Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170306
Jianwei Luo, Li Ma
In recently, image caption tasks are solved by using the LSTM to generate description. However, the model only accords image features and is hard to learn existing syntactic features, thereby lead to generate inaccurate description. In this paper, an image captioning model based on multi-head attention mechanism is presented. Specifically, the proposed model adopts Encoder-Decoder framework. A five-layer ResNet is used in Encoder module to extract image features. Multi-head attention layer and full connection feed forward layer are added to Decoder module. In addition, to capture the order of extracting feature sequences, the position-coded is used as a determining factor While calculating multi-head self-attention. Compared With the other current models based on various visual attention mechanisms, experimental results show that the proposed model has better performance.
{"title":"Image Caption Model Based on Multi-Head Attention and Encoder-Decoder Framework","authors":"Jianwei Luo, Li Ma","doi":"10.1109/ISKE47853.2019.9170306","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170306","url":null,"abstract":"In recently, image caption tasks are solved by using the LSTM to generate description. However, the model only accords image features and is hard to learn existing syntactic features, thereby lead to generate inaccurate description. In this paper, an image captioning model based on multi-head attention mechanism is presented. Specifically, the proposed model adopts Encoder-Decoder framework. A five-layer ResNet is used in Encoder module to extract image features. Multi-head attention layer and full connection feed forward layer are added to Decoder module. In addition, to capture the order of extracting feature sequences, the position-coded is used as a determining factor While calculating multi-head self-attention. Compared With the other current models based on various visual attention mechanisms, experimental results show that the proposed model has better performance.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718658","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170378
N. Jaber, A. Hussein, H. Almalikee
In the petroleum industry, data management reis is crucial to the success of petroleum projects. In particular, data acquisition, storage, and classification are major concerns of oil and gas companies. This study therefore focuses on the problem of predicting a petroleum point (a possible oil reservoir) using the data generated by well loggers from the propagation of a set of sensors at different depths within a well. The training of a feedforward neural network (FFNN) model by the LevenbergMarquardt (LM) algorithm involves a random allotment of weight/bias values over multiple epochs to reduce the variance between testing and training data. Random weight assignment degrades the performance of the model, as the variance between testing and training data will remain uncertain. In this paper, a novel method, called modified feedforward neural network (MFFNN) is proposed by freezing the weight/bias coefficients to predict petrol reservoirs with minimal error. The MFFNN outperforms existing conventional models and machine learning algorithms.
{"title":"A New Approach to Predict the Location of Petroleum Reservoirs Using FFNN","authors":"N. Jaber, A. Hussein, H. Almalikee","doi":"10.1109/ISKE47853.2019.9170378","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170378","url":null,"abstract":"In the petroleum industry, data management reis is crucial to the success of petroleum projects. In particular, data acquisition, storage, and classification are major concerns of oil and gas companies. This study therefore focuses on the problem of predicting a petroleum point (a possible oil reservoir) using the data generated by well loggers from the propagation of a set of sensors at different depths within a well. The training of a feedforward neural network (FFNN) model by the LevenbergMarquardt (LM) algorithm involves a random allotment of weight/bias values over multiple epochs to reduce the variance between testing and training data. Random weight assignment degrades the performance of the model, as the variance between testing and training data will remain uncertain. In this paper, a novel method, called modified feedforward neural network (MFFNN) is proposed by freezing the weight/bias coefficients to predict petrol reservoirs with minimal error. The MFFNN outperforms existing conventional models and machine learning algorithms.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132302721","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170374
Li Wang, Wei Xiang
Face recognition has made great progress due to the development of convolutional neural network (CNN), and face alignment is an important part of recognition, it is easily affected by gestures and occlusion. In this paper, we propose Residual Neural Network and Wing Loss for Face Alignment Network (RWAN), which consists of a plurality of stages, each stage ameliorates the position of facial landmarks estimated from the previous stage. Unlike the traditional cascading model, the network model uses the Residual Neural Network, which is easily to optimize and can improve accuracy by adding considerable depth, the internal residual block uses shortcut to alleviate the gradient explosion problem caused by increasing depth in deep neural network. Enter the face images and extract features from the entire image by introducing landmark heatmaps to obtain more accurate positioning. Using wing loss in the loss function part not only focuses on the landmark of large error points, but also on the small and medium errors of landmarks, it aims to improve the training ability of deep neural network with small and medium range error. The problem of unbalanced data not only confuses classification tasks, but also affects the accuracy of the model when face samples of different attitudes are not balanced in face key detection tasks. a simple but effective data enhancement method is proposed to deal with the problem, which solves the problem of unbalanced data processing by randomly rotating the training samples, amplifying and the like. The experimental results obtained by our method on the 300W dataset indicate the advantages of this method.
