发布求助
文献互助 智能选刊 最新文献

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)最新文献

筛选
英文 中文
Constraint Satisfaction Approaches in Cloud Resource Selection for Component Based Applications 基于组件的应用程序云资源选择中的约束满足方法
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516639
Flavia Micota, Madalina Erascu, D. Zaharie
{"title":"Constraint Satisfaction Approaches in Cloud Resource Selection for Component Based Applications","authors":"Flavia Micota, Madalina Erascu, D. Zaharie","doi":"10.1109/ICCP.2018.8516639","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516639","url":null,"abstract":"Cloud resource provisioning for applications con-sisting of interacting components requires solving a constrained optimization problem. In this paper two exact methods (Constraint Programming and Satisfiability Modulo Theory) and a newly proposed population-based metaheuristic are investigated with respect to their potential in finding low-cost assignment of components to virtual machines such that all constraints are satisfied. The results obtained for three case studies show that the exact methods are appropriate as long as the cloud provider's list of offers is rather small (a few dozens). On the other hand, the metaheuristic provides acceptable solutions, but not necessarily optimal, even in the case of hundreds of offers.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316072","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
An early fusion approach for multimodal emotion recognition using deep recurrent networks 基于深度递归网络的多模态情感识别早期融合方法
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516437
Beniamin Bucur, Iulia Somfelean, Alexandru Ghiurutan, C. Lemnaru, M. Dînsoreanu
{"title":"An early fusion approach for multimodal emotion recognition using deep recurrent networks","authors":"Beniamin Bucur, Iulia Somfelean, Alexandru Ghiurutan, C. Lemnaru, M. Dînsoreanu","doi":"10.1109/ICCP.2018.8516437","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516437","url":null,"abstract":"In this paper we compare different strategies for handling incomplete data and different classification architectures for emotion recognition from multimodal data, using an early fusion approach. In order to allow the different modalities to complement each other at feature level, the initial task was to align the data at the same frame rate. The source data possessed a high degree of incompleteness, which we addressed by different imputation approaches. Since the data was missing in blocks, we found that the best performing approach was to replace missing values with zeros. For the classification model, we experimented with LSTM and GRU networks, in both unidirectional and bidirectional flavors, and various hyper-parameter settings. We found that a bidirectional GRU model trained using a smaller batch size and more aggressive dropout produced the best classification performance.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117095646","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
Miniature Autonomous Vehicle Development on Raspberry Pi 在树莓派上开发微型自动驾驶汽车
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516589
Bianca-Cerasela-Zelia Blaga, M. Deac, Rami Al-Doori, M. Negru, R. Danescu
{"title":"Miniature Autonomous Vehicle Development on Raspberry Pi","authors":"Bianca-Cerasela-Zelia Blaga, M. Deac, Rami Al-Doori, M. Negru, R. Danescu","doi":"10.1109/ICCP.2018.8516589","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516589","url":null,"abstract":"Miniature self-driving cars are intended to facilitate the research and development in the domain of autonomous vehicles. Algorithms developed for the tasks of perception, navigation, and control on such platforms enable fast implementation and testing in scenarios similar to the real world. In this paper, we present a novel methodology for developing the assistance system for a 1/10 scale car, in which we use a simulated GPS to position the vehicle and to navigate on the test track. We propose a method for lane detection and tracking that is robust and accounts for the cases when one or both lines of the lane are missing or not seen in the image. We also present a new solution for detecting road traffic signs. In addition, we have implemented an application for map visualization, that enables us to test the correctness of our algorithms. Our miniature vehicle is capable of successfully navigating from a start point to a goal, while taking into account the lanes, intersections, traffic signs, and can perform lateral parking.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114343866","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: 11
Real-Time Temporal Frequency Detection in FPGA Using Event-Based Vision Sensor 基于事件视觉传感器的FPGA实时时间频率检测
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516629
Sahar Hoseini, B. Linares-Barranco
