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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Huang, Gengjun Long, Xiaosheng Mao, Yiming School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Software Engineering HuaZhong University of Science & Technology Wuhan China Bell Honor School Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781510663473

With wide-spread usage of style transfer, numerous methods for style transfer draw an increasing attention. Several methods to enhance the efficiency of style transformers have been made, one of them is StarGANv2, a method for multiple-style transfer, which can transform a batch of source pictures into other pictures with different styles. The main difference of StarGANv2 with other style transformers is that it uses style code to represent the styles to enable StarGANv2 to complete multiple-style transformation. The authors of StarGANv2 use CelebA-HQ and AFHQ dataset to train the model and test the model, and the results are pretty better than other style transformers. The goal of this paper is to exploit the effectiveness of StarGANv2 in the real-world scenes, such as over exposure or the angle facing the camera, The results validate the power of StarGANv2 where the model is robust enough to transfer the pictures into other styles. To achieve this, the authors of StarGANv2 use the photo clipped in videos which record real-world animals and form a new dataset. Then, the authors of StarGANv2 use the dataset to test the pre-trained model which is trained by AFHQ dataset and evaluate it according to FID metric. The authors of StarGANv2 draw a conclusion that StarGANv2 is robust in real world scenes. The meaning of this paper is that the authors get the real-world usage of StarGANv2 and have a test of StarGANv2's robustness in real world photos and validate the potential of StarGANv2 in real-world applications. © 2023 SPIE.

关键词： Generative adversarial networks

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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Design of Smart Home Control System Based on Raspberry Pi Design and Implementation 4

Design of Smart Home Control System Based on Raspberry Pi De...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Liu, Xuemin Zhang, Zhao School of Electronics and Information Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this paper, using the Internet of Things technology, a new smart home control system with Raspberry Pi as the central control and integrated multi-platform is designed. This system adopts Python language development, and realizes multi-platform adaptive user-side applications through WeChat Mini Programs. The system uses the message queue telemetry transmission protocol to realize LAN communication between the sensor node and the central control device. WebSocket protocol is used to realize the full duplex communication between the client and the central control device. The system includes environmental monitoring, equipment control, scene control, and family management functions, supplemented by voice recognition and face recognition technology. An actual system is used to verify the effectiveness of the work in this paper. © 2022 IEEE.

关键词： Internet of things

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Task Allocation Optimization Strategy in UAV-enabled Mobile Edge Computing System

Task Allocation Optimization Strategy in UAV-enabled Mobile ...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Yang, Sanhuan Su, Zhou Xu, Qichao Xing, Rui Fang, Dongfeng Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China California Polytechnic State University San Luis Obispo Department of Computer Science and Software Engineering United States

ISBN: (纸本)9798350333336

Unmanned Aerial Vehicles (UAVs) have been widely used as aerial computing devices in Internet of Things (IoT) to support time-sensitive and compute-intensive applications and services, e.g. automatic driving, data analysis, etc. In this paper, a UAV-enabled mobile edge computing (MEC) system is considered for computing offloading services, in which the offloaded tasks from ground devices are processed by UAVs. However, when multiple computing tasks exist, the task allocation for devices should be carried out at the lowest computing cost. To this end, we first propose a non-cooperative game model-based power allocation (NGPA) scheme, and the optimal power allocation strategy of devices is determined to minimize transmission energy consumption. Next, we model the problem of task allocation as a bilateral matching between devices and UAVs to minimize the overall computing cost. Then, a bilateral matching mechanism based on improved Hungarian algorithm is provided to obtain the optimal solution. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes on computing cost. © 2023 IEEE.

关键词： Mobile edge computing

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Noise-Augmented Contrastive Learning for Sequential Recommendation 24th

Noise-Augmented Contrastive Learning for Sequential Recomme...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： He, Kun Meng, Shunmei Li, Qianmu Liu, Xiao Beheshti, Amin Chi, Xiaoxiao Zhang, Xuyun School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819972531

Recently, contrastive learning has been widely used in the field of sequential recommendation to solve the data sparsity problem. CL4Rec augments data through simple random crop, mask, and reorder, while DuoRec proposes a model-level data augmentation method. However, these methods do not take into account the issue of noisy data in sequential recommendation, such as false clicks during browsing. The noise may lead to poor representations of learned sequences and negatively affect the augmented data. Current sequential recommendation methods tend to learn the user’s intention from their original sequences, but these methods have certain limitations as the user’s intention for the next interaction may change. Based on the above observations, we propose Noise-augmented Contrastive Learning for Sequential Recommendation (NCL4Rec). Our NCL4Rec proposes sequential noise probability-guided data augmentation. We introduce supervised noise recognition during training instead of obtaining it from original sequences. Moreover, we design positive and negative augmentations of the sequence and design unique noise loss function to train them. Through experiments, it is verified that our NCL4Rec consistently outperforms the current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Contrastive Learning Sequential Recommendation

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Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents

arXiv

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arXiv 2025年

作者： Keane, Jonathan Keyser, Sam Kedziora, Jeremy Department of Computer Science and Software Engineering Milwaukee School of Engineering MilwaukeeWI United States

The use of reward functions to structure AI learning and decision making is core to the current reinforcement learning paradigm;however, without careful design of reward functions, agents can learn to solve problems in ways that may be considered "undesirable" or "unethical. Without thorough understanding of the incentives a reward function creates, it can be difficult to impose principled yet general control mechanisms over its behavior. In this paper, we study methods for constructing guardrails for AI agents that use reward functions to learn decision making. We introduce a novel approach, which we call strategy masking, to explicitly learn and then suppress undesirable AI agent behavior. We apply our method to study lying in AI agents and show that it can be used to effectively modify agent behavior by suppressing lying post-training without compromising agent ability to perform effectively. © 2025, CC BY.

