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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiangru Lin Shenghua Zhong Yan Liu Gong Chen College of Computer Science Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science Shenzhen University Shenzhen China Department of Computer Science The Hong Kong Polytechnic University HK China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

The existing text-to-image generation methods based on stable diffusion yield better results in low-semantic prompt but often neglect the generation quality of high-semantic prompt such as emotional vocabulary, resulting in poor emotional image generation. In order to address this issue, we propose a novel approach called Sal-Guide Diffusion, which leverages saliency maps to guide emotional image generation with the goal of producing superior emotionally expressive images. In order to let the saliency maps guide the diffusion process, we introduce a lightweight adapter to extract emotional information from saliency maps and incorporate it into the diffusion process. Experimental results demonstrate that our proposed method generates higher-quality images across eight emotional dimensions, excelling in both generalization, emotional congruence, and subjective preference compared to stable diffusion or similar methods.

关键词： Vocabulary Image synthesis Buildings Diffusion processes Text to image Data mining

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Internet of Things for Agriculture Industry: A Bibliometric Analysis in the Last Five Years 11

Internet of Things for Agriculture Industry: A Bibliometric ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Puspitasari, Chasandra Hamim, Miftahul Adi, Puput Dani Prasetyo Arifuddin, Rahman Widartha, Vandha Pradwiyasma School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Pukyong National University Department of Information System Busan Korea Republic of

ISBN: (纸本)9798350389654

With the arrival of new technologies, the farm business is always changing. It is one of the most important in the world. The Internet of Things (loT) is one of the most exciting new technologies of the past few years. It could completely change the way we farm. We look at the literature about loT in agriculture over the last five years in this study. According to our findings, the use of loT in agriculture has been steadily increasing over the previous five years. This research provides a bibliometric evaluation of academic papers covering the years 2018-2022, with an emphasis on the Internet of Things (loT) in the agricultural sector. The analysis is performed using data obtained from the Scopus database. 120 documents were chosen and evaluated to acquire insights on the present state of research in this topic. Co-citation analysis identified wearable technology, farmers, loT devices, food, and wireless sensor networks (WSNs) as prominent research subjects in the agricultural industry's use of the Internet of Things (loT). The findings can provide valuable insights for scholars, practitioners, funding agencies, and policymakers in comprehending the patterns and focal points in this domain. The authors and institutes highlighted in this analysis are useful resources for collaborating and staying updated on the newest advances in the loT for Agriculture Industry. © 2024 IEEE.

关键词： Agricultural economics

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer systems science & engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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Keyphrase Extraction Using Pre-Trained Language Models BERT and Topic-Guided Graph Attention Networks

Keyphrase Extraction Using Pre-Trained Language Models BERT ...

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Data science and Its Applications (ICoDSA), International Conference on

作者： Novi Yusliani Siti Nurmaini Firdaus Firdaus Aini Nabilah Annisa Darmawahyuni Abdiansah Abdiansah Faculty Engineering Universitas Sriwijaya Palembang South Sumatera Informatic Engineering Faculty of Computer Science Universitas Sriwijaya Palembang South Sumatera Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang South Sumatera

ISBN: (数字)9798350365351

ISBN: (纸本)9798350365368

Keyphrase extraction aims to extract a set of keyphrases from the input document that highly summarizes the document. One of the issue in keyphrase extraction is how to extract representative keyphrases that is relevance and describe the information in the document. This study addresses pretrained language model (PLM) and topic-guided graph attention network (TgGAT) to extract a set of keyphrases from the input document. PLM can effectively capture knowledge from massive data and well perform on both language understanding and language generation tasks. BERT is one of PLM that designed to process bidirectionally, allowing BERT to understand word context based on the surrounding words. To enhance the keyphrase relevance, latent-dirichlet allocation used to learn the latent topic information and aiding the semantic understanding of the input document. For capturing semantic relationships between phrases in document based on their topics, this study used TgGAT. TgGAT models the anchoraware graph which organizes the initial anchor phrases. Experiment was conducted on a dataset containing 100 Indonesian accredited scientific article, Sinta 2 and Sinta 3, to evaluate the system“ $s$ performance. The experimental results show a precision value of 0.058, a recall value of 0.070, and an F1-score of 0.062 for the five generated keywords across the entire tested dataset. Further in-depth analyses shows that pattern of phrases used has the important role in determining the representative phrases in input document.

关键词： Semantics Bidirectional control Network architecture Data science Encoding Data models Data mining

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Dynamic Services Migration Based on Dueling Deep Q-Network in MEC

Dynamic Services Migration Based on Dueling Deep Q-Network i...

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International Conference on Communication Software and Networks, ICCSN

作者： Xinyang Zhang Chao Bu Key Laboratory of Computer Vision and System of Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin China

As an extended computing paradigm of cloud computing, Mobile Edge Computing (MEC) facilitates real-time service responses by deploying resources near network edges. However, services should frequently move among multiple edge computing servers because of the mobility of most users, which accordingly leads to increased network operation costs and influences service quality. In this paper, we formulate the service migration problem as a Markov Decision Process (MDP) and introduce the dueling Deep Q-Network (DQN) to solve the problem, so as to reduce the network operating cost without lowering the service quality. We also propose a trajectory prediction approach to further optimize the service migration. Simulation experimental results demonstrate that the proposed mechanism can achieve a lower network operation cost without reducing the service quality.

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MOEA/D With Spatial–Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 764-778页

作者： Wang, Xianpeng Zhao, Yumeng Tang, Lixin Yao, Xin Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Shenyang110819 China Northeastern University Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Lingnan University Department of Computing and Decision Sciences Hong Kong University of Birmingham School of Computer Science Birmingham United Kingdom

When solving dynamic multiobjective optimization problems, most evolutionary algorithms (EAs) attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this article proposes a spatial–temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multishort time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective EAs based on prediction strategies. 1997-2012 IEEE.

关键词： Tensors

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Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking system Using IoT 3

Fashionable Four Wheeler Parking to Reduce the Human Interve...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Manoj, P. Varun, C.M. Keerthi Kumar, N. Padhye, Pradip Kumari, K. Aruna Das, Puja Sarkar, Swagata Vidyavardhaka College of Engineering Department of Civil Engineering Mysore India R.M.K. Engineering College Department of Computer Science and Business System Kavaraipettai India Bms Institute of Technology and Management Department of Mechanical Engineering Bengaluru India School of Management Dr. Vishwanath Karad Mit World Peace University Pune India Gitam University Department of Mathematics Visakhapatnam India Hiralal Mazumder Memorial College for Women Department of Computer Science Kolkata India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350336238

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform. © 2023 IEEE.

关键词： Infrared detectors

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao 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 China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Zixiang Yan, Hao Wei, Changsong Wang, Junyu Xiao, Minheng College of Engineering and Computer Science Syracuse University SyracuseNY United States College of Engineering and Computer Science Syracuse Sichuan Chengdu China UCLA Computer Science Department University of California Los AngelesCA United States Department of Integrated System Engineering Ohio State University ColumbusOH United States

The behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle. However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios. In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem. Specifically, we used Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) for comparative experiments. DQN guides the agent to choose the best action by approximating the state-action value function, while PPO improves the decision-making quality by optimizing the policy function. We also introduce improvements in the design of the reward function to promote the robustness and adaptability of the model in real-world driving situations. Experimental results show that the decision-making strategy based on deep reinforcement learning has better performance than the traditional rule-based method in a variety of driving tasks. © 2024, CC BY.

关键词： Deep reinforcement learning

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