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19th International Conference on Web information systems and Applications, WISA 2022

作者： Lu, Xun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Multi-hop reasoning has been widely studied for its important application values in the domain of intelligent search and question answering. Real-world applications are often dominated by natural language input, and it is difficult to directly model the relation semantics in natural language to fit the multi-hop reasoning model. In addition, the extremely large scale and complex structure of knowledge graphs increase the challenge. We propose a natural language and knowledge graph fusion model (NLKGF) for multi-hop reasoning. NLKGF embeds knowledge graph by fusing natural language semantics during the graph propagation process and adds a relation attention mechanism to enable entity representations to perceive the contribution of different relations. A relation path encoder is designed to encode the relation path by an improved recurrent neural network, the reasoning entity is obtained by calculating the correlation score with the natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

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International Journal of Digital Earth 2023年第1期16卷 337-354页

作者： Lijia Cheng Bo-Hui Tang Zhiwei He Zhitao Fu Menghua Li Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunmingPeople’s Republic of China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China

The downward shortwave radiation(DSR)is a key input parameter for land surface models and climate *** on the daily averaged Global Land Surface Satellite downward shortwave radiation(GLASS-DSR)dataset over the Yunnan-Kweichow Plateau(YKP)from 1984 to 2018,this paper analyzes variation trend and breakpoints of *** results show that:annual averaged DSR decreases at a decreasing rate of-1.84 W·m^(-2)·decade^(-1) over the YKP from 1984 to 2018;the overall distribution of interannual averaged DSR shows higher in the mid-west,and gradually decreasing from west to northeast over the YKP;the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon;monthly averaged DSR reaches its maximum in May and its minimum in December;breakpoints are found in the seasonal and trend components of daily averaged *** driving factors are examined for their effects on DSR variation,including annual average temperature,precipitation,10 m wind speed,aerosol optical thickness(AOT),total cloud cover,elevation,slope,aspect,longitude,latitude,and climate *** to thefindings,AOT predominates in the spatio-temporal distribution of DSR over the *** study will contribute to studies related to climate change and highland radiation.

关键词： Yunnan-Kweichow plateau downward shortwave radiation time-series analysis breakpoints detection attribution analysis

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A Multi-label Classification Method for Positive and Unlabeled Dataset. 23

A Multi-label Classification Method for Positive and Unlabel...

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Proceedings of the 2023 4th Asia Service sciences and Software engineering Conference

作者： Hikaru Takahashi Zhao Qiangfu Computer Science and Engineering Computer Information System System Intelligence Lab. University of Aizu Japan Computer Science and Engineering System Intelligence Lab. University of Aizu Japan

ISBN: (纸本)9798400708534

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning.

关键词： Machine Learning Multi-class Classification Positive and Unlabeled Learning

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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

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Computer systems science & engineering 2022年第12期43卷 1175-1188页

作者： Norwini Zaidi Iskandar Ishak Fatimah Sidi Lilly Suriani Affendey System Development and Engineering Center Universiti Sains Islam Malaysia71800Negeri SembilanMalaysia Faculty of Computer Science and Information Technology Universiti Putra MalaysiaUPM Serdang43400SelangorMalaysia

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

关键词： Data migration data transformation column-oriented database relational database big data

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Traj-LLM: A New Exploration for Empowering Trajectory Prediction With Pre-Trained Large Language Models

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Lan, Zhengxing Liu, Lingshan Fan, Bo Lv, Yisheng Ren, Yilong Cui, Zhiyong School of Transportation Science and Engineering Beihang University Beijing China Beijing Key Laboratory of Traffic Engineering College of Metropolitan Transportation Beijing University of Technology Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System Beihang University Beijing China

Predicting the future trajectories of dynamic traffic actors is a cornerstone task in autonomous driving. Though existing notable efforts have resulted in impressive performance improvements, a gap persists in scene cognitive and understanding of complex traffic semantics. This paper proposes Traj-LLM, the first to investigate the potential of using pre-trained Large Language Models (LLMs) without explicit prompt engineering to generate future motions from vehicular past trajectories and traffic scene semantics. Traj-LLM starts with sparse context joint encoding to dissect the agent and scene features into a form that LLMs understand. On this basis, we creatively explore LLMs' strong understanding capability to capture a spectrum of high-level scene knowledge and interactive information. To emulate the human-like lane focus cognitive function and enhance Traj-LLM's scene comprehension, we introduce lane-aware probabilistic learning powered by the Mamba module. Finally, a multi-modal Laplace decoder is designed to achieve scene-compliant predictions. Extensive experiments manifest that Traj-LLM, fueled by prior knowledge and understanding prowess of LLMs, together with lane-aware probability learning, transcends the state-of-the-art methods across most evaluation metrics. Moreover, the few-shot analysis serves to substantiate Traj-LLM's performance, as even with merely 50% of the dataset, it surpasses the majority of benchmarks relying on complete data utilization. This study explores endowing the trajectory prediction task with advanced capabilities inherent in LLMs, furnishing a more universal and adaptable solution for forecasting agent movements in a new way. IEEE

关键词： Semantics

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Distributed IoT Community Detection via Gromov-Wasserstein Metric

