Reviewerly/exHarmony huggingface dataset:
The Effects of Consumer-grade Virtual Reality Headsets on Adult Visual Function

Introduction Consumer-grade virtual reality (VR) headset is being used with increasing frequency nowadays, however, the effect on visual function is not clear.Objectives We here investigate whether using VR headset changes adults' visual function and take into account the possible factors.Methods We compared the uncorrected distant visual acuity (UDVA), uncorrected near visual acuity (UNVA), best corrected visual acuity (BCVA), low contrast visual acuity (LCVA), glare visual acuity (GVA), refractive error (RE), amplitude of accommodation (ACC), and pupil diameter (PD) before and after using VR headset 10 mintues at a time twice per day for 2 successive weeks in 40 volunteers with a mean age of 28.6 years. Differences in these 8 parameters before vs. after VR headset use were analyzed using SPSS 22.0.Results In our study, we found that the amplitude of accommodation had significantly increased by 0.53 (F = 5.673; P = .006) after using, while visual acuity, refractive error and pupil diameter did not show statistically significant changes (P > .05). Correlation test showed that there was no significant correlation between any two parameters on visual function.Conclusion It is discovered that using a consumer-grade VR headset 10 minutes at a time twice daily for 2 weeks improved the amplitude of accommodation of adults dramatically, while neither visual acuity nor refractive error was affected.Abbreviations VR: visual reality; UDVA: uncorrected distant visual acuity; UNVA: uncorrected near visual acuity; BCVA: best corrected visual acuity; LCVA: low contrast visual acuity; GVA: glare visual acuity; RE: refractive error; ACC: amplitude of accommodation; PD: pupil diameter; 3D: three-dimensional; VDTS: visual display terminal syndrome; FOV: field of view; SEQ: spherical equivalent diopter.
A new transfer learning-based method for label proportions problem

Learning with label proportions (LLP), which only provides the unlabeled instances in the bag and the bag's label proportion, has been widely studied recently. However, most of the existing LLP methods do not consider the knowledge transfer from the source task to the target task. In addition, in the process of the collecting data, the data may be corrupted by noise, and this always leads to the uncertain data in its representation. This paper proposes a new transfer learning-based approach for the problem of learning with label proportions, which is called TL-LLP in brief, to transfer knowledge from the source task to the target task where both the source and target tasks contain uncertain data. We first formulate the objective model to deal with transfer learning and uncertain data for the label proportions problem at the same time. We then propose an iterative framework to solve the proposed objective model and obtain the accurate classifier for the target task. Extensive experiments have shown that the proposed TL-LLP method can obtain better performance and is less sensitive to noise compared with the existing LLP methods.
Improving Persian Digit Recognition by Combining Deep Neural Networks and SVM and Using PCA

One of the machine vision tasks is optical character recognition (OCR) that researchers in this field are trying to achieve a high performance and accuracy in the classification task. In this paper, we have used a fine tuned deep Neural networks for Hoda dataset, which is the largest dataset for Persian handwritten digit classification, to extract valuable discriminative features. then, these features are fed to a linear support vector machine (SVM) for classification part. In the next experiment, In order to improve the accuracy and computational load, we applied the Principal component analysis (PCA) to reduce the extracted features dimensions then we fed it to SVM. To the best of our knowledge the proposed method was better than other methods in terms of accuracy measure
Conversational Interface for Managing Non-trivial Internet-of-Things Systems

Internet-of-Things has reshaped the way people interact with their surroundings. In a smart home, controlling the lights is as simple as speaking to a conversational assistant since everything is now Internet-connected. But despite their pervasiveness, most of the existent IoT systems provide limited out-of-the-box customization capabilities. Several solutions try to attain this issue leveraging end-user programming features that allow users to define rules to their systems, at the cost of discarding the easiness of voice interaction. However, as the number of devices increases, along with the number of household members, the complexity of managing such systems becomes a problem, including finding out why something has happened. In this work we present Jarvis, a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept was used to carry out a quasi-experiment with non-technical participants that provides evidence that such approach is intuitive enough to be used by common end-users.
Foundations for Workflow Application Scheduling on D-Wave System

