Reviewerly/exHarmony huggingface dataset:
Microsofts Prospect in Gaming Industry: Strengths, Weaknesses, Opportunities and Threats in Acquisition of Activision Blizzard

In January 2022, Microsoft stunned the public by announcing that it would acquire Activision Blizzard for $68.7 billion, a move that would radically change the gaming market. Through SWOT analysis, this paper analyzes the advantages and disadvantages of Microsoft and Blizzard, as well as the opportunities and threats brought by the acquisition. The study found that while Microsoft has mastered core technologies in cloud computing, it is in a weak position to compete for the top spot in the global gaming market, with xbox selling far fewer games and exclusives than its rivals SONY and Nintendo. Activision Blizzard is a leading game manufacturer with a mature game production studio and a large player community, which maintains efficient and high-quality game output all year round. The combination of Activision Blizzard and Microsoft will bring more game choices to Xbox players. This acquisition is conducive to the integration of the metacomverse industry chain and brings Microsoft a huge profit opportunity, but at the same time it faces the liquidity risk caused by the failure to complete the acquisition.
Impact of Poverty, Economic Growth, and Information and Communication Technology on Human Development

Human development is one of the main problems in developing countries, as seen from the relatively low Human Development Index (HDI) level. This study examines how poverty, economic growth, and information and communication technology (ICT) influenced the quality of human development in Indonesia in 2014–2020. The research method used was panel data regression analysis with cross-sectional observations of 34 provinces. The study results show that poverty has a negative and significant impact on the quality of human development in Indonesia. Meanwhile, ICT has a positive and significant impact on the quality of human development. There is no significant evidence of the effect of economic growth on the quality of human development. The results of this study are helpful for the central and regional governments in making policies related to human development in Indonesia. This finding implies that coordination of poverty reduction efforts between central and local governments is essential for human development. In addition, the government needs to pay attention to the quality of the distribution of ICT availability between regions to improve the quality of human development in Indonesia.
A Property Constrained Video Summarization Framework via Regret Minimization

Video summarization has become one of the most effective solutions for quickly understanding a large amount of video data. Video properties such as importance, diversity, representativeness and storyness have been widely adopted for summarization based on kinds of features of video frames. To fully exploit these properties, in this paper we propose a property constrained video summarization framework to output fixed-size summaries based on the concept of regret minimization which is popular in the database community for solving multi-criteria decision making problems.
Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data

Emotions are a person's response to an event. Emotions can be expressed verbally or nonverbally. Over time people can express their emotions through social media. Considering that emotion is a reflection of society's response, it is important to classify emotions in society to find out the community's response as information for consideration in decision-making. This study is aimed to identify and analyze the datasets, methods, and evaluation metrics that are being used in the classification of emotional texts in textual data from research data from 2013 to 2022. Based on the inclusion and exclusion design in selecting literature, a total of 50 kinds of literature were used in extracting and synthesizing data. Analysis of the data shows that out of 50 pieces of literature, there are 36 works of literature that use public datasets while 14 kinds of literature use private datasets. In the method of developing models for classifying, the SVM and Naive Bayes models are the most popular among the other models. In evaluating the model, the F-measure or F1-score metric is the most widely used metric compared to other metrics. There are three main contributions identified in this study, namely methods, models, and evaluation
Low-light image enhancement by two-stream contrastive learning in both spatial and frequency domains (Erratum)

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.
STUDY ON THE PERFORMANCE OF SOME PARAMETRIC PROPORTIONAL HAZARD MODELS AND SEMI-PARAMETRIC MODEL IN THE ANALYSIS OF BREAST CANCER DATA

A study is conducted on medical records of 416 breast cancer patients. Analysis was performed using the R software version R3.6.3, and the level of significance was set at 0.05. The work employed three models which were based on Exponential, Weibull and Cox Regression models. The Weibull proportional model (AIC=1959.038) was the most appropriate model among the considered models, based on the Akaike information criterion (AIC). Results of the best fitted model showed that the survival time of breast cancer patients is significantly affected by age, age at diagnosis, and treatment taken at 95%.
Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study

