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2nd International Conference on Artificial Intelligence and data Sciences, AiDAS 2021

作者： Neoh, Desmond Kim Seng Lock, Eric Chun Meng Lew, Kem Ho Tang, Hong Yip Tong, Dong Ling Tan, Tun Tai Tseu, Kwan Lee Artificial Intelligence Lab First City University College Faculty of Computing and Engineering Petaling Jaya Malaysia Centre of IoT Big Data Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Perak Malaysia Global Software House Sdn Bhd Petaling Jaya Malaysia

ISBN: (纸本)9781665417266

Back in the days, documents, payment receipts, and books are being published in printed format. To make a digital format, readers need to manually type them into a computer. However, things have change rapidly especially in the information technology (IT) industry. Optical character recognition (OCR) has been introduced to simplify the digitalization process for users. However, OCR not able to translate scanned text images into different human readable languages. Therefore, this paper proposed an Android-based application programming interface (API) developed using Firebase as an improvement text recognition program that able to translate the scanned text images into language of user preference. This proposed API processes text images, detects text from the scanned images, and translate the text into user-preferred languages. The used data in this paper is the research papers available in the university library. © 2021 IEEE.

关键词： Digital libraries

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Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space

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Science China(Information Sciences) 2019年第7期62卷 90-98页

作者： Yushan ZHANG Han HUANG Hongyue WU Zhifeng HAO School of Statistics and Mathematics Guangdong University of Finance and Economics Big Data and Educational Statistics Application Laboratory Guangdong University of Finance and Economics School of Software Engineering South China University of Technology School of Mathematics and Big Data Foshan University

The pigeon-inspired optimization(PIO) algorithm is a newly presented swarm intelligence optimization algorithm inspired by the homing behavior of pigeons. Although PIO has demonstrated effectiveness and superiority in numerous fields, particularly in practical engineering optimization, there have been few results concerning the theoretical foundations of PIO. This paper conducts convergence analysis of basic PIO in a continuous search space in two aspects. First, we analyze the convergence of each pigeon's expected position using a difference equation and prove that the average position of each pigeon in the swarm will converge to the same value. To further study the stochastic global convergence property of the pigeon swarm, we apply the martingale theory to investigate the basic PIO swarm sequence, and achieve a sufficient condition to guarantee global convergence of the basic PIO. Our theoretical analysis shows that this convergence depends upon the accumulation of the minimum probability with which the pigeon swarm jumps to the global-optimal region at each iteration. The mathematical methods proposed in this study, particularly the martingale technique, also provide a new effective approach for the theoretical analysis of bio-inspired algorithms in continuous optimization.

关键词： pigeon-inspired optimization(PIO) convergence analysis martingale continuous optimization swarm intelligence

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Incremental Reduction Methods Based on Granular Ball Neighborhood Rough Sets and Attribute Grouping

SSRN

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SSRN 2023年

作者： Li, Yan Wu, Xiaoxue Wang, Xizhao Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal University Zhuhai Campus Zhuhai519087 China College of Mathematics and Information Science Hebei University Baoding071002 China Big Data Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China

In the framework of rough sets, incremental algorithms can effectively reduce repetitive computations for dynamic datasets by discovering the update principles of relevant *** the variations in attribute sets, incremental methods based on neighborhood granulation provide fast updates to attribute reduction in continuous-valued information systems. However, most of these methods assume that the sample distribution and neighborhood radius remain fixed with changes in the attribute set, which may mislead the granulation process and affect the model’s classification ability. In this paper, a new incremental reduction method based on granular balls and attribute grouping is proposed for dynamic information systems with multiple attribute additions. Different neighborhood radii are adaptively determined when the attribute set changes, and the number of neighborhood granules can also be effectively ***, the attributes are grouped based on the k-means algorithm, and only attributes from different groups or with small relevance to those in the current reduction set are considered to be incorporated as a reduction attribute, thus reducing the computation time and simultaneously preserving informative *** and nonlinear correlation coefficients are used to measure attribute relevance based on which three corresponding incremental reduction algorithms are ***, extensive experimental results on 12 benchmark datasets are shown to compare the proposed method with non-incremental and typical incremental methods in terms of time cost, classification accuracy, the size of attribute reduction, and coverage of the reduction. © 2023, The Authors. All rights reserved.

