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2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献

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Using AHP to Choose the Best Logistics Distribution Model 运用层次分析法选择最佳物流配送模式
M. Lai, Wu-Der Tsay, Zhen Wang
Choosing an appropriate distribution model for an e-commerce enterprise will undoubtedly greatly strengthen the core competitiveness of the enterprise. This paper aims to explore decision making of the best logistics distribution model to improve the distribution efficiency by taking the Dangdang E-commerce as an example. The research method used is Analytic Hierarchy Process (AHP), firstly to clarify the various impact indicators of the logistics distribution model selection, and then deeply analyze the factors affecting Dangdang's logistics distribution choices. The relative weights of each index are obtained through measurement, and then the scores are evaluated to obtain the priority of distribution model. The final delivery model provides Dangdang E-commerce recommendation.
电子商务企业选择合适的分销模式,无疑会大大增强企业的核心竞争力。本文旨在以当当电子商务为例,探讨最佳物流配送模式的决策,以提高配送效率。本文采用层次分析法(AHP)作为研究方法,首先厘清影响物流配送模式选择的各种指标,然后深入分析影响当当物流配送选择的因素。通过测量得到各指标的相对权重,然后对得分进行评价,得到分布模型的优先级。最终的配送模式为当当电子商务提供推荐。
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引用次数: 1
Examination of Indicators for Estimating Players' Strength by Using Computer Go 用计算机围棋评估棋手实力指标的检验
Yuuto Kosaka, Takeshi Ito
This study proposes a method to estimate players' skill using Computer Go. Computer Go has been considered one of the biggest challenges of artificial intelligence (AI) research. The AI of Go is based on the Monte Carlo tree search (MCTS) algorithm unlike the games of chess and shogi which are based on the game tree search using the evaluation function. Further, we apply the evaluation index used in the strength estimation method for shogi to the game of Go. We analyze the game records of KGS and YUUGEN-NO-MA with the evaluation index using the MCTS winning rate. It is concluded that stronger AI is necessary for identifying strength-estimating indicators.
本研究提出一种评估棋手围棋水平的方法。计算机围棋被认为是人工智能(AI)研究的最大挑战之一。围棋的人工智能是基于蒙特卡洛树搜索(Monte Carlo tree search, MCTS)算法,而国际象棋和将军棋则是基于使用评价函数的游戏树搜索。在此基础上,将将棋强度估计方法中的评价指标应用到围棋中。采用MCTS胜率评价指标对KGS和YUUGEN-NO-MA的比赛记录进行了分析。结论是需要更强的人工智能来识别强度估计指标。
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引用次数: 1
Machine Learning Based Path Prediction System - Adapting One Model for All Intersections 基于机器学习的路径预测系统-适用于所有路口的一个模型
Kai-Qi Huang, Min-Te Sun
To reduce the number of accidents, this thesis proposes a vehicle path prediction system to predict the future direction when a vehicle is about to cross an intersection. The GPS sensor is used to collect the dataset of vehicle trajectories at intersections. The trend of vehicle movements are derived from the heading in the trajectories, which is then combined with the vehicle speed to generate training data. In our path prediction algorithm, two ensemble learning algorithms, i.e., Random Forests and AdaBoost, are adopted for model training. The experiment results indicate that the Random Forest algorithm exhibits the best performance, and the Adaboost algorithm performs better than the base learner (i.e., Decision Tree).
为了减少事故的发生,本文提出了一种车辆路径预测系统,用于预测车辆即将通过十字路口时的未来方向。GPS传感器用于收集十字路口车辆轨迹数据集。从轨迹中的航向得到车辆的运动趋势,然后将其与车速相结合生成训练数据。在我们的路径预测算法中,采用随机森林和AdaBoost两种集成学习算法进行模型训练。实验结果表明,随机森林算法表现出最好的性能,Adaboost算法表现优于基础学习器(即决策树)。
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引用次数: 3
On the Effect of Data Imbalance for Multi-Label Pedestrian Attribute Recognition 数据不平衡对多标签行人属性识别的影响
T. Wang, Kai-Chen Shu, Chia-Hao Chang, Yi-Fu Chen
Pedestrian attribute recognition has many applications in surveillance and attribute based query, tracking, and person re-identification. The recent trend in deep-learning based pedestrian attribute recognition is to use a shared CNN backbone for feature extraction and multiple subsequent branches for the individual branches. While this allows the end-to-end learning to simultaneously recognize multiple attributes, the data imbalance problem of most attributes becomes a challenge that has not been studied sufficiently for this application. This paper presents studies on how the cost adjustment method affects several common evaluation metrics. We also propose a two-stage training procedure, where an additional fine-tuning stage on the classifier layers only with class-balanced data is shown to improve recognition performances.
