Reviewerly/exHarmony huggingface dataset:
An Improved Integrated Clustering Learning Strategy Based on Three-Stage Affinity Propagation Algorithm with Density Peak Optimization Theory

To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm (AP), an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory (DPKT-AP) was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected by density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm.
Continuing Our IEEE MTT-S Webinar Series [Education News]

The IEEE Microwave Theory and Techniques Society (MTT-S) Education Committee continues to offer high-quality webinars from experts in their respective fields through a series that started in 2016. The current webinars scheduled for the first half of 2021 can be found in Table 1, presented here. These speakers represent a diverse group of leading subject-matter experts from around the globe, including current or emeritus Distinguished Microwave Lecturers.
Chosen-Ciphertext Secure Key Encapsulation Mechanism in the Standard Model

Key Encapsulation Mechanism (KEM) is a foundational cryptography primitive, which can provide secure symmetric cryptographic key material for transmission by using public key algorithms.Until now, many Chosen-Ciphertext (IND-CCA) secure KEM schemes are constructed from Chosen-Plaintext (IND-CPA) or One-Way (OW-CPA) secure PKE via the generic Fujisaki-Okamoto (FO) transformations (TCC 2017).However, the security relies on the Random Oracle Model (ROM).To the best of our knowledge, there are no IND-CCA secure KEM schemes based on Learning Parity with Noise (LPN) assumption that can against post quantum attacks in the standard model.In this work, we propose the first direct construction of LPN-based KEM, which is secure in the standard model.In particular, we use double-trapdoor technique to answer adversary's decryption queries correctly and a Target Collision Resistant (TCR) hash function to check the validity of the ciphertext.The encapsulated key is determined by a special LPN problem (with no random oracle required).The scheme is IND-CCA secure against post-quantum attacks under the low-noise LPN assumptions by a series of games and the security reduction is tight.Compared with previous schemes on 128-bit security level, our CCA-secure scheme only holds 50.78MB public keys, 62.50MB secret keys and 4.54KB ciphertexts, which is more efficient than the schemes of Döttling et al. (
Online lectures: An implementation of full e-learning action research

This study aimed to determine whether there is an increase in student learning outcomes after the implementation of online learning using the WhatsApp platform in the Children with Special Needs (CSN) course. This research method was a classroom action research conducted online using the WhatsApp platform. The classroom action research was carried out in 2 cycles with the stages including planning, implementing, observing, and reflecting. The subjects of this study were 21 PGSD UBT students in semester 6 of the 2019/2020 academic year. The data collection techniques used were observation, interviews, questionnaires, and test questions. While the research instruments used were observation sheets, interview sheets, online learning implementation questionnaires, and test questions. The data obtained were analyzed using qualitative and quantitative. The results of the study showed that the percentage of learning outcomes before and after being given learning treatment using the WhatsApp platform experienced a significant increase and had reached 85% completeness criteria. The online learning through the WhatsApp platform has some advantages, such as ease of use and development of courage in the discussion process. While the weakness is related to the unstable network and the lack of direct experience obtained through observations related to CSN material that requires direct practice in the field. This study was conducted as a contribution to the selection of learning media in a situation of Covid-19 pandemic, which requires learning to be conducted online or remotely.
Extractive Text Summarization of Student Essay Assignment Using Sentence Weight Features and Fuzzy C-Means

One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.
GLObfus: An Enhanced Data Security Method to Protect Numerical Data in Public Cloud Storage

Cloud is an attractive computing paradigm for providing different kinds of computing services.Without realizing all day long, people are using cloud computing and their components.In the same way, without user's knowledge the data can be stolen at any time.Every second huge amount of data is uploaded in the cloud worldwide.Data storage and retrieval is a tedious task, but cloud makes it easy for all levels of users and also provides many service advantages to the end users.Apart from all these benefits, cloud pulls back because of security of data uploaded in the cloud.Security of data is a vital research area in cloud, and needs to be addressed.To enhance the security of data in the cloud, this paper proposes an enhanced data obfuscation method-GLObfus to protect the numerical data which are uploaded to the cloud.The proposed obfuscation method uses different mathematical calculations and functions to convert the data into meaningless data.The numerical data prepared to add are obfuscated prior to storage in the cloud area.The proposed technique is applied as a cloud utility and hosted on a cloud platform as a service and examined with current obfuscation strategies with respect to time and security.From the effects, it's far discovered that the proposed obfuscation technique-GLObfus is more efficient than the current obfuscation strategies with respect to obfuscation time and de-obfuscation time and security level.
MENGEMBANGKAN KEMAMPUAN MEMBACA ANAK USIA DINI DENGAN MULTIMEDIA INTERAKTIF

