Reviewerly/exHarmony huggingface dataset:
Research on Higher Order Artificial Neuron for E-Information Systems

In this paper, a novel model based on Complex Valued Single Neurons which make the composition of MLP and RBf, aggregate their response linearly and non-linearly in two new proposed single neurons respectively is presented. The learning and generalization capabilities of these neurons have been tested over various benchmark and real life problems. Since complex numbers have natural representation of phase and magnitude that locates the complex number uniquely on the plane, these neurons have shown their potent computational power over wide spectrum of geometrical transformations. The artificial neural network based applications in various enterprise information systems are growing interest for comprehensive coverage and understanding of organizational competitiveness. The neurobiologists studies have observed various ways for interaction of synaptic inputs in the dendritic tree which eventually reflect the computation performed by a neuron. The analysis of aggregation linearly or non-linearly has been a subject of interest for NN researchers. Most of the aggregated neuron models are based on ‘Multiplication’ in an attempt to consider non-linearity in ANN. Prior research work pertaining to implementation of the input derived from the relationship among the second and higher order polynomial as a set of inputs is available. The novel model presented in this paper has the potential to improve the ANN performance by virtue of faster response achieved as a result of aggregated input constituted of MLP and RBf
Application of Expectation Maximization Algorithm in Estimating Parameter Values of Maximum Likelihood Model

Parameter estimation is an estimation of the population parameter values ​​based on data or samples of population. Parameter estimation can be solverd by several methods, one of which is the Maximum Likelihood method. The focus of this research is to estimate the parameter value of a normal distribution data with Maximum Likelihood based on iteration algorithm. The iteration algorithm that will be used is the Expectation Maximation Algorithm with help of Matlab 2016a program. Based on the results obtained that the estimation value of the parameter and for an accident data in Indonesia based on age group with using Expectation Maximization algorithm is and with 2 iterations.
Motivational Factors for Knowledge Sharing and Trust

sharing knowledge is transmission of knowledge (implicit or tacit) from an organization, group, or person to another one. Through sharing knowledge, organizations are able to improve their effectiveness, saves cost of training and moderate risks due to lack of certainty. While managing knowledge, organizations find it difficult to motivate employees for sharing knowledge with others. Therefore, it is essential to recognize the elements impacting information sharing and trust. This paper attempts to understand trust and persuasive variables that impact information sharing conduct in associations. It is huge that there are a not many investigations because of inspirational factors on information sharing conduct through trust as an arbitrator. Right now, specialist proposed a hypothetical system that consolidated inspirational elements with Theory of Reasoned Action (TRA) to depict the relationship among inspiration (extraneous and inherent), trust and demeanors toward information sharing. This paper will be important to the experts as it gives a premise of understanding persuasive elements for information sharing and trust.
Touch Sound

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Introduction

In computer vision and machine learning, image classificationImage classification is an important and fundamental task, which assigns images to one of pre-defined groups based on the content in the images. It has a wide range of applications in various fields, such as medicine [28], biology [37], remote-sensing [22], and engineering [31]. Image classification is also a key component of many other visual tasks, such as object detection, video annotation, and image retrieval.
TeksDB

Key-value stores (KVS) are now an integral part of modern dataintensive systems. thanks to its simplicity, scalability, and efficiency over traditional database systems. Databases such as MySQL employ KVS (in this case, RocksDB as their backend storage instead of conventional database engines because KVS is less affected by data fragmentation. OLAP and big data analytics applications often use KVS as the backend store to make use of the efficient range_query operations of LSM(Log Structured Merge Tree)-based KVS. Furthermore, the increasing use of KVS as backend stores in distributed systems diversifies the workload for each local KVS, making it necessary to design a highly-concurrent data store that simultaneously processes mixed operations. The underlying technology trends also put immense pressure on the design of KVS. Nowadays, many core servers with hundred GBs of memory are common in production environments, and this enhanced density of hardware technologies (both in terms of the number of cores and memory capacity) leads to more data items residing in memory. This phenomenon places greater emphasis on the efficiency of the in-memory data structure in KVS, as more data accesses are serviced from its memory component, which is often called the memtable. The current memtable structure of KVS, however, generally assumes a limited memory capacity that only serves as a temporary write buffer, and the use of single-threaded writes or unscalable data structures for the memtable fails to fulfill this external demand.
PERFORMANCE OPTIMIZATION OF WEB-BASED APPLICATION

