Reviewerly/exHarmony huggingface dataset:
A two-part multi-algorithm concurrency control optimization strategy for distributed database systems

In this paper, we propose a novel holistic approach to address the issues of concurrency control after an exhaustive examination of the problem and the various forms it can transpire. The proposed strategy was formulated depending on different perspectives that are based on exploring a wide range of algorithms, methods, and strategies proposed in practice and theory that attempted to address the problem and its forms, but only partially succeeded in doing so. Here we proposed a two-part holistic strategy to optimize concurrency control in distributed environments that address a wide range of concurrency control anomalies by taking advantage of several concurrency control algorithms' strengths while minimizing their weaknesses. The novelty of our approach transpires from two interconnected parts that can be applied regardless of the type of distributed database environment. The first is a structured tier-based data classification system based on data sensitivity with respect to serializability requirements and ranges from strict to very relaxed forms of serializability constraints. The second is a concurrency management algorithm that allocates the appropriate concurrency control algorithm to each transaction depending on the type of transaction and/or type of data being accessed from the aforementioned tier-based classification method. Our proposed method also incorporates a priority allocation mechanism within the concurrency management algorithm. Priority is allocated to different tier transactions depending on the tier's level, which in turn reflects data importance and sensitivity. Although our proposed strategy remains an algorithmic approach as we encountered various challenges regarding performance testing of a novel multi-algorithm approach for handling concurrency control in distributed database systems. However, future work involves testing the performance of our proposed strategy either through real-time systems after considerable adjustments or by constructing an appropriate customized simulation framework. Finally, the potentials of the strategy presented here are very promising, hence, we recommend as we are also optimistic that other scholars are encouraged to further exploit the concept of using multiple concurrency control algorithms within the same distributed database environment.
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee

Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be learned using deep kernel survival models. In this paper, we present a new deep kernel survival model called a survival kernet, which scales to large datasets in a manner that is amenable to model interpretation and also theoretical analysis. Specifically, the training data are partitioned into clusters based on a recently developed training set compression scheme for classification and regression called kernel netting that we extend to the survival analysis setting. At test time, each data point is represented as a weighted combination of these clusters, and each such cluster can be visualized. For a special case of survival kernets, we establish a finite-sample error bound on predicted survival distributions that is, up to a log factor, optimal. Whereas scalability at test time is achieved using the aforementioned kernel netting compression strategy, scalability during training is achieved by a warm-start procedure based on tree ensembles such as XGBoost and a heuristic approach to accelerating neural architecture search. On four standard survival analysis datasets of varying sizes (up to roughly 3 million data points), we show that survival kernets are highly competitive compared to various baselines tested in terms of time-dependent concordance index. Our code is available at: https://github.com/georgehc/survival-kernets
Effective method for detecting error causes from incoherent biological ontologies

Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies based on four patterns. Each pattern is represented as a directed graph and the depth-first search strategy is used to find the axiom paths relevant to the MinAs of the unsatisfiable class. However, DOBP is inefficient when a debugging large incoherent ontology with a lot of unsatisfiable classes. To solve the problem, we first extract a module responsible for the erroneous classes and then compute the MinAs based on the extracted module. The basic idea of module extraction is that rather than computing MinAs based on the original ontology O, they are computed based on a module M extracted from O. M provides a smaller search space than O because M is considerably smaller than O. The experimental results on biological ontologies show that the module extracted using the Module-DOBP method is smaller than the original ontology. Lastly, our proposed approach optimized with the module extraction algorithm is more efficient than the DOBP method both for large-scale ontologies and numerous unsatisfiable classes.
4-Weighted Fractional Fourier Transform based Multiple Image Encryption Approach with PAN

In this manuscript, a new encryption approach for multiple images is proposed based on 4-weighted fractionalfrequency transform (4-WFRFT) domain. First, the low frequency-components of all the images are obtained byapplying Fourier Transform on each image, which positioned at corner position of image, shifted to the centralposition. Low-frequency component of each individual image is then scrambled with help of Arnold cat mapwith its parameters and combined all scrambled image to form a single image with the same size that of originalimage which is now ready for encryption process. Here, parameters of Arnold cat map and transform order of 4-WFRFT treated as secret keys which are converted from Permanent Account Number (PAN) of authorize person.The encrypted image information generated by authorize person can be recovered by applying PAN at receiverside.
FOLLOW-UP IN EDUCATION OF RESPONSIBILITY IN SENIOR PRESCHOOL AND JUNIOR SCHOOLCHILDREN

