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data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Xinyu Tian Zesen Zhuang Luyao Zhang Department of Computer Science Duke University Durham United States Pratt School of Engineering Duke University Durham United States Data Science Research Center and Social Science Division Duke Kunshan University Suzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

The Merge changes Ethereum from Proof-of-Work (PoW) to the more secure and less energy-intensive Proof-of-Stake (PoS) mechanism. However, the existence of malicious valida tors still threatens the security of Ethereum, primarily through a discouragement attack. How can we redesign the incentive mech-anism in PoS Ethereum for a more secure blockchain? For this quest, we, for the first time, apply the cutting-edge reinforcement mechanism design method-an interdisciplinary approach at the intersection of reinforcement learning (RL) and mechanism design-to staking mechanism designs. We abstract a generalized staking mechanism as a game environment and implement an RL method for the blockchain as a mechanism designer to explore the optimal incentive design. Our reinforcement mechanism design outperforms the status quo in cultivating honest validators. Furthermore, we identify Advantage Actor-Critic (A2C) as the most efficient RL algorithm among the three alternatives, which intuitively performs better when the initial proportion of honest validator is larger. Our interdisciplinary approach of generalized abstraction could be adapted to analyze the incentive design in any PoS blockchain and beyond.

关键词： Proof of stake Mechanism design Reinforcement learning Games Proof of Work Security Complex systems Optimization

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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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GCLS2: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics

arXiv

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arXiv 2024年

作者： Wen, Qi Zhang, Yiyang Ye, Yutong Zhou, Yingbo Zhang, Nan Lian, Xiang Chen, Mingsong MoE Engineering Research Center on HW/SW Co-design and Application East China Normal University Shanghai China Department of Computer Science Kent State University KentOH44242 United States

Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structural semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities. © 2024, CC BY.

关键词： Contrastive Learning

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Attention in Attention for PET-CT Modality Consensus Lung Tumor Segmentation

Attention in Attention for PET-CT Modality Consensus Lung Tu...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuzhou Zhao Xinyu Zhou Haijing Guo Qianyu Guo Yan Zuo Shaoli Song Shuyong Gao Wenqiang Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Department of Nuclear Medicine Fudan University Shanghai Cancer Center China Shanghai Engineering Research Center of AI Robotics Academy for Engineering and Technology Fudan University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Combination of multi-modal PET-CT imaging for lung tumor segmentation is significant for clinical treatment. Existing methods have not fully considered the impact of noise in PET-CT on the multi-modal interaction. To address this, we propose a novel Attention in Attention Network (aiANet). aiANet can mutually learn multi-modal characteristics for segmentation through its cross-learning modules. Within the cross-learning module, we introduce two nested-attention blocks, namely Attention in Self-Attention (aiSA) and Attention in Cross-Attention (aiCA), for multi-scale feature enhancement and multi-modal feature interaction. Importantly, since traditional attention weights calculated solely or unilaterally based on PET or CT can be vulnerable to the inevitable noisy information, we embed a novel Attention in Attention (aiA) module into aiSA and aiCA. The aiA module can seek cross-modal consensus for attention weights to alleviate their noise. Experimental results on clinical PET-CT data of lung cancer demonstrate the superiority of our method.

关键词： Image segmentation Computed tomography Aggregates Noise Lung cancer Imaging Noise measurement Tumors

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A Hydrodynamic Analysis of Water System in Dadahup Swamp Irrigation Area 1

A Hydrodynamic Analysis of Water System in Dadahup Swamp Irr...

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1st International Conference on computer science and Artificial Intelligence, ICCSai 2021

作者： Purboseno, Sentot Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Agricultural Engineering Yogyakarta55283 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Dadahup Swamp Irrigation Area (DIR) in Kapuas Regency, Central Kalimantan is developed for agricultural activities to provide food security after the pandemic. The water system consists of various channels, gates, and a flood embankment structure, which is also connected to three different rivers: Barito River, Mangkatif River, and Kapuas Mumpung River. To quantify flood risk assessments in the area, we perform a hydrodynamic analysis using the unsteady flow method via HEC-RAS software version 4.1.0. We use geometrical profiles from topography measurements and the outer boundary of the Dadahup DIR as input for the hydrologic model. Our results estimate the maximum surface water level from both cross and longitudinal sections in the channels and find that several points near the flood embankment area are prone to flood events. Our modeling approach provides a preliminary assessment for the local government to formulate a flood mitigation plan and policy in the area. © 2021 IEEE.

关键词： Rivers

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Safe and Reliable Training of Learning-Based Aerospace Controllers

Safe and Reliable Training of Learning-Based Aerospace Contr...