Many scientific processes and applications can be represented in the standardized form of workflows. One of the key challenges related to managing and executing workflows is scheduling. As an NP-hard problem with exponential complexity it imposes limitations on the size of practically solvable problems. In this paper, we present a solution to the challenge of scheduling workflow applications with the help of the D-Wave quantum annealer. To the best of our knowledge, there is no other work directly addressing workflow scheduling using quantum computing. Our solution includes transformation into a Quadratic Unconstrained Binary Optimization (QUBO) problem and discussion of experimental results, as well as possible applications of the solution. For our experiments we choose four problem instances small enough to fit into the annealer’s architecture. For two of our instances the quantum annealer finds the global optimum for scheduling. We thus show that it is possible to solve such problems with the help of the D-Wave machine and discuss the limitations of this approach.
BotDetector: An extreme learning machine‐based Internet of Things botnet detection model

Abstract The development of artificial intelligence has brought new methods for botnet detection. For better performance, deep learning (DL) is more and more widely employed to botnet detecting. The existing DL‐based botnet detection methods require lots of computing resources and running time. While in the real Internet of Things (IoT) environment, real‐time and low computing consumption are much needed. Therefore, the DL‐based methods seem to be powerless in real‐time IoT scenarios. For these reasons, this article proposes a botnet detection model based on extreme learning machine, named BotDetector, which can directly obtain network stream files and quickly learn without data processing to extract botnet traffic characteristics. Experiments show that BotDetector has a good performance, which can identify botnets accurately with great reduction the time consumption and resource consumption. Furthermore, BotDetector has strong applicability in real IoT scenes.
Learning from noisy out-of-domain corpus using dataless classification

Abstract In real-world applications, text classification models often suffer from a lack of accurately labelled documents. The available labelled documents may also be out of domain, making the trained model not able to perform well in the target domain. In this work, we mitigate the data problem of text classification using a two-stage approach. First, we mine representative keywords from a noisy out-of-domain data set using statistical methods. We then apply a dataless classification method to learn from the automatically selected keywords and unlabelled in-domain data. The proposed approach outperformed various supervised learning and dataless classification baselines by a large margin. We evaluated different keyword selection methods intrinsically and extrinsically by measuring their impact on the dataless classification accuracy. Last but not least, we conducted an in-depth analysis of the behaviour of the classifier and explained why the proposed dataless classification method outperformed supervised learning counterparts.
Automatic Recovery of Missing Issue Type Labels

Ag ile software organizations empower developers to make appropriate decisions rather than enforce adherence to a process, resulting in incomplete and noisy data in software archives. Since software analytics techniques are trained using this data, automated techniques are required to recover it.
Improved Genetic Algorithm to Solve Small Scale Travelling Salesman Problem

Genetic Algorithms are the best solution to solve NP-Hard problem. Travelling Salesman Problem (TSP) is one of them which can be solved by GA. TSP Algorithm takes more time to find results when the number of cities increases. TSP problem has many applications in science and engineering. Genetic Algorithm GA is a technique used to find the solution of large scale TSP problems in a reasonable time. In this paper, an improved genetic algorithm is proposed that can be used to find the solution of TSP problems. After implementing this algorithm, found that the proposed algorithm finds better solutions of some instances of TSP problems as compared to other existing algorithms. It's applied on standard TSP instances BAYG29, EIL51 and EIL76 and it found the better result of these type of NP-Hard problems.
Rate-optimal denoising with deep neural networks

Abstract Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying principle is that neural networks trained on large data sets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image. Given such a generator network, a noisy image can be denoised by (i) finding the closest image in the range of the generator or by (ii) passing it through an encoder-generator architecture (known as an autoencoder). However, there is little theory to justify this success, let alone to predict the denoising performance as a function of the network parameters. In this paper, we consider the problem of denoising an image from additive Gaussian noise using the two generator-based approaches. In both cases, we assume the image is well described by a deep neural network with ReLU activations functions, mapping a $k$-dimensional code to an $n$-dimensional image. In the case of the autoencoder, we show that the feedforward network reduces noise energy by a factor of $O(k/n)$. In the case of optimizing over the range of a generative model, we state and analyze a simple gradient algorithm that minimizes a non-convex loss function and provably reduces noise energy by a factor of $O(k/n)$. We also demonstrate in numerical experiments that this denoising performance is, indeed, achieved by generative priors learned from data.
A multiview approach based on naming behavioral modeling for aligning chinese user accounts across multiple networks

