Reviewerly/exHarmony huggingface dataset:
Implementation of an effective locomotion technique in Virtual Reality Stress Therapy

This paper discusses the issues and difficulties faced during the development process of several locomotion techniques that focuses on a Virtual Reality-based stress relaxation therapy. During the process, three techniques are developed and tested. These three techniques are joystick controller-based navigation, guided navigation and teleportation techniques. For each technique, the problems faced include the compatibility issues between the hardware and the software, scripting difficulties, game engine, and neural conflict and all these issues are also addressed in this paper. The discussion does not only highlight the problems faced but also suggests several solutions. In general, the objective of this development is to determine the most effective locomotion technique under the domain of VR stress therapy for relaxation. Research elements studied during the development includes determining the best technique that reduces or eliminates motion sickness during locomotion. This proposed technique is the novelty of this research. For this purpose, various locomotion techniques are studied and developed which is later used under a domain of stress therapy. Note that this research focused on issues related to the locomotion technique rather than the aspect of creating the virtual environment.
Real-Time Video Dehazing for Industrial Image Processing

In today's industries, automation, reliability, robustness and accuracy are pivotal problem to cut costs and increase productivity and quality. Visual sensor networks are vital control and monitoring tools for continues, on-line imaging and real time image processing in production and plant process. Most of the industrial videos are captured in hazy weather and usually degraded by suspended particles of atmosphere, such as smoke, fog, rain, and snow, which limits the visual quality of image. This hinders the ability of artificial intelligent driven systems to achieve automation, reliability and accuracy. Recovery of the clear visuals from the input hazy videos is challenging problem. Instead of relying on explicitly estimating the key component of atmospheric scattering model, we present end-to-end CNN model, which directly recovers the clear images from hazy images. This end-to-end architecture makes it an ideal pre-processing tool into other deep models for increasing the efficiency of various computer vision tasks in real time systems, such as Retina-Net for object detection, ResNet for object recognition. Experimental results demonstrate the effectiveness and robustness of proposed framework by outperforming the stat-of-the-art approaches in terms of time complexity and visual quality.
Information and Communication Technology for Small-Scale Farmers: Challenges and Opportunities

With the rapid growth in the world population, food production is going to be the biggest challenge for the 21st century. Industrialisation and urbanisation are taking away the available agricultural land and hence there is immense stress on the food production to cater the enormous growth of population. The farming community are struggling to meet the increased demand for food production due to limited agricultural land. Natural calamities, extreme weather events and wider variations in rainfall and temperature, destructing crops and reducing yields, thus affecting farmers’ incomes and livelihoods. Unsustainable agricultural practices further worsen the soil fertility and capacity to retain water, thus result in soil erosion. These problems can be minimised by utilising the Information and Communication Technology (ICT) to the farming community, especially small-scale farmers. The advances in the agricultural practices and up-to-date weather/climate information, immensely help the farmers to implement the best practices and contribute to sustainable agriculture. This chapter focuses on the need of ICT to provide the best sustainable practices and optimised water management, which can revolutionise farming technology. An assessment of various available technologies based on user-friendliness, affordability and pros and cons are discussed in detail for appraising their applications by the small-scale farmers.
Website localization

Abstract Traditionally, website translation has been considered a process moving from the global to the local, or from the major to the minor, and has largely been termed localization. Yet, the various meanings and interpretations of the term create the need for more specific terms that designate specific types of localization such as the cases where a locally generated website, created in a locale with ‘minor’ presence on the international market, needs to be provided in a language that is considered ‘major’ in cyberspace, e.g. websites into English as a lingua franca. In such cases, the website content transfer follows an opposite direction, i.e. from the local to the global. We would like to capitalize on the major-minor metaphor as a methodological tool for cases emerging as a quasi-opposite process to the prevailing conceptualization of localization, in an attempt to highlight differences which might justify new terminology. It will be argued that the concept of locale from which the term localization stems does not apply when a culturally undefined audience is addressed, and a new term will be proposed.
Accurate Model of the Internet Financial Poverty Alleviation Based on the Multi-agent and the Data Mining

