Reviewerly/exHarmony huggingface dataset:
6G as if People Mattered: From Industry 4.0 toward Society 5.0 : (Invited Paper)

While 5G was primarily developed to address the anticipated capacity growth demand and enable the increasing importance of IoT based industry verticals, 6G will require a substantially more holistic approach by embracing a much wider community. 6G will become more human-centered than 5G, putting people at the center of a future super-smart society. In this paper, we elaborate on the anticipated shift of research focus from an IoT based Industry 4.0 to a more human-centered Society 5.0 paradigm in future 6G mobile networks, whereby so-called cyber-physical-social systems (CPSS) will play a central role. To illustrate the potential of CPSS, we present two prominent examples of human-in-the-loop centric systems: The emerging Tactile Internet, one of the most interesting 5G low-latency applications, and the future Internet of No Things, an important stepping stone toward ushering in the 6G post-smartphone era. Finally, we take a brief trip into the emerging field of robonomics in the 6G era and touch on the potential of advanced blockchain technologies such as oracles to enable the on-chaining of blockchain-external off-chain information stemming from human users. In doing so, they leverage on human intelligence rather than machine learning only for the social integration of robots into human society.
The Application of Hough Transform and Canny Edge Detector Methods for the Visual Detection of Cumuliform Clouds

The increase in flying time of unmanned aerial vehicles (UAV) is a relevant and difficult task for UAV designers. It is especially important in such tasks as monitoring, mapping, or signal retranslation. While the majority of research is concentrated on increasing the battery capacity, it is also important to utilize natural renewable energy sources, such as solar energy, thermals, etc. This article proposed a method for the automatic recognition of cumuliform clouds. Practical application of this method allows diverting of an unmanned aerial vehicle towards the identified cumuliform cloud and improving its probability of flying into a thermal flow, thus increasing the flight time of the UAV, as is performed by glider and paraglider pilots. The proposed method is based on the application of Hough transform and Canny edge detector methods, which have not been used for such a task before. For testing the proposed method a dataset of different clouds was generated and marked by experts. The achieved average accuracy of 87% on the unbalanced dataset demonstrates the practical applicability of the proposed method for detecting thermals related to cumuliform clouds. The article also provides the concept of VilniusTech developed UAV, implementing the proposed method.
On the quality of quantitative instruments to measure digital competence in higher education: A systematic mapping study

In this study, we report on a Systematic Mapping Study (SMS) on how the quality of the quantitative instruments used to measure digital competencies in higher education is assured. 73 primary studies were selected from the published literature in the last 10 years in order to 1) characterize the literature, 2) evaluate the reporting practice of quality assessments, and 3) analyze which variables explain such reporting practices. The results indicate that most of the studies focused on medium to large samples of European university students, who attended social science programs. Ad hoc, self-reported questionnaires measuring various digital competence areas were the most commonly used method for data collection. The studies were mostly published in low tier journals. 36% of the studies did not report any quality assessment, while less than 50% covered both groups of reliability and validity assessments at the same time. In general, the studies had a moderate to high depth of evidence on the assessments performed. We found that studies in which several areas of digital competence were measured were more likely to report quality assessments. In addition, we estimate that the probability of finding studies with acceptable or good reporting practices increases over time.
Dynamics of Income Inequality, Investment, and Unemployment in Indonesia

This paper examines the effect of per capita income, investment, and unemployment on income inequality in Indonesia from 2011 to 2019. We use both static and dynamic panel data approaches covering 34 provinces in Indonesia. The results support the Kuznets hypothesis, whereby a more significant per capita income growth is associated with more substantial income inequality in a short period; however, this decreases over time (sign change). Furthermore, a larger real per capita income is associated with lower inequality when accompanied by progress in human capital. Alternatively, foreign direct investment (FDI) and infrastructure expenditure positively relate to income inequality, although FDI eventually helps lower inequality. Similarly, increases in domestic private investment can help to reduce income disparity. Meanwhile, unemployment is negatively associated with income inequality, suggesting that better jobs (rather than more jobs per se) are needed to improve income distribution in the country. Although per capita income, investment, and employment have improved substantially and helped Indonesia raise overall income, economic progress does not seem to have been inclusive. We argue that the panel dynamic model helps to capture the persistence effect of income distribution, suggesting a more precise estimation of income inequality issues than static models.
Implicit sentiment analysis based on graph attention neural network

