2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第2页

Component behavior discovery from software execution data 从软件执行数据中发现组件行为

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7849947

Cong Liu, B. V. Dongen, Nour Assy, Wil M.P. van der Aalst

{"title":"Component behavior discovery from software execution data","authors":"Cong Liu, B. V. Dongen, Nour Assy, Wil M.P. van der Aalst","doi":"10.1109/SSCI.2016.7849947","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849947","url":null,"abstract":"Tremendous amounts of data can be recorded during software execution. This provides valuable information on software runtime analysis. Many crashes and exceptions may occur, and it is a real challenge to understand how software is behaving. Software is usually composed of various components. A component is a nearly independent part of software that full-fills a clear function. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs. This paper presents an approach to utilize process mining as a tool to discover the real behavior of software and analyze it. The unstructured software execution data may be too complex, involving multiple interleaved components, etc. Applying existing process mining techniques results in spaghetti-like models with no clear structure and no valuable information that can be easily understood by end. In this paper, we start with the observation that software is composed of components and we use this information to decompose the problem into smaller independent ones by discovering a behavioral model per component. Through experimental analysis, we illustrate that the proposed approach facilitates the discovery of more understandable software models. All proposed approaches have been implemented in the open-source process mining toolkit ProM.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392473","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: 41

A systemic approach to automatic metadata extraction from multimedia content 从多媒体内容中自动提取元数据的系统方法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7849983

Christos Varytimidis, Georgios Tsatiris, Konstantinos Rapantzikos, S. Kollias

引用次数: 5

Robust data processing in the presence of uncertainty and outliers: Case of localization problems 存在不确定性和异常值的稳健数据处理:定位问题的案例

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7849985

Anthony Welte, L. Jaulin, M. Ceberio, V. Kreinovich

引用次数: 3

Comparing exploration strategies for Q-learning in random stochastic mazes 比较随机迷宫中q学习的探索策略

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7849366

A. Tijsma, Mădălina M. Drugan, M. Wiering

{"title":"Comparing exploration strategies for Q-learning in random stochastic mazes","authors":"A. Tijsma, Mădălina M. Drugan, M. Wiering","doi":"10.1109/SSCI.2016.7849366","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849366","url":null,"abstract":"Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax, ∈-greedy, and pursuit. For this purpose we adapted the UCB-1 and pursuit strategies to be used in the Q-learning algorithm. The mazes consist of a single optimal goal state and two suboptimal goal states that lie closer to the starting position of the agent, which makes efficient exploration an important part of the learning agent. Furthermore, we evaluate two different kinds of reward functions, a normalized one with rewards between 0 and 1, and an unnormalized reward function that penalizes the agent for each step with a negative reward. We have performed an extensive grid-search to find the best parameters for each method and used the best parameters on novel randomly generated maze problems of different sizes. The results show that softmax exploration outperforms the other strategies, although it is harder to tune its temperature parameter. The worst performing exploration strategy is ∈-greedy.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128568415","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: 93

Improving RL power for on-line evolution of gaits in modular robots 改进模块化机器人步态在线进化的强化学习功率

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7850166

Milan Jelisavcic, Matteo De Carlo, E. Haasdijk, A. Eiben

引用次数: 8

Broken bikes detection using CitiBike bikeshare system open data 利用CitiBike共享单车系统公开数据进行自行车破损检测

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7850091

Rémi Delassus, R. Giot, Raphael Cherrier, Gabriele Barbieri, Guy Melançon

引用次数: 6

Q-learning with experience replay in a dynamic environment 动态环境下的q学习与经验回放

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-06 DOI: 10.1109/SSCI.2016.7849368

