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arXiv 2025年

作者： Theerens, Adnan Saeys, Yvan Cornelis, Chris Department of Mathematics Computer Science and Statistics Ghent University Ghent Belgium Data Mining and Modeling for Biomedicine Group VIB Inflammation Research Center Belgium

This paper introduces feature subset weighting using monotone measures for distance-based supervised learning. The Choquet integral is used to define a distance metric that incorporates these weights. This integration enables the proposed distances to effectively capture non-linear relationships and account for interactions both between conditional and decision attributes and among conditional attributes themselves, resulting in a more flexible distance measure. In particular, we show how this approach ensures that the distances remain unaffected by the addition of duplicate and strongly correlated features. Another key point of this approach is that it makes feature subset weighting computationally feasible, since only m feature subset weights should be calculated each time instead of calculating all feature subset weights (2m), where m is the number of attributes. Next, we also examine how the use of the Choquet integral for measuring similarity leads to a non-equivalent definition of distance. The relationship between distance and similarity is further explored through dual measures. Additionally, symmetric Choquet distances and similarities are proposed, preserving the classical symmetry between similarity and distance. Finally, we introduce a concrete feature subset weighting distance, evaluate its performance in a k-nearest neighbors (KNN) classification setting, and compare it against Mahalanobis distances and weighted distance methods. © 2025, CC BY-NC-ND.

关键词： Choquet integral

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AMSER: Accelerate Mobile Speech Emotion Recognition with Signal Compression

AMSER: Accelerate Mobile Speech Emotion Recognition with Sig...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Lu Ran Wang Dian Ding Han Zhang Liyun Zhang Lanqing Yang Yi-Chao Chen Guangtao Xue Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems’ user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes AMSER, a real-time speech emotion recognition framework using signal compression and task offloading. AMSER utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24x faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 69.83% emotion recognition accuracy (IEMOCAP).

关键词： Emotion recognition Accuracy Computational modeling Speech recognition Artificial neural networks Speech enhancement Real-time systems Complexity theory Smart phones Singular value decomposition

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Hybrid Agile Model, the Synergy of Waterfall Model and Scrum in a Software Project

Hybrid Agile Model, the Synergy of Waterfall Model and Scrum...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Norzariyah Yahya Abdul Muizz Abdul Salam Siti Sarah Maidin Normi Sham Awang Abu Bakar Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Data Science and Information Technology (FDSIT) INTI International University Malaysia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Hybrid agile is one of the recent software engineering process model used by a software engineering team in a software development project. A hybrid agile model is used by software engineering team to meet the requirements of a software project. However, what does the hybrid agile model used by a software engineering team look like? A qualitative study was conducted to answer this question, and a series of interviews were chosen as a data collection instrument. The interview participants are practitioners involved in software projects using hybrid agile. The recorded interviews are transcribed and analysed using thematic analysis. The finding shows that the software engineering team uses the Waterfall model in the planning, design, deployment and maintenance phases, while scrum in the development and testing phases. The results have led to the hybrid agile model presented in this study, which leverages the strengths of each model to ensure the completion of a software project. Future iterations of this study will assess a project's software quality, comparing the proposed model with plan-driven and scrum.

关键词： Instruments Software quality Planning Maintenance Scrum (Software development) Interviews Information technology Software development management Software engineering Testing

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FloraNet: An Approach to Prediction of Sea Buckthorn Disease based on a Convolutional Neural Network and a Random Forest Method

FloraNet: An Approach to Prediction of Sea Buckthorn Disease...

