstract: In this paper, a synchronous motor controlled by a PID based on a PIC 18F452 microcontroller has been studied under three different working conditions using varying excitation currents. Due to the complexity of PID parameters such as integrative and derivative terms, their conversion to digital systems has proven difficult. Hence, the collection of errors in a specified time period has been multiplied be means of a sampling period rather than complex integral algorithms. The difference between the error rate and its previous value has been divided into a sampling period to obtain the derivative operation. Therefore, a PID controlled algorithm has been embedded into a microcontroller which is easily implemented without complex algorithms. In addition, the design of this study includes an LCD based visual interface, allowing users to instantly monitor the current, the voltage and the power factor of the synchronous motor.


title: Self-Organization In 1-d Swarm Dynamics

abstract: Self-organization of a biologically motivated swarm into smaller subgroups of different velocities is found by solving a 1-dimensional adaptive-velocity swarm, in which the velocity of an agent is averaged over a finite local radius of influence. Using a mean field model in phase space, we find a dependence of this group-division phenomenon on the typical scales of the initial swarm in the position and velocity dimensions. Comparisons are made to previous swarm models in which the speed of an agent is either fixed or adjusted according to the degree of direction consensus among its local neighbors. Key words: self-organization of swarm, phase space, multi-agent system, dynamical system, group-division.


title: Physiological Responses to Breaks in Presence: A Pilot Study

abstract: Introduction A participant in an Immersive Virtual Environment (VE) is subject to two streams of sensory data – from the real world in which the experience is taking place, and also from the virtual world displayed by the virtual reality system. A ‘break in presence’ (BIP) occurs when the participant stops responding to the virtual stream and instead responds to the real sensory stream. We examine whether BIPs correspond to real changes in physiological state that are detectable in physiological time series. We report on the results of an experiment where subjects reported BIPs in the same way as in Slater & Steed (2000) and where Skin Conductance Response (SCR GSR) and heartrate (HR) data were recorded using a ProComp+ device. We find that the evidence does not contradict the hypothesis that BIPs are observable as events in the SCR and HR time series. The evidence was based on 60 subjects for whom heart rate was recorded and 20 for whom SCR was recorded. Results The graphs above show the mean averaged waveforms within 4 seconds of a BIP, for both heart rate and SCR. For both heart rate and SCR there is a spike either just before or just after the BIP, a pattern which is not reflected in various control conditions, except that related with anxiety. Slater, M. & Steed, A.J. (200) A virtual presence counter, Presence: Teleoperators and Virtual Environments, 9(5): 413–434. Programme Book Page 42 of 78 Abstracts


title: Workspace augmentation of spatial 3-DOF cable parallel robots using differential actuation

abstract: In this paper, it is proposed to use spatial differentials instead of independently actuated cables to drive cable robots. Spatial cable differentials are constituted of several cables attaching the moving platform to the base but all of these cables are pulled by the same actuator through a differential system. To this aim, cable differentials with both planar and spatial architectures are first described in this work and then, their resultant properties on the force distribution is presented. Next, a special cable differential is selected and used to design the architecture of two incompletely and fully restrained robots. Finally, by comparing the workspaces of these robots with their classically actuated counterparts, the advantage of using differentials on their wrench-closure and wrench-feasible workspaces is illustrated.


title: Image Segmentation using Extended Edge Operator for Mammographic Images

abstract: Detection of edges in an image is a very important step towards understanding image features. Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. This can be used specifically for enhancing the tumor area in mammographic images. In this paper extended Sobel , Prewitt and Kirsch edge operators are proposed for image segmentation of mammographic images. Edges and tumor location can be seen clearly by using this method. For comparison purpose Gray level co-occurrence matrix, watershed algorithm, present Sobel, Prewitt and Kirsch edge operators are used and their results are displayed. Diagnostic imaging is an invaluable tool in medicine today. These imaging modalities provide an effective means for noninvasive mapping of the anatomy of a subject. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. With the increasing size and number of medical images, the use of computers in facilitating their processing and analysis has become necessary. Estimation of the volume of the whole organ, parts of the organ and/or objects within an organ i.e. tumors is clinically important in the analysis of medical image. The relative change in size, shape and the spatial relationships between anatomical structures obtained from intensity distributions provide important information in clinical diagnosis for monitoring disease progression. Therefore, radiologists are particularly interested to observe the size, shape and texture of the organs and/or parts of the organ. For this, organ and tissue morphometry performed in every radiological imaging centre. These routine assessments are commonly subjective and quantitative, and reports typically refer to lesions as large, small, and prominent. The clinical reports usually offer morphometric data in terms of change relative to a prior study. The recognition, labeling and the quantitative measurement of specific objects and structures are involved in the analysis of medical images. Therefore, to provide the information about an object clinically in terms of its size and shape, image segmentation and classification are important tools needed to give the desired information. Medical images edge detection is an important work for object recognition of the human organs such as lungs and ribs, and it is an essential pre-processing step in medical image


title: Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation

abstract: For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator (G) generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors.


title: Trusted Execution Environment: What It is, and What It is Not

abstract: Nowadays, there is a trend to design complex, yet secure systems. In this context, the Trusted Execution Environment (TEE) was designed to enrich the previously defined trusted platforms. TEE is commonly known as an isolated processing environment in which applications can be securely executed irrespective of the rest of the system. However, TEE still lacks a precise definition as well as representative building blocks that systematize its design. Existing definitions of TEE are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related concepts, such as secure execution environment (SEE). In this paper, we propose a precise definition of TEE and analyze its core properties. Furthermore, we discuss important concepts related to TEE, such as trust and formal verification. We give a short survey on the existing academic and industrial ARM TrustZone-based TEE, and compare them using our proposed definition. Finally, we discuss some known attacks on deployed TEE as well as its wide use to guarantee security in diverse applications.


