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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Du, Zhihua Huang, Tianyou Li, Jianqiang Uversky, Vladimir N. Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of South Florida Morsani College of Medicine Department of Molecular Medicine Usf Health Byrd Alzheimer's Research Institute FL United States

ISBN: (纸本)9798350337488

Transcription factors (TFs) are crucial proteins that regulate gene transcription by binding to specific sites on DNA, known as transcription factor binding sites (TFBSs). Identifying TFBSs enables the design of drugs to modulate gene expression, making it important for drug design and gene therapy. While deep learning-based methods have been proposed for predicting TFBSs, there is room for improvement. This study introduces TFBSnet, a novel deep learning-based technique that accurately predicts TFBSs by extracting diverse feature data from DNA sequences and utilizing a convolutional neural network (CNN) combined with SKNet. Experimental results show that TFBSnet outperforms existing methods. It also demonstrates accurate prediction of TF binding sites in human cells without label data and exceptional performance in predicting TFBSs in plant cells using 265 TFs in Arabidopsis. Ablation analysis highlights the integration of different features and advanced feature extraction by SKNet as contributors to TFBSnet's superior predictive capability. © 2023 IEEE.

关键词： deep learning DNA sequence neural network transcription factor binding site

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Multilingual Lexical Simplification via Zero-Shot Paraphrasing

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2200-2214页

作者： Kang Liu Jipeng Qiang Yun Li Yi Zhu Yunhao Yuan Kaixun Hua Xindong Wu Department of Computer Science Yangzhou University Yangzhou China Department of Industrial and Management Systems Engineering University of South Florida Tampa USA Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei China

Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings. Nonetheless, these methods necessitate distinct pretrained models tailored to diverse languages, often overlooking the imperative task of preserving a sentence’s meaning. In this paper, we propose a novel multilingual LS method via zero-shot paraphrasing (LSPG), as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. Once the input sentence is channeled into the paraphrasing, we embark on the generation of the substitutes. This endeavor is underpinned by a pioneering decoding strategy that concentrates exclusively on the lexical modifications of the complex word. To utilize the strong capabilities of large language models (LLM), we further introduce a novel approach PromLS that incorporates the results of LSPG to generate heuristic-enhanced context, enabling the LLM to generate diverse candidate substitutions. Experimental results demonstrate that LSPG surpasses BERT-based methods and zero-shot GPT3-based methods significantly in English, Spanish, and Portuguese. We also demonstrate a substantial improvement achieved by PromLS compared to the previous state-of-the-art LLM approach. LS approaches usually assume that complex words and their replacements are individual terms, concentrating on word-for-word substitutions. To tackle the more challenging task of multi-word lexical simplification, including phrase-to-phrase replacements, we extend LSPG and PromLS into MultiLSPG and MultiPromLS. MultiLSPG identifies multi-word expressions matched with their corresponding word counts in specific positions, while MultiPromLS, akin to PromLS, utilizes these candidates to generate a heuristi

关键词： Multilingual Decoding Translation Rhetoric Large language models Training Neural machine translation Encoding Speech processing Probability distribution

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Enhancing Waste Management Sustainability Through Smart and Incentivized Sorting

Enhancing Waste Management Sustainability Through Smart and ...

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Applied Computing (URC), Annual Undergraduate Research Conference on

作者： Muluberhan Woga Rediet Negash Ahmedin Bekera Nathanael Alemu Nazar Zaki Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University (UAEU) Al Ain UAE

ISBN: (数字)9798331527341

ISBN: (纸本)9798331527358

This paper presents an intelligent waste sorting system that utilizes computer vision and deep learning to accurately categorize waste items. Moreover, the system incentivizes proper waste disposal through a rewards scheme. Testing indicated over 90% accuracy in classifying waste into multiple categories. This sustainable solution has strong potential to address critical waste management challenges.

关键词： YOLO Technological innovation Accuracy Scalability User interfaces Robustness Waste disposal

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Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT 17

Smart Soft Sensor for Froth flotation quality monitoring usi...

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17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023

作者： Hasidi, Oussama Bendaouia, Ahmed Abdelwahed, El Hassan Abdellah, El Alaoui-Chrifi Moulay Boussetta, Abdelmalek Benzakour, Intissar Bourzeix, Francois Oubouhss, Kaotar Mohammed Vi Polytechnic University Lisi Laboratory Computer Science Departement Uca Analytics Lab Marrakech Morocco Cadi Ayyad University Lisi Laboratory Computer Science Departement Marrakech Morocco Reminex Managem Group R&d and Engineering Center Marrakech Morocco Mohammed Vi Polytechnic University Analytics Lab Rabat Morocco National School of Applied Science Uae Department of Electrical and Industrial Engineering Tangier Morocco

ISBN: (纸本)9798350370911

Smart sensor technologies have gained prominence in the field of machine vision-based applications. Within this context, the utilization of machine vision systems for monitoring and controlling flotation plants has become increasingly prevalent. This paper addresses the critical need for accurate froth image classification in the development of an online machine vision-based control system. This paper explores the recent advancements in machine learning, with a focus on deep learning techniques. Specifically, it introduces the application of a Vision Transformer (VIT) model as an alternative to the traditional Convolutional Neural Network (CNN) for classifying froth images collected from an industrial lead flotation circuit. The results of this research demonstrate the remarkable performance of the ViT model, achieving an impressive overall accuracy rate of 96% when combined with pre-trained feature extractor models. This achievement surpasses the capabilities of conventional artificial neural network (CNN) classifiers of the state of art in terms of both accuracy and computational efficiency. These findings underscore the promising potential of deep learning neural networks in froth image analysis, highlighting their significance in the advancement of smart sensor-based machine vision systems within the field of flotation plant control. © 2023 IEEE.

