Reviewerly/exHarmony huggingface dataset:
PERPUSTAKAAN ANAK KOTA PONTIANAK

The issue of low literacy culture and reading interest in Pontianak City can be overcome by introducing children a culture of literacy and reading interest from an early age. Provision of special library facilities for children that can accommodate children's reading and learning activities in accordance with their characters and needs is needed to improve literacy culture and reading needs of children. However, until now children’s library has not been available in Pontianak. Children's library is a library that is devoted to accommodating children's learning activities that are adjusted again to meet the special needs of children The activities in the children's library are educative learning and play activities and the provision of children's services as supporting activities. The design of the Pontianak City Children's Library is located on Ahmad Yani street. The design concepts are processed with colors and shapes on the building facade, the provision of a natural environment in the form of a garden and inner courtyard as well as the concept of indoor layout using an open plan system. This design process aims to create a children's library that expresses a conducive, imaginative and creative atmosphere that is appropriate to the characteristics of the child.
Tools for the Development of Super-Professional Competencies for Gifted Youth in the Conditions of an Unstable Labor Market

Instruments for developing super-professional skills, such as entrepreneurial activity, creativity, teamwork, communication skills, are especially necessary for graduates whose professions are at risk of a decrease in employment opportunities in a changing labor market due to a pandemic. The problem is especially acute for graduates of areas related to services, tourism, catering and others. The paper presents the Lobachevsky University experience in the implementation of mass education competition of projects with use online platform i-generation. Such tools are important for gifted students, which motivation depends on the recognition and validation of their high results, from professional and behavioral content that help identify giftedness.
Constructive Learning of Deep Neural Networks for Bigdata Analysis

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.
THE USE OF TEAM GAME TOURNAMENT (TGT) TO IMPROVE STUDENTSâ€™ PARTICIPANTION IN ENGLISH CLASS (A Classroom Action Research at the VIII grade of MTs Miftahul Huda Bulungan)

Abstract This research was aimed to describe to use of the Team Game Tournament (TGT) to improve studentsâ€™ participation in English class. This research was aimed to improve studentsâ€™ participation through Team Game Tournament (TGT) of the 8A students at MTs Miftahul Huda Bulungan. This research was conducted use classroom action research with 41 students of 8A grade at MTs Miftahul Huda as the subject. The writers used Deakin Model by Kemmis and McTaggart to collect data: (1) planning, (2) action and observation, (3) reflecting. The writer also used both of the qualitative data and quantitative data. The qualitative data was obtained by observation and the quantitative data was obtained by questionnaire. In the analysis data the writers compare the result of observation with lesson plan, and the questionnaire. The result of research showed that Team Game Tournament (TGT) can improve studentsâ€™ participation in English class. Students became more active because TGT method required students to work in group with other students and did a little game in class. The result was shown from the observation done during the teaching and learning process and through questionnaire that was done after the implementation of the method. The result of the questionnaire was 78,6%. It means that the implementation of Team Game Tournament (TGT) was success. Keywords: Classroom Action Research; Learning Method; Studentsâ€™ participation, Team Game Tournament
Comparative Study of CH4 Emission Factors for Domestic Waste Incineration Facilities by Sampling Method

In order to actively respond to the framework convention of climate change, it is necessary to estimate emissions using reliable emission factors. Compared to the energy and industry sectors that emit large amounts of greenhouse gases, the waste sector is relatively lacking in greenhouse gas studies. In particular, non-CO₂ in the waste sector has fewer studies of greenhouse gases than CO₂.<BR> In this study, CH₄ emission factors for domestic waste incineration facilities were compared according to sampling methods that included intermittent collection, continuous collection, and continuous measurement. Sampling methods suitable for developing CH₄ emission factors for domestic waste incineration facilities were derived. Statistically, there was no difference in CH₄ emission factors according to sampling method at the 95% confidence level. Therefore, intermittent collection, continuous collection, and continuous measurement sampling can be used to develop CH₄ emission factors for domestic waste incineration facilities.
ABSTRACTING INFLUENCE PATHS FOR EXPLAINING (CONTEXTUALIZATION OF) BERT MODELS

