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Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions 10th

Enhancing Multi-modal Regular Expression Synthesis via Larg...

引用

10th International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2024

作者： Tang, Zipan Yan, Yixuan Li, Rongchen Dong, Hanze Chen, Haiming Gao, Hongyu Beijing University of Technology Beijing China and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819606016

Real-world regular expressions (regexes) are widely used in practice. Since regexes are difficult to comprehend and write, automatically synthesizing regexes has been an important research problem. However, current techniques face challenges like weak capability in generalization and severely limited support of extended features of regexes. In this paper, we address these challenges via Large Language Models (LLMs) together with semantic manipulations of sub-expressions, and propose PowerSyn, a framework for regex synthesis based on both natural language descriptions and examples and supports extended features. Specifically, we design the prompt suitable for synthesizing regexes with LLMs and present novel algorithms to manipulate sub-expressions, which consider the matching relation, and are guided by examples. The evaluation results demonstrate the significant efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization 22nd

A Deep Reinforcement Learning Approach to Battery Managemen...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Ali, Nawazish Shaw, Rachael Mason, Karl School of Computer Science University of Galway GalwayH91 TK33 Ireland Atlantic Technological University GalwayH91 T8NW Ireland

ISBN: (纸本)9783031704147

Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment in the sector by maximizing the utilization of renewable energy sources. This research investigates the application of Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm (DRL), to enhance dairy farming battery management. We evaluate the algorithm’s effectiveness based on its ability to reduce reliance on the electricity grid, highlighting the potential of DRL to enhance energy management in dairy farming. Using real-world data our results demonstrate how the PPO approach outperforms Q-learning by 1.62% for reducing electricity import from the grid. This significant improvement highlights the potential of the Deep Reinforcement Learning algorithm for improving energy efficiency and sustainability in dairy farms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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Learning Using Generated Privileged Information by Text-to-Image Diffusion Models 27th

Learning Using Generated Privileged Information by Text-to-I...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Menadil, Rafael-Edy Georgescu, Mariana-Iuliana Ionescu, Radu Tudor Department of Computer Science University of Bucharest Bucharest Romania

ISBN: (纸本)9783031781827

Learning Using Privileged Information is a particular type of knowledge distillation where the teacher model benefits from an additional data representation during training, called privileged information, improving the student model, which does not see the extra representation. However, privileged information is rarely available in practice. To this end, we propose a text classification framework that harnesses text-to-image diffusion models to generate artificial privileged information. The generated images and the original text samples are further used to train multimodal teacher models based on state-of-the-art transformer-based architectures. Finally, the knowledge from multimodal teachers is distilled into a text-based (unimodal) student. Hence, by employing a generative model to produce synthetic data as privileged information, we guide the training of the student model. Our framework, called Learning Using Generated Privileged Information (LUGPI), yields noticeable performance gains on four text classification data sets, demonstrating its potential in text classification without any additional cost during inference. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A Multi-task Biomedical Named Entity Recognition Method Based on Data Augmentation 23rd

A Multi-task Biomedical Named Entity Recognition Method Base...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhao, Hui Zhao, Di Meng, Jiana Liu, Shuang Lin, Hongfei School of Computer Science and Engineering Dalian Minzu University Liaoning China School of Computer Science and Technology Dalian University of Technology Liaoning China Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co. Ltd Liaoning China