Web-based applications have become the new way for people to interact, be it work or business. Users always expect a fast response from the applications. The performance of web-based applications has always been a critical non-functional requirement. A better performing web-based system leads to better user experience which leads to repeated visits and in turn more revenues. Performance problems can bring all kinds of undesired consequences like bad reputation, loss of revenue and low productivity especially in an e-commerce system. Recent advancement leads to mobile device proliferation, increase in traffic volumes and richer content, which all increase application delivery challenges. Users not only expect fast application response time, but also expect higher throughput. In this work an e-commerce system is created, a performance tuning up was done on it to reduce the number of HTTP requests made and also reduce the size and number of pages and images in the system. Other ways of performance optimization were also considered. The system's performance was tested before and after performance tuning up, a load test was also done for 3000, 5000 and 10000 concurrent users. The results were compared to see the effect of the performance optimization techniques used.
Factors Affecting Job Performance: The Case of Jordanian Hotels Kitchen Staff

This paper aims at investigating the effects of work pressure, duties and responsibilities, career advancement opportunities, and the physical work environment on job performance of the hotels' kitchen staff. A structured questionnaire was used to collect information from the employees working in five-star hotels in Jordan. Multiple regression analysis was conducted to test the research hypotheses. Results of the current study revealed that there is a significant impact of work pressure and career advancement opportunities on job performance, whereas duties and responsibilities, and the physical work environment have not significant impact on it. Results of T-test and ANOVA showed that there is no significant difference in the impact of job performance that can be attributed to gender, age, educational level, personal income, marital status, job position, and position experience. The findings of this study will contribute a better understanding of the best work environment that could enhance the performance of the staff and increase guests' satisfactions.
SEMANTICAL ENRICHMENT OF WEB USER INTERFACES IN THE CROWD

Structured data on the Web have become very important in recent years and offer machine-readable semantics of the data contained.Schema.org is the most recognized vocabulary for structuring data on websites and serves beyond that also to organize and query Google's knowledge graph.However, Schema.org is mainly used today to semantically describe real-world entities and their relationships to one another.To describe the functionality and handling of websites has not been the key application area of Schema.orgyet, though it also includes an appropriate vocabulary.In addition, most Schema.orgtools are limited in terms of the Schema.orgclasses and properties they offer and do not support the semantical enrichment of web user interfaces.In this article we motivate the potential of the sematic enrichment of web user interfaces regarding their functionality and handling and present possible application areas to consume these structured data.We analyze the requirements on structured user interface data and investigate the suitability of Schema.org as a vocabulary to fulfil them.Since there are hundreds of classes and properties in Schema.org,we present a conceptual model of Schema.orgclasses and properties that are appropriate to enrich web user interfaces.We investigate to what extent interactive elements on HTML websites or applications can be automatically mapped to our conceptual model elements.As a result, we present a method to semi-automatically produce structured user interface data and illustrate our approach with a continuous use case.As a proof of concept, we introduce Schemator, a comfortable structured user interface data tool supporting our method, which can be used to crowdsource knowledge about the functionality and handling of websites and applications.
Case-Based Learning and Flipped Classroom for Teaching Administrative Processes

In times of confinement due to the COVID-19 pandemic, it was necessary for teachers and students to change the way they teach and learn; in that sense, this research work presents an experience of the application of Case-Based Learning and the Flipped Classroom. The work was applied with 26 students from the Professional School of Political Science and Governance of UNAMBA. The applied methodology was as follow: first, videos of 5 case studies were delivered to the students before the class session; second, in the class session each case was presented and analyzed; and third, the students created a video summary of the case to socialize with the other classmates. Subsequently, a 16-question satisfaction questionnaire was applied with 3 related factors: benefits of the Flipped Learning, use of videos, and collaboration and communication. Finally, the results show that 96.1% achieved positive evaluations in which the participants show their satisfaction with this applied strategy.
Multimedia Signal Processing in Cloud using Content Based Copy Detection