关键词： Reinforcement learning

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Fast Distributed Polynomial Multiplication Algorithm for Lattice-based Cryptographic Decryption In Blockchain Systems 22

Fast Distributed Polynomial Multiplication Algorithm for Lat...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhao, Hongjian Tao, Yunting Kong, Fanyu Zhang, Guoyan Zhang, Hanlin Yu, Jia Shandong University School of Software Jinan China Binzhou Polytechnic College of Information Engineering Binzhou China Shandong University Shandong Sansec Information Technology Co. Ltd School of Software Jinan China Shandong University School of Cyber Science and Technology Qingdao China Qingdao University College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331509712

Lattice-based Post-Quantum Cryptography (PQC) can effectively resist the quantum threat to blockchain's underlying cryptographic algorithms. Blockchain node decryption is one of the most commonly used cryptographic computations in blockchain systems, and polynomial multiplication, a time-consuming operation for decryption, is one of the factors limiting blockchain efficiency. This paper proposes a novel distributed computing algorithm for polynomial multiplication, applicable in blockchain decryption. By splitting polynomials into lower-degree terms and delegating tasks to distributed nodes, our approach reduces computation time. A novel verification strategy based on the Karatsuba algorithm ensures result accuracy. The experimental results demonstrate that our proposed scheme improves the execution efficiency of NTT and INTT operations by approximately 47.8% and 52.4%, and reduces Kyber decryption time by up to 23.5%. © 2024 IEEE.

关键词： Encryption algorithms

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Crysformer:An attention-based graph neural network for properties prediction of crystals

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Chinese Physics B 2023年第9期32卷 15-20页

作者：王田陈家辉滕婧史金钢曾新华 Hichem Snoussi Institute of Artificial Intelligence SKLSDEBeihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Academy for Engineering and Technology Fudan UniversityShanghai 200433China Charles Delaunay Institute University of Technology of TroyesTroyes Cedex 10004France

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory(DFT)-based ***,we utilize an attention-based graph neural network that yields high-accuracy *** approach employs two attention mechanisms that allow for message passing on the crystal graphs,which in turn enable the model to selectively attend to pertinent atoms and their local environments,thereby improving *** conduct comprehensive experiments to validate our approach,which demonstrates that our method surpasses existing methods in terms of predictive *** results suggest that deep learning,particularly attention-based networks,holds significant promise for predicting crystal material properties,with implications for material discovery and the refined intelligent systems.

关键词： deep learning property prediction crystal attention networks

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Glaucoma Detection Using Convolutional Neural Network (CNN) 20

Glaucoma Detection Using Convolutional Neural Network (CNN)

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Tohye, Tewodros Gizaw Qin, Zhiguang Leka, Habte Lejebo Hundera, Negalign Wake School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua China

ISBN: (纸本)9798350318982

Today, glaucoma detection is a severe problem. Because it was predicted that there would be 111.8 million cases of glaucoma worldwide by 2040, up from the expected 76.0 million cases in 2020. Blindness results if early treatment is not given. The CNN model has been used in a variety of studies to identify glaucoma. The results obtained thus far are insufficient to reliably identify glaucoma. In order to detect glaucoma, which is a binary classification, we are utilizing the CNN model. We divided the REFUGUE data set into tanning and testing, with 80% of the photos being utilized for tanning and 20% for testing. Our model's accuracy score was 99.8% for the training set and 95.4% for the testing set. We get result of precision=98% and recall= 97%. We compare the model with other state of arts (SOTA) our experimental results reveal that, the proposed model for glaucoma detection using convolutional neural network is more proficient in glaucoma classification. © 2023 IEEE.

关键词： Convolution

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Object Recognition and Tracking System to Assist Visually Impaired: A Neural Network-Based Deep RBM Technique 1

Object Recognition and Tracking System to Assist Visually Im...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Patil, Sonal Ramagundam, Shashishekhar Sukumar, P. Khan, Mohiuddin Ali Patil, Nisha Deepak Nishant, Neerav G H Raisoni Institute of Engineering and Business Management HoD of CSE-IT Department Jalgaon India Software and Dev Engg4- SDET/ IT COMCAST MonroviaMD United States Department of Computer Science and Engineering Tamilnadu Erode India Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia GCOERC Computer Engineering Nashik India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

Being able to see is fundamental to almost every aspect of our everyday lives, thus those who are visually impaired confront enormous obstacles. Thanks to recent developments in computer vision and computing, a system that can provide auditory input to visually impaired individuals regarding objects and their environments has been designed. Sequencing is crucial for preprocessing, feature selection, and model training. Although extremely accurate data input is still required for object detection, preparation is getting computationally cheaper. We were partially successful in reducing the image size by employing Gaussian Blur, which imparts a smoother appearance to the photos. We use color descriptors and Fourier transforms for feature extraction. Deep RBM training necessitates precise control over the attributes. In comparison to this method, current RBM and DNN algorithms look antiquated. The results demonstrated a notable enhancement in accuracy, reaching 98.31%. © 2024 IEEE.

关键词： Object detection

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