Reviewerly/exHarmony huggingface dataset:
Detection of Multilingual Spam SMS Using NaïveBayes Classifier

The exponential growth of mobile communication has led to an increase in the volume of spam SMS messages, posing a significant challenge to users. This research paper focuses on the detection of multilingual spam SMS using the Naive Bayes classifier. The proposed approach leverages the Naive Bayes algorithm's simplicity and efficiency to classify SMS messages as spam or non-spam, while specifically addressing the multilingual aspect. A comprehensive dataset of labeled SMS messages is collected and preprocessed, including text normalization, and tokenization, and language translation. Feature extraction techniques such as wordfrequency, keyword presence, and message length are employed to represent the SMS messages in a numerical format suitable for the Naive Bayes classifier. The conditional probabilities of features occurring in spam and non-spam messages are calculated using maximum likelihood estimation. To assess the system's performance, extensive testing is conducted, including accuracy assessment, precision computation, recall, and F1 score calculation. The system's ability to handle different languages and accurately detect multilingual spam SMS messages is thoroughly analyzed. The results demonstrate the efficacy of the novel approach in detecting multilingual spam SMS. The Naive Bayes classifier exhibits high accuracy and efficiency in classifying spam messages across various languages, offering users protection from unwanted and potentially harmful content. Furthermore, to facilitate easy utilization and accessibility, a user interface is developed to interact with the spam detection system. The user interface allows users to input messages and the language of the message and receive real-time feedback on the likelihood of a message being spam. The research findings highlight the significance of considering language diversity in spam detection systems and provide insights into the difficulties and opportunities associated with multilingual spam SMS detection. The proposed approach can serve as a groundwork for developing robust and language-aware spam detection systems, ultimately enhancing users' messaging experience and security.
Research on composite auto-focus method for on-machine interferometry measurement

White-light scanning interferometry (WSI) is an important tool in the surface topography measurement. The range of white light interference fringes is very narrow, which makes the traditional method of finding fringes extremely timeconsuming and inefficient, affecting the efficiency and automation of the measurement. Aiming at these problems, a composite auto-focus method for on-machine WSI is proposed in this paper. Utilizing the feature of constant relative axial position of cutting tool and measuring system, the position near the interference fringes is quickly located. Then the mountain climbing strategy is adopted to roughly locate the focusing position with the wide spectral light source. Finally, the narrow spectral light source is used to accurately locate the position of zero optical-path difference by finding the coherent envelope peak. The experiments shows that the proposed auto-focus method is effective to find clear fringes in the on-machine interferometry measurement and can achieve a nanoscale accuracy within microns range.
Decoding Acute Lymphoblastic Leukemia: Insights from Convolutional Neural Networks and Pretrained Model

Accurate detection of Acute Lymphoblastic Leukemia (ALL) is essential for prompt diagnosis and effective treatment. In this study, we introduce a deep learning method employing Convolutional Neural Networks (CNN) and pretrained models to enhance the precision of ALL detection. We leveraged a dataset comprising blood cell images for training and validation. Performance evaluations of three CNN models—ResNet-50, VGG16, and a bespoke CNN architecture—were conducted using metrics like accuracy, loss, and validation scores. Our findings reveal that VGG16 achieved a notable accuracy of 96.53%, while our custom CNN yielded 88.15%. Notably, VGG16, despite its high accuracy, exhibited signs of overfitting. In contrast, ResNet-50 underperformed, registering an accuracy of just 79.05%. This work underscores the potential of technological innovations in healthcare, especially in ALL diagnosis, and paves the way for more in-depth exploration of deep learning in cancer detection.
Adversarial Text Perturbation Generation and Analysis

With the evolution of applications of text generations in social networks, the genuineness of such text is questioned. Machine learning language based models such as GPT now can generate responses to complex questions which can be hardly distinguished from human-generated alternatives. In this scope, we utilized text-mutation to evaluate different text-based mutation operators that can be easily created and their impact on the output generated text. Our goal is to evaluate how can machine learning models distinguish those mutated versions from original versions. We reported results of several text-based mutation operators. We evaluated only a few examples of mutation operators and our goal is to eventually create a much larger list of operators. Those mutation operators can be used to distinguish human from machine-generated text.
Target Tracking Methods Based on Deep Learning