Summary Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter‐network applications, for example, cross‐network recommendation and cross‐network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users' accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this article, we first discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Second, we propose the framework of M ulti‐View C ross‐Network U ser A lignment (MCUA) method, which uses a multi‐view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top‐ k valuable features of different types of name matchings for MCUA over our experimental datasets.
Classification of Animals Using Toy Images

Through this work, we have built a supervised, based classification system for the classification of the images of real animals. The classification model is trained using the toy images of animals to account for factors other than just the physical appearance of animals. At first, the image is preprocessed to remove noise and enhanced using the adaptive histogram equalization and median filtering techniques. In the second stage, the preprocessed toy image is segmented using the k-means clustering technique. Segmentation separates the toy animal image from the background. The third stage involves extraction of hog features from the segmented image. In the final stage, the extracted features are used to classify the image using the supervised based multi-SVM classifier to appropriate animal class. The animal image is segmented and classified based on various characteristics/features extracted from the image itself. This project also aims at achieving nearly accurate animal classification considering factors other than just the physical appearance of the animal.
An effective framework for 3D shape measurement of specular surface based on the dichromatic reflection model

Fringe projection profilometry based on phase-shift method has been commonly used for three-dimensional measurement. However, measuring the specular surface is always a challenging task since the specular area cannot present original intensity information. This paper presents a novel framework for measuring objects with specular surfaces. Firstly, a set of phase-shifted fringe patterns with maximum intensity value is projected onto the specular surface. Then, the dichromatic reflection model is developed to theoretically decompose an image into specular and diffuse reflection components, which identifies specular pixels more robustly and effectively than judging by saturation only. Secondly, since the location and intensity of specular areas are acquired, the best projection intensity corresponding to the specular areas can be determined by fitting a power function that transforms captured specular reflection components to projected intensities, aiming to minimize specular areas and decrease specular reflection components. It should be stated here that the assumption based on this paper is that the fewer specular reflection components, the fewer measurement errors. Subsequently, the adaptive fringe patterns, which are constructed using the best projection intensity, are reprojected for phase recovery. Thirdly, it is reasonable that there still exist regions where the specular reflection components are too high. For these information-loss areas, the pixel inpainting technique is adopted when extracting the disparity map. Finally, the ultimate result combines adaptive fringe reconstruction with pixel inpainting. The experiment results verify that the proposed measurement framework can effectively and robustly perform three-dimensional reconstruction of various objects with specular surfaces.
Exploring Structural Knowledge for Automated Visual Inspection of Moving Trains

Deep learning methods are becoming the de-facto standard for generic visual recognition in the literature. However, their adaptations to industrial scenarios, such as visual recognition for machines, product streamlines, etc., which consist of countless components, have not been investigated well yet. Compared with the generic object detection, there is some strong structural knowledge in these scenarios (e.g., fixed relative positions of components, component relationships, etc.). A case worth exploring could be automated visual inspection for trains, where there are various correlated components. However, the dominant object detection paradigm is limited by treating the visual features of each object region separately without considering common sense knowledge among objects. In this article, we propose a novel automated visual inspection framework for trains exploring structural knowledge for train component detection, which is called SKTCD. SKTCD is an end-to-end trainable framework, in which the visual features of train components and structural knowledge (including hierarchical scene contexts and spatial-aware component relationships) are jointly exploited for train component detection. We propose novel residual multiple gated recurrent units (Res-MGRUs) that can optimally fuse the visual features of train components and messages from the structural knowledge in a weighted-recurrent way. In order to verify the feasibility of SKTCD, a dataset that contains high-resolution images captured from moving trains has been collected, in which 18 590 critical train components are manually annotated. Extensive experiments on this dataset and on the PASCAL VOC dataset have demonstrated that SKTCD outperforms the existing challenging baselines significantly. The dataset as well as the source code can be downloaded online (https://github.com/smartprobe/SKCD).
Human-in-the-Loop Conversation Agent for Customer Service

