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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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Optimizing Synchronization Delay for Digital Twin over Wireless Networks

Optimizing Synchronization Delay for Digital Twin over Wirel...

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

作者： Zhaohui Yang Mingzhe Chen Yuchen Liu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.

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A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease

arXiv

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arXiv 2023年

作者： Musto, Henry Stamate, Daniel Pu, Ida Stahl, Daniel Data Science and Soft Computing Lab Department of Computing Goldsmith University of London London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology Neuroscience Kings College London London United Kingdom

This paper explores deterioration in Alzheimer’s Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimer’s Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested cross-validation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76). © 2023, CC BY.

关键词： Deterioration

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity 22

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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22nd IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom 2023

作者： Al Sibahee, Mustafa A. Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq University of Basrah College of Education for Pure Sciences Department of Computer Science Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China

ISBN: (纸本)9798350381993

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections. © 2023 IEEE.

关键词： Cryptography

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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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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications

Exploring the Role of Internet of Things in Wireless Sensor ...

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Pioneering Developments in Computer science & Digital Technologies (IC2SDT), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N.R T. Sujatha S. Renuga Chithra R.S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Ponjesly College of Engineering Kanyakumari India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings.

关键词： Wireless sensor networks Protocols Wireless networks Sensor phenomena and characterization Routing Software Sensor systems Power grids Fourth Industrial Revolution Machinery

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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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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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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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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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