Reviewerly/exHarmony huggingface dataset:
Swarm Intelligence Decentralized Decision Making In Multi-Agent System

This research aims to understand how groups of agents can make decisions collectively without relying on a central authority. The research could focus on developing algorithms and models for distributed problem solving, such as consensus-reaching and voting methods, or for coordinating actions among agents in a decentralized manner. The research could also look into the application of these methods in various fields like distributed robotics, swarm intelligence, and multi-agent systems in smart cities and transportation networks. Swarm intelligence in decentralization is an emerging field that combines the principles of swarm intelligence and decentralized systems to design highly adaptive and scalable systems. These systems consist of a large number of autonomous agents that interact with each other and the environment through local communication and adapt their behaviors based on environmental cues. The decentralized nature of these systems makes them highly resilient and efficient, with potential applications in areas such as robotics, optimization, and block chain technology. However, designing algorithms and communication protocols that enable effective interaction among agents without relying on a centralized controller remains a key challenge. This article proposes a model for swarm intelligence in decentralization, including agents, communication, environment, learning, decision-making, and coordination, and presents a block diagram to visualize the key components of the system. The paper concludes by highlighting the potential benefits of swarm intelligence in decentralization and the need for further research in this area.
Detection of Deepfake Video using Deep Learning and MesoNet

Fraudsters are increasingly using evidence tampering to evade criminal charges and the acquisition of personal data for identity-related offenses. Deepfake is one of the most common strategies used today for identity theft and reputation defamation. To prevent the spread of these crimes, we need a system that can tell the difference between real and deep fake videos. Deep Neural Networks will be used in our system to identify and mark films as legitimate or manipulated, as well as the altered sections, by running the video through our trained Sequence Model, which can detect any discrepancies or alterations as a sequence of frames. LSTM will be used for temporal sequence analysis, and CNN will be employed for frame feature extraction. MesoNet is a neural network built primarily to identify deep fakes, but it would also be used for other purposes. MesoNet manages the noise produced by low-quality video processing, which impedes analysis. DeepFakes jeopardizes facial recognition and internet content. This deception is risky and can be exploited to impersonate a legitimate user. Our approach will propose a temporal-aware method for automatically detecting deepfake videos.
A Novel Intelligent Image Recognition Scheme based on Fully Convolutional Neural Network

As a deep feed-forward neural network, the convolutional neural network (CNN) model has achieved major breakthroughs in scheme of image recognition. Compared with the traditional classification methods (KNN, SVM, PSO), the fully automatic classification algorithms reply on convolutional network is separated from human identification, which is tedious, and at the same time backpropagation algorithm is used to automatically optimize the model parameters. Inspired by this, the paper proposes the novel intelligent image recognition scheme with the integrations of fully convolutional neural network. (FCNN). To solve the challenge that overall training accuracy of the general model is reduced due to the overfitting phenomenon caused by the commonality of patterns of the image features while training the models, this study introduced the AlexNet. The loss function is improved and optimized, and for preprocessing of the images, the compressive analysis model is integrated. Through the single and comparison tests, the performance of the designed algorithm is tested, and overall speaking, the recognition accuracy is better than the traditional methodologies.
Handling Secret Key Compromise by Deriving Multiple Asymmetric Keys based on Diffie-Hellman Algorithm

A collection of connected things or nodes that collect information and send it through communication channels is known as the Internet of Things (IoT). Maintaining the confidentiality and integrity of the transferred data is crucial in many applications. As a result, protection of private data from security risks is a top priority for IoT based applications. IoT objects are typically energy-constrained computing and storage devices. Therefore, during the communication of devices, authentication among devices is necessary. A public-private key is created and utilized for the duration of the session without taking into account the possibility of loss to an adversary. Once the key has been compromised, there is no way to know when it has been lost, and the attacker can read every message. This severe security lapse went on for a longer period of time undiscovered. Revoking the key and creating a new key, which is not advised to do frequently, is the only defense against this security breach. An alternative solution to this problem is deriving a new key at communicating sites rather than exchanging keys among them. The performance of the key-sharing algorithm to perform the key exchange is measured based on the time required to encrypt/decrypt and derive the key at the node.
Improved Human Detection and Classification using Supervised Machine Learning Algorithms

