Reviewerly/exHarmony huggingface dataset:
Recognizing Named Entities in Failure Analysis Reports

Failure Analysis (FA) is a complex laboratory activity that requires systematic documentation of all findings and conclusions obtained during an analysis to preserve knowledge acquired by engineers in this process. Current FA information systems store this data in different formats distributed across databases, file shares, wikis, or other human-readable forms. Given a large volume of generated FA data, navigating or searching for particular information is hard since machines cannot automatically process the stored knowledge and require frequent interaction with experts. This paper investigates two applications of modern Natural Language Processing (NLP) approaches to the Named Entity Recognition (NER) task. In particular, we study the performance of two techniques, spaCy, a highly regarded Python library, and the state-of-the-art BERT Language Models (LM), pretrained on semiconductors data. Our experiments show that spaCy reached precision, recall, and F1 scores of about 23%, whereas a BERT-based model achieved a precision of 51%, recall of 49%, and an F1 score of 50% on the test corpus.
Analysis and Research For Shield Tunnel Health Monitor Denoising and Refining Based On Wavelet Transform

During the process of signal acquisition, conversion, and transmission, due to the influence of equipment, environment, and human factors, the signal is inevitably subject to noise interference. Therefore, how to remove noise from signals and obtain interesting information is a key technology in signal processing. Wavelet transform is a time-frequency domain analysis method with fixed window area. It obtains different wavelet coefficients by scaling and shifting the parent wavelet. The data signal decomposed by wavelet transform consists of two parts. The modulus maxima method of wavelet is widely applied to analyze and reconstruct. The usage conditions and key computing parameters are also obtained. The wavelet transform method is successfully adopted to denoise the shield tunnel monitoring results, and the refined data is reliably provided for tunnel healthy diagnosis.
Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3 × fewer parameters and 2.2 × fewer FLOPs.
Unveiling Emotions through Handwriting: A Data Analysis Approach

Detection of unfavourable feelings through routine tasks like handwriting is beneficial for encouraging well-being. This paper presents a study on emotion detection using handwriting data analysis. By examining a dataset consisting of 129 users and their performance on seven different tasks, we gained valuable insights into the relationship between emotional states and an individual’s handwriting. The study employed various techniques, including feature engineering, correlation analysis, and dimensionality reduction using Linear Discriminant Analysis and Principal Component Analysis. A Random Forest model is implemented to predict emotional states based on handwriting features. The results demonstrate promising accuracy in classifying emotional states. This study emphasizes the potential of handwriting analysis as a means to understand and detect emotions, laying the groundwork for further research in this domain.
IndoBERT Based Data Augmentation for Indonesian Text Classification

Text data augmentation has been able to improve the performance of models or algorithms for text classification and sentiment analysis. In major cases, rule-based augmentation techniques can be easily applied to various languages such as Indonesia language. However, language model-based augmentation techniques rarely investigated for Indonesian language data. Therefore, this paper presents a text data processing model for Indonesian language, which has limited data, to perform text preprocessing and data augmentation techniques by selectively inserting words based on IndoBERT. This IndoBERT-based augmentation is able to generate data that still retains meaning and sentiment similar to the original data. The testing of this Twitter text dataset yielded results showing that the proposed augmentation technique was able to increase accuracy and outperform the Random Insert augmentation technique.
VeriReach: A Formally Verified Algorithm for Reachability Analysis in Virtual Private Cloud Networks

Virtual Private Cloud (VPC) has become a widely used cloud computing service, serving as a foundational web infrastructure for many organizations. Nevertheless, the growing problem of reachability issues poses significant threats to the security and reliability of VPC networks, potentially resulting in critical security concerns such as data breaches and service outages. Although there has been substantial progress in recent reachability analysis, existing methods lack validation of correctness. Moreover, current analyses are tailored for One-to-One reachability where both the source and the destination are fixed, and fail to efficiently answer One-to-Multi reachability queries, which involve computing all reachable destinations for a given source node. To address the above challenges, we propose VeriReach, the first formally verified algorithm that provides comprehensive and efficient reachability analysis in large-scale VPC networks. The reachability analysis result of VeriReach is proved to be equivalent to the original reachability semantics of the VPC networks, ensuring its correctness (i.e., soundness and completeness). The fine-grained formalization of VeriReach and its fully mechanized correctness proofs are carried out in Isabelle/HOL theorem prover with 282 lemmas/theorems and $\sim 4,900{\mathrm{LoC}}$. We further implement VeriReach in C++ and the evaluations indicate that VeriReach is more efficient and scalable than MonoSAT, the state-of-the-art SMT solver, when applied to large-scale VPC networks for reachability analysis.
Automatic Abstraction of Long Chinese Patent Texts Based on P-Bertsum Model

In response to the problems of loss of key information, deviation of the abstract from the core textual idea and excessive redundancy caused by the traditional text abstract extraction algorithm for long text processing of Chinese patents, the P-BertSum algorithm is proposed, which enables the algorithm to process long (over 1500 words) patent texts with high efficiency by building an extractive text abstract algorithm model for long Chinese patent text contents and generating high quality long (over 200 words) text summaries. The method is based on the improved BertSum algorithm model, using the new CLTPDS patented text dataset, processing long texts by Pooling, transforming Chinese input representations, generating sentence vectors using a pre-trained model, and capturing internal text features and text structure features of sentences to extract summaries. Experiments demonstrate that this method has improved the recall and F-value of ROUGE by more than 13 percentage points compared to existing methods.
Classification of Natural Disasters Using Decision Tree Techniques

