检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Wang, Xianlong Hu, Shengshan Zhang, Yechao Zhou, Ziqi Zhang, Leo Yu Xu, Peng Wan, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Information and Communication Technology Griffith University SouthportQLD4215 Australia

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, defense mechanisms such as adversarial training, image transformation techniques, and image purification have been proposed. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then introduce Gaussian noise to absorb the poisons and apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://***/CGCL-codes/ECLIPSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

TOKEN2VEC: A JOINT SELF-SUPERVISED PRE-TRAINING FRAMEWORK USING UNPAIRED SPEECH AND TEXT

arXiv

引用

arXiv 2022年

作者： Yue, Xianghu Ao, Junyi Gao, Xiaoxue Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China

Self-supervised pre-training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-text joint pre-training on unpaired speech and text. In this paper, we take the idea of self-supervised pre-training one step further and propose token2vec, a novel joint pre-training framework for unpaired speech and text based on discrete representations of speech. Firstly, due to the distinct characteristics between speech and text modalities, where speech is continuous while text is discrete, we first discretize speech into a sequence of discrete speech tokens to solve the modality mismatch problem. Secondly, to solve the length mismatch problem, where the speech sequence is usually much longer than text sequence, we convert the words of text into phoneme sequences and randomly repeat each phoneme in the sequences. Finally, we feed the discrete speech and text tokens into a modality-agnostic Transformer encoder and pre-train with token-level masking language modeling (tMLM). Experiments show that token2vec is significantly superior to various speech-only pre-training baselines, with up to 17.7% relative WER reduction. Token2vec model is also validated on a non-ASR task, i.e., spoken intent classification, and shows good transferability. Copyright © 2022, The Authors. All rights reserved.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

A Novel Machine Learning Approach for Fast and Efficient Detection of Mango Leaf Diseases

A Novel Machine Learning Approach for Fast and Efficient Det...

引用

Computing and Machine Intelligence (ICMI), International Conference on

作者： S M Masfequier Rahman Swapno Sm Nuruzzaman Nobel Md Babul Islam R. Haque V. P. Meena F. Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of EEE Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine Learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99 %. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Training Plant diseases Accuracy Plantations Machine learning Safety Reliability

来源：评论

学校读者我要写书评

暂无评论

Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

引用

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Feng, Houze Wang, Jingjing Chen, Jianrui Hou, Xiangwang Wang, Jiacheng Sun, Geng Niyato, Dusit School of Cyber Science and Technology Beihang University Beijing China Tsinghua University Department of Electronic Engineering Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798350368369

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments. © 2025 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Cloud Security System using Cryptography

A Hybrid Cloud Security System using Cryptography

引用

International Conference on Edge Computing and Applications (ICECAA)

作者： G. Sudhakar H. Azath Pachhaiammal Alias Priya M P. B. Edwinprabhakar Department of Computer Science and Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India School of Computer Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India

Information security is a major problem in cloud computing systems as the information may be physically placed anywhere in any data center throughout the networks. Due to the nature of cloud computing, user authentication, information integrity, and confidentiality are all brought up as significant challenges. In order to ensure confidentiality, integrity, and authentications while storing and accessing information from and to the data center as well as in peer contacts; it is advised to adopt a better, innovative, secure security method. Cloud security is a rapidly expanding area of computer and networks securities. In the cloud platforms paradigm, external data centers are used. It supports a range of programming languages used in the process of deploying web applications. The Cloud is built on a managed containers architecture that integrates information services and robust ecosystems for the deployment and operation of modern apps. In cloud computing, data securities are a major challenge that is dealt with using cryptographic methods. One potential method for encrypting data is the Advanced Encryption Standard (AES). After developing a cloud platform, the AES algorithm for encryption and the data Encryption Standard (DES) method for decryption will be used in this work to secure data. The performance study suggests that DES and AES cryptography can be used to secure data. Calculations of data encryption and decryption delay also show that increased data delay periods are caused by bigger data volumes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DL-BERT: a time-aware double-level BERT-style model with pre-training for disease prediction

DL-BERT: a time-aware double-level BERT-style model with pre...

引用

IEEE International Conference on Big data

作者： Xianlai Chen Jiamiao Lin Ying An Big Data Institute Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665480468

Disease prediction based on the Electronic Health Record (EHR) is an important task in healthcare. EHR records patients’ every visit by time, and there are many kinds of medical codes within a visit, therefore, EHR has characteristics of temporal irregularity and hierarchical structure. Some recent works employ BERT-style models to process EHR data for disease prediction. However, few of these models can give consideration to capture both the interaction between medical codes and the impact of temporal irregularity. To solve this problem, we propose the Double-Level BERT-style model (DL-BERT). Considering EHR’s hierarchical structure, the model contains a code-level and a visit-level representation layer which can learn the relationship between medical codes and temporal influence respectively. In the code-level representation layer, the model achieves the representation power by employing external medical ontologies to provide multi-resolution information of medical codes and the Transformer to embed medical codes. Besides, the model adopts two pre-training tasks to enhance the ability to capture the link between different kinds of codes. In the visit-level representation layer, DL-BERT utilizes a special time-aware Transformer to model temporal information. And the model adopts a visit-level pre-training task for better learning context information. Experiments are conducted on two real-world healthcare datasets and show that our model outperforms all baselines demonstrating the effectiveness of our model.

