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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Silva, K.G.G.H. Abeyasekare, W.A.P.S. Dasanayake, D.M.H.E. Nandisena, T.B. Kasthurirathna, Dharshana Kugathasan, Archchana Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Personalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its' ability to learn actions the way humans learn from experience, if utilized and evaluated properly it can result in a revolutionary effect on personalization. The methodology proposed in this research utilizes deep reinforcement learning where an artificial agent may be trained by interacting with its environment. Utilizing the experience gathered, the agent is able optimize in the form of rewards. The approach explained, can be utilized across applications which can be personalized. Several scenarios ranging from changing the layout of webpages, to rearranging icons on mobile home screens are discussed. The main objective is to develop an API for the web developers and smartphone manufacturers to utilize so that depending on the application personalization can be achieved by enhancing saliency, minimizing selection time, increasing engagement, or an arrangement of these. The technique can manage a variety of adaptations, such as how graphical elements are shown and how they behave. An experiment was conducted which showcased improved user experience considering the position change of the user interface elements thereby personalizing the layout. © 2021 IEEE.

关键词： Reinforcement learning

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An Entropy Based Unsupervised Visual Vocabulary Optimization for Visual Loop Closure Detection

An Entropy Based Unsupervised Visual Vocabulary Optimization...

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2023 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Li, Bo Yu, Xiangdong Feng, Xin He, Ying Zhu, Zhaohan Ge, Yongxin Geng, Li Department of Criminal Science Technology Chongqing Police College Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China School of Electrical Engineering Southwest Jiaotong University Sichuan China School of Big Data & Software Engineering Chongqing University Chongqing China New York City College of Technology City University of New York New York United States

ISBN: (纸本)9781510665040

In the task of simultaneous localization and mapping (SLAM), a mobile robot should be able to recognize the location that it has previously visited. This is termed as the loop closure detection. Current loop closure detection is implemented by matching of image features extracted from present image and previous images, where good feature representation is highly required. Bag-of-visual words (BoW) is known to be an effect feature representation for image and video matching task. However, to build the visual feature vocabulary in the unsupervised manner is still challenging. In this paper, we are aiming at evaluating the representability of the visual words and generating the discriminative vocabulary representation. We first present an entropy ranking based scheme to improve the spectral clustering algorithm by leveraging eigenvector selection and feature embedding simultaneously. We then propose to evaluate the representative ability of the visual vocabulary quantitatively based on the complexity of the decomposition of similarity matrix in the unsupervised manner. We perform experiments on the loop closure detection datasets. which contains strong perceptual aliasing in both indoor and outdoor scenarios. Results demonstrate that the proposed BoW based feature representation has strong representation ability in measuring the similarity of loop closure events and differentiating new visited places. © 2023 SPIE.

关键词： Entropy

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Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture

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Journal of Advanced Research 2024年 62卷 155-163页

作者： Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Kharel, Rupak Das, Ashok Kumar Shetty, Sachin Rodrigues, Joel J.P.C. School of Psychology and Computer Science University of Central Lancashire Preston United Kingdom Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Punjab Sahiwal 57000 Pakistan Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center and Center for Cybersecurity Education and Research Old Dominion University Suffolk 23435 VA United States COPELABS Lusófona University Campo Grande 376 Lisbon 1749-024 Portugal

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. Methods: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. Results: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. Conclusion: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed. © 2024

关键词： Digital twins Industrial Internet of Things (IIoT) Key agreement Mutual authentication Physically Unclonable Functions (PUFs)

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Improving efficiency in rationale discovery for Out-of-Distribution molecular representations

Improving efficiency in rationale discovery for Out-of-Distr...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiahui Zhang Jiahe Li Wenjie Du Shuai Zhang Di Wu Yang Wang School of Software Engineering University of Science and Technology of China Suzhou China School of Computer Science and Technology University of Science and Technology of China Suzhou China

Graph Neural Networks (GNNs), as the dominant approach in Molecular Representation Learning (MRL), have exhibited remarkable efficacy in diverse tasks, including molecular property prediction and drug discovery. Considering the dynamic and diverse nature of test molecules in real-world contexts, the previous independently and identically distributed (i.i.d.) assumption for training and test molecules is not align with the requirements of practical applications in chemistry. Rationalization has been proposed to enhance the Out-of-Distribution (OOD) generalization of GNN, but a critical concern remains unexplored: low efficiency in rationale discovery. We consider this bottleneck is due to large search space and overly flexible modeling. Here, we introduce a framework called Molecule Stratifier and Invariant Rational Improver (MSIRI) to overcome the challenge. Specifically, MSIRI adopt vector quantization to obtain invariant rationales and the search space is narrowed by substructure and two assumptions based on subgraph matching. Experimental results on ten datasets demonstrate that our approach both achieves significant improvements across various GNN backbones and outperforms other six Out-of-Distribution method, clearly demonstrating its effectiveness in addressing the OOD challenges in MRL.

