Reviewerly/exHarmony huggingface dataset:
Extended Fuzzy Analytical Hierarchy Process Approach in Determinants of Employees’ Competencies in the Fourth Industrial Revolution

This paper explored the education factors and ranked their impacts on the employees' competencies development in Vietnam.Factors contributing to the employees' competencies in the Vietnamese context are proposed based on the literature review under the justification of experts' interviews.Then, the extended fuzzy analytical hierarchy process (EFAHP) approach was used to prioritize the importance of the factors affecting the employees' competency.The research finding confirmed the decisive role of teachers with the greatest weight of impact on the employees' competency, though there was a shift of teacher's role to that of facilitator in the Fourth Industrial Revolution education.
Time Series Prediction Model of Grey Wolf Optimized Echo State Network

As a novel recursion neural network, Echo State Networks (ESN) are characterized by strong nonlinear prediction capability and effective and straightforward training algorithms. However, conventional ESN predictions require a large volume of training samples. Meanwhile, the time sequence data are complicated and unstable, resulting in insufficient learning of this network and difficult training. As a result, the accuracies of conventional ESN predictions are limited. Aimed at this issue, a time series prediction model of Grey Wolf optimized ESN has been proposed. W<sup>out</sup> of ESN was optimized using the Grey Wolf algorithm and predictions of time series data were achieved using simplified training. The results indicated that the optimized time series prediction method exhibits superior prediction accuracy at a small sample size, compared with conventional prediction methods.
Research on Network Security of Campus Network

Nowadays, network security incidents often occur, such as network worms, denial of service attacks, network fraud and so on. Nowadays, the methods of network security incidents are constantly innovating, resulting in data leakage, destruction and other incidents. Campus network is the basis of daily teaching, scientific research and normal operation of schools. It is of great significance to strengthen the construction of campus network security. Based on this, this paper studies the campus network security.
Cloud fraction retrieval and its variability during daytime from ground-based sky imagery over a tropical station in India

Total Sky Imager (TSI) is operated at a high altitude station in the Western Ghats of the Indian sub-continent from June 2012 to June 2013. The TSI image content is classified as clear, thin and thick clouds using red-blue ratio (RBR) threshold technique and then cloud fraction (CF) is retrieved. An algorithm is proposed to improve the accuracy of CF retrieval, by adapting the methodologies used in earlier studies and new developments are suggested in sections of the validation of clear sky image and the aerosol correction factor. Time series of thin, thick and total CF are constructed for each day at 5 min interval. These measurements at high temporal resolution are first of their kind over a tropical station in India. Both thick and thin CFs showed high temporal variability. The transition from one season to another season also showed distinct CF variability. Monthly mean CF showed a mono model distribution in the winter months (December and January) and a bimodal distribution in the pre-monsoon months (March, April, May) including February. During the monsoon season (JJAS) about 90% CF is observed throughout the day. The post-monsoon months (October, November) show a bimodal distribution with one peak in the morning hours and another peak in the afternoon hours. Unlike thin CF, total and thick CFs showed almost similar variability. The comparison of CF retrieved from MODIS (Terra and Aqua) and TSI showed a good correlation for overcast sky conditions, while partial cloudy skies showed less correlation due to the effect of satellite viewing angles. Such continuous measurements of CF at high temporal resolution are essential for cloud radiative forcing studies.
High Payload Steganography: Surface-Fitting The Transform Domain

Recent steganography schemes have shown that transform-based techniques can hide with relatively high capacity ratios while achieving acceptable stego quality. These techniques tend to hide in high-frequency areas exploiting some features provided by transform techniques such as the strong compaction property which concentrates the energy of a signal in low-frequency coefficients, leaving a large area of insignificant coefficients in the high-frequency area. Thus, conventional techniques replace these insignificant coefficients with the secret data. However, these high-frequency components of a signal do still contribute to obtain the composite signal. Thus, this paper proposes a hiding strategy which re-shapes the secret data to the same shape as the high-frequency components rather than simply replacing these components with secret data. This reshaping process should preserve the structure of the signal, and thus outstanding stego quality can be obtained while hiding with very high hiding capacities.
Blockchain for Open Data – Exploring Conceptual Underpinnings and Practice

There has been a sustainable development of the concept of blockchain as one of the key technology innovations changing the business landscapes. Blockchain has been used as a lever for enforcing accountability and responsiveness in different contemporary information and knowledge management environments. The core principle of blockchain is that it promotes the use of technology tools and platforms to achieve anonymous vetting of integrity for different types of information. Together with relatively new concepts such as Open Data, blockchain stands a chance to be practically utilised in different socio-economic establishments. The synthesis of blockchain and Open Data presently opens up requisite implementation of Freedom of Information (FoI) bills which many countries around the world have enacted into laws. A lot of researchers are jumping onto the bandwagon of exploring ways of how blockchain can be used in solving contemporary complex human problems. As a result, a lot of conceptual designs and underpinnings are coming up everyday from both practitioners and researchers. Anchoring the philosophical underpinning on descriptive informetrics, this chapter employs a focused and detailed bibliometric analysis of work that has been published in applied scholarly and practical outlets such as in Scorpus, Emerald, Elsevier, journals, etc., to delve deeper into the contours of blockchain. The chapter discusses formulaic definitions and concepts surrounding blockchain and Open Data with a special focus in the integration of the two concepts for practical application in real-world environments. It is hoped that exploring the formulaic underpinnings of blockchain and Open Data will open up avenues for consolidating their usage into the different contextual socio-economic set-ups.
Parametric Model-Based 3D Human Shape and Pose Estimation from Multiple Views

