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Sparse Context Transformer for Few-Shot Object Detection 21st

Sparse Context Transformer for Few-Shot Object Detection

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Jiu, Mingyuan Mei, Jie Sahbi, Hichem Jiang, Xiaoheng Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou China National SuperComputing Center in Zhengzhou Zhengzhou China Sorbonne University CNRS LIP6 Paris75005 France

ISBN: (纸本)9789819601240

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for the fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluated the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Enhanced Knowledge Tracing via Frequency Integration and Order Sensitivity 21st

Enhanced Knowledge Tracing via Frequency Integration and Ord...

引用

21st Pacific Rim international conference on Artificial Intelligence

作者： Cai, Songtao Li, Li Southwest Univ Sch Software Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9789819601158;9789819601165

Knowledge tracing (KT) is a pivotal component of online education systems, aiming to assess and predict students' knowledge states based on their learning history. Existing knowledge tracing models have achieved considerable success, yet they have neglected the frequency of interactions between students and knowledge points. Frequency information can aid in more stable modeling of students' knowledge states. In this study, we incorporate frequency data into our model's question embeddings, enabling it to consider the frequency of student-knowledge concept interactions. Moreover, the order within students' learning sequences is crucial, but knowledge tracing models based on the transformer architecture, with their attention mechanisms, are insensitive to the sequence order. We propose the N-transformer structure that combines transformer with order-sensitive RNNs, effectively enhancing the model's sensitivity to the order in students' learning sequences. Subsequently, we employ a simpler and more effective method for prior knowledge modeling, directly extracting prior knowledge related to the current exercise from the model's predicted knowledge state at the previous time step. Finally, we have designed a decay function for the attention mechanism based on the Ebbinghaus forgetting curve to simulate the students' forgetting behavior. Ultimately, we conducted experiments on four datasets, Our model has achieved up to a 3% increase in AUC compared to the baseline on some datasets.

关键词： Knowledge tracing Deep learning intelligent tutoring systems

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Smart Web Application for Recommending the Selection of Equipment for Organizing Activities in Educational Institutions 10

Smart Web Application for Recommending the Selection of Equi...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Khongtuan, Tanarin Mekruksavanich, Sakorn University of Phayao Technology and Digital Data Managementc School of Information and Communication Technology Phayao Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand

ISBN: (纸本)9798331543273

Higher education institutions strive to improve the quality of instruction and research to prepare students with essential 21st-century competencies. They promote academic and professional growth through teaching, research initiatives, community engagement, and the preservation of cultural values. One key tool in this effort is an educational resource recommendation system, critical in boosting students' academic outcomes, stimulating engagement in learning activities, and elevating educational standards. This study presents the design and evaluation of a personalized recommendation system for online educational resources, leveraging web technologies and mining techniques integrated within a digital learning infrastructure. The proposed system enhances the intelligence and adaptability of the network-based educational platform, caters to students' personalized learning requirements, and streamlines the process of accessing relevant information by minimizing the time required for resource discovery. © 2025 IEEE.

关键词： students

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Does data Balancing Impact stutter Detection and Classification? 21st

Does Data Balancing Impact Stutter Detection and Classificat...

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21st international conference on Distributed Computing and intelligent Technology

作者： Batra, Ashita Shrivastava, Pratyush Das, Pradip K. Indian Inst Technol Guwahati Gauhati 781039 India

ISBN: (纸本)9783031814037;9783031814044

stuttering, a speech impediment that significantly impacts an individual's personality and daily life, is a critical research area requiring technological solutions. Therefore, bringing our attention, we analyzed speech utterances of people who stutter, using recordings from the publicly available Sep-28k dataset, which consists of natural conversations from podcasts. A major focus has been put on analyzing the fact that data imbalance plays a role in learning the characteristics of a speech utterance. We have used a sampling technique by considering the middle value of samples calculated from the minority and the majority class and this serves as a combination of up-sampling and down-sampling. This helps us to avoid bias in the class-wise characteristic learning of the model. The proposed approach focuses on two feature extraction techniques, specifically Mel-Frequency Cepstral Coefficients (MFCCs), del-del MFCC and a comprehensive analysis using the openSMILE toolkit, selecting only the ComParE2016 features. We conducted a comparative study between various machine learning models like random forest (RF), support vector machine (SVM) and time-delayed neural network (TDNN) using both features. Our findings indicated that the stutter class outperformed the no stutter class by a significant margin for each classifier in binary classification. This prompted us to conduct two experiments: one with all classes and another with only stutter classes. The experimental analysis revealed an average F1-score with del-del MFCC features of 85.38% with RF, 90.10% with SVM when all classes are considered. Furthermore, 93.29% with RF, 93.71% with SVM when only stutter classes are considered. On the contrary, when we use ComParE2016 features we achieve an average accuracy of 86.79% with RF and 87.77% with SVM considering all classes. Whereas, 95.10% with RF, 94.34% with SVM when only stutter classes are considered. When TDNN was used with del-del MFCC, we achieved an accuracy of 79.

