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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9798350337907

The paper considers a mathematical model for managing a scientific digital ecosystem exemplified by the agriculture case;the model enables assessing the impact science exerts on the regions' socioeconomic position in order to build an adequate response to the advanced economies' regime of severe sanctions against Russia. For these purposes, a systemic analysis of the subject identification and classification of ecosystems was conducted to address the critical issue of determining the basic concepts and terms of the country's digitalization. © 2023 IEEE.

关键词： Agriculture

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SPTNET: Span-based Prompt Tuning for Video Grounding

SPTNET: Span-based Prompt Tuning for Video Grounding

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiren Zhang Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Technology Deakin University

When a Pre-trained Language Model (PLM) is adopted in video grounding task, it usually acts as a text encoder without having its knowledge fully utilized. Also, there exists an inconsistency problem between the pre-training and downstream objectives. To solve the issues, we propose a new paradigm, named Span-based Prompt Tuning (SPTNet). It can convert the video grounding task into a cloze form. Specifically, a query is first changed into a form with mask token by a template, then the video and the query embeddings are integrated through a cross-modal transformer. The start and end points of the query matching time span are predicted with the embedding of the mask token. Experimental results on two public benchmarks ActivityNet Captions and Charades-STA show that our SPTNet achieves surpassing performance compared with state-of-the-art methods.

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QSCL-EWIL: Quantum Stochastic Contrast Learning for Enhanced WiFi-Based Indoor Localization

arXiv

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arXiv 2025年

作者： Dastagir, Muhammad Bilal Akram Han, Dongsoo Al-Kuwari, Saif Farouk, Ahmed The School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar The Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt

WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings. However, existing systems face challenges such as data variability, environmental noise, and difficulty detecting floor and building levels, compounded by limited labeled data and high received signal strength (RSS) collection costs. This paper introduces quantum stochastic contrast learning (QSCL), a novel framework grounded in rigorous theoretical foundations. We present four theorems and one lemma that establish the probabilistic augmentation, diversity enhancement, relationship preservation, and resilience of QSCL under quantum noise, supported by formal proofs. Leveraging these foundations, QSCL utilizes quantum computing (QC) to generate strong data augmentations with stochastic perturbations, enhancing data diversity, while classical weak augmentations provide subtle variations for robust feature learning. We propose a spatial temporal adaptive attention (STAA) encoder that integrates convolutional layers with adaptive attention mechanisms to capture spatial and temporal dependencies in sequential data. Furthermore, a bidirectional contrastive loss function is introduced to capture forward and reverse relationships between augmented views, ensuring robust representations. Comprehensive evaluations on the UJIIndoorLoc and UTSIndoorLoc datasets validate QSCL, demonstrating superior performance with reduced labeled data and resilience to quantum noise such as bit-flip, dephasing, and measurement noise. The proposed framework significantly improves localization accuracy, floor and building detection, and generalizability in challenging indoor environments. © 2025, CC BY.

关键词： Quantum noise

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Stability in Games with Uncertain Factors

Stability in Games with Uncertain Factors

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Mikhail Gorelov Department of Information and Computing Systems Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

The maximal guaranteed result in a hierarchical game with an undetermined factor is found in the class of strategies with feedback. The stability of the problem under consideration concerning perturbations of the payo...

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Conditional Video-Text Reconstruction Network with Cauchy Mask for Weakly Supervised Temporal Sentence Grounding

Conditional Video-Text Reconstruction Network with Cauchy Ma...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jueqi Wei Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Technology Deakin University

Temporal sentence grounding aims to detect the target segment most related to a given query in an untrimmed video. To alleviate the expensive annotation cost for temporal labels, researchers paid more attention to weakly supervised setting. Prior studies neglected the utilization of video representation reconstruction, which led to an unbalanced alignment learning. Moreover, they used different strategies to generate proposals which ignored the temporal structure in a query. In this paper, we propose a novel Conditional Video-Text Reconstruction Network (CVTRN). It supports conditional reconstruction of video and text representation. Specifically, video and text features are fused to compute semantic alignment, which is the condition of reconstruction. A new mask strategy for mask conditioned sentence reconstruction is also devised. This strategy focuses more on boundary regions than the widely used Gaussian mask in previous methods. Experimental results on two public benchmark datasets show that our CVTRN outperforms the state-of-the-art methods.

