Reviewerly/exHarmony huggingface dataset:
Prediction of Second-Harmonic Generation Wave-Front Distribution by Extreme Learning Machine

In the applications of wave-front detection using second-harmonic generation, the spatial phase distribution needs to calculate accurately before and after frequency doubling in real-time. This letter presents a learning-based method called extreme learning machine to fit the corresponding relationship of phase between the fundamental frequency wave and the second-harmonic. The Zernike coefficients of the fundamental frequency wave wave-front and the second-harmonic wave-front are used as input data for Extreme Learning Machine model training and testing. The effects of the intensity-dependent phase shift and walk-off are also considered. The reliability of the trained Extreme Learning Machine model was accessed based on simulation results. The proposed method has shown distinct competitive advantages in real-time calculation efficiency. The well-trained Extreme Learning Machine model only needs 0.026 seconds to accurately predict the phase distribution of the fundamental frequency wave. The runtime is three orders of magnitude smaller than the traditional numerical calculation method.
ML-LRC: Low-rank-constraint-based Multi-label Learning with Label Noise

Existing multi-label learning algorithms mostly assume that all class labels in an obtained data set are completely accurate. This assumption is rarely valid. As the scale of the label space increases, owing to limited domain knowledge and differences among methods of data acquisition, some examples will be unlabeled or mislabeled. The introduction of such noise results in the poor performance of trained multi-label classifiers. This paper proposes a new method for multi-label learning with label noise, called low-rank-constraint-based multi-label learning with label noise (ML-LRC), by incorporating low-rank matrix recovery and features selection into the learning process. First, we assume that two strongly correlated labels share more samples than two weakly correlated labels. Based on this assumption, the ML-LRC applies low-rank constraints to the label matrix to mine the local correlations of class labels and remove noise from the observed label matrix. Second, we believe that each class label only correlates to some features. So we control the sparsity of the coefficient matrix to filter out label-specific features, which can reduce the complexity of the model and greatly improve the performance of the classifier. The accelerated proximal gradient method and alternating direction method of multipliers are adopted to solve the problem. Experiments conducted on four benchmark multi-label datasets demonstrate the competitive performance of our proposed method compared with several state-of-the-art methods.
Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences

First posted April 28, 2020 For additional information, contact: Director, Geology, Geophysics, and Geochemistry Science CenterU.S. Geological SurveyBox 25046, Mail Stop 973Denver, CO 80225 Some Earth science data, such as geochemical measurements of element concentrations, are non-stationary—the mean and the standard deviation vary spatially. It is important to estimate the spatial variations in both statistics because such information is indicative of geological and other Earth processes. To this end, an estimation method is formulated as a Bayesian hierarchical model. The method represents the spatially varying mean and the spatially varying standard deviation with basis functions; this formulation implicitly accounts for a spatially varying covariance function. A unique advantage of this method is that it can map the mean, the standard deviation, quantiles, and exceedance probabilities. The method is demonstrated by mapping titanium concentrations, which are measured in the coastal plain of the southeastern United States. Various checks demonstrate that the model fits the data and that the estimated statistics are geologically plausible.
Rancang Bangun Aplikasi Dilib-Us (Digital Library) Berbasis Android Menggunakan Metode Prototyping

Human life, both directly and indirectly, has been influenced by developments in the field of information and communication technology, one of which is in the library field. The library is one place where information and knowledge are easily obtained. But now libraries are still more often found in physical form where access to locations is still an obstacle. There are still many libraries that only provide a collection of printed books, making it difficult in the search process, and need more space to accommodate the collection of books. The manual lending and repayment process causes services to be less effective and efficient. The author tries to create an android application-based library system with the name Dilib-Us where there are search, loan and return features that can improve the quality of library services. This system applies the data collection method and prototype method as a system development model. The purpose of this study is to facilitate service users in finding information on libraries through the Dilib-Us application. It is expected that this application can provide facilities for prospective borrowers in the process of borrowing and returning books and for librarians in processing data, such as: adding, changing and deleting data.
ANALYSIS OF COGNITIVE ABILITIES OF SCHOOL-AGE CHILDREN BASED ON EATING HABITS AND NUTRITIONAL STATUS

