Reviewerly/exHarmony huggingface dataset:
A Brief Review on Rule Based Systems

Artificial Intelligence is science of systems which either learn by experience or by perform tasks according to the instructions already fed in their database. Rule-Based Systems are the simplest machines which are bench mark for Expert Systems when it comes to their architecture. It’s easy for an organization or a company to design an Expert system if the basics to design a rule-based system are clear in the minds of developers. AI is a discipline of uncertainties if one is trying to define it, but Rule-based systems are simplest form of AI which we are going to discuss in this search paper.
Multi-scale PIIFD for Registration of Multi-source Remote Sensing Images

This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion. Focusing on the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm is implemented. The key technologies include image scale-space for implementing multi-scale properties, Harris corner detection for keypoints extraction, and partial intensity invariant feature descriptor (PIIFD) for keypoints description. Eventually, a multi-scale Harris-PIIFD image registration algorithm framework is proposed. The experimental results of four sets of representative real data show that the algorithm has excellent, stable performance in multi-source remote sensing image registration, and can achieve accurate spatial alignment, which has strong practical application value and certain generalization ability.
On Mis-specification Tests for Stochastic Linear Regression Model

This research article explores some mis-specification tests for stochastic linear regression model viz. Durbin- Watson test, Ramsey's regression specification error test, Lagrange's multiplier test, and UTTS’ rainbow test. Any type of error occurred in the set of underlying assumptions of a stochastic linear regression model and the associated inferences lead to 'specification errors'. These errors will present particularly in specifying the error vector 􀁈 and the data matrix X. Generally specification errors are caused by the inclusion of irrelevant independent variables or exclusion of relevant independent variables in the stochastic linear regression model. Ivan Krivy et.al, in 2000 in their research article depicted two stochastic algorithms which are useful in estimating the parameters of nonlinear regression models. Russell Davidson et al. in 1984 (see [2]), in their paper, developed a simple computational procedure for performing a wide variety of model specification tests. In1993, LudgerRuschendorf et.al in their research article constructed as nonlinear regression representations of general stochastic processes so that they got a particular special regression representations on Markov chains and of certain m- dependent sequences.
Longitudinal Variational Autoencoder

Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies. A common approach to analyse high-dimensional data that contains missing values is to learn a low-dimensional representation using variational autoencoders (VAEs). However, standard VAEs assume that the learnt representations are i.i.d., and fail to capture the correlations between the data samples. We propose the Longitudinal VAE (L-VAE), that uses a multi-output additive Gaussian process (GP) prior to extend the VAE's capability to learn structured low-dimensional representations imposed by auxiliary covariate information, and derive a new KL divergence upper bound for such GPs. Our approach can simultaneously accommodate both time-varying shared and random effects, produce structured low-dimensional representations, disentangle effects of individual covariates or their interactions, and achieve highly accurate predictive performance. We compare our model against previous methods on synthetic as well as clinical datasets, and demonstrate the state-of-the-art performance in data imputation, reconstruction, and long-term prediction tasks.
PENGUKURAN ARAH KIBLAT TEMPAT IBADAH DENGAN APLIKASI ARAH KIBLAT DAN AZIMUT MATAHARI

This study examines the comparison of the Qibla direction using the Qibla direction application with the Sun Azimuth method. The problem is that there are many methods that can be used in measuring the Qibla direction especially in today's technological era. One such method is the Android-based Muslim Pro application. From these problems the question arises, namely how to position the Qibla direction using the Qibla direction application and the Sun Azimuth method, then what are the advantages and disadvantages of the Sun Azimuth method with the Muslim Pro Qibla direction application in determining the Qibla direction. This research is a field research. Data obtained through Ephemeris data, interviews and measurements using both methods. After the data collected, it is processed by means of Persistent observation, Triangulation, using reference materials. Then the data is analyzed in a cooperative way, namely comparing the measurement results of the Muslim Pro Qibla direction application with the measurement results of the Sun Azimuth method. This study found that there are differences in measurement results between the Sun Azimuth methods in the application of the Muslim Pro Qibla direction.
Grey Model Combined with Background Value Optimization by BP Neural Network on Seasonal-freezing Turfy Soil Settlement Prediction

