Owing to the remarkable growth of wireless communication and networking technologies, commercial unmanned aerial vehicles (UAVs) have newly arisen and employed in the significant parts of our sky. Abundant advancement is anticipated in the domain of UAV communication in the upcoming decades. The cooperation between multiple UAVs in the air can logically form a flying ad hoc network (FANET) by transferring information among them. FANETs can be used to achieve numerous missions and provide essential aid to ground networks. Nevertheless, they are opposed to several challenges and complications due to the movement of UAVs, the regular packet losses, and broken links between UAVs. Moreover, FANETs are operated with batteries, and energy consumption is a severe problem in FANETs. Furthermore, various activities of UAVs are responsible for energy consumption. This paper surveys different communication protocols and techniques expected to minimize energy consumption in FANETs and guarantee a high level of communication stability with increased network lifetime. Different energy conservation techniques for FANETs are qualitatively compared with each other. Open issues and research challenges are also discussed.
{"title":"Energy Conservation Techniques for Flying Ad Hoc Networks.","authors":"Sabitri Poudel, S. Moh, Jian Shen","doi":"10.1145/3426020.3426024","DOIUrl":"https://doi.org/10.1145/3426020.3426024","url":null,"abstract":"Owing to the remarkable growth of wireless communication and networking technologies, commercial unmanned aerial vehicles (UAVs) have newly arisen and employed in the significant parts of our sky. Abundant advancement is anticipated in the domain of UAV communication in the upcoming decades. The cooperation between multiple UAVs in the air can logically form a flying ad hoc network (FANET) by transferring information among them. FANETs can be used to achieve numerous missions and provide essential aid to ground networks. Nevertheless, they are opposed to several challenges and complications due to the movement of UAVs, the regular packet losses, and broken links between UAVs. Moreover, FANETs are operated with batteries, and energy consumption is a severe problem in FANETs. Furthermore, various activities of UAVs are responsible for energy consumption. This paper surveys different communication protocols and techniques expected to minimize energy consumption in FANETs and guarantee a high level of communication stability with increased network lifetime. Different energy conservation techniques for FANETs are qualitatively compared with each other. Open issues and research challenges are also discussed.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130872772","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}
Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application. To evaluate the performance of the proposed system, we performed verification based on the mean absolute error (MAE) metric. The evaluation results revealed an average accuracy of 87.43%.
情绪信息代表用户当前的情绪状态，可用于多种应用，例如根据用户情绪状态推荐音乐的文化内容服务和用户情绪监控。为了提高用户满意度，推荐方法必须理解和反映用户的特征和情况，如个人偏好和情绪。然而，大多数推荐方法不能准确反映这些特征，无法提高用户满意度。本文对人类的六种情绪(中性、快乐、悲伤、愤怒、惊讶和无聊)进行了广义的定义，以考虑用户的语音情绪信息并推荐匹配的内容。采用“遗传算法作为特征选择方法”(genetic algorithms as a feature selection method, GAFS)算法，根据语音情感信息对规范化语音进行分类。我们使用支持向量机(SVM)算法并选择一个最优核函数来识别六种目标情绪。各核函数的性能评价结果表明，径向基函数(RBF)核函数的情绪识别准确率最高，为86.98%。此外，基于情感信息，使用因子分析、对应分析和欧几里得距离对内容数据(图像和音乐)进行分类。最后，使用基于情绪分类的语音信息和通过协同过滤技术识别的情绪信息来预测用户的情绪偏好，并在移动应用程序中推荐与用户情绪匹配的内容。为了评估所提出系统的性能，我们基于平均绝对误差(MAE)度量进行了验证。评价结果显示平均准确率为87.43%。
{"title":"Emotion and Collaborative Filtering-Based Recommendation System","authors":"Tae-Yeun Kim, Sung-Hwan Kim","doi":"10.1145/3426020.3426119","DOIUrl":"https://doi.org/10.1145/3426020.3426119","url":null,"abstract":"Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application. To evaluate the performance of the proposed system, we performed verification based on the mean absolute error (MAE) metric. The evaluation results revealed an average accuracy of 87.43%.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091523","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}
Jihoon Lee, Seungmin Oh, Yeonggwang Kim, Dongsu Lee, Akm Ashiquzzaman, Jinsul Kim
Various smart farm technologies are currently being developed around the world to enhance agricultural competitiveness. Korea is also speeding up the development of Korean smart farm technology suitable for domestic environment, but it is difficult to develop high-reliability sensors and systems, and has problems such as preventing sensors from failing, so in this paper, environmental data values such as temperature, humidity, carbon dioxide, ammonia, etc. are sensed, refined, and pretreated to derive correlation and heat maps between sensors. This will not only predict the RUL (Remaining Useful Life) of the sensor using machine learning in the future, but also develop a reliable system by detecting failures and errors.
