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A Hybrid Framework for Spatial Interpolation: Merging data-driven with Domain Knowledge

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arXiv 2024年

作者： Zhang, Cong Du, Shuyi Song, Hongqing Wang, Yuhe National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology Beijing100190 China School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States

Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the features of these datasets are typically limited to the spatial coordinates of the scattered observation locations. In this paper, we propose a hybrid framework that integrates data-driven spatial dependency feature extraction with rule-assisted spatial dependency function mapping to augment domain knowledge. We demonstrate the superior performance of our framework in two comparative application scenarios, highlighting its ability to capture more localized spatial features in the reconstructed distribution fields. Furthermore, we underscore its potential to enhance nonlinear estimation capabilities through the application of transformed fuzzy rules and to quantify the inherent uncertainties associated with the observation datasets. Our framework introduces an innovative approach to spatial information estimation by synergistically combining observational data with rule-assisted domain knowledge. © 2024, CC BY.

关键词： Domain Knowledge

QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

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arXiv 2024年

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China MOE KLINNS Lab School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Feature Selection: A perspective on inter-attribute cooperation

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arXiv 2023年

作者： Sosa-Cabrera, Gustavo Gómez-Guerrero, Santiago García-Torres, Miguel Schaerer, Christian E. Polytechnic School National University of Asunción Mcal. López Central San Lorenzo2111 Paraguay CIMA Center for Research in Mathematics César López Moreira Asunción Capital 1766 Paraguay Data Science and Big Data Lab Universidad Pablo de Olavide Carretera de Utrera Provincia de Sevilla Seville41013 Spain

High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a learning algorithm. Over the decades, filter feature selection methods have evolved from simple univariate relevance ranking algorithms to more sophisticated relevance-redundancy trade-offs and to multivariate dependencies-based approaches in recent years. This tendency to capture multivariate dependence aims at obtaining unique information about the class from the intercooperation among features. This paper presents a comprehensive survey of the state-of-the-art work on filter feature selection methods assisted by feature intercooperation, and summarizes the contributions of different approaches found in the literature. Furthermore, current issues and challenges are introduced to identify promising future research and development. © 2023, CC BY.

关键词： Feature Selection

A Causality Network Construction Method for High Dimensional Time Series big data 5

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A Causality Network Construction Method for High Dimensional...

5th International Conference on data science and Information Technology, DSIT 2022

作者： Jing, Yi Liu, Zhenyu Gao, Jing Li, Tao Inner Mongolia Agricultural University Inner Mongolia Autonomous Reg. Key Lab. of Big Data Res. and Applic. for Agric. and Animal Husbandry Hohhot China The University of New Sales Wales The Faculty of Science Sydney Australia College of Computer and Information Engineering College of Life Sciences Inner Mongolia Agricultural University Hohhot China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

How to infer the causal relationship between variables in high-dimensional time series data and construct the causal network have always been a difficulty of research. The two major challenges are dimension reduction and causal inference. This paper proposes a causality network construction method for high-dimensional sequential big data. In this method, the dimensionality of feature variables is reduced by fuzzy C-means clustering twice and feature variables with similar and specific patterns are screened out. Then transfer entropy is used to infer causal relationships among variables and construct the time-series data network of causal relationships among variables. The proposed method was applied to a high-dimensional time-series data set which contains 32884 rats' gene expression values at 480 time points to infer the causal regulatory relationships among the genes, and efficiently construct the regulatory network of rats' rhythm genes. © 2022 IEEE.

关键词： big data

Prototype-supervised adversarial network for targeted attack of deep hashing

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arXiv 2021年

作者： Wang, Xunguang Zhang, Zheng Wu, Baoyuan Shen, Fumin Lu, Guangming Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data University of Electronic Science and Technology of China Koala Uran Tech

Due to its powerful capability of representation learning and high-efficiency computation, deep hashing has made significant progress in large-scale image retrieval. However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field. In this paper, we propose a novel prototype-supervised adversarial network (ProS-GAN), which formulates a flexible generative architecture for efficient and effective targeted hashing attack. To the best of our knowledge, this is the first generation-based method to attack deep hashing networks. Generally, our proposed framework consists of three parts, i.e., a PrototypeNet, a generator and a discriminator. Specifically, the designed PrototypeNet embeds the target label into the semantic representation and learns the prototype code as the category-level representative of the target label. Moreover, the semantic representation and the original image are jointly fed into the generator for flexible targeted attack. Particularly, the prototype code is adopted to supervise the generator to construct the targeted adversarial example by minimizing the Hamming distance between the hash code of the adversarial example and the prototype code. Furthermore, the generator is against the discriminator to simultaneously encourage the adversarial examples visually realistic and the semantic representation informative. Extensive experiments verify that the proposed framework can efficiently produce adversarial examples with better targeted attack performance and transferability over state-of-the-art targeted attack methods of deep hashing. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamming distance

