Reviewerly/exHarmony huggingface dataset:
An Inventive Technique of Fault Recognition and Data Recapture Construction for Gesture Assessment Technology

Specified the basic job of movement estimate (ME) inside a cinematic programmer, analyzing such a pattern is of need anxiety. Even though zeroing in upon this analyzing of ME inside a cinematic programming framework, that effort presents an error discovery and data retrieval (EDDR) plan, inside light of this residue and quotient (RQ) program, toward implant obsessed by ME for cinematic programming analyzing requests respectively. A mistake inside processing elements (PEs), for example main parts for a ME, it can be recognized and recuperated adequately through exploiting this projected EDDR plan. The test outcomes establish that this projected EDDR strategy for ME analyzing can discriminate blunders and recuperate information by an OK section above and effectiveness punishment. Critically, this projected EDDR formation achieves sufficiently to the extent that output and unwavering quality for ME analyzing requests respectively.
Image segmentation using active contour model driven by RSF and difference of Gaussian energy

As we all know, the region scalable fitting method is sensitive to initialisations and suffers from bad results in images with complex scene. In our article, we put forward a new framework by integrating region scalable fitting (RSF) term and difference of Gaussian (DOG) term for segmenting images. We first propose a DOG function which can enhance the contrast at the edges of objects. Then, the RSF energy term is introduced to drive the curve closer to the edge. In the next step, the regularisation term is established which can avoid of the process of reinitialisation. Compared with traditional classical methods, the proposed technique is more flexibility with initialisation and has better segmentation performance.
Najd-Related Transpressional Deformations in the Atalla Shear Zone (Eastern Desert, Egypt)

The Atalla Shear Zone (ASZ) is a NW-oriented mega-shear that belongs to the Najd-Shear Corridor in the Central Eastern Desert of Egypt. The present study focused on the structural and tectonic evolution of the ASZ throughout integrated space-born optical-based mapping and structural-field work. The ASZ area comprises a Neoproterozoic rock succession including serpentinites, island-arc assemblage of metasediments and metavolcanics, Dokhan Volcanics, Hammamat Sediments, and granitoids. A lithological mapping was carried out using Landsat-8 (ETM+) and ASTER, and the image processing techniques included false color composites (FCC), principal component analysis (PCA), and RGB band ratios (BR). The predominant trends were ENE-WSW and NE-SW. The transcurrent shearing related to ENE-WSW shortening in the ASZ resulted in prominent transpressive shear structures among which we find shear-related folds, imbricated fans and antiformal stacks, and passive-roofed- and domino-style-thrust duplexes. A reasonable structural history involving four successive deformation phases (D1–D4) was established. D1 was recorded within the amphibolitic enclaves found at Um Baanib area. D2 was a syn-accretion shortening stage, concurrent with the E–W assembly of Gondwanalands, and responsible for the W- and WSW-propagated thrusts and thrust-related folds. D3 and D4 were post-accretion phases that resulted in formation of the Najd-related ASZ with sinistral sense of shearing. The emplacement and exhumation of the Meatiq Gneiss Dome, and also the deposition of the Hammamat Volcanosedimentary Sequence in fault-controlled sags and pull-apart, are believed to be post-accretion and associated with the D3 phase.
Simultaneity in the Scientific Enterprise

&#x0D; &#x0D; &#x0D; In this article, we explore the concept of simultaneity in the scientific enterprise, defined herein as the near-coincident discovery of significant advances in the development of our scientific understanding of the world. We do this by examining two case studies of such coincident or near-coincident discoveries: the development of the so-called Lorentz transformation by H.A. Lorentz (1904) and A. Einstein (1905); and the Aharonov-Bohm effect discovered independently in chronological order by Franz(1939), Ehrenberg and Siday (1949) and Aharonov and Bohm (1959). It is now generally acknowledged that the Lorentz transformations were independently developed by both Lorentz and Einstein as they worked on different approaches to solve a similar problem – i.e., the preservation of the form of Maxwell's equations in coordinate systems moving relative to one another, while the relationship between the Ehrenberg-Siday and Aharonov-Bohm works is still controversial. In our view, these independent discoveries allow some speculation about the nature of human discovery and understanding of scientific truths as they progress through time.&#x0D; &#x0D; &#x0D;
Coordinate Mapping

In the previous chapters, we’ve seen that the video and depth cameras provide two-dimensional screen-friendly outputs, while the joint data are referenced in the three-dimensional physical world. A video frame is an image with X and Y data measured in pixels, while a skeleton frame includes X, Y, and Z values measured in meters. How is that possible, and what does it mean for the way you should approach your Kinect development process? In this chapter, we will demystify Kinect’s coordinate spaces and transform between 2D and 3D.
Cloud-Based ETL and AD Impression Prediction and Insight Visualization Using Tableau