The result of a natural hazard is a natural disaster. Earthquakes, landslides, tsunamis, and volcanoes are all natural disasters that cause economic, environmental, and human damage. The need of the hour is to predict such geological calamities. Furthermore, predicting these calamities is a complicated process that is influenced by a variety of physical and environmental factors. These natural risks can also be predicted using machine learning approaches. This proposed approach is primarily concerned with predicting loss owing to the impacted region. The system was created with the help of well-known classifiers like Decision Tree and Chai Square Technique based feature selection. In comparison to previous classifiers, our proposed classification method produces superior results. The afflicted data set is used to categories, and then the classifiers are compared to determine the disaster’s accuracy.
Automated Path Planning of Aerial Target for Weapon System Test and Evaluation

Flight path planning is a pre-requisite before every manned or unmanned aircraft’s take-off to meet the mission objectives taking into consideration all the vital internal and external factors by ensuring the safety of operation. The current work presents a simple, unique, and effective method to construct an aerial target’s flight plan using the user inputs and providing the user with an interactive interface to view, validate and check the feasibility of the formed trajectory based on the applied constraints.
It’s a Trap! Detection and Analysis of Fake Channels on Telegram

Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection and social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams.In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze fake channels on Telegram. To automatically detect fake channels, we propose a machine learning model that achieves an accuracy of 85.49%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.
Multi-View Fusion based Attribute Network for Multi-Query Vehicle Re-identification

In recent years, most re-identification methods use only a single query image to match the target vehicle. However, the limitation of single query image information hinders the performance of vehicle re-identification. In this case, we propose a Multi-View Fusion based Attribute Network (MVANet) for the Multi-Query Vehicle re-identification task. On the one hand, we fuse the information of multiple vehicle views by the Viewpoint Fusion Module to improve the information representation, thus solving the challenge of large intra-class variability caused by viewpoint differences. On the other hand, we use the Attribute Embedding Module to embed attribute information into visual features to address the challenge of small intra-class variability caused by similarity in appearance. In addition, we construct a new dataset HFVeRi for the Multi-Query Vehicle re-identification task.
Crime Detection Using Multi-Layer Perceptron in Social Media Platforms

Criminals have been increasingly utilizing Social Media Platforms (SMPs) to achieve a number of criminal goals. These goals can range from the establishment of criminal virtual groups to the sharing of user information and data breaches. Such criminal groups often use SMPs to share confidential information and coordinate criminal activities. The ease of access and the ability to remain anonymous makes SMPs a powerful tool for criminals. Data breaches are a major concern when it comes to SMPs. Criminals can use SMPs to gain access to personal and financial information, including credit card numbers and passwords. They can also use SMPs to spread malicious software and malware, which can be used to infect computers and mobile devices. The sharing of user information is also a concern for SMPs. Criminals can use SMPs to obtain personal information about individuals, which can be used for identity theft and other malicious activities. Furthermore, criminals can use SMPs to spread false information about individuals or organizations, which can lead to reputational damage. SMPs can be a powerful tool for criminals. Additionally, users should be careful when sharing personal information and be aware of any suspicious activity on SMPs. Hence, we suggested an ontology-based multilayer perceptron (MLP) classifier a feed forward artificial neural network algorithm MLP-NN) for criminal intention detection in SMPs, which creates program concepts for the choice of social network postings containing criminal slang terms and automatically categorizing these posts in line with illocutionary categories. The system uses trained models from previously published articles to accurately classify published posts with criminal purpose. The suggested method is examined and contrasted with different existing technologies. The results show that the suggested framework is effective in identifying crimes on social media.
Disentangled Hypergraph Collaborative Filtering for Social Recommendation

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.
Real-Time Anonymization of Sensitive Personal Data Using a Service-Based Architecture

Anonymization is an important aspect of data privacy protection, especially in the context of sensitive personal information collected through sensors. In this paper, we propose a new service-based architecture for anonymizing such data in real-time, ensuring that data is accessible to authorized users while maintaining privacy. Our architecture is based on the annotation of data at ingestion time, where privacy levels are assigned to sets of columns. The anonymization procedure is performed by compressing and encoding the data through an autoencoder model, where the encoder and decoder functions are defined as parametric functions composed of multiple hidden layers.
ExpoEv: Enhancing Social Recommendation Service with Social Exposure and Feature Evolution

Social networks are widely recognized as highly effective information sources for social recommendation services. However, previous social recommendation methods assumed that a user’s preference factor and social trust factor shared a common latent feature space. Additionally, few studies have explored the incorporation of social information into the item domain for recommendations. To address these gaps, we propose ExpoEv, a deep collaborative filtering recommendation model that integrates social exposure based on feature evolution for social recommendation services. Specifically, we propose a social exposure module for both user and item domains that considers the number of items that a user’s social friends interact with. Furthermore, we introduce a feature evolution component that enables the incorporation of social exposure information with social trust and attribute factors in the context of social recommendation services. Experiments demonstrate the effectiveness of our model in the quality of recommendation service.
Gas concentration prediction based on a combination of improved slime mould algorithm and temporal convolutional network