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3D Human Mesh Estimation from Virtual Markers

3D Human Mesh Estimation from Virtual Markers

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaoxuan Ma Jiajun Su Chunyu Wang Wentao Zhu Yizhou Wang School of Computer Science Center on Frontiers of Computing Studies Peking University Microsoft Research Asia Inst. for Artificial Intelligence Peking University Nat'l Eng. Research Center of Visual Technology

Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://***/ShirleyMaxx/VirtualMarker

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Efficient Collusion-Resisting Secure Sum Protocol

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Chinese Journal of Electronics 2023年第3期20卷 407-413页

作者： Youwen ZHU Liusheng HUANG Wei YANG Xing YUAN Department of Computer Science and Technology National High Performance Computing Center at Hefei University of Science and Technology of China Hefei China Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou China

Secure sum protocol is a significant secure multiparty computation protocol and it has various applications in privacy-preserving distributed multiparty computation. However, most existing secure sum protocols rarely considered how to resist underlying collusion which is a significant practical problem. Urabe et al. proposed a collusion-resistant secure sum protocol, but too much cost of communication and computation results in its low performance efficiency. In this paper, we propose security definitions to measure secure multiparty computation protocol's capability of resisting potential collusion. Then, we precisely analyze several previous secure sum protocols' capability of resisting collusion. In addition, considering realistic requirement to resist collusion and performance efficiency needs, we present a novel collusion-resisting secure sum protocol. Theoretical analysis and experimental results confirm that our secure sum protocol is efficient and has strong capability of resisting potential collusion such that it is much superior to previous ones. The communication overheads and computation complexity of our scheme both are linearity of the number of participants. Besides, our protocol's capability of resisting collusion is adjustable according to different security needs.

关键词： Protocols Costs Linearity Resists Computational efficiency Security Computational complexity

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Polynomial-Time Classical Simulation of Noisy Circuits with Naturally Fault-Tolerant Gates

arXiv

引用

arXiv 2024年

作者： Nelson, Jon Rajakumar, Joel Hangleiter, Dominik Gullans, Michael J. Joint Center for Quantum Information & Computer Science University of Maryland and NIST United States Department of Computer Science University of Maryland United States Simons Institute for the Theory of Computing University of California at Berkeley United States

We construct a polynomial-time classical algorithm that samples from the output distribution of low-depth noisy Clifford circuits with any product-state inputs and final single-qubit measurements in any basis. This class of circuits includes Clifford-magic circuits and Conjugated-Clifford circuits, which are important candidates for demonstrating quantum advantage using non-universal gates. Additionally, our results generalize a simulation algorithm for IQP circuits [RWL24] to the case of IQP circuits augmented with CNOT gates, which is another class of non-universal circuits that are relevant to current experiments. Importantly, our results do not require randomness assumptions over the circuit families considered (such as anticoncentration properties) and instead hold for every circuit in each class. This allows us to place tight limitations on the robustness of these circuits to noise. In particular, we show that there is no quantum advantage at large depths with realistically noisy Clifford circuits, even with perfect magic state inputs, or IQP circuits with CNOT gates, even with arbitrary diagonal non-Clifford gates. The key insight behind the algorithm is that interspersed noise causes a decay of long-range entanglement, and at depths beyond a critical threshold, the noise builds up to an extent that most correlations can be classically simulated. To prove our results, we merge techniques from percolation theory with tools from Pauli path analysis. © 2024, CC BY.

关键词： Polynomial approximation

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Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities

arXiv

引用

arXiv 2023年

作者： Saleh, Alaa Morabito, Roberto Tarkoma, Sasu Pirttikangas, Susanna Lovén, Lauri The Center for Ubiquitous Computing University of Oulu Finland The Department of Communication Systems EURECOM France The Department of Computer Science University of Helsinki Finland

In today’s digital world, Generative Artificial Intelligence (GenAI) such as Large Language Models (LLMs) is becoming increasingly prevalent, extending its reach across diverse applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models, highlighting the necessity for robust data communication infrastructures. Central to this need are message brokers, which serve as essential channels for data transfer within various system components. This survey aims to delve into a comprehensive analysis of traditional and modern message brokers, offering a comparative study of prevalent platforms. Our study considers numerous criteria including, but not limited to, open-source availability, integrated monitoring tools, message prioritization mechanisms, capabilities for parallel processing, reliability, distribution and clustering functionalities, authentication processes, data persistence strategies, fault tolerance, and scalability. Furthermore, we explore the intrinsic constraints that the design and operation of each message broker might impose, recognizing that these limitations are crucial in understanding their real-world applicability. Finally, this study examines the enhancement of message broker mechanisms specifically for GenAI contexts, emphasizing the criticality of developing a versatile message broker framework. Such a framework would be poised for quick adaptation, catering to the dynamic and growing demands of GenAI in the foreseeable future. Through this dual-pronged approach, we intend to contribute a foundational compendium that can guide future innovations and infrastructural advancements in the realm of GenAI data communication. © 2023, CC BY.

关键词： Fault tolerance

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Content Based Automated File Organization Using Machine Learning Approaches

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computers, Materials & Continua 2022年第10期73卷 1927-1942页

作者： Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120GyeonggidoKorea Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

In the world of big data,it’s quite a task to organize different files based on their *** with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer *** of file management related tasks will be solved if the files on any operating system are somehow categorized according to their ***,the browsing process can be performed quickly and *** research aims to design a system to automatically organize files based on their similarities in terms of *** proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer *** results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user *** results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their *** proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.

关键词： File organization natural language processing machine learning

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