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IEEE Internet of Things Journal 2024年第8期11卷 13281-13298页

作者： Chang, Shih Yu Chen, Yi Kao, Yi-Chih Chen, Hsiao-Hwa San Jose State University Department of Applied Data Science San JoseCA95192 United States Chengdu610103 China National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu300 Taiwan National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

The Internet of Things (IoT) network is a complex system interconnected by different types of devices, e.g., sensors, smartphones, computers, etc. Community detection is a critical component to understand and manage complex IoT networks. Although several community detection algorithms were proposed, they in general suffer several issues, such as lack of optimal solutions and scalability, and difficulty to be applied to a dynamic IoT environment. In this work, we propose a framework that uses distributed community detection (DCD) algorithms based on Gromov-Wasserstein (GW) metric, namely, GW-DCD, to support scalable community detection and address the issues with the existing community detection algorithms. The proposed GW-DCD applies GW metric to detect communities of IoT devices embedded in a Euclidean space or in a graph space. GW-DCD is able to handle community detection problems in a dynamic IoT environment, utilizing translation/rotation invariance properties of the GW metric. In addition, DCD approach and parallel matrix computations can be integrated into GW-DCD to shorten the execution time of GW-DCD. Finally, a new metric, i.e., GW driven mutual information (GWMI), is derived to measure the performance of community detection by considering internal structure within each community. Numerical experiments for the proposed GW-DCD were conducted with simulated and real-world data sets. Compared to the existing community detection algorithms, the proposed GW-DCD can achieve a much better performance in terms of GWMI and the runtime. © 2014 IEEE.

关键词： Internet of things

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Attention-based CNN for CT-Kidney Classification 6

Attention-based CNN for CT-Kidney Classification

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6th International Conference on Software engineering and Computer science, CSECS 2023

作者： Hu, Jiamu Guo, Yichen Li, Letao Toe, Teoh Teik Engineering with Information Engineering University of Electronic Science and Technology of China Chengdu China Nanyang Technological University NTU Business AI Lab Singapore

ISBN: (纸本)9798350306378

We constructed an attention-based convolutional neural network (CNN) model structure, and it was applied to classify CT-kidney. The concept of attention mechanism and its application in refining con-volution-extracted feature map is elaborated. The addictive attention mechanism is adopted to allocate more attention weight to more distinguish region on feature map, and lead to improvement in classification accuracy. Two experiments were conducted to verify our model structure. In the first experiment, the efficiency of attention-based CNN was validated as it reaches fully convergence within 8 epochs and achieve 100% accuracy on test set. The second experiment verify its improved performance, lightweight and easily train-able architecture in comparison with CNN and Resnet50 on Alzheimer's MRI dataset. In the future, this approach could potentially be employed to address challenges associated with CT radiography classification. © 2023 IEEE.

关键词： Convolutional neural networks

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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Computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Channel Fading Emulation for Highly Dynamic Communication Scenarios

Channel Fading Emulation for Highly Dynamic Communication Sc...

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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Fang, Sheng Yang, Ling Ma, Jian Xie, Yuetian Song, Maozhong Song, Lixin Hua, Boyu Zhu, Qiuming Shi, Dan College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Aviation Key Laboratory of Science and Technology On Aero Electromechanical System Integration Nanjing211106 China Electronic Science and Technology Beijing University of Posts & Telecommunications Beijing100876 China

ISBN: (纸本)9798350383317

Highly dynamic communication terminals have a crucial role in future communication networks. In this article, a channel emulator is designed and implemented on a Field Programmable Gate Array (FPGA) platform to generate channel fading for highly dynamic communication. To reproduce the fast time-varying channel characteristics, a hardware time-evolution method of channel parameters based on scenario-dependent information is proposed. The measurement results indicate that the proposed channel emulator achieves continuity in the time evolution of channel parameters, and the Doppler power spectral density of the emulated channel match the theoretical results, validating the effectiveness of the emulator. © 2024 IEEE.

关键词： Fading channels

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Commonsense Knowledge Construction with Concept and Pretrained Model 19th

Commonsense Knowledge Construction with Concept and Pretrain...

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19th International Conference on Web information systems and Applications, WISA 2022

作者： Cai, Hanjun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Commonsense knowledge (CSK) is the information that people use in daily life but do not often mention. It summarizes the practical knowledge about how the world works. Existing machines have knowledge but lack commonsense because they do not understand and master commonsense knowledge in the same way that humans do. In the latest works, crowdsourcing-based method is costly and has low coverage, knowledge base completion method can highly fit samples, and methods extracted from unstructured data have the defects of low quality. CG &BF is commonsense knowledge construction with a concept-based generator and a BERT-based filter. We utilize semantic search for node matching and entropy encoder for filtering triples with high abstraction. Two algorithms based on concept aggregation and path credibility are proposed to obtain high-quality CSK triples. We subsequently finetuning a BERT to filter incorrect triples. We obtain 500,000 CSK triples based on ConceptNet, which is superior to other construction methods in novelty and quality. In the reading comprehension task, the three-way attention network is selected as the basic model and the knowledge we generate enables the base model to perform better, which proves that the output of CG &BF has higher quality and ease of use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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