Nowadays making multimedia and multimedia altering has gotten simple because of effectively accessible handling instruments. Progressively over facilitating sites are unreservedly accessible. So clearly we experience circumstances wherein numerous recordings and pictures that are copyrighted are getting copied effectively. Such duplication isn't just unlawful yet in addition it makes an enormous misfortune unique content holder. On the off chance that at all we attempt to distinguish such copied content, process gets extremely mind boggling in light of enormous sizes and huge amount of multimedia documents present on web. So this procedure gets perplexing and computationally costly. Content based multimedia copy detection gives a powerful and improved instrument which can distinguish coordinating rate among tremendous multimedia duplicates from the content itself without having any reliance on any product. In the system of content based copy detection, marks are made from content itself before transferring on cloud like Picasa. In the proposed framework at whatever point new Multimedia article is transferred on the cloud initially its mark is made and on the off chance that the mark matches with put away database signature, at that point this will trap the site which copies copyrighted copy with no rights.
Load Balancing of Unbalanced Matrix Problem of Maximum Machines with Min Min Algorithm

Most eminent computing technology related to the cloud is an exclusively web-based approach where resources are hosted on a cloud; it prospers the resources. Cloud computing is the most promising technologies that offer a standard for bulky sized computing. That is a structure for enabling applications to execution on virtualized resources and accessed by a network protocol. It provides resource and services in a very elastic behavior that can be scaled according to the required of the clients. Limited numbers of devices execute less number of tasks at that time. So it is more complex to perform each task at once. Several devices execute each task, so it has required balancing total loads that reduce the completion time and executes each task in a definite way. We have said earlier that there are not feasible to stay behind an equal server to execute similar tasks. The tasks that are to be executed by machine in the cloud system must be less than the united VM for a time. Overloaded servers have to perform a less number of jobs. Here in our approach, we want to show a scheduling algorithm for balancing of loads and presentation with minimum execution time and makespan.
MySQL Shell

MySQL Shell is the second-generation command-line client which stands out compared to the traditional mysql command-line client by supporting the X protocol as well as the Python and JavaScript languages. It also comes with several utilities and is highly extensible. This makes it a great tool for not only day-to-day tasks but also when investigating performance issues.
Persistenza, precarietà e metamorfosi nella scrittura di Nelida Milani

In the writing of Nelida Milani different linguistic registers coexist: the Istro-Venetian dialect, the cultured citations, the Croatian. The outcome is that of an expressive vitality sometimes characterised by rhythmic cadences close to the prose of art and it expresses the potential of the Italian language on which Milani also reflects in her theoretical contributions. This paper intends to highlight how it is precisely the capacity to welcome ‘the other’ to find a writing capable of indicating new expressive paths to the language of Italian fiction.
A Decision Support System for Effective Academic Analysis by Using the Concept of Data Mining

In India, higher education institutions experience the need for effective analysis and decision support tools to assist in all academic processes.Management needs to take effective administrative decisions regarding the quality of education and efficient use of resources.Some of the higher learning institutions only manage financial, administrative and academic data used for common analysis works.The proposed conceptual system is designed to work as an integral part with its sub components for supporting academic and administrative decision making.For effective decision making, this system can be used to manage appropriate academic analytics & databases to collect, synthesize, and evaluate educational data.
Extreme Learning Machine with sigmoid activation function on large data

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.
SOFTSWISH NEURAL NETWORK APPROXIMATION WITH ZUI-CUI MODULUS

Until today, many formulas of neural networks are defined to be used for function approximation, they vary with respect to the weights, activation functions and other standards. Moreover, researchers have studied the approximation of different spaces of functions. In this paper, we approximate functions from multivariate spaces with a neural network with a new defined form of Swish function, named SoftSwish. Also, multivariate Zou-Cui modulus is introduced to express the degree of approximation by our Swish neural network that we call “SoftSwish Neural Network”.. Key words: Approximation, Neural Network, Swish, Modulus of Smoothness.
Enabling Gigabit Switches and Web Browsers using Poplin

Many statisticians would agree that, had it not been for the Turing machine, the key unification of Moore’s Law and intense encoding may never have occurred. In this paper, we disprove the analysis of information retrieval systems. In this work we verify that even though model checking can be made efficient, distributed, and pervasive, the well-known probabilistic algorithm for the refinement of kernels by Dennis Ritchie et al. is maximally proficient
Pareto front feature selection based on artificial bee colony optimization

