Reviewerly/exHarmony huggingface dataset:
Possibility of Gaining Full Access to the Server Through Vulnerability of the Website

We have got the ip address of the server and we have found the open ports and host discovery. Found that port 80 is open and has a website which is vulnerable to sql injection (add admin user) by the outdated CMS version of the website. We have added as admin user and got the reverse shell in the server through the website. We have explored the database and put the backdoor to listen the server. After testing this we have cleared the logs.
Data remnants analysis of document files in Windows: Microsoft 365 as a case study

In the era of digitization, electronic evidence has become increasingly important for investigations and legal proceedings. However, traditional digital forensic technologies, such as recovery and carving, face limitations because of difficulties acquiring unallocated areas intact. Furthermore, artifacts and files previously used for tracing can be easily deleted manually or via anti-forensic tools, which hinders traceability. This paper presents a novel framework to overcome these limitations. This method facilitates a more precise and comprehensive tracing of residual files through data remnants analysis, a forensic approach that investigates traces of deleted or overwritten data. By systematically constructing a dataset based on user action, we identify and analyze all data remnants within the system, thereby revealing file traces. The results of a case study on Microsoft 365 demonstrate our proposed framework's superior efficacy and accuracy compared to existing methods. Our approach offers valuable insights into data remnants analysis and contributes to digital forensic investigations conducted on Windows systems.
A graph neural network approach to detect original review spammers of astroturfing campaigns

Online reviews affect consumers’ buying decisions. When astroturfing happens, the posted fake reviews not only cause confusion to the consumers, but in extreme cases, harm the reputation of others. Identifying the original posters of fake review in a discussion forum from the interactions with the peers in a collusive effort is essential to an overall detection strategy. However, existing detection approaches that focus on the mechanic characteristics of the posted reviews may fall short, since fake reviews cause more harm when astroturfing campaign happens involving multiple accomplices. We propose a detection framework with graph neural network, which incorporates the original perpetrator’s stylometric patterns and relationships with other accomplices. The framework was tested against the data collected from real incidents. Multiple deep learning models with fusion techniques were tested. Managerial and theoretical implications are provided.
Explainable forecasting of global horizontal irradiance over multiple time steps using temporal fusion transformer

Accurate prediction of solar irradiance is essential for the successful integration of solar power plants into electrical systems. Despite recent advancements in deep learning technology yielding impressive results in solar forecasting, their lack of interpretability has hindered their widespread adoption. In this paper, we propose a novel approach that integrates a Temporal Fusion Transformer (TFT) with a McClear model to achieve accurate and interpretable forecasting performance. The TFT is a deep learning model that provides transparency in its predictions through the use of interpretable self-attention layers for long-term dependencies, recurrent layers for local processing, specialized components for feature selection, and gating layers to suppress extraneous components. The model is capable of learning temporal associations between continuous time-series variables, namely, historical global horizontal irradiance (GHI) and clear sky GHI, accounting for cloud cover variability and clear sky conditions that are often ignored by most machine learning solar forecasters. Additionally, it minimizes a quantile loss during training to produce accurate probabilistic forecasts. In this study, we evaluate the performance of hourly GHI forecasts on eight diverse datasets with varying climates: temperate, cold, arid, and equatorial, for multiple temporal horizons of 2, 3, 6, 12, and 24 h. The model is benchmarked against both climatological persistence for deterministic forecasting and Complete History Persistence Ensemble for probabilistic forecasting. To prove that our model is not location locked, it has been blind tested on four completely different datasets. The results demonstrate that the proposed model outperforms its counterparts across all forecast horizons.
GeoGebra Augmented Reality to Facilitate the Manipulation of Different Semiotic Representations of Paraboloids

In this research, we investigated whether the use of GeoGebra Augmented Reality (AR) can foster students in conceptualising a mathematical object by manipulating its different semiotic representations. We designed and implemented, with thirty university mathematics students, a teaching sequence on paraboloids using GeoGebra AR supported by GeoGebra 3D with the aim of fostering students??? work with different semiotic representations. All the activities carried out during the teaching case study were video-recorded, transcribed, and analysed according to Duval???s Theory. Results show how GeoGebra AR facilitated students in the manipulation of different semiotic representations of paraboloids involved by highlighting the cognitive activities of treatments and conversions which emerge while conceptualising the mathematical object at stake.
TECHNES: Penyedia Layanan Jasa Service Berbasis Aplikasi Android

