Reviewerly/exHarmony huggingface dataset:
Federated Knowledge Graphs Embedding.

In this paper, we propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and peer-to-peer manner while being privacy-preserving. FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. In order to protect the privacy of the training data, FKGE further implements a privacy-preserving neural network structure to guarantee no raw data leakage. We conduct extensive experiments to evaluate FKGE on 11 knowledge graphs, demonstrating a significant and consistent improvement in model quality with at most 17.85% and 7.90% increases in performance on triple classification and link prediction tasks.
RoboStar modelling stack

RoboStar technology for model-based Software Engineering for Robotics enables the construction of artefacts that capture and relate assumptions that can play a role in the reality. In this paper, we give a brief overview of the RoboStar approach.
A Quantum Walk Model for Idea Propagation in Social Network and Group Decision Making

We propose a quantum walk model to investigate the propagation of ideas in a network and the formation of agreement in group decision making. In more detail, we consider two different graphs describing the connections of agents in the network: the line graph and the ring graph. Our main interest is to deduce the dynamics for such propagation, and to investigate the influence of compliance of the agents and graph structure on the decision time and the final decision. The methodology is based on the use of control-U gates in quantum computing. The original state of the network is used as controller and its mirrored state is used as target. The state of the quantum walk is the tensor product of the original state and the mirror state. In this way, the proposed quantum walk model is able to describe asymmetric influence between agents.
Deformable Convolution Network based Invertibility-Driven Interpolation Filter for HEVC

Fractional-position motion compensation has been widely utilized in video coding standard to improve the inter prediction efficiency. In this paper, we study the three key components of the state-of-the-art method––Invertibility-driven Interpolation Filter (InvIF) and improve each of them to derive an Enhanced InvIF (EInvIF). Firstly, the deformable convolution layer is introduced to make the network's filters have the ability to change its shape and parameters to adapt to the video contents. Secondly, the generative adversarial network is utilized to increase the deep learning models' ability of approximating target distribution. Finally, the motion blur images are adopted as the regularization target instead of discrete cosine transform images. The proposed EInvIF has been integrated into HM-16.7, and the experimental results show that the proposed scheme can achieve 2.5% bitrate reduction on average.
On the use of stereo‐digital image correlation for the alignment of a fatigue testing machine in accordance with international standards: A feasibility study

Abstract Stereo‐digital image correlation (SDIC) is used for the analysis and quantification of parasite bending stresses in a fatigue specimen due to its mounting in a misaligned load frame. The aim of this work is to provide an easy way to estimate the induced parasite bending stresses due to the load frame misalignment, using the out‐of‐plane displacement measurements obtained by SDIC and beam theory. Analytical solutions of parasite stresses as a function of out‐of‐plane misalignments are provided for flat fatigue specimens having (a) tangentially blending fillets between the uniform test section and the ends and (b) continuous radius between the ends. Simple procedures for aligning a commercial load frame in accordance with international standards are also provided. The bending stresses profile along the specimen main axis obtained by numerical simulations have shown a very good agreement with the results obtained by the analytical models. The proposed methodology could complement standard alignment techniques based on samples instrumented with strain gauges or, in their absence, even replace established procedures.
Tantangan Pembelajaran Bahasa Inggris Pada Aspek Berbicara Pada Sekolah Pariwisata Dalam Masa Pandemi

During Covid-19 pandemic, online learning has been the only option to continue learning process. For tourism vocational school, in learning English for Foreign Language, especially in the speaking aspect, the learning process can be a challenge. This research is a qualitative research which aims to see the teacher's perspective regarding challenges of speaking learning process in EFL context. Six educators were interviewed to answer this question. Based on the results of interviews, it was obtained that teachers experienced various challenges during learning process which included technical difficulties, challenges during the learning process and challenges in assessment. Through this research, it is hoped that further strategies to solve these challenges can be formulated. Keyword : challenge, english for foreign language, online learning, speaking.
An economic theory of blockchain foundations

