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arXiv 2024年

作者： Xie, Yuting Guo, Xianda Wang, Cong Liu, Kunhua Chen, Long School of Computer Science and Engineering Sun Yat-Sen University China School of Computer Science Wuhan University China State Key Laboratory for Management and Control of Complex Systems The Institute of Automation Chinese Academy of Sciences China School of Mechanical and Automotive Engineering Qingdao University of Technology China

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance. © 2024, CC BY.

关键词： Automatic guided vehicles

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Lightweight Strip Steel Surface Defect Detection Algorithm Based on YOLOv8s 3

Lightweight Strip Steel Surface Defect Detection Algorithm B...

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3rd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2024

作者： Feng, Zhining Sun, Shibao Shi, Nianfeng Wang, Guoqiang Henan University of Science and Technology School of Information Engineering Luoyang China Henan University of Science and Technology School of Software Luoyang China Henan Key Laboratory of Green Building Materials Manufacturing and Intelligent Equipment School of Computer Information Engineering Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9798350355413

To address the issues of low accuracy, high number of parameters and computational cost in strip steel surface defect detection algorithms, a lightweight strip steel surface defect detection algorithm based on YOLOv8s is proposed. Initially, the Cross-stage Focused Linear Attention (C2F-FLA) module is introduced to enhance the feature extraction capability of the backbone network. Subsequently, to improve the capture of multi-scale defect contours and reduce the model's parameters and computational complexity, a Wavelet Convolution-Bidirectional Weighted Feature Pyramid Network (WT-BiFPN) is employed in the model's Neck section for adaptive fusion of features at different resolutions. Finally, Haar Wavelet Downsampling (HWD) is utilized to retain more defect-related information, further reducing the parameters and computational complexity. The proposed method was validated on the NEU-DET dataset, and the results demonstrate that compared to YOLOv8s, this approach achieves a 4.1% increase in mAP, a 44.4% reduction in parameters, and a 32.8% decrease in computational complexity. The method ensures lightweight implementation while improving detection accuracy, offering more possibilities for defect detection on edge terminal devices. © 2024 IEEE.

关键词： NP-hard

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Grasping Force Estimation for Markerless Visuotactile Sensors

arXiv

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arXiv 2024年

作者： Castaño-Amoros, Julio Gil, Pablo The AUROVA Lab Computer Science Research Institute University of Alicante Alicante03690 Spain The Department of Physics Systems Engineering and Signal Theory University of Alicante Alicante03690 Spain

Tactile sensors have been used for force estimation in the past, especially Vision-Based Tactile Sensors (VBTS) have recently become a new trend due to their high spatial resolution and low cost. In this work, we have designed and implemented several approaches to estimate the normal grasping force using different types of markerless visuotactile representations obtained from VBTS. Our main goal is to determine the most appropriate visuotactile representation, based on a performance analysis during robotic grasping tasks. Our proposal has been tested on the dataset generated with our DIGIT sensors and another one obtained using GelSight Mini sensors from another state-of-the-art work. We have also tested the generalization capabilities of our best approach, called RGBmod. The results led to two main conclusions. First, the RGB visuotactile representation is a better input option than the depth image or a combination of the two for estimating normal grasping forces. Second, RGBmod achieved a good performance when tested on 10 unseen everyday objects in real-world scenarios, achieving an average relative error of 0.125 ± 0.153. Furthermore, we show that our proposal outperforms other works in the literature that use RGB and depth information for the same task. © 2024, CC BY-NC-ND.

关键词： Depth perception

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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Congress on Evolutionary Computation

作者： Wenjing Hong Cheng Chen Zexuan Zhu Ke Tang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design.

关键词： Measurement Design methodology Evolutionary computation Search problems Linear programming Stability analysis Vaccines

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SPEAR: Design and Implementation of an Advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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Sustainable Expert systems (ICSES), International Conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants Machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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Corrigendum to ‘Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey’ [J. Netw. Comput., Volume 238, June 2025, 104152] (Journal of Network and computer Applications (2025) 238, (S1084804525000499), (10.1016/***.2025.104152))

引用

Journal of Network and computer Applications 2025年 239卷

作者： Ahmad, Rana Zeeshan Rizwan, Muhammad Yousuf, Muhammad Jehanzaib Khan, Mohammad Bilal Almadhor, Ahmad Gadekallu, Thippa Reddy Abbas, Sidra Department of Information Technology University of Sialkot Sialkot Pakistan College of Science and Engineering University of Derby United Kingdom Software Research Institute Athlone Institute of Technology Ireland School of Electronic Engineering Xidian University Xidian China Department of Computer Engineering and Networks College of Computer and Information Science Jouf University Sakaka Saudi Arabia The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science COMSATS University Islamabad Sahiwal Pakistan

The authors regret the mistake in affiliation of Author Sidra Abbas. The correct affiliation is as follows: Sidra AbbasDepartment of computer science, COMSATS University Islamabad, Sahiwal, Pakistansidraabbas@***:sidr...

