Pub Date : 2021-09-23DOI: 10.1504/ijes.2021.117951
S. Sahu, D. Mohapatra, S. K. Panda
In predictive analytics, many multi-disciplinary techniques have been used to analyse the known data in order to make a prediction about the unknown data. For this, an enormous amount of processed ...
在预测分析中，许多多学科的技术被用于分析已知数据，以便对未知数据做出预测。为此，大量的加工过的……
{"title":"NITIDS: a robust network intrusion dataset","authors":"S. Sahu, D. Mohapatra, S. K. Panda","doi":"10.1504/ijes.2021.117951","DOIUrl":"https://doi.org/10.1504/ijes.2021.117951","url":null,"abstract":"In predictive analytics, many multi-disciplinary techniques have been used to analyse the known data in order to make a prediction about the unknown data. For this, an enormous amount of processed ...","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121763881","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 : 2021-09-23DOI: 10.1504/ijes.2021.117938
Yongfang Qi, Liang-song Li, Guo-ping Li
This manuscript presents one method to solve the conformable fractional differential systems. In the first place, some results about conformable fractional are introduced. Secondly, the method used...
本文提出了一种求解符合分数阶微分系统的方法。首先介绍了适形分数的一些结果。其次，使用的方法是……
{"title":"Solution to the conformable fractional differential systems with higher order","authors":"Yongfang Qi, Liang-song Li, Guo-ping Li","doi":"10.1504/ijes.2021.117938","DOIUrl":"https://doi.org/10.1504/ijes.2021.117938","url":null,"abstract":"This manuscript presents one method to solve the conformable fractional differential systems. In the first place, some results about conformable fractional are introduced. Secondly, the method used...","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166035","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 : 2021-09-23DOI: 10.1504/ijes.2021.117948
Mahfuzulhoq Chowdhury
With the rise of advanced internet technologies and smart machines, several emerging internet of things (IoT) applications have been deployed that offer significant benefits to humans. Mobile cloud...
{"title":"Flexible heuristic-based prioritised latency-sensitive IoT application execution scheme in the 5G era","authors":"Mahfuzulhoq Chowdhury","doi":"10.1504/ijes.2021.117948","DOIUrl":"https://doi.org/10.1504/ijes.2021.117948","url":null,"abstract":"With the rise of advanced internet technologies and smart machines, several emerging internet of things (IoT) applications have been deployed that offer significant benefits to humans. Mobile cloud...","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076358","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 : 2021-06-30DOI: 10.1504/IJES.2021.116100
Jun Sun, Yan Li, Yatian Shen, Lei Zhang, Wenke Ding, Xianjin Shi, Xiajiong Shen, G. Qi, Jing He
Semantic relatedness between context information and entities, which is one of the most easily accessible features, has been proven to be very useful for detecting the semantic relation held in the text segment. However, some methods fail to take into account important information between entities and contexts. How to effectively choose the closest and the most relevant information to the entity in context words in a sentence is an important task. In this paper, we propose selection gate-based networks (SGate-NN) to model the relatedness of an entity word with its context words, and select the relevant parts of contexts to infer the semantic relation toward the entity. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrate that the proposed method is effective for relation classification, which can obtain state-of-the-art classification accuracy.
