Reviewerly/exHarmony huggingface dataset:
Classifying defective software projects based on machine learning and complexity metrics

Software defects can lead to software failures or errors at any time. Therefore, software developers and engineers spend a lot of time and effort in order to find possible defects. This paper proposes an automatic approach to predict software defects based on machine learning algorithms. A set of complexity measures values are used to train the classifier. Three public datasets were used to evaluate the ability of mining complexity measures for different software projects to predict possible defects. Experimental results showed that it is possible to min software complexity to build a defect prediction model with a high accuracy rate.
Data Mining

The main thrust of this chapter is the consolidation of large databases into usable information. The chapter begins with instructions on how to import a TXT file as a way of giving the reader a reasonably large database with which to experiment. The reader is told how to sort and filter a database. Consolidation techniques such as frequency tables and pivot tables are explained.
Medtronic Cobalt CRT-D

Biomedical Safety & Standards: April 15, 2021 - Volume 51 - Issue 7 - p 54 doi: 10.1097/01.BMSAS.0000742192.60382.92
Diamond search depend integrated projection error measurement motion estimation algorithm in video compression

In the preceding video compression method, some limitations required to be enhanced, i.e., the compression ratio has to be improved. To enhance the conventional method negative aspect, an innovative video compression method employing diamond search depend integrated projection error measurement (DIPEM) estimation algorithm is anticipated. To begin with, the input video frames are processed by employing watershed algorithm and then the video frames motion vectors are evaluated by diamond search dependent integrated projection error motion inference algorithm. Subsequent the motion vectors assessment, encoding and decoding process are performed by JPEG-LS method. The execution result depicts the efficiency of anticipated method, in compressing more number of videos and the performance is assessed with conventional video compression techniques. The comparison result demonstrates that our anticipated method acquires high-quality compression ratio and PSNR than the conventional techniques.
Kansas Field Research 2019

Existing methods to report phenology are expensive, labor-intensive, time-consuming, and often not very accurate, especially at some specific crop growth stages.The objective of this study was to develop large-scale phenology models via utilization of satellite imagery data and machine learning techniques for the southwest (SW) agricultural crop reporting district of Kansas.Different satellite images collected from Landsat were utilized as the main input to obtain different vegetation indices (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI; green chlorophyll vegetation index, GCVI; normalized difference water index, NDWI; and global vegetation moisture index, CVMI).Vapor Pressure Deficit (VPD), temperature, precipitation, and growing degree units (GDU) were evaluated for improving phenology prediction models.A large set of ground truth data with information about day of the year, crop phenology, and field location was provided by Crop Quest Inc. (Dodge City, KS) from 2014-2018 and utilized to train two different statistical models (Random Forest and Support Vector Machine) to catalog corn fields, and build a phenology evolution model for this crop.
Automatic Hate Speech Detection: A Literature Review

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A Pragmatic C ost Effective Model to Enhance the COCOMO Model in Software Estimation

This research work is aimed at to provide effective cost estimation methodology emphasize on cost effort and time . This paper summarizes the cost effort estimation of most conventionally used models like organic and semi-detached models using an improved version of genetic algorithm that enhances an empirical methodology to reduce the cost factor and time factor in software projects. Constructive cost model(Cocomo model) is broadly used for the fruitful valuation of cost estimation which is based on KLOC method(thousands of lines of code).This method yields beneficial result in case of lines of code method but lacks in terms of concept and logics. The same is estimated directly and is computed using the function point analysis. In the software development lifecycle, the software cost effort estimation is the most demanding process. The accuracy of the estimate in choosing the estimation model is an essential factor. Such conventional software effort estimation techniques fail to compute the accuracy of effort estimation and it is not up to the mark. So here, we tend to propose the cost reduction in the software projects by using the improved version of the known genetic algorithm.
Reasons for the failure of B.Tech Students in Mathematics using Combined Disjoint Blocked Fuzzy Cognitive Maps CDBFCM

