Reviewerly/exHarmony huggingface dataset:
Information System Web-Based HSSE Performance In PT.XYZ

Information Technology systems are developing very quickly and are a challenge for all types of industries. Data management of a company is very important in this digital 4.0 era. This system aims to help computerized data and information management is expected to reduce the risk of human error factors. The method begins with analyzing needs using the discussion method, translating data into UML (Unified Modeling Language) diagrams and implementing systems using web-based applications by taking data at PT XYZ, Department of Health, Safety, Security, Environment in processing HSSE Performance data which still uses semi-conventional applications and redundancies, so that it will produce an Information System that can make it easier for Company Management to Monitor the Achievement of HSSE Department Performance Targets which are presented visually in the form of a Dashboard. The design of the information system uses a prototype method that can be added or reduced according to the development process that can be adjusted to the needs of the company. This web-based application designs an HSSE Performance Information System to support operational data management in the company. The information system to be designed is the HSSE Performance information system in the HSSE Department, providing information results that can be used by leaders and management in making decisions.
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning

Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It’s particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4$\times$, time efficiency by 8$\times$, and reduces costs by more than 2$\times$ compared to extant cloud-based static methodologies.
Two-sample KS test with approxQuantile in Apache Spark®

The classical two-sample test of Kolmogorov-Smirnov(KS) is widely used to test whether empirical samples come from the same distribution. Even though most statistical packages provide an implementation, carrying out the test in big data settings can be challenging because it requires a full sort of the data. The popular Apache Spark system for big data processing provides a 1-sample KS test, but not the 2-sample version. Moreover, recent Spark versions provide the approxQuantile method for querying $\epsilon$-approximate quantiles. We build on approxQuantile to propose a variation of the classical Kolmogorov-Smirnov two-sample test that constructs approximate cumulative distribution functions (CDF) from $\epsilon$-approximate quantiles. We derive error bounds of the approximate CDF and show how to use this information to carry out KS tests. Psuedocode for the approach requires 15 executable lines. A Python® implementation appears in the appendix.
Efficient Extraction of Insights at the Edges of Distributed Systems

The recent advances in Graph Neural Networks (GNN) are poised to improve machine learning of IoT systems at the edge. Particularly, GNNs allow modeling the topology of distributed systems, including their physical laws, from sensors data. However, one of the main limitations of using GNNs arises from their adjacency matrix. The adjacency matrix of GNNs needs to be defined a priori and represents the connectivity between the edges of a network. Usually, the adjacency matrix of GNNs consists of binary values that are equal to 1 when two edges are physically connected and 0 otherwise. This representation considers connectivity in terms of proximity and assumes that they are of equal significance. However, in certain applications, areas that are not physically connected can share more properties than physically connected areas. This necessitates new methods for devising the adjacency matrix and leads us to propose an efficient approach for determining the adjacency matrix of GNNs. Our approach extends GNNs in two ways. First, we employ a mechanism that utilizes the time series data at the edges to determine the eigenvalues and eigenvectors associated with each edge, allowing us to compute the proportion of variance. Subsequently, we use the proportion of variance to construct our adjacency matrix. Second, we utilize Dynamic Time Warping (DTW) to cluster related time series at the edge and construct our adjacency matrix. We then integrate the newly derived adjacency matrix into the GNN operating with a sequence to sequence learner to infer insights at the edges. Through extensive experiments, we demonstrate the strength and performance of our proposed GNN approach.
Enhancing the Classification Accuracy using CNN, GANs, and data Augmentation Techniques

