Data Provenance has created an emerging requirement for technologies that enable end users to access, evaluate, and act on the provenance of data in recent years. In the era of Big Data, the amount of data created by corporations around the world has grown each year. As an example, both in the Social Media and e-Science domains, data is growing at an unprecedented rate. As the data has grown rapidly, information on the origin and lifecycle of the data has also grown. In turn, this requires technologies that enable the clarification and interpretation of data through the use of data provenance. This study proposes methodologies towards the visualization of W3C-PROV-O Specification compatible provenance data. The visualizations are done by summarization and comparison of the data provenance. We facilitated the testing of these methodologies by providing a prototype, extending an existing open source visualization tool. We discuss the usability of the proposed methodologies with an experimental study; our initial results show that the proposed approach is usable, and its processing overhead is negligible.
{"title":"A Data Provenance Visualization Approach","authors":"Ilkay Melek Yazici, Erkan Karabulut, M. Aktaş","doi":"10.1109/SKG.2018.00019","DOIUrl":"https://doi.org/10.1109/SKG.2018.00019","url":null,"abstract":"Data Provenance has created an emerging requirement for technologies that enable end users to access, evaluate, and act on the provenance of data in recent years. In the era of Big Data, the amount of data created by corporations around the world has grown each year. As an example, both in the Social Media and e-Science domains, data is growing at an unprecedented rate. As the data has grown rapidly, information on the origin and lifecycle of the data has also grown. In turn, this requires technologies that enable the clarification and interpretation of data through the use of data provenance. This study proposes methodologies towards the visualization of W3C-PROV-O Specification compatible provenance data. The visualizations are done by summarization and comparison of the data provenance. We facilitated the testing of these methodologies by providing a prototype, extending an existing open source visualization tool. We discuss the usability of the proposed methodologies with an experimental study; our initial results show that the proposed approach is usable, and its processing overhead is negligible.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"48 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":"124400773","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}
Min Zhou, Hao Luo, Zhigang Wu, Shuzhuang Zhang, Yingjun Qiu
Extracting underlying structures and significant communication patterns from Internet traffic data has become increasingly urgent and imperative for network operations and security management. In this paper, we proposed LPCT (Label Propagation based Community Tracking) to track the evolution of community in IP networks. In LPCT, we detect the community and preserve the labels of nodes for each snapshot by LAP (Label Propagation Algorithm), then initialize the labels of nodes as the preserved labels in the next community detection for next snapshot. By this way, we can track the evolution of community through the correspondence between label and community in two consecutive snapshots. We evaluate our method by using a NetFlow dataset collected from a boundary router in an actual environment. Experimental results show that our method outperform than other two community tracking methods (ALPA and CommTracker) in terms of NMI (Normalized Mutual Information) and speed. The NMI of LPCT is 30.6% more than that of ALPA and 50.3% more than that CommTracker. The tracking speed of LPCT is three times as fast as ALPA and twice as fast as CommTracker.
{"title":"Tracking the Evolution of Community in IP Networks","authors":"Min Zhou, Hao Luo, Zhigang Wu, Shuzhuang Zhang, Yingjun Qiu","doi":"10.1109/SKG.2018.00015","DOIUrl":"https://doi.org/10.1109/SKG.2018.00015","url":null,"abstract":"Extracting underlying structures and significant communication patterns from Internet traffic data has become increasingly urgent and imperative for network operations and security management. In this paper, we proposed LPCT (Label Propagation based Community Tracking) to track the evolution of community in IP networks. In LPCT, we detect the community and preserve the labels of nodes for each snapshot by LAP (Label Propagation Algorithm), then initialize the labels of nodes as the preserved labels in the next community detection for next snapshot. By this way, we can track the evolution of community through the correspondence between label and community in two consecutive snapshots. We evaluate our method by using a NetFlow dataset collected from a boundary router in an actual environment. Experimental results show that our method outperform than other two community tracking methods (ALPA and CommTracker) in terms of NMI (Normalized Mutual Information) and speed. The NMI of LPCT is 30.6% more than that of ALPA and 50.3% more than that CommTracker. The tracking speed of LPCT is three times as fast as ALPA and twice as fast as CommTracker.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","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":"125374218","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}
Facing the development of the times and technological progress needs new methods and technologies to enrich semantic analysis of scientific & technical literature. Term is the linguistic expression of the concepts in professional knowledge, which is accumulated through incremental exploration and research in specific fields. In the study of semantic analysis, term recognition is an important research subject. This research intended to apply deep neural network in term recognition. And the paper also introduced specific methods of semantic analysis based on the result of Chinese term recognition and implementation using specific scientific & technical literature. It gave an overview of theories and technologies related to the method and used the real and effective corpus for experiments.
