S. Hicks, Konstantin Pogorelov, T. Lange, M. Lux, Mattis Jeppsson, K. Randel, S. Eskeland, P. Halvorsen, M. Riegler
In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.
{"title":"Comprehensible reasoning and automated reporting of medical examinations based on deep learning analysis","authors":"S. Hicks, Konstantin Pogorelov, T. Lange, M. Lux, Mattis Jeppsson, K. Randel, S. Eskeland, P. Halvorsen, M. Riegler","doi":"10.1145/3204949.3208113","DOIUrl":"https://doi.org/10.1145/3204949.3208113","url":null,"abstract":"In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128245945","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}
Applications involving 3D scanning and reconstruction & 3D Tele-immersion provide an immersive experience by capturing a scene using multiple RGB-D cameras, such as Kinect. Prior knowledge of intrinsic calibration of each of the cameras, and extrinsic calibration between cameras, is essential to reconstruct the captured data. The intrinsic calibration for a given camera rarely ever changes, so only needs to be estimated once. However, the extrinsic calibration between cameras can change, even with a small nudge to the camera. Calibration accuracy depends on sensor noise, features used, sampling method, etc., resulting in the need for iterative calibration to achieve good calibration. In this paper, we introduce a skeleton based approach to calibrate multiple RGB-D Kinect cameras in a closed setup, automatically without any intervention, within a few seconds. The method uses only the person present in the scene to calibrate, removing the need for manually inserting, detecting and extracting other objects like plane, checker-board, sphere, etc. 3D joints of the extracted skeleton are used as correspondence points between cameras, after undergoing accuracy and orientation checks. Temporal, spatial, and motion constraints are applied during the point selection strategy. Our calibration error checking is inexpensive in terms of computational cost and time and hence is continuously run in the background. Automatic re-calibration of the cameras can be performed when the calibration error goes beyond a threshold due to any possible camera movement. Evaluations show that the method can provide fast, accurate and continuous calibration, as long as a human is moving around in the captured scene.
{"title":"Skeleton-based continuous extrinsic calibration of multiple RGB-D kinect cameras","authors":"Kevin Desai, B. Prabhakaran, S. Raghuraman","doi":"10.1145/3204949.3204969","DOIUrl":"https://doi.org/10.1145/3204949.3204969","url":null,"abstract":"Applications involving 3D scanning and reconstruction & 3D Tele-immersion provide an immersive experience by capturing a scene using multiple RGB-D cameras, such as Kinect. Prior knowledge of intrinsic calibration of each of the cameras, and extrinsic calibration between cameras, is essential to reconstruct the captured data. The intrinsic calibration for a given camera rarely ever changes, so only needs to be estimated once. However, the extrinsic calibration between cameras can change, even with a small nudge to the camera. Calibration accuracy depends on sensor noise, features used, sampling method, etc., resulting in the need for iterative calibration to achieve good calibration. In this paper, we introduce a skeleton based approach to calibrate multiple RGB-D Kinect cameras in a closed setup, automatically without any intervention, within a few seconds. The method uses only the person present in the scene to calibrate, removing the need for manually inserting, detecting and extracting other objects like plane, checker-board, sphere, etc. 3D joints of the extracted skeleton are used as correspondence points between cameras, after undergoing accuracy and orientation checks. Temporal, spatial, and motion constraints are applied during the point selection strategy. Our calibration error checking is inexpensive in terms of computational cost and time and hence is continuously run in the background. Automatic re-calibration of the cameras can be performed when the calibration error goes beyond a threshold due to any possible camera movement. Evaluations show that the method can provide fast, accurate and continuous calibration, as long as a human is moving around in the captured scene.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131748786","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}
Enrique Garcia-Ceja, M. Riegler, P. Jakobsen, J. Tørresen, T. Nordgreen, K. Oedegaard, Ole Bernt Fasmer
∑ depresjonen i høy grad kan forstås ved å se på sammenhengen den oppstår i; personens reaksjon på omgivelsene er viktig for utvikling og opprettholdelse av depresjon ∑ uhensiktsmessige forsøk på mestring, i form av unngåelse, inaktivitet eller grubling, bidrar til å opprettholde depresjon ∑ tanker er viktige, men en legger spesiell vekt på sammenhengen som tankene oppstår i og hvilken funksjon de har, forløpere og konsekvenser, mer enn på tankenes innhold
{"title":"Depresjon","authors":"Enrique Garcia-Ceja, M. Riegler, P. Jakobsen, J. Tørresen, T. Nordgreen, K. Oedegaard, Ole Bernt Fasmer","doi":"10.1145/3204949.3208125","DOIUrl":"https://doi.org/10.1145/3204949.3208125","url":null,"abstract":"∑ depresjonen i høy grad kan forstås ved å se på sammenhengen den oppstår i; personens reaksjon på omgivelsene er viktig for utvikling og opprettholdelse av depresjon ∑ uhensiktsmessige forsøk på mestring, i form av unngåelse, inaktivitet eller grubling, bidrar til å opprettholde depresjon ∑ tanker er viktige, men en legger spesiell vekt på sammenhengen som tankene oppstår i og hvilken funksjon de har, forløpere og konsekvenser, mer enn på tankenes innhold","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"106 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120835360","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}
Konstantinos Kousias, M. Riegler, Ö. Alay, A. Argyriou
Hyperparameter optimization is an important but often ignored part of successfully training Neural Networks (NN) since it is time consuming and rather complex. In this paper, we present HINDSIGHT, an open-source framework for designing and implementing NN that supports hyperparameter optimization. HINDSIGHT is built entirely in R and the current version focuses on Long Short Term Memory (LSTM) networks, a special kind of Recurrent Neural Networks (RNN). HINDSIGHT is designed in a way that it can easily be expanded to other types of Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNN) or feed-forward Deep Neural Networks (DNN). The main goal of HINDSIGHT is to provide a simple and quick interface to get started with LSTM networks and hyperparameter optimization.
