Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Trustworthy and Secure Review System for Decentralized e-Portfolio Platforms

E-portfolios have emerged as powerful tools in the education and professional development fields, enabling learners to showcase their skills, achievements, and credentials in a digital format. However, ensuring the security, authenticity and reliability of e-portfolio artifacts and credentials remains a challenge. Existing review systems face challenges such as lack of transparency, vulnerability to manipulation, and reliance on centralized trust. Therefore, this paper proposes B-TrustReview, a trustworthy and secure review system for decentralized e-portfolio platforms that leverages blockchain and smart contracts. By leveraging blockchain’s decentralized and tamper-resistant nature, the system enhances the security, transparency, and reliability of reviews. Every review is securely recorded on the blockchain, creating an auditable and transparent record of evaluations. Smart contracts govern the execution of the review process, automating the validation and verification of reviews based on predefined criteria. This automation reduces reliance on intermediaries and minimizes the risk of biased or manipulated reviews fostering transparency, accountability, and credibility in evaluating e-portfolios. Moreover, it enables secure and efficient transactions, ensuring the confidentiality of user data and protecting against unauthorized access.
Protecting Against Caller ID Spoofing Attacks Using In-band Signaling

Whenever one receives a phone call, the Caller ID is the first bit of information that is being processed. Based on this information, one may choose to accept the incoming call or to reject it. Furthermore, the Caller ID is the element that sets the trust premise of the call, even before the actual conversation begins. Unfortunately, the value of the Caller ID is neither guaranteed in any way by the telecommunications company nor is it imposed and can easily be spoofed by a malicious actor in order to start an attack that wouldn’t be possible otherwise. This paper presents a method of identifying a spoofed Caller ID, method that is applicable to smartphones with no need of additional hardware, no additional running costs or the implication of third-party entities.
Deep Learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation

With the exponential growth and rapid development in the fields of deep learning and neural networks, chatbots have gained a lot of popularity and have become a proven, and efficient tool to interact and provide service to users. Healthcare is one of the most promising fields where chatbots can be used more efficiently. This has become important, especially in the current medical landscape, where there is a shortage of doctors, and patients often have to wait long periods before getting any medical guidance. By using the power of transformer models and machine learning algorithms chatbots can help patients with personalized diagnoses and treatment recommendations, efficiently at ease and convenience. This helps the patients to access medical services anywhere, at any point in time. This paper proposes a well-planned systematic architecture for a medical chatbot that utilizes the potential of transformers, classification algorithms and machine-learning models. The architecture includes three main components: a Naïve Bayes Classifier, a Binary Tree classifier along with a Support Vector Classifier, and a sequence-to-sequence model. These algorithms are used to classify symptoms and determine the severity of a medical condition to provide patients with accurate medical diagnoses and treatment recommendations. Overall, the proposed architecture is built and designed to bridge the gap between doctors and patients by providing immediate access to medical advice, making it a promising tool for improving the quality and accessibility of healthcare services.
A Brief Survey of Watermarks in Generative AI

Generative AI technology is now capable of producing images and text at a level comparable to that of humans, showcasing its remarkable utility. However, this advancement comes with its share of challenges, such as misuse, prompting discussions on effective response strategies. Consequently, recommendations and regulations, including the adoption of watermark technology, are under deliberation on a country-by-country basis. Many companies are also integrating watermark technology into their services as a means of addressing this issue. This paper presents an analysis of the current status of watermark adoption across various countries and companies. Furthermore, it delves into further research topics that should be taken into account when implementing watermark technology. This analysis aims to provide valuable insights to those who are contemplating the implementation of watermarking in their future generative AI services.
Cloud Based Secured Hospital Ecosystem

Security issues that put a patient’s privacy and trust in a risk can be related to breaches in health records. If a portion of their medical information was unintentionally revealed, someone with a sensitive health condition might be far more concerned. Individuals react differently; some may become extremely worried and emotionally distressed, while others may become irritated or show no care at all. Patients only have to give the encrypting key for the approved physicians. The trouble of sifting through the encoded data, however, severely restricts availability of outsourced medical data. We suggest two Safe and Effective Active Searchable Symmetrical Encoding on Medical Cloud Information techniques in our project. First, we suggest active A Searchable symmetric encoding scheme that can attain two key safety properties, namely forward secrecy and backward secrecy. Our methods offer better storage, search, and update performance.
Research and Application of Prediction Model Based on Random Forest Algorithm

