Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Covid-19 Bansos Issues on Twitter using Chi-Square and Naïve Bayes

Social assistance or what is often called Bansos (Bantuan Sosial) is assistance in the form of goods or money from the government for the community that is temporary and selective. There were many reports from the public complaining that some had not received assistance or had not received it at all. This problematic social assistance has caused a stir in public reports on various social media, including Twitter. To find out the classification of public opinion related to social assistance on Twitter, it is necessary to do pre-processing and analysis of tweets. The dataset used is data crawled using the Twitter API and produces 702 tweet data in CSV format. Tweets retrieved based on the keyword 'bansos' in August 2021. The dataset is divided into two categories, positive and negative. Data with a total of 328 positive categories and 374 data of negative categories. The method applied in this study uses the Chi-Square feature selection method and the Naïve Bayes Classifier algorithm. The purpose of this research is to produce a website-based application that can classify tweets related to social assistance covid-19 into 2 categories, positive and negative by using the Chi-Square feature selection and the Naïve Bayes Classifier algorithm. The F1 score in the positive class is 85% and the negative class is 89% and produces an accuracy value of 87%. The results of the comparison between Naive Bayes and Naive Bayes using Chi-Square show that there is no difference in accuracy.
Questions of resource provision of teacher training for work in the conditions of distance education

At the present stage of the development of distance learning, the requirements for the training of teachers working in this environment are increasing. High-quality training of people with different abilities, interests and needs, considering the personality characteristics of students, as well as the specifics of distance learning can be carried out only by a professional teacher. Given the novelty of the situation in which teachers are involved, and the likelihood that teachers will face the prospect of teaching online in the future, it is extremely important to learn more about their experience and about the necessary professional qualities of teachers to implement distance learning. The research is aimed at establishing the actual state of readiness of teachers for distance learning of students on the basis of theoretical and empirical study and identifying the components of the resource support technology for the formation of teachers’ readiness for distance learning of students. The article states that the formation of teachers’ competencies in distance learning of students is possible in the process of implementing educational programs at universities, as well as professional development and retraining programs. The authors, based on the analysis of empirical data obtained during the survey of practicing teachers and future teachers on the use of distance learning technologies in the educational process, identified problems and difficulties in the implementation of distance learning, and determined the conditions necessary for the successful implementation of distance learning. Сonclusion is made about the need for further development of such issues as conducting a study to evaluate the developed technologies for the formation of a teacher’s readiness for distance learning of students, developed program and methodological materials.
Context-Encoded Code Change Representation for Automated Commit Message Generation

Changes in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions for code changes (commit messages) help people better understand the changes. However, due to the lack of motivation and time pressure, writing high-quality commit messages remains reluctantly considered. Several methods have been proposed with the aim of automated commit message generation. However, the existing methods are still limited because they only utilize either the changed codes or the changed codes combined with their surrounding statements. This paper proposes a method to represent code changes by combining the changed codes and the unchanged codes which have program dependence on the changed codes. Specifically, we first create program dependence graphs (PDGs) of source code before and after the change. After that, slices related to the changed code from these PDGs are extracted. These slices are then merged to represent the change. This method overcomes the limitations of current representations while improving the performance of 5/6 of state-of-the-art commit message generation methods by up to 15% in METEOR, 14% in ROUGE-L, and 10% in BLEU-4.
Quantum Computing Applications and Impact for Cyber Physical Systems

In the article the authors aim at presenting an overview of the current state of quantum computing, exploring qubits, gates, circuits, error correction, sample algorithms, and potential applications as well as an overview of current and near future applications. The article highlights the rapid growth in quantum computing research and argues that now is the opportune time to pursue practical applications. By delving into the fundamental aspects of quantum computing and examining the challenges and advancements in the field, the article provides a comprehensive understanding of the current landscape and emphasizes the importance of scaling and accelerating the development of quantum technologies to unlock their full potential
Special features in the formation of professional competence of students with “Pedagogical education (foreign languages)” as a major subject