With the development of model construction and theory system building on subgrade settlement prediction, a grey model is proposed in this paper for predicting the settlement deformation of turfy soil in seasonal freezing area based on the optimization of background value of BP neural network, BPGM for short. This model establishes two-dimension double-layer BP network by Matlab, considering the influence of time variable and filling height on subgrade settlement, and reconstructs the background value through constant concavity and function cot (x^α) transform. This paper takes the settlement deformation of seasonal freezing turfy soil around Changbai Mountain for instance, comparing the simulation results of four prediction methods, BPGM, theoretical calculation, traditional Lagrange interpolation grey prediction model and BP neural network direct predicting. As a consequence, BPGM has higher accuracy both of data fitting and extrapolation prediction, which illustrates BPGM proposed in this paper is feasible and might provide some reference value for the multi-factor dominated settlement prediction problem.
Prototipo de App de seguimiento escolar de Estudiantes de Ingenieria en Sistemas Computacionales del Tecnm Campus Oaxaca

The Project proposes a solution to the academic problems that arise in the student departments and in the accountability of the directors of the school, for failing to achieve the recommended indicators of the percentage of school leavers or the decrease in the percentages of school degree that are presented by the low performance in students with levels of conditionality (Regular student, Irregular student and Deserter); through a computer solution that provides the academic follow-up of students of Computer Systems Engineering (ISC) of the Tecnológico Nacional de México (TecNM) campus Oaxaca and support in addressing the problem. This prototype integrade some processes that are carried out manually, resulting in cost and time savings, added to the fact that the student will also have quick and detailed access to their academic situation and suggestions in other areas such as tutorial support (scholarships, counseling, psychological support) if required. The solution proposed "Early Warning System (SAT) for students of the ISC of the TecNM Campus Oaxaca through a web apps is based theoretically on research on standard processes to low performance identification, the classification according to the academic status that the accreditation guidelines of subjects issued by the National Technological of Mexico and the academic follow-up of students. The methodology used for the analysis and design of the system was applied research, the collection of information through questionnaires and interviews. At the end of the research, a web app for the academic follow-up of students was obtained to support the School Services Department, Division of Professional Studies and Academic Development as the Institutional Coordinator of the Tutoring Program; in which SCRUM was used as an agile development methodology for software development. It is considered according to the initial evidence that the work carried out contributes to the line of research on educational technology management, since the proposal optimizes the management of resources and time.
공급자 선정을 위한 주관적 가중치를 갖는 퍼지 ELECTRE-III 다기준 의사결정 기법의 개발

In this paper, among multi-criteria decision making (MCDM) procedures, the ELECTRE(Elimination and (Et) Choice Translating Reality)-III is revised to solve the supplier selection problem where both a criteria evaluation matrix and a decision matrix are linguistically evaluated by a group of decision makers. Final weights are obtained through subjective weights from criteria matrix and weights of the entropy from decision matrix. In addition, the fuzzy theory for dealing with ambiguity of linguistic variables is incorporated to develop the new procedure of the ELECTRE-III.The proposed procedure is tested through the numerical example which was used in the previous research with the fuzzy TOPSIS (technique for order preference by similarity to ideal solution). The results of the proposed procedure are compared to validate the outranking orders of suppliers. On the other hand, sensitivity analyses for the five cases with different subjective weights are carried to investigate the changes in the outranking orders of suppliers. In conclusion, the fuzzy ELECTRE-III procedure with entropy weights considering subjective judgement seems to be useful in the supplier selection problem.
The Learning Strategies in English Subject of Islamic Study Program at IAIN CURUP

The research objective was to find out the PAI students‟ strategies in learning English. The design of the research was presented in quantitative descriptive. The subject of this research was 2nd semester of PAI department students. To know what strategies used, the researcher used Stern theory about Language learning strategies which are divided into five categories namely management and planning, cognitive, communication, interpretational and affective. In collecting the data, the researcher used close-ended questionnaire. The result show that PAI students who got A score used 4 strategies in learning English they are management and planning strategies, cognitive strategies communicative strategies and affective strategies. But affective strategy is more dominant 83.7% as a strategy in learning English. PAI students who got B scores used 3 strategies in learning English they are management and planning strategies, cognitive strategies and affective strategies. Management and planning strategy is more dominant 74.4% as a strategy in learning English. PAI student who got C score 3 strategies in learning English they are cognitive strategies communicative strategies and affective strategies. But cognitive is more dominant 76% as a strategy in learning English. PAI student who got E score used 2 strategies in learning English they are interpretational strategies and affective strategies. But affective strategy is more dominant 90% as a strategy in learning English. Although, the strategies used by student who got A score and E score are alike, however students who got E score do not attend the class, the UTS and UAS properly.
A Literature Survey on Modern Heuristic Optimization with AI Applications