关键词： Granulation

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Detecting and Corrupting Convolution-based Unlearnable Examples

arXiv

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arXiv 2023年

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Approaches to Address Occlusion and Small Target Challenges in Restaurant Rat Detection

Approaches to Address Occlusion and Small Target Challenges ...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Yanqing Li Zhanxiang Lai Hanyu Lou Junhan He Yixin Zhong Wen Xin School of Public Health Xiamen University Xiamen China School of Marine Science and Technology Northwestern Polytechnical University Xi’an China School of Software Northwestern Polytechnical University Xi’an China School of Lilac Harbin Institute of Technology Harbin China School of Computer and Big Data Heilongjiang University Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

In this study, an improved target detection algorithm is proposed for the problem of rat detection in restaurant environments. The restaurant environment is characterized by obvious light changes, complex occlusion, and ground reflections, which bring more challenges to the accurate detection of rats. For this reason, we make the following improvements to the YOLOv5 algorithm. First, we introduce the Glare Suppression Module, which utilizes an adaptive feature correction method to reduce the interference of highly reflective areas such as oil and water stains on the ground on the detection model, and to improve the recognition ability of the rat target in low-contrast scenes. Secondly, combined with the Background Adaptive Modeling (BAM) technology, the stability and generalization ability of the model is improved by dynamically learning the background features of the restaurant environment to reduce the false detections caused by the activities of the restaurant personnel, changes in the tables and chairs, and other factors. The experimental results show that the improved YOLOv5 algorithm can effectively improve the precision and recall rate of rat detection, reduce the false detection rate, and ensure the real-time performance in the restaurant environment, which provides efficient and reliable technical support for restaurant hygiene monitoring and pest rat prevention and control.

关键词： YOLO Adaptation models Prevention and mitigation Rodents Rats Feature extraction Reflection Real-time systems Reliability Personnel

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Fast-Speed Image Recognition System on Retail Commodity Image

Fast-Speed Image Recognition System on Retail Commodity Imag...

引用

IEEE International Conference on e-Business engineering (ICEBE)

作者： Meiju Hu Xiaojing Zhong School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education

ISBN: (纸本)9781665444194

In recent years, image recognition technology has been widely used in commodity recognition in smart containers and retail shelves. However, as the number of images received by smart terminals is increasing, more and more commodity categories need to be recognized. The image recognition technology adopted by the industry now requires a large number of images, and the modeling time is very long, which cannot meet the requirements for fast and accurate recognition. In this paper, we design and implement a fast-speed image recognition system that can cover all categories of retail commodities to solve the problem. We use DenseNet to strengthen the feature extraction of commodities collected by cameras. At the same time, we utilize a large number of commodity images accumulated in the production system as the primary database to achieve the effect of identifying all categories of commodity. The proposed system can quickly model new commodity images and new packaging of commodities through matching with the database. The experimental results show that the system can overcome the limitation of the number of images and shorten the modeling time. Furthermore, the accuracy rate is not affected in a complex environment.

关键词： Industries Training Production systems Image recognition Costs databases Packaging

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ABFS: Natural Robustness Testing for LLM-based NLP software

arXiv

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arXiv 2025年

作者： Xiao, Mingxuan Xiao, Yan Ji, Shunhui Li, Yunhe Xue, Lei Zhang, Pengcheng Key Laboratory of Water Big Data Technology of Ministry of Water Resources the College of Computer Science and Software Engineering Hohai University No. 8 Focheng West Road Jiangning District Nanjing211100 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University No.66 Gongchang Road Xinhu Street Guangming District Shenzhen528406 China

