Reviewerly/exHarmony huggingface dataset:
Genetic Programming-Based Feature Learning for Facial Expression Classification

© 2020 IEEE. Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.
Prediction of soil physical properties by optimized support vector machines

A b s t r a c t.The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated.The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied.In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression.The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model.Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model.The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model.The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively.It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods.K e y w o r d s: soft
A Robust t-Test

The t-test is a work horse of a lot of statistical analysis in HCI. There are a lot of myths about how robust it is to deviations from normality and other assumptions. However, when faced with practical data, particularly those coming from usability studies, the claims of robustness do not stand up. This chapter reevaluates the t-test as a test for an effect on the location of data. This leads to considering robust measures of location, such as trimmed or Winsorized means and associated Yuen–Welch test as a robust alternative to the traditional t-test.
Regression and Matching with Observational Data

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
Engineering Students Attitude towards On-Line Shopping in Dindigul

The demonstration of buying items or administrations over the web is approached line shopping. In this age of quick moving way of life, building understudies are busier that what they were couple of years back. Today both urban and provincial regions appreciate web offices. It is absolutely hence building understudies are additionally buying the items or administrations on the web. The reason for this examination is to dissect factors influencing on internet shopping conduct of customers that may be a standout amongst the most essential issues of web based business and advertising field. To research these speculations 150 polls scattered among online customers for example designing undergrads in Dindigul. Respondents to the survey were purchasers of on-line in Dindigul which arbitrarily chose. At long last factor investigation was utilized on information so as to test conjectures of study
Study the Effect of Knowledge Sharing on Employees’ Creativity of IRI Police Engineering Administration

This article was executed by the title of studying the effect of knowledge sharing on employees’ creativity of IRI police engineering administration. Methodology: The methodology was applied and descriptive-correlative according to the objective and identity of issues. Statistical population of this study is all managers, assistances, and skilled experts in IRI police who were 140 that all were selected as statistical sample. Standard questionnaire was used in order to collect data. The ideas of experts and scholars were used to test the face and content validity of questionnaires. Cronbach’s alpha coefficient was used to test the reliability of questionnaires. The calculated alpha for questionnaires was obtained higher than 0.7. Results: Results showed that knowledge sharing has positive and significant effect on employees’ creativity. In other words, knowledge sharing improves employees’ creativity. Conclusion: According to the results of this research after indication of advantages and disadvantage of knowledge sharing, organization can identify proper strategy and approach about knowledge sharing, use the best methods of increasing knowledge sharing in organization, reinforce advantages and weaknesses, and also stop the obstacles reducing knowledge sharing in organization. Finally, it must be noticed that the knowledge sharing strategy is a continued attempt which must always be alive and in organization.
Competency building approach in education of pupils with autistic specter disorder

The content of special education in our time should contribute to the formation in children with disabilities of the ability to cope with daily social and personal tasks that arise when interacting with the environment - that is what "competence" means. In this article, based on the studied special psychological and pedagogical literature, the concepts of "competence", "competence" are considered, thecharacteristics of the "competence-based approach" are given in relation to teaching children with autism spectrum disorders.
Modeling Urban Solar Energy with High Spatiotemporal Resolution

Alternative sources of energy are being sought after in the world today, as the availability of fossil fuels and other non-renewable resources are declining. Solar energy offers a promising solution to this search as it is a less polluting renewable energy resource and can be easily converted into electricity through the usage of photovoltaic systems. This thesis focuses on the modeling of urban solar energy with high spatiotemporal resolution. A methodology was developed to estimate hourly solar PV electricity generation potential on rooftops in an urban environment using a 3-D model. A case study area of Ryerson University, Toronto was chosen and the incident solar radiation upon each building rooftop was calculated using a software tool called Ecotect Analysis 2011. Secondly, orthophotos of the case study area were digitized using Geographic Information Systems in order to eliminate undesirable rooftop objects within the model. Lastly, a software tool called HOMER was used to generate hourly solar PV electricity estimates using the values generated by the other two software tools as input parameters. It was found that hourly solar PV output followed the pattern of a binomial curve and that peak solar generation times coincided with summer peak electricity consumption hours in Ontario.
AN INTEGRATED APPROACH TO THE STUDY OF THE POEM OF N. IZHENDEY “THE VOICE OF THE UNBORN CHILD”