行人属性识别在监控和基于属性的查询、跟踪、人员再识别等方面有着广泛的应用。基于深度学习的行人属性识别的最新趋势是使用共享的CNN主干进行特征提取,使用多个后续分支进行单个分支。虽然这允许端到端学习同时识别多个属性,但大多数属性的数据不平衡问题成为该应用程序尚未充分研究的挑战。本文研究了成本调整方法对几种常用评价指标的影响。我们还提出了一个两阶段的训练过程,其中仅在类平衡数据的分类器层上进行额外的微调阶段可以提高识别性能。
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引用次数: 2
Using Machine Learning Algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting 机器学习算法在心脏骤停用药预警系统构建与预测中的应用
Hsiao-ko Chang, Cheng-Tse Wu, Ji-Han Liu, J. Jang
Target-In this paper, we focus on using medicine for patients who have cardiac arrest then must have to do Cardiopulmonary Resuscitation (CPR). We want to know the medicine influence in predicting state of an illness deterioration. Therefore, we proposes a Medication for Cardiac Arrest Early Warning System (MCAEWS). It's not only assist physicians to early diagnose of an illness and immediately warning, but also increase sensitivity, decrease false positive rate and mortality rate. The most important role is greatly improve medical quality. Methods-In this study, the data is from the emergency department of National Taiwan University Hospital (NTUH). It is from January 2014 to December 2015. The patients who stayed in the emergency detention area for more than six hours during this two years. The patients were included in the retrospective cohort study. To comparative measures for the machine learning models, we used such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area under the Precision-Recall Curve (AUPRC). Results-The data were analyzed for CPR and non-CPR groups respectively. Furthermore, we evaluated sensitivity and specificity. The Random Forest Algorithm (AUC: 0.98; AUP: 0.23) compare with others such as Logistic Regression Algorithm (AUC: 0.94; AUP: 0.13), Decision Tree (AUC: 0.97; AUP: 0.05), and Extreme Random Tree (AUC: 0.91; AUP: 0.08), it was significantly high performance. Conclusion-Increasing the drug factors in vital signs, that it effectively improved the accuracy of predicting cardiac arrest. The results of this study, it's help for emergency clinical Physicians and hospital quality management will validly solve clinical medical resource allocation issues and improve medical quality through decision support systems.
目的:在本文中,我们着重于使用药物的病人谁有心脏骤停,然后必须做心肺复苏(CPR)。我们想知道药物对预测疾病恶化状态的影响。因此,我们提出了一个心脏骤停药物预警系统(MCAEWS)。它不仅可以帮助医生早期诊断疾病并及时预警,而且可以提高灵敏度,降低假阳性率和死亡率。最重要的作用是大大提高医疗质量。方法:本研究资料来自国立台湾大学附属医院急诊科。从2014年1月到2015年12月。两年内在紧急拘留区停留超过6小时的患者。这些患者被纳入回顾性队列研究。为了比较机器学习模型的度量,我们使用了诸如Receiver Operating Characteristic Curve (AUROC)和Precision-Recall Curve (AUPRC)下的面积。结果:分别对心肺复苏术组和非心肺复苏术组进行数据分析。此外,我们评估了敏感性和特异性。随机森林算法(AUC: 0.98;AUC: 0.94)与Logistic回归算法(AUC: 0.94;AUP: 0.13),决策树(AUC: 0.97;AUP: 0.05),极端随机树(AUC: 0.91;AUP: 0.08),表现为显著的高性能。结论:增加生命体征中的药物因素,可有效提高心脏骤停预测的准确性。本研究结果有助于急诊临床医师和医院质量管理,通过决策支持系统有效解决临床医疗资源配置问题,提高医疗质量。
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引用次数: 3
A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization 基于时间序列数据分析、回归和正则化的PM2.5快速预报方法
Cyuan-Heng Luo, Hsuan Yang, Li-Pang Huang, Sachit Mahajan, Ling-Jyh Chen
The problem of air pollution has become a serious issue in developed as well as developing countries. Unfortunately, most of the current solutions are not very effective and this makes it important to have an efficient early warning system for monitoring and forecasting air quality. Our main focus is to build a real-time forecasting system with high accuracy, and deploy it in Taiwan. In this paper, we propose a forecast method called Adaptive Iterative Forecast (AIF), which can predict the value of PM2.5 for the next few hours (by linear programming, normalization and time-series) based on the trend of historical data. The goal of this research is to develop an efficient and accurate forecast model. Through various comparative analyses, we have proved that our model can achieve significant results. Based on the results, we have also built a real-time forecasting system which allows the users to stay aware of the air quality and plan their day to day life.
