Pub Date : 2020-06-01DOI: 10.1109/HORA49412.2020.9152933
Oğuz Gora, T. Akkan
Solar energy has a very high potential as a renewable energy source. Besides, it is observed that the desired levels are not achieved in the efficiency of the systems based on solar energy (especially the systems set-up with photovoltaic solar panels). Therefore, the studies on solar cell technology and other solutions related to efficiency have been continuing. As a preferred solution to increase efficiency of energy production systems based on solar energy is solar tracking. In this study, an embedded system with a wide-angle camera is used to define the sun trajectory. With this system, image data are recorded in the defined date and time range, and improvement studies are carried out to create the geometric form of the sun based on these images. Obtained images will be the basis for the progressive studies to identify the location of the sun in the sky angular manner.
{"title":"Image Acquisition with Wide-angle Camera for Sun Location Tracking","authors":"Oğuz Gora, T. Akkan","doi":"10.1109/HORA49412.2020.9152933","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152933","url":null,"abstract":"Solar energy has a very high potential as a renewable energy source. Besides, it is observed that the desired levels are not achieved in the efficiency of the systems based on solar energy (especially the systems set-up with photovoltaic solar panels). Therefore, the studies on solar cell technology and other solutions related to efficiency have been continuing. As a preferred solution to increase efficiency of energy production systems based on solar energy is solar tracking. In this study, an embedded system with a wide-angle camera is used to define the sun trajectory. With this system, image data are recorded in the defined date and time range, and improvement studies are carried out to create the geometric form of the sun based on these images. Obtained images will be the basis for the progressive studies to identify the location of the sun in the sky angular manner.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127533337","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152844
A. Ibrahim, Ahmed Tijani Salawudeen, O. Ucan, P. U. Okorie
In today’s age of wireless communication, Wireless Body Area Network (WBAN) which is an extension of the conventional Wireless Sensor Network (WSN) is attracting immense interest in academia as well as industry. This is due to its importance in providing smart heath care service. One of the major research issues are Quality-of-Service (QoS) provision and energy efficiency improvement. Since sensor nodes are highly resource constrained in terms of battery and it is impractical to recharge and replace them, it is imperative to develop techniques/routing protocols or other solutions in other to augment the battery life. For that reason, NEAT routing algorithm which is an improvement on RE-ATTMPT and CEMob protocols is proposed in this paper. NEAT prioritize data into low-emergency, high-emergency and regular-data. Unlike similar protocols, NEAT ignores the communication of regular-data and transmit high-emergency data via direct communication and low-emergency data is compared with the formerly sensed low-emergency data and if it is different, it is transmitted, otherwise it is not transmitted thus leading to significant energy saving. Simulation results obtained by MATLAB prove that NEAT protocol outperforms RE-ATTMPT and CEMob in terms of network lifetime and throughput.
{"title":"A Novel Energy-conscious threshold-based dAta Transmission routing protocol for wireless body area network (NEAT)","authors":"A. Ibrahim, Ahmed Tijani Salawudeen, O. Ucan, P. U. Okorie","doi":"10.1109/HORA49412.2020.9152844","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152844","url":null,"abstract":"In today’s age of wireless communication, Wireless Body Area Network (WBAN) which is an extension of the conventional Wireless Sensor Network (WSN) is attracting immense interest in academia as well as industry. This is due to its importance in providing smart heath care service. One of the major research issues are Quality-of-Service (QoS) provision and energy efficiency improvement. Since sensor nodes are highly resource constrained in terms of battery and it is impractical to recharge and replace them, it is imperative to develop techniques/routing protocols or other solutions in other to augment the battery life. For that reason, NEAT routing algorithm which is an improvement on RE-ATTMPT and CEMob protocols is proposed in this paper. NEAT prioritize data into low-emergency, high-emergency and regular-data. Unlike similar protocols, NEAT ignores the communication of regular-data and transmit high-emergency data via direct communication and low-emergency data is compared with the formerly sensed low-emergency data and if it is different, it is transmitted, otherwise it is not transmitted thus leading to significant energy saving. Simulation results obtained by MATLAB prove that NEAT protocol outperforms RE-ATTMPT and CEMob in terms of network lifetime and throughput.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106890","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152873
Hasen AlMomin, A. Ibrahim
Software Defined-Network (SDN) is still lately attracting much new research of interest. SDN networks introduce a new design that works on split the control plane from the data plane in order to allow a broader filed to program the network smoothly and efficiently to gain much simplicity, compared to the traditional networks. Any change in traditional networks required a re-configuration on a set of resources for the network. Whereas in new SDN network needs one person with knowledge on the control layer (controller) to manage all network resources and update rules with less time. One of the most critical attacks that increased lately is the Distributed Denial of Service (DDoS), which works to make the service unavailable for an unknown period. In this paper, we will suggest a method to detect a DDoS attack that targeting one or multiple victims concurrently by combining two algorithms of Machine Learning (ML), which is entropy and Principal Component Analysis (PCA). Also, we examined the efficiency of our schema through a Mininet emulator and a pox controller and using open vSwitch as a switch. We have obtained high detection accuracy to detect DDoS attacks.