The author describes the issues of effective implementation of continuity in the education of responsibility for children in preschools and primary schools.
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board at the time of submission.
Survey of Streaming Clustering Algorithms in Machine Learning on Big Data Architecture

Machine learning is becoming increasingly popular in a range of fields. Big data helps machine learning algorithms better timely and accurate recommendations than ever before. Machine learning big data phases and subsystems point the way to comparable issues and threats, as well as to associated research in various of previously new of unexplored areas. In latest days, data stream mining has become a popular research topic. The biggest challenge in streaming data mining is extracting important information directly from a vast, persistent, and changeable data stream in just one scan. Clustering is a powerful method for resolving this issue. Financial transactions, electronic communications, and other fields can benefit from data stream clustering. This research examines approaches for synthesizing enormous data streams, including projection clusters for high-dimensional data, scalability, and spreading computing, as well as the issues of big data and machine learning. The data abstraction and regular features of learning streams, such as abstraction wander, data flow system, and outlier observation, are discussed in this study. AutoCloud, which seems supported by previous inform prospect of typicality with eccentricity data science, will be used to discover deviations but instead resolve them using the support clustering approach, which is the process of computing difficulty and clustering exactness, will be detailed in this article. We present the MLAutoCloud algorithm, which will be used in machine learning PySpark frameworks. Implement the MLAutoCloud algorithm in the future to tackle the AutoCloud algorithm’s problems.
Acquiring the database “Internet resources in culture and arts” at the Russian State Art Library

The author discusses acquiring and developing the database “Internet resources in culture and arts” at the Russian State Art Library. The networked online resources are highly demanded by the users in science, academic and professional sphere, as well as by expert librarians. The stages of database design are specified; defining the subject scope, selection principles and search strategies are defined. The role of recommendations included into European MINERVA project “European MINERVA project «Cultural web-User Interaction” (2008). The criteria for selecting Internet-resources for cataloguing are analyzed; the description formats are evaluated, i. e. the Dublin Core and Russian communication format of machine-readable bibliographic records RUSMARC, with the preference given to the later. The difficulties in describing resources and filling in RUSMARC fields are identified. The general and specific problems of cataloging Internet resources were solved; the principles of constructing bibliographic record of online digital resource “Internet resources in culture and arts” were developed. The significance of regular audit of the information resource, its relevancy and accessibility is emphasized. The examples of queries to the database are provided. The content of the resources included into the database “Internet resources in culture and arts” expands the study field through adding the wider range of document sources.
Quantum network coding via time-bin multiplexing

<p indent="0mm">Quantum network coding is used to overcome the congestion problem in quantum networks, which can effectively improve the transmission efficiency and total throughput of quantum networks. Here, we propose a time-bin complexing network coding method for crossly transmitting two arbitrary single-qubit states over the butterfly network. To solve the congestion problem, a single photon with polarization and time-bin degrees of freedom acts as a bridge to entangle the two-dimensional quantum memories of two intermediate nodes in a heralded way. Finally, quantum 2-pair unicast communication is completed based on quantum teleportation and the heralded double entangled channels. Concerning dishonest intermediate nodes being concerned, a security-enhanced quantum 2-pair unicast communication protocol is presented. Security and performance are analyzed, and the result shows that the improved protocol is secure against the general entangle-measure attack of intermediate nodes. It can be extended to multiple unicast communication over a more general quantum network by exploiting the time-bin complexing technique.
Peningkatan Literasi Teknologi Informasi Bagi Perangkat Kelurahan di Lingkungan Kecamatan Ilir Barat Dua Kota Palembang

