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International Workshop on Quality of Service

作者： Shuxin Ge Xiaobo Zhou Tie Qiu Guobin Wu Xiaotong Wang Tianjin Key Laboratory of Advanced Networking (TANK) College of Intelligence and Computing Tianjin University Tianjin China Beijing DiDi Infinity Technology and Development Co. Ltd. Beijing China

The mobility on demand (MoD) system relieves traffic pressure by simultaneously dispatching multiple orders to a vehicle via ridesharing. However, since the supply-demand distribution varies over time, existing dispatching methods for minimum fleet failed to achieve long-term supply-demand equilibrium, and thus greatly reduces the order completion rate. In this paper, ElasticShare, a ride-sharing order dispatch method, is proposed to maximize the order completion rate under dynamic supply-demand distribution. First, we formalize the ridesharing order dispatching problem as an offline optimization problem and then prove it can be solved in an online manner by adding dummy orders and vehicles satisfying long-term supply-demand equilibrium conditions when dispatching. Second, based on the proof, we generate a certain number of dummies according to the supply-demand relation to promote or restrain ridesharing in an elastic manner. The characteristics of the dummies are determined by statistics and a specific selection method to ensure that the solution approximates the long-term supply-demand equilibrium. Next, we decouple the online problem into two sub-problems (order associating and order association dispatching), which are solved by a greedy algorithm and correctional order association dispatching algorithm, respectively. Finally, simulations with real-world data are used to validate the superiority of ElasticShare over state-of-the-art solutions in terms of the order completion rate.

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models 39

CoMT: A Novel Benchmark for Chain of Multi-modal Thought on ...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

ISBN: (纸本)157735897X

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) Visual Creation, (2) Visual Deletion, (3) Visual Update, and (4) Visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LVLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Modal analysis

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Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

arXiv

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arXiv 2022年

作者： Zheng, Yaojia Liu, Zhouwu Mo, Rong Chen, Ziyi Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing MOE China The First Affiliated Hospital Sun Yat-sen University Guangzhou China

Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs. Different from existing anomaly detection strategies which do not consider any property of unavailable abnormal data during model development, a task-oriented self-supervised learning approach is proposed here which makes use of available normal EEGs and expert knowledge about abnormal EEGs to train a more effective feature extractor for the subsequent development of anomaly detector. In addition, a specific two-branch convolutional neural network with larger kernels is designed as the feature extractor such that it can more easily extract both larger-scale and small-scale features which often appear in unavailable abnormal EEGs. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs for development of anomaly detector based on normal data and future anomaly detection for new EEGs, as demonstrated on three EEG datasets. The code is available at https://***/ironing/EEG-AD. © 2022, CC BY.

关键词： Electroencephalography

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Deterministic Algorithms for the Hidden Subgroup Problem

SSRN

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SSRN 2022年

作者： Ye, Zekun Li, Lvzhou Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China

We consider deterministic algorithms for the well-known hidden subgroup problem (HSP): for a finite group G and a finite set X, given a function f : G → X and the promise that for any (Formula Presented) for a subgroup H ≤ G, the goal of the decision version is to determine whether H is trivial, and the goal of the search version is to find H. An algorithm for the problem should query f(g) for g ∈ G at least as possible. Nayak [? ] asked whether there exist deterministic algorithms with (Formula Presented) query complexity for HSP. We answer this problem completely by proving the following results, which also extend the main results of Ref. [? ], since here the algorithms do not rely on any prior knowledge of H. (i) When G is a general finite Abelian group, there exists an algorithm with (Formula Presented) queries to decide the triviality of H and an algorithm to find H with (Formula Presented) queries.(ii) In general, there is no deterministic algorithm for the search version of HSP with query complexity of (Formula Presented), since there exists an instance of HSP that needs (Formula Presented) queries to find H. f(x) is said to be (Formula Presented) if for every positive constant C, there exists a positive constant N such that for (Formula Presented), which means g is a strict lower bound for f.} On the other hand, there exist instances of HSP with query complexity far smaller than (Formula Presented), whose query complexity is (Formula Presented) and even O(1). © 2022, The Authors. All rights reserved.

