Reviewerly/exHarmony huggingface dataset:
BENEFITS AND CHALLENGES OF CLOUD COMPUTING IN PRODUCTION AND SERVICE SECTOR IN IZMIR, THE CITY OF TURKEY

Purpose of the study: This research aims to understand the benefits and challenges of cloud and non-cloud adopter companies of production and service sectors on cloud computing and making recommendations based on companies’ perceptions of the issue in Izmir, Turkey.&#x0D; Methodology: This is a descriptive survey of businesses, forming a questionnaire for 99 companies in the service sector and 66 companies in the production sector to non-cloud or cloud user businesses in the city of Izmir in Turkey. SPSS 26.0 software is used to analyze the perceived benefits and challenges of cloud computing.&#x0D; Main Findings: The descriptive analysis results show that increasing productivity in business processes and working collaboratively from remote are the most important perceived benefits of adopting cloud computing in the production sector and service sector, respectively. On the other hand, business processes adoption issues and concerns about security are the significant perceived challenges and barriers of cloud computing adoption, respectively.&#x0D; Applications of this study: The study is useful for governments to understand the critical points of cloud computing and is believed to give governments the ability to prepare an appropriate environment for businesses. Cloud providers and companies also take a position to contribute to this environment by creating new I.T. products and purchasing these I.T. products conforming to their I.T. needs, respectively.&#x0D; Novelty/Originality of this study: This research provides companies in Izmir with a guide for the adoption considering the appropriate cloud computing deployment models such as public cloud, private cloud, community cloud and hybrid cloud for certain types of applications such as software as a service (SaaS), platform as a service(PaaS) and Infrastructure as a service (IaaS) for developing countries.
Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items

The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.
TO THE QUESTION OF BUILDING THE UNIVERSAL MODEL OF THE DEVELOPMENT OF WORLD PEDAGOGY AND EDUCATION

The purpose of the research is to build a universal dialectical model simulating the development of world pedagogy and education. The urgency of this task stems, on the one hand, from the system crisis of human civilization, and, on the other hand, from the need in building new educational meanings and methods enabling to overcome the crises. The research methods are based on theoretical and methodological procedure of analyzing the problem field of the research by moving from the general to the specific, that is, from theoretical aspects of the problem field to its practical aspects. One more used method is fractal-holographic modeling that helps to unify the processes of pedagogy and education development on local and global historic levels. The extrapolation procedure of system-theoretical reduction and simplification of the analyzed phenomena is also used, since the systems analysis is a means of combating complexity, a means of finding the simple in the complex.The universal models of pedagogy and education development on local and global levels are built. So, the results of the research, which reveal its scientific novelty, indicate that the unfolding of the studied dynamic phenomena can be represented as a single invariant process, which is simulated by a universal dialectical model, unifying this process due to the mechanism of fractal-holographic nesting of the stages of the development of any educational phenomenon. Thus, the understanding of unfolding the educational and pedagogical phenomena in historical context as certain invariant algorithmic dialectical process has been reached. Prospective directions for the study of these educational and pedagogical phenomena may be the researches focusing on more differentiated analysis of the problem field of the research.
Understanding Insider Threat Attacks Using Natural Language Processing: Automatically Mapping Organic Narrative Reports to Existing Insider Threat Frameworks

Traditionally cyber security has focused on defending against external threats, over the last decade we have seen an increasing awareness of the threat posed by internal actors. Current approaches to reducing this risk have been based upon technical controls, psychologically understanding the insider’s decision-making processes or sociological approaches ensuring constructive workplace behaviour. However, it is clear that these controls are not enough to mitigate this threat with a 2019 report suggesting that 34% of breaches involved internal actors. There are a number of Insider threat frameworks that bridge the gap between these views, creating a holistic view of insider threat. These models can be difficult to contextualise within an organisation and hence developing actionable insight is challenging. An important task in understanding an insider attack is to gather a 360-degree understanding of the incident across multiple business areas: e.g. co-workers, HR, IT, etc. can be key to understanding the attack. We propose a new approach to gathering organic narratives of an insider threat incident that then uses a computational approach to map these narratives to an existing insider threat framework. Leveraging Natural Language Processing (NLP) we exploit a large collection of insider threat reporting to create an understanding of insider threat. This understanding is then applied to a set of reports of a single attack to generate a computational representation of the attack. This representation is then successfully mapped to an existing, manual insider threat framework.
Game Edukasi Belajar Mengaji Menggunakan Metode Linear Congruent Method (LCM) Berbasis Android