A purpose of this study was to assess the impact of Artificial Intelligence (AI) on education premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction and learning. Here the implementation of this research work is on AI powered web based/distributed cloud software or a Web application to analyze the optimal use of AI tools instead of Traditional searching methodologies. Much of the research covered in this review could be applicable to developing strong AI.. In particular, this paper provides a full review of recent developments within the field of artificial intelligence and its applications. The work is targeted at new aspirants to the artificial intelligence field. It also reminds the researchers about some issues they have already known, thereby improving learners experience and overall quality of learning.
A MACHINE LEARNING METHOD TO IDENTIFY HATE SPEECH USING TRIMODEL APPROACH

Classification options were derived from the content of every tweet, as well as grammatical dependencies between words to acknowledge “othering” phrases, incitement to reply with antagonistic action, and claims of sensible or even discrimination against social teams. The results of the classifier were best employing a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. I incontestible however the results of the classifier will be robustly utilised during a applied math model wont to forecast the seemingly unfold of cyber hate during a sample of Twitter information. The applications to policy and deciding ar mentioned. I planned a cooperative multi-domain sentiment classification approach to coach sentiment classifiers for multiple domains at the same time. In our approach, the sentiment info in numerous domains is shared to coach a lot of correct and strong sentiment classifiers for every domain once labelled information is scarce. Specifically, I decompose the sentiment classifier of every domain into 2 parts, a world one and a domain-specific one. the worldwide model will capture the overall sentiment information and is shared by varied domains. The domain-specific model will capture the precise sentiment expressions in every domain. additionally, we tend to extract Tri_Model(Naive mathematician IBK,SVM)sentiment information from each labeled and unlabeled samples in every domain and use it to reinforce the educational of Tri_Model ( Naive bayes,IBK,SVM)sentiment classifiers. Besides, we tend to incorporate the similarities between domains into my approach as regularization over the Tri_Model(Naive bayes mathematician IBK,SVM)sentiment classifiers to encourage the sharing of sentiment info between similar domains. 2 styles of domain similarity measures ar explored, one supported matter content and therefore the different one supported sentiment expressions. Moreover, I introduce 2 economical algorithms to resolve the model of our approach. Experimental results on benchmark datasets show that our approach will effectively improve the performance of multi-domain sentiment classification and considerably vanquish baseline ways.
Efficient Quantization and Multi-Precision Design of Arithmetic Components for Deep Learning

We present a quantization algorithm to find the per-layer bit-width in deep neural networks (DNN) models and then propose multi-precision designs for two fundamental DNN arithmetic operations: multiplication and non-linear activation function computation. The multi-precision multiplier design with truncated partial product bits supports four precision modes (4-bit, 8-bit, 12-bit, and 16-bit) with shared hardware resource so that the power consumption in low-precision modes can be reduced by turning off unnecessary hardware circuits. For the evaluation of non-linear activation functions, we compare three different approaches in various precision requirements and observe that the best design method depends on the bit-accuracy.
A Comparative Study of Various Person Re-Identification in Videos

The various techniques for person re-identification in videos have been discussed and presented in this work. Many techniques like re-identification using neuromorphic criteria, deep metric learning with pose invariant case, transformation based on relaxation of label space, networks using multi-cameras, hybrid representation reinforced, bag of words model and log density gradient clustering are reviewed and illustrated in brief. This study can be used to further improve the existing techniques for person re-identification.
Effect of Seismotectonic And Engineering-Geology on Seismic Danger of Territories In India

Seismic hazard map seen in territory and adjacent areas has been prepared employing a deterministic supported the computation of synthetic seismograms. Data set consists of structural models, seismogenic zones, focal mechanisms and earthquake catalogues, using realistic strong ground motion modelling with the knowledge of the physical process of earthquake generation, the extent of seismicity and wave propagation in anelastic media. Prehistoric on earthquakes are often obtained through the utilization of palaeoseismic techniques. the prevailing procedures for assessing different natural hazards are essentially similar. the primary step is usually the definition of the source model in terms of the source location and therefore the frequency and size of probably damaging events; the second step relates to the propagation of disturbances from the source; and the third step, involves the event of an exposure model for a site or for a neighborhood. it's obvious that accuracy of assessment depends on the number and quality of input file. There are too many samples of an uncritical use of published data for hazard mapping, all of which illustrate the necessity for a careful revision and unification of the input information. Processing heterogeneous data leads only to misleading result.
ANALYTICAL STUDY ON DATA SECURITY FOR GRAPHICAL PASSWORD AUTHENTICATION USING DROPS

