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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Vittapu, Mani Sarma Dev, Anoop Shewale, Chaitali R. Majid, Mohd Aliff Abdul Kulkarni, Nandini Sunrise University Alwar Department of Computer Science and Engineering India Mallareddy Engineering College and Management Sciences Department of Computer Science and Engineering Kistapur Village Medchal Telangana Hyderabad India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Vishwakarma Institute of Technology Department of Information Technology Pune India University Teknology MARA Faculty of Hotel and Tourism Management Department of Foodservice Management Malaysia Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India

ISBN: (纸本)9798350370249

Cross-layer Protocols for proper resource Allocation in Mobile Networks are a singular technique to enhance the efficiency of wireless networks. The method utilizes multiple layers of the radio protocol stack better to coordinate cell node scheduling and resource allocation. Using combining communication, routing, and scheduling functionalities in an unmarried protocol, pass-Layer Protocols for proper resource Allocation can offer a sizable will increase in the number of facts that may be transferred and the overall latency and throughput of the community. This technique can also enhance the radio interface's operation by lowering interference and encouraging collaborative communications. Eventually, by using facts and managing records from the communique and routing layers, go-Layer Protocols for proper resource Allocation can reduce the complexity of the community, even offering greater scalability. This paper affords unique cross-Layer Protocols for resource Allocation in Cellular Networks. The proposed protocol permits integrated resource allocation in the cell network environment, considering communication and computation layers. © 2024 IEEE.

关键词： Network layers

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A Modular Open-Source Cryptographic Co-Processor for Internet of Things

A Modular Open-Source Cryptographic Co-Processor for Interne...

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Euromicro Symposium on Digital System Design

作者： Dina Hesse Maël Gay Ilia Polian Elif Bilge Kavun Owen Millwood Witali Bartsch Hörst Gortz Institute for IT Security Ruhr University Bochum Bochum Germany Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany Faculty of Computer Science and Mathematics University of Passau Passau Germany Department of Computer Science The University of Sheffield Sheffield UK WIZnet Germany GmbH Kleinniedesheim Germany

The security of computer systems can be increased effectively by using cryptographic co-processors to encapsulate secrets and speed-up the computationally intensive cryptographic functions. This can be especially advantageous for Internet of Things devices, as they usually have to be very efficient in cost, space and timing. However, these devices are also at greater risk of becoming targets of hardware attacks, as they handle sensitive data and are physically exposed to a nearly unrestricted population of users. This paper describes a modular cryptographic co-processor, allowing it to be applied in different scenarios and easily adjusted to concrete system specifications. The co-processor design is also open-source and freely available for anyone to further applications and modifications. It implements the basic cryptographic functions of symmetric encryption, hashing and a pseudo random number generation, with an interface to a true random number generator. In addition, the co-processor offers additional interfaces for key generation. A specific realization is presented in detail, compared to existing solutions, and its resilience against various attacks is discussed.

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Quantitative Evaluation to Detect Crack Depth in Beams Based on CNN-RNN-LSTM Approach

Quantitative Evaluation to Detect Crack Depth in Beams Based...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Gandhi, Mohammad Asif Swaminathen, A.N. Patil, Deepak T. Ravitheja, A. Kamali, R. Rajput, Akshay Anjuman-I-Islam's Kalsekar Technical Campus Department of Mechanical Engineering Maharashtra Panvel India Adi Shankara Institute of Engineering and Technology Kerala India Bharati Vidyapeeth College of Architecture Maharashtra Navi Mumbai India Svr Engineering College Department of Civil Engineering Andhra Pradesh Kurnool India Knowledge Institute of Technology Department of Computer Science and Engineering Tamilnadu Salem India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300857

In this study, the proposed approach offers a novel hybrid image-based deep learning method for autonomous concrete fracture identification. Cracks are easier to spot and false positives are reduced with hybrid images that include visual and infrared thermography data. In specifically, a Visible and infrared camera as well as a line laser make up a hybrid imaging system for spatially scanning unmanned vehicles can efficiently assess large-scale concrete-made constructions like bridges and dams. Crack detection is a commonplace task in many industrial domains, however the current method of relying on expert judgment is typically laborious, time consuming, and inaccurate. The practical benefits of automated decision-making increase as the size of the target concrete construction grows. Cracks can be automatically identified and seen using the proposed method. Preprocessing, segmentation, feature extraction, and model training are the four components of the proposed method. It performs clever edge recognition, scaling, and greyscale conversion as part of its preparation. Improved K-Means segmentation is used for the separation. It employs GLCM for feature extraction, and CNN-RNN-LSTM for model training. The new method is compared to two established approaches, CNN-RNN and CNN-LSTM, and is found to be superior. © 2023 IEEE.

