Reviewerly/exHarmony huggingface dataset:
DEFINITION OF THE CONCEPT OF EDUCATIONAL NETWORK DISCOURSE: DEFINITION OF THE CONCEPTS OF “EDUCATIONAL DISCOURSE”, “SCIENTIFIC DISCOURSE” AND “EDUCATION NETWORK DISCOURSE”

The article is devoted to distinguishing a new discourse - the educational network, which is a completely new unit that has not been studied before, from traditional scientific and pedagogical discourses and its characteristics according to the classical established scheme of structuring discourses. In the modern world of digitization and automation of various processes, science and education do not stand aside, and the teacher is at the forefront of changes - scientific and pedagogical discourses undergo not only formal, but also qualitative changes, which are caused by the search for new educational paradigms and concepts, principles and approaches to the organization of educational process, as well as methods, forms and means of teaching students, the introduction of technological innovations that will contribute to the improvement of the quality of foreign language philological education. All this could not fail to fundamentally change the structures of these discourses and lead to the formation of new ones. The purpose of the study is to separate the educational network discourse into a separate structural unit. The task: to analyze the online “life” of Ukrainian educators and, based on this research, to describe the structure of educational network discourse.The object of the study is the educational network discourse, and the subject of the study is the definition of the main differential features inherent in any discourse. The actual material is a collection of materials on social networks “Intagram”, “Facebook”, “YouTube”, “TikTok”. In total, more than 500 pages of both well-known and notable figures in the Ukrainian educational space (the official pages of I. D. Farion and O. M. Avramenko) and little-known ones have been processed. Among the general scientific methods, analysis, synthesis, induction, deduction, observation were used, in particular not included (observation of official pages); descriptive. The scientific novelty of the study lies in the fact that it is the first attempt to study the educational network discourse, which is widely represented in today’s society. Therefore, the educational network discourse is not purely institutional, but is an argumentative, informational, socially ritual institutional-household mediated type of discourse.
FLOOD DISASTER MITIGATION IN SONE DISTRICT, TANAH BUMBU REGENCY, SOUTH KALIMANTAN AS A SOURCE FOR LEARNING IPS CLASS VII

AbstrakFlood disasters that occur in Satui District almost occur every year, in 2019 there were six villages affected by flooding in Satui District, the purpose of this study was to find out the causes of floods that occurred in Satui District, and the efforts of the government and the community in minimizing the risk of loss in dealing with floods. flood disaster or better known as disaster mitigation. Mitigation activities include preparation before a disaster occurs, handling when a disaster occurs, evacuating residents affected by floods, and socializing disaster mitigation after a flood which can be socialized through social studies education that specifically discusses social issues. This study uses a qualitative approach. Data collection techniques include; interviews, observations and documentation of data analysis starting from reduction, presentation, and verification. The results of the study illustrate that the flood disaster that occurred in Satui District, Tanah Bumbu Regency is caused because it is a lowland if high rainfall will cause flooding, the government's efforts in implementing mitigation have not been maximized so it is necessary to disseminate information to the community by making flood disaster mitigation a learning resource for social studies class. VII junior high school level.
PREDICTION, MONITORING, AND EARLY WARNING SYSTEM OF WATER LEVELS IN FLOOD PROGRAM AREAS IN BANJARMASIN USING IoT AND LINEAR REGRESSION

Banjarmasin is a city of a thousand rivers, floods often occur due to high tides and accompanied by rain. An early warning system is needed for monitoring and predicting river tides, especially in rivers that are prone to disasters. This study aims to develop an early warning system for river water levels using IoT and predicting linear regression analysis. The research method is through the manufacture of the NodeMCU V3 early warning system device , Microcontroller, and Ultrasonic A02YYUW. Monitoring to collect water level data at high tide is carried out on the banks of the river, then predictions are made using simple linear regression analysis. The results showed that an early warning system for river water levels at high tide can be read in the A02YYUW ultrasonic sensor database which is sent to the Mysql database. Water surface distance data with tools are used to predict early warning, simple regression analysis shows significant results on the t test and simple linear regression equation = 29.472 - 0.061 X + e. This means that the river water level at high tide approaches an early warning tool, namely an ultrasonic sensor. The conclusion of this study is that an IoT-based early warning system combined with simple linear regression can monitor and predict river water level rise.
A Comparative Analysis of OS Forensics Tools

