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Understanding and improving fairness in cognitive diagnosis

Science China(Information Sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Tibetan Medicine Intelligent Consultation System Based on Graph Database

Tibetan Medicine Intelligent Consultation System Based on Gr...

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computer Science, Electronic Information Engineering and Intelligent Control technology (CEI), IEEE International Conference on

作者： Zexian Zhou Xiaojing Liu School of Computer Technology and Application Qinghai University Qinghai China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

With the widespread application of artificial intelligence technology, the medical informatization has entered a new stage. Although intelligent question-answering systems have achieved a certain extent of promotion and landing in various consulting fields, the Tibetan medicine field is still blank. This article designs an intelligent consultation system for Tibetan medicine knowledge and uses a graph database to store data, displaying the content of the graph database in the form of a dynamic web page for users. This is beneficial for the further promotion and dissemination of Tibetan medicine culture.

关键词： Knowledge engineering computer science Databases Web pages Personnel System analysis and design Biomedical imaging Intelligent control Diseases

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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing 3

Real-Time Data Streams: The Future of Predictive Maintenance...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Goyal, Manish Kumar Haripriya Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

Dynamic Predictive Modeling is a method that has brought about a revolutionary change in the field of preventive maintenance in the industrial sector. Using the benefits of real-time data streams and flexible algorithms, this cutting-edge technology enhances predictive maintenance processes. In this investigation, we evaluate the efficacy of Dynamic Predictive Modeling to that of numerous more conventional methods, including vibration analysis, acoustic emission analysis, thermography, oil analysis, ultrasonic testing, and data analytics. Accuracy, resource optimization, adaptability, downtime reduction, cost-effectiveness, and preventative maintenance are just a few of the criteria we use to evaluate the methods. The findings indicate that Dynamic Predictive Modeling outperforms its predecessors across a range of performance metrics. It can better adapt to the ever-changing industrial environment, allowing for more precise and timely breakdown forecasts. The allocation of resources and the economy of operation are both enhanced by the fact that maintenance procedures may be adjusted according to the actual condition of the equipment. Predictive maintenance is the most effective method for minimizing unscheduled downtime and increasing output in heavy industry. In a comparison of several approaches to predictive maintenance, Dynamic Predictive Modeling comes out on top with the highest aggregate score. A study reveals how an unconventional approach might improve the effectiveness and reliability of industrial operations. Increased equipment reliability, decreased maintenance costs, and a potential advantage in the manufacturing industry might arise from the use of dynamic predictive modeling. © 2023 IEEE.

关键词： Cost effectiveness

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Investigating Domain Adaptation for Medical Image Classification 5th

Investigating Domain Adaptation for Medical Image Classifica...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Finite Element Biomechanical Analysis of Pedicle Screw Redirection Insertion 24

Finite Element Biomechanical Analysis of Pedicle Screw Redir...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Ma, Shuangshuang Gao, Dedong Shan, Zhongshu Xu, Wenxu Lu, Zhirui School of Mechanical Engineering Qinghai University Qinghai Xining China Department of Orthopedics Qinghai Provincial People?s Hospital Qinghai Xining China School of Computer Technology and Application Qinghai University Qinghai Xining China

ISBN: (纸本)9798400717826

Spinal fusion surgery is a crucial method for treating spinal diseases. Pedicle screw fixation, commonly used in spinal fusion procedures, offers excellent biomechanical properties and stability, making it a frequent choice for orthopedic surgeons. However, due to individual anatomical differences, limited intraoperative space, and variations in the surgeon’s experience and technical skills, screws may deviate from the intended path, potentially leading to complications such as nerve damage and vertebral fractures, which could result in severe consequences for the patient. To address this issue, surgeons often resort to redirected insertion, readjusting the screw path to ensure safe and effective fixation. This study employs finite element analysis combined with computer modeling based on artificial intelligence to simulate the effects of different angles and instances of screw redirection on screw pull-out strength. The study aims to explore how redirected insertion impacts screw fixation strength and to analyze the influence of various redirection strategies on fixation stability. The results indicate that redirected insertion significantly affects screw pull-out strength. The use of artificial intelligence in the analysis not only improves the precision of model simulations but also provides scientific insights for optimizing screw placement strategies, effectively reducing the risk of screw loosening. For models with initial screw insertion, pull-out strength decreases as the offset angle increases. Correcting suboptimal initial screw placements can enhance fixation strength and reduce the risk of screw loosening. However, for models with two suboptimal insertions, even after further correction, the negative impact on screw fixation remains;thus, further redirection without screw replacement is not recommended. © 2024 Copyright held by the owner/author(s).