Abstract Sentiment analysis is one of the crucial tasks in the field of natural language processing. Implicit sentiment suffers a significant challenge because the sentence does not include explicit emotional words and emotional expression is vague. This paper proposed a novel implicit sentiment analysis model based on graph attention convolutional neural network. A graph convolutional neural network is used to propagate semantic information. The attention mechanism is employed to compute the contribution to the emotional expression of words. In order to solve the problem of multiple attention preserving repeated information, orthogonal attention constraint was used to make different attention store different emotional information; given the uneven distribution of emotional information, score attention constraint was proposed to make the model focus on a limited number of essential words. The performance of the proposed model was verified on implicit sentiment datasets. The F value reached 88.16%, which is higher than the benchmark model in the literature. The attention mechanism is analyzed to verify the effectiveness of orthogonal constraint and score constraint.
Mathematical Determination of the Upper and Lower Limits of the Diffuse Fraction at Any Site

A mathematical method for accurately determining the upper and lower diffuse-fraction (kd) limits that divide the sky into clear, intermediate, and overcast is developed. Fourteen sites around the world are selected for demonstrating the methodology. The upper and lower kd values for these sites are determined from scatter plots of direct-normal solar radiation vs. kd pairs over the typical meteorological year of each site. They vary between 0.73 and 0.80 for the upper and between 0.24 and 0.27 for the lower kd limits. Plots of sunshine duration (SSD) vs. kd are prepared for 12 of the 14 sites. These plots show a decreasing trend in SSD with increasing values of kd, as anticipated. According to local climatology, the number of the SSD values in each sky-condition classification varies from site-to-site.
Vulnerability Management Models Using a Common Vulnerability Scoring System

Vulnerability prioritization is an essential element of the vulnerability management process in data communication networks. Accurate prioritization allows the attention to be focused on the most critical vulnerabilities and their timely elimination; otherwise, organizations may face severe financial consequences or damage to their reputations. In addition, the large amounts of data generated by various components of security systems further impede the process of prioritizing the detected vulnerabilities. Therefore, the detection and elimination of critical vulnerabilities are challenging tasks. The solutions proposed for this problem in the scientific literature so far—e.g., PatchRank, SecureRank, Vulcon, CMS, VDNF, or VEST—are not sufficient because they do not consider the context of the organization. On the other hand, commercial solutions, such as Nessus, F-Secure, or Qualys, do not provide detailed information regarding the prioritization procedure, except for the scale. Therefore, in this paper, the authors present an open-source solution called the Vulnerability Management Center (VMC) in order to assist organizations with the vulnerability prioritization process. The VMC presents all calculated results in a standardized way by using a Common Vulnerability Scoring System (CVSS), which allows security analysts to fully understand environmental components’ influences on the criticality of detected vulnerabilities. In order to demonstrate the benefits of using the the open-source VMC software developed here, selected models of a vulnerability management process using CVSS are studied and compared by using three different, real testing environments. The open-source VMC suite developed here, which integrates information collected from an asset database, is shown to accelerate the process of removal for the critical vulnerabilities that are detected. The results show the practicability and efficacy of the selected models and the open-source VMC software, which can thus reduce organizations’ exposure to potential threats.
Optimal balancing &amp; efficient feature ranking approach to minimize credit risk

The banking industries are struggling with massive growth in the Non-Performing Assets (NPAs) that is raising the concerns of the financial institutions across the world. For gaining sustainable competitive advantages: detection, prediction, and prevention of credit Risks are becoming the foremost priorities for the banks. This data is vast, highly unstructured and imbalanced; thus, optimal balancing and efficient feature ranking are required, to predict the Credit Risk customers using Machine Learning techniques. Further, feature ranking algorithms are applied to identify the most vital characteristics of triggering the Credit Risk. The experiments have been conducted on credit Risk data set from a German bank, downloaded from the standard data repository of the UCI. Random Forest at optimal balancing ratio of 1:1.1335 has been found to be the best performing with a sensitivity of 81.6%, specificity value of 85.3%, the accuracy of 83.4%, MCC of 0.669 and AUC of 0.914.
Conditional probing: measuring usable information beyond a baseline

