Reviewerly/exHarmony huggingface dataset:
Recent Graph Neural Networks: A Survey

Graph neural networks (GNNs) are an extension of deep neural networks based on graph data, and have been widely used in various fields. However, due to the disadvantages of the existing GNN architecture, GNN is limited in many fields. Therefore, a large number of researchers have devoted their energy to the optimization of GNN. In this work, we provide a comprehensive survey of graph-structure optimization methods involving GNNs in various problems, and show the recent optimization methods comprehensively. We compare the literature on the basis of the taxonomy of the optimization direction, optimization effect and algorithm itself. Finally, we discuss several future research directions and related open problems.
Consensus algorithms in blockchain technology: A comparative study

Blockchain, nowadays, is a fastest growing distributed and secured technology that can be used in various fields. Bitcoin, that works on the PoW (Proof of Work) Consensus mechanism and commenced its services in 2009, is the most successful crypto currency today. The idea of Blockchain emerged out in 1991 by Stuart Haber and W. Scott Stornetta that a technology can run on a peer to peer network without any centralized authority. However it took almost 18 years to develop first Blockchain based application. In this type of network, each node contains the copy of Blockchain transactions that has been added to the system after validation. Consensus is a general agreement that has to be done between two parties in order to achieve an output. This mechanism guarantees that all the nodes in network are well coordinated and any illegal transaction must not occur in the system. Every Blockchain network uses different Consensus mechanism as per its requirements. In case of Blockchain technology, 51% of the present network nodes need to be agreed according to the contract in order to achieve the consensus. If a malicious node tries to control the network then it needs to gain the faith of 51% of the networks nodes that however is impossible. The main motive of Consensus mechanism is to secure the network and control any unauthorized activity that could be harmful for the network. Miners are the nodes that validate every new transaction that has to be linked in Blockchain. The consensus mechanism verifies that the block added in Blockchain is validated by the miners and a copy of this is distributed among all other nodes. Consensus algorithm becomes the backbone of a Blockchain network as it plays a major role in appending a block in a chain and ensures security. Consensus mechanisms can be differentiated in terms of difficulty level of computation, scalability, throughput, security and efficacy.
Research on the role of higher education in regional economic development

In order to achieve the overall goal of harmonious development of higher education and regional economy, this paper conducts an in-depth study of the role of higher education in regional economic development.First, it systematically analyzes the current situation of higher education and regional economic development.Secondly, it expounds the significance of colleges and universities in promoting local economic and social development under the current historical background from three aspects.Under the historical background of knowledge economy, the local economy and society are gradually transformed, and colleges and universities provide them with talent training and intellectual security; Colleges and universities will gradually form new growth points for local economic and social development in the process of their construction; Through higher education, we should train technicians with high moral quality and create an excellent humanistic environment to promote the healthy development of the socialist market economy.Through the in-depth study of the impact of the construction of colleges and universities on local economic and social development, we can improve the quality of the construction of colleges and universities, and then promote the development of local economic and social progress.
The Quality of Clustering Data Containing Outliers

This article evaluates the efficiency and performance of both clustering algorithms: an agglomerative hierarchical clustering AHC (with various linkage options and distance measures) and the $$K-Means$$ algorithm. We assess the quality of clustering using Davies-Bouldin and Dunn cluster validity indices. Our goal is to compare and analyze outlier detection algorithms depending on the applied clustering algorithm. We also wanted to verify whether the quality of clusters without outliers is higher than of those with outliers. In our research, we compare the LOF (Local Outlier Factor) and COF (Connectivity-based Outlier Factor) algorithms for detecting outliers (selecting $$1\%$$ , $$5\%$$ , and 10% of the most outlier instances in a given dataset). Next, we analyze how clustering quality has improved after excluding such outliers. In the experiments, three real datasets were used with a different number of instances. We wanted to investigate whether it is essential what clustering algorithm and outlier detection method we will use? Our goal was to check whether the clustering parameters impact the obtained clustering results. To the best of our knowledge, no research would combine these issues in one study.
Information Frictions, Belief Updating and Internal Migration: Evidence from Ghana and Uganda

Information frictions about the benefits of migration can lead to inefficient migration choices. We study the effects of a randomly assigned information treatment about regional income differentials in Ghana and Uganda to learn about participants' belief updating and subsequent changes in migration intentions and destination preferences. Participants react to the provided information by correcting their destination preferences towards regions with higher incomes, whereas their intent to migrate changes less. Participants' belief updating follows an asymmetric process restricted to individuals who initially underestimated regional differentials. The results suggest that income differentials matter for where to and less whether to migrate.
Modality Weighting in Multimodal Variational Autoencoders

