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arXiv 2023年

作者： Cheng, Zhiyong Han, Sai Liu, Fan Zhu, Lei Gao, Zan Peng, Yuxin Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences China School of Computing National University of Singapore Singapore School of Information Science and Engineering Shandong Normal University China Wangxuan Institute of Computer Technology Peking University China Peng Cheng Laboratory China

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users’ potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior’s embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively. © 2023, CC BY-NC-SA.

关键词： Collaborative filtering

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How is HCI taught, practiced, and supported in Malaysia? 22

How is HCI taught, practiced, and supported in Malaysia?

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Asian HCI Symposium'22

作者： Masitah Ghazali Ashok Sivaji Juliana Aida Abu Bakar Chui Yin Wong Noris Mohd Norowi School of Computing Universiti Teknologi Malaysia Malaysia MIMOS Technology Solutions Sdn Bhd Malaysia School of Creative Industry Management & Performing Arts Universiti Utara Malaysia Malaysia Intel Corporation Malaysia Multimedia Department Faculty of Computer Science and Information Technology Universiti Putra Malaysia Malaysia

ISBN: (纸本)9781450392501

This paper aims to describe how Human-Computer Interaction (HCI) is taught at academic and also how UI-UX is practiced in industry in Malaysia by a group of panelists from HCI academic and also industry UX practitioners from Malaysia. In addition, the authors also briefly describe how HCI and UI-UX is supported at the National Fourth Industrial Revolution (4IR) Policy in Malaysia. This workshop paper is presented at SEACHI 2022: International Southeast Asia CHI 2022 Workshop as part of the ACM CHI2022 conference.

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Classical to Quantum Transfer Learning Framework for Wireless Sensing Under Domain Shift

Classical to Quantum Transfer Learning Framework for Wireles...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yingxin Shan Peng Liao Xuyu Wang Lingling An Shiwen Mao Guangzhou Institute of Technology Xidian University Guangzhou China Knight Foundation School of Computing & Information Sciences Florida International University Miami FL USA School of Computer Science and Technology Xidian University Xi'an China Department of Electrical and Computer Engineering Auburn University Auburn AL USA

To implement ubiquitous wireless sensing, the domain shift problem (e.g., different environments, users, devices) for machine learning based approaches should be addressed. Some existing methods are proven to be effective, such as transfer learning and domain adaptation. Meanwhile, quantum machine learning, a combination of quantum computing and machine learning, has attracted much attention. More importantly, quantum transfer learning (QTL) has been successful for certain applications, e.g., image classification. In this paper, we explore a classical to quantum (C2Q) framework to address the domain shift problem in wireless sensing by exploiting the great potential of QTL. Specifically, we first analyze the data shift problem in various types of wireless datasets by calculating the Kullback-Leibler (KL) divergence of different domains. Then, a QTL framework is designed to introduce importance weighting and adversarial strategies. We finally evaluate the proposed framework using the representative human activity recognition task on three wireless sensing datasets. Experimental results demonstrate the feasibility of the framework and its great potential for solving the domain shift problem in wireless sensing.

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What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Networks

What can Discriminator do? Towards Box-free Ownership Verifi...

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International Conference on Computer Vision (ICCV)

作者： Ziheng Huang Boheng Li Yan Cai Run Wang Shangwei Guo Liming Fang Jing Chen Lina Wang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China College of Computer Science Chongqing University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis. However, well-trained GANs are under the threat of illegal steal or leakage. The prior studies on remote ownership verification assume a black-box setting where the defender can query the suspicious model with specific inputs, which we identify is not enough for generation tasks. To this end, in this paper, we propose a novel IP protection scheme for GANs where ownership verification can be done by checking outputs only, without choosing the inputs (i.e., box-free setting). Specifically, we make use of the unexploited potential of the discriminator to learn a hypersphere that captures the unique distribution learned by the paired generator. Extensive evaluations on two popular GAN tasks and more than 10 GAN architectures demonstrate our proposed scheme to effectively verify the ownership. Our proposed scheme shown to be immune to popular input-based removal attacks and robust against other existing attacks. The source code and models are available at https://***/AbstractTeen/gan_ownership_verification.

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Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning

arXiv

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arXiv 2024年

作者： Wu, Lintai Cheng, Xianjing Xu, Yong Zeng, Huanqiang Hou, Junhui Bio-Computing Research Center Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong Shenzhen Key Laboratory of Visual Object Detection and Recognition Guangdong Shenzhen518055 China School of Engineering Huaqiao University Quanzhou362021 China School of Information Science and Engineering Huaqiao University Xiamen361021 China

In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The task of self-supervised point cloud completion involves reconstructing missing regions of these incomplete objects without the supervision of complete ground truth. Current self-supervised methods either rely on multiple views of partial observations for supervision or overlook the intrinsic geometric similarity that can be identified and utilized from the given partial point clouds. In this paper, we propose MAL-SPC, a framework that effectively leverages both region-level and category-specific geometric similarities to complete missing structures. Our MAL-SPC does not require any 3D complete supervision and only necessitates a single partial point cloud for each object. Specifically, we first introduce a Pattern Retrieval Network to retrieve similar position and curvature patterns between the partial input and the predicted shape, then leverage these similarities to densify and refine the reconstructed results. Additionally, we render the reconstructed complete shape into multi-view depth maps and design an adversarial learning module to learn the geometry of the target shape from category-specific single-view depth images. To achieve anisotropic rendering, we design a density-aware radius estimation algorithm to improve the quality of the rendered images. Our MAL-SPC yields the best results compared to current state-of-the-art methods. We will make the source code publicly available at https://***/ltwu6/malspc. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Simulate the Machine Learning Algorithm to Organize the CRAHN Network System

