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arXiv 2021年

作者： Ali, Tahir Mohammad Rehman, Attique Ur Nawaz, Ali Butt, Wasi Haider Department of Computer Science Gulf University of Science and Technology Kuwait Department of Computer and Software Engineering CEME NUST Islamabad Pakistan

In most E-learning systems, educational activities are presented in a static way without bearing in mind the particulars or student levels and skills. Personalization and adaptation of an E-learning management system are dependent on the flexibility of the system in providing different learning and content models to individual students based on their characteristics. In this paper, we suggest an Adaptive E-learning system which is providing adaptability with support of justification-based truth maintenance system. The system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the student’s profile, interests, and previous results. The validation of proposed framework is performed by meta-model. © 2021, CC BY-SA.

关键词： Decision support systems

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Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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Mixture of Knowledge Minigraph Agents for Literature Review Generation

arXiv

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arXiv 2024年

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University the Department of Computing 999077 Hong Kong Shenzhen University the College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. © 2024, CC BY.

关键词： Reviews

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Role of masks in mitigating viral spread on networks

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Physical Review E 2023年第1期108卷 014306-014306页

作者： Yurun Tian Anirudh Sridhar Chai Wah Wu Simon A. Levin Kathleen M. Carley H. Vincent Poor Osman Yağan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 USA Thomas J. Watson Research Center IBM Yorktown Heights New York 10598 USA Department of Ecology & Evolutionary Biology Princeton University Princeton New Jersey 08544 USA Software and Societal Systems School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: The probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and tradeoffs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Last, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence.

关键词： Cascades Epidemic Percolation phase transition Spreading Multilayer & multiplex networks Poisson degree distribution networks Random graphs Social networks SIR model Spreading models Uncorrelated percolation

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P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

P1AC: Revisiting Absolute Pose From a Single Affine Correspo...

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International Conference on computer Vision (ICCV)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.

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TL-CLIP: A Power-specific Multimodal Pre-trained Visual Foundation Model for Transmission Line Defect Recognition

arXiv

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arXiv 2024年

作者： Zhang, Ke Zheng, Zhaoye Guo, Yurong Wang, Jiacun Yang, Jiyuan Xiao, Yangjie Department of Electronic and Communication Engineering North China Electric Power University Hebei071003 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Hebei Baoding071003 China Computer Science and Software Engineering Department Monmouth University West Long Branch07764 United States

Transmission line defect recognition models have traditionally used general pre-trained weights as the initial basis for their training. These models often suffer weak generalization capability due to the lack of domain knowledge in the pretraining dataset. To address this issue, we propose a twostage transmission-line-oriented contrastive language-image pretraining (TL-CLIP) framework, which lays a more effective foundation for transmission line defect recognition. The pre-training process employs a novel power-specific multimodal algorithm assisted with two power-specific pre-training tasks for better modeling the power-related semantic knowledge contained in the inspection data. To fine-tune the pre-trained model, we develop a transfer learning strategy, namely fine-tuning with pre-training objective (FTP), to alleviate the overfitting problem caused by limited inspection data. Experimental results demonstrate that the proposed method significantly improves the performance of transmission line defect recognition in both classification and detection tasks, indicating clear advantages over traditional pretrained models in the scene of transmission line inspection. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Framework for Secure Communication on Internet of Things (IoT) 5th

A Framework for Secure Communication on Internet of Things (...

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5th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2020

作者： Hosenkhan, Mohammad Reza Pattanayak, Binod Kumar Department of Software Engineering Faculty of Information and Communication Technology Universite Des Mascareignes Beau Bassin-Rose Hill Mauritius Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (纸本)9789813342989

Internet of things (IoT) encompasses millions of resource constrained wirelessly connected devices that are very often prone to external malicious attacks. Hence, there arises a need for protecting these devices from such attacks. In this paper, we have proposed a secure communication scheme for resource constrained scheme where a device is identified by a key pair, public key and private key, and its unique identification (ID) that is assigned to it by Google Cloud IoT Core and the device stores the private key for communication with the server. Key generation uses a cryptographic approach. The communication can be facilitated by the device communicating its private key along with a cryptographically signed Java-based Web Token (JWT) to the MQTT bridge following which the connection can be established. The same JWT can be used for further communication as well. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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HybridFlow-DNA: A Deep Generative Compression Framework for DNA Storage of Images

HybridFlow-DNA: A Deep Generative Compression Framework for ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Cihan Ruan Rongduo Han Shan Gao Lei Lu Wei Jiang Wei Wang Haoyu Wu Nam Ling Department of Computer Science and Engineering Santa Clara University Santa Clara CA USA College of Software Nankai University Tianjin China College of Life Sciences Nankai University Tianjin China Electrical and Computer Engineering Northeastern University MA USA Futurewei Technologies Inc. Santa Clara CA USA Roku Inc. San Jose CA USA

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

DNA storage has emerged as a promising solution to address the exponentially growing demand for storage capacity, offering advantages in density, stability, and long-term preservation potential. Currently, image compression for DNA storage has evolved into learned image compression (LIC), particularly through the application of deep learning methods based on artificial neural networks. The present study proposes a novel image compression framework for DNA Storage, named HybridFlow-DNA. HybridFlow-DNA is established by integration of VQGAN and MLIC with the adaptive dynamic DNA fountain encoding scheme. Experimental results demonstrate that HybridFlow-DNA achieves a high virtual information capacity while effectively maintaining the fidelity of the reconstruction of images.

关键词： Deep learning Image coding Circuits and systems DNA Information processing Feature extraction Robustness Image storage Integrated circuit reliability Image reconstruction

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A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen

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computers, Materials & Continua 2020年第10期65卷 597-616页

作者： Lejun Zhang Zhijie Zhang Weizheng Wang Rasheed Waqas Chunhui Zhao Seokhoon Kim Huiling Chen College of Information Engineering Yangzhou UniversityYangzhou225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou362000China University of Aizu Aizu Wakamatsu9658580Japan College of Information and Communication Engineering Harbin Engineering UniversityHarbin150001China Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China

As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually *** blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert ***,its combination with covert communication thus far has been mostly at the theoretical *** BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high *** present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed *** greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses *** the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission ***,a more efficient and feasible method for the application of covert communication on the blockchain is *** addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.

关键词： Covert communication blockchain Bitcoin address

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Smart Water Taps Using IoT Sensors and Deep Reinforcement Learning for Personalized Consumption Control

Smart Water Taps Using IoT Sensors and Deep Reinforcement Le...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Mageshkumar Naarayanasamy Varadarajan Viyyapu Lokeshwari Vinya R. Jenin Prabhu G. Swathi N. Mohankumar S. Velmurugan Lead Software Engineer Capital One Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Assistant Professor Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakundhala Engineering College Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Professor Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Professor Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.

关键词： Water heating Water conservation User interfaces Deep reinforcement learning Real-time systems Sensor systems Internet of Things

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