Reviewerly/exHarmony huggingface dataset:
Partitional Technique for Searching Initial Cluster Centers in K-means Algorithm

Although there are many clustering algorithms are introduced, the K-means clustering algorithm is the most universal algorithm because of its simplicity. Several modifications are also introduced to overcome the drawbacks of the K-means clustering algorithm. But still, it suffers from centroid initialization of clusters primarily which may help to elevate the efficiency of clustering. Hence this paper put forward a way of initializing cluster centroids smartly so that drawbacks of the previous algorithm may be eliminated. The proposed algorithm divides the dataspace into K (number of clusters) equally by calculating the center point of each partition set as initial cluster centers. Compared to the original and some other modified versions of the K-means grouping algorithm made by researchers, the proposed model of writing paper provides better results in terms of time complexity, space complexity, inter-cluster distance, etc.
NFTKART - A NFT Market place

A non-fungible token (NFT) is a new kind of blockchain-based token that is unique and indivisible. They were first introduced in late 2017. Fungible tokens have pioneered new use cases such as initial coin offerings, but the value of his NFTs as a component is still unknown. This study fills a gap in theoretical and practical knowledge by demonstrating the effectiveness of NFTs in the event ticketing arena. Using a rigorous design science research approach, we design, build, and extensively evaluate a prototype event ticket system based on NFTs. As a result, we demonstrate how NFTs can be used to tokenize digital products, reduce fraud, and increase control over secondary market transactions. We also provide generalizable information on the benefits and challenges of NFTs and their implications for researchers and practitioners. Key Word :NFTs, Blockchain, Polygon Testnet, Hardhat, Ethereum, NextJS, IPFS
Innovative Approaches in Cybersecurity: Addressing Future Trends, Challenges, and Incident Response

As organizations and individuals become increasingly reliant on digital technologies, the need for robust cybersecurity measures becomes more critical.Cybersecurity threats have evolved significantly over the past few years, with attacks becoming more sophisticated, frequent, and damaging.Therefore, it is essential to adopt innovative approaches to cybersecurity that can address emerging trends, challenges, and incident response.The rapid growth of the digital economy and the increasing reliance on technology has led to an unprecedented rise in cyber threats and attacks.Cybersecurity has become a pressing concern for individuals, businesses, and governments, with the potential to cause significant economic and social damage.The objective of this research paper is to explore innovative approaches in cybersecurity that can address future trends, challenges, and incident response.The paper provides a comprehensive review of the current state of cybersecurity, including emerging threats, vulnerabilities, and attack vectors.It also discusses the challenges facing traditional cybersecurity approaches and the need for innovative solutions.The research paper highlights several innovative approaches that can improve cybersecurity, such as using artificial intelligence and machine learning to detect and respond to cyber threats, implementing blockchain technology to secure data, and adopting a risk-based approach to cybersecurity.The paper also examines incident response strategies, including the importance of real-time monitoring, forensic analysis, and rapid recovery.Finally, the research paper emphasizes the need for collaboration between industry, academia, and government to develop and implement innovative cybersecurity solutions.The paper concludes that innovative approaches in cybersecurity are critical for addressing future trends and challenges and improving incident response.The research paper can serve as a valuable resource for individuals, businesses, and governments seeking to enhance their cybersecurity posture and protect against cyber threats.
Paper on Live Criminal detection Using Facial Recognition and Tracking Algorithms

This paper proposes an automated facial recognition system for criminal identification using the Haar featurebased cascade classifier.The system aims to address the increasing crime rate and limited availability of police personnel by automatically detecting and recognizing faces in real-time.By utilizing captured faces and criminal images from police stations, the system can match and identify criminals.The proposed system enhances and streamlines criminal identification processes in the Police Department through automated tasks.It employs face recognition technology to detect and recognize faces captured by CCTV cameras in public places, comparing them with a criminal database.When a match is found, the system displays the criminal's image and provides an alert message indicating their presence in the public place.At the current stage of development, the system achieves a matching rate of over 85% with the database images.The system processes video streams in real-time, converting them into frames, detecting faces, preprocessing and extracting features, and comparing them with the criminal database.If a match is found, the system sends an alert message containing the live location of the criminal to the relevant authorities.The proposed system utilizing Haar Cascade and CNN Algorithm offers an efficient solution for real-time criminal identification using video footage.
MODI Lipi Handwritten Character Recognition Using Convolutional Neural Networks (CNN)