Abstract: Purpose: to improve information technology literacy of employees in Ilir Barat Dua Subdistrict, Palembang City, especially regarding the use of software applications supporting office administration especially word processing, data processing, and presentation applications in order to improve employee performance. Method: The training was carried out face-to-face, in the form of demonstrations on how to use the menus in each application, questions/answers, and practice using application programs. The target of this activity was the village apparatus in the Ilir Barat Dua District, Palembang City, as many as 36 people. The training was carried out in the Computer Laboratory managed by the Quantitative and Qualitative Laboratory of the Faculty of Economics, Sriwijaya University. Results: After participating in this training, participants realized that this training contributed to improving their skills in using computer applications. Limitations: this activity only covers the use of office administration applications. Other materials should also be given, such as how to find data on the Internet Contribution: after participating in this training, the skills of employees have increased in using of computer applications to support office administration, so that they can contribute to improving employee performance. Keywords: 1. Information technology literacy 2. training 3. employee performance
SIRBİSTAN CUMHURİYETİ’NDE BİLGİ TOPLUMU VE BİLGİ GÜVENLİĞİ

Cybercrime or the computer-related crime is the most widespread form of transnational crime, which is in its social and economic characteristics significantly different from traditional and organized crime. Cyberspace offers countless opportunities for economic development, social interaction, and political cooperation, but also provides tools for illegal surveillance, personal data collection, influencing democratic processes, committing crimes, and exchanging numerous ways and methods of warfare. This paper provides an overview of legal documents in the Republic of Serbia related to information security, data security, and deviant behavior in cyberspace, with special emphasis on the analysis of the Strategy for Information Society Development and Information Security in the Republic of Serbia for 2021-2026. Using the criminological approach, this paper focuses on legislation concerning existing criminal offenses related to cybercrime and various forms of other criminal activities, but also on the international cooperation that Serbia achieves in the field of information and cyber security.
A Practical Study on Value-Added Assessment in Primary English Education

As a programmatic document to guide the reform of educational assessment, the “General Plan for Deepening the Reform of Educational Assessment in the New Era” clearly points out the requirement for exploring value-added assessment [1]. In the process of exploring, Tennessee Value-Added Assessment System (TVAAS), which was implemented in Tennessee, United States in 1992, has a certain referential significance to the practice of assessment reform in primary English education [2]. This study aims to build a value-added assessment model in line with China’s learning conditions by using big data and carry out pilot experiments in order to promote the development of educational assessment in primary schools.
The Study on Transitivity of Scientific Fiction: The Time Machine as an Example

Corpus as a tool has been introduced in literature and linguistics for many years, allowing linguistic features and authorial style more visible and appreciable to readers. Based on Biber’s MD/MF method and semantic field, this research is interested in linguistic features of science fiction and finds that transitivity is what makes science fiction distinguishable from science articles and novels. The current research is corpus-based, and corpora included in this research consist of Silence Spring (science article), The Time Machine (science fiction), and Jane Eyre (romance novel). This paper takes aims to investigate the 'transitivity' of science fiction by comparing three novels of different types. By means of the MD/MF method, the paper has found that transitivity in science fiction is achieved by TTR, mean word length, and mean sentence length. These findings may direct potential writers to focus on linguistic features worthy of notice, and readers can have a better understanding when they are reading novels.
Stability-Guided Multi-Guide Particle Swarm Optimization

This paper proposes a multi-guide particle swarm optimization (MGPSO) algorithm which does not require tuning of its control parameters. Control parameter values are randomly sampled to satisfy theoretically derived stability conditions, eliminating the need for computatinally expensive parameter tuning. In addition, the feasibility of utilizing dynamically decreasing tournament sizes in the selection of the archive guide, as well as a ring neighbourhood topology, is investigated. The results show that random control parameter sampling is a viable alternative to static tuning, most notably when applied to higher numbers of objectives. However, the results show no clear benefit or detriment to utilizing dynamic tournament selection sizes and ring neighbourhood topologies.
Using business simulators as factor of improving quality of educational services in university