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Fraction Arithmetic Operations Performed by $\mathbf{P}$ Systems

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Chinese Journal of Electronics 2023年第4期22卷 689-694页

作者： GUO Ping ZHANG Hong CHEN Haizhu CHEN Jingxin College of Computer Science Chongqing University Chongqing China Chongqing Key Laboratory of Software Theory and Technology Chongqing China Department of Software Engineering Chongqing College of Electronic Engineering Chongqing China Chongqing Branch Agricultural Bank of China Chongqing China

Arithmetical operations are fundamental in computing models. But arithmetic operations in membrane computing are restricted in integer field. In this paper, we present fraction arithmetic $\mathbf{P}$ systems for performing addition, subtraction, multiplication, division on fractions through designing the rules with priority. Some examples are given to illustrate how to compute the arithmetical fractions in these systems and show that the designed rules can carry out correct arithmetic computations of fractions.

关键词： Computational modeling Arithmetic

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TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation

arXiv

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arXiv 2024年

作者： Yang, Jiaqi Zhang, Yaning Hu, Jingxi He, Xiangjian Shen, Linlin Qiu, Guoping School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Science & Software Engineering Guang Dong Shen Zhen China

While large visual models (LVM) demonstrated significant potential in image understanding, due to the application of large-scale pre-training, the Segment Anything Model (SAM) has also achieved great success in the field of image segmentation, supporting flexible interactive cues and strong learning capabilities. However, SAM’s performance often falls short in cross-domain and few-shot applications. Previous work has performed poorly in transferring prior knowledge from base models to new applications. To tackle this issue, we propose a task-adaptive auto-visual prompt framework, a new paradigm for Cross-dominan Few-shot segmentation (CD-FSS). First, a Multi-level Feature Fusion (MFF) was used for integrated feature extraction as prior knowledge. Besides, we incorporate a Class Domain Task-Adaptive Auto-Prompt (CDTAP) module to enable class-domain agnostic feature extraction and generate high-quality, learnable visual prompts. This significant advancement uses a unique generative approach to prompts alongside a comprehensive model structure and specialized prototype computation. While ensuring that the prior knowledge of SAM is not discarded, the new branch disentangles category and domain information through prototypes, guiding it in adapting the CD-FSS. Comprehensive experiments across four cross-domain datasets demonstrate that our model outperforms the state-of-the-art CD-FSS approach, achieving an average accuracy improvement of 1.3% in the 1-shot setting and 11.76% in the 5-shot setting. © 2024, CC BY.

关键词： Semantic Segmentation

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A car-following model based on the optimized velocity and its security analysis

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Transportation Safety and Environment 2023年第4期5卷 127-134页

作者： Rong Fei Lu Yang Xinhong Hei Bo Hu Aimin Li School of Computer Science and Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an 710048ShaanxiP.R.China Software Design Department Hangzhou Holly Sys Automation Co.Ltd.No.29Jinye 1st RoadXi’an 710065ShaanxiP.R.China

An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving *** headway is introduced as a criterion for determining whether the car is *** the time headway is less discussed to ensure the model's safety and maintain the following state.A stability analysis of the model was carried out to determine than the minimum time headway(TH_(min))or more than the most comfortable time headway(TH_(com)),the acceleration constraints are the stability conditions of the *** EOVM is compared with the optimal velocity model(OVM)and fuzzy car-following model using the real *** show that the EOVM model has the smallest error in average,maximum and median with the real *** confirm the model's safety,design fleet simulation experiments were conducted for three actual scenarios of starting,stopping and uniform process.

