Reviewerly/exHarmony huggingface dataset:
Improved Ant Colony Optimization based Task Scheduling and Load Balancing Algorithm for Cloud Computing Infrastructure

Cloud Computing consists of virtual machines which are interconnected and provides distributed and parallel computing capabilities. Nowadays due to the advantages of cloud computing infrastructure, demands are also increasing and the advancement is also taking place to fulfil the demands and requirements. Task scheduling is an elementary issue in the cloud computing environment. Many researchers have attempted to solve this issue in order to this, many meta-heuristics approaches for task scheduling have been proposed. It is known that in the cloud, task scheduling is the NP-hard Optimization problem. Many environmental factors and types of tasks may also affect the performance of the task scheduler. In this paper, a task scheduling algorithm has been proposed which  is based on the meta-heuristic optimisation approach called Ant Colony Optimization (ACO). The proposed approach helps the cloud infrastructure to balance the load of the visual machine and physical machine. It balances the load of the cloud infrastructure with improving the makespan of the submitted tasks. The proposed algorithm is implemented using the Cloudsim Simulator. Further, the implementation results of the proposed algorithm are compared with the related work to find the performance of the proposed algorithm.
Implementation of MFCC And LVQ Methods For Learning English Pronounciation

English is one of the most popular international languages ​​spoken in various countries and is required in the working environment, Due to its popularity, there are several distinguishing accents of countries, Such as a British accent. The   accent is more popular than other accents because of its unique characteristics, that makes is difficult to learn and a learning media system is needed without the need for native speakers for learning. In the speech recognition system, the voice will be processed and matched to recognize the words spoken by the speaker so that the correct or wrong pronunciation can be known. In this study, MFCC is used as a feature extraction feature of sound and the classification method uses Learning Vector Quantization (LVQ) because it has good performance results in ideal or not ideal conditions. The system performance in the process of identifying British accents gets an average accuracy value of 80%, a precision value of 85%, and a recall value of 92%. From the precision and recall results obtained, the system gets an F-Measure value of 88%.
초등학생 채팅서비스의 학부모 인식조사를 통한 개선방안 연구 - 카카오톡 사례를 중심으로

Chatting services, which are popular but which have also caused numerous problems, such as exposure to harmful contents and cyber bullying due to the contents or functions, are unsuitable for elementary school students. In relation to this, there has been negative perception of the use of chat programs by students in elementary school. Here, we undertake research for the purpose of determining the problems of chatting services as used by elementary school students and devising improvements, starting the activities in the accounts of these students. This study focuses on concerns over Kakao Talk service functions and problems with certain policies, with a complementary survey about perceptions of the use of the Kakao Talk Service, targeting the parents of elementary school students. Interviews with a group of experts are also conducted. This study drew three improvements with regard to supplementing the weaknesses and range of these services for users that are elementary school students. First, in terms of the service provider, it is necessary to realize positive changes for the protection of minors by providing limited services and functions suitable for minors. Second, in terms of policy, in order to join a chatting service, an age restriction or parental consent should be requirements. Third, in terms of users, to grant the use of a chatting service by elementary school students, digital literacy education should be a prerequisite. To do this, a guideline and information for parents to educate them should be created and shared. The results of this study can contribute to several relevant areas pertaining to concerns about diverse functions added to the digital content activity of elementary school students, starting with their own smartphones and accounts. The study also suggests a service range suitable for elementary school students.rtphone and account, and also suggesting the service range suitable for elementary school students.
Extending Equational Monadic Reasoning with Monad Transformers.

There is a recent interest for the verification of monadic programs using proof assistants. This line of research raises the question of the integration of monad transformers, a standard technique to combine monads. In this paper, we extend Monae, a Coq library for monadic equational reasoning, with monad transformers and we explain the benefits of this extension. Our starting point is the existing theory of modular monad transformers, which provides a uniform treatment of operations. Using this theory, we simplify the formalization of models in Monae and we propose an approach to support monadic equational reasoning in the presence of monad transformers. We also use Monae to revisit the lifting theorems of modular monad transformers by providing equational proofs and explaining how to patch a known bug using a non-standard use of Coq that combines impredicative polymorphism and parametricity.
Role Of Cloud Computing in Education