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Emerging Technology (INCET), International Conference for

作者： J. Pushpakumari Nuthanakanti Bhaskar G. Divya Murali Kanthi K. Srujan Raju Jonnadula Narasimharao Dept. of Computer Science and Engineering (AIML & IOT) VNRVJIET Hyderabad Telangana India Dept. of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology CMR Technical Campus Hyderabad Telangana India Department of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In the current study, FloraNet was used to present an improved method for diagnosing leaf diseases in Sea Buckthorn. In the proposed model, Convolutional Neural Networks (CNN) and Random Forests (RF) are integrated. Essentially, CNN architecture is composed of a pair of convolutional layers, followed by a max-pooling layer; it is then followed by another pair of convolutional layers, before finishing off with a final pair of convolutional layers. Performance indicators such as Precision, Recall, F1-Score, or Accuracy are used to determine the performance of the model in terms of its performance. There were five disorders under investigation: Phakopsora, Erysiphe, Septoria, Colletotrichum, Phoma, as well as viral infections. Overall, the results show that the Micro Average accuracy for all classes is 95.11 %. According to the results, the individual accuracy ranges from 0.98 to 0.99 for each illness, while the F1-Scores range from 94.02% to 95.91% for each illness. This study concludes that the weighted average accuracy of 95.11 demonstrates a high level of proficiency of the model in identifying diseases, and indicates that the model is capable of identifying a wide variety of diseases. By using this integrated approach, agricultural scientists and researchers are able to detect Sea Buckthorn leaf diseases.

关键词： Measurement Accuracy Sea measurements Forestry Predictive models Solids Convolutional neural networks

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Content Moderation and the Formation of Online Communities: A Theoretical Framework

arXiv

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arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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ARE YOU SURE? ENHANCING MULTIMODAL PRE-TRAINING WITH MISSING MODALITIES THROUGH UNCERTAINTY ESTIMATION

arXiv

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arXiv 2025年

作者： Nguyen, Duy A. Do, Quan Huu Doan, Khoa D. Do, Minh N. Siebel School of Computing and Data Science University of Illinois Urbana-ChampaignIL United States College of Engineering and Computer Science VinUniversity Hanoi Viet Nam Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States

Multimodal learning has demonstrated incredible successes by integrating diverse data sources, yet it often relies on the availability of all modalities - an assumption that rarely holds in real-world applications. Pretrained multimodal models, while effective, struggle when confronted with small-scale and incomplete datasets (i.e., missing modalities), limiting their practical applicability. Previous studies on reconstructing missing modalities have overlooked the reconstruction’s potential unreliability, which could compromise the quality of the final outputs. We present SURE (Scalable Uncertainty and Reconstruction Estimation), a novel framework that extends the capabilities of pretrained multimodal models by introducing latent space reconstruction and uncertainty estimation for both reconstructed modalities and downstream tasks. Our method is architecture-agnostic, reconstructs missing modalities, and delivers reliable uncertainty estimates, improving both interpretability and performance. SURE introduces a unique Pearson Correlation-based loss and applies statistical error propagation in deep networks for the first time, allowing precise quantification of uncertainties from missing data and model predictions. Extensive experiments across tasks such as sentiment analysis, genre classification, and action recognition show that SURE consistently achieves state-of-the-art performance, ensuring robust predictions even in the presence of incomplete data. © 2025, CC BY.

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images

Generative Adversarial Networks for Spatio-Spectral Compress...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Martin Hermann Paul Fuchs Akshara Preethy Byju Alisa Walda Behnood Rasti Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCs E ); and ii) HiFiC with 3D convolutions (denoted as HiFiC 3D ) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCs E and HiFiC 3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC.

关键词： Convolutional codes Solid modeling Image coding Three-dimensional displays Convolution Redundancy Bit rate Transform coding Image reconstruction Hyperspectral imaging

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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IEEE Conference on Wireless Communications and Networking

作者： Houze Feng Jingjing Wang Jianrui Chen Xiangwang Hou Jiacheng Wang Geng Sun Dusit Niyato School of Cyber Science and Technology Beihang University Beijing China Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

关键词： Simulation Stability criteria Autonomous aerial vehicles Throughput Routing Routing protocols Vectors Delays Vehicle dynamics AODV

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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