Reviewerly/exHarmony huggingface dataset:
Strengthening Video Integrity and Anti-Duplication Measures with Blockchain Innovations

The emergence of the digital era has had a significant impact on the channels humans use to communicate. And with the increasing popularity of multimedia distribution channels, the law finds it difficult to adapt the traditional legal notions of privacy and intellectual property ownership rights to the digitalization of tangible goods and assets. As these modern digital assets, viz., videos and audio, do not constitute property in most countries, it is more challenging to employ conventional legal protection. Besides, several applications for copyright objection, duplication detection, and the current media can be manipulated or tampered with in different ways to erase any proof of content modification histories and propagate false objectives. Thus, it is crucial for organizations to detect and eliminate multimedia duplication to protect the intellectual property rights of a creator. In this paper, we propose a novel framework that allows one to manage data more efficiently by addressing copyright and data duplication issues over modern multimedia channels such as Facebook, YouTube, etc., leveraging blockchain technology. The proposed blockchain-based framework encapsulates the diverse content of every multimedia file with a hash using different cryptographic hashing technologies, such as perceptual hashing. Moreover, to make the entire process more secure, the proposed architecture develops a hash for every file using the Merkle Tree. The computed hash is then stored inside the decentralized database in order to preserve a historical audit trail. The proposed architecture holds promise to detect and address video duplication, ensuring secure and immutable protection against content tampering.
Visual Query System Based on Conceptual Graphs for Apache Cassandra

The new generation of database management systems, also known as NoSQL (Not-only-SQL) database management systems, appeared to solve the large variety of challenges raised by big data. On one hand, they offer unmatched performance advantages compared to relational database systems. On the other hand, NoSQL database management systems evolve at a very fast pace and are more challenging to understand and master, compared to relational database management systems. The fact that each NoSQL database management system has its own query language raises additional challenges for users (both experts and non-experts in computer science) in working or starting to work with them. Visual Query Systems can help in addressing this challenge by offering a more intuitive way of interacting with the database server. Based on a conceptual model which uses conceptual graphs to represent database structure and queries, we propose a Visual Query System for Apache Cassandra, the leading NoSQL database management system of the wide column stores DB-Engines ranking.
Visualisation of User Stories in UML Models: A Systematic Literature Review

The use of agile methodology in software development projects is growing rapidly among industry professionals and academia.The Unified Modelling Language (UML) conventionally accompanies agile software development to model the software requirements.The user story is fundamental and should be identified to communicate the basic requirements between the development team and the stakeholders before the UML model such as the use case diagram, class diagrams and many others can be designed.However, there are several challenges associated with this process such as poorly organised user stories, natural language complexity and high time consumption to create them.A systematic literature review is conducted to grasp more knowledge about the utilisation of natural language processing (NLP) for UML model generation.A total of 198 papers were initially found in four online databases, namely Scopus, IEEE Xplore, ScienceDirect and ACM Digital Library, from the period 2018-2022.After removing duplicates, applying inclusion and exclusion criteria, and conducting the full-text assessment, only 20 papers are included as the primary studies.The primary studies are reviewed to discover several important pieces of information, namely the challenges of designing UML models, NLP tools and techniques used to generate UML models, UML models generated, and validation methods used for measuring the accuracy of generated models.Finally, this study discusses important elements related to UML model generation using NLP tools and techniques.
Robust network based on embedded convolutional block attention module and earth mover’s distance for few-shot learning

Deep neural networks of training require a large quantity of effectively labeled datasets, and models trained with insufficient samples will cause a variety of problems, such as overfitting and poor generalization ability. Few-shot learning has been proposed to settle the problem, and the methods introduced in some papers have achieved impressive results. However, most of the methods have poor robustness due to the under-utilization of the global information and local information of the image. For solving the above problems, an improved classification network is proposed in this paper, which introduces two modifications. On the one hand, Convolutional Block Attention Module (CBAM) is embedded in the network for extracting global features, thus the model can quickly suit various tasks. On the other hand, the image is randomly divided into tiles and represented as a set of local features, the Earth Mover’s Distance (EMD) is employed to calculate the distance between embedded sets of two images. Finally, the weighted sum of these two parts’ classification errors, is computed as the total classification error of the proposed model. Experiments are conducted on two benchmark datasets: mini-ImageNet and tiered-ImageNet, and the results demonstrate that our proposed method achieves state-of-theart(SOTA) results in the field of few-shot image classification.
Quantum image encryption based on hyper-chaotic system and double-random phase encoding

