Reviewerly/exHarmony huggingface dataset:
EXPLORING STUDENTS’ TRANSLATION STRATEGY ON THE GLIMPSE OF US SONG BY JOJI

Translating texts from one language to another is often used as a resolution to overcome many challenges nowadays, allowing individuals to understand content that they may not have been able to otherwise. In the case of song lyrics, translation can be particularly useful in helping individuals understand the meaning and emotion behind the words. This study explored the translation strategies employed by ten Translation and Interpretation (TI) students in translating the lyrics of Joji's Glimpse of Us song, and investigated their translation products whether they meet the criteria of good translation. Using a qualitative-descriptive research design, the study analyzed 23 sentences from the source text, finding that the most common strategy used was the use of a more general word (44%), followed by paraphrasing with unrelated words (21%) , by paraphrasing using related word (18%), by addition (9%), and omission (4%). The least common strategy was the use of loan words or loan words with explanations (3%). The study also evaluated the accuracy, naturalness, and clarity of the students' translation products based on Mona Baker's theory and the criteria of good translation proposed by Larson (1984) out of 230 total data collected, the study found 148 acceptable translations (64%) and 82 unacceptable translations (36%). These findings can inform future teaching practices in TI programs and contribute to the field of translation studies.
Distributed Shared Memory : A Page-Based Approach

In 1986, Kai Li's PhD thesis, "Shared Virtual Memory on Loosely Coupled Microprocessors," introduced the concept of Distributed Shared Memory (DSM) systems, which has since facilitated further research in this field. One approach to implementing DSM in distributed systems is the page-based method, which utilizes virtual memory techniques to map pages of a process' address space onto the physical memory of multiple nodes. The system automatically moves pages between nodes as needed, ensuring coherence and consistency for shared page updates. Page-based DSM offers several advantages, including improved performance due to reduced communication overhead and better data locality, simplified programming models that resemble centralized systems, and improved scalability by allowing additional nodes to join the system and expand memory capacity. However, its suitability for certain distributed systems depends on their specific requirements and characteristics. Page-based DSM has been applied in various distributed systems, including high- performance computing, cloud computing, and distributed databases. Nonetheless, remote memory access overheads, cache coherence issues, and the necessity of a well-connected network remain potential limitations of this approach. Key words: Distributed memory, Shared memory, Paging, Virtual memory, memory coherence, memory consistency
Blockchain-Based Approach for Tracking Global Criminals

Blockchain is the latest technology, which is now widely used in various fields due to its benefits such as higher tamper-proof security. Each transaction is stored in an immutable distributed ledger on each blockchain node. Criminal records are confidential and should be kept safe from hackers and intruders. Law enforcement agencies or investigative agencies can use these security recordings to analyze and detect criminal activity. Due to the lack of a global online system, Investigative agencies are having difficulty locating and researching the past of anonymous foreign criminal who is committing crimes abroad. This problem will only be solved if investigative agencies have a common global criminal database and applications to extract information. Investigation agencies, Copes, and other organizations can access global crime data using this technology on the distributed node of the blockchain in their nation. Authorities can instantly and efficiently access global criminal histories from local blockchain databases and identify the names and details of anonymous individuals in response to any foreigners suspected of engaging in unsocial activities in their country of origin. This will reduce the time and process of gathering information from the country where the suspect is located. The system would eliminate the possibility of falsification and tamper with criminal records. Key Words: Blockchain, light, Global Criminal, Criminal information
CROSS-BORDER CYBERCRIME

Electronic piracy is considered one of the electronic crimes sweeping our world today, and it is a crime of a material nature, which is represented in every illegal act or behavior related to any destination or in any way with computers and computer networks, that causes the victim to suffer a loss, and the perpetrator obtains or is able to obtain Any gain. These crimes often aim at stealing information in computer devices, or aim indirectly at the persons and parties concerned with that information. Crime of this kind has several names, including computer and Internet crimes, high-tech crimes, cybercrime, and white-collar crimes. Attacks are often against moral entities related to their strategic value, such as information stores, and this is the most important characteristic that distinguishes cybercrimes from other crimes. It relates to moral entities with material value or purely moral value or both together, and this is its basis without which it is not possible to imagine the existence of an electronic crime, and if it were not for this basis, it would be one of the ordinary crimes that are subject to the criminal law.
Lifelong Learning for Document Image Binarization: An Experimental Study

