Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Extraction over Heterogeneous Data for Tunnel Engineering Scenarios Based on BiLSTM

With the continuous development of information technology, a substantial volume data related to tunnel engineering has been extensively documented and stored, containing a wealth of knowledge from engineering contexts. However, this data presents challenges due to its massive volume, intricacy, and susceptibility to frequent updates. To address this problem, a method for extracting knowledge related to tunnel engineering scenarios from diverse data sources is proposed using Bidirectional Long Short-Term Memory Networks (BiLSTM). The objective is to offer engineers and researchers in the field an efficient approach to automatically extract essential knowledge from intricacy data, thus facilitating information retrieval and application within the tunnel engineering domain. Firstly, the overview of the dataset, its sources, and detailed data annotation examples are provided. Subsequently, the technical approach of this study is elaborated, including the construction of the ontology model for the tunnel engineering scenarios and the specific procedures for extracting tunnel engineering elements. These methods encompass corpus annotation via rule-based matching and syntactic analysis, as well as the application of the BiLSTM model. Through experiments and analysis, the effectiveness and feasibility of the proposed approach are validated. Finally, the research work is summarized and potential future directions are outlined.
Frequency Hopping Scheduling Algorithm in Green LoRaWAN: Reinforcement Learning Approach

Long Range Wide Area Network(LoRaWAN) is suitable for wide area sensor networks due to its low cost, long range, and low energy consumption. A device can transmit without interference if it chooses a unique channel, spread factor(SF), and transmission power(TP). In dense networks, the devices run out of unique choices, leading to interference and retransmissions. Eventually, the battery levels of devices drop faster. Also, the selection of transmission parameters affects the Time on Air(ToA) of the signal, thus increasing the energy usage of the end device. Higher values of spread factor, transmission power, coding rate, channel frequencies, and lower values of bandwidth increase energy consumption. Moreover, devices cannot maintain the same values of transmission parameters for long due to duty cycle restrictions. We use two techniques to deal with this situation: Frequency hopping and reinforcement learning. We proposed a scheduling algorithm, ’LoRa-DDPGFHSS,’ based on Frequency Hopping Spread Spectrum(FHSS) and Deep Deterministic Policy Gradient(DDPG) reinforcement learning for dense networks. It schedules device transmission parameters by selecting a unique(channel, SF, TP, time slot) to avoid interference and lower energy consumption. We performed simulations of three scheduling algorithms: FREE [1], LoRaDDPG, and LoRa-DDPG-FHSS using the LoRaSim simulation tool. Our simulation analysis proves that LoRa-DDPG-FHSS improves energy efficiency by about 98% and Time on Air is reduced by 97% compared to FREE in a network with around 4000 devices.
Potato Disease Detection Using Deep Learning

Utilising deep learning methods, such as convolutional neural networks (CNNs), to recognise and categorise diseases affecting potato plants is known as “potato disease detection using deep learning. ” To identify patterns and features indicating various diseases. , deep learning models are trained on enormous datasets of potato plant photos, including both healthy and damaged plants. The deep learning models can precisely classify and detect the existence of numerous potato illnesses by examining the visual characteristics of the leaves or other sections of the plant. Using deep learning-based methods, it may be possible to accurately and automatically identify potato diseases, which will be helpful for early disease detection, disease prevention, and crop management. These techniques take advantage of deep learning algorithms, abilities to extract complex representations from unprocessed visual data and generate predictions based on discovered patterns. Researchers and farmers can monitor and identify diseases more effectively with the aid of deep learning, which will increase agricultural productivity and plant health.
Automatic Road Traffic Analyzer using Background Subtraction, Blob Analysis, and Tracking Algorithms

Due to the prodigious road traffic, many cities experience road congestions. Hence, there is a requirement of efficient and automatic road traffic analysis. The computer vision (CV) technique is one of the rapidly utilized fields for traffic analysis. This paper proposes a system that efficiently tracks and counts the number of vehicles for automatic traffic monitoring. The system is composed of several stages viz. demosaicing on Bayer filter pattern video frames, moving object algorithm for vehicle detection, and Blob analysis is for tracking and counting of vehicles on a road. The prepossessing is performed with the help of system generator development tool of Xilinx, and the tracking & counting of vehicles on a road are observed in the MATLAB Simulink environment. The algorithms are applied to all the frames in every video with the labeled vehicle is shown. An average accuracy of 98.78% is obtained on real time videos captured through different traffic conditions. The comparison with the similar reported algorithms validates the superiority of the proposed work for traffic analysis system.
Optimization of Hugging Face Transformers for Fake News Detection