In any small-scale or large-scale business, digital marketing plays a crucial role in the brand reach and success. But, the cost of digital marketing has increased significantly over the years as Internet grew from being a luxury a few years ago to a common commodity today. When an ad campaign is started, even though ad targeting technologies are already available, there is a significant amount of wastage of advertisement budget, as many users just look over the ad and never click. The idea behind this project is to help the businesses to invest wisely in digital marketing and target their audiences at a more personal level. This project will help businesses to identify the proper media platform for running the campaign for specific people. We use Google analytics to gather the data of the previous ad campaigns and develop predictive model which predicts the probability of the user clicking on an advertisement in a particular Web site. As the database contains data of several users, it is large and difficult to perform operations using the local resources; we are relying on cloud-based tools like snowflake for data warehousing and Talend to perform ETL tasks.
Introduction to Mobile Forensic Analysis

In this chapter on applied scientific experiments, firstly we will introduce mobile forensics using practical acquisition for iPhone 8 as sample works for the iOS system, and acquisition data from Samsung Galaxy S7 as sample works for the Android system. Then we will use the various forensic tools to make several backup copies of the iPhone 8, using forensic tools you will be introduced to later to take a look and analyze results that can be obtained for the data available from the backup files. Using basic forensic tools, you can see if it is possible to recover and extract the deleted files.
Landmark Recognition and Retrieval Using Resnet50 and Delf

Image recognition and retrieval In computer vision, is a useful yet difficult task. Landmark recognition predicts landmark labels directly from image pixels and Landmark retrieval finds similar images in a large database. In this proposed project, a Graphical User Interface (GUI) is created that allows uploading image, querying landmark and to get similar landmark images from the dataset. This project is done in two modules. The first module creates a model that recognises the correct landmark (if any) in a set of difficult test photographs, allowing users to better comprehend and organise their photo collections. In this project, a dataset containing historic monuments of India of around 100 classes is created with 1227 images. In the first module, i.e., Landmark recognition, the module is again divided into three sub-modules. The Data augmentation is performed on the dataset having classes. The resnet50 model training was done by changing the output layer with our classes and train the model with ImageNet weights and fit the model with train dataset. When a sample image is provided to the resnet50 model, the model gives the top 10 labels of the sample image. Consider two images for each label from the training dataset, Apply DELF on these images and sample image. The inliers are calculated for these images using the Ransac algorithm. Image label which has a good number of inliers with sample image is the landmark of that sample image. This is especially crucial for query photographs with landmarks, which make up a significant portion of what people want to capture. The Resnet50 model is applied to the Image data in the second module, and the output is saved in a data frame. A sample image is given as input, and the top 10 labels are obtained using the ResNet50 model. The relevant images of those top 10 labels are retrieved from data frame. Compare the 10 images with input image by inliers. The 9 images having maximum inliers are plot in the GUI window to user.
An Improved Bat Algorithm with a Dimension-Based Best Bat for Numerical Optimization

The bat algorithm (BA) is a recent swarm intelligence algorithm which can be a powerful tool for numerical optimization. However, the BA and most of its variants rely on a quite elitist strategy where the bat with the current global best fitness guides the population’s search direction. This may result in a slow convergence rate and low accuracy when searching in complex problem spaces. In this chapter, a dimension-based best bat for BA is formed by integrating both the current global best bat and favorable search information of the other bats in different dimensions separately. This strategy is embedded in the BA to guide the population to fly toward better directions. The proposed algorithm is tested on the IEEE CEC 2017 benchmark suite and is compared with some related algorithms. The experimental results demonstrate the effectiveness and robustness of the proposed BA enhancement.
An Innovative Rapid Cursor Control System Using Eye Movement for a Physically Challenged Person

People in large amount are affected with physical disabilities or other paralytic by injury, cannot use computers for basic tasks or access devices. Through a previous analysis study, it absolutely was complete that eyes are perfect for omnipresent computing since they move anyway for interaction. This paper helps to trace the user’s eye movement with a camera mounted over user’s face and convert them into the movement of mouse indicator on screen and translate it into click operation on screen. During this paper we've developed eye pursuit mouse tool that works with regular camera and may be used for many real time applications. This paper mainly focuses on forming a hands-free interaction for physically challenged person. Likewise, several had developed technique for serving to disabled individuals like X-ray photography from the brain, facial muscles signals and electrooculogram.
Hdr Tone Mapping Using a Generative Adversarial Network