While “attention is all you need” may be proving true, we do not yet know why: attention-based transformer models such as BERT are superior but how they contextualize information even for simple grammatical rules such as subject-verb number agreement(SVA) is uncertain. We introduce multi-partite patterns, abstractions of sets of paths through a neural network model. Patterns quantify and localize the effect of an input concept (e.g., a subject’s number) on an output concept (e.g. corresponding verb’s number) to paths passing through a sequence of model components, thus surfacing how BERT contextualizes information. We describe guided pattern refinement, an efficient search procedure for finding sufficient and sparse patterns representative of concept-critical paths. We discover that patterns generate succinct and meaningful explanations for BERT, highlighted by “copy” and “transfer” operations implemented by skip connections and attention heads, respectively. We also show how pattern visualizations help us understand how BERT contextualizes various grammatical concepts, such as SVA across clauses, and why it makes errors in some cases while succeeding in others.
Pelatihan Pendampingan Pembuatan Peta Tematik Menggunakan GPS dan Aplikasi CAD

One of the problems in the development of human resource professionalism so far is related to land issues (boundaries). The purpose of this activity is to provide training on the use of GPS and processing data using Autodesk map software. The training was attended by the village and sub-district staff of Samarinda City, as many as 50 people. The method used is 1) giving theoretical and practical knowledge about data collection and processing 2) assignment of making land boundaries per area, 3) presentation of results in the field and 4) output (boundary map). The results of this activity were: First, all participants of this training activity were very enthusiastic, enthusiastic, and had high motivation to take part in the implementation of the second boundary land making training activity, the participants who succeeded in making a land boundary map of 25 people or 50% of 50 participants. Third, the quality of the maps is quite good. They found differences in boundary map shape between version Samarinda City Bappeda and field data collection. Based on field data from the Bugis Kelurahan staff, the shape of the land boundary map in the Bugis sub-district in Samarinda almost resembles the land boundary map of Samarinda City of 2.5 ha, because Samarinda City Bappeda does not include the Segiri GOR Stadium as an area of Bugis sub-district that should be included as a map of the border. The map results from Samarinda Kota sub-district staff were recombined by sub-district and sub-district lurah to be determined as a map of sub-district Kota Samarinda and displayed in the A0 printout in each of the kelurahan offices located in sub-district Samarinda Kota.
Can Federated Learning Save the Planet?

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show FL, despite being slower to converge, can be a greener technology than data center GPUs. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.
Representation of Moving Object in Two-Dimensional Plane Through Object Detection Using Yolov3

Path detection is an emerging field which is challenging in itself. Here path detection is achieved through the coordinate values of anchor box which is used to bound the object throughout the video. The proposed work is implemented using YOLOv3 (You Only Look Once) network which is used mostly for real-time object detection. The proposed tracking and path detection model have been tested and implemented in real-time videos. The coordinate values are used further to generate a path of the moving object. The proposed algorithm here is tested on 20 number of videos of different resolution and frames successfully.
FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning

Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search efficiency. We propose here a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling language without loosing efficiency. FAPE's representation coherently integrates flexible timelines with hierarchical refinement methods that can provide efficient control knowledge. A novel reachability analysis technique is proposed and used to develop causal networks to constrain the search space. It is employed for the design of informed heuristics, inference methods and efficient search strategies. Experimental results on common benchmarks in the field permit to assess the components and search strategies of FAPE, and to compare it to IPC planners. The results show the proposed approach to be competitive with less expressive planners and often superior when hierarchical control knowledge is provided. FAPE, a freely available system, provides other features, not covered here, such as the integration of planning with acting, and the handling of sensing actions in partially observable environments.
A Recommendation System For Building School Teachers’ Multidisciplinary Skills

