Reviewerly/exHarmony huggingface dataset:
Teknoloji Muhasebesi

Increasing the stakeholders of the business, intensifying competition, changing the production structure and diversifying the types of decisions to be achieved in increasing the importance of financial information needed by both internal and external information users.It is very important to calculate the cost information correctly in presenting the fair condition of the business to the concerned.The correct calculation of the cost information depends on the establishment of the cost systems suitable for the production systems of the enterprises.In order to establish a suitable cost system, cost techniques appropriate for the change in the production structure of the enterprise should be used.The use of techniques suitable for the changing production structure under the influence of both Industry 4.0 and Covid-19 depends on the correct analysis of the change in cost structure.The change in cost structure arises from the decrease in the relative share of some direct costs in the total production cost, the increase in the amortization expenses within the technology cost and the associated general production costs.Due to this change, the increasing share of technology costs in the form of indirect costs and the ability to load directly on the products and services produced allow these costs to be monitored as direct technology costs in enterprises that perform technological transformation.At the same time, today's technology-intensive production systems make it necessary to calculate costs using technology-oriented techniques.In this calculation process, the human factor will be out of the system at the cost calculation stage and will only be effective in the decision processes.Performing cost calculations automatically will eliminate the errors caused by the human factor and provide full-time cost information.In the study, it has been revealed that the cost calculation should be technology oriented depending on the change in the cost structure of the changing production environments.
Recent Advancement in Anomaly Detection in Surveillance Videos

Detection of Anomaly is of a notable and emergent problem into many diverse fields like information theory, deep learning, computer vision, machine learning, and statistics that have been researched within the various application from diverse domains including agriculture, health care, banking, education, and transport anomaly detection. Newly, numbers of important anomaly detection techniques along with diverseness of sort have been watched. The main aim of this paper to come up with a broad summary of the present development on detection of an anomaly, exclusively for video data with mixed types and high dimensionalities, where identifying the anomalous behaviors and event or anomalous patterns is a significant task. The paper expresses the advantages and disadvantages of the detection methods the experiments tried on the publically available benchmark dataset to assess numerous popular and classical methods and models. The objective of this analysis is to furnish an understanding of recent computer vision and machine algorithms methods and also state-of-the-art deep learnings techniques to detect anomalies for researchers. At last, the paper delivered roughly directions for future research on an anomalies detection.
APPLYING EMPIRICAL THRESHOLDING ALGORITHM FOR A KEYSTROKE DYNAMICS BASED AUTHENTICATION SYSTEM

Through the application of a password-based authentication technique, users are granted permission to access a secure system when the username and password matches with that logged in database of the system. Furthermore, anyone who provides the correct username and password of a valid user will be able to log in to the secure network. In current circumstances, impostors can hack the system to obtain a userâ€™s password, while it has also been easy to find out a personâ€™s private password. Thus, the existing structure is exceptionally flawed. One way to strengthen the password-based authentication technique, is by keystroke dynamics. In the proposed keystroke dynamics based authentication system, despite the password match, the similarity between the typing pattern of the typed password and password samples in the training database are verified. The timing features of the userâ€™s keystroke dynamics are collected to calculate the threshold values. In this paper, a novel algorithm is proposed to authenticate the legal users based on the empirical threshold values. The first step involves the extraction of timing features from the typed password samples. The password training database for each user is constructed using the extracted features. Moreover, the empirical threshold limits are calculated from the timing features in the database. The second step involves user authentication by applying these threshold values. The experimental analyses are carried out in MATLAB simulation, and the results indicate a significant reduction in false rejection rate and false acceptance rate. The proposed methodology yields very low equal error rate of 0.5% and the authentication accuracy of 99.5%, which are considered suitable and efficient for real-time implementation. The proposed method can be a useful resource for identifying illegal invasion and is valuable in securing the system as a correlative or substitute form of client validation.
Deep Learning Framework to Detect the False Analysis of a Product given by Robots and Malicious Users