{"title":"Residual Neural Network and Wing Loss for Face Alignment Network","authors":"Li Wang, Wei Xiang","doi":"10.1109/ISKE47853.2019.9170374","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170374","url":null,"abstract":"Face recognition has made great progress due to the development of convolutional neural network (CNN), and face alignment is an important part of recognition, it is easily affected by gestures and occlusion. In this paper, we propose Residual Neural Network and Wing Loss for Face Alignment Network (RWAN), which consists of a plurality of stages, each stage ameliorates the position of facial landmarks estimated from the previous stage. Unlike the traditional cascading model, the network model uses the Residual Neural Network, which is easily to optimize and can improve accuracy by adding considerable depth, the internal residual block uses shortcut to alleviate the gradient explosion problem caused by increasing depth in deep neural network. Enter the face images and extract features from the entire image by introducing landmark heatmaps to obtain more accurate positioning. Using wing loss in the loss function part not only focuses on the landmark of large error points, but also on the small and medium errors of landmarks, it aims to improve the training ability of deep neural network with small and medium range error. The problem of unbalanced data not only confuses classification tasks, but also affects the accuracy of the model when face samples of different attitudes are not balanced in face key detection tasks. a simple but effective data enhancement method is proposed to deal with the problem, which solves the problem of unbalanced data processing by randomly rotating the training samples, amplifying and the like. The experimental results obtained by our method on the 300W dataset indicate the advantages of this method.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128222343","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170407
Yunfei Feng, Hongmei Chen
Mining community structure from network data set is an important research task in machine learning. Overlapping community detection is more complex due to the ambiguous of nodes which may be partitioned to different communities simultaneously. In this paper, an improved density peaks clustering is proposed to overlapping community detection. The rough set theory based uncertain similarity between nodes is defined in dual-nucleus subspace by fully considering the topological structure. Different strategies are used in density peaks clustering to improve the efficiency and the performance of the community division. Furthermore, rough set theory is employed to describe the overlapping nodes and rough set theory based overlapping community detection algorithm is proposed. Experiments are carried out on real-world social networks and artificial networks. The experimental results show that RSDPCD is effective.
{"title":"An Improved Density Peaks Clustering based on Rough Set Theory for Overlapping Community Detection","authors":"Yunfei Feng, Hongmei Chen","doi":"10.1109/ISKE47853.2019.9170407","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170407","url":null,"abstract":"Mining community structure from network data set is an important research task in machine learning. Overlapping community detection is more complex due to the ambiguous of nodes which may be partitioned to different communities simultaneously. In this paper, an improved density peaks clustering is proposed to overlapping community detection. The rough set theory based uncertain similarity between nodes is defined in dual-nucleus subspace by fully considering the topological structure. Different strategies are used in density peaks clustering to improve the efficiency and the performance of the community division. Furthermore, rough set theory is employed to describe the overlapping nodes and rough set theory based overlapping community detection algorithm is proposed. Experiments are carried out on real-world social networks and artificial networks. The experimental results show that RSDPCD is effective.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128392978","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}
Line Segment Representation (LSR) refers to represents a time series by a few of line segments, such that the original time series and the piecewise line segment series have shapes as similar as possible. Because of its simple expression, LSR based time series are often easier to be understood and computed for some time series datamining tasks than the original raw data. Two kinds of continuous LSR methods, namely, 11 trend filtering and mix-integer programming (MILP) method, are discussed in this paper. To overcome the poor representation ability of l1 trend filtering, and the high computational complexity of MILP, this paper proposes a hybrid method combining GA and linear programming (GA-LP) to find the optimal LSR time series efficiently. In GA-LP, locations of the breakpoints of the piecewise linear segment are fixed by GA, and values on these locations are fixed by a LP method. Numerical experiments reveal that GA-LP can reduce representation error by comparisons with l1 trend filtering and MILP method, and its computing time is much less than that of MILP.