Human body pose and shape estimation is an important and challenging task in computer vision. This paper presents a novel method for estimating 3D human body pose and shape from several RGB images, using detected joint positions in the images and based on a parametric human body model. Firstly, the 2D joint points of the RGB images are estimated using a deep neural network, which provides a strong prior on the pose. Then, an energy function is constructed based on the 2D joint points in the RGB images and a parametric human body model. By minimizing the energy function, the pose, shape and camera parameters are obtained. The main contribution of the method over previous work, is that the optimization is based on several images simultaneously using only estimated joint positions in the images. We have performed experiments on both synthetic and real image data-sets, that demonstrate that our method can reconstruct 3D human bodies with better accuracy than previous single view methods.
A Q-values Sharing Framework for Multiple Independent Q-learners

By using a multiagent reinforcement learning (MARL) framework, cooperative agents can communicate with one another to accelerate the joint learning. In the teacher-student paradigm applied in MARL, a more experienced agent (advisor) can advise another agent (advisee) which action to take in a state. However, when agents need to cooperate with one another, the advisee may fail to cooperate well with others since their policies may have changed. It requires a long period for an advisee to learn the same best actions as an advisor has learned, especially when the amount of advice is limited. We propose a partaker-sharer advising framework (PSAF) for independent Q-learners with limited communication in cooperative MARL. In PSAF, the overall learning process is shown to accelerate by multiple independent Q-learners' sharing their maximum Q-values with one another at every time step. We perform experiments in the Predator-Prey domain and HFO game. The results show that our approach significantly outperforms existing advising methods.
On the Invariance of the SELU Activation Function on Algorithm and Hyperparameter Selection in Neural Network Recommenders

In a number of recent studies the Scaled Exponential Linear Unit (SELU) activation function has been shown to automatically regularize network parameters and to make learning robust due to its self-normalizing properties. In this paper we explore the utilization of SELU in training different neural network architectures for recommender systems and validate that it indeed outperforms other activation functions for these types of problems. More interestingly however, we show that SELU also exhibits performance invariance with regards to the selection of the optimization algorithm and its corresponding hyperparameters. This is clearly demonstrated by a number of experiments which involve a number of activation functions and optimization algorithms for training different neural network architectures on standard recommender systems benchmark datasets.
Property in Digital Coins

Abstract Digital coins have burst into mainstream awareness recently, mainly as a result of high-worth ‘Initial Coin Offerings’ (‘ICOs’). The most immediate question in the legal treatment of digital coins is whether they are properly seen as digital ‘commodities’, and/or as ‘securities’, and/or as units of ‘money’. But the conceptual underpinnings of these categories are not clear, nor is it clear how these categories relate to each other; no legal system currently deals adequately with incorporeal objects as objects of property law. This category includes not only digital coins but also some forms of conventional money and securities. Establishing a satisfactory account of their treatment in property law is therefore a necessary first step to incorporating digital coins into private law theory. I argue that this task is best approached on the basis of a plausible ontology of incorporeal objects, including those embodied in paper (i.e. banknotes and conventional securities) and those that exist natively in ‘cyberspace’ (i.e. electronic ‘book-money’, modern securities, and now digital coins). We therefore urgently need to develop a plausible account of a how packets of data can be treated as an object of property rights. Using a comparative analysis of English law and Civilian law (particularly German) concepts of property as an entry point into this complex of problems, I explore the ontology of incorporeal objects and the role of documentation in their creation and maintenance as part of the ‘ontic furniture’ of our economic world. I explore the conceptual basis of property in digital coins in terms of a new category of property. Such a category is long overdue and will be increasingly important in the future.
New Era for Technology in Healthcare Powered by GDPR and Blockchain

The development of patients’ electronic healthcare records has been hampered by bureaucracy and by heavy regulation within the medical field. Medical data recorded in a patient health records is a valuable source of information that can be used by doctors and researchers to improve the quality of healthcare and the quality of life. Medical data should be owned and controlled by the patient instead of being scattered in different healthcare systems that does not support transfer and semantic interoperability between different healthcare providers. Blockchain technology has demonstrated in the financial field that it is possible to create a safe, secure and auditable mechanism by using a decentralized network along with a public ledger. But with the adoption of the newly General Data Protection Regulation, all technological advances discovered by the Blockchain are now blocked. In this paper we present the main aspect of the GDPR and Blockchain technology in healthcare and a way in which this two can work together.
Increasing concurrency in hyperledger fabric