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Visual Measurement of Foam Thickness in Flotation Process Based on Depth Stereo Matching

Visual Measurement of Foam Thickness in Flotation Process Ba...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xiuguang Zheng Xiaorui Li Zhaolin Yuan Xiaojuan Ban School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In the process of copper flotation, the traditional detection method has some defects in accurately detecting the depth of foam layer in the flotation cell, especially for the identification of foam information of highly reflective and attenuation materials in the flotation foam system. We aim to introduce a new synthetic dataset, specifically for the flotation foam system, and pay special attention to the foam information of high reflection and high attenuation materials, so as to promote it to the actual flotation foam system and improve the accuracy of liquid level detection. We combine the newly synthesized dataset with binocular ranging methods and propose a soft strategy method for depth estimation of flotation cell thickness using binocular stereo matching based on IoT technology. The experimental results show that our method has achieved remarkable results in the flotation foam system, can accurately measure the depth of the flotation cell, and provides reliable technical support for the control and optimization of the flotation process. Therefore, this binocular stereo matching depth estimation method based on IoT technology provides an effective way to solve the limitations of traditional flotation depth detection methods. It not only effectively monitors the current efficiency of the flotation cell but also automatically identifies instances of flotation cell froth overflowing, offering automated inspection and diagnostics.

关键词： Visualization Liquids Depth measurement Transportation Attenuation Real-time systems Reflection Reliability Synthetic data Thickness measurement

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Mental Disorder Detection using Non-Orthogonal Adaptive Constrained IVABMGGMM

Mental Disorder Detection using Non-Orthogonal Adaptive Cons...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Ali Algumaei Muhammad Azam Nizar Bouguila Concordia Institute for Information Systems Engineering Concordia University Quebec Montreal Canada School of Computer Science and Technology Algoma University Sault Ste. Marie Ontario Canada

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The constrained independent component analysis (cICA) algorithm has proven remarkable effectiveness in integrating prior information into the framework of independent component analysis (ICA). However, the necessity of orthogonality for decoupling the constraints on individual sources restricts the optimization space and consequently the separation performance of cICA algorithms. To overcome this limitation, we propose a decoupling method called non-orthogonal adaptive constrained independent vector analysis with a bounded multivariate generalized Gaussian mixture model (non-orthogonal acIVABMGGMM), designed to address the drawbacks of independent component analysis in modeling multivariate data. This structure has the capability to incorporate prior knowledge regarding the sources or the mixing coefficients into the cost function of IVA. A robust decoupling strategy is needed to enable this integration. We also identify the brain regions impacted by Alzheimer’s disease by using the extracted components from our non-orthogonal acIVABMGGMM model. We demonstrate the superior performance of non-orthogonal acIVABMGGMM over the basic IVA model through experiments conducted on both simulated and real medical imaging data.

关键词： Adaptation models Analytical models Mental disorders Image processing Independent component analysis Brain modeling Vectors

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A Hybrid Ai Framework for Multi-Label Toxicity Category Detection

SSRN

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SSRN 2023年

作者： Jayaraman, Ashok Kumar Trueman, Tina Esther Murugappan, Abirami Cambria, Erik Mitra, Satanik Department of Information Science and Technology Anna University Chennai India Department of Computer Science University of the People United States School of Computer Science and Engineering Nanyang Technological University Singapore Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur India

Nowadays, online social media platforms enable individuals or groups to freely express their opinions. Sometimes such opinions lead to personal attacks, harassment, and abusive language. Detecting and classifying toxicity is a challenging task of natural language processing. Therefore, we first focus on subsymbolic artificial intelligence (AI) for multi-label toxicity identification, where an instance is mapped to a set of pre-defined label categories. Second, we propose a hybrid AI framework to analyze only negative toxic comments. The hybrid AI framework is the combination of symbolic AI and subsymbolic AI. First, symbolic AI defines sentiment polarities of the given text as positive, negative, and neutral. Second, subsymbolic AI uses only the negative sentiment polarity texts to map a set of pre-defined label categories. In particular, we use the BERT fine-tuning model as subsymbolic AI for multi-label toxicity category detection. The proposed model is evaluated on multi-label metrics such as label-based, example-based, and rank-based metrics. Results indicate that the proposed BERT model outperforms stateof-the-art results. © 2023, The Authors. All rights reserved.

关键词： Toxicity

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Thermodynamic Interpolation: A generative approach to molecular thermodynamics and kinetics

arXiv

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arXiv 2024年

作者： Moqvist, Selma Chen, Weilong Schreiner, Mathias Nüske, Feliks Olsson, Simon Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg GothenburgSE-41296 Sweden Max-Planck-Institute for Dynamics of Complex Technical Systems Magdeburg Germany

Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In this work, we introduce Thermodynamic Interpolation (TI), which allows for generating sampling statistics in a temperature-controllable way. We introduce TI flavors that work directly in the ambient configurational space, mapping between different thermodynamic states or through a latent, normally distributed reference state. Our ambient-space approach allows for the specification of arbitrary target temperatures, ensuring generalizability within the temperature range of the training set and demonstrating the potential for extrapolation beyond it. We validate the effectiveness of TI on model systems that exhibit metastability and non-trivial temperature dependencies. Finally, we demonstrate how to combine TI-based sampling to estimate free energy differences through various free energy perturbation methods and provide corresponding approximated kinetic rates estimated through generator extended dynamic mode decomposition (gEDMD). Copyright © 2024, The Authors. All rights reserved.

关键词： Free energy

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