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2023 World Wide Web Conference, WWW 2023

作者： Sun, Kai Zhang, Richong Samuel, Mensah Nikolaos, Aletras Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781450394161

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023 ACM.

关键词： Sentiment analysis

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MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification 19

MalDIST: From Encrypted Traffic Classification to Malware Tr...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Bader, Ofek Lichy, Adi Hajaj, Chen Dubin, Ran Dvir, Amit Ariel Cyber Innovation Center Ariel University Department of Computer Science Ariel Israel Ariel Cyber Innovation Center Ariel University Data Science and Artificial Intelligence Research Center Department of Industrial Engineering & Management Ariel Israel

ISBN: (纸本)9781665431620

The world of malware is shifting towards using encrypted traffic. While encryption improves the privacy of users, it brings challenges in the fields of QoS, QoE, and cybersecurity. Recent state-of-the-art Deep-Learning architectures for encrypted traffic classifications demonstrated superb results in tasks of traffic categorization over encrypted traffic. In this paper, we leverage the feasibility to use such architectures for the tasks of malware detection and classification to gain insights into how well these architectures perform in the domain of malware traffic. Specifically, we present a Deep-Learning model for malware traffic detection and classification (MalDIST), which outperforms both classical ML and DL malware traffic classification models both in terms of detection and classification. © 2022 IEEE.

关键词： Deep learning

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Mukmin, Amirul Adnan Jalil, Abdul Pallawabonang, Mahabintang Prakasa, Esa Yuyun Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Handayani University of Makassar Dept. of computer system Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331508579

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation;however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts. © 2024 IEEE.

关键词： Speech recognition

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Leveraging YOLOv8 for Real-Time Parking Space Detection

Leveraging YOLOv8 for Real-Time Parking Space Detection

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Pallawabonang, Mahabintang Adnan, Muh. Zuhriyah, Sitti Mukmin, Amirul Prakasa, Esa Yuyun Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Handayani University of Makassar Dept. of Computers System Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331508579

This research develops a smart parking system based on computer vision integrated with the Firebase platform, utilizing the YOLOv8 algorithm and the OpenCV library. This system is designed to detect vehicles and determine the availability status of parking slots in real time through video recording analysis. The dataset was obtained from recorded parking activity videos, then processed using Roboflow for data labeling, and fine-tuned on Google Colab. The resulting model shows a vehicle detection accuracy of up to 99.1% with high stability, as indicated by the training loss graph. The process of mapping the parking area is carried out through an OpenCV-based application for the identification and configuration of parking slots. The status of each parking slot is automatically updated to the Firebase Realtime database, enabling efficient historical data recording and information management. Testing shows the system's reliable performance under various lighting conditions, with high capabilities in vehicle detection and real-time data management. This study makes a significant contribution to the development of parking management solutions through the integration of technology that supports operational efficiency and high accuracy. © 2024 IEEE.

关键词： Information management

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Tensor MIMO Perfect-Equalizer Design Using New Projection-Based Method and Approximation-Error Analysis

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IEEE Systems Journal 2024年第1期18卷 668-679页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Chungli32003 Taiwan National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu30010 Taiwan

As real environments are quite complex, multiple-input-multiple-output (MIMO) channels should be characterized by the multidimensional-multirelational models. Tensor linear algebra has been emerging as a powerful and promising tool for such MIMO channel modeling and estimation. However, to the best of our knowledge, there hardly exists any algebraic framework of MIMO channel equalization when the channel is modeled as an arbitrary-dimensional transfer tensor (a tensor whose entries are all rational z-functions). In this work, we first propose a new method based on the projection, namely, 'projection-based method,' to determine whether a given transfer tensor is a full-rank tensor or not and carry out the inversion of an invertible transfer tensor. Our idea is employing the projection subspace of the system transfer tensor to simplify the pertinent inversion process. Second, we design the finite-impulse-response (FIR) realization to approximate the inverse of an arbitrary transfer tensor such that all entries of the approximated inverse transfer tensor are finite-degree polynomials of z. Third, we also analyze the computational and memory complexities required by our proposed new projection-based method for checking the full-rank condition of a given transfer tensor and for undertaking the tensor inversion. Our developed new theories and analysis in this work can be applied to determine the FIR model-orders of an approximated inverse MIMO transfer tensor often encountered in signal processing, control, communications, etc. © 2007-2012 IEEE.

关键词： FIR filters

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Adnan, Muh Prakasa, Esa Yuyun Umar, Najirah Nasrullah Sadli, Adi Anatasya, A. Edeth Fuari Hazriani Razak, Mashur Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia Handayani University Makassar Dept. of computer system Makassar Indonesia

ISBN: (纸本)9798331508579

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures. © 2024 IEEE.

关键词： Error correction

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Ultra-Local Model Based Fixed- Time Control for the Wearable Exoskeleton with Prescribed Performance 42

Ultra-Local Model Based Fixed- Time Control for the Wearable...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Xianlei Zhang, Yan Hu, Qing Yang, Anjie Li, Xuan College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9789887581543

This paper studies the problem of trajectory tracking control for the wearable exoskeleton system subject to unknown dynamics. An ultra-local model is adopted to describe the dynamic relationship between control input and output. To achieve high-precision fast tracking, a new fixed-time control method is designed. Not only can the convergence time and tracking accuracy be given in advance, but also the complicated modeling process is avoided. A new performance function is constructed and the specified tracking accuracy is achieved within a given time. Through rigorous theoretical analysis, it can be concluded that all signals of the control system are fixed-time convergent, and the tracking error converges to a small neighborhood of the equilibrium within a fixed-time. Finally, the effectiveness of the control scheme is verified through a practical simulation example. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Exoskeleton (Robotics)

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MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

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Computer Modeling in engineering & sciences 2023年第9期136卷 2759-2785页

作者： Zhengkai Ding Qiming Fu Jianping Chen You Lu Hongjie Wu Nengwei Fang Bin Xing School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling *** reinforcement learning(DRL)methods have recently been proposed to address the HVAC control ***,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow *** this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal *** is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort *** addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

关键词： Deep reinforcement learning multi-zone residential HVAC multi-agent energy conservation comfort

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

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Geodesy and Geodynamics 2024年第1期15卷 100-105页

作者： Fei Ye Yunbin Yuan State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China BeiDou High-precision Satellite Navigation and Location Service Hunan Engineering Research Center Hunan Institute of Geomatics Sciences and TechnologyChangsha 410007China Hunan Continuously Operating Reference Stations(HNCORS)Data Center Hunan Institute of Geomatics Sciences and TechnologyChangsha 410007China

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame ***,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM ***,the weighted LS+AR hybrid method performs well for PM short-term ***,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR *** this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid *** using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid *** to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,***,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

关键词： Stochastic model LS+AR Short-term prediction The earth rotation parameter(ERP) Observation model

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