Pub Date : 2023-01-20DOI: 10.34133/icomputing.0012
Taoping Liu, L. Guo, Mou Wang, Chen Su, Di Wang, Hao Dong, Jingdong Chen, Weiwei Wu
{"title":"Review on Algorithm Design in Electronic Noses: Challenges, Status, and Trends","authors":"Taoping Liu, L. Guo, Mou Wang, Chen Su, Di Wang, Hao Dong, Jingdong Chen, Weiwei Wu","doi":"10.34133/icomputing.0012","DOIUrl":"https://doi.org/10.34133/icomputing.0012","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72482904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.34133/icomputing.0008
Hehe Fan, Tao Zhuo, Xiaoyu Feng, Guoshun Nan
{"title":"How Deep Neural Networks Understand Motion? Towards Interpretable Motion Modeling by Leveraging the Relative Change in Position","authors":"Hehe Fan, Tao Zhuo, Xiaoyu Feng, Guoshun Nan","doi":"10.34133/icomputing.0008","DOIUrl":"https://doi.org/10.34133/icomputing.0008","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73298405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-11DOI: 10.34133/icomputing.0010
P. Memmolo, D. Pirone, Daniele G Sirico, L. Miccio, V. Bianco, Ahmed B. Ayoub, D. Psaltis, P. Ferraro
{"title":"Loss minimized data reduction in single-cell tomographic phase microscopy using 3D Zernike descriptors","authors":"P. Memmolo, D. Pirone, Daniele G Sirico, L. Miccio, V. Bianco, Ahmed B. Ayoub, D. Psaltis, P. Ferraro","doi":"10.34133/icomputing.0010","DOIUrl":"https://doi.org/10.34133/icomputing.0010","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85949188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-03DOI: 10.34133/icomputing.0009
Deyi Li
{"title":"Cognitive Physics - The Enlightenment by Schrödinger, Turing, Wiener and Beyond","authors":"Deyi Li","doi":"10.34133/icomputing.0009","DOIUrl":"https://doi.org/10.34133/icomputing.0009","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85607704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-03DOI: 10.34133/icomputing.0007
Ido Czerninski, Y. Schechner
{"title":"PARS - Path Recycling and Sorting for Efficient Cloud Tomography","authors":"Ido Czerninski, Y. Schechner","doi":"10.34133/icomputing.0007","DOIUrl":"https://doi.org/10.34133/icomputing.0007","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82520205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities to understand the complexity of the brain and its emulation using computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, and the success of artificial neural networks has highlighted the importance of network architecture. It is now time to bring these together to better understand how intelligence emerges from the multiscale repositories in the brain. In this Perspective, we propose the Digital Twin Brain (DTB)—a transformative platform that bridges the gap between biological and artificial intelligence. It comprises three core elements: the brain structure, which is fundamental to the twinning process, bottom-layer models for generating brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint that preserves the brain’s network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately can propel the development of artificial general intelligence and facilitate precision mental healthcare.
{"title":"Digital twin brain: a bridge between biological intelligence and artificial intelligence","authors":"Hui Xiong, Congying Chu, Lingzhong Fan, Ming Song, Jiaqi Zhang, Yawei Ma, Ruonan Zheng, Junyang Zhang, Zhengyi Yang, Tianzi Jiang","doi":"10.34133/icomputing.0055","DOIUrl":"https://doi.org/10.34133/icomputing.0055","url":null,"abstract":"In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities to understand the complexity of the brain and its emulation using computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, and the success of artificial neural networks has highlighted the importance of network architecture. It is now time to bring these together to better understand how intelligence emerges from the multiscale repositories in the brain. In this Perspective, we propose the Digital Twin Brain (DTB)—a transformative platform that bridges the gap between biological and artificial intelligence. It comprises three core elements: the brain structure, which is fundamental to the twinning process, bottom-layer models for generating brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint that preserves the brain’s network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately can propel the development of artificial general intelligence and facilitate precision mental healthcare.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134967549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.34133/icomputing.0028
Mohammad Kordzanganeh, Daria Kosichkina, Alexey Melnikov
The use of quantum neural networks for machine learning is a paradigm that has recently attracted considerable interest. Under certain conditions, these models approximate the distributions of their datasets using truncated Fourier series. Owing to the trigonometric nature of this fit, angle-embedded quantum neural networks may have difficulty fitting nonharmonic features in a given dataset. Moreover, the interpretability of hybrid neural networks remains a challenge. In this study, we introduce an interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to (a) a classical multi-layered perceptron and (b) a variational quantum circuit, after which the 2 outputs are linearly combined. The quantum neural network creates a smooth sinusoidal foundation based on the training set, and the classical perceptrons fill the nonharmonic gaps in the landscape. We demonstrate this claim using 2 synthetic datasets sampled from periodic distributions with added protrusions as noise. The training results indicate that parallel hybrid network architecture can improve solution optimality on periodic datasets with additional noise.