{"title":"HINDSIGHT","authors":"Konstantinos Kousias, M. Riegler, Ö. Alay, A. Argyriou","doi":"10.1145/3204949.3208131","DOIUrl":"https://doi.org/10.1145/3204949.3208131","url":null,"abstract":"Hyperparameter optimization is an important but often ignored part of successfully training Neural Networks (NN) since it is time consuming and rather complex. In this paper, we present HINDSIGHT, an open-source framework for designing and implementing NN that supports hyperparameter optimization. HINDSIGHT is built entirely in R and the current version focuses on Long Short Term Memory (LSTM) networks, a special kind of Recurrent Neural Networks (RNN). HINDSIGHT is designed in a way that it can easily be expanded to other types of Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNN) or feed-forward Deep Neural Networks (DNN). The main goal of HINDSIGHT is to provide a simple and quick interface to get started with LSTM networks and hyperparameter optimization.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123281970","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}
In this paper we present a Multi-Profile Ultra High Definition (UHD) DASH dataset composed of both AVC (H.264) and HEVC (H.265) video content, generated from three well known open-source 4K video clips. The representation rates and resolutions of our dataset range from 40Mbps in 4K down to 235kbps in 320x240, and are comparable to rates utilised by on demand services such as Netflix, Youtube and Amazon Prime. We provide our dataset for both realtime testbed evaluation and trace-based simulation. The real-time testbed content provides a means of evaluating DASH adaptation techniques on physical hardware, while our trace-based content offers simulation over frameworks such as ns-2 and ns-3. We also provide the original pre-DASH MP4 files and our associated DASH generation scripts, so as to provide researchers with a mechanism to create their own DASH profile content locally. Which improves the reproducibility of results and remove re-buffering issues caused by delay/jitter/losses in the Internet. The primary goal of our dataset is to provide the wide range of video content required for validating DASH Quality of Experience (QoE) delivery over networks, ranging from constrained cellular and satellite systems to future high speed architectures such as the proposed 5G mmwave technology.