Learning the same subject from different sources is known as multimodal learning in the machine learning literature. In this approach some of the modalities may be more reliable than others and we may want to learn them more to a better understanding of the subject. In this study, we propose weighting the reliable modalities in multimodal learning process. We introduce both weighting with fixed coefficients and several methods for weighting automatically. We compare the proposed methods with classical methods on artificial Gaussian data sets and MNIST/FashionMNIST data sets and we have obtained significiant results.
PDAM Performance Clustering using K-Means

To increase the coverage and quality of drinking water services, PDAM conditions must be healthy to operate the Drinking Water Supply System (SPAM) effectively and efficiently through strong PDAM internal management. The evaluation of the performance of the PDAM's SPAM implementation annually produces PDAMs that have the performance of Healthy, Unhealthy, and Sick. The first thing to do is to determine the prioritized areas needed for further handling and improvement of PDAM performance. Clustering the provinces in Indonesia based on the categories of Healthy, Unhealthy, and Sick can solve this problem. The researcher proposes a K-Means model that uses the Square Euclidean Distance. The dataset used is PDAM Performance data sourced from the Agency for the Improvement of the Implementation of the Drinking Water Supply System (BPPSPAM) from the Ministry of Public Works and Public Housing website. The results show that the resulting model can classify PDAM performance between the features of Healthy with Unhealthy, Unhealthy with Sick, and Healthy with Sick. The classification looks more distributed in the relationship between the Unhealthy and Sick features. In cluster I, there are 14 data or provinces; in cluster II, there are ten provinces, and four provinces are in cluster III. So the total number of provinces included in the cluster is 25, and nine provinces are not included in any cluster.
Introducing a Gold Standard Corpus from Young Multilinguals for the Evaluation of Automatic UD-PoS Taggers for Italian

Part-of-speech (PoS) tagging constitutes a common task in Natural Language Processing (NLP) given its widespread applicability. However, with the advance of new information technologies and language variation, the contents and methods for PoS-tagging have changed. The majority of Italian existing data for this task originate from standard texts, where language use is far from multifaceted informal real-life situations. Automatic PoS-tagging models trained with such data do not perform reliably on non-standard language, like social media content or language learners’ texts. Our aim is to provide additional training and evaluation data from language learners tagged in Universal Dependencies (UD), as well as testing current automatic PoS-tagging systems and evaluating their performance on such data. We use Italian texts from a multilingual corpus of young language learners, LEONIDE, to create a tagged gold standard for evaluating UD PoS-tagging performance on non-standard language. With the 3.7 version of Stanza, a Python NLP package, we apply available automatic PoS-taggers, namely ISDT, ParTUT, POSTWITA, TWITTIRÒ and VIT, trained with diversified data, on our dataset. Our results show that the above taggers, trained on non-standard data or multilingual treebanks, can achieve up to 95% of accuracy on young multilingual learner data, if combined.
Dynamic-GTN: Learning an Node Efficient Embedding in Dynamic Graph with Transformer

Graph Transformer Networks (GTN) use an attention mechanism to learn the node representation in a static graph and achieves state-of-the-art results on several graph learning tasks. However, due to the computation complexity of the attention operation, GTNs are not applicable to dynamic graphs. In this paper, we propose the Dynamic-GTN model which is designed to learn the node embedding in a continous-time dynamic graph. The Dynamic-GTN extends the attention mechanism in a standard GTN to include temporal information of recent node interactions. Based on temporal patterns interaction between nodes, the Dynamic-GTN employs an node sampling step to reduce the number of attention operations in the dynamic graph. We evaluate our model on three benchmark datasets for learning node embedding in dynamic graphs. The results show that the Dynamic-GTN has better accuracy than the state-of-the-art of Graph Neural Networks on both transductive and inductive graph learning tasks.
An Overview of the Development of Speaker Recognition Techniques for Various Applications

Speech Enhancement (SE) is a significant research issue in audio signal processing where the goal is to enhance the clarity and quality of speech signals corrupted by noise. Because of its different applications, it becomes a compelling research topic nowadays. The focus of this paper is dedicated to one of these applications which is Speaker recognition. In this paper, the fundamentals and applications of speaker recognition are discussed. A brief study on the performance and the recognition accuracy of different speaker modeling techniques has been conducted. Furthermore, while there have been several studies about the technologies used in speaker recognition, there have been few studies about the applications of speaker recognition, and none of them considers combining or linking the applications and technology in the same study. This overview demonstrates the various technologies that can be used to achieve speaker recognition applications. It aims to give a new perspective of the existing technologies uses in various applications. The paper concludes with discussions on future trends and research opportunities in this area.
On The Neutrosophic Formula of Some Matrix Equations Derived from Data Mining Theory and Control Systems

