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IEEE Education engineering (EDUCON)

作者： Jorge Torres Gómez Nicolai Spicher Jan Haase School of Electrical Engineering and Computer Science TU Berlin Berlin Germany University Medical Center Göttingen Germany NORDAKADEMIE gAG Hochschule der Wirtschaft Elmshorn Germany

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Promoting science, technology, engineering and mathematics (STEM) education at K-12 level motivates pupils to pursue technical careers at universities, which is of high importance due to skilled labor shortage and other factors. In particular, in Germany the population with a tertiary education level is less than the average in the European Union (35–41 %. Motivating the study of technical careers, this paper features a model for promoting STEM at the K-12 education level by developing joint teaching platforms with students at technical universities. The model is derived from hands-on experience at TU Berlin targeting a climate change topic: As part of student semester projects, Bachelor students integrated a humidity sensor into a drone and conducted atmospheric measurements. The resulting hard and software components provided by the students allowed the preparation of a lesson plan to promote STEM at the K-12 level. In this way, we synergically connect the means from the university with hands-on activities as means to promote STEM topics with K-12 students. The resulting benefits are manifold; students at the university develop technical skills and become part of a social intention to promote STEM topics on the one hand. On the second hand, pre-university students might have first-hand experiences with hardware prototypes and their applicability to relevant topics like climate change surveillance. The developed model may illustrate straightforward means for educators to design lesson plans, including actual practices at university and pre-university levels.

关键词： Manifolds Climate change engineering profession Surveillance Sociology Prototypes Software

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Advancing Tourism Chatbots: Understanding Irony, Sarcasm, and Negative Emotions of Users

Advancing Tourism Chatbots: Understanding Irony, Sarcasm, an...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ommi Balamurali A.M. Abhishek Sai Sruthy Anand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The recent trend in chatbots for several application areas shows their potential to act as a support system to assist users with their needs. However, there is a need to direct efforts to build emotional intelligence into this software to enhance its usability and user experience. Building emotionally intelligent chatbots helps to interpret and respond to users according to their emotions, resulting in rich and healthy user experiences. Understanding irony, sarcasm and Negative emotions of the user are important aspects of sentimental analysis as they largely affect the emotional tone. This research utilises Long Short-Term Memory (LSTM) networks, to enhance chatbot sentiment analysis. In our experimentation, we leverage a dataset comprising over a million comments from Reddit, distinguishing between sarcastic and non-sarcastic expressions, to train and test our LSTM model. Along with the utilization of LSTMs Networks for sentiment analysis in the chatbot, the experiment incorporates BERT, a Transformer model architecture, to further enhance the chatbot’s capacity to recognize and react to sarcasm and satire. Using BERT’s capacity for contextual comprehension and pre-trained languages, the conversational agent can resonate with the user’s input tone and intent, whether they are sarcastic or not.

关键词： Sentiment analysis Social networking (online) Chatbots Transformers Data models User experience Software Artificial intelligence Usability Long short term memory

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems

arXiv

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arXiv 2023年

作者： Liu, Ruixuan Hu, Ming Xia, Zeke Xia, Jun Zhang, Pengyu Huang, Yihao Liu, Yang Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China School of Computer Science and Engineering Nanyang Technological University Singapore Computer Science and Engineering University of Notre Dame United States

Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive mobile computing devices, the performance of traditional homogeneous model-based Federated Learning (FL) is seriously limited. On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning. On the other hand, devices with high computing resources cannot train a high-performance large model with their insufficient raw data. To address the resource-constrained problem in mobile computing systems, we present a novel heterogeneous FL approach named AdapterFL, which uses a model reassemble strategy to facilitate collaborative training of massive heterogeneous mobile devices adaptively. Specifically, we select multiple candidate heterogeneous models based on the computing performance of massive mobile devices and then divide each heterogeneous model into two partitions. By reassembling the partitions, we can generate models with varied sizes that are combined by the partial parameters of the large model with the partial parameters of the small model. Using these reassembled models for FL training, we can train the partial parameters of the large model using low-performance devices. In this way, we can alleviate performance degradation in large models due to resource constraints. The experimental results show that AdapterFL can achieve up to 12% accuracy improvement compared to the state-of-the-art heterogeneous federated learning methods in resource-constrained scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile computing

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Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 827-838页

作者： Kui, Xiaoyan Ji, Zexin Zou, Beiji Li, Yang Dai, Yulan Chen, Liming Vera, Pierre Ruan, Su Central South University School of Computer Science and Engineering Changsha 410083 China Henri Becquerel Cancer Center Department of Nuclear Medicine Rouen France Hunan University of Chinese Medicine School of Informatics Changsha 410208 China Ecole Centrale de Lyon Department of Mathematics and Computer Science Ecully 69130 France University of Rouen-Normandy AIMS - QuantIF UR 4108 Rouen F-76000 France