Abstract. Blockchain (or crypto) foundations are nonprofit organizations that supply public goods to a crypto-economy. The standard theory of crypto foundations is that they are like governments with respect to a national or regional economy, i.e. raising a public treasury and allocating resources to blockchain specific capital works, education, R&D, etc., to benefit the community and develop the ecosystem. We propose an alternative theory of what foundations do, namely that the treasury they manage is a moat to raise the cost of exit or forking because the benefit of the fund is only available to those who stay with the chain. Furthermore, building and maintaining a large treasury is a costly signal that only a high quality chain could afford to do (Spence 1973). We review these two models of the economic function of a blockchain foundation - (1) as a private government supplying local public goods, and (2) as a moat to raise the opportunity costs of exit. We outline the empirical predictions each theory makes, and examine the implications for optimal foundation design. We conclude that foundations should be funded by a pre-mine of tokens, and work best when large, visible, transparent, rigorously managed, and with a low burn rate.
Classification of Quranic topics based on imbalanced classification

Imbalanced classification techniques have been applied widely in the field of data mining. It is used to classify the imbalanced classes that are not equal in the number of samples. The problem of imbalanced classes is that the classification performance tends to the class with more samples while the class with few samples will obtain poor performance. This problem can be occurred in the Qur’anic classification due to the different number of verses. Many studies classified Qur’anic verses, which depended on the traditional classification. However, no study classified Qur’anic topics based on the techniques of imbalanced classification. Therefore, this paper aims to apply the methods of imbalanced classification as synthetic minority over-sampling technique (SMOTE), random over sample (ROS), and random under sample (RUS) methods to classify the Qur’anic topics that are imbalanced. Many metrics were used in this research to evaluate the experimental results. These metrics are sensitivity/recall, specificity, overall accuracy, F-Measure, G-mean, and matthews correlation coefficient (MCC). The results showed that the Quranic classification performance improved when imbalanced classification techniques were applied
A Multi-Turn Emotionally Engaging Dialog Model.

Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing. Some of the recent work aims at incorporating affect information into sequence-to-sequence neural dialog modeling, making the response emotionally richer, while others use hand-crafted rules to determine the desired emotion response. However, they do not explicitly learn the subtle emotional interactions captured in human dialogs. In this paper, we propose a multi-turn dialog system aimed at learning and generating emotional responses that so far only humans know how to do. Compared with two baseline models, offline experiments show that our method performs the best in perplexity scores. Further human evaluations confirm that our chatbot can keep track of the conversation context and generate emotionally more appropriate responses while performing equally well on grammar.
New Thinking About Blockchain in the Fashion Industry

Blockchain is a relatively new and emerging technology that can be considered as a digital, decentralized, and distributed public ledger that records data, tracks transactions, and updates information. The majority of blockchain research has been conducted on bitcoin or other cryptocurrencies. Recently, however, researchers have recognized blockchain's potential in other areas, and the concept of blockchain technology is already starting to be applied in the fashion industry. While blockchain finds many applications related to sustainability and transparency, this research developed a blockchain model to map out the future fashion supply chain from a product attribute perspective.
Topic Modeling of the Research Papers' Citation Contexts: a Structure of an Author's Research Consumption

Citation contexts from research papers, as a rule, contain information about the reasons and the character of using the cited research outputs. By extracting this information from the citation contexts, one can create different data sets for scientometric studies. The paper systematizes general possibilities of using data from the citation contexts for the development of the author-citation network analysis. As one of applications, the paper presents an approach to constructing the thematic structure of a research consumption based on topic modelling of the citation contexts from researchers papers. The thematic structure features built in the forms of a "word tree" and a flowchart are discussed. Possible directions of development of this approach are considered. The proposed thematic structure of the research consumption is a promising new data source for both scientometric studies and creation of new research services.
Manipulative tactics employed by Azerbaijani authors in online media resources

Recent investigations have established that manipulation is the abuse of language realized through various consciously employed linguistic means aimed to influence the listeners’ or readers’ social, interpersonal and mental states and behaviours, thus misdirecting their actions. Intentionality as one of the basic parameters of manipulation, no doubt, exercises a destructive effect on an individual, group or society at large. Depending on the range of their manipulative attitudes and intentions, often reaching beyond the bounds of morality, manipulators tend to bend reality, distort facts and through seemingly persuasive argumentation and proofs present their subjective reality as truth. This presupposes the employment of certain manipulative tactics and techniques meant to guarantee the manipulator’s success. The present case study attempts to expose the manipulative techniques and tactics deliberately employed by the Azerbaijani author of the article “Armenian So-Called Genocide”, published in the Azerbaijani online news medium “Azvision.az”. The object of the paper consists of a deep and thorough analysis of the manipulative intentions and interpretations worked into the mentioned article. On the basis of critical discourse-analysis, the application of the methods of argumentation, with references to empirical evidence, assists not only in observation of the mechanisms of manipulative techniques and tactics the author implements, but also reveals the persistence of Azerbaijani political circles toward the fulfilment of political goals through violations and falsifications of historical facts and distortions of reality.
Survey on five nature-Inspired Optimization Algorithms

