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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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A GTFS data acquisition and processing framework and its application to train delay prediction

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International Journal of Transportation science and Technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory data fusion

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SEPSIS COLLAB: A Virtual Reality Training Simulation For Sepsis Treatment

SEPSIS COLLAB: A Virtual Reality Training Simulation For Sep...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Ayesha Williams-Bhatti David Carruthers Andrew Sean Wilson Department of Computing and Data Science Birmingham City University UK City Hospital Sandwell and West Birmingham Hospitals NHS Trust Birmingham UK

Sepsis is a main cause of death, by infection in the world. Despite modern treatments, mortality and morbidity could be reduced by improved training of the clinical teams. SEPSIS COLLAB (developed using Unity game engine and C# programming language for Meta Quest 2) is a unique virtual reality simulation for training both doctors and nurses to treat sepsis. It was evaluated with the help of 110 volunteers, in terms of usability (System Usability Scale) and knowledge acquisition. The latter used pre-/post-tests based on clinical treatment guidelines. Participants were divided into a control group (doctors n:35 and nurses n:20) who did not use SEPSIS COLLAB and a test group (doctors n:35 and nurses n:20) who did. The consensus regarding the usability of SEPSIS COLLAB was rated as excellent. Analysis showed significant improvement in the post-test results of the test group compared to their pre-test. There was no significant difference in the pre- or post-test scores of the control group. The test group performed significantly better in the post-test than the control group. These findings show SEPSIS COLLAB has potential to provide positive educational benefits to medical teams in their treatment of sepsis and act as a complement to traditional in-person training.

关键词： Training Solid modeling Three-dimensional displays Hospitals Knowledge acquisition Virtual reality User interfaces

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

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Procedia Computer science 2025年 258卷 4179-4189页

作者： Ashok Kumar Jayaraman Tina Ester Trueman Gayathri Ananthakrishnan Abirami Murugappan Erik Cambria Manikandan G Satanik Mitra HCLTech Engineering and R&D Services Chennai Tamil Nadu India Department of Computer Science University of the People California USA School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result.

关键词： Aspect-based sentiment analysis permutation language model deep learning transformers multiclass classification

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Assessing the Status of Mental Well-Being for Students of HEI: A data-Driven Approach 6th

Assessing the Status of Mental Well-Being for Students of HE...

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6th National Conference on Advances in Multidisciplinary Design, Analysis and Optimization, NCMDAO 2023

作者： Verma, Princy Pant, Millie Barua, Mukesh Kumar Department of Applied Mathematics and Scientific Computing IIT Roorkee Roorkee India Mehta Family School for Data Science and Artificial Intelligence IIT Roorkee Roorkee India Department of Management Studies IIT Roorkee Roorkee India

ISBN: (纸本)9789819611577

Most of the educational institutes particularly Higher Educational Institutions (HEIs) such that of IITs are prioritizing the mental well-being of students as an essential component for fostering both the overall development and academic success of the students as well as benefiting society as a whole. In HEIs. Students often encounter numerous challenges related to mental well-being. This study aims to recognize the trends and patterns of mental health challenges among students based on various factors including gender, age bracket, enrolled program, academic department, and year of academic study. data considered for this research is accumulated from the wellness center of a reputed HEI for 4219 students over a period of two years. Firstly, a basic exploratory data analysis (EDA) is performed on the dataset to obtain the initial insights into the data, and then data envelopment analysis (DEA) is performed to analyze the efficiency of the departments by including the state of mental well-being as one of the criteria along with other parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data curation

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Sneha BJ Shriyashreshta Ashwini Kodipalli Roshan Joy Martis Ushasree. A Shridhar B. Devamane Department of AI & Data Science Global Academy of Technology Bangalore India Department of Electronics & Communication Engineering Manipal Institute of Technology Bangalore India Department of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India Department of CS School of Computing Amrita Vishwavidyapeetam Mysuru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer.

关键词： Training Adaptation models Accuracy Reviews Computational modeling Artificial neural networks Predictive models data models Noise measurement Viruses (medical)

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Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling 39

Does GCL Need a Large Number of Negative Samples? Enhancing ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Yongqi Zhao, Jitao He, Dongxiao Jin, Di Huang, Yuxiao Wang, Zhen College of Intelligence and Computing Tianjin University Tianjin China Department of Data Science George Washington University WashingtonDC United States School of Cybersecurity Northwestern Polytechnical University Xi’an China