Abstract: The internet is expanding at a breakneck pace, as the number of crimes perpetrated using or against computers. The area of computer forensics has arisen in reaction to the rise of computer crime. Computer forensics is the meticulous collection and examination of electronic evidence that not only analyses the damage to a computer because of an electronic attack but also recovers lost data from such a system to convict a criminal. As a result, the standard forensic process that is required after an electronic attack involves collecting evidence from a computer system, analyzing, and presentation of the collected evidence in court. Forensics deals primarily with the recovery and analysis of latent evidence. The growth of digital forensics has substantially increased the requirement for effective tools. There are several tools available today which are used to investigate the OS of a given computer. The purpose of this paper is to compare OS forensics tools by evaluating their ease of use, functionality, performance, and product support and documentation. This research will provide a brief comparative analysis of two widely used OS forensic tools-OSForensics and autopsy based on various contradictory factors.
A Privacy Preserving Framework to Protect Sensitive Data in Online Social Networks

In this day and age, Internet has become an innate part of our existence. This virtual platform brings people together, facilitating information exchange, sharing photos, posts, etc. As interaction happens without any physical presence in the medium, trust is often compromised in all these platforms operating via the Internet. Although many of these sites provide their ingrained privacy settings, they are limited and do not cater to all users’ needs. The proposed work highlights the privacy risk associated with various personally identifiable information posted in online social networks (OSN). The work is three-facet, i.e. it first identifies the type of private information which is unwittingly revealed in social media tweets. To prevent unauthorized users from accessing private data, an anonymous mechanism is put forth that securely encodes the data. The information loss incurred due to anonymization is analyzed to check how much of privacy-utility trade-off is attained. The private data is then outsourced to a more secure server that only authorized people can access. Finally, to provide effective retrieval at the server-side, the traditional searchable encryption technique is modified, considering the typo errors observed in user searching behaviours. With all its constituents mentioned above, the purported approach aims to give more fine-grained control to the user to decide who can access their data and is the correct progression towards amputating privacy violation.
Text classification to predict skin concerns over skincare using bidirectional mechanism in long short-term memory

There are numerous types of skincare, each with its own set of benefits based on key ingredients. This may be difficult for beginners who are purchasing skincare for the first time due to a lack of knowledge about skincare and their own skin concerns. Hence, based on this problem, it is possible to find out the right skin concern that can be handled in each skincare product automatically by multi-class text classification. The purpose of this research is to build a deep learning model capable of predicting skin concerns that each skincare product can treat. By comparing the performance and results of predicting the correct skin condition for each skincare product description using both long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), The best results are given by Bi-LSTM, which has an accuracy score of 98.04% and a loss score of 19.19%. Meanwhile, LSTM results have an accuracy score of 94.12% and a loss score of 19.91%.
Pakistan sign language to Urdu translator using Kinect

The lack of a standardized sign language, and the inability to communicate with the hearing community through sign language, are the two major issues confronting Pakistan's deaf and dumb society. In this research, we have proposed an approach to help eradicate one of the issues. Now, using the proposed framework, the deaf community can communicate with normal people. The purpose of this work is to reduce the struggles of hearing-impaired people in Pakistan. A Kinect-based Pakistan sign language (PSL) to Urdu language translator is being developed to accomplish this. The system’s dynamic sign language segment works in three phases: acquiring key points from the dataset, training a long short-term memory (LSTM) model, and making real-time predictions using sequences through openCV integrated with the Kinect device. The system’s static sign language segment works in three phases: acquiring an image-based dataset, training a model garden, and making real-time predictions using openCV integrated with the Kinect device. It also allows the hearing user to input Urdu audio to the Kinect microphone. The proposed sign language translator can detect and predict the PSL performed in front of the Kinect device and produce translations in Urdu.
Research on Integrated Control Strategy of Reactive Power and Voltage for Distributed Generation

Abstract The access of distributed photovoltaic power sources will change the power flow distribution in the distribution network. When the line load is light and the photovoltaic access capacity is large, the distribution network will become a multi-source network, which will cause the reverse flow of the line, increase the line voltage, and affect the power quality of the distribution network. Aiming at this problem, this paper studies the voltage control strategy after photovoltaic grid-connected, and proposes a four-level reactive voltage control strategy to solve the voltage problem when photovoltaic large-scale grid-connected. Finally, the method is verified by an example.
Monocular 3D object detection via Mask‐Revised Network and quality perception loss

