Reviewerly/exHarmony huggingface dataset:
Validation of Satellite Rainfall Product (GPM-IMERG) an Bali and Nusa Tenggara: A Comparison of Normal Seasons, El Nino and La Nina Events

Bali and Nusa Tenggara are regions where monsoonal wind changes and strange interactions between the ocean and atmosphere influence rainfall. The purpose of this research is to evaluate Integrated Multi-Satellite Retrievals for GPM (IMERG) rainfall data using in-situ observations from Bali and Nusa Tenggara, Indonesia, while considering seasonal variations and the El Nino-Southern Oscillation (ENSO) phenomenon. The study combines rainfall data from synoptic stations with rain gauge measurements over ten years, from January 2012 to December 2021, to obtain more accurate verification results. The study's findings indicate that, apart from the transitional seasons, IMERG data provides substantial estimates of monthly rainfall accumulation with low error values for both light and heavy rainfall. The study also reveals that the islands' complexity and topography can impact each province's validation values. The verification results show excellent accuracy in flat terrain areas and moderate elevations, while performance decreases in regions with high altitudes. These findings are significant because IMERG data can estimate rainfall for regions lacking monitoring stations during specific seasons and active ENSO conditions. Thus, this information can serve as a valuable tool to address the issue of data unavailability in hard-to-access areas and contribute to optimizing water resource management and weather-related disaster mitigation. Keywords: Validation, Rainfall, IMERG, ENSO
INTERNET OF THINGS AND CLOUD COMPUTING IN LIGHT OF THE CORONA CRISIS

The term Internet of Things is a newly emerging term and includes many applications and devices under it. It depends primarily on the Internet, but it is tainted by some ambiguity in terms of its applications. These applications also depend for their services on cloud computing, which represents the new technological trend for future generations, and also provides services in various fields, and through this intervention we seek to clarify the true meaning of the Internet of Things and cloud computing while showing the relationship between them, and the reality of each of them in Algeria, where we reached a set of results, the most important of which is that most developed countries seek to employ Internet of things technology in all its organizations due to the latter's flexibility in work and efficiency in performance, And the adoption of cloud computing ensures keeping pace with the development in information systems technologies and the advantages it achieves that help organizations achieve their goals.
THE CONTRIBUTION OF ECONOMIC INTELLIGENCE TO IMPROVING THE SERVICES PROVIDED BY THE ECONOMIC INSTITUTION «CASE STUDY OF SONELGAS»

In light of the developments that the world is witnessing and the development taking place at the level of research and communication technology, economic institutions are keen to keep pace with these developments by developing their information programs that aim to improve the cost-effectiveness of services provided to customers. Economic intelligence in improving the various services provided by the economic institution, and we focused in this study on the use of programs and means of remote control in the use and maintenance of electrical networks in the Sonelgaz institution, through the contribution of economic intelligence in the transmission and transformation of electrical energy. The remote control system represents a cost-effective solution for controlling and operating electrical networks. The use of this system allows real-time control of the main business. In addition, the flexibility of operation provided by the remote control system allows the operator to quickly make and implement the necessary decisions, hence the importance of remote monitoring and control hardware software that makes it possible to control the state of networks and work quickly by avoiding as much costly interventions as possible and thus improve services. The researcher concluded a set of result and recommendations, the most important of which is that economic intelligence is an essential mechanism in improving the quality of the various services provided by the economic institution and providing the necessary information in order to use it to make optimal decisions for the institution.
THE STATE’S GENERAL BUDGET DEFICIT : A SOCIOECONOMIC STUDY OF THE PHENOMENON’S REPERCUSSIONS AND ITS TREATMENT IN NORTH AFRICAN COUNTRIES (MOROCCO - ALGERIA - TUNISIA - EGYPT AS A MODEL)

This study aims to analyze the phenomenon of the budget deficit and identify its causes in Morocco-Algeria-Tunisia-Egypt, and know the mechanisms that countries adopt to confront the deficit through a set of mechanisms. As it turns out that, the budget deficit is a structural deficit linked to the continued increase in public spending and the weakness of public revenues, as the economies of these countries remain hostage to policies with limited horizons in light of the weakness of alternative financing sources of a sustainable nature
E-MARKETING AND DISTANCE EDUCATION PLATFORMS IN LIGHT OF VIRTUAL COMMUNITIES