{"title":"A Genetic Algorithm Based Piecewise Linear Representation of Time Series","authors":"Xiyang Yang, Changxin Zhai, Fang Li, Longshu Liu, Youhua Zhang","doi":"10.1109/ISKE47853.2019.9170463","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170463","url":null,"abstract":"Line Segment Representation (LSR) refers to represents a time series by a few of line segments, such that the original time series and the piecewise line segment series have shapes as similar as possible. Because of its simple expression, LSR based time series are often easier to be understood and computed for some time series datamining tasks than the original raw data. Two kinds of continuous LSR methods, namely, 11 trend filtering and mix-integer programming (MILP) method, are discussed in this paper. To overcome the poor representation ability of l1 trend filtering, and the high computational complexity of MILP, this paper proposes a hybrid method combining GA and linear programming (GA-LP) to find the optimal LSR time series efficiently. In GA-LP, locations of the breakpoints of the piecewise linear segment are fixed by GA, and values on these locations are fixed by a LP method. Numerical experiments reveal that GA-LP can reduce representation error by comparisons with l1 trend filtering and MILP method, and its computing time is much less than that of MILP.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134371831","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170297
Xiaojun Ma, B. Peng, Xun Gong, Zeng Yu, Tianrui Li
Image segmentation is a key computer vision technique that divides the pixels of an image into different blocks of distinct transactions. The multi-scale segmentation method is one of the image segmentation methods, which can extract the object regions of different scales. It has the potential to fully exploit the application of high resolution and complex scene images and is the research hotspots direction of image segmentation technology. In this work, a feasible image scale-aware algorithm is proposed. By using the segmentation results of the existing multi-scale segmentation algorithm, the global region’s hierarchical region is merged by the quantitative description of each hierarchical region feature to achieve the optimal scale of multi-scale segmentation. We validate the proposed method on different algorithms and data sets. The results have shown that the proposed method can solve the error caused by manual threshold setting and achieve the optimal selection of individual goals to a certain extent.
{"title":"Hierarchical Region Merging for Multi-scale Image Segmentation","authors":"Xiaojun Ma, B. Peng, Xun Gong, Zeng Yu, Tianrui Li","doi":"10.1109/ISKE47853.2019.9170297","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170297","url":null,"abstract":"Image segmentation is a key computer vision technique that divides the pixels of an image into different blocks of distinct transactions. The multi-scale segmentation method is one of the image segmentation methods, which can extract the object regions of different scales. It has the potential to fully exploit the application of high resolution and complex scene images and is the research hotspots direction of image segmentation technology. In this work, a feasible image scale-aware algorithm is proposed. By using the segmentation results of the existing multi-scale segmentation algorithm, the global region’s hierarchical region is merged by the quantitative description of each hierarchical region feature to achieve the optimal scale of multi-scale segmentation. We validate the proposed method on different algorithms and data sets. The results have shown that the proposed method can solve the error caused by manual threshold setting and achieve the optimal selection of individual goals to a certain extent.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134265345","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170411
Qian Zhang, Di Zhang, Jie Lu, Guangquan Zhang, Wei Qu, M. Cohen
The recommender systems provide users with what they prefer and filter unnecessary information. In the fierce marketing environment, it is crucial to recommend items to users in an early stage to keep user’s interests and loyalty. With the fast product renewal, classical recommendation methods such as collaborative filtering cannot handle the cold-start item problem. In many real-world applications, content information of items or users is available and can be used to assist recommendation. Besides, user may interact with the items in different behaviors such as view, click or subscribe. How to use the complex content information and multiple user behaviors are real problems that are not well solved in applications. In this paper, we propose a content-based recommender system to deal with the practical problem. Boosting tree model also added to the system to avoid potential Spam. We applied our developed method to real estate application to recommend new property which just landed into the market to users. Experimental results with three data subsets and three recommendation scenarios demonstrate that the proposed method can outperform the baseline on recommendation accuracy. The results indicate that our method can effectively reduce potential Spam to users, so that user experience will be improved.
