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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Xie Yihong Lin Wenjie Cai Xuemiao Xu Huaidong Zhang Yong Du Shengfeng He South China University of Technology Independent Researcher State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Ministry of Education Key Laboratory of Big Data and Intelligent Robot Ocean University of China Singapore Management University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these one-to-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is then decomposed into three components: feature representation knowledge, inconsistent pairwise similarity differential knowledge, and consistent pairwise similarity differential knowledge. This strategic decomposition aligns the retrieval ranking of the query network with the gallery network effectively. Extensive experiments on various bench-mark datasets reveal that D3still surpasses state-of-the-art methods in asymmetric image retrieval. Code is available at https://***/SCY-X/D3still.

关键词： Computer vision Codes Accuracy Image retrieval Pattern recognition Matrix decomposition Knowledge transfer

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Improving Cancer Gene Identification by Enhancing Common Information Between the PPI Network and Gene Functional Association 39

Improving Cancer Gene Identification by Enhancing Common Inf...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Deng, Chao Li, Hongdong Wang, Jianxin School of Computer Science and Engineering Central South University Changsha 410083 China Hunan Provincial Key Lab on Bioinformatics Central South University Changsha 410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi 830091 China

ISBN: (纸本)157735897X

Identifying cancer genes is crucial for treatment and understanding pathogenesis. Recent methods typically leverage protein-protein interaction (PPI) networks or gene functional association data from annotated gene sets. There may be some shared neighborhood structure information between these two types of gene association data. While this common information may contain more accurate gene association information, existing methods often overlook this potential. To address this gap, we introduce DISFusion, which integrates multi-omics cancer data, PPI networks, and gene functional associations to identify cancer genes. A key innovation of DISFusion is the cross-view decorrelation loss, which enhances the common information between PPI networks and gene functional associations, thereby improving prediction accuracy. Extensive experiments indicate that DISFusion outperforms state-of-the-art methods and exhibits greater generalization ability. Moreover, analysis of CPTAC pan-cancer proteomic data highlights significant associations between the 30 novel cancer genes predicted by DISFusion and multiple cancer types, underscoring its practical utility. These findings validate the effectiveness of enhancing common information and provide new insights into cancer gene identification. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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OCRNet for Diabetic Foot Ulcer Segmentation Combined with Edge Loss 3rd

OCRNet for Diabetic Foot Ulcer Segmentation Combined with ...

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

作者： Yi, Huahui Xu, Wei Jiang, Zekun Gao, Jun Kang, Qingbo Lao, Qicheng Li, Kang West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu610041 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China West China Hospital-SenseTime Joint Lab Sichuan Chengdu610041 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

ISBN: (纸本)9783031263538

Diabetic foot ulcer is a serious manifestation of lesions on the diabetic foot that requires close monitoring and management. The research at hand investigates an approach on segmentation of diabetic foot ulcer area, conducted as part of the Diabetic Foot Ulcer Challenge (DFUC) 2022. We use OCRNet as the baseline for segmentation and a powerful ConvNeXt network was adopted as the backbone. To obtain better results, a boundary loss was introduced to further constrain the boundary of segmentation. In addition, gamma correction was used in the inference stage in order to reduce the difference in luminance between the training, validation and test sets. Our method won 2nd place in the DFUC2022 with a Dice score of 72.80%. Source code is available at: DFUC2022SegmentationOcrnet. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Hybrid Approach for Many-Objective Feature Selection in Intrusion Detection on Windows Operating Systems

A Hybrid Approach for Many-Objective Feature Selection in In...

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： J. Benítez Francisco P. Pinto-Roa Diego García-Torres Miguel B. D. Parameshachari Facultad de Ciencias y Tecnologías Universidad Autónoma de Asunción Asunción Paraguay Facultad Politécnica Universidad Nacional de Asunción Campus San Lorenzo Paraguay Data Science and Big Data Lab Pablo de Olavide University Seville Spain Nitte Meenakshi Institute of Technology Bengaluru India

The exponential increase in devices connected to the Internet has rendered them vulnerable to various types of cyberattacks, disrupting their proper functioning. As a result, it is crucial to have reliable Intrusion Detection Systems (IDS) that can identify malicious activities on the network. However, the unpredictable nature of network behavior and the large volume of data to be audited pose significant challenges. This work aims to address these challenges by eliminating redundant data features, identifying a subset of relevant and representative features that enhance the anomaly detection performance of an IDS using many-objective optimization algorithms. In this work, we have analyzed the recent TON IoT dataset, which collects monitoring data on devices using Windows and Linux operating systems. The NSGA-II, NSGA-III, RVEA, and MOEA/D were used as many-objective feature selection algorithms. Our computational simulations identified good subsets of features that showed better classification performance and accuracy in comparison to the complete set of features. Notably, the results obtained using the NSGA-II stood out from the others.

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KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph

arXiv

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arXiv 2024年

作者： Jiang, Jinhao Zhou, Kun Zhao, Wayne Xin Song, Yang Zhu, Chen Zhu, Hengshu Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China School of Information Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China NLP Center BOSS Zhipin China Career Science Lab BOSS Zhipin China

In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that design the interaction strategy between LLMs and KG, we propose an autonomous LLM-based agent framework, called KG-Agent, which enables a small LLM to actively make decisions until finishing the reasoning process over KGs. In KG-Agent, we integrate the LLM, multifunctional toolbox, KG-based executor, and knowledge memory, and develop an iteration mechanism that autonomously selects the tool then updates the memory for reasoning over KG. To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG, and synthesize a code-based instruction dataset to fine-tune the base LLM. Extensive experiments demonstrate that only using 10K samples for tuning LLaMA-7B can outperform state-of-theart methods using larger LLMs or more data, on both in-domain and out-domain datasets. Our code and data will be publicly released. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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CovKG: A Covid-19 Knowledge Graph for enabling multidimensional analytics on Covid-19 epidemiological data considering spatiotemporal, environmental, health, and socioeconomic aspects

