Reviewerly/exHarmony huggingface dataset:
Speed, Direction, Color and Type Identification of NHAI Expansions Using Deep Learning

Abstract: Vehicle numeration is associate interaction to appraise the road traffic thickness to judge the traffic conditions for shrewd transportation frameworks. With the broad use of cameras in metropolitan vehicle frameworks, the reconnaissance mission video has become a focal info supply to boot, constant traffic the board framework has become illustrious as lately owing to the accessibility of handheld/versatile cameras and machine learning investigation. In this work, propose video-based vehicle as well as technique in associate superhighway traffic video caught utilizing hand-held cameras. The primary and therefore the necessary step to estimate the vehicle flow; this later helps North American nation to count the vehicles victimization the virtual line Generally, we have a tendency to begin with the background subtraction to isolate moving objects. To facilitate crossing of vehicles with the road, we have a tendency to apply the detection of objects. Our system uses the LBPH (Local Binary Pattern Histogram) algorithmic program as a way to deduct the background, so as to use our numeration algorithmic program. Traffic observance is one space that utilizes Deep Learning for many functions. By exploitation cameras put in in some spots on the roads, several tasks like vehicle investigating, vehicle identification, traffic violation observance, vehicle speed observance, etc. will be completed. Deep Learning may be a common Machine Learning formula that's wide employed in several areas in current way of life. Its strong performance and ready-to-use frameworks and architectures allows many of us to develop varied Deep Learning-based code or systems to support human tasks and activities. During this paper, we tend to discuss a Deep Learning implementation to form a vehicle investigating system while not having to trace the vehicles movements. to reinforce the system performance and to cut back time in deploying Deep Learning design, therefore pre-trained model of YOLOv3 is employed during this analysis because of its sensible performance and moderate process time in object detection. This analysis aims to form a straightforward vehicle investigating system to assist human in classify and investigating the vehicles that cross the road. The investigating relies on four varieties of vehicle, i.e. car, motorcycle, bus, and truck, whereas previous analysis counts the automobile solely. because the result, our planned system capable to count the vehicles crossing the road supported video captured by camera with the very best accuracy of ninety seven.97%.
Collaborative Aspects of Solving Rail-Track Multi-sensor Data Fusion

Multi-sensor data fusion depicts a challenge if the data collected from different remote sensors present with diverging properties such as point density, noise and outliers. To overcome a time-consuming, manual sensor-registration procedure in rail-track data, the TransMVS COMET project was initiated in a joint collaboration between the company Track Machines Connected and the research institute Software Competence Center Hagenberg. One of the project aims was to develop a semi-automated and robust data fusion workflow allowing to combine multi-sensor data and to extract the underlying matrix transformation solving the multi-sensor registration problem. In addition, the buildup and transfer of knowledge with respect to 3D point cloud data analysis and registration was desired. Within a highly interactive approach, a semi-automated workflow fulfilling all requirements could be developed, relying on a close collaboration between the partners. The knowledge gained within the project was transferred in multiple partner meetings, leading to a knowledge leap in 3D point cloud data analysis and registration for both parties.
Fixing the Train-test Objective Discrepancy: Iterative Image Inpainting for Unsupervised Anomaly Detection

Autoencoders have emerged as a popular method for unsupervised anomaly detection, but they still have difficulty detecting local anomalies in real-world images due to a lack of modeling fine details. We have assessed this difficulty from a new perspective: a mismatch of training and testing objectives. Specifically, we expect autoencoders to encode an unseen locally anomalous image, reconstruct normal regions completely, and repair abnormal parts during testing, even though they merely aim to minimize total reconstruction errors during training. To address this issue, we reconstruct a potentially anomalous masked region from encoding a potentially normal unmasked region conditionally with a mask, similarly to image inpainting, during both training and testing. Because the ideal mask for anomalies is unknown in advance, we iteratively construct an adaptive mask from an earlier anomaly score of the reconstruction error. Our proposed Iterative Image Inpainting for Anomaly Detection (I3AD) updates image inpainting and masking by turns, which engenders the expected objective to maximize the anomaly score during testing. Evaluated by the MVTec Anomaly Detection dataset, our method outperformed baseline reconstruction-based methods in several categories and demonstrated remarkable improvement, especially in high-frequency textures.
On the Reform of Virtual Simulation Experiment Teaching Mode for Economics and Management Specialty: A Case Study