Target tracking has also received a great deal of attention from society. The basic problem of a target tracking algorithm is to select a target of interest in a video or image sequence, and in the next consecutive frames, find the exact position of that target and form its motion trajectory. In the early days of dealing with target tracking the optical flow method (Lucas-Kanade), Kalman filtering, kernel methods, etc. were usually used to achieve target tracking. But since 2016, deep learning has also made breakthroughs in target tracking as well as target detection. In this paper, we introduce the traditional target tracking algorithms and then describe and discuss in detail the target tracking algorithms around deep learning, to explore the excellence of deep learning in target tracking compared with the earlier tracking algorithms as well as the broad prospects for development in the future.
Investigating of the Degree of Distortion of Full-Color Images During Digital Watermark's Embedding by the Method of Discrete Transformations and the Efficiency of its Extraction

The paper presents the features of digital watermarking of a full-color image based on the method of discrete wavelet transform and discrete cosine transform, which is a procedure for embedding an invisible mark in the frequency dominant of a digital image. General information about the algorithm of realization of the mentioned method and its components is presented. The influence of the amplifying factor (coefficient) on the efficiency of watermark embedding and extraction and the degree of distortion of the processed image is presented. These dependencies were studied in order to find the optimal spectrum of digital color space for embedding the hidden mark in a full color image with optimal gain factor. Pearson's linear correlation coefficient was used to measure the degree of extraction efficiency, and the structural similarity index metric of digital images was used to measure the degree of distortion. The most suitable spectra as digital watermark carriers and the optimal gain values for these spectra were determined.
What Does the Upgraded State Educational Standard Hold in Store? (Analytical Review of a Survey of the Russian Teaching Community)

For the purpose of monitoring of the implementation of the upgraded State Educational Standards of primary and secondary education a survey of pedagogical community was held in spring, 2023. There were two groups of respondents: school administration and teachers of the 1st and 5th forms. The answers of the latter group are presented and analyzed in the article as their professional activities have been mainly impacted by the changes of the Standards. The majority of the questions had the so-called ‘your variant’ of the answer that have been also systematized and studied. In conclusion, the progressive trends as well as hitches of the Russian education are highlighted; the proposals for improvements of some aspects of secondary education are put forth.
A Context-Aware Approach for Filtering Empty Images in Camera Trap Data Using Siamese Network

This paper presents a method based on a Siamese convolutional neural network (CNN) for filtering empty images captured by camera traps. The proposed method takes into account information of the environment surrounding the camera by comparing captured images with empty reference images obtained regularly from the same capture locations. Reference images are expected to highlight local vegetation features such as rocks, mountains and lakes. By calculating the similarity between the two images, the Siamese network determines whether or not the captured image contains an animal. We present a protocol to provide image pairs to train the models, as well as the data augmentation techniques employed to enhance the training procedure. Three different CNN models are used as backbones for the Siamese network: MobileNetV2, ResNet50, and EfficientNetBO. In addition, experiments are conducted on three popular camera trap datasets: Snapshot Serengeti, Caltech and WCS. The results demonstrate the effectiveness of the proposed method due to the information of the capture location considered, and its potential for wildlife monitoring applications.
Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation

Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data. However, when those methods are applied to non-controllable environments, many different factors can degrade the model's expected performance, such as unlabeled datasets with different levels of domain shift and category shift. Particularly, when both issues occur at the same time, we tackle this challenging setup as Open Set Domain Adaptation (OSDA) problem. In general, existing OSDA approaches focus their efforts only on aligning known classes or, if they already extract possible negative instances, use them as a new category learned with supervision during the course of training. We propose a novel way to improve OSDA approaches by extracting a high-confidence set of unknown instances and using it as a hard constraint to tighten the classification boundaries of OSDA methods. Especially, we adopt a new loss constraint evaluated in three different means, (1) directly with the pristine negative instances; (2) with randomly transformed negatives using data augmentation techniques; and (3) with synthetically generated negatives containing adversarial features. We assessed all approaches in an extensive set of experiments based on OVANet, where we could observe consistent improvements for two public benchmarks, the Office-31 and Office-Home datasets, yielding absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and 5.8% for Accuracy and 4.7% for H-Score on Office-Home.
BoCS: Image Retrieval Using Explicable Methods

