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arXiv 2024年

作者： Sarkar, Aritra Kundu, Akash Steinberg, Matthew Mishra, Sibasish Fauquenot, Sebastiaan Acharya, Tamal Miszczak, Jaroslaw A. Feld, Sebastian Quantum Intelligence research team Quantum Machine Learning research group Quantum Computing division QuTech Netherlands Department of Quantum & Computer Engineering Delft University of Technology Netherlands Joint Doctoral School Silesian University of Technology Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland QTF Centre of Excellence Department of Physics University of Helsinki Finland

The standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms, represented as unitary matrices, to bounded depth quantum circuits depends strongly on the set of gates available for the decomposition routine. To investigate this dependence, we explore the design space of discrete quantum gate sets and present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The evaluation is conditioned on a set of unitary transformations representing target use cases on the quantum processors. The cost function considers three key factors: (i) the statistical distribution of the decomposed circuits’ depth, (ii) the statistical distribution of process fidelities for the approximate decomposition, and (iii) the relative novelty of a gate set compared to other gate sets in terms of the aforementioned properties. The developed software, called YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates through this tunable joint cost function. To identify these gate sets, we use the novelty search algorithm, circuit decomposition techniques (like Solovay-Kitaev, Cartan, and quantum Shannon decomposition), and stochastic optimization to implement YAQQ within the Qiskit quantum simulator environment. YAQQ exploits reachability tradeoffs conceptually derived from quantum algorithmic information theory. Our results demonstrate the pragmatic application of identifying gate sets that are advantageous to popularly used quantum gate sets in representing quantum algorithms. Consequently, we demonstrate pragmatic use cases of YAQQ in comparing transversal logical gate sets in quantum error correction codes, designing optimal quantum instruction sets, and compiling to specific quantum processors. © 202

关键词： Cost functions

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Traceable and Authenticable Image Tagging for Fake News Detection

arXiv

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arXiv 2022年

作者： Meng, Ruohan Zhou, Zhili Cui, Qi Lam, Kwok-Yan Kot, Alex School of Computer and Software Nanjing University of Information Science and Technology China Institute of Artificial Intelligence and Blockchain Guangzhou University China Computer Science and Engineering Nanyang Technological University Singapore Lab Nanyang Technological University Singapore Centre for Computer Vision and Deep Learning University of Windsor Canada

To prevent fake news images from misleading the public, it is desirable not only to verify the authenticity of news images but also to trace the source of fake news, so as to provide a complete forensic chain for reliable fake news detection. To simultaneously achieve the goals of authenticity verification and source tracing, we propose a traceable and authenticable image tagging approach that is based on a design of Decoupled Invertible Neural Network (DINN). The designed DINN can simultaneously embed the dual-tags, i.e., authenticable tag and traceable tag, into each news image before publishing, and then separately extract them for authenticity verification and source tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a parallel Feature Aware Projection Model (FAPM) to help the DINN preserve essential tag information. In addition, we define a Distance Metric-Guided Module (DMGM) that learns asymmetric one-class representations to enable the dual-tags to achieve different robustness performances under malicious manipulations. Extensive experiments, on diverse datasets and unseen manipulations, demonstrate that the proposed tagging approach achieves excellent performance in the aspects of both authenticity verification and source tracing for reliable fake news detection and outperforms the prior works. Copyright © 2022, The Authors. All rights reserved.

关键词： Image annotation

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GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs

arXiv

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arXiv 2025年

作者： Zhao, Songwei Jiang, Yuan Zhang, Zijing Yu, Yang Chen, Hechang School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Jilin University Changchun China School of Computer Science and Engineering Nanyang Technological University Singapore

Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Existing methods addressing this issue often overlook the importance of information granularity and rarely consider implicit relationships between distant nodes. To overcome these limitations, we propose the Granular and Implicit Graph Network (GRAIN), a novel GNN model specifically designed for heterophilous graphs. GRAIN enhances node embeddings by aggregating multi-view information at various granularity levels and incorporating implicit data from distant, non-neighboring nodes. This approach effectively integrates local and global information, resulting in smoother, more accurate node representations. We also introduce an adaptive graph information aggregator that efficiently combines multi-granularity and implicit data, significantly improving node representation quality, as shown by experiments on 13 datasets covering varying homophily and heterophily. GRAIN consistently outperforms 12 state-of-the-art models, excelling on both homophilous and heterophilous graphs. Copyright © 2025, The Authors. All rights reserved.

关键词： Network theory (graphs)

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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The Frontier of Geospatial AI Deep learning Applications in Data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Ajit Kumar Mishra Vikash Verma R. Manivannan N. Krishna Kumar Mudunuru Srividya Sanskriti Sharma Department of Electrical & Electronics Engineering IES College of Technology Bhopal M.P India Dept of Artificial Intelligence & Machine Learning GGITS Jabalpur M.P India School of Computing Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept.of CSE Guru Nanak Institutions Technical Campus Ibrahimpatam Hyderabad Telangana India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Business System GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI’s data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it’s versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN’s cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today’s challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era.

关键词： Deep learning Training Urban areas Transforms Feature extraction Solids Spatial databases Geospatial analysis Data mining artificial intelligence

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Innovations in Image Forensics: Designing a Smart learning Based Methodology for Forgery Detection over Digital Images

Innovations in Image Forensics: Designing a Smart Learning B...

