Reviewerly/exHarmony huggingface dataset:
TAVM: A Novel Video Summarization Model Based on Text, Audio and Video Frames

In today's digital world, the task of video summarization has gained immense importance within the realm of multimedia analysis. This relevance is largely driven by the exponential expansion in multimedia content consumption, encompassing audio, video, and images, which is readily available on-demand through various digital platforms. Automatic video summary is the process of creating a brief synopsis that summarizes the video by displaying its most useful and relevant elements, so consumers may rapidly comprehend the primary concept of a video without having to watch the entire material. Currently, the selection of the video segments to be included in the final summary is done in a variety of ways. The task is to analyze the video's multimedia data in search of relevant clues that will aid in decision-making. The proposed method TAVM (text, audio, and video mode) in this paper will provide the video summary using different multimedia elements of text, audio, and frames. The proposed TAVM method can be separated into three parts. The process begins with Video Processing, where the BEiT vision transformer is employed to recognize objects within the chosen frame. Following that, Audio Processing comes into play, which uses speech-to-text converters to transcribe the audio content. Finally, in the last step, the Summary Builder utilizes the GPT-3-based OpenAI API to generate a summary of the content. The experimental analysis on the benchmark dataset SumMe demonstrates the effectiveness of the proposed approach.
Convolutional Neural Network Based Alzheimer's Disease Detection Using OIASIS Dataset

Alzheimer's disease, an intricate neurological disorder, culminates in the degeneration of brain cells, a process intricately linked with the emergence of dementia-a primary consequence of this ailment. The ramifications of Alzheimer's disease reverberate through cognitive functions, particularly manifesting as an individual's struggle to retain recent memories, subsequently eroding their capacity to execute routine activities proficiently. This research paper delves into the application of Convolutional Neural Networks (CNNs) as a potent tool for the early-stage detection of Alzheimer's disease. Characterized by its debilitating effects on memory and cognitive functions, Alzheimer's disease occupies a significant position within the realm of neurodegenerative disorders. The gradual loss of brain cells, the fundamental building blocks of cognition, propels individuals toward the precipice of dementia-a multifaceted condition profoundly influencing cognitive abilities. This intricate interplay between Alzheimer's and dementia translates into a cascading effect on an individual's capacity to recollect recent events, further leading to a gradual erosion of their capability to perform seemingly mundane day-to-day tasks. Addressing this critical juncture, this research endeavors to unravel the potential of Convolutional Neural Networks (CNNs) in their capacity to diagnose Alzheimer's disease at its nascent stages. CNNs, a subset of artificial neural networks renowned for their prowess in image recognition and pattern detection, stand poised as an innovative technological avenue for early detection. Through the extraction of intricate patterns and nuanced features from medical imaging data, CNNs offer the promise of identifying subtle indicators of Alzheimer's disease even before noticeable symptoms emerge.
Machine Learning Techniques for Analysing Security Practises in Electronic Health Records

Electronic health statistics (EHR) security is essential because the fitness information saved and transmitted in these data is exclusive. Suppose you want to ensure the proper safety of the information stored in EHRs. In that case, machine-gaining knowledge of strategies is used to analyze the facts and detect any capacity safety problems before they emerge as real problems. Those techniques can identify any capacity weaknesses and help implement corrective measures. Moreover, system study can be used to detect any malicious pastime or ability intrusions that have already passed off. It can allow corporations to respond more speedily to potential security threats and save them from further damage. In conclusion, gadget-mastering techniques may shield the information saved in EHRs efficiently. They could stay a precious asset in enhancing the security of that information in the future.
Enhancing Breast Cancer Diagnosis Through Segmentation-Driven Generative Adversarial Networks for Synthetic Mammogram Generation

Breast cancer remains a global health challenge, necessitating innovative diagnostic strategies for early detection and precise treatment. This paper introduces a pioneering approach that leverages the transformative capabilities of Generative Adversarial Networks (GANs) to advance breast cancer diagnosis through Segmentation-Driven Synthetic Mammogram Generation. By seamlessly integrating accurate segmentation algorithms and generative AI, our framework addresses the scarcity of annotated medical images and enhances diagnostic accuracy. Synthetic mammograms, faithfully emulating real-world scenarios, are generated to enrich the training dataset, fostering a diversified learning environment for diagnostic models. This synergy of segmentation and synthesis not only empowers clinicians with a broader exposure to cases but also fuels the development of robust diagnostic models capable of tackling clinical challenges. Through an interdisciplinary lens, our approach ushers in a new era in medical imaging, illuminating a path toward improved patient outcomes and reshaping the landscape of breast cancer diagnosis. This paper paves the way for transformative advancements at the intersection of AI -driven image synthesis and clinical medicine.
Enhancing Sentiment Analysis with an AttentionBased Machine Learning Model

