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Applied Mathematical Modelling in the Physics Problem-Solving Classroom 4th

Applied Mathematical Modelling in the Physics Problem-Solvin...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Serpe, Annarosa Department of Mathematics and Computer Science University of Calabria CS Rende Italy

ISBN: (纸本)9783031812460

This paper aims to contribute to the literature on planning and implementing mathematical modelling tasks in the higher education curriculum. Specifically, we propose and discuss a lesson plan carried out within the course of Mathematics Education II - Master’s Degree in Mathematics, to train and qualify students on how to capture the interplay between Physics and Mathematics in the perspective of mathematical modelling and problem-solving. The goal is to make transparent what is meant by modelling by showing how it is possible to develop integrated learning environments beyond the traditional boundaries between subjects both at the subject level and at the level of required pedagogy, illustrating this characterisation with an example regarding a theme that ‘subsumes’ the two disciplines. It is important in the educational training of future teachers to explore ways based on influential theoretical expositions of the concept of interdisciplinarity. This is especially true when examples of didactic transposition are provided where Mathematics and Physics are understood as forms of knowledge that have relationships and differences, which are deeply intertwined and which co-evolve, mutually generating new problems that lead to permeating the labile boundaries between them to generate new knowledge. These examples of didactic transposition, therefore, are rich in interdisciplinarity connections, which show students the relevant aspects of the two disciplines from a historical and epistemological point of view and their connections. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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16th international conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / The 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

16th International Conference on Ambient Systems, Networks a...

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16th international conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

The proceedings contain 166 papers. The topics discussed include: smartphones and syntax: a quantitative study on harnessing the role of mobile-assisted language learning in the digital classroom and applications for language learning;context-driven service provisioning for ubiquitous systems;building heat-resilient communities: a collaborative approach to beat the heat;risk management in transportation systems: how big data can help predict behaviors and events;customized contraction hierarchies with flow-based natural cut heuristic;integrating establishments in an agent-based modeling framework for urban parcel shipments on the first and last mile;exploring the adoption and innovation of digital twins in healthcare;agent-based modeling of on-street parking supply and demand;and enhancing printed Lingala script recognition using deep learning techniques.

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Modified Higher-Order Dependencies in Networks for Anomaly Detection

Modified Higher-Order Dependencies in Networks for Anomaly D...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Kamala Challa Alladi Suresh Babu Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Anantapur Ananthapuramu India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Background: Anomaly detection is essential for detecting unusual behaviors in dynamic networks that may represent emerging security threats. Traditional models focus on First-Order Network representations that overlook the integration of higher-order dependencies. Methods: This work proposes a novel Modified Higher-Order Dependencies Network (HON) for anomaly detection. Initially, the input data is preprocessed by min-max normalization, followed by Random Oversampling to address the class imbalance. Then, this preprocessed data is sent to the novel Modified HON that finds the unusual behaviors of anomalies by modeling sequences with n-grams. An Improved Distance Measure is introduced as the loss function combines Improved Euclidean Distance and Interquartile Mean that improves the detection accuracy by prioritizing structural characteristics over node or edge attributes. Result: The proposed approach achieves 94.1% accuracy surpassing existing techniques by up to 12.5%. Conclusion: This technique offers better performance for anomaly detection that paves the way for advanced cybersecurity applications.

关键词： Accuracy Image edge detection Scalability Data preprocessing Euclidean distance Network security Loss measurement Time measurement Anomaly detection Tuning

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IoT-Based Remote Patient Monitoring systems: Revolutionizing Healthcare Management 1

IoT-Based Remote Patient Monitoring Systems: Revolutionizing...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kanade, Sujata Ramling Hassan, Sohail Malik, Kiren Khurshid Anjum, Sohail Das, Sreemoy Kanti Saleem, Sidra Tilak Maharashtra Vidyapeeth's Institute of Nursing Education and Research Pune India PhD Scholar of Medical Sciences Lincoln University College Malaysia Lincoln University College Department of Biochemistry Malaysia University of Health Sciences Department of Microbiology Lahore Pakistan

ISBN: (纸本)9798331518578

A developing medical technique called remote patient monitoring (RPM) utilizes sensors and Internet of Things (IoTs) gadgets to remotely look after patients' wellness. The medical field has an enormous opportunity to transform itself via breakthroughs that improve productivity, precision, and affordability in medical services. These developments have the potential to enhance patient health dramatically. For elderly people, as the prevalence of chronic illnesses rises, RPM has become essential for enhancing patient treatment and cutting expenses. Numerous metabolic data, such as blood oxygen degrees, respiration rates, temperature measurements, and ECG readings, can be collected and analyzed by IoT-based platforms. These devices can then give healthcare practitioners immediate updates so they may perform the necessary actions. An IoT-oriented framework for RPM for elderly people and prompt wellness issue identification in household healthcare environments is proposed in this research. The MQST system is used to send the gathered statistics to a host. The server classifies possible illnesses using a pretrained deep-learning algorithm built on a convolutional neural network. From ECG readings and fever or non-fever based on temperature measurements, the system may identify five distinct types of heart rates Additionally, the device reports the individual's oxygen saturation and heart pace, showing if they fall under acceptable parameters. If any serious anomalies are found, the program immediately links the person to the closest physician for additional diagnostics. © 2025 IEEE.