In the DROPS system, we isolate a record into pieces, and reproduce the divided data over the cloud nodes. Every one of the nodes stores just a solitary section of a specific data document that guarantees that even if there should arise an occurrence of a successful assault; no significant information is uncovered to the assailant. In cloud computing, the information is put away on outsider space which brings about a security concerns. Moreover, the DROPS philosophy doesn't depend on the conventional cryptographic techniques for the data security; consequently mitigating the arrangement of computationally costly procedures. Discontinuity is a procedure which cuts each sensitive record into a few sections so that it is difficult to accomplish absolute document in one attempt, and for each enlisted client a mystery key is produced so that to verify the data. T-shading idea is utilized for putting away the parts in nodes and for better unwavering quality and execution, assets are imitated at the excess areas and utilizing repetitive infrastructures.
Cryptographically Strong Elliptic Curves of Prime Order

The purpose of this paper is to generate cryptographically strong elliptic curves over prime fields Fp, where p is a Mersenne prime, one of the special primes or a random prime. We search for elliptic curves which orders are also prime numbers. The cryptographically strong elliptic curves are those for which the discrete logarithm problem is computationally hard. The required mathematical conditions are formulated in terms of parameters characterizing the elliptic curves.We present an algorithm to generate such curves. Examples of elliptic curves of prime order are generated with Magma.
SISTEM INFORMASI POINT REWARD PADA METRO INTERIOR FAKULTAS TEKNIK UNIVERSITAS BATAM BERBASIS WEBSITE DENGAN BAHASA PEMROGRAMAN PHP DAN MYSQL

CV. Mitra Perkasa Bintan is one of the contracting company that competes with other contracting companies in developing business in the field. CV. Mitra Perkasa Bintan serve some services are sales-natural materials, heavy equipment rental and leasing services of contractors. However, these companies have several problems in the transaction. All lease transactions is still conventional. Marketing of the rental of heavy equipment is also just around the location of the company so many people do not know the CV. Mitra Perkasa Bintan. Therefore need to be designed an application using Internet facilities that can be used customers to rent heavy equipment and save time and costs in conducting transactions between companies and customers. So CV. Mitra Perkasa Bintan requires a web-based online application to support the rental of heavy equipment as a container or an intermediary between company and customers in terms of trade are accompanied by pictures of heavy equipment to be leased. The purpose of this final project provides a solution to these problems so that customers can rent heavy equipment through the website. The system created is a web-based online applications using program PHP language and MySQL as database. With of building this web-based online application customers can rent heavy equipment through the website CV. Mitra Perkasa Bintan.
THE INFLUENCE OF EDUTAINMENT METHOD ON STUDENT LITERATION ABILITY

This study aimed to determine the method of learning paikem education on literacy capability to write the beginning of students in the eyes of Indonesian language in grade 2 SDN Pondok Kacang Timur 03. This research is quasi experimental design. This design is a nonequivalent control group design almost together with the pretest-posttest control group design implemented at SDN Pondok Kacang Timur 03 class 2a (30 students) and class 2b (30 students). Data collection used tests and behaviors. The results showed that edutainment learning method had an effect on writing literacy capability in Indonesian class of SDN Pondok Kacang Timur 03. The capability of initial writing literacy in the experimental class get the average score of pretests 75,3 and posttest 82,76. The average score control class the writing capability of pretest 70.5 and posttest 74.23. The result of the calculation of Thitung hypothesis (7,580) is greater than Ttable (2.002) at α = 0.05. Ho have been rejected cannot be paikem edutainment learning method to the literacy capability of writing the beginning of student
On some conjectures of P. Barry

We prove a number of conjectures recently stated by P. Barry, related to the paperfolding sequence and the Rueppel sequence. Furthermore, we study the regularity of sequences involved in the paper, and prove that for all q≥2, the sequence consisting of the positive integers whose odd part is of the form 4k+1 is not q-regular. Finally we establish the 2-regularity of two sequences of Hankel determinants.
Masked Face Recognition using ResNet-50

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.