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检索条件"任意字段=2015 International Conference on Big Data and Smart Computing, BIGCOMP 2015"

共 2627 条记录，以下是201-210 订阅

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Case Study and Qualitative Comparison of Global Legal Frameworks for Platform Labor

Case Study and Qualitative Comparison of Global Legal Framew...

引用

international conference on big data and smart computing (bigcomp)

作者： Byeongsu Koo Jooyeoun Lee Myoungsuk Chung Department of Science&Technology Policy Ajou University Suwon South Korea Department of Science&Technology Policy Ajou University Suwon Seoul

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

This study investigates the legal and policy responses of countries to platform labor, a significant employment form in the digital economy. Platform workers often fall into the gaps of traditional labor laws, facing challenges such as unclear legal status, limited social insurance coverage, insufficient algorithm transparency, and weak regulatory enforcement. Using fuzzy-set qualitative analysis, this study examines platform labor policies in the United States, Japan, and China. Japan recognizes platform workers as an intermediate category under the Specified Fiduciary System, offering partial legal protections. China adopts a comprehensive but weakly enforceable framework under the concept of New Employment Forms. Meanwhile, the U.S. demonstrates variability across states, with conflicts between recognizing platform workers as employees versus independent contractors. Based on these findings, the study proposes policy recommendations to improve platform worker protections, emphasizing clearer legal definitions, enhanced social insurance inclusion, greater algorithmic transparency, and stronger enforcement mechanisms.

关键词： Law Employment Insurance big data Protection

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Classification of Event Sequences Based on Temporal Relation Features

Classification of Event Sequences Based on Temporal Relation...

引用

international conference on big data and smart computing (bigcomp)

作者： Kai Cheng Graduate School of Information Science Kyushu Sangyo University Fukuoka Japan

Temporal data include not only time-stamped raw data but also time intervals for events with a non-zero duration. Classification of interval-based event sequences has been an active research topic in the data science community. One major research issue in sequence classification is to extract discriminative features that properly capture the underlying sequences for high accuracy classification. Previous research on sequence classification mainly focused on time series using time-point based features. In this paper, we propose to define features based on Allen’s temporal relations between time intervals. Based on our earlier work on temporal data modeling, we develop a novel scheme for sequence representation of event-intervals. We describe the detailed algorithms and report the experimental results.

关键词： Training databases Time series analysis Semantics Symbols Syntactics data science

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pmmeter: A Microbenchmark for Understanding Synchronization Cost on Persistent Memory

pmmeter: A Microbenchmark for Understanding Synchronization ...

引用

international conference on big data and smart computing (bigcomp)

作者： Hirotaka Yoshioka Yuto Hayamizu Kazuo Goda Masaru Kitsuregawa The University of Tokyo Tokyo Japan

Persistent memory is an emerging memory device, which offers durable, relatively large memory space cost-effectively. This technology potentially enables a new horizon for a broad spectrum of data-intensive applications. Historically, memory space has been deemed to be volatile for application programs. Only very recently the processor industry has introduced a variety of memory synchronization instructions to ensure the durability of persistent memory. Yet, no known programming practice yields how application programs can fully exploit those instructions. This paper proposes pmmeter, a new microbenchmarking tool that we have developed to measure the primary performance of persistent memory. pmmeter allows for clarifying the performance impact that the choice of synchronization instructions incurs on a given sequence of memory access. This paper presents our experiment to demonstrate that pmmeter unveils the synchronization cost of Intel Optane DCPMM.

关键词： Performance evaluation Industries Costs Source coding Programming big data Synchronization

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Diffusion-based portrait image generation scheme using plug-in adapters

Diffusion-based portrait image generation scheme using plug-...