This paper describes a prototype system for partial automation of customer service operations of a mobile telecommunications operator with a human-in-the loop conversational agent. The agent consists of an intent detection system for identifying the types of customer requests that it can handle appropriately, a slot filling information extraction system that integrates with the customer service database for a rule-based treatment of the common scenarios, and a template-based language generation system that builds response candidates that can be approved or amended by customer service operators. The main focus of this paper is on the system architecture and machine learning system structure design, and the observations of a limited pilot study performed to evaluate the proposed system on customer messages in Latvian. We also discuss the business requirements and practical application limitations and their influence on the design of the natural language processing components.
Nonlinear thermal coherent states

Coherent states and their generalizations are normally appropriate candidates for describing radiation fields. Despite this, the effect of thermal noise on generalized coherent states has not been extensively studied. In fact, thermal effects are unavoidable at finite temperatures, and they should be taken into account to have a better agreement with experimental results. In this regard, we use the concept of thermal coherent states, which are indeed the standard coherent states including thermal effects. So, in this paper, by using a nonlinear coherent states approach, we generalize the thermal coherent states to their nonlinear counterparts. In other words, we find a natural link between the thermal coherent states and the nonlinear coherent states associated with nonlinear oscillator algebra. Afterwards, the nonclassicality features of the obtained states are numerically investigated to explore the roles of both nonlinearity and thermal noise in physical properties. The results show that the thermal effects lead to the transition from nonclassical states to classical ones. Moreover, it is seen that the operator-valued intensity-dependent function plays a leading role in controlling the depth as well as the domain of nonclassicality aspects.
A Systematic Mapping Study of the Empirical Explicit Aspect Extractions in Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is described as one of the most vibrant research areas over the last decade. However, due to the exponential increase in aspect-based sentiment researches, there is a massive interest in advanced explicit aspect extraction (EAE) techniques. This interest brings about a huge amount of literature in the EAE domain. This study aims to investigate and identify the existing approaches, techniques, types of research, quantity of publications, publication trends and demographics shaping the EAE research domain in the last decade (2009 - 2019). Accordingly, an evidence-based systematic methodology was adopted to effectively capture all the relevant studies. The main findings revealed that, 1) there is considerable and continuous rise of EAE research activities around different parts of the globe in the last five years, particularly Asia, Middle-East, and European countries; 2) EAE research has been very limited among African countries which need to be addressed due its role on business intelligence as well as semantic values; 3) three research facets were highlighted based on this study, i.e. solution research, validation research, and evaluation research, in which solution research gets the highest attention; and finally 4) the EAE challenges, as well as feasible future recommendations, were highlighted in this study.
Spatio-temporal action detection and localization using a hierarchical LSTM

Video analysis is gaining importance in the recent past due to its usefulness in a wide variety of applications. The efficiency of a video analytics engine primarily depends on its ability to extract the spatio-temporal features, which has enough discriminative. Inspired by the way the human visual system operates, we propose a hierarchical architecture to capture the spatio-temporal information from a given input video at different time scales. The proposed architecture has a 3D Inception module followed by two layers of modified Convolutional Long Short Term Memory (ConvLSTM) as the fundamental unit. At each level, we consolidate the LSTM cell and hidden states to the next level by using an visual attention-based pooling approach. The proposed network is used for video action detection and localization application that is the foundational element for video analysis. UCF101 and AVA datasets are used to show that the recognition accuracy achieved by the proposed algorithm advances the state-of-the-art in spatio-temporal action detection and localization application.
Energy Trading in Microgrids using BlockChain Technology

Over the past decades, the concept of embedded energy production popularly known as microgrids is getting implemented in a wider range due to numerous benefits which include flexibility, efficiency, improved stability, and cost-effectiveness. Opportunities for microgrids in the electricity market are becoming higher these days due to which consumers are now becoming prosumers. Energy trading in microgrids happens in peer to peer manner where there is a need for third-party involvement. To eliminate this involvement, a technology is needed to make the entire system a decentralized one which is "Blockchain". In this paper, a systematic review is conducted on how blockchain technology can be implemented in the energy sector for mitigating the issues and how peer to peer energy trading happens in the microgrids thereby smart contracts are created for purpose of P2P energy trading using solidity tool.