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Augmented Reality With Authentic GeometryGo App to Help Geometry Learning and Assessments

IEEE Transactions on Learning Technologies 2023年第5期16卷 769-779页

作者： Hwang, Wu-Yuin Nurtantyana, Rio Purba, Siska Wati Dewi Hariyanti, Uun Suprapto National Dong Hwa University Department of Computer Science and Information Engineering College of Science and Engineering Hualien974 Taiwan National Central University Graduate Institute of Network Learning Technology Taoyuan City320 Taiwan Telkom University School of Computing Bandung40257 Indonesia Universitas Pelita Harapan Department of Master of Educational Technology Kota Tangerang15811 Indonesia Brawijaya University Information Systems Department Kota Malang65145 Indonesia State University Yogyakarta Department of Electronics Engineering Education Yogyakarta55281 Indonesia

We developed an Authentic GeometryGo (AGG) app to help students application of geometry in authentic contexts through authentic measurements and peer assessments. The study included an experimental group (EG) using an AGG app and a control group (CG) using paper-based methods. The results showed that the EG outperformed the CG in terms of estimation. Measuring authentic geometry shapes using the app could foster students' estimation. Furthermore, meaningful peer assessments with visual representations in authentic contexts can facilitate the evaluation of peers' work and improve students' geometry understanding. Therefore, we encourage educators to use augmented reality with appropriate learning activities to sharpen students' measurement quality and visual representations by measuring real geometry shapes and applying mathematics knowledge in authentic contexts. © 2008-2011 IEEE.

关键词： Augmented reality

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Evaluating Segmentation Accuracy with Diverse Prompt Strategies in Medsam

Evaluating Segmentation Accuracy with Diverse Prompt Strateg...

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IEEE International Symposium on Biomedical Imaging

作者： Muhammad Nouman Ghada Khoriba Essam A. Rashed Graduate School of Information Science University of Hyogo Kobe Japan School of Information Technology and Computer Science Nile University Giza Egypt Advanced Medical Engineering Research Institute University of Hyogo Himeji Japan

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Segment Anything Model (SAM) shows robustness in prompt-based segmentation of natural images. However, adapting SAM to the medical domain as a foundation model, MedSAM, presents unique challenges arising from clinical settings. This study evaluates MedSAM's performance across five medical imaging modalities (MRI, CT, ultrasound, endoscopy, and fundus IR) using four different prompting strategies: standard bounding boxes (SBB), rotated bounding boxes (RBB), contour points (CP), and simulated human annotations (SHA). MedSAM's performance is assessed under precise and perturbed conditions for each prompt type to evaluate its segmentation capabilities comprehensively. We empirically evaluate MedSAM's abilities and adaptability by experimenting with zero-shot learning and fine-tuning approaches. The results indicate that performance varies markedly with prompt type, accuracy, and imaging modality. The study concludes that MedSAM is sensitive to prompt precision and anatomical complexity. Fine-tuning improves performance, but further refinements are necessary to make it robust in various clinical settings. This work highlights the need for prompt engineering and provides practical insights to enhance segmentation accuracy in SAM-like foundation models.

关键词： Image segmentation Ethics Accuracy Ultrasonic imaging Foundation models Magnetic resonance imaging Robustness Prompt engineering Standards Biomedical imaging

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An Intelligent Task and Data Deployment Method for SIoT Systems

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IEEE Internet of Things Journal 2024年

作者： Ji, Hangxu Sun, Yongjiao Luan, Yuyao Yuan, Ye Wang, Guoren Wang, Qi Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University Foshan Graduate School of Innovation Foshan528311 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China

The SIoT system enables connectivity among smart devices by integrating social networks with the Internet of Things. This integration is essential for advancing intelligent services and applications, as well as enhancing the commercial value of data. Rational task and data deployment strategies allow different types of devices to perform optimally in their areas of expertise, reducing network load, improving the timeliness of data processing, and ensuring efficient collaboration across the entire system. However, the limited computational capacity and network bandwidth of SIoT devices result in communication delays that significantly impact job responsiveness and energy consumption. The heterogeneity in computational power and bandwidth across different devices leads to resource overload or underutilization with traditional data partitioning methods, further impacting the performance of SIoT systems. This paper proposes an intelligent task and data deployment method to address these issues. The proposed method abstracts the job execution process as JobGraph instance model and optimizes the mapping relationship of operators within TaskProcess. Additionally, an improved linear programming model is used to optimize the data distribution ratio among operators in heterogeneous computing environments. The proposed task deployment method achieves an average improvement of 19.6%-30.2% in job efficiency while reducing inter-device data transmission by 34.2%. In heterogeneous computing environments, the combination of the two deployment optimization methods further reduces job execution time, achieving efficiency improvements of over 2 times in optimal scenarios. © 2024 IEEE.

关键词： Linear programming

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Bridging Gender Disparities in Mental Health: A Bilingual Large Language Model for Multilingual Therapeutic Chatbots

Bridging Gender Disparities in Mental Health: A Bilingual La...