Higher education, being primarily professional education, aims to train a specialist with the necessary qualifications aimed at solving professional problems in a particular sector of the economy, and is also focused on the formation of a harmoniously developed personality. In this regard, pedagogical education, also focused on the training of a teacher of a certain qualification, puts the foundation of professional knowledge and practical pedagogical skills as pedagogical and methodological competencies as the basis of training, and for a teacher of a foreign language, the subject competence is the formation of a foreign language communicative competence of a certain level. This article describes the theoretical features of the formation of professional competence of a teacher in undergraduate studies as a basic level of professional education based on theoretical and methodological provisions and functional components of education; also, on the basis of an experimental study, the formed communicative and methodological levels of readiness of future teachers of a foreign language were determined, as a result of which a lack of methodological knowledge of future teachers in terms of the practical organization of the teacher's work was established. One can also note the absence in the education standards of specific requirements necessary for professional activity. In particular, for a foreign language teacher, the specific knowledge, skills and abilities required for further work are not indicated.
Investigation of Variables Affecting PISA Success Levels of Countries with Different Success Levels with Data Mining Methods

The aim of this research is to determine the important variables that predict the PISA 2018 reading comprehension achievement score of countries with different achievement levels, using 34 independent variables obtained from the student questionnaire given to the students who participated in PISA in 2018. For this purpose, 79 countries that participated PISA were ranked according to their success percentages then, these countries were sorted into lower, middle and upper group countries. A sample of lower, middle and upper group countries was formed then, three countries were selected from each of the lower group, middle group and upper group countries and a sample of lower, middle and upper group countries was formed. Data mining analyzes were carried out on the samples obtained by using the Classification and Regression Tree and Random Forest methods. It has been observed that the number of important variables that predict reading comprehension success can be reduced from 34 to three to eight. Like this; Data mining classification prediction models, which can predict the success level of PISA, were obtained by using a small number of variables. It has been determined that the models obtained have an acceptable level of predictive performance in predicting success in three categories (low, medium-high). The most important predictor variables obtained from the models are information and communication technologies resources, perception of reading difficulty, professional status expected from the student, perception of difficulty in the PISA test, reading pleasure, weekly test language learning time, disciplinary climate, socio-economic status index.
Privacy at Risk: Exploiting Similarities in Health Data for Identity Inference

Smartwatches enable the efficient collection of health data that can be used for research and comprehensive analysis to improve the health of individuals. In addition to the analysis capabilities, ensuring privacy when handling health data is a critical concern as the collection and analysis of such data become pervasive. Since health data contains sensitive information, it should be handled with responsibility and is therefore often treated anonymously. However, also the data itself can be exploited to reveal information and break anonymity. We propose a novel similarity-based re-identification attack on time-series health data and thereby unveil a significant vulnerability. Despite privacy measures that remove identifying information, our attack demonstrates that a brief amount of various sensor data from a target individual is adequate to possibly identify them within a database of other samples, solely based on sensor-level similarities. In our example scenario, where data owners leverage health data from smartwatches, findings show that we are able to correctly link the target data in two out of three cases. User privacy is thus already inherently threatened by the data itself and even when removing personal information.
Anki Tagger: A Generative AI Tool for Aligning Third-Party Resources to Preclinical Curriculum

Using large language models, we developed a method to efficiently query existing flashcard libraries and select those most relevant to an individual's medical school curricula.
Penggunaan Media Online dalam Proses Belajar Mengajar di Masa Pandemi Covid-19 : Studi Kasus di SD Talaga 2 Cikupa

The covid-19 pandemic conditions cause unprecedented changes in life, and so learning is made online from home through online media (online), where teachers are required to use technology to design learning media as an innovation. This type of research employs a qualitative approach with a descriptive method. Data retrieval takes place through interview techniques, observation and documentation. The data-analysis technique is done in a descriptive way by describing the student's interview results. Studies have found that teachers still encounter obstacles to the online use of the learning media and that it takes teacher creativity to create the learning media by using various platforms
Error-controlled decoding in phase unwrapping for phase-shifting projected fringe profilometry

A phase unwrapping method using the error-controlled fringe projection scheme for phase-shifting projected fringe profilometry is presented. Most unwrapping methods cannot cope with the problems caused by low signal-to-noise ratios. The presenting method can detect such kind of errors and recover the mistake.
A Blockchain-Based e-Government Service for Quantity Surveyors

In Spain, quantity surveyors are entitled to carry out official cadastral surveys, attestations, and certificate issuing according to a well-defined professional code. The Official Associations of Quantity Surveyors and Technical Architects (COAATs) are responsible for endorsing the documentation related to actions performed on buildings. An e-platform that enables immutability, traceability, and a unique property record among all the Spanish COAATs, with an affordable cost, is essential to streamline the involved processes.
Building a smarter surveillance system: real-time animal identification using deep learning

