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Maximizing Resource Efficiency in Cloud Data centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Adding Argumentation into Human Evaluation of Long Document Abstractive Summarization: A Case Study on Legal Opinions 4

Adding Argumentation into Human Evaluation of Long Document ...

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4th Workshop on Human Evaluation of NLP Systems, HumEval 2024

作者： Elaraby, Mohamed Xu, Huihui Gray, Morgan Ashley, Kevin Litman, Diane Department of Computer Science School of Computing and Information United States Learning Research and Development Center United States Intelligent Systems Program School of Computing Information University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9782493814418

Human evaluation remains the gold standard for assessing abstractive summarization. However, current practices often prioritize constructing evaluation guidelines for fluency, coherence, and factual accuracy, overlooking other critical dimensions. In this paper, we investigate argument coverage in abstractive summarization by focusing on long legal opinions, where summaries must effectively encapsulate the document’s argumentative nature. We introduce a set of human-evaluation guidelines to evaluate generated summaries based on argumentative coverage. These guidelines enable us to assess three distinct summarization models, studying the influence of including argument roles in summarization. Furthermore, we utilize these evaluation scores to benchmark automatic summarization metrics against argument coverage, providing insights into the effectiveness of automated evaluation methods. © 2024 European Language Resources Association (ELRA).

关键词： Human Evaluation Legal Summarization Summarization

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Schema-Driven Information Extraction from Heterogeneous Tables

Schema-Driven Information Extraction from Heterogeneous Tabl...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Bai, Fan Kang, Junmo Stanovsky, Gabriel Freitag, Dayne Dredze, Mark Ritter, Alan College of Computing Georgia Institute of Technology United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Artificial Intelligence Center SRI International Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9798891761681

In this paper, we explore the question of whether large language models can support cost-efficient information extraction from *** introduce schema-driven information extraction, a new task that transforms tabular data into structured records following a human-authored *** assess various LLM's capabilities on this task, we present a benchmark comprised of tables from four diverse domains: machine learning papers, chemistry literature, material science journals, and *** use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data *** experiments demonstrate that surprisingly competitive performance can be achieved without requiring task-specific pipelines or labels, achieving F1 scores ranging from 74.2 to 96.1, while maintaining cost ***, through detailed ablation studies and analyses, we investigate the factors contributing to model success and validate the practicality of distilling compact models to reduce API reliance. © 2024 Association for Computational Linguistics.

关键词： Data mining

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models 24

Explainable AI for Healthcare: An Approach Towards Interpret...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Ghani, Usman Jinnah University for Women Institute of Business Administration Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as 'normal' or 'pneumonia'. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness. © 2023 IEEE.

关键词： Deep learning Explainable AI Grad-CAM Healthcare LIME SHAP Transfer learning

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Energy Theft Detection Using the Wasserstein Distance on Residuals

Energy Theft Detection Using the Wasserstein Distance on Res...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Altamimi, Emran Al-Ali, Abdulaziz Malluhi, Qutaibah M. Al-Ali, Abdulla K. Qatar University Department of Computer Science and Engineering Doha Qatar Qatar University KINDI Center for Computing Research Doha Qatar

ISBN: (纸本)9781665490719

Detection of electricity theft improves the sustainability of the smart grid, helps electrical utilities mitigate their financial risks, and improves the overall management of resources. In this work, we utilize an LSTM neural network to forecast a given day's energy consumption and construct residuals. The residuals are then compared to previous residuals from normal days using the Wasserstein distance. If the Wasserstein distance for the residuals of a day exceeds a threshold, the day is highlighted to indicate suspected energy theft. Our framework can be built upon existing forecasting models with minimal computational overhead to calculate the Wasserstein distance. The framework is also highly explainable, which reduces the cost of false positives significantly. Our framework was evaluated using a public dataset and was able to detect six attack models of energy theft and faulty meters, with a false positive rate of 9% and an average F1 score of 0.91. © 2023 IEEE.

