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International Joint Conference on Neural Networks (IJCNN)

作者： Linhuang Wang Xin Kang Fei Ding Hai-Tao Yu Yunong Wu Kazuyuki Matsumoto Satoshi Nakagawa Fuji Ren Graduate School of Advanced Technology and Science Tokushima University Tokushima Japan Faculty of Library Information and Media Science University of Tsukuba Tsukuba Japan Dataa Robotics Chengdu China Graduate School of Information Science & Technology The University of Tokyo Tokyo Japan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Facial Expression Recognition (FER) poses significant challenges due to various imaging conditions, including diverse head poses, lighting conditions, resolutions, and occlusions. Additionally, different personal attributes such as age, gender, and racial background further contribute to the complexity of FER. To accurately extract meaningful expression features amidst these interfering factors to enhance recognition accuracy and the model’s generalization, we propose a Self Decoupling-Reconstruction Network (SDRNet). Specifically, our approach involves two learning processes. In the first phase, the network is trained to decouple facial images with expressions into expression and neutral components. This process involves reconstructing neutral facial images and the original input, ensuring the preservation of meaningful expression components devoid of interference in the decoupling process. In the second learning phase, we employ simple convolutional neural networks (CNNs) to recognize the extracted expression components. Our method has achieved state-of-the-art results across multiple widely used datasets, providing substantial evidence of its effectiveness. Additionally, we demonstrate the robust generalization performance of our approach through cross-database evaluations.

关键词： Image resolution Image recognition Head Face recognition Neural networks Lighting Imaging

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Machine Speech Chain with Emotion Recognition

Machine Speech Chain with Emotion Recognition

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Akeyla Pradia Naufal Dessi Puji Lestari Ayu Purwarianti Kurniawati Azizah Dipta Tanaya Sakriani Sakti School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics.

关键词： Training Emotion recognition Accuracy Error analysis Natural languages Training data Speech recognition Speech enhancement Data models Speech synthesis

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A Systematic Literature Review of Gamification in Massive Online Open Course 5

A Systematic Literature Review of Gamification in Massive On...

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5th IEEE International Conference on information Technology, information Systems and Electrical engineering, ICITISEE 2021

作者： Bangkit Saputra, Jeffri Prayitno Nizar Hidayanto, Achmad Prabowo, Harjanto Meyliana Universitas Amikom Purwokerto Doctor Of Computer Science Graduate Program Bina Nusantara University Faculty Of Computer Science Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia

ISBN: (纸本)9781665401968

The development of information and communication technology makes it easy for anyone to be able to take part in online-based learning. Currently, online-based learning has been offered by many institutions or commercial industries. Gamification is one of the strategies to increase students' learning motivation. In this study, we focus on examining the role of gamification and game elements are contained in gamification-based learning. From the results of our research, it shows that gamification in the online-based learning process is growing. Gamification is proven to have a positive impact on students' learning motivation, although it is still dependent on various factors and conditions. © 2021 IEEE.

关键词： Motivation

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PAFT: Prompt-Agnostic Fine-Tuning

arXiv

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arXiv 2025年

作者： Wei, Chenxing Shu, Yao Ou, Mingwen He, Ying Tiffany Yu, Fei Richard College of Computer Science and Software Engineering Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China China School of Information Technology Carleton University Canada

While Large Language Models (LLMs) adapt well to downstream tasks after fine-tuning, this adaptability often compromises prompt robustness, as even minor prompt variations can significantly degrade performance. To address this, we propose prompt-agnostic fine-tuning (PAFT), a simple yet effective approach that dynamically adjusts prompts during fine-tuning. This encourages the model to learn underlying task principles rather than overfitting to specific prompt formulations. PAFT operates in two stages: First, a diverse set of meaningful, synthetic candidate prompts is constructed. Second, during fine-tuning, prompts are randomly sampled from this set to create dynamic training inputs. Extensive experiments across diverse datasets and LLMs demonstrate that models trained with PAFT exhibit strong robustness and generalization across a wide range of prompts, including unseen ones. This enhanced robustness improves both model performance and inference speed while maintaining training efficiency. Ablation studies further confirm the effectiveness of PAFT. Copyright © 2025, The Authors. All rights reserved.

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Performance Analysis of Space-Air-Ground Integrated Network (SAGIN): UAV Altitude and Position Angle

Performance Analysis of Space-Air-Ground Integrated Network ...

