Real-time context-aware and personalized media streaming environments for large scale broadcasting applications My-e-Director 2012

2008 ◽  
Author(s):  
Nikolaos Papaoulakis ◽  
Nikolaos Doulamis ◽  
Charalampos Patrikakis ◽  
Emmanuel Protonotarios ◽  
Jonh Soldatos
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Media Streaming ◽  
Context Aware

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

scholarly journals Context-Aware and Class Imbalance Invariant Threat Severity Assessment for Heterogeneous IoT

2021 ◽  
Author(s):  
Nitish A ◽  
J. Hanumanthappa ◽  
Shiva Prakash S.P ◽  
Kirill Krinkin
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Class Imbalance ◽  
Threat Assessment ◽  
Severity Assessment ◽  
Context Aware ◽  
Root Cause ◽  
Network Intrusion ◽  
Classifier Performance ◽  
Learning Machine

<div>Due to demand for information ubiquity and large-scale automation, proliferating Internet-connected heterogeneous devices exhibit significant variations in data processing capacities, purposes, operating principles, underlying protocols, and dynamic contexts. As a result, adversarial entities exploit the increasing heterogeneous network (HetIoT) vulnerabilities, leading to frequent high-impact attacks due to anomalous device interactions and scarce knowledgebase. This paper presents a two-fold solution to the problem through a network intrusion detection and prevention framework for HetIoT, called \textit{HetIoT-NIDPS}. Firstly, we assign fault scores to the Expert-curated Knowledgebase (EK) framework, correlating with low-level alerts to assess threat severity and achieve context-awareness. Secondly, the proposed Beta distribution-based HetIoT traffic behavior approximation facilitates class imbalance invariance and improves classifier performance. Additionally, the HetIoT-NIDPS can detect zero-day attacks by identifying known attack variations upon encountering unseen traffic instances. Furthermore, the dynamic HetIoT contexts necessitate real-time threat assessment through online training---performed by analyzing small batches of network traffic samples. We propound the \textit{CorrELM} classifier based on the extreme learning machine algorithm and test the hypotheses on the Bot-IoT dataset. Finally, we prioritize the correlated alerts based on their severity, determined from root cause analysis and threat severity assessment tables. The results obtained prove that the proposed HetIoT-NIDPS framework is context-aware---producing reduced false alerts, class imbalance invariant---facilitating near real-time threat assessment with unbiased classifier performance, and generalizable---applicable to many NID datasets, which the existing techniques lack when combined.</div>

scholarly journals Context-Aware and Class Imbalance Invariant Threat Severity Assessment for Heterogeneous IoT

2021 ◽  
Author(s):  
Nitish A ◽  
J. Hanumanthappa ◽  
Shiva Prakash S.P ◽  
Kirill Krinkin
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Class Imbalance ◽  
Threat Assessment ◽  
Severity Assessment ◽  
Context Aware ◽  
Root Cause ◽  
Network Intrusion ◽  
Classifier Performance ◽  
Learning Machine

<div>Due to demand for information ubiquity and large-scale automation, proliferating Internet-connected heterogeneous devices exhibit significant variations in data processing capacities, purposes, operating principles, underlying protocols, and dynamic contexts. As a result, adversarial entities exploit the increasing heterogeneous network (HetIoT) vulnerabilities, leading to frequent high-impact attacks due to anomalous device interactions and scarce knowledgebase. This paper presents a two-fold solution to the problem through a network intrusion detection and prevention framework for HetIoT, called \textit{HetIoT-NIDPS}. Firstly, we assign fault scores to the Expert-curated Knowledgebase (EK) framework, correlating with low-level alerts to assess threat severity and achieve context-awareness. Secondly, the proposed Beta distribution-based HetIoT traffic behavior approximation facilitates class imbalance invariance and improves classifier performance. Additionally, the HetIoT-NIDPS can detect zero-day attacks by identifying known attack variations upon encountering unseen traffic instances. Furthermore, the dynamic HetIoT contexts necessitate real-time threat assessment through online training---performed by analyzing small batches of network traffic samples. We propound the \textit{CorrELM} classifier based on the extreme learning machine algorithm and test the hypotheses on the Bot-IoT dataset. Finally, we prioritize the correlated alerts based on their severity, determined from root cause analysis and threat severity assessment tables. The results obtained prove that the proposed HetIoT-NIDPS framework is context-aware---producing reduced false alerts, class imbalance invariant---facilitating near real-time threat assessment with unbiased classifier performance, and generalizable---applicable to many NID datasets, which the existing techniques lack when combined.</div>

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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