Semantic segmentation from aerial imagery is one of the most essential tasks in the field of remote sensing with various potential applications ranging from map creation to intelligence service. One of the most challenging factors of these tasks is the very heterogeneous appearance of artificial objects like buildings, cars and natural entities such as trees, low vegetation in very high-resolution digital images. In this paper, we propose an efficient deep learning approach to aerial image segmentation. Our approach utilizes the architecture of fully convolutional network (FCN) based on the backbone ResNet101 with additional upsampling skip connections. Besides typical color channels, we also use DSM and normalized DSM (nDSM) as the input data of our models. We achieve overall accuracy of 91%, which is in top 4 among 140 submissions from all over the world on the well-known Vaihingen dataset from ISPRS 2D Semantic Labeling Contest. Especially, our approach yields better results then all state-of-the-art methods in segmentation of car objects.
{"title":"Fully Residual Convolutional Neural Networks for Aerial Image Segmentation","authors":"D. V. Sang, N. D. Minh","doi":"10.1145/3287921.3287970","DOIUrl":"https://doi.org/10.1145/3287921.3287970","url":null,"abstract":"Semantic segmentation from aerial imagery is one of the most essential tasks in the field of remote sensing with various potential applications ranging from map creation to intelligence service. One of the most challenging factors of these tasks is the very heterogeneous appearance of artificial objects like buildings, cars and natural entities such as trees, low vegetation in very high-resolution digital images. In this paper, we propose an efficient deep learning approach to aerial image segmentation. Our approach utilizes the architecture of fully convolutional network (FCN) based on the backbone ResNet101 with additional upsampling skip connections. Besides typical color channels, we also use DSM and normalized DSM (nDSM) as the input data of our models. We achieve overall accuracy of 91%, which is in top 4 among 140 submissions from all over the world on the well-known Vaihingen dataset from ISPRS 2D Semantic Labeling Contest. Especially, our approach yields better results then all state-of-the-art methods in segmentation of car objects.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114493585","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}
Writing diary is not only a hobby but also provides a personal lifelog for better analysis and understanding of a user's daily activities and events. However, in a busy society, people may not have enough time to write in diary all their social interaction. This motivates our proposal to develop a ubiquitous system to automatically generate daily text diary using our novel method for image captioning from photos taken periodically from wearable cameras. We propose to incorporate common visual concepts extracted from a photo to enhance the details of the image description. We also propose a wide trail beam search strategy to enhance the naturalness of text caption. Our captioning method improves the results on MSCOCO dataset on four metrics: BLEU, METEOR, ROUGE-L, CIDEr. As compared to the method proposed by Xu et.al and Neuraltalk of Karpathy, our model has better performance on all four metrics. We also develop smart glasses and a prototype smart workplace in which people can have their personal diary generated from photos taken by smart glasses. Furthermore, we also apply a transformer machine translation model in order to translate captions into Vietnamese language. The results are promising and can be used for Vietnamese people.
{"title":"Personal Diary Generation from Wearable Cameras with Concept Augmented Image Captioning and Wide Trail Strategy","authors":"Viet-Khoa Vo-Ho, Quoc-An Luong, Duy-Tam Nguyen, Mai-Khiem Tran, Minh-Triet Tran","doi":"10.1145/3287921.3287955","DOIUrl":"https://doi.org/10.1145/3287921.3287955","url":null,"abstract":"Writing diary is not only a hobby but also provides a personal lifelog for better analysis and understanding of a user's daily activities and events. However, in a busy society, people may not have enough time to write in diary all their social interaction. This motivates our proposal to develop a ubiquitous system to automatically generate daily text diary using our novel method for image captioning from photos taken periodically from wearable cameras. We propose to incorporate common visual concepts extracted from a photo to enhance the details of the image description. We also propose a wide trail beam search strategy to enhance the naturalness of text caption. Our captioning method improves the results on MSCOCO dataset on four metrics: BLEU, METEOR, ROUGE-L, CIDEr. As compared to the method proposed by Xu et.al and Neuraltalk of Karpathy, our model has better performance on all four metrics. We also develop smart glasses and a prototype smart workplace in which people can have their personal diary generated from photos taken by smart glasses. Furthermore, we also apply a transformer machine translation model in order to translate captions into Vietnamese language. The results are promising and can be used for Vietnamese people.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130237580","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}
Phan The Duy, Do Thi Thu Hien, Do Hoang Hien, V. Pham
In the "Industry 4.0" era, blockchain as well as related distributed ledger technologies has been an unmissable trend for both academy and industry recently. Blockchain technology has become famous as the innovative technology that underlies cryptocurrencies such as Bitcoin and Ethereum platform. It also has been spreading with multiple industries exploring their capabilities and new blockchain use cases springing up on a daily basis. Its emergence has brought a great deal of impact on how the information will be stored and processed securely. Furthermore, almost of advocates say that blockchain will disrupt and change everything from education to financial payments, insurance, intellectual property, healthcare,... in the years to come. However, a comprehensive survey on potential and issues of blockchain adoption in academy and industry has not been yet accomplished. This paper tries to conduct a comprehensive survey on the blockchain technology adoption by discussing its influences as well as the opportunities and challenges when utilizing it in the real-world scenarios.
