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2023 Workshop on Artificial Intelligence Safety, SafeAI 2023

作者： Yang, Chenyang Brower-Sinning, Rachel Lewis, Grace A. Kästner, Christian Wu, Tongshuang School of Computer Science Carnegie Mellon University United States Carnegie Mellon Software Engineering Institute United States

In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show the potential of capabilities for reflecting model generalizability, which can provide guidance for the ML engineering process. We discuss challenges and opportunities for the integration of capabilities into ML engineering. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Specifications

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ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smoke Detection

ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Yuhan Wang, Zhongyuan Hua, Jie Liang, Jinbi Xu, Zengmin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350368741

In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air conditioner recycling. It contains a total of 10, 724 images covering three common scenarios of air conditioner refrigerant leak: loading port, refrigerant extraction, and disassembly. All images are sourced from 656 video segments of real air conditioner recycling scenarios, captured by surveillance cameras deployed on an environmental company's recycling production line. The annotations for the refrigerant leak smoke in the images are performed by industry experts to ensure high accuracy and consistency. To the best of our knowledge, ACRL-10K is the first dataset specifically designed for refrigerant leak smoke detection. Based on the ACRL-10K dataset, we present the performance of mainstream object detectors, including the YOLO series, as a baseline to conduct the following work: 1) preliminarily summarize the challenges of using the dataset for refrigerant leak smoke detection;2) show the detection results of benchmark methods;and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the ACRL-10K dataset would hopefully advance research and applications in refrigerant leak early warning during air conditioner dismantling and recycling processes. © 2025 IEEE.

关键词： Air Conditioner Dataset Object Detection Refrigerant Leak Smoke Detection

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Cryptcoder: An Automatic Code Generator for Cryptographic Tasks in Ethereum Smart Contracts 31

Cryptcoder: An Automatic Code Generator for Cryptographic Ta...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Xia, Libin Zhang, Jiashuo Wang, Che Tan, Zezhong Gao, Jianbo Guan, Zhi Chen, Zhong Peking University MoE Key Laboratory of High Confidence Software Technologies Beijing China School of Computer Science Peking University Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350330663

Cryptographic APIs provided by Ethereum are widely adopted in decentralized applications (DApps) for cryptographic operations. However, developers who lack expertise in cryptography frequently encounter difficulties when working with low-level cryptographic APIs, thereby producing insecure code. To address this issue, we introduce Cryptcoder, an automatic code generator designed to bridge the gap between low-level cryptographic APIs and high-level cryptographic tasks in Ethereum. The fundamental component of Cryptcoder is Cryptlang, a Solidity-compatible domain-specific language (DSL) designed for cryptographic tasks. Developers can utilize Cryptlang for the straightforward and secure implementation of cryptographic tasks, such as signatures and commitments, and employ Cryptcoder for the automatic conversion into Solidity code. The evaluation of Cryptcoder demonstrates both its functionality in generating Solidity code and an acceptable overhead, evidenced by a mere 4% average increase in gas costs compared to the reference code. A demonstration video of Cryptcoder is available at https://***/AxhCdGiu7dw. © 2024 IEEE.

关键词： Smart contract

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Research and application of data-driven carbon emission analysis and prediction 2

Research and application of data-driven carbon emission anal...

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2nd International Conference on Cloud Computing, Big Data Application and software engineering, CBASE 2023

作者： Wei, Dongmei Meng, Dan Southwest University of Finance and Economics School of Management Science and Engineering Chengdu611130 China Xihua University School of Computer and Software Engineering Chengdu610039 China

ISBN: (纸本)9798350331448

The issue of carbon emissions has become an important policy direction for countries around the world to promote healthy socio-economic development and address climate change issues. This article conducts feature extraction and data analysis on various major energy data in China from 1997 to 2017, and constructs a model for verifying and predicting total carbon emissions. The model can not only accurately reflect China's carbon emissions over the past twenty years, but also predict future carbon emissions. This not only provides scientific basis for government decision-making, but also enriches the application of data-driven and artificial intelligence methods in this field. © 2023 IEEE.

关键词： Machine learning

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CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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computer Modeling in engineering & sciences 2023年第4期135卷 45-64页

作者： Zhiyun Yang Qi Liu HaoWu Xiaodong Liu Yonghong Zhang School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK School of Automation Nanjing University of Information Science TechnologyNanjing210044China

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy *** relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather ***,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous *** address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar *** practical radar echo datasets were used involving the FREM and CIKM 2017 *** quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,*** particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

关键词： Radar echo extrapolation attention mechanism long short-term memory deep learning

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Mathematical Modeling and Analysis of Patterns in Structured Collections of Big Data 13

Mathematical Modeling and Analysis of Patterns in Structured...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Borys Udovyk, Iryna Shvachych, Hennadii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

This paper addresses the issue of creating and applying mathematical models and methods for finding generalized solutions when working with structured collections of 'big data'. We reviewed the modern methodologies used to solve problems of this class. The mathematical model presented describes an ordered set of all subsets formed from a finite ordered base set of arbitrary size and data type. We explored a set of functional dependencies of five discrete input variables to work with this mathematical model. Some of these functional dependencies are derived for specific solutions with specified boundary conditions. The paper also presents examples of how the derived functional dependencies are applied in the implementation of mathematical methods using this model. This required us to conduct a comparative assessment of the search time for a solution with and without the use of these mathematical methods. Comparative graphs are demonstrated to show the rate of increase in the number of operations depending on the size of the original finite base set with and without the use of these mathematical methods. As a result of this, logical conclusions are drawn regarding the impact of mathematical methods for working with structured collections on minimizing time and computational resources. © 2023 IEEE.

