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MFG-HUI: An Efficient Algorithm for Mining Frequent Generators of High Utility Itemsets 1

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10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Duong, Hai Tran, Thong Truong, Tin Le, Bac Department of Mathematics and Computer Science Dalat University Dalat Viet Nam Department of Computer Science Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

The discovery of frequent generators of high utility itemsets (FGHUIs) holds great importance as they provide concise representations of frequent high utility itemsets (FHUIs). FGHUIs are crucial for generating nonredundant high utility association rules, which are highly valuable for decision-makers. However, mining FGHUIs poses challenges in terms of scalability, memory usage, and runtime, especially when dealing with dense and large datasets. To overcome these challenges, this paper proposes an efficient approach for mining FGHUIs using a novel lower bound called lbu on the utility. The approach includes effective pruning strategies that eliminate non-generator high utility branches early in the prefix search tree based on lbu, resulting in faster execution and reduced memory usage. Furthermore, the paper introduces a novel algorithm, MFG-HUI, which efficiently discovers FGHUIs. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of efficiency and effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Large dataset

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Intentional Microgesture Recognition for Extended Human-computer Interaction

Intentional Microgesture Recognition for Extended Human-Comp...

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Human-computer Interaction Thematic Area Conference (HCI) Held as Part of the 25th International Conference on Human-computer Interaction (HCII)

作者： Kandoi, Chirag Jung, Changsoo Mannan, Sheikh VanderHoeven, Hannah Meisman, Quincy Krishnaswamy, Nikhil Blanchard, Nathaniel Colorado State Univ Ft Collins CO 80523 USA

ISBN: (纸本)9783031355950;9783031355967

As extended reality becomes more ubiquitous, people will more frequently interact with computer systems using gestures instead of peripheral devices. However, previous works have shown that using traditional gestures (pointing, swiping, etc.) in mid-air causes fatigue, rendering them largely unsuitable or long-term use. Some of the same researchers have promoted "microgestures"-smaller gestures requiring less gross motion-as a solution, but to date there is no dataset of intentional microgestures available to train computer vision algorithms for use in downstream interactions with computer systems such as agents deployed on XR headsets. As a step toward addressing this challenge, we present a novel video dataset of microgestures, classification results from a variety of ML models showcasing the feasibility (and difficulty) of detecting these fine-grained movements, present a demonstration of a novel keyframe detection method as a way to increase recognition accuracy, and discuss the challenges in developing robust recognition of microgestures for human-computer interaction.

关键词： Human-computer interaction Gesture recognition Microgestures

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CavFind: A Novel Algorithm to Detect Cavities on Protein Structure 1

引用

5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Adhikari, Swati Roy, Parthajit Department of Computer Science The University of Burdwan Golapbag West Bengal Purba Bardhaman713104 India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

Various essential functions in living organisms are performed by binding of proteins with other molecules (ligands). Proper detection and analysis of ligand binding locations (cavities) leads towards the success of the overall drug designing system. The main challenge towards that goal is that the problem is computationally hard. In the present study, a soft computing-based algorithm has been proposed that is capable of detecting cavities on protein’s structure. The proposed algorithm is based on Voronoi decomposition and implemented by applying the self-organizing map (SOM) clustering algorithm. The proposed algorithm is evaluated with 48 protein–ligand complexes and compared with two existing algorithms like Fpocket and ConCavity and compared with the results stored in two existing databases like BioLiP and CaviDB. The proposed algorithm is also capable of computing some descriptors of the detected cavities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Binding sites

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Leveraging computer Vision Networks for Guitar Tablature Transcription 40th

Leveraging Computer Vision Networks for Guitar Tablature Tra...

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40th computer Graphics International Conference (CGI)

作者： El Achkar, Charbel Couturier, Raphael Makhoul, Abdallah Atechian, Talar Antonine Univ UA TICKET Lab Baabda Lebanon Univ Franche Comte CNRS Inst FEMTO ST F-90000 Belfort France