Capturing data on animal movements, especially for nocturnal animals, is a time-consuming and arduous task for biologists. They typically rely on trail cameras, and have to manually search through low-quality video feeds to locate the desired footage. Leveraging advancements in artificial intelligence and hardware devices, such as microcontrollers with cameras, can help address this issue. Everyday consumers can also use this technology in their home surveillance cameras. In this study, the automation of animal identification in real-time using a night vision camera with a simple microcontroller such as ESP32 is explored. A motion detection algorithm on the device creates a trigger on the camera, when motion is detected. The captured image is sent to an image classification model to identify the presence of an animal and its type. The image classification model is deployed in the cloud as a REST API service. Finally, the predictions are displayed on the LCD screen on the device. We constructed two deep learning models, MobileNetV2 and ResNet50 for image classification and evaluated their performance. To test their accuracy, we utilized a validation set of images for three distinct species and a smaller test set of images for each of these species. We experimented with various hyper-parameters, such as epochs and learning rate to determine the model with best performance. ResNet50 model with 50 epochs and a learning rate of .0001 produced the most satisfactory results for our objectives. We then deployed the model as a REST API service for animal detection.
Cell classification framework using U-Net: convolutional networks for cervix cell segmentation

With the technological advancements in machine learning, it has become more prevalent to use learning techniques for clinical decision-making based on medical images. One of the state-of-the-art methods used for this purpose is Convolutional Neural Networks (CNN) for medical image segmentation and deep learning models for disease detection and classification. In this paper, we propose a framework for image segmentation using hierarchical CNNs to classify different types of cells using small frame images. This paper aims to generalize the segmentation of cancer cells, starting with cervix cancer. The first step of the framework is to achieve automatic nucleus and cell masking of the images using U-Net. The images are then segmented into “satisfactory” and “unsatisfactory” categories to determine whether these images can be used in our classification model. Using the hierarchical CNN, the satisfactory images are clustered based on cell types since the cell features that need to be considered vary between different cell types. Lastly, our classification model is trained with automatically segmented images to classify different cancer types based on cell images using various features, such as the area of the nucleus, the ratio of the nucleus area and cytoplasm area and the visual morphology of chromatin strands in the nucleus. To demonstrate the performance of the proposed framework, a labeled dataset, taken from the Detay Pathology and Cytology Laboratory, with over 100 images were used.
Safety path planning of cargo size based on improved ant colony algorithm

In order to solve potential safety problems that mobile robots can’t choose the best path real-time actual size according to the actual size of shipping goods and obstacles . This paper puts forward a new pheromone updating method based on ant colony algorithm, which set penalty factor to consider the influence of different sizes of goods transported by mobile robots and adjust the pheromone concentration adaptively. Firstly. This method could avoid the blind search of ant colony algorithm in the initial iteration by using Dijkstra algorithm to determine the initial path. In addition. This method improve the global planning ability of the algorithm in the middle iteration. After accurate division of raster map, the real-time penalty factor is formulated according to the actual size of mobile robot to improve the real-time pheromone concentration. At last, the experiment results shows that the path planning security of the algorithm has a great improved, and realize the function of updating the global path in real time according to the actual size of the cargo.
xkl: A legacy software for detailed acoustic analysis of speech made modern

The determination of the fundamental properties of speech relies on a fine and precise estimation of temporal and spectral properties of speech segments. Given the time-varying nature of speech, a digital estimation of its instantaneous spectrum is particularly challenging, and has been the object of investigation throughout the past 50 years. The xkl software, developed in the 80’s by the late Dennis Klatt at MIT, has superior capabilities in addressing the above question. Its use in the past 20 years was, however, limited by a lack of support for modern computing platforms. Revamping it will give access to a powerful tool that will eventually lead to new discoveries.
Edge Detection Algorithm for Power Operation Monitoring Video Based on AI Technology

In this paper, a video edge detection algorithm based on AI technology is presented to improve the accuracy of power operation safety monitoring. Firstly, the block and low rank tensor recovery video denoising algorithm is used to filter out internal noise in videos, which improves video clarity and makes video edge information more prominent. Secondly, the proximal support vector machine (PSVM) is used to detect the edges of denoised videos, thereby improving the effectiveness of video edge detection. The experimental results show that the proposed algorithm outperforms other algorithms in video edge detection, further ensuring the safety of power operations.
A Clustering-Based Approach for Effective Prevention of SQL Injections

