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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xiang, Yingjia Lu, Xuanyu Zhou, Wei Wen, Junhao School of Big Data and Software Engineering Chongqing University Chongqing China School of Computing China University of Geosciences Wuhan China

ISBN: (纸本)9781665410205

User behavior sequence modeling represents users' interests and preferences, which has a crucial impact on click-through rate (CTR) prediction models and recommendation systems. In practical scenarios, multiple types of interactive behaviors usually constitute users' complex interests and preferences. However, existing CTR prediction models based on multi-type interactive behavior modeling have two limitations: (1) they cannot handle noise in users' global interests, resulting in the inability to model accurate global interest preferences;(2) they struggle to depict users' local interest patterns and cannot model the complex patterns embedded in multi-behavior sequences. To overcome these limitations, an Adaptive Local and Global Interests Network For Click-Through Rate Prediction (ALGIN) is proposed, which effectively models global and local interests within multi-behavior interaction sequences through a unified framework. Firstly, ALGIN employs a Fourier Global Interest Modeling module to eliminate noise in the behavioral sequence;secondly, ALGIN designs a Multi-Scale Local Interest Extraction module to capture behavioral patterns within multi-type interactive sequences;finally, an Item-driven Interest Selector is used to integrate users' local interests with their global interests. Experiments on multiple public datasets demonstrate that the ALGIN model outperforms the current state-of-the-art (SOTA), and ablation experiments confirm the effectiveness of each module within the model. © 2024 IEEE.

关键词： Prediction models

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Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

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Computers, Materials & Continua 2024年第7期80卷 61-103页

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science FAST School of ComputingNational University of Computer and Emerging SciencesLahore54000Pakistan Department of Software and Communications Engineering Hongik UniversitySejong City30016Republic of Korea

Wireless Ad Hoc Networks consist of devices that are wirelessly *** Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc *** is used in wireless ad hoc *** is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined *** fetches data from a fixed *** redundancy,mobility,and location dependency are the main issues of the IP network *** these factors result in poor performance of wireless ad hoc *** main disadvantage of IP is that,it does not provide in-network ***,there is a need to move towards a new network that overcomes these *** Data Network(NDN)is a network that overcomes these *** is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user *** NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and *** considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless *** cachingmechanism-based results are also *** the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

关键词： Content centric network Internet of Things mobile ad hoc network named data network vehicular ad hoc network

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CLIP-Guided Bidirectional Prompt and Semantic Supervision for Dynamic Facial Expression Recognition 18

CLIP-Guided Bidirectional Prompt and Semantic Supervision fo...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Junliang Liu, Xu Liang, Yu Xian, Xiaole Xie, Weicheng Shen, Linlin Song, Siyang Shenzhen University School of Computer Science & Software Engineering China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350364132

Due to the insufficient semantic information supervision in existing works for dynamic facial expression recognition (DFER), videos with similar facial changes but different expressions may be easily confused. Thanks to the potential textual information for semantic supervision, contrastive language-image pretraining (CLIP) model provides a new direction for DFER. However, pre-trained CLIP based on image-text pairs has difficulty in capturing temporal features in the video domain. Therefore, we propose a novel visual language model that captures and aggregates dynamic features of expressions in semantic supervision via Inter-Frame Interaction Transformer (Inter-FIT) and Multi-Scale Temporal Aggregation (MSTA). Furthermore, though prompt learning is often used in CLIP to enhance semantic supervision, previous studies have only focused on the role of textual prompts, ignoring the importance of visual prompts in facilitating the relationality between the two. Therefore, we designed a Bidirectional Enhanced Prompt (BiEhPro) to facilitate the learning of this relationality between text and visual cues in enhancing semantic supervision. Extensive experiments and ablation studies on three benchmark datasets, i.e., DFEW, FERV39K, and MAFW, validate the effectiveness of our modules and algorithm. Code is publicly available at https://***/JunLiangZ/CLIP-Guided-DFER. © 2024 IEEE.

关键词： Semantics

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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True Rays Model Integration in OMNeT++ for IIoT Network Planning in Underground Mines 1

True Rays Model Integration in OMNeT++ for IIoT Network Plan...