In recent years, image generation has been growing at a very fast pace, demanding specific systems for managing large image datasets. For example, we can mention the content-based image retrieval (CBIR) systems. Usually, they use a representation of feature vectors based on the images' visual content to store/retrieve them and to perform demanded queries. Currently, neural networks perform the task of generating image representations with great mastery. However, these networks usually create methods that are difficult to understand or to explain, which for some applications, such as medical decisionmaking systems, can be a significant disadvantage. Thinking about the explainability aspect, in this work, we present a new technique based on the bag of visual words (BoVW) which, in addition to generating promising explainable methods, has long been the state of the art for generating image representations. The results showed that the presented method BoCS overcomes similar methods and still has the potential to be further explored.
Research on Integration of Testing and Training System and Its Key Technologies

With development of information technology, the demands for equipment test have been persistently increasing. The evaluation of the system’s capabilities in simulated environments that closely actual environment has emerged as a critical area of focus in the assessment of equipment performance. As a result of the high level of correspondence between testing and training, the integration of training system resources can significantly improve testing and evaluation efficiency while also promoting resource reuse. In light of current experience in merging testing and training, this study endeavors to explore the integration of testing and training through the use of distributed simulation technology and LVC technology. The research emphasizes system architecture design and key technologies. Taking the civil airport ground anti-interference equipment as an example, an integrated test and training system was designed. The results show that the system realizes the interoperability between LVC training equipment and the collection of key test data. The experiment verifies the feasibility of conducting operation test using training equipment through the integrated test and training system.
Pengembangan Aplikasi Penilaian Angka Kredit Dosen (Studi kasus: FMIPA Universitas Lampung)

According to Law Number 14 of 2005 on Teachers and Lecturers, lecturers are defined as professional educators and scholars whose primary responsibility is to transform, advance, and disseminate knowledge, technology, and the arts through education, research, and community service, often referred to as the Tri Dharma of Higher Education (Chapter 1, Article 1, paragraph 2). The assessment of lecturers' performance is deemed essential in the context of implementing the Tri Dharma of Higher Education to gauge the degree to which it is executed. In practice, lecturers are required to accrue credit points in line with the established standards specified in the operational guidelines for credit point assessment. Nevertheless, lecturers often encounter delays in the collection of these credit points, primarily due to the substantial amount of data used as assessment criteria and the manual creation of reports using Microsoft Excel. Consequently, this protracted process is responsible for the extensive time required for the preparation of Academic Qualification Credit Points (DUPAK) reports. To tackle this issue, the development of an Information System has been proposed to expedite and enhance the accuracy of data collection. The Information System is constructed using the prototype methodology, which encompasses three core phases: "Listening to Customer" to identify user requirements, "Building/Revising the Mock-Up" to develop the prototype, and "Testing the Mock-Up" to assess its compliance with user expectations. The system is coded using the PHP programming language, and the Laravel framework is employed to simplify the coding process. The system's functionality is rigorously tested using the Black Box Testing method with Equivalence Partitioning to ensure its reliability and accuracy. The outcome of this research is a Web-Based Lecturer Performance Evaluation Information System designed to streamline the DUPAK report creation process for lecturers, making it more efficient and precise.
FORMATION OF PROJECT CULTURE OF FUTURE TEACHERS USING INFORMATION AND COMMUNICATION TECHNOLOGIES