This article describes how the waterfall method approach was used for the creation of an Android app by Technes, an app-based service provider. The waterfall method is a methodical and linear approach to developing software that starts with analyzing requirements and progresses through design, testing, and implementation. In this article, we will discuss how the waterfall method was applied to the development of the Technes app to ensure it was well-organized and efficient. The waterfall methodology's requirements analysis, design, implementation, testing, and implementation phases are all thoroughly explained. Developing a solid understanding of the technical user requirements and deciding on the functionality that the app should have are the tasks involved in the requirements analysis process. An intuitive and responsive user interface is then produced during the design phase to enhance the user experience. The main concern of this article is how the waterfall methodology can help solve challenging problems when developing complex Android applications. We discuss many phases of the waterfall method such as requirements analysis, design, implementation, testing, and planning, and we illustrate how each stage contributes to the final outcome of the Technes application. The findings of the discussion show that the waterfall method can help organize, track, and evaluate each stage of app development in a clear and organized way. We also recognize the limitations of the waterfall method with respect to changing user requirements and backend operations, but these issues can be resolved with effective communication. The end result is a Technes application that fully meets user needs, has a responsive user agent, and offers a consistent user experience.
Parallel Clustering Algorithm for the k-medoids Problem in High-dimensional Space for Large-scale Datasets

We present a robust, parallel primal-dual heuristic algorithm for the k-medoids clustering problem, a widely utilized method in data mining and machine learning. Our approach surpasses current algorithms by effectively addressing their limitations, such as time-consuming distance matrix calculations, inefficient nearest-neighbor searches, and difficulties in handling large-scale datasets. To overcome these challenges, we employ an efficient parallel implementation, combined with a pioneering subgradient search algorithm. We evaluate our algorithm on the BIRCH and Stanford Dog datasets and demonstrate its superiority over existing k-medoids clustering algorithms in terms of solution quality and run time. Additionally, we introduce a novel vectorization technique that enables our algorithm to handle various types of data, such as images, text, and point data. Overall, our work contributes to the field of data mining and machine learning by providing an efficient and effective solution for the k-medoids clustering problem. The proposed algorithm offers improved performance, and versatility, making it a valuable tool for a wide range of applications.
Generative Pre-Trained Transformer for Kazakh Text Generation Tasks

This paper presents an empirical study evaluating text generation models for the low-resource and morphologically complex Kazakh language. In this study, we leveraged a transformer-based neural architecture. Initially, we trained a large language model for the Kazakh language using a substantial text corpus. Then, we fine-tuned the base model using a specialized question-answering dataset. The proposed pretrained language model (PLM) was evaluated on a conversation response generation task, investigating its performance across domains and languages. The findings of cross-lingual comparison underlines the challenges of achieving good results for low-resource languages like Kazakh with current solutions. The experimental findings revealed that despite the low-resource characteristics of Kazakh language, Kazakh's PLMs and its fin-tuned model for question answering were obtained and its performance for the BLEU score was 8.5%, which was better than the random guess 4.01%. Additionally, Two Kazakh text corpora were collected for this work. One of datasets comprised a large collection of Kazakh text from different domains and was used to train a large language model. Second datasets were specifically collected for the question-answering task for Kazakh and Russian languages.
Sliding Window Kalman Filter-Based DCQCN for RDMA Congestion Control

Remote Direct Memory Access (RDMA) is gradually being widely used in data center networks to achieve low data processing delay. The congestion control algorithm is the key for RDMA to realize packet loss-free, while DCQCN is a frequently-used algorithm. However, DCQCN and its advanced version have performance problems leading to slow rate recovery and unstable when network congestion. In this paper, we propose a Sliding Window Kalman Filter-based DCQCN congestion control algorithm. It consists of there parts: (1) the Sliding Window Kalman Filter predicts the latest congestion rate to maintain the network stability, (2) a novel rate control algorithm makes rate recovery faster, and (3) using feedback enables the sender to respond quickly. We evaluate the performance of SWKF-DCQCN in NS-3. The results show that SWKF-DCQCN can recover quickly and maintain stability after congestion, with a maximum improvement of , and the utilization rate of bottleneck link bandwidth can reach .
Improving the method of controlling the search for surface and underwater objects by a hybrid robotic complex