In modern education, methods of an integrated approach to learning are actively used.When studying literature, knowledge from the field of related sciences is acceptable: folklore, mythology, history, psychology, social science.This makes it possible to expand students' knowledge about the art world of a work, the national picture of the world of an ethnos, and socio-historical reality.The purpose of this work is to outline the role of the poem by N. Izhendey "The Voice of the Unborn Child" in the moral and aesthetic education of the younger generation, since the poem is included in the regional component of the school curriculum.
Cloud based Single Shot Multi Box Detection (SSD) Architecture Models for Jakarta Traffic Jam Monitoring

Traffic jam is still one of the main problems in Jakarta Indonesia. To encounter this problem, we proposed system to monitor the road and perform calculation on vehicles speed and road density. To achieve this, we used SSD to detect vehicle traffic on Jakarta’s road which obtained from public IP Camera. The main contribution of this work are utilization of public IP Camera and remote traffic analytics via cloud based artificial intelligence system. As a result, the program can perform monitoring on roads condition in real time. The accuracy of the system in average is 80% with highest accuracy achieved 92% to detect vehicle speed and road density.
List of Reviewers for this Special Issue

Journal of Management StudiesVolume 56, Issue 5 p. 1040-1041 List of Reviewer List of Reviewers for this Special Issue First published: 11 March 2019 https://doi.org/10.1111/joms.12442Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue5Special Issue: ‘Theories from the Lab’July 2019Pages 1040-1041 RelatedInformation
News: PhotonicsViews 3/2021

PhotonicsViewsVolume 18, Issue 3 p. 4-10 NewsFree Access News: PhotonicsViews 3/2021 First published: 16 June 2021 https://doi.org/10.1002/phvs.202170302AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume18, Issue3June/July 2021Pages 4-10 RelatedInformation
Naukotheka

An automated information system of electronic interaction forming a joint information and communication space for fundamental research and application development, as well as applying in the field of education by citizens, scientific specialists, Russian Academy of Sciences information systems, scientific organizations and institutions. In the near future Naukotheka has the potential to provide support of education and fundamental research for the countries of the Eurasian Economic Union at the level of platforms such as Scopus, Web of Science, Google Scholars, etc.
RIB: Analysis of I/O Automata

The evaluation of Boolean logic is a compelling quandary. Given the current status of peer-to-peer epistemologies, leading analysts famously desire the emulation of Web services, which em-bodies the essential principles of hardware and architecture. We introduce a novel approach for the analysis of the UNIVAC computer, which we call RIB.
Apperception

This paper describes a neuro-symbolic system for distilling interpretable logical theories out of streams of raw, unprocessed sensory experience. We combine a binary neural network, that maps raw sensory input to concepts, with an inductive logic programming system, that combines concepts into declarative rules. Both the inductive logic programming system and the binary neural network are encoded as logic programs, so the weights of the neural network and the declarative rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). We apply our system, the Apperception Engine, to the Sokoban domain. Given a sequence of noisy pixel images, the system has to construct objects that persist over time, extract attributes that change over time, and induce rules explaining how the attributes change over time. We compare our system with a neural network baseline, and show that the baseline is significantly outperformed by the Apperception Engine.
Efficient Mining of Frequent Item Sets on Large Uncertain Databases

In applications like location-based services, sensor monitoring systems and data integration diligence the data manipulated is highly ambiguous. mining manifold itemsets from generous ambiguous database illustrated under possible world semantics is a crucial dispute. Mining manifold Itemsets is technically brave because the ambiguous database can accommodate a fractional number of possible worlds. The mining process can be formed as a Poisson binomial distribution, by noticing that an Approximated algorithm is established to ascertain manifold Itemsets from generous ambiguous database exceedingly. Preserving the mining result of scaling a database is a substantial dispute when a new dataset is inserted in an existing database. In this paper, an incremental mining algorithm is adduced to retain the mining consequence. The cost and time are reduced by renovating the mining result rather than revising the whole algorithm on the new database from the scrap. We criticize the support for incremental mining and ascertainment of manifold Itemsets. Two common ambiguity models in the mining process are Tuple and Attribute ambiguity. Our approach reinforced both the tuple and attribute uncertainty. Our accession is authorized by interpreting both real and synthetic datasets.
Reusing Old Computers – An Environment Friendly Method to Produce Computational Performance

