Reviewerly/exHarmony huggingface dataset:
Transcending the Situation: On the Context-dependence of Practice-based Cognition

Socio-material practices entail what Per Linell (2009) terms ‘situationtranscendence’ in that they allow agents to engage in purposeful activity that exceeds the spatiotemporal constraints of a given situation. Radical perspectives in cognitive science offer insights into the cognitive dynamics involved: while those supportive of Distributed Cognition (DCog) analyze how cognition is distributed synchronously across situations, proponents of 4E Cognition show that cognition extends in a diachronic manner. Despite their differences, both perspectives acknowledge that agents must be sensitive to contextual information in order to accomplish cognitive tasks. In fact, both analyze the functionality of socio-cultural resources (e. g. computers, notebooks, instruments etc.) in relation to particular contexts and tasks, and focus on how such ressources give rise to the cognitive resources required for different kinds of cognitive activity. Having described key positions in the literature, the paper presents a case study on the practice of leakage detection in heating pipes as this is done by professionals in a Danish utility company. Accordingly, the paper explores situation-transcendence and, more specifically, how contextual constraints connect with and emerge from the synchronic and diachronic processes involved as professionals engage in data analysis, internal and external coordination and on-site exploration.
Educational design research, an innovative method to investigate virtual reality applications in disaster management training programs

Educational design research is a form of experiment that combines the stages of development, design and testing of a researched paradigm in a way that all theese processes are conducted in real learning environment. The investigated educational dilemmas are continuously modified and readjusted throughout the research process, so that researchers can test and generate theories simultaneously within naturalistic educational contexts.Design-based research is defined as an adaptive, collaborative, contextual and flexible process, easily adapting to any research objective through the adjacent methodological complex. It differrs from other research methods by addressing practical and theoretical objectives with the same importance, the final contribution being, in general, a validated practical application and a set of new theoretical knowledge.The aim of this paper is to present the exceptional advantages of this research method and to provide a source of ideas for researchers interested in developing scientific works based on this methodology. Demonstratively, we include an example of a research protocol adapted to this methodological concept, which aimed to investigate the potential use and integration of virtual reality technology in disaster management related training programs. This protocol constituted the basis of a complex inquiry process led by our research team and which we consider to be a perfect model for demonstrating the adaptability of the method to instructional research objectives.
SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER

The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%
Exploring Stylometric and Emotion-Based Features for Multilingual Cross-Domain Hate Speech Detection

In this paper, we describe experiments designed to evaluate the impact of stylometric and emotion-based features on hate speech detection: the task of classifying textual content into hate or non-hate speech classes. Our experiments are conducted for three languages – English, Slovene, and Dutch – both in in-domain and cross-domain setups, and aim to investigate hate speech using features that model two linguistic phenomena: the writing style of hateful social media content operationalized as function word usage on the one hand, and emotion expression in hateful messages on the other hand. The results of experiments with features that model different combinations of these phenomena support our hypothesis that stylometric and emotion-based features are robust indicators of hate speech. Their contribution remains persistent with respect to domain and language variation. We show that the combination of features that model the targeted phenomena outperforms words and character n-gram features under cross-domain conditions, and provides a significant boost to deep learning models, which currently obtain the best results, when combined with them in an ensemble.
Rapid Assessment of Breast Cancer Malignancy Using Deep Neural Network

Breast cancer is one of the deadly diseases that have high morbidity and mortality rate. Traditionally, doctors or radiologists should delineate the malignancy suspicious in a manual procedure such as manual segmentation or making diagnosis decisions. This medical examination may occur some problems such as time-consuming, tedious, and possible to produce subjective results. An alternative way to overcome this problem is that implementing technology to support this examination process. Hence, we propose a deep neural network model to reach the development of a rapid classification system for breast cancer. Our model was performed in the Breast Cancer Wisconsin (Diagnostic) dataset consisting of 569 instances and 30 attributes. Our proposed model was started by balancing the data in the preparation step. After conducting the preparation step, we performed a deep neural network model with three hidden layers and two dropout layers. Our experiment achieved the best performance compared to the previous study with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 98.536, 98.466, 98.765, 99.689, 94.118, and 98.500% respectively which indicates that our proposed method has suitable performance for assessing cancerous of breast cancer.
The Effect of Authentic Materials on Writing Performance across Different Levels of Proficiency