In order to enhance the security of encrypted images, a new quantum image encryption algorithm is proposed. This algorithm combines Chen's hyper-chaotic system with Double-random Phase Encoding (DRPE). The pixel values of the original quantum image are diffused using DRPE, while the pixel positions are scrambled through Cyclic Translation (CT). The hyper-chaotic sequences generated by Chen's hyper-chaotic system control the scrambling and diffusion operations. Numerical simulations and theoretical analyses have shown that this algorithm is highly effective in resisting illegal attacks. The encrypted image generated by this algorithm exhibits lower pixel correlation and more evenly distributed histogram. Overall, the proposed algorithm represents a robust and secure approach for image encryption
Exploring Common Areas and Types of Cybercrime in Today's Digital Landscape

Since the advent of the Internet, humanity has made great strides in enhancing global communication. But the rise of cybercrime is a serious problem in today's world. To commit a cybercrime, criminals often access a victim's private data over the internet and then use that data to injure the victim in some way, whether it be by smearing their reputation or physically harming them. Cybercrime and other crimes against women have risen in tandem with the development of new technologies. This is due to the fact that over half of all internet users know very little about how websites work. Numerous people, businesses, and even governments have been harmed by cybercrime in recent years. The many forms of cybercrime that exist today and the typical locations where they occur are briefly discussed in this paper. Some examples of cybercrime case studies are also provided.
Emerging Role of Healthcare Chatbots in Improving Medical Assistance

This study proposes integrating healthcare chatbots and assistive robots to address issues in densely populated areas with limited medical services. These robots offer benefits such as quicker and more accurate diagnoses, alleviating the workload of medical workers. The suggested healthcare chatbot enables efficient health inquiries through question-and-answer discussions. Users can input symptoms and receive ideas for suspected diseases, along with guidance on how to seek medical help. The chatbot uses natural language processing and machine learning. The chatbot compares symptoms to a database of recognized ailments, achieving an impressive 93% accuracy and outperforming existing models. The combination of accurate healthcare chatbots and assistive robots shows excellent potential for improving patient access and healthcare delivery, benefiting the medical sector.
Transforming Health Insurance Claims Adjudication with Blockchain-based Solutions

Over the past decade, the blockchain technology has gained a significant traction with the introduction of the “bitcoin”, the first cryptocurrency back in 2008. The rapid growth of “bitcoin” as a currency and the Bitcoin as a protocol has served as a wake-up call to the IT community which began to realize the real potential of blockchain technology. Blockchain technology is capable of filling many gaps that traditional IT solutions struggle to address. In the other hand the increasing complexity of adjudicating health insurance claims is an urge to introduce more secure and efficient claims adjudication system. Blockchain technology has the potential to reshape the claims adjudication process not only due to its decentralized and tamper-proof features but to its consensus protocols and much more other characteristics. This article studies some of the most recent researches on blockchain solution for health insurance claim adjudication, evaluates the its feasibility and identifies areas where blockchain technology could be applied to improve efficiency and accuracy. This study will also examine the potential benefits and challenges of implementing a blockchain-based system, like reducing administrative costs and improving fraud detection.
Improving Facial Recognition Accuracy Using Local Binary Patterns

The proposed method involves pre-processing of the dataset, which includes normalization and resizing of the images to a standard size. Local binary pattern is then used to extract features from the pre-processed images. These features are used to train a machine learning model, specifically a support vector machine, to recognize faces in the images. The effectiveness of the proposed approach was evaluated using a publicly available dataset, and the results indicate a high level of accuracy in face detection, with a recognition rate of over 90%. The approach was also tested on images with varying contrast and rotation angles, and the results demonstrate its ability to accurately identify faces under such conditions. The proposed method has practical applications in various areas such as surveillance, access control, and forensics. It has the potential to be integrated into security systems for real-time face recognition and identification, improving the accuracy and efficiency of such systems. Furthermore, the method can be extended to include more complex features for more accurate and robust face detection. Overall, this research contributes to the growing body of knowledge on face detection techniques and their practical applications.
Traffic sign detection algorithm for foggy weather scenarios