The upsurge of fake news in recent times, facilitated due to the swift dissemination of information on social media, has necessitated the development of advanced detection techniques. This research focuses on optimizing the Hugging Face Transformer models – a cutting-edge Natural Language Processing (NLP) tool – to enhance fake news detection. These models are widely recognized for their superior performance in understanding and generating human language. However, their application in fake news detection remains under-explored. Thus, this paper explores how Python, a high-level programming language known for its simplicity and robustness, can be used to fine-tune these models for this purpose. The primary objective is to improve their speed, efficiency, and accuracy in detecting fake news. We propose a comprehensive framework that uses Python-based methodologies to tweak various aspects of the Hugging Face Transformer models, such as their architecture, training paradigms, and hyperparameters. The expected outcome is a significant improvement in the models’ performance metrics, which will be evaluated using standard benchmarks within the fake-news detection domain. Overall, this research paves the way for harnessing the full potential of Hugging Face Transformers in curbing the menace of fake news, thus contributing to a more reliable and truthful digital information ecosystem.
Passive Underwater Target Classification using Sparse Models

Underwater audio signal classification has shown a wide range of applications in real-life scenarios, both for civilian and defence purposes. Passive sonars are ubiquitously used for underwater target recognition based on the sound waves emitted by the target. The radiated waves are classified to identify their source and to classify them as a threat or friend in a defence scenario. Machine learning algorithms are reported to impart good performance on audio classification problems. The paper proposes a sparse model-based algorithm as a means to classify the acoustic data received by passive sonar. A comparative study is conducted on an open dataset, namely the DeepShip dataset, a publicly available sonar audio dataset. Results show that the use of the proposed sparse model based algorithms for underwater passive target recognition is a promising direction for further research and applications.
The Analysis on Evaluating Mining Enterprises’ Ecological Management Information System

It is a very important job for the improvement of comprehensive appraise on mining enterprises’ ecological management information system. The evaluation of mining enterprises’ ecological management information system should be implemented: First of all, carry on overall analysis and appraisal to each subsystem that is operated; then, draw the value of evaluation index of each subsystem and arrange them in an order based on that. Finally, draw the lowest value in each subsystem according to the result of arranging in an order, judging by that we can improve the subsystem purposefully. The improved mining enterprises’management information system will ecologically adapt to the changing environment.
Research on Tibetan-Chinese Neural Machine Translation Based on GRU

The paper discusses the Tibetan-Chinese neural machine translation model based on LSTM and GRU, uses the Tibetan Dolma Tibetan word-splitting tool provided by the Chinese Academy of Sciences to split the Tibetan language as the input of the translation system, and analyzes it in comparison with the traditional recurrent neural network model. After experiments on the same Tibetan-Chinese dataset, it is shown that the BLEU value evaluated based on the LSTM NMT model is more than 5 BLEUS higher than that based on the traditional RNN NMT, and the BLEU value evaluated based on the GRU NMT model is more than 6 BLEUS higher than that based on the LSTM NMT.
The Fusion of AI and Music Generation: A Comprehensive Review

The integration of artificial intelligence (AI) and music generation has revolutionized the field of musical composition, ushering in a new era of creative possibilities. This comprehensive review explores two groundbreaking studies that illustrate the profound impact of AI in music: AI-driven music remixing and AI-generated music composition. The first approach employs state-of-the-art deep learning and neural network architectures to enhance music remixing by recognizing intricate audio patterns, resulting in remarkably inventive remixes. The second method harnesses generative adversarial networks (GANs) and reinforcement learning to compose original music pieces that rival human-created compositions. Both studies exemplify AI’s capacity to reshape the landscape of musical creativity, offering novel avenues for artistic expression. This paper not only examines these pioneering studies but also delves into the broader implications of AI in music, highlighting its potential to revolutionize music production and inspire a new generation of musicians.
Synergy of Artificial Intelligence and Music

This comprehensive paper delves into the intricate relationship between Music and Artificial Intelligence (AI). It presents an extensive overview of the current developments in this emerging field and explores the potential future applications. The paper goes beyond the surface, emphasizing that music is not just a melodious tune but a complex data set that AI can efficiently manipulate. It investigates how AI can affect human brain functionality through music, discusses AI’s role in automated music composition, and highlights its significance in assisting impaired individuals in playing music. Furthermore, the paper predicts the future of AI in the music industry, considering its possibility for transformation.
Development of an Efficient CNN model with Hyperparameter tuning for Early Prediction of Lung Diseases