{"title":"Multi-profile ultra high definition (UHD) AVC and HEVC 4K DASH datasets","authors":"Jason J. Quinlan, C. Sreenan","doi":"10.1145/3204949.3208130","DOIUrl":"https://doi.org/10.1145/3204949.3208130","url":null,"abstract":"In this paper we present a Multi-Profile Ultra High Definition (UHD) DASH dataset composed of both AVC (H.264) and HEVC (H.265) video content, generated from three well known open-source 4K video clips. The representation rates and resolutions of our dataset range from 40Mbps in 4K down to 235kbps in 320x240, and are comparable to rates utilised by on demand services such as Netflix, Youtube and Amazon Prime. We provide our dataset for both realtime testbed evaluation and trace-based simulation. The real-time testbed content provides a means of evaluating DASH adaptation techniques on physical hardware, while our trace-based content offers simulation over frameworks such as ns-2 and ns-3. We also provide the original pre-DASH MP4 files and our associated DASH generation scripts, so as to provide researchers with a mechanism to create their own DASH profile content locally. Which improves the reproducibility of results and remove re-buffering issues caused by delay/jitter/losses in the Internet. The primary goal of our dataset is to provide the wide range of video content required for validating DASH Quality of Experience (QoE) delivery over networks, ranging from constrained cellular and satellite systems to future high speed architectures such as the proposed 5G mmwave technology.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647119","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}
Huber Flores, Pan Hui, S. Tarkoma, Yong Li, T. Anagnostopoulos, V. Kostakos, Chu Luo, Xiang Su
IoT services hosted by low-power devices rely on the cloud infrastructure to propagate their ubiquitous presence over the Internet. A critical challenge for IoT systems is to ensure continuous provisioning of IoT services by overcoming network breakdowns, hardware failures, and energy constraints. To overcome these issues, we propose a cloud-based framework namely SensorClone, which relies on virtual devices to improve IoT resilience. A virtual device is the digital counterpart of a physical device that has learned to emulate its operations from sample data collected from the physical one. SensorClone exploits the collected data of low-power devices to create virtual devices in the cloud. SensorClone then can opportunistically migrate virtual devices from the cloud into other devices, potentially underutilized, with higher capabilities and closer to the edge of the network, e.g., smart devices. Through a real deployment of our SensorClone in the wild, we identify that virtual devices can be used for two purposes, 1) to reduce the energy consumption of physical devices by duty cycling their service provisioning between the physical device and the virtual representation hosted in the cloud, and 2) to scale IoT services at the edge of the network by harnessing temporal periods of underutilization of smart devices. To evaluate our framework, we present a use case of a virtual sensor created from an IoT service of temperature. From our results, we verify that it is possible to achieve unlimited availability up to 90% and substantial power efficiency under acceptable levels of quality of service. Our work makes contributions towards improving IoT scalability and resilience by using virtual devices.
{"title":"Sensorclone","authors":"Huber Flores, Pan Hui, S. Tarkoma, Yong Li, T. Anagnostopoulos, V. Kostakos, Chu Luo, Xiang Su","doi":"10.1145/3204949.3204952","DOIUrl":"https://doi.org/10.1145/3204949.3204952","url":null,"abstract":"IoT services hosted by low-power devices rely on the cloud infrastructure to propagate their ubiquitous presence over the Internet. A critical challenge for IoT systems is to ensure continuous provisioning of IoT services by overcoming network breakdowns, hardware failures, and energy constraints. To overcome these issues, we propose a cloud-based framework namely SensorClone, which relies on virtual devices to improve IoT resilience. A virtual device is the digital counterpart of a physical device that has learned to emulate its operations from sample data collected from the physical one. SensorClone exploits the collected data of low-power devices to create virtual devices in the cloud. SensorClone then can opportunistically migrate virtual devices from the cloud into other devices, potentially underutilized, with higher capabilities and closer to the edge of the network, e.g., smart devices. Through a real deployment of our SensorClone in the wild, we identify that virtual devices can be used for two purposes, 1) to reduce the energy consumption of physical devices by duty cycling their service provisioning between the physical device and the virtual representation hosted in the cloud, and 2) to scale IoT services at the edge of the network by harnessing temporal periods of underutilization of smart devices. To evaluate our framework, we present a use case of a virtual sensor created from an IoT service of temperature. From our results, we verify that it is possible to achieve unlimited availability up to 90% and substantial power efficiency under acceptable levels of quality of service. Our work makes contributions towards improving IoT scalability and resilience by using virtual devices.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116472913","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}
R. Mekuria, M. Mcgrath, Vincenzo Riccobene, Victor Bayon-Molino, C. Tselios, John Thomson, Artem Dobrodub
Most media streaming services are composed by different virtualized processing functions such as encoding, packaging, encryption, content stitching etc. Deployment of these functions in the cloud is attractive as it enables flexibility in deployment options and resource allocation for the different functions. Yet, most of the time overprovisioning of cloud resources is necessary in order to meet demand variability. This can be costly, especially for large scale deployments. Prior art proposes resource allocation based on analytical models that minimize the costs of cloud deployments under a quality of service (QoS) constraint. However, these models do not sufficiently capture the underlying complexity of services composed of multiple processing functions. Instead, we introduce a novel methodology based on full-stack telemetry and machine learning to profile virtualized or cloud native media processing functions individually. The basis of the approach consists of investigating 4 categories of performance metrics: throughput, anomaly, latency and entropy (TALE) in offline (stress tests) and online setups using cloud telemetry. Machine learning is then used to profile the media processing function in the targeted cloud/NFV environment and to extract the most relevant cloud level Key Performance Indicators (KPIs) that relate to the final perceived quality and known client side performance indicators. The results enable more efficient monitoring, as only KPI related metrics need to be collected, stored and analyzed, reducing the storage and communication footprints by over 85%. In addition a detailed overview of the functions behavior was obtained, enabling optimized initial configuration and deployment, and more fine-grained dynamic online resource allocation reducing overprovisioning and avoiding function collapse. We further highlight the next steps towards cloud native carrier grade virtualized processing functions relevant for future network architectures such as in emerging 5G architectures.