It’s no secret that trendy technology may be a propulsion in up to date education. The impact of cloud computing in education is clear. We observed that from improved accessibility, reduced prices and augmented collaboration, the cloud may be a supply of excellent for educational organizations and students. It is found that all students, whether or not they’re form or degree candidates, will take pleasure in innovations in cloud computing. Educational establishments are utilizing the cloud to extend property between colleges and their students. this can be an extended journey from the times of a disc storage system and serious textbooks that quickly went out of date. therefore what’s happening during this cloud-based instructional revolution? it's going to appear overwhelming for your institution to withdraw into application modernization, however the positive impact of cloud computing on learning is important.
Utilizing Noun-Verb Extraction in Enhancing Information Retrieval

The increasing growth of the news and a large number of users on information retrieval (IR) has resulted in making the retrieval of documents complex and more difficult. The IR process consists of pre-processing, extraction and representation, feature selection and indexing, querying, and retrieving results. The weakness of IR is concerned with the process of extraction, where most important words focused on verbs or nouns. Unsupervised Feature selection is an important task in content classification for being among the most popular and effective methods for retrieval reduction. The use of the verbs and nouns as extraction was recently introduced in the IR technique to avoid irrelevant and redundant unsupervised features. This paper aims to enhance IR using noun-verb extraction using Word-Net and Krill Herd Algorithm as Unsupervised Feature Selection (KHUFS) combine with Simulated Annealing as Unsupervised Feature Selection to find the suitable retrieval of ranking. The external of Mean Average Precision (MAP) and Mean Average Recall (MAR) internal of Mean Average Distance (MAD) as measurements were used to verify the proposed retrieval of ranking. The results demonstrate that the proposed nouns verbs method extraction outperformed other extraction methods, in which the proposed extraction was 26.15 % using MAP measure and using MAR was 45.41%. In comparison with other unsupervised feature selection algorithms such as Harmony Search, Simulated Annealing, Particle Swarm, and Genetic Algorithm, the combined combination outperformed other unsupervised feature selection algorithms with an accuracy 26.163 % MAP, and 11.653% MAR. On the other side, the effect of use proposed extraction on the proposed unsupervised feature selection was 39.563% MAP, and 8.96% MAR. The other evaluation using the number of features, the using combined Krill Herd with Simulated Annealing number of features has decreased to more than 50 %, which the total feature in the dataset was 10682 features and after used the proposed was 4723 features.
Socio-Adaptive Thinking of Searching for an Architectural Connection to Improve the Lives of Students with Special Physical Needs as a Special Kind of Philosophical Communication

An analysis of the spiritual situation in the modern, in particular the Western, world prompts the idea that a person's place in it is becoming increasingly uncertain and contradictory. This is especially evident in the context of various conflicts arising among people with disabilities due to infrastructure problems in educational institutions. It is obvious that disabled people and various categories of mentally and physically weakened people are a very significant part of our society, for which accessible and comfortable conditions of life and rehabilitation should be created. This requirement should be considered among the fundamental, and it should apply to many significant objects of the social infrastructure of modern cities, including their higher educational institutions. The major purpose of this article is to determine the ways of adapting the architectural environment of higher education institutions to the needs of persons with disabilities. The object of the research is the buildings and structures of higher education institutions. To achieve the goal of our research we have used a IDEF0 model in the context of achieving social adaptation and improving communication.
Comparison of Classification Algorithms for Sentiment Analysis on Movie Comments

The film industry is growing rapidly nowadays, various genres and storylines are nicely packaged to convey messages and entertain audiences. Sentiment analysis technology can be used for the advancement of the film industry as well as film recommendations that need to be presented next. This study aims to compare several algorithms used for sentiment analysis of movie reviews or comments. The algorithms used in this study are K-Nearest Neighbor (k-NN), NaA¯ve Bayes Classifier (NBC), and Logistic Regression. The experimental results using 25,000 film comment datasets show that Logistic Regression has the highest accuracy rate with an accuracy of 89%, compared to NaA¯ve Bayes' accuracy of 86%, while k-NN is 65.22%.
Power Meter Software Quality Analysis Based on Dynamic Binary Instrumentation

