2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第4页

Database caching for job-level computing 用于作业级计算的数据库缓存

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880170

H. Chiang, Ting-Han Wei, I-Chen Wu

{"title":"Database caching for job-level computing","authors":"H. Chiang, Ting-Han Wei, I-Chen Wu","doi":"10.1109/TAAI.2016.7880170","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880170","url":null,"abstract":"This paper improves upon Job-Level (JL) computing, a general distributed computing approach. In JL computing, a client maintains the overall search tree and parcels the overall search into coarse-grained jobs, which are then each calculated by pre-existing game-playing programs. In order to support large-scale problems such as solving 7×7 killall-Go, or building opening books for 9×9 Go or Connect6, JL computing is modified so that the entire search tree is stored in a database, as opposed to simply being stored in the client process' memory. However, the time cost of accessing this database becomes a bottleneck on performance when using a large number of computing resources. This paper proposes a cache mechanism for JL search trees. Instead of the previous approach, where the entire search tree is stored in the database, we maintain parts of the search tree in the memory of the client process to reduce the number of database accesses. Our method significantly improves the performance of job operations. Assuming that each job requires 30 seconds on average, the JL application with this cache mechanism can allow for the use of 5036 distributed computing resources in parallel without database accesses becoming the performance bottleneck.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124360748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Expert team finding for review assignment 为评审任务寻找专家团队

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7932314

Hongzhi Yin, B. Cui, Hua Lu, Lei Zhao

{"title":"Expert team finding for review assignment","authors":"Hongzhi Yin, B. Cui, Hua Lu, Lei Zhao","doi":"10.1109/TAAI.2016.7932314","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7932314","url":null,"abstract":"The peer-review process is the most widely accepted standard for validating products of researchers within the scientific community. It is also adopted by funding agencies. An essential component of peer-review is to find a certain number of experts to review a research paper or a grant proposal. Previous work mainly focuses on finding experts with the necessary expertise relevant to the paper or proposal while ignoring the diversity in the selected reviewers, which potentially leads to the conflict of interest (COI). In this paper, we propose a novel and unified framework that takes three major key factors into account for reviewer assignment: importance, diversity and expertise coverage of a group of reviewers. Our framework selects a panel of reviewers that not only cover all topics of a submission but also reduce various potential COIs. The proposed framework effectively integrates probabilistic topic model and activation spread model in the presence of a social network of researchers. To the best of our knowledge, this is the first work to study the diversity of reviewers and leverage its effect in the reviewer assignment. We conduct extensive experiments to evaluate the performance of our proposed framework for reviewer assignment. The experimental results show that our approach is very effective in finding panels of relevant, authoritative and diverse reviewers for given submissions to review.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122468638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

The development of a simulated car racing controller based on Monte-Carlo tree search 基于蒙特卡罗树搜索的模拟赛车控制器的开发

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880111

Jia-Hao Hou, Tsaipei Wang

引用次数: 3

Keynote3: Contention and disruption 主题3:竞争与颠覆

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/taai.2016.7880106

R. Susskind

{"title":"Keynote3: Contention and disruption","authors":"R. Susskind","doi":"10.1109/taai.2016.7880106","DOIUrl":"https://doi.org/10.1109/taai.2016.7880106","url":null,"abstract":"The development of science is clear. From 1950 to 1990 we lived in a world of Contention, with as main question: Will Contention between Paradigms lead to a Paradigm Shift? This development is nicely described by Popper (Logic of Scientific Discovery), Kuhn (The Structure of Scientific Revolutions), Lakatos (The Methodology of Scientific Research Programmes), and Feyerabend (Against Method). In the world of Games, this development is seen in the transition from Minimax to Monte Carlo Tree Search (MCTS). Apparently, the successor of Contention is called Disruption. Currently, we live in a world full of disruptions (1990–2030). In the lecture, I will show the current development by Daniel Dennett (Consciousness Explained, 1990), Richard Susskind (The Future of Law, 1998), Nick Bostron (Superintelligence, 2014), and my own thoughts on Intuition is Programmable (Van den Herik, 2016). The latter is extremely well identified by the power of Deep Learning in the Game of Go (congratulations to Aja Huang). Around 2030 we may expect to see a quantum computer in operation. It will not only produce prime numbers, but also give us the solution of the game of chess (draw or a win for White), and thereafter even for Go (i.e., at a later date). Next to game results, we will observe a continuous development: from decisions made by humans to decisions made by computers. Here, moral constraints are important. Examples will be given.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"1997 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128213684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trajectory pattern mining: Exploring semantic and time information 轨迹模式挖掘:探索语义和时间信息