{"title":"Selection gate-based networks for semantic relation extraction","authors":"Jun Sun, Yan Li, Yatian Shen, Lei Zhang, Wenke Ding, Xianjin Shi, Xiajiong Shen, G. Qi, Jing He","doi":"10.1504/IJES.2021.116100","DOIUrl":"https://doi.org/10.1504/IJES.2021.116100","url":null,"abstract":"Semantic relatedness between context information and entities, which is one of the most easily accessible features, has been proven to be very useful for detecting the semantic relation held in the text segment. However, some methods fail to take into account important information between entities and contexts. How to effectively choose the closest and the most relevant information to the entity in context words in a sentence is an important task. In this paper, we propose selection gate-based networks (SGate-NN) to model the relatedness of an entity word with its context words, and select the relevant parts of contexts to infer the semantic relation toward the entity. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrate that the proposed method is effective for relation classification, which can obtain state-of-the-art classification accuracy.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129346726","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 : 2021-06-29DOI: 10.1504/IJES.2021.116109
Min Wang, Zuo Chen, Zhiqiang Zhang, Sangzhi Zhu, Shenggang Yang
With the growing demand for data analysis, machine learning technology has been widely used in many applications, such as mass data summarising rules, predicting behaviours and dividing characteristics. The Ripper algorithm presents better pruning and stopping criteria than the traditional decision tree algorithm (C4.5), while its error rate less than or equal to C4.5 by O(nlog2n) time complexity. As a result of that, Ripper can maintain high efficiency even on the massive dataset which contains lots of noise. Adaboost is one of iterative algorithms, which combines a group of weak classifiers together to set up a strong classifier. In order to improve the accuracy of Ripper classification algorithm and reduce the computational complexity, this paper proposes a Ripper-Adaboost combined classification method (Ripper-ADB). The experiment result shows Ripper-ADB could improve the classifier and get higher classification accuracy than decision tree and SVM.
{"title":"A combination classification method based on Ripper and Adaboost","authors":"Min Wang, Zuo Chen, Zhiqiang Zhang, Sangzhi Zhu, Shenggang Yang","doi":"10.1504/IJES.2021.116109","DOIUrl":"https://doi.org/10.1504/IJES.2021.116109","url":null,"abstract":"With the growing demand for data analysis, machine learning technology has been widely used in many applications, such as mass data summarising rules, predicting behaviours and dividing characteristics. The Ripper algorithm presents better pruning and stopping criteria than the traditional decision tree algorithm (C4.5), while its error rate less than or equal to C4.5 by O(nlog2n) time complexity. As a result of that, Ripper can maintain high efficiency even on the massive dataset which contains lots of noise. Adaboost is one of iterative algorithms, which combines a group of weak classifiers together to set up a strong classifier. In order to improve the accuracy of Ripper classification algorithm and reduce the computational complexity, this paper proposes a Ripper-Adaboost combined classification method (Ripper-ADB). The experiment result shows Ripper-ADB could improve the classifier and get higher classification accuracy than decision tree and SVM.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121517642","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 : 2021-06-29DOI: 10.1504/IJES.2021.116134
Jun Guo, Zhixiong Jiang, Dingchao Jiang
Deep learning models for semantic image segmentation are limited in their hierarchical architectures to extract features, which results in losing contextual and spatial information. In this paper, a new attention-based network, MSANet, which applies an encoder-decoder structure, is proposed for image data segmentation to aggregate contextual features from different levels and reconstruct spatial characteristics efficiently. To model long-range spatial dependencies among features, the multi-level spatial attention module (MSAM) is presented to process multi-level features in the encoder network and capture global contextual information. In this way, our model learns multi-level spatial dependencies between features by the MSAM and hierarchical representations of the input image by the stacked convolutional layers, which means the model is more capable of producing accurate segmentation results. The proposed network is evaluated on the PASCAL VOC 2012 and Cityscapes datasets. Results show that our model achieves excellent performance compared with U-net, FCNs, and DeepLabv3.
{"title":"Multi-level spatial attention network for image data segmentation","authors":"Jun Guo, Zhixiong Jiang, Dingchao Jiang","doi":"10.1504/IJES.2021.116134","DOIUrl":"https://doi.org/10.1504/IJES.2021.116134","url":null,"abstract":"Deep learning models for semantic image segmentation are limited in their hierarchical architectures to extract features, which results in losing contextual and spatial information. In this paper, a new attention-based network, MSANet, which applies an encoder-decoder structure, is proposed for image data segmentation to aggregate contextual features from different levels and reconstruct spatial characteristics efficiently. To model long-range spatial dependencies among features, the multi-level spatial attention module (MSAM) is presented to process multi-level features in the encoder network and capture global contextual information. In this way, our model learns multi-level spatial dependencies between features by the MSAM and hierarchical representations of the input image by the stacked convolutional layers, which means the model is more capable of producing accurate segmentation results. The proposed network is evaluated on the PASCAL VOC 2012 and Cityscapes datasets. Results show that our model achieves excellent performance compared with U-net, FCNs, and DeepLabv3.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123819983","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 : 2021-06-29DOI: 10.1504/IJES.2021.116110
Huali Pan, Jingbo Wang, Zhijun Zhang
A deep analysis and discussion of matrix factorisation technologies are given in this paper taking into account the defects of traditional collaborative filtering recommendation algorithms. In addition, we provide an analysis of the effects of feature vector dimensions on the recommendation quality and efficiency of a probability matrix factorisation (PMF) algorithm. A PMF algorithm will lead to inaccurate recommendations if it does not consider possible dynamic changes in a user's interest over time. Accordingly, a TPMF model, a PMF algorithm integrated with time information, is proposed in this article. Its feasibility and effectiveness are empirically verified using movie recommendation datasets, and higher prediction accuracy is confirmed compared to existing recommendation algorithms.
