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Sentiment analysis of online product reviews based on SenBERT-CNN 19

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Sentiment analysis of online product reviews based on SenBER...

19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Wu, Fangyu Shi, Zhenjie Dong, Zhaowei Pang, Chaoyi Zhang, Bailing Zhejiang University Ningbo Research Institute PRC China NingboTech University School of Computer and Data Engineering China Hebei University of Economics and Business China Department of Computer Science and Software Engineering Xi'An Jiaotong-Liverpool University PRC China

ISBN: (纸本)9780738124261

Sentiment analysis, also known as opinion mining, is an important area of research to analyze people's opinions. In online e-commerce marketplace like Taobao, customers are allowed to comment on different products, brands and services using text and numerical ratings. Such reviews towards a product are valuable for the improvement of the product quality as they influence consumers' purchase decisions. In this paper, we introduce a novel model, SenBERT-CNN, to analyze customer's review. In order to capture more sentiment information in sentences, SenBERT-CNN model combines a pre-trained Bidirectional Encoder Representations from Transformers (BERT) network with Convolutional Neural Network (CNN). Specifically, we use BERT structure to better express sentence semantics as a text vector, and then further extract the deep features of the sentence through a Convolutional Neural Network. The effectiveness of the proposed method is validated through a collected product reviews of mobile phone from the e-commerce website, ***. © 2020 IEEE.

关键词： Sentiment analysis

Low-Complexity Channel Estimation for Massive MIMO Systems with Decentralized Baseband Processing

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arXiv 2022年

作者： Xu, Yanqing Wang, Bo Song, Enbin Shi, Qingjiang Chang, Tsung-Hui The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China The Shenzhen Research Institute of Big Data Shenzhen518172 China The Wireless Network RAN Algorithm Department Xi’an Huawei Technologies Co. Ltd. Xi’an710000 China The College of Mathematics School of Aeronautics and Astronautics Sichuan University Sichuan Chengdu610064 China The School of Software Engineering Tongji University Shanghai201804 China

The traditional centralized baseband processing architecture is faced with the bottlenecks of high computation complexity and excessive fronthaul communication, especially when the number of antennas at the base station (BS) is large. To cope with these two challenges, the decentralized baseband processing (DPB) architecture has been proposed, where the BS antennas are partitioned into multiple clusters and each is connected to a local baseband unit (BBU). In this paper, we are interested in the low-complexity distributed channel estimation (CE) method under such DBP architecture, which is rarely studied in the literature. The aim is to devise distributed CE algorithms that can perform as well as the centralized scheme but with a small inter-BBU communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose two distributed CE algorithms, namely the aggregate-then-estimate algorithm and the estimate-then-aggregate algorithm. In contrast to the existing distributed CE algorithm which requires iterative information exchanges among the nodes, our algorithms only require one roundtrip communication among BBUs. Extensive experiment results are presented to demonstrate the advantages of the proposed distributed CE algorithms in terms of estimation accuracy, inter-BBU communication cost and computation complexity. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel estimation

A mutual authentication lightweight rfid protocol for iot devices 2nd

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A mutual authentication lightweight rfid protocol for iot de...

2nd International Cognitive Cities Conference, IC3 2019

作者： Lu, Yong Wang, Anxi Liu, Shuai School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou550025 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou510632 China

ISBN: (纸本)9789811561122

The IoT data security is a hot topic at a data time. Personal privacy data may be easily leaked to data collectors in an open network environment. If the collectors get enough data, they can analyze the habit and interest of every IoT user, which may give helpful advice or cause endless harass. RFID authentication protocol in IoT is a powerful way to avoid the essential data leaked to data collectors. The previous protocols protect the security of RFID system. But they cannot guard the privacy of RFID system. The protocol we proposed achieves both the privacy and security. © Springer Nature Singapore Pte Ltd 2020.

