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arXiv 2023年

作者： Zhang, Xiaohan Li, Xingyu Sultani, Waqas Chen, Chen Wshah, Safwan Department of Computer Science and Vermont Complex Systems Center University of Vermont Burlington United States Lin Gang Laboratory Shanghai China Intelligent Machine Lab Information Technology University Pakistan Center for Research in Computer Vision University of Central Florida United States

Cross-View Geo-Localization (CVGL) estimates the location of a ground image by matching it to a geo-tagged aerial image in a database. Recent works achieve outstanding progress on CVGL benchmarks. However, existing methods still suffer from poor performance in cross-area evaluation, in which the training and testing data are captured from completely distinct areas. We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details. Our preliminary work [1] introduced a Geometric Layout Extractor (GLE) to capture the geometric layout from input features. However, the previous GLE does not fully exploit information in the input feature. In this work, we propose GeoDTR+ with an enhanced GLE module that better models the correlations among visual features. To fully explore the LS techniques from our preliminary work, we further propose Contrastive Hard Samples Generation (CHSG) to facilitate model training. Extensive experiments show that GeoDTR+ achieves state-of-the-art (SOTA) results in cross-area evaluation on CVUSA [2], CVACT [3], and VIGOR [4] by a large margin (16.44%, 22.71%, and 13.66% without polar transformation) while keeping the same-area performance comparable to existing SOTA. Moreover, we provide detailed analyses of GeoDTR+. Our code will be available at https://***/vail-uvm/geodtr_plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Antenna grounds

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BinaryAI: Binary Software Composition Analysis via intelligent Binary Source Code Matching 24

BinaryAI: Binary Software Composition Analysis via Intellige...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Jiang, Ling An, Junwen Huang, Huihui Tang, Qiyi Nie, Sen Wu, Shi Zhang, Yuqun Southern University of Science and Technology Shenzhen China Tencent Security Keen Lab Shanghai China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technol-ogy Department of Computer Science and Engineering Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China

ISBN: (纸本)9798400702174

While third-party libraries (TPLs) are extensively reused to enhance productivity during software development, they can also introduce potential security risks such as vulnerability propagation. Software composition analysis (SCA), proposed to identify reused TPLs for reducing such risks, has become an essential procedure within modern DevSecOps. As one of the mainstream SCA techniques, binary-to-source SCA identifies the third-party source projects contained in binary files via binary source code matching, which is a major challenge in reverse engineering since binary and source code exhibit substantial disparities after compilation. The existing binary-to-source SCA techniques leverage basic syntactic features that suffer from redundancy and lack robustness in the large-scale TPL dataset, leading to inevitable false positives and compromised recall. To mitigate these limitations, we introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. First, BinaryAI trains a transformer-based model to produce function-level embeddings and obtain similar source functions for each binary function accordingly. Then by applying the link-time locality to facilitate function matching, BinaryAI detects the reused TPLs based on the ratio of matched source functions. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task. Specifically, our embedding model outperforms the state-of-the-art model CodeCMR, i.e., achieving 22.54% recall@l and 0.34 MRR compared with 10.75% and 0.17 respectively. Additionally, BinaryAI outperforms all existing binary-to-source SCA tools in TPL detection, increasing the precision from 73.36% to 85.84% and recall from 59.81% to 64.98% compared with the well-recognized commercial SCA product Black Duck. E-https://*** © 2024 ACM.

关键词： Syntactics

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FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks 25

FediLive: A Framework for Collecting and Preprocessing Snaps...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Shaojie Min Shaobin Wang Yaxiao Luo Min Gao Qingyuan Gong Yu Xiao Yang Chen Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (纸本)9798400713316

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.

关键词： decentralized online social networks

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ReLU and Addition-based Gated RNN

arXiv

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arXiv 2023年

作者： Brännvall, Rickard Forsgren, Henrik Sandin, Fredrik Liwicki, Marcus Department of Computer Science RISE Research Institutes of Sweden Luleå Sweden Embedded Intelligent Systems LAB Luleå University of Technology Luleå Sweden

