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Robustness Analysis of Triangle Relations Attack in Social Recommender Systems

Robustness Analysis of Triangle Relations Attack in Social R...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Jia Wang Min Gao Zongwei Wang Runsheng Wang Junhao Wen School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Cloud computing is applied in various domains, among which social recommender systems are well-received because of their effectivity to provide suggestions for users. Social recommender systems perform well in alleviating cold start problem, but it suffers from shilling attack due to its natural openness. Shilling attack is an injection attack mainly acting on the training process of machine learning, which aims to advance or suppress the recommendation ranking of target items. Some researchers have studied the influence of shilling attacks in two perspectives simultaneously, which are user-item's rating and user-user's relation. However, they take more consideration into user-item's rating, and up to now, the construction of user-user's relation has not been explored in depth. To explore shilling attacks with complex relations, in this paper, we propose two novel attack models based on triangle relations in social networks. Furthermore, we explore the influence of these models on five social recommendation algorithms. The experimental results on three datasets show that the recommendation can be affected by the triangle relation attacks. The attack model combined with triangle relation has a better attack effect than the model only based on rating injection and the model combined with random relation. Besides, we compare the functions of triangle relations in friend recommendation and product recommendation.

关键词： Training Cloud computing Social networking (online) Computational modeling Machine learning Robustness Recommender systems

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Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal Knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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RCapsNet: A Recurrent Capsule Network for Text Classification

RCapsNet: A Recurrent Capsule Network for Text Classificatio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Junfeng Hu Jun Liao Li Liu Wenchao Ma School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we propose RCapsNet, a recurrent capsule network for text classification. Although a variety of neural networks have been proposed recently, existing models are mainly based either on RNN or on CNN, which are rather limited in encoding temporal features in these network structures. In addition, most of these models require to integrate prior linguistic knowledge into them, which is not practical for a non-linguistician to handcraft such knowledge. To address these issues on temporal relational variabilities in text classification, the RCapsNet is presented by employing a hierarchy of recurrent structure-based capsules. It consists of two components: the recurrent module considered as the backbone of the RCapsNet and the reconstruction module designed to enhance the generalization capability of the model. Empirical evaluations on four benchmark datasets demonstrate the competitiveness of the RCapsNet. In particular, it is shown that prior linguistic knowledge is dispensable for the training of our model.

关键词： Routing Task analysis Neurons Recurrent neural networks Linguistics Machine learning big data

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Impact of climate zone migration on geographical distribution of indigenous vegetation in Northeast China 2

Impact of climate zone migration on geographical distributio...

引用

2020 2nd International Conference on Advances in Civil engineering, Energy Resources and Environment engineering, ACCESE 2020

作者： Wu, Yunyang Li, Shengju Wang, Xue Zhang, Yuehua Gu, Ye Li, Lan College of Science Jiamusi University Jiamusi City Heilongjiang Province154007 China College of Big Data and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

Based on the relationship between climate and vegetation, this study uses the time lag of vegetation response to climate change, simulates the dynamic response process of different vegetation types to climate change, retrieves relevant literature and collates data, and deduces the actual occurrence and geographic distribution of vegetation under the current climate potential and future climate change scenarios. This experiment infer the adaptation relationship between plant geographical distribution and climatic conditions. The results show that the plants in Northeast China are generally more adaptable to climate change. The areas with poor adaptability are mainly at the junction of forest shrubs and grasslands. The geographical distribution of plants has changed, accounting for about 5%. The grassland ecosystem in the bush-grassland transition zone in the Greater Xing'an Mountains is also less adaptable to climate, accounting for about 35%. The vegetation in these areas tends to degenerate. The geographical distribution may change. The adjustment of plants in Northeast China to the future climate change is usually reasonable. 84% of the vegetation changes are positive changes. Especially in the eastern regions, the vegetation deduction conditions will be improved in the future, and the vegetation coverage in these regions may be improved. About 79% of the potential changes in vegetation can adapt to the future climate. Nevertheless, the grassland ecosystems in the Greater Xing'an Mountains and parts of Inner Mongolia are less adaptable to future climate change and tend to degrade. It provides new ideas for assessing vegetation adaptability or ecosystem adaptability. Research on the interaction between climate change and terrestrial ecosystems of different scales needs to be strengthened in the context of global change. © Published under licence by IOP Publishing Ltd.

关键词： Vegetation

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A deep learning model based on sparse matrix for point-of-interest recommendation 31

A deep learning model based on sparse matrix for point-of-in...

