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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Bejar, Althea Tiffanie A. Ouyang, Jon Kinsly T. Villabroza, Vince Miguel D. Correa, Aurelle Mikael Concepcion, Ronnie Palconit, Maria Gemel Vicerra, Ryan Rhay Duarte, Bernardo Fonseca, Vanessa F. De la Salle University Department of Manufacturing Engineering and Management Manila Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines Faculdade de Ciências da Universidade de Lisboa MARE - Marine and Environmental Sciences Centre ARNET - Aquatic Research Infrastructure Network Laboratory Lisbon1749-016 Portugal Universidade de Lisboa Faculdade de Ciências Departamento de Biologia Vegetal Lisbon Portugal Universidade de Lisboa Faculdade de Ciências Departamento de Biologia Animal Lisbon Portugal

ISBN: (纸本)9781665464932

Fish is a staple in the diet of nations globally and is cultured and harvested using various practices such as open sea fishing and aquaculture. In fact, the fisheries and aquaculture industry are valued at 250 billion as a whole and contribute to almost 2% of the Philippines' Gross Domestic Product;therefore, it is an industry with constant great demand. Aquaculture practice is where fish, shellfish, and other consumable marine species are cultivated by fishermen using traditional methods and experience typically manually operated via fish pens. However, occupational hazards exposed to fishermen in the industry often result in injury and long-term health effects. Thus, there is a need to semi-automate the labour-intensive task of fish harvesting by applying concepts from common current aquaculture practices and control systems engineering. This study develops two systems, namely: a fish pen elevating system and a single-input-multiple-output aquaculture pump system, for harvesting of tilapia in the Laguna de Bay, Philippines, based on the vastness of the lake, the quantity of open water fish pens located in the area, and the species being the most produced in this type of fish pen as a whole. The discussion of existing aquacultures around the world is also included in this study to show advancements in technology when it comes to this industry. The two proposed systems presented integrate the automation of the process where fish is brought to the surface via raising the base frame of the fish pen or its surrounding poles. The proper application of each system should result in more efficient harvests, fewer injuries to fishermen, and improved production volumes. © 2022 IEEE.

关键词： Fish

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Cooperative Multi-source Data Trading

Cooperative Multi-source Data Trading

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jin Cheng Ningning Ding John C. S. Lui Jianwei Huang School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Data Science and Analytics Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science and Engineering The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network and CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers’ cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data’s utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers’ revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller’s profit by up to 46.32% compared to traditional data trading methods in the current data market.

关键词： Couplings Games Big Data Data models Global communication Optimization

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial network

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computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

arXiv

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arXiv 2022年

作者： Sun, Lunan Yang, Yang Chen, Mingzhe Guo, Caili Saad, Walid Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Economic and social effects

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A game-theoretic framework for controlled islanding in the presence of adversaries

arXiv

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arXiv 2021年

作者： Niu, Luyao Sahabandu, Dinuka Clark, Andrew Poovendran, Radha Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States

Controlled islanding effectively mitigates cascading failures by partitioning the power system into a set of disjoint islands. In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adversary. We formulate the interaction between the grid operator and adversary using a game-theoretic framework. The grid operator first computes a controlled islanding strategy, along with the power generation for the post-islanding system to guarantee stability. The adversary observes the strategies of the grid operator. The adversary then identifies critical substations of the power system to compromise and trips the transmission lines that are connected with compromised substations. For our game formulation, we propose a double oracle algorithm based approach that solves the best response for each player. We show that the best responses for the grid operator and adversary can be formulated as mixed integer linear programs. In addition, the best response of the adversary is equivalent to a submodular maximization problem under a cardinality constraint, which can be approximated up to a (1 − 1e ) optimality bound in polynomial time. We compare the proposed approach with a baseline where the grid operator computes an islanding strategy by minimizing the power flow disruption without considering the possible response from the adversary. We evaluate both approaches using IEEE 9-bus, 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus power system case study data. Our proposed approach achieves better performance than the baseline in about 44% of test cases, and on average it incurs about 12.27 MW less power flow disruption. © 2021, CC BY.

关键词： Electric load flow

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Scalable planning in multi-agent MDPS

arXiv

引用

arXiv 2021年

作者： Sahabandu, Dinuka Niu, Luyao Clark, Andrew Poovendran, Radha Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Multi-agent Markov Decision Processes (MMDPs) arise in a variety of applications including target tracking, control of multi-robot swarms, and multiplayer games. A key challenge in MMDPs occurs when the state and action spaces grow exponentially in the number of agents, making computation of an optimal policy computationally intractable for medium- to large-scale problems. One property that has been exploited to mitigate this complexity is transition independence, in which each agent’s transition probabilities are independent of the states and actions of other agents. Transition independence enables factorization of the MMDP and computation of local agent policies but does not hold for arbitrary MMDPs. In this paper, we propose an approximate transition dependence property, called δ-transition dependence and develop a metric for quantifying how far an MMDP deviates from transition independence. Our definition of δ-transition dependence recovers transition independence as a special case when δ is zero. We develop a polynomial time algorithm in the number of agents that achieves a provable bound on the global optimum when the reward functions are monotone increasing and submodular in the agent actions. We evaluate our approach on two case studies, namely, multi-robot control and multi-agent patrolling example. © 2021, CC BY.

