检索结果-内蒙古大学图书馆

International Joint Conference on Neural Networks (IJCNN)

作者： Zeyu Huang Xiaofeng Zhang Jun Bai Wenge Rong Yuanxin Ouyang Zhang Xiong State Key Laboratory of Software Development Environment Beihang University Beijing China Sino-French Engineer School Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Solving math word problems (MWPs) is a challenging task. Some existing solvers retrieve textually similar problems and draw on their solutions to solve the given problem. However, textually similar questions are not guaranteed to have similar solutions, and vice versa. Therefore, this work investigates the logical consistency among different problems and proposes a novel framework to solve math word problems following logically consistent templates. Experimental results show that our method outperforms many strong baselines, including some pre-trained language model-based methods. Further analysis shows that our retrieval method does learn the logical similarity between Questions and plays a crucial role in our model's nerformance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Time-Series Key Points Detection Through Self-supervised Learning and Probability Compensation 28th

Towards Time-Series Key Points Detection Through Self-superv...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yuan, Mingxu Bi, Xin Huang, Xuechun Zhang, Wei Hu, Lei Yuan, George Y. Zhao, Xiangguo Sun, Yongjiao Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang110819 China Key Laboratory of Liaoning Province on Deep Engineering and Intelligent Technology Northeastern University Shenyang110819 China School of Computer Science and Engineering Northeastern University Shenyang China Thinvent Digital Technology Co. Ltd. Nanchang India College of Software Northeastern University Shenyang China

ISBN: (纸本)9783031306365

Key points detection is crucial for signal analysis by marking the identification points of specific events. Deep learning methods have been introduced into key points detection tasks due to their significant representation learning ability. However, in contrast to common time series classification and prediction tasks, the target key points correspond to significantly different time-series patterns and account for an extremely small proportion in a whole sample. Consequently, existing end-to-end methods for key points detection encounter two major problems: specificity and sparsity. Thus, in this work, we address these issues by proposing a probability compensated self-supervised learning framework named ProCSS. Our ProCSS consists of two major components: 1) a pretext task module pretraining an encoder based on self-supervised learning to capture effective time-series representations with a higher generalization ability;2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival methods for time-series key points detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Exploit your Latents: Coarse-Grained Protein Backmapping with Latent Diffusion Models 39

Exploit your Latents: Coarse-Grained Protein Backmapping wit...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Rongchao Huang, Yu Lou, Yiwei Xin, Yi Chen, Haixu Cao, Yongzhi Wang, Hanpin Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute of Geriatrics National Clinical Research Center of Geriatrics Disease Chinese PLA General Hospital Beijing China

ISBN: (纸本)157735897X

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Bc-Sse: A Blockchain-Aided, Secure and Practical Multi-Keyword Ranked Searchable Encryption Scheme

SSRN

引用

SSRN 2023年

作者： Zheng, Lixiang Zhang, Hanlin Lin, Jie Ge, Xinrui Kong, Fanyu College of Computer Science and Technology Qingdao University Qingdao266071 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Software Shandong University Jinan250100 China

Searchable encryption facilitates the retrieval of encrypted data by cloud servers, ensuring both user privacy and data usability. While several techniques exist that integrate blockchain with searchable encryption to counteract potential data alteration by malicious servers and to affirm the integrity of search results, a significant limitation persists: many of these techniques store user data directly on the blockchain. This approach escalates storage costs and results in increased gas consumption. In this paper, we present BC-SSE: a blockchainaugmented, efficient multi-keyword ranked searchable encryption system. This scheme vectorizes index data and query data with the vector space model and the TF-IDF model. Besides, it calculates the correlation score between documents to enable ranking functionality. Moreover, this scheme adopts the balanced binary tree structure to build an index and enhance retrieval efficiency. To avoid exceeding GasLimit, we devised a "chunk T transaction" method and employed the InterPlanetary File System (IPFS) to store ciphertext data and avoid failure risks while ensuring data storage availability. A comprehensive theoretical analysis of BC-SSE is provided, and we further validate our system’s efficacy through a series of experiments on real-world datasets, demonstrating the robustness and practicality of the BC-SSE framework. © 2023, The Authors. All rights reserved.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

