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Twin Transition or Competing Interests? Validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI)

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arXiv 2025年

作者： Bush, Annika Research Center Trustworthy Data Science and Security University Alliance Ruhr Faculty of Computer Science Technical University Dortmund Dortmund Germany

As artificial intelligence (AI) and sustainability initiatives increasingly intersect, understanding public perceptions of their relationship becomes crucial for successful implementation. However, no validated instrument exists to measure these specific perceptions. This paper presents the development and validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI), a novel 13-item instrument measuring how individuals view the relationship between AI advancement and environmental sustainability. Through factor analysis (N = 105), we identified two distinct dimensions: ‘Twin Transition’ and ‘Competing Interests’. The instrument demonstrated strong reliability (α = .89) and construct validity through correlations with established measures of AI and sustainability attitudes. Our findings suggest that individuals can simultaneously recognize both synergies and tensions in the AI-sustainability relationship, offering important implications for researchers and practitioners working at this critical intersection. This work provides a foundational tool for future research on public perceptions of AI’s role in sustainable development. © 2025, CC BY-NC-ND.

关键词： Sustainable development goals

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

LEARNING RELU networkS TO HIGH UNIFORM ACCURACY IS INTRACTABLE 11

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LEARNING RELU NETWORKS TO HIGH UNIFORM ACCURACY IS INTRACTAB...

11th International Conference on Learning Representations, ICLR 2023

作者： Berner, Julius Grohs, Philipp Voigtlaender, Felix Faculty of Mathematics University of Vienna Austria Research Network Data Science @ Uni Vienna University of Vienna Austria RICAM Austrian Academy of Sciences Austria Catholic University of Eichstätt-Ingolstadt Germany

Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a generalization error, that is, an expected value of a given loss function. However, for several applications - for example in a security-critical context or for problems in the computational sciences - accuracy in this sense is not sufficient. In such cases, one would like to have guarantees for high accuracy on every input value, that is, with respect to the uniform norm. In this paper we precisely quantify the number of training samples needed for any conceivable training algorithm to guarantee a given uniform accuracy on any learning problem formulated over target classes containing (or consisting of) ReLU neural networks of a prescribed architecture. We prove that, under very general assumptions, the minimal number of training samples for this task scales exponentially both in the depth and the input dimension of the network architecture. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： network architecture

High-dimensional classification problems with Barron regular boundaries under margin conditions

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arXiv 2024年

作者： García, Jonathan Petersen, Philipp Faculty of Mathematics University of Vienna Austria Faculty of Mathematics and Research Network Data Science @ Uni Vienna University of Vienna Austria

We prove that a classifier with a Barron-regular decision boundary can be approximated with a rate of high polynomial degree by ReLU neural networks with three hidden layers when a margin condition is assumed. In particular, for strong margin conditions, high-dimensional discontinuous classifiers can be approximated with a rate that is typically only achievable when approximating a low-dimensional smooth function. We demonstrate how these expression rate bounds imply fast-rate learning bounds that are close to n−1 where n is the number of samples. In addition, we carry out comprehensive numerical experimentation on binary classification problems with various margins. We study three different dimensions, with the highest dimensional problem corresponding to images from the MNIST data *** Codes 68T05, 62C20, 41A25, 41A46 © 2024, CC BY.

关键词： Multilayer neural networks

ATTACKING GRAPH NEURAL networkS WITH BIT FLIPS: WEISFEILER AND LEHMAN GO INDIFFERENT

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arXiv 2023年

作者： Kummer, Lorenz Moustafa, Samir Kriege, Nils M. Gansterer, Wilfried N. Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria

Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network’s weights and biases. Traditional weight-based fault injection attacks, such as bit flip attacks used for convolutional neural networks, do not consider the unique properties of graph neural networks. We propose the Injectivity Bit Flip Attack, the first bit flip attack designed specifically for graph neural networks. Our attack targets the learnable neighborhood aggregation functions in quantized message passing neural networks, degrading their ability to distinguish graph structures and losing the expressivity of the Weisfeiler-Lehman test. Our findings suggest that exploiting mathematical properties specific to certain graph neural network architectures can significantly increase their vulnerability to bit flip attacks. Injectivity Bit Flip Attacks can degrade the maximal expressive Graph Isomorphism networks trained on various graph property prediction datasets to random output by flipping only a small fraction of the network’s bits, demonstrating its higher destructive power compared to a bit flip attack transferred from convolutional neural networks. Our attack is transparent and motivated by theoretical insights which are confirmed by extensive empirical results. © 2023, CC BY.