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1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Suancha, Cristian Montanez, Oscar Medina, Fabian Fernandez, Eduardo Avendano Cespedes, Sandra School UPTC Systems and Computing Engineering Sogamoso Colombia School UPTC Electronic Engineering Sogamoso Colombia Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798331527587

This work develops a new module in the OMNeT++ simulator incorporating and validating the True Rays propagation model for underground environments. The integration enables the planning and evaluation of Industrial Internet of Things deployments of wireless sensor networks (WSNs) in mining scenarios. The tool can read the mine blueprints and deploy nodes at specified locations. It also defined the environment and network communication parameters for network coverage and performance analysis, allowing an understanding of how the WSN behaves in the underground mine before its deployment. The simulation predicts variables such as received power and path losses, optimizing sensing coverage and connectivity while reducing the need for site surveys. A large number of simulations using blueprints from real mines show that the signal propagation measurements are consistent with the theoretical models proposed in the state of the art. Experimental results were compared with a simulation WSN using LoRa, the maximum range obtained in simulation was 47 m, the measurements under real conditions in the underground only achieved 25 m of reach, but were limited at a threshold of -100 dBm to ensure connectivity and reliability of transmission among the nodes. As a result, the simulations and measurements performed better at the 433 MHz frequency, and the main contribution is a simulation tool simplifying planning IIoT networks in mining tunnels. © 2024 IEEE.

关键词： Sensor nodes

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Multi-Modal Incomplete Brain Tumor Segmentation with Mamba 24

Multi-Modal Incomplete Brain Tumor Segmentation with Mamba

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Li, Yudan Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun School of Electrical Engineering University of South China Hunan Hengyang China School of Computing/Software University of South China Hunan Hengyang China

ISBN: (纸本)9798400717826

Accurately segmenting brain tumors from multimodal MRI sequences is a key prerequisite for brain tumor diagnosis, prognosis assessment, and surgical treatment. However, in practical applications, one or more modal data is often missing due to image corruption, different acquisition protocols, artifacts, contrast agent allergies, or cost considerations. To address the challenges of brain tumor segmentation under modality loss, this paper proposes an innovative tumor feature perception strategy. The core of this strategy is to introduce a Mamba-based Encoder (MBE) architecture, which effectively improves the feature expression ability of each modality encoder under limited data conditions. In view of the irregularity of tumor morphology, a Modulation and Demodulation Fusion Block (MDFB) is designed to accurately capture the semantic features of the tumor from the missing multimodal image data, providing strong guidance for the network to locate the tumor area. Experimental results on the widely used BraTS2020 dataset demonstrate the effectiveness of MMITS, and the brain tumor segmentation effect under various incomplete modalities is better than the state-of-the-art methods. © 2024 Copyright held by the owner/author(s).

关键词： Multicarrier modulation

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CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua Science and Technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua Science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 647-660页

作者： Tian, Jiao Tsai, Pei-Wei Zhang, Kai Cai, Xinyi Xiao, Hongwang Yu, Ke Zhao, Wenyu Chen, Jinjun Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia Center for Health Computing Research Beijing Academy of Artificial Intelligence Beijing100084 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can detect this problem and make timely adjustments. In this article, we provide a new local-global class imbalance inconsistency quantification and utilize softmax as the activation and focal loss, a dynamically scaled cross-entropy loss, in federated XGBoost to mitigate local class imbalance. Moreover, we propose a simple but effective hyperparameter determination strategy based on local data distribution to adjust the sample weights among noncommunicating participants, synergetic focal loss, to solve the inconsistency of local and global class imbalance, a unique characteristic of federated learning. This strategy is perfectly integrated into the original classification algorithm. It requires no additional detectors or information transmission. Furthermore, a dynamical for loop is designed to capture an optimum hyperparameter combination. Finally, we conduct comprehensive tabular- and image-based experiments to show that synergetic focal loss used in federated XGBoost achieves faster convergency and significant accuracy improvement. Simulation results prove the effectiveness of the proposed principle of configuring sample weights. © 2023 IEEE.

关键词： Heuristic algorithms

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