臺灣博碩士論文加值系統

在本文中，我們探討了如何在動態環境中優化資源利用的三個面向，其中包含增進廣告影響的持續力，為了減少醫院負擔進行優化醫療資源配置，以及優化眾包資源配置策略。

首先針對延續效益的議題，我們探討了一種新穎的病毒式行銷法，目標是維持網路中的影響力。我們從真實的案例中學習，例如用於ALS的「冰桶挑戰」，並在市場行銷推廣中發現「來得快去得也快」的現象。這樣的自然現象在先前的文獻中完全沒有探討過，但它會違反許多試圖獲得長久關注和支持的行銷策略的直覺。因此，我們特別關注「影響力可持續性」的問題，以達成對網路有長期且有效的影響。給定一組初始種子S和一個門檻值ρ，影響力可持續性的目標是最佳化S中的種子激活時間，以便最大化影響持續時間，其中的每次持續時間代表激活超過ρ個的非激活節點的數量。由於其NP困難性質而影響力可持續性問題變
得更具有挑戰性。

其次，受全球暖化的影響，蚊子所造成（或藉由蚊子傳播的疾病）的流行病變得更趨嚴重，例如登革熱和茲卡病毒。據報導所言，由於難以預料的疾病大爆發，疫情可能給醫院管理帶來許多挑戰。此外，在傳播疾病（例如登革熱（迄今為止尚未確定適當的治療））期間的不完全護理可能導致應該避免的死亡率上升。本文提出了一種優化醫療資源配置的新方式，以在都市圈疫情爆發期間減緩醫院的超收病患情況。在本文中，我們是首篇探討了兩個重要問題的論文，包括評估服務質量的策略和動態調度醫療資源的解決方案，以及疫情爆發的空間變化。正如我們在台南（2015）發生登革熱爆發的真實數據的實驗結果中所證實的結
果，我們提出了我們的框架在醫療資源分配中部署動態資源配置策略的可能性。

最後，研究人員和科學家近年來一直在使用眾包平台來收集訓練數據上的標記。此過程具有成本效益和可擴展性，但研究表明，由於工人偏見，工作差異和任務難度等原因，真實姓推論的質量不穩定。在這項工作中，我們提出了一個混合系統，匯集了訓練有素的領域專家隊伍和眾多眾包平台，為行業級分類引擎收集高質量的培訓數據。我們展示如何通過質量控制策略獲得高品質的標籤數據，這些策略可動態且實惠有效地利用領域專家和眾包的優勢。

In this thesis, we discussed three significant aspects that need to be considered in the resource utilization of the evolving environment. There are mainly three portions in this thesis, including strengthening the duration of advertising impact, optimizing medical resource placement
to reduce hospital burden and improving resource allocation strategies in crowdsourcing.

First of all, for the topic of strengthens the sustainability, we study a novel paradigm of viral marketing with the goal to sustain the influential effectiveness in the network. We study from real cases such as the Ice Bucket Challenges for the ALS awareness, and figure out the ”easy come and easy go” phenomenon in the marketing promotion. Such a natural property is
fully unexplored in the literature, but it will violate the need of many marketing applications which attempt to receive the perpetual attention and support. We thus highlight the problem of Influential Sustainability, to pursue the long-term and effective influence on the network. Given the set of initial seeds S and a threshold ρ, the goal of Influential Sustainability is to best decide the timing to activate each seed in S so as to maximize the number of iterations in which each iteration will activate the number of inactive nodes more than ρ. The Influential Sustainability problem is challenging due to its #P-hard nature.

Secondly, with the effects of global warming, some epidemic diseases via mosquito (e.g. mosquito-borne diseases) become more serious, such as dengue fever and zika virus. It is reported that the epidemic disease may cause many challenges to the hospital management due to the unexpected burst with uncertain reasons. Furthermore, the imperfect cares during the propagation of epidemic diseases, such as dengue fever (so far the appropriate treatment is not well established), may lead to the increasing mortality rate which should be avoided. In this thesis, a novel paradigm for optimizing the placement of medical resource is proposed in pursuit
of reducing the overloading cases in hospitals during the epidemic outbreak in the urban area. In this thesis, we are the first thesis to explore two important issues, including the strategy to evaluate the service quality and the solution to dynamically dispatch the medical resource, along with the spatial variation of the epidemic outbreak. As validated in our experimental results in real data of dengue outbreak happening in Tainan (2015), we present the feasibility of our framework to deploy a dynamic placement strategy for medical resource assignment.

Lastly, researchers and scientists have been using crowdsourcing platforms to collect labeled training data in recent years. The process is cost-effective and scalable, but research has shown that the quality of truth inference is unstable due to worker bias, work variance, and task difficulty. In this work, we present a hybrid system that brings together a well-trained troop of domain experts and the multitudes of a crowdsourcing platform to collect high-quality training data for industry-level classification engines. We show how to acquire high quality labeled data through quality control strategies that dynamically and cost-effectively leverage the strengths of both domain experts and crowdsourcing.

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Resource Utilization for Influential Sustainability in Social Media . . . . 2
1.2.2 Resource Utilization for Easing the Hospital Congestion in Public Health
Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.3 Resource Utilization in Crowdsourcing . . . . . . . . . . . . . . . . . . . 5
1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Information Sustainability on Social Networks . . . . . . . . . . . . . . . . . . . . . . 8
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Influence Sustainability on Static Networks . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Greedy Heuristic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 19
vi
2.3.3 IS_Break Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.4 IS_UpperBound Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.5 IS_Last Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Dynamic Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.1 Observation on Algorithm Scalability . . . . . . . . . . . . . . . . . . . . 31
2.5.2 Observation on Propagation . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.5.3 Observation on Dynamic Sustainability Algorithm . . . . . . . . . . . . . 37
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 Spatiotemporal Resource Placement for Easing the Hospital Congestion . . . . . . . . 42
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Problems and Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.2 Objective and Allocation Algorithms . . . . . . . . . . . . . . . . . . . . 49
3.4 Approximate Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4.1 Problem Definition and Challenges . . . . . . . . . . . . . . . . . . . . . 56
3.4.2 Greedy Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4.3 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5.1 Data Description and Experimental Setup . . . . . . . . . . . . . . . . . 58
3.5.2 Observation on Algorithm Scalability . . . . . . . . . . . . . . . . . . . . 62
vii
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4 Sequential Resource Allocation for Crowdsourcing . . . . . . . . . . . . . . . . . . . . 67
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.3 The MAMO Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.1 Framework Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.3 Macro Assignment (MA) Strategies . . . . . . . . . . . . . . . . . . . . . 78
4.4 Micro Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4.1 Greedy Algorithmbased on RoI (Greedy-R) . . . . . . . . . . . . . . . . 80
4.4.2 Greedy Algorithmbased on Pre-confidence and RoI (Greedy-PR) . . . . 81
4.4.3 Dynamic Programming (DP) . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.1 Data Description and Experimental Setup . . . . . . . . . . . . . . . . . 82
4.5.2 Comparison between Different Scenarios . . . . . . . . . . . . . . . . . . 84
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

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