Quality Investing


title: Adaptive outlierness for subspace outlier ranking

abstract: Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. However, in recent applications traditional outlier mining approaches miss outliers as they are hidden in subspace projections. In this work, we propose a novel outlier ranking based on the degree of deviation in subspaces. Object deviation is measured only in a selection of relevant subspaces and is based on adaptive neighborhoods in these subspaces. We show that our approach outperforms competing outlier ranking approaches by detecting outliers in arbitrary subspaces.


title: Vehicle localization using an AVM camera for an automated urban driving

abstract: This paper presents a map-matching-based vehicle localization algorithm for application to automated driving on urban road. Vehicle position estimation of centimeter-level with low-priced commercial sensor setup is one of the key issue in urban automated driving. The information fusion method of localization algorithm utilizes vehicle chassis sensor and Around View Monitoring (AVM) module with four fish-eyed cameras. The proposed localization algorithm consists of three sections: a lane detection, a position correction, and a localization filter. A lane information is extracted from AVM image around the vehicle. This lane information is possible to correct vehicle position by the iterative closest point (ICP) algorithm which estimates the rigid transformation between the lane map and lanes obtained by AVM in real-time. The corrected vehicle position by this transformation is fused with the information of vehicle sensors based on an extended Kalman filter (EKF). In order to achieve higher accuracy, the covariance of the ICP algorithm is estimated by using Haralick's method. The performance of proposed localization is verified through vehicle experiments on proving ground and actual urban road.


title: Industry 4.0 and Lean Production — A matching relationship? An analysis of selected Industry 4.0 models

abstract: The increasing digitalization of business and society has led to drastic changes within companies. Nearly all enterprises are facing enormous challenges dealing with topics such as Industry 4.0/Industrial Internet. With the goal of supporting companies to handle these challenges and “move” in an Industry 4.0 environment, several frameworks or reference models already exist. Here, we share the results of a detailed analysis of selected Industry 4.0 models. In particular, we foster in our analysis Lean Production aspects since the basic principles of Lean Management/Lean Production in existence since the 1980s have yielded appropriate measures to optimize production. These principles can and should be addressed and included by Industry 4.0 models as well. Our study provides a classification of 31 Industry 4.0 models/frameworks as well as the identification of needs for further research to enhance existing Industry 4.0 models more holistically.


title: Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity

abstract: Semantic similarity measures are an important part in Natural Language Processing tasks. However Semantic similarity measures built for general use do not perform well within specific domains. Therefore in this study we introduce a domain specific semantic similarity measure that was created by the synergistic union of word2vec, a word embedding method that is used for semantic similarity calculation and lexicon based (lexical) semantic similarity methods. We prove that this proposed methodology out performs word embedding methods trained on generic corpus and methods trained on domain specific corpus but do not use lexical semantic similarity methods to augment the results. Further, we prove that text lemmatization can improve the performance of word embedding methods.


title: Key Management Protocol with Implicit Certificates for IoT systems

abstract: This paper proposes a Key Management Protocol for mobile and industrial Internet of Things systems, targeting, at the same time, robust key negotiation, lightweight node authentication, fast re-keying, and efficient protection against replay attacks. The proposed approach pragmatically leverages widely accepted Elliptic Curve Cryptography constructions, specifically the (Elliptic Curve) "Fixed" Diffie Hellman key exchange and the (Elliptic Curve) Qu-Vanstone implicit certificates. Our value added is their suitable integration into a security protocol exchange, designed at layer 2, in the 802.15.4 protocol stack, which permits to i) avoid Elliptic Point multiplications upon rekeying of previously paired devices, and ii) support mutual authentication while securing the protocol exchange. To prove its viability, the proposed Key Management Protocol has been implemented and assessed on severely constrained devices. As expected, but made explicit and quantified by our experimental performance evaluation, the usage of implicit certificates in conjunction with an optimized message exchange yields impressive gains in terms of airtime consumption with respect to state of the art schemes.


Sepsis-3 Is a Positive Evolution in Discrimination Between Septic and Nonseptic Patients As Well As Sepsis Entities, Not a Step Backward.


title: Photovoltaic DC-Building-Module-Based BIPV System—Concept and Design Considerations

abstract: The photovoltaic (PV) modules used in the building-integrated PV (BIPV) system, generally, can be installed in different orientations and angles. Moreover, performance of the PV modules is easy to be affected by partial shadows and mismatch of their electrical parameters. Consequently, the conventional power configurations are difficult to obtain higher energy efficiency and reliability. Some improved power configurations of BIPV system have been presented to solve these problems. The objective of the paper is to give an overview of the existing solutions, and then, proposes an efficient and cost-effective power configuration of BIPV system from the energy conversion and control point of view. The proposed power configuration consists of plenty of PV dc-building module (PV-DCBM) and a centralized inverter. Each PV-DCBM includes a high step-up dc-dc converter integrated with a PV module into an individual electrical device. The PV-DCBMs are parallel connected, and then, connected to a common dc bus. The centralized inverter is connected to the grid. The design criterions, optimum design considerations, and design procedure of the proposed PV-DCBM-based BIPV system are proposed in this paper. The experimental results are presented to verify the validity and feasibility of the novel concept.


title: Engineering a trans-tibial prosthetic socket for the lower limb amputee.

abstract: INTRODUCTION ::: This review addresses the different prosthetic socket designs for trans-tibial amputees, the biomechanics behind the designs and the current state of the field. Of particular focus is the classic patella-tendon bearing (PTB) socket and the more recent sockets manufactured using pressure casting techniques and the theory, biomechanics and clinical implications of the two designs. Methods to examine and compare these designs are also addressed. ::: ::: ::: MATERIALS AND METHODS ::: Journal papers by various investigators which have clinical significance/impact on the field of trans-tibial socket design were chosen for this review. Articles were chosen over a period of over 50 years to demonstrate the evolution of knowledge. ::: ::: ::: RESULTS ::: The engineering of the trans-tibial socket has been largely subjected to empirical derivations and biomechanical theory that remains, for the most part, unproven. The fundamental principles of the PTB socket have been widely refuted. Hydrostatic theory based on pressure casting techniques, on the other hand, provides an optimal scenario to produce a more uniform stump/socket interface pressure. ::: ::: ::: CONCLUSION ::: Preliminary studies indicate the pressure casting technique has the potential to produce comfortable sockets, providing an alternative to the PTB design. Various studies have been attempted to quantitatively compare the 2 types of socket designs. However, further quantitative biomechanical studies are needed to explain the fundamental theory surrounding the pressure cast technique. Methods that could help further understand the pressure cast concept include amputee gait analysis, stump/socket interface pressure measurements, computer aided socket design and finite element modelling techniques.


title: Towards Synthesizing Complex Programs from Input-Output Examples

abstract: In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples. Albeit its significant progress, the programs that can be synthesized by state-of-the-art approaches are still simple in terms of their complexity. In this work, we move a significant step forward along this direction by proposing a new class of challenging tasks in the domain of program synthesis from input-output examples: learning a context-free parser from pairs of input programs and their parse trees. We show that this class of tasks are much more challenging than previously studied tasks, and the test accuracy of existing approaches is almost 0%. ::: We tackle the challenges by developing three novel techniques inspired by three novel observations, which reveal the key ingredients of using deep learning to synthesize a complex program. First, the use of a non-differentiable machine is the key to effectively restrict the search space. Thus our proposed approach learns a neural program operating a domain-specific non-differentiable machine. Second, recursion is the key to achieve generalizability. Thus, we bake-in the notion of recursion in the design of our non-differentiable machine. Third, reinforcement learning is the key to learn how to operate the non-differentiable machine, but it is also hard to train the model effectively with existing reinforcement learning algorithms from a cold boot. We develop a novel two-phase reinforcement learning-based search algorithm to overcome this issue. In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500x longer than the training samples.