The use of authentic materials for language teaching is common. Yet, there is not much information about what type of instructional materials benefits learners. The purpose of the study is to find empirical evidence of whether nonâ€“fiction authentic materials are more superior that fiction authentic material. The researchers used an experimental design to answer the research question. The population of the study was all students who took an essay writing course at University PGRI Wiranegara in the academic year 2019-2020. The sample was taken by using a systematic random sampling method which results in two equal numbers of students in two experimental groups. Group A was taught with non-fiction authentic material, while group B was taught with the fiction authentic material. The researchers used ANOVA to analyze the data in SPSS 22. The process of teaching and learning followed the writing as a process approach and was done in a blended-learning setting because of the pandemic of COVID 19. The result shows that there is no significant interaction between content type and proficiency level (F(2,30) = 1.347,Â pÂ = .275). The main effect ofÂ content type on writing performance was not significant (F(1,30)=.001,Â pÂ .973), but the main effect of proficiency level onÂ writing performance is significant such that the students who have a higher level of writing proficiency have better performance than the students who have a lower level of writing proficiencyÂ Â (FÂ (2,30) = 5.653,Â pÂ .008). The researchers conclude that both types of authentic materials are equally effective to improve the studentsâ€™ essay writing performance.
Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

The data owner administers their data to the public cloud due to regulatory. Even this data encoded once it easily transmitted to the cloud. In order to ensure the privacy and security, cloud subscribers need a very different type of online data. This specific information must be germane to the recipient's query. The user to inspect the query too in the cloud with umpteen keywords can accomplish this. With the intention to defend the privacy of data, confidential data encoded before subcontracting by the metadata owner, attempting to make the traditional and efficient plaintext keyword search tactic pointless. Therefore, it is crucial to explore a secure data search service for encrypted user data. Due to the growing of massive number of digital users and legal documents in the cloud, umpteen keywords forced in the search request and documents returned in the order of their relevance to these keywords.Cloud assistance end-user demands cloud information by various paternoster inquiry, whichlabelled as umpteenkeywordstratified exploration over scrambled information. In the cloud server, all the client inquiries exchanged. Server looks through the significant matter by utilizing the harmonic equivalency and sends the applicable outcomes to the client. Information got from cloud server is in the scrambled configuration. To get to control to the client,Information proprietor furnishes with key for unscrambling of information. Message Authentication Code(MAC) calculation is utilized to check and confirm trustworthiness of information. Thusly this paper depicts the method of giving security to redistribute information on cloud and checking the trustworthiness of information using blowfish algorithm. Comparison results shows the improvement in efficiency of Data Retrieval and Time efficiency.
Introduction to Machine Learning and Deep Learning

The subject of deep learning has gained immense popularity recently, and, in the process, has given rise to several terminologies that make distinguishing them fairly complex. One might find the task of neatly separating each field overwhelming, with the sheer volume of overlap between the topics.
Measure of Similarity between GMMs by Embedding of the Parameter Space That Preserves KL Divergence

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.
The research on using robust functions for neural networks

The paper is devoted to analyzing the ability of using robust functions for building neural networks. The research highlights different robust functions in terms of applying them for obtaining a robust modification of the back-propagation algorithm. The algorithm requires that the used loss function should be infinitely or continuously differentiable. The analysis of twelve different functions has been done. The derivate of Charbonnier function has been obtained by the author. The results of analysis shows which functions can be used for the further investigation and which ones should be excluded from it.
A Digital and Lightweight Delay-Based Detector against Fault Injection Attacks