{"title":"Exploration on Chinese Term Recognition and Semantic Analysis of Scientific & Technical Literature","authors":"Wen Zeng, Junsheng Zhang, Yunliang Zhang","doi":"10.1109/SKG.2018.00047","DOIUrl":"https://doi.org/10.1109/SKG.2018.00047","url":null,"abstract":"Facing the development of the times and technological progress needs new methods and technologies to enrich semantic analysis of scientific & technical literature. Term is the linguistic expression of the concepts in professional knowledge, which is accumulated through incremental exploration and research in specific fields. In the study of semantic analysis, term recognition is an important research subject. This research intended to apply deep neural network in term recognition. And the paper also introduced specific methods of semantic analysis based on the result of Chinese term recognition and implementation using specific scientific & technical literature. It gave an overview of theories and technologies related to the method and used the real and effective corpus for experiments.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"8 3 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":"114898862","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}
The main aim of Software Engineering is to develop a software system, which fulfils the user requirements within time and budget constraints. This paper uses the multi-dimensional Resource Space Model to manage multiple types of software engineering processes and maps their features into multiple dimensions for supporting analysis, development and maintenance of software system. Two case studies show that the Resource Space Model is feasible to use for managing the software processes and data.
{"title":"Managing Software Processes with the Multi-Dimensional Resource Space Model","authors":"M. Rafi","doi":"10.1109/SKG.2018.00018","DOIUrl":"https://doi.org/10.1109/SKG.2018.00018","url":null,"abstract":"The main aim of Software Engineering is to develop a software system, which fulfils the user requirements within time and budget constraints. This paper uses the multi-dimensional Resource Space Model to manage multiple types of software engineering processes and maps their features into multiple dimensions for supporting analysis, development and maintenance of software system. Two case studies show that the Resource Space Model is feasible to use for managing the software processes and data.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"298 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":"116115000","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}
In recent years the country has issued a series of policies to support the development of wisdom agriculture, promote agricultural industrial upgrading, and actively promote the rural development of electronic commerce. The electronic commerce enterprises also sight to rural e-commerce. In this paper firstly the development of the national rural “electric Internet plus agriculture” under the background of policy support, and the actively layout of rural electronic commerce is introduced. Then the current situation of the development of rural e-commerce is analyzed, from the basic network facilities, e-commerce talent, “last mile“ of the logistics problems and farmers, enterprises and other aspects of the understanding of the existing problems in the development of rural electricity providers. Finally, the development strategy of rural e-commerce in the future is put forward.
{"title":"Research on the Problems and Strategies of Rural E-Commerce in the Age of Internet + Agriculture","authors":"Yilan Zhang, Yiqing Lu","doi":"10.1109/SKG.2018.00041","DOIUrl":"https://doi.org/10.1109/SKG.2018.00041","url":null,"abstract":"In recent years the country has issued a series of policies to support the development of wisdom agriculture, promote agricultural industrial upgrading, and actively promote the rural development of electronic commerce. The electronic commerce enterprises also sight to rural e-commerce. In this paper firstly the development of the national rural “electric Internet plus agriculture” under the background of policy support, and the actively layout of rural electronic commerce is introduced. Then the current situation of the development of rural e-commerce is analyzed, from the basic network facilities, e-commerce talent, “last mile“ of the logistics problems and farmers, enterprises and other aspects of the understanding of the existing problems in the development of rural electricity providers. Finally, the development strategy of rural e-commerce in the future is put forward.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","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":"128657807","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}
SSD (Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed. FSSD (Feature Fusion Single Shot Multibox Detector) proposed feature fusion module which can improve the performance significantly. RFB Net(Receptive Field Block Net for Accurate and Fast Object Detection) proposed RFB module to simulate Receptive Fields (RFs) in human visual systems and gain higher accuracy. In this paper, we proposed FRFB Net (Integrate Receptive Field Block Feature into Fusion Net for Single Shot Multibox Detector), an enhanced FSSD with a RFB module,which not only fully utilize the pyramidal features, but also change the RFs of the fused feature map. To make the model more robust,we use Gaussian Blur to process training images,in addition to use the data augmentation in SSD.On the Pascal VOC 2007 test, our network can achieve 79.6 mAP with the input size $300times 300$ using a single Nvidia 1080 GPU with any bells and whistles. In addition, our result on COCO is also better than FSSD, achieves 2.7mAP improvement compared to FSSD. Our FRFBNet outperforms a lot of state-of-the-art object detection algorithms in accuracy and speed.