Binarization is one of the ubiquitous processes in the field of document image analysis. In recent years, there has been an increasing number of approaches based on deep learning, which in turn require labeled data of each document collection. In many real cases, however, the possible domains of document collections are not known in advance but appear incrementally. Adapting a model to accommodate new domains is known as lifelong (or continual) learning. In this scenario, neural networks suffer from catastrophic forgetting; i.e., when learning about a new domain, the knowledge from the previous ones is lost. This paper presents an experimental study of alternatives to prevent catastrophic forgetting in a continuous learning scenario for document binarization. We present results with different evaluation protocols considering four different document collections of heterogeneous features. Our results show that while there is no single approach that consistently outperforms the others, all of the proposed mechanisms help prevent catastrophic forgetting to some extent. Interestingly, we found that relatively simple mechanisms such as transfer learning tend to perform better on average than specifically designed strategies for continual learning.
Evaluating Domain Generalization in Kitchen Utensils Classification

The remarkable performance of deep learning models is heavily dependent on the availability of large and diverse amounts of training data and its correlation with the target application scenario. This is especially crucial in robotics, where the deployment environments often differ from the training ones. Domain generalization (DG) techniques investigate this problem by leveraging data from multiple source domains so that a trained model can generalize to unseen domains. In this work, we thoroughly evaluate the performance in the classification of kitchen utensils of several state-of-the-art DG methods. Extensive experiments on the seven domains that compose the Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation) dataset show that the effectiveness of some of the DG methods varies across domains, with none performing well across all of them. Specifically, most methods achieved high accuracy rates in four of the seven datasets, and while there was a reasonable improvement in the most difficult domains, there is still ample room for further research.
Ar-PuFi: A short-text dataset to identify the offensive messages towards public figures in the Arabian community

The fight against offensive speech on the Internet necessitates increased efforts from linguistic analysis and artificial intelligence perspectives to develop countermeasures and preventive methods. Reliable predictions can only be obtained if these methods are exposed to a representative sample of the domain or environment under consideration. Datasets serve as the foundation for significant developments in this field because they are the main means of obtaining appropriate instances that reveal the multiple and varied faces of the offensive speech phenomenon. In this sense, we present Ar-PuFi, a dataset of offensive speech towards Public Figures in the Arabian community. With 24,071 comments collected from TV interviews with Egyptian celebrities belonging to six domains of public interest, Ar-PuFi is currently the largest Arabic dataset in terms of its category and size. The examples were annotated by three native speakers over the course of two months and are provided with two-class and six-class annotations based on the presence or absence of explicit and implicit offensive content. We evaluated the performance of a diverse set of classification models employing several text representations of actual examples (e.g., N-gram, TF/IDF, AraVec, and fastText), and AraBERT achieved the baseline for the new dataset in both offensive detection and group classification. Additionally, we apply the Pointwise Mutual Information (PMI) technique to comments within the target domain in order to derive a lexicon of offensive terms associated with each domain of ArPuFi. We further explored whether active learning (AL) or meta-learning (ML) frameworks could be used to reduce the labeling effort required for our dataset without affecting prediction quality and found that, though AL can reduce the amount of data annotations by 10% over the ML approach, neither approach requires less than about 70% of the full dataset to achieve baseline performance. Finally, we took advantage of the availability of relevant datasets and conducted a cross-domain experiment to back up our claims not only about the uniqueness of our dataset but also about the difficulty of adapting Arabic dialects against one another.
Multisensor-Based Maneuvering Target Tracking Algorithm under Non-Gaussian Measurement Noise

Abstract The UAVs have become a huge threat in the battlefield. The excellent maneuverability makes them competent for the actual combat operations such as investigation and strike. Therefore, accurate tracking of UAVs is critical. Centralized radar/IR fusion system has been widely concerned to track the UAVs because of the complementary characteristics between these two sensors. The appearance of non-Gaussian measurement noise produces a negative impact on tracking accuracy. This paper proposes a novel IMM-CMCSCKF algorithm which can improve the tracking accuracy of radar/IR fusion system for maneuvering target under non-Gaussian measurement noise. The heterogeneous measurements from radar and IR are firstly fused. Then, CMCSCKF algorithm is presented based on MCC to deal with non-Gaussian measurement noise. To improve maneuvering target tracking accuracy, CMCSCKF is embed into IMM algorithm. Numerical simulation verifies the effectiveness of the proposed IMM-CMCSCKF.
TEACHING ENGLISH IN VIRTUAL LEARNING ENVIRONMENTS IN LATIN AMERICA