ISBN: (纸本)9789819783663

The rapid development of artificial intelligence has led to an explosion of literature in the biomedical field, and Biomedical Named Entity Recognition (BioNER) can quickly and accurately identify key information from unstructured text. This task has become an important topic to promote the rapid development of intelligence in the biomedical field. However, in the Named Entity Recognition (NER) of the biomedical field, there are always some problems of unclear boundary recognition, the underutilization of hierarchical information in sentences and the scarcity of training data resources. Based on this, this paper proposes a multi-task BioNER model based on data augmentation, using four data augmentation methods: Mention Replacement (MR), Label-wise token Replacement (LwTR), Shuffle Within Segments (SiS) and Synonym Replacement (SR) to increase the training data. The syntactic information is extracted by incorporating the input sentence into the Graph Convolutional Network (GCN), and then the tag information encoded by BERT is interacted through a co-attention mechanism to obtain an interaction matrix. Subsequently, NER is performed through boundary detection tasks and span classification tasks. Comparative experiments with other methods are conducted on the BC5CDR and JNLPBA datasets, as well as the CCKS2017 dataset. The experimental results demonstrate the effectiveness of the model proposed in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Syntactics

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Assessing the Complexity of Gaming Mechanics During science Learning 12th

Assessing the Complexity of Gaming Mechanics During Science ...

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12th International Conference on Games and Learning Alliance (GALA)

作者： Dever, Daryn A. Wiedbusch, Megan Park, Saerok Llinas, Andrea Lester, James Azevedo, Roger Univ Cent Florida Orlando FL 32828 USA North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9783031490644;9783031490651

Game-based learning environments (GBLEs) incorporate game mechanics, i.e., learning and assessment mechanics, to increase domain knowledge while maintaining learner engagement. Although GBLEs have been developed to improve science learning, learners have attained lower science achievement scores over the past decade as they progress through school. As such, there is a need to better understand how learners use game mechanics as they learn about science content. This study aimed to understand how learners generally use and transition between learning and assessment mechanics while learning about science with a GBLE and how those transitions were related to learning outcomes (i.e., learning gains, game success). High-school students (N = 137) were recruited to play Crystal Island, a GBLE about microbiology. Results found that participants used static learning mechanics (e.g., virtual books about microbiology) most often, followed by game and content assessment mechanics, and lastly followed by aid and dynamic learning mechanics. Further results found that several sequential transition probabilities were related to lower learning outcomes with a few transitions positively relating to game completion success. Findings from this study also show that the type of game mechanic, as well as the direction of transitions across game mechanics significantly relate to learning outcomes. These findings provide insights into how to develop scaffolding techniques for improving science learning outcomes.

关键词： Game-based Learning Gaming Mechanics Transition Matrices science Learning

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A Unified Machine Learning Framework for Multi-subtype Tumour Classification Across Diverse Datasets 12th

A Unified Machine Learning Framework for Multi-subtype Tumo...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Yadav, Ankur Daescu, Ovidiu Department of Computer Science The University of Texas at Dallas 800 W Campbell Rd. RichardsonTX75080 United States

ISBN: (纸本)9783031827679

Machine learning models are increasingly employed in the classification of digitized medical tissue images, including for identifying cancer types and subtypes. Most models focus on a target tumor type, using datasets from a single source, which limits generalization. To overcome this, we formulated unified frameworks and applied them to bone, colon, and prostate cancer datasets from varied origins. Rigorous testing concluded that framework 1 achieved an overall accuracy of 88.48%, while framework 2, with classification corrections, achieved an enhanced overall accuracy of 90.28%. The area under the curve (AUC) values exceeded 0.97, with average specificity and sensitivity surpassing 0.98 and 0.99 across frameworks for individual classes. Additionally, when Framework 2 was applied to an unseen breast cancer dataset, it demonstrated a notable accuracy of 88.8% for normal vs. tumor tile classification, indicating its efficacy on unseen datasets. The results reflect the potential of engineered feature extraction in predictive models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection 27th

SuperSimpleNet: Unifying Unsupervised and Supervised Learni...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rolih, Blaž Fučka, Matic Skočaj, Danijel Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)9783031781919

The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that’s increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of these industries, which encompass high performance, consistency, and fast operation, along with the capacity to leverage the entirety of the available training data. Addressing these gaps, we introduce SuperSimpleNet, an innovative discriminative model that evolved from SimpleNet. This advanced model significantly enhances its predecessor’s training consistency, inference time, as well as detection performance. SuperSimpleNet operates in an unsupervised manner using only normal training images but also benefits from labelled abnormal training images when they are available. SuperSimpleNet achieves state-of-the-art results in both the supervised and the unsupervised settings, as demonstrated by experiments across four challenging benchmark datasets. Code: https://***/blaz-r/SuperSimpleNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation 17th