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31st International Conference on software engineering and Knowledge engineering, SEKE 2019

作者： Zeng, Jun Tang, Haoran Li, Yinghua He, Xin School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)1891706489

Point-of-interest (POI) recommendation that consists of location-based social networks (LBSNs) and provides personal services for users has become an important part in the field of recommendation system. Due to the sparseness of user check-in matrix, POI recommendation faces great challenges. However, most researches just consider of spatial and temporal impact on recommendation and do not solve the problem of sparsity. This paper proposes a POI recommendation model called RBMNMF which is based on sparse matrix of user check-ins. Firstly, by stacking restricted Boltzmann machines (RBM), the potential relationship between users and POIs is learned and multiple user-POI matrices are extracted. Second, fill the original sparse matrix by using non-negative matrix factorization (NMF). Finally, fuse those prediction matrices to generate final POI recommendation for users, which is benefit for solving the problem of sparsity effectively. Experiments on real-world data set prove that the model we propose has a better accuracy than traditional algorithms. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Matrix algebra

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A Small Sample Image Recognition Method Based on ResNet and Transfer Learning

A Small Sample Image Recognition Method Based on ResNet and ...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： XiaoZhen Han Ran Jin College of Big Data and Software Engineering Zhejiang Wanli University Ningbo

ISBN: (数字)9781728160429

ISBN: (纸本)9781728160436

With the vigorous development of artificial intelligence big data era and the advent of 5G era, the amount of network information shows a blowout-like growth. As a result, the accurate query of information is faced with unprecedented challenges. The image as a material reproduction of visual perception, has a large number of retrieval requests all the time, but the traditional image target recognition annotation is mainly based on pixel-level supervised learning. In the face of massive high-quality image recognition, it is very difficult for users to query the target content accurately and quickly. Therefore, this paper studies the animal classification model based on Convolutional Neural Network (CNN), using transfer learning to pre-train the characteristics of the network and combined with the hybrid classification model of CNN. In the experiment, CATS/DOGS were used as the data set, and PyTorch was used to train the network model. Experimental research shows that the accuracy of 96.43% is achieved by using CNN+ transfer learning algorithm, which is significantly higher than that of traditional methods. For small-scale data sets, it effectively solves the non-transferability of manual feature extraction, and improves the accuracy and robustness.

关键词： Feature extraction Image recognition Training Convolutional neural networks Convolution Animals Machine learning

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Breast Cancer Risk Prediction based on Six Machine Learning Algorithms

Breast Cancer Risk Prediction based on Six Machine Learning ...

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2020 IEEE Asia-Pacific Conference on Computer Science and data engineering, CSDE 2020

作者： Ahmed, Md.Razu Ali, Md. Asraf Roy, Joy Ahmed, Shakil Ahmed, N. Daffodil International University Department of Software Engineering Dhaka Bangladesh Massey University Department of Electrical and Mechanical Engineering Auckland New Zealand Massey University Big Data Lab Sncc Auckland New Zealand

ISBN: (纸本)9781665419741

Breast Cancer is the second most important cause of death among women. As per the clinical expert, breast cancer is one of prominent cancers after lung cancer. However, early detection of this type of cancer in its initial stage helps to save lifes and increases lifespan. The survival chance of a patient can increase if there is a classifier that helps with a quick prediction of breast cancer. Therefore, a smart framework is required that can effectively detect and predict with high accuracy early stage of breast cancer. In this article, six machine learning classification algorithms, namely Logistic Regression (LR), K-Nearest Neighbours (kNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) are implemented in order to evaluate the performance and the prediction power of the model. The main target of this work is to compare these algorithm performances using the Wisconsin Breast Cancer (original) dataset. The number of performance metrics such as accuracy, precision, recall, f-1 score, and specificity are taken into consideration Our analysis of the results shows that the Support Vector Machine achieved the highest accuracy of 97.07% with the least error rate and Naive Bayes gives the lowest accuracy of 96%. All these experiments were carried out using SciKit. © 2020 IEEE.

关键词： Support vector machines

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Knowledge-Guided And Hyper-Attention Aware Joint Network For Benign-Malignant Lung Nodule Classification

Knowledge-Guided And Hyper-Attention Aware Joint Network For...

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IEEE International Conference on Image Processing

作者： Weixin Xu Kun Wang Jingkai Lin Yuting Lu Sheng Huang Xiaohong Zhang School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Accurate identification and early diagnosis of malignant lung nodules are crucial for improving the survival rate of patients with lung cancer. Deep learning methods have recently been proven success in computer-aided diagnostic tasks. However, to the best of our knowledge, the features of tissues and vessels will disturb the model resulting in inaccurate classification of the nodules. To reduce the interference and capture crucial contextual information from different channels in a more efficient way, we introduce a Hyper-Attention Mechanism(HAM) that can be easily integrated into convolutional neural networks(CNNs). Moreover, without incorporating prior-domain knowledge, traditional methods lack interpretability, which is difficult to understand and utilize them in the clinic by radiologists. Based on this, we propose a novel Knowledge-Guided model to predict malignant pulmonary nodules from chest CT data, which inject external medical knowledge into CNNs to guide the training process. We evaluate the proposed model on the LIDC-IDRI dataset and demonstrate its effectiveness by achieving comparable state-of-the-art performance.

关键词： Cancer Feature extraction Lung Semantics Computed tomography Visualization Training

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

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IEEE International Conference on data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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Hierarchical Context Modeling Network for Landmark Recognition

Hierarchical Context Modeling Network for Landmark Recogniti...

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IEEE International Conference on data Mining (ICDM)

作者： Xing Bao Huan Zheng Zhao Zhang Zhengjun Zha Meng Wang School of Information Science and Technology University of Science and Technology of China Hefei China Aerospace Information Research Institute Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China

Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

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