To develop this article, a documentary review of the elaboration and production of research works related to the study of English Teaching in Virtual Learning Environments was carried out in order to know through a bibliometric study the main characteristics of 409 publications registered in Scopus database during the period 2018-2022 at the Latin American level. The results of this database were organized in graphs and figures categorizing the information by variables such as Year of Publication, Country of Origin and Area of Knowledge which allowed to identify through a qualitative analysis, the position of different authors against the proposed theme. The main findings found through this research was that Brazil stood out for having the highest scientific production, leading the list with 104 publications. Likewise, the Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of the variables were the social sciences with 239 published documents.
https://sifisheriessciences.com/journal/index.php/journal/article/view/1294

Wireless experts worldwide have become interested in using Autoencoders (AEs) for modelling communication systems as an end-to-end reconstruction task.This approach optimizes both the transmitter and receiver components simultaneously, offering flexibility and convenience for representing complex channel models.Traditional communication systems rely on conventional models and assumptions that limit their utilization of limited frequency resources and hinder their ability to adapt to new wireless applications.However, with the rise of Artificial Intelligence, new wireless systems are capable of learning from wireless spectrum data and optimizing their performance.In this paper, the use of deep learning with autoencoders is explored to create an end-to-end communication system that replaces traditional transmitter and receiver activities.The autoencoder architecture effectively addresses channel impairments and enhances overall performance.Simulation results indicate that autoencoders surpass conventional communication systems in terms of Block Error Rate performance, even when facing impairments in the autoencoder's channel layer and using different neural network optimization algorithms.
Breast Cancer Image Semantic Segmentation with Attention U-Net

Semantic segmentation is to segment objects in an image into meaningful units. Among them, the basic idea of U-Net is to use low-dimensional as well as high-dimensional information to extract image features and enable accurate location identification. In this paper, we present a new model that combines Attention Gates with U-Net and evaluate the results through semantic segmentation with breast cancer datasets. To this end, this study proposes and tests a methodology for breast cancer image segmentation based on Attention U-Net. In conclusion, when comparing the performance with the existing U-Net, It can be seen that IoU is 0.069 higher than the existing U-Net. Thus, the proposed model enables better image semantic segmentation.
Impulse noise threshold detection and Noise removal using Non-local Grid Network

The method of removing the existing Impulse noise was to find the threshold and apply the median filter to remove the noise. In this paper, we try to find reference values in neural networks. In order to find noise, we used a non-local method to consider not only the information of the region but also the entire region. We then utilized Yolo's grid cell structure to make stronger predictions considering various sizes and different areas. Accordingly, qualitative and quantitative evaluation results show better performance than conventional median filters.
Analisa Bonus Demografi Dengan Algoritma Machine Learning Di Kabupaten Gorontalo Utara

Nationally, Indeks has entered the demographic bonus era since 2012 and is predicted to end in 2037. The demographic Bonus is the period during which 100 productive people are dependent on the unproductive population of a country below 50. The results of the 2020 population census recorded that the population in Gorontalo province reached 1,171,681 people, a data source from the Central Statistics Agency (BPS) of Gorontalo province. Not increasing opportunities in such a rapid digital era makes employment decline, especially in labor-intensive activities. On the other hand, the advancement of information technology opens up many new business opportunities. This is the challenge of this nation in welcoming the demographic bonus. Demographic Bonus in Gorontalo. The problem of the impact of the demographic bonus in addition to the ongoing covid 19 pandemic also holds many problems that will also have an impact on uneven economic recovery, unemployment,stunting, and poor nutrition. The government’s efforts to overcome and control population growth have had an impact on changes in the demographic structure of the population in Gorontalo. The purpose of this study is to produce a product description of knowledge pattern analysis of population data in the province of North Gorontalo from data extraction with machine learning algorithms. Clustering analysis, the experimental results with K-Means model k value with the best bouldin indeks feature is at K=3, for classification analysis with decision tree model of the demographic dataset of North Gorontalo region has an accuracy of 89.29% while using the random forest model reaches 100% accuracy.
Comparison of Edge Detection of Images with Various Types of Noise and Denoising Methods

