Pub Date : 2021-12-11DOI: 10.5121/csit.2021.112105
Daniela Moctezuma, Víctor Muñiz, Jorge Garcia
Social media data is currently the main input to a wide variety of research works in many knowledge fields. This kind of data is generally multimodal, i.e., it contains different modalities of information such as text, images, video or audio, mainly. To deal with multimodal data to tackle a specific task could be very difficult. One of the main challenges is to find useful representations of the data, capable of capturing the subtle information that the users who generate that information provided, or even the way they use it. In this paper, we analysed the usage of two modalities of data, images, and text, both in a separate way and by combining them to address two classification problems: meme's classification and user profiling. For images, we use a textual semantic representation by using a pre-trained model of image captioning. Later, a text classifier based on optimal lexical representations was used to build a classification model. Interesting findings were found in the usage of these two modalities of data, and the pros and cons of using them to solve the two classification problems are also discussed.
{"title":"Multimodal Data Evaluation for Classification Problems","authors":"Daniela Moctezuma, Víctor Muñiz, Jorge Garcia","doi":"10.5121/csit.2021.112105","DOIUrl":"https://doi.org/10.5121/csit.2021.112105","url":null,"abstract":"Social media data is currently the main input to a wide variety of research works in many knowledge fields. This kind of data is generally multimodal, i.e., it contains different modalities of information such as text, images, video or audio, mainly. To deal with multimodal data to tackle a specific task could be very difficult. One of the main challenges is to find useful representations of the data, capable of capturing the subtle information that the users who generate that information provided, or even the way they use it. In this paper, we analysed the usage of two modalities of data, images, and text, both in a separate way and by combining them to address two classification problems: meme's classification and user profiling. For images, we use a textual semantic representation by using a pre-trained model of image captioning. Later, a text classifier based on optimal lexical representations was used to build a classification model. Interesting findings were found in the usage of these two modalities of data, and the pros and cons of using them to solve the two classification problems are also discussed.","PeriodicalId":190330,"journal":{"name":"Web, Internet Engineering & Signal Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128655984","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 : 2021-12-11DOI: 10.5121/csit.2021.112104
Anam Hashmi, Bilal Alam Khan, Omar Farooq
In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.
{"title":"A Study of the Classification of Motor Imagery Signals using Machine Learning Tools","authors":"Anam Hashmi, Bilal Alam Khan, Omar Farooq","doi":"10.5121/csit.2021.112104","DOIUrl":"https://doi.org/10.5121/csit.2021.112104","url":null,"abstract":"In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.","PeriodicalId":190330,"journal":{"name":"Web, Internet Engineering & Signal Processing","volume":"299 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114049835","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 : 2021-12-11DOI: 10.5121/csit.2021.112107
K. Tani, Nobuyuki Umezu
We propose a gesture-based interface to control a smart home. Our system replaces existing physical controls with our temporal sound commands using accelerometer. In our preliminary experiments, we recorded the sounds generated by six different gestures (knocking the desk, mouse clicking, and clapping) and converted them into spectrogram images. Classification learning was performed on these images using a CNN. Due to the difference between the microphones used, the classification results are not successful for most of the data. We then recorded acceleration values, instead of sounds, using a smart watch. 5 types of motions were performed in our experiments to execute activity classification on these acceleration data using a machine learning library named Core ML provided by Apple Inc.. These results still have much room to be improved.
{"title":"Temporal-Sound based User Interface for Smart Home","authors":"K. Tani, Nobuyuki Umezu","doi":"10.5121/csit.2021.112107","DOIUrl":"https://doi.org/10.5121/csit.2021.112107","url":null,"abstract":"We propose a gesture-based interface to control a smart home. Our system replaces existing physical controls with our temporal sound commands using accelerometer. In our preliminary experiments, we recorded the sounds generated by six different gestures (knocking the desk, mouse clicking, and clapping) and converted them into spectrogram images. Classification learning was performed on these images using a CNN. Due to the difference between the microphones used, the classification results are not successful for most of the data. We then recorded acceleration values, instead of sounds, using a smart watch. 5 types of motions were performed in our experiments to execute activity classification on these acceleration data using a machine learning library named Core ML provided by Apple Inc.. These results still have much room to be improved.","PeriodicalId":190330,"journal":{"name":"Web, Internet Engineering & Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116045232","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 : 2021-12-11DOI: 10.5121/csit.2021.112102
T. Tiropanis, A. Poulovassilis, Adrian Chapman, George Roussos
Search has been central to the development of the Web, enabling increasing engagement by a growing number of users. Proposals for the redecentalisation of the Web such as SOLID aim to give individuals sovereignty over their data by means of personal online datastores (pods). However, it is not clear whether search utilities that we currently take for granted would work efficiently in a redecentralised Web. In this paper we discuss the challenges of supporting distributed search on a large scale of pods. We present a system architecture which can allow research, development and testing of new algorithms for decentralised search across pods. We undertake an initial validation of this architecture by usage scenarios for decentralised search under user-defined access control and data governance constraints. We conclude with research directions for decentralised search algorithms and deployment.