Product evaluations are precious for upcoming clients in supporting them make choices. To this, numerous mining techniques have been proposed, wherein judging a evaluation sentence’s orientation (e.g. Outstanding or bad) is considered as one of their key worrying conditions. Lately, deep studying has emerged as a powerful technique for fixing sentiment kind issues. A neural network intrinsically learns useful instance routinely without human efforts. But, the fulfilment of deep getting to know pretty is primarily based totally on the supply of big-scale education data. We recommend a unique deep studying framework for product review sentiment classification which employs prevalently to be had rankings as susceptible supervision signs and symptoms. The framework consists of steps: (1) studying a high level representation (an embedding region) which captures the general sentiment distribution of sentences thru score facts; (2) such as a class layer-on top of the embedding layer and use labelled sentences for supervised fine-tuning. We discover styles of low stage community structure for modelling evaluation sentences, specifically, convolution function extractors and prolonged brieftime period memory. To have a take a look at the proposed framework, we gather a data set containing 1.1M weakly classified evaluate sentences and eleven, 754 labelled review sentences from Amazon. Experimental effects display the efficacy of the proposed framework and its superiority over baselines. In this future work todetect false reviews given by robots or by malicious people by taking amount, sometimessome companies may hire people to boost their product ranking higher by assigning fake rating and this malicious people or robots give continuous ranking or review to such product and we can detect such fake rating by analysingratingandremove suchfake rating to give only genuine reviews to users.
An Efficient Technique to Remove Gaussian Noise and Improve the Quality of Magnetic Resonance Image

Medical Resonance Imaging (MRI) is very useful in different medical applications for diagnosis the diseases in human body. But the main problem arising in MRI images is presence of various noises. These noises are affecting the originality of the MRI images and producing erroneous results. Here Brain MRI images are used for analysis of noise. Brain images are fractal in nature and especially those images are affected by various noises. In this paper we discussed about the Gaussian noise in brain MRI image. The effect of this noise is reduced by wavelet based thresholding techniques. They are namely Visu shrink, SURE shrink and Bayes shrink. These three methods are applied to the brain MRI images and obtained results are compared by PSNR(Peak signal to Noise ratio), MSE(Mean Square Error),Absolute error, Fractal dimension, IEF(Image Enhancement Factor),Normalized cross correlation and structural content.
Agile Marketing Conceptual Framework for Private Higher Education Institutions

Agile marketing is an iterative approach towards marketing strategy planning and implementation using frameworks like Scrum, Kanban and other Agile methodologies.This study aims to understand how agile can be adopted as a marketing strategy in higher education industry especially in Private Higher Education Institutions (PHEIs).This is because private higher education industry is fast becoming a very competitive business.Therefore, agile marketing is important in order to make sure PHEIs use effective marketing strategy for sustainable customer relationships and sales over a longer period of time.In addition, with great competition in higher education industry, universities are challenged in their branding campaigns to recruit the best and brightest students, staff, and sustain their reputation.The literature shows that agile can be an effective tool in marketing strategies.However, research on the impact of agile marketing on brand equity is still in its infancy.Hence, this study will look into how agile marketing plays important roles in enhancing the brand equity in PHEIs.This study reviews various literatures to propose a conceptual framework for agile marketing.Overall findings are expected to serve as a basis for universities to foster agile marketing in enhancing its brand and reputation.
A Nature Based Computing Technique for Image Watermarking using Bacterial Foraging Optimization, Wavelet and Cosine Transform

The Watermarking technique is used for the purpose of broadcast monitoring, copyright protection, integrity verification and authentication. Its application extends in fingerprinting and content description. In literature several methods like DWT, DCT are presented and embedded which occupies maximum energy. The watermarking techniques use lossy data compression due to strong energy compaction. The new methods and optimization techniques are required. The paper presents a new design and implemented by nature based computing. The proposed method combines the advantage of the wavelet transform and cosine transform. The proposed Bacterial Foraging Optimization technique hybrid with DWT and DCT increase the performance of watermarking of digital images. The high-frequency area of the image is used in this methodology. The method is comparable for Genetic Algorithms (GAs). Selected type of image of PSNR &amp; NCC is processed in MATLAB. The effective results are compared with DWT-DCT algorithm, DWT and multiple images. NCC (normalized cross correlation), PSNR (Peak signal to noise ratio) value and IF (image fidelity) for different techniques are compared. The DWT-DCT-BFO based watermarking performance is found best.
Object Tracking using Supervised Level_Set Model (SLSM)