One of the important scientific tasks of organising the control of searching for surface and underwater objects is to ensure the efficiency and trouble-free movement of a hybrid robotic complex in the modes of transition and search operation. An analysis of modern methods of controlling the search for surface and underwater objects by robotic vehicles in search operations, scientific and environmental research, monitoring, inspection and military underwater operations shows the need to use hybrid robotic systems. As part of the intellectualisation of the route planning process, it is relevant to solve the problem of synthesising an optimal trajectory to ensure that the search is carried out in all designated areas of the operation. The paper solves the problem of organising the management of searching for surface and underwater objects by a hybrid robotic complex, namely, building a route in the area of interest as an optimal spline trajectory of a moving object. The possibility of constructing a navigation function through the effective use of the curvature of the spline trajectory as a reproductive template for constructing an axonometric projection is determined. To accomplish this task, the paper improves the method of controlling the search for surface and underwater objects by a hybrid robotic complex by formalising the processes of planning the route of the hybrid robotic complex and the processes of information processing, forecasting the navigation situation and choosing a strategy for preventing dangerous situations by combining intelligent and numerical methods. The developed algorithm for constructing a search route for a hybrid robotic complex allows automated formation of spline trajectories in real time with synchronous submission of information about the parameters of the route to the control system of the hybrid robotic complex. This approach makes it possible to automate the search operation process and apply it in the CoCoRo and e-Navigation concepts
The impact of poor reading skills on Grade 12 isiXhosa Home Language exam results

AbstractIsiXhosa is one of the 11 official languages offered as a subject in the South African school curriculum at Home Language (HL) level. The examination of this subject in grade 12 (the school-exit year) consists of three papers: Paper 1 (P1), which focuses on reading for understanding and language usage; Paper 2 (P2), which focuses on reading for analysis using prescribed genres; and Paper 3 (P3), which focuses on writing. The Department of Basic Education (DBE) publishes a diagnostic report after every examination session, following the completion of marking of the final examinations, detailing the performance of the grade 12 learners in each subject written. The 2018 and 2019 reports show that the performance of candidates in isiXhosa HL is poor and suggests that they cannot read for understanding. This study sought to analyse the impact of poor literacy skills on grade 12 candidates’ performance in the isiXhosa HL final examinations. This was done by analysing the DBE’s 2018 and 2019 isiXhosa HL Diagnostic Reports for P1 and determining how a lack of reading skills affects learners’ comprehension and analysis skills. The South African Curriculum and Assessment Policy Statements (CAPS) emphasise the importance of reading for different purposes. The findings of this study suggest that learners do not have a solid foundation in reading and textual analysis and that reading is not given priority in the classroom, which ultimately leads to poor performance. The article concludes that reading and academic performance go hand in hand and that learners should be exposed to different texts; this can be achieved when teachers prepare well for their reading lessons and use not only the prescribed works. Furthermore, every learner should be given a chance to read, as no learner will ever perform well in their academics if their reading skills are poor.
Exploration on the application scenarios and inner mechanisms of educational metaverse in college teaching

Since the global outbreak of COVID-19, the traditional face-to-face classes in colleges have been seriously challenged, and the discussion about the function and application prospects of the educational metaverse has become a hot topic. Some scholars have analyzed and predicted the use of metaverse in the field of education, covering various aspects of basic education, inclusive education, vocational education, and lifelong education. However, the higher education stage has been little studied. In order to rationalize as much as possible the real problems faced in the current educational metaverse, and in the absence of sufficient reference materials, this paper composes the concept and development of educational metaverse through literature analysis and theoretical research, defines the characteristics and current patterns of educational metaverse, analyzes four application scenarios of educational metaverse in colleges, such as contextualized learning, personalized learning, gamified learning , teacher training, and finally reveals the inner mechanism of metaverse empowering college teaching from three aspects: the embodiment, immersion, and concretization of educational metaverse. The paper shows a new progress and blueprint for the development of information technology in college education, hoping to contribute to the development of the education meta-universe and to provide some materials for later researchers' research and practice concerning college education.
Lattice-Based Adaptation Model for Developing Adaptive Learning Games