关键词： Codes Medical services Ontologies Predictive models Big data Transformers data models

来源：评论

学校读者我要写书评

暂无评论

Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Maori Views

引用

JMIR Formative Research 2024年 8卷 e59811页

作者： Jayamini, Widana Kankanamge Darsha Mirza, Farhaan Bidois-Putt, Marie-Claire Naeem, M. Asif Chan, Amy Hai Yan Department of Computer Science School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Kelaniya Sri Lanka School of Pharmacy Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Department of Data Science & Artificial Intelligence National University of Computer and Emerging Sciences (NUCES) Islamabad Pakistan

Background: Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Maori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including artificial intelligence (AI) and machine learning (ML) models, are increasingly popular for asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. Objective: This study aimed to explore the views and perceptions that Maori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and ensure Maori are represented in ML models without worsening existing health inequities. Methods: Semistructured interviews were conducted with 20 Maori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. Results: We identified four key themes: (1) concerns about AI use, (2) interest in using technology to support asthma, (3) desired characteristics of AI-based systems, and (4) experience with asthma management and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Maori experience of colonization. Most of the participants were interested in using technology to support their as

关键词： artificial intelligence asthma risk prediction health system development machine learning maori perceptions mobile phone

来源：评论

学校读者我要写书评

暂无评论

Improving E-Commerce Fraud Detection: A GAN and Reinforcement Learning Approach Integrated with Personality Analysis for Secure Digital Economy

Improving E-Commerce Fraud Detection: A GAN and Reinforcemen...

引用

Visual Analytics and data Visualization (ICVADV), International Conference on

作者： Deependra Nath Pathak Ankit Kumar Kriti Srivastava Radha Ranjan Kirandeep Kaur Ramanjeet Singh Amity Law School Amity University Patna Danapur Bihar India Computer Science and Engineering Chandigarh University Mohali Punjab India Department of CSE (Data Science) Dwarkadas J Sanghvi College of Engineering Maharashtra India Amity Law School Amity University Patna Bihar University School of Business Chandigarh University Mohali Punjab India Chandigarh University Mohali Punjab India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The rapid growth of online e-commerce makes platforms more vulnerable to fraud. Fraudulent behavior hurts e-commerce rating systems and user experiences. Fraudsters change their approaches to avoid detection using complicated adversarial methods. This requires efficient and effective mechanisms to identify and control such actions. Preprocessing, feature extraction, and model training comprise the suggested method. During the preprocessing step, duplicates are removed, repeated matches are ignored, and finally, the remaining pairs of persons are joined. Combining word2vec and doc2vec with TF-IDF produces helpful feature extraction representations. A RL-GAN is used to trains the model. The suggested RL-GAN model outperformed solo RL and GAN models. Its 93.49% accuracy rate shows its effectiveness in detecting e-commerce fraud. Results reveal that the suggested strategy can resist new online fraud. The RL-GAN model uses cutting-edge preprocessing, feature extraction, and training to detect e-commerce fraud.

关键词： Training Accuracy Visual analytics data visualization Reinforcement learning Resists Generative adversarial networks Feature extraction Fraud Electronic commerce

来源：评论

学校读者我要写书评

暂无评论

Real-time Student Classroom Feedback with the RFMNet Framework in a Smart Classroom

Real-time Student Classroom Feedback with the RFMNet Framewo...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xianpiao Tang Pengyun Hu Dewei Yu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

Facial expression recognition is the ability to interpret human feelings from facial features. Teachers can use it to visualize the state of emotion and estimate students' learning effects to take corresponding intervention strategies to improve teaching. The current technology has shortcomings such as network performance degradation and feature information loss, which cannot meet the needs of sentiment analysis of student feedback. Therefore, RFMNet, a novel network model based on deep learning theory, is proposed in this paper. Featuring higher extraction ability, it is conducive to accurately analysing students' expressions. First of all, this model introduces a relation-aware global attention (RGA) module to better integrate the more discriminative expression features in the image and achieve more refined sentiment analysis. Second, the Mish activation function and the focal loss function are employed. The Mish activation function is introduced to avoid disappearing feature information due to neuron deactivation when the ReLU gradient is 0. The model achieves an average recognition accuracy of 89.62%, as confirmed through a sequence of trials conducted on the public dataset FERPlus. The model helps achieve intelligent processing of teaching information and obtaining real-time feedback by detecting, extracting, and analysing the students' facial expressions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Hepatocellular Carcinoma Diseases Based on Methylation data and Screening of Hub Genes 2021

Prediction of Hepatocellular Carcinoma Diseases Based on Met...

引用

2021 5th International Conference on computer Science and Artificial Intelligence

作者： Yawei Zhang Fangtao Ren Xi Liu Fan Zhang School of computer and information engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC.

关键词： DNA Methylation Machine learning Artificial neural networks Hepatocellular carcinoma

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：