In many applications, surveillance, robots, and autonomous driving, human detection is an essential role. Deep learning techniques have made significant strides in recent years when it comes to quickly and accurately detecting humans. This research proposes a deep learning-based investigation into human detection. This study specifically concentrates on how well YOLO, SSD, and Faster R-CNN perform among other cutting-edge deep learning models for human detection. Using benchmark datasets, these models are assessed and their advantages and disadvantages are considered. Furthermore, by adding contextual information into the detection process, this study suggests a unique method for enhancing human detection accuracy in low-light situations. The proposed tests demonstrate that the proposed strategy outperforms other existing approaches. Aerial picture detection, multi-person detection, and real-time detection are some of the issues and future directions are discussed in the final section. The research findings show how deep learning may be used to perform human detection and offer guidance for further study in this field.
Blockchain-based Privacy-Preserving System for Internet of Things (IoT)

The shortcomings of conventional healthcare systems are becoming more apparent as the Internet of Things (IoT) becomes ever more pervasive in people's daily lives. The standard procedure for dealing with confidential data frequently results in unwanted public disclosure. Blockchain's agreements make it possible to automate the safe transfer of patient data based on their individual authorization settings. To create a completely untraceable trading platform, blockchain technology is frequently implemented. To protect user data in the Internet of Things (IoT), a privacy-protecting protocol is developed using the blockchain. It is important to consider that the rogue cloud server might potentially develop a database of user behaviour profiles, which could be a serious breach of privacy. Confidentiality makes it more difficult to resolve disagreements about private data exchange transactions. When users of metadata are falsely accused, their rights are difficult to defend. Also, smart contracts are used to enforce security protocols and data privacy for multisharing adaptable access control. There is no reason to doubt the safety of the proposed work. The proposed work is efficient and applicable, as demonstrated by the efficiency evaluation and experimental findings.
ACM Conference on Recommender Systems September 18--22, 2023 at Singapore

ACM Conference on Recommender Systems (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. RecSys 2023 will be held in Singapore and will bring together the major international research groups working on recommender systems, along with many of the world's leading companies active in e-commerce and other adjacent domains. It is a SIGCHI-sponsored conference, that will attract also several leading industry sponsors. The proceedings will be published by ACM as part of the ACM Digital Library.
Securing Images using Encryption &amp; Decryption

A quick and reliable security systems or APIs are required since the communication domain and interactive media data, such as movies and photographs, are expanding exponentially. Encryption is one method of protecting our data from unauthorized users when communicating and sharing files. The proposed API in this paper aims to secure images by utilizing a well-known Data Encryption Standard (DES) algorithm. The National Institute of Standards and Technology adopted IBM's Data Encryption Standard (DES) algorithm, a symmetric-key block cypher, in the early 1970s (NIST). Using 48-bit keys, the method transforms plain text, which is provided in 64-bit blocks, into ciphertext. Because DES can only convert plain text into secure cypher text, the Image encryption API first converted the image into a binary array so that the DES algorithm could be applied to the image in binary form. Using the API, each image to be transferred is first encrypted by selecting an encryption key. The image can only be opened by the receiver using the same key and API that the sender used during encryption. The primary need for this API or model is to ensure communication security and privacy.
Design and Comparative Analysis of a User-Friendly Telegram Bot for Image Steganography using F5 and LSB Algorithms

Secure communication has become increasingly important with the rise of online communication and data privacy concerns. Image steganography is a popular method for secure communication, but existing systems face several challenges. One of the primary challenges is maintaining image quality while hiding a significant amount of data within the image. Additionally, many existing systems are not user-friendly or system-independent, making them less accessible to a wider range of users. Security and detection of hidden messages are also challenges that need to be addressed. To address these challenges, user-friendly Telegram bot for image steganography that uses the F5 algorithm for JPEG images and the LSB algorithm for PNG images is presented in the study. The objective is to provide a secure and user-friendly platform for image steganography that is easily accessible and system-independent. The use of Telegram as the platform for the bot provides a familiar and widely-used interface that many users are comfortable with. Additionally, the use of encryption ensures that hidden messages are transmitted securely and cannot be easily intercepted or detected. This survey demonstrates that the proposed system provides high-quality steganography images that are difficult to detect, addressing the challenge of detection. Overall, the proposed system aims to overcome the challenges faced by existing image steganography systems and provide a reliable and effective method for secure communication.
Block Chain based Malware Detection System for Smartphone Application