An ancient Indian script from Maharashtra is called MODI.During Chhatrapati Shivaji Maharaj's rule, this script was frequently used to create official documents.The structural characteristics of MODI make character recognition challenging, and there is no image database.In this study, we built a CNN model for character recognition and extended the dataset of the MODI script using data augmentation methods.Due of the low picture dataset multiple images provided by the MODI script, we added data to the dataset and trained the CNN model on a created dataset.About 91.62% of the time, the trained model correctly recognizes Handwritten MODI characters.
VISION BASED SURVEILLANCE SYSTEM FOR THEFT DETECTION IN SUPERMARKETS

Shoplifting, break-in and robbery are burdensome and challenging for human abilities to maintain.This system precisely aims to provide the solution by detecting these mentioned activities with comparatively less involvement of beings.To markup on these issues, surveillance systems for theft detection in supermarkets are introduced.It can be utilized in two methods, watching in real-time and the latter one is looking for evidence of what happened in the past.In this paper, we are presenting a system which is capable of alerting the owner about the activities in his\her absence.Also, static inputs which were recorded in the past can be uploaded.Frames are extracted from the video which makes the machine understand in a simplified manner.For object detection and assisting in an image, we have used InceptionV3.The model calculates the probability by considering the inputs while comparing them with the trained data.The main idea of the system is that they are trained using data sets about the classes and will be able to differentiate among them and alert the user in time, with an accuracy of 99.86%.
Encryption Using AES Algorithm, Steganography, and Chaos Technology

The research offers a new image encryption system based on the Arnold chaotic sequence and a modified AES algorithm.The algorithm offers advantages over existing techniques by reducing time complexity, providing diffusion ability, and resisting differential attacks.The Arnold chaos sequence is used to generate the secret key which will encrypt the image, making it difficult for attackers to determine the key used for encryption.The proposed algorithm provides a large key space, which makes it resistant to brute-force attacks.Because it is highly sensitive to beginning settings and input images, attackers will have a difficult time recovering the original image without the correct key.Because of the algorithm's exceptional diffusion qualities, even minor changes in the source image or key result in huge changes in the encrypted image.Simulation results show the proposed algorithm's effectiveness in securing image data, providing robust security against a wide range of attacks while maintaining high efficiency.Due to its low time complexity, the proposed algorithm is appropriate for real-time image encryption applications.
Enhanced Credit Card Fraud Detection: A Novel Approach Integrating Bayesian Optimized Random Forest Classifier with Advanced Feature Analysis and Real-time Data Adaptation

In the financial industry, credit card fraud is a widespread issue that costs both individuals and businesses a lot of money. Using their capacity to spot patterns and abnormalities in huge datasets, machine learning algorithms have demonstrated their effectiveness as tools for fraud detection. This paper suggests a cutting-edge method, specifically an enhanced Bayesian random forest classifier, to improve the detection of credit card fraud. We solve the shortcomings and difficulties of conventional random forest classifiers by applying Bayesian optimization to optimize the model's hyperparameters. Using a publicly available dataset on credit card fraud, we ran experiments to assess the efficacy of the suggested strategy. The effectiveness of our improved Bayesian random forest classifier was evaluated in comparison to cutting-edge methods. The findings demonstrate a fantastic area under the curve (AUC) of 0.99 and a remarkable accuracy of 99.6%, demonstrating the superiority of our proposed classifier above traditional random forest and benchmark techniques. We also investigate the model's interpretability by looking at the importance of characteristics in fraud detection. This research improves the proposed approach's openness and interpretability by offering useful insights into the underlying components that contribute to fraud detection. Our work shows the effectiveness of the optimized Bayesian random forest classifier, but it's crucial to recognize its limitations and the room for improvement in the future. The application of this method to situations other than credit card fraud detection could be explored in more detail. Additionally, it is important to test the proposed classifier's scalability and robustness on bigger and more varied datasets. Ultimately, our study aids in the creation of trustworthy and efficient fraud detection tools for the financial industry. For fraud analysts and investigators, the proposed Bayesian optimized random forest classifier can be used as a decision support tool. Its versatility makes it a plausible answer for a variety of fraud detection problems that go beyond credit card theft.
KanSan: Kannada-Sanskrit Parallel Corpus Construction for Machine Translation