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Pioneering Developments in computer Science & Digital Technologies (IC2SDT), International Conference on

作者： G. Ramkumar K. Pithamber C.H. Srilakshmi S. Rathika B Anni Princy Vijay Anand Kandaswamy Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Dundigal Hyderabad India Department of Computer Science and Business Systems R.M.D. Engineering College Kavaraipettai Tamil Nadu India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Detecting forgery in digital images is crucial for several reasons. Firstly, with the widespread use and accessibility of image editing tools, individuals can easily manipulate digital images, raising concerns about the authenticity and integrity of visual information. This study introduces a novel approach, termed SFVGG, for detecting forgery in digital images by leveraging a smart learning methodology. The SFVGG method integrates the robust features of SIFT, the clustering capabilities of Fuzzy C-Mean clustering, and the deep learning process of VGG19. Through a synergistic combination of these techniques, the proposed forgery detection system achieves a comprehensive and effective solution. The methodology was implemented using Python, showcasing its practical applicability. Remarkably, the SFVGG framework demonstrates a high accuracy of 94.5%, underscoring its effectiveness in identifying forged content within digital images. This research represents a significant advancement in digital image forgery detection, offering both a sophisticated methodology and practical implementation.

关键词： Deep learning Visualization Technological innovation Accuracy Digital images Digital forensics Educational technology Forgery

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A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation

arXiv

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arXiv 2023年

作者： Liu, Siwei Wang, Xi Macdonald, Craig Ounis, Iadh Mohamed bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar Abu Dhabi44737 United Arab Emirates University College London School of Computer Science Gower Street LondonWC1E 6BT United Kingdom University of Glasgow School of Computing Science Lilybank Gardens GlasgowG12 8QQ United Kingdom

The use of pre-training is an emerging technique to enhance a neural model’s performance, which has been shown to be effective for many neural language models such as BERT. This technique has also been used to enhance the performance of recommender systems. In such recommender systems, pre-training models are used to learn a better initialisation for both users and items. However, recent existing pre-trained recommender systems tend to only incorporate the user interaction data at the pre-training stage, making it difficult to deliver good recommendations, especially when the interaction data is sparse. To alleviate this common data sparsity issue, we propose to pre-train the recommendation model not only with the interaction data but also with other available information such as the social relations among users, thereby providing the recommender system with a better initialisation compared with solely relying on the user interaction data. We propose a novel recommendation model, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data at the pre-training stage in a Graph Neural Network (GNN). Afterwards, in the subsequent fine-tuning stage, our SGP model adopts a Gaussian Mixture Model (GMM) to factorise these pre-trained embeddings for further training, thereby benefiting the cold-start users from these pre-built social relations. Our extensive experiments on three public datasets show that, in comparison to 16 competitive baselines, our SGP model significantly outperforms the best baseline by upto 7.7% in terms of NDCG@10. In addition, we show that SGP permits to effectively alleviate the cold-start problem, especially when users newly register to the system through their friends’ *** Codes 68P20 © 2023, CC BY.

关键词： Recommender systems

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An Efficient and Intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization

An Efficient and Intelligent IoT-Based Security Model for En...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： H B Gangadharaswamy Narendra Kumar N Sharmila M Shilpa S Mallikarjunaswamy R Shivaji N M Basavaraju P Balachandra M Komala Dept of Computer Science and Engineering JSS Academy of Technical Eduction Bengaluru India Artificial Intelligence and Machine Learning RNS Institute of Technology Bengaluru India Dept of Electronics and Electrical Engineering JSS Science and Technology University Mysuru India Dept of Information Science and Engineering Bengaluru Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering JSS Academy of Technical Eduction Bengaluru India Dept Electronics and Telecommunication Engineering Jagadambha college of Engineering & Technology India Dept of Mathematics SJB Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python’s image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it’s automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs.

关键词： Cloud computing Power demand Costs Simulation Memory Infrared image sensors Motion detection Hybrid power systems Security Intelligent sensors

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Game Theory Based Spectrum Sharing Techniques for Cognitive Radio Networks with Multiple Primary Users 3

Game Theory Based Spectrum Sharing Techniques for Cognitive ...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Pradhan, Rangaballav Minhas, Deepak Nirmalrani, V. Sudhamsu, Gadug Savita Rampal, Sourav Centre for Artificial Intelligence and Machine Learning Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Sathyabama Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

The recreation concept has been delivered in cognitive radio networks (CRNs) as an effective approach for spectrum sharing amongst more than one number one user (PUs). This method permits secondary customers (SUs) to utilize the unused spectrum sources of the PUs while minimizing interference to their primary transmissions. This is especially important in CRNs, where the constrained availability of spectrum assets is a first-rate assignment. In this paper, we advocate sport idea-primarily based spectrum sharing techniques for CRNs with multiple PUs. We formulate the problem as a non-cooperative game wherein SUs choose their transmission strategies to maximize their utilities while thinking about the restrictions imposed by the PUs. The proposed techniques consist of coalitional sport-based total power control, software-primarily based negotiation, and cooperative spectrum sharing through aspect charge. Simulation outcomes display that these strategies can achieve better spectrum utilization and equity among SUs compared to standard methods while also ensuring minimum interference to the PUs. © 2025 IEEE.

关键词： Radio communication

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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

arXiv

引用

arXiv 2022年

作者： Hedström, Anna Weber, Leander Bareeva, Dilyara Krakowczyk, Daniel Motzkus, Franz Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin10587 Germany Department of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin10587 Germany Department of Computer Science University of Potsdam Potsdam14476 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus-a comprehensive, evaluation toolkit in Python that includes a growing, wellorganised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/). © 2022, CC BY-NC-SA.

关键词： Python

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