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arXiv 2025年

作者： He, Qisheng Summerfield, Nicholas Wang, Peiyong Glide-Hurst, Carri Dong, Ming Department of Computer Science Wayne State University MI United States Department of Human Oncology University of Wisconsin-Madison WI United States Department of Medical Physics University of Wisconsin-Madison WI United States

Recent studies have shown that diffusion models produce superior synthetic images when compared to Generative Adversarial Networks (GANs). However, their outputs are often non-deterministic and lack high fidelity to the ground truth due to the inherent randomness. In this paper, we propose a novel High-fidelity Brownian bridge model (HiFi-BBrg) for deterministic medical image translations. Our model comprises two distinct yet mutually beneficial mappings: a generation mapping and a reconstruction mapping. The Brownian bridge training process is guided by the fidelity loss and adversarial training in the reconstruction mapping. This ensures that translated images can be accurately reversed to their original forms, thereby achieving consistent translations with high fidelity to the ground truth. Our extensive experiments on multiple datasets show HiFi-BBrg outperforms state-of-the-art methods in multi-modal image translation and multi-image super-resolution. © 2025, CC BY.

关键词： Generative adversarial networks

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Scalable Subspace Clustering via Purity Kernel Tensor Learning

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rachit Adhvaryu S Rajes Kannan Sanjay C. P Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Sri Balaji University Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor Learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications.

关键词： Tensors Accuracy Scalability Noise Clustering algorithms Machine learning Robustness Computational efficiency Noise measurement Kernel

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ExplainitAI: When do we trust artificial intelligence? The influence of content and explainability in a cross-cultural comparison

arXiv

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arXiv 2025年

作者： Kang, Sora Potinteu, Andreea-Elena Said, Nadia Human-Computer Interaction+Design Lab Seoul National University Seoul Korea Republic of Perception and Action Lab Leibniz-Institut für Wissensmedien Tübingen Germany Applied Cognitive Psychology Department of Psychology University of Tübingen Tübingen Germany

This study investigates cross-cultural differences in the perception of AI-driven chatbots between Germany and South Korea, focusing on topic dependency and explainability. Using a custom AI chat interface, ExplainitAI, we systematically examined these factors with quota-based samples from both countries (N = 297). Our findings revealed significant cultural distinctions: Korean participants exhibited higher trust, more positive user experience ratings, and more favorable perception of AI compared to German participants. Additionally, topic dependency was a key factor, with participants reporting lower trust in AI when addressing societally debated topics (e.g., migration) versus health or entertainment topics. These perceptions were further influenced by interactions among cultural context, content domains, and explainability conditions. The result highlights the importance of integrating cultural and contextual nuances into the design of AI systems, offering actionable insights for the development of culturally adaptive and explainable AI tailored to diverse user needs and expectations across domains. © 2025, CC BY-NC-SA.

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DFE: Deep Flow Embedding for Robust Network Traffic Classification

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 1597-1612页

作者： Wang, Zhijiong Zhang, Anguo Li, Hung Chun Yin, Yadong Chen, Wei Lam, Chan Tong Mak, Peng Un Vai, Mang I. Gao, Yueming Pun, Sio Hang Zhuhai UM Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory Zhuhai519031 China Lingyange Semiconductor Inc. Zhuhai519030 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics 999078 China Fuzhou University School of Physics and Information Engineering Fuzhou350108 China Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Human Phenome Institute Shanghai200437 China Fudan University Human Phenome Institute Shanghai200437 China Macau Polytechnic University Faculty of Applied Sciences 999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China Fuzhou University Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350108 China

People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery. © 2013 IEEE.

关键词： Network embeddings

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Designing semantic substrates for visual network exploration

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Information Visualization 2007年第4期6卷 281-300页

作者： Aris, Aleks Shneiderman, Ben Computer Science Department Human-Computer Interaction Lab. University of Maryland College Park MD United States Computer Science Department Human-Computer Interaction Lab. University of Maryland College Park MD 20742 United States

A semantic substrate is a spatial template for a network, where nodes are grouped into regions and laid out within each region according to one or more node attributes. This paper shows how users can be given control in designing their own substrates and how this ability leads to a different approach to network data exploration. Users can create a semantic substrate, enter their data, get feedback from domain experts, edit the semantic substrate, and iteratively continue this procedure until the domain experts are satisfied with the insights they have gained. We illustrate this process in two case studies with domain experts working with legal precedents and food webs. Guidelines for designing substrates are provided, including how to locate, size, and align regions in a substrate, which attributes to choose for grouping nodes into regions, how to select placement methods and which attributes to set as parameters of the selected placement method. Throughout the paper, examples are illustrated with NVSS 2.0, the network visualization tool developed to explore the semantic substrate idea. © 2007 Palgrave Macmillan Ltd. All rights reserved.

