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Advanced insulating materials contributing to“carbon neutrality”:Opportunities,issues and challenges

High Voltage 2022年第4期7卷 607-609页

作者： Chuanyang Li Yunlong Zi Yang Cao Giovanni Mazzanti Davide Fabiani Di Zhang Yunqi Xing Jinliang He State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing100084China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyClear Water BayHong Kong SARChina Electrical and Computer Engineering University of Connecticut371 Fairfield WayStorrsCT 06269USA Department Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Electrical and Computer Engineering Department Naval Postgraduate School1 University CircleMontereyCA 93943USA State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin300131China

The vision of 2050 worldwide carbon-neutral ignites a revo-lution in all *** innovations in both electrical power equipment and electrified transportation assets are shaping our future energy *** materials,as the core element of energy infrastructure,are facing unprece-dented challenges that stand in the pathways to global carbon ***,researchers from both academic in-stitutions and industrial sections are working to turn the challenges into opportunities.

关键词： structure. neutral carbon

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

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Automatic Identification of Chinese Paired Discourse Connectives

Automatic Identification of Chinese Paired Discourse Connect...

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International Conference on Semantic Computing

作者： Nelson Filipe Costa Yushun Cheng Thomas Chapados Muermans Blaise Hanel Leila Kosseim Department of Computer Science and Software Engineering Computational Linguistics at Concordia (CLaC) Laboratory Concordia University Montréal Canada

This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations between them. Most DCs consist of a consecutive words (eg. as a result); however paired DCs are composed of non-consecutive words that together signal the discourse relation (eg. on one hand … on the other hand). Although paired DCs are not common in English, they are very frequent in Chinese. The contribution of this paper in two-fold: First, we propose a methodology for the automatic identification of Chinese paired DCs. Second, we present a new corpus based on the Chinese Discourse Treebank (CDTB) [1] annotated with paired DCs. To identify paired DCs, we experimented with two main approaches: hypothesis testing and supervised machine learning. Although the hypothesis testing approaches led to lower than expected results, the simple machine learning models achieved F-scores between 72.5%–75.6% with no fine-tuning.

关键词： Computational modeling Semantics Machine learning Testing

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Word Embeddings and Validity Indexes in Fuzzy Clustering

arXiv

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arXiv 2022年

作者： Toufani-Movaghar, Danial Feizi-Derakhshi, Mohammad-Reza Computer Intelligent System Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran

In the new era of internet systems and applications, a concept of detecting distinguished topics from huge amounts of text has gained a lot of attention. These methods use representation of text in a numerical format-called embeddings-to imitate human-based semantic similarity between words. In this study, we perform a fuzzy-based analysis of various vector representations of words, i.e., word embeddings. Also we introduce new methods of fuzzy clustering based on hybrid implementation of fuzzy clustering methods with an evolutionary algorithm named Forest Optimization. We use two popular fuzzy clustering algorithms on count-based word embeddings, with different methods and dimensionality. Words about covid from Kaggle dataset gathered and calculated into vectors and clustered. The results indicate that fuzzy clustering algorithms are very sensitive to high-dimensional data, and parameter tuning can dramatically change their performance. We evaluate results of experiments with various clustering validity indexes to compare different algorithm variation with different embeddings accuracy. © 2022, CC0.

关键词： Fuzzy clustering

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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

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Chinese Journal of Electronics 2023年第2期21卷 202-208页

作者： Huihui Li Xuanqin Mou Zhen Ji Hang Yu Yan Li Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Embedded System Design Shenzhen China

A novel wideband 5.8GHz CPW-fed antenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numerically using the Method of moment (MOM) and the Finite element method (FEM). With the antenna size limited to $30\times 30 \text{mm}^{2}$ , the −10dB bandwidth obtained by MOM is 3.235GHz (5.765∼9GHz) and the −9.5dB band-width obtained by FEM is 2.74GHz (5.32∼8.06GHz), corresponding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured −10dB bandwidth is 2.68GHz (5.63GHz∼8.31GHz), 46.2% of the 5.8GHz frequency. Furthermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than −10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： Antenna measurements Resonant frequency Loss measurement Frequency measurement Finite element analysis Broadband antennas RFID tags

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Clustering With Adaptive Graph Learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of engineering & Technology 2023.

关键词： Fuzzy clustering

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A Review of Semantic-Based Reasoning Framework for Context-Aware Mobile-Crowdsourcing

A Review of Semantic-Based Reasoning Framework for Context-A...