title: A DTLS based end-to-end security architecture for the Internet of Things with two-way authentication

abstract: In this paper, we introduce the first fully implemented two way authentication security scheme for the Internet of Things (IoT) based on existing Internet standards, especially the Datagram Transport Layer Security (DTLS) protocol. The proposed security scheme is based on the most widely used public key cryptography (RSA), and works on top of standard low power communication stacks.We believe that by relying on an established standard, existing implementations, engineering techniques and security infrastructure can be reused, which enables easy security uptake. We present an implemented system architecture for the proposed scheme based on a low-power hardware platform suitable for the IoT. We further demonstrate its feasibility (low overheads and high interoperability) through extensive evaluation.


title: A two-stage 3D Unet framework for multi-class segmentation on full resolution image

abstract: Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances. However, a common problem when dealing with large, high resolution 3D data is that the volumes input into the deep CNNs has to be either cropped or downsampled due to limited memory capacity of computing devices. These operations lead to loss of resolution and increment of class imbalance in the input data batches, which can downgrade the performances of segmentation algorithms. Inspired by the architecture of image super-resolution CNN (SRCNN) and self-normalization network (SNN), we developed a two-stage modified Unet framework that simultaneously learns to detect a ROI within the full volume and to classify voxels without losing the original resolution. Experiments on a variety of multi-modal volumes demonstrated that, when trained with a simply weighted dice coefficients and our customized learning procedure, this framework shows better segmentation performances than state-of-the-art Deep CNNs with advanced similarity metrics.


title: TEM Horn Antenna Loaded With Absorbing Material for GPR Applications

abstract: A novel exponentially tapered TEM horn antenna is presented. It is fed directly by a coax through a compact transition section and possesses a wide bandwidth, good radiation characteristic, and easy integration with microwave circuits. Two specially tapered metal flares have been designed to make a wideband characteristic and reduce the reflection. To overcome aperture reflections at low frequencies and unbalanced aperture field at higher frequencies, an arc surface is added at the end of the flare plates, and an absorbing material is loaded on the outer surface of the flare section. Measured results show that the presented antenna has a wide bandwidth from 0.83 to more than 12.8 GHz under a return loss <;-10 dB, small input reflection, a moderate gain from 0 dBi at 0.83 GHz to 10 dBi at 12 GHz, and a good radiation characteristics in the time domain, which can be suitable for ground penetrating radar (GPR) applications.


title: A Survey of Various Bilateral Filtering Techniques

abstract: Noise not only deteriorates image quality but also may result in loss of important information hidden in images (i.e. medical applications). Various types of noises are available in literature such as Gaussian noise, impulse noise, mixed noise etc. In order to remove these noises from images, bilateral filters and its variants are used. This paper surveys the impact of these techniques using several performance metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Mean Absolute Error (MAE) and Time Complexity. For this purpose a simulator is designed in MATLAB to implement these techniques. The results show that Joint Bilateral Filter (JBF) technique is the best technique for removing Gaussian noise, Modified Double Bilateral Filter (MDBF) technique provides good results for removing impulse noise and Switching Bilateral Filter (SBF) technique work well for mixed noise as seen from results.


title: Inverse filtering of room acoustics

abstract: A novel method is proposed for realizing exact inverse filtering of acoustic impulse responses in room. This method is based on the principle called the multiple-input/output inverse theorem (MINT). The inverse is constructed from multiple finite-impulse response (FIR) filters (transversal filters) by adding some extra acoustic signal-transmission channels produced by multiple loudspeakers or microphones. The coefficients of these FIR filters can be computed by the well-known rules of matrix algebra. Inverse filtering in a sound field is investigated experimentally. It is shown that the proposed method is greatly superior to previous methods that use only one acoustic signal-transmission channel. The results prove the possibility of sound reproduction and sound reception without any distortion caused by reflected sounds. >


Extending DBpedia with List Structures in Wikipedia Articles


title: Theory and measurement of backscattering from RFID tags

abstract: This paper presents a method for measuring signal backscattering from RFID tags, and for calculating a tag's radar cross section (RCS). We derive a theoretical formula for the RCS of an RFID tag with a minimum-scattering antenna. We describe an experimental measurement technique, which involves using a network analyzer connected to an anechoic chamber with and without the tag. The return loss measured in this way allows us to calculate the backscattered power and to find the tag's RCS. Measurements were performed using an RFID tag operating in the UHF band. To determine whether the tag was turned on, we used an RFID tag tester. The tag's RCS was also calculated theoretically, using electromagnetic simulation software. The theoretical results were found to be in good agreement with experimental data


title: Improved Adhesive Strength and Toughness of Polyvinyl Acetate Glue on Addition of Small Quantities of Graphene

abstract: We have prepared composites of polyvinyl acetate (PVAc) reinforced with solution exfoliated graphene. We observe a 50% increase in stiffness and a 100% increase in tensile strength on addition of 0.1 vol % graphene compared to the pristine polymer. As PVAc is commonly used commercially as a glue, we have tested such composites as adhesives. The adhesive strength and toughness of the composites were up to 4 and 7 times higher, respectively, than the pristine polymer.


title: Optimal Timing to Adopt an Environmental Policy in a Strategic Framework

abstract: In this paper, the problem of optimal timing, when to adopt an environmental policy in a strategic framework is considered. Using real options theory and some basic tools of game theory, we show that, under certain assumptions, a country behaving strategically should wait longer before adopting such a policy than if it behaves unstrategically or within a larger entity. Such a postponed decision is sub-optimal as regards to the environment protection.


title: Speeding up Convolutional Neural Network Training with Dynamic Precision Scaling and Flexible Multiplier-Accumulator

abstract: Training convolutional neural network is a major bottleneck when developing a new neural network topology. This paper presents a dynamic precision scaling (DPS) algorithm and flexible multiplier-accumulator (MAC) to speed up convolutional neural network training. The DPS algorithm utilizes dynamic fixed point and finds good enough numerical precision for target network while training. The precision information from DPS is used to configure our proposed MAC. The proposed MAC can perform fixed point computation with variable precision mode providing differentiated computation time which enables speeding up training for lower precision computation. Simulation results show that our work can achieve 5.7x speed-up while consuming 31% energy compared to baseline for modified Alexnet on Flickr image style recognition task.


