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Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

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Customized scheduling for shared bus with deadlines

Customized scheduling for shared bus with deadlines

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作者： Jin, Yong Xu, Jia Xu, Lijie Liu, Linfeng Xiao, Fu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science & Engineering Changshu Institute of Technology Jiangsu Changshu China

Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S-MBSA for the shared bus with strong endurance and E-MBSA for the large-scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S-MBSA and E-MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue. © 2022 John Wiley & Sons Ltd.

关键词： Scheduling

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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features

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Frontiers of Computer Science 2023年第1期17卷 123-132页

作者： Ke-Jia CHEN Mingyu WU Yibo ZHANG Zhiwei CHEN School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China

Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency component upsampling, named SR-AFU. The network is composed of multiple cascaded dilated convolution residual blocks (CDCRB) to extract multi-resolution features representing image semantics, and multiple multi-size convolutional upsampling blocks (MCUB) to adaptively upsample different frequency components using CDCRB features. The paper also defines a new loss function based on the discrete wavelet transform, making the reconstructed SR images closer to human perception. Experiments on the benchmark datasets show that SR-AFU has higher peak signal to noise ratio (PSNR), significantly faster training speed and more realistic visual effects compared with the existing methods.

关键词： super-resolution multi-resolution features adaptive frequency upsampling wavelet transformation

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Relation Classification based on Selective Entity-Aware Attention 25

Relation Classification based on Selective Entity-Aware Atte...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Ding, Haijie Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China

ISBN: (纸本)9781665405270

Relation classification aims to classify the entity pairs into a certain relation, which is an important task of natural language processing. The latest end-to-end models based on attention mechanism still have shortcomings, i.e., the attention will be gradually weakened when processing long sequences, and they cannot make use of the hidden type information of the entities. To solve these problems, we propose a relation classification model based on the selective entity-aware attention mechanism, which consists of context encoder and entity-aware attention network. In the context encoder, contextual word semantics are learned through self-attention. Entity selection is applied to adapt the fact that different words can determine each other's importance. Latent types of entities are taken as auxiliary information to make full use of the entities' hidden features. Experiments on the SemEval-2010 Task 8 dataset and TACRED show that our model outperforms the baselines without implementing any external resources or NLP tools, and the entity-aware attention indeed improve the model's performance. © 2022 IEEE.

关键词： Semantics

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CETO-Sim: A Simulation Platform for Cloud-Edge Task Offloading 25

CETO-Sim: A Simulation Platform for Cloud-Edge Task Offloadi...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yao, Rui Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China

ISBN: (纸本)9781665405270

In order to verify the effectiveness of task offloading algorithms in large-scale cloud and edge computing systems, scholars usually use simulation platforms to conduct extensive experiments. However, existing simulation platforms have unclear application scenarios and relatively homogeneous functions, which cannot fully exploit the respective advantages of cloud and edge computing. To address the above issues, this paper designs a new simulation platform, CETO-Sim (Cloud-Edge Task Offloading Simulation), for the cloud-edge aggregation computing scenario, which is convenient for users to verify the effectiveness of the task offloading algorithm. The platform adopts a modular design strategy, allowing users to invoke the various interfaces provided by the platform to simulate and build different physical entities, and to freely combine them to simulate different computing environments. The platform contains six components: task management component, network topology component, end-user component, scheduling policy component, server component and operation component. The platform simulates the task offloading process through the interaction between these components, while recording the changes of various parameters of tasks and simulated entities in real time, and finally printing out the experimental results in the form of logs. This paper presents an example of task offloading using the particle swarm algorithm in a cloud-side aggregated computing environment, demonstrating that the CETO-Sim platform has the advantages of clear architecture, simple operation, easy deployment, accurate experimental results and high simulation efficiency, and also verifying the feasibility of deploying the platform in other scenarios. © 2022 IEEE.

