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共 1846 条记录，以下是171-180 订阅

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Improve Exploration of Arithmetic Optimization Algorithm by Opposition-based Learning

Improve Exploration of Arithmetic Optimization Algorithm by ...

引用

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Xia Lin Haomiao Li Xin Jiang Yuchao Gao Jinran Wu Yang Yang College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China School of Mathematical Sciences Queensland University of Technology Brisbane Australia

ISBN: (纸本)9781665426565

An improved version of the arithmetic optimization algorithm (AOA) based on the opposition-based learning (OBL) strategy called OBLAOA is proposed in this paper. The proposed OBLAOA algorithm consists of two stages, and in the second stage we adds OBL to update the AOA population in each iteration. The improved AOA is compared with the original AOA by using 12 benchmark functions in different dimensions to validate the improvement on exploration with the OBL. Eventually ,we get a conclusion that the OBLAOA is committed to take both candidate solutions and their opposite solutions into consideration, which shows greater opportunity to reach the global optimal and faster convergence acceleration than AOA.

关键词： Image segmentation conferences Sociology Benchmark testing Feature extraction Statistics informatics

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Optimizing schedule length for DAG type applications with energy consumption constraint in heterogeneous computing systems 7

Optimizing schedule length for DAG type applications with en...

引用

7th ieee international conference on progress in informatics and computing, PIC 2020

作者： He, Yu Chen, Jinchao Du, Chenglie Han, Pengcheng Northwestern Polytechnical University Department of Computer Science Xi'an710072 China

ISBN: (纸本)9781728170862

Schedule length minimization of a parallel application with precedence constrained tasks in heterogenous computing systems is always a research hotspot of distributed computing. Unfortunately, accelerating the execution of tasks requires faster processors and consequently produces more energy. Users are faced to a time-energy tradeoff in selecting processors and execution frequencies for tasks. A common way to address this trade-off is to minimize schedule length under energy constraints. Up to now only a few works have been done to address this problem. Nevertheless, the energy preassignment methods of these works are unfair for low priority tasks in various degrees. Furthermore, existing works pay little attention to the priority assignment strategy, which can be critical for task scheduling. Hence, we proposed a novel approach named DAG Scheduling with Energy Consumption Constraint (DSECC) to solve this problem. DSECC combines several priority assignment strategies to assign priorities for tasks, and preassigns energy for tasks based on the average energy consumption of tasks on different processors. A large number of experiments based on randomly generated DAG show that compared against state-of-the-art algorithms, our proposed algorithm can obtain smaller schedule length and generate less energy consumption. © 2020 ieee.

关键词： Energy utilization

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Adaptive Total Variation Regularized for Hyperspectral Unmixing

Adaptive Total Variation Regularized for Hyperspectral Unmix...

引用

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Chenguang Xu Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9781665426565

The purposed of hyperspectral unmixing is to estimate the spectral signatures composing the data (endmembers) and their abundance fractions. However, most of the traditional sparse unmixing methods are effective in the case of high signal-to-noise ratio (SNR), but is not good in the case of high noise. In order to solve this problem, we innovatively integrates adaptive total variation (ATV) regularization into hyperspectral sparse unmixing and propose a new hyperspectal sparse unmixing model named adaptive total variation regularized for sparse unmixing (SU_ATV). The model can adaptively adjust the horizontal difference and vertical difference of TV, can better optimize the efficiency of TV to improve the anti-noise performance. The experimental results show that SU_ATV has good anti-noise performance to the sparse unmixing.

关键词： Adaptation models TV conferences informatics Hyperspectral imaging Signal to noise ratio

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A Hybrid Brain-Inspired computing Architecture towards Artificial General Intelligence

A Hybrid Brain-Inspired Computing Architecture towards Artif...

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19th ieee international conference on Cognitive informatics and Cognitive computing (ICCI*CC)

作者： Shi, Luping Tsinghua Univ Dept Precis Instrument Beijing Innovat Ctr Future Chips Opt Memory Natl Engn Res CtrCtr Brain Inspired C Beijing 100084 Peoples R China

ISBN: (纸本)9781728195940

Recently, artificial intelligence has made rapid progresses. However, existing AI systems still encounter difficulties even for somethings that humans can easily do. The ultimate way to solve these problems is to develop artificial general intelligence (AGI). Brain inspired computing (BIC) systems are one of the most promising technologies to integrate computer science and neuroscience to facilitate the development of AGI. In this talk, three issues will be discussed: (1) why do we need BIC system? (2) the current status and latest progress in BIC chips, software, and systems; (3) how to develop BIC systems to support AGI with limited understanding of the brain mechanisms. A hybrid and scalable exploration platform of AGI is demonstrated by an unmanned bicycle control system. The main challenges, possible solutions and strategies to develop BIC systems to stimulate AGI development will be addressed.

关键词： Artificial general intelligence brain inspired computing unmanned bicycles

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The Construction of Learning Diagnosis and Resources Recommendation System Based on Knowledge Graph

The Construction of Learning Diagnosis and Resources Recomme...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Kaiyu Dai Yiyang Qiu Rui Zhang Software School of Fudan University Shanghai China National Demonstration Center for Experimental Computer Education School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9781665426565

With the deepening integration of artificial intelligence, ICT in education is approaching to the stage of smart education, the main purpose of which is to realize learning personalization. This paper constructs an intelligent tutoring system to allow teacher establish the course knowledge model visually based on ontology. This system evaluates the learning situation of students using a test auto-generated by a global prediction accuracy optimization algorithm. The learning diagnosis module is implemented according to the learning situations of students and the structure analysis of knowledge graph based on node contribution. The resource recommendation module is implemented through the importance ranking of learning resources. The prototype system is constructed and the experiments are conducted. The results show that our approach can achieve personalized learning well in a certain range.

