Reviewerly/exHarmony huggingface dataset:
THE MOST SIGNIFICANT FEATURES OF INNOVATIVE PEDAGOGICAL TECHNOLOGIES IN HIGHER SCHOOL

Having determined the place of pedagogical technology in the hierarchy of concepts, it is necessary to clarify its semantic meaning, which is as follows: firstly, in the features of translating the teacher's idea into the language of the preliminary design of the pedagogical process; secondly, the main points in the project are the structure and content of the educational and cognitive activity of the student, and not the pedagogical influence of the teacher; thirdly, the construction of the teacher's activity is carried out in certain integrity and sequence, and the implementation involves the achievement of the desired result and is predictable. In many senses of the interpretation of the concept of innovation and in the definition of different levels of novelty. It is emphasized that the new can be used in the first place or in synthesis even with something already known.
EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

In the realm of cybersecurity, the escalating sophistication of adversarial attacks poses a significant threat, particularly in the context of machine learning models. Traditional defensive mechanisms often fall short in identifying and mitigating such attacks, primarily due to their static nature and inability to adapt to the evolving strategies of adversaries. This limitation underscores the necessity for more dynamic and responsive approaches. Addressing this critical gap, our research introduces an innovative Active Machine Learning Adversarial Attack Detection framework process. Central to our approach is the strategic amalgamation of data collection and preprocessing techniques. We meticulously gather a diverse dataset encompassing both genuine and adversarial user feedback, which is then carefully annotated to differentiate between the two scenarios. This data undergoes rigorous preprocessing, including tokenization and conversion into numerical features through methods like TF-IDF and word embeddings, paving the way for more nuanced analysis. The core of our model employs a variety of machine learning algorithms—Logistic Regression, Random Forest, SVM, CNN, and XGBoost—each fine-tuned through meticulous hyperparameter optimizations. The novelty of our approach, however, lies in the integration of an active learning strategy for efficient results. By employing uncertainty sampling and query-by-committee, our model actively identifies and learns from instances of highest informational value, continuously evolving in its detection capabilities. Our framework further stands out in its post-training phases. The models are not only retrained with newly labeled data but are also subjected to a comprehensive evaluation on separate test datasets. Metrics such as accuracy, precision, recall, F1-score, and AUC are meticulously computed, ensuring the robustness of our results. Deployed in a real-time environment, the model demonstrates remarkable efficacy in detecting adversarial attacks in user feedback. Continuous monitoring and periodic retraining allow the model to adapt and respond to new adversarial tactics. The impact of our work is quantitatively significant—our model outperforms existing methods with a 9.5% improvement in precision, 8.5% higher accuracy, 8.3% increased recall, 9.4% greater AUC, 4.5% higher specificity, and a 2.9% reduction in detection delays for different scenarios.
Risk Prioritization using A FUZZY BASED Approach in Software Development Design Phase

The success of a software project's objective is directly proportional to the degree to which it satisfies all of the stakeholders' concerns regarding the project's requirements, including the budget, schedule, and overall performance. Risks can occur throughout the software development lifecycle (SDLC) phases and affect every phase. The design phase of the SDLC yields an overview of the software and can be defined as the software's blueprint. Different types of software have their own unique design phases and have different types of risks. With the high number of interacting components, complex systems have a greater propensity to be more volatile, which increases the risk. It is necessary to prioritize the risks in order of their severity levels. The issue at hand is the lack of effective methods to prioritize and mitigate the risk. Recent studies have suggested several methods for prioritizing risks, but it is clear that few of these have been implemented. These methods are overly complicated, time-consuming, prone to inconsistency, and challenging to put into practice. This paper proposes a novel Fuzzy-based approach to risk prioritization in the software design phase using MATLAB software. Fuzzy-based models have been shown to be more accurate than other techniques when using standard datasets to prioritize risks. Fuzzy-based methods that have been proposed take into account the characteristics of risks by modelling those characteristics as fuzzy
Survey Analysis on Secured user Authentication through Biometric Recognition