{"title":"Detection of Distributed Denial of Service Attacks through a Combination of Machine Learning Algorithms over Software Defined Network Environment","authors":"Hasen AlMomin, A. Ibrahim","doi":"10.1109/HORA49412.2020.9152873","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152873","url":null,"abstract":"Software Defined-Network (SDN) is still lately attracting much new research of interest. SDN networks introduce a new design that works on split the control plane from the data plane in order to allow a broader filed to program the network smoothly and efficiently to gain much simplicity, compared to the traditional networks. Any change in traditional networks required a re-configuration on a set of resources for the network. Whereas in new SDN network needs one person with knowledge on the control layer (controller) to manage all network resources and update rules with less time. One of the most critical attacks that increased lately is the Distributed Denial of Service (DDoS), which works to make the service unavailable for an unknown period. In this paper, we will suggest a method to detect a DDoS attack that targeting one or multiple victims concurrently by combining two algorithms of Machine Learning (ML), which is entropy and Principal Component Analysis (PCA). Also, we examined the efficiency of our schema through a Mininet emulator and a pox controller and using open vSwitch as a switch. We have obtained high detection accuracy to detect DDoS attacks.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122527225","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152931
H. Genç, Serkan Aydin, Hasan Erdal
Quantum computers are expected to offer an effect similar to the influence of the early integrated circuit computers. According to current systems, it is predicted that they will play an effective role in the emergence of a stronger technology with an increasing speed. Some high-tech companies have quantum computer designs that they actively put into use from the research and development phase to the problem-solving phase and these computers use different architectures. Unlike the others, IBM launched a cloud-based software infrastructure in 2016 and first introduced its 5-qubit quantum computer, which consists of sequential quantum ports architecture, to the use of researchers and interested parties via its web servers. Programming is done by using quantum gates via a web interface called quantum composer. Significant progress has also been made in virtual reality technologies and virtual reality based browser platforms have been developed. In this paper, studies on the using and training of the IBM quantum composer platform through a browser designed on the basis of virtual reality are presented.
