2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)最新文献_第10页

Trust management in cloud computing: A survey 云计算中的信任管理:调查

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397227

Merrihan B. Monir, M. H. AbdElaziz, Abdelaziz A. Abdelhamid, El-Sayed M. Ei-Horbaty

引用次数: 16

Code clone detection using coarse and fine-grained hybrid approaches 使用粗粒度和细粒度混合方法的代码克隆检测

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397263

Abdullah M. Sheneamer, J. Kalita

引用次数: 21

A study on PCG as a biometric approach PCG作为生物识别方法的研究

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397215

M. Bassiouni, W. Khalifa, EL-Sayed A. El Dahshan, Abdel-Badeeh M. Salam

引用次数: 4

An intelligent optimization approach for storage space allocation at seaports: A case study 港口存储空间分配的智能优化方法:一个案例研究

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397198

Gamal Abd El-Nasser A. Said, El-Sayed M. El-Horbaty

引用次数: 5

Analysis of Automatic Multi-label GrabCut using NPR for natural image segmentation 基于NPR的自动多标签GrabCut自然图像分割分析

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397235

D. Khattab, H. M. Ebeid, M. Tolba, A. S. Hussein

引用次数: 0

Finding the pin in the haystack: A Bot Traceback service for public clouds 大海捞针:公共云的Bot追溯服务

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397230

E. Hossny, S. Khattab, F. Omara, H. Hassan

引用次数: 0

A performance study of the chain sampling algorithm 链式采样算法的性能研究

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397265

Rayane El Sibai, Yousra Chabchoub, J. Demerjian, Zakia Kazi-Aoul, Kabalan Barbar

{"title":"A performance study of the chain sampling algorithm","authors":"Rayane El Sibai, Yousra Chabchoub, J. Demerjian, Zakia Kazi-Aoul, Kabalan Barbar","doi":"10.1109/INTELCIS.2015.7397265","DOIUrl":"https://doi.org/10.1109/INTELCIS.2015.7397265","url":null,"abstract":"On-line data stream analysis is an important challenge today because of the always-increasing rates of the streams issued from multiple heterogeneous sources, in many application domains. To reduce the amount of the data stream, several sampling methods were designed by the data stream research community. We focus in this paper, on the chain sampling algorithm proposed by Babcock et al. The aim of this algorithm is to select randomly and at any time, a given fixed proportion from the most recent items of the stream contained in the last sliding window. This algorithm is well adapted to the stream context, as only one pass over the data is performed. Moreover it uses a small memory, as it does not store all the items of the current sliding window. We show in this paper that the chain sampling algorithm suffers from some collision or redundancy problems. The collision occurs when the same item is selected as a sample more than once during the execution of the algorithm. We propose two approaches to overcome this weakness and improve the chain sampling algorithm. The first one is called “inverting the selection for a high sampling rate” and the second one is inspired from the “divide to conquer strategy”. Different experimentations are performed to show the efficiency of these two improvements, in particular their impact on the execution time of the algorithm.","PeriodicalId":6478,"journal":{"name":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"91 1","pages":"487-494"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78245454","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: 7

Cyber fuzzy assessment methods 网络模糊评价方法

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397189

M. Voskoglou

{"title":"Cyber fuzzy assessment methods","authors":"M. Voskoglou","doi":"10.1109/INTELCIS.2015.7397189","DOIUrl":"https://doi.org/10.1109/INTELCIS.2015.7397189","url":null,"abstract":"The assessment of a system's performance is a very important task for its operation, because the results obtained by this action help the designer/user of the system to correct its weaknesses, thus making it more effective. The assessment methods usually utilized in practice are based on the principles of classical, bivalent logic (yes-no). However, in our everyday life they frequently appear assessment situations involving a degree of uncertainty and (or) ambiguity. Fuzzy logic, due to its nature of characterizing a case with multiple values, offers rich resources for dealing with such kind of situations. This gave us several times in past the impulse to apply principles of fuzzy logic for assessment purposes using as tools the corresponding system's total uncertainty (e.g. see [2] and its relevant references, Section of [3], etc) the Center of Gravity (COG) defuzzification technique (e.g. Section of [3], [4], etc) as well as the Triangular (TFAM) (e.g. [1]) and Trapezoidal (TRFAM) (e.g. [5]) Fuzzy Assessment Models, which are recently developed variations of the COG technique. In this presentation we shall use the Fuzzy Numbers (FNs), and in particular the Triangular (TFN) (e.g. [6]) and Trapezoidal (TpFN) Fuzzy Numbers, as an alternative assessment tool. FNs play a fundamental role in fuzzy mathematics, analogous to the role played by the ordinary numbers in classical mathematics. Our results are illustrated by an example, while this alternative assessment approach is compared with the assessment methods of the bivalent (calculation of the means, GPA index) and fuzzy logic (see above) that we have already used in earlier works.","PeriodicalId":6478,"journal":{"name":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"113 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79836326","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

Online multi-person tracking-by-detection method using ACF and particle filter 基于ACF和粒子滤波的在线多人检测跟踪方法

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397272

T. Kokul, A. Ramanan, U. Pinidiyaarachchi

{"title":"Online multi-person tracking-by-detection method using ACF and particle filter","authors":"T. Kokul, A. Ramanan, U. Pinidiyaarachchi","doi":"10.1109/INTELCIS.2015.7397272","DOIUrl":"https://doi.org/10.1109/INTELCIS.2015.7397272","url":null,"abstract":"Automatically detecting and tracking multiple persons in videos is one of the main research interest in computer vision based applications. This paper presents a tracking-by-detection approach for tracking people in dynamic backgrounds with frequent occlusions by combining pre-trained generic person detector, online trained person-specific detector and a motion tracker. The popular aggregate channel features (ACF) are used to train the detectors and target specific particle filter is used as motion tracker. In order to learn right appearance of a target person, person-specific detector learns positive samples from prior frames which are detected by both generic person detector and person-specific detector. Data associations among the coincident detections of the detectors and tracker are used to update the person-specific detector and motion tracker. The person-specific detector searches the target person in a reduced region, which is defined by the associate motion tracker. A careful combination of detections of both detectors and tracker are used to locate the correct target person in the video sequence. Experiments have been carried out on Caltech pedestrian benchmark dataset. The proposed method shows better performance against state-of-the-art tracker while maintaining the tracking speed in real-time.","PeriodicalId":6478,"journal":{"name":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"55 1","pages":"529-536"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89008483","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: 9

Development of a novel EEG wave controlled security system 一种新型脑电波控制安全系统的研制

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2015-12-01 DOI: 10.1109/INTELCIS.2015.7397207

Pritham Gajakumar Shah, Krishna Chaithanya Vastare, Xu Huang, Ajithkumar Srikumar, Suraj Mademur Sreenivasa, Adarsh Puvvadi Ram Mohan Kumar, Karthik Rajashekhar Kodada

引用次数: 3