In the current digital era, information availability is abundant, and news serves as a primary source of up-to-date and reliable information for the public. However, with the increasing volume of information, a robust evaluation method is necessary to ensure accurate and dependable news labeling. This research employs a machine learning approach, utilizing three common classification algorithms: Naive Bayes, SVM, and Random Forest, to evaluate news labels based on their titles. The dataset utilized in this study is obtained from Jakarta AI Research and consists of 10,000 samples covering various news topics. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics to gain a comprehensive understanding of the classification algorithm's performance. The results of this research demonstrate that the SVM algorithm exhibits the best performance, achieving an accuracy rate of 92.92%. Random Forest follows with an accuracy rate of 91.21%, and Naive Bayes with an accuracy rate of 89.61%. These findings provide deep insights into the effectiveness of the machine learning approach in evaluating news labels based on their titles. Furthermore, the study highlights the importance of considering other evaluation metrics such as precision, recall, and F1-Score to obtain a more holistic understanding of the algorithm's performance. Further research is encouraged to involve additional classification algorithms and more diverse and extensive datasets to enhance the comprehension of news label evaluation comprehensively. Such endeavors can significantly contribute to the development of automated systems for classifying news with higher accuracy and reliability in the future
ASSESSMENT OF HEAVY METAL POLLUTION AND CONTAMINATION IN WASTE DUMPSITES WITHIN GUSAU AREA, NORTHWEST NIGERIA

The aims of this research work are to determine the concentrations of metals present in waste dump soils, to evaluate the metal contamination of the soils using the pollution indices and to identify whether the sources of heavy metals were natural and/or anthropogenic. Analysis of the soil was carried out using EDXRF technique. Three points were assessed from each three wastes dumpsites (Hira Dekodi, Low Cost and Old Market), using portable XRF equipment for the in-situ data acquisition. The data obtained were subjected to statistical analysis and environmental pollution index models. The data showed mean concentrations of the heavy metals analyzed in the following order Fe &gt; Zn &gt; Mn &gt; Cr &gt; Pb &gt; Ni. The mean concentrations of these three metals (Pb, Zn and Cu) exceeded their average crustal abundance, which is an indication of possible contamination. Environmental pollution models applied to the geochemical dataset revealed Zn, Pb and Cu as considerable/moderately contaminated, which generally indicated severe potential contamination in the investigated area. Other metals such as Mn, Cr, Ni and Fe showed low contamination factors. However, from the results of geo-accumulation/ecological risk index of the heavy metals, the study areas were unpolluted and indicated low ecological risk factors with respect to the analyzed parameters. The chemical assay revealed positive and significant relationship between Cu, Pb, Mn and Fe while Cr showed positive and significant relationship with Mn, Fe and Cu. However, Zn shows significant level of relationship only to Mn. Virtually, all the heavy metals showed...
PENGARUH VARIASI WAKTU SENTRIFUGASI NILAI HEMATOKRIT MENGGUNAKAN METODE MIKROHEMATOKRIT DI UNIVERSITAS BINAWAN

Hematocrit is the total volume of red blood cells in 100 ml of blood expressed as a percentage of blood volume. Hematocrit examination is needed to help diagnose diseases such as anemia and dengue hemorrhagic fever (DHF) to determine the total volume of red blood cells or erythrocytes. The normal value for hematocrit examination is 40-54% for males and 37-47% for females. The purpose of this study was to determine the effect of variations in centrifugation time on hematocrit values using the microhematocrit method. This type of research is quantitative using experimental method with cross sectional design. The method used for hematocrit examination is the microhematocrit method with variations in centrifugation time of 2 minutes, 3 minutes, 4 minutes, and 5 minutes as a control with a speed of 12,000 rpm. The respondents of this study were 35 respondents and the samples of this study were 35 venous blood samples taken from Binawan University students, Medical Laboratory Technology Study Program batch 2021 and 2022. The average result of the 2-minute centrifugation hematocrit value examination is 37.87%, 3-minute centrifugation is 37.57%, 4-minute centrifugation is 37.30%, and 5-minute centrifugation is 37.25%. The Wilcoxon test results show the Sig. (2-tailed) of 2-minute centrifugation compared with 5 minutes is 0.00 (&lt;0.05), 3-minute centrifugation compared with 5 minutes is 0.001 (&lt;0.05), 4-minute centrifugation compared with 5 minutes is 0.560 (&gt;0.05). The results of this study showed that hematocrit values with centrifugation times of 2 minutes and 3 minutes compared to 5 minutes had significant differences in hematocrit values.
PENERAPAN METODE TSAQIFA DALAM KERAMPILAN MEMBACA AL-QURAN DI DESA SEPAT, MASARAN, SRAGEN, JAWA TENGAH