This paper proposes a digital and lightweight delay- based detector against fault injection attacks (FIAs) which could induce clock glitches. The detector has a simple structure and exploits a reconfigurable delay chain to monitor the clock signal disturbances. The delay chain is designed according to the protected circuit's clock period and timing slack, such that the detector can work in a range of delay values. This property makes the detector tolerate the aging issue. The experiments on iterative DES cipher circuit verify that the detector is effective to detect clock glitches. The overheads of area and power consumption introduced by the detector are 0.058% and 4.5%, respectively.
A PMBoK Extension Proposal for Data Visualization in Software Project Management

Although the human brain stores images more easily than text, most of the tools adopted for software project management are based on textual reports.The number of software projects that fail is huge, and the lack of understanding of the project complexity by the stakeholders is among the reasons for project failure.Data visualization techniques and tools can help to identify the project issues and reduce misunderstandings.In this paper, we investigate how project management can benefit from data visualization.To do so, we adopted a hybrid research approach composed by a systematic mapping study, a survey, and three focus group sessions.As a result, we identify a set of the 16 visualization techniques and tools that can be used to support software project management and we propose a PMBoK extension that provides a reference for practitioners who are planning to use data visualization to support software project management.
AN EMPIRICAL STUDY ON DATA MINING TECHNIQUES AND ITS APPLICATIONS

Data mining is the process of bring out the information from huge amount of data.Data mining is also referred as Knowledge Discovery from the Data(KDD), it is the convenient extraction of patterns representing knowledge implicitly stored or captured in Large Databases, Data Warehouses, the Web, other massive Data Repositories or Information Streams.In this paper we have discourse various Data Mining Concepts, Process, Techniques and Data Mining Applications.
THE ROLE OF EDUCATIONAL PEDAGOGICAL TECHNOLOGIES IN THE PROFESSIONAL TRAINING OF THE FUTURE TEACHER IN HIGHER EDUCATION INSTITUTIONS

Proceedings Article published 2020 in THEORETICAL AND PRACTICAL FOUNDATIONS OF SOCIAL PROCESS MANAGEMENT
PointNet-Based Jitter Decomposition on Point Cloud of Jitter Histogram

Jitter is one of the key factors affecting bit error rate (BER) on high-speed links. A novel method of jitter decomposition by PointNet using 2D point cloud of jitter histogram is proposed for decomposing the time interval error (TIE) jitter into deterministic jitter (DJ) and random jitter (RJ). The proposed method uses transformNet (T-Net) and multi-layer perceptron (MLP) to learn global point cloud features and uses average pooling to aggregate information from all the points. Experimental results show that the proposed approach can decompose jitter into DJ and RJ, and is better than the traditional jitter decomposition method i.e. time lag correlation (TLC) and Time-Domain PLL Jitter Decomposition (T-D PLL). In addition, results show that the performance of the proposed method is better than CNN, PointRNN, and PointANN.
Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods

In this study, different models were created to reduce bias by ensemble learning methods. Reducing the bias error will improve the classification performance. In order to increase the classification performance, the most appropriate ensemble learning method and ideal sample size were investigated. Bias values and learning performances of different ensemble learning methods were compared. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n: 6000, n: 8000, n: 10000, and n: 20000 sample sizes. When the learning performances were compared, AdaBoost ensemble learning method and RBF classifier achieved the best performance with n: 250 sample size (ACC = 0.956, AUC: 0.987). The AdaBoost ensemble learning method and REPTree classifier achieved the best performance with n: 20000 sample size (ACC = 0.990, AUC = 0.999). In conclusion, for reduction of bias, methods based on stacking displayed a higher performance compared to other methods.
DRS+: Load Shedding Meets Resource Auto-Scaling in Distributed Stream Processing