High-resolution medical images can provide more detailed information for better diagnosis. Conventional medical image super-resolution relies on a single task which first performs the extraction of the features and then upscaling based on the features. The features extracted may not be complete for super-resolution. Recent multi-task learning, including reconstruction and super-resolution, is a good solution to obtain additional relevant information. The interaction between the two tasks is often insufficient, which still leads to incomplete and less relevant deep features. To address above limitations, we propose an iterative collaboration network (ICONet) to improve communications between tasks by progressively incorporating reconstruction prior to the super-resolution learning procedure in an iterative collaboration way. It consists of a reconstruction branch, a super-resolution branch, and a SR-Rec fusion module. The reconstruction branch generates the artifact-free image as prior, which is followed by a super-resolution branch for prior knowledge-guided super-resolution. Unlike the widely-used convolutional neural networks for extracting local features and Transformers with quadratic computational complexity for modeling long-range dependencies, we develop a new residual spatial-channel feature learning (RSCFL) module of two branches to efficiently establish feature relationships in spatial and channel dimensions. Moreover, the designed SR-Rec fusion module fuses the reconstruction prior and super-resolution features with each other in an adaptive manner. Our ICONet is built with multi-stage models to iteratively upscale the low-resolution images using steps of 2x and simultaneously interact between two branches in multi-stage supervisions. Quantitative and qualitative experimental results on the benchmarking dataset show that our ICONet outperforms most state-of-the-art approaches. © 2015 IEEE.

关键词： medical imaging multi-task learning reconstruction Super-resolution

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IoT-based Efficient Waste Management on University Campus: Enhancing Monitoring and Optimization through Machine Learning

IoT-based Efficient Waste Management on University Campus: E...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Sumathi, M. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Meghana, J. Department of Studies in Computer Science University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)'s flawless

关键词： Electronic Waste

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A Priority-Aware AI-Generated Content Resource Allocation Method for Multi-UAV Aided Metaverse

A Priority-Aware AI-Generated Content Resource Allocation Me...

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IEEE Conference on Wireless Communications and Networking

作者： Zheng Zhang Jingjing Wang Jianrui Chen Zhengru Fang Chunxiao Jiang Zhu Han School of Cyber Science and Technology Beihang University Beijing China Department of Computer Science City University of Hong Kong Hong Kong China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments.

关键词： Metaverse Simulation Markov decision processes Reinforcement learning Autonomous aerial vehicles Diffusion models Real-time systems Communications technology Resource management Next generation networking

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Usability Evaluation of Kids’ Learning Apps

Usability Evaluation of Kids’ Learning Apps

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Amer Ibrahim Murad Al-Rajab Khalid Hamid Muhamamd Aqeel Salman Muneer Mehvish Parveen Maryam Saleem College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates Assistant Professor Computer Scienc and IT College of Engineering Abu Dhabi University Department of Computer Science Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Science NCBAE Lahore Pakistan

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids’ fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically.

关键词： Electronic learning User interfaces Security Usability Standards Business

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EchoDiffusion: Waveform Conditioned Diffusion Models for Echo-Based Depth Estimation 39

EchoDiffusion: Waveform Conditioned Diffusion Models for Ech...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Wenjie Yin, Jun Ma, Long Yu, Peng Jiang, Xiaoheng Tian, Zhen Xu, Mingliang School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou450001 China National Supercomputing Center in Zhengzhou Zhengzhou450001 China

ISBN: (纸本)157735897X

To extract spatial information, depth estimation using conventional echo-based methods typically employs models with encoder-decoder architectures, such as UNet. However, these methods may face challenges in extracting fine details from echo waveforms and handling multi-scale feature extraction with high precision. To address these challenges, we introduce EchoDiffusion, a framework that incorporates diffusion models conditioned on waveform embeddings for echo-based depth estimation. This framework employs the Multi-Scale Adaptive Latent Feature Network (MALF-Net) to extract multi-scale spatial features and perform adaptive fusion, encoding the echo spectrograms into the latent space. Additionally, we propose the Echo Waveform Detail Embedder (EWDE), which leverages a pre-trained Wav2Vec model to extract detailed spatial information from echo waveforms, using these details as conditional inputs to guide the reverse diffusion process in the latent space. By embedding the echo waveforms into the reverse diffusion process, we can more accurately guide the generation of depth maps. Our extensive evaluations on the Replica and Matterport3D datasets demonstrate that EchoDiffusion establishes new benchmarks for state-of-the-art performance in echo-based depth estimation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Zhang, Yizhe Zhou, Tao Wang, Shuo Wu, Ye Gu, Pengfei Chen, Danny Z. The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai Key Laboratory of MICCAI Shanghai China The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image segmentation tasks. In contrast, medical image segmentation tasks often rely on domain-specific knowledge (DSK). In this paper, we propose a novel method that combines the segmentation foundation model (i.e., SAM) with domain-specific knowledge for reliable utilization of unlabeled images in building a medical image segmentation model. Our new method is iterative and consists of two main stages: (1) segmentation model training;(2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge. These two stages are repeated until no more samples are added to the labeled set. A novel optimal-matching-based method is developed for combining the SAM-generated segmentation proposals and pixel-level and image-level DSK for constructing annotations of unlabeled images in the iterative stage (2). In experiments, we demonstrate the effectiveness of our proposed method for breast cancer segmentation in ultrasound images, polyp segmentation in endoscopic images, and skin lesion segmentation in dermoscopic images. Our work initiates a new direction of semi-supervised learning for medical image segmentation: the segmentation foundation model can be harnessed as a valuable tool for label-efficient segmentation learning in medical image segmentation. © 2023, CC BY.

关键词： Image segmentation

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