This research is motivated by the fact that learning still uses conventional learning. The media used is still using books or student worksheets. This causes teaching and learning activities for children to become bored and less varied. This is due to the lack of teacher media to convey learning to students. So that teachers prefer to use media that is easy and sober so that the development of children's reading skills has not yet developed optimally&#x0D; The aim of this research is to find out the effect of interactive multimedia on children's reading ability. The method used in this research is quantitative Pre Experimental with the design of "the one group pretest posttest". The sample in this study were 18 children in group A Kindergarten consisting of 10 male students and 8 female students. The data collection technique used is the method of observation and documentation. The results of the analysis show that the t test results Sig (0,000) &lt;0.05 means there is an influence. This means that interactive multimedia affects the reading of group A children.
Dynamic Three-Dimensional Surface Reconstruction Approach for Continuously Deformed Objects

For dynamic three-dimensional (3-D) surface reconstruction of continuously deformed objects, an efficient method employing one fringe pattern is proposed. First, a two-dimensional wavelet transform profilometry (2-D WTP) employing an improved cost function is adopted to obtain the wrapped phase, which has the better noise suppression ability. Then, a new 3-D phase unwrapping algorithm is introduced to obtain the unwrapped phase, which includes four steps. a) A new 3-D residues recognition method is proposed. b) A 3-D branch cut construction algorithm based on an improved GA is proposed. c) A 3-D phase derivative variance quality map is established. d) A 3-D flood filling phase unwrapping algorithm guided by 3-D quality map avoiding 3-D branch cut (3-D FGQAB) is introduced to obtain the unwrapped phase. Simulation and experimental comparisons are carried out, the results show that the proposed method can be applied to dynamic 3-D reconstruction of the continuously deformed objects.
BERT for Opinion Mining and Sentiment Farming

BERT which is published by Google AI language stands for Bidirectional Encoder Representations from Transformers is a language representation model recently developed has caused a flurry in Machine learning community.The design of BERT consists of pre-training the unlabeled text contextually from both the directions left and right for all the layers, unlike the recent language representation models.For a BERT model which is pre-trained, inclusion of one output layer to the state-of-the-art models for fine tuning is done which facilitates different tasks for language understanding such as opinion mining and question answering so that the task specific architectures are not to be modified.BERT is a key technical innovation which applies bidirectional training of a popular attention model called as Transformer language modelling.The results in the paper represent a language model BERT in which training is done bi-directionally and a deeper sense of context for the language understanding is developed.In the paper, a detailed description of a novel technique of BERT in which training involves Masked Language Model (MLM) and Next Sentence Prediction (NSP) bidirectionally which was previously impossible.
Faktor-Faktor yang Memengaruhi Tingkat Kriminalitas di Indonesia Tahun 2018

&lt;p&gt;Crime still often occurs easily in daily life in Indonesia. This study aims to determine factors that influence the level of crime in Indonesia in 2018 and the magnitude of the influence of each factor. The data used in this study are secondary data obtained from the Statistics Indonesia (BPS). The method used in this study is path analysis, and the variables used are population, education, unemployment rate, poverty rate, and crime. As a result, population and poverty influenced crime while education influenced poverty significantly.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: crime, path coefficient, path analysis&lt;/p&gt;
Efektifitas Model Quantum Teaching Terhadap Pembelajaran Matematika Siswa di Sekolah Dasar

This research stems from the difficulty of students of SDN Salupompong, Mamuju Regency, West Sulawesi in following the learning process, this is due to the lack of development of teaching models and methods by teachers which impact on relatively low student learning outcomes. Therefore, this study aims to determine the effectiveness of the Quantum Teaching model in class IV SDN Salupompong, Mamuju Regency, West Sulawesi. This study used an experimental research method with descriptive analysis and imperative analysis carried out with four learning meetings and two test meetings. The learning outcomes obtained were 3,7, student activity was 3,53, and student response was 3,44, from the three indicators of the effectiveness of the Quantum Teaching learning model, it can be obtained an average effectiveness of 3,55 with the very effective category.
Software Engineering in Saudi Arabia: A Bibliometric Assessment

This paper presents a bibliometric assessment of the Software Engineering (SE) community in the Kingdom of Saudi Arabia (KSA). The assessment was based on the number of SE papers published by KSA-based SE researchers in SE-related venues and indexed in Scopus between years 1984-2019. The assessment aimed to measure the volume of research contribution produced by KSA-based researchers and institutions to the SE field. 802 SE papers were published by KSA-based SE researchers and the top active institution in the domain is King Fahd University of Petroleum and Minerals (KFUPM) and the top active researcher is Mohammad Alshayeb who is affiliated to KFUPM. The results estimated that KSA produced around %0.62 of the world-wide SE knowledge which indicates that KSA SE community needs to increase the volume of its publications to be more active in the worldwide SE community. The results also show that from 2007 onwards, the annual publication trend of KSA SE community has been growing in a healthy rate reaching 113 published papers in 2019. Additionally, 56% of the papers were internationally-authored and the highest international collaborations were with researchers from the USA, Tunisia and the UK respectively. In general, KSA is performing well comparing to three of its neighboring countries (UAE, Jordan and Egypt) in terms of the quantity of the published SE papers and received citations. However, in comparison to developed countries, the results suggest that more work on the quantity, quality and visibility of the SE papers authored by KSA-based researchers is needed.
Adaptive Models of Information Management in Crisis