Abstract The accuracy of the monocular 3D detection tasks based on the Pseudo‐LiDAR method is improved greatly. However, the depth map obtained by depth estimation contains a lot of noise, which limits the detection accuracy. To address this problem, an efficient monocular 3D target detection method combined with a Mask‐Revised Network and Quality Perception Loss is proposed. The method adaptively encodes the image into the mask and adjusts the feature weight from the region of interest by the visual attention mechanism. Then, the 2D bounding box confidence and 3D bounding box quality are used to calculate each confidence of the 3D bounding box to realize the quality perception of prediction results. The proposed algorithm is evaluated on the KITTI test datasets, and the results show that the proposed method achieves state‐of‐the‐art performance on the task of monocular 3D target detection and outperforms the existing methods by about 2.75% on AP 40 for Car categories.
JPR volume 59 issue 4 Cover and Back matter

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Goal-Oriented Coordination with Cumulative Goals

Multi-actor coordination is in general a complex problem, with inefficient and brittle solutions. This paper addresses the problem of abstract coordination of multiple actors at the goal-level, which often enables us to avoid these problems. Goals in multi-actor settings often rely on the cumulative efforts of a group of distinct actors whose individual, measurable contributions accumulate to lead to the fulfillment of the goal. We formalize these as cumulative goals in a mathematical framework that permits us to measure individual actor contributions on a variety of scales (including qualitative ones). We view these as softgoals, a conception general enough to incorporate the more traditional hardgoals. Within this mathematical setting, we address the coordination problem as one of deciding which actor is allocated to fulfilling each goal of interest (and the associated problem of goal model maintenance). We also report some empirical evaluation results.
Enhanced Machine learning algorithms Lightweight Ensemble Classification of Normal versus Leukemic Cel

Leukemia is type of cancer in blood which impacts the lymphatic framework and the bone marrow and also impacts white blood cells. Leukemia, in contrast to other types of cancer, does not produce solid tumors; instead, it produces a huge number of aberrant white blood cells that crowd out the healthy blood cells. Machine learning algorithms that are widely utilized in the treatment of leukemia, whether it is to classify the various forms of leukemia or to determine whether a patient has the disease. It is a malignant kind of cancer that results in a number of medical issues. Expert hematologists and pathologists manually examine blood samples under the microscope to make a diagnosis. Techniques like image processing and pattern recognition can be utilized to help these experts. In order to attain excellent performance in the categorization of malignant leukocytes challenge, this paper suggests straightforward modifications to conventional neural network topologies. Consequently, there is considerable interest in the trustworthy and precise recognition of nonmalignant and malignant cells. Leukemia can be automatically detected using computer-aided diagnostic (CAD) models, which can help doctors and be useful for leukemia early identification. In this single-center study, we attempted to develop a deep learning model for classification of leukemic B-lymphoblasts. Data augmentation methods were utilized to manage the little dataset size and an exchange learning technique was utilized to accelerate learning and upgrade the presentation of the recommended network in order to create a trustworthy and accurate deep learner. The outcomes demonstrate that our suggested approach surpassed seperate networks with a test accuracy of 95.59% in the Leukemic B-lymphoblast examination, and was caable to merge characteristics extracted from the top deep learning models.
Influence of Team Negotiations on Collaborative Value within Ashoka Fellows' Organizations in Africa

Purpose: The study sought to determine the influence of team negotiations on collaborative value within Ashoka Fellows' Organizations in Africa&#x0D; Methodology: The study applied pragmatism philosophy to offer several ways to bridge dichotomies in mixed methods approaches to social science. Explanatory sequential mixed-method research design consisting of two distinct phases, namely quantitative and qualitative, was adopted. Both qualitative and quantitative study methods were adopted. In the quantitative study, the target population constituted all the 154 Ashoka Fellows' Organizations working in 19 countries in Africa. Data was collected using a structured questionnaire administered online to the founders (Ashoka Fellows) or the Ashoka Fellows' Organizations' CEOs. One hundred responded by filling out the questionnaire, which translated to a 64.9% response rate. Additionally, qualitative data applied purposive sampling and selected six Ashoka regional team leaders in Africa for in-depth interviews. They all were available for the interviews translating to a 100% response rate. Data analysis techniques combined descriptive and inferential statistics. Statistical Package for Social Sciences and SmartPLS 3 software were used to analyze the collected data.&#x0D; Findings: The study results revealed that team negotiation significantly influences collaborative value within Ashoka Fellows’ Organizations in Africa. Team Negotiations have a significant influence on collaborative value with an R2 = 0.214, chi-square X2 (10, N=100) = 72.319, p&lt;.05, SRMR=0.090, Rms-theta=0.234, and NFI=0.773. The null hypothesis was rejected.&#x0D; Unique Contribution to Theory, Practice and Policy: The study recommends that negotiating teams should ensure that they know as much as possible about the other side of the negotiation. The teams' perception is vital in the negotiating team's preparedness as they should be aware of what is necessary at every stage of the negotiating process. When negotiating, the focus should be on looking for alternative ways of dealing with the problem before finding a solution that satisfies all members. The evaluation of options during team negotiations should be objective. At large, the negotiating teams should ensure that all members feel comfortable with the solution to the problem raised before making the steps to implement the solution.&#x0D; &#x0D;
OPTIMIZATION OF E-LEARNING AND PERFORMANCE USING IOT AND 6G TECHNOLOGY

