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Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society

作者： Oluseun Olulana Kathleen Cachel Fabricio Murai Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester MA Data Science Program Worcester Polytechnic Institute Worcester MA and Computer Science Department Worcester Polytechnic Institute Worcester MA

As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://***/sewen007/***

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Enhancing cybersecurity resilience through advanced red-teaming exercises and MITRE ATT&CK framework integration: A paradigm shift in cybersecurity assessment

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditya Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As cybersecurity threats evolve alarmingly, conventional defense strategies are becoming increasingly ineffective. In response to this urgent challenge, our study presents a transformative approach to red-teaming exercises by integrating the MITRE ATT&CK framework. This innovative integration leverages real-world attacker tactics and behaviors to create highly realistic scenarios that rigorously test defenses and uncover previously unidentified vulnerabilities. Our comprehensive evaluation demonstrates a significant enhancement in the realism and effectiveness of red-teaming, leading to improved vulnerability identification and the generation of actionable insights for proactive remediation. This study uniquely contributes by providing a structured, data-driven methodology that aligns current defenses with emerging threat tactics and promotes continuous improvement in cybersecurity resilience. Such advancements are essential for organizations seeking to stay ahead of attackers and maintain robust cyber defenses in dynamic threat environments. The significance of this study lies in offering a structured approach to strengthen cybersecurity resilience against evolving threats and providing a model for continual defense enhancement. Future research should investigate the impact of this integration on long-term cybersecurity strategies. © 2024 The Authors

关键词： Continuous defense improvement Cyber resilience Cybersecurity MITRE ATT&CK framework Red-teaming exercises

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Ensuring Ethical, Transparent, and Auditable Use of Education Data and Algorithms on AutoML 23

Ensuring Ethical, Transparent, and Auditable Use of Educatio...

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Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing

作者： Kylie Griep Leah Stone Stephen Edwards Malik Hill Jialin Cui Chengyuan Liu Qinjin Jia Yang Song Department of Computer Science University of North Carolina Wilmington USA Master of Data Science Program University of North Carolina Wilmington USA Department of Computer Science NC State University USA

ISBN: (纸本)9798400709241

Automated machine learning (AutoML) creates additional opportunities for less advanced users to build and test their own data mining models. Even though AutoML creates the models for the user, there is still technical knowledge and tools needed to evaluate those models, and due to the black-box nature of the machine learning models, problems can arise with regard to algorithmic biases and fairness. Such biases can escalate in future applications, necessitating a structured approach for fairness evaluation in AutoML. This involves defining fairness criteria, selecting appropriate metrics, assessing fairness across groups, and addressing biases. In the realm of educational data mining, where AutoML is prevalent, biases related to attributes like gender or race can lead to unethical outcomes. Since fairness metrics vary in definition and strength, and some may even contradict others, making fairness evaluation more complex. In this paper, ten fairness metrics were chosen, explored, and implemented on four AutoML tools, Vertex AI, AutoSklearn, AutoKeras, and PyCaret. We identified two open educational datasets and built both prediction and classification models on those AutoML frameworks. We report our work in evaluating different machine learning models created by AutoML and provide discussions about the challenges in evaluating fairness in those models and our effort to mitigate and resolve the problems of algorithmic bias in educational data mining.

关键词： AutoML Automated machine learning algorithmic bias educational data mining fairness

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The "LLM World of Words" English free association norms generated by large language models

arXiv

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arXiv 2024年

作者： Abramski, Katherine Improta, Riccardo Rossetti, Giulio Stella, Massimo University of Pisa Department of Computer Science Pisa Italy University of Trento Department of Psychology and Cognitive Science Trento Italy National Research Council of Italy Institute of Information Science and Technologies Pisa Italy

Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the absence of large-scale LLM-generated free association norms that are comparable with human-generated norms is an obstacle to this new research direction. To address this limitation, we create a new dataset of LLM-generated free association norms modeled after the "Small World of Words" (SWOW) human-generated norms consisting of approximately 12,000 cue words. We prompt three LLMs, namely Mistral, Llama3, and Haiku, with the same cues as those in the SWOW norms to generate three novel comparable datasets, the "LLM World of Words" (LWOW). Using both SWOW and LWOW norms, we construct cognitive network models of semantic memory that represent the conceptual knowledge possessed by humans and LLMs. We demonstrate how these datasets can be used for investigating implicit biases in humans and LLMs, such as the harmful gender stereotypes that are prevalent both in society and LLM outputs. © 2024, CC BY.

关键词： Semantics

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Trends in Research Publication Topics Related to Artificial Intelligence for Medicine in Medical Education: A Bibliometric Analysis 25

Trends in Research Publication Topics Related to Artificial ...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Adi, Puput Dani Prasetyo Purnomo, Agung Amalia, Mufidah Nur Hamim, Miftahul Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Research Center for Telecommunication Bandung Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Entrepreneurship Business Creation Binus Business School Jakarta11480 Indonesia

ISBN: (纸本)9798350378573

This bibliometric analysis aims to explore the trends in research publication topics related to Artificial Intelligence (AI) for medicine in medical education over the last five years (2019-2023). By synthesizing data from a variety of scholarly articles, this study seeks to identify key themes, emerging areas of interest, and research trends within this specific domain. The research question focuses on understanding the evolution of research topics in AI for medicine in medical education, while the objective is to conduct a comprehensive analysis using bibliometric techniques. Data selection from the Scopus database yielded a total of 389 documents from 290 sources. There are 1889 Authors, with author collaboration points on Co-Authors per Doc of 5.11. Regarding the quantity of published documents, the USA, China, and Canada have the highest documents. The software employed for bibliometric analysis were R and VOSviewer. By leveraging bibliometric analysis, this research aims to address the existing gap in the literature and provide valuable insights for researchers, educators, and practitioners interested in AI applications in medical education. The present solution exploration will offer recommendations for future research directions and priorities within the field, contributing to the advancement of knowledge and practice in the integration of AI technologies in medical education. © 2024 IEEE.