{"title":"A Recommender System for Cold-start Items: A Case Study in the Real Estate Industry","authors":"Qian Zhang, Di Zhang, Jie Lu, Guangquan Zhang, Wei Qu, M. Cohen","doi":"10.1109/ISKE47853.2019.9170411","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170411","url":null,"abstract":"The recommender systems provide users with what they prefer and filter unnecessary information. In the fierce marketing environment, it is crucial to recommend items to users in an early stage to keep user’s interests and loyalty. With the fast product renewal, classical recommendation methods such as collaborative filtering cannot handle the cold-start item problem. In many real-world applications, content information of items or users is available and can be used to assist recommendation. Besides, user may interact with the items in different behaviors such as view, click or subscribe. How to use the complex content information and multiple user behaviors are real problems that are not well solved in applications. In this paper, we propose a content-based recommender system to deal with the practical problem. Boosting tree model also added to the system to avoid potential Spam. We applied our developed method to real estate application to recommend new property which just landed into the market to users. Experimental results with three data subsets and three recommendation scenarios demonstrate that the proposed method can outperform the baseline on recommendation accuracy. The results indicate that our method can effectively reduce potential Spam to users, so that user experience will be improved.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133075449","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170351
Zhuang Liu, Kaiyu Huang, Ziyu Gao, Degen Huang
Machine Comprehension (MC) of text is the problem to answer a query based on a given document. Although MC has been very popular recently, it still have some serious weaknesses which rely only on query-to-document interaction or its learning is just heavily dependent on the training data. To take advantage of external knowledge to improve neural networks for MC, we propose a novel knowledge enhanced recurrent neural model, called knowledge-aware LSTM (k-LSTM), an extension to basic LSTM cells, designed to exploit external knowledge bases (KBs) to improve neural networks for MC task. To incorporate KBs with contextual information effectively from the currently text, k-LSTM employs an compositional attention mechanism to adaptively decide whether to attend to KBs and which information from external knowledge is useful. Furthermore, we present our knowledge enhanced neural network, called Knowledge-guided DIM Reader (K-DIM Reader), which is a novel knowledge-aware compositional attention neural network architecture, employing the k-LSTM in our framework. By stringing external background knowledge together and imposing compositional attention interaction that regulate their interaction, K-DIM Reader effectively learns to perform reading comprehension processes that are directly inferred from the data in an end-to-end approach. We show our proposed models strength, robustness and interpretability on the challenging MC datasets, achieving significant improvements on SQuAD dataset [1] and obtaining new state-of-the-art results on both Cloze-style datasets, CBTest [2] and CNN news [3]. In particular, we further extend 6 popular end-to-end neural MC models using k-LSTM incorporating knowledge into models for improving MC, and evaluate their performance on both well-known MC datasets. We demonstrate that neural model with external knowledge improves performance on MC task.
