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IEEE International Conference on Data Mining (ICDM)

作者： Xin Gao Zhengye Zhu Xu Chu Yasha Wang Wenjie Ruan Junfeng Zhao Key Lab of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University School of Computer Science Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China University of Exeter Exeter UK

Despite the success of deep neural networks on text classification, their large capacity also leads to capturing task-irrelevant patterns such as label noise. Label noise is usually introduced into the data during label collection and causes nontrivial declines in performance due to the memorization effect. Though effort has been devoted to combating the label noise in other systems such as image classification, high-quality input features are necessary for discovering task-relevant patterns before memorizing the label noise. However, such a high-quality input feature requirement is hard to be satisfied for text classification due to the nature of natural language. To combat the label noise with low-quality input features in the text classification, we propose a novel framework that exploits external category descriptions to construct prototypes that can be used to denoise the input representation and alleviate the over-fitting. However, there still remains a challenge that the external category descriptions from other corpora could be semantically discrepant with the underlying task-specific classes in the training corpus. To align their semantics, we propose two regularizers that penalize sample-wise semantic-based deviations at the local level and class-wise structure-based deviations at the global level, respectively. Our extensive experiments across two open datasets and one real-world case study demonstrate that our method is superior to state-of-the-art baselines under various settings of label noise.

关键词： Training Deep learning Text categorization Semantics Neural networks Natural languages Prototypes

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Strategies for predictive power: Machine learning models in city-scale load forecasting

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 6卷

作者： Nooruldeen, Orhan Baker, Mohammed Rashad Aleesa, A.M. Ghareeb, Ahmed Shaker, Ehab Hashim Software Department College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of Mechanical Engineering University of Kirkuk Kirkuk Iraq Department of Computer Techniques Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq

This study focuses on enhancing machine learning (ML) algorithms' performance in predicting daily loads for Kirkuk, Iraq—an essential element in energy planning, resource allocation, and policymaking. We explore single and ensemble learning algorithms, including AdaBoost, Bagging, Support Vector Regression (SVR), and Decision Tree (DT). To assess accuracy, we employ critical metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). MSE and RMSE gauge precision through squared differences between predictions and actual energy consumption. MAPE reveals percentage deviations, while R² quantifies model fit, indicating its ability to capture energy consumption variance. Our results highlight Bagging's superiority, particularly in day-ahead forecasts, demonstrating superior accuracy over Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). These findings underscore the importance of feature reduction methods in enhancing performance. Bagging exhibits promise across various feature reduction scenarios, proficiently predicting energy consumption. Furthermore, the AdaBoost algorithm demonstrates commendable performance. The application of voting ensemble learning emerges as a particularly insightful approach, effectively reducing the squared differences and deviations in energy consumption forecasts. The significant implications of these findings suggest that the models, with their impressive performance, could serve as valuable tools for energy planners and policymakers in Kirkuk, playing a key role in optimizing resource allocation for efficient energy utilization. © 2023 The Author(s)

关键词： Electricity planning Ensemble learning Feature reduction Kirkuk city Load forecasting Machine learning

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Evaluation of the Energy Efficiency of software Calculations in the Design of Microcontroller Devices

Evaluation of the Energy Efficiency of Software Calculations...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer science

作者： Andrii Topalov Taras Styslo Vitalii Tkach Volodymyr Peschanenko Oksana Styslo Rostyslav Skrypyuk Department of computerized control systems Admiral Makarov National University of Shipbuilding Mykolaiv Ukraine Department of IT King Danylo University Ivano-Frankivsk Ukraine Department of computer science and software engineering Kherson State University Ivano-Frankivsk Ukraine Department of automation and computer-integrated technologies Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The paper presents a study of the effectiveness of software from the point of view of minimizing the energy consumption of microprocessor devices. In this case, the programming of the microcontroller in various programming languages for arithmetic operations, as well as data output through the port, is considered. In the course of the tests, the peculiarities of the application of this or that program and their operation, taking into account the energy saving of the microcontroller, are noted.

关键词： Energy consumption computer languages Microcontrollers Energy conservation Programming Market research software Energy efficiency Telecommunications Arithmetic

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Mixture of Knowledge Minigraph Agents for Literature Review Generation 39

Mixture of Knowledge Minigraph Agents for Literature Review ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

ISBN: (纸本)157735897X

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues

arXiv

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arXiv 2025年

作者： Yu, Fangxu Jiang, Lai Huang, Shenyi Wu, Zhen Dai, Xinyu National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Department of Computer Science and Engineering Shanghai Jiao Tong University China University of California San DiegoCA United States

