Reviewerly/exHarmony huggingface dataset:
The Resolution of Niho’s Last Conjecture Concerning Sequences, Codes, and Boolean Functions

A new method is used to resolve a long-standing conjecture of Niho concerning the crosscorrelation spectrum of a pair of maximum length linear recursive sequences of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> -1 with relative decimation d=2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m+2</sup> -3, where m is even. The result indicates that there are at most five distinct crosscorrelation values. Equivalently, the result indicates that there are at most five distinct values in the Walsh spectrum of the power permutation f(x)=x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> over a finite field of order 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> and at most five distinct nonzero weights in the cyclic code of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> -1 with two primitive nonzeros α and α <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> . The method used to obtain this result proves constraints on the number of roots that certain seventh degree polynomials can have on the unit circle of a finite field. The method also works when m is odd, in which case the associated crosscorrelation and Walsh spectra have at most six distinct values.
Research into Online Teaching in Private Colleges Under the Background of Internet Big Data

With the development of modern information technology, online teaching is a new educational method. Teachers and students can use high-quality information resources, and break the limitations of time and space, to achieve personalized teaching.[1] During the current epidemic prevention and control period, online teaching can ensure “no suspension of classes, no suspension of teaching”, which is also a measure of not affecting students' papers, employment and other work, by making a good using of Internet big data. This paper first analyzes the current problems of online teaching in Colleges and universities, and then puts forward effective measures for the existing problems in order to maximize the effect of online teaching.
BMIM: Generating Adversarial Attack on Face Recognition via Binary Mask

Face recognition has received interest among researchers due to the model vulnerabilities towards adversarial threats, which are imperceptible to human eye. In this research, we proposed binary mask iterative method (BMIM). In this method, we generate the attack by occluding the face landmark to fool the face recognition model. To conduct the extensive experiment, we used three face recognition models i.e. MobileFace, MobileNet, and SphereFace on Labeled Face in the Wild (LFW) dataset. We evaluate the robustness of these models against dodging and impersonate black-box attacks under $L_{\infty}$ norms and improve the transferability of existing attacks. The experimental results show that our method achieved desired results.
Teaching English writing with fun to elementary students

Background: Elementary school students often complain on writing assignments from their teachers. As a consequence, the elementary students are not enthusiastic about understanding English writing.&#x0D; Purpose: This study aims to identify the technique of teaching English writing for elementary students.&#x0D; Design and methods: The method used is library research method as an activity carried out systematically to collect, process, and conclude data using certain methods/techniques.&#x0D; Results: The study reveals elementary students in low grade (I, II, III) uses creative writing, media word games and high class (IV, V, VI) uses collaborative writing.
Universities’ Training Programs Congruence to the Needs of the Regional Labour Market: Sectoral Analysis

The authors of this article are carrying out the analysis of the students’ training programs congruence to the sectoral structure of employment in 84 Russian regions using the sectoral model concept and the index of structural differences. The paper presents a methodological approach to assessing the congruence of training programs with the needs of the regional labor market in Russia. The level of regional higher education systems differentiation, the sectoral structure of graduates, and the level of their employment in the regional context were determined applying economic and statistical methods. The ratio of the graduates’ supply to their demand analyzed, there is presented a classification of regions according to the levels of this ration, conclusions drawn on the balance of higher education systems in the regions, taking into account the needs for training. A structural and professional disproportion between regional labor markets and regional systems of higher education in most regions of Russia is diagnosed, as well as a number of imbalances concerning specialists’ training in the secondary sector of the economy, which is decisive for the development of high-tech in dustries and provides the need for personnel within the new digital economy. The directions for overcoming dysfunction of the relationship between universities and the labor market are identified. The results of the study make it possible to make decisions on improving the policy of distributing the planned contingent of students by training programs in accordance with the needs of regional economies.
A graphical study of comparative probabilities

We consider a set of comparative probability judgements over a finite possibility space and study the structure of the set of probability measures that are compatible with them. We relate the existence of some compatible probability measure to Walley's behavioural theory of imprecise probabilities, and introduce a graphical representation that allows us to bound, and in some cases determine, the extreme points of the set of compatible measures. In doing this, we generalise some earlier work by Miranda and Destercke on elementary comparisons.
FU Covid-19 AI Agent built on Attention algorithm using a combination of Transformer, ALBERT model, and RASA framework

