Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00008
K. Brezinski, K. Ferens
Malware classification remains at the forefront of ongoing research as the prevalence of metamorphic malware introduces new challenges to anti-virus vendors and firms alike. One approach to malware classification is Static Analysis - a form of analysis which does not require malware to be executed before classification can be performed. For this reason, a lightweight classifier based on the features of a malware binary is preferred, with relatively low computational overhead. In this work a modified convolutional neural network (CNN) architecture was deployed which integrated a complexity-based evaluation based on box-counting. This was implemented by setting up max-pooling layers in parallel, and then extracting the fractal dimension using a polyscalar relationship based on the resolution of the measurement scale and the number of elements of a malware image covered in the measurement under consideration. To test the robustness and efficacy of our approach we trained and tested on over 9300 malware binaries from 25 unique malware families. This work was compared to other award-winning image recognition models, and results showed categorical accuracy in excess of 96.54%.
{"title":"Complexity-Based Convolutional Neural Network for Malware Classification","authors":"K. Brezinski, K. Ferens","doi":"10.1109/CSCI51800.2020.00008","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00008","url":null,"abstract":"Malware classification remains at the forefront of ongoing research as the prevalence of metamorphic malware introduces new challenges to anti-virus vendors and firms alike. One approach to malware classification is Static Analysis - a form of analysis which does not require malware to be executed before classification can be performed. For this reason, a lightweight classifier based on the features of a malware binary is preferred, with relatively low computational overhead. In this work a modified convolutional neural network (CNN) architecture was deployed which integrated a complexity-based evaluation based on box-counting. This was implemented by setting up max-pooling layers in parallel, and then extracting the fractal dimension using a polyscalar relationship based on the resolution of the measurement scale and the number of elements of a malware image covered in the measurement under consideration. To test the robustness and efficacy of our approach we trained and tested on over 9300 malware binaries from 25 unique malware families. This work was compared to other award-winning image recognition models, and results showed categorical accuracy in excess of 96.54%.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127688826","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00263
Wangjie Xu, T. Fujimoto, Ziran Fan
This paper focuses on the problem of in-house split information systems and the resulting decline in operational efficiency. According to the result of the authors' investigation for the business environment of small and medium-sized enterprises, the simplification of information and communication among employees has become an issue to be solved in many enterprises today in order to improve operational efficiency. Based on this, in this paper, we design a composite platform that integrates information tools commonly used in companies. By covering all information-related operations with one platform, we support improvement of companies’ operational efficiency. We also show the effectiveness of the proposed platform by comparing it with existing information systems. We mainly examine the design concept of the composite platform and its construction requirements.
{"title":"Designing a composite platform for operational efficiency","authors":"Wangjie Xu, T. Fujimoto, Ziran Fan","doi":"10.1109/CSCI51800.2020.00263","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00263","url":null,"abstract":"This paper focuses on the problem of in-house split information systems and the resulting decline in operational efficiency. According to the result of the authors' investigation for the business environment of small and medium-sized enterprises, the simplification of information and communication among employees has become an issue to be solved in many enterprises today in order to improve operational efficiency. Based on this, in this paper, we design a composite platform that integrates information tools commonly used in companies. By covering all information-related operations with one platform, we support improvement of companies’ operational efficiency. We also show the effectiveness of the proposed platform by comparing it with existing information systems. We mainly examine the design concept of the composite platform and its construction requirements.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133078078","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00049
Damián Martínez Díaz, Francisco LUNA ROSAS, Julio Cesar Martínez Romo, Marco Antonio Hernandez Vargas, Ivan CASTILLO ZUÑIGA
There is a suicide every 40 seconds in the world and it is the third cause of death for young people between 15 and 19 years old worldwide. For every suicide, many more attempt it, which is why suicide prevention remains an universal challenge and has been recognized by the World Health Organization (WHO) as a public health priority. Experts say that one of the best ways to prevent suicide is for people who are going through this urge to take their own lives to listen to people who are close to them and social networks such as Twitter or Facebook are in a unique position to help these people connect in real time in difficult situations that people with these suicidal tendencies are going through, but also represents a potential risk to receive information that could later prove harmful, either by stressing the same information or by taking some suicidal ideas. In this research we propose a model to optimize the global time processing in the detection of patterns related to suicide in the social network Twitter. Our results show that the proposed model can be a good alternative when it comes to optimizing the response time in this type of problems.