title: MCGraph: multi-criterion representation for scene understanding

abstract: The field of scene understanding endeavours to extract a broad range of information from 3D scenes. Current approaches exploit one or at most a few different criteria (e.g., spatial, semantic, functional information) simultaneously for analysis. We argue that to take scene understanding to the next level of performance, we need to take into account many different, and possibly previously unconsidered types of knowledge simultaneously. A unified representation for this type of processing is as of yet missing. In this work we propose MCGraph: a unified multi-criterion data representation for understanding and processing of large-scale 3D scenes. Scene abstraction and prior knowledge are kept separated, but highly connected. For this purpose, primitives (i.e., proxies) and their relationships (e.g., contact, support, hierarchical) are stored in an abstraction graph, while the different categories of prior knowledge necessary for processing are stored separately in a knowledge graph. These graphs complement each other bidirectionally, and are processed concurrently. We illustrate our approach by expressing previous techniques using our formulation, and present promising avenues of research opened up by using such a representation. We also distribute a set of MCGraph annotations for a small number of NYU2 scenes, to be used as ground truth multi-criterion abstractions.


title: What make long term contributors: Willingness and opportunity in OSS community

abstract: To survive and succeed, software projects need to attract and retain contributors. We model the individual's chances to become a valuable contributor through their capacity, willingness, and the opportunity to contribute at the time of joining. Using issue tracking data of Mozilla and Gnome, we find that the probability for a new joiner to become a Long Term Contributor (LTC) is associated with her willingness and environment. Specifically, during their first month, future LTCs tend to be more active and show more community-oriented attitude than other joiners. Joiners who start by commenting on instead of reporting an issue or ones who succeed to get at least one reported issue to be fixed, more than double their odds of becoming an LTC. The macro-climate with high project relative sociality and the micro-climate with a large, productive, and clustered peer group increase the odds. On the contrary, the macro-climate with high project popularity and the micro-climate with low attention from peers reduce the odds. This implies that the interaction between individual's attitude and project's climate are associated with the odds that an individual would become a valuable contributor or disengage from the project. Our findings may provide a basis for empirical approaches to design a better community architecture and to improve the experience of contributors.


title: Learning sleep stages from radio signals: a conditional adversarial architecture

abstract: We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.


title: Sub-event detection from tweets

abstract: Social media plays an important role in communication between people in recent times. This includes information about news and events that are currently happening. Most of the research on event detection concentrates on identifying events from social media information. These models assume an event to be a single entity and treat it as such during the detection process. This assumption ignores that the composition of an event changes as new information is made available on social media. To capture the change in information over time, we extend an already existing Event Detection at Onset algorithm to study the evolution of an event over time. We introduce the concept of an event life cycle model that tracks various key events in the evolution of an event. The proposed unsupervised sub-event detection method uses a threshold-based approach to identify relationships between sub-events over time. These related events are mapped to an event life cycle to identify sub-events. We evaluate the proposed sub-event detection approach on a large-scale Twitter corpus.


title: 70W GaN-HEMT Ku-Band Power Amplifier in MIC Technology

abstract: In this paper the design, implementation, and experimental results of a Ku -band 70W GaN-HEMT power amplifier (PA) for satellite communication are presented. A two-stage design approach with two 250nm bare-die devices has been chosen to achieve a considerably high saturated gain of 15 dB over the whole extended Ku -band (13.75–14.5 GHz). The circuit was realized in a hybrid microwave integrated circuit technology on an alumina substrate. The PA shows a measured performance of more than 50W output power for a continuous-wave signal with a power-added efficiency (PAE) higher than 23%. Modulated measurements demonstrate an average output power of more than 30W (70W peak) and 21% PAE, while holding the Eutelsat linearity requirements.


Insect classification using image processing and bayesian network


title: Evaluating the Level of Evidence of Qualitative Research

abstract: Guidelines for evaluating the levels of evidence based on quantitative research are well established. However, the same cannot be said for the evaluation of qualitative research. This article discusses a process members of an evidence-based clinical practice guideline development team with the Association of Women's Health, Obstetric and Neonatal Nurses used to create a scoring system to determine the strength of qualitative research evidence. A brief history of evidence-based clinical practice guideline development is provided, followed by discussion of the development of the Nursing Management of the Second Stage of Labor evidence-based clinical practice guideline. The development of the qualitative scoring system is explicated, and implications for nursing are proposed.


title: "...As broad as our life experience": visions of feminist political economy, 1972-1991:

abstract: The six previous special issues of the Review are analyzed both as texts that illuminate the concerns of specific historical moments and as part of the ongoing development of feminist political economy. Over the course of these issues, the diversity of women's experiences forms a basis for critiques of existing theory and the development of feminist analytical constructs.


title: Fast vehicle detection in UAV images

abstract: Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) images remains a challenge, due to its very high spatial resolution and very few annotations. Although numerous vehicle detection methods exist, most of them cannot achieve real-time detection for different scenes. Recently, deep learning algorithms has achieved fantastic detection performance in computer vision, especially regression based convolutional neural networks YOLOv2. It's good both at accuracy and speed, outperforming other state-of-the-art detection methods. This paper for the first time aims to investigate the use of YOLOv2 for vehicle detection in UAV images, as well as to explore the new method for data annotation. Our method starts with image annotation and data augmentation. CSK tracking method is used to help annotate vehicles in images captured from simple scenes. Subsequently, a regression based single convolutional neural network YOLOv2 is used to detect vehicles in UAV images. To evaluate our method, UAV video images were taken over several urban areas, and experiments were conducted on this dataset and Stanford Drone dataset. The experimental results have proven that our data preparation strategy is useful, and YOLOv2 is effective for real-time vehicle detection of UAV video images.


title: Multiagent Learning: Basics, Challenges, and Prospects

abstract: Multiagent systems (MAS) are widely accepted as an important method for solving problems of a distributed nature. A key to the success of MAS is efficient and effective multiagent learning (MAL). The past twenty-five years have seen a great interest and tremendous progress in the field of MAL. This article introduces and overviews this field by presenting its fundamentals, sketching its historical development and describing some key algorithms for MAL. Moreover, main challenges that the field is facing today are indentified.