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IEEE International Conference on Communications in China (ICCC)

作者： Jingjing Tan Fengxiao Tang Ming Zhao Nei Kato School of Computer Science and Engineering Central South University Changsha China Graduate School of Information Sciences Tohoku University Sendai Japan

With the explosive growth of mobile data traffic, the Space-Air-Ground integrated network (SAGIN) has attracted the attention of academia and industry. As the communication infrastructure in the air network of the SAGIN system, Unmanned Aerial Vehicles (UAVs) are considered the air relay nodes to access ground and space networks. Due to the mobility of satellites in the space network, the formed variable position angle between the UAV and the satellite and the fixed UAV altitude can reduce the air-to-space transmission capability. Moreover, a UAV’s battery is usually very limited, which may cause SAGIN transmission energy interruptions when the battery runs out. To address these issues in the SAGIN environment, we propose an integrated UAV channel fading and UAV energy consumption model to analyze the transmission performance. Based on the network performance analysis results, we derive the optimal UAV altitude expression. The numerical results verify the accuracy of the derived expressions and evaluate the impact of some key parameters on the optimal UAV altitude in the SAGIN uplink.

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A Multi-UAV Charging Station Enabling Free Landing by Grid Pattern Transmitter

A Multi-UAV Charging Station Enabling Free Landing by Grid P...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Jungho Kim Hyunkyeong Jo Seoktae Seo Bonyoung Lee Hyungki Min Franklin Bien School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Program in Information & Communication Technology Convergence Ulsan National Institute of Science and Technology Ulsan Korea

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper proposed a novel landing platform with wireless power transfer (WPT) to charge multiple unmanned aerial vehicles (UAVs) without restrictions on landing locations or alignment. A transmitter is designed using wires to charge a wide area. To overcome the disadvantages of system made by wires, a grid pattern transmitter and double power line are proposed. These structures provide multiple charging with free positioning of receivers and excite the transmitter lines in wide area properly. The optimized system is composed of 0.15m square cells, providing a 0.9 m by 0.9 m charging area. By experiment, it can efficiently charge four 0.1 m square receivers located anywhere in transmitter. The overall efficiency of four receivers is 37.5%. Also, the experimental results verify that it is unnecessary to align the receiver within the cell, as it has a 2.62% error due to rotation and a 2.94 % error due to position within the cell.

关键词： Coils Magnetic flux leakage Transmitters Wires Windings Receivers Wireless power transfer Autonomous aerial vehicles Current distribution Power electronics

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On a New Rough Clustering Based on Optimization

On a New Rough Clustering Based on Optimization

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Sakuya FURUTA Yasunori ENDO Master's Program in Risk and Resilience Engineering Degree Programs in Systems and Information Engineering Graduate School of Science annd Technology University of Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The purpose of this study is to develop and investigate a new rough clustering based on optimization. We propose a restructured algorithm using only one variable, instead of the algorithm described using two variables in existing methods. We also perform numerical examples using artificial data and the Iris and Wine datasets. This research has the potential to be used as a method for determining false negatives and false positives in cancer diagnosis.

关键词： Iris Accuracy Clustering algorithms Computational complexity Optimization Cancer

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor Systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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Mixed Reality System for Teaching and Learning: A Systematic Literature Review 5

Mixed Reality System for Teaching and Learning: A Systematic...

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5th IEEE International Conference on information Technology, information Systems and Electrical engineering, ICITISEE 2021

作者： Suryodiningrat, Satrio Pradono Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar Bina Nusantara University Faculty Of Computing And Media Computer Science Department BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (纸本)9781665401968

Mixed reality is becoming more popular, and a lot of technology companies trying to develop and enhance mixed reality technology and using it not just in manufactures or industries but lately the technology is used for teaching and learning, either in school or for training purposes. While traditional teaching and learning processes are not effective anymore and most of the time, the learning objectives are not reached. This paper has an objective to point out the benefits of using mixed reality for teaching and learning. 99 articles have been collected and reviewed carefully as a quality assessment process, there are 41 articles has been selected for further review and top 5 benefits will be discussed. This review informs educators and scholars that there are common benefits, and the most popular benefit is better learning engagement. © 2021 IEEE.

关键词： Mixed reality

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Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

Usefulness of Honeypots Towards Data Security: A Systematic ...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Kalpin Erlangga Silaen Meyliana Meyliana Harco Leslie Hendric Spits Warnars Harjanto Prabowo Achmad Nizar Hidayanto Maria Susan Anggreainy Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Information System Faculty of Computer Science Universitas Indonesia Depok Indonesia

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity.

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