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arXiv 2024年

作者： Liu, Jiajun Ke, Wenjun Wang, Peng Wang, Jiahao Gao, Jinhua Shang, Ziyu Li, Guozheng Xu, Zijie Ji, Ke Li, Yining School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Institute of Computing Technology Chinese Academy of Sciences China College of Software Engineering Southeast University China

Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating catastrophic forgetting of old knowledge but neglect efficient learning for the emergence of new knowledge. However, in real-world scenarios, knowledge graphs (KGs) are continuously growing, which brings a significant challenge to fine-tuning KGE models efficiently. To address this issue, we propose a fast CKGE framework (FastKGE), incorporating an incremental low-rank adapter (IncLoRA) mechanism to efficiently acquire new knowledge while preserving old knowledge. Specifically, to mitigate catastrophic forgetting, FastKGE isolates and allocates new knowledge to specific layers based on the fine-grained influence between old and new KGs. Subsequently, to accelerate fine-tuning, FastKGE devises an efficient IncLoRA mechanism, which embeds the specific layers into incremental low-rank adapters with fewer training parameters. Moreover, IncLoRA introduces adaptive rank allocation, which makes the LoRA aware of the importance of entities and adjusts its rank scale adaptively. We conduct experiments on four public datasets and two new datasets with a larger initial scale. Experimental results demonstrate that FastKGE can reduce training time by 34%-49% while still achieving competitive link prediction performance against state-of-the-art models on four public datasets (average MRR score of 21.0% vs. 21.1%). Meanwhile, on two newly constructed datasets, FastKGE saves 51%-68% training time and improves link prediction performance by 1.5%. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Holographic Integrated Data and Energy Transfer

arXiv

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arXiv 2024年

作者： Huang, Qingxiao Hu, Jie Zhao, Yizhe Yang, Kun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China School of Computer Science and Electronic Engineering University of Essex United Kingdom

Thanks to the application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain by enabling the ability to generate any current distribution on the surface. With the aid of electromagnetic (EM) manipulation capability of H-MIMO, integrated data and energy transfer (IDET) system can fully exploits the EM channel to realize energy focusing and eliminate inter-user interference, which yields the concept of holographic IDET (H-IDET). In this paper, we invetigate the beamforming designs for H-IDET systems, where the sum-rate of data users (DUs) are maximized by guaranteeing the energy harvesting requirements of energy users (EUs). In order to solve the non-convex functional programming, a block coordinate descent (BCD) based scheme is proposed, wherein the Fourier transform and the equivalence between the signal-to-interference-plus-noise ratio (SINR) and the mean-square error (MSE) are also conceived, followed by the successive convex approximation (SCA) and an initialization scheme to enhance robustness. Numerical results illustrate that our proposed H-IDET scheme outperforms benchmark schemes, especially the one adopting traditional discrete antennas. Besides, the near-field focusing using EM channel model achieves better performance compared to that using the traditional channel model, especially for WET where the EUs are usually close to the transmitter. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Revisiting Knowledge Transfer for Success Enterprise System Project

Revisiting Knowledge Transfer for Success Enterprise System ...

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2021 International Conference on Information technology, ICIT 2021

作者： Hussien, Jamal Abdullateef, Mansoor Kahtan, Hasan Sulaiman, Riza Bangi Malaysia Abdulgabber Abdulhak Consultant Researcher Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665428705

This paper re-examines the relationship between the Knowledge Transfer (KT) and the project management process groups. Knowledge Transfer as one of the knowledge management life-cycle is supposed to have a remarkable impact on the enterprise system success. However, project failure is one of the main problems in enterprise systems management and lack of proper documentation on the results of previous projects and knowledge transfer are the main reasons. In this paper, we have found that enterprise system project divided into three stages, with (6) process groups in the Pre-Implementation stage, (6) process groups during-Implementation stage, and (3) process groups in the post-Implementation, however, there are eleven (11) roles who are evolving to anticipate the fifth results (Project Proposal, Evaluation Ratings, Project Charter, Project Plan, Project Documents), to increase the understanding of knowledge in each project stages to improve the enterprise system project success. © 2021 IEEE.

关键词： Project management

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Research on recommendation algorithm combining item social regularization and variational encoder 5

Research on recommendation algorithm combining item social r...

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5th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Fang, Yiqiu Ge, Na Ge, Junwei Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China Chongqing University of Posts and Telecommunications College of Software Engineering Chongqing China

Variational autoencoder is a very concise and effective unsupervised learning method, which can achieve excellent performance when applied in the field of recommendation systems. At present, most of the research on recommendation algorithms consider the user's social relationship, and rarely consider the social relationship between items. Focused on the problem of how to improve the recommendation accuracy of the recommendation system under the condition of sparse scoring data, a recommendation algorithm (SR-CVAE) for fusion item social relationship regularization and variational encoder are proposed. In this paper, the user-item feedback information, item content information, social relationship between items, and tag information are combined, and integrated into a variational encoder with a hidden variable generation model, and then combing the variational encoder with the discriminant model and applying it to the collaborative filtering recommendation algorithm to improve the accuracy of the algorithm. Experimental results on two CiteULike datasets show that the proposed algorithm has better interpretation and recommendation performance than other models. © 2021 IEEE.