ISBN: (纸本)157735897X

Graph Contrastive Learning (GCL) aims to self-supervised learn low-dimensional graph representations, primarily through instance discrimination, which involves manually mining positive and negative pairs from graphs, increasing the similarity of positive pairs while decreasing negative pairs. Drawing from the success of Contrastive Learning (CL) in other domains, a consensus has been reached that the effectiveness of GCLs depends on a large number of negative pairs. As a result, despite the significant computational overhead, GCLs typically leverage as many negative node pairs as possible to improve model performance. However, given that nodes within a graph are interconnected, we argue that nodes cannot be treated as independent instances. Therefore, we challenge this consensus: Does employing more negative nodes lead to a more effective GCL model? To answer this, we explore the role of negative nodes in the commonly used InfoNCE loss for GCL and observe that: (1) Counterintuitively, a large number of negative nodes can actually hinder the model’s ability to distinguish nodes with different semantics. (2) A smaller number of high-quality and non-topologically coupled negative nodes are sufficient to enhance the discriminability of representations. Based on these findings, we propose a new method called GCL with Effective and Efficient Negative samples, E2Neg, which learns discriminative representations using only a very small set of representative negative samples. E2Neg significantly reduces computational overhead and speeds up model training. We demonstrate the effectiveness and efficiency of E2Neg across multiple datasets compared to other GCL methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Deep coastal sea elements forecasting using UNet-based models

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Knowledge-Based Systems 2022年 252卷

作者： Fernández, Jesús García Abdellaoui, Ismail Alaoui Mehrkanoon, Siamak Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Due to the recent development of deep learning techniques applied to satellite imagery, weather forecasting that uses remote sensing data has also been the subject of major progress. The present paper investigates multiple hours ahead coastal sea elements forecasting in the Netherlands using UNet based architectures. The hourly satellite image data from the Copernicus observation program spanned over a period of two years has been used to train the models and make the forecasting, including seasonal forecasting. Here, we propose 3D dimension Reducer UNet (3DDR-UNet), a variation of the UNet architecture, and further extend this novel model using residual connections, parallel convolutions and asymmetric convolutions which result in introducing three additional architectures, i.e. Res-3DDR-UNet, InceptionRes-3DDR-UNet and AsymmInceptionRes-3DDR-UNet respectively. In particular, we show that the architecture equipped with parallel and asymmetric convolutions as well as skip connections outperforms the other three discussed models. © 2022 The Author(s)

关键词： Convolution

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Displacement-Sparse Neural Optimal Transport

arXiv

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arXiv 2025年

作者： Chen, Peter Xie, Yue Zhang, Qingpeng Department of Mathematics Columbia University New York United States HKU Musketeers Foundation Institute of Data Science Hong Kong HKU Shanghai Intelligent Computing Research Center Shanghai China

Optimal Transport (OT) theory seeks to determine the map T : X → Y that transports a source measure P to a target measure Q, minimizing the cost c(x, T(x)) between x and its image T(x). Building upon the Input Convex Neural Network OT solver (Makkuva et al., 2020;Amos et al., 2017) and incorporating the concept of displacement-sparse maps (Cuturi et al., 2023), we introduce a sparsity penalty into the minimax Wasserstein formulation, promote sparsity in displacement vectors ∆(x):= T(x) − x, and enhance the interpretability of the resulting map. However, increasing sparsity often reduces feasibility, causing T#(P) to deviate more significantly from the target measure. In low-dimensional settings, we propose a heuristic framework to balance the tradeoff between sparsity and feasibility by dynamically adjusting the sparsity intensity parameter during training. For high-dimensional settings, we directly constrain the dimensionality of displacement vectors by enforcing dim(∆(x)) ≤ l, where l d. Among maps satisfying this constraint, we aim to identify the most feasible one. This goal can be effectively achieved by adapting our low-dimensional heuristic framework without resorting to dimensionality reduction. We validate our method on both synthesized sc-RNA and real 4i cell perturbation datasets, demonstrating improvements over existing methods. © 2025, CC BY.

关键词： Dimensionality reduction

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A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems

A Fast Feature Selection for Interpretable Modeling Based on...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Andrea Tangherloni Paolo Cazzaniga Nicolò Stranieri Francesca M. Buffa Marco S. Nobile Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Computing Sciences Bocconi University Milan Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Large datasets are often beneficial for the generation of predictive models using machine learning approaches. However, it is often the case that not all variables in the dataset contain useful information. In fact, some variables might be useless, redundant, misleading, or even harmful to performance, both in terms of accuracy and computational effort. Because of that, Feature Selection (FS) is one of the most delicate and important steps in machine learning. This is even more relevant in the case of interpretable models based on Fuzzy Inference Systems (FIS). The reasons are two-fold: on the one hand, FIS are generally built on top of a data partitioning based on clustering, which can suffer from high dimensionality; on the other hand, the knowledge base of the FIS, to be concretely understandable, should not contain rules involving too many variables. FS can be performed using multiple approaches, most notably filter and wrapper methods. The latter are often based on evolutionary algorithms, where a population of candidate solutions (each representing a possible set of selected variables) evolves towards the optimal selection. Although wrapper methods can be effective, they are, in general, computationally expensive. In this work, we propose a completely different – and more computationally effective – algorithm based on Random Forest (RF) models. Specifically, we exploit RFs to rank variables according to their importance. Then, we use that information to perform a statistical analysis and determine the minimal set of features necessary to build an accurate FIS. We show the effectiveness of our approach by using two (semi)synthetic datasets built on real-world datasets, and we validate our approach by applying the FS method to a medical dataset.

关键词： Radio frequency Fuzzy logic Accuracy Statistical analysis Computational modeling Predictive models Feature extraction Inference algorithms Noise measurement Random forests

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