关键词： computer vision

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Ensuring IoT Controller Reliability with Colored Generalized Stochastic Petri Net

Ensuring IoT Controller Reliability with Colored Generalized...

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International computer science and engineering Conference (ICSEC)

作者： Kruntarat Samngamnoi Sutinun Pomsiri Apipath Kamput Chanon Dechsupa Department of Computer Science Intelligent Software Engineering Research Group College of Computing Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochastic Petri Net (CGSPN), a formal language, facilitate correctness checks of such systems. This study proposes a verification approach by translating a C++-based system controller of a self-service machine into a CGSPN models and validating it using the Snoopy Tool. Mapping techniques employed to transform components in the controller into CGSPN models are provided. Results demonstrate the method’s efficacy in verifying system safety properties, simulating system events, and enabling quantitative verification.

关键词： Codes Petri nets Stochastic processes C++ languages Transforms Control systems Internet of Things Reliability Washing machines Self-service

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Towards Safe Online Machine Learning Model Training and Inference on Edge Networks

Towards Safe Online Machine Learning Model Training and Infe...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Md Al Maruf Akramul Azim Nitin Auluck Mansi Sahi Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Departmcnt of Computer Science and Engineering Indian Institute of Technology Ropar India

With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and inference is essential. Although data and model parallelism offer significant advantages for large machine learning model training, adopting parallel computing architecture in edge networks is challenging. It introduces safety concerns while splitting and integrating machine learning models over different computing nodes, which can pose risks to the integrity and reliability of the system. Therefore, online model training and inference in edge networks require a safe parallel computing architecture to achieve improved performance with optimal resource utilization. To address this challenge, we propose an efficient machine learning model partitioning algorithm that considers the safety constraint and requirements of edge networks and includes the triple-modular redundancy (TMR) technique for trusted computation. Our proposed approach achieves a significant speedup of approximately 56.3% in net training time compared to the non-partitioning approach, making it more efficient and suitable for real-time applications in edge networks.

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a c... 详细信息

关键词： Dispersion compensation

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Research on Visual Slam Technology for UAV Positioning and Navigation

Research on Visual Slam Technology for UAV Positioning and N...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： ZHOU WANG LIU ZHIHAO TIAN YING YANG YUJUN School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China College of Computer and Artificial Intelligence (Software College) Huaihua University Huaihua China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation.

关键词： Visualization Simultaneous localization and mapping Navigation Information processing Media Autonomous aerial vehicles Robots

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Structure of Mo2Cx and Mo4Cx Molybdenum Carbide Nanoparticles and Their Anchoring Sites on ZSM-5 Zeolites

Structure of Mo2Cx and Mo4Cx Molybdenum Carbide Nanoparticle...

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2023 AIChE Annual Meeting

作者： Gao, Jie Zheng, Yiteng Fitzgerald, George B. de Joannis, Jason Tang, Yadan Wachs, Israel E. Podkolzin, Simon G. Department of Chemical Engineering and Materials Science Stevens Institute of Technology HobokenNJ07030 United States Accelrys Software Inc. San DiegoCA92121 United States Operando Molecular Spectroscopy & Catalysis Laboratory Department of Chemical Engineering Lehigh University BethlehemPA18015 United States

Mo carbide nanoparticles supported on ZSM-5 zeolites are promising catalysts for methane dehydroaromatization. For this and other applications, it is important to identify the structure and anchoring sites of Mo carbide nanoparticles. In this work, structures of Mo2Cx (x = 1, 2, 3, 4, and 6) and Mo4Cx (x = 2, 4, 6, and 8) nanoparticles are identified using a genetic algorithm with density functional theory (DFT) calculations. The ZSM-5 anchoring sites are determined by evaluating infrared vibrational spectra for surface OH groups before and after Mo deposition. The spectroscopic results demonstrate that initial Mo oxide species preferentially anchors on framework Al sites and partially on Si sites on the external surface of the zeolite. In addition, Mo oxide deposition causes some dealumination, and a small fraction of Mo oxide species anchor on extraframework Al sites. Anchoring modes of Mo carbide nanoparticles are evaluated with DFT cluster calculations and with hybrid quantum mechanical and molecular mechanical (QM/MM) periodic structure calculations. Calculation results suggest that binding through two Mo atoms is energetically preferable for all Mo carbide nanoparticles on double Al-atom framework sites and external Si sites. On single Al-atom framework sites, the preferential binding mode depends on the particle composition. The calculations also suggest that Mo carbide nanoparticles with a C/Mo ratio greater than 1.5 are more stable on external Si sites and, thus, likely to migrate from zeolite pores onto the external surface of the zeolite. Therefore, in order to minimize such migration, the C/Mo ratio for zeolite-supported Mo carbide nanoparticles under hydrocarbon reaction conditions should be maintained below 1.5. © 2023 American Institute of Chemical Engineers. All rights reserved.

关键词： Atoms

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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