Owing to the exceptional performance of Large Language Models (LLMs) in Natural Language Processing (NLP) tasks, LLM-based NLP software has rapidly gained traction across various domains, such as financial analysis and content moderation. However, these applications frequently exhibit robustness deficiencies, where slight perturbations in input (prompt+example) may lead to erroneous outputs. Current robustness testing methods face two main limitations: (1) low testing effectiveness, limiting the applicability of LLM-based software in safety-critical scenarios, and (2) insufficient naturalness of test cases, reducing the practical value of testing outcomes. To address these issues, this paper proposes ABFS, a straightforward yet effective automated testing method that, for the first time, treats the input prompts and examples as a unified whole for robustness testing. Specifically, ABFS formulates the testing process as a combinatorial optimization problem, employing Best-First Search to identify successful test cases within the perturbation space and designing a novel Adaptive control strategy to enhance test case naturalness. We evaluate the robustness testing performance of ABFS on three datasets across five threat models. On Llama2-13b, the traditional StressTest achieves only a 13.273% success rate, while ABFS attains a success rate of 98.064%, supporting a more comprehensive robustness assessment before software deployment. Compared to baseline methods, ABFS introduces fewer modifications to the original input and consistently generates test cases with superior naturalness. Furthermore, test cases generated by ABFS exhibit stronger transferability and higher testing efficiency, significantly reducing testing costs. © 2025, CC BY-NC-ND.

关键词： software testing

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Video Summarization with a Dual Attention Capsule Network

Video Summarization with a Dual Attention Capsule Network

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International Conference on Pattern Recognition

作者： Hao Fu Hongxing Wang Jianyu Yang School of Big Data and Software Engineering Chongqing University Chongqing China School of Rail Transportation Soochow University Suzhou Jiangsu China

In this paper, we address the problem of video summarization, which aims at selecting a subset of video frames as a summary to represent the original video contents compactly and completely. We propose a simple but effective supervised approach with a dual attention capsule network towards this end. Unlike existing LSTM based methods, it pays attention to short- and long-term dependencies among video frames through an elaborate dual self-attention architecture, which can handle longer-term dependencies and admit parallel computing. To reconcile the outputs of dual self-attention, we rely on a two-stream capsule network to learn the underlying frame selection criteria. Experiments on real-world datasets show the advantages of the proposed approach compared with state-of-the-art methods.

关键词： Fuses Computer architecture Parallel processing Benchmark testing Pattern recognition Labeling

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Deepnodule: Multi-Task Learning of Segmentation Bootstrap for Pulmonary Nodule Detection

Deepnodule: Multi-Task Learning of Segmentation Bootstrap fo...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jingqin Li Kun Wang Dan Yang Xiaohong Zhang Chen Liu School of Big Data and Software Engineering Chongqing University Chongqing China The First Affiliated Hospital of Army Medical University Chongqing China

ISBN: (纸本)9781728176055;9781728176062

Pulmonary nodule detection and segmentation are the necessary successively steps in lung cancer screening with low-dose computed tomography (CT) scans. However, the state-of-the-art models focus on solving tasks separately, thereby ignore the correlation between each task. Besides, most nodule detectors adopt anchor-based method falling to achieve good performance in low FPs per scan. To overcome those barriers, we present a novel multi-task 3D convolutional network (DeepNodule) for simultaneous nodule detection and segmentation in a shared-and-fined manner. Meanwhile, we utilize the center-point of the predicted segmentation masks to refine the bounding box coordinate and get a more precise nodule location. Furthermore, we design a 3D Gated Channel Transformation convolutional attention block for learning nodule features better. Experiments conducted on LUNA16 dataset demonstrates that DeepNodule obtains competitive performance, with the sensitivity of nodule candidate detection achieving 92.0%, and the accuracy of nodule segmentation reaching 80.04%.

关键词： Three-dimensional displays Correlation Convolution Computed tomography Lung cancer Lung Logic gates

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