{"title":"Automated profiling of virtualized media processing functions using telemetry and machine learning","authors":"R. Mekuria, M. Mcgrath, Vincenzo Riccobene, Victor Bayon-Molino, C. Tselios, John Thomson, Artem Dobrodub","doi":"10.1145/3204949.3204976","DOIUrl":"https://doi.org/10.1145/3204949.3204976","url":null,"abstract":"Most media streaming services are composed by different virtualized processing functions such as encoding, packaging, encryption, content stitching etc. Deployment of these functions in the cloud is attractive as it enables flexibility in deployment options and resource allocation for the different functions. Yet, most of the time overprovisioning of cloud resources is necessary in order to meet demand variability. This can be costly, especially for large scale deployments. Prior art proposes resource allocation based on analytical models that minimize the costs of cloud deployments under a quality of service (QoS) constraint. However, these models do not sufficiently capture the underlying complexity of services composed of multiple processing functions. Instead, we introduce a novel methodology based on full-stack telemetry and machine learning to profile virtualized or cloud native media processing functions individually. The basis of the approach consists of investigating 4 categories of performance metrics: throughput, anomaly, latency and entropy (TALE) in offline (stress tests) and online setups using cloud telemetry. Machine learning is then used to profile the media processing function in the targeted cloud/NFV environment and to extract the most relevant cloud level Key Performance Indicators (KPIs) that relate to the final perceived quality and known client side performance indicators. The results enable more efficient monitoring, as only KPI related metrics need to be collected, stored and analyzed, reducing the storage and communication footprints by over 85%. In addition a detailed overview of the functions behavior was obtained, enabling optimized initial configuration and deployment, and more fine-grained dynamic online resource allocation reducing overprovisioning and avoiding function collapse. We further highlight the next steps towards cloud native carrier grade virtualized processing functions relevant for future network architectures such as in emerging 5G architectures.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122403005","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}
Débora Pereira Salgado, F. Martins, T. B. Rodrigues, Conor Keighrey, R. Flynn, E. Naves, Niall Murray
The1 key aim of various assistive technology (AT) systems is to augment an individual's functioning whilst supporting an enhanced quality of life (QoL). In recent times, we have seen the emergence of Virtual Reality (VR) based assistive technology systems made possible by the availability of commercially available Head Mounted Displays (HMDs). The use of VR for AT aims to support levels of interaction and immersion not previously possibly with more traditional AT solutions. Crucial to the success of these technologies is understanding, from the user perspective, the influencing factors that affect the user Quality of Experience (QoE). In addition to the typical QoE metrics, other factors to consider are human behavior like mental and emotional state, posture and gestures. In terms of trying to objectively quantify such factors, there are wide ranges of wearable sensors that are able to monitor physiological signals and provide reliable data. In this demo, we will capture and present the users EEG, heart Rate, EDA and head motion during the use of AT VR application. The prototype is composed of the sensor and presentation systems: for acquisition of biological signals constituted by wearable sensors and the virtual wheelchair simulator that interfaces to a typical LCD display.
{"title":"A QoE assessment method based on EDA, heart rate and EEG of a virtual reality assistive technology system","authors":"Débora Pereira Salgado, F. Martins, T. B. Rodrigues, Conor Keighrey, R. Flynn, E. Naves, Niall Murray","doi":"10.1145/3204949.3208118","DOIUrl":"https://doi.org/10.1145/3204949.3208118","url":null,"abstract":"The1 key aim of various assistive technology (AT) systems is to augment an individual's functioning whilst supporting an enhanced quality of life (QoL). In recent times, we have seen the emergence of Virtual Reality (VR) based assistive technology systems made possible by the availability of commercially available Head Mounted Displays (HMDs). The use of VR for AT aims to support levels of interaction and immersion not previously possibly with more traditional AT solutions. Crucial to the success of these technologies is understanding, from the user perspective, the influencing factors that affect the user Quality of Experience (QoE). In addition to the typical QoE metrics, other factors to consider are human behavior like mental and emotional state, posture and gestures. In terms of trying to objectively quantify such factors, there are wide ranges of wearable sensors that are able to monitor physiological signals and provide reliable data. In this demo, we will capture and present the users EEG, heart Rate, EDA and head motion during the use of AT VR application. The prototype is composed of the sensor and presentation systems: for acquisition of biological signals constituted by wearable sensors and the virtual wheelchair simulator that interfaces to a typical LCD display.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644906","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}
Daniel Kümper, Thorben Iggena, R. Tönjes, E. Pulvermüller
Heterogeneous sensor device networks with diverse maintainers and information collected via social media as well as crowdsourcing tend to be elements of uncertainty in IoT and Smart City networks. Often, there is no ground truth available that can be used to check the plausibility and concordance of the new information. This paper proposes the Valid.IoT Framework as an attachable IoT framework component that can be linked to generate QoI vectors and Interpolated sensory data with plausibility and quality estimations to a variety of platforms. The framework utilises extended infrastructure knowledge and infrastructure-aware interpolation algorithms to validate crowdsourced and device generated sensor information through sensor fusion.