关键词： Energy utilization

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Evaluation and modeling of students’ persistence and wheel-spinning propensities in formative assessments

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Smart Learning Environments 2023年第1期10卷 63页

作者： Yang, Albert C. M. Ogata, Hiroaki Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

Persistence represents a crucial trait in learning. A lack of persistence prevents learners from fully mastering their current skills and makes it difficult for them to acquire new skills. It further hinders the administration of effective interventions by learning systems. Although most studies have focused on identifying non-persistence and unproductive persistence behaviors, few have attempted to model students’ persistence propensity in learning. In the present study, we evaluated students’ persistence propensity in formative assessments by using an item response theory model with their attempt data. In addition, we modeled their wheel-spinning propensity. The students (N = 115) of first-level mathematics classes at a high school in Japan underwent the aforementioned formative assessments;their log data were collected. Persistence propensity was found to be correlated with frequency-related statistics, and wheel-spinning propensity was correlated with correctness-related statistics. However, persistence and wheel-spinning propensities were not correlated. A comparison of the students’ scores with various persistence and wheel-spinning propensities revealed that both traits considerably influenced their academic performance. The present study provides insights into the use of attempt data to evaluate various characteristics crucial for learning, which are otherwise difficult to evaluate. © 2023, The Author(s).

关键词： Formative assessment Item response theory Learner modeling Persistence Wheel-spinning

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Agent-Based Social Simulation for Policy Making 18th

Agent-Based Social Simulation for Policy Making

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18th European Advanced Course on Artificial Intelligence, ACAI 2021

作者： Lorig, Fabian Vanhée, Loïs Dignum, Frank Department of Computer Science and Media Technology Internet of Things and People Research Center Malmö University Malmö Sweden Department of Computing Science Umeå University Umeå Sweden

ISBN: (纸本)9783031243486

In agent-based social simulations (ABSS), an artificial population of intelligent agents that imitate human behavior is used to investigate complex phenomena within social systems. This is particularly useful for decision makers, where ABSS can provide a sandpit for investigating the effects of policies prior to their implementation. During the Covid-19 pandemic, for instance, sophisticated models of human behavior enable the investigation of the effects different interventions can have and even allow for analyzing why a certain situation occurred or why a specific behavior can be observed. In contrast to other applications of simulation, the use for policy making significantly alters the process of model building and assessment, and requires the modelers to follow different paradigms. In this chapter, we report on a tutorial that was organized as part of the ACAI 2021 summer school on AI in Berlin, with the goal of introducing agent-based social simulation as a method for facilitating policy making. The tutorial pursued six Intended Learning Outcomes (ILOs), which are accomplished by three sessions, each of which consists of both a conceptual and a practical part. We observed that the PhD students participating in this tutorial came from a variety of different disciplines, where ABSS is mostly applied as a research method. Thus, they do often not have the possibility to discuss their approaches with ABSS experts. Tutorials like this one provide them with a valuable platform to discuss their approaches, to get feedback on their models and architectures, and to get impulses for further research. © 2023, Springer Nature Switzerland AG.

关键词： Intelligent agents

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Epic-Sounds: A Large-Scale Dataset of Actions that Sound 48

Epic-Sounds: A Large-Scale Dataset of Actions that Sound

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Huh, Jaesung Chalk, Jacob Kazakos, Evangelos Damen, Dima Zisserman, Andrew University of Oxford Visual Geometry Group Department of Engineering Science United Kingdom University of Bristol Department of Computer Science United Kingdom Samsung Ai Center Cambridge United Kingdom

ISBN: (纸本)9781728163277

We introduce EPIC-SOUNDS, a large-scale dataset of audio annotations capturing temporal extents and class labels within the audio stream of the egocentric videos from EPIC-KITCHENS-100. We propose an annotation pipeline where annotators temporally label distinguishable audio segments and describe the action that could have caused this sound. We identify actions that can be discriminated purely from audio, through grouping free-form descriptions into classes. For actions that involve objects colliding, we collect human annotations of the materials of these objects (e.g. a glass object being placed on a wooden surface), which we verify from visual labels, discarding ambiguities. Overall, EPIC-SOUNDS includes 78.4k categorised segments of audible events and actions, distributed across 44 classes, as well as 39.2k non-categorised segments, totalling 117.6k segments spanning 100 hours of audio, capturing diverse actions that sound in home kitchens. We train and evaluate two state-of-the-art audio recognition models on our dataset, highlighting the importance of audio-only labels and the limitations of current models to recognise actions that ***-SOUNDS and baseline source code is available from: https://***/epic-sounds. © 2023 IEEE.

关键词： Large dataset

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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