Dietary habit is behaviors that are appropriate for the selection of the right foods so that they will provide good nutritional status. Nutrients that consist of macronutrients such as carbohydrates, proteins, fats, and micro substances such as vitamins and minerals needed by the body for the process of growth and development. The purpose of this study was to determine the relationship between eating patterns with the nutritional status and cognitive abilities of elementary school children. Cross-Sectional research design. The population in this study were all students as many as 367 students, the sampling used was a Stratified random sampling technique, a sample of 192 respondents. Data analysis uses the Spearman Rank Rho statistical test. The results of the analysis of dietary variables with nutritional status were obtained ρ (0.003) &lt;α (0.05), so H0 was rejected, meaning that there was a relationship between eating patterns and nutritional status of school-age children, with a value of r = 0.210 which showed a high positive correlation. The results of the analysis of nutritional status variables with cognitive abilities obtained results ρ = 0.00, &lt;α = 0.05. meaning that there is a relationship between nutritional status and cognitive learning achievement in elementary school children. Normal nutritional status can be obtained by applying a positive diet so that the intake that enters the body in accordance with the needs of the body. Children who have normal nutritional status have good cognitive abilities. Parents are the most important support in optimizing children's growth and development
Determination of Trace Elements Concentration in Soil Samples of Fika Agrarian Community of Yobe State, Nigeria, Using MP-AES Analytical Technique

Soil is conceivably the most endangered component of our environment which is open to potential contamination by a variety of different pollutants arising from majorly human activities such as nuclear, industrial, agriculture, among others (Djingova & Kuleff, 2000;Bermea, et al., 2002& Kalantari, et al., 2006).Soil is a composite system which consists of organic and inorganic matter that directly and indirectly supports plant and animal life and is a crucial component of our rural and urban environments (Emanuel, 2015).The accumulation of certain elements, in particular trace elements in agricultural soil is of increasing concern due to food safety issues, potential health risks and its detrimental effects on soil ecosystems (McLaughlin et al., 1999).The main natural sources of elements in soils are weathering of parent material and soil erosion (Emmanuel, 2014).The trace elements are primary content of rocks released due to weathering processes and their moderate concentration levels are usually safe but an increase in concentration of these elements in the environment can be significantly destructive to plants and animal life (Goleakar et al., 2013;Macfarlane & Burchett, 2000).Baseline data on trace element levels in soil is beneficial to all agrarian communities particularly in fertilizer applications, with view to identifying suitable agricultural activities on soil as well as in resource identification, management and land use planning (Wilcke, et al., 1998).The association of elements in varying concentrations in soils is indicative of the mineral content, which in turn serves as a measure of soil fertility (Abubakar, 2007).Hence, there is the crucial need to ensure that adequate information on the elemental concentration of soil is available to the agrarian communities so as to identify which part of the soil is best for planting certain crops and the types of fertilizer to be applied to the soil (Emanuel, 2015).Despite the importance of this data on trace elements to agrarian communities, none of such study was conducted in Fika local government area of Yobe state.Notwithstanding, only few relevant studies were conducted in the Northeast
Formation of spectral triplets induced by parity deformation in a quantum dot–cavity system

In this work, we conduct an investigation on the optical emission properties of a pumped-dissipative QD–cavity system within the Lindblad master equation approach, where the cavity mode is modeled through the R-deformed realization of the Heisenberg algebra (RDHA). We found that the parity deformation parameter associated with the RDHA gives rise to a collective effect in the photonic emission of the system by reordering of the one-photon optical transitions, and it is demonstrated that there are two critical values for this parameter where a new type of spectral triplet arises in the emission spectrum. Our results reveal that depending on the critical value of the deformation parameter, the spectral triplets observed in the emission spectrum exhibit signatures of the weak- and strong-coupling regimes simultaneously.
Salute to the genetic tambourine man

We followed the Genetic Tambourine Man[1] Professor Nirupam Chakraborti since 2003 and tried to respond to his calls for special issues on applications of genetic algorithms in materials science, w...
Analysis of the Current Situation and Improvement Strategies of College English Informatization Teaching Based on MOOC Platform

The advent of the information age has brought new changes to all walks of life, and also established a new platform for autonomous learning for the majority of learners.In the context of the information age, college English teaching is also undergoing reforms in response to changes, and is constantly moving towards information and technology development.Combining the rise and promotion of the new "Mu-class" teaching methods and methods in the Internet era, it also provides a new path for college English information-based teaching.This article starts discussion and research on this.
Joint multi-party remote preparation of arbitrary qudit states via a &amp;lt;italic&amp;gt;d&amp;lt;/italic&amp;gt;-level six-qudit Cluster state

We propose two protocols for joint, multi-party remote preparation of qudit states by using a d -level six-qudit Cluster state as the quantum channel. The first protocol is used for the joint remote preparation of an arbitrary two-qudit state via the d -level six-qudit Cluster state. The second is used for bi-directional joint remote preparation of an arbitrary single-qudit state via a d -level six-qudit Cluster state. All senders share information about the prepared state. The first sender applies Positive Operator-Valued Measurement (POVM) on her entangled particles in accordance with her information about the prepared state, while the second sender performs projective measurements in accordance with the information about the prepared state and the first sender’s measurement results. The receiver can prepare the original state by performing a corresponding unitary operation according to the measurement results from all the senders. These two protocols have the advantage of being able to provide a high channel capacity by using a d -level Cluster state as the quantum channel.
The Neural Network Conversation Model enables the Commonly Asked Student Query Agents

