Reviewerly/exHarmony huggingface dataset:
An Analysis of One-Shot Augmented Learning: A Face Recognition Case Study

Nowadays artificial intelligence models increase in complexity and people tend to doubt how these systems work and how vulnerable they are when they use these systems. The systems indeed use user data, nonetheless, it is possible to reduce this data with techniques like one-shot learning. This study aims to compare how one-shot learning works in face recognition compared to the one-shot augmented learning that uses data augmentation techniques. The research shows that in many face recognition models the data augmentation technique is highly effective. Also, the study allows us to determine the best face recognition model for low data training.
Construction and Application of Media Corpus Based on Big Data

Aiming at the problems of poor data quality and low application rate in the construction of existing media corpus, this paper proposes the construction and application research of media corpus based on big data. Media corpus data are collected, the data are divided into four categories, the heuristic data item column sorting algorithm is introduced to sort all collection processes, the minimum value of data item collection rate is determined, on this basis, the maximum value of quantity in media corpus is determined, and data collection is realized in media corpus data through sliding window. Then, the state characteristics and probability distribution of feature data are determined by dynamic Bayesian network, the relationship between the state variables and dimensions of media corpus data is determined, and the media corpus data state is processed by component to complete the preprocessing of media corpus data; finally, through the application research of storage and encryption of the designed database through big data technology, the storage structure data and encryption secret key are designed to realize the construction and application of media corpus. The experimental results show that the data quality of the media corpus constructed by the proposed method is high, and its application rate has been improved to a certain extent.
Recognition Of Facial Expressions Using A Deep Neural Network

Facial expression recognition technology has boomed over the past few years because of human-computer engagement. Computer vision advancements have made it possible that machines can now understand the human’s actions., expressions, etc. Research in this area is also a hot topic because it offers a wide range of applications and shows that CNN provides impressive results compared to traditional methods. So keeping it as a motivation, in our work, we aimed for such Deep CNN architecture, which can work on real-world images like images having various resolution, angles, poses, illumination, and brightness, etc. So for this, we have implemented our CNN architecture with the Kaggle challenge presented dataset FER-2013 and trained the model to recognize the basic seven expressions. The proposed approach seems to be effective since we were able to achieve a validation accuracy of 70.15%. This approach not only can be applied to other datasets but also in real-world applications.
Remote Monitoring of Outdoor High Voltage Insulator using Deep Learning-based Image Processing

The outdoor high voltage insulators are some of the essential parts for electrical and mechanical assistance of transmission lines. Monitoring the insulators’ health frequently on a regular basis is an indispensable routine for ensuring uninterrupted power transmission. Traditionally, the monitoring is accomplished manually by linemen, which is time-consuming and implies additional logistics costs due to the long distances and various terrains associated with transmission lines. One of the most economic solutions can be condition monitoring by analysis of insulators’ images using Unmanned Aerial Vehicles (UAVs). In this regard, this study focuses on outdoor high-voltage insulators’ health monitoring by deep learning-based classification of hazardous surface conditions. To this end, Convolutional Neural Networks (CNNs) with hyperparameter optimization as well as fine-tuning pretrained deep learning models are employed to classify insulator surface conditions into one of the following four categories: clean, or covered either with snow, ice, or dust. Applying cross-validation external to the CNN hyperparameter optimization and the fine-tuning process of pretrained models yielded remarkable accuracies of 92.07% and 97.80%, respectively.
A Fast Block-Based Technique to Detect Copy-Move Forgery in Digital Images

Images have a use in almost each and every field of the world, from institutions to the court of law. This huge acceptance of the images in every field gave birth to numerous image editing tools and hence, various forgeries. Copy-move is a very popular forgery technique. Generally, keypoint-based and block-based methods are used for the copy-move forgery detection (CMFD). In this paper, a block-based approach for CMFD is implemented in which quantized discrete cosine transform (DCT) coefficients are used to detect forgery over an image. As, many forgers apply various post-processing like JPEG compression, blurring to make forged image traceless. Thus, these quantized DCT coefficients help in detecting the approximate match between the regions by setting user defined threshold parameter.
Permutation-Based Lightweight Authenticated Cipher with Beyond Conventional Security