The Agent system is an important part of the artificial intelligence, and the multi-agent system focuses on the cooperation mechanism between the agents. Most of the existing multi-agent systems are described by the human beings in the form of the language. Firstly, a multi-agent collaboration model is proposed and formally described. With the development of the social science and technology, the Internet technology has been gradually popularized throughout the country, bringing numerous opportunities and unlimited possibilities to the development of various industries. At the same time, the new thinking mode of the “Internet+” has promoted the improvement of the poverty alleviation work. The “Internet plus targeted poverty alleviation” mode has put wings to the poverty alleviation work, no longer limited by the geographical and the time constraints, and has continuously improved the poverty alleviation work and opened up a new path to help the poor.
Effective Use of Augmentation Degree and Language Model for Synonym-based Text Augmentation on Indonesian Text Classification

Machine learning based text processing relies on a qualified text dataset. Text augmentation research aims to enrich text dataset in order to gain higher performance compared to the one using original text dataset. We have conducted text augmentation process on Indonesian text classification by replacing certain words with their synonyms. The process consists of determining the number of words to be substituted in the sentence and selecting the substitute word from the synonym list. The first process, determining the number of words to be substituted, is done using augmentation degree. The second process, selecting the best substitute word, is done using language model. The synonym list is built from thesaurus. We compared several options in building language model. Statistical model is built using combinations of n-gram and smoothing while simple neural model is built using gram value of 3 and 5. The neural model uses pre trained word embedding as input. 5-gram neural model excels other language model setup by significant value of perplexity. Using the best language model, augmented dataset is generated and applied on two classification task of aspect-based sentiment analysis: aspect categorization and sentiment classification. Experiments were done using augmentation degree of 0.1 to 1. The best augmentation degree yields a better 3-4% on classification model's performance.
The Effects of Missing Data Characteristics on the Choice of Imputation Techniques

One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness of values in databases in medical datasets. In this paper, we propose the need to examine the causes of missing data in a medical dataset to ensure that the right imputation method is used in solving the problem. The mechanism of missingness in datasets was studied to know the missing pattern of datasets and determine a suitable imputation technique to generate complete datasets. The pattern shows that the missingness of the dataset used in this study is not a monotone missing pattern. Also, single imputation techniques underestimate variance and ignore relationships among the variables; therefore, we used multiple imputations technique that runs in five iterations for the imputation of each missing value. The whole missing values in the dataset were 100% regenerated. The imputed datasets were validated using an extreme learning machine (ELM) classifier. The results show improvement in the accuracy of the imputed datasets. The work can, however, be extended to compare the accuracy of the imputed datasets with the original dataset with different classifiers like support vector machine (SVM), radial basis function (RBF), and ELMs.
Single Image Dehazing Using Sky Adaptive Fusion

Hazy days often appears in our daily life, which reduces the visibility of images captured from various cameras. High quality dehazing algorithms are in great demand. Most of the common approaches are based on the dark channel prior (DCP). However, DCP is not valid in the sky region and make the estimated transmission close to zero, which lead to color distortions and noise appear in the sky region. To deal with the above problem, this paper proposes a bright channel prior (BCP) to for computing the transmission map in the sky region. After that, the transmission maps obtained using DCP and BCP are fused effectively using the proposed gradient-based fusion scheme. The haze-free images are finally recovered using the fused transmission map and input hazy image. Experimental results demonstrate that the proposed method outperforms the DCP method especially on preserving the color consistence of sky regions.
On the Teaching Reform of Mathematics Course in Independent Colleges

This paper summarizes the teaching reform of mathematics courses carried out by our higher mathematics teaching team in view of the work carried out and experience gained in recent years. Mathematics teachers in colleges and universities should change their educational concepts, incorporate the idea of mathematical models, Encourage and guide students to participate in mathematical model competitions for college students, adopt a mixed teaching mode of online and offline, innovate and turn over the teaching form of the classroom, make micro-classes and develop online teaching platforms, and strengthen the construction of Resource pool. These are effective ways to improve the mathematics level and ability of our students. The cultivation of students’ innovative spirit and ability should be put in the first place in mathematics teaching.
A Secure Audio Steganography Scheme using Genetic Algorithm

Steganography is an active research area in the field of information security. Audio steganography refers to the process of embedding a secret message into an audio signal for secure message transmission. In this paper, we propose a novel secure and robust audio steganography scheme with good embedding rate. The key idea behind the proposed scheme is that a secure encryption scheme is used in the steganography scheme to encrypt the secret message. Further, a random least significant bit plane will be selected by using the genetic algorithm. The encrypted secret message bits will be embedded into the selected bit-plane. The genetic algorithm helps to reduce the distortions on the stego audio after the data hiding process. The selection of higher bit-planes for data hiding process will help to achieve better robustness against noises. The experimental study of the proposed scheme on a variety of audio signals shows that the proposed scheme performs better than the well-known least significant bit steganoaraphy scheme.
Deep Feature Weighting Based on Genetic Algorithm and Naïve Bayes for Twitter Sentiment Analysis