Potentialized by Natural Language Processing (NLP) technology, we can build a chatbot or an AI Agent to automatically address the need to automatically get credible and timely information, especially in the fight against epidemics. However, Vietnamese understanding is still a big challenge for NLP. This paper introduces an AI Agent using the Attention algorithm and Albert model to implement the question/answering task in the Covid-19 field for the Vietnamese language. In the end, we also built two other modules, one for Vietnamese diacritic auto-correction and another for updating Covid-19 statistics (using RASA framework), to deploy a Covid-19 chatbot application on mobile devices.
Sentiment Time Series Calibration for Event Detection

Event detection based on sentiment time series, which describe the trend of users' emotions or attitudes towards specific topics over time, has been widely applied in the analysis of social network or text mining. Most of the contributions directly generate time series sequences by classifiers. However, due to the missing corpus labels or the limited performance of the classifier, such generated sentiment time series may not correspond to the actual values, especially when the sentiment value changes drastically, called extreme value. We propose a new method to calibrate sentiment times series for event detection based on evaluation on a sampling dataset. Theoretical analysis of the calibration method is explicated, and it is proved that the sampling error of the performance indicators can be limited to a minimal range for extreme values, thus sentiment value error can be reduced. Experiments on simulated datasets and real-world datasets illustrate the effectiveness and robustness of our method.
An Intelligent Error Correction Algorithm for Elderly Care Robots

With the development of deep learning, gesture recognition systems based on the neural network have become quite advanced, but the application effect in the elderly is not ideal. Due to the change of the palm shape of the elderly, the gesture recognition rate of most elderly people is only about 70%. Therefore, in this paper, an intelligent gesture error correction algorithm based on game rules is proposed on the basis of the AlexNet. Firstly, this paper studies the differences between the palms of the elderly and young people. It also analyzes the misread gesture by using the probability statistics method and establishes a misread-gesture database. Then, based on the misreading-gesture library, the maximum channel number of different gestures in the fifth layer is studied by using the similar curve algorithm and the Pearson algorithm. Finally, error correction is completed under the game rule. The experimental results show that the gesture recognition rate of the elderly can be improved to more than 90% by using the proposed intelligent error correction algorithm. The elderly-accompanying robot can understand people’s intentions more accurately, which is well received by users.
WITHDRAWN: Deep learning method for identification and selection of relevant features

Feature Selection has turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technology is a useful asset to choose features; anyway, not all calculations are on an equivalent balance with regards to the selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway, neural systems are black-box models and not many endeavors have been made to examine the fundamental procedure. In this proposed work a new calculation to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues that enable us to think about every calculation on various fronts: exhibitions, calculation time, and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.
Z-Adaptive Fuzzy Inference Systems

Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training. To overcome this limitation, we propose a Z-Adaptive Fuzzy Inference System (ZAFIS) that systematically learns the parameters of Z-numbers from input-output data pairs. We first convert the second component of Z-numbers to a crisp number. We then use this number as a weight for the first fuzzy membership part of Z-numbers. Finally, the resultant membership is placed in a fuzzy inference system, and the parameters of the system are learned based on the input-output data pairs using a gradient descent algorithm. The proposed method is evaluated on several functions (sine, increasing sine, Hermite, Gabor, and a nonlinear function) with/without added noise scenarios. The results show that the ZAFIS is more robust against the noisy inputs and is superior to the Fuzzy Inference Systems (FISs) in terms of MSE.
Design and analysis of gray code generator as test pattern generator

Gray code is a process of encoding integers as a sequence of binary bits. These binary bits are arranged in such a way that two adjacent integers differ by a single binary digit. There are different types of grey code such as reflected binary, uniform balanced, antipodal, and so on. This paper mainly focuses on the reflected binary grey code. The reflected binary code is light weighted. The primary function of counters is to produce a specified output sequence and they are also called pattern generators. This paper contains the comparison of different bits of grey counters. The parameters of comparison are static power, dynamic power, utilization, and on-chip temperature.
Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning

We present the experiment results to use the YOLOv3 neural network architecture to automatically detect tumor cells in cytological samples taken from the skin in canines. A rich dataset of 1219 smeared sample images with 28,149 objects was gathered and annotated by the vet doctor to perform the experiments. It covers three types of common round cell neoplasms: mastocytoma, histiocytoma, and lymphoma. The dataset has been thoroughly described in the paper and is publicly available. The YOLOv3 neural network architecture was trained using various schemes involving original dataset modification and the different model parameters. The experiments showed that the prototype model achieved 0.7416 mAP, which outperforms the state-of-the-art machine learning and human estimated results. We also provided a series of analyses that may facilitate ML-based solutions by casting more light on some aspects of its performance. We also presented the main discrepancies between ML-based and human-based diagnoses. This outline may help depict the scenarios and how the automated tools may support the diagnosis process.
Evaluation of Blockchain Algorithm for Smart Rental Application using Smart Contract

Blockchain technology has emerge over the years since the introduction of Bitcoin and Ethereum cryptocurrency, to the point the value of these cryptocurrencies are as high as a bar of gold. Since Ethereum has been an open source development platform, the industry has taken advantage of decentralized application or DApp where by the application is based on Smart Contract, a set of programming written code becomes the law of in agreement. Hence, the same can implement in House Rental industry where all contract agreement between tenant and landlord were conducted using pen and paper method. Even though, there are applications for this segment in the industry but most of the applications were not able keep data tracking and secure information. In this paper, a comparison is made to propose which algorithm is best to be used in the blockchain application and a better understanding how blockchain works.
GWO Optimized K-Means Cluster based Oversampling Algorithm

Skewed data distribution prevails in many real world applications. The skewedness is due to imbalance in the class distribution and it deteriorates the performance of the traditional classification algorithms. In this paper, we provide a Grey wolf optimized K-Means cluster based oversampling algorithm to handle the skewedness and solve the imbalanced data classification problem. Experiments are conducted on the proposed algorithm and compared it with the benchmarking popular algorithms. The results reveal that the proposed algorithm outperforms the other benchmarking algorithms.
A Gap in the Analysis of Technical Debt in Procedural Languages: An Experiential Report on Go

Technical debt analysis of procedural languages has not kept up with the growth in popularity of procedural languages. This article provides a perspective from one language: Go. We describe analysis complications and the potential for future developments to help practitioners overcome this gap.
A Real-time Interactive Multi-Model (RT-IMM) Target Tracking Method

The IMM target tracking theory employs the invariant Markov transfer probability matrix and the residual model in the model probability update, which lacks real-time adaptability. In this paper, Bayesian estimation theory is utilized to update the target state distribution by integration with multi-model tracking results, and the model probability of next moment is updated according to the model likelihood function. The likelihood function theory is applied to model probability interactive update, and the current filtering model target state distribution is employed to update the Markov transfer probability matrix among models. The proposed method is compared with conventional IMM method through Monte Carlo simulation. The simulation results show that the accuracy of the proposed method is better than conventional IMM, and it can track the maneuvering targets and fluctuating targets effectively.
Multi-dimensional Ranking via Majorization

University ranking are ubiquitous as there are dozens of them. University ranking are not only used as tools in academia and funding agencies, but they are popular reading in newspapers and magazines as well. Also in academics is of major importance for decision-making. University ranking have been heavily criticized for a number of reasons, including that they are based on arbitrary choices about the key indicators, their special weights and so on. For these reasons, we argue that there is no meaning in ranking universities in an absolute ordered manner; instead universities should be ranked in "sets of equivalence". To this end, we introduce the mathematical concept of majorization and perform experiments on a dataset extracted from NTU Ranking. This approach is applicable to other entities, such as authors or other higher order multi-dimensional entities.
A Hybrid Face Recognition Scheme in a Heterogenous and Cluttered Environment