{"title":"A movie recommendation model combining time information and probability matrix factorisation","authors":"Huali Pan, Jingbo Wang, Zhijun Zhang","doi":"10.1504/IJES.2021.116110","DOIUrl":"https://doi.org/10.1504/IJES.2021.116110","url":null,"abstract":"A deep analysis and discussion of matrix factorisation technologies are given in this paper taking into account the defects of traditional collaborative filtering recommendation algorithms. In addition, we provide an analysis of the effects of feature vector dimensions on the recommendation quality and efficiency of a probability matrix factorisation (PMF) algorithm. A PMF algorithm will lead to inaccurate recommendations if it does not consider possible dynamic changes in a user's interest over time. Accordingly, a TPMF model, a PMF algorithm integrated with time information, is proposed in this article. Its feasibility and effectiveness are empirically verified using movie recommendation datasets, and higher prediction accuracy is confirmed compared to existing recommendation algorithms.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128573730","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 : 2021-06-29DOI: 10.1504/IJES.2021.116111
Gaofeng Luo, She-Xiang Jiang, L. Zong
Quantum information processing can overcome the limitations of classical computation. Consequently, image filtering using quantum computation has become a research hotspot. Here, a quantum algorithm is presented on the basis of the classical image filtering principle to detect and cancel the noise of an image. To this end, a quantum algorithm that completes the image filtering task is proposed and implemented. The novel enhanced quantum representation of digital images is introduced. Then, four basic modules, namely, position-shifting, parallel-CNOT, parallel-swap, and compare the max, are demonstrated. Two composite modules that can be utilised to realise the reversible logic circuit of the proposed quantum algorithm are designed on the basis of these basic modules. Simulation-based experimental results show the feasibility and the capabilities of the proposed quantum image filtering scheme. In addition, our proposal has outperformed its classical counterpart and other existing quantum image filtering schemes supported by detailed theoretical analysis of the computational complexity. Thus, it can potentially be used for highly efficient image filtering in a quantum computer age.
{"title":"Quantum image filtering and its reversible logic circuit design","authors":"Gaofeng Luo, She-Xiang Jiang, L. Zong","doi":"10.1504/IJES.2021.116111","DOIUrl":"https://doi.org/10.1504/IJES.2021.116111","url":null,"abstract":"Quantum information processing can overcome the limitations of classical computation. Consequently, image filtering using quantum computation has become a research hotspot. Here, a quantum algorithm is presented on the basis of the classical image filtering principle to detect and cancel the noise of an image. To this end, a quantum algorithm that completes the image filtering task is proposed and implemented. The novel enhanced quantum representation of digital images is introduced. Then, four basic modules, namely, position-shifting, parallel-CNOT, parallel-swap, and compare the max, are demonstrated. Two composite modules that can be utilised to realise the reversible logic circuit of the proposed quantum algorithm are designed on the basis of these basic modules. Simulation-based experimental results show the feasibility and the capabilities of the proposed quantum image filtering scheme. In addition, our proposal has outperformed its classical counterpart and other existing quantum image filtering schemes supported by detailed theoretical analysis of the computational complexity. Thus, it can potentially be used for highly efficient image filtering in a quantum computer age.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130344955","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 : 2021-06-29DOI: 10.1504/IJES.2021.116113
Ehsan Ali, W. Pora
Although there are many 8-bit IP processor cores available, only a few, such as Xilinx PicoBlaze and Lattice Mico8 firm-cores are reliable enough to be used in commercial products. One of the drawbacks is that their codes are confined to vendor-specific primitives. It is inefficient to implement a PicoBlaze processor on non-Xilinx FPGA devices. In this paper we propose a systematic approach that transforms primitive-level designs (firm-cores) to vendor independent designs (soft-cores), while modularising them during the process. This makes modification and implementation of designs on any FPGA devices possible. To demonstrate the idea, our soft-core version of PicoBlaze is implemented on a Lattice iCE40LP1k FPGA device and is shown to be fully compatible with the original PicoBlaze macro. Rigorous verification mechanisms have been employed to ensure the validity of the porting process; therefore, the quality of transformation matches the industry expectation.