关键词： Computational complexity

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A convolutional generative adversarial framework for data augmentation based on a robust optimal transport metric 23

A convolutional generative adversarial framework for data au...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Su, Liyilei Fu, Xianjun Hu, Qingmao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang College of Security Technology Wenzhou325016 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665494571

Enhancement of the vanilla generative adversarial network (GAN) to preserve data variability in the presence of real world noise is of paramount significance in deep learning. In this study, we proposed a new distance metric of cosine distance in the framework of optimal transport (OT), and presented and validated a convolutional neural network (CNN) based GAN framework. In comparison with state-of-the-art methods based on Graphics Processing Units (GPU), the proposed framework could maintain the data diversity and quality best in terms of inception score (IS), Fréchet inception distance (FID) and enhancing the classification network of bone age, and is robust to noise degradation. The proposed framework is independent of hardware and thus could also be extended to more advanced hardware such as specialized Tensor Processing Units (TPU), and could be a potential built-in component of a general deep learning networks for such applications as image classification, segmentation, registration, and object detection. © 2021 IEEE.

关键词： Measurement Deep learning Tensors Smart cities Neural networks Graphics processing units Object detection

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Conditional Image Hiding Network Based on Style Transfer

SSRN

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SSRN 2023年

作者： Zhang, Fenghua Feng, Bingwen Xia, Zhihua Weng, Jian Lu, Wei Chen, Bin College of Information Science and Technology Jinan University Guangzhou510632 China School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China School of Cyber Security Guangdong Polytechnic Normal University Guangzhou510665 China

Various data hiding methods have been suggested to hide secret images within stego images. However, many of them could be easily detected by steganalytic tools due to their large hiding information. In this paper, we enhance the undetectability of image hiding network by mapping latent representation conditional on secret information. We extend the idea of image generation-based steganography and propose a Transformer-based image hiding network that can hide a secret image with the same size as the target image. The proposed scheme uses style transferring to help map latent representation. The hiding network of the proposed scheme consists of three modules: encoding, transfer, and synthesis modules. The encoding module extracts the latent representations from content and secret images, the transfer module stylizes the latent representation, and the synthesis module fuses the the latent representations to synthesize a target image with the secret image hidden in it. A new synthesis module as well as the corresponding extraction network is designed to improve recovery accuracy. The proposed scheme shows high image quality on both target images and recovered secret images. Furthermore, it can resist steganalytic tools and thus provides good security. © 2023, The Authors. All rights reserved.

关键词： Steganography

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cRVR: A Cache-Friendly Approach to Enhancing Request Privacy for Online Video Services

arXiv

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arXiv 2023年

作者： Zhang, Xianzhi Xiao, Linchang Zhou, Yipeng Hu, Miao Wu, Di Lui, John C.S. Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China School of Computing Faculty of Science and Engineering Macquarie University 2122 Australia Department of Computer Science & Engineering Chinese University of Hong Kong Hong Kong

—As users conveniently stream their favorite online videos, video request records are automatically stored by video content providers, which have a high chance of privacy leakage. Unfortunately, most existing privacy-enhancing approaches are not applicable for protecting user privacy in video requests, because they cannot be easily altered or distorted by users and must be visible for content providers to stream correct videos. To preserve request privacy in online video services, it is possible to request additional videos that are irrelevant to users’ interests so that content providers cannot precisely infer users’ interest information. However, a naive redundant requesting approach would significantly degrade the performance of edge caches and increase bandwidth overhead. In this paper, we are among the first to propose a Cache-Friendly Redundant Video Requesting (cRVR) algorithm for User Devices (UDs) and its corresponding caching algorithm for the Edge Cache (EC), which can effectively mitigate the problem of request privacy leakage with minimal impact on the EC’s performance. To tackle the problem, we first develop a Stackelberg game to analyze the dedicated interaction between UDs and EC, and obtain their optimal strategies to maximize their respective utility. For UDs, the utility function is a combination of both video playback utility and privacy protection utility. We prove the existence and uniqueness of the equilibrium of the Stackelberg game. Extensive experiments are conducted with real traces to demonstrate that cRVR can effectively protect video request privacy by reducing up to 59.03% of privacy disclosure compared to baseline algorithms. Meanwhile, the caching performance of EC is only slightly affected. Copyright © 2023, The Authors. All rights reserved.

关键词： Differential privacy

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An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients’ Motor Execution from Multi-Channel EEG-Recordings

An Efficient Deep Transfer Learning Network for Characteriza...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Oluwarotimi Williams Samuel Mojisola Grace Asogbon Frank Kulwa Alistair A. McEwan Sunday Timothy Aboyeji Rami Khushaba Peng Fang Guanglin Li School of Computing and Data Science Research Centre University of Derby Derby United Kingdom Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS Shenzhen China Australian Centre for Field Robotics Sydney University Chippendale NSW Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients’ limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP’s motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP’s motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function.

关键词： Training Accuracy Transfer learning Stroke (medical condition) Motors Assistive robots Electroencephalography Decoding Trajectory Robots

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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