When analyzing the quality of the power meter software, traditional software safety detection tools have limitations such as difficulty in obtaining source code, low test efficiency, and blindness in the test process . Therefore, the binary dynamic instrumentation technology for embedded software is studied, the fast execution path storage and analysis algorithm is designed, the embedded software quality evaluation model is proposed, and a complete solution to the safety detection problem of the electric energy meter is given. Compared with the traditional solution, this paper implements fuzz testing without the need of source code, calculates test coverage, thus improves the reliability of the test, and can scientifically evaluate the software security quality.
Using Blockchain Technology to Establish a Deferred Commodity (Service) Platform with a Performance Guarantee Mechanism: Take the Supplementary Education Industry as an Example

Deferred commodity (services) are prepaid by consumers before they get the service. In order to avoid business bankruptcy, the Executive Yuan stipulates that all businesses selling deferred commodity (services) must declare a performance guarantee mechanism in their contracts; such businesses must also pledge in the bank when they apply for bank card swiping to provide consumer installment services. In this way, businesses got to pledge two funds to legally start business and provide credit card services. We uses blockchain technology to establish a deferred commodity (service) trading platform with a performance guarantee mechanism under the premise that the industry does not need to pledge. The solution builds an federated blockchain in the banks, and pays the fees paid by consumers through e-wallets and escrows them in the bank. After the industry provides services, the bank allocates funds to the industry in stages according to the transaction agreement. If the industry fails to perform services to consumers in accordance with the contract, the bank may terminate the allocation and return the remaining fees to the consumers. This study applies the scene of the supplementary education industry, constructs a model to technically demonstrate the operation mode of the platform, and creates a more secure and credible trading platform.
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees

Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. As training most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex objectives, there is a pressing need for such convergence guarantees. In this paper, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared model is asynchronously updated by concurrent processes. To this end, we use a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes. Under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum. From a practical perspective, one issue with the family of algorithms that we consider is that they are not efficiently supported by machine learning frameworks, which mostly focus on distributed data-parallel strategies. To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks for a partitioned but shared model in single- and multi-GPU settings. Based on this implementation, we achieve on average1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks, without compromising accuracy.
Analysis of the Results of Experimental Research of Stage Culture Formation of Future Teachers of Music Art in the Vocal Training Process

The article analyzes the organization of pedagogical experiment on the formation of the stage culture of future teachers of music in the vocal training process. The pedagogical experiment included three stages: ascertaining, forming, comparative. The stages of the formative expression are characterized – motivational, informative and creative-reflexive. Methods of experimental research are described. The effectiveness of experimental and experimental work, which is carried out on the basis of a comparative analysis of the stage and figurative culture of future teachers of musical art of experimental and control groups, is tested.The content and method of formation of stage-figurative culture of future teachers of music art in the process of vocal training during the forming experiment are characterized according to certain stages.At the first (motivational and motivational) stage, the formation of motivation of future teachers of music art to develop their own stage-figurative culture, motives and needs for the formation of stage, vocal and interpretative skills, value orientations for the improvement of this important quality was carried out.During realization of the second (informative) stage of realization of the method of formation of stage culture of the future teacher of musical art in the process of vocal training, attention was focused on enrichment of knowledge about stage and vocal skills, interpretation of musical image, formation of knowledge on stage-figurative culture (cultural studies, theatrical sciences, hermeneutics, art pedagogies, etc.); expanding thesaurus and identifying opportunities for practical application of knowledge in pedagogical activity.During the realization of the third (creative-reflexive) stage, attention was paid to the formation of vocal, stage, interpreting skills, development of vocal technique, artistic expressiveness, communicativeness, empathy, expression, etc., in order to influence the feelings, intellect of the students.The results of a formative experiment allow to confirm the effectiveness of sound pedagogical conditions and methods of forming the stage-figurative culture of future music teachers in the process of vocal training.
LibGuides: Teen Space: Homework Help

Your guide to all things teen at the Huntington Beach Public Library! Homework Help Resources and Assistance
An Efficient Data Mining Approach on Compressed Transactions

In an era of knowledge explosion, the growth of data increases rapidly day by day. Since data storage is a limited resource, how to reduce the data space in the process becomes a challenge issue. Data compression provides a good solution which can lower the required space. Data mining has many useful applications in recent years because it can help users discover interesting knowledge in large databases. However, existing compression algorithms are not appropriate for data mining. In [1, 2], two different approaches were proposed to compress databases and then perform the data mining process. However, they all lack the ability to decompress the data to their original state and improve the data mining performance. In this research a new approach called Mining Merged Transactions with the Quantification Table (M2TQT) was proposed to solve these problems. M2TQT uses the relationship of transactions to merge related transactions and builds a quantification table to prune the candidate itemsets which are impossible to become frequent in order to improve the performance of mining association rules. The experiments show that M2TQT performs better than existing approaches.
The Relationship between Agile Practices and A Team’s Maturity