{"title":"Modular transformation of embedded systems from firm-cores to soft-cores","authors":"Ehsan Ali, W. Pora","doi":"10.1504/IJES.2021.116113","DOIUrl":"https://doi.org/10.1504/IJES.2021.116113","url":null,"abstract":"Although there are many 8-bit IP processor cores available, only a few, such as Xilinx PicoBlaze and Lattice Mico8 firm-cores are reliable enough to be used in commercial products. One of the drawbacks is that their codes are confined to vendor-specific primitives. It is inefficient to implement a PicoBlaze processor on non-Xilinx FPGA devices. In this paper we propose a systematic approach that transforms primitive-level designs (firm-cores) to vendor independent designs (soft-cores), while modularising them during the process. This makes modification and implementation of designs on any FPGA devices possible. To demonstrate the idea, our soft-core version of PicoBlaze is implemented on a Lattice iCE40LP1k FPGA device and is shown to be fully compatible with the original PicoBlaze macro. Rigorous verification mechanisms have been employed to ensure the validity of the porting process; therefore, the quality of transformation matches the industry expectation.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124235598","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 : 2021-06-29DOI: 10.1504/IJES.2021.116135
Lan Yang, Zhixiong Jiang, Hongbo Zhou, Jun Guo
Semantic image segmentation makes a pixel-level classification play an essential role in scene understanding. Recently, most approaches exploit deep learning neural networks, especially convolutional neural networks (CNNs), to tackle the image segmentation challenge. Common issues of these CNN-based methods are the loss of spatial features during learning representations and the limited capacity for capturing contextual information in a large receptive field. This paper proposes a diffusion convolutional network (DCNet) to combine the CNN and graph convolutional neural network (GCNN) for semantic image segmentation. In the proposed model, diffusion convolution is formulated as a graph convolutional layer to aggregate structural and contextual information without losing spatial features. The final segmentation results on the PASCAL VOC 2012 and Cityscapes datasets show better performance than baseline approaches and can be competitive with state-of-the-art methods.
{"title":"DCNet: diffusion convolutional networks for semantic image segmentation","authors":"Lan Yang, Zhixiong Jiang, Hongbo Zhou, Jun Guo","doi":"10.1504/IJES.2021.116135","DOIUrl":"https://doi.org/10.1504/IJES.2021.116135","url":null,"abstract":"Semantic image segmentation makes a pixel-level classification play an essential role in scene understanding. Recently, most approaches exploit deep learning neural networks, especially convolutional neural networks (CNNs), to tackle the image segmentation challenge. Common issues of these CNN-based methods are the loss of spatial features during learning representations and the limited capacity for capturing contextual information in a large receptive field. This paper proposes a diffusion convolutional network (DCNet) to combine the CNN and graph convolutional neural network (GCNN) for semantic image segmentation. In the proposed model, diffusion convolution is formulated as a graph convolutional layer to aggregate structural and contextual information without losing spatial features. The final segmentation results on the PASCAL VOC 2012 and Cityscapes datasets show better performance than baseline approaches and can be competitive with state-of-the-art methods.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133165098","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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