{"title":"Design of Virtual Reality Browser Platform for Programming of Quantum Computers via VR Headsets","authors":"H. Genç, Serkan Aydin, Hasan Erdal","doi":"10.1109/HORA49412.2020.9152931","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152931","url":null,"abstract":"Quantum computers are expected to offer an effect similar to the influence of the early integrated circuit computers. According to current systems, it is predicted that they will play an effective role in the emergence of a stronger technology with an increasing speed. Some high-tech companies have quantum computer designs that they actively put into use from the research and development phase to the problem-solving phase and these computers use different architectures. Unlike the others, IBM launched a cloud-based software infrastructure in 2016 and first introduced its 5-qubit quantum computer, which consists of sequential quantum ports architecture, to the use of researchers and interested parties via its web servers. Programming is done by using quantum gates via a web interface called quantum composer. Significant progress has also been made in virtual reality technologies and virtual reality based browser platforms have been developed. In this paper, studies on the using and training of the IBM quantum composer platform through a browser designed on the basis of virtual reality are presented.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114388351","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152600
A. Yavuz, A. Er
Bu araştırma öğrenme sürecinde kişinin beyin sıcaklığının ne oranda ve nasıl değiştiğini incelemek amacıyla yapıldı. Ayrıca görevlerin zorluk derecesi ve kişinin bu görevdeki yetisi ile beyin sıcaklık değişiminin ilgisi araştırıldı. Bu amaçla temassız sıcaklık tespiti için termal görüntü işleme kullanıldı. Beynin belirli kısımlarının sıcaklık değişimleri, görev aktivitesi anında incelendi. Bu çalışma ile ön lob sıcaklığının diğer bölümlerin sıcaklıklarına nazaran daha anlamlı olduğu görüldü. Görevlerin zorluk derecesi, görev süresi, kişinin bu görevdeki yetisi ve başarısı ile bölge sıcaklığının bağlantılı olduğu anlaşıldı. Görev başarısı ve becerisi arttıkça ölçülen sıcaklıların maksimum ve minimum arasındaki farkların asgari seviyede olduğu gözlemlendi. Görev tekrar sayısı artmasıyla görev esnasında elde edilen maksimum sıcaklık ile minimum sıcaklık farkı yaklaşık olarak 2.5 – 3 °C ‘den 0.5 – 1 °C bandına gerilediği gözlemlenmiştir. Çalışma sonucunda, deneklerin görev yetisi hakkında sadece sıcaklık ölçümü yapılarak karar verilebileceği görüldü.
{"title":"Termal Görüntü İşleme Kullanılarak Zihinsel İş Yükünün Değerlendirilmesi","authors":"A. Yavuz, A. Er","doi":"10.1109/HORA49412.2020.9152600","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152600","url":null,"abstract":"Bu araştırma öğrenme sürecinde kişinin beyin sıcaklığının ne oranda ve nasıl değiştiğini incelemek amacıyla yapıldı. Ayrıca görevlerin zorluk derecesi ve kişinin bu görevdeki yetisi ile beyin sıcaklık değişiminin ilgisi araştırıldı. Bu amaçla temassız sıcaklık tespiti için termal görüntü işleme kullanıldı. Beynin belirli kısımlarının sıcaklık değişimleri, görev aktivitesi anında incelendi. Bu çalışma ile ön lob sıcaklığının diğer bölümlerin sıcaklıklarına nazaran daha anlamlı olduğu görüldü. Görevlerin zorluk derecesi, görev süresi, kişinin bu görevdeki yetisi ve başarısı ile bölge sıcaklığının bağlantılı olduğu anlaşıldı. Görev başarısı ve becerisi arttıkça ölçülen sıcaklıların maksimum ve minimum arasındaki farkların asgari seviyede olduğu gözlemlendi. Görev tekrar sayısı artmasıyla görev esnasında elde edilen maksimum sıcaklık ile minimum sıcaklık farkı yaklaşık olarak 2.5 – 3 °C ‘den 0.5 – 1 °C bandına gerilediği gözlemlenmiştir. Çalışma sonucunda, deneklerin görev yetisi hakkında sadece sıcaklık ölçümü yapılarak karar verilebileceği görüldü.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128463345","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152915
Ahmet Ali Süzen, Burhan Duman, Betül Şen
Hardware, low power consumption, high accuracy and performance are crucial factors for deep learning applications. High level graphics processing units (GPU) are commonly used in high performance deep learning applications. However, it is a lot in terms of cost and power consumption to build a high-performance platform. In this study, performances of single-board computers in NVIDIA Jetson Nano, NVIDIA Jetson TX2 and Raspberry PI4 through CNN algorithm created by using fashion product images dataset are compared. 2D CNN model has been developed so as to classify 13 different fashion products in tests. Data set is comprised of 45K pictures. Parameters for performance analysis has been defined as consumption (GPU, CPU, RAM, Power), accuracy and cost. Data set is divided into parts of 5K, 10K, 20K, 30K and 45K in training and test of the model in order to expand on the differences of single-board computers. Eventually, performance of the embedded system boards in different data set in CNN algorithm is analyzed. It is, thus, aimed to attain high accuracy preference by minimum hardware requirements in deep learning applications.