Societies differ in their specificities and forms, and there are the real societies in which we live, as a group of individuals living in a specific geographical region, where a fabric of social relations arises, adhering to a single social order and sharing certain values and objectives. A realistic society, as is customary, is a coherent construction composed of interrelated parts that are functionally supportive, consisting of physical spaces and forming different entities and structured by the laws of gravity, space and time. There are also virtual societies in the form of digital information spaces that seek to globalize beyond the physiological world. These societies have imposed themselves in the light of contemporary social changes experienced by various countries of the world due to the coronavirus epidemic that has brought them into and expanded their spaces by attempting to bring down "traditional" concepts at the heart of the concepts of digital space consisting of virtual societies. It shows the simulation of the vocabulary of the physiological world. This resulted in several vocabulary, namely, e-education, virtual tourism, e-shopping, chat rooms, etc., which makes physical space content similar to information space content but from a different environment is real and the second is virtual. We will stand in this intervention: at both e-shopping vocabulary and chat rooms we will try to study their most important developments in virtual communities
Word-level machine translation for bag-of-words text analysis:: Cheap, fast, and surprisingly good

The quality of automated machine translation is rapidly approaching that of professional human translation. However, the best methods remain costly in terms of money, computational resources, and/or time, particularly when applied to large volumes of text. In contrast, word-level translation is both free and fast, simply mapping each word in a source language deterministically to a target language. This paper demonstrates that high-quality word-level translation dictionaries can be generated cheaply and easily, and that they produce translations that can serve reliably as inputs into some of the most common automated text analysis methods. It advances the field on two fronts: it assesses different techniques for creating word-level translation dictionaries, and it systematically compares the similarity of word-level translations against those produced by either state-of-the-art neural machine translation or professional human translation. Comparisons are performed for three common text analysis tasks — sentiment analysis, dictionary-based content analysis, and topic modeling — across a total of eleven different source languages and two target languages (English and French). Across all languages and tasks, word-level dictionaries perform sufficiently well to make them an attractive alternative when resource constraints make neural machine translation inaccessible. The translation dictionaries as well as the code used to generate and validate them are available on Github.
StableGCN: Decoupling and Reconciling Information Propagation for Collaborative Filtering

Graph Convolutional Networks (GCNs) have been widely applied to collaborative filtering, where each layer typically contains neighborhood aggregation and feature transformation. Recent studies have found that feature transformation contributes little to the final recommendation performance. They however eliminated it directly without further exploration, leading to a degradation of model expressive power. In this paper, we show that this problem arises from inconsistent information propagation process, in which the dominance of feature transformation prevents features from being properly smoothed by neighborhood aggregation. To this end, we present StableGCN to decouple and reconcile this contradictory process in an orderly rather than intertwined manner. The coarse-grained node features are first refined by an elaborate extractor, and then smoothed by a specific kind of GCN concerning feature denoising. Consequently, feature transformation and neighborhood aggregation can support each other without sacrificing expressive power. Extensive experiments on six public datasets demonstrate the effectiveness and state-of-the-art performance of StableGCN.
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification

Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models. We present FocusedCleaner as a poisoned graph sanitizer to effectively identify the poison injected by attackers. Specifically, FocusedCleaner provides a sanitation framework consisting of two modules: bi-level structural learning and victim node detection. In particular, the structural learning module will reverse the attack process to steadily sanitize the graph while the detection module provides the “focus” – a narrowed and more accurate search region – to structural learning. These two modules will operate in iterations and reinforce each other to sanitize a poisoned graph step by step. As an important application, we show that the adversarial robustness of GNNs trained over the sanitized graph for the node classification task is significantly improved. Extensive experiments demonstrate that FocusedCleaner outperforms the state-of-the-art baselines both on poisoned graph sanitation and improving robustness.
Research on Book Publishing Enabled by Digital Twin and NFT

With the explosion of “metacmos”, the idea of various technologies enabling reality has gradually entered the public view. Whether it's digital twins, big data, or blockchain, they're essentially technology enabling the physical world. The “digital twins” of physical publications are constructed through digital twinning technology, followed by data storage. Not only can ensure that the book board type, the content is not damaged by the passage of time. It can also use the NFT blockchain technology to ensure that the “digital book” data itself will not be stolen or tampered with, with a verified identification certificate and an independent collection number. As the vision of virtual space life becomes more and more real, people collecting “precious physical books” from ancient times may gradually give way to the collection of “digital books” as a collection value due to the emergence of technology. It is foreseeable that the application of digital technology will bring a series of irreversible changes to the production, sales and service of the publishing industry.
Comprehensive performance assessment of Multi-neural ensemble model for mortality prediction in ICU