关键词： Decision making

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OMNI: OPEN-ENDEDNESS VIA MODELS OF HUMAN NOTIONS OF INTERESTINGNESS

arXiv

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arXiv 2023年

作者： Zhang, Jenny Lehman, Joel Stanley, Kenneth Clune, Jeff Department of Computer Science University of British Columbia Vector Institute Canada Stochastic Labs United States Maven United States Department of Computer Science University of British Columbia Vector Institute Canada CIFAR AI Chair Canada

Open-ended algorithms aim to learn new, interesting behaviors forever. That requires a vast environment search space, but there are thus infinitely many possible tasks. Even after filtering for tasks the current agent can learn (i.e., learning progress), countless learnable yet uninteresting tasks remain (e.g., minor variations of previously learned tasks). An Achilles Heel of open-endedness research is the inability to quantify (and thus prioritize) tasks that are not just learnable, but also interesting (e.g., worthwhile and novel). We propose solving this problem by Open-endedness via Models of human Notions of Interestingness (OMNI). The insight is that we can utilize foundation models (FMs) as a model of interestingness (MoI), because they already internalize human concepts of interestingness from training on vast amounts of human-generated data, where humans naturally write about what they find interesting or boring. We show that FM-based MoIs improve open-ended learning by focusing on tasks that are both learnable and interesting, outperforming baselines based on uniform task sampling or learning progress alone. This approach has the potential to dramatically advance the ability to intelligently select which tasks to focus on next (i.e., auto-curricula), and could be seen as AI selecting its own next task to learn, facilitating self-improving AI and AI-Generating Algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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An Efficient Error Estimation Method in Quantum Key Distribution

arXiv

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arXiv 2024年

作者： Wang, Yingjian Njoku, Buniechukwu Bassoli, Riccardo Hai, Yilun Kondepu, Koteswararao Fitzek, Frank H.P. Deutsche Telekom Chair of Communication Networks Dresden University of Technology Dresden Germany Department of Computer Science and Engineering Indian Institute of Technology Karnataka Dharwad India

Error estimation is an important step for error correction in quantum key distribution. Traditional error estimation methods require sacrificing a part of the sifted key, forcing a trade-off between the accuracy of error estimation and the size of the partial sifted key to be used and discarded. In this paper, we propose a hybrid approach that aims to preserve the entire sifted key after error estimation while preventing Eve from gaining any advantage. The entire sifted key, modified and extended by our proposed method, is sent for error estimation in a public channel. Although accessible to an eavesdropper, the modified and extended sifted key ensures that the number of attempts to crack it remains the same as when no information is leaked. The entire sifted key is preserved for subsequent procedures, indicating the efficient utilization of quantum resources. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum cryptography

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Implicit and Explicit Commonsense for Multi-sentence Video Captioning

arXiv

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arXiv 2023年

作者： Chou, Shih-Han Little, James J. Sigal, Leonid Department of Computer Science University of British Columbia Canada Vector Institute for AI Canada Canada CIFAR AI Chair Canada

Existing dense or paragraph video captioning approaches rely on holistic representations of videos, possibly coupled with learned object/action representations, to condition hierarchical language decoders. However, they fundamentally lack the commonsense knowledge of the world required to reason about progression of events, causality, and even the function of certain objects within a scene. To address this limitation we propose a novel video captioning Transformer-based model, that takes into account both implicit (visuo-lingual and purely linguistic) and explicit (knowledge-base) commonsense knowledge. We show that these forms of knowledge, in isolation and in combination, enhance the quality of produced captions. Further, inspired by imitation learning, we propose a new task of instruction generation, where the goal is to produce a set of linguistic instructions from a video demonstration of its performance. We formalize the task using the ALFRED dataset [54] generated using an AI2-THOR environment. While instruction generation is conceptually similar to paragraph captioning, it differs in the fact that it exhibits stronger object persistence, as well as spatially-aware and causal sentence structure. We show that our commonsense knowledge enhanced approach produces significant improvements on this task (up to 57% in METEOR and 8.5% in CIDEr), as well as the state-of-the-art result on more traditional video captioning in the ActivityNet Captions dataset [29].∗ Copyright © 2023, The Authors. All rights reserved.