Machine Translation (MT) is the process of automatic conversion of text from the source language into a target language preserving the meaning in the source text. Large parallel corpora are the essential resources to build any MT model. However, most of the languages are under-resourced due to lack of computational tools and digital resources with respect to parallel corpora for MT. Further, translation of under-resourced languages with complex morphological structures are more challenging. In view of these factors, this paper describes the practical approaches to develop MT systems for Kannada-Sanskrit language pair from scratch. This work comprises of the construction of KanSan - a parallel corpus for Kannada-Sanskrit language pair and implementation of MT baselines for translating Kannada text to Sanskrit text and vice versa. The models, namely: Recurrent Neural Network (RNN), Bidirectional Recurrent Neural Network (BiRNN), transformer-based Neural Machine Translation (NMT) with and without subword tokenization, and Statistical Machine Translation (SMT) are implemented for MT of Kannada text to Sanskrit text and vice versa. The performance of MT models is measured in terms of Bilingual Evaluation Understudy (BLEU) score. Among all the models, the transformer-based model with subword tokenization performed best with BLEU scores of 9.84 and 12.63 for Kannada to Sanskrit and Sanskrit to Kannada MT respectively.
Students' Attitude to Distance Learning as a Component of their Mobility

The presented article examines the problem of students’ readiness to distance learning from the point of view of developing their mobility, as a priority characteristic of a modern specialist. The relevance of the problem is due to the processes of informatization of education as a modern trend in the development of education and the rapid introduction of distance learning in connection with the COVID-19 pandemic. In this context, mobility is a significant quality of a specialist’s personality, which is manifested in readiness for various spatial movements and adaptation to different situations. The article presents the results of an empirical study of students of specialty 016 Special education, based on the results of an online survey. The results of the study showed the predominance of positive attitude of students to distance learning, the main advantages of which are convenience, time savings and the ability to get an education at home. The main disadvantage, in addition to poor-quality internet connection, is the lack of live communication, which is also the main need of students. Despite the positive assessment of distance learning, students do not consider it as high-quality and do not want to teach children with special educational needs remotely. In order to form students’ readiness towards distance learning and remote work, it is advisable to define the formation of mobility as the quality of a specialist in the process of professional training, since distance or blended learning is a modern form of education and has a significant potential for further development.
Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement

To reduce the cargo loss rate caused by abnormal consumption behavior in smart retail cabinets, two problems need to be solved. The first is that the diversity of consumers leads to a diversity of actions contained in the same behavior, which makes the accuracy of consumer behavior identification low. Second, the difference between normal interaction behavior and abnormal interaction behavior is small, and anomalous features are difficult to define. Therefore, we propose an anomalous behavior detection algorithm with human–object interaction graph convolution and confidence-guided difference enhancement. Aiming to solve the problem of low accuracy of consumer behavior recognition, including interactive behavior, the human–object interaction graph convolutional network is used to recognize action and extract video frames of abnormal human behavior. To define anomalies, we detect anomalies by delineating anomalous areas of the anomaly video frames. We use a confidence-guided anomaly enhancement module to perform confidence detection on the encoder-extracted coded features using a confidence full connection layer. The experimental results showed that the action recognition algorithm had good generalization ability and accuracy, and the screened video frames have obvious destruction characteristics, and the area under the receiver operating characteristic (AUROC) curve reached 82.8% in the detection of abnormal areas. Our research provides a new solution for the detection of abnormal behavior that destroys commodity packaging, which has considerable application value.
FLSwitch: Towards Secure and Fast Model Aggregation for Federated Deep Learning with a Learning State-Aware Switch

Security and efficiency are two desirable properties of federated learning (FL). To enforce data security for FL participants, homomorphic encryption (HE) is widely adopted. However, existing solutions based on HE treat FL as a general computation task and apply HE protections indiscriminately at each step without considering FL computations’ inherent characteristics, leading to unsatisfactory efficiency. In contrast, we find that the convergence process of FL generally consists of two phases, and the differences between these two phases can be exploited to improve the efficiency of secure FL solutions. In this paper, we propose a secure and fast FL solution named FLSwitch by tailoring different security protections for different learning phases. FLSwitch consists of three novel components, a new secure aggregation protocol based on the Pailliar HE and a residue number coding system outperforming the state-of-the-art HE-based solutions, a fast FL aggregation protocol with an extremely light overhead of learning on ciphertexts, and a learning state-aware decision model to switch between two protocols during an FL task. Since exploiting FL characteristics is orthogonal to optimizing HE techniques, FLSwitch can be applied to the existing HE-based FL solutions with cutting-edge optimizations, which could further boost secure FL efficiency.
Capturing Antique Browsers in Modern Devices: A Security Analysis of Captive Portal Mini-Browsers