Introduction. The execute-order-validate blockchain architecture, introduced by Hyperledger Fabric (HLF) [1], allows parallel transactions execution and validation, thus raising the need for transaction isolation. Today transaction isolation is attained by locking the entire state database (DB) during simulation of transactions and state update. This lock is one of the major bottlenecks as observed by previous work [3]. This work presents a new lock-free approach for providing transaction isolation.
Don't Put All Your Strategies in One Basket: Playing Green Security Games with Imperfect Prior Knowledge

Security efforts for wildlife monitoring and protection of endangered species (e.g., elephants, rhinos, etc.) are constrained by limited resources available to law enforcement agencies. Recent progress in Green Security Games (GSGs) has led to patrol planning algorithms for strategic allocation of limited patrollers to deter adversaries in environmental settings. Unfortunately, previous approaches to these problems suffer from several limitations. Most notably, (i) previous work in GSG literature relies on exploitation of error-prone machine learning (ML) models of poachers' behavior trained on (spatially) biased historical data; and (ii) online learning approaches for repeated security games (similar to GSGs) do not account for spatio-temporal scheduling constraints while planning patrols, potentially causing significant shortcomings in the effectiveness of the planned patrols. Thus, this paper makes the following novel contributions: (I) We propose MINION-sm, a novel online learning algorithm for GSGs which does not rely on any prior error-prone model of attacker behavior, instead, it builds an implicit model of the attacker on-the-fly while simultaneously generating scheduling-constraint-aware patrols. MINION-sm achieves a sublinear regret against an optimal hindsight patrol strategy. (II) We also propose MINION, a hybrid approach where our MINION-sm model and an ML model (based on historical data) are considered as two patrol planning experts and we obtain a balance between them based on their observed empirical performance. (III) We show that our online learning algorithms significantly outperform existing state-of-the-art solvers for GSGs.
Survey of Online Social Networks Threats and Solutions

In the last few years, a new form of Internet-based services has started, which is called Online Social Networks (OSNs). The impact of OSNs on human lives is foreseen to be very large with unprecedented amount of data and users. OSN users share their ideas, photos, daily life events, feelings and news. Since OSNs' security and privacy challenges are more potential than ever before, it is necessary to enhance the protection and filtering approaches of OSNs contents. This paper provides a comprehensive analysis of OSNs' threats and challenges, and categorize them into: account-based, URL-based and content-based threats. We analyze the existing protection methods and highlight their limitations and weaknesses. Finally, we provide a set of recommendations for a security system that fill the gaps of the currently applied methods.
Measuring Immediate Adaptation Performance for Neural Machine Translation

Patrick Simianer, Joern Wuebker, John DeNero. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction

Purpose Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices. Design/methodology/approach A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms. Findings Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t -test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study. Originality/value The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t -test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.
ITM-CNN: Learning the Inverse Tone Mapping from Low Dynamic Range Video to High Dynamic Range Displays Using Convolutional Neural Networks

While inverse tone mapping (ITM) was frequently used for graphics rendering in the high dynamic range (HDR) space, the advent of HDR TVs and the consequent need for HDR multimedia contents open up new horizons for the consumption of ultra-high quality video contents. Unfortunately, previous methods are not appropriate for HDR TVs, and their inverse-tone-mapped results are not visually pleasing with noise amplification or lack of details. In this paper, we first present the ITM problem for HDR TVs and propose a CNN-based architecture, called ITM-CNN, which restores lost details and local contrast with its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. Our ITM-CNN is a powerful means to solve the lack of HDR video contents with legacy LDR videos.
RARD II: The 94 Million Related-Article Recommendation Dataset.

The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from Mr. DLib, a recommender-system as-a-service in the digital library and reference-management-software domain. As such, RARD II complements datasets from other domains such as books, movies, and music. The dataset encompasses 94m recommendations, delivered in the two years from September 2016 to September 2018. The dataset covers an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual (implicit) ratings matrices, RARD II includes the original recommendation logs, which provide a unique insight into many aspects of the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approaches for predicting algorithm performance. In this paper, we summarise the key features of this dataset release, describe how it was generated and discuss some of its unique features. Compared to its predecessor RARD, RARD II contains 64% more recommendations, 187% more features (algorithms, parameters, and statistics), 50% more clicks, 140% more documents, and one additional service partner (JabRef).
Analyzing the Evolution of Javascript Applications

Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications, including IoT applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.
Machine learning based Insider Threat Modelling and Detection

Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.
A Novel Method for Assessing the Spatial Presence of Omnidirectional Videos: Comparing Virtual and Real World

With the development of immersive media technologies, omnidirectional video service has been popularized at an accelerated speed due to the sense of presence it offered. Accurately measuring the user's subjective sense of presence on virtual reality (VR) devices are urgently required by video service providers and VR device manufacturers to further improve their quality of service. So far, there is no standardized methodology available for the conduction of the subjective experiment toward the user's sense of presence when viewing omnidirectional videos. In this paper, we designed a novel subjective assessment method, which evaluates the presence of omnidirectional videos where the scenes have been previously experienced in the real world by subjects. In this setup, subjects can easily and accurately form their sense of presence when viewing omnidirectional videos using previous observations of the real environment as references. The experimental results demonstrated that the proposed method is able to obtain reliable quantitative results toward the sense of presence.