{"title":"Valid.IoT: a framework for sensor data quality analysis and interpolation","authors":"Daniel Kümper, Thorben Iggena, R. Tönjes, E. Pulvermüller","doi":"10.1145/3204949.3204972","DOIUrl":"https://doi.org/10.1145/3204949.3204972","url":null,"abstract":"Heterogeneous sensor device networks with diverse maintainers and information collected via social media as well as crowdsourcing tend to be elements of uncertainty in IoT and Smart City networks. Often, there is no ground truth available that can be used to check the plausibility and concordance of the new information. This paper proposes the Valid.IoT Framework as an attachable IoT framework component that can be linked to generate QoI vectors and Interpolated sensory data with plausibility and quality estimations to a variety of platforms. The framework utilises extended infrastructure knowledge and infrastructure-aware interpolation algorithms to validate crowdsourced and device generated sensor information through sensor fusion.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129281158","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}
Savino Dambra, Giuseppe Samela, L. Sassatelli, R. Pighetti, R. Aparicio-Pardo, A. Pinna-Dery
Streaming Virtual Reality (VR), even under the mere form of 360° videos, is much more complex than for regular videos because to lower the required rates, the transmission decisions must take the user's head position into account. The way the user exploits her/his freedom is therefore crucial for the network load. In turn, the way the user moves depends on the video content itself. VR is however a whole new medium, for which the film-making language does not exist yet, its "grammar" only being invented. We present a strongly inter-disciplinary approach to improve the streaming of 360° videos: designing high-level content manipulations (film editing) to limit and even control the user's motion in order to consume less bandwidth while maintaining the user's experience. We build an MPEG DASH-SRD player for Android and the Samsung Gear VR, featuring FoV-based quality decision and a replacement strategy to allow the tiles' buffers to build up while keeping their state up-to-date with the current FoV as much as bandwidth allows. The editing strategies we design have been integrated within the player, and the streaming module has been extended to benefit from the editing. Two sets of user experiments enabled to show that editing indeed impacts head velocity (reduction of up to 30%), consumed bandwidth (reduction of up to 25%) and subjective assessment. User's attention driving tools from other communities can hence be designed in order to improve streaming. We believe this innovative work opens up the path to a whole new field of possibilities in defining degrees of freedom to be wielded for VR streaming optimization.
{"title":"Film editing: new levers to improve VR streaming","authors":"Savino Dambra, Giuseppe Samela, L. Sassatelli, R. Pighetti, R. Aparicio-Pardo, A. Pinna-Dery","doi":"10.1145/3204949.3204962","DOIUrl":"https://doi.org/10.1145/3204949.3204962","url":null,"abstract":"Streaming Virtual Reality (VR), even under the mere form of 360° videos, is much more complex than for regular videos because to lower the required rates, the transmission decisions must take the user's head position into account. The way the user exploits her/his freedom is therefore crucial for the network load. In turn, the way the user moves depends on the video content itself. VR is however a whole new medium, for which the film-making language does not exist yet, its \"grammar\" only being invented. We present a strongly inter-disciplinary approach to improve the streaming of 360° videos: designing high-level content manipulations (film editing) to limit and even control the user's motion in order to consume less bandwidth while maintaining the user's experience. We build an MPEG DASH-SRD player for Android and the Samsung Gear VR, featuring FoV-based quality decision and a replacement strategy to allow the tiles' buffers to build up while keeping their state up-to-date with the current FoV as much as bandwidth allows. The editing strategies we design have been integrated within the player, and the streaming module has been extended to benefit from the editing. Two sets of user experiments enabled to show that editing indeed impacts head velocity (reduction of up to 30%), consumed bandwidth (reduction of up to 25%) and subjective assessment. User's attention driving tools from other communities can hence be designed in order to improve streaming. We believe this innovative work opens up the path to a whole new field of possibilities in defining degrees of freedom to be wielded for VR streaming optimization.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116271361","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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