Reviewerly/exHarmony huggingface dataset:
Auto-luminance-Based Face Image Recognition System

For getting the improvement over the recognition accuracy of face recognition system, a method based on auto-luminance is proposed. Firstly, the face is detected from the query via input images, real-time web cam using the Viola–Jones face detection algorithm, then detected face is pre-processed with auto-luminance and histogram equalization method. Then, we use HOG feature descriptor measuring gradient amplitude and the orientation of the gradients. These features with the description are then classified with the help of SVM machine learning classifier to discriminate the different classes. By using this approach of auto-luminance with histogram equalization and HOG feature extraction when performed at input images, live web cam and the well-known ORL cropped face database, it is cogitated that this algorithm has a higher face recognition rate than the conventional one when applied pre-processing on them.
Deep learning does not replace Bayesian modeling: Comparing research use via citation counting

Abstract One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence, and statistics, but like most “meme” class broad generalizations the concept does not hold up to scrutiny. Memes do not generally matter since the experts will always know better; but in the case of Bayesian software like Stan and PyMC3, even their developers and advocates bemoan the apparent dominance of deep learning as manifested in popular culture, breathtaking performance, and most problematically from funding agency peer review that impacts our ability to further advance the field. The facts, however, do not support the assumed dominance of deep learning in science upon closer examination. This letter simply makes the argument by the crudest of possible metrics, citation count, that once the discipline of Computer Science is subtracted, Bayesian software accounts for nearly a third of research citations. Stan and PyMC3 dominate some fields, PyTorch, Keras, and TensorFlow dominate others with lot of variations in between. Bayesian and deep‐learning approaches are related but very different technologies in goals, implementation, and applicability with little actual overlap‐‐so this is not a surprise. For example, deep learning cannot bring the explainability of applied math/statistics and Bayesian methods do not scale to deep‐learning data sets. While deep‐learning behemoths like Facebook and Google use and support Bayesian efforts, the Bayesian packages scientists actually use are academic/volunteer efforts punching far above their weight class, and they need financial support. It would behoove funders to fully understand the impact and role of Bayesian methods in resource allocation.
Training multilayer neural networks

A single-layer neural network is the simplest type of neural networks laying the foundation for understanding other kinds of neural networks. There are no hidden layers in this network that only contains input and output layers. After the advent of the single-layer neural network, it took nearly 30 years for the multilayer neural perceptron (MLP) networks to emerge due to the lack of the right methods for reversing the calculated error to the hidden layers of MLPs. Unlike the single-layer neural networks, multilayer networks can be employed to learn nonlinear problems, as well as problems with multiple decisions. This chapter introduces the multilayer neural networks and how to perform the learning process with them. At the end of this chapter, the Keras library is utilized to gradually learn how to design a multilayer neural network for handwriting recognition.
STUDY TOUR IN THE PROCESS OF ACTIVE LEARNING

This report considers the essence of a study tour as a strategy for active learning. The increasingly evolving consumer society, as well as the depolarization and deideologization of student education in the 21st century, are leading to spiritual deficits that can be filled up spontaneously by undervalued values. There is also the worrying phenomenon that students are reading less and less as a result of which their motivation for learning is greatly reduced.The modern school is tasked with engaging students in well-motivated and intensive study work that encourages them to engage in independent cognitive activity. Active learning is a process that involves different methods, techniques and forms for solving learning tasks in order to improve the quality of learning. Using them provokes interest in students, awakens a desire for active participation in an activity, as a result of which they gain experience, based on specific knowledge and practical activity. Active learning is a two-way process that combines, on the one hand, the active actions of the teacher aimed at the perception of objects by students and, on the other, the active participation of students in this process. The teacher with his story and explanations helps the students to perceive the object observed.The professional competences of the teacher determine the effective selection of methods and techniques that guarantee the successful conduct of the excursion.The article classifies the types of excursions on different bases. The methodology and technique of conducting a study tour are described. A variant technology for conducting a study tour has been developed; The stages are separated; the methods, forms and methods used are indicated.During the excursion, students gain knowledge through active learning under natural conditions. The phenomena are perceived in their entirety, which helps to build students' perceptions in an attractive way.Through the study tour, it influences the emotional sphere of the individual and promotes a wide variety of impressions. The study tour becomes the basis for further work in the classroom by collecting extensive material.It has a beneficial effect on the aesthetic upbringing of students and their social skills.The study tour creates favorable conditions for the successful application of the acquired knowledge in practice. This interactive form of learning facilitates the expression of cross-curricular relationships.
Feature Hashing with Insertion and Deletion of Features

Feature hashing algorithms [14], [21] are well-known algorithmic techniques for handling large dimensionality of the datasets. These methods reduce the dimensionality of data points while preserving the pairwise distance, or similarity between them. However, to the best of our knowledge, these feature hashing algorithms are not adaptable to the dynamic insertion and deletion of features. In this work, we suggest algorithms using which the existing feature hashing algorithms can be made adapted to dynamic feature insertion and deletion. Our algorithms fit in the framework of both real-valued [21], and binary [14] feature hashing algorithms. We show a theoretical analysis of our algorithms, and complement it with experiments on real-world datasets. Our algorithms suggest comparable accuracy w.r.t. baselines while simultaneously offering a significant speed-up in the running time. Our proposal is easy to implement and can be adopted in practice.
Delve into the Realms with 3D Forms: Visualization System Aid Design in an IOT-Driven World

Since its inception, Augmented Reality (AR) has shown immense promise in several applications. This project aims to amalgamate computer vision and AR techniques to create a platform that has applications in several industries like retail, gaming, cosmetics, electronics manufacturing, etc. The project implements steps like object detection—where the model detects and describes pertinent features from the input image. Oriented FAST and Rotated BRIEF (ORB) is the algorithm chosen for the same, due to its robustness. ORB is an open-source algorithm developed at OpenCV Labs. Additionally, the project also implements Feature matching in order to determine the spatial orientation of the target object in the camera feature. The algorithm selected for this is FLANN (Fast Approximate Nearest Neighbor Search Library). Moreover, this project uses Raspberry-Pi which provides excellent computing abilities and a high-definition camera can be mounted onto it. This camera is the input feed through which the image fed to the model can be augmented on an object.
Design and Development of AI based Approach for Histopathology Cancer Screening and Identification

Cancer is a heterogeneous disorder comprising various types and sub-types. Early detection, screening, and diagnosis of cancer types are necessary for facilitating cancer research in early diagnosis, management, and the evolution of successful therapies. Existing methodologies were only able to classify and diagnose a single variety of cancer based on a homogeneous dataset but more focused on predicting patient survivability then cure. This research defines a machine learning-based methodology to develop an universal approach in diagnosis, detection, symptoms-based prediction, and screening of histopathology cancer, their types, and sub types using a heterogeneous dataset based on images and scans. In this architecture, we use VGG-19 based 3D-Convolutional Neural Network for deep feature extraction and later perform regression using a random forest algorithm. We create a heterogeneous dataset consisting of results from laboratory tests, imaging tests and biopsy reports, not only relying on clinical images. Initially, we categorize tumors and lesions as benign or malignant and classify the malignant lesions into their sub-types, detecting their severity and growth rate. Our system is designed to predict risk at multiple time-points, leverage optional risk factors if they are available and produce predictions that are consistent across mammography machines. We found the classification accuracy for categorizing tumors as cancerous to be 95% whereas the accuracy for classification of malignant lesions into their sub-types to be 94%..
Procedures for the Acquisition of Syntax

Presupposing some survival value in being able to anticipate the appearance of particular kinds of objects, imagine that the creatures have a lexicon which enables them to name each of the objects on the conveyer belt. In natural language, the word symbols of speech still appear one after the other in a linear temporal sequence, but the relationships among concepts and things communicated by natural-language speakers are much more varied and complicated. Skinner suggested that the language user can learn to discriminate the grammatical categories of individual words. Linguists and some psychologists have readily adopted underlying structures as formal description of language data while disregarding the question of how such structures, generating algorithms, might be acquired by the principles of learning. The set of grammatically correct sentences of a natural language is somewhat analogous to set A in the lever sequence experiment. Words and connection codes are entered, reinforced, and extinguished during the language-acquisition process.
HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extraction

Because of the inaccuracy of event element extraction caused by long text, the errors of pre-training and fine-tuning stages, and the single sample of semantic features in the current Chinese event element extraction model, Hierarchical Decomposition MacBERT and Fast Gradient Method-based Chinese event element extraction algorithm by jointing Conditional Random Field (HDMacBERT-FGM-CRF) is proposed. This model extends absolute location encoding based on hierarchical decomposition so that the HDMacBERT model can directly process more than 512 Token texts. The HDMacBERT model based on the Mask strategy of similar words is used to mitigate errors. The FGM-based approach implements adversarial networks and increases the diversity of semantic feature samples. The P, R and $${\mathrm{F}}_{1}$$ of the HDMacBERT-FGM-CRF model proposed in this paper are 2.52%, 4.03% and 3.26% higher than those of the classical Chinese event element extraction model BERT-CRF on the DuEE dataset. The experimental results show that the proposed model can learn more comprehensive deep-level semantic information of text and improve Chinese event element extraction.
Home Security Using Facial Recognition System

Security is very important issue in today’s time. Face recognition, or facial recognition, is one of the largest areas of research within computer vision. The development of facial recognition systems has also developed and has been implemented for home door locking systems and is an option that is quite simple and easy to use and is quite accurate in recognizing the face of homeowners. We can now use face recognition to unlock our mobile phones, verify identification at security gates, and in some countries, make purchases. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. This paper consists of face recognition and Machine Learning for detecting an intruder and recognition.
Peer review declaration

Abstract All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Open • Conference submission management system: EIConferences@126.com • Number of submissions received: 220 • Number of submissions sent for review: 180 • Number of submissions accepted: 154 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 70 • Average number of reviews per paper: 2 • Total number of reviewers involved: 5 groups with a total of 30 reviewers (each group consists of a lead reviewer and 5 reviewers, each reviewer reviews about 12 papers on average) • Any additional info on review process: Authors will submit their papers through the official email address of the conference, and the deadline for submission is March 15, 2021. The person in charge of the mailbox of the conference committee will sort out all the submissions, divide them into groups and distribute them equally to 5 review groups. The review groups will return comments one after another within 2 weeks, and the person in charge of the conference committee will feedback the comments to the authors. Each paper was reviewed by two reviewers who gave their own comments: Papers with good quality and themes will be accepted directly, and the authors can register for the next stage of the conference; for papers with medium quality, revision suggestions will be given, and then the review groups will decide whether to accept them or not after the authors revise and return; For papers with very poor quality, the review groups will give rejection suggestions. All involved reviewers are recognized specialists in fields covered by the Conference. The final decision regarding acceptance/revision/rejection was based on reviews received from the reviewers. If the two reviewers cannot agree on a recommendation, the final at the sole discretion of the lead reviewer. The authors themselves do not have any decision on whether their papers are accepted or not. The final approved papers will be registered until April 10, 2021. The conference committee will send the invitation to the accepted authors and submit the information of the authors’ papers to the publishers. Contact person for queries Name: Xiangtao Wang Affiliation:Shaanxi higher education alliance Email: lidueiconferences@126.com
Autonomous Strategic Defense: An Adaptive Clustering Approach to Capture Order Optimization

The proliferation of UAV technology has introduced a new risk to the security of high-value assets. Emerging advancement in cooperative multi-agent control of UAVs presents a means of automating a defensive response to these new threats. A practical realization of an automated defense strategy is limited by the computational constraints of onboard computers. The UAV’s onboard computer must solve multiple non-convex NP-hard navigation optimization problems to maximize the effectiveness of its defensive strategy. One such problem is the challenge of finding the optimal flight path for a single defender that must capture multiple slower invaders. This problem has been labeled as the n-Invader Capture Order Problem, abbreviated as n-ICOP. This research proposes an approximation method for reducing the solution space of n-ICOP. Given a specific constraint on computational resources, the method can adaptively reduce the computational load while optimizing the accuracy of the approximation. The new method splits the n-ICOP into a grouping problem and an ordered set problem, like the clustered variant of the Traveling Salesman Problem. The optimal grouping of invaders is estimated efficiently through the k-means clustering algorithm. The estimated grouping scheme reduces the complexity of an approximated n-ICOP solution because all strategies that separate members of a group are excluded from the search space. Simulations of the approximated n-ICOP solution were performed on a large data set of randomized defender-invader scenarios. Analysis suggests that this novel algorithm can reliably generate near-optimal strategies at a small fraction of the computational cost of a full exact solution. The results of the simulated trials demonstrate that the reduction in search space is substantial for the vast majority of randomized scenarios. This significant improvement in computational efficiency, with a sufficient degree of reliability, provides a practical means of solving for feasible n-ICOP solutions in a computationally limited environment.
Verification column