空气污染问题在发达国家和发展中国家都已成为一个严重的问题。不幸的是,目前的大多数解决方案都不是很有效,这使得建立一个有效的早期预警系统来监测和预测空气质量变得非常重要。我们的主要目标是建立一个高精度的实时预报系统,并在台湾部署。本文提出了一种自适应迭代预测(AIF)的预测方法,该方法可以根据历史数据的趋势(通过线性规划、归一化和时间序列)预测未来几个小时的PM2.5值。本研究的目的是建立一个高效、准确的预测模型。通过各种对比分析,我们证明了我们的模型可以取得显著的效果。根据结果,我们还建立了一个实时预报系统,让用户随时了解空气质量,并计划他们的日常生活。
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引用次数: 14
Alternative Multitask Training for Evaluation Functions in Game of Go 围棋评估函数的可选多任务训练
Yusaku Mandai, Tomoyuki Kaneko
For the game of Go, Chess, and Shogi (Japanese Chess), deep neural networks (DNNs) have contributed to building accurate evaluation functions, and many studies have attempted to create the so-called value network, which predicts the reward of a given state. A recent study of the value network for the game of Go has shown that a two-headed neural network with two different objectives can be trained effectively and performs better than a single-headed network. One of the two heads is called a value head and the other head, the policy head, predicts the next move at a given state. This multitask training makes the network more robust and improves the generalization performance. In this paper, we show that a simple discriminator network is an alternative target of multitask learning. Compared to the existing deep neural network, our proposed network can be designed more easily because of its simple output. Our experimental results showed that our discriminative target also makes the learning stable and the evaluation function trained by our method is comparable to the training of existing studies in terms of predicting the next move and playing strength.
对于围棋、国际象棋和日本象棋,深度神经网络(dnn)有助于构建准确的评估函数,许多研究试图创建所谓的价值网络,预测给定状态的奖励。最近一项关于围棋价值网络的研究表明,具有两个不同目标的双头神经网络可以有效地训练,并且比单头神经网络表现更好。两个头像中的一个被称为价值头像,另一个头像,策略头像,预测在给定状态下的下一步行动。这种多任务训练使网络具有更强的鲁棒性,提高了泛化性能。在本文中,我们证明了一个简单的鉴别器网络是多任务学习的一个备选目标。与现有的深度神经网络相比,由于输出简单,我们的网络可以更容易地设计。我们的实验结果表明,我们的判别目标也使学习变得稳定,并且我们的方法训练的评价函数在预测下一步和打法强度方面可以与现有研究的训练相媲美。
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引用次数: 4
AlphaZero for a Non-Deterministic Game 用于非确定性游戏的AlphaZero
Chu-Hsuan Hsueh, I-Chen Wu, Jr-Chang Chen, T. Hsu
The AlphaZero algorithm, developed by DeepMind, achieved superhuman levels of play in the games of chess, shogi, and Go, by learning without domain-specific knowledge except game rules. This paper investigates whether the algorithm can also learn theoretical values and optimal plays for non-deterministic games. Since the theoretical values of such games are expected win rates, not a simple win, loss, or draw, it is worthy investigating the ability of the AlphaZero algorithm to approximate expected win rates of positions. This paper also studies how the algorithm is influenced by a set of hyper-parameters. The tested non-deterministic game is a reduced and solved version of Chinese dark chess (CDC), called 2×4 CDC. The experiments show that the AlphaZero algorithm converges nearly to the theoretical values and the optimal plays in many of the settings of the hyper-parameters. To our knowledge, this is the first research paper that applies the AlphaZero algorithm to non-deterministic games.