Conventional Tone Mapping Operators (TMOs) usually fail to retain the same performance across different scenes, while Deep Learning (DL) TMOs are typically computationally expensive. In this work, we introduce a novel, lightweight, Generative Adversarial Network TMO (TM-GAN) that efficiently adapts to various scenes and delivers the same high-quality results across them. Our network utilizes bottleneck depth-separable convolution blocks with skip connections, where batch normalization and PReLU are used to stabilize training. Our discriminator classifies images patches that make the network resolution agnostic and finally a novel loss function is proposed to account for both structural and perceptual image properties. TM-GAN is trained with professionally color graded HDR frames that we tone mapped with state-of-the-art non-deep learning TMOs. Objective and subjective evaluations showed that TM-GAN outperforms state-of-the-art TMOs, indicating that TM-GAN not only choose the best TMO per scene but it combined their principles to deliver images of higher perceptual quality.
Real Time Multi Object Detection &amp; Tracking on Urban Cameras

Processing video from urban video surveillance cameras requires the use of algorithms for multi-object detection and tracking on video in real-time. However, existing computer vision algorithms require the use of powerful equipment and are not sufficiently optimized to process multiple video streams simultaneously. This article proposes an approach to using the tracker in conjunction with the YoloV4 object detector for real-time video processing on medium-power equipment. Paper also presents the solution for difficulties that arise during work with optical flow. The results of the comparison of the accuracy and speed of image processing of the applied approach with such trackers as IOU17, SORT, KCF, and MOSSEE are also presented.
Improved Bald Eagle Search Optimization for Enhancing the Performance of Supervised Classifiers in Dementia Diagnosis

Evolutionary algorithms are widely used to improve the performance of machine learning techniques through various approaches. This paper focuses on increasing the accuracy of the machine learning techniques in diagnosing dementia through the usage of Improved Bald Eagle Search Optimization as a transformation technique. In the proposed Improved Bald Eagle Search Optimization, the control parameters are updated using the entropy-based gradient to reduce the error rate of the classification algorithm. Magnetic resonance images of 52 dementia and 52 non-dementia subjects obtained from the OASIS database are considered in this research work. Raw intensity values are taken as features and given as input to the transformation technique based on Improved Bald Eagle Search Optimization. Then the transformed values are classified through one of the three different supervised classifiers namely Naïve Bayes, Linear Discriminant Analysis, and Support Vector Machine. The accuracy of Support Vector Machine is 64% when no transformation technique is used and it is increased to 88% when Improved Bald Eagle Search Optimization is used for transforming the features.
Improving our record on innovation

Summary - The UK has great depth of talent in scientific research - In innovation, the UK could do better - The decarbonisation imperative will rely heavily on innovation - The skills agenda will be a key part in delivering the Strategy - We need to diversify the range of institutions that are involved.
A Deep Learning Approach for Face Mask Detection

The whole world is passing through a very difficult time since the outbreak of Covid-19. Wave after wave of this pandemic hitting people very hard across the globe. We have lost around 3.8 million lives so far to this pandemic. Moreover, the impact of this pandemic and the pandemic-induced lockdown on the lives and livelihoods of the people in the developing world is very significant. Till now there is no one-shot remedy available to stop this pandemic. However, spread can be controlled by social distancing, frequent hand sanitization, and using a face mask in public places. So, in this paper, we proposed a model to detect face mask of people in public places. The proposed model uses OpenCv module to pre-process the input images, it then uses a deep learning classifier MobileNetV3 for face mask detection. The accuracy of the proposed model is almost 97%. The proposed model is very light and can be installed on any mobile or embedded system.
Platform Regulation of Hate Speech – A Transatlantic Speech Compromise?

Continental European law on hate speech may be read - against the historically strict hierarchical societies - as forms of levelling up of ‘dignity’ which was traditionally reserved to the upper classes. This democratisation of dignity which underlies European understanding of citizenry, recognises public insult and humiliation as tools for disempowerment and exclusion, and as inconsistent with legal and political equality. Yet, this construction of empowered citizenship enabled by the State inter alia through hate speech laws is at odds with the American view of empowered citizenry. There free speech jurisprudence is based on ‘liberty’ and deeply inscribed with settler mentality, one of self-reliance and self-rule and distrust of government, whether the old-world governments left behind, or the colonial administrations belatedly encountered in the new world. This paper argues that this binary opposition in the divergent treatment of hate speech hides non-binary preoccupations that reflect different primary fears which do not fall along the same ‘scale’. European liberal democracies fear the consequences of hate speech being left uncensored in the public domain (a WHAT concern), whilst America fears the consequences of content interventions by government (a WHO concern). Flowing from this, this paper proposes that the German Network Enforcement Law (NetzDG) of 2017 which requires major platforms to moderate content in response to user takedown notices based on German legally imposed speech standards delivers a compromise solution, a bridge, between American and European speech traditions. The mechanism which NetzDG adopts, namely public standards embedded in private processes, is arguably uniquely adept at simultaneously assuaging the primary European fear about the absence of effective speech controls in the public domain and the primary American fear about the presence of governmental censorship.
TO THE ORGANIZATION OF MODULAR LEARNING IN MATHEMATICS COURSE AT UNIVERSITY