关键词： Simulation platform

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VECSim: A Simulation Platform for Multi-bitrate Video Caching Collaboratively in Edge Computing Network 25

VECSim: A Simulation Platform for Multi-bitrate Video Cachin...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chen, Peipei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China

ISBN: (纸本)9781665405270

The development of the mobile Internet has led to a shape increase in video traffic data. New Mobile Edge Computing (MEC) technology can reduce network operation and service delivery delays, so as to improving the QoE (Quality of Experience) of user. In MEC environment, this article builds a simulation platform for multi-bitrate video caching and scheduling scenarios VECSim (Video Edge CloudSim). VECSim provides a simulation environment specific to verify multi-bitrate video caching collaboratively and scheduling strategy in the Mobile Edge Computing scenarios. Unlike simple numerical simulations and complex real-world deployments, VECSim provides a simple interface that can be called by the user, besides, it can be used to provide larger-scale, more flexible request scenarios which are closer to the actual environment. Based on the edge computing simulation platform EdgeCloudSim, this platform adds and modifies quite a few functions so that it can be effectively used in multi-bitrate video caching and scheduling scenarios, which now includes seven main modules: Server Management Modules, Network Module, User Mobility Module, Task Loader Generator Module, Video Storage Module, Edge Client Management Module, and Edge Orchestrator Module. With this platform, researchers can easily and quickly verify their video caching and scheduling strategies. © 2022 IEEE.

关键词： Mobile edge computing

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Point Cloud Compression Method Based on Curvature Grading and Inter-point Compression for Industrial Products 15

Point Cloud Compression Method Based on Curvature Grading an...

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15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Ran Nanjing University of Posts and Telecommunications School of Computer Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350308693

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression. © 2023 IEEE.

关键词： Grading

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A Privacy Leak Detection Mechanism based on Service Binding 10

A Privacy Leak Detection Mechanism based on Service Binding

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10th International Symposium on System security, Safety, and Reliability, ISSSR 2024

作者： Wang, Boyang He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid. © 2024 IEEE.

关键词： Static analysis

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FBOD: An Outlier Detection Algorithm based on data Features Suitable for processing Large-scale datasets on Distributed Platforms 10

FBOD: An Outlier Detection Algorithm based on Data Features ...

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10th International Symposium on System security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Song, Hu Cheng, Xinyun Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

The primary objective of outlier detection is to identify values that are significantly different from other data in the dataset. However, most of the current algorithms are effective for small-scale data and their performance is highly dependent on the choice of parameters. To this, the paper proposes an innovative outlier detection algorithm, named FBOD, which is based on data features. Specifically, FBOD utilizes the number of combinations of attribute values in each tuple across the dataset as a key feature of the tuple. This feature reflects the degree of differentiation of each data tuple from the overall dataset. By identifying data features that are significantly different from objects in the entire dataset, FBOD can accurately determine outliers. Based on the knowledge of attribute importance in Rough Set Theory, FBOD can efficiently reduce the data dimensionality, so high-dimensional datasets can be handled by FBOD. Furthermore, this paper employs a distributed parallelism approach in the algorithm. The performance of FBOD is evaluated on several datasets, including standard datasets and artificial datasets, and is compared with common outlier detection algorithms. Experimental results indicate that, compared to existing typical algorithms, FBOD achieves higher outlier detection accuracy without relying on a prior knowledge of datasets or parameter selection. It also proves the suitability of FBOD for large-scale dataset processing on distributed platforms. © 2024 IEEE.

关键词： Anomaly detection

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AtM-DNN: A Multimodal Attention Fusion Network with Auxiliary Function for Sentiment Classification 25

AtM-DNN: A Multimodal Attention Fusion Network with Auxiliar...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Huang, Ji Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Multimodal sentiment classification is an important research attracting many scientists' attention in natural language processing. In most multimodal sentiment research, each modal of the dataset is labeled with a unified label. However, this unified label of multimodal data may limit the model to obtain the different information between multimodal in the training process. To address the above issues, this paper proposes AtM-DNN, a model based on multimodal attention fusion network in collaboration with auxiliary function for multimodal sentiment classification. Different single-modal encoders are used to map the original data of different modal into vectors of different information dimensions. Attention mechanism is introduced to control the information weight of each modal in the interaction, so as to remove the redundancy of information. Besides, each modal is introduced into the loss function as an auxiliary function to assist the calculation of the final loss function. Experiments on CH-SIMS dataset show that the attention mechanism of each modal can exactly improve the accuracy and F1 of sentiment classification of each single-modal, and also can improve the accuracy and F1 of multimodal sentiment classification. Although the collaboration of auxiliary function can improve the effect of multimodal sentiment classification, it is likely to reduce the effect of each single-modal. And these models are better than the single-modal sentiment classification model. © 2022 IEEE.

关键词： Neural networks

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