关键词： Bibliographies

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Investigating the User Experience and Identifying User Needs for Kitchen Appliances Using Thematic Analysis

Investigating the User Experience and Identifying User Needs...

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2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022

作者： Jung, W.C. Lazaro, M.J. Lee, J.H. Kim, G.B. Yang, E.J. Yun, M.H. Seoul National University Department of Industrial Engineering Seoul Korea Republic of Seoul National University Interdisciplianary Program in Cognitive Science Seoul Korea Republic of

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Kitchen appliances are essential to accomplish cooking tasks efficiently. Advancements in technology have led to changes in the needs and expectations of the users of commonly used kitchen appliances. Thus, this qualitative research aims to investigate the user experience and identify the user needs for common kitchen appliances (microwave, cooktop, oven, and dishwasher) by conducting semi-structured interviews with users who frequently use the appliances. Results of the thematic analysis showed that UX factors such as informativeness, comfortability, and safety were prominent sub-themes across all kitchen appliances. Based on the findings, we presented design recommendations on how the product can be improved. © 2022 IEEE.

关键词： Kitchens

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Object Size Recognition as Intra-class Variations using Transfer Learning

Object Size Recognition as Intra-class Variations using Tran...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Rissa Rahmania Harco Leslie Hendric Spits Warnars Benfano Soewito Ford Lumban Gaol Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

The ability to differentiate various products in the retail store plays an essential role to provide effectiveness to customers and reduce or even eliminate long queues. However, traditional machine learning algorithms are incapable of recognizing many subordinate categories in various retail product. This paper aims to recognize the retail product recognition algorithm based on the YOLOv7 model in terms of intra-class variations, with the sub-categories of brand and size. We used two schemes of the dataset to compare recognition performance between them. Firstly, the YOLOv7 is applied in the two schemes of the dataset that is annotated with the subordinate category to detect the brand as meta category. Secondly, the proposed method is applied by adding the object size classification into the YOLOv7 model where the square area of the bounding box was calculated to classify the product according to size. Confidence score and square area are used to verify the object and to obtain the product size, which represents sub-category of the product. The experimental results show that our proposed method achieves higher recall compared to baseline object detection.

关键词： computer science Machine learning algorithms Automation Transfer learning Object detection computer architecture Information technology

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Analysis of Feature Selection Methods On Clove Quality Classification

Analysis of Feature Selection Methods On Clove Quality Class...

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International Conference on Information Technology (INCIT)

作者： Firman Tempola Aina Musdholifah Retantyo Wardoyo Doctoral Program Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Proper feature selection is becoming increasingly important in the context of clove quality classification in Indonesia; effective feature selection can identify the most relevant attributes of clove quality. This study investigates 3 famous feature selection methods, namely mutual information, recursive feature selection, and lasso regression, for classifying clove quality using various models, i.e. Naïve Bayes, K-NN, and Decision Tree. The study results showed that the mutual information feature selection method impacted the K-NN method by 1.05%; for naïve Bayes and decision trees, the accuracy did not increase. For the recursive feature elimination (RFE) feature selection method, the accuracy of K-NN increased by 2.16%, the decision tree by 0.37%, and naïve Bayes accuracy did not increase. In addition, using Lasso Regression feature selection, the accuracy of naïve Bayes increased by 2.97%, K-NN by 2.28%, and decision tree accuracy decreased.

关键词： Analytical models Accuracy Lighting Machine learning Nearest neighbor methods Feature extraction Bayes methods Information technology Mutual information Regression tree analysis

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 IEEE.

关键词： Energy management systems

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Multiclassifier for Aspect-Based Sentiment Analysis on Indonesian Reviews of Kredit Pintar Online Lending App

Multiclassifier for Aspect-Based Sentiment Analysis on Indon...

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International Conference of Information and Communication Technology (ICoICT)

作者： Nanda Yuniar Aina Musdholifah Undergraduate Program in Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Aspect-based sentiment analysis is carried out to determine user sentiment's polarity while still considering the aspects of the discussion so that all information in the reviews can be represented. The multiclassifier in this research means that two classifiers are used, the first is an aspect classifier to perform aspect classification, and the second is a sentiment classifier to perform sentiment classification based on its aspect. Two approaches were studied according to their classification work, namely, individual model in the form of an aspect classification and sentiment classification based on the aspects individually and sequentially model by testing the aspect and sentiment models sequentially. This study evaluates the performance of Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) algorithms and their combination in sequential testing. In addition, to overcome imbalance dataset, the use of oversampling types is also compared. In the individual approach, the SVM model with random oversampling showed the best performance with average accuracy, precision, recall, and f1 score of 92%, 86%, 80%, and 83% for the aspect model and 86%, 81%, 80%, and 80% for the sentiment model, respectively. While in the sequential approach, the combination of SVM-SVM with oversampling shows the best performance with an average accuracy, precision, recall, and f1-score of 79%, 56%, 50%, and 53%, while the performance of other model combinations is only in the maximum range of 77% accuracy, 55% precision, 51% recall, and 52% f1-score.

关键词： Support vector machines Sentiment analysis Accuracy Reviews Vectors Bayes methods Information and communication technology Classification algorithms Testing

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