Educational systems are in serious need of personalized platforms, that could help to build students’ multidisciplinary skills. A recommendation system focused on multidisciplinary learning objects could be a solution to the issue. Moscow electronic school repository is analyzed and patterns of its users’ behaviors are described. Those patterns are observed based on the character and structure of actions available to the users, such as creating, copying, using, accessing, and viewing learning objects. The platform users constitute a network community, using similar objects and showing similar interests and thus building network relationships. These networks can be analyzed both at the macro and micro levels, thus visualizing a personal profile of a user in the system. Data analysis showed 7 clusters of users, most of who are not very active, while a moderate number of them exhibit so-called lurking behavior. They look through the learning resources, sometimes use them, but seldom create their own content. Our research found that a trend to create multidisciplinary objects is observed among active users, while lurkers are likely to create mostly monodisciplinary objects. The ratio of multidisciplinary objects can be increased by supporting delurking behaviors among users. Our findings can be useful both for educators and developers of platform learning solutions.
AUTOMATED LIVER CANCER IMAGES CLASSIFICATION USING SUPPORT VECTOR MACHINE WITH KERNEL PRINCIPLE COMPONENT ANALYSIS

The current degree of hepatocellular carcinoma and cholangiocarcinoma is the two main forms of diagnosis. These tumours are highly heterogeneous and are characterised by various morphological or clinical factors, which represent different oncological agents and the complex pathways to tumorigenesis.In this paper, we propose to classify broad sets of images of hepatic cancer as an automatic classification model (ACM). The images in the Kernel Principle Component Analysis (KPCA) model are prepared for noise removal and the features are extracted. The images are eventually categorised with the SVM classification system. ACM is a conventional model for the extraction of functions and classification in images of liver cancer. It is checked in real time to identify the correct lesion into the vast array of images of liver cancer. The experimental findings are reliably compared with current methods. Experimental results indicate that ACM is more accurately, explicitly and responsively categorised than traditional classifiers.
Digitalisasi Sistem Administrasi Perpustakaan Studi Kasus SMK Negeri 1 Sambi Boyolali

The digitization of the library administration system is the process of implementing and utilizing technology in library data processing. Massive technological advances, system digitization are the main reasons for increasing system efficiency and effectiveness. One of them is the digitization of the library administration system. There are still many libraries with manual systems, all of which still use handwritten and paper. The data has not been integrated, which resulted in making reports requiring a lot of time and difficulty in obtaining library data information. This can be overcome by building a website-based digitalization library system that can be accessed easily and integrated data management. The system development uses the PHP programming language which is applied to the laravel framework with the SDLC prototype model method. The result of the development of this system is a library administration system that can facilitate officers in managing library data and make it easier for the head of the library to monitor library data. Based on black box testing, functionally this system can process book data, members, create library cards, can manage data on library visits and borrowing transactions, returns and fines. The library administration system can be accepted by users as proven by testing the System usability scale (SUS), resulting in an average of 76.5, including in the good and acceptable categories.
Fresnel Integral Computation Techniques

This work is an extension of previous work by Alazah et al. [M. Alazah, S. N. Chandler-Wilde, and S. La Porte, Numerische Mathematik, 128(4):635-661, 2014]. We split the computation of the Fresnel Integrals into 3 cases: a truncated Taylor series, modified trapezoid rule and an asymptotic expansion for small, medium and large arguments respectively. These special functions can be computed accurately and efficiently up to an arbitrary precision. Error estimates are provided and we give a systematic method in choosing the various parameters for a desired precision. We illustrate this method and verify numerically using double precision.
Coloured Graphical Models and their Symmetries

Coloured graphical models are Gaussian statistical models determined by an undirected coloured graph. These models can be described by linear spaces of symmetric matrices. We outline a relationship between the symmetries of the graph and the linear forms that vanish on the reciprocal variety of the model. In particular, we give four families for which such linear forms are completely described by symmetries.
Early Detection Of Acute Myocardial Infarction Using The Dempster Shafer Method

