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arXiv 2021年

作者： Díaz Ferreyra, Nicolás E. Hecking, Tobias Aïmeur, Esma Heisel, Maritta Hoppe, H. Ulrich University of Duisburg-Essen Department of Computer Science and Applied Cognitive Science Duisburg47057 Germany Institute for Software Technology Intelligent and Distributed Systems German Aerospace Center Köln51147 Germany Department of Computer Science and Operations Research University of Montréal Canada

Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination. Copyright © 2021, The Authors. All rights reserved.

关键词： Access control

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Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN

arXiv

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arXiv 2024年

作者： Li, Hao Deuser, Fabian Yin, Wenping Luo, Xuanshu Walther, Paul Mai, Gengchen Huang, Wei Werner, Martin School of Engineering and Design Technical University of Munich Bavaria Munich85521 Germany Institute of Distributed Intelligent Systems University of the Bundeswehr Munich Bavaria Neubiberg85579 Germany School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou221116 China Spatially Explicit Artificial Intelligence Lab Department of Geography and the Environment University of Texas at Austin AustinTX78712 United States College of Surveying and Geo-Informatics Tongji University Shanghai200092 China

Nature disasters play a key role in shaping human-urban infrastructure interactions, Effective and efficient response to natural disasters is essential for building resilience and sustainable urban environment. Two types of information are usually the most necessary and difficult to gather in disaster response. The first information is about the disaster damage perception, which shows how badly people think that urban infrastructure has been damaged. The second information is geolocation awareness, which means how people's whereabouts are made available. In this paper, we proposed a novel disaster mapping framework, namely CVDisaster, aiming at simultaneously addressing geolocalization and damage perception estimation using cross-view Street-View Imagery (SVI) and Very High-Resolution satellite imagery. CVDisaster consists of two cross-view models, where CVDisaster-Geoloc refers to a cross-view geolocalization model based on a contrastive learning objective with a Siamese ConvNeXt image encoder and CVDisaster-Est is a cross-view classification model based on a Couple Global Context Vision Transformer (CGCViT). Taking Hurrican IAN as a case study, we evaluate the CVDisaster framework by creating a novel cross-view dataset (CVIAN) and conducting extensive experiments. As a result, We show that CVDisaster can achieve highly competitive performance (over 80% for geolocalization and 75% for damage perception estimation) with even limited fine-tuning efforts, which largely motivates future cross-view models and applications within a broader GeoAI research community. The data and code are publicly available at: https://***/tumbgd/CVDisaster. © 2024, CC BY.

关键词： Satellite imagery

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One-shot Implicit Animatable Avatars with Model-based Priors

arXiv

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arXiv 2022年

作者： Huang, Yangyi Yi, Hongwei Liu, Weiyang Wang, Haofan Wu, Boxi Wang, Wenxiao Lin, Binbin Zhang, Debing Cai, Deng State Key Lab of CAD & CG Zhejiang University China Max Planck Institute for Intelligent Systems Tübingen Germany University of Cambridge United Kingdom Xiaohongshu Inc China School of Software Technology Zhejiang University China Fullong Inc China

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pretrained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJUMoCAP, Human3.6M, and DeepFashion, show that ELICIT outperforms strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://huangyangyi. ***/ELICIT © 2022, CC BY.

关键词： Semantics

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DISC-LawLLM: Fine-tuning Large Language Models for intelligent Legal Services

arXiv

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arXiv 2023年

作者： Yue, Shengbin Chen, Wei Wang, Siyuan Li, Bingxuan Shen, Chenchen Liu, Shujun Zhou, Yuxuan Xiao, Yao Yun, Song Huang, Xuanjing Wei, Zhongyu Research Institute of Intelligent Complex Systems Fudan University China Shanghai Center for Mathematical Sciences Fudan University China School of Software Engineering Huazhong University of Science and Technology China School of Data Science Fudan University China School of Computer Science Fudan University China Rule of Law Institute Northwest University of Political and Law China New York University Shanghai China