We replace the multiplication and sigmoid function of the conventional recurrent gate with addition and ReLU activation. This mechanism is designed to maintain long-term memory for sequence processing but at a reduced computational cost, thereby opening up for more efficient execution or larger models on restricted hardware. Recurrent Neural Networks (RNNs) with gating mechanisms such as LSTM and GRU have been widely successful in learning from sequential data due to their ability to capture long-term dependencies. Conventionally, the update based on current inputs and the previous state history is each multiplied with dynamic weights and combined to compute the next state. However, multiplication can be computationally expensive, especially for certain hardware architectures or alternative arithmetic systems such as homomorphic encryption. It is demonstrated that the novel gating mechanism can capture long-term dependencies for a standard synthetic sequence learning task while significantly reducing computational costs such that execution time is reduced by half on CPU and by one-third under encryption. Experimental results on handwritten text recognition tasks furthermore show that the proposed architecture can be trained to achieve comparable accuracy to conventional GRU and LSTM baselines. The gating mechanism introduced in this paper may enable privacy-preserving AI applications operating under homomorphic encryption by avoiding the multiplication of encrypted variables. It can also support quantization in (unencrypted) plaintext applications, with the potential for substantial performance gains since the addition-based formulation can avoid the expansion to double precision often required for multiplication. © 2023, CC BY.

关键词： Cryptography

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VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

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Frontiers of computer Science 2023年第6期17卷 13-24页

作者： Xumeng WANG Ziliang WU Wenqi HUANG Yating WEI Zhaosong HUANG Mingliang XU Wei CHEN College of Computer Science Nankai UniversityTianjin 300381China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Digital Grid Research Institute China Southern Power GridGuangzhou 510670China Huawei Technologies Co. Ltd.Hangzhou 310052China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Engineering Research Center of Ministry of Education on Intelligent Swarm Systems Zhengzhou UniversityZhengzhou 450001China National Supercomputing Center in Zhengzhou Zhengzhou 450001China Laboratory of Art and Archaeology Image Zhejiang UniversityHangzhou 310058China

Visualization and artificial intelligence(AI)are well-applied approaches to data *** one hand,visualization can facilitate humans in data understanding through intuitive visual representation and interactive *** the other hand,AI is able to learn from data and implement bulky tasks for *** complex data analysis scenarios,like epidemic traceability and city planning,humans need to understand large-scale data and make decisions,which requires complementing the strengths of both visualization and *** studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as ***,how can AI and visualization complement each other and be integrated into data analysis processes are still *** this paper,we define three integration levels of visualization and *** highest integration level is described as the framework of VIS+AI,which allows AI to learn human intelligence from interactions and communicate with humans through visual *** also summarize future directions of VIS+AI to inspire related studies.

关键词： visualization artificial intelligence data analysis knowledge generation

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Directed Acyclic Transformer for Non-Autoregressive Machine Translation 39

Directed Acyclic Transformer for Non-Autoregressive Machine ...

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39th International Conference on Machine Learning, ICML 2022

作者： Huang, Fei Zhou, Hao Liu, Yang Li, Hang Huang, Minlie The CoAI Group Tsinghua University China Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University China ByteDance AI Lab

Non-autoregressive Transformers (NATs) significantly reduce the decoding latency by generating all tokens in parallel. However, such independent predictions prevent NATs from capturing the dependencies between the tokens for generating multiple possible translations. In this paper, we propose Directed Acyclic Transfomer (DA-Transformer), which represents the hidden states in a Directed Acyclic Graph (DAG), where each path of the DAG corresponds to a specific translation. The whole DAG simultaneously captures multiple translations and facilitates fast predictions in a non-autoregressive fashion. Experiments on the raw training data of WMT benchmark show that DA-Transformer substantially outperforms previous NATs by about 3 BLEU on average, which is the first NAT model that achieves competitive results with autoregressive Transformers without relying on knowledge distillation. Copyright Â© 2022 by the author(s)

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FedPerfix: Towards Partial Model Personalization of vision Transformers in Federated Learning

arXiv

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arXiv 2023年

作者： Sun, Guangyu Mendieta, Matias Luo, Jun Wu, Shandong Chen, Chen Center for Research in Computer Vision University of Central Florida United States Intelligent Systems Program University of Pittsburgh United States Department of Radiology Biomedical Informatics and Bioengineering University of Pittsburgh United States

Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed to improve the efficiency of PFL by selectively updating local model parameters instead of aggregating all of them. However, previous work on partial model personalization has mainly focused on Convolutional Neural Networks (CNNs), leaving a gap in understanding how it can be applied to other popular models such as vision Transformers (ViTs). In this work, we investigate where and how to partially personalize a ViT model. Specifically, we empirically evaluate the sensitivity to data distribution of each type of layer. Based on the insights that the self-attention layer and the classification head are the most sensitive parts of a ViT, we propose a novel approach called FedPerfix, which leverages plugins to transfer information from the aggregated model to the local client as a personalization. Finally, we evaluate the proposed approach on CIFAR-100, OrganAMNIST, and Office-Home datasets and demonstrate its effectiveness in improving the model’s performance compared to several advanced PFL methods. Code is available at https://***/imguangyu/FedPerfix Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Lightweight Attention Network for Camouflaged Object Fixation Prediction

A Lightweight Attention Network for Camouflaged Object Fixat...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Qingbo Wu Guanxing Wu Shengyong Chen Key Laboratory of Computer Vision and Systems (Ministry of Education) Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper proposes a novel encoder-decoder architecture for camouflaged object fixation prediction (COFP), which builds on a large-kernel decomposition technique and depth-wise separable convolution embedded attention. Specifically, the encoder with the large-kernel decomposition adopts a Visual Attention Network (VAN) as the backbone to extract more accurate features, and the decoder predicts fixation maps from concealed objects using the attention module incorporating depth-wise separable convolution. Both designs aim to reduce the number of learnable parameters, making the model lighter and simultaneously maintaining the performance of COFP. In addition, we propose using the reverse Kullback-Leibler Divergence (KLDiv) as the loss function. Extensive experiments demonstrate that the proposed method performs well against SOTA approaches in quality and quantity.

关键词： Industrial electronics Visualization Accuracy Convolution Feature extraction Decoding

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OPTIHROV: Optically Linked Hybrid Autonomous/Remotely Operated Vehicle, Beyond Teleoperation in a New Generation of Underwater Intervention Vehicles

OPTIHROV: Optically Linked Hybrid Autonomous/Remotely Operat...

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OCEANS

作者： Roger Pi Joan Esteba Patryk Cieslak Narcís Palomeras Pedro J. Sanz Raúl Marín Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) Universitat de Girona Girona Interactive and Robotic Systems Lab (IRS-Lab) Universitat Jaume I Castelló de la Plana

OPTIHROV is a proof-of-concept project to demonstrate a telerobotic system, based on an untethered, OPTIcally-connected, Hybrid Autonomous-ROV, that aims to replace light intervention ROVs operating at close range to networked infrastructures. The system is designed to bridge the gap between current ROVs, tethered to a Docking Station, and the dream of the future Intervention Autonomous Underwater Vehicle (I-AUV). OPTIHROV is a telerobotic system to perform inspection and light intervention tasks in potentially unknown and unstructured environments. It uses human-robot interaction (HRI) methods that allow the user to remotely guide the robot by launching a set of sensory-motion primitives performed by the robot *** paper presents a proof of concept demonstration of the OPTIHROV concept, where the wireless Hybrid Remotely Operated Vehicle (HROV), located in the test tank of the Universitat de Girona (UdG), is controlled/supervised from the Universitat Jaume I (UJI) in Castelló de la Plana, about 330 km away.

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End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization

arXiv

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arXiv 2022年

作者： Vödisch, Niclas Unal, Ozan Li, Ke Van Gool, Luc Dai, Dengxin The Computer Vision Lab ETH Zürich Switzerland Autonomous Intelligent Systems University of Freiburg Germany Processing Speech and Images KU Leuven Belgium Vision for Autonomous Systems MPI for Informatics Saarbrücken Germany

Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distributed. Those fixed configurations are task-agnostic, so simply using them can lead to sub-optimal performance. In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application. Specifically, we propose a reinforcement learning-based learning-to-optimize (RL-L2O) framework to automatically optimize the beam configuration in an end-to-end manner for different LiDAR-based applications. The optimization is guided by the final performance of the target task and thus our method can be integrated easily with any LiDAR-based application as a simple drop-in module. The method is especially useful when a low-resolution (low-cost) LiDAR is needed, for instance, for system deployment at a massive scale. We use our method to search for the beam configuration of a low-resolution LiDAR for two important tasks: 3D object detection and localization. Experiments show that the proposed RL-L2O method improves the performance in both tasks significantly compared to the baseline methods. We believe that a combination of our method with the recent advances of programmable LiDARs can start a new research direction for LiDAR-based active perception. The code is publicly available at ***/vniclas/lidar_beam_selection. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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