Virtual simulation experiment teaching mode uses computer simulation technology to virtualize the experimental subject and the experimental environment, enabling students to do actual operation in a virtual scene. Taking the Economics and Management Specialties of Guangxi University of Finance and Economics as an example, this study made a thorough analysis of virtual simulation experiment teaching mode.It further put forward a new model of virtual simulation experiment teaching from four aspects: experiment teaching method, experiment content design, experiment process simulation and experiment evaluation. Through specific practice, it is consistent with the existing teaching mode, and more diversified in experimental content and experimental evaluation. The new mode of virtual simulation experiment teaching was applied in the comprehensive training teaching of 2018 interdisciplinary innovation and entrepreneurship. The tracking results have seen the following improvements after the application of the new mode: 1) the increased proportion of class hours of virtual simulation experiment;2) the increased proportion of information technology experiment content; 3) more winners of competition; 4) more efficient evaluation of experimental teaching. Moreover, the implications of the reform of virtual simulation experiment teaching were discussed.
Inventors must be human, US federal court rules

Under the US Patent Act, inventors must be “a natural person,” a federal court ruled on Aug. 5. The case originated when the US Patent and Trademark Office (USPTO) denied patent applications in which physicist and Imagination Engines CEO Stephen Thaler listed an artificial intelligence system as the sole inventor. Thaler unsuccessfully sought to have the denial overturned through USPTO procedures. He then sued the office in federal court and lost, and he appealed that decision. In the latest ruling, Judge Leonard P. Stark of the US Court of Appeals for the Federal Circuit writes , “The Patent Act expressly provides that inventors are ‘individuals.’ ” Stark adds, “The Supreme Court has held that, when used in statutes, the word ‘individual’ refers to human beings unless there is ‘some indication Congress intended’ a different reading.” Thaler is also suing the US Copyright Office for denying his registration applications that listed AI
Marketing deception practices through social media channels and its reflection on the e-purchase decision: An exploratory study of the opinions of a sample of e-shopping customers in Nineveh Governorate

The research aims to find out the availability of marketing deception practices through social media channels from the point of view of the research sample, and to determine its reflection on their purchasing decision and during the three main stages of purchase. For the purpose of achieving the objectives of the research and answering the research questions, the descriptive analytical approach was adopted to describe the study variables. The field of research was the e-procurement customers in Nineveh Governorate. The two researchers relied on the electronic questionnaire as a main tool for collecting the necessary data and information to complete the research. A valid response was obtained for the analysis (821), and the data was subjected to the appropriate statistical treatments using the statistical program (SPSSV.28). The research reached several results, most notably the existence of practices of marketing deception, as well as the existence of two correlations and an effect between marketing deception and the e-purchase decision through social media channels. The research recommended the need to stay away from e-marketing deception practices by marketing websites, as well as the need for official authorities to intervene in setting rules and laws for e-marketing in a way that protects customer rights.
Real-time person detection from UAV images using performant neural networks

Detection of persons from restricted areas with the help of UAVs is one of the current trends in ensuring the security of regions of interest. For the processing of images acquired by UAVs of special interest are machine learning techniques with an emphasis on neural networks. For this task, two neural networks are proposed and tested in this paper: YOLO v5m and Faster R-CNN. They were learned and tested on two data sets: HERIDAL and a proprietary video stream data set. Both networks gave good results, and the best results were obtained on their own data set (because the images in HERIDAL were taken at a higher height).
Cataloging Quality and the Views of Catalogers in Hellenic Academic Libraries

This study focuses on cataloging quality and how it is defined by information professionals, specifically university library catalogers. Although there is no single and objective definition of ‘cataloging quality,’ research aims to specify its core characteristics. The goal is to define the modern cataloging environment, as well as the tools and opportunities it provides, and to improve the success of academic library services for both professional catalogers and users, who are the final consumers of the information. Regarding methodology, a sample survey was chosen. The survey results revealed that the quality of cataloging is determined by several factors, including technical features of the data, adherence to standards, the cataloging process, user satisfaction, and the development of a general quality culture.
Negative result about the construction of genuinely entangled subspaces  from unextendible product bases

Unextendible product bases (UPBs) provide a versatile tool with various applications across different areas of quantum information theory. Their comprehensive characterization is thus of great importance and has been a subject of vital interest for over two decades now. An open question asks about the existence of UPBs, which are genuinely unextendible, i.e., they are not extendible even with biproduct vectors. In other words, the problem is to verify whether there exist genuinely entangled subspaces (GESs), subspaces composed solely of genuinely multiparty entangled states, complementary to UPBs. We solve this problem in the negative for many sizes of UPBs in different multipartite scenarios. In particular, in the all-important case of equal local dimensions, we show that there are always forbidden cardinalities for such UPBs, including the minimal ones corresponding to GESs of the maximal dimensions.
An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm

Load training is an important part of the daily training of aerobics athletes. Therefore, the research on the characteristics of aerobics movement of athletes in load training has been paid extensive attention by teaching units. Deep learning algorithm in artificial intelligence algorithm is used to study the characteristics of calisthenics teaching and training, which is a beneficial exploration to improve the scientific of calisthenics training programs. The basic principles of the neural network algorithm and implementation process are described. After the basic structure and characteristics of the network are analyzed, a comprehensive solution to optimize and improve the regularized deep belief network algorithm is proposed. The results show that compared with other learning algorithms, the feature classification error rate of the optimized regularized deep belief network algorithm is 6% lower than that of other algorithms. Although the training speed of the model decreases, the convergence period of the deep neural network algorithm can better achieve the extraction accuracy of abstract features of training data when the deep learning period increases. Being less affected by input parameters, the algorithm will have better stability and be more conducive to extracting the features favorable to classification. In the field of deep learning, the advantages of the deep neural networks can effectively classify the training characteristics of aerobics athletes and provide scientific decision-making basis for subsequent teaching and training.
Thinking like a lawyer

On December 9, 2021, Apache announced a zero-day vulnerability in Apache Log4j [3]. As the news broke, tech companies and computing administrators scrambled to push out recommended mitigations. If you are reading this, chances are you already heard about this vulnerability and have made sure to update all of your systems. It is estimated that millions of companies are vulnerable to data breach through exploits of this widely used logging software. When millions of companies are vulnerable to breach it means that lots of people are going to have their data stolen and will suffer financial losses.
News Bias Detection Using Transformers

The main motive of news is to inform people without any highlighting personal opinions. A lot of news that spreads on social media is first introduced by news articles from unreliable sources. A lot of the news that is spread by these sources is only to generate clicks from the masses and has no intention to educate the common people. A news bias detection system aims to find a robust solution to bifurcate between biased news and genuine news. The goal is to make a single reliable platform that accumulates news from multiple sources and gives them a bias rating. We use the classification tokens generated by the BERT architecture to classify the bias of news articles. We then deploy this model through an API and call it through a web-app. The user interface will make browsing news convenient.
Comparative study of satellite multispectral image data processing with MapReduce and classification algorithm

Now that Big Data has amassed a significant amount of data, it is available in both structured and unstructured formats. Unstructured data processing is difficult to generate by individuals (e.g. Twitter data) or even sensors (e.g. satellites, videos) with data sizes ranging from gigabytes to terabytes and peta-bytes. We can easily analyse and classify different trends in unstructured data sets if the right analytical approach is used, while keeping data quality and size in mind. Early warning forecasts, which are based on satellite imagery and radar sensor data, are a major problem in the real world. To obtain a better understanding of Big data a proper architecture for the analysis of various classifications of satellite imagery patterns using Hadoop technology should be proposed. Different classification methods for different satellite imagery pattern classification methods are segregated in the proposed architecture to reduce time and improve efficiency and will get scalability results to increase the performance of classification patterns to large data sets.
Robots Designed for 'Adult Recreation' - a buyer's guide

You&apos;d be forgiven, if you believe certain sections of the Internet, for thinking that robots designed for &apos;adult recreation&apos; are ubiquitous. But do they even exist? Every technology needs a stroke of luck to get moving, and the Covid-19 pandemic has, by all accounts, provided a huge boost to the domestic adult entertainment business, and the use of robots - sexbots - is part of this. Certainly, artificial partners have come a long way since the blow-up doll, although these souped-up balloons are still available for the cash-strapped lover. But what exactly is on offer as the 21st century creaks and shudders towards its second quarter?
Construction and Research of Converged Media Random Matrix Model and Multivariate Evaluation System