{"title":"Real-Time Temporal Frequency Detection in FPGA Using Event-Based Vision Sensor","authors":"Sahar Hoseini, B. Linares-Barranco","doi":"10.1109/ICCP.2018.8516629","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516629","url":null,"abstract":"A dynamic vision sensor (DVS) is a new type of vision sensor in which each pixel acts as a motion sensor and generates highly time-accurate events when it detects movement in the scene. The high temporal precision of these types of vision sensors allows the extraction of different low-level temporal features, which is not possible when using a frame-based camera. Hierarchical vision-processing systems use low-level features to recognize a higher level of abstraction. One of the lowlevel features that can be extracted with DVS is the temporal frequency. This feature can be used along with other visual features for more accurate object recognition when the object has rotating parts, such as a quadcopter. This work is an extension of our previous work, wherein we proposed an algorithm to extract this temporal low-level feature by using a DVS. In this work, we proposed a digital circuit with a small footprint to extract the frequency of rotating objects in real time with very low latency. We have synthesized the digital circuit in Spartan-6 field-programmable gate array (FPGA) and also in UMC 180-nm technology to measure the performance, power consumption, and occupied area. MATLAB and Verilog codes for this work are available for academic purposes upon request.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114239814","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: 6
Automatic Detection of Tumor Cells in Microscopic Images of Unstained Blood using Convolutional Neural Networks 利用卷积神经网络自动检测未染色血液显微图像中的肿瘤细胞
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516638
Ioana Mocan, R. Itu, A. Ciurte, R. Danescu, R. Buiga
{"title":"Automatic Detection of Tumor Cells in Microscopic Images of Unstained Blood using Convolutional Neural Networks","authors":"Ioana Mocan, R. Itu, A. Ciurte, R. Danescu, R. Buiga","doi":"10.1109/ICCP.2018.8516638","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516638","url":null,"abstract":"Accessible high-performance computing power has recently spiked interest in medical image analysis and processing. Biomedical image segmentation has been used to aid in the process of medical analysis and diagnosis. In this paper we present a novel approach to identifying circulating tumor cells (CTCs) using convolutional neural networks on Dark Field microscopic images of unstained blood. We use a modified U-Net that is able to automatically perform image segmentation in order to detect CTCs. We perform detection on our own dataset containing input images and ground truth label images. Detection is done on small image patches using a sliding window mechanism in order to reduce computation time. The final result is reconstructed from the patches and further refined using post-processing. The total number of CTCs is computed from the segmented image using the Hough circle algorithm. We were able to obtain over 99.8% accuracy using our data set.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134447033","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: 4
Decentralized Swarm Aggregation and Dispersion with Inter-Member Collision Avoidance for Non-holonomic Multi-Robot Systems 非完整多机器人系统成员间避碰的分散群聚集与分散
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516604
Dan M. Novischi, A. Florea
{"title":"Decentralized Swarm Aggregation and Dispersion with Inter-Member Collision Avoidance for Non-holonomic Multi-Robot Systems","authors":"Dan M. Novischi, A. Florea","doi":"10.1109/ICCP.2018.8516604","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516604","url":null,"abstract":"In this paper we present a formation control approach for team of unicycle mobile robots that exhibits both aggregation and dispersion behaviors. The approach is based on a attractive/repulsive potential function that models the inter- action between the robots using only local information, while also accounting for the physical limitations of the actuators. Throughout the paper we provide several simulation results and further analyze the swarm cohesiveness which shows asymptotic stability of the proposed controller.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122009303","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
False Positive Mitigation in Behavioral Malware Detection Using Deep Learning 使用深度学习的行为恶意软件检测中的误报缓解
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516611
Alexru Mihai Lungana-Niculescu, Adrian Colesa, Ciprian Oprișa
{"title":"False Positive Mitigation in Behavioral Malware Detection Using Deep Learning","authors":"Alexru Mihai Lungana-Niculescu, Adrian Colesa, Ciprian Oprișa","doi":"10.1109/ICCP.2018.8516611","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516611","url":null,"abstract":"The malicious software is in a continuous development and the anti-malware technologies are advancing as well to keep up. There are proactive detection technologies, based on the analysis of a sample behavior, that succeed in detecting zero-day malware, the downside being the false positives rate. The current paper proposes an approach for mitigating the false positives by introducing a deep learning classifier. This classifier provides a ’’second opinion’’ for the samples that would have been detected by the current state of the art approach. The proposed approach is able to reduce the false positives rate by 97‥, while only losing 12‥ of the legitimate detection.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122390247","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