The development of models to estimate the mortality rate of critically ill patients in the intensive care unit(ICU) is significantly enhanced by technologies based on artificial intelligence. But it can be quite difficult because most medical data are typically unbalanced and incomplete.This paper aims to address the missing data using advanced imputation algorithm like single center imputation from multiple chained equation (SICE) and synthetic minority oversampling technique with edited nearest neighbor (SMOTE-ENN) method is used to solve the imbalanced data problem. Wrapper-based genetic feature selection method is used for the feature selection. With a highly skewed dataset, the objective of this work is to propose an improved ensemble classifier that utilizes a combination of boosting and stacking ensemble strategies. Our work proposed a Stacking-based multi-layer perceptron ensemble learning method on bench-marking dataset namely 2020 WiDS (Women in Data Science) Challenge and got an accuracy of 92.45%. The results obtained using randomized five-fold cross-validation and hold-out techniques reveal that the performance of the proposed ensemble algorithm was better than that of all other predictors.In comparison to prior deep learning-driven mortality classification research, we proposed a comprehensive structure that encompasses a novel feature pre-processing methods and stacking ensemble algorithm for classification.
AutoSF: Adaptive Distributed Model Training in Dynamic Edge Computing

Distributed learning on edges aims at training the AI model collaboratively in a network of edge devices via frequent model aggregations. Achieving the desired training performance requires the aggregation structure and frequency to fit well with the dynamic edge environment. Existing works often consider the optimization of either aggregation structure or frequency, assuming that the edge environment is stable and deterministic. In this paper, we propose a novel approach, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoSF</i> , to automatically optimize the aggregation structure and frequency jointly in dynamic edge computing so as to minimize the global loss function. The main idea of AutoSF is that when the edge environment changes, the automated machine learning approach is triggered to find out the near-optimal aggregation structure and frequency that adapt to time-varying edge resources. When the environment keeps unchanged, a heuristic approach is used to tune the aggregation structure and frequency to further tame the heterogeneity caused by data distributions. We validate the effectiveness of AutoSF via numerical experiments with real datasets on our self-developed edge computing testbed. Evaluation results demonstrate that AutoSF outperforms the benchmark approaches by up to 16.3× speedups in convergence speed and 31.0 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> increases in training accuracy.
The Dilemma and Reflection of Short Video Infringement from the Perspective of Game Theory: Publicity and Protection of Film and Television Works

In the context of the popularization of digital technology, more and more film and television works are troubled by the infringement of short video, and publishers have to rely on short video platforms for publicity while gaining losses. The infringement of short video needs to be regulated, but as a propaganda tool in the Internet era, short video does bring traffic to film and television works and platforms. It is difficult for copyright holders, short video platforms and copyright users to balance in the game of traffic and interests. Therefore, a balance should be sought between the three, and a win–win situation should be sought by breaking through the traditional business model and formulating reasonable game rules. This paper takes the infringement phenomenon of short video as the breakthrough point and analyzes it, and proposes to solve the problem of propaganda and protection of film and television works through short video platform by making reasonable game rules through technology and strengthening control.
Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal

Abstract Anti-jamming cyclic frequency feature extraction is an important link in identifying communication interference signals, which is of great significance for eliminating electronic communication interference factors and improving the security of electronic communication environment. However, when the traditional feature extraction technology faces a large number of data samples, the processing capacity is low, and it cannot solve the multi-classification problems. For this type of problem, a method of electronic communication signal anti-jamming cyclic frequency feature extraction based on particle swarm optimization-support vector machines (PSO-SVM) algorithm is proposed. First, the SVM signal feature extraction model is proposed, and then the particle swarm optimization (PSO) algorithm is used. Optimize the kernel function parameter settings of SVM to raise the classifying quality of the SVM model. Finally, the function of the PSO-SVM signal feature extraction model is tested. The results verify that the PSO-SVM model begins to converge after 60 iterations, and the loss value remains at about 0.2, which is 0.2 lower than that of the SVM technique. The exactitude of signal feature extraction is 90.4%, and the recognition effect of binary phase shift keying signal is the best. The complete rate of signal feature extraction is 85%. This shows that the PSO-SVM model enhances the sensitivity of the anti-jamming cyclic frequency feature, improves the accuracy of the anti-jamming cyclic frequency feature recognition, reduces the running process, reduces the time cost, and greatly increases the performance of the SVM method. The good model performance also improves the application value of the method in the field of electronic communication.
Modular learning technology based on a subject-activity approach