Lightweight authenticated ciphers are specially designed as authenticated encryption (AE) schemes for resource-constrained devices. Permutation-based lightweight authenticated ciphers have gained more attention in recent years. However, almost all of permutation-based lightweight AE schemes only ensure conventional security, i.e., about <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>c</mi><mo>/</mo><mn>2</mn></math> -bit security, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>c</mi></math> is the capacity of the permutation. This may be vulnerable for an insufficiently large capacity. This paper focuses on the stronger security guarantee and the better efficiency optimization of permutation-based lightweight AE schemes. On the basis of APE series (APE, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><msup><mrow><mtext>APE</mtext></mrow><mrow><mi>R</mi><mi>I</mi></mrow></msup></math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"><msup><mrow><mtext>APE</mtext></mrow><mrow><mi>O</mi><mi>W</mi></mrow></msup></math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"><msup><mrow><mtext>APE</mtext></mrow><mrow><mi>C</mi><mi>A</mi></mrow></msup></math> ), we propose a new improved permutation-based lightweight online AE mode <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"><msup><mrow><mtext>APE</mtext></mrow><mrow><mo>+</mo></mrow></msup></math> which supports beyond conventional security and concurrent absorption. Then, we derive a simple security proof and prove that <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"><msup><mrow><mtext>APE</mtext></mrow><mrow><mo>+</mo></mrow></msup></math> enjoys at most about <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"><mi mathvariant="normal">min</mi><mfenced open="{" close="}" separators="|"><mrow><mi>r</mi><mo>,</mo><mi>c</mi></mrow></mfenced></math> -bit security, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"><mi>r</mi></math> is the rate of the permutation. Finally, we discuss the properties of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"><msup><mrow><mtext>APE</mtext></mrow><mrow><mo>+</mo></mrow></msup></math> on the hardware implementation.
“Look Me in the Eyes!” Analyzing the Effects of Embodiment in Humanized Human-Machine Interaction in Heavy Trucks

Personal assistants like Alexa, Siri, and co. persuade users to treat technology like a friend. Studies indicate that humanized features like voice, the display of emotions and empathy help people to have more trust and pleasure and feel companionship when interacting with the technology. The professional truck driver has various challenges such as being isolated for several hours while performing a monotonous driving task, which is exacerbated by having to monitor increasing automation, growing pressure due to increasing demand, and just-in-time delivery, we believe that humanized human-machine interaction can improve the working situation of truck drivers. Therefore, we created a socially interactive device called ICo (the Intelligent Co-driver), which supports and accompanies the driver while driving. In a driving simulator study with 34 professional drivers, we investigated within a Wizard of Oz setup to what extent the addition of human characteristics through mimicry and gesture changes the driving performance, user experience, and technology acceptance. No differences could be shown in the subjective data, but the objective data captured in the study suggests that humanization affects driving performance and driver engagement.
The Issue of Adaptive Learning as Educational Innovation

Covid-19 has catalyzed positive disruptive change in the contemporary education system. It has accelerated the necessity of an innovation implementation process, technology-enhanced learning environment, customized and student-centered learning. It has enabled educators to tailor course content and pathways to students’ strengths, skills, interests and learning profiles. Adaptive learning is a contemporary method orchestrating human and digital resources to meet the needs of individual learners. The paper gives a review of the innovation process in education and the implementation of theoretical adaptive learning framework in Russian universities. It identifies innovators and early adopters who can assist two Russian Universities in attempts to implement new educational approaches. The authors also consider if there are any correlations between professors’ self-categorization and their practical application of adaptive learning in terms of the pace of learning, type of content presentation, knowledge state and students’ interests. Moreover, the researchers investigate and evaluate the Public Relations students’ perceived importance of factors affecting their learning: subject choice, subject knowledge acquisition, a type of subject content (text, audio, video), an individual pace in studying the subject, a lecturer’s guidance and group size.
Artificial Intelligence and Blockchain as Disruptive Technologies in Adolescent Lives

COVID-19 and future pandemics drastically change the way of life globally. Research has predominantly focused on the use and integration of disruptive technologies in industry and commerce. Little of the recent studies focused on the implementation of artificial intelligence and blockchain technologies in educational applications. The chapter focuses on how these can be implemented, from development, deployment, use, and maintenance of applications. A computer program's lifespan is usually spent during its use. The qualitative case study was administered using a digital learning platform that provides interactive learning for primary and secondary learners. The disruptive technologies inform new teaching methodologies and the development of student-centered algorithms for learning. Further research includes privacy issues in the implementation of disruptive technologies and data-sharing governance issues and evaluating the effectiveness of artificial intelligence and blockchain-based learning platforms.
An Agricultural Traceability Permissioned Blockchain with Privacy-Aware