Koran refers to the activity of reading the Qur'an. Koran is a skill that must be mastered by every Muslim and must be taught from an early age to children from the preschool age is the formation of the main character. But at this age still playing and when studying the Koran is still conventional with limited teaching methods. The method of learning for preschoolers requires interactive learning in the form of interactive audio visual education game. In the midst of a lot of research on educational learning games of the Koran, researchers focus on using the Linear Congruent Method (LCM). This linear congruent method model is used to randomize the quiz questions that will be presented. In developing each component of this educational learning learning game software using the Rapid Application Development (RAD) model. The results achieved from this study are in the form of teaching learning applications that are ready for use. This application can be used as the best solution to make it easier for preschool children to study Koran and data from distribution test results in graphical form showing system assessments with the percentage of satisfaction of this application using the Computer System Usability Questionnaire (CSUQ) to get a value with an average of 86.46% in. in the very decent category.
Ocean Big Data Service Technology in a Distributed Network Environment

Liang, X.-B. and Wang, K., 2020. Ocean big data service technology in a distributed network environment. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 576–579. Coconut Creek (Florida), ISSN 0749-0208.Ocean big data is distributed, which has the characteristics of integrated application. In the distributed network environment, one must avoid information barriers, which will avoid the repeated construction of ocean big data service technology. Therefore, based on the construction of marine information infrastructure and demand orientation, the relationship between marine data sharing and information services can be demonstrated. Through the construction of the framework of ocean big data platform, this article develops ocean big data under the distributed network environment, which is very helpful for the application of ocean data. Through diversified ocean big data, this article establishes an efficient distributed computing ocean big data cloud platform, which is an important development direction of intelligent ocean construction. Through technical support, massive data resources can be used, which will tap the potential value of ocean data.
Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment

Security and ethics are both core to ensuring that a machine learning system can be trusted. In production machine learning, there is generally a hand-off from those who build a model to those who deploy a model. In this hand-off, the engineers responsible for model deployment are often not privy to the details of the model and thus, the potential vulnerabilities associated with its usage, exposure, or compromise. Techniques such as model theft, model inversion, or model misuse may not be considered in model deployment, and so it is incumbent upon data scientists and machine learning engineers to understand these potential risks so they can communicate them to the engineers deploying and hosting their models. This is an open problem in the machine learning community and in order to help alleviate this issue, automated systems for validating privacy and security of models need to be developed, which will help to lower the burden of implementing these hand-offs and increasing the ubiquity of their adoption.
Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

The Codebook model is one of the popular real-time models for background subtraction to detect moving objects. In this paper, we propose two techniques to adapt the original Codebook algorithm to multispectral images: dynamic mechanism and fusion strategy. For each channel, only absolute spectral value is used to calculate the spectral similarity between the current frame pixel and reference average value in the matching process, which can simplify the matching equations. Besides, the deciding boundaries are obtained based on statistical information extracted from the data and always adjusting themselves to the scene changes. Results demonstrate that with the proposed techniques, we can acquire a comparable accuracy with other methods using the same multispectral dataset for background subtraction.
Graph-Based Image Retrieval: State of the Art

The paper deals with the problem of semantic Image Retrieval. Indeed, the image has recently gained popularity in several domains such as medical domain, marketing, etc. Image plays a very vital role in documentation. However, finding visual and relevant information in an image is a huge task for Image Retrieval community and a very discussed issue in digital image processing. In fact, image can be extracted from a big collection of images, in the purpose of responding to user's need. Image Retrieval processes based on classical techniques may not be sufficient to user. For several years, great efforts have been devoted to integrate semantic aspect, in order to enhance relevance of the result and ensure high-level content consideration in image. This paper presents a state of the art of Image Retrieval approaches using graph theory due to the growing interest given to graphs in terms of performance, representation and its ability to ingrate semantic aspect. We review a number of recently available graph-based approaches in Image Retrieval aiming to determine factors adding semantic aspect in Image Retrieval system.
Development of Ethnobotany-Based Booklets as Learning Tools for Communities

Ethnopharmaceutical research-based booklets are teaching material that contains important information that is presented in a concise and interesting manner for the general public to study.This research aims to develop a booklet for ethnopharmaceutical research-based communities in the Bromo Tengger Semeru National Park area.This research method uses the development adopted from the ADDIE learning model.Data is collected through identification of plants in the field and group discussion forums with the Tengger community.While the booklet's eligibility data was validated by media experts and content material experts, as well as the effectiveness of the booklet being tested on community groups.The results of this study indicate that there are about 40 types of medicinal plants commonly used by the Tengger community.The results of media and content material validation obtained 88.95% and 80% results, which are included in the very valid and quite valid criteria and can be used.The results of the booklet trial on the community group including readability and practicality tests obtained 86.42% and 91.65% which were included in the criteria that were read well and were very practical.Booklet development from the results of research can be a learning media that is more easily understood in learning biology related to plant diversity and utilization.
Ensemble Methods to Improve Accuracy of a Classifier

