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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Kushala, V.M. Supriya, M.C. Divakar, H.R. Sri Siddhartha Academy of Higher Education Department of Computer Science Karnataka Tumkur India Sri Siddhartha Institue of Technology Department of Information Science Karnataka Tumkur India PES College of Engineering Department of Master of Computer Applications Karnataka Mandya India

ISBN: (纸本)9781665456357

Agriculture being a prominent occupation of India with its contribution to GDP, effective, prominent technological methodology needs to be adopted for the process of agriculture to be more sustainable and eco-friendlier. Adoption and awareness towards organic fertilizers is a key concept in sustainable agriculture. Agriculture is the most dynamic field with multi-dimensional factors involving in the procedure. To handle the factors involved in the cultivation of a crop a knowledge base with organic fertilizers information and its availability is much needed information for the farmers in adopting them. To achieve this, we built a domain ontology for agriculture by considering factors like soil information, organic fertilizers, diseases, geographical information, crop demand, fertilizers availability. A semantic web helps in serving the ontology to the farmers. © 2022 IEEE.

关键词： Knowledge management

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A MIMD rendering algorithm for distributed memory architectures 93

A MIMD rendering algorithm for distributed memory architectu...

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Proceedings of the 1993 symposium on Parallel rendering

作者： Thomas W. Crockett Tobias Orloff Institue for Computer Applications in Science and Engineering NASA Langley Research Center Minerva Software

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EMVGAN: Emotion-Aware Music-Video Common Representation Learning via Generative Adversarial Networks 5

EMVGAN: Emotion-Aware Music-Video Common Representation Lear...

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5th International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia, MMArt-ACM 2022

作者： Tsai, Yu-Chih Pan, Tse-Yu Kao, Ting-Yang Yang, Yi-Hsuan Hu, Min-Chun Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science National Tsing Hua University Taiwan Institue of Information Systems and Applications National Tsing Hua University Taipei Taiwan Research Center for IT Innovation Academia Sinica Taiwan

ISBN: (纸本)9781450392402

Music can enhance our emotional reactions to videos and images, while videos and images can enrich our emotional response to music. Cross-modality retrieval technology can be used to recommend appropriate music for a given video and vice versa. However, the heterogeneity gap caused by the inconsistent distribution between different data modalities complicates learning the common representation space from different modalities. Accordingly, we propose an emotion-Aware music-video cross-modal generative adversarial network (EMVGAN) model to build an affective common embedding space to bridge the heterogeneity gap among different data modalities. The evaluation results revealed that the proposed EMVGAN model can learn affective common representations with convincing performance while outperforming other existing models. Furthermore, the satisfactory performance of the proposed network encouraged us to undertake the music-video bidirectional retrieval task. © 2022 ACM.

关键词： Generative adversarial networks

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More than Extracting "Important" Sentences: the Application of PEGASUS 26

More than Extracting "Important" Sentences: the Application ...

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26th International Conference on Technologies and applications of Artificial Intelligence, TAAI 2021

作者： Yang, Ting-Hao Lu, Ching-Ching Hsu, Wen-Lian National Tsing Hua University Institue of Information Systems and Applications Hsinchu City Taiwan National Tsing Hua University Institute of Taiwan Langauges and Langauge Teaching Hsinchu City Taiwan Asia University Department of Computer Science and Information Engineering Taichung City Taiwan

ISBN: (纸本)9781665408257

Pre-trained language models may reduce the amount of training data required. Among the models, PEGASUS, a recently proposed self-supervised approach, is trained to generate the pseudo-summary given the partially masked document. PEGASUS uses gap sentence generation for summarization. The most important sentences are masked, and then PEGASUS predicts the masked sentences as the output summary. In this study, however, we apply PEGASUS in a novel downstream task. We reformulate the task to generate the masked question part in a primary math word problem. In the past research, PEGASUS has shown good potentials on the few-shot datasets, so we try a smaller set of primary math text problems as well. The fine-tuning dataset sizes used in this study are 1000, 500, 50, 10 samples. Their performance are measured by a non-weighted average of the ROUGE-1, ROUGE-2, and ROUGE-L scores. The results show the outstanding performance of PEGASUS applied in our novel downstream task. © 2021 IEEE.

