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SSRN 2024年

作者： Cao, Mingwei Liu, Bowen Yi, Jun Yu, Ye Zhao, Haifeng Lyu, Zhihan Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Game Design Uppsala University Visby62167 Sweden

In recent years, deep learning-based multi-view stereo (MVS) reconstruction methods have made significant progress. However, they still face challenges such as low accuracy and incomplete scene reconstruction due to the differences between the captured images from various viewpoints. To address these challenges, we propose a multi-stage MVS reconstruction network that utilizes offset regression and uncertainty distance estimation to weaken the impact of the differences. Firstly, the multi-view images are input into a dynamic feature extraction network to obtain robust image features. Secondly, we utilize the uncertainty distance estimation method to dynamically calculate the depth sampling range for each pixel. This method considers the probability distribution and contextual information of adjacent pixels to improve the depth estimation accuracy. Thirdly, we present an offset regression algorithm that combines the advantages of both classification and regression methods. It further refines the predicted depth values, bringing them closer to the ground truth values. Finally, we tested the method on public datasets and compared it to traditional MVS reconstruction methods. Results show that the new method outperforms the current methods in terms of the accuracy and completeness of point clouds while using less memory space. The source code is available at: https://***/liubowen1233/MS-MVS. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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An Efficient Safety and Authorized Helmet Detection Using Deep Learning Approach

An Efficient Safety and Authorized Helmet Detection Using De...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Susa, Julie Ann B. Maaliw, Renato R. Ceribo, Carla May C. MacAlisang, Jonel Fabro, Bernard C. College Of Engineering Southern Luzon State University Lucban Quezon Philippines Technological University Of The Philippines Technology Licensing Office- Itso Manila Philippines Eulogio 'Amang' Rodriguez Institute Of Science And Technology Computer Engineering Program Manila Philippines

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

In recent years, riding a motorcycle has become one of the most convenient ways for consumers to go to their destination. Helmets are very important and necessary for the safety of motorcyclists, however, officers find it difficult to enforce the laws regarding the wearing of authorized helmets. This system was established to determine whether or not a helmet is allowed. The authorized helmet for riders is the full-face helmet. This vision-based system could aid in identifying riders who are wearing helmets that aren't fitting properly. The system used the YOLOv3 model and a deep learning approach to use the helmet detection model in this work. As a result, the model with the best training received an mAP of 97%. This proposed device may be used on busy routes to monitor motorcyclists to see if they are wearing an authorized helmet. © 2022 IEEE.

关键词： Safety devices

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Using Real-Time Integrated computer Vision and Deep Learning for Advanced Factory Safety

Using Real-Time Integrated Computer Vision and Deep Learning...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Yu-Cheng Chang Chao-Yi Huang Fang-Yuan Yang Wei Chien Zong-Rong Wu School Art and Design Sanming University Fujian China Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as “normal operations” and “unexpected incidents” to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety.

关键词： computer vision Prevention and mitigation Employment Real-time systems Production facilities Safety Internet of Things

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CMIVGSD: CircRNA-miRNA Interaction Prediction Based on Variational Graph Auto-Encoder and Singular Value Decomposition

CMIVGSD: CircRNA-miRNA Interaction Prediction Based on Varia...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Qian, Yurong Zheng, Jingjing Zhang, Zhe Jiang, Ying Zhang, Jiaxuan Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China University of California San Diego Department of Cognitive Science La Jolla United States

ISBN: (纸本)9781665401265

A large amount of evidence shows that circular RNAs(circRNAs) participate in transcription and translation regulation and function as 'micro RNA(miRNA)-sponges'. Recognizing circRNA-miRNA interaction is helpful to understand the function of circRNAs, especially its role in complex diseases. Obtaining interactive information based on traditional biological experiments is usually small-scale, time-consuming, and laborious. Considering that there are few calculation methods, it is urgent to develop efficient and accurate methods to extract the interaction between circRNA and miRNA. In this work, we proposed a computational framework called CMIVGSD, which uses singular value decomposition and graph variational auto-encoders to predict circRNA-miRNA interaction. To our best knowledge, CMIVGSD is the first calculation framework to predict circRNA-miRNA interaction. CMIVGSD uses the singular value decomposition (SVD) algorithm to obtain linear features from the circRNA-miRNA interaction matrix. We have constructed the similarity networks of circRNA and miRNA, respectively. The graph variational auto-encoder (VGAE) is employed to mine the non-linear features of circRNA-miRNA in similarity networks. Finally, we combine linear and non-linear features and use LightGBM to predict interaction scores. We performed five-fold cross-validation experiments. Experimental results show that our proposed method is better than other methods. The case study further proves the effectiveness of CMIVGSD in predicting circRNA-miRNA interaction. © 2021 IEEE.