引用

international conference on big data and smart computing (bigcomp)

作者： Junha Hwang Jonghwa Shim Eunbeen Kim Eenjun Hwang School of Electrical Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Portrait image generation is the task of creating a new face image that reflects modification conditions while preserving the identities of the reference portrait images. Although recent deep learning-based generative models have shown excellent image generation performance, they suffer from inefficiencies in this task, including fine-tuning using multiple reference images for each person. To address these issues, we propose a novel diffusion-based portrait image generation method using two plug-in adapters: a semantic adapter and a spatial adapter. The semantic adapter integrates text conditions into the facial identity from a single reference image, while the spatial adapter adjusts the spatial configuration of the same reference face image. In experiments using the LAION-Face dataset, the proposed method achieved $16.7 \%$ improvement in Id-Pres and $6.35 \%$ improvement in CLIP-TI over state-of-the-art portrait generation methods. Also, we demonstrate through ablation studies that both adapters are essential for efficient portrait image generation.

关键词： Adaptation models Image synthesis Computational modeling Semantics big data Diffusion models data models Faces

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Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet

Machine Fault Diagnosis Using EMD-Gammatone Texture Represen...

引用

international conference on big data and smart computing (bigcomp)

作者： Mahe Zabin Anika Nahian Binte Kabir Muhammad Khubayeeb Kabir Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science & Technology KAIST Daejeon South Korea Department of Computer Science and Engineering Brac University Dhaka Bangladesh Artificial Intelligence and Big Data Department Woosong University Daejeon South Korea

Machine fault diagnosis involves the intricate process of detecting and isolating faults, which is particularly challenging due to various sources of noise and the complex nature of the faults. In automatic fault diagnosis, feature engineering becomes daunting because of the time-varying characteristics of the fault signals. As artificial intelligence continues to advance, deep learning models have been increasingly employed for machine fault classification. However, a significant limitation of state-of-the-art models is their high computational complexity, making them unsuitable for deployment on portable devices. In this paper, a lightweight fault diagnosis model is proposed that consists of a self-attention SqueezeN et architecture along with a hybrid texture representation technique using empirical mode decomposition (EMD) and gammatone-spectrogram (GS) filter. In the model, initially, the dominant signal is extracted from the ID audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD and then the dominant signals are converted to 2D texture maps applying the GS filter. Then, the generated texture maps are fed to the modified self-attention SqueezeNet classifier, with reduced model width and depth as input for training and validation. In the experimental evaluation, two public benchmark datasets- ToyADMOS and MIMII are used to validate the model. Finally, the model classifies the machine audio faults using the trained features. The experimental results demonstrated that the hybrid EMD-Gammatone texture imaging outperforms the other state-of-the-art methods like MFCC, Gammatone, and Hilbert Huang Transform with a self-attention (SA) based SqueezeNet architecture. The EMD-Gammatone spectrum-based feature extraction accurately detected the faults by exhibiting an accuracy of 89.32% and 96.46% for MIMII and ToyADMOS datasets, respectively. In addition, with the EMD-Gammatone Spectrogram images, the proposed model outperforms the conventional

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Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars

Generative Pre-training for Paraphrase Generation by Represe...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Bui, Tien-Cuong Le, Van-Duc To, Hai-Thien Cha, Sang Kyun Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ Grad Sch Data Sci Seoul South Korea

ISBN: (纸本)9781728189246

Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems. Despite some encouraging results, recent methods either confront the problem of favoring generic utterance or need to retrain the model from scratch for each new dataset. This paper presents a novel approach to paraphrasing sentences, extended from the GPT-2 model. We develop a template masking technique, named first-order masking, to masked out irrelevant words in exemplars utilizing POS taggers. So that, the paraphrasing task is changed to predicting spans in masked templates. Our proposed approach outperforms competitive baselines, especially in the semantic preservation aspect. To prevent the model from being biased towards a given template, we introduce a technique, referred to as second-order masking, which utilizes Bernoulli distribution to control the visibility of the first-order-masked template's tokens. Moreover, this technique allows the model to provide various paraphrased sentences in testing by adjusting the second-order-masking level. For scale-up objectives, we compare the performance of two alternatives template-selection methods, which shows that they were equivalent in preserving semantic information.

关键词： Natural language processing Computational linguistics Neural Networks

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A BERT-enhanced Graph Neural Network for Knowledge Base Population

A BERT-enhanced Graph Neural Network for Knowledge Base Popu...