Enhancements on a Transition-Based Approach for AMR Parsing Using LSTM Networks 使用LSTM网络的基于转换的AMR解析方法的增强
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516606
Roxana Pop, Anda Dregan, F. Macicasan, C. Lemnaru, R. Potolea
{"title":"Enhancements on a Transition-Based Approach for AMR Parsing Using LSTM Networks","authors":"Roxana Pop, Anda Dregan, F. Macicasan, C. Lemnaru, R. Potolea","doi":"10.1109/ICCP.2018.8516606","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516606","url":null,"abstract":"This work proposes two enhancements to a system of generating Meaning Representations (AMR) graphs from English textual data. We first enhance a transition-based approach with additional actions that aim to handle particularities in the structure of the AMR. We analyze actions to address multi-aligned nodes and non-projective word orders, and explore several algorithms for action sequence generation, which incorporate the newly proposed actions. Secondly, we explore strategies for tackling AMR re-entrant concepts, which represent co-references in the associated textual data. We choose to handle co-reference detection and resolution via specific pre-processing and post-processing operations.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815053","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
Semantic segmentation-based traffic sign detection and recognition using deep learning techniques 基于语义分割的交通标志检测与识别的深度学习技术
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516600
Calin Timbus, Vlad-Cristian Miclea, C. Lemnaru
{"title":"Semantic segmentation-based traffic sign detection and recognition using deep learning techniques","authors":"Calin Timbus, Vlad-Cristian Miclea, C. Lemnaru","doi":"10.1109/ICCP.2018.8516600","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516600","url":null,"abstract":"We present a method for detecting and classifying traffic signs based on two deep neural network architectures. A Fully Convolutional Network (FCN) - based semantic segmentation model is modified to extract traffic sign regions of interest. These regions are further passed to a Convolutional Neural Network (CNN) for traffic sign classification. We propose a novel CNN architecture for the classification step. In evaluating our approach, we contrast the efficiency and the robustness of the deep learning image segmentation approach with classical image processing filters traditionally applied for traffic sign detection. We also show the effectiveness of our CNN-based recognition method by integrating it in our system.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126147750","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: 4
EEG Multi-Objective Feature Selection Using Temporal Extension 基于时间扩展的脑电多目标特征选择
2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516613
L. Ferariu, Corina Cimpanu, Tiberius Dumitriu, F. Ungureanu
{"title":"EEG Multi-Objective Feature Selection Using Temporal Extension","authors":"L. Ferariu, Corina Cimpanu, Tiberius Dumitriu, F. Ungureanu","doi":"10.1109/ICCP.2018.8516613","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516613","url":null,"abstract":"Nowadays Electroencephalogram (EEG) devices allow the recording of signals that can be used to extract information necessary to identify different types of cognitive processes. In EEG classification, Feature Selection (FS) represents a pivotal phase, as these problems request the processing of a large amount of high-dimensional patterns. In this paper, FS has been solved by an embedded multi-objective genetic optimization procedure which evolves a population of potential solutions (subsets of features), subject to the simultaneous minimization of the misclassification ratio and number of selected attributes. Random Forests (RF) classifiers are adopted, due to their fast training and their compatibility with spread classes of very diverse patterns. The main contribution presented in this paper consists in introducing an inertial behavior to feature extraction. The available feature set is extended with features from previous time frames, and FS is performed on this extended set. In this context, the experimental analysis illustrates the impact of the temporal extension on FS. Additionally, two enhancements are proposed for the multi-objective optimization, to support an effective Pareto-ranking of the solutions in the expanded exploration search space. Thus, the number of trees in the embedded RF classifier is gradually increased, for reducing the computational load requested for the evaluation of the misclassification ratio, without impeding the exploration. Also, the preference for the minimization of misclassifications is set by introducing a dynamic objective function for describing the parsimony of the selected subset of attributes. The proposed FS is experimentally demonstrated on EEG data collected during mathematical tasks of gradual complexities.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126933370","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信