Browsing by Author "Shuai, Hong-Han"

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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
(2018-07-01)
Shuai, Hong-Han
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Shen, Chih-Ya
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Yang, De-Nian
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Lan, Yi-Feng Carol
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Lee, Wang-Chien
The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.
BeautyGlow: On-Demand Makeup Transfer Framework with Reversible Generative Network
(2019-01-01)
Chen, Hung-Jen
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Hui, Ka-Ming
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Wang, Szu-Yu
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Tsao, Li-Wu
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Shuai, Hong-Han
As makeup has been widely-adopted for beautification, finding suitable makeup by virtual makeup applications becomes popular. Therefore, a recent line of studies proposes to transfer the makeup from a given reference makeup image to the source non-makeup one. However, it is still challenging due to the massive number of makeup combinations. To facilitate on-demand makeup transfer, in this work, we propose BeautyGlow that decompose the latent vectors of face images derived from the Glow model into makeup and non makeup latent vectors. Since there is no paired dataset, we formulate a new loss function to guide the decomposition. Afterward, the non-makeup latent vector of a source image and makeup latent vector of a reference image and are effectively combined and revert back to the image domain to derive the results. Experimental results show that the transfer quality of BeautyGlow is comparable to the state-of-the-art methods, while the unique ability to manipulate latent vectors allows BeautyGlow to realize on-demand makeup transfer.
Communications and Networking Technologies for Intelligent Drone Cruisers
(2019-01-01)
Wang, Li-Chun
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Lai, Chuan-Chi
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Shuai, Hong-Han
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Lin, Hsin-Piao
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Li, Chi-Yu
Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of self-configuration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following live technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.
Customer Purchase Behavior Prediction from Payment Datasets
(2018-01-01)
Wen, Yu-Ting
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Yeh, Pei-Wen
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Tsai, Tzu-Hao
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Peng, Wen-Chih
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Shuai, Hong-Han
With the advances in the development of mobile payments, a huge amount of payment data are collected by banks. User payment data offer a good dataset to depict customer behavior patterns. A comprehensive understanding of customers' purchase behavior is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. For example, by exploring customer behavior patterns, given a target store, a set of potential customers is able to be identified. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Here we propose a probability graphical model that exploits the payment data to discover customer purchase behavior in the spatial, temporal, payment amount and product category aspects, named STPC-PGM. As a result, the mobility behavior of an individual user could be predicted with a probabilistic graphical model that accounts for all aspects of each customer's relationship with the payment platform. To achieve real time advertising, we then develop an online framework that efficiently computes the prediction results. Our experiment results show that STPC-PGM is effective in discovering customers' profiling features, and outperforms the state-of-the-art methods in purchase behavior prediction. In addition, the prediction results are being deployed in the marketing of real-world credit card users, and have presented a significant growth in the advertising conversion rate.
Distributed and scalable sequential pattern mining through stream processing
(2017-11-01)
Chen, Chun-Chieh
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Shuai, Hong-Han
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Chen, Ming-Syan
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電機工程學系
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Department of Electrical and Computer Engineering
Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential pattern mining on the cloud (SPAMC), has already addressed the scalability problem. It supports the MapReduce cloud computing architecture for mining frequent sequential patterns on large datasets. However, this existing algorithm does not address the iterative mining problem, which is the problem that reloading data incur additional costs. Furthermore, it did not study the load balancing problem. To remedy these problems, we devised a powerful sequential pattern mining algorithm, the sequential pattern mining in the cloud-uniform distributed lexical sequence tree algorithm (SPAMC-UDLT), exploiting MapReduce and streaming processes. SPAMC-UDLT dramatically improves overall performance without launching multiple MapReduce rounds and provides perfect load balancing across machines in the cloud. The results show that SPAMC-UDLT can significantly reduce execution time, achieves extremely high scalability, and provides much better load balancing than existing algorithms in the cloud.