Learning to read the Qur'an is actually not difficult, boring, or time-consuming. There are only three important stages that need to be mastered, which are mastering the Arabic letters (Hijaiyyah) and their variations, mastering the diacritical marks (harakat), and actively practicing them. Moreover, the Qur'an itself possesses miraculous and supra-rational energy. In the process of learning, we do not need to worry about difficulties, because Allah has guaranteed ease in learning the Qur'an, as stated in the Qur'an: "And We have certainly made the Qur'an easy for remembrance, so is there any who will remember?" (Surah al-Qamar: 17). The truth of Allah's promise can be proven by ourselves, as the Qur'an is indeed easy to learn for anyone who is willing to take it as a lesson. Research will be conducted on the implementation of the Tsaqifa Method, an alternative method for learning to read the Qur'an for adults who are not yet able to read, with only 5 meetings. This method was developed by Ustadz Umar Taqwim, S.Ag. and published by the Adz Dzikr Islamic Foundation, Center for Qur'anic Reading and Writing Learning (PPBTA) Tsaqifa Method in Magelang. This method is suitable for those who are busy and have limited time to learn the Qur'an. The aim of this research is to improve the skills of reading the Qur'an in Dukuh Wonorejo, Sepat Masaran Village, Sragen through the implementation of the Tsaqifa Method. Therefore, the title of this research is "The Implementation of the Tsaqifa Method in Improving Qur'anic Reading Skills in Sepat Masaran Village, Sragen." From the results of the assessment of the score range of Qur'anic reading training using the Tsaqifa method, it can be seen that the score range of 90-100 (fluent) was achieved by 4 participants, the score range of 80-90 (standard) was achieved by 4 participants, and the score range of 70-80 (satisfactory) was achieved by 18 participants. From this analysis, it can be concluded that the Tsaqifa method has an effect that is spread across a wide age range, from children to the elderly. Although there is variation in the participants' ability to read the Qur'an, comments from the trainer indicate that the majority of participants have shown progress and need further practice at home to improve their Qur'anic reading skills. Overall, the Tsaqifa method can be considered effective in helping participants understand and read the Qur'an, regardless of age and initial skill level of the participants. The improvements achieved by participants who have undergone this training can be seen from the comments of the trainer, which show that most participants have shown progress in their ability to read the Qur'an.
A Graph Neural Network Recommendation Based on Long- and Short-Term Preference

The recommendation system (RS) on the strength of Graph Neural Networks (GNN) perceives a user-item interaction graph after collecting all items the user has interacted with. Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession. However, user preferences are dynamic. With the passage of time and some trend guidance, users may generate some short-term preferences, which are more likely to lead to user-item interactions. A GNN recommendation based on long- and short-term preference (LSGNN) is proposed to address the above problems. LSGNN consists of four modules, using a GNN combined with the attention mechanism to extract long-term preference features, using Bidirectional Encoder Representation from Transformers (BERT) and the attention mechanism combined with Bi-Directional Gated Recurrent Unit (Bi-GRU) to extract short-term preference features, using Convolutional Neural Network (CNN) combined with the attention mechanism to add title and description representations of items, finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations. In experiments conducted on five publicly available datasets from Amazon, LSGNN is superior to state-of-the-art personalized recommendation techniques.
An approach to simulate basic concepts of mathematics with object-oriented programming

Traditional approaches to using data structures mainly focus on the improvement in algorithm efficiency but rarely take advantage of object-oriented programming that can simulate the definition of mathematic concepts in a way that is similar to human thinking mode, so traditional approaches cannot satisfy the need of simulating sets and their features and operations for mathematics studies. In this paper, the author pointed out the disadvantages of traditional ways, proposed a series of hypotheses describing the relationship between sets and classes such as inheriting and inclusion relationships and a way based on those hypotheses using features of object-oriented programming, and used C#, one of the best object-oriented programming languages, to simulate sets and their features. In addition, for each hypothesis, the author raised examples with C# codes that realize the theory in this paper, clearly showing the approach proposed in this paper and why it is both efficient and elegant.
Algorithmic applications and principles of superconducting quantum computers

Nowadays, with the continuous progress of science and technology, the role of quantum computers has received more attention and gradually become a part of national strategies. Many countries, such as the United States, China, and Japan, have invested a lot of resources in research and development, striving to make breakthroughs and take the lead in this field, in order to meet the coming of the future computing era. Superconducting quantum computers are the most widely used computers at the moment. Based on the introduction of Josephson junction, the physical principle of superconducting quantum computers, this paper analyses the advantages and disadvantages of multiple quantum bits as well as the algorithms of various quantum gates in superconducting quantum computers. Moreover, the author also looks forward to the development of the related technologies of superconducting quantum computers. Finally, corresponding suggestions are put forward for some current problems.
Pemanfaatan Online Platform dalam Pembelajaran Teknik Informatika