In recent years, users' have expressed their opinion about certain product or service, especially in microblogging sites like Twitter. The analysis of opinion or sentiment is important for organizations to identify correct measures to improve their products or services. To analyze sentiments, the Naive Bayes (NB) model is one of the more popular methods due to low computational time and understandability. However, NB assumes that each feature does not have dependencies with others, which may not be fulfilled in text analysis. To reduce the effect of this assumption, Genetic Algorithm (GA) is used as a feature weighting process. Features that are highly correlated with the class label and lowly correlated with other features are given more weights. Experiments are conducted on the Twitter Airline database to validate the proposed model. Result shows that average accuracy of weighted NB model is 73.72 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , compared to standard NB model's 73.55%. It is concluded that feature weighted NB has minor advantage over standard NB model.
Private Information Delivery

We introduce the problem of private information delivery (PID), comprised of K messages, a user, and N servers (each holds M≤K messages) that wish to deliver one out of K messages to the user privately, i.e., without revealing the delivered message index to the user. The information theoretic capacity of PID is defined as the maximum number of bits of the desired message that can be privately delivered per bit of total communication to the user. For the PID problem with K messages, N servers and M messages stored per server, we show that the capacity is M/K when N≥gcd K/gcd(K,M)-(M/gcd(K,M)-1) (⌊K/M⌋-1).
The Concept of Criticality in Reinforcement Learning

This paper introduces a novel idea in human-aided reinforcement learning - the concept of criticality. The criticality of a state indicates how much the choice of action in that particular state influences the expected return. In order to develop an intuition for the concept, we present examples of plausible criticality functions in multiple environments. Furthermore, we formulate a practical application of criticality in reinforcement learning: the criticality-based varying stepnumber algorithm (CVS) - a flexible stepnumber algorithm that utilizes the criticality function, provided by a human, in order to avoid the problem of choosing an appropriate stepnumber in n-step algorithms such as n-step SARSA and n-step Tree Backup. We present experiments in the Atari Pong environment demonstrating that CVS is able to outperform popular learning algorithms such as Deep Q-Learning and Monte Carlo.
Validation of a novel personalized therapeutic virtual gaming system

The overall purpose of this study is to explore the potential of a personalized virtual gaming system to support and enhance treatment of children with cerebral palsy (CP). The iVG4Rehab system (Intelligent therapeutic Virtual Gaming System for Rehabilitation) is capable of dynamically adjusting game parameters in accordance with the abilities and therapeutic needs of its users. This paper presents the results of a validation study of typically developing children as a first step towards investigating its potential to enhance therapy for children with CP. The results demonstrated that the game was enjoyable, was not perceived to require excessive effort and was sensitive to changes in participant performance, particularly under conditions that were easy (accuracy mode with no weights) and that were more difficult (dwell mode with weights).
Corporate Risk Analysis Approach for Information Leakage

In this paper, We showed a method of risk management against information leakage of companies which has been frequent in recent years. We classified the risk of information leakage into four types from impact and probability, and showed each type of countermeasures through case studies. We also clarified the risk management procedure using ArchiMate. Risk analysis, risk assessment, and selection of risk measures depend on the company's situation. This paper concludes that companies are required to manage the risk of information leakage appropriate to the situation.
PoolSim: A Discrete-Event Mining Pool Simulation Framework

In Proof-of-Work cryptocurrencies, fair reward distribution within mining pools has become a popular area of research. Aside from a theoretical grounding, mining pool reward scheme research has commonly involved discrete event simulations of deterministic miner behaviour under different reward schemes. However, until now researchers have been left with the tedious task of developing their own mining pool simulation software, a rather time-consuming and potentially extensive undertaking, as miner behaviour becomes more complex. We present PoolSim, an open-source and very extensible discrete event simulation framework for modelling different behaviours of miners under any reward distribution scheme. By utilising this framework for different hypothetical mining scenarios, we showcase that mining pool reward scheme analysis indeed remains an exciting area for future research. Further, we believe that PoolSim will vastly increase productivity of researchers focusing on mining pools.
Opinion Mining on Movie Reviews