The study focuses on applying attention mechanisms in sentiment analysis tasks specifically on Twitter data. Sentiment analysis involves determining the sentiment expressed in a given text, such as positive, negative, or neutral. Twitter data presents unique challenges for sentiment analysis due to its brevity, noise, and informal language. The attention of mechanism is employed to recognize and highlight the most relevant words in a tweet, contributing to sentiment classification. The model employs a recurrent neural network architecture with attention, allowing it to learn the importance of different words or phrases in sentiment classification. The current model used support vector machines, random forests, and decision trees as its algorithms. The field of deep learning has seen a lot of breakthroughs. SemEval-2016 Twitter dataset, where the embeddings have been improved [1]. The chosen features, which combine recurrent neural networks and attention mechanisms, are used to distinguish between classes of sentiment that are positive, negative, and neutral. [2]. The study conducts experiments using a large dataset of Twitter messages annotated with sentiment labels. The proposed model utilizes attention mechanisms to effectively capture and emphasize relevant parts of the Twitter text. A lengthy short-term memory network is employed by the model to extract sequential information and context from tweets. The attention mechanism assigns higher weights to informative words, enabling the model to capture context and sentiment nuances in short Twitter messages. To assess the usefulness of the attention-based model, its performance is contrasted with that of conventional sentiment analysis methods. The outcomes show that when performing sentiment analysis on Twitter data, the attention-based model performs better than more established techniques.
Framework for Optimizing Cloud Workflow Scheduling with the Krill Herd Algorithm

Cloud computing has revolutionized the way compu-tational resources are provisioned and managed, offering scalability, flexibility, and cost-efficiency. Effective workflow scheduling in dynamic cloud environments is paramount to harness these benefits. In this paper, we introduce a novel workflow scheduling framework centered on the Krill Herd Algorithm (KHA). Our approach aims to optimize task allocation and resource utilization while meeting service-level agreements (SLAs) in diverse cloud computing scenarios. The paper begins with a detailed system model, providing insights into the dynamic nature of cloud environments and the associated scheduling challenges. We present the methodology, highlighting the adaptability and efficiency of KHA in addressing real-time changes and resource allocation dynamics. To evaluate our framework, comprehensive experiments are conducted, leveraging key performance metrics, including Task Completion Time (TCT), Resource Utilization (RU), Makespan (MS), and SLA Compliance. Results reveal the superiority of our approach over existing methods, with consistent improvements in task completion efficiency, resource optimization, reduced turnaround times, and SLA adherence.
Utilizing Blockchain and Deep Learning for Decentralized Discovery of Deceptive Practices in Healthcare Insurance

To address the complex issues of detecting fraudulent practices in healthcare insurance, this research employs sophisticated machine learning, especially the Long Short-Term Memory (LSTM) model, to provide a complete framework for reliable fraud detection. The study meticulously examines performance metrics such as accuracy, precision, recall, and the F1 score by employing and evaluating the LSTM model across two distinct datasets-Dataset A (198810 samples) and Dataset B (434319 samples)-illuminating the model's capacity to detect fraudulent activities while minimizing misclassifications. The evaluation process, as shown by confusion matrices displayed as percentages, reveals the model's strengths and points up areas for improvement. This study makes an important contribution to the field of fraud detection by providing practical insights to strengthen healthcare insurance against misleading practices. This research proclaimers a paradigm shift by combining innovative methodologies, extensive dataset curation, and stringent evaluation, ushering in increased security, transparency, and efficacy in healthcare insurance fraud detection, ultimately fostering a future of resilient and precise fraud detection mechanisms.
Phishing URL Detection and Reporting System Using Machine Learning Approach

Phishing attacks continue to pose a threat to the internet users due to the consequences they bring, including the compromise of sensitive information, financial harm and reputational damage. In this research paper, we propose to create a free access, ad-free and non-profit website that uses machine learning algorithms to identify phishing URLs. This website shall be designed to provide users with a reliable and user-friendly tool to evaluate the legitimacy of the URLs they come from. Using different data-sets containing phishing and legitimate URLs, extract important features to train the classification model. The model will be embedded in website, which will evaluate user-submitted URLs and provides true phishing detection. There exists report, which claims on the website where users can contribute to the collective fight against phishing attacks by reporting phishing URLs they encountered. In this way users actively contribute to the development of the and improved efficiency of the system.
Continual Reinforcement Learning for Autonomous Driving with Application on Velocity Control under Various Environment