An item discovery framework discovers objects in this present reality in an advanced picture or video, in which the article can have a place with any of articles to be specific people, vehicles, and so on. So as to distinguish an article in a picture or video the frameworkneeds couple of parts so as to finish the errand of recognizing an item, an element finder, a theorem and theorem checker.In this work survey of different strategies which are utilized to distinguish an article, limit an item, order an item, extricate highlights, appearance data in pictures and recordings. The remarks are dependent on the considered writing and major problems are likewise recognized significant to the item location. A thought regarding the conceivable answer for multiple class_object identification is likewise exhibited. This work is appropriate for specialists who are learners in this area.. We initially portray the proposed system of two-stage supervised level set model in target following, at that point give summed up multi-stage adaptation for managing multiple-target . Positive decline is utilized to modify the learning after some time, empowering following to proceed under fractional and add up to impediment. Test results in various testing arrangements approve the viability inproposed strategy
THE ROLE OF ORGANIZATIONAL COMMITMENT IN MEDIATING THE EFFECT OF JOB CHARACTERISTICS AND ORGANIZATIONAL SUPPORT ON EMPLOYEE LOYALTY OFMUHAMMADIYAH ACEH UNIVERSITY

This study intends to examine the role of organizational commitment to mediate the effect of job characteristics and organizational support on employee loyalty at the University of Muhammadiyah Aceh (Unmuha).The study population comprised all 193 Unmuha employees.Unmuha employees are structural lecturers and educational staffswho are scattered in each work unit.Data were analyzed by Structural Equation Modeling (SEM).The direct test results found that job characteristics and organizational support significantly affect organizational commitment; job characteristics, organizational support and organizational commitment significantly affect employee loyalty; and the indirect test result found that organizational commitment mediates the effect of job characteristics and organizational support on employee loyalty.These findings reinforce the notion that the model of increasing employee loyalty, especially in Unmuha, is strongly influenced by job characteristics and organizational support, supported by an organizational commitment that acts as a partial mediator.Further researchers can develop this proven model by adding new variables such as organizational culture and talent management to further complement the employee loyalty model.
An Efficient Ancient Tamil Script Classification System using Gradient Boosted Tree Algorithm

A variety of historical information’s has been derived and obtained from the various forms of inscriptions existing worldwide. Regional languages of certain selected sources have been employed in the inscription writing procedures. One such regional language with a wealthy literature and heritage that can be suitably opted for the inscription writing process is ‘Tamil’. Encryption of the writing was done with the help of certain materials such as metals, conch shells, stones, palm leaf, and copper plate. These are found to be rich in the data contents related to the fields of history, religious, administrative, astronomy, culture, economic tax and educational conditions. This paper incorporates certain feature extraction methods like Shape and Hough transform and Gradient Boosting tree as classifiers for identifying the ancient Tamil script.
"Artificial Intelligence Verification Platform using Block Chain with Distributed Ledger Technology"

Developments in (AI) or (DLT) are now provoking lively debate in practice and academia. AI uses data to achieve goals that were previously believed to be only possible for humans. In an unsettled context, distributed ledger technology has the potential to establish consensus about information among a group of contributors. Both technologies that are needed in similar and common systems are investigated in this study. As a result, the safe pattern (DLT) or the creation of a federated learning system spread over many nodes. It has the ability to lead technological convergence, which has previously covered the technique for the major revolution in technology (IT). Earlier work summarizes many possible benefits of the convergence of DLT and AI, but only provides a limited theoretical framework to describe future real-world DLT and AI merger instances. The primary goal of this research is to disseminate by maintaining systematic previous works and offering conscientious derivative future research opportunities. This study contributes to overcoming existing constraints in AI and DLT convergence for both technologies.
Sculpture in Modern Environment: Educational Technology

The article looks at the issues connected with art education aimed at training professional sculptors and qualified spectators. It outlines the main problems of sculptural thinking in the Russian mentality. The current situation is described in terms of shifting paradigms related to filling the everyday environment of Russian cities with sculptural objects. The article provides results of the research into preferences of population groups most knowledgeable about art, such as experts in art education, teachers and professional sculptors. A stable tradition of visual thinking rather than using tactile imagery has been identified, as well as preferences for the realistic paradigm of art and academic traditions at all stages of sculptors’ training. At the same time, the article shows positive changes in the social demand towards small-scale sculpture that is actively present in the living environment of a modern person. The main problems of sculptor training at different levels of education are indicated. The author suggests a new model of teaching sculpture based on the actualization of mythological traditions in the cultural paradigm of postmodernism.
Attempts to modernize XML conversion at PubMed Central

PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine. PMC receives about 70,000 XML articles every month and uses XSLT to convert them into its preferred format. In 2021, PMC started to explore options to modernize its extensive conversion codebase leveraging XSLT 3.0. This paper describes XML conversion and its challenges at PMC. It then outlines the first approach that PMC is evaluating: breaking a single conversion operation into multiple, dynamic transformations using fn:transform , one of the powerful new tools available with XSLT 3.0.
Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura. The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.
Missing Value Aware Optimal Feature Selection Method for Efficient Big Data Mining Process

Big mining plays a more critical role in the real world environment due to presence of large volume of data with different varieties and type. Handling these data values and predicting the information would be the more difficult task which needs to be concerned more to obtain the useful knowledge. This is achieved in our previous research work by introducing the Enhanced Particle Swarm Optimization with Genetic Algorithm – Modified Artificial Neural Network (EPSOGA -MANN) which can select the optimal features from the big volume of data. However this research work might be reduced in its performance due to presence of missing values in the dataset. And also this method is more complex to perform due to increased computational overhead of ANN algorithm. This is resolved in the proposed research method by introducing the method namely Missing Value concerned Optimal Feature Selection Method (MV-OFSM). In this research method Improved KNN imputation algorithm is introduced to handle the missing values. And then Dynamic clustering method is introduced to cluster the dataset based on closeness measure. Then Anarchies Society Optimization (ASO) based feature selection approach is applied for performing feature selection in the given dataset. Finally a Hybrid ANN-GA classification technique is applied for implementing the classification. The overall performance evaluation of the research method is performed in the matlab simulation environment from which it is proved that the proposed research method leads to provide the better performance than the existing research technique.
Feature Engineering for Enhanced Model Performance in Software Effort Estimation

Many new methodologies have been defined in the last two decades in the domain of Software Effort Estimation. They include manual methods based on expert judgment, analogy-based models, parametric models, regression models, machine learning models, and more recently, deep learning models. Except for manual methods, all other models depend heavily on data. Lack of quality data in this domain is a motivation to explore means to optimize the sparse data available. Machine learning algorithms depend on domain features, and their ability to represent and model the domain, to solve the problems irrespective of whether it is classification or regression, image, or voice synthesis. There is continued research for the best representation of the issue through the right feature space. While most of the traditional research rely on the original dataset and concentrate more on feature selection, modern-day approaches explore creating additional features that have the potential to extend the models representational space.This research builds on our last research exploring the potential to improve Software Effort Estimation accuracy by employing engineered features in addition to the original ones. The features are created manually based on the literature. Through the engineered features, we captured additional representational features such as missingness and proportion of categorical data available in the dataset. We present the rationale for the features generated and compare the prediction accuracy between a model using the original dataset and the engineered data set.Our experiments in Feature Engineering is innovative in the Software Estimation domain and the results conclusive establishing its use in predicting Software Effort. We report an improved accuracy of 38% with engineered features at PRED(15), and 11% improvement at PRED(20). The quantitative growth that we have been able to achieve in terms of accuracy is promising enough for this to be adopted as a standard in future research on the subject and practical applications.
Impulse Noise Removal in Mammograms using Bi-Dimensional Empirical Mode Decomposition and Fast Adaptive Bilateral Filter

The work aims to detect and correct the noisy mammogram images corrupted by impulse noise. This is achieved in two phases – identification of noise-affected pixels and renovation of those pixels in an image. The pixels which are disturbed by impulse noise are identified by Bi-dimensional Empirical Mode Decomposition (Bid-EMD). The restoration of these pixels and noise removal are done by fast adaptive bilateral filter (fABF). The proposed work for impulse noise removal is examined on digital mammogram images of Digital Database for Screening Mammography (DDSM) database. The proposed approach is compared with other existing state-of-the-art schemes using peak signal to noise ratio (PSNR) and image enhancement factor (IEF) performance measures. The study of performance of the proposed scheme provides enhanced outcome than the other algorithms used for impulse noise removal.