{"title":"Optimizing global processing time in the detection of patterns related to suicide in social networks","authors":"Damián Martínez Díaz, Francisco LUNA ROSAS, Julio Cesar Martínez Romo, Marco Antonio Hernandez Vargas, Ivan CASTILLO ZUÑIGA","doi":"10.1109/CSCI51800.2020.00049","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00049","url":null,"abstract":"There is a suicide every 40 seconds in the world and it is the third cause of death for young people between 15 and 19 years old worldwide. For every suicide, many more attempt it, which is why suicide prevention remains an universal challenge and has been recognized by the World Health Organization (WHO) as a public health priority. Experts say that one of the best ways to prevent suicide is for people who are going through this urge to take their own lives to listen to people who are close to them and social networks such as Twitter or Facebook are in a unique position to help these people connect in real time in difficult situations that people with these suicidal tendencies are going through, but also represents a potential risk to receive information that could later prove harmful, either by stressing the same information or by taking some suicidal ideas. In this research we propose a model to optimize the global time processing in the detection of patterns related to suicide in the social network Twitter. Our results show that the proposed model can be a good alternative when it comes to optimizing the response time in this type of problems.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134055587","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00036
Salahaldeen Duraibi, Wasim Alhamdani, Frederick T. Sheldon
In this paper, we explore the use of the deep learning approach for replay spoof detection in speaker verification systems. Automatic speaker verifications (ASVs) can be easily spoofed by previously recorded genuine speech. In order to counter the issues of spoofing, detecting spoofing attacks play an important role. Hence, we consider the detection of replay attack spoofing that is the most easily accomplished spoofing attack. In this light, we propose a deep neural network-based (DNN) classifier using a hybrid feature from Mel-frequency cepstral coefficient (MFCC) and constant Q cepstral coefficient (CQCC). Several experiments were conducted on the latest version of ASVspoof 2017 dataset. The results are compared with a base line system that uses the Gaussian mixture model (GMM) classifier with different features that include MFCC, CQCC, and the hybrid feature of the two. The experiment results reveal that the DNN classifier outperforms the conventional GMM classifier. It was found that the hybrid-based features are superior to single features, such as CQCC and MFCC in terms of equal error rate (ERR). In addition, like many previous researchers have found, it turned out that high-frequency regions of speech utterance convey much more discriminative information for replay attack detection.
{"title":"Replay Spoof Attack Detection using Deep Neural Networks for Classification","authors":"Salahaldeen Duraibi, Wasim Alhamdani, Frederick T. Sheldon","doi":"10.1109/CSCI51800.2020.00036","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00036","url":null,"abstract":"In this paper, we explore the use of the deep learning approach for replay spoof detection in speaker verification systems. Automatic speaker verifications (ASVs) can be easily spoofed by previously recorded genuine speech. In order to counter the issues of spoofing, detecting spoofing attacks play an important role. Hence, we consider the detection of replay attack spoofing that is the most easily accomplished spoofing attack. In this light, we propose a deep neural network-based (DNN) classifier using a hybrid feature from Mel-frequency cepstral coefficient (MFCC) and constant Q cepstral coefficient (CQCC). Several experiments were conducted on the latest version of ASVspoof 2017 dataset. The results are compared with a base line system that uses the Gaussian mixture model (GMM) classifier with different features that include MFCC, CQCC, and the hybrid feature of the two. The experiment results reveal that the DNN classifier outperforms the conventional GMM classifier. It was found that the hybrid-based features are superior to single features, such as CQCC and MFCC in terms of equal error rate (ERR). In addition, like many previous researchers have found, it turned out that high-frequency regions of speech utterance convey much more discriminative information for replay attack detection.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121286175","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00135
Shinjin Kang, Sookyun Kim
In this paper, we introduce a system that can detect the space outlier utilization of residents in indoor environment at low cost. Our system facilitates autonomous data collection from mobile app logs and the Google app server and generates a high-dimensional dataset required to detect outlier behaviors. For this, we used density-based clustering algorithm with t-distributed stochastic neighbor embedding (t-SNE). Our system enables easy acquisition of large volumes of data required for outlier detection. Our methodology can assist spatial analysis for indoor environments housing residents and help reduce the cost of space utilization feedback.