The quick rise of smartphone malware indicates that cybercriminals are switching from traditional personal computer systems to mobile devices. For adequate security, technology specialists are currently creating new anti-malware techniques. This study proposes the use of a block chain to identify malicious smartphone applications. Two internal and external proprietary block chains are combined to create a private dual block chain and a block chain alliance for the decision-making process in the system. While the external block chain stores the blocks for apps of existing models, the private internal block chain stores include blocks drawn from both statically and dynamically driven function removers. Additionally, the proposed system blocks the outside parties, enabling anti-malware vendors to provide more precise solutions. Because cloud resources can control loads and durability, resource monitoring may be dynamic in nature. The additional feature of the proposed system is that authentication techniques like cryptography and digital signature can be integrated. Finally, the effectiveness of the suggested structure is assessed in terms of throughput and precision rate for malware detection.
Transfer of Information using Blockchain Technology

Data sharing is crucial for advancing commercial breakthroughs, scientific research, and public education. But data owners haven't been able to transfer their information due to worries about data protection, costs, and a lack of safe data-sharing options. Several research works have suggested blockchain-based data-sharing solutions as a means of resolving these problems because to its capacity to increase data-sharing process control and transparency. Although there are models for decentralized data sharing, nothing is known about how to motivate these systems to allow for large-scale data sharing. Anyone can investigate several incentive systems for decentralized data-sharing platforms in this research. Several payment methods between data owners and data requesters are automated via smart contracts. By the simulation of incentive mechanisms on a data-sharing platform built on the blockchain, people assess several cost pricing scenarios for the monetization of data. This concept demonstrates that a cost compensation approach for the data owner may balance the overall incentives for all platform participants while quickly covering the cost of data sharing
Table Detection &amp; Data Extraction from Documents using Object Detection

Table detection and data extraction from documents is a crucial task in the field of computer vision and document analysis. The goal is to automatically detect tables in documents and extract relevant information from them. This can be achieved through object detection techniques, which use machine learning algorithms to identify objects in images and extract information from them. The process involves training a model on a dataset of table images, and then using the trained model to detect tables in new images. Once tables are detected, data extraction algorithms can be used to extract text and numerical information from the cells of the table. This information can then be used for a variety of applications, such as database creation, business intelligence, and data analysis.
Indian Sign Language Recognition for Static Gestures using DenseNet169 Model

It is usually difficult to communicate with someone who has hearing loss. Sign language is a way of communication used by dumb and deaf people. Each country has its own unique sign language. Being an Indian, it’s a pride and identity to develop a system for Indian sign language (ISL) recognition. This proposed system aims to examine the difficulties in classifying signs in ISL and intends to take a step forward in this field by utilizing machine learning (ML) models for gesture prediction. This model is trained using a convolutional neural network (CNN), and then the Densenet169 architecture is utilized to recognize the images. This model has been tested and has yielded the highest accuracy of 99.95% in the classification of signs.
Multilabel News Category Classification using Machine Learning

Multi-label text classification is a technique used to categorize texts into multiple labels or categories. Such classification problems are difficult as well as captivated with several parameters and changes per the matter. News categories classification is one of the most popular and necessary multi-label text classification problems because the same news might or might not belong to multiple genres at constant times. The majority of the factors of news genre classification are based on parameters like news headlines, articles, and their short descriptions. The project analyzes news articles using a variety of machine learning and natural language processing approaches. The goal is to take a big collection of article headlines, group them into similar, related stories, and then analyze those groups further to try to develop a statistic for how well the collection of articles covers various points of view on the issue. This uses a variety of Natural Language Processing techniques to pre-process the text in order to improve the amount of importance that can be deduced. It is implemented in Python. It makes use of freely accessible NLP libraries like NLTK.
Offline Recognition Of Handwritten Text Using Combination Of Neural Networks