Mathijs Pieters, M. Wiering

{"title":"Q-learning with experience replay in a dynamic environment","authors":"Mathijs Pieters, M. Wiering","doi":"10.1109/SSCI.2016.7849368","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849368","url":null,"abstract":"Most research in reinforcement learning has focused on stationary environments. In this paper, we propose several adaptations of Q-learning for a dynamic environment, for both single and multiple agents. The environment consists of a grid of random rewards, where every reward is removed after a visit. We focus on experience replay, a technique that receives a lot of attention nowadays, and combine this method with Q-learning. We compare two variations of experience replay, where experiences are reused based on time or based on the obtained reward. For multi-agent reinforcement learning we compare two variations of policy representation. In the first variation the agents share a Q-function, while in the second variation both agents have a separate Q-function. Furthermore, in both variations we test the effect of reward sharing between the agents. This leads to four different multi-agent reinforcement learning algorithms, from which sharing a Q-function and sharing the rewards is the most cooperative method. The results show that in the single-agent environment both experience replay algorithms significantly outperform standard Q-learning and a greedy benchmark agent. In the multi-agent environment the highest maximum reward sum in a trial is achieved by using one Q-function and reward sharing. The highest mean reward sum is obtained with separate Q-functions and separate rewards.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128600951","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: 20

HERMES: A high-level policy language for high-granularity enterprise-wide secure browser configuration management HERMES:用于高粒度企业级安全浏览器配置管理的高级策略语言

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849914

Ananth A. Jillepalli, D. Leon, Stuart Steiner, Frederick T. Sheldon

{"title":"HERMES: A high-level policy language for high-granularity enterprise-wide secure browser configuration management","authors":"Ananth A. Jillepalli, D. Leon, Stuart Steiner, Frederick T. Sheldon","doi":"10.1109/SSCI.2016.7849914","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849914","url":null,"abstract":"In this article, we describe the characteristics, structure, and uses of HERMES. HERMES is a high-level security policy description language. Its characteristics are: (1) enable the specification of organizational domain knowledge in a hierarchical manner; (2) enable the specification of security policies at desired granularity levels within the organizational IT and OT infrastructure; (3) enable security policies to be automatically instantiated into security configurations; (4) it is human-centered and designed for ease of use; (5) it is application and device independent. We show an example of using HERMES to write a high-level policy and show examples of how such policy can be instantiated into a domain and device, user and role, application and action specific security configuration. We also describe the integration of HERMES within the HiFiPol:Browser policy management system. We believe HERMES is a necessary step toward securing the client side of the web ecosystem and prevent or mitigate the current onslaught of web browser-based attacks, such as phishing.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121040772","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

Sensor-based change detection schemes for dynamic multi-objective optimization problems 基于传感器的动态多目标优化问题变化检测方案

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849963

Shaaban A. Sahmoud, H. Topcuoglu

引用次数: 19

Applying Computational Intelligence for enhancing the dependability of multi-cloud systems using Docker Swarm 利用Docker Swarm应用计算智能增强多云系统的可靠性

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850194

N. Naik

{"title":"Applying Computational Intelligence for enhancing the dependability of multi-cloud systems using Docker Swarm","authors":"N. Naik","doi":"10.1109/SSCI.2016.7850194","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850194","url":null,"abstract":"Multi-cloud systems have been gaining popularity due to the several benefits of the multi-cloud infrastructure such as lower level of vendor lock-in and minimize the risk of widespread data loss or downtime. Thus, the multi-cloud infrastructure enhances the dependability of the cloud-based system. However, it also poses many challenges such as nonstandard and inherent complexity due to different technologies, interfaces, and services. Consequently, it is a challenging task to design multi-cloud dependable systems. Virtualization is the key technology employed in the development of cloud-based systems. Docker has recently introduced its container-based virtualization technology for the development of software systems. It has newly launched a distributed system development tool called Swarm, which allows the development of a cluster of multiple Swarm nodes on multiple clouds. Docker Swarm has also incorporated several dependability attributes to support the development of a multi-cloud dependable system. However, making Swarm cluster always available requires minimum three active manager nodes which can safeguard one failure. This essential condition for the dependability is one of the main limitations because if two manager nodes fail suddenly due to the failure of their hosts, then Swarm cluster cannot be made available for routine operations. Therefore, this paper proposes an intuitive approach based on Computational Intelligence (CI) for enhancing its dependability. The proposed CI-based approach predicts the possible failure of the host of a manager node by observing its abnormal behaviour. Thus, this indication can automatically trigger the process of creating a new manager node or promoting an existing node as a manager for enhancing the dependability of Docker Swarm.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125741643","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: 23