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arXiv 2024年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Research Network Data Science Austria University of Vienna Faculty of Computer Science Research Network Data Science Austria

Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the "XAI Novice Question Bank," an extension of the XAI Question Bank [60] containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task-based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences. Copyright © 2024, The Authors. All rights reserved.

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Layered data-Centric AI to Streamline data Quality Practices for Enhanced Automation 19th

Layered Data-Centric AI to Streamline Data Quality Practice...

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19th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： Akmal, Muhammad Uzair Asif, Saara Koval, Leonid Mathias, Selvine G. Knollmeyer, Simon Grossmann, Daniel AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt85049 Germany Faculty of Computer Science and Data Processing Technische Hochschule Ingolstadt Ingolstadt85049 Germany

ISBN: (纸本)9783031815416

Most artificial intelligence (AI) applications are designed under the model-centric AI (MCAI) approach, where data scientists aim to optimize the machine learning (ML) models starting with fixed, preprocessed data. However, businesses often struggle with limited datasets, changes in data over time, and limited ML knowledge, making it difficult to maintain data quality. One potential approach is data-centric AI (DCAI), which systematically improves the data quality used to build AI systems. However, rapid growth in data volume leads to challenges in selecting the most suitable operations for enhancing data quality thereby maintaining data accuracy, completeness, consistency, and reliability in real-world applications. To address this gap, we propose a novel framework, namely Layered data-Centric AI (LDCAI) by expanding upon the existing DCAI Pipeline. LDCAI employs a three-layered approach, starting with the DCAI layer which generates the initial training data. This data is then analyzed and refined in the data Analysis (DA) layer by a team of data scientists and domain experts. The refined data is later sent to the data Quality Control (DQC) layer for additional quality checks and improvements by data scientists. The entire process can be iterated as needed to continuously improve data quality, which can be fed back into the DCAI layer’s model to achieve better predictions and outcomes. The implementation is illustrated through a scalable and customizable architecture designed for optimizing data quality within AI systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data accuracy

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Design, Mathematical Modeling, and Control of an Underactuated 3-DOF Experimental Helicopter

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IEEE Access 2024年 12卷 55568-55586页

作者： Makki, Osamah Talal Moosapour, Seyyed Sajjad Mobayen, Saleh Nobari, Jafar Heyrani Shahid Chamran University of Ahvaz Faculty of Engineering Ahvaz*** Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu Yunlin640301 Taiwan K. N. Toosi University of Technology Department of Electrical and Computer Engineering Tehran19697 Iran

This article covers the design, implementation, mathematical modelling, and control of a multivariable, underactuated, low-cost, three-degrees-of-freedom experimental helicopter system (namely a 3-DOF helicopter). The system is considered an essential benchmark for the development and evaluation of control algorithms and identification methods as it mimics real helicopters and UAVs. The system is multivariable and difficult to control due to its underactuation characteristics. In addition, it presents further challenges in modelling and control analysis in terms of high nonlinearity, coupling, uncertainty, and unmodeled dynamics. In this scene, the system is designed, developed, and operated virtually in SolidWorks using the necessary hardware and software. A new accurate multivariable coupled nonlinear mathematical model is derived and proposed. The model takes into account the complex dynamics and interactions between the system components, providing a more comprehensive representation of the real system compared to previous models. System parameters are then identified using the Sum of the Square Error (SSE) algorithm, and the mathematical model is compared to the practical results in many different scenarios via Simulink. Experimental tests confirmed the mathematical model and robust performance of the implemented experimental 3-DOF helicopter system, as well as the ability of the proposed controller to stabilize the helicopter and track different trajectories in the presence of uncertainties and cross-coupling effects. © 2013 IEEE.

关键词： Iron

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Investigating Vulnerabilities of Information Solicitation Process in RPL-Based IoT Networks 11th

Investigating Vulnerabilities of Information Solicitation P...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Sahay, Rashmi Sushant, Cherukuri Gaurav Department of Computer Science and Engineering Faculty of Science and Technology IcfaiTech The ICFAI Foundation For Higher Education Hyderabad India Department of Data Science and Artificial Intelligence Faculty of Science and Technology IcfaiTech The ICFAI Foundation For Higher Education Hyderabad India

ISBN: (纸本)9789819967018

The Routing Over Low power and Lossy network (ROLL) working group of IETF proposed the IPv6 Routing Protocol over Low Power and Lossy network (RPL) to fulfill multiple traffic requirements of the Internet of Things (IoT) environments. RPL structures ubiquitous IoT networks in the form of Directed Acyclic Graphs (DAGs) that comprises basic sensor nodes (motes), motes with routing capability, and one or more root (sink) nodes. RPL ensures optimal topological organization of IoT networks by its rank and version number properties. However, vulnerabilities in the RPL routing process pose threats to several protocol violations in the IoT environment. In RPL, information solicitation is the process by which sensor nodes seek network configuration information required to join an IoT network. The paper investigates the vulnerabilities associated with the information solicitation process in RPL-based IoT networks that leads to flooding of control messages. Consequently, there is energy and queue buffer depletion of sensor nodes in IoT networks. On the one hand, queue buffer depletion may lead to the dropping of packets by sensor nodes. On the other hand, energy depletion of sensor nodes may altogether make them non-functioning. Therefore, IoT network administrators must pay attention to the device’s power consumption. Recent research focuses on energy depletion due flooding of control packets. Nevertheless, flooding also affects other significant parameters like, the network convergence time, beacon interval and packet arrival rate. In this work, we perform an in-depth evaluation of the performance of RPL-based IoT networks under the threat of vulnerabilities associated with information solicitation process. The results of our analysis will assist in devising mitigation mechanisms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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Design and Development of Stress Monitoring System in Use Case: Twitter #Dek65 15