As the blockchain technology maturing, data publishing platform based on blockchain also more and more. However, the privacy of the blockchain becomes one of the key problems. In order to solve the problem of privacy protection, the paper proposed a privacy-awake aggregate signature scheme based on the bilinear mapping. This signature scheme aggregates the signatures of multiple users, shortens the signature verification time and reduces the use of memory space. At the same time, in order to protect the privacy of users, this signature scheme uses pseudonyms to protect the real identity of users. In this paper, the privacy-awake aggregate signature scheme is applied to the permissioned blockchain, and a traceable permissioned blockchain data publishing platform with privacy-awake is designed. The platform can be applied to agricultural product traceability, realizing product traceability and the anonymous release of agricultural product and trading records.
Smaller Residual Network for Single Image Depth Estimation

We propose a new framework for estimating depth information from a single image. Our framework is relatively small and straightforward by employing a two-stage architecture: a residual network and a simple decoder network. Our residual network in this paper is a remodeled of the original ResNet-50 architecture, which consists of only thirty-eight convolution layers in the residual block following by pair of two up-sampling and layers. While the simple decoder network, stack of five convolution layers, accepts the initial depth to be refined as the final output depth. During training, we monitor the loss behavior and adjust the learning rate hyperparameter in order to improve the performance. Furthermore, instead of using a single common pixel-wise loss, we also compute loss based on gradient-direction, and their structure similarity. This setting in our network can significantly reduce the number of network parameters, and simultaneously get a more accurate image depth map. The performance of our approach has been evaluated by conducting both quantitative and qualitative comparisons with several prior related methods on the publicly NYU and KITTI datasets.
Information Security Situation Awareness Based on Big Data and Artificial Intelligence Technology

The information security threats faced by enterprises are increasing rapidly, and the technology of security attacks is also diversified and developed at a high level. The application of big data technology and artificial intelligence technology in all walks of life continues to deepen. While greatly improving social productivity, it also brings certain information security issues. Security situation awareness technology has become a new research hotspot in the field of network security. Using big data-related technologies to analyze, filter, merge, and identify known and unknown security threats is a new research discovery. This research builds a new cascaded network security situational awareness model based on the traditional and fusion decision tree algorithms. We use an induction algorithm to generate a decision tree on the preprocessed data to classify the data according to the decision rules. Research shows that a new network security situation awareness model is constructed using decision tree calculations. Compared with the traditional model, the classification effect of this model is better.
GEVE: A generative adversarial network for extremely dark image/video enhancement

A variety of information of the real-time scenes is carried by the images and videos. Processing these images and videos in an intelligent way helps in many domains such as computer vision, object detection, deep learning and 3D reconstruction leaving its large usage in applications such as auto pilots, augmented reality, smart vehicles, etc. The quality of image and videos plays a vital role in case of real-time systems. One such scenario is where the images are captured without sufficient illumination. Images captured in cameras where sufficient light is not present, leads to noisy and information-loss images. It is a fact that, dark images have mainly two aspects which make its study a difficult task. They are at its low dynamic range and high propensity for generating high noise levels. Hence, an approach based on deep learning based system is adopted. For this purpose, Generative Adversarial Network (GAN) based Extremely Dark Video Enhancement Network (GEVE) model is proposed. The main objective of GEVE is to team the model with low /normal- light image pairs. Thus, GAN network learns the translation from light feeble images and images captured under normal illumination and automatically translate original images taken under extremely low light conditions into images of quality. It is clearly observed that the proposed GEVE outperforms the known state-of-art techniques. We are the view that the proposed system is an ideal candidate to handle dark image/video frames.
Question Answering for Visual Navigation in Human-Centered Environments

In this paper, we propose an HISNav VQA dataset – a challenging dataset for a Visual Question Answering task that is aimed at the needs of Visual Navigation in human-centered environments. The dataset consists of images of various room scenes that were captured using the Habitat virtual environment and of questions important for navigation tasks using only visual information. We also propose a baseline for a HISNav VQA dataset, a Vector Semiotic Architecture, and demonstrate its performance. The Vector Semiotic Architecture is a combination of a Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model allows representing various aspects of an agent’s knowledge, and Vector Symbolic Architectures serve on a low computational level. The Vector Semiotic Architecture addresses the symbol grounding problem that plays an important role in the Visual Question Answering Task.
Secure Ramp Merging using Blockchain

