检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Zhang, Litian Zhang, Xiaoming Zhou, Ziyi Zhang, Xi Li, Chaozhuo Yu, Philip S. School of Cyber Science and Technology Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Beijing University of Posts and Telecommunications Beijing China Department of Computer Science University of Illinois at Chicago Chicago United States

Amidst the rapid propagation of multimodal fake news across various social media platforms, the identification and filtering of disinformation have emerged as critical areas of academic research. A salient characteristic of fake news lies in its diversity, encompassing text-image incongruity, content-knowledge inconsistency, and content fabrication. However, existing endeavors are generally tailored to a specific subset of fake news, leading to limited universality. Moreover, these models primarily rely on scarce and exorbitant manually labeled annotations, which is incapable of providing sufficient learning signals to detect a variety of fake news. To address these challenges, we propose a novel knowledge-aware multimodal pre-training paradigm for fake news detection, dubbed KAMP. Our motivation lies in incorporating unsupervised correlations through pre-training tasks as complementary to alleviate the dependency on annotations. KAMP consists of a novel multimodal learning model and various delicate pre-training tasks to simultaneously capture valuable knowledge from single modality, multiple modalities, and background knowledge graphs. Our proposal undergoes comprehensive evaluation across two widely utilized datasets, and experimental results demonstrate the superiority of our proposal. © 2024, The Authors. All rights reserved.

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

arXiv

引用

arXiv 2023年

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China School of Information and Communication Technology Griffith University Australia RIKEN AIP Japan The University of Tokyo Japan

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spatial regions to guide talking portrait modeling. Specifically, to improve the accuracy of dynamic head reconstruction, a compact and expressive NeRF-based Tri-Plane Hash Representation is introduced by pruning empty spatial regions with three planar hash encoders. For speech audio, we propose a Region Attention Module to generate region-aware condition feature via an attention mechanism. Different from existing methods that utilize an MLP-based encoder to learn the cross-modal relation implicitly, the attention mechanism builds an explicit connection between audio features and spatial regions to capture the priors of local motions. Moreover, a direct and fast Adaptive Pose Encoding is introduced to optimize the head-torso separation problem by mapping the complex transformation of the head pose into spatial coordinates. Extensive experiments demonstrate that our method renders better high-fidelity and audio-lips synchronized talking portrait videos, with realistic details and high efficiency compared to previous methods. Code is available at https://***/Fictionarry/ ER-NeRF. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

BCPPAS : Blockchain-Based Cross-Domain Identity Authentication Scheme for IoT with Privacy Protection

BCPPAS : Blockchain-Based Cross-Domain Identity Authenticati...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Qianqian Li Yubing Han Qi Liu Guijuan Wang Hao Yu Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In Internet of Things(IoT) systems, ensuring the secure exchange of information between devices from different domains is crucial. Current cross-domain authentication schemes based on a single blockchain struggle to meet the confidentiality requirements for data and information exchange in large-scale IoT systems. This paper proposes a blockchain-based identity authentication scheme (BCPPAS) for IoT, featuring dual-chain collaboration, and designs a novel certificateless aggregate signature algorithm to address complex certificate management and key escrow issues. The edge server is capable of aggregating different signatures to achieve batch authentication, markedly improving authentication efficiency and reducing computational and storage overhead. Also, BCPPAS is designed to avoid costly bilinear pairing operations, providing less computational overhead for IoT devices with limited resources. To protect the privacy of IoT devices, BCPPAS uses the pseudonym instead of the real identity. Finally, efficiency of BCPPAS are demonstrated through theoretical analysis and experiments.

关键词： Privacy Federated learning Aggregates Authentication Blockchains Computational efficiency Internet of Things Servers Protection Information exchange

来源：评论

学校读者我要写书评

暂无评论

Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

Instance-aware Dynamic Prompt Tuning for Pre-trained Point C...

引用

International Conference on computer Vision (ICCV)

作者： Yaohua Zha Jinpeng Wang Tao Dai Bin Chen Zhi Wang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Harbin Institute of Technology Shenzhen Research Center of Artificial Intelligence Peng Cheng Laboratory

Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of full fine-tuning in downstream tasks leads to large per-task storage overhead for model parameters, which limits the efficiency when applying large-scale pre-trained models. Inspired by the recent success of visual prompt tuning (VPT), this paper attempts to explore prompt tuning on pre-trained point cloud models, to pursue an elegant balance between performance and parameter efficiency. We find while instance-agnostic static prompting, e.g. VPT, shows some efficacy in downstream transfer, it is vulnerable to the distribution diversity caused by various types of noises in real-world point cloud data. To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models. The essence of IDPT is to develop a dynamic prompt generation module to perceive semantic prior features of each point cloud instance and generate adaptive prompt tokens to enhance the model's robustness. Notably, extensive experiments demonstrate that IDPT outperforms full finetuning in most tasks with a mere 7% of the trainable parameters, providing a promising solution to parameter-efficient learning for pre-trained point cloud models. Code is available at https://***/zyh16143998882/ICCV23-IDPT.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework

Enhanced Student Stress Prediction in Higher Education Using...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati K. Amudha D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance.

