Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Improve User Experience of Mouse Control using CNN Based Hand Gesture Recognition

This paper presents a method that controls a mouse pointer using hand gestures and further improves the user experience by resolving the issue of mouse displacement due to abrupt hand movement. The approach is capable of detecting a set of predefined gestures such as clicking, scrolling, and dragging, allowing for a more natural and intuitive interaction with the computer. This research includes the study and use of the Mediapipe library, which is used to track the hand. The Mediapipe hand detection model is based on a machine learning algorithm that uses a variant of a SSD architecture, which is trained using a large dataset of hand images. The training data was annotated with bounding boxes that indicate the location of the hands in the images. The model was trained to predict the bounding boxes for the hands in new images using a combination of CNNs and other DL techniques. Using this library, many methods were developed to control the mouse using gestures. However, it has been observed that in existing systems, if hand positions change abruptly, it causes pointer displacement that negatively affects the user experience. The proposed system resolves this issue of mouse pointer displacement to further enhance the user experience in a variety of use cases that require abrupt and natural hand movements during the course of action. The proposed system solves the issue of mouse displacement by tracking the coordinates of hand movement and calculating the new position of the mouse pointer using the distance moved by the hand, rather than just using the position of the hand with respect to the screen. Using this method, the user can also control the mouse using a small part of the camera's wide angle to map it on any point on the screen. Experimental results show that the proposed solution is effective and efficient in terms of user experience in controlling the mouse pointer and holds potential for wider use in a variety of applications of human-computer interaction.
Exploring AndroidManifest.xml for Automated Android Apps Classification

An important step in publishing an Android app on Google Play Store is selecting its category from a pre-defined list of categories. Recent work focused on automating the process of classifying Android apps to prevent misclassification. Using information mainly extracted from the stores or through expensive methods from app resources, researchers applied machine learning techniques to automate the classification. In this research, we focus mainly on extracting features from the AndroidManifest.xml and from other components of the app package. The extracted features are then fed into a 2layer hierarchy of neural networks. Our experiments show an accuracy of about 80% for classification of a data set of more than 270,000 apps from 8 categories. The proposed framework shows promising results and enables a complete on-device solution for app categorisation.
Interpreting and understanding the image using deep learning and imprecise information from the monitoring system

The interpretation and understanding of images using deep learning algorithms in monitoring systems is an important area of research in computer vision. In this paper, we propose a novel approach for interpreting and understanding images of human behaviour captured from cameras and drones in monitoring systems. In the work, we presented a pedestrian detection method. This is the first element of a larger system of interpreting the behaviour of traffic participants. We begin by pre-processing the images to remove noise and unwanted artefacts. We then use labelled and unlabelled data to train a deep learning algorithm to accurately classify and predict human behaviour. To account for imprecise information, we incorporate probabilistic models into the algorithm to allow it to make predictions with uncertainty. To evaluate the performance of our proposed approach, we use standard metrics, such as precision, recall, and F1 score, and compare our results to existing methods. We also identify the limitations of existing approaches and propose new methods for interpreting and understanding images that can improve their accuracy and reliability. Our proposed approach has the potential to provide a more accurate and robust method for interpreting and understanding images of human behaviour in monitoring systems. It can be used in various applications like traffic monitoring, security systems, and surveillance. Our results demonstrate the importance of incorporating imprecise information into deep learning algorithms to improve their performance and highlight the potential for future research in this area.
Unveiling Conversational Patterns: Intent Classification and Clustering in Reddit’s Firearm Trade Community

