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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41-50 订阅

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TCAC-GAN: Synthetic Trajectory Generation Model Using Auxiliary Classifier Generative Adversarial Networks for Improved Protection of Trajectory data

TCAC-GAN: Synthetic Trajectory Generation Model Using Auxili...

引用

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

作者： Shin, Jihwan Song, Yeji Ahn, Jinhyun Lee, Taewhi Kwangwoon Univ Dept Artificial Seoul South Korea Jeju Natl Univ Dept Management Informat Syst Jeju South Korea Telecommunicat Res Inst Blockchain & Big Data Res Dept Daejeon South Korea

ISBN: (纸本)9781665475785

Mobile social networking (MSN) is gaining significant popularity owing to location-based services (LBS) and personalized services. This direct location sharing increases the risk of infringing the user's location privacy. In order to protect the location privacy of users, many studies on generating synthetic trajectory data using generative adversarial networks (GANs) are being conducted. However, GAN generates limited synthesis trajectory data due to mode collapse problem. In this paper, we propose a trajectory category auxiliary classifier-GAN (TCAC-GAN) that generates synthetic trajectory data with improved utility and anonymity by reducing mode collapse using ACGAN. In experiments, the performance of utility and anonymity of TCAC-GAN is compared with LSTM-TrajGAN.

关键词： generative adversarial networks ACGAN trajectory data synthetic trajectory generation privacy protection

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CLIP: Train Faster with Less data

CLIP: Train Faster with Less Data

引用

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

作者： Khan, Muhammad Asif Hamila, Ridha Menouar, Hamid Qatar Univ Qatar Mobil Innovat Ctr QMIC Doha Qatar Qatar Univ Elect Engn Doha Qatar

ISBN: (纸本)9781665475785

Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In data-centric approaches, the focus is to refine and improve the quality of the data to improve the learning performance of the models rather than redesigning model architectures. In this paper, we propose CLIP i.e., Curriculum Learning with Iterative data Pruning. CLIP combines two data-centric approaches i.e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed. The proposed scheme applies loss-aware dataset pruning to iteratively remove the least significant samples and progressively reduces the size of the effective dataset in the curriculum learning training. Extensive experiments performed on crowd density estimation models validate the notion behind combining the two approaches by reducing the convergence time and improving generalization. To our knowledge, the idea of data pruning as an embedded process in curriculum learning is novel.

关键词： Convergence crowd counting curriculum learning data-centric pruning

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A Comparative Study on Bengali Speech Sentiment Analysis Based on Audio data

A Comparative Study on Bengali Speech Sentiment Analysis Bas...

引用

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

作者： Shruti, Abanti Chakraborty Rifat, Rakib Hossain Kamal, Marufa Alam, Md. Golam Rabiul BRAC Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh

ISBN: (纸本)9781665475785

Sentiment analysis is one of the most researched areas for every language. Due to the rise of AI, the use of speech in every sector is rapidly growing so is the importance of Speech Sentiment Analysis. Despite being the seventh most spoken language in the world, Bengali speech sentiment analysis studies are not much enriched. This study compared the Bengali speech sentiment analysis using machine learning and CNN, LSTM, and Bi-LSTM models. We have used the SUBESCO and BanglaSER datasets for training our models where the KNN model outperformed other models with an accuracy of 90%. Later, we evaluated the performance of the models with our custom-made test dataset. Experimental results show that AdaBoost and Bi-LSTM model performed best with 45% accuracy. Moreover, to understand the feature effect on the output, we used the interpretable SHAP model in the ML model outcomes as they provide the best results allowing us to have an explainable advantage to determine the results.

关键词： Bangla Sentiment Analysis Machine Learning CNN SHAP MFCC KNN AdaBoost Random Forest Explanable AI LSTM Bi-LSTM

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data-driven Digital Therapeutics Analytics

Data-driven Digital Therapeutics Analytics

引用

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

作者： Lee, Uichin Jung, Gyuwon Park, Sangjun Ma, Eun-Yeol Kim, Heeyoung Lee, Yonggeon Noh, Youngtae Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Korea Adv Inst Sci & Technol Ind & Syst Engn Daejeon South Korea KENTECH Energy AI Naju Jeollanam Do South Korea

ISBN: (纸本)9781665475785

Digital therapeutics (DTx), in contrast to traditional treatments such as pills, use software installed in smartphones or wearable devices as a medical device to cure diseases and improve health conditions, which represents a significant departure from existing wellness products such as Fitbits. DTx requires clinical validation of efficacy through systematic clinical trials, as do conventional therapeutics. Mobile DTx apps transform conventional treatment approaches such as counseling, self-help, and self-tracking into app-based micro-interventions that can be delivered via notifications, short videos, and chatbots. This article presents a data-driven DTx analytics framework for analyzing and optimizing DTx delivery processes in everyday life contexts by leveraging passive sensor data analysis and human-in-the-loop interaction support.