Connected vehicles nowadays can provide a variety of useful and advanced services to their owners, manufacturers, transportation authorities, and other mobility service providers. Securing the complex sensing and networking protocols that enable these applications is an important and difficult problem. In this paper, we use blockchain which is traditionally used in applications from cryptocurrencies to smart contracts, as a potential solution to CV security. Specifically, we exploit the immutability of blockchain to ensure safety from falsified information and attacks. We demonstrate these properties by developing an algorithm that uses blockchain to maintain trusted communications between vehicles in the context of a cooperative ramp merging application.
Gradient-Based Supported Model Computation in Vector Spaces

We propose a method for computing supported models of normal logic programs in vector spaces using gradient information. First, the program is translated into a definite program and embedded into a matrix representing the program. We introduce a loss function based on the implementation of the immediate consequence operator $$T_P$$ by matrix-vector multiplication with a suitable thresholding function, and we incorporate regularization terms into the loss function to avoid undesirable results. The proposed thresholding operation is an almost everywhere differentiable alternative to the non-linear thresholding operation. We report the results of several experiments where our method shows promising performance when used with adaptive gradient update.
Covariance Matrix Adaptation Evolution Strategy for Convolutional Neural Network in Text Classification

Text classification has become relevant in recent years because of its usefulness in supporting different text mining solutions. Neural networks for this purpose have benefited from the creation of word embedding for learning semantics among words in a corpus. However, artificial neural network training by conventional methods present several theoretical and computational limitations. In this work, we develop a hybrid training method that combines gradient-based methods and Covariance Matrix Adaptation Evolution Strategy to train Convolutional Neural Network for the text classification task. For this, the training process is divided into two stages taking advantage of the speed of the gradient-based methods for learning the parameters of the convolutional filters and the application of the Covariance Matrix Adaptation Evolution Strategy for learning the weights of the fully connected layer. Our proposal was evaluated using a Spanish dataset for text classification, taken off the EcuRed Cuban Encyclopedia, divided into five classes. The proposed method increases the accuracy significantly of the convolutional network applied to the text classification.
Chatbot Implementation in a Steel Company in Russia

Chatbots (sometimes just called “bots”) are the subject of much corporate and public interest today. Many enterprises are looking to get started with chatbot development initiatives to improve communication efficiency as well as reduce operating costs. Current research indicates constantly growing interest in this area and forecasts that 70% of office employees will interact with chatbots daily in 2022. This chapter reports on the challenges inherent in chatbot integration projects and identifies key operational factors for successful chatbot projects, as well as highlighting issues of strategic significance. Different technology adoption and project management models are explored, analysed, and applied in the context of chatbot implementation, and based on an in-depth case study, a model is put forward to aid the manageability of chatbot implementation in other similar environments.
Fast and Fusion: Real-Time Pedestrian Detector Boosted by Body-Head Fusion

As pedestrian detection plays a critical role in various real-world applications, real-time processing becomes a natural demand. However, the existing pedestrian detectors either are far from being real-time or fall behind in performance. From our observation, two-stage detectors yield stronger performance but their speed is limited by the propose-then-refine pipeline; Current single-stage detectors are significantly faster yet haven't addressed the occlusion problem in the pedestrian detection task, which leads to poor performance. Thus, we propose FastNFusion, which enjoys the merit of anchor-free single-stage detectors and mitigates the occlusion problem by fusing head features into the body features using body-head offset since heads suffer from occlusion more rarely. Particularly, our body-head fusion module introduces marginal computational overhead, which enables the real-time processing. In addition, we design an auxiliary training task to further boost the learning of full-body bounding box and body-head offset prediction, which is cost-free during inference. As a result, FastNFusion improves the \(\hbox {MR}^{-2}\) by 3.6% to 41.77% on the CrowdHuman dataset, which is state-of-the-art, while runs at 16.8 fps on single Tesla P40.
Recognition of Moving Human Targets by Through the Wall Imaging RADAR Using RAMA and SIA Algorithms

In this research work, we are locating the moving objects or targets inside a building. It is sensor technology, and standoff distance outstandingly increases for the classical urban battlefield. In this study, a thermal image has to be developed based on cross-range vs down range mechanism; these tracks the moving targets with a video frame rate. This mechanism had to design based on S-band frequency and TDMA-MIMO antenna array. The continuous wave modulating scheme acquires the image and display at the frame rate of 10.80 Hzs. The maximum allowed range is 20 m concrete wall and thickness of 20 cm. This modern system can locate humans either in moving or in standing positions, behind 20 m thickness of the concrete wall. The results challenging the present technologies and outperforms the performance metrics.