At the present time most of the B.Tech. students are failing in Mathematics subject. The reason is they are not having fundamentals. Lack of practice also one reason. Because most B.Tech. Students are with attitude problem. In this paper we are going to investigate the causes for the failure of B.Tech Students in Mathematics with the help of Combined Disjoint Block Fuzzy Cognitive Maps (CDBFCM). W . B . V a s a n t h a K a n d a s a m y , A . V i c t o r D e v a d o s s s t a r t e d t h e t e c h n i q u e . T h i s t e c h n i q u e w i l l b e e f f i c i e n t i f t h e n u m e r a l o f c o n c e p t s a r e b i g i n f i g u r e a n d w e h a v e t o c l u s t e r t h e m . T h e t r o u b l e s a r e g o i n g t o b e d i s c u s s e d h e r e with the assist of Combined Disjoint Block Fuzzy Cognitive Maps (CDBFCM). F i n a l l y , w e a r e g o i n g t o i d e n t i f y t h e most important causes for the failure of B.Tech. students in Mathematics. For this we used neutrosophic device. There are five sections. Section one provides details regarding Fuzzy Cognitive Maps and the the causes for the failure of B.Tech. students in Mathematics. Section two provides basic concepts of Fuzzy Cognitive Maps, Combined Disjoint Block Fuzzy Cognitive Maps. Process of finding the unseen outline was given in section three. The difficulties are given in section four. After the completion of work decisions are given in the last section.
Media and Information Literacy

Paul G. Zurkowski was the first to introduce the term information literacy in 1974 to describe the techniques and skills necessary for utilizing “a wide range of information tools and primary sources”. It also included the ability to measure information value to “mold information to [meet] needs” and to create solutions to problems.1-2 Since then, media literacy and information literacy has gained a base in advanced countries. But UNESCO is now promoting the composite concept of media and information literacy (MIL). To promote MIL and MIL research, it has founded the MIL Alliance and the Media and Information Literacy and Intercultural Dialogue (MILID UniTwin) to enable people to use information critically, ethically and legally in quality decision-making. Other useful initiatives of UNESCO include “MIL MOOC; Media and Information Literacy: Critical-thinking, Creativity, Literacy, Intercultural, Citizenship, Knowledge and Sustainability (MIL CLICKS); MIL Policy and Strategy Guidelines; MIL Curriculum for Teachers; MIL Indicators; MIL Framework; and MIL Week Feature Conference and Youth Agenda Forum. The International Federation of Library Association and Institutions (IFLA) has its programme entitled 'Freedom of Access to Information and Freedom of Expression (FAIFE)' and developed the MIL Guidelines. IFLA/FAIFE' programme is committed to ensure freedom of access to information and freedom of expression to empower people to realize their “right to be.”3 There are many models and standards to promote information literacy and MIL but MIL is not yet effectively imbedded in the developing countries. Hence, to sensitize the stakeholders about the concept and practice of MIL in India, this theme issue of the DESIDOC Journal of Information Technology (DJLIT) has been planned.
Data Controller, Processor or a Joint Controller: Towards Reaching GDPR Compliance in the Data and Technology Driven World

The present chapter aims to examine critically the existing legal provisions on the concepts of controller, processor and joint controller, as interpreted by the relevant non-binding guidelines and case-law, and to propose a new “value chain” method for allocating responsibilities among joint controllers which is considered more effective and appropriate for the technology and data driven world. It also examines the corresponding data protection responsibilities of the different data processing actors, in particular through the prism of the new accountability principle, which arguably includes not only obligations for means, but also result-oriented obligations for compliance in terms of data subjects’ effective and complete protection.
A parallel approach for web session identification to make recommendations efficient

A sequence of web pages visited by the clients over a particular timeframe is called a session. Web log mining is done to analyse the behaviour of the users, using the web access patterns. Sessions are identified as the significant part of the construction of the recommendation model. The novel part of the work makes use of backward moves made by the user, considering both the referrer url and the requested url extracted from the extended web log for session identification which is not taken into consideration in the existing heuristic-based approach. Two noteworthy issues in session identification are: 1) framing excessively numerous smaller length sessions; 2) taking longer time for identifying the sessions. In the proposed work, the length of the sessions are maximised using split and merge technique and the time taken for session identification is reduced using thread parallelisation. For efficient storage and retrieval of information the hash map data structure is used. The proposed work shows significant improvement in performance in terms of execution time, standard error, correlation coefficient and the objective value.
Conference reports

In this installment of the conference report column, Diego Calvanese, Paul Fodor, and Marco Montali report on RuleML+RR 2019, the Third International Joint Conference on Rules and Reasoning. RuleML+RR is the leading international joint conference in the field of rule-based reasoning. This event took place in Bolzano, Italy in September 16--19, 2019. As Diego, Paul, and Marco describe in their detailed report, RuleML+RR 2019 was held as part of BRAIN 2019 (the Bolzano Rules and Artificial Intelligence Summit) and included a series of sub-events, tutorials, and invited keynotes. I am most grateful to Diego, Paul, and Marco for their contribution. I look forward to receiving your reports and/or personal impressions on conferences and scientific meetings broadly related to SIGLOG. In these times of limited international travel, reports on virtual conferences and workshops would be most welcome. Ideas and suggestions for future installments of the column are also most welcome.
EXTENT OF IMPLEMENTATION, MONITORING AND EVALUATION OF SCHOOL LEARNING ACTION CELL (SLAC) AS A COST-EFFECTIVE LEARNING AND DEVELOPMENT STRATEGY FOR TEACHERS DEVELOPMENT