The ability to understand and predict the mental states of oneself and others, known as the Theory of Mind (ToM), is crucial for effective social interactions. Recent research has emerged to evaluate whether Large Language Models (LLMs) exhibit a form of ToM. Although recent studies have evaluated ToM in LLMs, existing benchmarks focus predominantly on physical perception with principles guided by the Sally-Anne test in synthetic stories and conversations, failing to capture the complex psychological activities of mental states in real-life social interactions. To mitigate this gap, we propose PERSUASIVETOM, a benchmark designed to evaluate the ToM abilities of LLMs in persuasive dialogues. Our framework introduces two categories of questions: (1) ToM Reasoning, assessing the capacity of LLMs to track evolving mental states (e.g., desire shifts in persuadees), and (2) ToM Application, evaluating whether LLMs can take advantage of inferred mental states to select effective persuasion strategies (e.g., emphasize rarity) and evaluate the effectiveness of persuasion strategies. Experiments across eight state-of-the-art LLMs reveal that while models excel on multiple questions, they struggle to answer questions that need tracking the dynamics and shifts of mental states and understanding the mental states in the whole dialogue comprehensively. Our aim with PERSUASIVETOM is to allow an effective evaluation of the ToM reasoning ability of LLMs with more focus on complex psychological activities. Our code is available at https://***/YuFangxu/PersuasiveToM. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Nanoinformatics: Opportunities and challenges in the development and delivery of healthcare products in developing countries 4

Nanoinformatics: Opportunities and challenges in the develop...

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4th International Conference on science and Sustainable Development: Advances in sciences and Technology for Sustainable Development, ICSSD 2020

作者： Nwankwo, Wilson Ukhurebor, Kingsley Eghonghon Software Engineering and Cyberphysical Systems Unit Department of Computer Science Edo University Iyamho Edo State Nigeria Environmental AndTelecommunication Unit Department of Physics Edo University Iyamho Edo State Nigeria

The world is witnessing sustained effects of information technologies across all works of life. Though some of these influences are markedly negative and detrimental to the socio-economic prospects and progress of the society, the positive effects are often impressive especially where they are integrated for the betterment and greater good of the larger society. One of the fastest growing technologies is nanotechnology. Nanotechnology has numerous prospects and applicability across various sectors ranging from electronics, telecommunications, agriculture and food production, biotechnology and genetics, oil prospecting and production, remote sensing, drug production, to name a few. This novel technology readily finds usefulness and several researches are ongoing, geared at developing new tools and techniques that would improve its acceptance across the concerned domains. It is this quest that has culminated into the ongoing effort in nanoinformatics, an interdisciplinary study and a subdomain of informatics simply regarded as the conscientious application of informatics tools and technologies to the analysis, design and development of systems on the broad spectrum of nanomaterials including their physicochemical and environmental characteristics as well as their interactions, interrelationships, and applications within a given domain. This paper presents a review of some opportunities for individuals, experts, and the society especially in the production of cost-effective nanotechnology-based healthcare products. Notwithstanding the aforesaid opportunities which could be harnessed and sustained in any developing country like Nigeria, this paper identifies and buttresses core challenges that could confront the adoption of good nanoinformatics methodologies. This paper concludes that a developing country (Nigeria in perspective) could benefit from nanoinformatics if there are stronger ties among the key stakeholders involved in healthcare products delivery in the socie

关键词： Developing countries

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models Data models Internet of Things Logic Business

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Leveraging Bitcoin Mining Machines in Demand-Response Mechanisms to Mitigate Ramping-Induced Transients

SSRN

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SSRN 2025年

作者： Ginzburg-Ganz, Elinor Eyal, Ittay Baimel, Dmitry Santosh, Leena Belikov, Juri Levron, Yoash Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Shamoon College of Engineering Beer-Sheva84100 Israel Pandit Deendayal Energy University India Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Several previous works examine the use of cryptographic mining machines, for instance Bitcoin mining machines, in demand-response mechanisms, as part of the portfolio of assets managed by the grid operator. In this paper we extend this formulation by highlighting the effects of fast ramping transients, which may often occur in power grids rich with renewable energy sources. The resulting optimization problem is solved based on Pontryagin's minimum principle. We use this solution to examine the profitability and usage of these machines in a real-world settings, based on data from the California ISO and the ``Noga'' grid operator. In our analysis, we compare the effects of several key parameters, such as the electricity price, and the machines' price, hashrate and monetary revenue. These are examined for several different machine types that are available in the market today. Our main conclusion is that the profitability of the discussed mechanism is highly influenced by the cost of the mining machines, and the percentage of renewable sources within the energy mix, where some scenarios are more profitable then others. © 2025, The Authors. All rights reserved.

关键词： Demand response

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USBDAN: Unsupervised Scale-aware and Boundary-aware Domain Adaptive Network for Gastric Tumor Segmentation

USBDAN: Unsupervised Scale-aware and Boundary-aware Domain A...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yongtao Zhang Ning Yuan Bing Liu Aocai Yang Hongwei Yu Kuan Lv Jixin Luan Pianpian Hu Haijun Lei Tianfu Wang Guolin Ma Baiying Lei School of Biomedical Engineering Health Science Centers Shenzhen University Department of Medical Imaging Heping Hospital Affiliated to Changzhi Medical College Changzhi China Department of Radiology China-Japan Friendship Hospital Beijing China School of Computer Science and Software Engineering Shenzhen University Shenzhen China

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.

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Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

arXiv

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arXiv 2022年

作者： Wang, Yanyun Du, Dehui Liu, Yuanhao Department of Computer Science The University of Hong Kong Hong Kong Software Engineering Institute East China Normal University Shanghai China

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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