Due to more bad weather conditions such as fog, rain and snow in the road driving environment, resulting in low target recognition accuracy and missed detection problems in the traffic sign detection algorithm in autonomous driving technology, this paper proposed an improved YOLOv7-based traffic sign detection algorithm. Firstly, the MP conv structure of YOLOv7 was improved and the AC mix attention mechanism was embedded to reduce the feature loss caused during the network feature processing and improve the feature expression capability of the model. Secondly combined with the traffic sign image features, the Kmeans++ algorithm was introduced to re-cluster the anchor frame size and the soft-NMS non-maximum suppression algorithm was introduced. Finally, an image data enhancement algorithm was written in Python to simulate the generation of traffic sign images for foggy scenes to enhance the dataset. Experiments were conducted on the Chinese traffic sign dataset CCTSDB, and the results showed that the improved YOLOv7 algorithm achieved 93.8% recognition accuracy, 91.5% recall, 95.7% average accuracy and 99 frames in complex scenes such as fog, and all indicators were improved compared with the mainstream detection algorithms, which can achieve traffic sign detection in bad weather such as fog.
Enhancing Review Authenticity Using Transformers: Web Extension for Detecting AI-Generated Fake Reviews vs Human-Written Feedback

The importance of the online review system is growing along with the significant increase in internet users. The reliability of online reviews is crucial for businesses since they have a significant influence on their reputation and revenue. It is essential in influencing how people see a good or service, as they are the ones through which customers share their first-hand experience. This study suggests a flexible and user-friendly web plugin while shedding light on the most effective method for locating and eliminating genuine evaluations. The web plugin will have a significant impact on customers and help them form better opinions about a product or service. A supervised learning model is used while deploying the proposed plugin. The user just has to enable the plugin first. The dataset is then retrieved using web scraping tools. Further, in order to extract sound characteristics from the data, it is examined and deconstructed using Natural Language Processing methods. Later predictions are made on the data. Three models namely- BERT, DeBERTa, and XLNet are used for review classification. Based on the experimental results of the study, DeBERTa has a dataset accuracy of 980/0, which is the maximum out of all the three models used. The major goal of this project is to develop a fake survey filtering system that will give clients more trustworthy data and prevent enterprises from losing money dramatically.
An image retrieval method based on fusion of multiple features

In image retrieval tasks, two main indicators are focused on: accuracy and efficiency. In general, the more the number of features, the higher the retrieval accuracy but the lower efficiency. Focusing on ensuring retrieval efficiency while improving retrieval accuracy, this paper designs an unsupervised image retrieval system that integrates multiple features. On UKbench, Holidays, UC Merced Land Use, and RSSCN7, the method proposed was validated. On UKbench, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves N-S by 0.1882, 0.2499, and 0.1985 respectively, while saving retrieval time by 0.0115s. On Holidays, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves MAP by 7.11%, 9.05%, and 7.27% respectively, while saving retrieval time by 0.0021s. On UC Merced Land Use, compared to methods based on shuffleNetV2, VGG16 and Resnet18, the method proposed improves accuracy by an average of 3.31%, 11.84%, and 6.29% respectively, while saving retrieval time by 0.0007s. On RSSCN7, compared to methods based on shuffleNetV2, VGG-16 and Resnet18, the method proposed improves accuracy by an average of 2.19%, 9.19%, and 7.18% respectively, while saving retrieval time by 0.0002s. The experimental results show that the proposed method can effectively improve retrieval accuracy and to some extent improve retrieval efficiency
Efficient Cryptosystem for Image Security Based on Enhanced Chaotic Maps

In this modern age, digital communication has revolutionized the way we connect with each other, making communication faster, more convenient, and more accessible than ever before. While transmitting data such as images, audio, video etc., through the insecure communication channel, the possibility of data capturing, identity theft, and tempering increases. In this view, data security becomes the primary objective before transmitting them. To avoid this problem, we have developed an effective image encryption technique based on enhanced one-dimensional chaotic maps that consist of three stages; in the first stage, we generate the control parameters for enhanced sine map (ESM) and enhanced tent map (ETM) from the user-defined key string and original images that help in making the cryptosystem image and user-defined key sensitive. The second stage corresponds to the generation of two key images and indexing sequences after iterating the ESM and ETM using the evaluated control parameters and initial parameters. The final stage of the proposed cryptosystem contains the two-phase instantaneous scrambling and substitution operation and in between the two phases, the full-scale scrambling operation is performed which is a creative and new way to increase the encryption effect, resulting in the final encrypted image. The evaluation observation reveals that the proposed system can withstand all plain text and differential attacks effectively compared to other state-of-the-art techniques. Moreover, the key space of the proposed system is greater than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> that makes it difficult for an attacker to break the encryption.
Indoor object recognition based on YOLOv5 with EIOU loss function