The successful implementation of School Learning Action Cell (SLAC) LACs as seen to be the most cost-effective continuing professional development process that improve the teaching-learning process is dependent on how school leaders and teachers understand the process.The consistency of its implementation and monitoring is somehow difficult to achieve when school heads and teachers have limited grasp of the processes and framework of SLACs.To answer the problems, three sets of survey questionnaires were distributed to 249 school heads and 3,907 teachers in the 37 districts Surigao del Sur Division.Data gathered were analyzed using descriptive statistics.The results revealed that the school heads understanding and Teachers on SLAC as a strategy for improving instructional delivery was good while teachers understanding was fair.The findings provided evidence that there was only a limited understanding of teachers in the implementation of school learning action cell.Feedback gathered from questionnaires show that teachers did not see how school heads focused the implementation and monitoring of SLAC in schools.Based on these findings, it is recommended that school heads and teachers must be provided with intensive capacity building on conducting and monitoring of school learning action cell to equip them with knowledge and skills of implementing LAC as a strategy of improving the teaching and learning delivery.It is also recommended to strengthen program implementation review to improve the process of conducting LACs in schools and learning centers.
MENDETEKSI OBJEK BERDASARKAN WARNA DENGAN SEGMENTASI WARNA HSV MENGGUNAKAN APLIKASI MATLAB

Object recognition using computer vision techniques is a necessary process to detect objects in a 3-dimensional image in order to obtain accurate results. By utilizing the color segmentation method with HSV color, it will produce an image segment object in the form of a blob so that it can be detected by the computer. Based on the test results, the color sample will produce a value that will be the reference for the filter range in the segmentation process. In the process, it will be known how many objects were detected according to the specified color.
Implementation of Dynamic Artificial Intelligence in Game Development

The aim of this paper is to create an adaptive Artificial Intelligence implemented within our own video game which learns from players and uniquely adapts its playstyle in order to counter the play style of the current player in real time. This is done by creating a basic AI using the AI packages that come with Unreal Engine, which is what is being used to implement this technique. This AI is then trained with many different possible moves in the game represented within a tree. The end goal is to have the AI learn the moves that the user is using most frequently or most effectively and begin countering it more and more effectively as the level rises. This is done by training the AI to various movesets and giving it a sizeable sample space in order to understand and predict as required
Improving the accuracy of item recommendations by combining collaborative and content-based recommendations: a hybrid approach

Recommender systems facilitate the users by providing the ample information of the items or the products they are interested. Users would not be aware of item details without the help of recommender systems due to the size of information available on the web. Collaborative filtering and content-based filtering are the two traditional filtering techniques of recommender systems. Both the filtering techniques have their advantages and certainly the disadvantages too. This can be solved by combining both the filtering techniques and improves the accuracy of recommendations. This leads to system as a hybrid recommender system. This paper presents a novel hybrid approach by combining a dynamic item-based collaborative filtering with the content-based filtering. Time variance and machine learning algorithms are applied on the filtering techniques to overcome the problems in recommendations. The approach is demonstrated using the MovieLens data sets to ensure the effectiveness of the proposed hybrid system.
High-Order Surrogacy for the Audiovisual Display of Dance

The current pandemic (COVID-19) has had considerable impact on many fronts, not least on the physical presence of humans, affecting how we relate to one another and to the natural environment. To investigate these two interactions, the notion of surrogacy, originally described by Smalley as remoteness between source and sonic gesture, is considered and extended to include bodily gesture, for the rendering of contemporary dance performances into abstract audiovisual compositions/objects. To this end, for a given dance performance, sonification of the motion capture data is combined with video-frame processing of the video recording. In this study, we focus on higher order surrogacy and associate this with 1) a soundscape ecology-inspired approach to sonification, whereby three species of sounds coexist and adapt in the environment according to the symbiotic paradigm of mutualism, and 2) a wave space method to sonify their coevolution. Aesthetic implications of this procedure in the context of multimodal, telematic/remote and virtual systems are discussed as disembodied presence emerges as a dominant trope in our daily experience.
Study and prioritising factors of productivity of the employees of steel manufacturing industry, Kanjikode by extended ACHIEVE model