{"title":"Pig Farm Environment Sensor Data Correlation and Heatmap Analysis for Predicting Sensor Remaining Useful Life✱","authors":"Jihoon Lee, Seungmin Oh, Yeonggwang Kim, Dongsu Lee, Akm Ashiquzzaman, Jinsul Kim","doi":"10.1145/3426020.3426136","DOIUrl":"https://doi.org/10.1145/3426020.3426136","url":null,"abstract":"Various smart farm technologies are currently being developed around the world to enhance agricultural competitiveness. Korea is also speeding up the development of Korean smart farm technology suitable for domestic environment, but it is difficult to develop high-reliability sensors and systems, and has problems such as preventing sensors from failing, so in this paper, environmental data values such as temperature, humidity, carbon dioxide, ammonia, etc. are sensed, refined, and pretreated to derive correlation and heat maps between sensors. This will not only predict the RUL (Remaining Useful Life) of the sensor using machine learning in the future, but also develop a reliable system by detecting failures and errors.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114109500","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}
V. Huynh, Hyung-Jeong Yang, Gueesang Lee, J. H. Kim, Soohyung Kim
In this paper, we present our approach to estimate the trustworthiness intensity, a kind of affective state, in advertisements. Our method explored multi-modal (audio, video, text) fusion with LSTM to learn the relationship between frames in video, and attention mechanism to fuse the learned representation of these features. We achieved a CCC score of 0.3426 on validation set of MuSe-Car dataset which outperform baseline methods. In terms of test set, we reached a promising result of 0.3353.
{"title":"Multimodal Fusion with Attention Mechanism for Trustworthiness Prediction in Car Advertisements","authors":"V. Huynh, Hyung-Jeong Yang, Gueesang Lee, J. H. Kim, Soohyung Kim","doi":"10.1145/3426020.3426079","DOIUrl":"https://doi.org/10.1145/3426020.3426079","url":null,"abstract":"In this paper, we present our approach to estimate the trustworthiness intensity, a kind of affective state, in advertisements. Our method explored multi-modal (audio, video, text) fusion with LSTM to learn the relationship between frames in video, and attention mechanism to fuse the learned representation of these features. We achieved a CCC score of 0.3426 on validation set of MuSe-Car dataset which outperform baseline methods. In terms of test set, we reached a promising result of 0.3353.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123294206","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}
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.
{"title":"Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals","authors":"S. A. Khoshnevis, In-ho Ra, R. Sankar","doi":"10.1145/3426020.3426160","DOIUrl":"https://doi.org/10.1145/3426020.3426160","url":null,"abstract":"Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"22 22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123423471","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}
Geunha You, Gyoosik Kim, Jihyeon Park, Seong-je Cho, Minkyu Park
Code obfuscation is a technique that makes programs harder to understand. Malware writers widely the obfuscation technique to evade detection from anti-malware software, or to deter reverse engineering attempts for their malicious code. If we de-obfuscate the obfuscated code and restore it to the original code before obfuscation was applied, we can analyze the obfuscated malware effectively and efficiently. In this paper, we apply ReDex optimizer for reversing the control-flow obfuscation performed by the Obfuscapk system on open-source Android applications. We then analyze the effectiveness and limitations of ReDex in terms of its deobfuscation ability to reverse the control-flow obfuscation of Android apps. The experimental results show that ReDex can recover 1089 of 1108 apps obfuscated with control-flows obfuscation techniques of Obfuscapk obfuscator. During the process of optimizing bytecode, ReDex reduces the number of methods and fields significantly while it has a limitation in removing dead codes related to both useless goto statements and random nop instructions.