Online discussion boards have become a tool for traffickers to widen their reach in the illegal firearm trade. This provides opportunities for authorities to detect firearm trafficking networks, as well as interdict arms shipments arranged by such networks. The present work offers a fresh solution to the pressing problem of spotting dubious firearm transaction chats on online discussion boards. The suggested methodology integrates state-of-the-art capabilities, such as natural language understanding and unsupervised clustering methods, to support the creation of more efficient cybercrime solutions. The necessity to target and flag questionable discussions about the trading of firearms in Internet forums is the main issue this work addresses. The major claim in this study centers on the use of intent recognition sequence patterns within dialogues and unsupervised clustering of such sequences. This method groups conversations based on the general direction and subject. The research methodology involves several key steps: i) data collection and manual annotation from the subreddit r/GunDeals, chosen due to its proximity to the primary subject of illicit firearm tracking, ii) training an intent classification Transformer-based model to generate intent sequences for each conversation, iii) preprocessing the intent sequences for clustering and encoding them using a polynomial method. iv) implementing clustering techniques while tuning various hyperparameters to optimize the results. The proposed methodology effectively classifies conversations into meaningful clusters, providing actionable insights. This research contributes to the body of knowledge by presenting a novel approach to detecting suspicious firearm trade conversations online. The practical implications are significant, as this work can be leveraged by law enforcement agencies to enhance their Internet scanning capabilities. The ability to efficiently process large volumes of text conversations and flag specific content can aid in addressing illicit firearm trading more effectively.
AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation

Cyber Threat Intelligence (CTI) reporting is pivotal in contemporary risk management strategies. As the volume of CTI reports continues to surge, the demand for automated tools to streamline report generation becomes increasingly apparent. While Natural Language Processing techniques have shown potential in handling text data, they often struggle to address the complexity of diverse data sources and their intricate interrelationships. Moreover, established paradigms like STIX have emerged as de facto standards within the CTI community, emphasizing the formal categorization of entities and relations to facilitate consistent data sharing. In this paper, we introduce AGIR (Automatic Generation of Intelligence Reports), a transformative Natural Language Generation tool specifically designed to address the pressing challenges in the realm of CTI reporting. AGIR’s primary objective is to empower security analysts by automating the labor-intensive task of generating comprehensive intelligence reports from formal representations of entity graphs. AGIR utilizes a two-stage pipeline by combining the advantages of template-based approaches and the capabilities of Large Language Models such as ChatGPT. We evaluate AGIR’s report generation capabilities both quantitatively and qualitatively. The generated reports accurately convey information expressed through formal language, achieving a high recall value (0.99) without introducing hallucination. Furthermore, we compare the fluency and utility of the reports with state-of-the-art approaches, showing how AGIR achieves higher scores in terms of Syntactic Log-Odds Ratio (SLOR) and through questionnaires. By using our tool, we estimate that the report writing time is reduced by more than 40%, therefore streamlining the CTI production of any organization and contributing to the automation of several CTI tasks.
Weapon Detection from Surveillance Footage in Real-Time Using Deep Learning

Weapon violence is a serious threat nowadays due to the rise in technology and criminal intelligence. Manual surveillance is a very tedious task because these activities are atypical in comparison with everyday activities. By introducing machine learning techniques to detect such activities, we can minimise the risk of human errors and prevent details from going unnoticed. Current systems fail when it comes to efficiency of the system and ease of use. We have implemented various models like CNN, YOLOV7, YOLOV8 and VGG for weapon detection and identified their strengths and limitations. A complete system for detection of weapons using these models and raising an alarm has been implemented here to overcome the problems of manual surveillance. Results are presented in a table focusing on metrics like precision and recall as these metrics prove to be more insightful and reliable as compared to accuracy for object detection.
Classifying Email as High and Low Risk: An Effective Approach to Spam Email Classification

Email is one of the most widely used and popular forms of communication due to its accessibility on a worldwide scale, the relative speed at which messages can be transferred, and the low sending costs. Today, a large portion of the population depends on the messages or emails sent by spammers, and it gives them a great opportunity to send spam messages to people about their interests. The rise in email-based threats is directly attributed to the flaws in e-mail protocols and the rise in electronic commerce and financial activities. Spam emails are sneaking into users' mailboxes without their consent. They utilize more network resources and require more time to check and delete spam emails. Spam overflows inboxes with absurd emails. greatly reduces the speed of our internet. stealing vital information, such as contact information, from the user In the digital age, spam email has grown to be a serious issue, and it is crucial to identify and filter spam emails in order to preserve the integrity of communication channels. Traditional techniques of classifying email simply consist of identifying email as spam or not spam, We introduced the concept of further classifying spam email as high risk or low risk. Ensemble learning is used for binary classification as spam or ham, it includes three models, namely Multinomial Naive Bayes, SVM, and Decision Tree, which gave an accuracy of 98.95%. For further classification of the spam mail into two subcategories as high or low risk, SVM produced an outcome of 84.37% accuracy.
Remote Patient Monitoring using the Internet of Things and Artificial Intelligence: A Study