This research paper’s primary objective revolves around addressing the critical issue of handling imbalanced data sets, particularly in the context of image classification tasks with an uneven distribution of images. The imbalanced data set can lead to biased predictions, which can have adverse impacts in numerous domains such as medical diagnostics and agriculture. This clearly shows the urgency of addressing this problem and finding effective solutions to mitigate its impact. Hence, this study began with an imbalanced image data set, resulting in biased accuracy, and to address this problem, this research has proposed an innovative two-step approach using data augmentation and generative adversarial networks (GANs) to balance the data set. This balanced data set was then used to train the CNN classification model, achieving impressive results with an accuracy of 97.03%, a precision of 0.75, a recall of 0.83, and an F1-score of 0.79. Hence, the proposed methodology significantly improves classification accuracy and performance, demonstrating the potential of this approach to rectify imbalances in image classification tasks.
Enhancing Cryptanalysis of DES Encryption using Neural Networks and Firefly Algorithms

In today’s cryptographic landscape, evaluating encryption algorithms’ integrity is crucial. Neural networks offer promise for extracting information from Data Encryption Standard (DES) encrypted data. This report explores diverse methods of DES attacks using deep learning techniques. The primary aim is to decode DES-encrypted data with neural networks, extending this to contemporary encryption algorithms. The proposed system uses Long Short-Term Memory (LSTM) networks to improve plaintext reconstruction from DES-encrypted data. It evaluates LSTM network performance metrics and seeks to predict critical patterns. Findings reveal the neural network’s impressive ability to predict plaintext with 98% accuracy after 250k training iterations. However, performance varies with the encryption key, and complete plaintext recreation remains challenging. This method also experiments using the Firefly algorithm to enhance key prediction, improving overall efficiency. Artificial Neural Networks (ANNs) for classification tasks on encrypted data show promise for practical applications. Future work can focus on enhancing classification accuracy, exploring diverse data types, and studying the critical impact on performance metrics.
Exploring Social Media Trends - A Kannada Dataset Analysis

Platforms for social media are dynamic tools that help ideas spread and develop quickly. This study introduces a novel way of locating popular Kannada-language subjects on social networking sites. This study also involves data preprocessing, feature extraction, and data visualization approaches to reveal underlying patterns and insights using a sizable dataset made up of Kannada text, tweets, hashtags, and news headlines. This method efficiently incorporates both sophisticated Machine Learning models, such as N-grams and word tokenization, and Deep Learning models, including sentence transformers and U-map embeddings. The examination of coherence and silhouette scores is used to validate the models. The main goal of this research is to offer an in-depth analysis of issues that regularly come up in Kannada debates on social media. This enables organizations, researchers, and content producers to make well-informed decisions, comprehend user opinion more thoroughly, and keep up with rapidly changing technological developments. In essence, this study helps provide a thorough understanding of the constantly changing digital ecosystem.
Physics-based Underwater Image Enhancement

Underwater images suffer from serious degradation because of the complex imaging environment. However, the existing methods ignore the degradation caused by the physical factors. It is helpful to improve underwater images via estimate these factors. In this paper, we propose a generative approach for underwater image enhancement, called Physicsbased WaterGAN (PWGAN), which considers the factors that affect the visibility of underwater objects in reality, including the scattering coefficient of different degrees of purity water and the depth. This paper focuses on exploring the scattering coefficient and depth information contained in the degraded and clean underwater images. In our network, the generated results of the two GANs are fine-tuned using the underwater scattering coefficient obtained from the underwater image/the depth information obtained from the clean image to make the results realistic. Experiments show that the PWGAN model has good generalization ability, can adapt to different underwater datasets, and eliminates color degradation of degraded images taken in various underwater environments.
Medical Recommendations: Leveraging CRNN with Self-Attention Mechanism for Enhanced Systems