关键词： Training Accuracy Redundancy Human factors Machine learning Mental health Convolutional neural networks Integrated circuit modeling Stress Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Ethical Aspects of ChatGPT in software Engineering Research

arXiv

引用

arXiv 2023年

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland School of Computer Science Wuhan University Wuhan430072 China

ChatGPT can improve software Engineering (SE) research practices by offering efficient, accessible information analysis and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Further, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. Additionally, we employed Interpretive Structure Modeling (ISM) approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level based decision model. We further conducted a Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles through adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. Impact Statement—This paper establishes the impact of employing ChatGPT in SE research while carefully addressing ethical challenges like privacy, data security, and bias. By developing a taxonomy through a comprehensive literature survey and an extensive questionnaire-based survey, we have created a guideline based on the identified motivators, demotivators, and ethical principles. Using ISM and MICMAC approa

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Algorithmic Cluster Expansions for Quantum Problems

arXiv

引用

arXiv 2023年

作者： Mann, Ryan L. Minko, Romy M. Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW2007 Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of abstract polymer models formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probability amplitudes of a class of quantum circuits close to the identity, (2) approximating expectation values of a class of quantum circuits with operators close to the identity, (3) approximating partition functions of a class of quantum spin systems at high temperature, and (4) approximating thermal expectation values of a class of quantum spin systems at high temperature with positive-semidefinite operators. Further, we obtain hardness of approximation results for approximating probability amplitudes of quantum circuits and partition functions of quantum spin systems. This establishes a computational complexity transition for these problems and shows that our algorithmic conditions are optimal under complexity-theoretic assumptions. Finally, we show that our algorithmic condition is almost optimal for expectation values and optimal for thermal expectation values in the sense of zero freeness. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Fast rare itemset mining in uncertain database

Fast rare itemset mining in uncertain database

引用

Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Weidong Qian Han Li Huang Huang Maocai Yuan Yuan Xu Ganjiang Sun China Ship Scientific Research Center Wuxi China School of Computer Science Northwestern Polytechnical University Xi'an China Wuxi Orient Software Technology Co. Ltd. Wuxi China

Rare itemset mining in uncertain database finds itemsets which have their support no less than a given probability minimum threshold. Discovering probabilistic rare itemset from a uncertain database can help user in better decision making by focusing on information that it is important but rare to something. Usually, frequent itemset and its relevant researches have gotten a lot of attention in the previous works, and much more efficient and effective algorithms was proposed to obtain valued knowledge under various constrained scenarios. Compared with frequent itemset mining, especially probabilistic rare itemset mining are rarely studied. Therefore, we present the concept of mining rare itemsets in uncertain data based on rare probabilities, and propose two algorithms, i.e. the bottom-up algorithm and the top-down algorithm. On this basis, two pruning strategies are designed to reduce the computational cost by traversing from long itemsets to short itemsets. Finally, we validate and evaluate the correctness and effectiveness of the algorithms in several experiments.

关键词： Itemsets Databases Decision making Focusing Artificial neural networks Probabilistic logic Generators

来源：评论

学校读者我要写书评

暂无评论

Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LogFold: Enhancing Log Anomaly Detection Through Sequence Folding and Reconstruction

LogFold: Enhancing Log Anomaly Detection Through Sequence Fo...

引用

Asia-Pacific Conference on software Engineering

作者： Xiaonan Shi Rui Li Qingfeng Du Cheng He Fulong Tian School of Software Engineering Tongji University Shanghai China Khoury College of Computer Science Northeastern University Boston United States Di-Matrix Information Technology Co. Ltd Shanghai China

Modern large-scale systems and networks necessitate automated anomaly detection to support the high availability and quality of services. Since logs are an essential data source that can accurately reflect the state of a system, log anomaly detection has attracted a lot of attention from researchers in both academia and industry. As the technology of artificial intelligence advances, plenty of work has adopted deep learning to detect log anomalies and achieved promising results. Nevertheless, it usually suffers from a lack of labels, excessive log sequence length, and low throughput problems when deploying to real-world systems. To address these challenges, we propose Log-Fold, an unsupervised Transformer-based log anomaly detection approach. In LogFold, we propose fold embedding, which can compress long log sequences to enhance the efficiency of anomaly detection. And we design a sequence reconstruction technique to enhance the effectiveness of anomaly detection. Our evaluation shows LogFold achieves 90.55% and 99.90% Fl-score on HDFS and BGL datasets, respectively, outperforming state-of-the-art methods. Besides, the fold embedding layer achieves compression rates of 36.55% and 64.86% on HDFS and BGL datasets, respectively, which helps to improve the throughput of LogFold.

关键词：

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：