关键词： Learning systems

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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Automatic Leaf Recognition Based on Attention DenseNet 5th

Automatic Leaf Recognition Based on Attention DenseNet

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wu, Huisi Shi, Zhouan Huang, Haiming Wen, Zhenkun Sun, Fuchun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science and Technology Tsinghua University Beijing100083 China

ISBN: (纸本)9789811623356

Automatic leaf recognition algorithm is widely used in plant taxonomy, horticulture teaching, traditional Chinese medicine research and plant protection, which is one of the research hotspots in information science. Due to the diversity of plant leaves, the variety of leaf forms, and the susceptibility to seasonal and other external factors, there is often a small inter-class variance and a large intra-class variance, which brings great challenges to the task of automatic leaf recognition. To solve this problem, we propose a leaf recognition algorithm base on the attention mechanism and dense connection. Firstly, base on dense connection, DenseNet is applied to realize the cross-layer learning of our model, which effectively improves the generalization ability of the network to the intra-class variance. At the same time, the learning ability of our model to the discriminative features such as the veins and textures of plant leaves is also improved. Secondly, we also employ the attention mechanism to further enhance the ability of our network in learning discriminative features of plant leaves. The experimental results show that our Attention DenseNet achieves a high accuracy of leaf recognition in our plant leaf database, including the challenging cases. Visual and statistical comparisons with state-of-the-art methods also demonstrate its effectiveness. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Horticulture

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Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka 12

Smart Agriculture Prediction System for Vegetables Grown in ...

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12th IEEE Annual Information technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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Analysis and Research on Students' Learning Behavior Based on Lag Sequence Analysis 4

Analysis and Research on Students' Learning Behavior Based o...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Han, Jiazheng Wei, Zhengang Ocean University of China Dept. of Computer Application Technology Qingdao China Ocean University of China Dept. of Software Engineering Qingdao China

ISBN: (纸本)9781665406925

with the effective practical application of data mining technology in other fields, many researchers have used data mining technology to analyze the learning process data in the field of education and teaching, and explore the hidden and potentially valuable information and knowledge. This paper mainly mines the association in students' learning behavior data through lag sequence analysis algorithm (LSA), and shows learners' learning trajectory, willingness and cognitive process in the learning process. Help to grasp the potential behavior mode of students' learning and effectively guide the development and implementation of follow-up teaching work. © 2021 IEEE.

关键词： Data mining

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Attack against UAV Infrared Object Detection Based on Gradient Descent Method

Attack against UAV Infrared Object Detection Based on Gradie...

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International Conference on Information Systems and computer Aided Education (ICISCAE)

作者： Mingkang Xu Keyu Chen Haoli Xu Xing Yang Jun Lu Bin Qu Haoqi Gao Zhiyang Hu Mengjiang Wu Jun Chen State Key Laboratory of Pulsed Power Laser College of Electronic Engineering National University of Defense Technology Hefei China National University of Defense Technology Hefei China College of Computer Science and Software Enginering Hohai University Nanjing China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

At present, infrared intelligent detection technology has been widely used in various fields. Due to the development of its technology, how to interfere with important targets such as intelligent recognition of vehicles has become a hot issue in the current technical field. Therefore, this study is based on the near-infrared band. We generate vehicle target confrontation samples to interfere with infrared detector devices. This paper proposes to first collect the single-band image of the protection target through the infrared UAV, and aligns it to align the position of the protection target in the single-band image to obtain the salient features of the protection target. Then, based on the loss function calculated by machine learning method, the adversarial samples of single-band images are generated by using the basic iterative method. The constraint function is used to jointly optimize the loss function, so that the attack of the adversarial samples on the single-band detection system of the infrared UAV can reach the target value, and the adversarial samples of the infrared image in this state are obtained. Then, it is arranged in multiple positions on the target vehicle to complete the interference to the infrared detection system. We first need to complete the simulation attack experiment in the digital domain, and then complete the confrontation with the infrared detector by using a special material that meets the requirements in the physical domain. The experimental results show that the attack success rate of this method has a certain effect. This paper has a certain reference for the future research of vehicle infrared reconnaissance and anti-reconnaissance.

关键词： Infrared detectors Accuracy Target recognition Autonomous aerial vehicles Linear programming Stability analysis Protection Optimization Thermal stability Convergence

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