引用

arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation

arXiv

引用

arXiv 2022年

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Tao, Dacheng The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China The School of Computer Science Faculty of Engineering The University of Sydney Australia

Sequence-to-sequence (seq2seq) learning is a popular fashion for large-scale pretraining language models. However, the previous seq2seq pretraining models generally focus on reconstructive objectives on the decoder side and neglect the effect of encoder-side supervision, which we argue may lead to sub-optimal performance. To verify our hypothesis, we first empirically study the functionalities of the encoder and decoder in seq2seq pretrained language models, and find that the encoder takes an important but under-exploitation role than the decoder regarding the downstream performance and neuron activation. Therefore, we propose an encoding-enhanced seq2seq pretraining strategy, namely E2S2, which improves the seq2seq models via integrating more efficient self-supervised information into the encoders. Specifically, E2S2 adopts two self-supervised objectives on the encoder side from two aspects: 1) locally denoising the corrupted sentence (denoising objective);and 2) globally learning better sentence representations (contrastive objective). With the help of both objectives, the encoder can effectively distinguish the noise tokens and capture high-level (i.e., syntactic and semantic) knowledge, thus strengthening the ability of seq2seq model to accurately achieve the conditional generation. On a large diversity of downstream natural language understanding and generation tasks, E2S2 dominantly improves the performance of its powerful backbone models, e.g., BART and T5. For example, upon BART backbone, we achieve +1.1% averaged gain on the general language understanding evaluation (GLUE) benchmark and +1.75% F0.5 score improvement on CoNLL2014 dataset. We also provide in-depth analyses to show the improvement stems from better linguistic representation. We hope that our work will foster future self-supervision research on seq2seq language model pretraining. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving 39

Dual-View Interaction-Aware Lane Change Prediction for Auton...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yuhuan Zhang, Zhen Bai, Rufan Liu, Han Wang, Wei Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering Southeast University China School of Software Dalian University of Technology China School of Medical Technology Beijing Institute of Technology China

ISBN: (纸本)157735897X

As artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for Predicting Air Quality Index in Smart Cities

A Machine Learning Approach for Predicting Air Quality Index...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Swamynathan N. Sneha S. P. Ramesh R. Niranjana D. David Neels Ponkumar R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions; however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction.

关键词： Support vector machines Adaptation models Atmospheric modeling Biological system modeling Computational modeling Artificial neural networks Predictive models Air quality Data models Indexes

来源：评论

学校读者我要写书评

暂无评论

Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

引用

6th International Symposium on computer and Information Processing Technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

来源：评论

学校读者我要写书评

暂无评论

An Unsupervised Convolutional Adversarial Anomaly Detection Model for IoT Data Infrastructure

An Unsupervised Convolutional Adversarial Anomaly Detection ...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Peian Wen Zhenyu Yang Lei Wu Sibo Qi Juan Chen School of Computer and Software Engineering Xihua University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

Anomaly detection is critical to ensure the IoT (Internet of Things) data infrastructures' Quality of Service. However, due to the complexity of incon-spicuous(indistinct) anomalies, high dynamicity, and lack of anomaly labels in the operational IoT systems and cloud infrastructures, multivariate time series anomaly detection becomes more difficult. Existing approaches are rarely effective in meeting these challenges. In this paper, We propose a novel convolutional adversarial model based on CNN adversarial training with POT (Peak Over Threshold) dynamic threshold selection (CAT-POT) for multivariate time series anomaly detection in the IoT data. We build on the original autoencoder, replacing the fundamental linear changes with a convolutional neural network and adding a convolutional decoder to make an adversarial training architecture. The ability of the model to identify “slight” anomalies is enhanced through adversarial training, combined with POT dynamically selected thresholds to detect anomalies. We conduct ex-periments on five subsets of various open datasets, and the overall performance of CAT-POT is better than that of the baseline method. The average F1 value of CAT-POT on all datasets is 0.889, which is 5.1% higher than the suboptimal model, with the best improvement reaching 21.5%.

关键词： Training Time series analysis Quality of service Data models Real-time systems Decoding Complexity theory

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：