关键词： Linguistics

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Distributed Learning by Local Training ADMM 63

Distributed Learning by Local Training ADMM

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Kth Royal Institute of Technology 2School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Trieste Italy

ISBN: (纸本)9798350316339

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments. © 2024 IEEE.

关键词： Peer to peer networks

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User-Attention Based Product Aesthetics Evaluation with Image and Eye-Tracking Fusion Data Analysis 15

User-Attention Based Product Aesthetics Evaluation with Imag...

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15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Xing, Baixi Song, Xinjie Chen, Qimeng Shi, Lei Pan, Yanhong Wang, Kaiqi Tang, Mengyue Industrial Design Institute Zhejiang University of Technology Hangzhou China School of Design and Architecture Zhejiang University of Technology Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798350326178

Users' viewing behavior could affect their perception and evaluation of design works. Taking into account users' visual attention as a subjective cognition cue, we used eye-tracking evidence to identify users' focus areas for further analysis. We conducted experiments to extract the image features of design images and the reviewers' eye-tracking data, aiming to predict the product design ranking in the competition through fusion data analysis. In particular, we collected 1,504 product design images from a design competition. Four deep convolutional neural networks were selected to explore the best aesthetics computation model. The experimental results show that using design images and eye-tracking data fusion can improve the model prediction performance. Finally, MobileNet-V3 achieves the highest classification accuracy of 74.75%. This suggests the proposed method can provide useful insights into personalized aesthetics evaluation and user-centered design perception. © 2023 IEEE.

关键词： Eye tracking

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The -model expansion approach for solving the (2+1)-dimensional NLSE with Kerr law nonlinearity and STD with multiplicative white noise in birefringent fibers

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Optical and Quantum Electronics 2024年第3期56卷 1-25页

作者： AlQahtani, Salman A. Shohib, Reham M. A. Alngar, Mohamed E. M. Alawwad, Abdulaziz 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 Higher Institute of Foreign Trade & Management Sciences New Cairo Academy Cario Egypt Basic Science Department Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

With incorporating of multiplicative white noise, we assess the Itô-coupled NLSE and STD in two and one dimensions with Kerr law nonlinearity and birefringent fibers. Light dynamics in birefringent fibers, including the propagation of soliton waves, the instability of modulation, and the occurrence of stochastic events, all have a connection to this issue. The study aims to facilitate in the growth of fiber lasers, benefiting fields such as in the fields of material processing, medicine, and communications. To achieve this, the $$\Phi ^{6}$$ -model approach to expansion is adopted. In addition to Jacobi-elliptic solutions, this work’s analysis also yields dark solitons, bright solitons, singular solitons, and periodic solutions. Because of such results, fiber laser systems in industries like manufacturing, medicine, and communications may be improved upon. The Jacobi-elliptic solution, as well as bright and dark solitons, may be seen in both one- and two-dimensional representations.

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A Workflow Management System for Reproducible and Interoperable High-Throughput Self-Driving Experiments

SSRN

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SSRN 2024年

作者： Mione, Federico M. Kaspersetz, Lucas Luna, Martin F. Aizpuru, Judit Scholz, Randolf Borisyak, Maxim Kemmer, Annina Schermeyer, Marie Therese Martinez, Ernesto Carlos Neubauer, Peter Cruz Bournazou, M. Nicolas Avellaneda 3657 Santa Fe Argentina Technische Universität Berlin Institute of Biotechnology Chair of Bioprocess Engineering Berlin Germany University of Hildesheim Institute of Computer Science Hildesheim Germany

To foster self-driving experimentation and address the reproducibility crisis in bioprocess development in a collaborative environment, a modular Workflow Management System (WMS) is required. In this work, a WMS based on Directed Acyclic Graphs that offers a modular and flexible design for plug-and-play integration of computational tools is presented. A case study is used to demonstrate that the implementation of an experimental-computational WMS in robotic experimental facilities promotes the application of Findable, Accessible, Interoperable and Re-usable principles, allowing researchers to readily share protocols, models, methods and data. As a proof of concept, we successfully managed three different computational workflows simultaneously for the online re-design of feeding rates in 24 parallel E. coli fed-batch cultivations producing elastin-like proteins. This approach provides a solid foundation for increasing scientific data generation in robotic experimental facilities, fostering open collaboration, and addressing the challenges of reproducibility in research. © 2024, The Authors. All rights reserved.

关键词： Robotics

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