As a result of the exponential increase in the global population, many individuals have limited access to medical care. Robust medicine recommendation systems are needed to provide timely and accurate drug recommendations, improving patient outcomes and optimizing healthcare utilization. This research paper aims to propose a recommendation system for medical drugs that utilize sentiment analysis and captures main features using CRNN with a self-attention mechanism. This self-attention was instrumental in enabling the system to concentrate on critical segments of the text, while the CRNN architecture facilitated the capture of both spatial and temporal features of the input. The system yielded 96% accuracy, alongside precision, recall, and F1 scores of 95.4%, 93.8%, and 94.6%, surpassing multiple baseline models and hybrid models in recommending top drugs accurately for particular conditions. Hence, by providing efficient and effective drug recommendations, the proposed system can significantly benefit medical professionals and patients, particularly those in underserved communities.
Deep Neural Pretraining for Improving Diagnostic and Therapeutic Entity Recognition in Electronic Medical Records

The predominance of compound agency, a lack of utterance components, and unclear entity boundaries all contribute to difficulties in distinguishing diagnostic and treatment entities in EMRs. Furthermore, obtaining electronic medical records in China may be difficult. A deep learning strategy that uses deep neural pretraining to improve diagnostic and therapeutic entity recognition in EMRs is proposed. A BERT-CRF model example is shown using a dataset of 10,000 EMRs. This paradigm combines the benefits of pretraining with the adaptability of CRF. The outcomes of the study show that deep brain pretraining might be used to improve the performance of NLP applications in the healthcare business. The use of a domain-specific corpus learning technique and an entity recognition model trained on a training dataset improves word embeddings. The suggested system achieved an accuracy (0.973), precision (0.909), recall (0.896), f1-score (0.902) and MCC (0.893) respectively.
Towards Big Data Analytics over Mobile User Data using Machine Learning

Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.
Network Security Strategy Based on Cloud-Edge Collaboration in Electric Power Internet of Things

In recent years, with the application of Multi-access edge computing (MEC) technology in the power Internet of Things (PIOT), the power Internet of Things has gradually formed a development trend of large connections, wide coverage, real-time, and intelligence. However, the introduction of MEC makes the power Internet of Things involve more information transmission and data processing, and how to strengthen the preventive measures for data security and privacy protection is also a key challenge. Aiming at the problem that the edge nodes in the power Internet of Things are vulnerable to attacks, this paper proposes a network security strategy for the power Internet of Things based on cloud-edge collaboration. This strategy updates their respective trust values in real time through the communication and interaction between nodes, and quickly detects abnormal nodes in the network based on the trust values. At the same time, this paper implements the abnormal node detection smart contract proposed in this paper based on the Hyperledger Fabric blockchain platform. The experimental results show that the designed abnormal node detection smart contract accurately detects the abnormal nodes in the network, and successfully synchronizes the results with all nodes, effectively ensuring the operation security of the power Internet of Things.
An Abstractive Text Summarization Using Decoder Attention with Pointer Network

Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods.
Scalable Real-Time Multi Object Tracking for Automated Driving Using Intelligent Infrastructure

Automated driving highly relies on object detection and tracking. While vehicle autonomous solutions represent the state of the art, connected systems are increasingly being developed and researched. Many current approaches to Multi Object Tracking focus on the accuracy, whereas such a system working as the backbone of a live and offline digital twin of a test field mainly needs to be scalable to a large amount of perceived objects and still real-time capable. This paper presents a novel approach to Multi Object Tracking that can reach framerates of up to 604 Hz spanning a digital test field of 0.5 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> size with still reasonable accuracy on simulated data. The approach can also be applied to real world data where our framework reaches a similar accuracy as the common baseline AB3DMOT on the nuScenes dataset, being 588 % faster. Using a new motion model in combination with an Unscented Kalman Filter, we can further increase the framework's accuracy and robustness while still reaching a framerate of 544 Hz on nuScenes and 367 Hz on the ACCorD test field.
AGM: Adaptive Graph Enhanced Graph Matching for Planar Tracking