关键词： Temperature measurement

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Detection of Multiple Small Biased GPS Spoofing Attacks on Autonomous Vehicles 4

Detection of Multiple Small Biased GPS Spoofing Attacks on A...

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4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Mohammadi, Ahmad Hemmati, Vahid Ahmari, Reza Owusu-Ambrose, Frederick Mahmoud, Mahmoud Nabil Homaifar, Abdollah North Carolina A&T State University Electrical and Computer Engineering Greensboro United States North Carolina A&T State University Computer Science Greensboro United States

ISBN: (纸本)9798331518882

This paper introduces an algorithm designed to detect GPS spoofing attacks in Autonomous Driving systems (ADS). The algorithm combines data from in-vehicle sensors, including the speedometer and gyroscope. This data is then fed into a Deep Neural Network (DNN) to predict the vehicle's displacement between timestamps. These predictions are continuously compared with GPS-provided displacement and velocity to detect the turn-by-turn, stop, and overshoot GPS spoofing attacks. Also, the same data is processed through an analytical model that uses the vehicle's dynamic movement equations to compute its position and velocity and to juxtapose them with GPS-provided position and velocity to detect the aforementioned spoofing attacks. A threshold is predetermined by using clean datasets to calculate the maximum possible deviation between in-vehicle sensor and GPS provided position/displacement and speed. Subsequently, spoofing attack detection uses this threshold for its continuous, real time comparison. Additionally, the proposed algorithm is able to detect a sequence of multiple biased attacks that fall below the mentioned threshold but can create a large deviation between the position/displacement and speed provided by GPS and Inertial measurement Unit (IMU). To validate the effectiveness of the work, datasets simulating four distinct spoofing scenarios such as turn-by-turn, overshoot, stop and multiple biased attacks were generated using real-world data from [1]. The analyses shows that the Data-driven model successfully identifies the turn-by-turn, stop, overshoot and multiple biased spoofing attacks with the accuracies of 100%, 99.95%, 99.91%, 97.83%, respectively. © 2025 IEEE.

关键词： Global positioning system

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CSA4Rec: Collaborative Signals Augmentation Model Based on GCN for Recommendation 25th

CSA4Rec: Collaborative Signals Augmentation Model Based on ...

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25th international conference on Web Information systems Engineering, WISE 2024

作者： Liu, Haibo Yu, Lianjie Si, Yali Liu, Jinglian School of Cyber Security and Computer Hebei University Baoding Baoding China School of Computer Science and Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9789819605699

Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. The augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN model on raw data, before making the final prediction. Despite their success, three problems have always existed. First, during the pre-training phase, over-smooth and data sparsity have a serious impact on the quality of augmented graph data. Second, the influence degree of higher-order collaborative signals are difficult to control, resulting in unstable embeddings. Last, the output of the pre-trained model is only used to augment the data and is not utilized in subsequent training phases, leading to performance penalty. In this study, we propose an efficient Collaborative Signals Augmentation model based on GCN for Recommendation (CSA4Rec). In the pre-training phase, graph contrastive learning is utilized to generate distinct embeddings to address over-smooth and data sparsity. To manipulate the higher-order collaborative signals effectively, the signals are generated from raw data in the modeling phase and aggregated independently by GCN in the training phase. The Residual-like connection is employed to leverage the output of the pre-trained model to improve the convergence speed in the next training phase. Extensive experiments demonstrate the effectiveness and rationality of CSA4Rec. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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TOWARDS CLINICALLY FEASIBLE NONINTRUSIVE QUALITY AND INTELLIGIBILITY INDICES FOR HEARING AIDS

TOWARDS CLINICALLY FEASIBLE NONINTRUSIVE QUALITY AND INTELLI...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ashkanichenarlogh, Vahid Folkeard, Paula Parsa, Vijay National Centre for Audiology Western University London Canada Department of Electrical and Computer Engineering Western University London Canada School of Communication Sciences and Disorders Faculty of Health Sciences Western University London Canada