单镜头多盒检测(Single Shot Multibox Detector, SSD)是目前精度高、速度快的目标检测算法之一。FSSD (Feature Fusion Single Shot Multibox Detector)提出的特征融合模块可以显著提高检测性能。RFB Net(Receptive Field Block Net for Accurate and Fast Object Detection)提出了RFB模块来模拟人类视觉系统中的感受场(Receptive Fields, RFs)并获得更高的精度。本文提出了一种带有RFB模块的增强FSSD (integrated Receptive Field Block Feature into Fusion Net for Single Shot Multibox Detector)，它不仅充分利用了锥体特征，而且改变了融合特征映射的RFs。为了增强模型的鲁棒性，除了在SSD中使用数据增强外，我们还使用高斯模糊对训练图像进行处理。在Pascal VOC 2007测试中，我们的网络可以使用单个Nvidia 1080 GPU实现79.6 mAP，输入大小为$300 × 300$。此外，我们在COCO上的结果也优于FSSD，比FSSD提高了2.7mAP。我们的FRFBNet在精度和速度上优于许多最先进的目标检测算法。
{"title":"FRFB: Integrate Receptive Field Block Into Feature Fusion Net for Single Shot Multibox Detector","authors":"Yu Zhu, Jiong Mu, Haibo Pu, Baiyi Shu","doi":"10.1109/SKG.2018.00032","DOIUrl":"https://doi.org/10.1109/SKG.2018.00032","url":null,"abstract":"SSD (Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed. FSSD (Feature Fusion Single Shot Multibox Detector) proposed feature fusion module which can improve the performance significantly. RFB Net(Receptive Field Block Net for Accurate and Fast Object Detection) proposed RFB module to simulate Receptive Fields (RFs) in human visual systems and gain higher accuracy. In this paper, we proposed FRFB Net (Integrate Receptive Field Block Feature into Fusion Net for Single Shot Multibox Detector), an enhanced FSSD with a RFB module,which not only fully utilize the pyramidal features, but also change the RFs of the fused feature map. To make the model more robust,we use Gaussian Blur to process training images,in addition to use the data augmentation in SSD.On the Pascal VOC 2007 test, our network can achieve 79.6 mAP with the input size $300times 300$ using a single Nvidia 1080 GPU with any bells and whistles. In addition, our result on COCO is also better than FSSD, achieves 2.7mAP improvement compared to FSSD. Our FRFBNet outperforms a lot of state-of-the-art object detection algorithms in accuracy and speed.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"20 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":"121535236","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}
Christian Urcuqui, Yor Castaño, J. Delgado, Andrés Navarro, Javier Díaz, Beatriz Muñoz, J. L. Orozco
Parkinson's disease (PD) is the second most common neurodegenerative disorder. Changes in gait kinematics and its spatiotemporal features are hallmarks for the diagnosis of PD. Lower limbs movement analysis is intricate and usually requires a gait and biomechanics laboratory; these complex systems are not always available in the medical consultation. This paper evaluates and proposes a machine learning classifier for the analysis of people diagnosed with PD through their gait information. This model has an accuracy of 82%, a false negative rate of 23% and a false positive rate of 12%, results were obtained from a training process that incorporates a low cost system that uses RGBD cameras (MS Kinect) as the main motion capture and the best features detected during an exploratory data analysis. Our study was evaluated using data harvested through the system mentioned and measurements from 60 volunteers; there were 30 subjects with PD and 30 healthy subjects.