DRESSING FOR ATTENTION: OUTFIT BASED FASHION POPULARITY PREDICTION
(2019-01-01)
Lo, Ling
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Liu, Chia-Lin
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Lin, Rong-An
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Wu, Bo
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Shuai, Hong-Han
Accurate analysis of fashion trends is crucial. However, existing predictive algorithms of fashion popularity are restricted to be feasible on the coarse style level but not a finer item level. That is, they are only predictive in the future popularity of a given type of fashion styles (e.g., Rocker), but cannot be precisely down to a particular outfit look chosen by individuals. This paper thus proposes the first solution directly aimed at predicting the fine-grained fashion popularity of an outfit look by taking social media as the learning source. Particularly, a deep temporal sequence learning framework is developed and the proposed framework is evaluated on a real dataset of 380,000 street fashion images collected from the fashion website lookbook.nu. The experimental results show that our proposed framework outperforms the state-of-the-art approaches, with a relative increase of 11.51% to 27.62% (MSE metric) and 7.02% to 32.61% (CSE metric) in the prediction accuracy.
Eavesdropping Prevention for Heterogeneous Internet of Things Systems
(2018-01-01)
Liao, Chen-Hung
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Shuai, Hong-Han
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Wang, Li-Chun
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交大名義發表
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National Chiao Tung University
With the rapid growth of Internet of Things (IoT), the idea of integrating a variety of IoTs has been proposed to support a variety of novel applications. However, IoT devices are often deployed in an open environment in which IoTs are inclined to be malfunctioned. Although data reliability can be achieved by data recovery with conventional replication, the communication between IoTs is susceptible to eavesdropping. Since most of the current work focuses on designing the architectures for IoTs under the specific scenarios, the eavesdropping prevention for heterogeneous IoT systems remains unexplored. Therefore, in this paper, we consider using the network-coding-based distributed storage systems for security and show that repair bandwidth can be reduced by increasing storage per node. Moreover, we theoretically derive the relation between repair bandwidth and storage in heterogeneous IoT systems. Finally, we show the relation between storage cost and reliability with regard to different amounts of IoT devices.
FashionOn: Semantic-guided Image-based Virtual Try-on with Detailed Human and Clothing Information
(2019-01-01)
Hsieh, Chia-Wei
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Chen, Chieh-Yun
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Chou, Chien-Lung
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Shuai, Hong-Han
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Liu, Jiaying
The image-based virtual try-on system has attracted a lot of research attention. The virtual try-on task is challenging since synthesizing try-on images involves the estimation of 3D transformation from 2D images, which is an ill-posed problem. Therefore, most of the previous virtual try-on systems cannot solve difficult cases, e.g., body occlusions, wrinkles of clothes, and details of the hair. Moreover, the existing systems require the users to upload the image for the target pose, which is not user-friendly. In this paper, we aim to resolve the above challenges by proposing a novel FashionOn network to synthesize user images fitting different clothes in arbitrary poses to provide comprehensive information about how suitable the clothes are. Specifically, given a user image, an in-shop clothing image, and a target pose (can be arbitrarily manipulated by joint points), FashionOn learns to synthesize the try-on images by three important stages: pose-guided parsing translation, segmentation region coloring, and salient region refinement. Extensive experiments demonstrate that FashionOn maintains the details of clothing information (e.g., logo, pleat, lace), as well as resolves the body occlusion problem, and thus achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
FIT-ME: IMAGE-BASED VIRTUAL TRY-ON WITH ARBITRARY POSES
(2019-01-01)
Hsieh, Chia-Wei
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Chen, Chieh-Yun
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Chou, Chien-Lung
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Shuai, Hong-Han
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Cheng, Wen-Huang
The image-based virtual try-on system has raised research attention recently, but it still requires to upload an image of a user with the target pose. We present a novel learning model, Fit-Me network, to seamlessly fit in-shop clothing into a person image and simultaneously transform the pose of the person image to another given one. The proposed Fit-Me network helps users not only save the time used to change clothes physically but also provide comprehensive information about how suitable the clothes are. By facilitating the arbitrary pose transformation, we can generate consecutive poses to help users get more information for deciding whether to buy the clothes or not from different aspects.
Garment Detectives: Discovering Clothes and Its Genre in Consumer Photos
(2019-01-01)
Hidayati, Shintami C.