Secured user authentication is the process of verifying the user authenticity. Biometric authentication is the human identification system employed to match the biometric characteristics of user for verifying the authenticity. Biometric identifiers are exclusive, making it harder to hack accounts using them. Common types of biometrics comprise the fingerprint scanning verifies authentication based on a user's fingerprints Face recognition and voice recognition are employed in real-time application for improving the security level in different application scenarios. Face recognition is a method of identifying or verifying the individual identity using their face expression. Voice recognition is the ability of machine to receive and interpret the dictation to understand. Many researchers carried out their research on different face and voice recognition methods. But, recognition accuracy was not improved with minimum time consumption by existing biometric recognition method. In this research, different recognition methods are reviewed using biometric recognition method for user authentication. The recognition methods are efficiently on human faces dataset with respect to performance metrics like recognition accuracy, error rate, and recognition time.
Principles of digital diagnostics of the health level of the human biosystem as an element of artificial intelligence

The analysis of the level of health was carried out using methods for assessing the responses of the main human biosystems in regulating and predicting the development of the biosystem within the framework of control equations and proposed methods for processing diagnostic laboratory analysis data in determining the assessment of the state of the energy potential of the human body (including the patient) and assigning a life development strategy, if necessary, treatment
Snowball-Miner: Integration of Deep Learning for Extraction of Cyber Threat Intelligence from Dark Web

In Cyber threat intelligence is a crucial component in defending against cybersecurity threats. Cyber security dark web, security Blogs, Hackers’ community, news forums, Open-Source Intelligence (OSINT) are known as the harbor of illicit activities and serve as a breeding ground for cybercriminals. Extracting actionable intelligence from the dark web is challenging due to its anonymous and encrypted nature. State-of-art work proposed machine learning and deep learning approach to aggregate the dark web for cyber threat intelligence from data present in the dark web. This paper proposes, a novel approach utilizing Snowball-Miner for cyber threat intelligence discovery from the dark web. The model is trained on a diverse dataset consisting of dark web forums, hidden .onion based marketplaces and other underground platforms using Snowball-crawler. However, we have employed hybrid convolutional model CNN-LSTM and CNN-GRU adopting doc2vec word embedding to classify into four domains viz Energy Sector, Finance, Illicit Activities and illegal Services. From our experiment it emerged that, CNN-LSTM outperforms as 96.37% for classification of domain specific threat documents. Furthermore, after data preparation we implemented NLP technique and extracted the domain specific Indicator of Compromise (IoCs) using RegEx parser and Subject, Object and Verb (SOV) semantics dependency analysis. Finally, we have integrated IoCs and Threat keywords with respective domains to generate domain specific threat intelligence which enhance the quality of the domain specific CTI based on R-dimension (Relevance).
Natural Language Processing for Prediction of Election Results on Twitter Engagement and Polls

With the ability to predict political outcomes and provide insights into public opinion, using Twitter data to predict election results has gained popularity. Twitter offers a massive supply of data for analysis due to its enormous user base and real-time nature. Researchers use sentiment analysis tools to categorize tweets as good, harmful, or neutral and follow sentiment patterns over time. Network analysis finds influential users and digs deeper into the dynamics of political discourse. The accuracy of predictions is improved by combining traditional polling data with machine learning methods. Twitter data analysis has the potential to offer insightful information for election campaigns and improve political strategies despite issues like representativeness and identifying genuine sentiment. Ongoing research focuses on refining methodologies and addressing limitations, advancing the reliability of election prediction using Twitter data. The paper shows the results of election prediction for Indian political parties based on Twitter data
Scrutinization of Video posts on Social Media for Authenticity

Multimedia posts, especially video posts are easy to understand, attractive and are considered as proof of evidence for an event that occurred. Videos are one of the frequently shared posts on social media. Videos and images speak louder than words. They are very much believed by people and forwarded. Videos reach every person despite of language or literacy barriers. Technological improvements lead to easy generation of fraudulent videos to create a negative impact on people and society. Celebrities and politicians are highly affected because of fake video generation and dissemination. The video posts uploaded and forwarded on social media should be analyzed and identified to stop propagation before they create harm to the society. The proposed work converts the video clips into a sequence of frames. The keyframes are then identified by the use of histogram comparison. A CNN model is built with optimum layers for image classification. The keyframes or images are then classified using the CNN model to identify fraudulent content. The proposed work is light in terms of processing when compared to existing or conventional video classification. The work uses FaceForensics++, DeepFake and Celeb-DF V2 datasets and achieved 99.7%, 99.8% and 98.01% accuracy to identify fraudulent video posts.
Smart and Secure Blockchain Structure to Track Vehicle Record-keeping in the Sultanate of Oman