This paper is dedicated to studying the neutrosophic formula of some famous matrix equations used in theoretical data mining algorithms and control systems by using neutrosophic matrices and refined neutrosophic matrices over neutrosophic real fields. On the other hand, we concentrate on the neutrosophic formula of the Sylvester equation, and Lyapunov equation, where we study their formulas and properties in terms of theorems in the neutrosophic real number field and refined real number field. Also, we illustrate many different examples to clarify the validity of our work.
Formation of a modern digital educational environment: interdisciplinary approach to research

The purpose of the study is to take an integrated approach to studying the process of methodological development of digitalization in the education system. As a result, the formed approaches to the study of the development of design and research activities of students in a digital format allow us to determine the strategy, to see the necessary tasks and problems of this process as one of the factors in the innovative development of society as a whole. The obtained materials, which assess the nature of the professional needs of the pedagogical community in the field of information and communication relations, make it possible to form the necessary dynamics of the process and didactic conditions for the introduction of digital technologies in the educational process. The findings allow us to reveal not only the methodological, but also the subjective component of the introduction of innovative technologies in the research, cognitive activity of students in the framework of the implementation of additional education.
Design of Secured Lightweight PRNG Circuit using LFSR for Portable IoT Devices

This paper proposes the pseudo random number generator (PRNG) circuit with three linear feedback shift register (LFSR) design, which generates cryptographically more secured pseudo random number than a conventional LFSR system. In this configuration, the three different LFSRs (4, 5 and 7-bit) are triggered with same positive edged clock pulse. A 1-bit comparator is employed as intermediate stage to compare the outputs of 4 and 5-bit LFSRs. The subsequent output of the 1-bit comparator has been XORed with the feedback XOR output of the 7-bit LFSR. This introduces the desired inequality in recurrence relations of the three LFSRs used to enhance the overall linear complexity of the proposed LFSR circuit. The power consumption of the proposed compact LFSR circuit is less than the conventional 16-bit LFSR.
Visual Security Evaluation of Perceptually Encrypted Images based on Multi-Task Learning

Over past decades, many image encryption algorithms have been proposed, among which we can cite the perceptual/selective encryption methods which have attracted wide attention. Such methods allow for adjusting the scrambling intensity, it is therefore essential to have a reliable visual security metric to adjust the scrambling intensity on the one hand and to evaluate the visual security of encrypted images on the other hand. Usually, these tasks are performed based on classical randomness-based measures or image quality assessment metrics. However, these methods have shown their inadequacy as a visual security metric, as they do not address content intelligibility, which represents an essential security requirement. Moreover, these methods are either dedicated to the prediction of visual security (VS) or visual quality (VQ), but not both. In this paper, we propose a no-reference (NR) visual security metric for perceptually encrypted images based on deep multi-task learning, which we dub the Multi-Task Visual Security (MTVS) metric. The proposed metric consists of one shared convolutional neural network (CNN) followed by two separate sub-networks of fully-connected (FC) layers, where one sub-network is responsible for predicting the VS score, while the other is for predicting the VQ score. Experiments were performed on two publicly perceptually encrypted image databases and the results show that the proposed metric yields superior performance on both VS and VQ prediction tasks. The source code and models are available at: https://github.com/Mamadou-Keita/MTVS.
MicroStream vs. JPA: An Empirical Investigation

MicroStream is a new in-memory data engine for Java applications. It directly stores the Java object graph in an optimized way, removing the burden of having to map data from the Java object model to the relational data model and vice versa, a problem well known as the impedance mismatch. Its vendor claims that their product outperforms JPA-based systems realized with Hibernate. They furthermore argue that it is well-suited for implementing microservices in a cloud-native way where each service complies with the decentralized data management principle of microservices. Our work empirically assessed the performance of MicroStream by implementing two applications. The first one is a modified version of MicroStream’s BookStore performance demo application. We used it to reproduce the data the MicroStream developers used as backing for their performance claims. The second application is an OLTP system based on the TPC-C benchmark specification. MicroStream does not provide any sophisticated features for concurrent data access management. Therefore, we created two distinct MicroStream-based approaches for our OLTP application. For the first solution, we used a third-party transaction management system called JACIS. The second solution relies on structured modelling and Java 1.0 concurrency concepts. Our results show that MicroStream is indeed up to 427 times faster when comparing the service execution time on the server with the fastest JPA transaction. From a user’s perspective, where network overhead, scheduling etc. impact the overall server response time, MicroStream is still up to 47% faster than a comparable JPA-based solution. Furthermore, we implemented concurrent data access by using an approach based on structured modelling to handle lock granularity and deadlocks.
Analysing Foreground Segmentation in Deep Learning Based Depth Estimation on Free-Viewpoint Video Systems