由DeepMind开发的AlphaZero算法在国际象棋、将棋和围棋等游戏中达到了超人的水平,通过学习,除了游戏规则之外没有特定领域的知识。本文研究了该算法是否也可以学习非确定性博弈的理论值和最优玩法。由于这类游戏的理论值是预期胜率,而不是简单的赢、输或平局,因此值得研究AlphaZero算法近似位置预期胜率的能力。本文还研究了一组超参数对算法的影响。被测试的非确定性游戏是中国黑棋(CDC)的简化解版,称为2×4 CDC。实验表明,AlphaZero算法收敛于理论值,并且在许多超参数的设置中发挥了最优的作用。据我们所知,这是第一篇将AlphaZero算法应用于非确定性博弈的研究论文。
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引用次数: 4
Privacy-Preserving SRS Data Anonymization by Incorporating Missing Values 结合缺失值的SRS数据匿名化保护隐私
Wen-Yang Lin, Kuang-Yung Hsu, Zih-Xun Shen
Spontaneous Reporting Systems (SRSs) refer to systems used to collect voluntary reporting of adverse drug events (ADEs), which usually contain sensitive personal privacy information. Although many scholars have proposed various privacy protection models, they overlooked characteristics of SRS data. We previously have proposed a feasible privacy model and anonymization method dedicate to SRS data. However, this method is only applicable to complete data, not considering the fact that SRS data contain a lot of missing data. In this paper, we propose a new privacy model Closed MS(k, θ*)-bounding and a new anonymization method, Closed-MSpartition, to process SRS data with missing values. We used US FDA's FAERS data to evaluate our proposed method from the aspects of information loss, privacy risk, and data utility. The results show that our proposed new method can effectively prevent attackers from learning personal privacy without sacrificing data quality and utility.
自发报告系统(srs)是指用于收集药物不良事件(ade)自愿报告的系统,其中通常包含敏感的个人隐私信息。虽然许多学者提出了各种隐私保护模型,但都忽略了SRS数据的特点。我们之前已经针对SRS数据提出了一种可行的隐私模型和匿名化方法。但是,这种方法只适用于完整的数据,没有考虑到SRS数据中存在大量的缺失数据。本文提出了一种新的隐私模型Closed MS(k, θ*)-bounding和一种新的匿名化方法Closed- mpartitioning来处理存在缺失值的SRS数据。我们使用美国FDA的FAERS数据从信息丢失、隐私风险和数据效用方面评估我们提出的方法。结果表明,该方法在不牺牲数据质量和实用性的前提下,能够有效防止攻击者窃取个人隐私。
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引用次数: 3
Ant Colony Optimization with Negative Feedback for Solving Constraint Satisfaction Problems 求解约束满足问题的负反馈蚁群算法
Takuya Masukane, Kazunori Mizuno, Hirotoshi Shinohara
As meta-heuristics to solve large-scale constraint satisfaction problems (CSPs), ant colony optimization (ACO) has recently been drawing attentions. In most of algorithms based on ACO, candidate assignments are constructed by taking account of data called pheromone graph. The pheromone graph is updated getting positive feedbacks from candidate assignments with the least number of constraint violations. However, it might be easy to get stuck in locally optimal solutions considering only a single perspective. In this paper, we propose a method that adopting new pheromone graph in addition to the original pheromone graph. This new pheromone graph is updated getting negative feedback from candidate assignments with the greatest number of constraint violations. This new pheromone graph, called a negative pheromone graph, is updated getting negative feedback from candidate assignments with the largest number of constraint violations. Also, the standard pheromone graph is updated by considering negative pheromones as well. By using pheromones updated from two perspectives, more effective search can be conducted. Moreover, in this paper, we conducted experiments on graph coloring problems. Graph coloring problem is one of CSPs. We demnonstrated that our model, which is applied to the cunning ant system, can be effective than other ACO-based methods for large-scale and hard graph coloring problems whose instance appears in the phase transition region.
蚁群算法作为求解大规模约束满足问题(csp)的元启发式算法,近年来受到广泛关注。在大多数基于蚁群算法中,候选分配是通过考虑信息素图来构建的。更新信息素图,从违反约束次数最少的候选分配中获得正反馈。然而,只考虑单一视角很容易陷入局部最优解。本文提出了在原信息素图的基础上采用新的信息素图的方法。这个新的费洛蒙图被更新,从具有最大数量的约束违规的候选分配中获得负反馈。这个新的费洛蒙图被称为负费洛蒙图,它被更新,从违反约束最多的候选分配中获得负反馈。此外,标准费洛蒙图也会通过考虑负费洛蒙进行更新。利用从两个角度更新的信息素,可以进行更有效的搜索。此外,在本文中,我们对图的着色问题进行了实验。图的着色问题是一类csp问题。结果表明,该模型应用于狡猾蚂蚁系统,对于出现在相变区域的大规模难图着色问题,比其他基于蚁群算法的方法更有效。
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引用次数: 0
期刊
2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)
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