In the country of Sultanate of Oman, the Royal Oman Police (ROP) is responsible for carrying out the Inspection, Registration/Renewal, Transfer of ownership, Licensing of a vehicle and the Investigation of traffic accidents. The traditional system used for storing this information does not guarantee whether any information has been tampered with or manipulated. Having a tamper-proof methodology like Blockchain to capture this important information in a distributed ledger register can enable all the participants in the Registration Chain (Supplier of the vehicle, Owner of the vehicle, various Government Agencies and public at large) to gain confidence, build trust and authenticate records that are maintained by the centralised agency. In this paper, a groundbreaking solution built on Hyperledger Besu, a permissioned Ethereum blockchain platform, is introduced that offers a revolutionary approach to vehicle registration and management. It ensures secure and immutable vehicle registration, utilizing IPFS for document storage and ERC-20 tokens to track vehicle history. This Ethereum-powered system facilitates transparent ownership transfers, comprehensive vehicle history tracking, streamlined insurance renewals, seamless vehicle inspections, real-time tracking, and blockchain-based verification, all contributing to heightened efficiency and trust in vehicle processes for the Omani government. By integrating blockchain technology, this solution addresses the challenges of data integrity, security, and transparency that have long plagued the vehicle registration and transfer processes in Oman. This paper highlights the potential of blockchain to revolutionize governmental services and improve the overall efficiency and trustworthiness of vehicle-related transactions, ultimately benefiting both the government and citizens alike.
Status and problem of Integrated Library Management system among Engineering college Libraries in TamilNadu with Special Reference to Kancheepuram district

Libraries are becoming more and more dependent on information and communication technology (ICT), especially with regard to automation and technological know-how. The prevailing paper is displays the reputation and troubles of Library automation in aided university Libraries affiliated with Anna College In Kancheepuram district. The check was changed to unquestionably state that the Kancheepuram district' 87.50% of libraries are automated. And basic problems that are currently not automated include a lack of staff, a lack of infrastructure, a lack of funding, and the absence of human education in libraries. A status view of the software packages that libraries and modules use is also provided by this. The availability, applicability, and issues encountered during the process of implementing and using library automation software applications are the study's specific areas of focus.
Knowledge Management and Organizational Performance: The Mediating Role of Dynamic Capabilities

This research examines the effect of knowledge management on organizational performance, with dynamic capability as a mediating variable. A quantitative research approach was applied. A survey was conducted using purposive random sampling. Data were collected manually by using a questionnaire. SmartPLS3 was used to analyze the data. The results showed that dynamic capability has been verified to mediate the relationship between knowledge management and performance. Greater knowledge management leads to higher performance, especially if the mediated dynamic capability is also greater. The managerial implications were that knowledge management and dynamic capability have prominent roles in organizational performance. Knowledge management and dynamic capability allow managers to optimize their creativity and innovation to develop ideas, which helps maximize the growth of organizational performance. The results supported the knowledge-based view (KBV). KBV serves as the foundation for comprehension of knowledge-based organizational theory. Knowledge significantly affects sustainable performance.
Implementasi Model Pembelajaran Inkuiri dalam Menulis Kritis melalui Teknologi Literasi Digital

This research aims to describe the implementation of the inquiry learning model in critical writing through digital literacy technology. This research is also to determine the implementation of inquiry learning in critical writing by utilizing digital literacy technology. This research method uses research and development methods used in this research. Based on development standards for learning system design, this investigation was carried out procedurally. The procedure includes preparation, implementation and evaluation stages. The data collection in this research was through classroom observations, student and teacher interviews, as well as analysis of student written work produced through digital literacy technology. Data analysis in this research involved collecting, processing and interpreting data from students' literacy activities and written production. Data can be presented in the form of interactions on digital literacy platforms, student responses, or the quality of written results. The results of this research show that the inquiry learning model can be useful in making it easier for students to write critically, supported by the existence of digital literacy technology which has developed rapidly. Digital literacy technologies enable quick and easy access to a wide range of information sources in critical writing contexts.
The Influence of Use of the Imla' Method on Student Learning Outcomes in the Subject of Reading Write Al-Qur'an at Private MTS Nahdhatul Islam Manceg