title: An open access repository of images on plant health to enable the development of mobile disease diagnostics

abstract: Human society needs to increase food production by an estimated 70% by 2050 to feed an expected population size that is predicted to be over 9 billion people. Currently infectious diseases reduce the potential yield by an average of 40% with many farmers in the developing world experiencing yield losses as high as 100%. Infectious diseases of crops are not new and historic examples such as the Irish Potato Famine of 1845-49 demonstrate this. But what is new is the widespread distribution of smartphones among crop growers around the world with an expected 5 billion smartphones by 2020. This offers the potential of turning the smartphone into a valuable tool for diverse communities growing food. One potential application is the development of mobile disease diagnostics through machine learning and crowdsourcing. Computer vision and machine learning have shown their potential to automatically classify images. To do this for plant diseases requires a training set that facilitates the development of the algorithms. Here we announce the release of >50,000 expertly curated images on healthy and infected leaves of crops plants through the existing platform www.PlantVillage.org. We describe both the data and the platform. These data are the beginning of an on-going, crowdsourcing effort to enable computer vision approaches to help solve the problem of yield losses in crop plants due to infectious diseases.


title: Weighted Graph Cuts without Eigenvectors A Multilevel Approach

abstract: A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods - in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods such as Metis have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis, and gene network analysis.


title: Geo-localization of street views with aerial image databases

abstract: We study the feasibility of solving the challenging problem of geolocalizing ground level images in urban areas with respect to a database of images captured from the air such as satellite and oblique aerial images. We observe that comprehensive aerial image databases are widely available while complete coverage of urban areas from the ground is at best spotty. As a result, localization of ground level imagery with respect to aerial collections is a technically important and practically significant problem. We exploit two key insights: (1) satellite image to oblique aerial image correspondences are used to extract building facades, and (2) building facades are matched between oblique aerial and ground images for geo-localization. Key contributions include: (1) A novel method for extracting building facades using building outlines; (2) Correspondence of building facades between oblique aerial and ground images without direct matching; and (3) Position and orientation estimation of ground images. We show results of ground image localization in a dense urban area.


title: Transposable element polymorphisms recapitulate human evolution

abstract: Background ::: The human genome contains several active families of transposable elements (TE): Alu, L1 and SVA. Germline transposition of these elements can lead to polymorphic TE (polyTE) loci that differ between individuals with respect to the presence/absence of TE insertions. Limited sets of such polyTE loci have proven to be useful as markers of ancestry in human population genetic studies, but until this time it has not been possible to analyze the full genomic complement of TE polymorphisms in this way.


title: microRNA-218 Inhibits Oxygen-induced Retinal Neovascularization via Reducing the Expression of Roundabout 1

abstract: BACKGROUND ::: The mechanisms of pathological retinal neovascularization (RNV) remain unknown. Several microRNAs were reported to be involved in the process of RNV. Oxygen-induced retinopathy (OIR) is a useful model to investigate RNV. Our present work explored the expression and the role of microRNA-128 (miR-218) in oxygen-induced RNV. ::: ::: ::: METHODS ::: OIR was used to establish RNV model. The expression level of miR-218 in the retina from OIR mice was assessed by quantitative real-time reverse transcriptase polymerase chain reaction. Fluorescein angiography was performed in retinae of OIR mice, and RNV was quantified by hematoxylin and eosin staining to evaluate the effect of pCDH-CMV-miR-218 intravitreal injection on RNV in OIR mice. Roundabout 1 (Robo1) expression was detected by Western blotting in mouse retinal vascular endothelial cells expressing a high or low level of miR-218 and retinal tissues from OIR mice. Cell migration was evaluated by scratch wound assay. ::: ::: ::: RESULTS ::: In OIR mice, the expression level of miR-218 was significantly down-regulated (P = 0.006). Retinal Robo1 expression was significantly increased at both mRNA and protein levels (P = 0.001, 0.008; respectively). miR-218 intravitreal injection inhibited retinal angiogenesis in OIR mice, and the restoration of miR-218 in retina led to down-regulation of Robo1. ::: ::: ::: CONCLUSIONS ::: Our experiments showed that restoration of miR-218 inhibited retinal angiogenesis via targeting Robo1. MiR-218 contributed to the inhibition of retinal angiogenesis and miR-218 might be a new therapeutic target for preventing RNV.


title: Design for auto-tuning PID controller based on genetic algorithms

abstract: This paper presents a method of designing proportional-integral-derivative (PID) controller based on genetic algorithms (GA). Ziegler-Nichols tuning formula has been used for predicting the range of gain coefficients of GA-PID controller. The use of GA for optimizing the gain coefficients of PID controller has considerably improved the performance of PID controller. Simulation studies on a second-order and a third-order control system demonstrate that the proposed controller provides high performance of dynamic and static characteristics.


title: Achilles: An autonomous lightweight ankle exoskeleton to provide push-off power

abstract: This paper presents the Achilles exoskeleton, an autonomous ankle exoskeleton that can generate 52% of the positive plantarflexion power around the ankle of a 80 kg individual with only 1.5 kg of mass added around the ankle joint. The mass of the exoskeleton is lower and the power density is higher than that of existing autonomous exoskeletons. This high power density was achieved by designing a series elastic actuator that consists of an electric motor and ball-screw gear with a carbon fiber reinforced leaf-spring as lever-arm. A dynamic model that includes the motor and gear properties, spring stiffness, and exoskeleton geometry was used to optimize the design parameters for positive power injection. Doing this for multiple combinations of preselected motors and gears and comparing their support to weight ratio, revealed the best drive combination. The performance of the realized exoskeleton was assessed in several tests. The actuator can track the optimized actuator stroke trajectory with a following error that has a RMS of 2.3 mm, it can track force reference signals with amplitudes of 1 N to 100 N with a bandwidth between 8.1 Hz and 20.6 Hz, and it outputs a maximum mechanical power of 80.2 W. These results show that the device is suitable for fulfilling its purpose: reducing the metabolic cost of walking with an autonomous device.


title: A Real-Time Augmented Reality System to See-Through Cars

abstract: One of the most hazardous driving scenario is the overtaking of a slower vehicle, indeed, in this case the front vehicle (being overtaken) can occlude an important part of the field of view of the rear vehicle's driver. This lack of visibility is the most probable cause of accidents in this context. Recent research works tend to prove that augmented reality applied to assisted driving can significantly reduce the risk of accidents. In this paper, we present a real-time marker-less system to see through cars. For this purpose, two cars are equipped with cameras and an appropriate wireless communication system. The stereo vision system mounted on the front car allows to create a sparse 3D map of the environment where the rear car can be localized. Using this inter-car pose estimation, a synthetic image is generated to overcome the occlusion and to create a seamless see-through effect which preserves the structure of the scene.