{"title":"A survey on opportunities and challenges of Blockchain technology adoption for revolutionary innovation","authors":"Phan The Duy, Do Thi Thu Hien, Do Hoang Hien, V. Pham","doi":"10.1145/3287921.3287978","DOIUrl":"https://doi.org/10.1145/3287921.3287978","url":null,"abstract":"In the \"Industry 4.0\" era, blockchain as well as related distributed ledger technologies has been an unmissable trend for both academy and industry recently. Blockchain technology has become famous as the innovative technology that underlies cryptocurrencies such as Bitcoin and Ethereum platform. It also has been spreading with multiple industries exploring their capabilities and new blockchain use cases springing up on a daily basis. Its emergence has brought a great deal of impact on how the information will be stored and processed securely. Furthermore, almost of advocates say that blockchain will disrupt and change everything from education to financial payments, insurance, intellectual property, healthcare,... in the years to come. However, a comprehensive survey on potential and issues of blockchain adoption in academy and industry has not been yet accomplished. This paper tries to conduct a comprehensive survey on the blockchain technology adoption by discussing its influences as well as the opportunities and challenges when utilizing it in the real-world scenarios.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123359944","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}
Hieu Mac, Dung Truong, Lam Nguyen, Hoa Nguyen, H. Tran, Duc Tran
Web attacks have become a real threat to the Internet. This paper proposes the use of autoencoder to detect malicious pattern in the HTTP/HTTPS requests. The autoencoder is able to operate on the raw data and thus, does not require the hand-crafted features to be extracted. We evaluate the original autoencoder and its variants and end up with the Regularized Deep Autoencoder, which can achieve an F1-score of 0.9463 on the CSIC 2010 dataset. It also produces a better performance with respect to OWASP Core Rule Set and other one-class methods, reported in the literature. The Regularized Deep Autoencoder is then combined with Modsecurity in order to protect a website in real time. This algorithm proves to be comparable to the original Modsecurity in terms of computation time and is ready to be deployed in practice.
{"title":"Detecting Attacks on Web Applications using Autoencoder","authors":"Hieu Mac, Dung Truong, Lam Nguyen, Hoa Nguyen, H. Tran, Duc Tran","doi":"10.1145/3287921.3287946","DOIUrl":"https://doi.org/10.1145/3287921.3287946","url":null,"abstract":"Web attacks have become a real threat to the Internet. This paper proposes the use of autoencoder to detect malicious pattern in the HTTP/HTTPS requests. The autoencoder is able to operate on the raw data and thus, does not require the hand-crafted features to be extracted. We evaluate the original autoencoder and its variants and end up with the Regularized Deep Autoencoder, which can achieve an F1-score of 0.9463 on the CSIC 2010 dataset. It also produces a better performance with respect to OWASP Core Rule Set and other one-class methods, reported in the literature. The Regularized Deep Autoencoder is then combined with Modsecurity in order to protect a website in real time. This algorithm proves to be comparable to the original Modsecurity in terms of computation time and is ready to be deployed in practice.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124205649","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}
Shunta Fukumoto, Ryo Nishide, Yumi Takaki, C. Ohta, K. Oyama, T. Ohkawa
In the management of beef cattle breeding, estrus detection is especially important for improving productivity. Generally, pedometers have been used since the past few decades to measure the increase in momentum, which is likely to occur during estrus. Cattle are known to be social animals which take unique actions such as the approaching or mounting other cattle during estrus. However, the sociality of cattle has not been focused much on detecting estrus accurately. In this paper, we propose the method to detect estrus in relation to the approaching behavior between cattle. We extract the approaching behaviorbased on position information from GPS devices attached to the cattle. Our method focuses on moving direction to detect even small approaching actions with which the momentum may not increase clearly. Furthermore, we conducted an experiment based on the records of artificial insemination to examine the efficiency of our method. As a result, we verified some cases with a distinctive characteristic related to approaching behavior, which occurred during the days when the pregnancy was confirmed from the artificial insemination records.
{"title":"Quantifying the Approaching Behaviors for Interactions in Detecting Estrus of Breeding Cattle","authors":"Shunta Fukumoto, Ryo Nishide, Yumi Takaki, C. Ohta, K. Oyama, T. Ohkawa","doi":"10.1145/3287921.3287944","DOIUrl":"https://doi.org/10.1145/3287921.3287944","url":null,"abstract":"In the management of beef cattle breeding, estrus detection is especially important for improving productivity. Generally, pedometers have been used since the past few decades to measure the increase in momentum, which is likely to occur during estrus. Cattle are known to be social animals which take unique actions such as the approaching or mounting other cattle during estrus. However, the sociality of cattle has not been focused much on detecting estrus accurately. In this paper, we propose the method to detect estrus in relation to the approaching behavior between cattle. We extract the approaching behaviorbased on position information from GPS devices attached to the cattle. Our method focuses on moving direction to detect even small approaching actions with which the momentum may not increase clearly. Furthermore, we conducted an experiment based on the records of artificial insemination to examine the efficiency of our method. As a result, we verified some cases with a distinctive characteristic related to approaching behavior, which occurred during the days when the pregnancy was confirmed from the artificial insemination records.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133944252","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}
Closeness centrality is an substantial metric used in large-scale network analysis, in particular social networks. Determining closeness centrality from a vertex to all other vertices in the graph is a high complexity problem. Prior work has a strong focuses on the algorithmic aspect of the problem, and little attention has been paid to the definition of the data structure supporting the implementation of the algorithm. Thus, we present in this paper an efficient algorithm to compute the closeness centrality of all nodes in a social network. Our algorithm is based on (i) an appropriate data structure for increasing the cache hit rate, and then reducing amount of time accessing the main memory for the graph data, and (ii) an efficient and parallel complete BFS search to reduce the execution time. We tested performance of our algorithm, namely BigGraph, with five different real-world social networks and compare the performance to that of current approaches including TeexGraph and NetworKit. Experiment results show that BigGraph is faster than TeexGraph and NetworKit 1.27-2.12 and 14.78-68.21 times, respectively.
{"title":"An Efficient Parallel Algorithm for Computing the Closeness Centrality in Social Networks","authors":"P. Du, N. Chau, K. Nguyen, N. Nguyen","doi":"10.1145/3287921.3287981","DOIUrl":"https://doi.org/10.1145/3287921.3287981","url":null,"abstract":"Closeness centrality is an substantial metric used in large-scale network analysis, in particular social networks. Determining closeness centrality from a vertex to all other vertices in the graph is a high complexity problem. Prior work has a strong focuses on the algorithmic aspect of the problem, and little attention has been paid to the definition of the data structure supporting the implementation of the algorithm. Thus, we present in this paper an efficient algorithm to compute the closeness centrality of all nodes in a social network. Our algorithm is based on (i) an appropriate data structure for increasing the cache hit rate, and then reducing amount of time accessing the main memory for the graph data, and (ii) an efficient and parallel complete BFS search to reduce the execution time. We tested performance of our algorithm, namely BigGraph, with five different real-world social networks and compare the performance to that of current approaches including TeexGraph and NetworKit. Experiment results show that BigGraph is faster than TeexGraph and NetworKit 1.27-2.12 and 14.78-68.21 times, respectively.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124030942","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}
One of the significant goals of personalized medicine is to provide the right treatment to patients based on their molecular features. Several big projects have been launched and generated a large amount of -omics and drug response data for cell lines of the human. These projects are very useful for testing of drug responses on cell lines before employing clinical trials on humans. However, a range of drugs and cell lines have not been tested yet. Thus, many computational methods attempt to predict such the responses to maximize the treatment efficiency and to minimize side-effects. These methods use not only known drug -- cell lines responses but also the similarity between drugs and between cell lines. Nevertheless, -omics data for cell lines which is used to calculate the cell-line similarities usually varies among platforms leading to heterogeneous results. Therefore, in this study, we propose a drug response prediction method (MCDRP) based on a matrix completion technique using only known drug -- cell lines response information to predict drug responses for untested cell lines. The method can impute responses for not only one at time but also all drugs simultaneously. In comparison with other methods, we found that our method achieved better performance for IC50 response measurement.
{"title":"A matrix completion method for drug response prediction in personalized medicine","authors":"Giang T. T. Nguyen, Duc-Hau Le","doi":"10.1145/3287921.3287974","DOIUrl":"https://doi.org/10.1145/3287921.3287974","url":null,"abstract":"One of the significant goals of personalized medicine is to provide the right treatment to patients based on their molecular features. Several big projects have been launched and generated a large amount of -omics and drug response data for cell lines of the human. These projects are very useful for testing of drug responses on cell lines before employing clinical trials on humans. However, a range of drugs and cell lines have not been tested yet. Thus, many computational methods attempt to predict such the responses to maximize the treatment efficiency and to minimize side-effects. These methods use not only known drug -- cell lines responses but also the similarity between drugs and between cell lines. Nevertheless, -omics data for cell lines which is used to calculate the cell-line similarities usually varies among platforms leading to heterogeneous results. Therefore, in this study, we propose a drug response prediction method (MCDRP) based on a matrix completion technique using only known drug -- cell lines response information to predict drug responses for untested cell lines. The method can impute responses for not only one at time but also all drugs simultaneously. In comparison with other methods, we found that our method achieved better performance for IC50 response measurement.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127955635","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}
Genetic Programming (GP) is a technique that allows computer programs encoded as a set of tree structures to be evolved using an evolutionary algorithm. In GP, code bloat is a common phenomenon characterized by the size of individuals gradually increasing during the evolution. This phenomenon has a negative impact on GP performance in solving problems. In order to address this problem, we have recently introduced a code bloat control method based on semantics: Substituting a subtree with an Approximate Terminal (SAT-GP). In this paper, we propose an extension of SAT-GP, namely Substituting a subtree with an Approximate Subprogram (SAS-GP). We tested this method with different GP parameter settings on a real-world time series forecasting problem. The experimental results demonstrate the benefit of the proposed method in reducing the code bloat phenomenon and improving GP performance. Particularly, SAS-GP often achieves the best performance compared to other tested GP systems using four popular performance metrics in GP.
{"title":"Semantics Based Substituting Technique for Reducing Code Bloat in Genetic Programming","authors":"Thi Huong Chu, Nguyen Quang Uy, V. Cao","doi":"10.1145/3287921.3287948","DOIUrl":"https://doi.org/10.1145/3287921.3287948","url":null,"abstract":"Genetic Programming (GP) is a technique that allows computer programs encoded as a set of tree structures to be evolved using an evolutionary algorithm. In GP, code bloat is a common phenomenon characterized by the size of individuals gradually increasing during the evolution. This phenomenon has a negative impact on GP performance in solving problems. In order to address this problem, we have recently introduced a code bloat control method based on semantics: Substituting a subtree with an Approximate Terminal (SAT-GP). In this paper, we propose an extension of SAT-GP, namely Substituting a subtree with an Approximate Subprogram (SAS-GP). We tested this method with different GP parameter settings on a real-world time series forecasting problem. The experimental results demonstrate the benefit of the proposed method in reducing the code bloat phenomenon and improving GP performance. Particularly, SAS-GP often achieves the best performance compared to other tested GP systems using four popular performance metrics in GP.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132823061","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}
We are now living in the significant era where technologies are advancing very quickly. Among the leading technologies, Internet of Things or IoT in short is standing at the peak of inflated expectation in the Gartner Hype Cycle [1]. The application of IoT has evolved from home appliances in the smart home environment such as smart tv, smart refrigerator, smart door and has grown into smart building, smart city, smart healthcare, smart industry. We could imagine that everything could go online to cut cost, add value to businesses and increase users convenience. The prediction has been made [2] that there will be 50 Billion IoT devices by 2020. Along the new revolution there has also been threats that significantly warned us if we didn't take security into account some bad things could happen, such as Mirai botnet [3] attacked dyn DNS server by using vulnerable IoT devices which turned smart devices became exploitable or vulnerable devices and named as the weakness link of the network. At the same time another hype among technology communities of using Blockchain Technology to secure the Internet of Things is getting mainstream. Blockchain is not a silver bullet to secure Internet of Things, in this paper we will propose an architecture model of using a Blockchain called Hyperledger Fabric to secure the Internet of Things network in a smart classroom scenario and integrated with The Thing Networks (TTN) cloud server and demonstrates the opportunities and challenges of this concept by taking security properties such as confidentiality, integrity, availability and non-repudiation to evaluate the concept.