关键词： Digital Therapeutics DTx data Analysis Proactive Engagement Management

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Predicting Next Application Most Likely Used with Word Embedding and Time-Series data Encoding

Predicting Next Application Most Likely Used with Word Embed...

引用

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

作者： Song, Taieui Gweon, Gahgene Seoul Natl Univ Dept Intelligence & Informat Seoul South Korea

ISBN: (纸本)9781665475785

Predicting Next Application Most Likely Used, referred to as NAMLU, is a problem predicting what application a user will use at the next using prediction model. In this study, we propose the Time Series Deep learning (TED) model, which has two components that yield better model performance for the NAMLU task than the NAP (Natural App Processing), which is the existing state-of-the-art model. The TED model proposes the following architectural changes to address two challenges (1) To reduce data representation building cost, we use word embedding and time-series data encoding (2) To increase the model's inference speed, we use CNN-based deep learning architecture. Two experiments were conducted to measure the best data embedding and encoding representation as well as the TED model performance. The experiments showed that using Doc2Vec for embedding and using Gramian Angular Field (GAF) for encoding yields the best performance at 84.5% (Recall@5). This result is about 5% points higher than the SOTA model, NAP. In addition, through experiments, it was confirmed that using the transfer learning model improves prediction performance by 4% compared to the user independent model.

关键词： Next App Most Likely Used Word Embedding Time-series data Encoding Convolutional Neural Network Mobile

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Can Eye Gaze Improve Emotional State Detection on Off the Shelf smart Devices

Can Eye Gaze Improve Emotional State Detection on Off the Sh...

引用

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

作者： Kim, Jiwan Lee, Doyoung Kim, Jaeho Oakley, Ian UNIST Interact Lab Ulsan South Korea Hanyang Univ Digital Healthcare Ctr Seoul South Korea UNIST Embedded Al Lab Ulsan South Korea

ISBN: (纸本)9781665475785

smartphones and wearable technology have revolutionized digital healthcare through the use of rich sensor data. Digital phenotyping, a field that uses smartphone data to detect or recognize cognitive, behavioral, or affective states and traits, is often based on data from sensors (such as inertial or touch sensors), activity or user logs, and user-generated content. In this paper, we propose the use of eye gaze as a new digital biomarker for affect detection, leveraging the advanced capabilities of off-the-shelf smart devices. We designed two studies in the Instagram use scenario to detect emotional state change using gaze data. The first study took place in a controlled setting and help us understand the value of gaze features for affect detection. In this study, we achieved a peak accuracy of 76.4% for binary valence detection. The second study will be conducted over a longer period in real-world settings, with a larger population, to assess the effectiveness of our approach. By including this new sensing modality in affective digital phenotyping, we plan to improve the reliability and robustness of emotional state detection.

关键词： digital phenotyping smartphones gaze

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Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Adverse Drug Reaction Posts Detection with a Bi-LSTM based a...

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

作者： Lee, Chung-Chun Lee, Seunghee Song, Mi Hwa Lee, Suehyun Konyang Univ Coll Med Dept Biomed Informat Daejeon South Korea Konyang Univ Med Ctr Healthcare Data Sci Ctr Daejeon South Korea Semyung Univ Div Informat & Commun Sci Jecheon South Korea

ISBN: (纸本)9781665475785

Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.

关键词： ADR classification pharmacovigilance concept pipeline SNS

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Towards an Effective Over-The-Top Platform Service: A Machine Learning Approach for Box Office Analysis

Towards an Effective Over-The-Top Platform Service: A Machin...