SQL injection attack happens due to the insertion of a malicious SQL query into the application server via the input data from the attacker. A successful SQL injection attack can read and/or modify sensitive data stored in the database, execute administrative commands on the database such as DB shutdown, and in some cases issue harmful commands to the operating system. Owing to this, injection-based security vulnerabilities rank in the top ten security pitfalls identified by Open Web Application Security Project. Due to the significance of the security of web-based database applications, prevention of SQL injection at the right time is of paramount importance. A lot of research has been carried out on SQL injection attack detection and prevention. However, there is not much work done on clustering SQL injections into different types. Since different types of SQL injections can be countered effectively using different techniques, grouping of SQL injections can help organizations to allocate resources judiciously for effective prevention of SQL injection attacks. In this paper, we present an empirical study to identify the best suited unsupervised machine learning algorithm for clustering SQL injections, with the intention to find the frequency of occurrence of different types of injections. This approach would aid organizations to strengthen their security measures against particular types of injections in a more focused way. We also give a detailed analysis of the efficacy of the clustering algorithms used.
An image compression and encryption scheme for similarity retrieval

With the development of cloud computing, people usually outsource encrypted images for saving storage and protecting privacy. However, traditional image encryption methods not only hinder the availability of images such as similarity retrieval, but also degrade the compression performance. To address this issue, we propose a retrievable image compression and encryption method(RICE). RICE takes into account the contradiction of image compression, availability and security, then propose a cascaded information bottleneck model, which includes the compression information bottleneck and the security and availability information bottleneck. The former is converted into a rate distortion problem and its optimal solution is sought by a convolutional neural network(CNN)-based compression network which includes channel space attention module and discrete wavelet transform(DWT) module. To solve the later, we propose a feature partition method to find a retrieval subset that balances the contradiction between security and availability, and design a DNA-based deterministic encryption method for this subset to support ciphertext retrieval. The ciphertext of the retrieved subset is sent to the proposed similarity search fully connected network(SimFcNet) to improve the retrieval accuracy. The remaining subset is encrypted by Non-deterministic encryption to further improve security. In general, the method RICE we proposed supports similarity retrievable in compressed domain ciphertext, and can achieve excellent performance. Experimental results show that our method is 36.56% higher than JPEG2000 at compression ratio of 60:1 in MS-SSIM, the accuracy of ciphertext retrieval can reach 0.828, and the security of ciphertext is close to that of traditional encryption methods.
EMOTIONAL DEPENDENCY AND THE IMPACTS ON THE LIVES OF PEOPLE: AN INTEGRATIVE LITERATURE REVIEW

Introduction: emotional dependency is a pattern of behavior where a person develops a strong need to be emotionally attached to another person, constantly seeking the approval, validation, and emotional security on the part of that person. This can lead to difculty in terms of making independent decisions, low self-esteem and intense fear of abandonment. Objective: to conduct a systematic literature review on emotional dependency and its impacts on the lives of people. Methodology: this is a systematic literature review, based on descriptive research, whose articles were selected in May 2023 in the LILACS, APA PsycNet and PubMed databases, from the combination of the descriptors “Psychological dependency”; “Emotional dependency”; “Interpersonal dependency”; and “Affective dependency”. As for the selection of works, those published in English, Spanish and Portuguese were determined. Acritical analysis was performed, and the articles related to the theme and made available in full were ltered. Accordingly, 16 articles were selected for this study. Results and Discussion: it is perceived that there is a scarcity of articles on emotional dependency and that the studies found present divergences in the used terminology. Emotional dependency can be divided into passive or active, being associated with different controlling proles, such as natural victim, intentional victim, avenger and narcissist. Conclusion: emotional dependency occurs when a person is dependent on the state or actions of another person, resulting in an emotional prison that limits freedom and authenticity, leading to a lack of permission to move on.
Application of Time Token Learning to Improve Elementary Students' Communication Skills

Students' skills in communicating teaching materials are still in the low category, so they need active learning strategies, one of which is time tokens. The purpose of this research is to find out the results of applying the time token learning strategy to elementary school-age children. This study used a qualitative research method with a descriptive approach. The results of this study are (1) children can improve their speaking skills more easily with the help of the time token strategy; and (2) children can express their opinions well, and listen to and respect the opinions of their peers.