{"title":"Knowledge-Aware LSTM for Machine Comprehension","authors":"Zhuang Liu, Kaiyu Huang, Ziyu Gao, Degen Huang","doi":"10.1109/ISKE47853.2019.9170351","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170351","url":null,"abstract":"Machine Comprehension (MC) of text is the problem to answer a query based on a given document. Although MC has been very popular recently, it still have some serious weaknesses which rely only on query-to-document interaction or its learning is just heavily dependent on the training data. To take advantage of external knowledge to improve neural networks for MC, we propose a novel knowledge enhanced recurrent neural model, called knowledge-aware LSTM (k-LSTM), an extension to basic LSTM cells, designed to exploit external knowledge bases (KBs) to improve neural networks for MC task. To incorporate KBs with contextual information effectively from the currently text, k-LSTM employs an compositional attention mechanism to adaptively decide whether to attend to KBs and which information from external knowledge is useful. Furthermore, we present our knowledge enhanced neural network, called Knowledge-guided DIM Reader (K-DIM Reader), which is a novel knowledge-aware compositional attention neural network architecture, employing the k-LSTM in our framework. By stringing external background knowledge together and imposing compositional attention interaction that regulate their interaction, K-DIM Reader effectively learns to perform reading comprehension processes that are directly inferred from the data in an end-to-end approach. We show our proposed models strength, robustness and interpretability on the challenging MC datasets, achieving significant improvements on SQuAD dataset [1] and obtaining new state-of-the-art results on both Cloze-style datasets, CBTest [2] and CNN news [3]. In particular, we further extend 6 popular end-to-end neural MC models using k-LSTM incorporating knowledge into models for improving MC, and evaluate their performance on both well-known MC datasets. We demonstrate that neural model with external knowledge improves performance on MC task.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122230571","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170359
Linghao Zhang, Bingde Lu, Tao Zhao, Hongjun Wang
Blockchain is very important in finance field and electronic business field, so many researchers are attracted to study the technologies of blockchain. Since the transactions in blockchain takes much time, and they make the blockchain poor efficiency, business processes across organizations require the transactions as soon as possible. Concurrency is attracted much attention and is very important in blockchain field. In this paper, a novel decentralized blockchain network model with high concurrency is proposed. First, the idea of the proposed model is stated. Second, the high concurrency blockchain network model is proposed. Third, the corresponding algorithms are designed according to the proposed model. Furthermore, the experiment is conduced and the results show that proposed model works well.
{"title":"A Novel Decentralized Blockchain Networks Model with High Concurrenc(Blockchain Networks Model with High Concurrency)","authors":"Linghao Zhang, Bingde Lu, Tao Zhao, Hongjun Wang","doi":"10.1109/ISKE47853.2019.9170359","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170359","url":null,"abstract":"Blockchain is very important in finance field and electronic business field, so many researchers are attracted to study the technologies of blockchain. Since the transactions in blockchain takes much time, and they make the blockchain poor efficiency, business processes across organizations require the transactions as soon as possible. Concurrency is attracted much attention and is very important in blockchain field. In this paper, a novel decentralized blockchain network model with high concurrency is proposed. First, the idea of the proposed model is stated. Second, the high concurrency blockchain network model is proposed. Third, the corresponding algorithms are designed according to the proposed model. Furthermore, the experiment is conduced and the results show that proposed model works well.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125522227","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}
Pub Date : 2019-11-01DOI: 10.1109/ISKE47853.2019.9170370
Brigitte Hass, C. Yuan, Zhong Li
With the introduction of digital media and the rapid spreading of the digitalization process in our society, there is an increased need in the use of online methods for the automatic evaluation of learning outcomes. Such kinds of electronic assessments (abbr. E-Assessment) are of particular importance in the areas of science and engineering education, where students have to learn and exercise on programming techniques. In this work, we have reviewed and analyzed existing approaches aiming at automatic verification of computer programs for teaching and learning purposes. Based on the capabilities and characteristics of these systems, they have been clustered into three categories. After the study of the strengths and limitations of these approaches, we put forward our view on several aspects which are relevant for an E-Assessment system. Our further contribution lies in the discussion of relevant research questions as well as the potential impacts of E-Assessment in future academic teaching.
{"title":"On the Automatic Assessment of Learning Outcome in Programming Techniques","authors":"Brigitte Hass, C. Yuan, Zhong Li","doi":"10.1109/ISKE47853.2019.9170370","DOIUrl":"https://doi.org/10.1109/ISKE47853.2019.9170370","url":null,"abstract":"With the introduction of digital media and the rapid spreading of the digitalization process in our society, there is an increased need in the use of online methods for the automatic evaluation of learning outcomes. Such kinds of electronic assessments (abbr. E-Assessment) are of particular importance in the areas of science and engineering education, where students have to learn and exercise on programming techniques. In this work, we have reviewed and analyzed existing approaches aiming at automatic verification of computer programs for teaching and learning purposes. Based on the capabilities and characteristics of these systems, they have been clustered into three categories. After the study of the strengths and limitations of these approaches, we put forward our view on several aspects which are relevant for an E-Assessment system. Our further contribution lies in the discussion of relevant research questions as well as the potential impacts of E-Assessment in future academic teaching.","PeriodicalId":399084,"journal":{"name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130422159","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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