title: An Analysis of The Effect of Fashion Consciousness as A Mediating Factor on Hijab Fashion Consumption in Indonesia

abstract: This research aims to analyze the influence factors in fashion consciousness on consumers in Indonesia, especially in relation to consumption of hijab-fashion. This matter is affected by the increasing of halal fashion industry in the world. Beside that, Indonesia is a country with large number of consumption in Hijab-fashion industry. So it is important to know about the contribution of aspects of fashion consciousness to the interest of these onsumen in consuming hijab-fashion. This study focus to the factors that influence fashion consciousness and consumption of hijab-fashion in Indonesia. These factors are religiosity, dressing style, source of knowledge, fashion motivation, and uniqueness also tha fashion consciousness as a intervening variable. This study use a structural equation model by analyzing the facts that have occurred. In this study, the primary data was collected using questionaire with a total of 483 respondents. In this study the first interesting finding was that religiosity has a negative effect to hijab fashion consciousness. Furthermore, there is a positive significant influence on dressing style and fashion motivation in fashion consciousness, but there is no significant influence on the source of knowledge and the uniqueness in the fashion consciousness. This study also found that fashion consciousness has a significant effect on the consumption of hijab-fashion in Indonesia. By looking at the results of this study, it is hoped that it can be useful especially for hijab-fashion manufacturers and industru in Indonesia in considering a more effective strategy in attracting the attention of Muslim consumers in Indonesia. Producers are expected to be able to provide the consumer expectations, especially for religious consumers. Because religious consumers will be more critical in decision related to the selection of fashion models and consumption of hijab-fashion. Thus, producers must provide fashionable product innovations in accordance with the criteria and regulations stipulated in the Islamic religion. So that religious consumers feel comfortable in consuming the modest of hijab-fashion.


title: Environmental Kuznets Curve Hypothesis

abstract: This chapter deals with the environmental Kuznets curve (EKC) hypothesis, as one hypothesis that speaks to the possibility of sustainable development; this chapter also provides a brief overview and current research trends. The EKC hypothesis implies there is an inverted U-shaped relationship between environment degradation and economic development. The concept of the EKC originally began with data observation, and has attracted considerable attention from many researchers concerning the existence of the EKC—since it is very naive—and the thinking that relates to it. This chapter also surveys the main EKC research, which has been undertaken from both empirical and theoretical perspectives. As for the empirical perspective, the author compares previous studies that focus on the income level inherent in the EKC turning point. From the theoretical perspective, the author briefly introduces the Stokey (International Economic Review, 39, 1–31, [43]) model, which addresses stock pollutants. Finally, the author takes up criticisms and problems inherent in previous empirical and theoretical studies of the EKC.


title: Implications of Placebo and Nocebo Effects for Clinical Practice: Expert Consensus

abstract: © 2018 S. Karger AG, Basel. Copyright: All rights reserved. Background: Placebo and nocebo effects occur in clinical or laboratory medical contexts after administration of an inert treatment or as part of active treatments and are due to psychobiological mechanisms such as expectancies of the patient. Placebo and nocebo studies have evolved from predominantly methodological research into a far-reaching interdisciplinary field that is unravelling the neurobiological, behavioural and clinical underpinnings of these phenomena in a broad variety of medical conditions. As a consequence, there is an increasing demand from health professionals to develop expert recommendations about evidence-based and ethical use of placebo and nocebo effects for clinical practice. Methods: A survey and interdisciplinary expert meeting by invitation was organized as part of the 1st Society for Interdisciplinary Placebo Studies (SIPS) conference in 2017. Twenty-nine internationally recognized placebo researchers participated. Results: There was consensus that maximizing placebo effects and minimizing nocebo effects should lead to better treatment outcomes with fewer side effects. Experts particularly agreed on the importance of informing patients about placebo and nocebo effects and training health professionals in patient-clinician communication to maximize placebo and minimize nocebo effects. Conclusions: The current paper forms a first step towards developing evidence-based and ethical recommendations about the implications of placebo and nocebo research for medical practice, based on the current state of evidence and the consensus of experts. Future research might focus on how to implement these recommendations, including how to optimize conditions for educating patients about placebo and nocebo effects and providing training for the implementation in clinical practice.


abstract: This paper reports the design, a part of the fabrication process, especially ion doping method by oblique ion implantation, and experimental results of a 6-axis force-torque sensor chip composed of 16 piezoresistive beams. The sensor chip's area, 2mm square in size, is one-third of that of the minimum 6-axis sensor chip ever reported. It will enhance the mounting density of sensor array and also reduce the cost in case of volume production. These sensor chips were fabricated by MNOIC, 8-inch MEMS foundry in Japan, and their characteristic variations are enough small to make practical use of them.


title: Exploring Contextual Engagement for Trauma Recovery

abstract: A wide range of research has used face data to estimate a person's engagement, in applications from advertising to student learning. An interesting and important question not addressed in prior work is if face-based models of engagement are generalizable and context-free, or do engagement models depend on context and task. This research shows that context-sensitive face-based engagement models are more accurate, at least in the space of web-based tools for trauma recovery. Estimating engagement is important as various psychological studies indicate that engagement is a key component to measure the effectiveness of treatment and can be predictive of behavioral outcomes in many applications. In this paper, we analyze user engagement in a trauma-recovery regime during two separate modules/tasks: relaxation and triggers. The dataset comprises of 8M+ frames from multiple videos collected from 110 subjects, with engagement data coming from 800+ subject self-reports. We build an engagement prediction model as sequence learning from facial Action Units (AUs) using Long Short Term Memory (LSTMs). Our experiments demonstrate that engagement prediction is contextual and depends significantly on the allocated task. Models trained to predict engagement on one task are only weak predictors for another and are much less accurate than context-specific models. Further, we show the interplay of subject mood and engagement using a very short version of Profile of Mood States (POMS) to extend our LSTM model.


title: A Cross-Cultural Adaptation of the ICECAP-O: Test–Retest Reliability and Item Relevance in Swedish 70-Year-Olds

abstract: Background: While there is a plethora of Quality of Life (QoL) measures, the Investigating Choice Experiments for the Preferences of Older People—CAPability index (ICECAP-O) is one of the few that taps into the concept of capability, i.e., opportunities to 'do' and 'be' the things that one deems important in life. We aimed to examine test–retest reliability of the ICECAP-O in a Swedish context and to study item relevance. Methods: Thirty-nine 70-year-olds who took part in a population-based health study completed the Swedish version of the ICECAP-O on two occasions. We analyzed the test–retest reliability for the index and for the individual items. Participants also rated the relevance of each item on a visual analogue scale (0–100). Results: Test–retest reliability for the index score was in good agreement with an ICC of 0.80 (95% CI 0.62–0.90). However, Kappa was low for each item and ranged from 0.18 (control) to 0.41 (role). For attachment, we found a systematic disagreement with lower ratings at the second test occasion. Participants gave their highest relevance rating to attachment and lowest to enjoyment. Conclusion: The Swedish version of the ICECAP-O had good test–retest agreement, similar to that observed for the English version. Item level agreement was problematic, however, highlighting a need for future research.