Deep learning (DL), an evolving technology, allows computers to learn autonomously from historical data. Furthermore, it employs a number of methodologies to generate the precise models and create predictions based on facts or old data. A deep learning system produces the predictions based on mathematical models of prediction which is built from past information once it gets new information. Nowadays, the use of deep learning in medical systems is crucial in various applications such as recognition, segmentation, and classification. Worldwide, lung illness is a prevalent occurrence in human beings which includes Covid-19, pneumonia, emphysema, asthma, lung cancer, etc. The early identification of lung diseases helps to detect the disease more accurately. Several deep learning techniques, including convolutional neural network, vanilla neural networks, visual geometry group-based networks and Caps Net, are used to predict the lung illness. To forecast different lung diseases from chest X-ray and CT scan pictures, typical CNN’s performance and learning framework is insufficient. This proposed work aims to build a lung disease detection model using CNN with various hyperparameter tuning to enhance the accuracy of the model. Also, the accuracy is associated with advanced methods to enhance the performance of the proposed work.
Towards a Longitudinal Comparison Between Different Strategies for Android Malware Detection

The growing popularity of the Android platform makes it a target of malware authors. The effective identification of such malware is an ongoing challenge. Several methods using machine learning have been proposed to prevent this threat. These methods are usually conventionally evaluated without considering the extent of performance over time. Given the evolving nature of both malware and benign apps, conventional evaluation may lack information. To imitate reality, this study compares the longitudinal performance of different machine learning models, using different strategies that combine permissions and API calls as features extracted through static analysis. Thus, to determine which strategy of features on which classifier are most effective to characterize malware for building a robust malware detector. To achieve this goal, on the one hand, we use a large real-world app set consisting of 100K (50k benign, 50k malware) apps date-labeled, collected across ten years, first seen between 2013 and 2022. On the other hand, each feature's strategy is fed into five classifiers (i.e., SVM, RF, LR, DT, and ANN), using old apps for the training and new apps for the evaluation. Among the assessed machine learning models, the SVM achieves the most promising results over time by employing the combination strategy of the high difference usage of API calls and permissions.
Stability Constrained Voltage Control for Transmission Grids

With increasing penetration of renewable energy sources (RESs), the significant volatility and time-varying characteristics of RESs introduce new challenges to the conventional methods of continuously assessing voltage stability in transmission grids. In response to these challenges, we introduce a novel voltage stability metric referred to as the K-index, along with a synchronized voltage control strategy which enhances the stability of the system’s voltage, as quantified by the K-index. In the proposed scheme, we integrate RESs with established voltage control instruments, notably synchronous generators (SGs). Our objective encompasses the dual task of managing monitored voltages within permissible thresholds while elevating the broader spectrum of voltage stability. Notably, the controller demands only essential grid model data, encompassing voltage, reactive power, and K-index metrics within the system, and employs real-time measurements to craft refreshed set-points for SGs. The preeminence of our proposed algorithm is substantiated through its simulation within a succinct 4-bus system.
Sensitivity of Threshold Value ‘r’ in Approximate Entropy based Brain-Computer Interface

Approximate entropy(ApEN) is an algorithm to determine complexity in a data series based on the existence of patterns. Its calculation requires the determination of two unknown parameters, m, and r. Even though the r recommended in the range of 0.1 to 0.2 times standard deviation works well for a variety of applications, in certain cases, it causes misinterpretation as the largest possible complexity in a data series may not always comes with this range of r. It led to motivating another recommendation for r, choosing the one that gives maximum ApEN. This paper addresses the implementation of motor imagery (MI) classification with ApEN as a method of feature extraction. While assessing the classification accuracy by varying r from 0.1 to 0.4, it has been shown that neither of the two recommendations for r leads to the best result. The hypothesis behind this is, in the MI classification problem, beyond the maximum ApEN, it is the ApEN discrimination between classes during the MI activity that decides its performance. Therefore, r is recommended to be the one with large ApEN discrimination during MI activity. Results show the highest accuracy at this r. To trace out the time interval of maximum MI activity, another feature extraction strategy based on auto-regressive (AR) modeling coefficients is implemented. Finally, a combined feature extraction strategy based on AR and ApEN is evaluated, and improved accuracy over individual AR or ApEN strategies can be observed only for r from the proposed recommendation.
Evaluating the Impact of a 3D Simulation Model on the Performance of Vehicular Networks

A crucial aspect of vehicular networks is the wireless communication between a vehicle and the road infrastructure, which is strongly influenced by various three-dimensional factors, including the height of buildings, the presence of natural and artificial obstacles, the altitude, and the positioning of the antennas. 2D simulators fail to capture these influences fully, as they simplify and flatten the terrain. This paper presents the extension of the Veins vehicular network simulators to take into account the third dimension. We describe the key algorithms we implemented, the limited increased simulation time, and most of all the improved accuracy in evaluating the coverage of a mobile vehicular network.
Road Flaw Detection using CNN and Transfer Learning Techniques