关键词： conferences Education Prototypes Learning (artificial intelligence) Ontologies Prediction algorithms informatics

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Multi-Level Drowsiness Detection Based on Deep Feature Fusion of Eye and Head Pose

Multi-Level Drowsiness Detection Based on Deep Feature Fusio...

引用

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Fang Ye Shunxin Li Xin Yuan Longfei Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665426565

Drowsiness detection is a significant problem, most existing non-intrusive methods estimate drowsiness only by single images, without leveraging the temporal information available in the frame sequence. The lack of temporal information leads to the inability of drowsiness detection to indicate consecutive behaviors. To this end, we present a drowsiness detection method, which takes into account both eye and head pose deep feature representation by conducting feature fusion. Then, the fused feature is fed into the LSTM (Long Short-Term Memory) network to enhance the accuracy of the drowsiness detection model through temporal information. The experimental results on the NHTU-DDD dataset and the self-constructed dataset show that the proposed method outperforms six existing advanced approaches.

关键词： Head conferences Feature extraction informatics

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Modeling of a system for intelligent animal husbandry 7

Modeling of a system for intelligent animal husbandry

引用

7th international conference on Big Data, Knowledge and Control Systems Engineering, BdKCSE 2021

作者： Valchev, Evgeni Malinov, Pencho Glushkova, Todorka Nikolov, Vasil Doukovska, Lyubka Monov, Vladimir Plovdiv University 'Paisii Hilendarski' Faculty of Mathematics and Informatics Plovdiv Bulgaria Agricultural University Faculty of Agronomy Department of Animal Sciences Plovdiv Bulgaria Bulgarian Academy of Sciences Institute of Information and Communication Technologies Sofia Bulgaria

ISBN: (纸本)9781665410427

Modern realities and the rapid development of digital technologies in the era of Industry 4.0 bring to the fore the task of developing intelligent systems for precision agriculture. Grazing livestock has proven its importance in obtaining biologically clean food products in an ecologically clean environment. The report examines the possibilities for developing an intelligent environment for grazing cows, presents the main directions of work on the implementation of such a system and shares the results obtained so far from the application of the developed prototype. © 2021 ieee.

关键词： Sensor networks

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Towards Engineering Fair Ontologies: Unbiasing a Surveillance Ontology

Towards Engineering Fair Ontologies: Unbiasing a Surveillanc...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Evangelos Paparidis Konstantinos Kotis Intelligent Systems Lab University of the Aegean Lesvos Greece

ISBN: (纸本)9781665426565

Capturing knowledge in ontology-based AI applications may significantly propagate technical/statistical, cultural/social, cognitive/psychological, or other types of bias, to un-fair AI models and to their generated decisions. Biased ontologies (and consequently, knowledge graphs) engineered for intelligent surveillance applications can introduce technical barriers in fair capture of offenders, thus it must be researched as a first priority problem and a constant concern for explicit actions to be taken in the era of a more secure and fair world. In this paper we report preliminary research conducted on the novel topic of engineering fair ontologies and present first experiments with a prototype ontology and knowledge graph in the surveillance domain. Engineering fair ontologies is a quite new research topic, thus, the related work is at early stages. Having said that, in this paper we already highlight a recommended methodological approach for unbiasing ontologies, demonstrated in the surveillance domain, and we identify specific key research issues and challenges for further investigation by the ontology engineering community.

关键词： Knowledge engineering Surveillance conferences Prototypes Ontologies informatics

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Optimal Scale Combinations Selection for Incomplete Generalized Multi-scale Decision Systems

Optimal Scale Combinations Selection for Incomplete Generali...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Qiong Mou Yunlong Cheng College of Mathematics and Physics Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665426565

In the real world, objects are usually measured at different scales and information is often incomplete. The main objective of this study is how to quickly obtain the optimal scale combinations of incomplete generalized multi-scale decision systems (IGMDSs). First, the concept of IGMDSs is introduced, and the sequential three-way decision model of scale space is developed. Second, a stepwise optimal scale selection algorithm is proposed to obtain an optimal scale combination of IGMDS quickly. Finally, to describe the relationships among the scale combinations, the adjacency matrix of the Hasse diagram and updating method for the adjacency matrix are proposed. Accordingly, an efficient optimal scale combinations selection algorithm based on sequential three-way decision is proposed to obtain all optimal scale combinations of IGMDS. Experimental results demonstrate that the proposed algorithms can significantly reduce computational time.

关键词： conferences informatics

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Multi-Round Recommendations for Stable Groups

Multi-Round Recommendations for Stable Groups

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Ilmo Heiska Kostas Stefanidis Faculty of Technology and Communication Sciences Tampere University Tampere Finland

ISBN: (纸本)9781665426565

Recommender systems have been used for suggesting the most suitable products and services for users in diverse scenarios. More recently, the need for making recommendations for groups of users has become increasingly relevant. In addition, there are applications in which recommendations are required in a consecutive sequence. Group recommendations present a challenge for recommender systems: how to balance the preferences of the individual members of a group. On the other hand, when making recommendations for a group for multiple rounds, a recommender has a possibility to dynamically try to balance the preference differences between the group members. This paper introduces two novel methods for multi-round group recommendation scenarios: the adjusted average aggregation method and the average-min-disagreement aggregation method. Both methods aim to provide a group with highly relevant results for the group, while remaining fair for all group members. We experimentally evaluate our approach for groups with different characteristics and show that our methods outperform baseline solutions in all scenarios.

关键词： conferences informatics Recommender systems

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