scholarly journals Privacy-Preserving Surveillance as an Edge Service Based on Lightweight Video Protection Schemes Using Face De-Identification and Window Masking

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 236
Author(s):  
Alem Fitwi ◽  
Yu Chen ◽  
Sencun Zhu ◽  
Erik Blasch ◽  
Genshe Chen
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Privacy Preserving ◽  
Hybrid Architecture ◽  
Foreground Object ◽  
The Public ◽  
Protection Scheme ◽  
Suburban Areas ◽  
Cloud Server ◽  
Human Faces

With a myriad of edge cameras deployed in urban and suburban areas, many people are seriously concerned about the constant invasion of their privacy. There is a mounting pressure from the public to make the cameras privacy-conscious. This paper proposes a Privacy-preserving Surveillance as an Edge service (PriSE) method with a hybrid architecture comprising a lightweight foreground object scanner and a video protection scheme that operates on edge cameras and fog/cloud-based models to detect privacy attributes like windows, faces, and perpetrators. The Reversible Chaotic Masking (ReCAM) scheme is designed to ensure an end-to-end privacy while the simplified foreground-object detector helps reduce resource consumption by discarding frames containing only background-objects. A robust window-object detector was developed to prevent peeping via windows; whereas human faces are detected by using a multi-tasked cascaded convolutional neural network (MTCNN) to ensure de-identification. The extensive experimental studies and comparative analysis show that the PriSE scheme (i) can efficiently detect foreground objects, and scramble those frames that contain foreground objects at the edge cameras, and (ii) detect and denature window and face objects, and identify perpetrators at a fog/cloud server to prevent unauthorized viewing via windows, to ensure anonymity of individuals, and to deter criminal activities, respectively.

Exploring Visual Representation of #ShoutYourAbortion Hashtag Movement and the Public’s Responses on Instagram: Descriptive and Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Sunmi ‍Lee ◽  
Yunhwan Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Social Media ◽  
Neural Network Model ◽  
Predictive Analytics ◽  
Visual Representation ◽  
Point Of View ◽  
The Public ◽  
Intelligence Services ◽  
Human Faces

BACKGROUND Hashtag movement has become one of the major ways of online movement, but few studies have examined how social media photos were used for the movement. Also, it has not been actively investigated how photo features were related to the public’s responses in hashtag movements. OBJECTIVE The aim of the present research was to explore Instagram photos with #ShoutYourAbortion hashtag, as an example of hashtag movements via photos, in terms of their visual representation and the relationships between photo features and the public’s responses to the photos. METHODS Instagram photos with #ShoutYourAbortion hashtag, 11,176 in total, were downloaded, and their content and embedded texts were analyzed using online artificial intelligence services. The photos were clustered into subgroups based on the features extracted using a pretrained convolutional neural network model. The resulting clusters were compared in terms of their content tags, embedded texts, and photo features which were manually extracted at the content and pixel levels. The public’s responses were measured by engagement and comment sentiment. Correlational analysis and predictive analytics were conducted to examine the relationships between photo features and the public’s responses. RESULTS It was found that the photos in the text category took the largest share (57.19%), and the embedded texts were mainly about stories told in first person point of view as a woman. A possible evidence of hashtag hijacking was observed. The photos were grouped into two clusters; the first cluster comprised photos which exhibit text materials on them, while the second cluster consisted of photos which contain human faces with texts. The photos in the first cluster were brighter, while the photos in the second cluster were more colorful than the others. And public responses were found to be related to photo features such as size of faces, happy emotion, and share of warm colors. Engagement was predicted from the photo features with an acceptable level of accuracy, while comment sentiment was not. CONCLUSIONS This This study has shown the visual representation of #ShoutYourAbortion hashtag movement. It has also shown how photo features at content and pixel levels were related to the public’s responses to the photos. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public. CLINICALTRIAL Not Applicable

Public finance flow in contemporary Russia

2020 ◽  
Vol 26 (11) ◽  
pp. 2501-2523
Author(s):  
V.V. Smirnov
Keyword(s):  
Neural Network ◽  
Public Finance ◽  
Systems Approach ◽  
Management Decisions ◽  
Public Authorities ◽  
Cluster Analyses ◽  
The Public ◽  
National Wealth ◽  
Economic Formation

Subject. This article discusses the issues related to public finance. Objectives. The article aims to identify the determinants, indicators, and priorities of the public finance flow in contemporary Russia. Methods. For the study, I used the methods of statistical, neural network, and cluster analyses, and the systems approach. Results. The article identifies and describes the determining indicators of the main aggregates and balances of public finance, sources, and the use of funds. It establishes a link between the main aggregates and balances of public finance, defining the form and content of Russian capitalism. Conclusions. Understanding the issue and problem of public finance flow in contemporary Russia helps identify the reasons for the inability to transit to a capitalist socio-economic formation. The provisions of the study expand the scope of knowledge and develop the competence of public authorities to make management decisions on the distribution and redistribution of the value of a public product and part of the national wealth.

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

scholarly journals Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

2020 ◽  
Vol 12 (5) ◽  
pp. 784 ◽  
Author(s):  
Wei Guo ◽  
Weihong Li ◽  
Weiguo Gong ◽  
Jinkai Cui
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Semantic Information ◽  
Aerial Images ◽  
Training Strategy ◽  
The Public ◽  
Multi Scale ◽  
Shallow Layer ◽  
The Neural Network ◽  
Feature Pyramid

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

scholarly journals Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 26
Author(s):  
Na Li ◽  
Lianguan Huang ◽  
Yanling Li ◽  
Meng Sun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Locality Sensitive Hashing ◽  
Sensitive Information ◽  
The Public ◽  
Big Data Mining ◽  
Euclidean Distances ◽  
Computational Resources

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

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