Adaptive learning applications that provide personalization of the learning process are one of the fastest growing types of educational software. The article analyzes the models and methods of adaptation for the development of adaptive learning games in which the learning process is implemented in a game context. A portable adaptation model is proposed, applicable for the development of adaptive learning games of various genres. The learning course for adaptive learning games is represented by a space, formed by embedding the initial course structure into the lattice, the structural ordering of which allows the formation of personalized learning strategies determined by the current state of the learner and the structure of the space itself. An approach for dynamic content matching of non-linear learning scenarios and gameplay is proposed, which preserves the logic of both the learning process and the game process. An embedded adaptation module for adaptive learning games has been designed and implemented, which makes it possible to reduce the complexity of development and improve the quality of such games in terms of the effectiveness of the learning process. An example of applying the proposed module to the development of an adaptive game for studying the object-oriented paradigm and the C# language is described.
The Structure Oriented Evaluation of Five Courses Teach by the Single Teacher

In Mongolian National University of Education sometime need to teach a single teacher several courses in parallel during one academic semester. In this paper describes the evaluation process and data analyze of the five different courses teach by the single teacher. For evaluation process is applied the structure-oriented evaluation model. 69 students responded to survey checklist. All evaluation scores were higher than 0.73 but less than 0.87 which confirmed prediction of evaluator teacher. The teacher is predicted that evaluation results cannot but enough high due to the complex of teaching many different courses in parallel. Main aim of this study was figure out opinions of students and compare the evaluation result with prediction of the teacher. Based on this fact make in light some teaching issues to stakeholders.
Business Process Optimization of Technological Map in Farm Management System

Technological maps are the primary planning document and economic analysis in an agricultural enterprise and its divisions, serve as the basis for the development and adoption of specific management decisions in the crop production industry, financial and long-term plans of the enterprise. Technological map planning is currently carried out by compilation using various tools or in paper form, and can take up to several working weeks, depending on the land bank of the enterprise and the depth of planning. This paper describes the developed method for analyzing the costs of performing the stage of field work within the framework of the technological map of cultivation of an agricultural crop, based on the processing of statistical data on the results of the actual performance of field work. The developed method has been tested at an enterprise with an average field area in Russia (6,500 hectares). After the introduction of a management system at the enterprise with a planning tool for technological maps of growing crops, the time spent on planning was reduced by 50%. When using the proposed method, this indicator improved by another 20%.
The New Method of Predicting the Importance of Patented Technologies

Building of all spheres of life at a qualitatively new technological level and possession of one's own technological keys to the creation of goods and services of the next generations is necessary to ensure one of the key principles of the development of the state, namely the achievement of technological sovereignty. In modern realities, the development of enterprises cannot be carried out without coordination with partners from Russia, as well as China, India and other countries. The selection of potential partners can be carried out on the basis of the revealed significance of their patented technological solutions. Further ranking of potential partners can be carried out on the basis of the revealed significance of their patented technological solutions. At the same time, it is proposed to use three criteria: the mass nature of the subject of the patented invention in the current period, the predicted mass nature of the subject (technology) in the future period, the success of the patent in the information field. The novelty of the developed method of forecasting the significance of patented technologies is the use of the generated metrics of innovation potential (prospects) to analyze the global patent array according to the sphere of interests of key enterprises of the Volgograd region. The developed software module provides the following functions: a) parsing of patent documents is carried out from Yandex Patents and Google Patents; b) the formation of a list of IPC classes corresponding to the spheres of interests of enterprises of the Volgograd region, and the extraction of patents of these classes from Google Patents; c) the determination of the mass content of the subject of the invention in the current period is carried out by clustering the patent array based on the lists of keywords provided by Google Patents; d) the predicted mass content of the subject (technology) in the future the period is determined by the ARIMA method; e) success in the information area is determined based on the information provided by Google Patents about the citation of the patent.
On Capturing Functional Style of Texts with Part-of-speech Trigrams

This article is dedicated to automatic detection of natural language texts functional style. Part-of-speech N-grams are selected as text features for capturing word order, which depends on functional style in Russian. The introduced approach was approbated within the task of texts classification by functional style and within a content-oriented book recommender system, which uses basic and modified probabilistic topic modeling and selects writings basing on their styles similarity to the input. Successful style-based books selection showed that an expectation gap for recommender systems can be filled, since it became possible to match books by style, successfully crossing the genre boundaries. The results are applicable for texts classification by functional style in libraries and publishers software, for personalized writings selection in recommender systems for news, scientific articles and fiction, as well as for further style modelling in dialogue systems and conversational communicative robots in order to select appropriate style depending on the interlocutor’s one and on the polilogue pragmatics and participants’ roles.