Reviewerly/exHarmony huggingface dataset:
Encrochat: The Hacker with a Warrant and Fair Trials?

This paper introduces the Encrochat operation as an example of the technological complexity, the cross-border, and the cross-disciplinary elements of one contemporary digital investigation. The use of encryption for large-scale criminal activity and organized crime requires law enforcement to act pro-actively to secure evidence, to rely on cross-border evidence exchange, and to use more efficient digital forensic techniques for decryption, data acquisition, and analysis of volumized evidence. The Encrochat investigation also poses the question whether the traditional fair trial principle can still ensure minimum state intrusion and upholding of legitimacy in the new ubiquitous investigation process, where digital forensics methods and tools for hacking and data acquisition are used to identify and arrest thousands of suspects and collect evidence in real-time during criminal activity. The operation is examined through the lens of the right to a fair trial, as codified in Art. 6 ECHR, in order to exemplify three challenging aspects. Firstly, in cross-border investigations there are no binding digital forensics standards in criminal proceedings or forensic reports exchange policy which demands reliability and compliance with Art. 6 ECHR-based evidence rules. Secondly, the defense’s stand is not sufficiently addressed in current digital evidence legislation or mutual trust-based instruments at the EU level. Finally, the judicial process lacks scalable procedures to scrutinize digital evidence processing and reliability and is exposed to technology dependences. The identified gaps and their practical impact require a novel approach to digital evidence governance.
Elliptic Curve Cryptography for Defense Use

With the rapid growth in the development in communication technology recently had lead the researchers to shift their focus towards the security of vital data that transmitted over public network such as internet. In the modern era a vital research area is emerging in data security over unsecured public networks. There is a lot of scope for research in areas such as military communication, business communication etc. In the defence communication data is very vital and critical thus needs to be protected from intruders. Even though there are a lot of technological developments in hardware and software which leads to narrowing down the data transmission rate and storage capabilities in defence systems. However there is still a persistent threat to the defence systems, need to be protected against physical threats as well as attacks by enemies in data transmission. Hence a need is arise to develop a customized algorithm for data security in E.W. (Electronic Warfare) Systems. The transmission of data between the JAM and the DFS systems are a matter of great concern as it affects the national security. So to provide a better security Elliptic Curve Cryptography is proposed to be use in the encryption of vital defence data. One of the most important advantages of ECC is, it can provides better security strength as compared to RSA with smaller key size. Thus the proposed approach aims an effective solution for secured data transmission between the communicating parties in the defence system.
Gradient Clipping in Deep Learning: A Dynamical Systems Perspective

Neural networks are ubiquitous components of Machine Learning (ML) algorithms.However, training them is challenging due to problems associated with exploding and vanishing loss-gradients.Gradient clipping is shown to effectively combat both the vanishing gradients and the exploding gradients problems.As the name suggests, gradients are clipped in order to prevent large updates.At the same time, very small neural network weights are updated using larger step-sizes.Although widely used in practice, there is very little theory surrounding clipping.In this paper, we analyze two popular gradient clipping techniques -the classic norm-based gradient clipping method and the adaptive gradient clipping technique.We prove that gradient clipping ensures numerical stability with very high probability.Further, clipping based stochastic gradient descent converges to a set of neural network weights that minimizes the average scaled training loss in a local sense.The averaging is with respect to the distribution that generated the training data.The scaling is a consequence of gradient clipping.We use tools from the theory of dynamical systems for the presented analysis.
Pola Aktivitas Pengunjung Terhadap Ruang Terbuka Publik Pada Kawasan Pantai Melasti, Bali