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Conference on Digital Avionics Systems (DASC)

作者： Udayan Mandal Guy Amir Haoze Wu Ieva Daukantas Fletcher Lee Newell Umberto Ravaioli Baoluo Meng Michael Durling Kerianne Hobbs Milan Ganai Tobey Shim Guy Katz Clark Barrett Center for AI Safety Stanford University Stanford USA School of CS & Engineering The Hebrew University of Jerusalem Jerusalem Israel Department of Computer Science IT University of Copenhagen Copenhagen Denmark Google Mountain View USA GE Aerospace Research Niskayuna USA Air Force Research Laboratory US Air Force Dayton USA Department of Computer Science Stanford University Stanford USA Department of Data Science Stanford University Stanford USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and sasfety-critical domains, in which a single mistake can have dire consequences. In this paper, we present novel advancements in both the training and verification of DRL controllers, which can help ensure their safe behavior. We showcase a design-for-verification approach utilizing k-induction and demonstrate its use in verifying liveness properties. In addition, we also give a brief overview of neural Lyapunov Barrier certificates and summarize their capabilities on a case study. Finally, we describe several other novel reachability-based approaches which, despite failing to provide guarantees of interest, could be effective for verification of other DRL systems, and could be of further interest to the community.

关键词： Training Space vehicles Neural networks Aerospace electronics Deep reinforcement learning Control systems Aerospace safety Reliability Reachability analysis Artificial intelligence

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Waveform Boundary Detection for Partially Spoofed Audio

Waveform Boundary Detection for Partially Spoofed Audio

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zexin Cai Weiqing Wang Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Data Science Research Center Duke Kunshan University Kunshan PR China

The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of the utterance is replaced, either with synthetic or natural audio clips, has recently been reported as one scenario of audio deepfakes. As deepfakes can be a threat to social security, the detection of such spoofing audio is essential. Accordingly, we propose to address the problem with a deep learning-based frame-level detection system that can detect partially spoofed audio and locate the manipulated pieces. Our proposed method is trained and evaluated on data provided by the ADD2022 Challenge. We evaluate our detection model concerning various acoustic features and network configurations. As a result, our detection system achieves an equal error rate (EER) of 6.58% on the ADD2022 challenge test set, which is the best performance in partially spoofed audio detection systems that can locate manipulated clips.

关键词： Deepfakes Adaptation models Error analysis Signal processing Feature extraction Acoustics Security

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Kbpt: Knowledge-Based Prompt Tuning for Zero-Shot Relation Triplet Extraction

SSRN

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SSRN 2023年

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data & Internet Audience Shanghai200072 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Knowledge representation is becoming an effective way of information extraction. However, considerable studies ignored its application scenarios in the zero-shot setting. In this paper, we propose a novel framework for prompt language models based on external ontology knowledge called Knowledge-Based Prompt Tuning for the Zero-shot Relation Triplet Extraction (KBPT), which encourages further investigation in low-resource regimes to address the data scarcity problem in Relation Triplet Extraction (RTE). The major task of relation triplet extraction in zero-shot learning is to extract multiple triplets that are consisted of head entities, tail entities, and relation labels from an input sentence where the extracted relation labels do not exist in the training set. The fundamental idea of prompt tuning is to construct a prompt template, then append it behind the input text as the pre-trained language models (PLMs) input, thus, transforming the classification task into a masked language model prediction. However, our proposed model does not involve the mask language model prediction but a well-designed prompt template in a structured text format to generate synthetic training data containing the unseen relation category. Concretely, we utilize the relation labels and incorporate virtual tokens sources from the relation semantics to construct a structured prompt template for generating synthetic training instances. Moreover, to further enrich and supplement prior knowledge, we draw on ontology schema based on external knowledge bases to enhance the capability of semantic representation in the prompt template. To address the problem of knowledge heterogeneity, we synergistically optimize these embedding representations by way of collective training. In addition, we carefully design a Multiple Triplets Decoding (MTD) algorithm to break through the limitation of extracting multiple relation triplets in a sentence, and our proposed model is model-agnostic and can be orthogon

关键词： Ontology

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Role of Contemporary Techniques in Agriculture Development: A Systematic Review 2

Role of Contemporary Techniques in Agriculture Development: ...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Popli, Renu Singh, Daljeet Kumar, Rajeev Saini, Gurpreet Singh Garg, Kanwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Center for Space Research DRD Lovely Professional University Phagwara India School of Electronics and Electrical Engineering Lovely Professional University Phagwara India Kurukshetra University Department of Computer Science and Application Kurukshetra India

ISBN: (纸本)9781665437899

Gone are the days when agriculture was said to be a profession of uneducated people who used only basic mechanical tools to survive. Use of technology in agriculture has transformed it into a high-tech, smart and efficient business. The abrupt increase in population has pushed the efficiency of agriculture activities to achieve higher and better yield from given set of resources. In this paper, a comprehensive survey on the role of contemporary techniques used in agricultural development is presented. Various aspects of smart agriculture involving the use of Wireless Sensor Networks (WSNs) to gather information, big data analysis for processing the collected information, Internet of Things (IOT) framework for communication, image processing techniques and mobile phone application developed for agriculture are discussed. Landmark studies published in each of these related fields are also presented. © 2022 IEEE.

关键词： Wireless sensor networks

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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