At present, large-scale object classification and recognition tasks inevitably encounter problems of training efficiency and model accuracy. The solution of this problem highly depends on the definition of loss function. A better loss function can get a more accurate model with fewer epoches, which undoubtedly helps to save computility and time cost and improve the security of the model. Nowadays, the task of indoor object recognition plays an important role in the fields of computer vision aided blind people, home robots and so on. However, the task of indoor object recognition is plagued by the above problems due to its huge category of recognition objects. Starting with the task of indoor object recognition, this paper innovatively uses EIOU loss function and YOLOv5 deep learning convolutional neural network in this field, which improves training efficiency and recognition accuracy. Some original bounding box regression loss functions of YOLO series (such as GIOU, DIOU, CIOU) have defects in that the prediction box coincides with the center point of the truth value box, and the horizontal vertical ratio is the same. The paper uses the EIOU loss function to solve this problem. This paper focuses on the network structure of YOLOv5, the defects of YOLOv5's native loss function, the calculation method and advantages of EIOU loss function, and compares the performance gap between EIOU and CIOU. Finally, the EIOU loss function is used to complete the indoor article identification task.
Data-Centric AI in Mechanical Engineering

Data-centric AI approach focuses on techniques to gather data from multiple sources, its management, and its utilization. This approach understands the meaning of high-quality data. The high-quality data, well-curated data will in turn help in better model development and accurate outcomes.
A federated and explainable approach for insider threat detection in IoT

An insider threat is a malicious action launched by authorized personnel inside the organization. Since insider actions may only leave a small digital footprint in the system, it is considered a major cybersecurity challenge in different applications. Along with the rapid growth of the Internet of Things (IoT) and the extensive attack surface in this technology, many concerns have been raised regarding the potential insider threats in IoT environments. Several studies have been conducted on Machine Learning (ML)-based insider threat detection solutions which are focused on the models’ performance while the trustability of these models is neglected. Trustworthy Learning refers to a new trend in ML that focuses on ways to ensure that the data collection and data analysis procedures in ML techniques follow ethical applications and are trustable to human users. This approach enforces the acceptance and successful adoption of ML-based solutions. This study aims to propose an improved trustworthy insider threat detection method that ensures two of the trustworthy learning requirements: Privacy and Explainability. The proposed solution protects the privacy of the utilized data and is capable of explaining why certain behaviors are detected as a threat. The proposed solution also leverages data collaboration between different data owners to increase the volume of data used in the training process and enhance the performance of the ML model. Experimental results show the proposed solution outperforms the learning models trained by individual data holders.
Towards Realizing Lightweight Consensus Protocol for IoT

Internet of Things (IoT) refers to the network of heterogeneous computing devices that are web-enabled and have the capabilities of sensing, collecting, processing, and disseminating data. The IoT devices are embedded with various sensors or actuators supported by communication hardware and protocols to ensure their deployment for real-world applications. Furthermore, these devices can use logic building with sensor inputs and on connecting actuators to generate relevant output. With numerous applications but the unavailability of basic security policies, interoperability issues, and resource constraint environment, IoT devices nowadays face lots of difficulty in terms of ensuring communication security. Also, the use of simple processors and stripped-down operating systems which may not support sophisticated security approaches, make IoT systems vulnerable to security attacks. The advent of blockchain technology paved the way to secure data or information using distributed hashed ledger of transactions. With blockchain, one can ensure the secure sharing and dissemination of data between the entities running blockchain network. However, deploying blockchain with IoT systems may hinder their performance and efficiency because of resource constraint characteristics of heterogeneous devices. In this research work, we have explored a lightweight blockchain-based approach using AERGO blockchain to secure data transfer between IoT devices. The same has been confirmed for validity and effectiveness using a test-bed setup of IoT devices.
Penerapan PIECES Framework dalam Analisis Kepuasan Pengguna F-Learn UKSW saat Pandemi Covid-19

This research aimed to analyze the satisfaction level of Satya Wacana Christian University (SWCU) F-Learn users in daring learning during the Covid-19 pandemic using the PIECES framework. This method was chosen because it has proven its effectiveness in evaluating system user satisfaction in identifying problems, opportunities and development directions for the system being developed. This research analyzes six variables: performance, information and data, economics, control and security, efficiency, and service. The research sample for this was SWCU students. The total sample is 160 respondents. Data was measured using a Likert scale. The results of testing on the variables performance, information and data, control and security, efficiency, and service, users feel satisfied, while testing the economics variables, F-Learn users feel very satisfied. Additional testing on the performance of F-Learn confirmed that F-Learn has an excellent performance in terms of the speed of responding to commands, as well as in terms of control F-Learn is in category B (good). Overall, the average level of satisfaction of F-Learn users is 4.02. It means that overall F-Learn users, namely SWCU students, were satisfied when using F-Learn during the implementation of the daring learning policy during the pandemic.