Employee productivity is a key factor for the success of manufacturing companies. Performance analysis studies with a wide range of approaches are used in an attempt to improve employee productivity. However, these studies take only one or two factor into consideration, which may not provide a comprehensive solution to the productivity problem they face. An extended ACHIEVE model by the name MACHIEVE model has been proposed to overcome this, with additional factor M-'Material'. Survey analysis based on this new model has been performed among employees in the steel manufacturing industry in Kanjikode. This is a structural equation modelling analysis which used filled questionnaire data of randomly selected 420 employees from among a population of 1,280 employees. The results indicated that all eight factors of MACHIEVE model has impact on employee productivity. The analysis also suggested that the factors C-Clarity and H-Help have the greatest impact on labour productivity.
Pemanfaatan Moodle pada Identifikasi Kendala dalam Soal Reading dengan Format Tes TOEIC

Most TOEIC test takers who score low are unfamiliar with the format of the test. This study aims to identify the problems that will likely become obstacles in the Reading Section of the TOEIC test. The data come from the results of the English I online final exam in the second term of 2017/2018 as well as the English II online mid-term and final exam in the first term of 2018/2019, all of which were held in Politeknik Negeri Batam. The exams are part of Moodle-based e-learning courses. The data consist of questions which have a low percentage of correct answers. The analysis was done by classifying the questions based on the five abilities measured in the Reading Section (vocabulary, grammar, specific information, inference, and connection). Based on the analysis, it can be observed that the biggest challenge comes from grammar, while the search for specific information poses little to no problem at all. It means the students still consider Reading Section of TOEIC test requires no special attention.&#x0D;
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?

Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in model over-relying on features in the low-frequency domain, sacrificing performance against low-frequency corruptions, highlighting a connection between frequency and robustness. Here, we take one step further to more directly study the frequency bias of a model through the lens of its Jacobians and its implication to model robustness. To achieve this, we propose Jacobian frequency regularization for models' Jacobians to have a larger ratio of low-frequency components. Through experiments on four image datasets, we show that biasing classifiers towards low (high)-frequency components can bring performance gain against high (low)-frequency corruption and adversarial perturbation, albeit with a tradeoff in performance for low (high)-frequency corruption. Our approach elucidates a more direct connection between the frequency bias and robustness of deep learning models.
Educational Inequality before and During the Covid-19 Pandemic in Social and Virtuality

Decades ago, technology was seen as something futuristic, today we are facing a new learning reality: virtuality. This systematic review article aims to determine the importance of providing answers to the existing inequality in many of the homes where a student is found without internet and without technological tools that lead to the new paradigms of learning based on computational means. research, a descriptive systematic review and qualitative approach was used, which is achieved using the methodology of searching for information from the articles, with the Prism method, obtaining the information in the different repositories in which these investigations are deposited, such as Scopus, Scielo, Redaly or World wid Escience. Significant learning shows the relevance and significance of studying educational inequalities and the skills acquired by few students according to their economy.
Currency Dominance and National Power in the Era of Distributed Ledger Technology and Cryptocurrency

Currency dominance has been the symbol of national power, influence, and dominance. After the Second World War, the Dollar has maintained its unrivaled influence as a currency reserve by central banks and as a global transaction currency. Recently, cryptocurrency and the distributed ledger system were seen as a challenge. However, due to the challenge it poses to the sovereignty of nation-states, central banks have resorted to developing the central bank digital currencies (CBDCs). China is the only major economy to have tested a CBDC, a symbol of its increasing economic power and innovation. Contrary to Mearsheimer’s theory of offensive realism, developments show that China can use its offensive economic capabilities to build a regional order through the belt and road initiative (BRI). With the recent release of its Central Bank Electronic Payment and the Blockchain-based Network System, China can seek to regionalize the use of its renminbi (RMB) and rival the power of the Dollar.
High-speed three-dimensional shape measurement based on tripartite complementary Gray-coded light

In phase-shifting profilometry based on the Gray code, the jump error is inevitably generated and is further amplified in dynamic scenes. To tackle this problem, we propose the robust tripartite complementary Gray code method (TCG). Without projecting additional patterns, TCG uses different combinations of Gray code to calculate three complementary orders able to avoid jump error in the unwrapping process. TCG is efficient and robust, as it fully utilizes the redundant information of the Gray code. Experimental results demonstrate that TCG can realize high-efficiency and high-speed three-dimensional shape measurement at a rate of 500 fps.