scholarly journals End-to-End Network Slices: From Network Function Profiles to Fine-Grained SLAs

2019 ◽  
Author(s):  
Raphael V. Rosa ◽  
Christian Esteve Rothenberg
Keyword(s):  
Augmented Reality ◽  
State Of The Art ◽  
Vehicular Communications ◽  
Automated Extraction ◽  
Network Slicing ◽  
Fine Grained ◽  
Network Function ◽  
End To End ◽  
Future Work ◽  
Administrative Domains

Towards end-to-end network slicing, diverse envisioned 5G services (eg, augmented reality, vehicular communications, IoT) Call for advanced multi-administrative domain service deployments, open challenges from vertical Agreement (SLA) -based orchestration hazards. Through different proposed methodologies and demonstrated prototypes, this work showcases: the automated extraction of network function profiles; the manners to analyze how such profiles compose programmable network slice footprints; and the means to perform fine-grained auditable SLAs for end-to-end network slicing among multiple administrative domains. Sustained on state-of-the-art networking concepts, this work presents contributions by detecting roots on standardization efforts and best-of-breed open source embodiments, each one standing prominent future work topics in shape of its shortcomings.

scholarly journals Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 597 ◽  
Author(s):  
Nicholus Mboga ◽  
Stefanos Georganos ◽  
Tais Grippa ◽  
Moritz Lennert ◽  
Sabine Vanhuysse ◽  
...  
Keyword(s):  
Democratic Republic Of Congo ◽  
State Of The Art ◽  
Computational Cost ◽  
Convolutional Networks ◽  
Object Based Image Analysis ◽  
Fully Convolutional Networks ◽  
Object Based ◽  
Geographic Object ◽  
End To End ◽  
Future Work

Land cover Classified maps obtained from deep learning methods such as Convolutional neural networks (CNNs) and fully convolutional networks (FCNs) usually have high classification accuracy but with the detailed structures of objects lost or smoothed. In this work, we develop a methodology based on fully convolutional networks (FCN) that is trained in an end-to-end fashion using aerial RGB images only as input. Skip connections are introduced into the FCN architecture to recover high spatial details from the lower convolutional layers. The experiments are conducted on the city of Goma in the Democratic Republic of Congo. We compare the results to a state-of-the art approach based on a semi-automatic Geographic object image-based analysis (GEOBIA) processing chain. State-of-the art classification accuracies are obtained by both methods whereby FCN and the best baseline method have an overall accuracy of 91.3% and 89.5% respectively. The maps have good visual quality and the use of an FCN skip architecture minimizes the rounded edges that is characteristic of FCN maps. Additional experiments are done to refine FCN classified maps using segments obtained from GEOBIA generated at different scale and minimum segment size. High OA of up to 91.5% is achieved accompanied with an improved edge delineation in the FCN maps, and future work will involve explicitly incorporating boundary information from the GEOBIA segmentation into the FCN pipeline in an end-to-end fashion. Finally, we observe that FCN has a lower computational cost than the standard patch-based CNN approach especially at inference.

scholarly journals Network Slicing to Enable Resilience and High Availability in 5G Mobile Telecommunications

2018 ◽  
Vol 246 ◽  
pp. 03028
Author(s):  
Qi He ◽  
Yunxia Ju ◽  
Jianguo Wang ◽  
Gang Zhao ◽  
Haiyong Qin ◽  
...  
Keyword(s):  
Smart Grids ◽  
State Of The Art ◽  
Network Function Virtualization ◽  
Software Defined Networks ◽  
Network Slicing ◽  
Network Function ◽  
Fifth Generation ◽  
Personalized Services ◽  
Health Applications ◽  
Open Issues

In the upcoming fifth-generation (5G) ecosystem, the delivery of a variety of personalized services is envisioned. With the development of software-defined networks and network function virtualization technologies, networks display increasingly flexible features, such as programmability. Network slicing is a state-of-the-art technology that provides services tailored to the specific demands of users, such as smart grids and e-health applications. In this article, we introduce the network slicing concept and its application and discuss related work. In addition, we propose an architecture for network slicing by combining software-defined networks and network function virtualization technologies. Finally, we note important challenges and open issues in the development and application of these technologies.

scholarly journals Development of Visual and Audio Speech Recognition Systems Using Deep Neural Networks

2021 ◽  
Author(s):  
Denis Ivanko ◽  
Dmitry Ryumin
Keyword(s):  
Speech Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Recognition Task ◽  
Reading Task ◽  
Lip Reading ◽  
Recognition Systems ◽  
End To End ◽  
Future Work ◽  
Single Modality

In this paper we design end-to-end neural network for the low-resource lip-reading task and audio speech recognition task using 3D CNNs, pre-trained CNN weights of several state-of- the-art models (e.g. VGG19, InceptionV3, MobileNetV2, etc.) and LSTMs. We present two phrase-level speech recognition pipelines: for lip-reading and acoustic speech recognition. We evaluate different combinations of front-end and back-end modules on the RUSAVIC dataset. We compare our results with traditional 2D CNN approach and demonstrate the increase in recognition accuracy up to 14%. Moreover, we carefully studied existing state-of-the-art models to be use for augmentation. Based on the conducted analysis we have chosen 5 most promising model’s architectures and evaluated them on own data. We have tested our systems on a real-word data of two different scenarios: recorded in idling vehicle and during actual driving. Our independently trained systems demonstrated acoustic speech accuracy up to 90% and lip-reading accuracy up to 61%. Future work will focus on the fusion of visual and audio speech modalities and on speaker adaptation. We expect that fused multi-modal information will help to further improve recognition performance compared to a single modality. Another possible direction could be the research of different NN-based architectures to better tackle end-to-end lip-reading task.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Alternatives

2020 ◽  
pp. 112-138
Author(s):  
Holly M. Smith
Keyword(s):  
Action Theory ◽  
Human Action ◽  
Fine Grained ◽  
Moral Properties ◽  
Future Work

Consequentialists have long debated (as deontologists should) how to define an agent’s alternatives, given that (a) at any particular time an agent performs numerous “versions” of actions, (b) an agent may perform several independent co-temporal actions, and (c) an agent may perform sequences of actions. We need a robust theory of human action to provide an account of alternatives that avoids previously debated problems. After outlining Alvin Goldman’s action theory (which takes a fine-grained approach to act individuation) and showing that the agent’s alternatives must remain invariant across different normative theories, I address issue (a) by arguing that an alternative for an agent at a time is an entire “act tree” performable by her, rather than any individual act token. I argue further that both tokens and trees must possess moral properties, and I suggest principles governing how these are inherited among trees and tokens. These proposals open a path for future work addressing issues (b) and (c).

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals The NECOS Approach to End-to-End Cloud-Network Slicing as a Service

2021 ◽  
Vol 59 (3) ◽  
pp. 91-97
Author(s):  
Stuart Clayman ◽  
Augusto Neto ◽  
Fabio Verdi ◽  
Sand Correa ◽  
Silvio Sampaio ◽  
...  
Keyword(s):  
Network Slicing ◽  
End To End ◽  
Cloud Network

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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