ISBN: (纸本)9783031500688;9783031500695

Generating music-related notations offers assistance for musicians in the path of replicating the music using a specific instrument. In this paper, we evaluate the state-of-the-art guitar tablature transcription network named TabCNN against state-of-the-art computer vision networks. The evaluation is performed using the same dataset as well as the same evaluation metrics of TabCNN. Furthermore, we propose a new CNN-based network named TabInception to transcribe guitar-related notations, also called guitar tablatures. The network relies on a custom inception block converged by dense layers. The TabInception network outperforms the TabCNN in terms of multi-pitch precision (MP), tablature precision (TP), and tablature F-measure (TF). Moreover, the Swin Transformer achieves the best score in terms of multi-pitch recall (MR) and tablature recall (TR), while the Vision Transformer achieves the best score in terms of multi-pitch F-measure (MF). Motivated by the previous insights, we train the networks with more epochs and propose another network named Inception Transformer (InT) to surpass all the estimation metrics of TabCNN using a single network. The InT network relies on an inception block converged by a Transformer Encoder. The TabInception and the InT network outperformed all estimation metrics of TabCNN except the tablature disambiguation rate (TDR) when trained using a bigger epoch size.

关键词： Guitar Tablature Transcription computer Vision Automatic Music Transcription

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Probabilistic Offloading Algorithm for Opportunistic Networks Integrating Node Influence Prediction 18th

Probabilistic Offloading Algorithm for Opportunistic Networ...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Tang, Qi Xu, Xiaodong Li, Shuai Seah, Winston Xu, Gang College of Computer Science Inner Mongolia University Hohhot010021 China The Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia University Hohhot010021 China Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9783031714634

In Opportunistic Network based mobile data diversion algorithms, the need for multi-hop transmission makes the selection of the next hop node critical. The traditional Prophet algorithm calculates the encounter delivery probability of a node based on historical encounter information as a forwarding condition. However, algorithms for data diversion are more concerned with the issue of nodes’ propagation influence in the network. Therefore, in this paper, we propose a metric to measure the dynamic influence of a node (sir) and use the CNN-BiLSTM-Attention model to predict the sir value of a node during the current time period in the route. After that, a data diversion algorithm based on deep learning prediction of node influence is proposed in combination with encounter delivery probability. The experimental results show that the NS-Prophet algorithm performs well in terms of performance metrics in opportunistic networks, and the triage efficiency in cellular networks is also improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data diversion deep learning prediction node influence opportunistic networks

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Enhancing Interpretability in CT Reconstruction Using Tomographic Domain Transform with Self-supervision 20th

Enhancing Interpretability in CT Reconstruction Using Tomog...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Huang, Baiyin Tan, Boheng Tang, Xiaoqin Xiao, Guoqiang College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9789819970247

Computed tomography (CT) reconstruction faces difficulties in dealing with artifacts caused by imperfect imaging processes. Deep learning-based CT reconstruction models have been proposed to address these challenges, but they often lack interpretability since they use end-to-end neural networks to directly transform signals from sinograms to CT sections. Additionally, supervised methods are commonly used to guide network training, yet obtaining supervision information can be challenging in biomedical imaging systems. To overcome these limitations, we propose a new domain transform CT reconstruction framework that includes self-supervision. Our approach interprets sinogram signals as tomographic information in the CT section domain, which is then used to formulate pixel intensities with a simple mean operation. A refinement network is utilized to improve the quality of the CT images, which are further processed using the Radon transform to achieve simulated sinograms without requiring additional supervision. Our experimental results demonstrate the effectiveness of the proposed framework in both anatomical structure reconstruction and artifact reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Deep learning

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Cost-Efficient Crowdsourcing for Span-Based Sequence Labeling: Worker Selection and Data Augmentation 23rd

Cost-Efficient Crowdsourcing for Span-Based Sequence Labeli...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Wang, Yujie Huang, Chao Yang, Liner Fang, Zhixuan Huang, Yaping Liu, Yang Yu, Jingsi Yang, Erhong School of Computer and Information Technology Beijing Jiaotong University Beijing China National Language Resources Monitoring and Research Center for Print Media Beijing Language and Culture University Beijing China Department of Computer Science The University of California Davis United States School of Information Science Beijing Language and Culture University Beijing China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China