The article discusses the essence, purpose, and objectives of modular learning, which is one of the types of personality-oriented learning technology. The sequence of transition to modular learning, the technology of the subject-activity approach, with the use of which continuity, intensification, individualization of learning is ensured, is presented.
Integration Methods as a Means of Improving the Level of Teachers’ Professional Training

This article suggests one of the possible ways of integrating teaching methods from the integration methods developed by the authors, namely: the integration based on the dominant teaching method. The article aims to substantiate the author’s methods of integrative teaching based on the dominant method and present the results of the approbation of these methods in higher schools. The project was implemented within the course “Medical Biophysics”. An integration algorithm as a dominant teaching method is proposed. The project contained several specific techniques. In the first specific method, the situational method is chosen as the dominant one. For the second one, the method of trials and coincidences is dominant. For diagnostics as a complex topic in medicine, physics, chemistry and other sciences, the problem method as dominant is dominant. In all methods, several other methods are gradually integrated into the dominant method, depending on the content and nature of the educational material. The practical significance of the project is to create algorithms for integrating teaching methods and the possibility of their transformation into other educational topics. The approbation shows an increase in the indicators of saving time and flexibility of knowledge.
On the problem of scientific and theoretical research on the development of divergent thinking of primary class students

In our study, it is important to understand the development process of thinking in primary school children, because in our study we are talking about the formation of divergent thinking of elementary school students in extracurricular activities. Starting from the chosen logic, first of all, we will clarify the essence of the concept of "development", which has various interpretations in the scientific literature. We have made comments on the approach to philosophical understanding of development as an irreversible process aimed at changing material and ideal objects, changing matter and consciousness, and the process of becoming a person undergoing qualitative and quantitative changes under the influence of internal impulses.
AN ANALYSIS OF TEACHING WRITING STRATEGY BY ONLINE DURING COVID-19 PANDEMIC AT SMK PGRI 2 GIRI BANYUWANGI

This research aimed to know the strategies used and the problems faced by the teachers in teaching writing by online at SMK PGRI 2 Giri Banyuwangi. The scope of this study is to analyze teaching writing by online during covid-19 pandemic at SMK PGRI 2 Giri Banyuwangi and limit only to the strategy used in teaching writing by online during covid-19 pandemic. This study used qualitative research. The subjects of the research are three of English teachers at SMK PGRI 2 Giri Banyuwangi. The instruments used to collect the data is an interview. The technique of data analysis which used in this research is interactive model of analysis from Miles and Huberman. They are: reducing the data, display the data and drawing and verifying conclusion. The finding of the research showed the respodents use two teaching writing strategies during pandemic, those are teacher presentation and self-awareness exercises / tests (Large and Small Group Discussion). The teachers deliver the material using presentation by Zoom and then share and collect the task in Google Classroom. In certain condition, students can come to the school for doing small group discussion with the teachers directly. Therefore, there are three problems faced by teacher during teaching writing by online, there are internet connection, students’ lack of interest and inappropriate teaching strategy.
Constraints on extraposition: no difference between relative and comparative clauses

This paper presents four judgment experiments that investigated the acceptability of sentences in which a comparative clause modified an NP embedded within a complex NP. The comparative clause occurred either adjacent to its head noun or in extraposed position. The first three experiments addressed the claim of Haider (1997) that comparative clause extraposition is subject to a c-command constraint on S-structure whereas relative clause extraposition is not. To test this claim, Experiments 1–3 compared sentences with comparative clauses to corresponding sentences with relative clauses. Extraposition was found to be as acceptable as non-extraposition when extraposition was across a verb but less acceptable when extraposition was across an adjective. Thisheld for comparative and relative extraposition alike. The results of the first three experiments do therefore not support the claim that comparative clause extraposition is subject to tighter syntactic constraints than relative clause extraposition. The fourth experiment tested the claim of Shannon (1992) that extraposition from a topic phrase is less acceptable than extraposition from a phrase in focus. This experiment investigated comparative clause extraposition from the subject across two types of predicates: individual-level predicates, for which the subject is the default topic, and stage-level predicates, which come without an (overt) topic by default. The results show that extraposition across a stage-level predicate is more acceptable than extraposition across an individual-level predicate and therefore support the claim that extraposition from a topic is disfavored.