The rapid development of technologies and digitalization of all segments of the economy form new requirements of the business community for personnel, which obliges the education system to comply with modern trends and provide training for highly qualified specialists. The variety of methods and forms of training students of higher educational institutions provides an opportunity for variability in choosing the optimal way to organize the educational process, however, the active introduction of innovations in business processes actualizes the problem of insufficient practical orientation of university graduates to work in the real sector of the economy. There has been illustrated a learning pyramid (cone of experience, cone of retention) developed in the 1950s by an American scientist E. Dale, which displays the share of information perceived by a person during training as a percentage and the effectiveness of various teaching methods. In this connection, in this article, the authors consider the use of business simulations in the educational process as a way to improve the quality of educational services and increase the interest and motivation of the student body. It is substantiated that the use of this method contributes to an increase in interest in the educational process both on the part of the teaching staff and on the part of students who experience significant difficulties in mastering new material. There has been substantiated the importance of introducing the modern teaching methods into the activities of universities, as well as to identify the main problems of using business simulators in modern conditions. The results obtained are based on current scientific articles by Russian and foreign scientists who have studied the process of improving the quality of educational services in the digital economy, as well as innovative teaching methods in higher educational institutions. The application of the main tools for the digitalization of the educational process regulated by federal regulatory documents has been actualized.
PENGELOMPOKAN WILAYAH BERDASARKAN FAKTOR PENDUKUNG PENDIDIKAN DENGAN JUMLAH SEKOLAH DAN JUMLAH GURU MENGGUNAKAN ALGORITMA K-MEANS

Education is one field that has a role in creating human resources and national development. The national education system states that the government can ensure equal distribution of education, quality improvement, the relevance and efficiency of education management. The implementation of education in regions of Indonesia has obstacles, including in remote areas have not been evenly distributed in accessing education services. The provision of schools and teaching staff is a supporting factors for teaching and learning activities. All regions, including sub-districts in Cianjur Regency. One of the efforts for equity is to use the k-means method to classifysub-districts in Cianjur Regency in the provision of schools and teachers by the Government. The data used number of schools and teachers at the elementary school, junior high school, high school and vocation high school level in every sub-district in Cianjur Regerency in 2020. The results that have been researched show two clusters.Cluster 1 or an area that has several schools and the number of teachers lacked in Cianjur Regency consisting of 21 sub-districts and cluster 2 or an area had a better number of schools and the number of teachers than cluster 1 in Cianjur Regency consisting of 11 sub-districts.
Deep learning method for visual question answering in the digital radiology domain

Computer vision applications in the medical field are widespread, and language processing models have gained more and more interest as well. However, these two different tasks often go separately: disease or pathology detection is often based purely on image models, while for example patient notes are treated only from the natural language processing perspective. However, there is an important task between: given a medical image, describe what is inside it – organs, modality, pathology, location, and stage of the pathology, etc. This type of area falls into the so-called VQA area – Visual Question Answering. In this work, we concentrate on blending deep features extracted from image and language models into a single representation. A new method of feature fusion is proposed and shown to be superior in terms of accuracy compared to summation and concatenation methods. For the radiology image dataset VQA-2019 Med [1], the new method achieves 84.8 % compared to 82.2 % for other considered feature fusion methods. In addition to increased accuracy, the proposed model does not become more difficult to train as the number of unknown parameters does not increase, as compared with the simple addition operation for fusing features.
Single Phase to Ground Fault Location of Distribution Network Based on Combined-GAT

Background: At present, small current grounding system has been widely used in distribution network of China. Affected by the complex topology of the distribution network and other factors, single-phase grounding fault has become the most prone type of electrical short-circuit fault in China. Objective: Considering that the traditional fault selection and location methods are difficult to mine the effective information of fault quantity, a new method is proposed in this paper to achieve accurate fault location on the basis of ensuring timeliness. Methods: In this paper, the physical topology of the distribution network is regarded as a graph, the overhead lines and cables of the main equipment are regarded as the nodes in the graph, and the problem of fault node location is corresponding to the task of graph attention classification. Considering the average degree and homogeneity of the given network topology, an improved graph attention network is built to realize fault node location. Results: This paper verifies the effectiveness of the proposed model for fault location through simulation in PSCADA. In addition, the applicability of the proposed model in the case of changes in the distribution network structure is verified. It verifies that the proposed method achieves high positioning accuracy.