ISBN: (纸本)9789819783663

This paper introduces a novel crowdsourcing worker selection algorithm, enhancing annotation quality and reducing costs. Unlike previous studies targeting simpler tasks, this study contends with the complexities of label interdependencies in sequence labeling. The proposed algorithm utilizes a Combinatorial Multi-Armed Bandit (CMAB) approach for worker selection, and a cost-effective human feedback mechanism. The challenge of dealing with imbalanced and small-scale datasets, which hinders offline simulation of worker selection, is tackled using an innovative data augmentation method termed shifting, expanding, and shrinking (SES). Rigorous testing on CoNLL 2003 NER and Chinese OEI datasets showcased the algorithm’s efficiency, with an increase in F1 score up to 100.04% of the expert-only baseline, alongsidecost savings up to 65.97%. The paper also encompasses a dataset-independent test emulating annotation evaluation through a Bernoulli distribution, which still led to an impressive 97.56% F1 score of the expert baseline and 59.88% cost savings. Furthermore, our approach can be seamlessly integrated into Reinforcement Learning from Human Feedback (RLHF) systems, offering a cost-effective solution for obtaining human feedback. All resources, including source code and datasets, are available to the broader research community at https://***/blcuicall/nlp-crowdsourcing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Crowdsourcing

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Constant-Size Group Signatures with Message-Dependent Opening from Lattices 1

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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Chen, Simin Chen, Jiageng Miyaji, Atsuko Chen, Kaiming School of Computer Science Central China Normal University Wuhan China Graduate School of Engineering Osaka University Osaka Japan

ISBN: (数字)9783031455131

ISBN: (纸本)9783031455124

Group signatures allow users to sign messages on behalf of the group without prevealing their identities. However, the opening authority can trace signatures back to their source, raising concerns about privacy. To address this issue, Sakai et al. proposed a cryptographic primitive called Group Signature with Message-Dependent Opening (GS-MDO), which balances privacy and traceability. Libert et al. introduced GS-MDO using lattices, but the scheme’s signature sizes vary with the number of group users. In this paper, we present the first constant-size group signature with message-dependent opening using lattices. Our scheme’s signature, public key, and secret key sizes are reduced to O~ (λ), and are independent of the number of group users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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Cache Behavior Analysis with SP-Relative Addressing for WCET Estimation 10th

Cache Behavior Analysis with SP-Relative Addressing for WC...

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

作者： Xiao, Shangshang Sun, Mengxia Zhang, Wei Zhan, Naijun Ju, Lei School of Cyber Science and Technology Shandong University Qingdao China Quan Cheng Laboratory Jinan China School of Computer Science Peking University Beijing China

ISBN: (纸本)9789819606016

Accurately bounding the worst-case execution time (WCET) is crucial for efficient real-time system design. Precisely analyzing whether a memory reference results in a cache miss or a cache hit significantly impacts the accuracy of the estimated WCET bound, as the latency of a cache miss is typically two orders of magnitude higher than that of a cache hit access. SP-relative addressing enables dynamic memory allocation on the stack, enhances thread safety, an therefore is widely adopted in embedded real-time systems. However, existing cache behavior analysis requires obtaining the exact address of each memory reference and pessimistically assumes that SP-relative addressing could access any memory address, resulting in an overestimated WCET bound. In this paper, we propose a new WCET analysis framework that first identifies the program segments in which the SP value remains constant for a accurate cache behavior analysis, and then comprehensively compute the WCET of the whole program based on the constraints between the SP value defined by the program’s control flow. Experimental results show that, our method can tighten the estimated WCET bound by up to 36.31% and achieve an average improvement of 11.85%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Embedded systems

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NAOL: NeRF-Assisted Omnidirectional Localization 27th

NAOL: NeRF-Assisted Omnidirectional Localization

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yu, Muzhou Cai, Zhihan Wu, Kailu Cao, Dapeng Ma, Kaisheng School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100080 China

ISBN: (纸本)9783031781278

Visual localization is a challenging task involving precise camera position and orientation estimation from an image. Specifically, existing panorama datasets suffer from a limitation in the number of available omnidirectional images, resulting in a sparse distribution and insufficient diversity compared to the well-distributed dataset. This makes omnidirectional localization hard for conventional feature-based visual localization algorithms. In this paper, we introduce a novel and efficient approach named NAOL, specifically designed to address the omnidirectional localization problem. Our proposed pipeline unfolds in two pivotal stages: stage one employs a visual-based algorithm for preliminary coarse pose estimation. Acknowledging the number of omnidirectional images is limited and the distribution is sparse, in stage two, we take an innovative step to augment the dataset and refine the coarse pose estimations by stage one. We introduce the Depth-supervised Panorama Neural Radiance Fields (DP-NeRF), a novel approach designed for training on a single omnidirectional image with depth, thereby enriching the dataset while enabling an iterative algorithm grounded in DP-NeRF to enhance localization accuracy. Our experimental results validate the efficacy of the NAOL algorithm in performing visual localization in scenarios with sparse panorama datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Visualization

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