In this paper, based on the open source UC dataset, the original dataset was first subjected to data preprocessing operations such as logarithmic transformation, outlier and missing value processing, and dimensionality reduction to obtain complete, valid, intuitive, and concise data. To avoid multicollinearity among variables, the variance expansion coefficient of each variable was calculated and found to be less than 5, so there was no multicollinearity. Then lasso regression feature extraction was performed to reduce the number of variables to 13, thus reducing the complexity of the model. A random forest prediction model was constructed, and its average relative error was 0.04217 according to the model evaluation criterion 0.99157, with an error precision of 1. The model has good precision and is useful for prediction research in other fields.
ADA: Augmented-Reality Development Adaptor for Supporting AR glass

AR technology and AR devices have rapidly evolved, and the AR market is also very active. However, content creators face challenges in adapting their creations to different devices, as each device requires a distinct development approach. Moreover, hardware manufacturers often encounter difficulties in providing sustained software support for content development.In this study, we define data profiles for four distinct AR devices and propose the ADA SDK within the Unity framework. This ADA SDK aims to facilitate content development for multiple AR devices, providing content creators with a more streamlined approach. We demonstrate the potential for a single content piece developed using the ADA SDK to be adaptable across various devices through simulated testing within a simple Unity application. Building upon this data and SDK, we anticipate the emergence of a platform that seamlessly integrates AR capabilities, catering to a diverse range of devices. Furthermore, we expect this research to contribute to increased adoption of AR devices by small and medium-sized manufacturing companies, ultimately fostering the activation of the AR market.
Adaptive client training scale orchestration for federated learning

Federated learning (FL), in contrast to traditional centralized learning, has gained significant attention as it enables the training of high-performance neural networks by maintaining user data locally while exchanging model updates between the server and clients, i.e., end-to-end network exchange. However, in practical environment, when considering various anonymous participants’ computational resources, only a minority of clients can comply with the constraints imposed during the server-side training process. To address this issue, we propose Adaptive Scaling-Federated Learning (AS-Fed) based on Deep Q-Network (DQN) to dynamically orchestrate client’s local data, allowing the inclusion of a larger number of clients in the training procedure. Our experimental results demonstrate that the proposed AS-Fed approach outperforms the legacy scheme, achieving higher normalization performance during the training process.
TAKA Cybersecurity Chatbot

The internet has become an integral part of our daily lives, with smartphones and websites being used for communication, entertainment, shopping, banking, and more. However, this increased dependence on technology has also increased the risk of cyber threats such as hacking, identity theft, and malware attacks. Cybersecurity awareness and education are essential to safeguard against cyber threats. Collaboration between security experts can help prevent attacks and safeguard against cybercrime. Despite the numerous platforms available across various domains, we have discovered that there is currently no platform that can effectively bridge the gap between common people, developers, and cyber security researchers to protect people on the internet. This has led us to develop TAKA (meaning of TAKA in Japanese is to GIVE. Our research work aims to GIVE Security to every person in the world through our chatbot service), a conversational AI that provides continuous assistance to its users in protecting themselves from various cyber threats that occur on the Internet on a daily basis. TAKA is a conversational AI built using the RASA chatbot framework, designed to provide various cyber security services to protect its users. The chatbot offers protection against phishing links and emails, spam messages and spam calls, a range of inbuilt cyber security tools, and includes resources for security researchers and daily security and vulnerability news for developers.
Cybersecurity and Electoral Processes. An Analysis of Block Chain Enabled Biometric Voter System and Risk Control in Kenya’s 2022 Electoral Process and the United States Election System Infrastructure.