The aim of this research is to determine student learning outcomes in Al-Qur'an reading and writing subjects. before and after using the Imla' method and whether or not there is an influence of the Imla' method on student learning outcomes in the Al-Qur'an reading and writing course in class VIII at the Nahdhatul Islam Mancang Private MTs. This research uses quantitative experimental methods, with the research design used is a quasi-experimental design. The subjects in this research were school principals, teachers in the subject of reading Al-Qur'an sincerely and all class VIII students at MTs Nahdhatul Islam Mancang. This research obtained results that show that there is an influence on the Imla' method through the results of the difference in the average value of the experimental class of 84.70 after the application of student-made image media with the average value of the control class being 68.16. Meanwhile, through hypothesis testing using the T test The obtained t value is 72.17 with a significance of 0.000 &lt; 0.005, so it is stated that there is an influence (Ha) accepted. After the implementation of the Imla' method, student learning outcomes in Al-Qur'an reading and writing subjects increased compared to previous results, after the implementation of the Imla' method it showed that there was an increase. The population used was 60 class VIII students. The research sample was 30 students in class VIIIA (experimental class) and 30 students in class VIIIB (control class).
Feature Decomposition and Attribute Augmentation for Attribute-based Person Search

The attribute-based person search task aims to find matching pedestrian images by text attributes, which is relevant in scenarios where no query image is given. However, the existing methods exhibit inferior performance due to their inadequate representation of local fine-grained features, which hinders their ability to effectively model intra-class variations. In addition, there is a zero-shot retrieval problem due to the large number of unseen categories in the test set, resulting in suboptimal generalization performance. In this paper, we propose a novel Feature Decomposition and Attribute Augmentation (FDAA) framework to solve the above problems. Firstly, by decomposing the global features of the image from different directions, the local features are extracted at multiple granularities, thus effectively improving the discrimination ability of the model. Secondly, an attribute augmentation strategy is proposed that can effectively expand the combination of attributes during training and improve the generalization ability of the model. Extensive experiments on the PETA, Market-1501 Attribute, and PA100K datasets demonstrate the effectiveness of our proposed method, outperforming state-of-the-art methods.
AttentiveNet: Detecting Small Objects for LiDAR Point Clouds by Attending to Important Points

With the development of autonomous driving, object detection in LiDAR point clouds is receiving increasing attention. Though many excellent works have been proposed, small object detection still remains challenging because insufficient points of these small objects are scanned, leaving limited information for detectors. In this paper, we propose AttentiveNet, aiming to facilitate small object detection by enriching point information of small objects and focusing on the most important points. The proposed Small object Enhancement Module (SEM) effectively alleviates the issues of insufficient information provided by scanned small objects through an "enriching-attending" strategy. The "enriching-attending" strategy operates by first supplementing points for small objects and then extracting features using a 3D spatial attention mechanism, which helps avoid noise from some inappropriately added points. Furthermore, we propose the Local Attentive 2D Backbone (LAB) to perform self-attention on projected 2D feature maps, catering to the locality of small objects. Extensive experimental results on the KITTI dataset substantiate that the AttentiveNet outperforms existing popular 3D object detection algorithms in detecting 3D small objects in LiDAR point clouds.
Enhancing Data Security in Cloud Computing: A Comparative Analysis of Encryption Techniques

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework designed to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations.
Modelling the Process of Building Digital Security in Commodity Markets using Blockchain

Blockchain technology is acquiring momentous popularity in commodity markets over the past few years. A commodity can be regarded as an article of trade between an importer and exporter nation. Several types of commodities—such as agricultural (i.e., wheat, soyabean, corn, rice, spices, oil, meal products, sugar, dairy, marine, and plantation) and non-agricultural (i.e., base metals and petroleum products)—can be traded. The process of commodity trading or shipment between nations involves several actors like exporters, customs, importer, insurance, and importer bank and activities like bills of lading, letter of credit, factoring, exporting credit, and insurance—which are paper based, interdependent, and require human involvement. Blockchain, on the other hand, is a decentralized and distributed ledger technology that is open, immutable or tamperproof, efficient, verifiable, and permanent. The integration of blockchain in commodity markets can revolutionize the entire trading or shipment process. Considering the abovementioned problems, this study attempts to offer a solution by performing business process modelling in commodity markets using blockchain. The inferences made in this study prove to be quite promising wherein paper-based trading processes can be replaced with paperless trading processes via smart contracts—self-executable codes—which operate on top of blockchain, thereby eliminating intermediaries or middlemen and improving not only efficiency but also transparency of existing trading processes.