Analysis of sentiments is also called as opinion mining. Sentiment analysis is in a general sense stressed over examination of sentiments and feelings from substance. Sentiment analysis finds and legitimizes the sentiment of the individual with respect to a given wellspring of substance. Electronic life contains huge proportion of the inclination data as tweets, online diaries, and updates on the status, posts, etc. Sentiment analysis of the things considered, made data incredibly useful to reveal the opinions of the people. A model of analysis of sentiment of tweets with respect to latest reviews of Bollywood movies is proposed here. With the help of classifiers, for instance, Support vector machine and Naïve Bayes, it precisely categorizes the tweets as positive tweet, negative tweet or neutral tweet in order to give sentiment to each tweet. Further, specific movie rating is predicted using neural networks for better results.
Automatic 2D-to-3D Video Conversion using 3D Densely Connected Convolutional Networks

We present a 3D densely connected convolutional networks(DenseNet3D) for the task of automatic 2D-to-3D video conversion. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth as supervision, our method uses spatial transformer networks to combine the separate stages so that our model can be trained end-to-end and directly use stereo image pairs as supervision. We further propose a 3D densely connected convolutional networks(DenseNet3D), which replace the original convolution layer with 3D convolution in densely connected convolutional networks to capture the spatiotemporal characteristics of videos. The experiment shows that the network has better results and faster speed than existing state-of-the-art methods.
Blocking Artifacts Removal of DCT based Highly Compressed Images

In this paper, an adaptive method of removing the blocking artifacts from JPG standard based highly compressed images have been presented. In the case of JPG based highly compressed images, there is always the appearance of some boundary like artifacts called Blocking artifacts. It is always necessary to remove these type of artifacts. Therefore, in order to remove all these artifacts, a post-de-blocking method is essential. In the proposed work, to remove these blocking artifacts an adaptive method which is based on local content has been used. The simulation results of the considered method have been compared with other methods based on MSSIM, PSNR, MSE, where the proposed method has performed better than other methods.
Multi-View and Multi-Information Clustering for Semi-Supervised Person Re-Identification

Deep learning based methods for person re-identification (re-id) have aroused extensive attention in recent years. However, most works adopt fully-supervised learning, which heavily rely on a large amount of labeled training data. And collecting labeled samples is quite time consuming. To address this problem, we present a semi-supervised framework for person re-id. The key point in this work is to estimate the label of unlabeled data, thus a multi-view and multi-information clustering (MVMIC) method is proposed. First, multi-view feature representation is obtained by two Convolutional Neural Networks, then KNN graphs can be constructed by the feature representation. Finally, multi-information is collected from the KNN graphs to select positive pairs and clustering will be achieving. Experimental results on two large-scale datasets demonstrate the superiority of the proposed method.
About on Organizational Impact on the Adoption of New Technologies in Tourism

The aim of this paper is to investigate whether the adoption of a blockchain technology can facilitate improvements in the governance and management of tourism businesses and what are the opportunities, risks and benefits by adopting this technology to create new marketplaces. Innovation adoption has been widely debated among scholars in order to identify variables and models that boost adoption processes within touristic firms. Although tblockchain is still at an early-age of development, it is also beginning to influence the tourism sector through cryptocurrencies, smart contracts, and decentralized applications. Given the importance of intermediaries in tourism sector, systems that will maintain these professional figures facilitating the transactions between the parties are being developed. This conceptual paper intends to propose a discussion on the potentiality of the application of blockchain in the tourism sector. It also suggests a number of research directions that have not been adequately investigated yet. In particular, scholars do not seem to have caught all the implications of this innovation adoption, especially for SMEs in tourism.
Improving the Performance of MapReduce-based Change Detection Using Sampling

Video data have become critical source of information that can be used in many video surveillance applications. Processing large video data can benefit from a technology such as Apache Hadoop that provides a transformative solution for distributed storing and processing of large data. The goal of this paper is to discuss how to improve the performance of Change Detection while processing large video data on Hadoop clusters. The proposed approach uses two sampling techniques along with MapReduce programing model to improve the execution time of change detection. The experiments have been conducted in the Cloud (Amazon EC2). The experimental results exhibit that the proposed approach improves the execution time of MapReduce-based change detection.