关键词： stuttering Speech disorder feature extraction MFCC ComParE TDNN Sep-28k

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THE BENEFITS OF TRANSFORMING engineering EDUCATION AND TRAINING WITH METAVERSE TECHNOLOGIES 21

THE BENEFITS OF TRANSFORMING ENGINEERING EDUCATION AND TRAIN...

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21st international conferences on Mobile learning, ML 2025 and 10th international conference on Educational Technologies, ICEduTech 2025

作者： Nedeva, Veselina Dineva, Snezhana Ducheva, Zlatoeli Trakia University – Stara Zagora Faculty of Technics and Technology 38 Graf Ignatiev Str. Yambol8600 Bulgaria

ISBN: (纸本)9789898704665

After analysing various publications and leveraging our direct experiences, we have identified the benefits and advantages of incorporating the metaverse. The Metaverse and related technologies such as Virtual Reality, Augmented Reality, and Artificial Intelligence offer innovative approaches to transforming engineering education. These technologies overcome the limitations of traditional methods by providing interactive and accessible learning opportunities. This study integrates insights from a comprehensive bibliometric analysis of research publications for the period 2013–2024, an extensive content analysis of selected academic articles, and the practical experiences of the Faculty of engineering and Technology. Our research is informed by professional engagements with e-learning platforms such as MOODLE and by observations from mobility exchanges at Karlsruhe Institute of Technology, where faculty members gained valuable insights into virtual and immersive education practices. The data reveals significant growth in interest and impact in recent years, with a peak in publications and citations. The analysis highlights how Metaverse and immersive technologies enhance student engagement, foster better understanding of complex concepts, and enable safe, realistic simulations. In addition to identifying opportunities for transforming traditional teaching methods, this study provides recommendations for integrating the Metaverse into engineering education. It also examines current technology trends and best practice applications, while addressing key challenges and outlining future prospects. By combining research insights with practical expertise, this paper underscores how Metaverse technologies can revolutionise engineering education and offer sustainable solutions to existing challenges. © 2025 Proceedings of the international conferences on Mobile learning 2025 and Educational Technologies 2025. All rights reserved.

关键词： Virtual environments

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multi-GAT: Integrative Analysis of scRNA-seq and scATAC-seq data Using Graph Attention Networks for Cell Annotation 21st

multi-GAT: Integrative Analysis of scRNA-seq and scATAC-seq ...

引用

21st Pacific Rim international conference on Artificial Intelligence

作者： Jia, Shangru Tsunoda, Tatsuhiko Sharma, Alok Univ Tokyo Grad Sch Frontier Sci Dept Computat Biol & Med Sci Lab Med Sci Math Tokyo Japan Univ Tokyo Sch Sci Dept Biol Sci Lab Med Sci Math Tokyo Japan RIKEN Lab Med Sci Math Ctr Integrat Med Sci Yokohama Kanagawa Japan Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia Korea Univ Coll Informat Seoul South Korea

ISBN: (纸本)9789819601158;9789819601165

Single-cell RNA sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) provide complementary views of cellular states by capturing transcriptomic and chromatin accessibility landscapes, respectively [1]. Combining these modalities offers a comprehensive understanding of cellular functions and regulatory mechanisms. Here, we present multi-GAT, a model specifically designed for integrative analysis of scRNA-seq and scATAC-seq data using Canonical Correlation Analysis (CCA) followed by Graph Attention Network (GAT) to predict cell types. This approach leverages shared nearest neighbors and contrastive learning to enhance model performance. Multi-GAT effectively captures the complex relationships between transcriptomic and chromatin accessibility data, achieving robust cell type annotation across different single-cell modalities. The experimental results demonstrate that multi-GAT surpasses several baseline methods in accuracy, precision, and F1-score on the benchmark dataset.

关键词： Graph Attention Networks Single-cell transcriptomics Canonical Correlation Analysis Cell Annotation Contrastive learning

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Are Dense Retrieval Models Few-Shot Learners? 21st

Are Dense Retrieval Models Few-Shot Learners?