ISBN: (纸本)9798350368741

Speech quality and intelligibility are of significant importance during clinical hearing aid (HA) fitting and verification. Validated intrusive objective predictors of intelligibility and quality such as the Hearing Aid Speech Perception Index (HASPI) and the Hearing Aid Speech Quality Index (HASQI) have not been widely adopted for implementation within clinically available HA test systems. Recent advances in nonintrusive measures, such as those from Clarity Prediction Challenges (CPCs) and HASA-Net, are also not yet accessible to clinicians. Moreover, most of these advancements rely on datasets from simulated HAs, not the commercial devices used by audiologists. This work aims to develop non-intrusive quality and intelligibility indices using custom databases of noisy speech recorded in a HA test box. Similar to the successful models from previous CPCs, the indices were derived using a novel non-intrusive model leveraging automatic speech recognition and self-supervised learning techniques. The proposed non-intrusive model was trained to predict the intrusive HASPI/HASQI values and later validated against subjective intelligibility data obtained from a group of listeners with hearing loss. The proposed model resulted in strong correlations with HASQI (95.96%) and HASPI (97.59%), and a moderate correlation with the subjective intelligibility scores (76.63%). © 2025 IEEE.

关键词： Hearing Aids Nonintrusive Self-Supervised Learning Speech Quality and Intelligibility measurement Transformers

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Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation

Evaluating the Impact of Discriminative and Generative E2E S...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bharadwaj, Rangavajjala Sankara Mallela, Jhansi Aluru, Sai Harshitha Yarra, Chiranjeevi Language Technology Research Centre International Institute of Information Technology Hyderabad India

ISBN: (纸本)9798350368741

Automatic syllable stress detection is a crucial component in computer-Assisted Language Learning (CALL) systems for language learners. Current stress detection models are typically trained on clean speech, which may not be robust in real-world scenarios where background noise is prevalent. To address this, speech enhancement (SE) models, designed to enhance speech by removing noise, might be employed, but their impact on preserving syllable stress patterns is not well studied. This study examines how different SE models, representing discriminative and generative modeling approaches, affect syllable stress detection under noisy conditions. We assess these models by applying them to speech data with varying signal-to-noise ratios (SNRs) from 0 to 20 dB, and evaluating their effectiveness in maintaining stress patterns. Additionally, we explore different feature sets to determine which ones are most effective for capturing stress patterns amidst noise. To further understand the impact of SE models, a human-based perceptual study is conducted to compare the perceived stress patterns in SE-enhanced speech with those in clean speech, providing insights into how well these models preserve syllable stress as perceived by listeners. Experiments are performed on English speech data from non-native speakers of German and Italian. And the results reveal that the stress detection performance is robust with the generative SE models when heuristic features are used. Also, the observations from the perceptual study are consistent with the stress detection outcomes under all SE models. © 2025 IEEE.

关键词： Speech enhancement models Syllable stress detection

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Time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation 27th

Time-Aware Intent Contrastive Learning with Rare-Class Samp...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Liu, Yichen Ren, Qianqian Lv, Xingfeng Department of Computer Science and Technology Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031781681

User interactions with items are driven by diverse intentions, and effectively modeling these intentions can greatly enhance recommendation systems’ efficacy. In this paper, we propose a Time-aware Intent Contrastive Learning (TICL) framework for sequential recommendation. Unlike existing methods, TICL employs contrastive learning in a hierarchical manner over augmented views, enabling robust sequence representation at each timestamp. The framework incorporates two contrastive tasks: temporal-aware and intent-wise contrastive learning. To address data sparsity across different time frames, two novel data augmentation operators, namely Down-Sample and Time-Warping are introduced. Additionally, the Rare-class Sample Generator (RSG) is presented to tackle the challenges posed by temporal data imbalance. By integrating temporal intentional data into the sequential recommendation framework, TICL significantly enhances recommendation accuracy and relevance. Empirical analysis on real-world datasets demonstrates that TICL outperforms existing baseline methods and exhibits resilience to data sparsity and interaction noise, leading to improved model performance where the maximum improvement can reach to 46.24%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Investigating Reinforcement Learning for Histopathological Image Analysis

Investigating Reinforcement Learning for Histopathological I...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Mohamad, Mohamad Ponzio, Francesco Gassier, Maxime Pote, Nicolas Ambrosetti, Damien Descombes, Xavier Université Côte d’Azur INRIA CNRS I3S INSERM IBV Sophia Antipolis France Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Pathology Bichat Hospital Assistance Publique-Ĥopitaux de Paris Paris France Department of Pathology CHU Nice Université Côte d’Azur Nice France

In computational pathology, whole slide images represent the primary data source for AI-driven diagnostic algorithms. However, due to their high resolution and large size, these images undergo a patching phase. In this paper, we approach the diagnostic process from a pathologist’s perspective, modeling it as a Sequential decision-making problem using reinforcement learning. We build a foundational environment designed to support a range of whole slide applications. We showcase its capability by using it to construct a toy goal conditioned Navigation environment. Finally, we present an agent trained within this environment and provide results that emphasize both the promise of reinforcement learning in histopathology and the distinct challenges it faces. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Deep reinforcement learning

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