{"title":"Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN","authors":"Ahmet Ali Süzen, Burhan Duman, Betül Şen","doi":"10.1109/HORA49412.2020.9152915","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152915","url":null,"abstract":"Hardware, low power consumption, high accuracy and performance are crucial factors for deep learning applications. High level graphics processing units (GPU) are commonly used in high performance deep learning applications. However, it is a lot in terms of cost and power consumption to build a high-performance platform. In this study, performances of single-board computers in NVIDIA Jetson Nano, NVIDIA Jetson TX2 and Raspberry PI4 through CNN algorithm created by using fashion product images dataset are compared. 2D CNN model has been developed so as to classify 13 different fashion products in tests. Data set is comprised of 45K pictures. Parameters for performance analysis has been defined as consumption (GPU, CPU, RAM, Power), accuracy and cost. Data set is divided into parts of 5K, 10K, 20K, 30K and 45K in training and test of the model in order to expand on the differences of single-board computers. Eventually, performance of the embedded system boards in different data set in CNN algorithm is analyzed. It is, thus, aimed to attain high accuracy preference by minimum hardware requirements in deep learning applications.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144455","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152875
Ümit Turkut, Adem Tuncer, Hüseyin Savran, Sait Yilmaz
Recommendation systems are frequently preferred in recent years ensuring customer satisfaction and accelerating sales. Thanks to these systems, it is aimed to accelerate the decision-making process of customers. Recommendation systems have become a necessary part, especially in online shopping. Most of the recommendation systems used in many different areas have been attracting attention, focusing on fashion, and clothing recently. In this paper, a deep learning-based online recommendation system has been proposed with a Convolutional Neural Network (CNN). Classes of different patterns in the CNN architecture have been determined according to users' and designers' pattern preferences. The deep learning model recommends patterns considering color compatibility for textile products. The proposed model has been trained and tested using our own pattern dataset including 12000 images. Experiments on pattern datasets show the effectiveness of our proposed approach.
{"title":"An Online Recommendation System Using Deep Learning for Textile Products","authors":"Ümit Turkut, Adem Tuncer, Hüseyin Savran, Sait Yilmaz","doi":"10.1109/HORA49412.2020.9152875","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152875","url":null,"abstract":"Recommendation systems are frequently preferred in recent years ensuring customer satisfaction and accelerating sales. Thanks to these systems, it is aimed to accelerate the decision-making process of customers. Recommendation systems have become a necessary part, especially in online shopping. Most of the recommendation systems used in many different areas have been attracting attention, focusing on fashion, and clothing recently. In this paper, a deep learning-based online recommendation system has been proposed with a Convolutional Neural Network (CNN). Classes of different patterns in the CNN architecture have been determined according to users' and designers' pattern preferences. The deep learning model recommends patterns considering color compatibility for textile products. The proposed model has been trained and tested using our own pattern dataset including 12000 images. Experiments on pattern datasets show the effectiveness of our proposed approach.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123957400","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152893
Mohammad Shahjahan Majib, T. S. Sazzad, M. Rahman
Tumor indicates unfettered presence of a cluster of cells in a specific area of the body part. Brain tumor is considered one of the most common tumors for both men and women and can lead to high death risk if patients fail to obtain appropriate medical treatment. In order to diagnose brain tumors, electronic modalities are integrated and among them MRI is a popular one. For MRI brain tumor region analysis segmentation, detection and classification are considered as important steps in digital imaging pathology laboratory. Existing state-of-the-art approaches demand widespread amount of supervised training data from pathologists and may still accomplish poor results in images from unseen tissue types. A suitable framework has been presented in this study to identify brain tumor cells for MRI images. In this study for the first time in compare to all other existing accessible approaches morphological operations has been incorporated to eliminate undesirable regions and to assist segmentation and identification of region of interests. Compared with existing state-of the-art supervised models, our method generalizes considerably improved identified results on brain tumor cells deprived of training data. Even with training data, our approach attains the identical performance without supervision cost. This study results indicates an accuracy rate above 96.23% accuracy associated to existing works.