Road cracks can have very dangerous results. Road flaw detection is a crucial aspect of ensuring road safety and maintenance. Identifying cracks and taking the necessary steps are requirements for preventing such dangers. Manual approaches are costly and time consuming. Using pricey laser equipment vehicles for operation and the associated upfront costs is also not very feasible. This paper proposes an automation-based approach to identify various types of flaws like potholes, cracks, and patchy terrain on road using machine learning techniques. The convolutional neural network (CNN) and transfer learning techniques are utilized to carry out the mentioned work. To get the precise features from the image data, first the data is preprocessed and then transfer learning is applied using different pre-trained CNN models. Thereafter, to accurately detect the road flaws, the model is fine-tuned using the transfer learning and the dataset collected by us. The proposed approach is evaluated on metrics such as precision, recall, and F1 score. The performance results show that the proposed approach performs well while achieving an F1 score of 95%. Therefore, reducing the risk of accidents and improving road safety.
Machine learning Model for footballer position classification

Artificial intelligence is widely applied in sports science, especially in football, including match result prediction, and analysis of the performance of footballer. In team sports, the classification of footballer position can provide guidance for the selection of talents. However, there is still a lack of work in determining the position of the footballer, which is one of the leading problems for coaches in football. At present, it mainly relies on the preferences of footballers and arrangements of coaches. Therefore, the aim of this study is to predict the position of footballer using the dataset of football matches. In this study, we used web crawler to receive match dataset from the football analysis website whoscored. We tried to analyse the position of footballer and the factors affecting their positions. To achieve this aim, a two-stage application is followed. In the first stage, feature engineering was established to select important features. In the second stage, machine learning model was established. The result shows that compared with other models, the model based on GRU-attention has the highest accuracy (92.16%), the lowest loss (0.0144) and the fastest time. The AUC values of forwards, midfielders and defenders are 98%, 92% and 92%. The result verifies the effectiveness of the model.
Named Entity Recognition for Ancient Chinese Based on Knowledge Embedding

With the acceleration of the digitization process of ancient literature, the automatic extraction of entity information in ancient literature can enable researchers to study ancient history and literature deeply, which is of great significance to the study of ancient literature. Currently exist insufficient characterization of Chinese character information features in ancient literature entity recognition and boundary recognition problem. According to the problem, this paper propose an ancient Named Entity Recognition (NER) model based on knowledge embedded. Through the construction of entity recognition model based on training, in which the lexicon knowledge and PinYin knowledge, and explore a variety of fusion mechanism make the input information to learn the corresponding external knowledge. The model was trained in the ancient ZuoZhuan dataset and the GWPC2023 dataset with other methods. The F1 value reached the optimum compared with the baseline model and other methods. The F1 value of ZuoZhuan and GWPC2023 datasets increased by 1.7% and 1.8%, respectively. The results verified the superiority and validity of the model.
A Novel Approach for Specification and Verification of Symmetric Distributed Algorithms Using SPIN

This paper focuses on the challenges of modeling and verifying symmetric distributed algorithms with point-to-point and bidirectional communications using the SPIN model checker. In this paper, we first state the problems of implementing the model of such algorithms in the Promela language. Then, we present a solution for each of these problems, where these solutions present a hybrid data structure that facilitates the definition of any set of variables or channels as a matrix. Also, we model and verify one of the valuable and practical algorithms called the causal broadcast (CB) algorithm as a case study using the proposed method. In addition, we specify the main properties of the CB algorithm, namely validity, integrity, causal delivery, and termination, in Linear Temporal Logic (LTL) and then verify them using the SPIN model checker.
Automatic Nuclei Segmentation Method using NLM Filter for Denoising

Nuclei provide plenty of information about the micro-environment. An automatic nuclei segmentation approach can reduce the effort of pathologists and provide precise microenvironment observations for clinical investigations. Even though the current deep-learning (DL) based nuclei segmentation approaches perform better than the traditional ones in most cases, this task is still difficult, especially when the nuclei are cohesive and overlapping with each other. In this present work, we have suggested a novel nuclei segmentation method, where we combine the convolution neural network (CNN) based U-Net model with a Nonlocal Means (NLM) filter for pre-processing the source image. The NLM filter can eliminate the noise from the input image while retaining the edge details. The pre-processing step employed improves the effectiveness of the suggested segmentation method. The efficacy of the suggested method for segmenting nuclei is compared with the existing methods on the same dataset. The proposed method attained IOU(JI), Accuracy, Precision, and F1 score values as 0.8055, 96.29%, 0.8819, and 0.8944 respectively. The empirical results show that our proposed method performs better than the other existing techniques.