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Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Enriching molecular graph representation via substructure learning

Enriching molecular graph representation via substructure le...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, HongHao Zhang, Acong Li, Ping Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China China Mobile Jiangxi Co. Ltd. Network Department Ganzhou China

ISBN: (纸本)9798350386226

Continuous molecular graph representations are highly useful for effective molecule property predictions. However, learning graph-specific structure information remains challenging. Current graph neural network models typically aggregate all node embeddings of a graph into a single vector, which, while being an efficient compressor, may not capture the high-level function-related substructure information, e.g., motifs. Moreover, the nodes that are loosely relevant to the graph representation may lead to the sub-optimal representation learning. Towards these limitations, in this paper we propose a new approach to enrich molecular graph representation, where the most relevant motif are selected to represent the graph at a high level and the key-node spanned subgraph is constructed to filter out irrelevant nodes. By comparing to recently proposed unimodal graph neural network models and multi-modal methods, we show that our method achieves state-of-the-art performance on molecular property prediction task. © 2024 IEEE.

关键词： Molecular docking

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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

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computer Modeling in engineering & sciences 2022年第4期131卷 23-26页

作者： Qi Liu Xiaodong Liu Radu Grosu Ching-Nung Yang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburgh018373UK Institute of Computer Engineering Vienna University of TechnologyWien1040Austria Department of Computer Science and Information Engineering National Dong Hwa UniversityHualien97401Taiwan

Cyber Physical Systems(CPS)have been appealing in recent years as a result of the rapid emergence of unique hardware and software compositions that create smart,autonomously behaving ***-to-end procedures and new types of user-machine interaction are made possible via the *** the one hand,these CPS applications have the potential to deliver crucial services in a variety of developing application areas,including energy management,healthcare,traffic monitoring,industrial assessment and surveillance.

关键词： CPS hardware services

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Self-powered tactile sensor for real-time recognition of Morse code based on machine learning

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Nano Research 2025年第2期18卷 760-768页

作者： Shenxing Tan Yang Jiang Xujiang Chao Fei Liang Ripeng Li Tao Jiang Hai-Dong Yu Zhong Lin Wang Guangzhou Institute of Blue Energy Guangzhou 510555China Frontiers Science Center for Flexible Electronics Xi'an Institute of Flexible Electronics(IFE)and Xi'an Institute of Biomedical Materials&EngineeringNorthwestern Polytechnical UniversityXi'an 710072China Beijing Key Laboratory of Micro-Nano Energy and Sensor Center for High-Entropy Energy and SystemsBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Engineering University of Chinese Academy of SciencesBeijing 100049China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Developing lightweight,green,and flexible wearable electronics with high sensitivity and multifunctional sensing capabilities is of important significance in the field of outdoor sports,such as mountaineering,animal tracking and *** work proposes a silk fibroin fibers-based triboelectric nanogenerator(SF TENG)to harvest tiny energy from human fingertip tapping and act as a self-powered tactile *** SF-TENG adopts a green,efficient,and low-cost fabrication strategy,in which a breathable and electropositive silk fibroin fiber membrane and a silver conductive layer are prepared by electrostatic spinning and magnetron sputtering,and combined with a conductive cloth and a breathable tape to form a flexible sensor that can be attached to a human *** thin and soft portable TENG device,having a thickness of only 0.3 mm and a mass of 354 mg at the dimension of 4.5 cm×4.5 cm,can generate a maximum power density of 1.0 mW·m^(–2).Furthermore,the SF-TENG has excellent sensitivity of 1.767 mV·Pa^(–1) with good cyclic *** superior sensing characteristics provide new avenues for Morse code applications toward outdoor wearable autonomous *** proposed SF-TENG offers promising solutions in multi-scenario outdoor sport,human-machine interface interaction,and security systems.

关键词： flexible electronics triboelectric nanogenerator silk fibroin Morse code translation

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Towards Efficient Auction Design with ROI Constraints 23

Towards Efficient Auction Design with ROI Constraints

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Tang, Xinyu Lv, Hongtao Gao, Yingjie Wu, Fan Liu, Lei Cui, Lizhen School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University Shandong Research Institute of Industrial Technology Jinan China

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3/2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5/4;when it falls below 2, the 3+γ lower bound becomes (Equation presented). © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Autonomous agents

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Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-view Stereo 12th

Adaptive Cost Aggregation in Iterative Depth Estimation for...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Xiang Bai, Xiao Wang, Chen School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China

ISBN: (纸本)9783031463075

The deep multi-view stereo (MVS) approaches generally construct 3D cost volumes to regularize and regress the depth map. These methods are limited with high-resolution outputs since the memory and time costs grow cubically as the volume resolution increases. In this paper, we presented an multi-stage iterative depth map estimation method for MVS. In our network, the cost volume is iteratively processed by lightweight 2D convolution based GRU modules, and the multi-stage coarse-to-fine structure is adopted to speed up the depth estimation process. To further improve the 3D reconstruction quality, we make improvements from two different perspectives of adaptive cost aggregation: a view-adaptive weighting module is proposed to account for the occlusion problem in cost volume fusion, and a spatial-adaptive deformable geometric feature encoding module is introduced to the cost volume feature encoding before feeding into GRUs for stronger modeling capability. Experiments on the DTU dataset demonstrated the effectiveness of the proposed network in accuracy with remarkable efficiency performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Iterative methods

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Deep Bayesian Image Set Classification Approach for Defense against Adversarial Attacks

Deep Bayesian Image Set Classification Approach for Defense ...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Mirnateghi, Nima Shah, Syed Afaq Ali Bennamoun, Mohammed Edith Cowan University Center for Ai and Machine Learning Australia The University of Western Australia School of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350382204

Deep learning has become an integral part of various pattern recognition and computer vision systems in recent years due to its outstanding achievements in object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to being fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in physical-world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image-set-based adversarial defense approach. Image set classification has shown exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defense framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defense methods and single-shot recognition task. The empirical results demonstrate superior performance on the CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets. Our code is available at https://***/ai-voyage/***. © 2023 IEEE.

关键词： Object recognition

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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