{"title":"Search in a Redecentralised Web","authors":"T. Tiropanis, A. Poulovassilis, Adrian Chapman, George Roussos","doi":"10.5121/csit.2021.112102","DOIUrl":"https://doi.org/10.5121/csit.2021.112102","url":null,"abstract":"Search has been central to the development of the Web, enabling increasing engagement by a growing number of users. Proposals for the redecentalisation of the Web such as SOLID aim to give individuals sovereignty over their data by means of personal online datastores (pods). However, it is not clear whether search utilities that we currently take for granted would work efficiently in a redecentralised Web. In this paper we discuss the challenges of supporting distributed search on a large scale of pods. We present a system architecture which can allow research, development and testing of new algorithms for decentralised search across pods. We undertake an initial validation of this architecture by usage scenarios for decentralised search under user-defined access control and data governance constraints. We conclude with research directions for decentralised search algorithms and deployment.","PeriodicalId":190330,"journal":{"name":"Web, Internet Engineering & Signal Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128818108","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 : 2021-12-11DOI: 10.5121/csit.2021.112106
Matheus G. do Nascimento, Paulo B. Lopes
This research proposes to evaluate the level of thermal comfort of the environment in real time using Internet of Things (IoT), Big Data and Machine Learning (ML) techniques for collecting, storage, processing and analysis of the concerned information. The search for thermal comfort provides the best living and health conditions for human beings. The environment, as one of its functions, must present the climatic conditions necessary for human thermal comfort. In the research, wireless sensors are used to monitor the Heat Index, the Thermal Discomfort Index and the Temperature and Humidity Index of remote indoor environments to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms are also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment to determine preventive actions or optimize the environment control for reducing energy consumption.
{"title":"Thermal Comfort of the Environment with Internet of Things, Big Data and Machine Learning","authors":"Matheus G. do Nascimento, Paulo B. Lopes","doi":"10.5121/csit.2021.112106","DOIUrl":"https://doi.org/10.5121/csit.2021.112106","url":null,"abstract":"This research proposes to evaluate the level of thermal comfort of the environment in real time using Internet of Things (IoT), Big Data and Machine Learning (ML) techniques for collecting, storage, processing and analysis of the concerned information. The search for thermal comfort provides the best living and health conditions for human beings. The environment, as one of its functions, must present the climatic conditions necessary for human thermal comfort. In the research, wireless sensors are used to monitor the Heat Index, the Thermal Discomfort Index and the Temperature and Humidity Index of remote indoor environments to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms are also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment to determine preventive actions or optimize the environment control for reducing energy consumption.","PeriodicalId":190330,"journal":{"name":"Web, Internet Engineering & Signal Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115425354","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 : 2021-12-11DOI: 10.5121/csit.2021.112101
Lakshmaiah Alluri, Hemant Jeevan Magadum
This Small Delay Tracing Defect Testing detect small delay defects by creating internal signal races. The races are created by launching transitions along simultaneous two paths, a reference path and a test path. The arrival times of the transitions on a ‘convergence’ or common gate determine the result of the race. On the output of the convergence gate, a static hazard created by a small delay defect presence on the test path which is directed to the input of a scan-latch. A glitch detector is added to the scan latch which records the presence or absence of the glitch.
{"title":"Small Delay Tracing Defect Testing","authors":"Lakshmaiah Alluri, Hemant Jeevan Magadum","doi":"10.5121/csit.2021.112101","DOIUrl":"https://doi.org/10.5121/csit.2021.112101","url":null,"abstract":"This Small Delay Tracing Defect Testing detect small delay defects by creating internal signal races. The races are created by launching transitions along simultaneous two paths, a reference path and a test path. The arrival times of the transitions on a ‘convergence’ or common gate determine the result of the race. On the output of the convergence gate, a static hazard created by a small delay defect presence on the test path which is directed to the input of a scan-latch. A glitch detector is added to the scan latch which records the presence or absence of the glitch.","PeriodicalId":190330,"journal":{"name":"Web, Internet Engineering & Signal Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122004566","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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