Electoral Processes Infrastructure Security and Integrity has been an emerging challenge due to the verifiability disputes that arise in competitive elections. This is despite investments in Secure and electoral technology that meets the Confidentiality, Integrity and Availability (CIA triad) of information security. To ensure electoral infrastructure efficiency, information security and voters data confidentiality, governments have invested in Blockchain modeled technology, Biometric Voter Registration and Authentication as well as secure electronic voting mechanisms. This paper studies the Information Security issues raised in the Data Collection, Results Transmission Servers (RTS), Management in the Kenyan electoral process of 2022 where Biometric Voter Registration (BVR) Technology was used to authenticate the voter registration and a hybrid System used in voting and transmitting results. Comparatively, this paper also investigates the United States electoral voting platforms, and documented attempts to interfere with the elections Cyber Security Infrastructure through Malware Attacks and Spearphishing on State and Local Networks. The demonstrates the shows the cost and benefit of traditional data centers against block chain secured BVR and virtualized platform. To enhance electoral integrity and security, the findings recommend a proposed change in the Information Management policies and protocols in the administration of electoral hardware and software assets as well as unrestricted access for Results Transmission Audit.
Network Data Generation using IP2Vec Embedding

Network data generation is crucial in applying deep learning techniques for predicting and managing 5G/6G networks. In this paper, we propose to incorporate IP2Vec in generative adversarial networks (GAN)-based network data generation models. Our evaluation demonstrates that utilizing IP2Vec can effectively enhance the performance of GANs in generating network data.
Harm of Giants in Cloud computing Market

In the realm of traditional IT, dominance was primarily held by IT service providers. However, the landscape has drastically changed in the artificial intelligence sector, with market power shifting towards global behemoths such as Google, Apple, and Amazon. These industry giants have solidified their dominance by capitalizing on electronic data, presenting a challenge in terms of regulating their growing market control. This article aims to explore the consolidation of monopolistic power among a handful of cloud service providers and the resulting ramifications on the market. Additionally, it delves into the comprehensive inquiry conducted by the U.S. Judiciary Committee in 2019. This investigation scrutinized the market influence and business practices of tech titans including Amazon, Apple, Facebook, and Google. The insights gleaned from this investigation are analyzed to shed light on the issue at hand. Furthermore, this article puts forth strategies for the regulation of business activities within the cloud market ecosystem. It suggests potential enhancements to antitrust regulations that can effectively address the emerging concerns. The goal is to strike a balance between fostering innovation and competition while preventing unchecked monopolistic control.
Health status assessment of scraper conveyor electrical system based on cooperative game theory and cloud model

The paper is based on cooperative game theory and cloud model theory to evaluate the health status of the electrical system of the scraper conveyor in the underground fully mechanized mining face. Firstly, establish a hierarchical analysis model for the electrical system of the scraper conveyor, and use a cooperative game method that combines Analytic Hierarchy Process, Entropy Weight Method, and Grey Correlation Degree Method to calculate the composite weights of each indicator. Then, the membership formula is combined with cloud model parameters to obtain the membership values of each indicator corresponding to each state level, and the maximum membership method is used to obtain the final evaluation result. The research results indicate that the proposed health status assessment method can intuitively obtain the health status information of various indicators in the electrical system of the scraper conveyor, and effectively complete its health status assessment.
Generating Textual Video Summaries using Modified Bi-Modal Transformer and Whisper Model

Online video material availability has grown significantly over the past few years, making it challenging for viewers to consume and process information effectively. Video summaries written in text form have shown to be a helpful technique for solving this problem. The key aspects of a video can be accurately and briefly summarised by utilizing methods like video summarising and natural language processing. The purpose of the model is to display a short textual summary of the video to the user before the user watches the complete video. It is frequently difficult and time-consuming for students to locate a suitable educational video. It is occasionally seen on video streaming platforms such as YouTube that the title and the content of the video are not precisely the same. So the model will generate a textual summary of the video and the user can read the summary and get to know about the content of the video. Also, this approach to summarizing videos is helpful in other tasks like video content moderation and could also enable visually-impaired people to consume video material and improve their quality of life.
Smart BAT - Smart Budget Analyzer and Tracker