关键词： Graph neural networks

Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge 30

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Medical Entity Disambiguation with Medical Mention Relation ...

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lu, Wenpeng Zhang, Guobiao Peng, Xueping Guan, Hongjiao Wang, Shoujin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9782493814104

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between medical mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK's superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

Soliton Solutions for Coupled Nonlinear Generalized Zakharov Equations with Anti-cubic Nonlinearity Using Various Techniques

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International Journal of Applied and Computational Mathematics 2024年第1期10卷 1-30页

作者： AlQahtani, Salman A. Alngar, Mohamed E. M. New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Basic Science Department Faculty of Computers and Artificial Intelligence Modern University for Technology and Information Cairo 11585 Egypt

This study introduces novel exact solutions for the coupled nonlinear generalized Zakharov equations with anti-cubic nonlinearity. Utilizing a variety of mathematical approaches, including the extended trial function method, the rational exp (-ψ(ξ))-expansion method, and three well-known auxiliary equation methods, a spectrum of solutions is derived. These solutions encompass dark solitons, bright solitons, singular solitons, Jacobi elliptic functions, periodic waves, and rational solutions. The unique contribution lies in the use of a phase variable θ(ξ) instead of a phase constant, providing fresh insights into wave behavior. A comparative analysis underscores the originality of the derived solutions, setting this work apart from prior studies. These findings advance our understanding of nonlinear dynamics and mathematical modeling, with potential applications in various domains. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Coupled nonlinear generalized Zakharov equations Extended trial function method Rational exp(- ψ(ξ))-expansion method Soliton solutions Three famous auxiliary equation methods

Separate Source Channel Coding Is Still What You Need:An LLM-Based Rethinking

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ZTE Communications 2025年第1期23卷 30-44页

作者： REN Tianqi LI Rongpeng ZHAO Mingmin CHEN Xianfu LIU Guangyi YANG Yang ZHAO Zhifeng ZHANG Honggang College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Shenzhen CyberAray Network Technology Co. Ltd.Shenzhen 518000China China Mobile Research Institute Beijing 100053China The Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511453China Zhejiang Lab Hangzhou 311121China Faculty of Data Science City University of MacaoMacao 999078China

Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ***,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for *** demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over *** proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point *** comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication *** other words,separate source channel coding is still what we need.

关键词： separate source channel coding(SSCC) joint source channel coding(JSCC) end-to-end communication system Large Language Model(LLM) lossless text compression Error Correction Code Transformer(ECCT)

Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph

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arXiv 2024年

作者： Roth, Andreas Bause, Franka Kriege, Nils M. Liebig, Thomas Faculty of Computer Science TU Dortmund University Dortmund Germany Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channels, a phenomenon known as rank collapse, and oversmoothing as a special case. Most approaches to mitigate over-smoothing extend common message-passing schemes, e.g., the graph convolutional network, by utilizing residual connections, gating mechanisms, normalization, or regularization techniques. Our work contrarily proposes to directly tackle the cause of this issue by modifying the message-passing scheme and exchanging different types of messages using multi-relational graphs. We identify a sufficient condition to ensure linearly independent node representations. As one instantion, we show that operating on multiple directed acyclic graphs always satisfies our condition and propose to obtain these by defining a strict partial ordering of the nodes. We conduct comprehensive experiments that confirm the benefits of operating on multi-relational graphs to achieve more informative node representations. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution