Pub Date : 2017-09-01DOI: 10.1109/CICN.2017.8319363
Tunç Uzlu, E. Saykol
Rust, as being a systems programming language, offers memory safety with zero cost and without any runtime penalty like high level languages while providing complete memory safety unlike others like C, C++ or Cyclone. Todays world is in a transition from dumb devices to smart devices that are connected to the Internet all the time. Low cost embedded hardware is a key element for this kind of devices. Software needs to be smaller, lighter and power efficient. How one can operate with such limited hardware while preserving reliability? At the end, high level designs require runtime penalties while low level designs are known for memory unsafety and complicated design paradigms. Rust is higher level than other systems programming languages, has a rich standard library and compile-time abstractions for blazingly fast execution. While being completely available in mobile world, Internet of Things (IoT) devices are to be operated by all known mobile hardware as well. To this end, Rust, pushes limits of systems programming for two different views; first, at the core of hardware, running as daemon and talking to firmware, second, as a mobile controller software talking to mobile operating system. In this study, we summarize some concepts, employed in Rust, in terms of embedded systems development to clarify the appropriateness of using Rust within IoT world.
{"title":"On utilizing rust programming language for Internet of Things","authors":"Tunç Uzlu, E. Saykol","doi":"10.1109/CICN.2017.8319363","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319363","url":null,"abstract":"Rust, as being a systems programming language, offers memory safety with zero cost and without any runtime penalty like high level languages while providing complete memory safety unlike others like C, C++ or Cyclone. Todays world is in a transition from dumb devices to smart devices that are connected to the Internet all the time. Low cost embedded hardware is a key element for this kind of devices. Software needs to be smaller, lighter and power efficient. How one can operate with such limited hardware while preserving reliability? At the end, high level designs require runtime penalties while low level designs are known for memory unsafety and complicated design paradigms. Rust is higher level than other systems programming languages, has a rich standard library and compile-time abstractions for blazingly fast execution. While being completely available in mobile world, Internet of Things (IoT) devices are to be operated by all known mobile hardware as well. To this end, Rust, pushes limits of systems programming for two different views; first, at the core of hardware, running as daemon and talking to firmware, second, as a mobile controller software talking to mobile operating system. In this study, we summarize some concepts, employed in Rust, in terms of embedded systems development to clarify the appropriateness of using Rust within IoT world.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396706","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319354
Z. Aydın, O. Kaynar, Yasin Görmez
Three-dimensional structure prediction has crucial importance for bioinformatics and theoretical chemistry. One of the main steps of three-dimensional structure prediction is dihedral (torsion) angle prediction. As new feature extraction methods are developed the dimension of the input space increases considerably yielding longer model training and less accurate models due to noisy or redundant features. In this study, feature selection is employed for dimensionality reduction on one of the established benchmarks of protein 1D structure prediction. Experimental results show that the feature selection improves the accuracy of protein dihedral angle class prediction by 2% and can eliminate up to %82 of the features when random forest classifier is used. Accurate prediction of dihedral angles will eventually contribute to protein structure prediction.
{"title":"Feature selection for protein dihedral angle prediction","authors":"Z. Aydın, O. Kaynar, Yasin Görmez","doi":"10.1109/CICN.2017.8319354","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319354","url":null,"abstract":"Three-dimensional structure prediction has crucial importance for bioinformatics and theoretical chemistry. One of the main steps of three-dimensional structure prediction is dihedral (torsion) angle prediction. As new feature extraction methods are developed the dimension of the input space increases considerably yielding longer model training and less accurate models due to noisy or redundant features. In this study, feature selection is employed for dimensionality reduction on one of the established benchmarks of protein 1D structure prediction. Experimental results show that the feature selection improves the accuracy of protein dihedral angle class prediction by 2% and can eliminate up to %82 of the features when random forest classifier is used. Accurate prediction of dihedral angles will eventually contribute to protein structure prediction.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752501","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319350
Z. Aydın, Ömmu Gülsüm Uzut
Protein secondary structure prediction is an important step in estimating the three dimensional structure of proteins. Among the many methods developed for predicting structural properties of proteins, hybrid classifiers and ensembles that combine predictions from several models are shown to improve the accuracy rates. In this paper, we train, optimize and combine a support vector machine, a deep convolutional neural field and a random forest in the second stage of a hybrid classifier for protein secondary structure prediction. We demonstrate that the overall accuracy of the proposed ensemble is comparable to the success rates of the state-of-the-art methods in the most difficult prediction setting and combining the selected models have the potential to further improve the accuracy of the base learners.
{"title":"Combining classifiers for protein secondary structure prediction","authors":"Z. Aydın, Ömmu Gülsüm Uzut","doi":"10.1109/CICN.2017.8319350","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319350","url":null,"abstract":"Protein secondary structure prediction is an important step in estimating the three dimensional structure of proteins. Among the many methods developed for predicting structural properties of proteins, hybrid classifiers and ensembles that combine predictions from several models are shown to improve the accuracy rates. In this paper, we train, optimize and combine a support vector machine, a deep convolutional neural field and a random forest in the second stage of a hybrid classifier for protein secondary structure prediction. We demonstrate that the overall accuracy of the proposed ensemble is comparable to the success rates of the state-of-the-art methods in the most difficult prediction setting and combining the selected models have the potential to further improve the accuracy of the base learners.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131803172","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319353
Augustine Nnamdi Ekweariri, Kamil Yurtkan
Facial expression, a non-verbal communication, is a means through which humans convey their inner emotional state, thus playing an important role in social interaction and interpersonal relations. Facial expression recognition plays a significant role in human-computer interaction as well as various fields of behavioral science. There are six known classes of emotional state which are anger, disgust, fear, happiness, sadness and surprise, associated with their respective facial expressions, according to Ekman's studies. Humans recognize facial expressions almost effortlessly and without delay, but this is quite challenging for digital computers. The paper presents facial expression recognition using local binary patterns. The main contribution of the paper is the feature selection applied, in which the high variance LBP pixels are selected to represent faces. By selecting the high variance pixels based on LBPs, the recognition rates were improved significantly. The tests are completed on the BU-3DFE database. The experiments show that after applying feature selection, the recognition rates are improved by 11%.
{"title":"Facial expression recognition using enhanced local binary patterns","authors":"Augustine Nnamdi Ekweariri, Kamil Yurtkan","doi":"10.1109/CICN.2017.8319353","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319353","url":null,"abstract":"Facial expression, a non-verbal communication, is a means through which humans convey their inner emotional state, thus playing an important role in social interaction and interpersonal relations. Facial expression recognition plays a significant role in human-computer interaction as well as various fields of behavioral science. There are six known classes of emotional state which are anger, disgust, fear, happiness, sadness and surprise, associated with their respective facial expressions, according to Ekman's studies. Humans recognize facial expressions almost effortlessly and without delay, but this is quite challenging for digital computers. The paper presents facial expression recognition using local binary patterns. The main contribution of the paper is the feature selection applied, in which the high variance LBP pixels are selected to represent faces. By selecting the high variance pixels based on LBPs, the recognition rates were improved significantly. The tests are completed on the BU-3DFE database. The experiments show that after applying feature selection, the recognition rates are improved by 11%.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115963289","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319360
P. Goyal, Anurag Goyal
With the ever expanding sphere of Internet and its applications, the scope of Networking, data transfer and data security too have tremendously increased. This has led to sophisticated tools that are though useful in cyber mitigation but are also widely used by cyber criminals to eavesdrop or gain illegal access. This Statement stands true for Network monitoring and Packet Sniffing tools. Though, they were designed to assist the network administrators in better assessing the servers, traffic and diagnosing the issues but they have become the favorite tool of hackers to scan a particular network and sniff on unprotected data. White Hat hackers use these tools to prevent such attacks by criminals as they identify and filter out malicious packets and their source. In this paper, we have thoroughly compared two of the most widely used open source packet sniffing and network monitoring tools-Wireshark and Tcpdump.
{"title":"Comparative study of two most popular packet sniffing tools-Tcpdump and Wireshark","authors":"P. Goyal, Anurag Goyal","doi":"10.1109/CICN.2017.8319360","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319360","url":null,"abstract":"With the ever expanding sphere of Internet and its applications, the scope of Networking, data transfer and data security too have tremendously increased. This has led to sophisticated tools that are though useful in cyber mitigation but are also widely used by cyber criminals to eavesdrop or gain illegal access. This Statement stands true for Network monitoring and Packet Sniffing tools. Though, they were designed to assist the network administrators in better assessing the servers, traffic and diagnosing the issues but they have become the favorite tool of hackers to scan a particular network and sniff on unprotected data. White Hat hackers use these tools to prevent such attacks by criminals as they identify and filter out malicious packets and their source. In this paper, we have thoroughly compared two of the most widely used open source packet sniffing and network monitoring tools-Wireshark and Tcpdump.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132494550","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319366
N. A. Othman, I. Aydin
In recent years, the security constitutes the most important section of our lives. Automation of a home is an exciting field for security applications. This area has developed with new technologies like Internet of things (IoT). In IoT, each device behaves as a small part of an internet node and each node communicate and interact. Currently, security cameras are used in order to construct safety areas, cities, and homes. The camera records the images and, when a problem occurs, the problem is detected by monitoring the old record. In this study, an IoT-based system is combined with computer vision in order to detect the people. A Raspberry PI 3 card with the size of a credit card was used for this purpose. A motion is detected by the PIR sensor mounted on the Raspberry PI. PIR sensor helps to monitor and get alerts when movement is detected. Afterward, human is detected in the captured image and sends images to a Smartphone by using telegram application.
{"title":"A new IoT combined body detection of people by using computer vision for security application","authors":"N. A. Othman, I. Aydin","doi":"10.1109/CICN.2017.8319366","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319366","url":null,"abstract":"In recent years, the security constitutes the most important section of our lives. Automation of a home is an exciting field for security applications. This area has developed with new technologies like Internet of things (IoT). In IoT, each device behaves as a small part of an internet node and each node communicate and interact. Currently, security cameras are used in order to construct safety areas, cities, and homes. The camera records the images and, when a problem occurs, the problem is detected by monitoring the old record. In this study, an IoT-based system is combined with computer vision in order to detect the people. A Raspberry PI 3 card with the size of a credit card was used for this purpose. A motion is detected by the PIR sensor mounted on the Raspberry PI. PIR sensor helps to monitor and get alerts when movement is detected. Afterward, human is detected in the captured image and sends images to a Smartphone by using telegram application.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"47 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124968777","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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