Distributed stream processing is nowadays a leading paradigm for managing massive streaming data and performing real-time analytics on such streams. Since data volume and distribution in the input streams often change over time, a dynamic resource scheduler is often employed to ensure that the system meets response time constraints while being cost effective in terms of resource usage. Currently, resource schedulers in distributed streaming systems generally assume that each input must be completely processed, and allocate resources accordingly. In practice, this assumption can often be relaxed, since many stream analytics tasks do not require exact outputs. To our knowledge, however, no existing resource scheduler takes this fact into consideration, leading to unnecessary over-provisioning of resources. This paper presents DRS+, a novel dynamic resource scheduler that integrates load shedding into resource auto-scaling strategies. DRS+ is based on a unified model that establishes the relationship between response time, result accuracy and resource consumption, given the current workload statistics. Using this model, DRS+ computes the best resource allocation plan and load shedding strategy, and executes them through an efficient protocol that minimizes the computation and communication overhead at each operator. We have implemented DRS+ based on Apache Storm, and evaluated it using real dataset. The results demonstrate that DRS+ achieves low resource consumption and high result utility, while satisfying real-time response constraints.
Handwriting Character Recognition Javanese Letters Based on Artificial Neural Network

Abstract— Javanese character is one   of   Indonesia's cultural heritages that must be preserved. Manuscript form of the Javanese character is one of the priceless inheritances. Javanese characters are often called the Hanacaraka font. The development of technology especially in the field of image processing is one method to preserve the culture.  The development of image processing methods for detecting characters from images with fewer errors is a great   task. The purpose of this research is to create a system of Javanese character recognition from the handwriting into Latin character, so that the young generation can learn the form of Javanese character easily. The method used in this research is back-propagation as a classification method. The trial result of this recognition is to have accuracy 74 %.
Obesity Risk Prediction using Wifi based Human Activity Recognition Techniques

WiFi based technologies can be found everywhere and are an integral part of home and work environments. WiFi signals carry information related to the channel they are travelling through. Thus WiFi signals can be used to observe the changes in its vicinity. As WiFi signals are closely integrated with human workplace, the signals is used to observe the human activity in the vicinity of the WiFi routers, that is, the transmitter and the receiver. The activity once correctly identified can be used to store the changes in the activity and how often one performs such activity or is there a sudden change. using these metrics and quantifying the changes can help calculate and predict the heath of the individual and provide better statistics to health professionals to identify any ailment that may have caused a change in the individuals behaviours making them seek medical attention.
A Method for Combining Classical and Deep Machine Learning for Mobile Health and Behavior Monitoring

This paper summarizes the doctoral dissertation of the author, which presents a method for fusing classical and deep machine learning for mobile health and behavior monitoring with wearable sensors.
A systematic overview of secure image steganography

&lt;p&gt;Information is a vitalthing that needs to be secured and well protected during transmission between two or more parties over the internet. This can be achieved by steganography technology. Steganography is the concealing science in which the information is concealed inside other information in a way that the concealed information cannot be detectable by the human eye. Many ways are available to hide data inside a cover media for example text, image, and audio steganography, but image steganography is the most utilized technique among the others. Secure image steganography has a high-security level than traditional technique by combining steganography with cryptography due to encrypting secret information by cryptography algorithm before embedding it into the cover media by steganography algorithm. In this paper,a systematic review has been presented about secure image steganography and its renowned types. Many researchers proposed secure image steganography by using various cryptography and steganography algorithms which have been reviewed. The least significant bit ‘LSB’ was the renowned steganography algorithm which has been used by researchers due to its simplicity, while various cryptography algorithms like advanced encryption standard (AES) and blowfishhave been used to propose secure image steganography in the reviewed papers. The comparison among the reviewed papers indicated that the LSB with hash-RSA gave a greater peak signal-noise ratio ‘PSNR’ value than the others which was 74.0189 dB.&lt;/p&gt;
СТАН ДОСЛІДЖЕНОСТІ РОЗВИТКУ ПРОФЕСІЙНОЇ МАЙСТЕРНОСТІ МАЙБУТНІХ УЧИТЕЛІВ МИСТЕЦЬКИХ ДИСЦИПЛІН У СТРУКТУРІ КУРСУ «ПЕДАГОГІКА МИСТЕЦТВА»

Proceedings Article published 2020 in MODERN PROBLEMS IN SCIENCE