Volumetric video acquisition systems enable realistic virtual experiences such as Free-Viewpoint Video (FVV). Stereo matching is a well known way of obtaining this volumetric information as depth images, calculating the disparity be-tween two stereo color images. On these applications, the background of the scene captured is static and does not change, so foreground information is much more valuable. We propose adding foreground segmentation to help learning based algorithms, such as deep learning models, improve results previously obtained. We utilized the framework De-tectron2 to model foreground segmentation by detecting people. Additionally, we built a large stereo dataset focused on FVV systems. Finally, we modified a successful deep learning model from the state-of-the-art, CREStereo, to add foreground segmentation and performed supervised training on it to estimate disparity, obtaining promising results.
Towards Regulated Deep Learning

Regulation of Multi-Agent Systems (MAS) and Declarative Electronic Institutions (DEIs) was a multidisciplinary research topic of the past decade involving (Physical and Software) Agents and Law since the beginning, but recently evolved towards News-claimed Robot Lawyer since 2016. One of these first proposals of restricting the behaviour of Software Agents was Electronic Institutions.However, with the recent reformulation of Artificial Neural Networks (ANNs) as Deep Learning (DL), Security, Privacy, Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community.Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of Artificial Neural Networks as Agent-based Training of a special type of regulated Artificial Neural Network that we call Institutional Neural Network (INN).The main purpose of this paper is to bring attention to Artificial Teaching (AT) and to give a tentative answer showing a proof-of-concept implementation of Regulated Deep Learning (RDL). This paper introduces the former concept and provides $\mathcal{I}$, a language previously used to model declaratively and extend Electronic Institutions, as a means to regulate the execution of Artificial Neural Networks and their interactions with Artificial Teachers (ATs).
ANALISIS EFEKTIVITAS PENGGUNAAN SISTEM INFORMASI AKUNTANSI (APLIKASI PASSION) TERHADAP PRODUK GADAI KCA PADA PT. PEGADAIAN PERSERO CABANG NAGOYA

This research aims to find out the Effectiveness of Accounting Information System for KCA Pawn Services at PT. Pegadaian Persero Nagoya Branch. This research uses a quantitative descriptive method. With data collection techniques in the form of primary and secondary, by means of observation, interview, and documentation. The results of this research show that the accounting information system is applied by PT. Pegadaian Pesero Nagoya Branch has been running effectively as seen from several components to support the intelligence of the information system, namely related parts (HR), documents used, accounting records used, loan dispossession procedures provided and rent modal at pawnshops. Admission procedures from transfers at pawnshops, lelang admission and sales procedures on pawnshops as well as turnover income from KCA pawns.
A Systematic Review of Research on the Use and Impact of Technology for Learning Chinese

In light of technological development enforced by the Covid-19 pandemic, learning Chinese has become more digitalised. Confucius institutes went online and now follow 2021 to 2025 Action Plans for the Construction of Teaching Resources for International Chinese Education and International Chinese Online Education. New ways of learning Chinese emerged, such as educational games and intelligent tutoring systems (ITS), some of them based on artificial intelligence. The aim of this systematic review is to examine recent (from 2017 to 2022) research published in ScienceDirect and Scopus databases on the use and impact of educational games and ITS in Chinese language learning. A total of 29 selected studies were analysed. Based on the overall findings, games and ITS are effective tools for Chinese learning, that impact studentsâ€™ motivation, self-efficacy progress, and learning satisfaction. However, more in-depth research should explore how games and ITS can best be implemented to teach Chinese to foreigners.
Demonstration of a Polarization-encoding Quantum Key Distribution System

In this work, a fully functional polarization-encoding quantum key distribution system (QKD) is demonstrated. At the transmitter side, the polarization states are prepared using an algorithm to determine the voltages to be applied on the electronic polarization controller (EPC), to generate up to four polarization states. At the receiver, a second EPC is used to perform the basis alignment. The symbol synchronization is achieved using a reference optical signal that is wavelength division multiplexed with the quantum signal which carries the polarization-encoded qubit. Frame synchronization is achieved at a protocol level. The presented QKD system works at a repetition rate of 500 Hz, with an average quantum bit error rate of 1.35%.