The article deals with the problem business processes revaluation and modelling of changes in economic crisis conditions. Factors of a modern condition formation for intangible assets use and IT technologies and their value for information management are analyzed. Features of adaptive management in the conditions of quarantine, when all processes are moving to online domains, and criteria of changes estimation are defined. The necessity of key business processes definition competences and choice of strategies for changes in information management is proved.
Analyze Classification Act of Data Mining Schemes

In the current era, the elevated amount of data related to various working field is globally available that utilize by huge research community for fetching an unseen knowledge. Related field study reveals that classification schemes of data mining have highly exploited by investigators for labeling data and to recognize the category/class of fresh entered statistics but with differ operational structure each handy scheme puts fluctuate upshot even evaluates with same data sets. Hence, an assortment of an exclusive classification scheme is still a research challenge of related field. In term to discover and demonstrates suitability/unsuitability of accessible classification scheme, this paper presents a comprehensive assessment among some of the most adopted classification methods in company of few benchmarked datasets access from UCI repository.
Superendividamento e crise econômica do novo coronavírus: impactos nas mulheres no litoral do Paraná

In order to better understand the current scenario, this article investigated the economic impacts of the coronavirus pandemic on women from the coast of Paraná.For this, we did an exploratory descriptive research with 221 women, between May and July 2020.The research showed that the pandemic had several consequences in the lives of these women.In the psychological field, the interviewees reported a high degree of anxiety, fear, stress and insomnia.These problems were triggered by social distance and uncertainty about the future.In many cases, psychological problems may have generated an increase in consumerism, which may have contributed to the indebtedness of these women.38.9% of the interviewees said they were the main breadwinner of their houses, while 39.9% declared themselves indebted.Divorced women had the highest average debt.Schooling is also a factor to be considered, since we observed that indebtedness compromises less the income of women with higher education.The study also revealed that only 19.5% of the women interviewed keep some kind of formal debt control.Therefore, we concluded that the use of basic economic planning tools can be an alternative to reduce the levels of indebtedness among the women interviewed.
Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.
Complete trace models of state and control

Abstract We consider a hierarchy of four typed call-by-value languages with either higher-order or ground-type references and with either $$\mathrm {call/cc}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>call</mml:mi> <mml:mo>/</mml:mo> <mml:mi>cc</mml:mi> </mml:mrow> </mml:math> or no control operator. Our first result is a fully abstract trace model for the most expressive setting, featuring both higher-order references and $$\mathrm {call/cc}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>call</mml:mi> <mml:mo>/</mml:mo> <mml:mi>cc</mml:mi> </mml:mrow> </mml:math> , constructed in the spirit of operational game semantics. Next we examine the impact of suppressing higher-order references and callcc in contexts and provide an operational explanation for the game-semantic conditions known as visibility and bracketing respectively. This allows us to refine the original model to provide fully abstract trace models of interaction with contexts that need not use higher-order references or $$\mathrm {call/cc}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>call</mml:mi> <mml:mo>/</mml:mo> <mml:mi>cc</mml:mi> </mml:mrow> </mml:math> . Along the way, we discuss the relationship between error- and termination-based contextual testing in each case, and relate the two to trace and complete trace equivalence respectively. Overall, the paper provides a systematic development of operational game semantics for all four cases, which represent the state-based face of the so-called semantic cube.
Horizontal Scaling for Containerized Application Using Hybrid Approach

Cloud computing is a paradigm to provide services to end-users through the Internet. The availability of services to end-users is dependent on various factors such as the availability of computing resources as well as the number of users to access those services. To manage the real-time fluctuating workload cloud providers use elasticity mechanisms. Elasticity is one of the important characteristics of cloud computing that dynamically allocates computing resources to manage the fluctuating workload. The failure of allocation/de-allocation of computing resources at the right moment leads to SLA violation, degradation of services performance, maximum power consumption, minimum throughput, and maximum response time. To address these challenges, we have proposed a hybrid approach to perform horizontal elasticity. The proposed approach uses both reactive and proactive approaches for provisioning/de-provisioning of computing resources. The simulation results of the proposed model show that performance of system has improved in terms of CPU utilization, response time, and throughput.
IMPROVEMENT OF THE METHODOLOGY OF RESEARCHING PROBLEMS OF EDUCATION

The article in the discourse of historical development presents the consistent development of scientific approaches in education based on their methodological improvement.To solve the problem, two features of the development of theoretical knowledge about education were taken as a basis: 1-the ever-increasing deepening of theoretical research and 2-the use of all possible innovations in the field of methodology by the humanities, including those obtained as a result of integration processes in science.It is shown that modern research methodology is necessary for the design of future education.The inclusion of the tasks of comprehending the methodology in the content of education is noted.An analysis of the modern content of the updated methodological categories is given: goals, objectives, methods, etc.The growth in the share and influence of educational sciences is indicated.It is proved that the development of the theory of education of the 20th century is reflexively consistent with the methodology of classical science.The methodological features of the humanistic approach in the second half of the XX century are presented.It is shown that the reflection included in the model of the "trajectory of individual development" brings the content of modern education to a higher innovative methodological level.