title: Multi-Frequency Measurements of Complex Permittivity of Dielectric Plates using Higher-Order Modes of a Balanced-Type Circular Disk Resonator

abstract: A multi-frequency measurement method is proposed to determine the complex permittivity of dielectric plates. In these measurements, a balanced-type circular disk resonator excited in the center by coaxial cables is used to excite selectively only the TM0m0 modes having different values of integer m. The frequency dependence of the complex permittivity of the plates is obtained from the measured values of the resonant frequency and the unloaded Q of the TM0m0 modes for only one circular disk resonator. The numerical results based on the rigorous analysis are presented to explain the effects of the fringing field and coupling holes including coaxial cable. The usefulness of this method is verified by the measured results of polyolefin and PTFE plates.


title: Feature selection for text classification with Naïve Bayes

abstract: As an important preprocessing technology in text classification, feature selection can improve the scalability, efficiency and accuracy of a text classifier. In general, a good feature selection method should consider domain and algorithm characteristics. As the Naive Bayesian classifier is very simple and efficient and highly sensitive to feature selection, so the research of feature selection specially for it is significant. This paper presents two feature evaluation metrics for the Naive Bayesian classifier applied on multi-class text datasets: Multi-class Odds Ratio (MOR), and Class Discriminating Measure (CDM). Experiments of text classification with Naive Bayesian classifiers were carried out on two multi-class texts collections. As the results indicate, CDM and MOR gain obviously better selecting effect than other feature selection approaches.


title: Node Immunization on Large Graphs: Theory and Algorithms

abstract: Given a large graph, like a computer communication network, which $k$ nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? This problem, referred to as the node immunization problem, is the core building block in many high-impact applications, ranging from public health, cybersecurity to viral marketing. A central component in node immunization is to find the best $k$ bridges of a given graph. In this setting, we typically want to determine the relative importance of a node (or a set of nodes) within the graph, for example, how valuable (as a bridge) a person or a group of persons is in a social network. First of all, we propose a novel ‘bridging’ score $\Delta \lambda$ , inspired by immunology, and we show that its results agree with intuition for several realistic settings. Since the straightforward way to compute $\Delta \lambda$ is computationally intractable, we then focus on the computational issues and propose a surprisingly efficient way ( $O(nk^2+m)$ ) to estimate it. Experimental results on real graphs show that (1) the proposed ‘bridging’ score gives mining results consistent with intuition; and (2) the proposed fast solution is up to seven orders of magnitude faster than straightforward alternatives.


title: Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information

abstract: In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation , which aims to recommend products from e-commerce websites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we propose learning both users’ and products’ feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.


title: Toward a Comprehension Challenge, Using Crowdsourcing as a Tool

abstract: Human readers comprehend vastly more, and in vastly different ways, than any existing comprehension test would suggest. An ideal comprehension test for a story should cover the full range of questions and answers that humans would expect other humans to reasonably learn or infer from a given story. As a step toward these goals we propose a novel test, the Crowdsourced Comprehension Challenge (C3), which is constructed by repeated runs of a three-person game, the Iterative Crowdsourced Comprehension Game (ICCG). ICCG uses structured crowdsourcing to comprehensively generate relevant questions and supported answers for arbitrary stories, whether fiction or nonfiction, presented across a variety of media such as videos, podcasts, and still images.


title: Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets

abstract: When the number of projections does not satisfy the Shannon/Nyquist sampling requirement, streaking artifacts are inevitable in x-ray computed tomography (CT) images reconstructed using filtered backprojection algorithms. In this letter, the spatial-temporal correlations in dynamic CT imaging have been exploited to sparsify dynamic CT image sequences and the newly proposed compressed sensing (CS) reconstruction method is applied to reconstruct the target image sequences. A prior image reconstructed from the union of interleaved dynamical data sets is utilized to constrain the CS image reconstruction for the individual time frames. This method is referred to as prior image constrained compressed sensing (PICCS). In vivo experimental animal studies were conducted to validate the PICCS algorithm, and the results indicate that PICCS enables accurate reconstruction of dynamic CT images using about 20 view angles, which corresponds to an undersampling factor of 32. This undersampling factor implies a potential radiation dose reduction by a factor of 32 in myocardial CT perfusion imaging.


title: A Study on Defect Density of Open Source Software

abstract: Open source software (OSS) development is considered an effective approach to ensuring acceptable levels of software quality. One facet of quality improvement involves the detection of potential relationship between defect density and other open source software metrics. This paper presents an empirical study of the relationship between defect density and download number, software size and developer number as three popular repository metrics. This relationship is explored by examining forty-four randomly selected open source software projects retrieved from SourceForge.net. By applying simple and multiple linear regression analysis, the results reveal a statistically significant relationship between defect density and number of developers and software size jointly. However, despite theoretical expectations, no significant relationship was found between defect density and number of downloads in OSS projects.


title: Learning to Adapt: Meta-Learning for Model-Based Control.

abstract: Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations can cause proficient but narrowly-learned policies to fail at test time. In this work, we propose to learn how to quickly and effectively adapt online to new situations as well as to perturbations. To enable sample-efficient meta-learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach trains a global model such that, when combined with recent data, the model can be be rapidly adapted to the local context. Our experiments demonstrate that our approach can enable simulated agents to adapt their behavior online to novel terrains, to a crippled leg, and in highly-dynamic environments.


title: Machine learning and time series: Real world applications

abstract: There are in-numerous applications that deal with real scenarios where data are captured over time making them potential candidates for time series analysis. Time series contain temporal dependencies that divide different points in time into different classes. This paper aims at reviewing marriage of a concept i.e. time series modeling with an approach i.e. Machine learning in tackling real life problems. Like time series is ubiquitous and has found extensive usage in our daily life, machine learning approaches have found its applicability in dealing with complex real world scenarios where approximation, uncertainty, chaotic data are prime characteristics.