Design and Development of Stress Monitoring System in Use Ca...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Sukumthammarat, Nessara Nimma, Sasima Songmuang, Pokpong Thammasat University College of Interdisciplinary Studies Data Science and Innovation Pathum Thani Thailand Thammasat University Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9781665477123

Twitter is a social media platform where users can post, make a conversation, comments, and share experiences that express their emotions and sentiments. Our objective is to monitor and analyze the #Dek65 hashtag's Twitter messages. We take 166,110 Twitter messages on the #Dek65 hashtag from August 2021 to July 2022 and bring them to analyze attitudes, thoughts, emotions, and stress during the preparation for university entrance exams and the Thai education system. We designed and developed a system by creating a model that can sentiment message, a model for cluster topics from the negative message then represent sentiment messages in a way that is simple to understand through visualization. We do this to make stakeholders can monitor and aware of the problems in the Thai education system. © 2023 IEEE.

关键词： Social networking (online)

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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Multi-modal Fusion and 1D-CNN Based Elderly Fall Risk Prediction during Treadmill Walking 10

Multi-modal Fusion and 1D-CNN Based Elderly Fall Risk Predic...

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10th IEEE Smart World Congress, SWC 2024

作者： Huang, Xuan Wu, Bo Zhou, Xiaokang Nishimura, Shoji Waseda University Faculty of Human Sciences Tokorozawa Japan Tokyo University of Technology School of Computer Science Hachioji Japan Kansai University Faculty of Business Data Science Osaka Japan Riken Center for Aip Tokyo Japan

ISBN: (纸本)9798331520861

In today's society, there has been an increase in the number of accidents among the elderly (60 and over) in places such as health clubs, where the main cause of accidents is due to the loss of balance on treadmills. However, traditional solutions are inefficient while consuming human resources and lagging in their inability to predict effectively and prevent upcoming accidents. A user-friendly system that can detect the movement status of the elderly in real-time and predict and alarm the fall risk is needed. Therefore, to protect the safety of the elderly when using the treadmill, in this paper we design a set of elderly exercise experiments at an elderly welfare center in Japan and propose a multi-modal fusion and 1D-CNN based elderly fall risk prediction system to integrate the short-term (e.g., skeletal features and plantar pressures in five minutes) and long-term (e.g., heart rate and sleep quality for three months) data. Finally, we have shown and discussed an application scenario with the simulation of our proposed system. The system will optimize the elderly users' exercise experience when exercising on a treadmill, reduce the risk of falls, and provide guidance for the design of future fitness equipment for the elderly. © 2024 IEEE.

关键词： Risk perception

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FedDGA: Federated Multitask Learning Based on Dynamic Guided Attention

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 268-280页

作者： Sun, Haoyun Zhao, Hongwei Xu, Liang Zhang, Weishan Guan, Hongqing Yang, Su China University of Petroleum Qingdao266580 China Shandong Data Open Innovative Application Laboratory Qingdao266580 China WINDAKA Technology Company Ltd. Qingdao266580 China Beijing University of Science and Technology Beijing100083 China Swansea University Department of Computer Science Faculty of Science and Engineering SwanseaSA2 8PP United Kingdom

The proliferation of privacy-sensitive data has spurred the development of federated learning (FL), which is an important technology for state-of-the-art machine learning and responsible AI. However, most existing FL methods are constrained in their applicability and generalizability due to their narrow focus on specific tasks. This article presents a novel federated multitask learning (FMTL) framework that is capable of acquiring knowledge across multiple tasks. To address the challenges posed by non-IID data and task imbalance in FMTL, this study proposes a federated fusion strategy based on dynamic guided attention (FedDGA), which adaptively fine-tunes local models for multiple tasks with personalized attention. In addition, this article designed dynamic batch weight (DBW) to balance the task losses and improve the convergence speed. Extensive experiments were conducted on various datasets, tasks, and settings, and the proposed method was compared with state-of-the-art methods such as FedAvg, FedProx, and SCAFFOLD. The results show that our method achieves significant performance gains, with up to 11.1% increase in accuracy over the baselines. © 2020 IEEE.

关键词： Scaffolds

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A Novel Convolutional-Transformer Neural Network Architecture for Diagnosis of Pneumothorax 28

A Novel Convolutional-Transformer Neural Network Architectur...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Sanati, Amir Dashtestani, Mansoureh A. Rostami, Habib Azad, Saeed Talatian Persian Gulf University Faculty of Intelligent System Engineering and Data Science Computer Engineering Department Bushehr Iran

ISBN: (纸本)9798350338195

Pneumothorax is a life-threatening and urgent chest disease than can be detected using Chest X-Ray (CXR) image. CXR images are low resolution and diagnosis of pneumothorax based on them is error prone. Deep learning-based computer aided diagnosis systems can improve diagnosis performance of pneumothorax. Convolutional Neural Networks (CNNs) are default networks in deep learning-based medical image process. However, CNNs fail to capture long range features. On the other side, Transformer are proposed to exploit long range feature, but they cannot capture local features. In this paper, we propose a general method with a convolution and a transformer module which can classify CXR images to diagnose pneumothorax by extracting local features, global features and global features attended by local ones using a novel architecture. Results show that the proposed method outperforms base architectures and the other previous works. © 2023 IEEE.

关键词： Convolutional neural networks

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