Recent technical developments and improved connectivity have made artificial intelligence (AI) and Internet of Things (IoT) more widely adopted across numerous industries. In this study, the effects of IoT and AI technologies on the healthcare industry are systematically assessed. The reform of the healthcare sector, which brings about better efficiency, lower costs, and a greater emphasis on patient care, is the cornerstone of a smart city. It is essential to have a thorough understanding of the various frameworks used in smart cities if you want to use IoT and AI for remote healthcare monitoring (RHM) systems. These frameworks encompass supporting technologies, devices, systems, models, layouts, use cases, and software applications. Each component of clinical decision support systems is meticulously examined to provide patients with personalized treatment, lifestyle recommendations, and care strategies. The employed technology facilitates healthcare applications and enables the analysis of vital functions such as blood glucose levels, heart rate, and body temperature. In view of this, the present study presents a survey that aims to identify the most relevant online resources pertaining to health information on the internet.
Music Generation using Human-In-The-Loop Reinforcement Learning

This paper presents an approach that combines Human-In-The-Loop Reinforcement Learning (HITL RL) with principles derived from music theory to facilitate real-time generation of musical compositions. HITL RL, previously employed in diverse applications such as modelling humanoid robot mechanics and enhancing language models, harnesses human feedback to refine the training process. In this study, we develop a HILT RL framework that can leverage the constraints and principles in music theory. In particular, we propose an episodic tabular Q-learning algorithm with an epsilon-greedy exploration policy. The system generates musical tracks (compositions), continuously enhancing its quality through iterative human-in-the-loop feedback. The reward function for this process is the subjective musical taste of the user.
AI-Generated Images as an Emergent Record Format

AI-generated Images are disrupting existing approaches to verifying the trustworthiness of visual media. The application of generative AI in fields in which images are trusted visual evidence of persons, actions and events is drawing the attention of archival scientists and AI researchers. A literature review of AI-generated images as an emergent record format, identified an absence of archival and recordkeeping knowledge. Analysis of the results revealed six thematic categories: authenticity and verifiability; manipulation and misinformation; bias and representation; attribution and intellectual property; transparency and explainability; and ethical considerations. These themes inform the development of research questions and the next phase of the study that includes the application of theory and methods of archival diplomatics and computational archival science.
Simplifying Watermark Removal: Efficiently Enhancing Visual Integrity through Traditional Image Processing Techniques

This research paper presents a watermark removal technique for digital images using image processing methods, specifically thresholding with the Otsu method. Unlike complex deep learning algorithms, this approach focuses on simplicity and computational efficiency while achieving effective results. The Otsu method automatically determines an optimal threshold to separate the watermark from the background based on image segmentation. Experimental results demonstrate the efficacy of the proposed technique, showcasing high accuracy in removing watermarks with minimal distortion. Moreover, the method's computational efficiency makes it suitable for real-time applications. This research contributes to the field by highlighting the potential of traditional image processing techniques. The simplicity and interpretability of the approach make it accessible to researchers and practitioners with limited expertise in advanced methods. The results indicate promising performance in watermark removal for a diverse dataset of digital images with different watermark types and complexities. This research contributes valuable insights into effective and efficient watermark removal techniques, providing a practical solution for copyright protection, image authentication, and content restoration tasks.
Kannada Words Sense Disambiguation using Regular Expression

Word sense disambiguation is a crucial task in natural language processing that involves determining the correct sense of a word in a given context. This paper focuses on Kannada word sense disambiguation using regular expression. Regular expression involves pattern-matching technique that allows the identification of specific patterns in text. In this paper, propose a method to disambiguate Kannada words using regular expression, where identify the most appropriate sense of a word based on its context in the sentence. The approach leverages the syntactic and semantic features of words to identify the most appropriate sense. Evaluate the performance of proposed method on a benchmark dataset and compare it with existing state-of-the-art techniques. The outcome include that the method achieves high accuracy and outperforms existing approaches.
Mobile Application for M-learning among Students in Higher Education