Reinforcement learning based methods are extensively studied in autonomous driving. However, most existing methods, suffering from catastrophic forgetting, only work properly under certain scenarios. Therefore, this paper proposes a continual reinforcement learning approach to improve the control adaptability of autonomous driving systems. Specifically, the framework is based on a Soft Actor-Critic algorithm, which includes a shared feature extractor with regularization loss to learn task-specific output mapping. A linear multi-policy heads structure is designed to continuously learn different tasks without interference. Velocity control under various environment is taken as a case study, and three velocity control tasks with a high degree of overlap in the observation spaces are designed. In addition, multi-modal optimization objectives considering safety, efficiency and comfort for the three tasks are designed to evaluate the effectiveness of the proposed approach. The simulation studies are conducted in CARLA, which demonstrates the efficiency of the proposed method in terms of improving the continual learning capability of the model. Overall, the proposed continual reinforcement learning framework contributes to the development of adaptive autonomous driving systems to a large extent.
Non-uniform Image Down-sampling for Efficient Dense Perception

Dense perception tasks such as Semantic Segmentation, Depth Estimation and Object Detection can provide rich perceptual information for self-driving vehicles. As the Deep Neural Network is becoming a trend for these tasks, most of these networks face the great challenge of taking a balance between performance and speed. Typically, downsampling high-resolution input images before feeding it into the network is an effective way to reduce computation overhead but it also sacrifices accuracy. Based on the fact that information is not uniformly distributed in the input image, it might be helpful to downsample the image using a spatial Non-Uniform Sampling (NUS) method, so as to minimize the information loss in the downsampling process. In this paper, we proposed a general and end-to-end trainable NUS-based framework coined DiffeoSTN for dense prediction tasks. Importantly, we reveal that the key ingredient to obtain superior performance through end-to-end training is a reverse (up)sampler corresponding to the NUS downsampler. Specifically, we leverage an intuitive force attraction downsampler and a fixed-point upsampler in the framework. We demonstrate consistently favorable performance compared to non-end-to-end-trained and uniform samplers on multiple datasets.
Understanding the relationship between cybersickness and usability through seven human factors dimensions: An exploratory comparison of two virtual reality training applications

Virtual Reality (VR) technology has become an increasingly popular tool in various training applications. It can provide trainees with a controlled environment to practice scenarios that may be too dangerous or expensive to replicate in real life. Yet, while VR training provides many benefits, it is essential to ensure that it is used appropriately and consider the potential risks associated with such technology, namely cybersickness. If not taken into account, cybersickness can seriously threaten the usability of VR training applications, preventing them from reaching their full potential. Similarly, poor usability can have negative impacts on users' well-being. This paper provides an overview of the usability and cybersickness characteristics of VR training. Then, it looks into seven human factors (HF) dimensions impacting cybersickness and provides recommendations to mitigate its effects on VR users. Finally, it shows the importance of considering both usability and cybersickness aspects to achieve safe and effective training in VR. To this end, two VR applications are compared, one used for learning assembly operations and the other to learn aircraft operating procedures. Their usability issues and degree of cybersickness are then discussed using the HF dimensions.
INTELLIGENT VIDEO SURVEILLANCE SYSTEMS: A SURVEY

Over the years, the need for intelligent video surveillance has increased in other to enhance security and safety in the society. Government, private organization and individual need to make sure its properties are kept safe from intruders and as such intelligent video surveillance plays a key role in ensuring that this is achieved. Intelligent video surveillance is embedded with the capability of providing real time intelligent surveillance and also automatically provide analysis of video and image data without human operation. In the development of such systems, computer vision, machine learning and deep learning plays a vital role in achieving this. Therefore, this paper provides a general survey on intelligent video surveillance system, overview of background concept and discussion on object detections and classification, tracking and deep networks. Also, this paper presents an efficient and faster object detection and classification techniques for intelligent video surveillance.
Block Chain Technology: Architecture, Application and Limitations