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Hua, Kai-Lung
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Tsao, Yu
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Shuai, Hong-Han
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Liu, Jiaying
Clothing image analysis has shown its potential for use in a wide range of applications such as personalized clothing recommendation. Given a consumer photo, this paper addresses the problem of finding clothes and recognizing the genre of that clothes. This problem is very challenging due to large variations of uncontrolled realistic imaging conditions. To tackle these challenges, we formulate a novel framework by integrating local features of multi-modality as the instances of the price-collecting Steiner tree (PCST) problem to discover clothing regions, and exploiting visual style elements to discover the clothing genre. The experimental results show that our fully automatic approach is effective to identify irregular shape of clothing region, and it significantly improves the accuracy of clothing genre recognition for images taken in unconstrained environment.
Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform
(2018-01-01)
Hsieh, Yu-Heng
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Chen, Chun-Chieh
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Shuai, Hong-Han
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Chen, Ming-Syan
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電子工程學系及電子研究所
Sequential pattern mining can be applied to various fields such as disease prediction and stock analysis. Many algorithms have been proposed for sequential pattern mining, together with acceleration methods. In this paper, we show that a heterogeneous platform with CPU and GPU is more suitable for sequential pattern mining than traditional CPU-based approaches since the support counting process is inherently succinct and repetitive. Therefore, we propose the PArallel SequenTial pAttern mining algorithm, referred to as PASTA, to accelerate sequential pattern mining by combining the merits of CPU and GPU computing. Explicitly, PASTA adopts the vertical bitmap representation of database to exploits the GPU parallelism. In addition, a pipeline strategy is proposed to ensure that both CPU and GPU on the heterogeneous platform operate concurrently to fully utilize the computing power of the platform. Furthermore, we develop a swapping scheme to mitigate the limited memory problem of the GPU hardware without decreasing the performance. Finally, comprehensive experiments are conducted to analyze PASTA with different baselines. The experiments show that PASTA outperforms the state-of-the-art algorithms by orders of magnitude on both real and synthetic datasets.
Multi-Source Learning for Sales Prediction
(2017-01-01)
Tsai, Kuen-Han
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Wang, Yau-Shian
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Kuo, Hsuan-Yu
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Tsai, Jui-Yi
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Chang, Ching-Chih
With the convenience and popularity of Internet, the sales on e-commerce platforms have grown significantly. This exponential growth generates massive chunks of data, which provides the opportunity to utilize the historical data for predicting the customers' behaviors and can thus offer better services. One of the major tasks is to correctly estimate the coming sales of products since the e-retailers can reserve the products in a smart way. However, even with massive data, it is still challenging to estimate the sale amount of each product due to 1) the complicated language structures, 2) difficulties in integrating the features, and 3) missing values. To address these issues, we propose a framework for predicting the sales, which contains two phases: 1) feature extraction from product attributes and reviews, and 2) tensor decomposition for multi-source learning. The experimental results show that our framework outperforms baselines by 73%.
Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions
(2018-01-01)
Shuai, Hong-Han
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Lien, Yen-Chieh
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Yang, De-Nian
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Lan, Yi-Feng
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Lee, Wang-Chien
While the popularity of online social network (OSN) apps continues to grow, little attention has been drawn to the increasing cases of Social Network Addictions (SNAs). In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages. Our idea, based on behavioral therapy, is to incrementally substitute highly addictive newsfeeds with safer, less addictive, and more supportive newsfeeds. To realize this idea, we propose a novel framework, called Newsfeed Substituting and Supporting System (N3S), for newsfeed filtering and dissemination in support of SNA interventions. New research challenges arise in 1) measuring the addictive degree of a newsfeed to an SNA patient, and 2) properly substituting addictive newsfeeds with safe ones based on psychological theories. To address these issues, we first propose the Additive Degree Model (ADM) to measure the addictive degrees of newsfeeds to different users. We then formulate a new optimization problem aiming to maximize the efficacy of behavioral therapy without sacrificing user preferences. Accordingly, we design a randomized algorithm with a theoretical bound. A user study with 716 Facebook users and 11 mental healthcare professionals around the world manifests that the addictive scores can be reduced by more than 30%. Moreover, experiments show that the correlation between the SNA scores and the addictive degrees quantified by the proposed model is much greater than that of state-of-the-art preference based models.