关键词： Multi agent systems

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Assigning Entities to Teams as a Hypergraph Discovery Problem

arXiv

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arXiv 2024年

作者： de Arruda, Guilherme Ferraz He, Wan Heydaribeni, Nasimeh Javidi, Tara Moreno, Yamir Eliassi-Rad, Tina CENTAI Institute Turin Italy Network Science Institute Northeastern University BostonMA United States Department of Electrical and Computer Engineering University of California San Diego United States University of Zaragoza Zaragoza50018 Spain Department of Theoretical Physics University of Zaragoza Zaragoza50018 Spain Khoury College of Computer Sciences Northeastern University BostonMA United States Santa Fe Institute Santa FeNM United States

We propose a team assignment algorithm based on a hypergraph approach focusing on resilience and diffusion optimization. Specifically, our method is based on optimizing the algebraic connectivity of the Laplacian matrix of an edge-dependent vertex-weighted hypergraph. We used constrained simulated annealing, where we constrained the effort agents can exert to perform a task and the minimum effort a task requires to be completed. We evaluated our methods in terms of the number of unsuccessful patches to drive our solution into the feasible region and the cost of patching. We showed that our formulation provides more robust solutions than the original data and the greedy approach. We hope that our methods motivate further research in applying hypergraphs to similar problems in different research areas and in exploring variations of our methods. © 2024, CC BY.

关键词： Simulated annealing

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A Construction of IoT Malicious Traffic Dataset and Its Applications 1st

A Construction of IoT Malicious Traffic Dataset and Its Ap...

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Yiping Zhou, Yiyang Yin, Jie Yang, Yichen Xie, Wenwei Liang, Guangjun Zhang, Lanping Department of Criminal Science and Technology Jiangsu Police Institute Nanjing China Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Wuxi Public Security Bureau Intelligence Command Center Wuxi China Network Security Trend Micro Incorporated Nanjing China College of Telecommunications and Information Engineering NJUPT Nanjing China

ISBN: (纸本)9789819712762

The current Internet of Things (IoT) malicious traffic dataset mainly relies on raw binary data at the traffic packet level and structured data at the session flow level for learning training and predictive classification, each of which has shortcomings and is not conducive to the construction of IoT malicious traffic classification models. In this paper, based on the packet and session stream datasets in the Iot-23 dataset, an importance-based feature extraction method is given, and a new malicious traffic dataset construction process is further proposed. The newly constructed malicious traffic dataset solves the shortcoming of mismatch between the number of features and the prediction performance in the traffic packet and session flow datasets. Experiments show that compared with a single dataset, the dataset constructed by the new method has richer and more important features, which improves the accuracy of malicious traffic classification and the generalization ability of the model, and is more conducive to the research of IoT malicious traffic classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Deep Joint Source-Channel Coding for Efficient and Reliable Cross-Technology Communication

Deep Joint Source-Channel Coding for Efficient and Reliable ...

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IEEE International Conference on Communications (ICC)

作者： Shumin Yao Xiaodong Xu Hao Chen Yaping Sun Qinglin Zhao Department of Broadband Communication Peng Cheng Laboratory Shenzhen China Beijing University of Posts and Telecommunications Beijing China Future Network of Intelligent Institute (FNii) the Chinese University of Hong Kong (Shenzhen) Shenzhen China School of Computer Science and Engineering Macau University of Science and Technology Avenida Wei Long Taipa Macau China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification. However, it has not been widely adopted in real-world applications due to its inefficiency and unreliability. To address this issue, this paper proposes a deep joint source-channel coding (DJSCC) scheme to enable efficient and reliable CTC. The proposed scheme builds a neural-network-based encoder and decoder at the sender side and the receiver side, respectively, to achieve two critical tasks simultaneously: 1) compressing the messages to the point where only their essential semantic meanings are preserved; 2) ensuring the robustness of the semantic meanings when they are transmitted across incompatible technologies. The scheme incorporates existing CTC coding algorithms as domain knowledge to guide the encoder-decoder pair to learn the characteristics of CTC links better. Moreover, the scheme constructs shared semantic knowledge for the encoder and decoder, allowing semantic meanings to be converted into very few bits for cross-technology transmissions, thus further improving the efficiency of CTC. Extensive simulations verify that the proposed scheme can reduce the transmission overhead by up to 97.63% and increase the structural similarity index measure by up to 734.78%, compared with the state-of-the-art CTC scheme.

关键词： Wireless communication Knowledge engineering Semantics Neural networks Receivers Encoding Hardware

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