An urgent problem of teaching mathematics in non-mathematical areas of higher education training is the inconsistency between the content, methods of teaching mathematics and the required learning outcomes. The purpose of this study is to develop and test a mathematics course modular model, in which the content, teaching methodology and examination technique correspond to the development of bachelors’ competencies set by FSES of non-mathematical training areas at university and which is based on the general methodological principles of the system, modular and technological approaches. The proposed model was tested while teaching bachelors in engineering areas of training. The model showed that the proposed teaching methodology and examination technique allowed taking into account different levels of student learning and contributed to increasing their motivation to study mathematics in the process of professional competence development.
Role of Deep Learning for Smart Health Care

Deep learning has become one of the trendiest fields in recent years. Nonetheless, it is an appealing and hard undertaking to do tasks related to computer vision, natural language processing, speech recognition, bioinformatics, and smart health care. Acquiring information and significant knowledge from complex, high-dimensional, and heterogeneous biomedical data sources remains a critical test in changing health care. Different kinds of data have been arising in present-day biomedical exploration, including electronic health records, imaging, sensor data, and information, which are perplexing, heterogeneous, inadequately explained and for the most part unstructured. Conventional data mining and machine learning approaches regularly need to initially perform feature designing to acquire robust and more vigorous features from those data and afterward build prediction or clustering models on top of them. There are several severe difficulties on the two stages in the case of complex data and lacking adequate domain information. Based on machine learning, medical IoT devices, detection and diagnosis through imaging, automated surgeries and other several techniques have been in use in real-world and some are still under development. A few potential issues like security, QoS improvement, and arrangement demonstrate the vital piece of deep learning. We reveal working principle, deep learning for health care including images and text, smart devices and privacy issues in health care data.
Simulation Verification of Equipment Maintenance Characteristics Based on Big Data

The research on the simulation and verification technology of equipment maintenance characteristics based on big data is aimed at the possible failures and their causes in the actual operation training process of the technology-intensive complex system integrating mechanical, electronic, hydraulic, information and control technologies. The failure mode and impact analysis method (FMEA) is adopted for large equipment. This paper studies the electromechanical and electro-hydraulic faults in large weapon equipment, analyzes the basic properties and propagation characteristics of the faults, establishes the fault attribute description model database, carries on the fault propagation simulation analysis by using neural network. This study makes full use of the characteristics of big data virtuality, increases the number of repeated verification, reduces unnecessary costs and saves costs.
A New Optimization Algorithm Based on Red-Tailed Hawk Hunting Model

This study suggests a new, nature-inspired metaheuristic optimization algorithm, the red-tailed hawk algorithm (RTH). It mimics the hunting method of the red-tailed hawk for the prey. This algorithm is split into three stages. In the high soaring stage, the red-tailed explores the search space and chooses the area that contains the greatest amount of food. In the low soaring stage, the red-tailed moves inside the selected area to select the best position for the hunt. The red-tailed swings and hits its prey in the stooping and swooping stage. This paper evaluates the RTH's performance on three kinds of optimization problems: 22 standard benchmark functions that include unimodal functions, multimodal functions, and fixed-dimensional multimodal functions, seven real-world engineering problems, and the extraction of the proton exchange membrane fuel cell (PEMFC) parameters. This algorithm provides excellent convergence speed for many optimization problems with high accuracy. The achieved results show that the proposed RTH algorithm outperforms sophisticated metaheuristic algorithms and traditional approaches.
Data centre energy efficiency optimisation in high-speed packet I/O frameworks

To meet the demands for faster network packet processing requirements in data centres, packet I/O accelerator solutions have emerged which are dedicated to enabling faster packet forwarding capabilities. Often, such I/O frameworks are implemented in software that is hosted on a Linux operating system and for many of the solutions the software is run in the system's userspace, thus bypassing the operating system's kernel to facilitate a streamlined packet throughput rate. However, operating outside of the kernel loses the real-time interrupt facility, so many of the I/O accelerator schemes rely on a very high-speed polling solution to react to receive packet traffic arrivals; the continuous fast polling rate results in the CPU running hot and wasting energy. An experimental approach to reduce such energy inefficiencies is introduced based on an adaptive polling mechanism (APM). The investigation results show potential energy CPU savings ranging between 9% and 76%.