On Accelerating Multi-Layered Heterogeneous Network Embedding Learning
(2018-01-01)
Shuai, Hong-Han
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Tsai, Cheng-Ming
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Hsu, Yun-Jui
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Hsiao, Ta-Che
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電機工程學系
With the rapid growth of online social networks and IoT networks, mining valuable knowledge from the graph data become important. Meanwhile, as machine learning algorithms show their powers in prediction, different machine learning algorithms are proposed for different applications, e.g., personal recommendation, price prediction, communication anomaly detection. However, it is challenging to extract network features from graph data as the inputs for machine learning algorithms. One of the promising approaches is to use graph embedding approach, which extracts the valuable information of networks from each node into low dimensional vectors. However, the graph embedding approaches on a large-scale network require tremendous training time. Therefore, in this paper, we propose NOde Differentiation for Graph Embedding (NODGE) to prioritize the nodes, while high priority nodes are allocated with more resources to train their representations. We also theoretically analyze the proposed NODGE. Experimental results show that the proposed method reduces the training time of state-of-the-art method by at least 30.7%.
On Finding Socially Tenuous Groups for Online Social Networks
(2017-01-01)
Shen, Chih-Ya
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Huang, Liang-Hao
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Yang, De-Nian
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Shuai, Hong-Han
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Lee, Wang-Chien
Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.
Optimizing Social-Topic Engagement on Social Network and Knowledge Graph
(2019-01-01)
Teng, Ya-Wen
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Shi, Yishuo
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Tsai, Jui-Yi
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Shuai, Hong-Han
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Tai, Chih-Hua
Existing research on social networks manifests two crucial criteria to improve activity engagement of users: (1) user interests in the activity topics and (2) opportunities of making new friends with some acquaintances. However, current online platforms still involve massive manual selection for activity attendees and contents without proper recommendations. In this paper, therefore, we formulate a new activity organization problem, named Social Knowledge Group Query (SKGQ), to recommend attendees and topic-related contents simultaneously. We prove that SKGQ is NP-hard and design an approximation algorithm, named Social cOntent Knowledge Exploration (SOKE), to jointly choose the activity attendees and topic-related contents according to social-oriented and topic-oriented strategies. Simulation results manifest that the solution acquired by SOKE is close to the optimal solution and outperforms various baselines.
RNN-Assisted Network Coding for Secure Heterogeneous Internet of Things With Unreliable Storage
(2019-10-01)
Liao, Chen-Hung
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Shuai, Hong-Han
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Wang, Li-Chun
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電機工程學系
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Department of Electrical and Computer Engineering
With the rapid growth of Internet of Things (IoT), integrating a variety of IoT can result in novel applications. However, IoT devices are often deployed in an open environment where IoT are inclined to be malfunctioned. Although data reliability can be achieved by data recovery with conventional replication, the communication between IoT is susceptible to eavesdropping. Therefore, in this paper, we study the eavesdropping prevention of data repair in IoT environments based on network coding. We theoretically derive the relation between security level and storage in heterogeneous IoT systems. To further reduce the repair bandwidth, we exploit recurrent neural network for the storage failure prediction. Under the condition when failure probability and workloads of storage devices are considered, two allocation algorithms are proposed to avoid data repair. Finally, we show the relation between storage cost and reliability with different numbers of IoT devices. Experimental results manifest that the proposed allocation algorithms can outperform the baseline case by 18.4% in terms of the security level.
Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization
(2019-01-01)
Lai, Hsu-Chao
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Shuai, Hong-Han
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Yang, De-Nian
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Huang, Jiun-Long
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Lee, Wang-Chien
Recent technological advent in virtual reality (VR) has attracted a lot of attention to the VR shopping, which thus far is designed for a single user. In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions. We formulate the Multiview-Enabled Configuration Recommendation (MECR) problem to rank a set of displayed items for a VR shopping user. We design the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, Multi-View Display (MVD) configurations, and multiple feedback with content features. Experimental results manifest that the proposed approach outperforms personalized recommendations and group recommendations by at least 30.8% in large-scale datasets and 63.3% in the user study in terms of hit ratio and mean average precision.