Inf. Sci.最新文献_第4页

A novel extended multimodal AI framework towards vulnerability detection in smart contracts 面向智能合约漏洞检测的新型扩展多模态AI框架

Inf. Sci. Pub Date : 2023-03-01 DOI: 10.2139/ssrn.4331099

Wanqing Jie, Qi Chen, Jiaqi Wang, Arthur Sandor Voundi Koe, Jin Li, Pengfei Huang, Yaqi Wu, Yin Wang

引用次数: 6

A robust mixed error coding method based on nonconvex sparse representation 一种基于非凸稀疏表示的鲁棒混合错误编码方法

Inf. Sci. Pub Date : 2023-03-01 DOI: 10.2139/ssrn.4201627

W. Lv, Chao Zhang, Huaxiong Li, Bojuan Wang, Chunlin Chen

引用次数: 1

Priority ranking for the best-worst method 最佳-最差方法的优先级排序

Inf. Sci. Pub Date : 2023-03-01 DOI: 10.2139/ssrn.4331045

Jiancheng Tu, Zhibin Wu, W. Pedrycz

引用次数: 7

Stratified multi-density spectral clustering using Gaussian mixture model 基于高斯混合模型的分层多密度谱聚类

Inf. Sci. Pub Date : 2023-03-01 DOI: 10.2139/ssrn.4331043

Guanli Yue, Ansheng Deng, Yanpeng Qu, Hui Cui, Xueying Wang

引用次数: 3

Urban Regional Function Guided Traffic Flow Prediction 城市区域功能导向交通流预测

Inf. Sci. Pub Date : 2023-03-01 DOI: 10.48550/arXiv.2303.09789

Kuo Wang, Lingbo Liu, Yang Liu, Guanbin Li, Fan Zhou, Liang Lin

{"title":"Urban Regional Function Guided Traffic Flow Prediction","authors":"Kuo Wang, Lingbo Liu, Yang Liu, Guanbin Li, Fan Zhou, Liang Lin","doi":"10.48550/arXiv.2303.09789","DOIUrl":"https://doi.org/10.48550/arXiv.2303.09789","url":null,"abstract":"The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata.","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":"59 1","pages":"308-320"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76295537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks 神经网络事后不确定度量化的测试时间Dropout注入

Inf. Sci. Pub Date : 2023-02-06 DOI: 10.48550/arXiv.2302.02924

Emanuele Ledda, G. Fumera, F. Roli

{"title":"Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks","authors":"Emanuele Ledda, G. Fumera, F. Roli","doi":"10.48550/arXiv.2302.02924","DOIUrl":"https://doi.org/10.48550/arXiv.2302.02924","url":null,"abstract":"Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during inference for evaluating uncertainty. This approach, which we call dropout injection, provides clear benefits over its traditional counterpart (which we call embedded dropout) since it allows one to obtain a post hoc uncertainty measure for any existing network previously trained without dropout, avoiding an additional, time-consuming training process. Unfortunately, no previous work compared injected and embedded dropout; therefore, we provide the first thorough investigation, focusing on regression problems. The main contribution of our work is to provide guidelines on the effective use of injected dropout so that it can be a practical alternative to the current use of embedded dropout. In particular, we show that its effectiveness strongly relies on a suitable scaling of the corresponding uncertainty measure, and we discuss the trade-off between negative log-likelihood and calibration error as a function of the scale factor. Experimental results on UCI data sets and crowd counting benchmarks support our claim that dropout injection can effectively behave as a competitive post hoc uncertainty quantification technique.","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":"1 1","pages":"119356"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88351458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Sequential Deep Learning Algorithm for Sampled Mixed-integer Optimisation Problems 抽样混合整数优化问题的顺序深度学习算法

Inf. Sci. Pub Date : 2023-01-25 DOI: 10.48550/arXiv.2301.10703

M. Chamanbaz, Roland Bouffanais

引用次数: 1

An online decision-making strategy for routing of electric vehicle fleets 电动汽车车队路线的在线决策策略

Inf. Sci. Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4087063

J. Futalef, Diego Muñoz-Carpintero, Heraldo Rozas, Marcos E. Orchard

引用次数: 6

Community-aware empathetic social choice for social network group decision making 社区意识共情社会选择对社会网络群体决策的影响

Inf. Sci. Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4331049

Zhan Bu, Shanfan Zhang, Shanshan Cao, Jiuchuan Jiang, Yichuan Jiang

引用次数: 0

Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss 基于内容的对手类自适应边缘损失医学图像检索

Inf. Sci. Pub Date : 2022-11-22 DOI: 10.48550/arXiv.2211.15371

Ş. Öztürk, Emin Çelik, T. Çukur

{"title":"Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss","authors":"Ş. Öztürk, Emin Çelik, T. Çukur","doi":"10.48550/arXiv.2211.15371","DOIUrl":"https://doi.org/10.48550/arXiv.2211.15371","url":null,"abstract":"Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearance to suspected cases can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large data repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on dense embedding vectors that represent image features to allow quantitative similarity assessments. Triplet learning has emerged as a powerful approach to recover embeddings in CBIR, albeit traditional loss functions ignore the dynamic relationship between opponent image classes. Here, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. OCAM uses a variable margin value that is updated continually during the course of training to maintain optimally discriminative representations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against baselines.","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":"22 1","pages":"118938"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84292996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13