Few Exemplar-Based General Medical Image Segmentation via D...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Chen Huang, Qiming Hou, Yuqi Wu, Jiangxing Zhang, Fan Chang, Hyung Jin Jiao, Jianbo School of Computer Science University of Birmingham Birmingham United Kingdom School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9789819609000

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without extensive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Accelerating Medical Knowledge Discovery Through Automated Knowledge Graph Generation and Enrichment 6th

Accelerating Medical Knowledge Discovery Through Automated K...

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6th International Conference on Knowledge Graphs and Semantic Web, KGSWC 2024

作者： Khalid, Mutahira Rahman, Raihana Abbas, Asim Kumari, Sushama Wajahat, Iram Bukhari, Syed Ahmad Chan TIB - Leibniz Information Centre for Science and Technology Lower Saxony Hannover30165 Germany Department of Statistics Harvard University CambridgeMA United States School of Computer Science University of Birmingham Edgbaston Birmingham United Kingdom Routing and Planning Amazon BellevueWA United States Institute for Biotechnology St. John’s University New York United States Division of Computer Science Mathematics and Science St. John’s University New York United States

ISBN: (纸本)9783031812200

Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automation and completeness. Despite efforts in automation and the utilization of expert-created ontologies, gaps in connectivity remain prevalent within KGs. In response to these challenges, we propose an innovative approach termed "Medical Knowledge Graph Automation (M-KGA)". M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, thereby enhancing the completeness of knowledge graphs through the integration of pre-trained embeddings. Our approach introduces two distinct methodologies for uncovering hidden connections within the knowledge graph: a cluster-based approach and a node-based approach. Through rigorous testing involving 300 frequently occurring medical concepts in Electronic Health Records (EHRs), our M-KGA framework demonstrates promising results obtained 50% accuracy, 57% F1-score, 50% recall and 65% precision on a cluster base approach. Similarly, we achieve 85% accuracy, 87% F1-score, 89% recall and 88% precision on a node base approach indicating its potential to address the limitations of existing knowledge graph automation techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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Acoustic Side-Channel Attacks on a computer Mouse 21st

Acoustic Side-Channel Attacks on a Computer Mouse

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21st Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA)

作者： Conti, Mauro Duroyon, Marin Orazi, Gabriele Tsudik, Gene Univ Padua Padua Italy Delft Univ Technol Delft Netherlands Univ Calif Irvine Irvine CA USA FDM Business Serv Milan Italy

ISBN: (纸本)9783031641701;9783031641718

Acoustic Side-Channel Attacks (ASCAs) extract sensitive information by using audio emitted from a computing devices and their peripherals. Attacks targeting keyboards are popular and have been explored in the literature. However, similar attacks targeting other human-interface peripherals, such as computer mice, are under-explored. To this end, this paper considers security leakage via acoustic signals emanating from normal mouse usage. We first confirm feasibility of such attacks by showing a proof-of-concept attack that classifies four mouse movements with 97% accuracy in a controlled environment. We then evolve the attack towards discerning twelve unique mouse movements using a smartphone to record the experiment. Using Machine Learning (ML) techniques, the model is trained on an experiment with six participants to be generalizable and discern among twelve movements with 94% accuracy. In addition, we experiment with an attack that detects a user action of closing a fullscreen window on a laptop. Achieving an accuracy of 91%, this experiment highlights exploiting audio leakage from computer mouse movements in a realistic scenario.

关键词： Cybersecurity Human Interface Devices computer Mouse Acoustic Signals Acoustic Side-channel Input Devices Audio Leakage Machine Learning Mouse Movement

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