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Identifying outliers in astronomical images with unsupervised machine learning

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arXiv 2022年

作者： Han, Yang Zou, Zhiqiang Li, Nan Chen, Yanli School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data. However, it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload. Supervised learning is also unsuitable for this purpose since designing proper training sets for unanticipated signals is unworkable. Motivated by these challenges, we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers. For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE) + KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods. Results show that attCAE KNN achieves the best recall (78%), which is 53% higher than the classical KNN method and 22% higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior to KNN (4 hours) and equal to CAE+KNN (10 minutes) for accomplishing the same task. Thus, we believe it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN to available survey datasets to assess its applicability and reliability. Copyright © 2022, The Authors. All rights reserved.

关键词： k-nearest neighbors

ER: Equivariance Regularizer for Knowledge Graph Completion

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arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the "size" of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods. © 2022, CC BY.

关键词： Knowledge graph

Design and Optimization of Evaluation Metrics in Object Detection and Tracking for Low-Altitude Aerial Video 1st

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Design and Optimization of Evaluation Metrics in Object Dete...

1st International Conference on big data and security, ICBDS 2019

作者： Wang, Li Shu, Xin Zhang, Wei Chen, Yunfang Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The combination of Unmanned Aerial Vehicle (UAV) technology and computer vision has become popular in a wide range of applications, such as surveillance and reconnaissance, while popular evaluation measures are sometimes not applicable for specific tasks. In order to evaluate visual object detection and tracking algorithms of low-altitude aerial video properly, we first summarize the evaluation basis of computer vision tasks, including ground truth, prediction-to-ground truth assignment strategy and distance measures between prediction and ground truth. Then, we analyze the advantages and disadvantages of visual object detection and tracking performance measures, including average precision (AP), F-measure, and accuracy. Finally, for the low-altitude (nearly 100 m) surveillance mission of small unmanned aerial vehicles, we discuss the threshold optimization method of popular measures and the design strategy of application measures. Our work provides a reference in the aspect of performance measures design for researchers of UAV vision. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

Geometry Interaction Knowledge Graph Embeddings

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arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean space (zero curved), hyperbolic space (negatively curved) or hyperspherical space (positively curved) to maintain their specific geometric structures (e.g., chain, hierarchy and ring structures). However, the topological structure of KGs appears to be complicated, since it may contain multiple types of geometric structures simultaneously. Therefore, embedding KGs in a single space, no matter the Euclidean space, hyperbolic space or hyperspheric space, cannot capture the complex structures of KGs accurately. To overcome this challenge, we propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. Theoretically, our proposed GIE can capture a richer set of relational information, model key inference patterns, and enable expressive semantic matching across entities. Experimental results on three well-established knowledge graph completion benchmarks show that our GIE achieves the state-of-the-art performance with fewer parameters. © 2022, CC BY.

关键词： Knowledge graph

Community Detection Based on DeepWalk in Large Scale Networks 1st

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Community Detection Based on DeepWalk in Large Scale Network...

1st International Conference on big data and security, ICBDS 2019

作者： Chen, Yunfang Wang, Li Qi, Dehao Zhang, Wei Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The large scale and complex structure of real networks bring enormous challenges to traditional community detection methods. In order to detect community structure in large scale networks more accurately and efficiently, we propose a community detection algorithm based on network embedding representation method. This algorithm first selects the DeepWalk network embedding method to represent the vertices in the network graph as low-dimensional vector representations which reflect the topology in the network. Then this algorithm treats each vertex as a sample, and each dimension of the vector representation as a feature. Finally, the sample data are input into the variational Gaussian mixture clustering model to obtain the clusters in the network. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large scale networks. Through further analysis of the excavated community structure, the organizational characteristics within the community are better revealed. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network embeddings

Three-way conflict analysis model via the best-worst method: Balancing subjective preferences and objective data on incomplete and dispersed systems

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International Journal of Approximate Reasoning 2025年 186卷

作者： Junjie Zhu Qinghua Zhang Nanfang Luo Fan Liu Longjun Yin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China Tourism Multi-source Data Perception and Decision Technology Key Laboratory of the Ministry of Culture and Tourism Chongqing 400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing 400065 China

In real-world environments, different issues contribute to conflicts with varying weights. However, current conflict analysis weighting models face significant limitations when dealing with incomplete data and dispersed knowledge. Objective methods are sensitive to missing values and struggle to accurately capture authentic preferences, while subjective approaches lack systematic evaluation criteria, leading to substantial randomness in weight assignments. Therefore, a three-way conflict analysis model via the best-worst method is proposed, which is combined with a correlation coefficient method to balance subjective preferences and objective data. First, the trisection of agent pairs is derived through Bayesian minimum risk. Subsequently, a new conflict distance function is defined on the incomplete information system to provide a more precise measurement of conflict degrees. Then, for incomplete and dispersed information systems, a maximal coalition-based agent partitioning algorithm is designed, along with a new weighted voting mechanism to aggregate dispersed knowledge. Finally, the scientific transparency of the weighting process, as well as the robustness and feasibility of the model, are demonstrated through experimental analysis.

关键词： Distributed database systems

Forensicability Assessment of Questioned Images in Recapturing Detection

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arXiv 2022年

作者： Chen, Changsheng Zhao, Lin Cai, Rizhao Yu, Zitong Huang, Jiwu Kot, Alex C. The Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Department of Electrical and Electrics Engineering Nanyang Technological University Singapore639798 Singapore

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing (FAS) and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

QoI-aware incentive for multimedia crowdsensing enabled learning system

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QoI-aware incentive for multimedia crowdsensing enabled lear...

作者： Gu, Yiren Shen, Hang Bai, Guangwei Wang, Tianjing Liu, Xuejun College of Computer Science and Technology Nanjing Tech University Nanjing211816 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize social welfare while meeting the requirements in quality, timeliness, correlation, and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Machine learning

Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Liu, Jiamou Zhang, Ziang Li, Meng Zhu, Liehuang Russello, Giovanni The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China The School of Computer Science The University of Auckland Auckland New Zealand Southeast Institute of Information Technology Beijing Institute of Technology Fujian China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China

With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This has led them to explore alternative approaches such as outsourced learning and federated learning. While these methods address the feasibility of model training effectively, they introduce concerns about the trustworthiness of the training process since computations are not performed locally. Similarly, there are trustworthiness issues associated with outsourced model inference. These two problems can be summarized as the trustworthiness problem of model computations: How can one verify that the results computed by other participants are derived according to the specified algorithm, model, and input data? To address this challenge, verifiable machine learning (VML) has emerged. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology. We first analyze the potential verifiability issues that may exist in different machine learning scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then conduct a detailed analysis and classification of existing works based on their technical approaches. Finally, we discuss the key challenges and future directions in the field of ZKP-based VML. © 2023, CC BY.

关键词： Machine learning