Maintaining your daily expenses manually by entering all the details of the items purchased can often feel like an intimidating task. This research discusses the difficulties in budget tracking and suggests a fresh budget tracking program to simplify difficulties such as maintaining the expenses manually using pen and paper, spreadsheet or a diary. Departing from these traditional manual approaches the authors have proposed an innovative solution for this tedious task. In this research paper, we have proposed an automated system that combines Optical Character Recognition and Machine Learning to tackle the challenges associated with transforming physical bills into digital format. The key feature of the application includes optical character recognition which will scan the bill and extract all the items present on the bill and automatically categorize the items into various categories where the item belongs. The application also provides some additional features such as future expense prediction and a detailed report generation. In this study, authors emphasizes the significance of effective budget tracking in the modern world and offers a creative solution by creating a user-centered budget tracking application. We hope that by meeting the needs of the average customer and providing a flawless experience, our application can considerably enhance people’s financial well-being.
Underwater Object Detection using EfficientDet

Underwater object detection has numerous applications, including marine research, habitat inspection, mineral identification, and more. Taking high-quality photographs or videos underwater can be difficult because water absorbs and scatters light, which can make images appear unclear or distorted. As water depth increases, longer wavelengths of light are absorbed, resulting in predominantly blue-green images, while reds are absorbed due to their higher wavelengths. This leads to image degradation, including low contrast, color distortion, and poor visibility, making it challenging to detect objects in underwater images. Therefore, enhancing underwater images is crucial to improve their quality for various applications while retaining the valuable information they contain. However, low light conditions can negatively affect detection performance, and the image’s light composition significantly impacts subject detection. To enhance the detection outcomes, it is possible to utilize low-light image enhancement as a preliminary mechanism aimed at ameliorating the quality of the image Nevertheless, current enhancement methods may not work well in all situations due to the complexity of the wavelength environment. To tackle this issue, our solution involves a twostage process comprising of an enhancement phase followed by a detection phase. In the enhancement phase, we use multiple enhancement methods to automatically enhance low-light images and generate corresponding weights that are better suited for detection.
ChatGPT for Creative Writing and Natural Language Generation in Poetry and Prose

Machine learning models, such as ChatGPT, are increasingly being used in creative writing. ChatGPT, a language model developed by OpenAI, can generate human-like writings in a variety of styles, including poetry and fiction. The purpose of this research is to investigate the possibilities of ChatGPT in creative writing and natural language text production. The study contains an in-depth examination of the elements considered by ChatGPT while generating poetry and fiction, as well as an exploration of different language models. The study also looks at the challenges and limitations of using ChatGPT in creative writing. The findings of this study have the potential to provide light on the use of machine learning in creative writing and natural language text generation.
Improved Classification Algorithm for Restricted Coulomb Energy-based Neural Network

In this paper, we propose an improved classification scheme for the restricted coulomb energy neural network (the RCE NN). The core concept of our proposed algorithm lies in performing a K-Nearest Neighbor (KNN) search to determine the majority class among the neighbors in cases where the RCE network’s decision is not reliable. Experimental results demonstrate that the proposed algorithm achieves higher classification accuracy compared to the standard RCE NN algorithm.
Improving Product Traceability and Security in Supply Chain Management using BlockChain

In present supply chains have great value and have a great competitive advantage. Modern supply chains have grown into incredibly detailed value networks that are a major source of competitive advantage. By creating a permanent, shareable, auditable record of products along their supply chain to maximise overall value generated, cost quality improvement, quick delivery, delivery optimisation, cost reduction, and flexibility, blockchain technology has the potential to improve product traceability, authenticity, and legality more effectively. Once inputted, data cannot be removed. A smart contract is a contract that can be automatically executed and must be executed according to the terms of the contract. Computers can automatically execute smart contracts, but some functions still require human input and control. Complex transactions can be automated using blockchain technology in combination with smart contracts. The data security, authenticity, time management, and transaction processes of conventional supply chain management systems will all be considerably enhanced by the integration of blockchain technology in the project. Data is shielded from illegal access via blockchain along with the drawbacks of the current system. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. This enhances process accessibility, effectiveness, and timeliness overall. Supply chain management is managing inflow and outflow of goods and services. In order to increase customer value and gain a competitive advantage in the market, it involves actively streamlining a company’s supply-side operations. The reduction of third-party involvement in the Delivery management and the improvement of data security are the main goals of this article.