The sixth-generation (6G) has stricter criteria for the online learning capability and high interpretability of taught algorithms. It is anticipated that machine learning would be crucial for making network effective and flexible, however the most promising technologies are frequently considered as secret elements because of their profound designs’ major areas of strength for and. To make AI calculations more reasonable to 6G-empowered web of things (IoT) organizations, the motivation behind this paper is to analyse their translations. This article presents two different ways for acquiring translations: the free technique and the Joint strategy. Probes numerous IoT network datasets exhibit that the recommended techniques produce unrivalled execution regarding the two clarifications and forecasts.
IOT Based Privacy Preserving Machine Learning Algorithm for High-Speed Encryption

Many of our ordinary things will soon be linked, due to contemporary Internet of Things (IoT) technology. These items will be able to connect and interact with one other as well as their surroundings, automating much of our job. Security, seamless authentication, resilience, and simplicity of maintenance are essential for IoT node communication. Blockchain appears as a possible option to provide these vital features. Blockchain’s decentralised nature has enabled it to address several IoT security, maintenance, and authentication issues. As a consequence, the number of blockchain-based Internet of Things applications has increased dramatically in recent years. This study presents a blockchain-based industrial IOT method using RSA encryption. RSA encryption results in fast encryption.
Blockchain Technology – Understanding Its Application in Humanitarian Supply Chains

This paper provides a holistic view of blockchain technology promises in humanitarian operations. Blockchain provides most value in the areas of trust, transparency, and visibility, yet it’s still unregulated and its technical complexities compounded with a lack of knowledge, technical skills and training pose stringent challenges in its application. Our review offers valuable and unique insight for academic and supply chain practitioners on the extant status and its potential areas of application in humanitarian settings while laying a firm foundation for further research.
Research into Early Stage Identification of Entrepreneurs: Educational Tools Using Virtual and Augmented Reality

One of the big challenges facing youth over all the world is unemployment. There are many reasons that unemployment exists specially within this community. It is mainly due to lack of jobs, knowledge, professional skills, and inability of the youth to recognise their potential as entrepreneurs. This is off course impact on national economy and there is a need to help this community to identify their individual potential at early stage and develop the necessary educational and training tools that can help them to overcome such unemployment challenge. This ongoing research programme is mainly focused on researching into early-stage identification of young entrepreneurs and aiming to develop the tools that can support them in short and long development. This paper presents the initial outcomes of whether virtual reality and augmented reality tools can offer advanced educational and training tools, better suited to entrepreneurs than conventional classroom-based training. It is important to mention that key part of this research programme is also focused on integrating Artificial Intelligences tools. This is to enhance the tools learning experience and meet individual needs. The outcomes of this studies are reported in the other paper published in this conference.
Deduction of Edge Signs in Bitcoin Alpha Social Network Modelled as a Signed Graph

Signed graphs have a wide array of applications in the social networking domain as the industry and platforms highly rely on forming trust links between users so as to smooth out the process of interacting virtually. Signed graphs facilitate this process by providing a mode of representing such networks as graphs that have edges with a positive/negative sign that help in defining the nature of relationship between the nodes(that can represent the users of the platform or any respective representatives of the platform) of the graph. In this paper we have dealt with the platform of bitcoin alpha that is now coming into notice due to cryptocurrency’s rising popularity. Trading online can be risky and thus the entire platform is functional on the principle of trust/distrust between such anonymous users. We have attempted to formulate the social network of bitcoin alpha platform into a signed graph and predict the links to establish trust/distrust between any two users in the entire graph using concepts of balanced and unbalanced graph theories, and fairness and goodness measures of vertices. Fairness of a user denotes how reliable the rating given by that particular user to others is, whereas goodness measures how likeable or trustworthy a particular user of the website is. Using these metrics, we have attempted to solve this problem.