Melasti Beach was used as the object of research because it has a high level of visits, recorded during January and February 2021, the number of visitors who came to Melasti Beach was 86,214 people. The density of beach visitor activities and the existence of religious ritual activities in it are considered to represent the pattern of visitor activity in public open spaces. Seeing the high number of visits, the author is interested in conducting this research to find out what activities are taking place in it and also the pattern of activities in it. This aims to obtain a pattern of circulation of activities that take place in the research location to be a reference for designers in planning public open space facilities. The theory used in this research is behavior setting, which is studying the relationship between human behavior and the environment that accommodates these activities. The method of data collection is by observing the movement patterns of visitor activities and interviews and using questionnaires distributed to people who have visited Melasti Beach. Technical analysis of the data used is descriptive analysis is done to obtain the use of space based on a person's activity pattern which is described through behavior mapping. It takes several variables to analyze this research, including actors, activities, flow, time, and form. The results of this study are the activity patterns of Melasti Beach visitors which are divided into 3 types: high-intensity activity patterns occur during the day off, moderate intensity occurs on weekdays and low intensity occurs in the afternoon to evening.
Digitalization of the most important enlightenment in today's minds

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Intelligent Continuous Monitoring to Handle Data Distributional Changes for IoT Systems

Intelligent continuous monitoring of an IoT system to identify the operational changes, encompassing both normal and abnormal scenarios, with drift in sensing device is a challenging problem. It demands capability of learning continuously with multiple interventions or shifts, without forgetting past events information. However, forgetting the past learned knowledge, known as catastrophic forgetting, impacts significantly on the performance of continuous monitoring. In this work, we propose a generative neural network based model to handle various operational changes. Here, one objective is to learn continually by capturing past data distributional knowledge, while adapting new data signatures. Other objective, is to handle changes in data distribution like, to identify drifts, as well as, variations in diverse operational conditions of the system. We have experimented using vibration sensor based public real-world bearing data and performed extensive analysis incorporating synthetic drifts. Proposed method outperforms existing benchmark performances with clear separation of drift in learned representation.
Modern trends in educational services of universities

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
How to break the chain of problems in education?

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science

After the many ups and downs over the past 70 years of AI and 50 years of data science (DS), AI/DS have migrated into their new age. This new-generation AI/DS build on the consilience and universology of science, technology and engineering. In particular, it synergizes AI and data science, inspiring Trans-AI/DS (i.e., Trans-AI, Trans-DS and their hybridization) thinking, vision, paradigms, approaches and practices. Trans-AI/DS feature their transformative (or transformational), transdisciplinary, and translational AI/DS in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. Here, we discuss these important paradigm shifts and directions. Trans-AI/DS encourage big and outside-the-box thinking beyond the classic AI, data-driven, model-based, statistical, shallow and deep learning hypotheses, methodologies and developments. They pursue foundational and original AI/DS thinking, theories and practices from the essence of intelligences and complexities inherent in humans, nature, society, and their creations.
Appearance Awared Detector for MOT: An Enhanced ReID Branch for Tracking Memorize

The traditional ByteTrack approach to multi-target tracking, focusing on simple, effective algorithms. It performs well in short-term multi-target tracking tasks with 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on MOT17 30 FPS and is currently ranked number one in the MOTChallenge rankings. But for situations where the camera is moving, or for completing target recovery tasks after a brief loss of position and track information, conventional MOT modules often do not perform as well as they should. To make bytetrack perform better when the motion background pattern is more complex, we used the re-identification module and added appearance information to enhance ByteTrack’s MOT process. We verified our improvement on MOT17, which achieved higher results than the original on the metrics Precision and Recall, and was also more suitable for more complex scenarios.
Vehicle Object Detection Based on Deep Learning

With the continuous improvement of science and technology and living standards, cars have become a necessary means of transportation for people to travel, which is bound to be followed by traffic accidents, so it is particularly necessary to detect or identify cars. Aiming at the above problems, this paper focuses on the application of deep learning in vehicle recognition problems, and is committed to finding a vehicle recognition algorithm with high recognition rate and stability. This paper focuses on the analysis of SSD algorithm and its basic theory convolutional neural network. Finally, this paper uses the pictures of stationary vehicles and a film video respectively to identify the stationary and moving state of vehicles. In the process of image recognition, the rationality and accuracy of the proposed method are verified according to the accuracy rate given above the image.
Motivation to Use Gamification Elements in E-Learning for Formal and Non-Formal Education