引用

21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Cheng, Haipeng Sun, Si Yang, Shanglin Zheng, Benchang Yang, Wei Huang, Hu China Academy of Launch Vehicle Technology Beijing China Intelligent Game and Decision Laboratory Beijing China

ISBN: (纸本)9789819601189

Few-shot dense retrieval (DR) aims to generalize effectively to novel query scenarios by learning from a few samples. Despite its importance, there has been limited study on the few-shot learning abilities of DR models. One primary reason for this gap is the absence of standardized datasets and evaluation protocols. As a result, current methods often resort to random sampling from regular datasets to create "few-data" setups, making robust and reliable evaluations challenging. This paper introduces a customized FewDR dataset and evaluation benchmark for few-shot DR. Specifically, FewDR categorizes query-answer entity pairs based on their entity relations into classes and organizes queries and relevant passages containing both entities into shots. We then use class-wise shot sampling on FewDR to establish a standardized "few-shot" setup. This evaluation setting reduces variability across different sampling trials, enhancing the robustness of evaluating DR’s few-shot performance. Our extensive empirical findings across 17 DR models reveal the considerable challenges these models face in standard few-shot learning scenarios. Our code and data are publicly available at https://***/SunSiShining/FewDR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Empowering data Literacy Among High School Learners: Insights from a Linked Open data Workshop 14th

Empowering Data Literacy Among High School Learners: Insight...

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14th international conference on Methodologies and intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Antelmi, Alessia Esposito, Pasquale Università degli Studi di Torino Torino Italy Università degli Studi di Salerno Fisciano Italy

ISBN: (纸本)9783031855603

The growing accessibility of (linked) open data offers a rich opportunity for stakeholders to harness their potential value. Nonetheless, significant obstacles like inadequate technical skills and a lack of awareness hinder their effective utilization. As a result, there is a pressing need for learners to cultivate competencies in data and information literacy, which are integral components of 21st-century skill sets. To foster discussion on initiatives to raise awareness of linked open data and enhance users’ capabilities to work with them, we proposed a workshop to introduce Italian high school learners to retrieve information from encyclopedic sources modeled as knowledge graphs. Specifically, the workshop guided the learners through the process of querying the Wikidata endpoint via KGSnap!, a block-based programming interface that allows lay users to build and run queries over a SPARQL endpoint. This article describes the activities of the workshop, delineating its protocol, participants, and outcomes. The results indicate that participants were highly engaged and achieved accurate results in the assigned tasks within a short timeframe, suggesting promising expectations for further initiatives in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Challenges and Approaches to the Artificial Intelligence in Education 27th

Challenges and Approaches to the Artificial Intelligence in ...

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27th international conference on Interactive Collaborative learning-ICL

作者： Zeleneva, Iryna Parkhomenko, Anzhelika Subbotin, Sergey Tabunshchyk, Galyna Holub, Tetiana Kulykovskyi, Ruslan Selivanov, Maksym Natl Univ Zaporizhzhia Polytech Zhukovskogo Str 64 UA-69063 Zaporizhzhia Ukraine

ISBN: (纸本)9783031856518;9783031856525

The intensive development of artificial intelligence (AI) and its implementation in various areas of our lives cannot ignore the aspect of higher education. The problem has become so urgent and this is demonstrated by the European Parliament's recent adoption of a basic set of rules governing AI. In this regard, many researchers are paying attention to finding a balance between the opportunities and risks associated with the use of artificial intelligence tools (AIT). The topic of the work is relevant and is devoted to the analysis of the current state, as well as consideration of practical aspects of an educational process using AIT. The paper presents the results of the development of disciplines aimed at students studying AI methods and tools. Examples of the use of generative AIT in several academic disciplines for bachelor's and master's degrees are also shown. A questionnaire was developed and a survey of students was conducted, which made it possible to identify priority areas and formulate recommendations for the use of AIT in the educational process.

关键词： Generative Artificial Intelligence Artificial Intelligence Tools intelligent Chat Bot

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Towards Responsible Decisions with Limited Training data Using Human-in-the-Loop 20th

Towards Responsible Decisions with Limited Training Data Us...

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Mahidadia, Ashesh Bain, Michael Suryanto, Hendra Kang, Byeong Guan, Charles Compton, Paul Rich Data Corporation Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information and Communication Technology University of Tasmania Hobart Australia

ISBN: (纸本)9789819600250

A fundamental issue with machine learning is that the training data must contain sufficient examples of every data pattern of interest for the essentially statistical techniques of machine learning to derive a model that accurately covers all data patterns. For example, in real-world situations, samples from minority populations may not be adequately represented in available training datasets, resulting in models that are biased in favour of majority populations. We present an approach to partially address this problem by using domain knowledge to construct rules to correctly classify, specifically, those cases representing rare patterns in training data that the machine learning model has incorrectly classified. We use Ripple-Down Rules (RDR) to add rules, as it is a proven approach to knowledge acquisition and incremental maintenance that enables rules to be very easily and quickly added to a knowledge base on a case-by-case basis. This paper extends previous work using a human expert by using both artificial and real-world data and a simulated expert to add rules in multiple controlled experiments. Results show that domain knowledge can be successfully used in our approach to mitigate performance issues arising from a limited representation of a subgroup in the available training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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