Education is a vital aspect of the development of oneself as well as the nation one resides in, it is a way to improve oneself and aid the world in advancements in technology and providing a better future for everyone. However, despite the many breakthroughs in science and humanity, education has remained essentially the same, with no real usage of the technological advancements that the education system themselves have developed. The research is to develop a mobile application that improves the education system. Case study research and online survey based on groups of users that are targeted will be conducted for acquiring accurate data regarding M-Learning and the factors which contribute to its uniqueness in education. After that, a prototype of the proposed application will be developed to help in appraising the qualities of the application. Subsequently, it is the implementation of the application with its additional advantages to real-world educating usage for improving the student experience in learning.
Frequent Subgraph Mining in Dynamic Databases

Frequent subgraph mining is fundamental in graph mining, with wide-ranging applications in domains such as biology, chemistry, and social network analysis. Most existing algorithms are tailored for static graph databases. Real-world databases often exhibit dynamic attributes, such as data that may change over time. Existing methods for mining frequent subgraphs in databases with dynamic attributes primarily cater to dynamic graph databases, in which graphs evolve over time. However, in practice, a category of graph databases allows for adding or removing graphs. We refer to these databases as dynamic ones, which can be incrementally or decrementally updated while the remaining graphs do not change. This paper introduces frequent subgraph mining in this type of database and proposes the corresponding algorithm called DyFSM. We design a set called Fringe, which comprises DMFSand DMIS. DMFSis a novel concise representation based on the DFS code and can efficiently recover all frequent subgraphs. DMISis a set of subgraphs from which all infrequent subgraphs can be extended. Fringefacilitates updating frequent subgraphs in the renewed database. In our experiments, we collect four real-world graph datasets and conduct experiments using DyFSM. The results validate the accuracy and show good performance of our algorithm.
TensorBank: Tensor Lakehouse for Foundation Model Training

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we introduce TensorBank – a petabyte scale tensor lakehouse capable of streaming tensors from Cloud Object Store (COS) to GPU memory at wire speed based on complex relational queries. We use Hierarchical Statistical Indices (HSI) for query acceleration. Our architecture allows to directly address tensors on block level using HTTP range reads. Once in GPU memory, data can be transformed using PyTorch transforms. We provide a generic PyTorch dataset type with a corresponding dataset factory translating relational queries and requested transformations as an instance. By making use of the HSI, irrelevant blocks can be skipped without reading them as those indices contain statistics on their content at different hierarchical resolution levels. This is an opinionated architecture powered by open standards and making heavy use of open-source technology. Although, hardened for production use using geospatial-temporal data, this architecture generalizes to other use cases like computer vision, computational neuroscience, biological sequence analysis and more.
Multi-Objective Optimization Algorithm in Big Data Environment

None of the basic system software in big data domain can identify and use such algorithms. So the study evaluates hardware compression the performance of the method in big data environment is of great significance. The algorithm takes into account both the completion time of the optimization task and the load equilibrium value of the heterogeneous fog cluster, and adopts a nonlinear decreasing method to update the inertial weight value. The effectiveness of the proposed algorithm is verified by MATLAB simulation. The experimental results show that the algorithm improves the convergence rate at the same time as obtaining more search space at the early stage. Based on the KAEzip experiment, the performance of the classic Spark benchmarking program was evaluated in Spark. Tests of KAEzip show that: 1) Hardware compression algorithm can effectively improve Spark performance, such as KAEzip has up to 13.8% compression performance advantage over snappy, up to 7% decompression advantage and up to 5.7% performance potential in the actual application scenario; 2) The mismatch between the data transfer rate of the disk and the performance of the hardware compression algorithm is an important factor that restricts the performance of the hardware compression algorithm.
Document Verification System and Validation Against QR Codes

As the need for products and services is growing at a bizarre rate and with this shift of adopting online platforms, it becomes important for businesses to identify their customers via online portals and authenticate the documents. The paper includes a proposed model which consists of three major stages viz. document acquisition, document verification and document validation. Initially the system separates the document from its background. Secondly, the system extracts features, looks for signs of tampering of the document thus verifying the authenticity of the documents and lastly validating it against the data procured from the QR/Barcodes present on the document. In this paper, we propose a method for robust document verification system and validation against QR codes. Also the paper proposes a combined system, which offer techniques for document validation and verification, to see if the image has been tampered or not. The experimental results is described, for the proposed method.