关键词： optimized velocity constraint optimization security analysis

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Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning

arXiv

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arXiv 2024年

作者： Gilkarov, Daniel Dubin, Ran The Department of Computer Science Ariel Cyber Innovation Center Ariel University Ariel Israel The Department of Computer and Software Engineering Ariel Cyber Innovation Center Ariel University Ariel Israel

The potential for exploitation of AI models has increased due to the rapid advancement of Artificial Intelligence (AI) and the widespread use of platforms like Model Zoo for sharing AI models. Attackers can embed malware within AI models through steganographic techniques, taking advantage of the substantial size of these models to conceal malicious data and use it for nefarious purposes, e.g. Remote Code Execution. Ensuring the security of AI models is a burgeoning area of research essential for safeguarding the multitude of organizations and users relying on AI technologies. This study leverages well-studied image few-shot learning techniques by transferring the AI models to the image field using a novel image representation. Applying few-shot learning in this field enables us to create practical models, a feat that previous works lack. Our method addresses critical limitations in state-of-the-art detection techniques that hinder their practicality. This approach reduces the required training dataset size from 40000 models to just 6. Furthermore, our methods consistently detect delicate attacks of up to 25% embedding rate and even up to 6% in some cases, while previous works were only shown to be effective for a 100%-50% embedding rate. We employ a strict evaluation strategy to ensure the trained models are generic concerning various factors. In addition, we show that our trained models successfully detect novel spread-spectrum steganography attacks, demonstrating the models' impressive robustness just by learning one type of attack. We open-source our code to support reproducibility and enhance the research in this new field. © 2024, CC BY-NC-ND.

关键词： Zero-shot learning

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Crowdsourcing to Service Users: Work for Yourself and Get Reward

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IEEE Transactions on Services Computing 2022年 1-1页

作者： Hou, Jing Sun, Li Shu, Tao Department of Computer Science Florida Polytechnic University Lakeland Florida United States Computer Science and Software Engineering Auburn University 1383 Auburn Alabama United States 36849 the department of computer science and software engineering Auburn University 1383 Auburn Alabama United States

Numerous service providers rely on crowdsourcing from service users, rather than a less-specific, more public group, to provide better services to the users themselves. In the majority of studies on the incentive mechanism for crowdsourcing, however, the users' intrinsic desire/demand for better service, which could have been exploited to enhance their involvement in crowdsourcing, has been largely overlooked. Therefore, conclusions are limited regarding the optimal incentives and the benefits of crowdsourcing on the service quality and thus the market share. In this paper, we study the incentive mechanism for crowdsourcing that combines a financial reward in the form of service price discounts and users' intrinsic demand for better service. Our focus is on leveraging the users dual role, i.e., the interdependence between the service and the users. We show that the dynamic market converges to a unique equilibrium under mild conditions, with the consideration of varying service usage levels and privacy concerns of the users. Besides, counter-intuitively, failure to take into account the users' intrinsic reward leads to too little extrinsic incentive. Moreover, our results showed how the competition reshapes the markets, which cannot be intuitively or trivially predicted without a thorough analysis. IEEE

关键词： Crowdsourcing

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DCNet: Full waveform inversion with difference convolution

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Journal of Applied Geophysics 2025年 240卷

作者： Fu, Li-Sha Hu, Chang-Yang Tan, Leting Liao, Huawei Zhang, Qiang Min, Fan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China College of Electronics and Information Engineering SiChuan University Chengdu610065 China The Southwest Oil & Gas Field CDB Operating Company China

Deep learning full waveform inversion (DL-FWI) is attracting broad attention due to its powerful feature extraction and nonlinear mapping capabilities. It learns stratigraphic features from seismic data without the need for a high quality initial velocity model and manual intervention. However, popular DL-FWI methods often exhibit velocity deviations and boundary localization errors at geological boundaries. Thus, we propose a new encoder-decoder network based on two types of difference convolution modules to address these issues. First, we design a novel oblique difference convolution (ODConv) inspired by an existing central difference convolution (CDConv). It effectively extracts edge details by calculating the difference between pixel pairs along different directions. Second, we incorporate CDConv and ODConv into the depth-wise separable structure to obtain the CDConv module and ODConv module, respectively. These modules are lightweight, effective, and able to mitigate gradient vanishing. Finally, we introduce a focal frequency loss to focus on frequencies that are hard to synthesize by down-weighting easy frequencies. It effectively improves the recovery of high wavenumber components in the velocity model. Experiments on SEGSimulation, OpenFWI and Marmousi II datasets show that our method outperforms state-of-the-art data-driven methods. The source code is available at ***/FanSmale/DCNet. © 2025 Elsevier B.V.

关键词： Network coding

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