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Hyperbolic Pattern Detection in Ground Penetrating Radar Images Using Faster R-CNN

Hyperbolic Pattern Detection in Ground Penetrating Radar Ima...

International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Pachara Srimuk Akkarat Boonpoonga Kamol Kaemarungsi Krit Athikulwongse Danai Torrungrueng Nattawat Chantasen Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Teacher Training in Electrical Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Computer Engineering Faculty of Engineering Rajamangala University of Technology Phra Nakhon Bangkok Thailand

This study presents the application of Faster RCNN, a popular Region Based Convolutional Neural Network, for detecting hyperbolic patterns in Ground Penetrating Radar (GPR) images. GPR is an important tool for subsurface imaging in various fields such as geology, archaeology, and engineering. However, the analysis of GPR images can be challenging due to noise, small objects, and variations in object sizes. To evaluate the performance of the proposed method, 369 simulated GPR B-scan images were generated using GprMax simulation software. These images included single, double, and triple hyperbolic patterns. The results showed that preprocessing improved the detection accuracy and led to higher Intersection over Union (IoU) scores. The experimental results demonstrate that Faster R-CNN is an effective tool for hyperbolic pattern detection in GPR images and provides a promising direction for future research in the field.

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Ion-Trap Chip Architecture Optimized for Implementation of Quantum Error-Correcting Code

arXiv

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arXiv 2025年

作者： Lee, Jeonghoon Jeon, Hyeongjun Kim, Taehyun Department of Computer Science and Engineering Seoul National University Korea Republic of NextQuantum Seoul National University Korea Republic of Institute of Applied Physics Seoul National University Korea Republic of Inter-university Semiconductor Research Center Seoul National University Korea Republic of Institute of Computer Technology Seoul National University Korea Republic of Automation and System Research Institute Seoul National University Korea Republic of

We propose a new ion-trap architecture optimized for the efficient execution of both transversal and non-transversal gate operations in a two-dimensional color code. By differentiating the regions for transversal gates from those for non-transversal gates and syndrome extraction, which require distinct qubit connectivities, our chip layout minimizes ion shuttling and simplifies physical implementations. We also provide a dedicated transpiler and scheduler for this architecture, wherein the scheduler coordinates the sequence of operations and inserts the necessary swap and shuttling operations. Finally, we developed an error analyzer to evaluate the chip’s performance across a variety of quantum algorithms. Simulation results confirm that our architecture can significantly increase success rates and reduce gate error probabilities, particularly lowering the effective two-qubit gate error probability to about 10−8 when a quantum error-correcting code of 31 physical qubits is employed. Our findings clearly show that the improvement in success rates clearly outweighs the runtime overhead, demonstrating that strategic hardware-scheduler co-design can advance quantum systems towards reliable, large-scale computing, potentially surpassing classical capabilities. © 2025, CC BY.

关键词： Qubits

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AmBC-NOMA-Aided Short-Packet Communication for High Mobility V2X Transmissions

arXiv

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arXiv 2024年

作者： Pei, Xinyue Wang, Xingwei Chen, Yingyang Pei, Tingrui Wen, Miaowen School of Computer Science and Engineering Northeastern University Shenyang110819 China The College of Information Science and Technology Jinan University Guangzhou510632 China The National Engineering Technology Research Center for Mobile Ultrasonic Detection South China University of Technology Guangzhou510640 China

In this paper, we investigate the performance of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA)-assisted short packet communication for high-mobility vehicle-to-everything transmissions. In the proposed system, a roadside unit (RSU) transmits a superimposed signal to a typical NOMA user pair. Simultaneously, the backscatter device (BD) transmits its own signal towards the user pair by reflecting and modulating the RSU’s superimposed signals. Due to vehicles’ mobility, we consider realistic assumptions of time-selective fading and channel estimation errors. Theoretical expressions for the average block error rates (BLERs) of both users are derived. Furthermore, analysis and insights on transmit signal-to-noise ratio, vehicles’ mobility, imperfect channel estimation, the reflection efficiency at the BD, and blocklength are provided. Numerical results validate the theoretical findings and reveal that the AmBC-NOMA system outperforms its orthogonal multiple access counterpart in terms of BLER performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

arXiv

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arXiv 2025年

作者： Bi, Zhangqian Wan, Yao Chu, Zhaoyang Hu, Yufei Zhang, Junyi Zhang, Hongyu Xu, Guandong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is computationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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On Pipelined GCN with Communication-Efficient Sampling and Inclusion-Aware Caching