关键词： Semantic Web

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Using the interactive software FossilSketch to teach micropaleontology to undergraduate students

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Journal of Geoscience Education 2025年第2期73卷 133-153页

作者： Stepanova, Anna Belanger, Christina Anwar, Saira Stanley, Christine Nath, Ankur Cherian, Josh Hammond, Tracy Department of Computer Science and Engineering Texas A&M University College Station TX United States Department of Geology and Geophysics Texas A&M University College Station TX United States Department of Multidisciplinary Engineering Texas A&M University College Station TX United States Department of Educational Administration and Human Resource Development Texas A&M University College Station TX United States Institute for Engineering Education and Innovation Texas A&M University College Station TX United States

Micropaleontology is a critical tool for determining the ages of geologic records, reconstructing ancient environments, and monitoring modern ecosystem health. However, most students are not exposed to micropaleontology in their college coursework. To enable non-expert instructors to integrate microfossil identification training in their undergraduate courses, we developed FossilSketch, an interactive web-based educational tool that introduces students to the basics of micropaleontology and guides students through a scaffolded learning experience that develops microfossil identification skills. Here we test the impact of FossilSketch on students’ ability to learn micropaleontology skills, such as identification of microfossils to genus level and basics of fossil data analysis, using data on students’ performance and survey responses collected in an undergraduate paleontology course for geology majors at a large public university. A total of 112 students took part in this study. Analysis of classroom assessments showed that junior and senior geology majors who used FossilSketch were better able to understand the process of microfossil identification, recognize morphological characteristics, and achieve a correct identification than those who did not use FossilSketch. Students who used FossilSketch needed to ask the teaching assistant fewer questions and felt better prepared for specimen-based work than students who did not use FossilSketch. These results suggest that FossilSketch improves students’ understanding of the microfossil identification process. © 2024 National Association of Geoscience Teachers.

关键词： micropaleontology Taxonomy undergraduate education

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Enhancing Consumer Electronics Security in Smart Homes with t-SNE Feature Selection and Seagull Optimized Deep Learning Model

Enhancing Consumer Electronics Security in Smart Homes with ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Akshat Gaurav Brij B. Gupta Kwok Tai Chui Ronin Institute Montclair NJ USA Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan & University of Economics and Human Science Warsaw Poland Hong Kong Metropolitan University (HKMU) Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This work presents a t-SNE-based feature selection and Seagull Optimized Deep Learning Model to improve consumer electronics security in smart homes. Optimized using the Seagull Optimization Algorithm (SOA), the suggested Convolutional Neural Network (CNN) model showed exceptional performance with an accuracy of 92% in identifying cyber risks. The model routinely exceeded in both accuracy and loss measures when tested against conventional machine learning and deep learning models. T-SNE helped to choose important criteria that provide strong identification of harmful activity. Our method presents a major development in smart home security and a dependable instrument for reducing cybersecurity threats in a linked world.

关键词： Deep learning Accuracy Machine learning algorithms Smart homes Feature extraction Loss measurement Convolutional neural networks computer security Consumer electronics Optimization

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Effects of Alternative Scatterplot Designs on Belief 25

Effects of Alternative Scatterplot Designs on Belief

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Gabriel Strain Andrew J. Stewart Caroline Jay Charlotte Rutherford Paul A. Warren Department of Computer Science Faculty of Science and Engineering University of Manchester Manchester United Kingdom Division of Psychology Communication and Human Neuroscience Schoolof Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom

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Dynamic Localisation of Spatial-Temporal Graph Neural Network

arXiv

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arXiv 2025年

作者： Duan, Wenying Guo, Shujun Zhou, Zimu Huang, Wei Rao, Hong He, Xiaoxi Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau China

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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ON ACCELERATING EDGE AI: OPTIMIZING RESOURCE-CONSTRAINED ENVIRONMENTS

arXiv

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arXiv 2025年

作者： Sander, Jacob Cohen, Achraf Dasari, Venkat R. Venable, Brent Jalaian, Brian Department of Computer Science University of West Florida PensacolaFL United States Department of Mathematics and Statistics University of West Florida PensacolaFL United States DEVCOM Army Research Laboratory Aberdeen Proving Ground MD United States Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States Department of Computer Science Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge. © 2025, CC BY.

关键词： Budget control

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