Recognition of handwriting is a critical challenge in artificial intelligence having implications for numerous areas such as document analysis, optical character recognition, including the processing of natural languages. In this study, a novel deep neural network technique for offline handwritten textual data recognition is described. Due to the volume of data available today and the numerous algorithmic improvements ongoing, it is now simpler to construct deep neural networks. Recent advancements in GPU accessibility and numerous cloud-based solutions like AWS or Amazon Web Services, as well as Google's cloud computing platform that offer resources to do so have increased the processing power that is required to simulate neural networks. It has shown great promise to achieve high accuracy in handwriting recognition tasks by incorporating a few deep learning approaches, including Region-based Convolutional Neural Networks (RCNN), Convolutional Neural Networks (CNN) along with Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The Connectionist Temporal Classification (CTC) method is also frequently used to train deep learning models for sequence identification and loss estimation.
Mastering Fencing Techniques with Machine Learning: A Video-Based Classification and Correction System

In fencing and other sports, athletes must continually execute numerous movements absent of expert supervision. In this article, we address this issue by creating a smart coach that classffies and corrects fencing movements using video footage. To avoid contextual bias, a variety of machine learning models, including LSTM, CNN-BiLSTM and attention based models were trained on fencers’ keypoints. For this purpose, we collected and annotated a video dataset featuring more than 1200 videos of four fencing movements and corresponding error patterns. This will be published along with this work under the Creative Commons License 4.0. The actions in this dataset can be classified with the masked self-attention architecture attaining a macro-averaged F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of over 99% on the test set. In an additional effort to show the impact of label quality, the performances are boosted on average by 3% with the introduction of multi-labeling.
Image Forgery Detection using Block Matching and K-Means Clustering Techniques

The increasing prevalence of digital image forgery has led to the development of various techniques for detecting such manipulations. This study proposes a method for detecting copy-move forgery is a sort of image forgery that involves copying and pasting a segment of a picture onto another location inside the same image. Block matching and K-means clustering are the two primary phases of the suggested methodology. The picture is separated into blocks in the block matching process, and each block is compared to other blocks to identify regions that are similar. Using normalized cross-correlation, which assesses the similarity between two blocks, this stage is completed. The comparable blocks are clustered together using the K-means algorithm in the K-means clustering stage. The clusters that are produced are then examined to find areas that could have been copied and pasted. The experimental results show that the proposed technique detects copy-move forgeries with excellent accuracy even in the presence of various types of noise and post-processing. The approach is computationally efficient and may be utilized for large pictures.
Enhanced Deep Learning Architectures for Spectrum Sensing in Cellular Networks

The expansion of 5G technologies and the Internet of Things (IoT) increases the demand for spectrum efficiency. In future smart city and Industrial IoT (IIoT) applications, the number of wireless users and IoT devices will be excessive. The effect will be spectrum congestion. Moreover, the existing wireless technology has security flaws and inadequate service quality. Cognitive Radio (CR) technology intends to enhance the functioning of the existing system and meet the growing bandwidth needs of users. Spectrum awareness with identification of various signal patterns, is crucial in a cellular system environment. In this work, two deep neural network architectures are presented to distinguish 5G NR (new Rradio) signals from Long-Term Evolution (LTE) signals. This paper presents AlexNet and SqueezeNet architectures for the classification of NR signal with LTE signal. The analysis is conducted by training the classifiers with three distinct optimizers, including RMSprop (root mean squared propagation), ADAM (adaptive moment estimation) and SGDM (stochastic gradient descent with momentum), In addition, performance study is conducted at three distinct training frequencies to assess the classifiers’ superiority.
Private Document Vault with Server-Side Encryption in Cloud AWS S3 Bucket

The most requested future-oriented innovation in the world is distributed computing. Distributed storage is one of the most famous managements presented in cloud computing. Information is stored on various external servers as opposed to the fixed servers used in traditionally located information capacity in distributed storage. All the information stored on various external servers leaves the client unattended, and no one knows exactly where the information is stored. Rejected by distributed storage vendors who claim they can protect your information, but no one trusts them. Information stored via the cloud and traveling in cleartext design within an organization is a security risk. This research study proposes a strategy for allowing clients to securely store and access information from distributed stores. This technology ensures the security and protection of information stored in the cloud. Another advantage of this strategy is that it is encrypted using cryptographic methods. In this technology, the files are encrypted using both symmetric and asymmetric algorithms, which is nothing but Hybrid Encryption and as a result the end user will get a pair of keys that will help in decrypting the files. If a data breach occurs the first layer of security will get damaged but the second layer of security helps in saving the files from data breach.