On Pipelined GCN with Communication-Efficient Sampling and I...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shulin Wang Qiang Yu Xiong Wang Yuqing Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Graph convolutional network (GCN) has achieved enormous success in learning structural information from unstructured data. As graphs become increasingly large, distributed training for GCNs is severely prolonged by frequent cross-worker communications. Existing efforts to improve the training efficiency often come at the expense of GCN performance, while the communication overhead persists. In this paper, we propose PSC-GCN, a holistic pipelined framework for distributed GCN training with communication-efficient sampling and inclusion-aware caching, to address the communication bottleneck while ensuring satisfactory model performance. Specifically, we devise an asynchronous pre-fetching scheme to retrieve stale statistics (features, embedding, gradient) of boundary nodes in advance, such that the embedding aggregation and model update are pipelined with statistics transmission. To alleviate communication volume and staleness effect, we introduce a variance-reduction based sampling policy, which prioritizes inner nodes over boundary ones for reducing the access frequency to remote neighbors, thus mitigating cross-worker statistics exchange. Complementing graph sampling, a feature caching module is co-designed to buffer hot nodes with high inclusion probability, ensuring that frequently sampled nodes will be available in local memory. Extensive evaluations on real-world datasets show the superiority of PSC-GCN over state-of-the-art methods, where we can reduce training time by 72%-80% without sacrificing model accuracy.

关键词： Training Estimation error Accuracy Graph convolutional networks Computational modeling Probability Benchmark testing

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Multi-factor based face validation attendance system with contactless design in training event 18

Multi-factor based face validation attendance system with co...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Pichetjamroen, Sasakorn Rattanalerdnusorn, Ekkachan Vorakulpipat, Chalee Pichetjamroen, Achara National Electronics and Computer Technology Center Information Security Research Team Pathumthani Thailand Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9780738111278

Various methods for face validation-based authentication systems have been applied in a number of access control applications. However, using only one biometric factor such as facial data may limit accuracy and use, and is not practical in a real environment. This paper presents the implementation of a face time attendance system with an additional factor, a QR code to improve accuracy. This two- factor authentication system was developed in the form of a kiosk with a contactless process, which emerged due to the COVID-19 pandemic. The experiment was conducted at a well- known training event in Thailand. The proposed two-factor system was evaluated in terms of accuracy and satisfaction. Additionally, it was compared to a traditional single-factor system using only face recognition. The results confirm that the proposed two-factor scheme is more effective and did not incorrectly identify any users. © 2021 IEEE.

关键词： Face recognition

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Influence of Pulse Discharging on Lithium-Ion Battery

Influence of Pulse Discharging on Lithium-Ion Battery

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International Electrical engineering Congress (iEECON)

作者： Hsu Myat Naing Chinda Charoenphonphanich Masaki Yamakita Pera Tanateerapong Burin Kerdsup Manop Masomtob Department of Mechanical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan National Astronomical Research Institute of Thailand (NARIT) Chiangmai Thailand National Electronics and Computer Technology Center(NECTEC) National Science and Technology Development Agency Pathum Thani Thailand National Energy and Technology Center(ENTEC) National Science and Technology Development Agency Pathum Thani Thailand

This paper aims to investigate the impact of switching frequencies in pulse discharging of batteries by testing with Lithium-ion cells. Applying lithium-ion batteries in high power applications is needed to be managed according to the demand of load power and current profile. The pulse current discharging technique with different frequencies is expected to improve the charging/ discharging capacity and energy of lithium-ion batteries. In this paper, lithium-ion cells were tested with pulse current at various switching frequencies with 75% duty cycle during discharging. The results of pulse discharging with different switching frequencies were compared with constant current discharging method by evaluating capacity and energy. From this research, the results indicated that pulse discharging at low frequencies generated high discharging capacity than constant current discharging.