title: Substrate-Integrated-Waveguide-Fed Array Antenna Covering 57–71 GHz Band for 5G Applications

abstract: A wideband linearly polarized antenna element with complementary sources is proposed and exploited for array antennas. The element covers a bandwidth of 38.7% from 50 to 74 GHz with an average gain of 8.7 dBi. The four-way broad wall coupler is applied for the 2 $\times $ 2 subarray, which suppresses the cross-polarization of a single element. Based on the designed 2 $ \times $ 2 subarray, two larger arrays have been designed and measured. The $4 \times 4$ array exhibits 26.7% bandwidth, fully covering the 57–71 GHz unlicensed band. The $8 \times 8$ array antenna covers a bandwidth of 14.5 GHz (22.9%) from 56.1 to 70.6 GHz with a peak gain of 26.7 dBi, and the radiation efficiency is around 80% within the matching band. It is demonstrated that the proposed antenna element and arrays can be used for future 5G applications to cover the 22% bandwidth of the unlicensed band with high gain and low loss.


title: Aspect Analysis for Opinion Mining of Vietnamese Text

abstract: Aspect extraction is one of most challenging tasks in opinion mining. Many researches have attempted to solve this problem for English text. For less popular languages such as Vietnamese, their complex structure causes difficulties in management or semantic analysis tasks. In this paper, we propose an approach to extracting and classifying aspect-terms for Vietnamese language. The semi-supervised learning GK-LDA is proved to have better performance than the traditional topic modeling LDA. In the aspect inference, we use dictionary-based method which can extract noun-phrases for obtaining better performance than just extract word seeds or use a complete sentence to infer aspects. Our experimental results show that our proposed method can effectively perform the aspect extraction and classification task. Even though our approach is initially proposed for handling Vietnamese text, we believe that it is also applicable to other languages.


title: A Shift-Reduce Parsing Algorithm for Phrase-based String-to-Dependency Translation

abstract: We introduce a shift-reduce parsing algorithm for phrase-based string-todependency translation. As the algorithm generates dependency trees for partial translations left-to-right in decoding, it allows for efficient integration of both n-gram and dependency language models. To resolve conflicts in shift-reduce parsing, we propose a maximum entropy model trained on the derivation graph of training data. As our approach combines the merits of phrase-based and string-todependency models, it achieves significant improvements over the two baselines on the NIST Chinese-English datasets.


title: Water Dynamics at the Root of Metamorphosis in Living Organisms

abstract: Liquid water has been recognized long ago to be the matrix of many processes, including life and also rock dynamics. Interactions among biomolecules occur very differently in a non-aqueous system and are unable to produce life. This ability to make living processes possible implies a very peculiar structure of liquid water. According to modern Quantum Field Theory (QFT), a complementary principle (in the sense of Niels Bohr) holds between the number N of field quanta (including the matter field whose quanta are just the atoms/molecules) and the phase Ф. This means that when we focus on the atomic structure of matter it loses its coherence properties and, vice versa, when we examine the phase dynamics of the system its atomic structure becomes undefined. Superfluid liquid Helium is the first example of this peculiar quantum dynamics. In the present paper we show how consideration of the phase dynamics of liquid water makes the understanding of its peculiar role in the onset of self-organization in living organisms and in ecosystems possible.


title: Use Cases of Pervasive Artificial Intelligence for Smart Cities Challenges

abstract: Software engineering has been historically topdown. From a fully specified problem, a software engineer needs to detail each step of the resolution to get a solution. The resulting program will be functionally adequate as long as its execution environment complies with the original specifications. With their large amount of data, their ever changing multi-level dynamics, smart cities are too complex for a topdown approach. They prompt the need for a paradigm shift in computer science. Programs should be able to self-adapt on the fly, to handle unspecified events,, to efficiently deal with tremendous amount of data. To this end, bottom-up approach should become the norm. Machine learning is a first step,, distributed computing helps. Multi-Agent Systems (MAS) can combine machine learning, distributed computing, may be easily designed with a bottom-up approach. This paper explores how MASs can answer challenges at various levels of smart cities, from sensors networks to ambient intelligence.


title: Quantum Maxwell-Bloch equations for spatially inhomogeneous semiconductor lasers

abstract: We present quantum Maxwell-Bloch equations (QMBE) for spatially inhomogeneous semiconductor laser devices. The QMBE are derived from fully quantum mechanical operator dynamics describing the interaction of the light field with the quantum states of the electrons and the holes near the band gap. By taking into account field-field correlations and field-dipole correlations, the QMBE include quantum noise effects which cause spontaneous emission and amplified spontaneous emission. In particular, the source of spontaneous emission is obtained by factorizing the dipole-dipole correlations into a product of electron and hole densities. The QMBE are formulated for general devices, for edge emitting lasers and for vertical cavity surface emitting lasers, providing a starting point for the detailed analysis of spatial coherence in the near field and far field patterns of such laser diodes. Analytical expressions are given for the spectra of gain and spontaneous emission described by the QMBE. These results are applied to the case of a broad area laser, for which the frequency and carrier density dependent spontaneous emission factor beta and the evolution of the far field pattern near threshold are derived.


Bad for Data, Good for the Brain : Knowledge-First Axioms For Visualization Design


title: Cognitive Radio for Aeronautical Communications: A Survey

abstract: Novel air traffic management (ATM) strategies are proposed through the Next Generation Air Transportation and Single European Sky for ATM Research projects to improve the capacity of the airspace and to meet the demands of the future air traffic. The implementation of the proposed solutions leads to increasing use of wireless data for aeronautical communications. Another emerging trend is the unmanned aerial vehicles. The unmanned aerial systems (UASs) need reliable wireless data link and dedicated spectrum allocation for its operation. On-board broadband connectivity also needs dedicated spectrum to satisfy the quality of service requirements of the users. With the growing demand, the aeronautical spectrum is expected to be congested. However, the studies revealed that the aeronautical spectrum is underutilized due to the static spectrum allocation strategy. The aeronautical communication systems, such as air–air and air–ground communication systems, inflight infotainment systems, wireless avionics intra-communications, and UAS, can benefit significantly from the introduction of cognitive radio-based transmission schemes. This paper summarizes the current trends in aeronautical spectrum management followed by the major applications and contributions of cognitive radio in solving the spectrum scarcity crisis in the aeronautical domain. Also, to cope with the evolving technological advancement, researchers have prioritized the issues in the case of cognitive radio that needs to be addressed depending on the domain of operation. The proposed cognitive aeronautical communication systems should also be compliant with the Aeronautical Radio Incorporated and Aerospace Recommended Practice standards. An overview of these standards and the challenges that need immediate attention to make the solution feasible for a large-scale operation, along with the future avenues of research is also furnished.