{"title":"Behavior-based Outlier Detection for Indoor Environment","authors":"Shinjin Kang, Sookyun Kim","doi":"10.1109/CSCI51800.2020.00135","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00135","url":null,"abstract":"In this paper, we introduce a system that can detect the space outlier utilization of residents in indoor environment at low cost. Our system facilitates autonomous data collection from mobile app logs and the Google app server and generates a high-dimensional dataset required to detect outlier behaviors. For this, we used density-based clustering algorithm with t-distributed stochastic neighbor embedding (t-SNE). Our system enables easy acquisition of large volumes of data required for outlier detection. Our methodology can assist spatial analysis for indoor environments housing residents and help reduce the cost of space utilization feedback.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121431010","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00266
Noah W. Scott, D. Hodson, Richard Dill, M. Grimaila
Serialization is the process of translating a data structure into a format that can be stored and/or transmitted, and then subsequently reconstructed at a later time to create an identical clone of the original. The use of data serialization assures data objects can be transmitted, stored, and reliably reconstructed across differing computer architectures, even with different data type sizes or endianness, with no additional effort.Serializing the data in an architecture-independent format prevents the problems of byte ordering, memory layout, or representing data structures in different programming languages. This is especially important in the context of live, virtual, and constructive (LVC) simulation environments where multiple geographically separated computers, each with many independent threads, are connected and must communicate with as little latency as possible to remain near "real-time" like in terms of responsiveness.In this paper, we demonstrate the use of Serde, a Rust-based systems programming language crate, to serialize and deserialize IEEE standard Distribute Interactive Simulation (DIS) Protocol Data Units (PDUs) to support DIS-based network interoperability. The results show that Serde is an efficient mechanism for serialization/deserialization when using the inherently safe Rust programming language.
{"title":"Using Serde to Serialize and Deserialize DIS PDUs","authors":"Noah W. Scott, D. Hodson, Richard Dill, M. Grimaila","doi":"10.1109/CSCI51800.2020.00266","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00266","url":null,"abstract":"Serialization is the process of translating a data structure into a format that can be stored and/or transmitted, and then subsequently reconstructed at a later time to create an identical clone of the original. The use of data serialization assures data objects can be transmitted, stored, and reliably reconstructed across differing computer architectures, even with different data type sizes or endianness, with no additional effort.Serializing the data in an architecture-independent format prevents the problems of byte ordering, memory layout, or representing data structures in different programming languages. This is especially important in the context of live, virtual, and constructive (LVC) simulation environments where multiple geographically separated computers, each with many independent threads, are connected and must communicate with as little latency as possible to remain near \"real-time\" like in terms of responsiveness.In this paper, we demonstrate the use of Serde, a Rust-based systems programming language crate, to serialize and deserialize IEEE standard Distribute Interactive Simulation (DIS) Protocol Data Units (PDUs) to support DIS-based network interoperability. The results show that Serde is an efficient mechanism for serialization/deserialization when using the inherently safe Rust programming language.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128972201","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00242
Sakir Yucel
The boundaries on different domains are blurring as corporations and infrastructure providers are collaborating to offer end-to-end services over the networks and infrastructures of various network, service, cloud, content delivery and other infrastructure providers as well as the customer premises. To capture opportunities and shine in competitive service market, infrastructure and service providers need to excel in addressing the changing customer requirements and in the operations and management of the resources. They should welcome effective collaboration with other network and infrastructure providers in delivering quality services to customers. Effective sharing of the infrastructure resources is essential in meeting the customer demands and reducing the cost. Server placement problem for end-to-end virtual services becomes a crucial optimization challenge for providers in such collaborative environments. We formulate the collaborative virtual server placement problem and suggest density-based clustering algorithms to address this problem.
{"title":"Density-Based Server Placement for Collaborative Virtual Services","authors":"Sakir Yucel","doi":"10.1109/CSCI51800.2020.00242","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00242","url":null,"abstract":"The boundaries on different domains are blurring as corporations and infrastructure providers are collaborating to offer end-to-end services over the networks and infrastructures of various network, service, cloud, content delivery and other infrastructure providers as well as the customer premises. To capture opportunities and shine in competitive service market, infrastructure and service providers need to excel in addressing the changing customer requirements and in the operations and management of the resources. They should welcome effective collaboration with other network and infrastructure providers in delivering quality services to customers. Effective sharing of the infrastructure resources is essential in meeting the customer demands and reducing the cost. Server placement problem for end-to-end virtual services becomes a crucial optimization challenge for providers in such collaborative environments. We formulate the collaborative virtual server placement problem and suggest density-based clustering algorithms to address this problem.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128818041","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00127
Kabir Nagrecha, Pratyush Muthukumar, Emmanuel Cocom, Jeanne Holm, Dawn Comer, Irene Burga, M. Pourhomayoun
The devastating impacts of air pollution have be-come more and more evident in recent years. As our measurement technologies improve, we gain better insight into the true impact of this deadly, yet often ignored, threat. The first step in reducing the damages caused by this problem is being able to analyze and predict its patterns. The problem of predicting air quality and the presence of particulate matter lies in the nature of the data needed to create an accurate system. The sheer number of factors affecting air quality mean that previously proposed approaches often utilize a great many sources of data, aiming to incorporate images, wind graphs, traffic information, and more. Yet in truth, most areas outside large metropolises lack ready access to high-quality data, preventing them from ever implementing an effective system. We propose a system utilizing a 1-D deep convolutional neural network to analyze past sensor readings and predict air pollutant concentrations up to a day in the future at a 3-hour resolution. We specifically developed this model for predicting PM2.5 values. The system receives PM2.5 sensor values and discovers temporal pattern in the data, which will be later used for prediction. By removing the dependency on complex data inputs, the system becomes accesible and easily implementable for any region. Despite this simplified approach, the results are comparable to — and often better than — any current state-of-the-art predictive systems in this domain.