引用

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

作者： Leem, Subeen Oh, Jisong Moon, Jihoon Soonchunhyang Univ Dept Med Sci Asan South Korea Soonchunhyang Univ Dept AI & Big Data Asan South Korea

ISBN: (纸本)9781665475785

We developed a machine learning approach for trend analysis of box office performance on online platforms. We first collected the top 300 box office data points, including movie name, release date, production country, genre, director, actor, and screening time from the Korean Film Council online integrated computer network. We preprocessed these data to configure a total of 1030 input variables and then used a deep autoencoder and uniform manifold approximation and projection to reduce them to two input variables. Next, we used a k-means clustering algorithm to cluster movies with similar characteristics on the reduced data. We constructed an extreme gradient boosting (XGBoost) model using the 1030 original variables and cluster results as input and output variables for each cluster. We finally demonstrated the interpretability of the XGBoost model using Shapley additive explanations to understand the clustering results. The proposed method is expected to assist in the marketing strategy of domestic entertainment companies because it can be used for casting for movie production, determining release time, and adjusting running time by determining elements that determine box office hits.

关键词： box office analysis data reduction data clustering ensemble learning explainable artificial intelligence

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AnyFace: A data-Centric Approach For Input-Agnostic Face Detection

AnyFace: A Data-Centric Approach For Input-Agnostic Face Det...

引用

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

作者： Kuzdeuov, Askat Koishigarina, Darina Varol, Huseyin Atakan Nazarbayev Univ Inst Smart Syst & AI Astana Kazakhstan Nazarbayev Univ Dept Robot & Mechatron Astana Kazakhstan

ISBN: (纸本)9781665475785

Face detection is a mandatory step in many computer vision applications, such as face recognition, emotion recognition, age detection, virtual makeup, and vital sign monitoring. Thanks to advancements in deep learning and the introduction of annotated large-scale datasets, numerous applications have been developed for human faces. Recently, other domains, such as animals and cartoon characters, have started gaining attention but still lag far behind human faces. The biggest challenge is the limited number of annotated face datasets in these domains. The manual labeling of large-scale datasets is tedious and requires substantial human labor. In this regard, we present an input-agnostic face detector to ease the annotation of various face datasets. We propose a simple but effective data-centric approach instead of building a specific neural network architecture. Specifically, we trained a face detection model, YOLO5Face, on human, animal, and cartoon face datasets. The experiments show that the model can achieve accurate results in all domains. In addition, the model achieved decent results for animals and cartoon characters different from the ones in the training set. This implies that the model can extract agnostic facial features. We have made the source code and pre-trained models publicly available at https://***/IS2AI/AnyFace to stimulate research in these fields.

关键词： face detection input-agnostic models datacentric AI deep learning

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Deep Learning-based AOI System for Detecting Component Marks

Deep Learning-based AOI System for Detecting Component Marks

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

作者： Chang, Yi-Ming Lin, Ti-Li Chi, Hung-Chun Lin, Wei-Kai Univ Sci Ind Smart Mfg Dept Nantou Taiwan

ISBN: (纸本)9781665475785

While the automated optical inspection (AOI) was adopted for the surface mounted technology (SMT) on printing circuit board assembly (PCBA), it inspected that the products with miniaturized components and high-density placement of components on boards are becoming important issues. Especially, due to the manufacturing trend in the high-mix and low volume, the detection of the component marks with wide variation are growing into even more challengeable. As for the defect detection of AOI machine by using the similarity technology, it is very sensitive to different marks, including the different brightness of the font, spacing, brand, word shift and direction. Therefore, it implies that the AOI performance which is reduced as the AOI engineer fails to solve the demanding problem, and results in more time on checking the alarmed images by a human operator. Also, it affects the production yield rate and overall equipment effectiveness. Consequently, in this paper, a novel architecture is proposed to solve such a problem. That is, the classification method combined with similarity measurement on basis of the deep learning structure. The technique we used takes the MobileNet lightweight model to modify as the output with double stages to prevent the escape from occurring. The proposed method is approved by the manufacturing line to produce the products due to its no escape occurred, easier model training and faster implement. The proposed method provides a high improved rate of the average 90% with the zero-leakage rate in SMT process according to the experimental results.

关键词： surface mount technology (SMT) printing circuit board (PCB) printing circuit board assembly (PCBA) styling automated optical inspection (AOI) convolutional neural networks (CNN)

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