In recent years, graph matching has made substantial progress in planar object tracking, offering end-to-end learning capabilities. However, these methods often fall short in real-world tracking tasks due to their reliance on static, manually constructed graphs that fail to effectively account for the dynamic distribution of feature points and their relationships within individual video frames. To address these challenges, we introduce the adaptive graph matching (AGM) Network, a resilient object tracking model designed to handle complex transformations and changes in object appearance with significant accuracy. The cornerstone of AGM lies in its innovative construction of dynamic graphs that model the feature points across video frames. Nodes represent key feature points in the graph, while edges adaptively form based on the spatial relationships and descriptors of these points. This allows the graph to adjust flexibly to the properties and relationships inherent in the feature points of each frame, enhancing its fidelity to the actual structure and changes of the planar object across the video sequence. AGM employs self-attention mechanisms to guide the adaptive graph construction, and cross-attention mechanisms to capture the relationships between adjacency matrices and their associated features. Our ablation study shows that these self and cross-attention mechanisms play an integral role in enhancing the model’s tracking performance, providing a more robust solution to real-world complications such as motion blur and object occlusion. We trained AGM and validated its performance on three datasets. The results show that AGM consistently outperforms other state-of-the-art models, demonstrating the effectiveness of our approach.
Multiclass Context Analysis of Hindi Statements Using Natural Language Processing

India has many different spoken languages. The majority of the populace communicates in their native tongues. In every device, support for these languages is available. Hindi language posts and material are becoming more and more prevalent. However, tools for categorizing and analyzing the context and intent have primarily been created for international languages like English. Because so many people increasingly utilize local languages on social media, there is a greater demand for support for text classification and analysis in local languages. In this work an application is created which can analyze the context of the particular hindi text entered. The application has XLM-ROBERTA model running in the background for which transfer learning is used to train it.
Educational Software and Security Vulnerabilities: an experimental study

The educational domain comprises fragmented solutions with different services, tools, and plugins. As a complex system, it raises several security and threat prevention concerns. We conducted an exploratory study to characterize security vulnerabilities and their impacts on Learning Management Systems. We focus on an intelligent educational solution using the risk assessment methodology HEAVENS 2.0. We identify vulnerabilities in architectural elements and detail two of them. Risks are not acceptable, and security measures must be adopted to avoid damage to students, tutors, and teachers. The results highlight the security vulnerabilities and the consequences of threats to users, hoping to motivate future research
From Text to Maps: Automated Concept Map Generation Using Fine-tuned Large Language Model

Concept maps (CMs) are tools for visualizing relationships between ideas, facilitating more effective comprehension and learning. However, the automatic generation of CMs from unstructured text presents a challenge, often requiring semantic markup and subsequent complex processing. This paper introduces a novel approach to address this hurdle by harnessing the capabilities of fine-tuned Large Language Models (LLMs). Our innovative methodology uses these models to extract structured propositions from unstructured text, subsequently serving as the foundation for constructing a CM. This process reverses the transformation of CM relations into first-order logic propositions, a concept explored in our previous work. To achieve this, we train the LLM using fine-tuning techniques, leveraging the latest advancements in artificial intelligence and machine learning. We evaluate our proposed solution based on precision and recall metrics, comparing our outcomes against models crafted by experts. Notably, the results indicate that our method can contribute significantly to advancements in the automatic generation of CMs, illustrating another application bolstered by recent breakthroughs in artificial intelligence. As a stepping stone in this promising direction, future research should continue to refine the model and explore potential applications across diverse domains.
Design and Implementation of Access Control for Pension Data : A Smart Contract Approach

With the development of the Blockchain 3.0 era, there is an increased focus on combining smart contracts with other technologies. In the pension data scenario, traditional access control methods are no longer applicable due to complex data objects, numerous data operations, high granularity requirements and the need to support scalability. To address these issues, a more secure and flexible access control scheme is required. Therefore, this research abstractly describes the specific scenarios under pension data applications, combines smart contracts with an attribute-based access control model, designs and implements three smart contracts based on the Hyperledger Fabric blockchain architecture: Drive Contract, Access Contract, and Policy Contract. The solution has completed performance testing and can remain stable in the face of a large number of concurrent requests.