A public ledger in which all transactions are recorded as blocks is known as a block chain. It is a chain that is constantly growing, with new blocks being automatically added in accordance with the volume of transactions. In the block chain we will use cryptographic techniques such as digital signatures and different consent algorithms for user security and the consistency of the stored data. Consider any block chain that follows the following characteristics. They are transparency, invisibility, continuity and decentralization with these characteristics that a block chain can work efficiently and save costs. Because a Block chain is a growing list of public access records, each user has a public and private key to view and sign transactions without any intermediary. It is also applicable to and usable in other contexts, such as smart contracts, Iot, organizations financial. Because a transaction cannot be manipulated once placed in the block chain, organizations must be honest when creating and signing transactions. Although it has great potential for building a system of future transactions, it faces some difficulties. The main issue is archiving because a transaction volume rises, block chain size also grows.. In second place comes the loss of privacy in which the data are subject to losses and third comes the part of the algorithm. Here we cannot say which of all the algorithms, for example, pow, which is a test of labor wasting electricity and the POS test of participation, which makes the rich richer in the phenomenon. Therefore, depending on the application, it is necessary to use and implement our block chain accordingly
Taxonomy of Adversarial Attacks on Text-to-Image Generative Models

We introduce a first of its kind taxonomy of attacks on recent text-to-image generative models, providing a comprehensive understanding of the vulnerabilities of these models to adversarial attacks. We demonstrate the success of these attacks on a popular model and highlight how existing guardrails are insufficient. By identifying potential vulnerabilities and promoting transparency and accountability in the development and deployment of image generation models, this work encourages stakeholders to take proactive steps to mitigate risks and ensure safe and responsible use of these models. This work also highlights the importance of effective defense strategies and can help identify the most robust and reliable models.
Spacecraft autonomous mission planning connotation

In the chapter, the definition and development process of mission planning are summarized, the development needs of spacecraft autonomous mission planning are integrated, the concept of spacecraft autonomous mission planning is proposed, and the key points for development are clarified.
Forging payment cards and cybercrime

Payment card forging and high-tech crime are deeply rooted problems in today's society. These sophisticated forms of crime utilize advanced techniques and high-tech tools to illegally access financial resources and commit fraud. Payment card forgery involves the creation of fake copies of debit or credit cards with the intent of conducting illegal financial transactions. Access to card data is achieved through various methods, including skimming (illegally collecting card data), phishing (fraud through fake emails or web pages), or the physical theft of cards. Simultaneously, high-tech crime encompasses a wide range of activities aimed at the misuse of digital technologies and networks to achieve financial gain or harm to individuals, companies, or states. These crimes often include computer fraud, cyber-attacks, and digital fraud. This paper aims to highlight the importance and seriousness of payment card forgery, explore different methods and patterns of these criminal activities, and emphasize their specific connection with high-tech crime. Different methodologies were applied in the research including quantitative and qualitative content analysis, comparative analysis, as well as descriptive and analytical statistics. The obtained results clearly indicate the growing importance of this problem both in the legislative and in the criminological contexts, with a constant increase in the number of committed criminal acts. Additionally, the research highlights the inextricable link between payment card forgery and various forms of high-tech crime, which often intertwine and together constitute an overarching challenge to the justice system and the security of society. Finally, the paper will consider various strategies and methods that society and the state can use to counter the spread of these criminal activities. The ultimate goal is to preserve the safety and integrity of the financial system and protect the interests of individuals.
Generation Forecasting of Solar PV in Distribution Network using ANN

Solar PV plays vital role in renewable electricity generation, due to its numerous advantages. The solar power generation is only available at day time and it also affected by the various parameters. The storage systems i.e. batteries are required if the requirement of the power is during night from the Solar PV system. The prediction of the solar PV generation is an important issue in order to match the desired demand. Also the need of power generation prediction is essential in energy industry. The machine learning model is the powerful tool for this purpose. In this paper the study of solar PV generation forecasting is done using artificial neural network model. The prediction becomes more accurate by using the machine learning methods using vast data. In this study, neural network-based forecasting model for time series models is performed using neural network. The effectiveness of the system is shown in results.
High Utility Pattern Mining: A Comprehensive Survey

High-utility pattern mining, the process of searching highly relevant patterns in databases. Pattern mining has gained popularity as a study area due to its many appliance in fields like bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis. Effective decision- making requires high utility patterns, and demand for these patterns has grown recently. By classifying HUPM algorithms into a wide taxonomy, current comprehensive survey offers a overall, broad and strategic summary of advanced approaches in UPM. With a discussion of their benefits and disadvantages, a detailed analysis of current high-utility pattern mining techniques is offered. The study also covers current issues and practical difficulties in this field.