One of the challenges in academic counselling is to provide an automated service system for students.There several query questions asking the faculty staffs about related-academic services each semester.Offered the communication interface more convenience, the novel approach based on neural network model is introduced to investigate the automated conversational agent.The pre-defined dialogue sentences were collected manually from the student query questions and used as the training dataset.The questions have been varied and grouped by topic-categorizing queried from the registration help desk of the department.Artificial intelligence and machine learning have contributed each other to build the conversational agent so-call KUSE-ChatBOT plugged and used in the modern messenger application, LINE.The system is also included the dialogue backend management system to use in further deep learning model updating.Tensorflow, the machine learning development platform originated by Google, was performed and obtained the learning model using Python development kits.The LINE Messaging APIs is then contributed as the user interface where users could have FAQs' conversation via the LINE application.The KUSE-ChatBOT is outperformed and efficient by providing automated consultation to the students precisely with the accuracy rate over 75 percent.The system could assist the staffs to be able to lessen the workload of answering the same question repeatedly and give response to the student timely.
Breast Cancer Diagnosis Based on Genetic-fuzzy Logic and ANFIS Using WBCD

Breast cancer is conspicuously one of the most common diseases that cause death for women.Besides, it is increasing with high rates.Consequently, Breast Cancer must be discovered in early stages to avoid death or losing part of the body due to late diagnosis.Thus, there are many researches for computerizing breast cancer diagnosis with different techniques.It reduces human decision rate in order to decrease the mortality rate through the disease.Therefore, we have a major motivation for this highly significant work.The primary focus of the research is to produce a multi-model that can predict the diagnosis whether benign (noncancerous) or malignant (cancerous) nature of a tumor with high accuracy using two methods.The first method is a combining of two major methodologies, namely the fuzzy based systems and the evolutionary genetic algorithms (GFIS).The second method intends to an integrated view of implementing an adaptive neuro-fuzzy inference system (ANFIS) with feature selection using principle component analysis (PCA).Wisconsin breast cancer database (WBCD) is applied because it contained records of patients with known diagnosis.The proposed target of this research compares breast cancer diagnosis based on physical characteristics of the tumour between GFIS and ANFIS.GFIS has achieved a high performance with 97.7% however ANFIF has achieved the highest accuracy with 99.1%.
The Influence of Survey, Question, Read, Recite, Review Method Toward Reading Comprehension Ability Mediated by Working Memory

Reading comprehension plays a vital role in students' academic achievement. This empirical study aims to determine the influence of survey, question, read, recite, and review (SQ3R) method on reading comprehension ability. The impact of mediation on semantic and phonological working memory toward reading comprehension ability after being given the SQ3R method. The empirical study was Quasi-experimental with untreated control group design with dependent pretest and posttest samples. The total participants are 57 students in elementary school. Thirty of the subjects were selected based on colored progressive matrices and reading comprehension measurement in equal. In this case, 15 students as treatment groups and 15 students as a control group. Backward digit span task was used to measure phonological working memory, sentence completion, responsibility for semantic working memory, and reading comprehension test for reading comprehension ability. Causal analysis technique by SPSS MACRO PROCESS was used to examine the hypothesis of the research. The results revealed that there was an influence SQ3R method toward reading comprehension ability. Semantic working memory and phonological working memory are mediate between SQ3R and reading comprehension, but the mediating effect of semantic working memory is more reliable than phonological working memory. This research indicated that the effectivity of the SQ3R method to improve reading comprehension was mediated by working memory capacity in Bajo’s children.
Ekstarsi Ciri Citra Ultrasonografi Abdomen pada Regional 3, 6 dan 8 Menggunakan Metode Gray Level Co-Occurance Matrix (GLCM)