关键词： Natural language processing systems

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A Novel Approach to Optimized Cluster Head Selection in Heterogeneous WSNs for IoT Deployments

A Novel Approach to Optimized Cluster Head Selection in Hete...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Amit Kumar Jain Megha Mudholkar Pankaj Mudholkar M. K. Sharma Mohit Tiwari Vandana Roy Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India Department of Computer Engineering Marwadi University Rajkot Gujarat India Faculty of Computer Applications Marwadi University Rajkot Gujarat India Mathematics Chaudhary Charan Singh University Meerut Uttar Pradesh India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Department of Electronics Communication Gyan Ganga Institue of Technology and Sciences Jabalpur M.P. India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Despite the study proposing the LEACH methodology, its ineffectiveness has been shown. A non-deterministic polynomial (NP) is a difficult issue to optimise for CH. Using an Artificial Immune System (AIS) and Particle Swarm Optimisation (PSO), this study suggests a new routing algorithm. This paper also discusses the best route choice, which may increase network lifetime and reduce energy consumption. We make good use of a lot of metaheuristics techniques, including PSO and AIS. Using principles from neuroscience and social science, the PSO algorithm tackles challenges in a wide range of fields, with a focus on computing and engineering. Particles are creatures that move around the multi-dimensional search distance, where every particle represents a possible solution to the multi-dimensional optimisation issue. The most recent development in AI, AIS takes its cues from the workings of the immune response in living organisms. Immune systems control both the inborn and adapted immunity response's defence mechanisms. Since the latter includes metaphors like variety, self-control, recognition, and memory growth, it is more significant. The AIS outperforms the Heterogeneous Low Energy Adaptive Clustering Hierarchy (LEACH-HPR) and particle swarm optimisation (PSO) in terms of results from experiments. Artificial Immune System (AIS) retains an extensive amount of active nodes while maintaining its efficiency with a 98.5% accuracy rate. AIS is more energy efficient, which means nodes last longer, but it's not much better than PSO and LEACH-HPR in terms of accuracy.

关键词： Wireless sensor networks Accuracy Artificial immune systems Metaheuristics Signal processing algorithms Routing Polynomials Internet of Things Artificial intelligence Particle swarm optimization

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A mathematical model for efficient extraction of key locations from point-cloud data in track area

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Industrial Artificial Intelligence 2023年第1期1卷 1-14页

作者： Chen, Shuyue Wu, Jiaolv Lu, Jian Wang, Xizhao College of Mathematics and Statistics Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institue of Information Technology Shenzhen China Shenzhen No. 3 Vocational School of Technology Shenzhen China College of Engineering Huaqiao University Quanzhou China Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Lab. of Intelligent Information Process Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

During the construction of a metro system, it is inevitable that deviations will occur between the excavated tunnel and the original designed scheme. As such, it is necessary to adjust the designed scheme to accommodate these discrepancies. Specifically, the adjustment of the designed scheme involves a rigorous process of repeatedly selecting and verifying the feasibility of the proposed modifications using point-cloud data obtained from the tunnel. However, this process can be considerably time-consuming due to the large-scale and potentially redundant nature of the point-cloud data. This paper proposes a mathematical model for point-cloud data acquired in measuring a mined tunnel, which may deviate from the originally designed one. The modeling, which mainly includes determining its normal plane, and building the equation of tunnel point-cloud data, is to quickly extract several key locations in the tunnel surface for modifying the original design in order to achieve a minimum error between the modified design and the mined tunnel. In comparison with the conventional processing of extracting several key locations directly from point-cloud data, our model shows a significant promotion of extraction efficiency under an acceptable error bound. The model is tested in a real tunnel point-cloud data and the testing results confirm the increase of fitting accuracy and the decrease of computational load.

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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