关键词： Internet of things

Model-enhanced Vector Index

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arXiv 2023年

作者： Zhang, Hailin Wang, Yujing Chen, Qi Chang, Ruiheng Zhang, Ting Miao, Ziming Hou, Yingyan Ding, Yang Miao, Xupeng Wang, Haonan Pang, Bochen Zhan, Yuefeng Sun, Hao Deng, Weiwei Zhang, Qi Yang, Fan Xie, Xing Yang, Mao Cui, Bin School of Computer Science Key Lab of High Confidence Software Technologies Peking University China Microsoft United States Aerospace Information Research Institute Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Carnegie Mellon University United States National University of Singapore Singapore National Engineering Laboratory for Big Data Analysis and Applications Peking University China

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with endto- end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Modelenhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embeddingbased models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

A New Simple Stochastic Gradient Descent Type Algorithm With Lower Computational Complexity for Bilevel Optimization

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arXiv 2023年

作者： Huo, Haimei Liu, Risheng Su, Zhixun The School of Mathematical Sciences Dalian University of Technology Dalian116024 China The DUT-RU International School of Information Science and Engineering Dalian University of Technology China The Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China The State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian116024 China The Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian116024 China

Bilevel optimization has been widely used in many machine learning applications such as hyperparameter optimization and meta learning. Recently, many simple stochastic gradient descent(SGD) type algorithms(without using momentum and variance techniques) have been proposed to solve the bilevel optimization problems. However, all the existing simple SGD type algorithms estimate the hypergradient via stochastic estimation of Neumann series. In the paper, we propose to estimate the hypergradient via SGD-based Estimation(i.e., solving the linear system with SGD). By using warm start initialization strategy, a new simple SGD type algorithm SSGD based on SGD-based Estimation is proposed. We provide the convergence rate guarantee for SSGD and show that SSGD outperforms the best known computational complexity achieved by the existing simple SGD type algorithms. Our experiments validate our theoretical results and demonstrate the efficiency of our proposed algorithm SSGD in hyperparameter optimization applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear systems

Joint computation offloading and service caching in Vehicular Edge Computing via a dynamic coevolutionary multiobjective optimization algorithm

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Expert Systems with Applications 2025年 284卷

作者： Qiu, Qijie Ye, Yulong Li, Lingjie Xiao, Zhijiao Lin, Qiuzhen Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Birmingham Birmingham United Kingdom College of Big Data and Internet Shenzhen Technology University Shenzhen China Shenzhen China

With the development of autonomous driving technologies, Vehicular Edge Computing (VEC) has become a powerful computing paradigm that improves vehicle performance and edge resource efficiency by introducing Computation Offloading and Service Caching (CO-SC). However, due to the mobility and dynamics of vehicular networks, the problem of jointly optimizing CO-SC under dynamic VEC systems is extremely challenging, as CO-SC decisions must be adjusted in real-time based on the current location and state of the vehicular network. To alleviate this problem, in this paper, we first formulate the joint CO-SC problem as a dynamic multi-objective optimization problem with the objective of simultaneously minimizing delay, energy consumption, and load balance. Then, this paper proposes a dynamic coevolutionary multi-objective optimization algorithm (DCMOA) to search for optimal CO-SC decisions under dynamic VEC systems. Specifically, we design a multi-population coevolutionary approach following the idea of divide-and-conquer to simplify the complexity of target problem, and then utilize knowledge transfer between subpopulations to coevolve each subpopulation. Besides, we devise an effective dynamic feedback mechanism to achieve timely feedback on environmental changes. Extensive simulations on real-world datasets of telecom base stations and cab trajectories in Shanghai, China show that our proposed method outperforms several state-of-the-art peer methods. © 2025 Elsevier Ltd

关键词： Multiobjective optimization

Decentralized linear MMSE equalizer under colored noise for massive MIMO systems

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arXiv 2021年

作者： Zhao, Xiaotong Guan, Xin Li, Mian Shi, Qingjiang School of Software Engineering Tongji University Shanghai China School of Elec. Info. and Comm. Huazhong University of Science and Technology Wuhan China Shenzhen Research Institute of Big Data Shenzhen China

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antenna increases, centralized baseband processing architectures encounter two bottlenecks, i.e., the tremendous data interconnection and the highdimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear minimum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks. Copyright © 2021, The Authors. All rights reserved.

关键词： White noise

Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

K-Core Decomposition on Super Large Graphs with Limited Resources

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arXiv 2021年

作者： Gao, Shicheng Xu, Jie Li, Xiaosen Fu, Fangcheng Zhang, Wentao Ouyang, Wen Tao, Yangyu Cui, Bin School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Tencent Inc. China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China School of CS Key Laboratory of High Confidence Software Technologies Peking University China Institute of Computational Social Science Peking University Qingdao China

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods