Reviewerly/exHarmony huggingface dataset:
Astigmatism correction of convex aspheres using the subaperture stitching hindle test

Abstract We propose an astigmatism correction method for the subaperture stitching Hindle test to measure hyperbolic convex aspheres. Astigmatic wavefront errors arise from misaligned Hindle setups, mechanical runout errors of the rotational motion for stitching, and the surface error of the target itself. Because these errors are combined, they cannot be separated in the conventional subaperture stitching Hindle tests. We exploited the rotational periodicity of each error to distinguish the surface figure error from other astigmatic error sources and rectified the Hindle test results with a third-order astigmatism. Using the subaperture stitching Hindle test, we averaged two sets of measurement data with a 180° rotational phase difference between them to calculate the astigmatic surface error. The proposed method was verified experimentally by comparing it with the results from a commercial stitching interferometer from QED Technologies; only subnanometer differences in the root-mean-square values were obtained. Therefore, the proposed method calibrated the system errors from the test surface wedge and the rotational decenter easily, thereby reducing the mechanical costs and alignment efforts and making it more accessible than a sophisticated mechanism.
Analysis of the Stability of quadcopter and Control using PID controller

A UAV is an avion which fly without a human pilot which can be controlled by autopilot hardware. In this work, a type of aerial vehicle called quadcopter is considered which is very agile and unstable. A helicopter can be lifted by aerodynamic principle acting upon wings but quadcopter can't lift like helicopter any one of the motor failure or speed of the four motor mismatches will lead to a severe accident. As a result, it’s highly challenging for a human to control the flying quadcopter by controlling every motor directly, for this reason, they move for classic piloting method to achieve extreme reaction speed and attention. The scope of this paper is to develop an autonomous quadcopter using a PID controller which has a capacity of stable flying in the air with the help of using a microcontroller-based control mechanism. That can be done by measuring Euler angles to produce a control signal to the quadcopter to control its position and velocity. The Quadcopter is designed to be small enough so that cost would be minimized.
HYPER-REF: A General Model of Reference for First-Order Logic and First-Order Arithmetic

Abstract In this article I present HYPER-REF, a model to determine the referent of any given expression in First-Order Logic (FOL). I also explain how this model can be used to determine the referent of a first-order theory such as First-Order Arithmetic (FOA). By reference or referent I mean the non-empty set of objects that the syntactical terms of a well-formed formula (wff) pick out given a particular interpretation of the language. To do so, I will first draw on previous work to make explicit the notion of reference and its hyperintensional features. Then I present HYPER-REF and offer a heuristic method for determining the reference of any formula. Then I discuss some of the benefits and most salient features of HYPER-REF, including some remarks on the nature of self-reference in formal languages.
Schulkommunikation im digitalen Wandel – Der Lockdown als Akzelerator von Digital Literacy?

The discussion about media literacy today goes far beyond the traditional understanding of media competence. At the same time, very different meanings are attributed to the term. This book brings together 14 contributions on different dimensions of media literacy: in addition to definitions of terms, it addresses media and information literacy in dealing with journalistic content; it discusses media literacy in the contexts of social media, organisations and in specific roles; and sheds light on technological, social and aesthetic aspects of media literacy. These contributions build a bridge between the findings of media and communication research, educational science and the concept of media literacy.With contributions byOthmar Baeriswyl, Davide Cino, Urs Dahinden, Caroline Dalmus, Mark Eisenegger, Noemi Festic, Brigitte Gasser, Laura Gründler, Julia Grundisch, Anastasija Grynko, Uwe Hasebrink, Martin Hermida, Markus Hodel, Angelica Hüsser, Kiran Kappeler, Guido Keel, Carmen Koch, Franziska Kohler, Nadine Klopfenstein, Michael Latzer, Franziska Oehmer, Stefano Pedrazzi, Nicole Rosenberger, Colette Schneider, Jörg Schneider, Pascal Schneiders, Lisa Schwaiger, Antonia Steigerwald and Christine Trültzsch-Wijnen.
Usability evaluation methodology for scalable multiuser games

The aim of this paper is to propose means for objective evaluation metrics in case of multiuser online games and to offer a viable solution with a scalable architecture for the coordination services for this specific type of games.This paper presents a covering analysis on the high usability of such a solution with justification based on objective usability metrics, reusable for different game designs and scenarios.The article justifies the need of switching to a set of proposed metrics as means to usability evaluation instead of relying strictly on user feedback based on empirical analysis of the game.
Fighting behaviour detection in video using convolutional neural network

With the rapid development of computer networks and artificial intelligence technology, video surveillance technology has developed rapidly and tends to be intelligent. This paper aims to use deep learning technology to achieve abnormal detection of fighting behaviour in specific video surveillance scenarios, and then analyse and process more complex human gesture behaviour recognition problem. The collected initial video data was well handled with data pre-processed and enhancement, the corresponding model was also further optimised. Experimental results show that the improved convolutional neural network model accuracy rate reached 92.53%, while using migration technology to quote classic. The convolutional neural network structure VGG16 and GoogleNet model can reach 98.71% and 99.60% accuracy.
Traffic Incident Detection

Traffic incident detection is an essential part of traffic control and management systems. In the past several decades, automatic incident detection (AID) has attracted much attention from traffic researchers as an exciting research area, and a number of incident detection algorithms have been proposed and tested. The target of this introduction is to provide an overview of traffic incident detection, and consist of the following parts. First, give the definition of traffic incident, the impact of incidents as well as the benefits of clearing an incident early. Second, introduce what automatically incident detection is. Third, provide an overview of traffic incident detection methods. Last, present how to evaluate the performance of incident detection algorithms.
&lt;i&gt;A Philosophical Analysis of the Updating Rule in a Bayesian Perceptual Learning Model&lt;/i&gt;

Bayesian models of human object perception have been widely studied. Helmholtz’s idea of perception as unconscious inference is formalized by Bayes’ theorem. Human object perception is now widely called Bayesian inference or statistical inference, while obtaining a Bayesian quantitative model of human perceptual learning has become a primary goal for the consciousness scientists who utilize Helmholtz’s idea.
9. Administrasi Layanan Khusus

The purpose in writing this article is to find out matters relating to the administration of special services. Special service administration is to provide special services or an effort that is not directly related to the teaching process in the classroom, but specifically given by schools to their students so that they are more optimal in the implementation of learning. This special service administration is made to facilitate learning and can also help students carry out special needs. The types of special service administration are libraries, laboratories, uks, school cafeterias, religious facilities, cooperatives and transformations. The role of the teacher in the administration of special services is the involvement of the teacher in library administration, for example introducing books to students, knowing the types and mastering general criteria that determine the merits of a library of books and promoting the library both for use and for guidance.
A Path Planning Scheme with Consideration of Absolute Localization Accuracyfor Exploration UAVs

Exploration UAVs (Unmanned Aerial Vehicles) are required to fly autonomously, because GPS cannot be used on Mars. Hence, localization is one of the major topics to be developed. In general, visual odometry is available for relative position estimation. However, it is difficult to estimate absolute position. This paper deals with the path planning in consideration of the absolute localization. This paper uses Mutual Information for image matching, which is robust to scene changes. MCL (Monte Carlo Localization) is applied for the absolute localization. Then, this paper proposes a new RRT (Rapidly-exploring Random Tree) extension method to generate a path with a large effect of the absolute localization. The simulation results show that the proposed method reduces the uncertainty of the position and generates a path that can increase the probability of reaching the goal.
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Double-blind • Conference submission management system: AI Scholar System • Number of submissions received: 186 • Number of submissions sent for review: 176 • Number of submissions accepted: 99 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 53.2% • Average number of reviews per paper: 2 • Total number of reviewers involved: 50 • Any additional info on review process: • Step 1. Each of selected paper will be reviewed by two/three professional experts in the related subject area. • Step 2. Review Reports received from the experts will be judged by one of the editors either Review Reports are logical or not? • Step 3. If not logical, then editor can assign new reviewer or can also judge at his/her own. • Step 4. If logical, then Review Reports will be sent to authors to modify the manuscript accordingly. • Step 5. Authors will be required to revise their papers according to the points raised. • Step 6. Revised version will then be evaluated by the editor for the incorporation of the points raised by the reviewers. • Step 7. Then the editor will send the revised manuscript to the reviewers again for re-evaluation. • Step 8. If the reviewers approve the revise version of the manuscript, then will be accepted for publication. • Contact person for queries: Xuexia Ye publication@keoaeic.org AEIC Academic Exchange Information Centre
Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values ​​of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values ​​are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%
Heuristic method for bitsliced representation of randomly generated 8×8 cryptographic S-Box

The article is devoted to the issues of increasing the security and efficiency of software implementation for the symmetric block ciphers. For the implementation of cryptoalgorithms on low-end CPUs (8/16/32-bit microcontrollers), it is important to provide increased resistance to power consumption analysis attacks. With regard to the implementation of ciphers on high-end CPUs (x86, ARM Cortex-A), it is important to eliminate the vulnerability primarily to timing and cache attacks. The authors used a bitslice approach to securely implement block ciphers, which has potential advantages such as high speed and low computing resources. However, the known bitsliced methods have a significant limitation, since they work with deterministic S-Boxes or arbitrary S-Boxes of smaller sizes. The paper proposes a new heuristic method for bitsliced representation of cryptographic 8×8 S-Boxes containing randomly generated values. These values defy description using algebraic expressions. The method is based on the decomposition of the truth table, which describes the S-Box, into two parts. One part of the table forms logical masks, and the other is split into bit vectors. To find a logical description of these vectors an exhaustive search is used. After finding the description of all vectors, these two parts of the table are combined into one using logical operations. The use of this method oriented on software implementation in the logical basis {AND, OR, XOR, NOT} ensures the minimization of arbitrary 8×8 S-Boxes. The proposed method can be implemented using standard logical instructions on any 8/16/32/64-bit processors. It is also possible to use logical SIMD instructions from the SSE, AVX, AVX-512 extensions for x86-64 processors, which provides high performance due to the use of long registers. The corresponding software has been developed that implements the method of searching for bitsliced representations of a given S-Box, and also automatically generates C++ code for it based on SSE, AVX and AVX-512 instructions. The effectiveness of the method on the S-Box of known block ciphers, in particular the Ukrainian encryption standard "Kalyna", has been investigated. It was found that the developed algorithm requires almost half as many gates for the bitsliced description of an arbitrary S-Box than the best of known algorithm (370 gates versus 680, respectively). For ciphers that use two or four S-Box tables, joint minimization can yield up to 330 or 300 gates per table, respectively. Keywords: bitslicing; S-Box; logical minimization; SIMD; x86-64 CPU; software implementation; block ciphers.
Ensemble of Gray Fuzzy Cognitive Maps Application to Solve the Problem of Predicting University Activity

An approach to solving the problem of predicting the performance indicators of a university as a semi-structured system based on cognitive modeling methods is proposed. The university activity model is presented in the form of an ensemble of gray fuzzy cognitive maps, which allows considering the uncertainty of experts’ opinions about the state of cognitive map concepts and gives more reliable modeling results. The ensemble of cognitive maps simultaneously uses a finite set of gray cognitive maps built based on expert assessments, the output signals of which are combined into a combined assessment, which is superior in quality to the results obtained using local assessments of the cognitive maps included in the ensembles. Special constructions in the form of interval estimates for assessing the connections between concepts, used in a fuzzy ensemble of cognitive maps, ensured the construction of an adequate cognitive model. The proposed approach allows us to find the most acceptable scenario for planning the increment of baseline indicators with given constraints to target values by identifying hidden factors affecting them and impulse influences on them, ensuring guaranteed achievement of the set goal and obtaining more accurate results.
Digital Medical Record System using Blockchain Technology: A Survey

Abstract:In these modern times where diseases, viral flu, and infections are too common for the human being, to tackle this problem sometimes it gets hard, as the patient has incomplete documents of their diagnostics in case the document get lost for certain reason or patient left some important reports at home, and carrying a file every single time when patients visit a doctor is quite a burden, and some doctors give fake medication to the patients, and as patient migrate to a new place and visits a doctor then that particular doctor would need all patients previous medication and hereditary diseases information if any, doctor has to go through the whole check-up in order to get any allergies for certain medication or any lab reports if there are missing any. Keywords: Blockchain, Hyperledger Fabric, Medical Records, Orderers, Radiologist, Tester, Patient, Doctor
Efficient-net Speaker Recognition Master—The New Speaker Recognition System Built Base on Efficient-net

Speaker recognition is a kind of voiceprint recognition, that is, its theoretical basis is that each voice has a unique feature, through which different voices can be effectively distinguished. That means it is a problem of “one-to-one discrim-ination” to determine whether a particular speech is said by a particular person. In the classic VoxCeleb2: Silent Speaker Recognition, the author implemented Speaker Recognition by the Convolutional Neural Network, a neural network for computer vision, under noisy and unconstrained conditions. The main purpose of this paper is to solve the problem of insufficient speaker recognition accuracy. During the experiment, we use some recent achievements in the field of machine learning to improve the efficiency and accuracy of speaker recognition. In the experiment, I design and compare the impact of Res-net 34s and Efficient-net on accuracy on a pure Chinese voice dataset, CN-Celeb. Comparing to the traditional neural network Res-NET, Efficient-net achieves higher accuracy and efficiency, especially in the test of Efficient-net B4 version, I got the accuracy of top-1 at 84. 20% and top5 at 96. 42%. This paper consists of: introduction, CN-Celeb, neural networks, experimental processes and conclusions.
Low Time Complexity Model for Email Spam Detection using Logistic Regression

Spam emails have recently become a concern on the Internet.Machine learning techniques such as Neural Networks, Naïve Bayes, and Decision Trees have frequently been used to combat these spam emails.Despite their efficiency, time complexity in high-dimensional datasets remains a significant challenge.Due to a large number of features in high-dimensional datasets, the intricacy of this problem grows exponentially.The existing approaches suffer from a computational burden when thousands of features are used (high-time complexity).To reduce time complexity and improve accuracy in high-dimensional datasets, extra steps of feature selection and parameter tuning are necessary.This work recommends the use of a hybrid logistic regression model with a feature selection approach and parameter tuning that could effectively handle a big dimensional dataset.The model employs the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to mitigate the drawbacks of Term Frequency (TF) to obtain an equal feature weight.Using publicly available datasets (Enron and Lingspam), we compared the model's performance to that of other contemporary models.The proposed model achieved a low level of time complexity while maintaining a high level of spam detection rate of 99.1%.
Perbandingan Neural Network Backpropagation dan Extreme Learning Machine pada Robot Manipulator

Robotics technology and artificial intelligence devices are growing rapidly in the medical field. The work of medical workers will be made easier by the presence of this technology. It is also applied directly to patients. Patients suffering from various diseases, of course, need an appropriate solution. A robotic finger manipulator can be applied to a patient with disabilities to assist him in placing or retrieving items. In the manipulator robot, there are sensors, controllers, and actuators. The stimulation of the muscles in the forearm is detected by an electromyograph sensor. The resulting muscle stimulation is classified by the controller into servo motor movement. The motor represents the fingers. The classification method uses a neural network backpropagation and an extreme learning machine which is compared to the performance. Classification using neural network backpropagation has a success rate of 65.3%. While the classification using the extreme learning machine has a success rate of 78.7%.
Dynamic Universal Maturity Assessment System

After the introduction of computer, there has been an influx of applications. Every application sates different human needs, however, each one of them uses software maturity model to assess and deliver a reliable and scalable application. Since, every organization uses a varied model, or calculation standard, a universal standard is necessary. This research paper proposes an ubiquitous formula that, not only solves the above ambiguity but also considers every aspect of development basis on their individual prominence. Each valuable process is divided into sub categories and given respective weightage based on the use case of the application, such a dichotomy dramatically increases the assessment accuracy of the software application and provides a rate of maturity on a scale of 0-10 where the former being the lowest and later being highest level of maturity.
Jaringan Syaraf Tiruan Untuk Memprediksi Nilai Siswa SMA Menggunakan Backpropagation

A student's mastery of a subject greatly influences the marking given by the teacher / teacher concerned. The need for instructors or teachers to monitor every value of students who are taught science in their respective fields. With the rapid development of technology, it is very helpful for teachers in knowing or predicting the value that students will get related. This study aims to apply the performance of backpropagation artificial neural networks in predicting the value of students of SMA N 1 Sidamanik with various models and minimizing their errors. In this study the authors used data on student grades from SMA N 1 Sidamanik. In processing data values, the authors use artificial neural networks with backpropagation algorithms as logical steps to predict student National Exam Scores in SMA N 1 Sidamanik. The main problem in this study is the decline in student grades in some subjects, in the future students will experience difficulties in reaching the desired university or high school.
Grammatical Errors Made by The Eleventh Grade Students in English Argumentative Writing

Grammatical errors that the English learners make in language learning are difficult to be avoided.To reduce those errors the learners should pay more attention in mastering grammar so it will not be fossilized in their minds.This paper aims at investigating grammatical error made by Eleventh Grade Students in English argumentative writing.This study is a descriptive qualitative by collecting data from students of Saint Hendrikus Senior High School writing test.The result shows that the course students with additional English Course do not make many errors in argumentative essay rather than another group.However, the frequency of the error is not too wide in every item between two groups.The misuse of using tense has almost the same frequency; 60% and 57% between two groups and it happened because the less comprehension of using adverb of time in a sentence.The less error happened only on using gerund 0% and 3%.