{"title":"Parallel Hybrid Networks: an interplay between quantum and classical neural networks","authors":"Mohammad Kordzanganeh, Daria Kosichkina, Alexey Melnikov","doi":"10.34133/icomputing.0028","DOIUrl":"https://doi.org/10.34133/icomputing.0028","url":null,"abstract":"The use of quantum neural networks for machine learning is a paradigm that has recently attracted considerable interest. Under certain conditions, these models approximate the distributions of their datasets using truncated Fourier series. Owing to the trigonometric nature of this fit, angle-embedded quantum neural networks may have difficulty fitting nonharmonic features in a given dataset. Moreover, the interpretability of hybrid neural networks remains a challenge. In this study, we introduce an interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to (a) a classical multi-layered perceptron and (b) a variational quantum circuit, after which the 2 outputs are linearly combined. The quantum neural network creates a smooth sinusoidal foundation based on the training set, and the classical perceptrons fill the nonharmonic gaps in the landscape. We demonstrate this claim using 2 synthetic datasets sampled from periodic distributions with added protrusions as noise. The training results indicate that parallel hybrid network architecture can improve solution optimality on periodic datasets with additional noise.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135699130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.34133/icomputing.0006
Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Salim Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence, and internet of things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human–computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: Intelligent computing is not only intelligence oriented but also intelligence driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy, and an abundance of innovations in the theories, systems, and applications of intelligent computing is expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.
{"title":"Intelligent Computing: The Latest Advances, Challenges, and Future","authors":"Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Salim Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan","doi":"10.34133/icomputing.0006","DOIUrl":"https://doi.org/10.34133/icomputing.0006","url":null,"abstract":"Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence, and internet of things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human–computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: Intelligent computing is not only intelligence oriented but also intelligence driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy, and an abundance of innovations in the theories, systems, and applications of intelligent computing is expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135913079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.34133/icomputing.0041
Chenyang Wu, Zongzhang Zhang
Reinforcement learning (RL) is indispensable for building intelligent decision-making agents. However, current RL algorithms suffer from statistical and computational inefficiencies that render them useless in most real-world applications. We argue that high-value information in the real world is essential for intelligent decision-making; however, it is not addressed by most RL formalisms. Through a closer investigation of high-value information, it becomes evident that, to exploit high-value information, there is a need to formalize intelligent decision-making as bounded-optimal lifelong RL. Thus, the challenge of achieving intelligent decision-making is summarized as effectively surfing information, specifically regarding handling the non-IID (independent and identically distributed) information stream while operating with limited resources. This study discusses the design of an intelligent decision-making agent and examines its primary challenges, which are (a) online learning for non-IID data streams, (b) efficient reasoning with limited resources, and (c) the exploration–exploitation dilemma. We review relevant problems and research in the field of RL literature and conclude that current RL methods are insufficient to address these challenges. We propose that an agent capable of overcoming these challenges could effectively surf the information overload in the real world and achieve sample- and compute-efficient intelligent decision-making.
{"title":"Surfing Information: The Challenge of Intelligent Decision-Making","authors":"Chenyang Wu, Zongzhang Zhang","doi":"10.34133/icomputing.0041","DOIUrl":"https://doi.org/10.34133/icomputing.0041","url":null,"abstract":"Reinforcement learning (RL) is indispensable for building intelligent decision-making agents. However, current RL algorithms suffer from statistical and computational inefficiencies that render them useless in most real-world applications. We argue that high-value information in the real world is essential for intelligent decision-making; however, it is not addressed by most RL formalisms. Through a closer investigation of high-value information, it becomes evident that, to exploit high-value information, there is a need to formalize intelligent decision-making as bounded-optimal lifelong RL. Thus, the challenge of achieving intelligent decision-making is summarized as effectively surfing information, specifically regarding handling the non-IID (independent and identically distributed) information stream while operating with limited resources. This study discusses the design of an intelligent decision-making agent and examines its primary challenges, which are (a) online learning for non-IID data streams, (b) efficient reasoning with limited resources, and (c) the exploration–exploitation dilemma. We review relevant problems and research in the field of RL literature and conclude that current RL methods are insufficient to address these challenges. We propose that an agent capable of overcoming these challenges could effectively surf the information overload in the real world and achieve sample- and compute-efficient intelligent decision-making.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74764103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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