关键词： Lithium-ion batteries Electrical engineering Switching frequency Testing

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The Effect of Face-to-Face Interaction on Older Adults’ Attitudes Toward Robots in Human-computer Interaction 25th

The Effect of Face-to-Face Interaction on Older Adults’ Att...

引用

25th International Conference on Human-computer Interaction, HCII 2023

作者： Yang, Chien-Chun Yeh, Su-Ling Chien, Sung-En Huang, Tsung-Ren Chang, Yu-Ling Goh, Joshua O. S. Chen, Yi-Chuan Fu, Li-Chen Department of Psychology College of Science National Taiwan University Taipei Taiwan Department of Medicine Mackay Medical College New Taipei City Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan Graduate Institute of Brain and Mind Sciences College of Medicine National Taiwan University Taipei Taiwan Department of Neurology College of Medicine National Taiwan University Hospital National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan MOST Joint Research Center for AI Technology and All Vista Healthcare Taipei Taiwan

ISBN: (纸本)9783031359910

Interpersonal interactions are often expected to be polite and follow communicative norms to avoid potential social conflicts by employing strategies such as face work. Although face work has been well-documented in human-human interactions, it is unclear whether people would be polite to the robots and give them higher ratings in face-to-face interactions than in paper questionnaires. In addition, according to the Socioemotional Selectivity Theory, older adults are more likely to be polite to reduce potential interpersonal tensions and maintain emotional harmony, which causes them to respond kindly to others. The current study investigated how face-to-face human-robot interactions influence people’s subjective evaluations of the robot and, more critically, whether older and younger adults perform differently. We recruited older and younger adults and assessed their attitudes toward the robot through face-to-face interaction and a paper questionnaire. The results revealed that the older adults rated the robot more positively than the younger adults. Critically, the older adults rated the robot’s personal association and perceived ease of use higher when responding to the robot directly than when answering the paper questionnaire;however, the younger adults did not show this difference. The findings emphasize the importance of face-to-face interaction and social norms in human-robot interactions, especially for older adults. Our findings have implications for human-computer interaction researchers and practitioners who are interested in developing social robots that are sensitive to social norms for different age groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human robot interaction

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

How to Select Pre-Trained Code Models for Reuse? A Learning ...

引用

IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Zhangqian Bi Yao Wan Zhaoyang Chu Yufei Hu Junyi Zhang Hongyu Zhang Guandong Xu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is compu-tationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks.

关键词： Phase change materials Analytical models Adaptation models Codes Costs Computational modeling Training data Machine learning Software Software development management

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Multimodal Cross-Lingual Features and Weight Fusion for Cross-Cultural Humor Detection 4

Multimodal Cross-Lingual Features and Weight Fusion for Cros...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Xie, Heng Cui, Jizhou Cao, Yuhang Chen, Junjie Tao, Jianhua Fan, Cunhang Liu, Xuefei Wen, Zhengqi Lu, Heng Yang, Yuguang Lv, Zhao Li, Yongwei Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China School of Information Science and Technology ShanghaiTech University Shanghai China Ximalaya Inc. Shanghai China College of Computer and Information Engineering Tianjin Normal University Tianjin China Department of Automation Tsinghua University Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400702709

Sentiment analysis plays a crucial role in interpreting human interactions across different modalities. This paper addresses the Cross-Cultural Multilingual Humour Detection Sub-Challenge in the MuSe 2023 Multi-modal Sentiment Analysis Challenge, which aims to identify humor instances in cross-cultural situations using multimodal data. In this paper, we explore the unique window length processing of audio data to more accurately and fully capture humor information. Furthermore, we employ a data augmentation technique for text to bridge the gap between the train set and the test set. This augmentation involves converting German and English samples in the database to each other, as well as translating both languages into Chinese. These techniques aim to enhance the model's generalization across different languages and alleviate the scarcity of humor samples. For multimodal fusion, we use the late fusion technique applied to different Gated Recurrent Unit (GRU) models with modality-specific weights obtained through gradient descent. Experimental results demonstrate the effectiveness of our approach, achieving an Area Under the Curve (AUC) score of 0.872 on the MuSe-Humor test set. This performance ranked second place in the sub-challenge. © 2023 ACM.

关键词： Sentiment analysis

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