关键词： Screws

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Personalized Assistance Through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the Human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely HUMANVOICE. The most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. The Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. The system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Engineering education

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Inculcating Algebra Properties in the Context of Image Processing

Inculcating Algebra Properties in the Context of Image Proce...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agarwal, Trapty Raja Praveen, K.N. Asif Iqbal, Mohammad Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350357769

Strong detection with mitigation systems are crucial given the growing danger of computer cluster breaches and worldwide assaults. Given the rising frequency and magnitude of network assaults and abnormalities, the intrusion detection systems (IDS) is an essential tool for recognizing them. In this study, we provide a novel method for security breach detection using three different DNN (deep neural network) vision-based approaches. The classification process uses each of the four parts (Red, Colored, Aquamarine's and Alpha) or circuit-generated pictures as a foundation, making it easier to train and validate the reputable ResN et50 visual analysis framework. We assess the performance of our suggested approach on two well-known datasets: UNSW-NB15 and Regularly Utilize Management Defeat of Service (DoS). The results demonstrate our method's outstanding effectiveness, as it identified broad exploits in the UB test with a 99.8% success rate. Moreover, our method achieves 99.7% accuracy in detecting devices unauthorized to enter restricted areas and diagnosing comparable intrusion data pertaining to cars. With this effort, cybersecurity defenses against emerging attacks have taken a major step forward. © 2023 IEEE.

关键词： DLN Firmware and Image Classification IDS NTF ResNet 5.0 UB Test UNSW-NB15 Zip Files

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Enhancing Facial Expression Recognition with a Directional Attentive Neural Network 3

Enhancing Facial Expression Recognition with a Directional A...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Masson, Palak Agrawal, Rishi Sharma, Amit Parashar, Amit Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Greater Noida India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University School of Computer Application Phagwara India Pranveer Singh Institute of Technology Basic Science & Humanities-Chemistry Kanpur India

ISBN: (纸本)9798350384598

The proposed DANN was developed for better recognition of facial expression. The proposed DANN combines the attention and directional filters, which filter out features that focus on the informative facial regions while capturing the directional patterns in the facial queue. The average accuracy after benchmarking all the 10 experiment trials was in the very high level with the proposed DANN on datasets combining CK+, FER2013, and RAF-DB, with an average accuracy of 88.04%. The model showed a good trade-off between precision and recall: its average weighted F1 score was 0.881, while the average precision and recall were 0.883. This implies that the model will maximize the identification of true positive samples while, at the same time, minimizing that of false positives and false negatives. All these results are indicative of the robustness and reliability of the DANN architecture in classifying the facial expressions correctly across the different datasets and different experimental settings. The results suggest the high potential of DANN in furthering the state-of-the-art facial expression recognition with prosperous affective computing, human-computer interaction, and psychology. © 2024 IEEE.

关键词： Affective computing Attention mechanisms Deep learning Directional Attentive Neural Network Facial expression recognition

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Long-range Brain Graph Transformer 38

Long-range Brain Graph Transformer

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shuo Jin, Shan Li, Ming Sarwar, Tabinda Xia, Feng School of Computer Science and Technology Dalian University of Technology China Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multilevel communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://***/yushuowiki/ALTER. © 2024 Neural information processing systems foundation. All rights reserved.

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