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international conference on big data and smart computing (bigcomp)

作者： Heechul Lim Min-Soo Kim Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology Republic of Korea Information and Communication Engineering Korea Advanced Institute of Science and Technology Republic of Korea

We present BGKBP, a deep-learning algorithm based on BERT, and a graph neural network for knowledge base population (KBP). Our experiments showed that a straightforward application of BERT and GNN on a large knowledge base (e.g., Wikidata) improves KBP quality and outperforms the previous state-of-the-art methods. We developed four techniques to improve the BGKBP’s KBP capability: (1) serialization, (2) fine-tuning, (3) node regression, and (4) text augmentation. BGKBP achieved the best F1 scores of 0.723 and 0.495 on entity linking and new entity detection, respectively. We further showed that using text augmentation (BGKBP-TA) achieved the best F1 score of 0.547 on relation linking, which is more difficult than entity linking because of the various representations of some of the relations.

关键词： Computational modeling Knowledge based systems Sociology Bit error rate big data Graph neural networks data models

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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2025 IEEE international conference on big data and smart computing, bigcomp 2025

作者： Zabin, Mahe Ayon, Syed Tasnimul Karim Siraj, Farhan Md Shuvo, Mehedi Hasan Choi, Ho-Jin Uddin, Jia Korea Advanced Institute of Science and Technology School of Computing Daejeon Korea Republic of BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Department of Computer Science and Engineering Dhaka Gazipur Bangladesh Woosong University AI and Big Data Department Endicott College Daejeon Korea Republic of

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis. © 2025 IEEE.

关键词： Deep learning

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MOAT:An improved multi-modal matching algorithm based on object attention and tensor fusion

MOAT:An improved multi-modal matching algorithm based on obj...

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international conference on big data and smart computing (bigcomp)

作者： Qian Xiong Xuanyi Wang Chao Jing School of Computer Science and Engineering Guilin University of Technology 12 Jiangan Road Guilin Guangxi China

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

This work proposes an improved multi-modal matching algorithm based on object attention and tensor fusion (MOAT). The key improvements in proposed algorithm consists of two parts: (1) In order to construct a semantic association model between multi-modal data, the object attention model with same feature is adopted for different modal features to generate modal features with semantic associations. (2) Based on the established semantic association model, the goal is to find the trade-off between matching accuracy and model complexity. Then, a multi-modal tensor fusion model is proposed built upon bilinear fusion, which mainly learns the matching score strategy for matching without constructing a common semantic space. Finally, MOAT is verified through two public datasets, MS COCO and Flickr30k, *** experimental results demonstrate that MOAT shows a better performance in recall with maximum improvement of $3.5 \%$ and $4.2 \%$ compared to CLIP model in image and text matching tasks.

关键词： Tensors Accuracy Attention mechanisms Computational modeling Semantics big data data models Complexity theory

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Improving Generation of Sentiment Commonsense by Bias Mitigation

Improving Generation of Sentiment Commonsense by Bias Mitiga...

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international conference on big data and smart computing (bigcomp)

作者： JinKyu Lee Jihie Kim Department of Artificial Intelligence Dongguk University Seoul Korea

Commonsense knowledge graphs (CSKG) are crucial for artificial intelligence systems to understand natural language. Recently, with the construction of COMET (Commonsense Transformer) and ATOMIC2020, a comprehensive coverage commonsense reasoning knowledge graph, CSKG research is increasingly vital in natural language understanding and reasoning. Since sentiment commonsense knowledge is understudied yet, our work focuses on improving the generation of sentiment commonsense in ATOMIC2020. We first show a problem in natural language generation that degrades the accuracy due to the unbalanced sentiment distribution in the dataset. Next, we propose the EDA (Easy data Augmentation) and UDA(Unsupervised data Augmentation) based methods that improve the accuracy through biased mitigation of the unbalanced dataset. Our experimental results show that EDA method has little effect on the accuracy, while UDA-based method has some accuracy improvements in ROUGE-I, ROUGE-2, and ROUGE-L.

关键词： Comets Tail Knowledge graphs big data Transformers Natural language processing Task analysis

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