According to the indicators, the evaluation index system of convergent media communication efficiency is constructed, and its corresponding hierarchical structure model is constructed. According to the nine-level scale in the analytic hierarchy process, the indicators are scored by designing an expert questionnaire with weights of relevant indicators, and a pairwise comparison judgment matrix is constructed from the questionnaire data obtained through the expert consultation questionnaire. This paper uses the YAAHP analytic hierarchy process software to calculate the matrix results and test the final results. If the obtained results are within the consistency test range, it will pass; otherwise it will not pass. The fine-tuning matrix is processed, and the weight vector is calculated to obtain the final indicator weight result. After studying a class of using matrix factorization to enhance the independence of random matrix column vectors, a Gram-Schmidt orthogonalization of random matrices is proposed to minimize the intrinsic correlation of the matrix. After analyzing the reconstruction characteristics of the orthogonal matching pursuit algorithm, a new signal reconstruction algorithm is given by combining the new optimization method with the reconstruction algorithm. Referring to the relevant theories of physics and molecular dynamics, this paper draws on the relevant description of the information fusion theory, introduces the basic concepts of “energy” and “collision,” and constructs the energy description model before data object fusion and the collision model when data objects are fused. By combining the practice of the radio mobile client log access and record data resources that have completed situation assessment and state transition, it effectively handles many low-efficiency regional data object processing problems generated in the process of data object fusion and deepens data object fusion.
Investigation of Efficient SKC Cryptic Algorithm for Image Encipherment and Decipherment Using SMCrypter

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Analysis of continuity between the current 2015 revised national curriculum standard and the subject-area curriculum

Objectives The purpose of this study is to analyze the continuity between the current 2015 revised national curriculum standard and the subject-area curriculum. The current 2015 revised curriculum standards put an emphasis on the composition of the curriculum and presupposes the direction of teaching, learning, and evaluation in the organization and operation of the school curriculum. Considering that standards plays the same role like the constitution in school education activities, it is necessary to closely connect the development and operation direction of each curriculum presented in ‘emphasis of curriculum composition’, ‘teaching and learning’, and ‘evaluation’ with all the subject-area curriculum’s direction of ‘teaching⋅learning’, and ‘evaluation’.&#x0D; Methods This study has analyzed the continuity between the current 2015 revised curriculum standards and the evaluation criterion of the elementary school’s Korean language and mathematics subjects. So, the evaluation criterion presented in the standards was first analyzed, and then the evaluation criterion for Korean and mathematics subjects were reviewed. Lastly, based on these reviews, the evaluation criterion were analyzed according to the criterion of continuity and actual situation was examined.&#x0D; Results As a result, it was found that there was a gap between the evaluation method⋅precautions and the evaluation direction. In particular, educational goal-education content-teaching⋅learning-evaluation consistency, and the use of evaluation results showed insufficient connection with curriculum improvement, descriptive evaluation, and essay evaluation. Therefore, it can be said that it is necessary to apply it through the recomposition of evaluation standards that strengthen the connection with the standards in the operation of the curriculum. Also, in the case of presenting the evaluation criterion, there were cases where the evaluation method with the same meaning was presented in different terms.&#x0D; Conclusions So, it is necessary to have overall unity and comprehensive completeness so that there is no difference in interpretation through constant discussion and conversation between curriculum developers. Among the curriculum standards, students' academic achievement standards can function as goals for managing educational outcomes. Therefore, the evaluation criteria presented in the curriculum should be presented more clearly so that it can play a role in presenting the direction of teaching and learning.
Correction to: Multimodal Augmented Reality and Subtle Guidance for Industrial Assembly – A Survey and Ideation Method

In the version of this chapter that was originally published the heading contains a typographical error. This has now been corrected.
A method of circle center fitting for water wall cladding

In the process of automatic laser cladding of the water wall, the determination of the cladding path is a vital step. As the cladding progresses, the central angle of the measurable pipe gradually decreases, and the pipe will be slightly deformed due to thermal stress. A method for obtaining the center of the pipe is proposed to guarantee a high fitting accuracy under the condition of small arc. The principle of the method is to introduce Lagrangian constraint parameters λx, λy to the circle center obtained by a fitting curve. Then the gradient descent method is performed on the parameters to find the optimal solution for the center of the section circle. For the problem of poor fitting accuracy for small arc, the simulation and experimental results show that when the central angle is 10 degrees, the center of the fitting circle's error is 0.2326 mm. It possesses a higher fitting circle center accuracy for water wall cladding.