In order to improve the accuracy of gas concentration prediction, a gas concentration prediction method based on Improved Sticky Mushroom Algorithm (ISMA-TCN) is proposed. Firstly, Pearson correlation coefficients are used to reduce the dimensionality of gas concentration feature data, reduce redundant data and improve accuracy, and perform improved soft threshold wavelet denoising; then combine sine chaos, Corsi variation strategy and Gaussian variation strategy to improve the sticky fungus algorithm (SMA), and compare with the original ABC, SMA, PSO and SCA algorithms by 2 benchmark test functions to prove Its optimization-seeking ability is greatly improved; meanwhile, replacing the excitation function ReLU function in TCN model as Swish function improves the complementary zero term in TCN to the average value. Finally, the parameters of the improved temporal convolutional network (ITCN) are dynamically sought by the improved viscous algorithm, and the combined ISMA-ITCN prediction model is obtained and compared with others, and the evaluation indexes of the prediction results are higher than those of ISMA-ITCN respectively. the results prove that the proposed method can effectively improve the accuracy of gas concentration.
Probabilistic Data Association Performance During Launch Vehicle Tracking

Probabilistic Data Association (PDA) is Bayesian approach of deriving likelihood function for all measurements reported by radar. Tracking of Launch Vehicle with skin mode radar involves processing of multiple returns from rocket, separated stages and rocket plumes, which makes association of track measurements more uncertain. Using PDA we bring down the uncertainty of measurements by computing posterior probability for all measurements reported by radar during real time. Measurement with highest posterior probability is selected and given as input to processing filter. In this paper we analyze Probabilistic Data Association (PDA) performance during tracking of Launch Vehicle by Multi Object Tracking Radar.
Utilizing Different Predictive Methods to Optimize Hyperledger Fabric Instance

This paper explores the use of reinforcement learn-ing and various machine learning techniques to optimize the configurations of Hyperledger Fabric v2 Channels and Orderers. Our goal is to increase the average throughput and success rate. To achieve this, we train multiple optimization agents using different algorithms, such as Q-learning and Deep Q Network. These agents interact with a Hyperledger Fabric v2 instance in a single host environment, using FabCar as the smart contract and Hyperledger Caliper as the benchmark platform.Based on the data collected from our reinforcement learning agents, we observed that the agents tend to choose larger batch sizes to optimize their results. Our training data also suggests that it is possible to achieve an average throughput of more than 98% for send rates up to 200 TPS. Our prediction results demonstrate that, even with the limitation of training time, reinforcement learning agents can increase the average throughput by around 59% and the average success rate by around 18%, while machine learning models can increase the average throughput by around 45% and the average success rate by around 8%, compared to the baseline results provided by the default configuration.
A Hybrid Model for Object Detection Based on Feature-Level Camera-Radar Fusion in Autonomous Driving

Preventing car collisions through object detection has always been a major research direction in the field of autonomous driving. Recent years, camera-based object detection technology has achieved great success. However, its performance is still insufficient under poor lighting or weather conditions. Therefore, the fusion of various sensor information has become a new trend in object detection for autonomous driving. This paper proposes a hybrid object detection model that fuses millimeter-wave radar and camera at the feature level. The model uses a traditional convolutional neural network to extract features from data collected by the radar and camera, and performs multi-scale deep fusion. Subsequently, a multi-scale deformable attention module is used to process the fused feature maps for object detection. We tested this model on the nuScenes for autonomous driving, which includes night and rainy scenes. The hybrid model achieved a mean average precision (mAP) of 47.8%, which is 1.4% higher than that of the baseline object detection model.
A Deep Learning Based Cross Model Text to Image Generation using DC-GAN

In recent times, Generative Adversarial Networks have successfully synthesized images through text descriptions. In the domain of image processing, deep convolutional generative adversarial networks (DCGANs) have recently demonstrated promising results. In this technique, two major approaches are used. The first approach in this method utilizes a text transformer using XLNet to extract semantically significant text descriptions using a stronger text encoder. The better text encoder will be allowing the generator to gather more insightful text information with the goal of generating realistic and text-matching visuals. The second method uses the Deep Convolutional Block to efficiently merge text and picture information. It is known as the Deep Convolutional Text to Image Generative Adversarial Network (DCGAN). We experiment with the DCGAN with OXFORD 102 flower dataset to generate the relevant visuals according to the text descriptions. The quality of the images gained from DCGAN has improved with the OXFORD 102 Flower dataset of inception score of 4.580 in order to show the DCGAN network how deeply varies from other networks. The intensive experiments also provide a PSNR value with the Deep Convolutional Layer of 3.983 and an SSIM value of 0.508 and the stronger text encoder improves text-to-image synthesis performance. The proposed method is successfully boosted the inception score by 9.39% by depicting XLNet as text encoder.