{"title":"A Framework To Detect Brain Tumor Cells Using MRI Images","authors":"Mohammad Shahjahan Majib, T. S. Sazzad, M. Rahman","doi":"10.1109/HORA49412.2020.9152893","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152893","url":null,"abstract":"Tumor indicates unfettered presence of a cluster of cells in a specific area of the body part. Brain tumor is considered one of the most common tumors for both men and women and can lead to high death risk if patients fail to obtain appropriate medical treatment. In order to diagnose brain tumors, electronic modalities are integrated and among them MRI is a popular one. For MRI brain tumor region analysis segmentation, detection and classification are considered as important steps in digital imaging pathology laboratory. Existing state-of-the-art approaches demand widespread amount of supervised training data from pathologists and may still accomplish poor results in images from unseen tissue types. A suitable framework has been presented in this study to identify brain tumor cells for MRI images. In this study for the first time in compare to all other existing accessible approaches morphological operations has been incorporated to eliminate undesirable regions and to assist segmentation and identification of region of interests. Compared with existing state-of the-art supervised models, our method generalizes considerably improved identified results on brain tumor cells deprived of training data. Even with training data, our approach attains the identical performance without supervision cost. This study results indicates an accuracy rate above 96.23% accuracy associated to existing works.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132203400","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152852
Lekë Pepkolaj, Siditë Duraj, V. Toma, Dritan Gerbeti
This article describes the main results obtained from analyzes of face-to-face experiences in high school mathematics. These experiences are realized by two models of cooperative learning: the collaborative model and the peer tutoring. Both models differ in the required roles, in the task types given to students and in the teacher's role change. From the analysis viewpoint of the peer collaboration model the positive aspects are seen such as: disciplinary skills, decision making skills, strengthening of knowledge, social interaction, meanwhile the negative aspects are: group assessment, unintentional waste of time. The peer tutoring model was more effective when it happened without given roles. Both models highlighted effective results through dialogue between students and arguments of a metacognitive rather than cognitive nature. The presented analysis may be important in identifying the effectiveness of working with groups under these two models and the possibility of implementing them in an online learning format.
{"title":"A collaborative learning experience in high school mathematics","authors":"Lekë Pepkolaj, Siditë Duraj, V. Toma, Dritan Gerbeti","doi":"10.1109/HORA49412.2020.9152852","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152852","url":null,"abstract":"This article describes the main results obtained from analyzes of face-to-face experiences in high school mathematics. These experiences are realized by two models of cooperative learning: the collaborative model and the peer tutoring. Both models differ in the required roles, in the task types given to students and in the teacher's role change. From the analysis viewpoint of the peer collaboration model the positive aspects are seen such as: disciplinary skills, decision making skills, strengthening of knowledge, social interaction, meanwhile the negative aspects are: group assessment, unintentional waste of time. The peer tutoring model was more effective when it happened without given roles. Both models highlighted effective results through dialogue between students and arguments of a metacognitive rather than cognitive nature. The presented analysis may be important in identifying the effectiveness of working with groups under these two models and the possibility of implementing them in an online learning format.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132332671","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 : 2020-06-01DOI: 10.1109/HORA49412.2020.9152859
O. A. Soysal, Mehmet Serdar Guzel
With deep learning achieving more successful results than traditional machine learning methods, researches in the field of computer vision have evolved towards this area. However, in order to obtain successful models in deep learning methods, it needs a large number of training samples similar to traditional machine learning methods. In order to meet this requirement, auxiliary information of visual data has been used in recent years. Zero-shot learning methods focused on the compatibility functions of image embeddings and class embeddings, and researches aimed at better representation of class embeddings on visual data. In this paper, recent studies on zero-shot learning have been examined and evaluated.
{"title":"An Introduction to Zero-Shot Learning: An Essential Review","authors":"O. A. Soysal, Mehmet Serdar Guzel","doi":"10.1109/HORA49412.2020.9152859","DOIUrl":"https://doi.org/10.1109/HORA49412.2020.9152859","url":null,"abstract":"With deep learning achieving more successful results than traditional machine learning methods, researches in the field of computer vision have evolved towards this area. However, in order to obtain successful models in deep learning methods, it needs a large number of training samples similar to traditional machine learning methods. In order to meet this requirement, auxiliary information of visual data has been used in recent years. Zero-shot learning methods focused on the compatibility functions of image embeddings and class embeddings, and researches aimed at better representation of class embeddings on visual data. In this paper, recent studies on zero-shot learning have been examined and evaluated.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130212039","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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