Disposing the old computer parts to the environment, the toxic chemicals from the hardware mix-up with soil and makes the solid water poisonous. Most of the computers which are made up of a mix of toxic chemicals like lead, mercury, cadmium and similar materials are very much harmful to the environment when they are thrown to the environment without proper disposal. With the rapidly growing number of PCs and different electronics regularly filling landfill regions, any chance to drag out the lifecycle of these components ought to be taken. This paper aims at using such old computing parts which are usable for computations. Also, instead of using heavy PC on the client side, a replacement of thin PC and then client PC will reduce the hardware elements also the power consumption of these machine. Also there is a high demand of computing power in research projects which takes hours of time to produce results. A low scale compute cluster made upon this reusable computer serves this need also. This compute clusters are provided as a virtual server over a private cloud.
Supplemental Material: Three stages of arc migration in the Carboniferous-Triassic in northern Qiangtang, central Tibet, China: Ridge subduction and asynchronous slab rollback of the Jinsha Paleotethys

Sampling details (Table S1), zircon geochronological data (Table S2), geochemical data (Table S3), and a summary of zircon morphology and geochronology (Table S4) for Carboniferous-Triassic samples from the northern Qiangtang terrane. Geochemical data set of Carboniferous-Triassic magmatic rocks from the northern Qiangtang terrane (Table S5).
Enhancement of the Homomorphism Encryption Based Mechanism by Solving the Integrity Problems Effectively on Multi Keyword Search in Cloud Computing

Cloud computing gives part of advantages to endeavors to offload their information and software services to cloud administrations by saving them lot of money that must be spent on foundation setup cost. Despite that, while offloading the information privacy and protection is a vital concern. Correspondingly for example when the emergency clinics and medicinal services association transfer their patient's subtleties to cloud and when the information is included, it will influence the protection of the patients. So privacy is a vital concern while offloading the information to cloud. Most of solutions for privacy depend on encryption and information to be offloaded is encoded and put away in cloud. Lots of encryption techniques like cryptography, symmetric key etc. methods are used for encrypting the information before uploading to cloud. However, the reaction in this encryption system is that, the encrypted information isn't structure preserving and it is not suitable for ranking or searching. To solve the complications of this searching and ranking due to disordering a homomorphism encryption based mechanism is proposed but this solution also suffers from the problem of integrity attacks on result. So this paper discussed the enhancement of the homomorphism encryption based mechanism by solving the integrity problems effectively.
Improved grey wolf optimisation algorithm for heterogeneous cloud environment task scheduling

The attraction towards cloud computing by industry and individuals increases everyday as the benefits and advantages are much reliable and convenient to user to make the process simple. Software and data giants like Google, Microsoft, and Apple are efficiently utilising the cloud features and the research towards improving its efficiency and utilisation is going on worldwide. Cloud computing has large computational data intensive task and by reducing the complexity of task scheduling the efficiency could be improved. This research identifies the issues the existing task scheduling model and provides an optimised scheduling algorithm. Conventional models such as particle swarm optimisation and PBEES algorithm are compared with proposed improved grey wolf optimisation model experimentally to achieve 96% of utilisation efficiency. This reduces the computation cost and provides high performance computing with reliability among the clients and service providers.
A computational perception of locating multiple longest common subsequence in DNA sequences

Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCS) is one of the indispensable issue to be unravelled viably in computational science. Discovering LCS is fundamental undertaking in deoxyribonucleic acid (DNA) arrangement investigation and other molecular biology. In this paper, new calculation for discovering LCS of two DNA successions and its area is proposed. The objective of this created framework is to discover the area and length of all subsequences which introduces in the two arrangements. To achieve this, DNA sequences are stored in an array and the comparison of DNA sequences are performed using matching algorithm. At the end of matching process, group of subsequence are obtained. Then the length and location of the matched subsequence are computed. After completing the matching process, longest common subsequence(s) is located. In this proposed work, maximally obtained length of LCS is 8. Finally, the computation time is calculated for locating LCS in DNA sequences. In addition to this, computation time is analysed by gradually increasing the length (in characters count) of DNA sequences from 100, 200, 300, 400 and 500. It concludes that computation time for locating LCS in various lengths of DNA sequences took few seconds difference only.
Can Genetic Programming Do Manifold Learning Too?

© Springer Nature Switzerland AG 2019. Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.
Scholarship in the Twenty-First Century: Surviving the Next Millennium

The history of scholarship has remained intact over much of the last five centuries. Now, as we approach the twenty-first century, the future of scholarship is undergoing dramatic changes. This presentation examines the impact of technology on scholarship in the twenty-first century as well as the changing roles of the library, publisher and vendor in the information cycle. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Notions of Norm-Attainability and Orthogonality in Banach Spaces

A. New notions of norm-attainability and orthogonality are given in this paper.We first consider Range-Kernel orthogonality in norm-attainable classes then we characterize orthogonality conditions for Jordan elementary operators in a Banach space setting.