This study aims to design an android application to detect indications of Acute Myocardial Infarction (AMI) using the Dempster Shafer method. The system is built with initial symptoms input parameters and risk factor indicated by AMI. The system output consists of 2 classes, namely AMI and non-AMI. The test results obtained system accuracy of 98%.
Grammatical Intricacy and Lexical Density on Genres in National Examination Texts

The objective of this study is to analyze the grammatical intricacy and lexical density on genres in national examination texts by using the theory from Gerot and Wignell (1994). The result of data analysis is twelve texts of genres on reading section and three texts of genres in listening section. From the reading section analysis, the writers find the number of content words tends to be higher than the number of grammatical intricacy per clause complex and the number of lexical density tends to be higher than the number of grammatical intricacy from all texts. It means they tend to be included in written text. The text of reading section in national examination contains written language. From the listening section analysis, the writers find the different result of calculating grammatical intricacy and lexical density in each text, but it has the same result as reading section. The number of content words tends to higher than the number of grammatical intricacy per clause complex and the number of lexical density tends to be higher than the number of grammatical intricacy from all texts. They tend to be included in written text. The texts of listening section in national examination also contain written language. In conclusion, the reading section is ideal that contains written texts, but the listening section is not ideal because it should tend to contain spoken texts.
Randomized Transferable Machine

Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is truly \emph{domain-invariant}, and thus directly train a transfer model $\mathcal{M}$ on source domain. In this paper, we consider a more realistic scenario where the new feature representation is suboptimal and small divergence still exists across domains. We propose a new learning strategy with a transfer model called Randomized Transferable Machine (RTM). More specifically, we work on source data with the new feature representation learned from existing feature-based transfer methods. The key idea is to enlarge source training data populations by randomly corrupting source data using some noises, and then train a transfer model $\widetilde{\mathcal{M}}$ that performs well on all the corrupted source data populations. In principle, the more corruptions are made, the higher the probability of the target data can be covered by the constructed source populations, and thus better transfer performance can be achieved by $\widetilde{\mathcal{M}}$. An ideal case is with infinite corruptions, which however is infeasible in reality. We develop a marginalized solution with linear regression model and dropout noise. With a marginalization trick, we can train an RTM that is equivalently to training using infinite source noisy populations without truly conducting any corruption. More importantly, such an RTM has a closed-form solution, which enables very fast and efficient training. Extensive experiments on various real-world transfer tasks show that RTM is a promising transfer model.
Methods of Designing Digital Learning Technologies for Developing Primary School Pre-Service Teachers’ 21st Century Skills

The survey looks into the issue of designing digital learning technologies for training primary school pre-service teachers. The paper discloses the authors’ conception of designing digital learning technologies for training primary school pre-service teachers, based on the digital humanistic pedagogy, the multiple intelligence theory, and the theory of open education. The authors present the model of designing digital learning technologies for training primary school pre-service teachers and diagnostic tools, which includes the criteria and indicators of the development of skills to use digital technologies for fostering students’ skills. The effectiveness of the authors’ methods which have interdisciplinary character and can be implemented into the study of a number of courses, is proved while conducting learning and research projects in formal and informal education.
Removable weak keys for discrete logarithm-based cryptography

We describe a novel type of weak cryptographic private key that can exist in any discrete logarithm-based public-key cryptosystem set in a group of prime order p where $$p-1$$ has small divisors. Unlike the weak private keys based on numerical size (such as smaller private keys, or private keys lying in an interval) that will always exist in any DLP cryptosystems, our type of weak private keys occurs purely due to parameter choice of p, and hence, can be removed with appropriate value of p. Using the theory of implicit group representations, we present a method to determine whether a public key comes from a weak private key subject to a given computational bound, and if so, recover the private key from the corresponding public key. We analyze several elliptic curves proposed in the literature and in various standards, giving counts of the number of keys that can be broken with relatively small amounts of computation.. Our results show that many of these curves, including some from standards, have a considerable number of such weak private keys. We also use our methods to show that none of the 14 outstanding Certicom Challenge problem instances are weak in our sense, up to a certain weakness bound.