In this paper, we show comparison results of edge detection of images that have additive gaussian noise or salt and pepper noise by using various techniques of noise removal such as filtering, morphology and deep learning based ones. In particular, this present work provides comparison results of noise removal by using gaussian filter, open and close operations of morphology and auto-encoder model followed by carrying out edge detection. Robert cross, Sobel, Prewitt and Canny detectors are used for edge detection of the images with noise removal. Experimental results show that noise removal results are different with characteristics of noise and techniques applied for noise removal. In addition, deep learning based technique, auto-encoder does not always shows superior results of noise removal, particularly in the case of existence of salt-pepper noise. In the experiments, gaussian noise or salt-pepper noise is used and peak signal noise ratio (PSNR) is used for quantitative comparison and the results of edge detection is qualitatively compared from visual perspective.
FACTORS AFFECTING MANAGEMENT INFORMATION SYSTEMS: SOFTWARE, DATABASES AND INFORMATION TECHNOLOGY (LITERATURE REVIEW)

Management Information System (MIS) is one of the most important achievements in the field of work administration, which aims to provide reliable, accurate, relevant and complete information to managers to improve organizational performance in organizations. This research reviews other research in the field of MIS adoption in organizations. Synthesizing from the previous literature with several books, articles and related studies, this paper proposes a theoretical framework. Previous research or relevant research is very important in a research or scientific article. Previous research or relevant research serves to strengthen the theory and phenomena of the relationship or influence between variables. This article reviews the factors that influence Information Systems in the academic field, namely: Software, Databases and Information Technology, a literature study on Information Systems Management. The purpose of writing this article is to build a hypothesis of the influence between variables to be used in further research. The results of this literature review article are: 1) Software has an effect on Information Systems; 2) Database affects the Information System; and 3) Information Technology has an effect on Information Systems.
Text to Speech Using Finite State Automata on Health Data

Computer science and engineering has provided many benefits that can be applied in our lives, such as in the field of automata theory, one of the largest areas related to the efficiency of an algorithm in solving problems in computational models. Text to Speech is a technology that converts text into sound using a phonetization system, phonemes that are arranged to form a speech to make computers able to communicate and interact with everyday spoken language. Data that need to be interpret is the health data such as body temperature, heart rate per minute, and oxygen levels. Text to Speech is very useful to be applied to blind aids who need information in the form of sound because of their limitations. For this reason, it is necessary to make an application that can read text-based data that is stored into a voice that can be heard by the blind, the method used in this study is Finite State Automata (FSA) which is used to split Indonesian words into words according to its syllable patterns and facilitate the pronunciation process which is included in the blind aids so that it is expected to help the visually impaired to be able to find out their health condition. in this study, the test was carried out using the Confusion Matrix method, while the results obtained were 97% accurate.
INNOVATIVE TECHNOLOGIES AS A MEANS OF OPTIMIZING THE PROCESS OF TEACHING CLASSICAL DANCE

This article examines the essence of the use of ICT in the modern educational process in the choreography class. The author presents the advantages and disadvantages of the use of technologies, as well as an overview of some special programs that can be used by teachers and students during classes and during students' pedagogical practice. There is an urgent need to train teachers in the use of ICT in educational interaction with pupils at various stages of studying choreographic material. Modern pedagogy faces the problematic issue of training competitive specialists with a high level of professional culture, the ability for intellectual, spiritual and professional self-improvement. Сhoreographic education, as well as other educational fields, needs to modernize teaching methods and techniques, update the technical side of the educational process, improve the qualifications of teachers, expand their knowledge and skills in using ICT. The use of technology in education contributes to the development of students' internal motivation, their independence and self-discipline, reveals their creative potential, and engages inactive and shy students in joint activities. Technology has become an integral part of people's lives and as a society we have become dependent on technology for everything from simple everyday tasks to complex activities. As technology changes, we as a society must learn to adapt to it. The art world is also involved in this trend, and in fact, technology seems to be opening new doors for new kinds of learning and new audiences. Technology helps bring dance to millions of viewers through television, film and the Internet. These events changed the way many people approached dance. They also changed the idea of what dance is, what it can be. The long-term effects of these changes are yet to be seen, but in a relatively short period of time it appears that dance is gaining a lot of popularity in the media thanks to various shows and programs. With the help of video conferencing technology, you can conduct dance classes with guest teachers located in other cities or countries. Choreographers can also set up or rehearse work with dancers located in different geographic regions. Thus, the use of video conferencing technology is an important educational and artistic tool, as it expands the possibilities for dancers and choreographers. Dance presented through electronic media does not replace live dance, it is its continuation. This is an effective way to draw more attention to the choreographic form of art in the hope that more people will appreciate it, learn more about it and want to try their hand at dancing.