Simulate the Machine Learning Algorithm to Organize the CRAH...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： P Prabakaran L. Chandra Sekhar Reddy LVD Ravikumar Manish Kumar Verma Department Of Computer Applications School of computing sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of CSE(Data Science) CMR College of Engineering & Technology Medchal Hyderabad India Department of Information Technology Shri Vishnu Engineering College For Women Bhimavaram India Department of Psychology Lovely Professional University Phagwara India

Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be dynamically capable construct autonomous and decentralized networks without negatively impacting licensed main user (PU) systems. For this reason, an effective spectrum necessitates a system structure based on artificial intelligence. This research provides a model for network planning, learning, and dynamic configuration that is based on a distributed autonomous CRAHN network system that uses reinforcement learning. The proposed optimization techniques for spectrum sensing, ad hoc network design, and context-aware signal categorization are all derived from the system model and are based on machine learning. The cognitive and detection engines may be used to examine the spectrum utilization and neighbour network status in the immediate area. To adapt to the ever-changing nature of the wireless environment, the suggested policy engine may generate network operating policies, identify policy conflicts, and infer the best course of action. Together with the erudition engine, whereby apply the recommended machine-learning methods, the decision engine arrives at the best possible settings for the CRAHN. In addition, guarantee peaceful cohabitation with surrounding systems to have excellent signal context recognition ability.

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Progressive Human Motion Generation Based on Text and Few Motion Frames

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Artificial Intelligence Guangdong Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangdong Guangzhou510275 China Sun Yat-sen University School of Computer Science and Engineering China Guangdong Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Peng Cheng Laboratory China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code is available here. © 1991-2012 IEEE.

关键词： Controllable Motion Generation Human Motion Text2Motion Generation

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An Efficient Approach for Merging Multidimensional Blockchains in Mobile IoT

An Efficient Approach for Merging Multidimensional Blockchai...

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International Conference on Distributed computing in Sensor Systems (DCOSS)

作者： Hussein Zangoti Niki Pissinou Charles A Kamhoua School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia US Army Research Laboratory Network Security Branch Adelphi MD USA

Graph-based or multidimensional blockchains have been proposed to improve the scalability and efficiency of existing blockchain applications. However, when implemented in mobile Internet of Things (mIoT) networks, these blockchain systems can frequently split and merge and cause the merging algorithm to process a large number of similar blocks (a block is similar or identical when it exists once in multiple blockchains). The presence of similar blocks hinders the merging process, as it consumes time and computational resources to scan, validate, and potentially merge similar blocks. This paper presents an efficient approach for merging graph-based or multidimensional blockchains in mIoT networks by avoiding similar blocks and effectively detecting and merging new blocks that were created after the split. Our proposed merging algorithm employs depth-first search and Merkle tree techniques to minimize the time and computational resources spent on identical blocks. Finally, we evaluate the performance of our method in highly mobile networks and demonstrate that it can execute the merge with a more than 72% reduction in time in comparison to merging algorithms without block similarity handling.

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A Comprehensive Survey on Intelligent Reflecting Surfaces and Unmanned Aerial Vehicles Integration for Enhanced Communication in Massive Networks

A Comprehensive Survey on Intelligent Reflecting Surfaces an...

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Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Hani Attar Jafar Ababneh Mohamed Hafez Ali Rachini Ahmed Solyman Ramy M. Bahy Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia Department of Computer Science and Information Technology Holy Spirit University of Kaslik (USEK) Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK Technical Research Center Cairo Egypt

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. Through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. This paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study.

关键词： NOMA Reviews Wireless networks Autonomous aerial vehicles Energy efficiency Real-time systems Security Millimeter wave communication Visible light communication Optical surface waves

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High Sensitivity and Wide Response Range Artificial Synapse Based on Polyimide with Embedded Graphene Quantum Dots

SSRN

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SSRN 2023年

作者： Kou, Lijie Ye, Nan Waheed, Anjam Auliya, Rahmat Zaki Wu, Chaoxing Ooi, Poh Choon Li, Fushan Computing and Information Science College Fuzhou Institute of Technology Fuzhou350506 China Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia School of Physics and Information Engineering Fuzhou University Fuzhou350002 China The National University of Malaysia Malaysia

Artificial electronic synapses are commonly used to simulate biological synapses to realize various learning functions, which is regarded as one of the key technologies in the next generation of neurological computation. In this work, a simple solution spin coating technique prepared memristors based on the simple structure of polyimide (PI):graphene quantum dots(GQDs). The devices exhibit remarkably stable exponentially decaying postsynaptic suppression current over time, as interpreted in the spike-timing-dependent plasticity phenomenon. Furthermore, with the increase of the applied electrical signal over time, the conductance of the electrical synapse gradually changes, and the electronic synapse also shows plasticity dependence on the amplitude and frequency of the pulse applied. In particular, the devices with the structure of Ag/PI:GQDs/ITO prepared in this study can produce a stable response to the stimulation of electrical signals between millivolt to volt, not only showing high sensitivity but also a wide range of "feelings", which makes the electronic synapses take a step forwards to emulate biological synapses. Meanwhile, the electronic conduction mechanisms of the device are also studied and expounded in detail. The findings in this work lay a foundation for developing brain-like neuromorphic modeling in artificial intelligence. © 2023, The Authors. All rights reserved.

关键词： Memristors

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