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880171

Chien-Cheng Chen, Meng-Fen Chiang

{"title":"Trajectory pattern mining: Exploring semantic and time information","authors":"Chien-Cheng Chen, Meng-Fen Chiang","doi":"10.1109/TAAI.2016.7880171","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880171","url":null,"abstract":"With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134275331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Keynote2: Intercultural collaboration as a multi-agent system 主题2:作为多主体系统的跨文化协作

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/taai.2016.7880105

引用次数: 0

BigExplorer: A configuration recommendation system for big data platform BigExplorer:大数据平台配置推荐系统

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880179

Chao-Chun Yeh, Jiazheng Zhou, Sheng-An Chang, Xuan-Yi Lin, Yichiao Sun, Shih-Kun Huang

{"title":"BigExplorer: A configuration recommendation system for big data platform","authors":"Chao-Chun Yeh, Jiazheng Zhou, Sheng-An Chang, Xuan-Yi Lin, Yichiao Sun, Shih-Kun Huang","doi":"10.1109/TAAI.2016.7880179","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880179","url":null,"abstract":"With the complexity big data platform architectures, data engineer provides the infrastructure with computation and storage resource for data scientist and data analyst. With those supports, data scientists can focus their domain problem and design the intelligence module (e.g., prepare the data, select/train/tune the machine learning modules and validate the result). However, there is still a gap between system engineer team and data scientists/engineers team. For system engineers, they don't have any knowledge about the application domain and the propose of the analytic program. For data scientists/engineers, they don't know the configuration of the computation system, file system and database. Some application performance issues are related with system configurations. Data scientist and data engineer do not have information and knowledge about the system properties. In this paper, we propose a configuration layer with the current big data platform (i.e., Hadoop) and build a configuration recommendation system to collect data, pre-process data. Based on the processed data, we use semi-automatic feature engineer to provide features for data engineers and build the performance model with three different machine learning algorithms (i.e., random forest, gradient boosting machine and support vector regression). With the same two benchmarks (i.e., wordcount and terasort), our recommended configuration archives remarkable improvement than rule of thumb configuration and better than their improvements.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129537815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Exploring multi-view learning for activity inferences on smartphones 探索智能手机上多视角学习的活动推断

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880160

Gunarto Sindoro Njoo, C. Lai, Kuo-Wei Hsu

{"title":"Exploring multi-view learning for activity inferences on smartphones","authors":"Gunarto Sindoro Njoo, C. Lai, Kuo-Wei Hsu","doi":"10.1109/TAAI.2016.7880160","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880160","url":null,"abstract":"Inferring activities on smartphones is a challenging task. Prior works have elaborated on using sensory data from built-in hardware sensors in smartphones or taking advantage of location information to understand human activities. In this paper, we explore two types of data on smartphones to conduct activity inference: 1) Spatial-Temporal: reflecting daily routines from the combination of spatial and temporal patterns, 2) Application: perceiving specialized apps that assist the user's activities. We employ multi-view learning model to accommodate both types of data and use weighted linear kernel model to aggregate the views. Note that since resources of smartphones are limited, activity inference on smartphones should consider the constraints of resources, such as the storage, energy consumption, and computation power. Finally, we compare our proposed method with several classification methods on a real dataset to evaluate the effectiveness and performance of our method. The experimental results show that our approach outperforms other methods regarding the balance between accuracy, running time, and storage efficiency.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126955574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Job-level computing with BOINC support 具有BOINC支持的作业级计算

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880173

Chia-Chuan Chang, Ting-Han Wei, I-Chen Wu

引用次数: 1

Learning to select actions in starcraft with genetic algorithms 学习用遗传算法在星际争霸中选择行动

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 1900-01-01 DOI: 10.1109/TAAI.2016.7880180

W. Hsu, Ying-ping Chen

引用次数: 5