The implementation of gamification in e-learning is not new, both within the context of formal and informal. Formal education is a learning activity organized by both public and private parties that refers to the official education system of a country. Meanwhile, informal education is another learning activity that does not refer to the official education system of a country. Not only visually appealing, but gamification elements also have a certain motivation and emotional value for the user. This study seeks to compare the use of gamification elements and their motivation between formal and nonformal education. The method used was a Systematic Literature Review using the Kitchenham method. From the selected databases, 65 articles were obtained that had implemented gamification in e-learning. The gamification elements were grouped by motivation and emotion based on the Octalysis framework. The results showed that ownership and possession were the most prominent motivations for implementing gamification.
A Cooperative Action Research on the Topic Selection Activities Free School Year System Program of Special School: Focusing on the theme of the Cafe Management Project

Purpose: he purpose of this study was to implement a cafe management project that utilized the theme of topic selection activities as economic education in a special school and to explore the practical knowledge gained by special teachers during this process. Method: A collaborative action study was conducted with a professional learning community consisting of subject only teachers and a homeroom teacher teaching for six students in the first grade middle school classroom. The collected data were analyzed comprehensive analysis procedure based on practical compromise, 46 semantic units, 13 subcategories, and four categories were derived after the analysis. Results: According to the results first, curriculum for the cafe management project was necessary to reorganize the curriculum between subjects based on the curriculum in special schools, Second, when operating the project as economic education, it is necessary to balance the contents, such as production, consumption, and distribution, Third, it was necessary to activate cooperative action research through a professional learning community in order to operate programs that guaranteed autonomy, such as topic selection activities however, the cases and information wore lacking. Conclusion: This study provided valuable preliminary result about specific field cases for the free school year system topic selection activities and examined practical Challenges.
The genetic algorithm and <scp>BP</scp> neural network in financial supply chain management under information sharing

Abstract In today's global business competition environment, the significant changes in the market environment make it very difficult for supply chain enterprises to gain a place in the fierce market only by their strength. This article will do the following research work to effectively play the role of supply chain management. First, the concepts, and characteristics, related applications of supply chain information sharing (IS) and genetic algorithm back propagation (GABP) neural networks, are expounded. Then, the feasibility of applying BP neural network (BPNN) to evaluate supply chain IS is analysed. Next, the assessment indicator system for IS is constructed from four aspects: IS infrastructure, IS member characteristics, IS content, and the quality of supply chain enterprises. Finally, the principle and algorithm of BPNN are analysed, the network model is constructed, the BPNN process based on GA is designed, and every step in the process is explained in detail. The sample input is provided for the network model by obtaining and sorting data. The supply chain IS assessment model based on the GABP neural network is constructed. The results show that the assessment model of supply chain IS based on the GABP neural network has high accuracy, and the highest network accuracy reaches 95.01%, which proves that the assessment model based on BPNN has certain advantages for the supply chain IS of the financial industry.
Development of a geodatabase for retrieval of geochemical data from oil wells: A case study from the Gachsaran oilfield; SW Iran

Managing geochemical data is crucial in the petroleum industry, as the exploration and development of oilfields typically require a large volume of data from geochemical analyses. Specifically, geochemical data relevant to oil wells, source rocks, and reservoir rocks are often related to location dimensions. Therefore, the geodatabase can be a powerful tool in collecting, storing, retrieving, processing, and analysis of these data. In this study, a novel geodatabase approach was utilized to store, display, and present geochemical data of petroleum samples from oil wells. The geodatabase scheme comprised two types of data layers: spatial data (e.g., oil well, oilfield boundary, formations including source rocks/reservoir rocks and sector) and non-spatial data (e.g., oil samples and geochemical information). The PostgreSQL/PostGIS object-relational database management system was used to design and implement the geodatabase, while QGIS was employed to connect to the database and visualize the geometrical data of the Gachsaran oilfield. The QGIS Data Plotly plugin was then used to interpret geochemical data. According to the findings of this study, the PostgreSQL/PostGIS open-source database is a suitable method for spatial data management of oil well geometrical data. In addition, the geochemical evaluation of possible source rocks and determination of geochemical characteristics of reservoir oil were analyzed using this geodatabase. Moreover, integrating oil geochemical data into a geodatabase prevents the scattering of geochemical data and facilitates data interpretation.
Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.&#x0D;
A Novel Binary DE Based on the Binary Search Space Topology