ltrasonography is the most popular modality used for clinical applications to detect abnormalities related to abdominal organs such as the liver, kidneys, uterus and other organs. In the context of organ recognition, image processing techniques are needed to improve image quality without eliminating important information contained in it. The purpose of this study was to identify prominent features of organs found in regional 3 (left liver lobe), 6 (left kidney and spleen) and 8 (bladder and prostate) from the abdomen using the gray level co-assurance matrix (GLCM) method. with features that are used, among others: contrast, correlation, energy and homogenity in the direction 0°, 45°, 90° and 135°. The results obtained show that each organ has a GLCM value that varies between one organ and another organ, namely regional Hypochondrium Dexta for left liver lobe organ, contrast 〖5.1473 x10〗^(-4)-〖1 x10〗^(-3), corrlation 0.9786-0.9890, energy 0.9073-0.9754 and homognity 0.9995 - 0.9997. Lumbaris Sinistra Regional for left ginjla organ, contrast 〖3.8526 x10〗^(-4)- 〖6.2959 x10〗^(-4), corrlation 0.9737 - 0.9862, energy 0.9737 - 0.9862 and homognity 0.9997-0.9998 while spleen, contrast 〖6.0350 x10〗^(-4) - 9.3762 x 〖10〗^(-4), corrlation 0.9640-0.9812, energy 0.9493-0.9816 and homognity 0.9995 - 0.9997. Regional Hipogastrum in bladder organs, contrast 4.0614〖 x10〗^(-4)- 9.9296 〖x10〗^(-4), corrlation 0.9770 - 0.9890, energy 0.9085 - 0.9884 and homognity 0.9995 - 0.9998 for prostate organs, contrast 3.0383 〖x10〗^(-4)-3.1532 〖x10〗^(-4), corrlation 0.9727 - 0.9738, energy 0.9877 - 0.9885 and homognity 0.9998. The range values represent the organs studied. The results of the research obtained indicate that the features of GLCM can be used to identify organs in regions 3, 6 and 8.
Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization

Based on the constrained non-negative matrix factor algorithm, the article presents a new approach to facial recognition recognition. Our proposed method incorporated two tasks in an automatic expression analysis system: facial feature extraction and classification into expressions. To obtain local and geometric structure information in the data as much as possible, we amalgamate max-margin relegation into the constrained NMF optimization, resulting in a multiplicative updating algorithm is additionally proposed for solving optimization quandary. Experimental results on JAFFE dataset demonstrate that the effectiveness of the proposed method with improved performances over the conventional dimension reduction methods.
Formalization of Cognitive-Agent Systems, Trust, and Emotions

A cognitive agent is an agent characterized by properties that are generally attributed to humans. Cognition is viewed here as a general mechanism of reasoning (in contrast with reactive agents) about knowledge. Such agents can perceive their environment, reason about fact or epistemic states of other agents, have a decision making process, etc. This article presents the main concepts used in cognitive agents formalizations, and speak about two particular concepts related to humans: trust and emotion. The language used for cognitive agents is here a logical language because it particularly fits well for both knowledge representation and reasoning formalization. But, even if trust and emotion can be both easily formalized by logical languages, we show that some numerical models are also well adapted.
Optimization Naive Bayes Algorithm Using Particle Swarm Optimization in the Classification of Breast Cancer

Methods of data mining classification are used in various fields of research.Naive Bayes is one of the most used algorithms of data mining classification, especially in the medical science because Naive Bayes is considered good method for the data concerned with a statistical diagnosis.Optimization of diagnosis results needs to be done in terms of various weaknesses, including data passing certain classes even though the data it is irrelevant or relevant so the need to be optimized by feature selection.Optimization was done using Particle Swarm Optimazation algorithm for feature selection in breast cancer classification using Naive Bayes.The Naive Bayes method is used for the classification of breast cancer, while the Particle Swarm Optimization Algorithm is used for the selection of irrelevant attribute features in order to obtain optimal diagnosis results.The results of the Naive Bayes method were 95.49% while after being optimized with the Particle Swarm Optimazation Algorithm the result was 98.19%.
Information security: anti-virus protection technologies.

The article deals with the normative and methodological bases of classification of information security threats related to the use of malware against information systems. The threats to the impact on the information systems of certain types of malware are highlighted. In the process, antivirus experts desing and develop new methodologies to make them stronger, more and more every day. The purpose of this paper is to reviev these methodologies and outline their strengths and weaknesses to encourage those and interested in more investigation on these areas.
AI 32TFLOPS Autonomous Driving Processor on AI-Ware with Adaptive Power Saving

AI processors are extending the application area into mobile and edge devices. The requirement of low power consumption which has been an essential factor in designing processors is now becoming the most critical factor for mobile AI processors to be viable. The high performance requirement exacerbates the energy crisis caused by a large area due to a lot of processing engines required for implementing AI processors. We present the design of an AI processor targeting both CNN and MLP processing in autonomous vehicles. The proposed AI processor integrates Super-Thread-Core composed of 16384 nano cores in mesh-grid network for neural network acceleration. The performance of the processor reaches 32 Tera FLOPS enabling hyper real-time execution of CNN and MLP. Each nano core is programmable by a sequence of instructions compiled from the neural network description by the proprietary AI-Ware. The mesh-array of nano cores at the heart of neural computing accounts for most of the power consumption. The AI-ware compiler enables adaptive power gating by dynamically compiling the commands based on the temperature profile reducing 50% of total power consumption.