The purpose of this study is to investigate the relationship between Agile practices and a team’s maturity. This is a quantitative study that was carried out specifically to software developing organizations. The questionnaire was made up of questions to determine the Agile practices and characteristics of a mature Agile team. The questionnaire also included questions from the Tuckman model for team maturity. The results show that the way Agile practices are used can influence the team’s performance and maturity level. Through the use of quantitative research, it was noted that there is a correlation between Agile practices and a team’s maturity. This research study opens the minds of Information Technology (IT) professionals on how to build their teams in order to increase their skill levels to enhance project success. This research study has concluded that less mature teams need to be trained well enough with regard to Agile practices from an early stage to help them transition to a level of maturity that allows these teams to be self-managing and efficient within an organization.
Course syllabus ontology for supporting e-learning program

In current information technology, every researcher put his effort to minimize the effects of having a huge amount of processed data, but in the same time the data could be produced by the users was not used and took a large space. The information was generated by different resources in different fields such as industries, telecommunications, and education. Therefore, this project is focused on the educational field, and in more specific is the syllabus of courses and how is it going to be defined and stored, which each component has a link to another component. Therefore, the student will be aware of the contents of the courses, and how each component was related to each other to form a better information to the student and lecturer. This will generate a clear picture for the students and lecturer on the course before the learning process started. The Protege-OWL and SPARQL tools were used to develop the course syllabus ontology, and the evaluation of prototype application's usability has shown the increase of awareness of course content accessibility among the students and lecturers.
AMBIGUITY CONCEPT IN STEREO MATCHING PIPELINE

Abstract. In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integral metric, to assess the quality of the produced disparity map. This metric is derived from the concept of ambiguity, which characterizes the property of the cost curve profile. It aims to quantify the difficulty in identifying the correct disparity to select. The quality of ambiguity integral metric is evaluated through the ROC curve methodology and compared with other confidence measures. In regards to other measures, the ambiguity integral measure shows a good potential. We also integrate this measure through various steps of the stereo matching pipeline in order to improve the performance estimation of the disparity map. First, we include ambiguity integral measure during the Semi Global Matching optimization step. The objective is to weight, by ambiguity integral measure, the influence of points in the SGM regularization to reduce the impact of ambiguous points. Secondly, we use ambiguity as an input of a disparity refinement deep learning architecture in order to easily locate noisy area and preserve details.
Best Trees Extraction and Contextual Grammars for Language Processing

In natural language processing, the syntax of a sentence refers to the words used in the sentence, their grammatical role, and their order. Semantics concerns the concepts represented by the words ...
Psychological and pedagogical basis of innovative methods in higher school

Review on the collective monograph of I.V. Abakumova, A.K. Belousova, E.S. Zorina, J. Maksimovic, E.A. Nikolaeva, E.V. Nurmukhamedova, L. Stosic, O. D. Fedotova “Psychological and pedagogical basis of innovative methods in Higher school” Scientific Editor - Academician of Russian Academy of Education, Dr. of Biological Science, Professor P.N. Ermakovhttps://www.knorus.ru/catalog/psihologiya/518898-psihologo-pedagogicheskie-osnovy-innovacionnyh-metodov-obucheniya-v-vysshey-shkole-aspirantura-magistratura-monografiya/
Load Balancing in Cloud Computing using Honey Bee Swarm Intelligence Technique

In cloud computing, job scheduling is one of the prominent research areas. Many endeavors have been made to provide efficient scheduling algorithms for optimizing the utilization of resources in cloud computing. It has been observed while scheduling in real time scenario, the system gets overloaded and the performance declines and compromises at the Quality of Service (QoS).Therefore some efficient load balancing technique is required to be deployed at the intra-datacenter level to prevent the system from violating the Service level agreement (SLA). Many swarm intelligence techniques have been rendered and studied in the previous year's which provide an intelligent insight for balancing the load on the system. In this paper Honey Bee foraging behavior inspired algorithm has been studied/presented to balance the load on the system, to ensure the optimal utilization of resources and to improve QoS in cloud computing environment.