{"title":"Reversing Obfuscated Control Flow Structures in Android Apps using ReDex Optimizer","authors":"Geunha You, Gyoosik Kim, Jihyeon Park, Seong-je Cho, Minkyu Park","doi":"10.1145/3426020.3426089","DOIUrl":"https://doi.org/10.1145/3426020.3426089","url":null,"abstract":"Code obfuscation is a technique that makes programs harder to understand. Malware writers widely the obfuscation technique to evade detection from anti-malware software, or to deter reverse engineering attempts for their malicious code. If we de-obfuscate the obfuscated code and restore it to the original code before obfuscation was applied, we can analyze the obfuscated malware effectively and efficiently. In this paper, we apply ReDex optimizer for reversing the control-flow obfuscation performed by the Obfuscapk system on open-source Android applications. We then analyze the effectiveness and limitations of ReDex in terms of its deobfuscation ability to reverse the control-flow obfuscation of Android apps. The experimental results show that ReDex can recover 1089 of 1108 apps obfuscated with control-flows obfuscation techniques of Obfuscapk obfuscator. During the process of optimizing bytecode, ReDex reduces the number of methods and fields significantly while it has a limitation in removing dead codes related to both useless goto statements and random nop instructions.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126275912","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}
The integration of Distributed Generators (DGs) in the reconfigurable microgrid is widely adopted to enhance the power delivery performance. This paper investigates the performance behavior of the hybrid DG which uses the benefits of two kinds of DGs and overcomes their limitations. In this work, a total of five objective functions are considered: minimization of power loss, total generation cost, total emission cost and voltage deviation, and the maximization of the percentage DG penetration. The performance investigation of DGs is carried out by considering the system under five different test cases including the uncertainty in power supply and demand. The modeling of the demand variation is done with the support of the Probability Density Function (PDF). The many objective multi-indicator Stochastic Ranking Approach (SRA) is used for optimization purposes. The simulations are performed on IEEE 33-bus Radial Distribution System (RDS) in order to assess the capability of the proposed investigation.
{"title":"Performance Enhancement Realization of Hybrid DGs in Microgrid under Uncertainties","authors":"T. Srinivasan, Hyuntae Kim, In-ho Ra","doi":"10.1145/3426020.3426161","DOIUrl":"https://doi.org/10.1145/3426020.3426161","url":null,"abstract":"The integration of Distributed Generators (DGs) in the reconfigurable microgrid is widely adopted to enhance the power delivery performance. This paper investigates the performance behavior of the hybrid DG which uses the benefits of two kinds of DGs and overcomes their limitations. In this work, a total of five objective functions are considered: minimization of power loss, total generation cost, total emission cost and voltage deviation, and the maximization of the percentage DG penetration. The performance investigation of DGs is carried out by considering the system under five different test cases including the uncertainty in power supply and demand. The modeling of the demand variation is done with the support of the Probability Density Function (PDF). The many objective multi-indicator Stochastic Ranking Approach (SRA) is used for optimization purposes. The simulations are performed on IEEE 33-bus Radial Distribution System (RDS) in order to assess the capability of the proposed investigation.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121232623","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}
H. Nguyen, Nu-ri Shin, Gwanghyun Yu, Gyeong-Ju Kwon, Woon-Young Kwak, Jinyoung Kim
In this paper, the defective product classification based on deep learning for a smart factory is introduced. The proposed system contains PLC (Programmable Logic Controller), Artificial Intelligence (AI) embedded board and cloud service. The AI embedded board is connected and communicated to receive and send commands to PLC via SPI (Serial Peripheral Interface) protocol. The pre-trained defective product classification model is uploaded, saved on a cloud server and downloaded to AI Embedded board for each particular product. The core technique of the system is the AI-based embedded board. Due to the limitation of label data, we use transfer learning method to retrain deep neural networks (DNN). We implement and compare the classification results on different deep neural network including ResNet, DenseNet, and GoogLeNet. We trained these networks by GPU server on casting product classification data. After that, the pre-trained models are optimized and applied on practical embedded board. The experimental results show that our system is able to classify defective products with high accuracy and fast speed.