© 2017 Elsevier Inc. Feature selection has two major conflicting aims, i.e., to maximize the classification performance and to minimize the number of selected features to overcome the curse of dimensionality. To balance their trade-off, feature selection can be handled as a multi-objective problem. In this paper, a feature selection approach is proposed based on a new multi-objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators. Two different implementations of the proposed approach are developed: ABC with binary representation and ABC with continuous representation. Their performance are examined on 12 benchmark datasets and the results are compared with those of linear forward selection, greedy stepwise backward selection, two single objective ABC algorithms and three well-known multi-objective evolutionary computation algorithms. The results show that the proposed approach with the binary representation outperformed the other methods in terms of both the dimensionality reduction and the classification accuracy.
Prelims

Citation (2020), "Prelims", Popkova, E.G. and Bogoviz, A.V. (Ed.) Circular Economy in Developed and Developing Countries: Perspective, Methods and Examples, Emerald Publishing Limited, Bingley, pp. i-xxiii. https://doi.org/10.1108/978-1-78973-981-720201001
A Study on Content Curation Plan of Local Cultural Resources Archives: Focusing on Region N Culture Service

최근 들어 지역 활성화는 전국 지방자치단체들의 초미의 관심사가 되고 있다. 이러한 지역 활성화는 그동안 경제개발 논리를 중심으로 전개되어 왔다가 최근에 이르러서야 문화가치 중심의 논리로 전환되고 있다. 문화가치 중심의 지역 활성화를 이루기 위해서는 우선 지역문화자원의 발굴이 중요하다. 지역문화자원의 발굴은 잠재적 가치를 지닌 지역문화자원들을 아카이빙하는 것으로부터 비롯된다. 이러한 점에서 최근 한국문화원연합회에서 추진한 지역N문화 아카이브는 대단히 중요한 의미를 지닌다. 지역N문화 아카이브는 전국의 지방문화원들이 그동안 조사, 연구, 집적해 왔던 지역문화자원들을 한데 모은 아카이브이다. 여러 가지 메뉴들을 통해 지역문화자원들을 다양한 방식으로 서비스하고 있다. 그러나 현재의 지역N문화 아카이브는 기존의 지방문화원들이 제시한 맥락 속에서 서비스되고 있다는 점에서 한계를 지니고 있다. 즉 잠재적 가치를 지닌 새로운 문화자원을 발굴하고 활용하기에는 미흡한 측면이 있다. 따라서 이글에서는 다양한 맥락 속에서 지역문화자원을 탐색할 수 있도록 콘텐츠 큐레이션 방안을 제시하였다. 이는 지역의 다양한 자원들이 지닌 이야기를 분절화하고, 그 안에서 주요한 항목들을 추출해 내어, 기타 자원들과의 관계망을 형성하는 방식이다. 그렇게 함으로써 새로운 시각에서 지역문화자원을 바라보고, 문화콘텐츠를 개발할 수 있도록 하자는 것이다. 이는 풍부한 파생콘텐츠를 발굴할 수 있다는 장점을 지니고 있어서, 지역문화콘텐츠 개발 또는 트랜스미디어 콘텐츠 개발에 큰 효과를 나타낼 것으로 기대할 수 있다.In recent years, regional revitalization has become a super concern of local governments. Such regional revitalization has been centered on economic development until recently, and has only recently been converted to cultural value-oriented. In order to achieve regional vitalization centered on cultural values, it is important to discover local cultural resources. Discovery of local cultural resources comes from archiving local cultural resources with potential value. In this regard, the Region N Culture Archives recently promoted by the Federation of Korea Culture Center has a very important meaning. The Region N Culture Archives is an archive of local cultural resources that have been investigated, researched, and accumulated by local culture centers throughout the country. Through various menus, local cultural resources are served in various ways. However, current Region N Culture Archives have limitations in that they are serviced in the context suggested by local culture centers. In other words, it is insufficient to discover and utilize new cultural resources with potential value. Therefore, this paper suggests a curation method for content to explore local cultural resources in various contexts. It is a way of segmenting the stories of various local resources, extracting key items from them, and forming a network of other resources. By doing so, we can look at local cultural resources from a new perspective and develop cultural contents. This has the advantage of discovering abundant derivative contents, and can be expected to have a great effect on local cultural contents development or transmedia contents development.
Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.