A decision tree algorithm is developed for a B-School which can be used as a strategy to identify potential placeable candidates at the time of admission itself based on their past academic performance.Building the classification model is the easier part.How to get the best performance from the model is the challenging part.While the classification tree is simple and easy to interpret, it is not competitive as compared to other supervised learning approaches mostly in terms of prediction accuracy.The disadvantage of a single tree can be overcome if there is an approach which can produce multiple trees and combine them to yield a better prediction.Ensemble methods are supervised learning algorithms for combining multiple learners, trees in this case, to produce a strong learner.There will be some loss of interpretability but the improvement in accuracy will outweigh this.Baggingand random forests approaches are used to improve the accuracy of the positive class.
Systematic Approach for Development of Knowledge Base in Higher Education

Internet of Things and Big Data are revolutionary technologies bringing innovation in almost every sector. These innovative technologies have of late been implemented in the education sector with significant success in achieving the goal of “education par excellence”. A lot of research has been conducted to yield the benefits of IoT in the education sector and many models have been proposed in the past by various researchers all around the world. This research introduces a systematic manner of collecting the data using sensing devices from various cohorts of students studying at “Middle East College” and how this data can be utilized to infer various postulations related to the enhancement of teaching and learning. Moreover, visualization of the facts that may provide preventive and preemptive capabilities to manage and support them in decision making is also explored in great detail. This research work is an effort to contribute to the development of a strong educational system through innovation and inquiry. This research work is also intended to contribute to developing the foundation of “knowledgebase” for Middle East College.
An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Abstract Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.
Investigating Bias and Fairness in Facial Expression Recognition

Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and corresponding expression labels. However, virtually none of these datasets have been acquired with consideration of fair distribution across the human population. Therefore, in this work, we undertake a systematic investigation of bias and fairness in facial expression recognition by comparing three different approaches, namely a baseline, an attribute-aware and a disentangled approach, on two well-known datasets, RAF-DB and CelebA. Our results indicate that: (i) data augmentation improves the accuracy of the baseline model, but this alone is unable to mitigate the bias effect; (ii) both the attribute-aware and the disentangled approaches fortified with data augmentation perform better than the baseline approach in terms of accuracy and fairness; (iii) the disentangled approach is the best for mitigating demographic bias; and (iv) the bias mitigation strategies are more suitable in the existence of uneven attribute distribution or imbalanced number of subgroup data.
The Implementation Tandur Quantum Teaching Learning Model to Improve Social Learning Achievement

Social studies is a subject with memorizing learning that scared some students of it is applied without considering students need and students’ various learning style. Indirectly this situation would affect the students’ learning outcomes. Besides, there are some difficulties happen in social studies for advance class, such as: a) Students have low ability on developing traits and attitude in daily life; b) IPS only taught in a very limited time. So, it is hard to cover all the important values. Therefore, learning process should emphasize on an enjoyable process that relates to ability and Students learning style. The purpose of this research is to analyze the impact of the Quantum Teaching Technique TANDUR towards social studies for elementary school students' learning achievement. The type of research is the basic pattern of The Posttest Only Control Group with a quasi-experiment. The research Will conduct on the sixth grade of Elementary School Students in SD 07 Sungai Raya Kubu Raya Regency, Kalimantan Barat. The population of the research consists of 106 Students, with 70 Students as the sample. Data analysis used in this research is MANOVA (Multivariant Analysis of Variance) assisted by SPSS 17.00 by windows. The result shows that IPS learning achievement between Students with Quantum Teaching Technique TANDUR learning model is more significant than Students with a conventional model (F 1 3.682 and sig = 0,000; p &lt; 0,05). This research would give an overview of teacher's innovation by applying various teaching models to create meaningful learning to improve Students learning achievement.
Attention as Activation

Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-linear gating function. Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms. In particular, we propose a local channel attention module for the simultaneous non-linear activation and element-wise feature refinement, which locally aggregates point-wise cross-channel feature contexts. By replacing the well-known rectified linear units by such ATAC units in convolutional networks, we can construct fully attentional networks that perform significantly better with a modest number of additional parameters. We conducted detailed ablation studies on the ATAC units using several host networks with varying network depths to empirically verify the effectiveness and efficiency of the units. Furthermore, we compared the performance of the ATAC units against existing activation functions as well as other attention mechanisms on the CIFAR-10, CIFAR-100, and ImageNet datasets. Our experimental results show that networks constructed with the proposed ATAC units generally yield performance gains over their competitors given a comparable number of parameters.
Gesture Recognition for Initiating Human-to-Robot Handovers

Human-to-Robot handovers are useful for many Human-Robot Interaction scenarios. It is important to recognize when a human intends to initiate handovers, so that the robot does not try to take objects from humans when a handover is not intended. We pose the handover gesture recognition as a binary classification problem in a single RGB image. Three separate neural network modules for detecting the object, human body key points and head orientation, are implemented to extract relevant features from the RGB images, and then the feature vectors are passed into a deep neural net to perform binary classification. Our results show that the handover gestures are correctly identified with an accuracy of over 90%. The abstraction of the features makes our approach modular and generalizable to different objects and human body types.