Deep Learning-based Activity Detection for Grant-free Random Access

10.36227/techrxiv.16834552 ◽

2021 ◽

Author(s):

Taufik Abrao ◽

João Henrique Inácio de Souza

Keyword(s):

Deep Learning ◽

Random Access ◽

Network Models ◽

Efficient Solutions ◽

Supervised Machine Learning ◽

Data Detection ◽

Detection Accuracy ◽

Activity Detection ◽

Neural Network Models ◽

The Impact

<div> <div> <div> <p>The cellular internet-of-things wireless network is a promising solution to provide massive connectivity for machine- type devices. However, designing grant-free random access (GF- RA) protocols to manage such connections is challenging, since they must operate in interference-aware scenarios with sporadic device activation patterns and a shortage of mutually orthogonal resources. Supervised machine learning models have provided efficient solutions for activity detection, non-coherent data detection, and non-orthogonal preamble design in scenarios with massive connectivity. Considering these promising results, in this paper, we develop two deep learning (DL) sparse support recovery algorithms to detect active devices in mMTC random access. The DL algorithms, developed to deploy GF-RA protocols, are based on the deep multilayer perceptron and the convolutional neural network models. Unlike previous works, we investigate the impact of the type of sequences for preamble design on the activity detection accuracy. Our results reveal that preambles based on the Zadoff-Chu sequences, which present good correlation properties, achieve better activity detection accuracy with the proposed algorithms than random sequences. Besides, we demonstrate that our DL algorithms achieve activity detection accuracy comparable to state-of-the-art techniques with extremely low computational complexity. </p> </div> </div> </div>

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Detecting internet attacks using a neural network

Dynamics of Complex Systems - XXI century ◽

10.18127/j19997493-202102-04 ◽

2021 ◽

Author(s):

E.Yu. Silantieva ◽

V.A. Zabelina ◽

G.A. Savchenko ◽

I.M. Chernenky

Keyword(s):

Neural Network ◽

Deep Learning ◽

Open Source Software ◽

Network Traffic ◽

Network Models ◽

Detection Accuracy ◽

Learning Models ◽

Neural Network Models ◽

Robust Identification ◽

Variational Autoencoder

This study presents an analysis of autoencoder models for the problems of detecting anomalies in network traffic. Results of the training were assessed using open source software on the UNB ICS IDS 2017 dataset. As deep learning models, we considered standard and variational autoencoder, Deep SSAD approaches for a normal autoencoder (AE-SAD) and a variational autoencoder (VAE-SAD). The constructed deep learning models demonstrated different indicators of anomaly detection accuracy; the best result in terms of the AUC metric of 98% was achieved with VAE-SAD model. In the future, it is planned to continue the analysis of the characteristics of neural network models in cybersecurity problems. One of directions is to study the influence of structure of network traffic on the performance indicators of using deep learning models. Based on the results, it is planned to develop an approach of robust identification of security events based on deep learning methods.

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Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

Concurrent Engineering ◽

10.1177/1063293x211003194 ◽

2021 ◽

pp. 1063293X2110031

Author(s):

Maolin Yang ◽

Auwal H Abubakar ◽

Pingyu Jiang

Keyword(s):

Deep Learning ◽

Complex Network ◽

Network Models ◽

Neural Network Models ◽

Complex Network Theory ◽

New Type ◽

Manufacturing Resource ◽

Manufacturing Resources ◽

Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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Feature extraction-based image steganalysis using deep learning

WEENTECH Proceedings in Energy ◽

10.32438/wpe.182021 ◽

2021 ◽

pp. 188-198

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Secure Communication ◽

Information Technologies ◽

Network Models ◽

Error Rates ◽

Multimedia Data ◽

Secret Message ◽

Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

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Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

Industrial and Systems Engineering Review ◽

10.37266/iser.2021v9i1.pp15-31 ◽

2021 ◽

Vol 9 (1) ◽

pp. 15-31

Author(s):

Ali Arishi ◽

Krishna K Krishnan ◽

Vatsal Maru

Keyword(s):

Raw Materials ◽

Early Stage ◽

Supply And Demand ◽

Network Models ◽

Combine System ◽

Neural Network Models ◽

Digital Twin ◽

Twin Model ◽

The Impact ◽

Reaction Patterns

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

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Deep Network Guided Proof Search

10.29007/8mwc ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarah Loos ◽

Geoffrey Irving ◽

Christian Szegedy ◽

Cezary Kaliszyk

Keyword(s):

Deep Learning ◽

Program Analysis ◽

Network Models ◽

Theorem Prover ◽

Neural Network Models ◽

Two Phase ◽

First Order ◽

Proof Search ◽

System Verification ◽

Theory Reasoning

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

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An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Future Internet ◽

10.3390/fi13120306 ◽

2021 ◽

Vol 13 (12) ◽

pp. 306

Author(s):

Ahmed Dirir ◽

Henry Ignatious ◽

Hesham Elsayed ◽

Manzoor Khan ◽

Mohammed Adib ◽

...

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Smart Cities ◽

Weather Conditions ◽

Research Area ◽

Transportation Systems ◽

Detection Accuracy ◽

Adverse Weather ◽

Object Counting ◽

The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

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SEALing Neural Network Models in Encrypted Deep Learning Accelerators

10.1109/dac18074.2021.9586199 ◽

2021 ◽

Author(s):

Pengfei Zuo ◽

Yu Hua ◽

Ling Liang ◽

Xinfeng Xie ◽

Xing Hu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Models ◽

Neural Network Models

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Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Machine Learning and Knowledge Extraction ◽

10.3390/make1010027 ◽

2019 ◽

Vol 1 (1) ◽

pp. 450-465 ◽

Cited By ~ 8

Author(s):

Abhishek Sehgal ◽

Nasser Kehtarnavaz

Keyword(s):

Deep Learning ◽

Real Time ◽

Open Source Software ◽

Mobile Applications ◽

Network Models ◽

Software Tools ◽

Learning Tools ◽

Smartphone Apps ◽

Learning Models ◽

Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

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