{"title":"Sensor-Based Air Pollution Prediction Using Deep CNN-LSTM","authors":"Kabir Nagrecha, Pratyush Muthukumar, Emmanuel Cocom, Jeanne Holm, Dawn Comer, Irene Burga, M. Pourhomayoun","doi":"10.1109/CSCI51800.2020.00127","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00127","url":null,"abstract":"The devastating impacts of air pollution have be-come more and more evident in recent years. As our measurement technologies improve, we gain better insight into the true impact of this deadly, yet often ignored, threat. The first step in reducing the damages caused by this problem is being able to analyze and predict its patterns. The problem of predicting air quality and the presence of particulate matter lies in the nature of the data needed to create an accurate system. The sheer number of factors affecting air quality mean that previously proposed approaches often utilize a great many sources of data, aiming to incorporate images, wind graphs, traffic information, and more. Yet in truth, most areas outside large metropolises lack ready access to high-quality data, preventing them from ever implementing an effective system. We propose a system utilizing a 1-D deep convolutional neural network to analyze past sensor readings and predict air pollutant concentrations up to a day in the future at a 3-hour resolution. We specifically developed this model for predicting PM2.5 values. The system receives PM2.5 sensor values and discovers temporal pattern in the data, which will be later used for prediction. By removing the dependency on complex data inputs, the system becomes accesible and easily implementable for any region. Despite this simplified approach, the results are comparable to — and often better than — any current state-of-the-art predictive systems in this domain.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115929970","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00081
Tasnim Gharaibeh, E. Doncker
As COVID-19 patients flood hospitals worldwide, physicians are trying to search for effective antiviral therapies to save lives. However, there is currently a lack of proven effective medications against COVID-19. Multiple COVID-19 vaccine trials and treatments are underway, but yet need more time and testing. Furthermore, the SARS-CoV-2 virus that causes COVID-19 replicates poorly in multiple animals, including dogs, pigs, chickens, and ducks, which limits preclinical animal studies. We built an unsupervised deep learning model (CDVec) to produce word-embeddings using word2vec from a corpus of articles selectively focusing on COVID-19 candidate drugs that appeared in the literature to identify promising target drugs that could be used in COVID-19 treatment.
{"title":"Unsupervised Learning with Word Embeddings Captures Quiescent Knowledge from COVID-19 Drugs Literature","authors":"Tasnim Gharaibeh, E. Doncker","doi":"10.1109/CSCI51800.2020.00081","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00081","url":null,"abstract":"As COVID-19 patients flood hospitals worldwide, physicians are trying to search for effective antiviral therapies to save lives. However, there is currently a lack of proven effective medications against COVID-19. Multiple COVID-19 vaccine trials and treatments are underway, but yet need more time and testing. Furthermore, the SARS-CoV-2 virus that causes COVID-19 replicates poorly in multiple animals, including dogs, pigs, chickens, and ducks, which limits preclinical animal studies. We built an unsupervised deep learning model (CDVec) to produce word-embeddings using word2vec from a corpus of articles selectively focusing on COVID-19 candidate drugs that appeared in the literature to identify promising target drugs that could be used in COVID-19 treatment.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117066475","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00313
W. Phillips, L. Deligiannidis
The use of digital photo albums, social media posts, and embedded videos connect people closer to their memories. We believe the next generation of visual albums will immerse individuals through modern virtual reality technologies. This paper examines new methods for enhancing photo albums and visual content; achieved in highly interactive and realistic environments where users are presented with interactable frames, 360 imagery, videos, and dynamic exhibits.
{"title":"Next Generation of Gallery Sharing in VR","authors":"W. Phillips, L. Deligiannidis","doi":"10.1109/CSCI51800.2020.00313","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00313","url":null,"abstract":"The use of digital photo albums, social media posts, and embedded videos connect people closer to their memories. We believe the next generation of visual albums will immerse individuals through modern virtual reality technologies. This paper examines new methods for enhancing photo albums and visual content; achieved in highly interactive and realistic environments where users are presented with interactable frames, 360 imagery, videos, and dynamic exhibits.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114527609","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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