This paper presents a literature review about Particle Swarm Optimization (PSO), Firework, Firefly, Clonal Selection, and Cuckoo Search algorithms, which are among the most common natural-inspired optimization algorithms.These algorithms were tried on different benchmark functions.The obtained results were analyzed, and the performance was compared.The results showed that PSO and Firefly Search algorithms provided the best performance in the studied cases.
Factors Affecting Students’ Intention of Gamification for Learning Model in the Covid- 19 Pandemic Era at Indonesia: A Confirmatory Factor Analysis

The current study explores the measurement of gamification-based distance learning education on campus in the Covid-19 period.Thriving from the regular distance learning during the pandemic Covid-19, the element of interactive study and fun through games will help the students resolve their process of learning.The well famous Technology Acceptance Model, to include the perceived interactivity, is used as the measurement model.The assessment uses the confirmatory factor analysis, and nine hypotheses are stated.The research collects a total of 150 higher education students as the instrument of the sample.The data is analysed by a structural equation model to evaluate hypotheses testing.The result shows the suitability model in explaining the students' intention up to use gamification-based education.A total of seven out of nine hypotheses are accepted.AT presents the most dominant influence value to BI with the beta value of 0.87.The overall model explains 76 percent of total intention in the measurement model.The theoretical and practical implications are further explained.
Design and Development of Citizen Surveillance and Social-Credit Information System for Bangladesh

Most of the areas of Bangladesh face a wide range of threats, ranging from terrorism and civil unrest to kidnapping, rape and murder. To reduce the impact of these threats, it is critical for the authorities to capture real-time information onwhat is happening in and around the city. Therefore, there is a growing requirement for utilizing the new and emerging technologies to make our cities safer. The development of the monitoring system in the technological field has become a blazing topic in the milieu of scholars and AI techniques have become an essential part of the future era. With the help of AI &amp; deep learning, we can evaluate the social behavior (Action Recognition) of people, and by implementing the social credit score system, we can mark the citizen’s activities &amp; analyze them. To detect and recognize the actions of a human we used YOLOV2 architecture and also incorporated the SQL database (SQLite) to record the social credit score. A citizen can visualize their credit score from an HTML based website.
Design and implementation of a mobile device APP for network-based EEW systems: application to PRESTo EEWS in Southern Italy

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;A fundamental feature of any Earthquake Early Warning System is the ability of rapidly broadcast earthquake information to a wide audience of potential end users and stakeholders, in an intuitive, customizable way. Smartphones and other mobile devices are nowadays continuously connected to the internet and represent the ideal tools for earthquake alerts dissemination, to inform a large number of users about the potential damaging shaking of an impending earthquake.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Here we present a mobile App (named ISNet EWApp) for Android devices which can receive the alerts generated by a network-based Early Warning system. Specifically, the app receives the earthquake alerts generated by the PRESTo EWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in Southern Italy. In the absence of alerts, the EWApp displays the standard bulletin of seismic events occurred within the network. In the event of a relevant earthquake, instead, the app has a dedicated module to predict the expected ground shaking intensity and the available lead-time at the user position and to provide customized messages to inform the user about the proper reaction during the alert.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;We first present the architecture of both network-based system and EWApp, and then and describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Friend closeness based user matching cross social networks

<abstract> The typical aim of user matching is to detect the same individuals cross different social networks. The existing efforts in this field usually focus on the users' attributes and network embedding, but these methods often ignore the closeness between the users and their friends. To this end, we present a friend closeness based user matching algorithm (FCUM). It is a semi-supervised and end-to-end cross networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. Due to the expensive costs of labeling new match users for training FCUM, we also design a bi-directional matching strategy. Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity. </abstract>
Multiple Instance Learning with Differential Evolutionary Pooling