A Physics-based and data-driven Approach for Localized Statistical Channel Modeling

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arXiv 2023年

作者： Zhang, Shutao Ning, Xinzhi Zheng, Xi Shi, Qingjiang Chang, Tsung-Hui Luo, Zhi-Quan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel’s angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths’ powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method. © 2023, CC BY.

关键词： Statistical Physics

MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

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MGCT: Mutual-Guided Cross-Modality Transformer for Survival ...

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Mingxin Liu, Yunzan Cui, Hui Li, Chunquan Ma, Jiquan Heilongjiang University Department of Computer Science and Technology Harbin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia University of South China Institute of Cardiovascular Disease Hengyang Medical School The First Affiliated Hospital Hengyang China University of South China Cardiovascular Lab of Big Data and Imaging Artificial Intelligence Hengyang Medical School The First Affiliated Hospital Hengyang China University of South China School of Basic Medical Sciences Hengyang Medical School Department of Biochemistry and Molecular Biology Hengyang China

ISBN: (纸本)9798350337488

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000×150,000 pixels;(2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data;and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods. © 2023 IEEE.

关键词： Computational Pathology Multimodal Deep Learning Survival Prediction Weakly-Supervised Learning

A Multi-Step Speed Prediction Method for Traffic Networks Based on MAF-GCN-Seq2Seq Model 2

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A Multi-Step Speed Prediction Method for Traffic Networks Ba...

2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Long, Yi Xiao, Hongbo Xiao, Jianhua Peng, Xiaoning School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9781665464017

Accurately predicting the future trend of traffic flow is of great significance to the planning of urban traffic. The great challenge of predicting traffic flow stems from the complexity and uncertainty of traffic flow. To address the complexity of traffic flow, this paper proposes a MAF-GCN 2 Seq model. The model uses a moving average filter to filter the time series data, extracts the spatial features between each node through a graph convolutional neural network, and forms a time series matrix for input into the encoder (GRU), and introduces a self-attention mechanism to mine the spatial features. time-domain features for multi-step traffic speed prediction. In order to verify the advanced nature of the model, this paper uses the traffic flow data set in Shenzhen to predict the traffic flow. Through the comparison experiments, it can be seen that the prediction accuracy of the MAF-GCN 2 Seq model reaches 90%, which has obvious advantages compared with other models. © 2022 IEEE.

关键词： Convolutional neural networks

Securing Sdn/Nfv-Enabled Campus Networks with Software-Defined Perimeter-Based Zero-Trust Architecture

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SSRN

SSRN 2023年

作者： Ruambo, Francis A. Zou, Deqing Lopes, Ivandro O. Yuan, Bin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on Big Data Security Wuhan China National Engineering Research Center for Big Data Technology and System Wuhan China Services Computing Technology and System Lab Wuhan China Cluster and Grid Computing Lab Wuhan China Mbeya university of Science and Technology Mbeya131 Tanzania United Republic of Nucleo Operacional para a Sociedade de Informacao Cape Verde Songshan Laboratory Zhengzhou China

Network softwarization is a breakthrough in designing modern networks and providing numerous new network operations and services. This change is exemplified by Software Defined Networks (SDN) and Network Function Virtualization (NFV). As a result, the combination of SDN and NFV (SDN/NFV) frameworks and individual SDN and NFV frameworks have gained substantial traction in network security over the past decade, significantly supporting on-demand protection measures. However, the inherent security shortcomings of SDN, NFV, and SDN/NFV frameworks provide the fundamental issue in implementing security procedures via network softwarization. To solve this issue, we present a Software-Defined Perimeter (SDP)-powered Zero-Trust Architecture (ZTA) to improve security in the SDN/NFV-enabled campus network holistically. In addition to this, we also propose a threat model and quantitatively assess the suggested approach's performance and security. Furthermore, we highlight potential zero-trust network security function implementations in SDN/NFV-enabled infrastructure to increase protection for endpoint devices, network resources, and flows for detecting threats and responding effectively with minimal latency and overhead. © 2023, The Authors. All rights reserved.

关键词： Access control