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Learning and Technology Conference (L&T)

作者： Ahmed Tamer Elboardy Ziad Mohamed Ghada Khoriba Tamer Arafa Essam A. Rashed School of Information Technology and Computer Science Nile University Giza Egypt Graduate School of Information Science University of Hyogo Kobe Japan Advanced Medical Engineering Research Institute University of Hyogo Himeji Japan

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Significant gender disparities and a general lack of support systems compound the state of mental health in developing countries. Women, in particular, face unique challenges due to societal norms and expectations that often restrict their access to mental health services. Additionally, the cultural stigma associated with mental health issues affects both men and women but can be particularly debilitating for women who may be less likely to seek help or disclose their struggles. This study explores the application of Jais-13B, a pre-existing bilingual Large Language Model (LLM) proficient in English and Arabic, tailored to address mental health inquiries. We employed a rich dataset encompassing both languages, essential for fine-tuning and enhancing Jais-13B’s responsiveness and empathetic engagement in mental health contexts. The optimization focused on improving the model’s ability to deliver accurate and contextually aware responses, which is crucial for effective mental health support. Performance evaluation using the cross-entropy loss function demonstrated that Jais-13B achieved an accuracy of 70.58%, indicating its capability to produce relevant and precise answers to mental health questions. These results showcase Jais-13B’s potential to enhance digital mental health interventions significantly, providing accessible care solutions across diverse linguistic landscapes. This study reaffirms the effectiveness of bilingual LLMs in mental health applications and provides insights into their practical implementation, setting a foundational benchmark for future research in multilingual therapeutic chatbots.

关键词： Performance evaluation Ethics Adaptation models Sentiment analysis Accuracy Large language models Mental health Chatbots Multilingual Context modeling

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Towards an ultrafast 3D imaging scanning LiDAR system:a review

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Photonics Research 2024年第8期12卷 1709-1729页

作者： ZHI LI YAQI HAN LICAN WU ZIHAN ZANG MAOLIN DAI SZE YUN SET SHINJI YAMASHITA QIAN LI H.Y.FU Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Department of Electrical Engineering and Information Systems The University of TokyoTokyo 113-8656Japan Research Center for Advanced Science and Technology The University of TokyoTokyo 153-8904Japan School of Electronic and Computer Engineering Peking UniversityShenzhen 518055China

Light detection and ranging(LiDAR),as a hot imaging technology in both industry and academia,has undergone rapid innovation and *** current mainstream direction is towards system miniaturization and *** are many metrics that can be used to evaluate the performance of a LiDAR system,such as lateral resolution,ranging accuracy,stability,size,and *** recently,with the continuous enrichment of LiDAR application scenarios,the pursuit of imaging speed has attracted tremendous research ***,for autonomous vehicles running on motorways or industrial automation applications,the imaging speed of LiDAR systems is a critical *** this review,we will focus on discussing the upper speed limit of the LiDAR *** on the working mechanism,the limitation of optical parts on the maximum imaging speed is *** beam scanner has the greatest impact on imaging *** provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning ***,we highlight the spectral scanning LiDAR as a new paradigm of ultrafast ***,to further improve the imaging speed,we then review the parallel detection methods,which include multiple-detector schemes and multiplexing ***,we summarize the LiDAR systems with the fastest point acquisition rate reported *** the outlook,we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.

关键词： LiDAR scanner system

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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Pre-planning of trajectory and orbiting around greenhouses to shorten operation time for fruit-set-reagent spraying robot

Pre-planning of trajectory and orbiting around greenhouses t...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Yusuke Oikawa Akihisa Ohya Ayanori Yorozu Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan Dpertment of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350394276

ISBN: (纸本)9798350394283

This paper presents a motion strategy for a fruitset-reagent spraying operation using a mobile manipulator-type robot. It is very time-consuming to search for and detect flowers and plan the manipulator’s trajectory on time. Therefore, we propose a strategy to effectively execute the manipulator’s trajectory planning in advance based on the flower positions obtained by the robot running in advance, and to execute the spraying and detecting of flowers by orbiting around greenhouse. As a result, the hourly spraying efficiency was improved compared to related methods. This achievement is important in that it enables on-time spreading process in an environment where the operation time is limited.

关键词： Mechatronics Trajectory planning Green products Spraying Flowering plants Orbits Trajectory

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Selective Multi-pulse Pulse Position Modulation for Lighting Constrained Visible Light Communications

Selective Multi-pulse Pulse Position Modulation for Lighting...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yusuke Kozawa Department of Computer and Information Sciences Graduate School of Science and Engineering Ibaraki University Hitachi City Ibaraki Pref. Japan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Lighting-constrained visible light communications (VLC) can provide both high-speed wireless communications and dimming control of the lighting state of indoor environments. Here, a dimming control method based on multi-pulse pulse position modulation (MPPM) has been investigated to simultaneously achieve high-data-rate transmissions and dimming control. MPPM data is transmitted on r slots selected from the available ones. Dimming-control-based MPPM can also be realized by changing the value of r. When the dimming level changes, the data transmission rate changes, resulting in a significant degradation of the average data transmission efficiency averaged over the data transmission efficiency of each dimming stage of VR-MPPM. This paper proposes a selective VR-MPPM (SVR-MPPM) scheme to improve this average data transmission efficiency by selecting a value of r that achieves the highest possible transmission efficiency while satisfying a wide dimming range.

关键词： Wireless communication Degradation Spectral efficiency Circuits and systems Measurement uncertainty Modulation Lighting

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Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mollah, Muhammad Baqer Wang, Honggang Karim, Mohammad Ataul Fang, Hua University of Massachusetts Dartmouth Department of Electrical and Computer Engineering MA02747 United States Yeshiva University Katz School of Science and Health Department of Graduate Computer Science and Engineering NY10016 United States University of Massachusetts Dartmouth Department of Computer and Information Science MA02747 United States

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications. © 2015 IEEE.

关键词： Beamforming

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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13571-13584页

作者： Liu, Wei Liu, Jierong Peng, Tao Wang, Guojun Balas, Valentina Emilia Geman, Oana Chiu, Hung-Wen School of Information and Communication Engineering Hainan University Hainan Haikou570228 China School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China College of Biomedical Information and Engineering Hainan Medical University Street Hainan Haikou571199 China Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad310130 Romania Department of Health and Human Development Stefan cel Mare University of Suceava Suceava720229 Romania Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network layers

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