In this compact but extensive and authoritative survey, the author provides a personal overview of recent progress in the highly valuable as well as scientifically challenging area of verifying randomised distributed algorithms, including an explicit invitation for researchers to join the effort of the community!
Noisy Dense Label Prediction with Noise-Robust Loss Functions

Image segmentation is an actively research field in the domain of deep learning and computer vision. Due to its use in a wide range of applications such as medical imaging problems, automated disease detection, self-driving vehicles and so on, image segmentation is widely used in dealing with building extraction tasks. Although deep learning models such as U-Net performs well with building extraction tasks that require semantic segmentation, presence of noisy data can still affect the performance of these models. Moreover, it is not unusual to have noisy labels present in data sets that are available online or collected automatically via the Internet. As such, we commonly come across label noise present in data sets used for building extraction tasks that can decrease the performance of a learning task. In this paper, we implement multiple well-known loss functions used for image segmentation on an existing U-Net model for learning building extraction and compare the results of each of the loss functions’ performance. Furthermore, we implement some of the widely used loss functions such as cross entropy (CE), Dice loss (DL) and Tversky loss (TL) on a noise-robust intermediate false positive–false negative matrix model to evaluate the performances of these loss functions when dealing with noisy data.
Identity

The project IDENTITY was commissioned in 2018 by the Museum of Modern Art in Arnhem for the development of a pilot version of a high-quality interactive virtual exhibition in WebVR. This virtual space needed to provide an alternative platform during a transitional period in which the museum was closed for renovation. Our approach is based on the belief that the most critical aspect of any exhibition design is the way in which the works are contextualized. The added value of VR lies precisely in the fact that the design of the virtual environment can make a substantial contribution to the interrelation of the exhibited works.
Questions Boards Should Ask

Boards of directors do not need to be technical experts to oversee or discover cybersecurity risks in organizations. However, they need to ask probing questions to ascertain the maturity level and fundamental challenges in the way the organization understands and manages cybersecurity risk. There is only one fact they do need to know: getting into any organization is possible because nothing is truly secure.
Basic concepts

Before diving into the depths of the Common Lisp condition system, we will first introduce three programming concepts that collectively form the foundation of the condition system: dynamic variables, performing non-local transfers of control, and lexical closures. Understanding these lower-level techniques is important to understanding the functioning of the CL condition system as a higher-level mechanism; therefore, readers who are already proficient with these techniques may consider skipping the relevant sections and continue from the next chapter of the book.
Pengamanan Data melalui Model Super Enkripsi Autokey Cipher dan Transposisi Kolom

One of the essential instruments in the cyber era is data. Therefore, maintaining data security is an important thing to do. One way that can be done to maintain data security is through cryptography. In cryptography, two basic techniques are commonly used, namely substitution techniques and transposition techniques. One of the weaknesses of the basic cryptographic techniques is the lower level of data security. This study proposed a super encryption model in securing data by combining cryptographic algorithms with substitution techniques, i.e., autokey cipher and transposition, i.e., columnar transposition cipher. This study used the Avalanche Effect method as a measurement tool for the proposed super encryption model. The test results have shown that the proposed super encryption model can provide a better level of security. The avalanche effect test on the five data test shows that the average AE value of the proposed super encryption model is 30.76%. This value is higher than the single autokey cipher algorithm of 1.66% and column transposition with a value of 18.03%. Other results from the five data test have shown that the proposed model has a high level of accuracy of 100% in terms of the decryption process results, which is the same as the initial data before going through the encryption process.&#x0D;
Integrating digital teaching package for the German language classroom

This paper intends to examine the perceptions of teachers and students of the use of Digitales Unterrichtspaket or Digital Teaching Package (DUP) in the German language online learning. In this study, a total of 20 students and three teachers are involved. A questionnaire consisting of closed and open-ended questions was distributed to the students and teachers. Data analysis was conducted by examining the results of descriptive statistics and by examining the participants' open-ended responses. The results showed that students and teachers perceived DUP as an attractive and useful tool for online learning. Teachers do not have to spend a lot of time preparing instructional materials and students are more involved in the learning process. DUP facilitates interactions that lead to successful learning. These findings help teachers evaluate their instruction with DUP during the pandemic.