我们现在生活在一个科技飞速发展的重要时代。在领先的技术中，物联网(Internet of Things，简称IoT)在Gartner Hype Cycle中处于膨胀预期的顶峰[1]。物联网的应用已经从智能电视、智能冰箱、智能门等智能家居环境中的家电发展到智能建筑、智慧城市、智慧医疗、智慧产业。我们可以想象，所有东西都可以放到网上，以降低成本，为企业增值，并增加用户的便利性。据预测[2]，到2020年将有500亿台物联网设备。在新的革命中，也有一些威胁，如果我们不考虑安全问题，一些不好的事情可能会发生，例如Mirai僵尸网络[3]通过使用易受攻击的物联网设备攻击dyn DNS服务器，使智能设备成为可利用或易受攻击的设备，并被命名为网络的薄弱环节。与此同时，另一种利用区块链技术来保护物联网的炒作正在成为主流。区块链并不是保护物联网安全的灵丹妙策，在本文中，我们将提出一种架构模型，使用称为超级账本结构的区块链来保护智能教室场景中的物联网网络，并与物联网(TTN)云服务器集成，并通过采用机密性、完整性、可用性和不可否认性等安全属性来评估这一概念，从而展示这一概念的机遇和挑战。
{"title":"Blockchain and Internet of Things Opportunities and Challenges","authors":"Kimheng Sok, Jean-Noel Colin, Kimtho Po","doi":"10.1145/3287921.3287933","DOIUrl":"https://doi.org/10.1145/3287921.3287933","url":null,"abstract":"We are now living in the significant era where technologies are advancing very quickly. Among the leading technologies, Internet of Things or IoT in short is standing at the peak of inflated expectation in the Gartner Hype Cycle [1]. The application of IoT has evolved from home appliances in the smart home environment such as smart tv, smart refrigerator, smart door and has grown into smart building, smart city, smart healthcare, smart industry. We could imagine that everything could go online to cut cost, add value to businesses and increase users convenience. The prediction has been made [2] that there will be 50 Billion IoT devices by 2020. Along the new revolution there has also been threats that significantly warned us if we didn't take security into account some bad things could happen, such as Mirai botnet [3] attacked dyn DNS server by using vulnerable IoT devices which turned smart devices became exploitable or vulnerable devices and named as the weakness link of the network. At the same time another hype among technology communities of using Blockchain Technology to secure the Internet of Things is getting mainstream. Blockchain is not a silver bullet to secure Internet of Things, in this paper we will propose an architecture model of using a Blockchain called Hyperledger Fabric to secure the Internet of Things network in a smart classroom scenario and integrated with The Thing Networks (TTN) cloud server and demonstrates the opportunities and challenges of this concept by taking security properties such as confidentiality, integrity, availability and non-repudiation to evaluate the concept.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133971490","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}
Although there is a great interest in developing automatical machines for classifying rice seed varieties, it is still unclear if differences in performance of existing techniques come from better feature descriptors or if this is due to varying inter-class or intra-class among the examined species. In this paper, we present a novel method for inspecting purity of rice seed species from the largest number of rice species dataset. The proposed method is conducted utilizing both morphological and geometrical features extracted from high resolution RGB images. Particularly, we take into account relevant pre-processing techniques so that the collected seeds are normalized by their biological structure. As a consequent, the geometrical features at local part of a seed can measured precisely. In addition, whereas existing methods include a limitation number (or a few) of examined species, we construct a dataset a much larger number of species. Because of a sufficient number of species, we can analyze the dependence of a classification performance on similarities of species (or their distinguishable), or types of the extracted features. In the evaluations, we confirm that both morphological features and geometrical features are informative. Combinations of them achieve the highest performances. Extensive evaluations on several schemes of different classifiers as well as several sub-datasets which consist of varying similarity of species are taken into account. These evaluations confirm stability and feasibility of the proposed method.
{"title":"Inspecting rice seed species purity on a large dataset using geometrical and morphological features","authors":"Hai Vu, Van Ngoc Duong, Thuy Thi Nguyen","doi":"10.1145/3287921.3287983","DOIUrl":"https://doi.org/10.1145/3287921.3287983","url":null,"abstract":"Although there is a great interest in developing automatical machines for classifying rice seed varieties, it is still unclear if differences in performance of existing techniques come from better feature descriptors or if this is due to varying inter-class or intra-class among the examined species. In this paper, we present a novel method for inspecting purity of rice seed species from the largest number of rice species dataset. The proposed method is conducted utilizing both morphological and geometrical features extracted from high resolution RGB images. Particularly, we take into account relevant pre-processing techniques so that the collected seeds are normalized by their biological structure. As a consequent, the geometrical features at local part of a seed can measured precisely. In addition, whereas existing methods include a limitation number (or a few) of examined species, we construct a dataset a much larger number of species. Because of a sufficient number of species, we can analyze the dependence of a classification performance on similarities of species (or their distinguishable), or types of the extracted features. In the evaluations, we confirm that both morphological features and geometrical features are informative. Combinations of them achieve the highest performances. Extensive evaluations on several schemes of different classifiers as well as several sub-datasets which consist of varying similarity of species are taken into account. These evaluations confirm stability and feasibility of the proposed method.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123969343","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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