Differential evolution is known as a simple and well-argued evolutionary algorithm that demonstrates the high performance in many hard black-box optimization problems with continuous variables. The main feature of differential evolution is the difference-based mutation. The mutation explores the search space using the distribution of points in the population and usually can well adapt to the objective function landscape. There exist some modifications of differential evolution for the binary search space. The proposed approach involves the understanding of the binary space topology for developing a better analogue of the difference-based mutation. We have compared the proposed binary differential evolution algorithm with the standard binary genetic algorithm using a set of binary test problems, including hard deceptive problems. The preliminary experimental results shows that new binary differential evolution is a competitive search algorithm and outperforms the binary genetic algorithm in reliability for some problems but yields it in the required number of function evaluations. The proposed approach for developing mutation in DE can be expanded to all other mutation schemes for increasing the performance.
Supporting Democracy through Content-Neutral Social Media Policies

The internet and social media carry vast amounts of new information every second. To make these flows manageable, platforms engage in content moderation, using algorithms and humans to decide which content to recommend and which to remove. These decisions have profound effects on our elections, democratic debate, and human well-being. The U.S. government cannot directly regulate these decisions due to the scale of the content and the First Amendment. Rather than focusing exclusively on whether or what content gets moderated, policy-makers should focus on ensuring that incentives and processes create an information infrastructure that can support a robust democracy. These policies are most likely to be content-neutral. Three content-neutral mechanisms are promising targets for policy: process, transparency, and de-amplification.
The Performance Gap of Policy Information Systems: A Knowledge Infrastructure Assessment Framework

Digital technologies’ development and their ubiquity has significantly changed the way information is collected and shared. These changes are also observed in the ways that knowledge and information used to design and implement Science, Technology, and Innovation (STI) policies are increasingly made accessible via digital platforms. Lacking, however, are evaluation frameworks to measure the performance and effectiveness of public information systems used for STI policy work. This limits what we know about what aspects work for whom, when, and why. As a response to this gap limiting our collective capacity for improving their utility, this paper presents an assessment framework tool for STI policy-focused digital knowledge platforms. Our proposition is informed by theoretical lessons from the areas of work on Knowledge Infrastructures (KIs) and Next Generation Repositories (NGRs), and practical experiences from policy professionals working in STI policy domains. The tool’s architecture is structured around three interdependent thematic pillars of performance in the production, aggregation, distribution, and maintenance of knowledge in digital information platforms: the communities pillar, the technical systems pillar, and the sustainability pillar. To test the design and utility of our proposed evaluation framework, we applied it on a digital platform of STI policy instruments maintained by the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the Global Observatory for Science, Technology, and Innovation Policy (GO-SPIN) platform. We conclude with reflections on future areas for evaluation framework development.
Comparing ensemble methods combined with different aggregating models using micrograph cell segmentation as an initial application example

Strategies such as ensemble learning and averaging techniques try to reduce the variance of single deep neural networks. The focus of this study is on ensemble averaging techniques, fusing the results of differently initialized and trained networks. Thereby, using micrograph cell segmentation as an application example, various ensembles have been initialized and formed during network training, whereby the following methods have been applied: (a) random seeds, (b) L1-norm pruning, (c) variable numbers of training examples, and (d) a combination of the latter 2 items. Furthermore, different averaging methods are in common use and were evaluated in this study. As averaging methods, the mean, the median, and the location parameter of an alpha-stable distribution, fit to the histograms of class membership probabilities (CMPs), as well as a majority vote of the members of an ensemble were considered. The performance of these methods is demonstrated and evaluated on a micrograph cell segmentation use case, employing a common state-of-the art deep convolutional neural network (DCNN) architecture exploiting the principle of the common VGG-architecture. The study demonstrates that for this data set, the choice of the ensemble averaging method only has a marginal influence on the evaluation metrics (accuracy and Dice coefficient) used to measure the segmentation performance. Nevertheless, for practical applications, a simple and fast estimate of the mean of the distribution is highly competitive with respect to the most sophisticated representation of the CMP distributions by an alpha-stable distribution, and hence seems the most proper ensemble averaging method to be used for this application.