In the modern technocratic society, the development of which is characterized by extreme mobility and changeability, the project type of culture begins to dominate, becoming one of the central mechanisms of the future, revealing at the same time the universal and synthetic nature of project activity, in which a combination of technocratic and humane, research and prognostic, informational and educational and socially transformative beginning, and this leads to the fact that every teacher needs to master the project activity in its various variants. The success of solving this task largely depends on the use of modern information and communication technologies in the process of professional training of future teachers. These technologies make the process of training future teachers manageable and individually differentiated, with a high specific weight of independent and educational activities. The article examines the possibilities of using interactive learning technologies and their influence on the formation of the project culture of future teachers. It was found that the use of information and communication technologies in the educational process releases creative forces, improves the effectiveness and quality of education due to novelty and maintaining interest, contributes to the optimization of educational activities, and the improvement of tools for the development of the project culture of future teachers. Information and communication technologies combine three fundamental components of modern pedagogy: development of creative thinking, formation of project thinking, mastery of information and communication technologies as a new instrumental sphere of creative cognitive activity. It was established that the use of information and communication technologies in the process of professional training contributes to the formation of new pedagogical thinking, creativity, and the desire to master innovative technologies in future teachers, which definitely improves the process of forming the project culture of future teachers. We see the prospects for further research in the definition and substantiation of the pedagogical conditions for the formation of the project culture of the future teacher in the process of professional training.
EXPLORING THE EFFECTIVENESS OF SITUATIONAL TASKS IN CHEMISTRY TEACHER TRAINING WITHIN THE FRAMEWORK OF DISTANCE LEARNING

This article puts forward the view that situational tasks help enhance professional teacher training. It also outlines the background of the “situational task” concept. Employing an activity-based approach, the article justifies three levels (generative, partially exploratory, creative) of situational tasks and exemplifies them through the educational module “Chemistry Teaching Methods”. It shows that situational tasks are particularly important, with their practical nature, depicting real-life scenarios of future professional activity, encouraging the development of intellectual capabilities, the capacity to assess data, make comparisons, identify the key points, explain, work with supplementary materials. Besides, prominence is given to the use of knowledge and techniques from multiple educational areas, the encouragement of curiosity and enthusiasm for the subject and the teacher’s support in tackling tasks. The application of systemic and systematic use of situational tasks is highlighted for its superior benefits when compared to traditional generative-informational approaches. The article proves that the use of situational learning technology assists in the development of analytical, cognitive, practical, communicative and creative skills in future teachers. At the same time, this technology combines the components of problem- and project-based learning, as well as ICT. To illustrate, a situational task is constructed from a specific educational situation (utilizing the case method). Situational learning is an approach which facilitates the tight integration of theoretical knowledge into the practical training of future teachers. As a method of assessment, it is successful in both traditional and distance learning, decreasing the risk of cheating. Integrating situational tasks in professional teacher training offers extensive opportunities to develop diverse groups of key competences, obtain essential professional knowledge, skills and abilities, as well as sharpen analytical thinking. Further research should focus on the technological and procedural aspects associated with the application of situational tasks in chemistry teacher training.
PEDAGOGICAL DEONTOLOGY IN THE INCLUSIVE PROCESS: THEORETICAL ASPECT

The article focuses on the moral and ethical aspect of the pedagogical activity of a teacher of an educational institution with inclusive education. The purpose of the publication is to reveal the peculiarities of the interaction of the teacher and students with developmental disorders through the prism of moral and ethical, universal, social, and professional values. With the help of methods of observation, questionnaires, surveys, pedagogical experiment, processing of the received data, existing violations of professional ethics, principles of tolerance in the process of teaching and educating students with special educational needs were established, and attention was focused on increasing the level of professional culture and skill of modern teachers. It has been established that the harmonious development of schoolchildren, including those with special educational needs, depends on the psychologically favorable atmosphere created in the educational institution, sincere relationships, and constant psychological and pedagogical support. The relationship between professional abilities and skills, the level of culture, professional orientation and personal qualities of the teacher in creating a favorable tolerant educational environment and providing quality educational services to students with developmental disorders is demonstrate. The essence of the concept of "pedagogical deontology" is revealed, its structural components and their significance in the practical activity of a teacher of an educational institution with inclusive education are highlighted. The peculiarities of the professional activity of a modern teacher of an inclusive school are analyzed, the main ethical categories of pedagogical deontology are singled out as a factor influencing the success of education, upbringing and personality development of the student. The main directions of scientific research in this field are revealed, attention is focused on the views of scientists regarding the specifics of inclusive education and the need for the formation of inclusive competence, the teacher's compliance with the chosen profession and qualification requirements, requests and expectations of education seekers and their parents. It has been established that the basis of the professional activity of a teacher of an educational institution with inclusive education is the idea of equality, freedom and respect for the individual, child-centeredness, which should be the guiding principle in practical work with all students.