{"title":"Exploring Machine Learning to Analyze Parkinson's Disease Patients","authors":"Christian Urcuqui, Yor Castaño, J. Delgado, Andrés Navarro, Javier Díaz, Beatriz Muñoz, J. L. Orozco","doi":"10.1109/SKG.2018.00029","DOIUrl":"https://doi.org/10.1109/SKG.2018.00029","url":null,"abstract":"Parkinson's disease (PD) is the second most common neurodegenerative disorder. Changes in gait kinematics and its spatiotemporal features are hallmarks for the diagnosis of PD. Lower limbs movement analysis is intricate and usually requires a gait and biomechanics laboratory; these complex systems are not always available in the medical consultation. This paper evaluates and proposes a machine learning classifier for the analysis of people diagnosed with PD through their gait information. This model has an accuracy of 82%, a false negative rate of 23% and a false positive rate of 12%, results were obtained from a training process that incorporates a low cost system that uses RGBD cameras (MS Kinect) as the main motion capture and the best features detected during an exploratory data analysis. Our study was evaluated using data harvested through the system mentioned and measurements from 60 volunteers; there were 30 subjects with PD and 30 healthy subjects.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"101 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":"129370799","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}
This paper surveys research on the Resource Space Model (RSM). RSM is a multi-dimensional, classification-based, content-based and high-level semantic space model for organizing and managing various resources through multi-dimensional abstraction and specialization. RSM has more powerful resource representation ability than traditional resource management model. It has applications not only in resource management and retrieval, but also in other areas, such as automatic text summarization and question answering system.
{"title":"Resource Space Model: A Survey","authors":"Jian Zhou","doi":"10.1109/SKG.2018.00051","DOIUrl":"https://doi.org/10.1109/SKG.2018.00051","url":null,"abstract":"This paper surveys research on the Resource Space Model (RSM). RSM is a multi-dimensional, classification-based, content-based and high-level semantic space model for organizing and managing various resources through multi-dimensional abstraction and specialization. RSM has more powerful resource representation ability than traditional resource management model. It has applications not only in resource management and retrieval, but also in other areas, such as automatic text summarization and question answering system.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"6 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":"130728512","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}
Author name ambiguity can significantly impact the accuracy of a bibliographic retrieval system, especially when author name served as a search keyword. In this paper, we propose an unsupervised approach addressing the name ambiguity problem by linking papers to their corresponding authors based on clustering result of word embeddings. Each cluster represents a collection of words in a certain research area. Papers and authors which to be disambiguated are then assigned a probability of each research area they belong to. We put those probabilities and some metadata of papers and authors as features into a graphic model and do the collective inference. Experiment shows that our entirely unsupervised method perform well for a Chinese Bibliographic Retrieval System even with a huge amount of noisy in its database.
{"title":"An Unsupervised Framework for Author-Paper Linking in Bibliographic Retrieval System","authors":"Xin Ding, Hui Zhang, Xiaoyu Guo","doi":"10.1109/SKG.2018.00028","DOIUrl":"https://doi.org/10.1109/SKG.2018.00028","url":null,"abstract":"Author name ambiguity can significantly impact the accuracy of a bibliographic retrieval system, especially when author name served as a search keyword. In this paper, we propose an unsupervised approach addressing the name ambiguity problem by linking papers to their corresponding authors based on clustering result of word embeddings. Each cluster represents a collection of words in a certain research area. Papers and authors which to be disambiguated are then assigned a probability of each research area they belong to. We put those probabilities and some metadata of papers and authors as features into a graphic model and do the collective inference. Experiment shows that our entirely unsupervised method perform well for a Chinese Bibliographic Retrieval System even with a huge amount of noisy in its database.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","volume":"30 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":"114466348","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}
Rapid growth of multi-modal documents containing images on the Internet makes multi-modal summarization necessary. Recent advances in neural-based text summarization show the strength of deep learning technique in summarization. This paper proposes a neural-based extractive multi-modal summarization method based on multi-modal RNN. Our method first encodes documents and images with a multi-modal RNN, and then calculates the summary probability of sentences through a logistic classifier using text coverage, text redundancy, and image set coverage as features. We extend the DailyMail corpora by collecting images from the Web. Experiments show our method outperforms the state-of-the-art neural summarization methods.
{"title":"Extractive Text-Image Summarization Using Multi-Modal RNN","authors":"Jingqiang Chen, H. Zhuge","doi":"10.1109/SKG.2018.00033","DOIUrl":"https://doi.org/10.1109/SKG.2018.00033","url":null,"abstract":"Rapid growth of multi-modal documents containing images on the Internet makes multi-modal summarization necessary. Recent advances in neural-based text summarization show the strength of deep learning technique in summarization. This paper proposes a neural-based extractive multi-modal summarization method based on multi-modal RNN. Our method first encodes documents and images with a multi-modal RNN, and then calculates the summary probability of sentences through a logistic classifier using text coverage, text redundancy, and image set coverage as features. We extend the DailyMail corpora by collecting images from the Web. Experiments show our method outperforms the state-of-the-art neural summarization methods.","PeriodicalId":265760,"journal":{"name":"2018 14th International Conference on Semantics, Knowledge and Grids (SKG)","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":"131356020","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}
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