While implementing Multiple Instance Learning (MIL) through Deep Neural Networks, the most important task is to design the bag-level pooling function that defines the instance-to-bag relationship and eventually determines the class label of a bag. In this article, Differential Evolutionary (DE) pooling—an MIL pooling function based on Differential Evolution (DE) and a bio-inspired metaheuristic—is proposed for the optimization of the instance weights in parallel with training the Deep Neural Network. This article also presents the effects of different parameter adaptation techniques with different variants of DE on MIL.
Increasing the Steganographic Resistance of the LSB Data Hide Algorithm

The robustness of the security algorithm is one of the most important properties that determines how difficult it is to break it.Increasing the robustness of the algorithm directly affects the degree of secrecy when it is used for confidential transmission.The paper analyzes the steganographic algorithm Least Significant Bit, represents a method of counteracting the algorithm of the "visual attack" and statistical methods used against stego-containers generated using the LSB algorithm.To prove the increase in resistance, the study used the PSNR index, Chi-square test.The proposed technique involves the use of a uniform distribution and compression method.The paper presents the results of computer experiments demonstrating the effectiveness of the proposed technique.
The Voltage Effect in Behavioral Economics

All happy families are alike; each unhappy family is unhappy in its own way. -Leo Tolstoy, Anna Karenina
Arabizi across Three Different Generations of Arab Users Living Abroad: A Case Study

The study explores the sociolinguistic phenomenon, Arabizi, from a new perspective by investigating the case of three Arabizi users coming from different generations: Azer (42 years old), Nada (29 years old), and Mira (21 years old). The main research question of the study is how generational differences between Arabizi users shape their experiences with Arabizi in terms of 1) how they learned it, 2) how they currently use it, 3) how they perceive its effect on the Arabic language, 4) their attitude towards its users, 5) and their expectations for its future use. Semi-structured interviews were employed to gain a deeper insight into participants’ experiences with Arabizi, taking into consideration their linguistic, educational, work, and social backgrounds. The significance of this case study lies in how Arabizi users’ surroundings and life experiences influence their linguistic choices and perceptions. Findings provide insight into how Arabizi is considered advantageous compared to using English language. It is also perceived to be a phase for youngsters that will subside in the future as technology develops. The study concludes with recommendations for future research on how Arabizi transforms as its users grow older.
Efficient compound values in virtual machines

Compound values are not universally supported in virtual machine (VM)-based programming systems and languages. However, providing data structures with value characteristics can be beneficial. On one hand, programming systems and languages can adequately represent physical quantities with compound values and avoid inconsistencies, for example, in representation of large numbers. On the other hand, just-in-time (JIT) compilers, which are often found in VMs, can rely on the fact that compound values are immutable, which is an important property in optimizing programs. Considering this, compound values have an optimization potential that can be put to use by implementing them in VMs in a way that is efficient in memory usage and execution time. Yet, optimized compound values in VMs face certain challenges: to maintain consistency, it should not be observable by the program whether compound values are represented in an optimized way by a VM; an optimization should take into account, that the usage of compound values can exhibit certain patterns at run-time; and that necessary value-incompatible properties due to implementation restrictions should be reduced.We propose a technique to detect and compress common patterns of compound value usage at run-time to improve memory usage and execution speed. Our approach identifies patterns of frequent compound value references and introduces abbreviated forms for them. Thus, it is possible to store multiple inter-referenced compound values in an inlined memory representation, reducing the overhead of metadata and object references. We extend our approach by a notion of limited mutability, using cells that act as barriers for our approach and provide a location for shared, mutable access with the possibility of type specialization. We devise an extension to our approach that allows us to express automatic unboxing of boxed primitive data types in terms of our initial technique. We show that our approach is versatile enough to express another optimization technique that relies on values, such as Booleans, that are unique throughout a programming system. Furthermore, we demonstrate how to re-use learned usage patterns and optimizations across program runs, thus reducing the performance impact of pattern recognition.We show in a best-case prototype that the implementation of our approach is feasible and can also be applied to general purpose programming systems, namely implementations of the Racket language and Squeak/Smalltalk. In several micro-benchmarks, we found that our approach can effectively reduce memory consumption and improve execution speed.
The Rise of UX and How It Drives XR User Adoption

This chapter is dedicated to the economic context of UX and XR. What does UX mean for the game industry, and how has the digital landscape changed that game engine toolsets and frameworks became more important for the success of digital XR products? What are the future-proof use cases and emerging standards from recent VR history, and what design philosophy is most relevant for the future role of XR designers?