We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://***/FudanDISC/DISC-LawLLM. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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FEDCSD: A FEDERATED LEARNING BASED APPROACH FOR CODE-SMELL DETECTION

arXiv

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arXiv 2023年

作者： Alawadi, Sadi Alkharabsheh, Khalid Alkhabbas, Fahed Kebande, Victor Awaysheh, Feras M. Palomba, Fabio Awad, Mohammed Center for Applied Intelligent Systems Research School of Information Technology Halmstad University Halmstad Sweden Department of Software Engineering Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Jordan Internet of Things and People Research Center Malmö University Sweden Department of Computer Science Blekinge Institute of Technology Sweden Institute of Computer Science Delta Research Centre University of Tartu Estonia SeSa Lab University of Salerno Italy Arab American University Palestine Palestine

software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model’s highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer software selection and evaluation

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

arXiv

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arXiv 2022年

作者： Ding, Lige Zhao, Dong Wang, Zhaofeng Wang, Guang Tan, Chang Fan, Lei Ma, Huadong Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge02139 United States iFLYTEK Research IFLYTEK Co. Ltd Anhui Hefei230088 China

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people’s lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

arXiv

引用

arXiv 2022年

作者： Suo, Jiashun Wang, Tianyi Zhang, Xingzhou Chen, Haiyang Zhou, Wei Shi, Weisong Engineering Research Center of Cyberspace Yunnan University Kunming650091 China School of Software Yunnan University Kunming650091 China Research Center of Distributed Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer and Information Sciences University of Delaware NewarkDE19716 United States

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2, 898 infrared thermal images extracted from 43, 470 frames in hundreds of videos captured by UAVs in various scenarios including schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, such as flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://***/suojiashun/HIT-UAV-Infrared-Thermal-Dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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A Life Cycle Oriented Data-Driven Architecture for an Advanced Circular Economy

引用

Procedia CIRP 2021年 98卷 318-323页

作者： Lars Kintscher Sebastian Lawrenz Hendrik Poschmann Institute of Distributed Systems Ostfalia University of Applied Sciences Salzdahlumer Straße 46/48 38302 Wolfenbüttel Germany Institue for Software and Systems Engineering Clausthal University of Technology Arnold-Sommerfeld-Straße 1 38678 Clausthal-Zellerfeld Germany Institute of Production Technology Ostfalia University of Applied Sciences Salzdahlumer Straße 46/48 38302 Wolfenbüttel Germany

Product recycling will become more important in the future to meet the needs of a growing industry. The challenge is to combine this heterogeneous information flows and establish a way for a holistic life cycle management in the circular economy. Therefore, this contribution proposes an automated and data-driven architecture of connecting all the stakeholders to exchange (life cycle) data in order to foster transition towards a circular economy. A general framework is implemented and evaluated on the example of an electric vehicle battery.

关键词： Industry 4.0 intelligent systems recycling information marketplace sustainability engineering

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Method for detecting surface defects of ceramic tile based on improved Cascade RCNN

Method for detecting surface defects of ceramic tile based o...

引用

Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Yu Cao Yu Wang Haiwen Feng Ting Wang School of Software College Shenyang University of Technology Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

In view of the problems of small scale and low contrast of ceramic tile defects, various types of ceramic tile surface defects, and difficulty in realizing high-precision ceramic tile defect detection, a ceramic tile surface defect detection model based on improved Cascade RCNN is proposed to locate and identify the types of ceramic tile surface defects in different texture backgrounds. The improved ResNest network is used to improve the classification ability of the algorithm and optimize the performance of the model. The improved feature pyramid enhanced the feature extraction ability of the algorithm, and improved the accuracy of detecting small-scale defects and low-contrast defects of ceramic tiles. The whole connection structure of the last layer of cascade detectors was modified to double-head structure, which improved the ability of detectors to perform classification and regression tasks, and solved the problem of various kinds of defects on the surface of ceramic tiles. After data collection, 2810 tiles defect pictures were obtained, and then 7934 tiles defect slices were obtained by image segmentation, and the slices were made into tiles defect data sets. Experiments show that this method can achieve 77.8 % MAP and 93.9% average positive detection rate, which is higher than Faster RCNN and original Cascade RCNN.

关键词： Training Image segmentation Tiles Detectors Feature extraction Data models Ceramics

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