{"title":"Deep learning-based defective product classification system for smart factory","authors":"H. Nguyen, Nu-ri Shin, Gwanghyun Yu, Gyeong-Ju Kwon, Woon-Young Kwak, Jinyoung Kim","doi":"10.1145/3426020.3426039","DOIUrl":"https://doi.org/10.1145/3426020.3426039","url":null,"abstract":"In this paper, the defective product classification based on deep learning for a smart factory is introduced. The proposed system contains PLC (Programmable Logic Controller), Artificial Intelligence (AI) embedded board and cloud service. The AI embedded board is connected and communicated to receive and send commands to PLC via SPI (Serial Peripheral Interface) protocol. The pre-trained defective product classification model is uploaded, saved on a cloud server and downloaded to AI Embedded board for each particular product. The core technique of the system is the AI-based embedded board. Due to the limitation of label data, we use transfer learning method to retrain deep neural networks (DNN). We implement and compare the classification results on different deep neural network including ResNet, DenseNet, and GoogLeNet. We trained these networks by GPU server on casting product classification data. After that, the pre-trained models are optimized and applied on practical embedded board. The experimental results show that our system is able to classify defective products with high accuracy and fast speed.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127443360","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}
The power systems are deeply reforming to meet future power demands. With the continuous emergence of new technologies, the novel power system represented by microgrid has received more attention, and the research on the integration of emerging technologies of microgrid has become more focused. In this paper, a microgrid communication framework based on 5G technology is proposed, which makes full use of the low communication delay of 5G technology and the computation capacity of cloud/edge computing to implement the reconfiguration of microgrid deployed with DG(s). Lastly, we estimate the computing power of the cloud servers to predict the loads, and preprocess the restoration Optimal Configuration Table (OCT) scheme for instant fault restoration in the microgrid.
{"title":"Enhanced Microgrid Functions for Topology Reconfiguration and Fault Restoration","authors":"Xiancheng Wang, In-ho Ra","doi":"10.1145/3426020.3426165","DOIUrl":"https://doi.org/10.1145/3426020.3426165","url":null,"abstract":"The power systems are deeply reforming to meet future power demands. With the continuous emergence of new technologies, the novel power system represented by microgrid has received more attention, and the research on the integration of emerging technologies of microgrid has become more focused. In this paper, a microgrid communication framework based on 5G technology is proposed, which makes full use of the low communication delay of 5G technology and the computation capacity of cloud/edge computing to implement the reconfiguration of microgrid deployed with DG(s). Lastly, we estimate the computing power of the cloud servers to predict the loads, and preprocess the restoration Optimal Configuration Table (OCT) scheme for instant fault restoration in the microgrid.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"19 10 Suppl 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993773","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}
Cooperation or Cooperative behavior constrained between any two nodes or groups always result in constant scrutiny for reconfiguration. This continual reconfiguration creates a new modulus for expansion and thus detecting community structure can fundamentally become a problem of identifying groups and a leader in a network. In a network, the influencer is commonly termed as leader and the leader node is a node that has high attraction to increase, i.e., high degree of centrality. In this paper, we devised an efficient method to detect influencers in a network through cooperative and spread strategies. This dynamic strategy technique is used to detect subevents and anomalies through social and physical sensor data. This paper contributes toward a dynamic game theory approach for information maximization by maximizing the influence features over the network for higher information delivery over the dynamic network.
{"title":"Cooperative Influence Learning","authors":"Harshit Srivastava, In-ho Ra, R. Sankar","doi":"10.1145/3426020.3426159","DOIUrl":"https://doi.org/10.1145/3426020.3426159","url":null,"abstract":"Cooperation or Cooperative behavior constrained between any two nodes or groups always result in constant scrutiny for reconfiguration. This continual reconfiguration creates a new modulus for expansion and thus detecting community structure can fundamentally become a problem of identifying groups and a leader in a network. In a network, the influencer is commonly termed as leader and the leader node is a node that has high attraction to increase, i.e., high degree of centrality. In this paper, we devised an efficient method to detect influencers in a network through cooperative and spread strategies. This dynamic strategy technique is used to detect subevents and anomalies through social and physical sensor data. This paper contributes toward a dynamic game theory approach for information maximization by maximizing the influence features over the network for higher information delivery over the dynamic network.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129841173","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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