The recognition of human faces is an old application which can detect, track, and identify or verify human faces from a still or motioned picture stored digitally and is taken by digital camera. Though a significant development has already been seen in this domain, still some challenging issues are present which are yet to be addressed. Some of such issues are recognizing human faces in low intensity lighting condition, the presence of noise in images, scales, masquerade, etc. The present work focuses on using a conglomerate approach using deep learning algorithms with a support vector machine (SVM) to recognize faces. In the present paper, it has proposed a model to do facial image detection by using of deep neural network (DNN) with triplet loss function to extract features and support vector machine (SVM) classification algorithms to identify the images. The aim of the present work is to recognize the people from a variety of sources like videos, pictures and even sketches in a heterogeneous environment. When a selected image is fed to the system the model can be able to recognize detected faces. After that these detected faces are extracted and supplied to the recognizer which works with these faces and recognizes them as the respective persons. This recognition is done using SVM classifier which is very efficient when it comes to image processing. DNN and SVM have significantly improved the performance in recognition of faces in a cluttered and heterogeneous environment.
Extracting video games rating labels from transcript files

Video games industry represents one of the most profitable activities connected with entertainment and visual arts. Video game industry involves a great number of different professionals who work together to create products expected to reach people in many countries. A substantial part of these people are teenagers, strongly attracted and influenced by video games. For these reasons, various systems of labels have been created. These systems are based on different criteria, but they have in common the presence of descriptors, or labels, whose indicate the type of contents in the game. These labels give an age range indicator to inform the buyers and users to the more suitable age for the product. One of these systems is called PEGI, and we will primarily take it in consideration for the purposes of our study. The rating procedure includes a large process of manual control of each submitted game. In order to help this large and demanding process, we propose a system of video games rating based on automatic classification of the products performed over the “transcript” or script, files that display the full transcription of dialogues in a video game. The proposed automatic classification algorithm is based on specialized dictionaries enriched with a vector semantics algorithm, and is able to provide an age rating and a genre classification of video games. It works in a more efficient way in games with a consistent amount of dialogues. The experimentation of the proposed algorithm is returning encouraging results.
A study on human reaction time in a mixed reality environment

Mixed or augmented reality is a technology that introduces digital elements into a real scene. In many applications, these systems can be used to supplement or augment the information provided to the user. The impact of mixed reality on the performance of a human subject is still under investigation. In this contribution, we go towards this goal by analyzing the reaction time of a user in a mixed reality environment. For this purpose, a subjective experiment has been designed and implemented. Both accuracy and reaction time features have been evaluated. Experimental results show that the interaction methodology with the virtual world, used in the experiment, is not yet fully mature and that a more friendly virtual-interaction methodology can lead to shorter reaction times.
Study on the Change of Photon Number with Humidity in UV Detection of External Insulation Corona Discharge and Its Correction Method

In order to make the photon number of the UV imaging detector detectable under different air relative humidity, select the tip discharge as the experimental model, explore the change characteristics of the relative humidity of the air to the photon number detection, establish a suitable function model, and use Matlab. The software fits the experimental data to obtain a formula for correcting the photon number with the relative humidity of the air. The verification results show that the photon number is corrected with the air relative humidity correction formula, which can meet the needs of actual engineering measurement.
Tingkat Kerawanan dan Arahan Pengendalian Pengurangan Risiko Bencana Banjir di Kecamatan Mandonga, Kota Kendari

The purpose of this study was to analyze the level of vulnerability and direction of flood control in Mandonga District, Kendari City. The study used a quantitative descriptive design and a survey. The population and sample of the research in Mandonga District, the informants were determined by purposive sampling. Data were analyzed spatially (GIS namely ArcView 3.2) and descriptive qualitative. Flood vulnerability in Mandonga District, Kendari City is in “high” vulnerability covering an area of 137.02 ha or 31.55%; “Medium” vulnerability covering an area of 79.07 ha or 18.21%; and “low” vulnerability covering an area of 218.14 ha or about 50.24%. The directions for flood control that can be taken are construction and repair/maintenance of infrastructure such as drainage; river normalization; manufacture of injection wells (Atificial Recharge) and infiltration wells; integrated sewer; application of bioretical technology; creation of green open areas (RTH); and afforestation or reforestation.
A paradox about sets of properties

A paradox about sets of properties is presented. The paradox, which invokes an impredicatively defined property, is formalized in a free third-order logic with lambda-abstraction, through a classically proof-theoretically valid deduction of a contradiction from a single premise to the effect that every property has a unit set. Something like a model is offered to establish that the premise is, although classically inconsistent, nevertheless consistent, so that the paradox discredits the logic employed. A resolution through the ramified theory of types is considered. Finally, a general scheme that generates a family of analogous paradoxes and a generally applicable resolution are proposed.