Granting access to public Wi-Fi networks heavily relies on captive portals that are accessible using dedicated browsers. This paper highlights that such browsers are crucial to captive portals’ security, yet have not been emphasized in prior research. To evaluate the security of captive portal mini-browsers, we built an assessment tool called Wi-Fi Chameleon and evaluated them on 15 popular devices. Our evaluation revealed that they all lacked the essential security mechanisms provided by modern browsers. Indeed, many provided no warnings even when using HTTP or encountering invalid TLS certificates, and some did not isolate sessions, enabling attackers to silently steal users’ sensitive information (e.g., social networking accounts and credit card numbers) typed in captive portals and stored in their browsing histories. Moreover, even if a captive portal mini-browser is equipped with all security protections that modern browsers provide, users are still susceptible to existing captive portal attacks. We discuss the best practice of a secure captive portal mini-browser and two possible approaches to mitigate the vulnerabilities. For end-users, we proposed a browser extension for immediate deployability. For access points and captive portal mini-browser vendors, we proposed a comprehensive solution compatible with RFC 8952, the standard of captive portals.
Optimized Feature Representation for Odia Document Clustering

Document clustering is the task of organizing textual content into groups so that they are more similar to one another than to those in other groups. Several text clustering algorithms have been proposed recently by various researchers. However, the majority of them limited their research to English-language documents. Odia is the language spoken by the people of Odisha, and its appearance on the digital platform is on the rise recently. This paper proposes an optimized feature representation using PCA of Odia documents for efficient document clustering. The proposed work first extracts four different features from Odia sentences: word-level TF-IDF, character-level TF-IDF, word embedding, and sentence embedding vectors. With a silhouette coefficient of 0.964, Rand index of 0.352, normalized mutual information score of 0.001, and Davies–Bouldin index of 0.022, it was found that the use of PCA-based optimized word-level TF-IDF features performed better than other feature representations.
Exploring the Potential of Sixth Sense: A Wearable Technology Enabling Natural Interaction with Digital Information

Sixth Sense is a remarkable wearable technology that seamlessly connects the physical and digital worlds, fostering a more natural and intuitive interface between humans and digital information.Worn around the neck, this innovative device incorporates a data projector, camera, and internet connection, leveraging advanced image processing and gesture recognition technologies.By offering an open user interface, Sixth Sense enables users to effortlessly engage with data through natural hand motions, surpassing the limitations of traditional text and graphic-based interfaces.This ground breaking technology has not only revolutionized Human-Computer Interaction but has also opened up new possibilities for enhancing the efficiency and effectiveness of digital interactions.This paper explores the key features and potential applications of Sixth Sense, highlighting its significance in bridging the gap between humans and digital information.
INVESTIGATION OF THE METHOD OF EVALUATING THE EFFECTIVENESS OF THE INFORMATION SECURITY SYSTEM BASED ON FUZZY INFERENCE

As organizations increasingly rely on digital technology to operate, protecting their information and data has become a critical concern. Information security systems are designed to safeguard digital assets against unauthorized access, use, disclosure, disruption, modification, or destruction. However, evaluating the effectiveness of an information security system can be challenging due to the complexity of the system and the diversity of threats it faces. In recent years, researchers have proposed using fuzzy inference to evaluate the effectiveness of information security systems. Fuzzy inference is a mathematical approach that can handle uncertain and imprecise information, making it well-suited for evaluating the effectiveness of information security systems. This research aims to develop a method for evaluating the effectiveness of an information security system based on fuzzy inference. The proposed method uses a set of performance indicators to measure the effectiveness of the system, such as the number of security incidents detected, the response time to security incidents, and the number of false positives and false negatives [1]. These indicators are then combined using fuzzy inference to generate an overall effectiveness score for the system. The proposed method will be evaluated using a real-world case study of an information security system deployed in an organization. The effectiveness score generated by the fuzzy inference method will be compared to the results obtained using traditional evaluation methods, such as the cost-benefit analysis or the return-on-investment analysis. The results of the study will demonstrate the effectiveness and usefulness of the proposed method for evaluating information security systems.
Mitigating Query-based Neural Network Fingerprinting via Data Augmentation

Protecting the intellectual property (IP) of deep neural network (DNN) models becomes essential and urgent with the rapidly increasing cost of DNN training. Fingerprinting is one promising IP protection method that queries suspicious models with specific fingerprint samples to infer and verify IP by comparing the predictions with pre-defined labels. Based on utilizing unique features of target models, various DNN fingerprinting methods are proposed to effectively verify the IP of models remotely with a meager false-positive ratio. In this paper, we propose a novel attack to mitigate query-based fingerprinting methods based on data augmentation methods. We propose a randomized transformation on input samples to significantly mislead the fingerprint samples’ prediction and compromise the IP verification. Then, our attack can keep the model utility with an acceptable accuracy drop in the data-free scenario (i.e. without any samples) or achieve much higher precision in the data-limited scenario (i.e. with a small number of samples with the same distribution). An intensive evaluation of three well-known model structures and three well-known datasets shows that our attack can effectively mitigate five query-based DNN fingerprinting methods in top-tier conferences.