关键词： Singular value decomposition

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Qori Alfatah Putra Christy Atika Sari Eko Hari Rachmawanto Nur Ryan Dwi Cahyo Edy Mulyanto Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science National University of Science and Technology DhiQar Iraq

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers.

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Spectral Extremal Connectivity of Two-State Seizure Brain Waves

arXiv

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arXiv 2025年

作者： Talento, Mara Sherlin D. Richards, Jordan Pinto-Orellana, Marco Huser, Raphael Ombao, Hernando C. Statistics Program Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia Neuro-AI Laboratory Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh EdinburghEH9 3FD United Kingdom Department of Biomedical Engineering University of California Irvine92697 United States

Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of connectivity in the tails of the distribution. This limitation becomes important when investigating extreme neural events that are characterized by large signal amplitudes. The focus of this paper is to examine connectivity in the tails of the distribution, as this reveals salient information that may be overlooked by standard methods. We develop a novel notion of spectral tail association of periodograms to study connectivity in the network of electroencephalogram (EEG) signals of seizure-prone neonates. We further develop a novel non-stationary extremal dependence model for multivariate time series that captures differences in extremal dependence during different brain phases, namely burst-suppression and non-burst-suppression. One advantage of our proposed approach is its ability to identify tail connectivity at key frequency bands that could be associated with outbursts of energy which may lead to seizures. We discuss these novel scientific findings alongside a comparison of the extremal behavior of brain signals for epileptic and non-epileptic patients. © 2025, CC BY.

关键词： Electroencephalography

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Arabic text sentiment analysis and emotion classification using transformers

Arabic text sentiment analysis and emotion classification us...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nada Mossad Youssef Mohamed A Fares Ahmed B. Zaky Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt The Department of Electrical Engineering The Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately.

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Blockchain-based Smart Contract in Service-Oriented Business Processes 7

Blockchain-based Smart Contract in Service-Oriented Business...

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7th International Conference on Information Technology Systems and Innovation, ICITSI 2022

作者： Parjuangan, Sabam Suhardi Nughara, I Gusti Bagus Baskara Khoirunnisa, Elvin Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia University of Twente Faculty of Electrical Engineering Mathematics Computer Science Enschede Netherlands

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

Service systems' business processes show the existence of mutual interaction between users. Running a particular business subdomain requires the role of each user. This role is in line with the concept contained in blockchain-based smart contracts. However, determining the subdomains that require smart contract coding is challenging in developing business processes that will run on the blockchain. We aim to determine the service-oriented business process (in the case of marketplace service) and the subdomain of the business process that requires smart contract coding. This article is essential because it shows how developing IT services without a smart contract differs from developing IT services with a smart contract. The main differences between developing services that need smart contracts and those that do not are that certain process can be run automatically and the record of activity in blockchain cannot be changed by anyone (tamper-proof). We outlined methods for designing marketplace business processes and defined subdomains to be encoded into smart contract code. After reviewing the three marketplace platforms, we determined eight subdomains of the marketplace business process. Two subdomains require smart contract coding from eight, namely the payment and complaint subdomains. © 2022 IEEE.

关键词： Smart contract

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SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering 39

SCANS: Mitigating the Exaggerated Safety for LLMs via Safety...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cao, Zouying Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)157735897X

Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions. However, recent researches reveal safety-aligned LLMs tend to reject benign queries due to the exaggerated safety issue, limiting their helpfulness. In this paper, we propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns in aligned LLMs. First, SCANS extracts the refusal steering vectors within the activation space and utilizes vocabulary projection to anchor some specific safety-critical layers which influence model refusal behavior. Second, by tracking the hidden state transition, SCANS identifies the steering direction and steers the model behavior accordingly, achieving a balance between exaggerated safety and adequate safety. Experiments show that SCANS achieves new state-of-the-art performance on XSTest and OKTest benchmarks, without impairing their defense capability against harmful queries and maintaining almost unchanged model capability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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