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International Journal of Information Management data Insights 2025年第1期5卷

作者： Deb Nath, Rudra Pratap Raihan, S.M. Shafkat Das, Tonmoy Chandro Pedersen, Torben Bach Ghose, Debasish Big Data Information and Knowledge Engineering Lab Department of Computer Science and Engineering University of Chittagong Chattogram 4331 Bangladesh Department of Computer Science Aalborg University Aalborg 9220 Denmark School of Economics Innovation and Technology Kristiania University College State Three Bergen 0153 Norway

The Covid-19 pandemic is influenced by many environmental, health, and socioeconomic aspects such as air pollution, comorbidity, occupation, etc. To better manage future pandemics, decision-makers need comprehensive data on Covid-19 mortality and morbidity. Most Covid-19 data sources focus on spatiotemporal aspects, and existing research often overlook the combined impact of multiple interconnected factors. This study introduces a Covid-19 Knowledge Graph (CovKG) derived from 20 data sources, enabling multidimensional analysis of epidemiological data, including time, location, temperature, comorbidity, occupation, and others. CovKG is modeled using RDF, connected to 10,951 external resources, and semantically enriched with data Cube (QB) and QB for OLAP (QB4OLAP) vocabularies to adhere to the FAIR principles and ensure OLAP compatibility. Finally, we perform a qualitative and comparative evaluation and extract statistical insights across multiple dimensions of Covid-19 epidemiology. When assessed, CovKG answers 100% of competency queries, outperforming other data stores that only answer 39%. CovKG and its analytical interface are available at https://***/datasets/CovKG/. © 2025 The Authors

关键词： Covid-19 FAIR principles Knowledge Graph Linked data Multidimensional analysis Semantic technology

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DUALVC 2: DYNAMIC MASKED CONVOLUTION FOR UNIFIED STREAMING AND NON-STREAMING VOICE CONVERSION

arXiv

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arXiv 2023年

作者： Ning, Ziqian Jiang, Yuepeng Zhu, Pengcheng Wang, Shuai Yao, Jixun Xie, Lei Bi, Mengxiao School of Computer Science Northwestern Polytechnical University Xi’an China Fuxi AI Lab NetEase Inc. Hangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China

Voice conversion is becoming increasingly popular, and a growing number of application scenarios require models with streaming inference capabilities. The recently proposed DualVC attempts to achieve this objective through streaming model architecture design and intra-model knowledge distillation along with hybrid predictive coding to compensate for the lack of future information. However, DualVC encounters several problems that limit its performance. First, the autoregressive decoder has error accumulation in its nature and limits the inference speed as well. Second, the causal convolution enables streaming capability but cannot sufficiently use future information within chunks. Third, the model is unable to effectively address the noise in the unvoiced segments, lowering the sound quality. In this paper, we propose DualVC 2 to address these issues. Specifically, the model backbone is migrated to a Conformer-based architecture, empowering parallel inference. Causal convolution is replaced by non-causal convolution with a dynamic chunk mask to make better use of within-chunk future information. Also, quiet attention is introduced to enhance the model’s noise robustness. Experiments show that DualVC 2 outperforms DualVC and other baseline systems in both subjective and objective metrics, with only 186.4 ms latency. Our audio samples are made publicly available1 Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling

Weakly-Supervised Action Localization by Hierarchically-stru...

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International Conference on Computer Vision (ICCV)

作者： Guiqin Wang Peng Zhao Cong Zhao Shusen Yang Jie Cheng Luziwei Leng Jianxing Liao Qinghai Guo School of Computer Science and Technology Xi’an Jiao Tong University School of Mathematics and Statistics Xi’an Jiao Tong University National Engineering Laboratory for Big Data Analytics Xi’an Jiao Tong University ACS Lab Huawei Technologies

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods.

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GraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow

GraphFly: Efficient Asynchronous Streaming Graphs Processing...

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Supercomputing Conference

作者： Dan Chen Chuangyi Gui Yi Zhang Hai Jin Long Zheng Yu Huang Xiaofei Liao National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665454452

Existing streaming graph processing systems typically adopt two phases of refinement and recomputation to ensure the correctness of the incremental computation. However, severe redundant memory accesses exist due to the unnecessary synchronization among independent edge updates. In this paper, we present GraphFly, a high-performance asynchronous streaming graph processing system based on dependency-flows. GraphFly features three key designs: 1) Dependency trees (D-trees), which helps quickly identify independent graph updates with low cost; 2) Dependency-flow based processing model, which exploits the space-time dependent co-scheduling for cache efficiency; 3) Specialized graph data layout, which further reduces memory accesses. We evaluate GraphFly, and the results show that GraphFly significantly outperforms state-of-the-art systems KickStarter and GraphBolt by 5.81× and 1.78× on average, respectively. Also, GraphFly scales well with different sizes of update batch and compute resources.

关键词： Costs High performance computing Memory management Layout data models Synchronization

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Efficient mobile code offloading in heterogeneous wireless networks

Efficient mobile code offloading in heterogeneous wireless n...

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作者： Lu, Feng Liu, Ruoxue Li, Wei Jin, Hai Zomaya, Albert Y. Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Center for Distributed and High Performance Computing School of Information Technologies The University of Sydney Sydney Australia

Mobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with *** technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data-intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m2. Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3× over the single application and can save an average of 25% power over the single application. © 2019 John Wiley & Sons, Ltd.

关键词： Heterogeneous networks

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