The aim of this article is to elucidate the benefits of utilizing online platforms in the teaching of information technology. We summarize the key findings within this article, encompassing crucial aspects such as global accessibility, learning flexibility, interactive education, global collaboration, content quality, and the comprehension of ethics and cybersecurity. The method employed in this article is a literature review and understanding based on current research and practices in information technology education. We gathered information from various relevant sources and compiled it into a comprehensive summary.This article synthesizes the foremost advantages of employing online platforms, provides examples of successful implementation, and addresses the challenges faced within the realm of information technology education. The conclusion drawn is that the utilization of online platforms in information technology education yields significant positive impacts, facilitating global access, fostering flexibility, promoting interactive learning, encouraging global collaboration, ensuring content quality, and enhancing cybersecurity awareness. The future of education in this field will continue to be interconnected, open, and responsive to technological advancements. This transformation brings about substantial positive changes in how we learn and teach.
INNOVATIVE TECHNOLOGIES IN HIGHER EDUCATION INSTITUTION

The article aim: define the concept of «innovation», conduct a comprehensive analysis of modern innovative educational technologies used to improve the quality of education, consider the legislative framework in the field of education and in the field of innovation. Methods applied: analysis of scientific literature, systematization and generalization of research materials, which allowed to identify the state of the problem of the use of innovative technologies in the modern educational process; pedagogical observation and systematization in order to obtain research results on the active introduction of innovative pedagogical technologies, methods and techniques in the higher education system. The classification of innovations is presented. The role of innovations in education in economic development is shown. Modern educational and pedagogical technologies are considered. The main directions of implementation of information and communication technologies in the field of education are shown. Innovative methods and educational technologies are analyzed: e-learning, blockchain, interactive learning technologies, VR, augmented reality technologies, multimedia technologies. The use of the described innovative technologies in teaching allows achieving the following results: to ensure the mastery of knowledge and skills by each student; to develop learning independence; to form a zone of immediate professional development that ensures further individual work on the educational program; to create conditions for each student to realize the personal content of the results of learning activities based on their self-control, self-assessment and self-correction; to reduce the number of errors in solving practical problems in other disciplines. Innovative technologies are an ordered set of actions, operations and procedures that ensure the achievement of a diagnosed and predicted result.
PENGARUH MODEL PEMBELAJARAN Two Stay Two Stray TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN PKn DI KELAS V SD NEGERI 094155 RAMBUNG MERAH

This research aims to determine how big the influence of the Two Stay Two Stray Learning Model is on the Learning Outcomes of Class V Students Theme 2 Sub-theme 1 Clean Air for Health on Civics learning at SDN 0941155 Rambulng Melrah. There are 2 hypotheses in this research, namely: (Ha) There is an influence of the Two Stay Two Stray model on student learning outcomes in Civics subjects in class V of SD Negeri 094155 Rambung Merah FY 2023/2024. (Ho) There is no influence of the Two Stay Two Stray model on student learning outcomes in Civics subjects in class V of SD Negeri 094155 Rambung Merah T.A 2023/2024. This research was carried out in the 2023/2024 academic year in October. This research method is an experiment with a pre-experimental design research type with a One-Group Pretest-Posttest Design research design. The sample for this research was 30 students consisting of 11 male students and 19 female students. The data collection techniques used are tests and documentation. The data was processed using the N-Gain test statistical analysis technique. Based on the normality test results obtained, the learning outcomes of class V students were normally distributed with the final value of the N-Gain test having a significance of 0.57 &gt; 0.05. From these results, it can be seen that Ha was accepted, which means that there is an influence of using the Two Stay Two Stray learning model on student learning outcomes in Civics subjects in Class V of SD Negeri 094155 Rambung Merah FY 2023/2024..
Enhanced Advanced Encryption Standard

The analysis of AES (Advanced Encryption Standard) including the byte substitution method, the permutation stage made by shifting the data matrix rows and the column mixing step, shows some of the weak points of this algorithm. We propose enhancing some features of AES by the mean of more powerful arrays and polynomials that make the algorithm more robust against various cryptographic attacks including the differential one. We analyze the algorithm over the Galois Field with 256 integer values, denoted as GF(256), and we propose to use Costas arrays for byte substitution and permutation, and a polynomial with primitive roots for mixing the columns of the data matrix, getting an enhanced version of the encryption algorithm, with better features and an increased degree of non-linearity.