关键词： Big data

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PSA-CLNet: Pedestrian Search Method Based on Polarized Self-Attention and COIM Loss 9

PSA-CLNet: Pedestrian Search Method Based on Polarized Self-...

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9th International Conference on Virtual Reality, ICVR 2023

作者： Fu, Junjie Liu, Rui Yi, Pengfei Dong, Jing Zhou, Dongsheng Wei, Xiaopeng Dalian University National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350345810

Pedestrian search is a combined task of pedestrian detection and pedestrian re-identification, i.e., how to find a specific pedestrian in a panoramic picture. However, due to the impact of different imaging conditions and environments, the accuracy of pedestrian search is still low at present, which is difficult to meet the needs of practical applications. To improve the accuracy of pedestrian search, this paper proposes PSA-CLNet pedestrian search method, which integrates polarized self-attention mechanism (PSA) and cosine online instance matching (COIM) loss. Firstly, the channel attention is added to enhance the pedestrian feature information and improve the expression ability of the feature information. Secondly, spatial attention is added to enhance the areas that have more contribution to the pedestrian search task to increase the correlation between the pictures and improve the recognition ability. Meanwhile, this paper proposes a COIM loss based on the online instance matching (OIM) loss. The loss function can enlarge the class distance between pedestrians and background, and reduce the class distance between pedestrians. This could improve the accuracy of pedestrian search algorithm effectively. Finally, this paper uses ResNet as the main backbone network to verify the proposed method. The pedestrian search method proposed in this paper could achieve 96.60% of mAP index and 96.83% of top-1 index in CUHK-SYSU dataset, and achieve 48.78% of mAP index and 88.38% of top-1 index in PRW dataset. © 2023 IEEE.

关键词： COIM loss pedestrian search PSA

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Weakly Supervised Myeloma Cells Segmentation based on Point Annotation

Weakly Supervised Myeloma Cells Segmentation based on Point ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Zhao, Jia Tong, Guanjie Qiu, Xinyun Su, Huaqiang Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen University Medical School Shenzhen China

ISBN: (纸本)9798350337488

Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN. © 2023 IEEE.

关键词： Cell Segmentation Point Annotation Weakly-supervised

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Motion path planning method based on map reference points

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Multimedia Tools and Applications 2024年 1-21页

作者： Yu, Wenbin Fan, Cheng Zhang, Chengjun Chen, Yadang Li, Yangsong Zhang, Yifan Yin, Na Cen, Xiaolin School of Software Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Wuxi Institute of Technology Nanjing University of Information Science and Technology Jiangsu Wuxi214000 China Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China School of Computer Science Northeastern University Boston02101 United States Computer Science and Engineering The University of New South Wales SydneyNSW02101 Australia

In recent years, significant progress has been made in the development of the Rapidly-exploring Random Trees (RRT) algorithm, a probabilistic sampling-based search approach. This algorithm effectively addresses complex challenges in path planning within high-dimensional spaces characterized by intricate constraints. However, the RRT algorithm faces certain limitations, including sluggish computational performance in handling intricate calculations, susceptibility to becoming trapped in confined path regions, and difficulties in generating randomized paths across a given map. To overcome these issues, this study introduces a motion path planning method based on map reference points. Initially, the algorithm selects strategic map reference points, followed by the utilization of an enhanced RRT algorithm for fundamental path selection. Real-world scenario data is collected to empirically evaluate the algorithm’s performance. The outcomes of the experiments reveal that, within the same experimental setting, the proposed method outperforms conventional motion path planning algorithms by effectively circumventing obstacles. Furthermore, it substantially reduces the resource wastage caused by global random sampling in the RRT algorithm, leading to expedited path planning and improved pathfinding success rates. Additionally, the proposed approach enhances the RRT algorithm’s adaptability to some extent, particularly in negotiating narrow passages and intricate environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision trees

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Low-Quality Ultrasound Image Enhancement Using Spatially Variable Neural Implicit Network

Low-Quality Ultrasound Image Enhancement Using Spatially Var...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xiaojie Xia, Jing Wu, Mei Gao, Shanshan Zhou, Yuanfeng Shandong University School of Software Jinan China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore The Second Hospital of Shandong University Ultrasound Department Jinan China Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350386226

Ultrasound images are widely used because they are easy to use and non-invasive. However, differences in acquisition equipment and frequency settings can result in varying image quality. To address this issue, we propose an ultrasound image enhancement method based on spatially variable neural implicit networks. Our approach consists of several steps: First, the encoder of an arbitrary-scale super-resolution network processes low-quality ultrasound images to generate a feature matrix. Concurrently, the parameter generation network decomposes the low-quality image into a parameter matrix. The pixel prediction module of the arbitrary-scale super-resolution network then uses the parameter matrix to map encoded features to pixel values. To further align the predicted pixel values with the distribution of high-quality ultrasound images and reduce interference from artifacts, we design a spectrum discriminator network based on the fast Fourier transform. This network is highly effective at capturing high-quality information and minimizing the impact of redundant information. In both qualitative and quantitative comparisons with current state-of-the-art image enhancement methods and arbitrary magnification techniques, our approach demonstrates superior performance. © 2024 IEEE.

关键词： Ultrasonic imaging

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