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International Conference on Information Technology in Asia (CITA)

作者： Nana Hauwa Abdulsalam Sufri Muhammad Raihani Mohamed Department of Software Engineering and Information System Universiti Putra Malaysia Serdang Selangor Malaysia Department of Computer Science Universiti Putra Malaysia Serdang Selangor Malaysia

Technology has increased the interest and demand for pervasive systems which require contextual information to function at optimal capacity. There have been numbers of research in context-aware systems that has limited focused on the semantic-based approach in the crowdsourcing domain. Thus, it promotes challenges in the context and service acquisition and representation for reasoning control mechanism. This paper aims to review semantic-based reasoning framework with a focus on the mobile crowdsourcing domain. Different domains acquire different contextual information, either extrinsic or intrinsic. The review of the frameworks has help to formulate a process framework that applied semantic approach that has the important component for context-aware reasoning process. The framework can be used in the context-aware mobile crowdsourcing domain or can be generalized to other domain to aid reasoning control. Its advantage over other crowdsourcing frameworks which is focuses not only on context and service acquisition but also the representation on the acquired information.

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: the next leap toward native language-based and explainable machine learning framework

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Automated software engineering 2023年第2期30卷 1-28页

作者： Dessureault, Jean-Sébastien Massicotte, Daniel LSSI - Laboratory of Signal and System Integration Electrical and Computer Engineering Department Université du Québec á Trois-Rivières Trois-Rivières Canada

The machine learning frameworks flourished in the last decades, allowing artificial intelligence to get out of academic circles to be applied to enterprise domains. This field has significantly advanced, but there is still some meaningful improvement to reach the subsequent expectations. The proposed framework, named AI $$^{2}$$ , uses a natural language interface that allows non-specialists to benefit from machine learning algorithms without necessarily knowing how to program with a programming language. The primary contribution of the AI $$^{2}$$ framework allows a user to call the machine learning algorithms in English, making its interface usage easier. The second contribution is greenhouse gas (GHG) awareness. It has some strategies to evaluate the GHG generated by the algorithm to be called and to propose alternatives to find a solution without executing the energy-intensive algorithm. Another contribution is a preprocessing module that helps to describe and to load data properly. Using an English text-based chatbot, this module guides the user to define every dataset so that it can be described, normalized, loaded, and divided appropriately. The last contribution of this paper is about explainability. The scientific community has known that machine learning algorithms imply the famous black-box problem for decades. Traditional machine learning methods convert an input into an output without being able to justify this result. The proposed framework explains the algorithm’s process with the proper texts, graphics, and tables. The results, declined in five cases, present usage applications from the user’s English command to the explained output. Ultimately, the AI $$^{2}$$ framework represents the next leap toward native language-based, human-oriented concerns about machine learning framework.

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Learning Pareto Set for Multi-Objective Continuous Robot Control

arXiv

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arXiv 2024年

作者： Shu, Tianye Shang, Ke Gong, Cheng Nan, Yang Ishibuchi, Hisao Department of Computer Science and Engineering Southern University of Science and Technology China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science City University of Hong Kong Hong Kong

For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-objective control problem is continuous and complex, traditional multiobjective reinforcement learning (MORL) algorithms search for many Pareto-optimal deep policies to approximate the Pareto set, which is quite resource-consuming. In this paper, we propose a simple and resource-efficient MORL algorithm that learns a continuous representation of the Pareto set in a high-dimensional policy parameter space using a single hypernet. The learned hypernet can directly generate various well-trained policy networks for different user preferences. We compare our method with two state-of-the-art MORL algorithms on seven multi-objective continuous robot control problems. Experimental results show that our method achieves the best overall performance with the least training parameters. An interesting observation is that the Pareto set is well approximated by a curved line or surface in a highdimensional parameter space. This observation will provide insight for researchers to design new MORL algorithms. © 2024, CC BY.

关键词： Reinforcement learning

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An Intelligent Transportation systems-Based Machine Learning-Enhanced Traffic Prediction Model using Time Series Analysis and Regression Techniques*

An Intelligent Transportation Systems-Based Machine Learning...

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IEEE International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Klea Elmazi Donald Elmazi Eugen Musta Fatjon Mehmeti Florenc Hidri Department of Computer Engineering Faculty of Engineering University of Rijeka University Luarasi Tirana Albania Department of Computer Engineering Faculty of Computer Science and IT Artificial Intelligence Laboratory University Metropolitan Tirana Tirana Albania Department of Finance Faculty of Economy Canadian Institute of Technology Tirana Albania Department of Computer Engineering University Metropolitan Tirana Tirana Albania Department of Software Engineering Faculty of Engineering Canadian Institute of Technology Tirana Albania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Traffic congestion represents a daunting challenge for all facets of urban development, as well as represents a universal problem in all urban areas, to various extents. In recent years, many cities have adopted the use of Intelligent Transport systems (ITS) to manage traffic congestion. These systems are indeed useful, but they are mainly geared to predict real-time traffic congestion, yielding to a certain short-sightedness in our prediction model. Through the use of Time Series Analysis, and Regression models for traffic congestion prediction, we posit that we can address the issue. The data that these models were trained on derives from two datasets, namely a dataset from Kaggle and another from the road traffic footage of Tirana. The data then points to Gated Recurrent Units (GRU) being a more accurate time series category, and Support Vector Regression (SVR) to be a better performer than linear regression.

关键词： Support vector machines Analytical models Technological innovation Time series analysis Urban areas Transportation Predictive models

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