Probing experiments investigate the extent to which neural representations make properties-like part-of-speech-predictable.One suggests that a representation encodes a property if probing that representation produces higher accuracy than probing a baseline representation like non-contextual word embeddings.Instead of using baselines as a point of comparison, we're interested in measuring information that is contained in the representation but not in the baseline.For example, current methods can detect when a representation is more useful than the word identity (a baseline) for predicting part-ofspeech; however, they cannot detect when the representation is predictive of just the aspects of part-of-speech not explainable by the word identity.In this work, we extend a theory of usable information called V-information and propose conditional probing, which explicitly conditions on the information in the baseline.In a case study, we find that after conditioning on non-contextual word embeddings, properties like part-of-speech are accessible at deeper layers of a network than previously thought.
A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships

Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only one or a few variables missing. This motivates the use of imputation. We present a new imputation method that uses a version of nearest neighbour imputation, whereby missing data from one individual is replaced with data from another individual, their nearest neighbour. For each individual with missing data, the subsets of variables to be used to select the nearest neighbour are chosen by sampling without replacement the complete data and estimating a best fit Bayesian network. We show that this approach leads to marked improvements in the recall and precision of directed edges in the final network identified, and we illustrate the approach through application to data from a recent study investigating the causal relationship between methylation and gene expression in early inflammatory arthritis patients. We also describe a second improvement in the form of a pseudo-Bayesian approach for upweighting certain network edges, which can be useful when there is prior evidence concerning their directions.
High Key Rate Quantum Conference Key Agreement With Unconditional Security

Quantum cryptography is a major ingredient of the future quantum internet that promises various secure communication tasks. Quantum conference key agreement (CKA) is an important cryptographic primitive of quantum cryptography, which provides the conference key among multiple users simultaneously. However, quantum CKA is currently far from practical application due to the low conference key rate. Here, we propose a quantum CKA protocol of three users with information-theoretic security. Our protocol only requires phase-randomized weak coherent sources and threshold single-photon detectors, and is anticipated to be experimentally demonstrated over 600 km under current technology. Our scheme can be widely implemented in the approaching large-scale quantum network.
Towards a unified assessment framework of speech pseudonymisation

Anonymisation and pseudonymisation are two similar concepts used in privacy preservation for speech data. With no established definitions for these tasks, nor standard approaches to assessment, this paper provides definitions and presents two complementary assessment frameworks. The first is based on voice similarity matrices which provide both an immediate visualisation of privacy protection performance at the speaker level and two objective measures in the form of de-identification and voice distinctiveness preservation. The approach readily highlights imbalances in system performance at the speaker level. The second, referred to as the zero evidence biometric recognition assessment (ZEBRA) framework, is based on information theory and measures the amount of private information disclosed in speech data. The paper presents also an extension to the original ZEBRA framework. It aims to reflect the robustness of the privacy safeguard when a privacy adversary adapts to the protected speech. We demonstrate the application of both frameworks to assess pseudonymisation performance on the two VoicePrivacy 2020 challenge baseline solutions plus a third one. The two frameworks were designed independently of each other. The ZEBRA framework is fully consistent with the Bayesian decision theory and the other framework focuses instead on speaker-wise visualisations of a system performance. Thus, while metrics derived from them bear similarities, they expose differences in safeguard behavior. The assessment of pseudonymisation remains challenging and merits greater attention in the future.
An Over Sampling Method of Unbalanced Data Based on Ant Colony Clustering

Aiming at the low classification accuracy of unbalanced data sets, an improved SMOTE over-sampling algorithm ACC-SMOTE (Ant Colony Clustering Synthetic Minority Oversampling Technology) based on ant colony clustering is proposed.On the one hand, the improved ant colony clustering algorithm is used to divide a small number of samples into different sub-clusters, fully considered the imbalance between inter-cluster and intra-cluster data, and SMOTE algorithm is used to oversample the samples according to the proportion of sub-clusters, to reduce the imbalance of intra-class data.On the other hand, Tomek Links data cleaning technology is used to correct the oversampled samples in time, the quality of synthetic samples is guaranteed by eliminating noise in data sets and overlapping samples generated by sampling methods.The training data set and the test data set used in this paper are both UCI data sets.The experimental results show that this algorithm can significantly improve the classification accuracy of a few classes, thus improving the classification performance of the classifier.
EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks

The rapid advancement in artificial intelligence (AI) and wide deployment of Internet of Video Things (IoVT) enable situation awareness (SAW). The robustness and security of IoVT systems are essential for a sustainable urban environment. While blockchain technology has shown great potential in enabling trust-free and decentralized security mechanisms, directly embedding cryptocurrency oriented blockchain schemes into resource-constrained Internet of Video Things (IoVT) networks at the edge is not feasible. By leveraging Electrical Network Frequency (ENF) signals extracted from multimedia recordings as region-of-recording proofs, this paper proposes EconLedger, an ENF-based consensus mechanism that enables secure and lightweight distributed ledgers for small-scale IoVT edge networks. The proposed consensus mechanism relies on a novel Proof-of-ENF (PoENF) algorithm where a validator is qualified to generate a new block if and only if a proper ENF-containing multimedia signal proof is produced within the current round. The decentralized database (DDB) is adopted in order to guarantee efficiency and resilience of raw ENF proofs on the off-chain storage. A proof-of-concept prototype is developed and tested in a physical IoVT network environment. The experimental results validated the feasibility of the proposed EconLedger to provide a trust-free and partially decentralized security infrastructure for IoVT edge networks.
ANALISIS MANFAAT PENGGUNAAN E-MODUL INTERAKTIF SEBAGAI MEDIA PEMBELAJARAN JARAK JAUH DI MASA PANDEMI COVID-19

Interactive e-module is one of the learning media that has many benefits. In this e-module there are features such as text, images, animations and videos that make it easier for students to understand lessons, especially during distance learning. This research was conducted through a literature study method from various literatures such as books, articles from electronic journals, trusted websites and various statistical data sourced from the internet which will then be investigated through several stages which will then be examined through several stages, including: descriptive analysis, content analysis, and critical analysis. Based on the results of research through library research, the use of interactive e-modules can increase students' learning motivation, students' scientific literacy skills, student learning outcomes, students' independence and students' critical thinking skills. From the results of the study, we can see that this e-module can be a choice of learning media that can be used by teachers in distance learning.
CAVE-based immersive learning in undergraduate courses: examining the effect of group size and time of application

Abstract Immersive virtual reality is increasingly regarded as a viable means to support learning. Cave Automatic Virtual Environments (CAVEs) support immersive learning in groups of learners, and is of potential interest for educational institutions searching for novel ways to bolster learning in their students. In previous work we have shown that the use of a CAVE-based virtual learning environment yielded higher learning gains compared to conventional textbook study. Yet, few prior studies have explored the circumstances that yield a trade-off between learning gains and the practical feasibility of providing immersive learning to large student numbers. To gain insight into these circumstances the current study examined two factors: (1) group size (small, medium and large), and (2) time of application (pre-, mid- and late-term of a course). Results indicated learning gains were present for all group sizes and application time periods, with highest learning gains in smaller groups. Learning gains were consistent across application time periods. Additionally, structural equation modeling was applied to assess how learning may result from the use of immersive virtual reality. Results indicated technological virtual reality features predicted learning outcomes via self-reported usability but less so via self-reported presence. Based on the findings, recommendations are presented for effective immersive learning for different group size and application time period configurations. Taken together, the current study elucidates factors affecting learning in immersive virtual reality and facilitates its use in educational practice.
Auction method to prevent bid-rigging strategies in mobile blockchain edge computing resource allocation

To introduce blockchain technology to the mobile internet and Internet of Things, offloading proof-of-work tasks to edge computing nodes is considered an effective solution. Among these solutions, pricing edge computing resources through auctions has been widely accepted as a simple and efficient method. However, in these auctions, it is difficult to identify bid-rigging behavior by users, and the lack of technical means to avoid such behavior can results in a loss of revenue for edge service providers (ESPs). In this study, we first model the optimal bid-rigging mechanism in a mobile blockchain edge computing resource auction. Then, we introduce an auction method that an ESP can use to prevent the bid-rigging strategy in resource auctions. Finally, we propose a solution for the optimal reserve price of the SA (simulated annealing) algorithm, which calculates the optimal reserve price according to bidders’ willingness to pay and the number of bid-rigging participants to improve the revenue of the ESP. Through experiments, it is verified that the proposed method meets the requirements of IR (individual rationality) and IC (incentive compatibility) in resource allocation, and effectively impacts of the number of bidders who adopt a bid-rigging strategy in auctions on ESP revenue. Furthermore, the efficiency and effectiveness of the algorithm are analyzed using data. When the cooling coefficient is less than 0.95, the time complexity of the algorithm is O(logN). Using this algorithm to conduct auctions can increase revenue by approximately 2%, requiring the ESP to pay only a small computational cost.