Reviewerly/exHarmony huggingface dataset:
Bounded Verification of Doubly-Unbounded Distributed Agreement-Based Systems

The ubiquity of distributed agreement protocols, such as consensus, has galvanized interest in verification of such protocols as well as applications built on top of them. The complexity and unboundedness of such systems, however, makes their verification onerous in general, and, particularly prohibitive for full automation. An exciting, recent breakthrough reveals that, through careful modeling, it becomes possible for verification of interesting distributed agreement-based (DAB) systems, that are unbounded in the number of processes, to be reduced to model checking of small, finite-state systems. It is an open question if such reductions are also possible for DAB systems that are doubly-unbounded, in particular, DAB systems that additionally have unbounded data domains. We answer this question in the affirmative in this work for models of DAB systems, thereby broadening the class of DAB systems which can be automatically verified. We present a new symmetry-based reduction and develop a tool, Venus, that can efficiently verify sophisticated DAB system models.
COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)

ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.
NeRF-Editing: Geometry Editing of Neural Radiance Fields

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users.
Automatic Lemmatization of Old English Class III Strong Verbs (L-Y) with ALOEV3

This article presents ALOEV3, a lemmatizer based on Morphological Generation that allows for the type-based automatic lemmatization of Old English Class III strong verbs beginning with the letters L–Y. The lemmatizer operates on the basis of the inflectional, derivational and morpho-phonological alternation rules characteristic of this class. The generated form-types are checked against the two most reputed Old English corpora, namely the Dictionary of Old English Corpus and The York-Toronto-Helsinki Parsed Corpus of Old English Prose to validate their attestations and assign the corresponding lemma. Results show that 97 percent of the validated forms are successfully assigned a single lemma. The remaining inflectional forms (38 out of 1,256) show competition between two lemmas, which implies that despite the high level of accuracy of the lemmatizer, contextual, token-based analysis is still needed for disambiguation. However, the research shows that competition only occurs in a limited set of lemma pairs and their derivatives. Although the research focuses on but one strong verb class, it confirms that exploring the avenues of automatic lemmatization will contribute to the field of Old English lexicography by either lemmatizing attested inflectional form types or by highlighting areas for manual revision.
THE CHALLENGES FACED BY EFL TEACHERS IN IMPLEMENTING SCHOOL LITERACY PROGRAM DURING THE COVID-19 PANDEMIC

School Literacy Program is expected to be able to develop students' interest in reading. However, in practice, schools can still not achieve these goals indicated by the existence of challenges in its implementation. Thus, this study aimed at describing the challenges faced by EFL teachers in implementing school literacy programs during the COVID-19 pandemic. Conducting a descriptive qualitative study, four EFL junior high school teachers were interviewed using a semi-structured interview. The results of the study show that the EFL teachers were challenged by four factors in implementing the school literacy program during COVID-19, namely, 1) students' interest in reading, 2) the technical challenges, 3) the teacher's lack of understanding about the school literacy program, 4) time management. These four factors were considered some of the challenges, such as students read in order to fulfil the obligation to fill out daily journals, not because of their own desires, technological illiteracy, and inadequate source of online reading. Therefore, the practitioners of school activities can respond and take thoughtful actions regarding the problems they face in implementing the school literacy program.
Street Scene understanding via Semantic Segmentation Using Deep Learning

Scene classification is an essential conception task used by robotics for understanding the environment.• The deep learning technique has been proved as a great role in the challenging scene understanding application.• Using data augmentation to increase dataset size • Using K-means clustering as a preprocessor for the input dataset • The proposed hydride model is generated by combined two of the deep, deep neural networks as an xception and U-net models.Scene classification is an essential conception task used by robotics for understanding the environment.Like the street scene, the outdoor scene is composed of images with depth that has a greater variety than iconic object images.Image semantic segmentation is an important task for Autonomous driving and Mobile robotics applications because it introduces enormous information needed for safe navigation and complex reasoning.This paper provides a model for semantic segmentation of outdoor sense to classify each object in the scene.The proposed network model generates a hybrid model that combines U-NET with Xception networks to work on 2.5 dimensions cityscape dataset, which is used for 3D applications.This process contains two stages.The first is the pre-processing operation on the RGB-D dataset (data Augmentation and k-means cluster).The second stage designed the hybrid model, which achieves a pixel accuracy is 0.7874.The output module is generated using a computer with GPU memory NVIDIA GeForce RTX 2060 6G, programming with python 3.7.
Image Processing for Criminal Pattern Detection Using Machine Learning in the Dark Web

Data mining has been used to present difficulties in this chapter in order to reduce crime. Crime is a major issue for which we must pay a high price in a variety of ways. Here, the authors look at how machine-learning techniques may be used in an image processing method to aid in the detection of and speed up the investigative process by identifying criminal tendencies. The authors look at the supervised approaches, as well as some enhancements, to help in the identification of criminal trends. They tested their approaches with real-world crime data from a police department, and they were satisfied with the results. They also employed clustering approaches to improve the prediction accuracy of criminal record categorization. They also utilized a clustering approach to assist classify crime records and improve prediction accuracy. They created a weighting system for the characteristics below. The machine-learning framework works with geospatial crime plotting and helps detectives and other law enforcement professionals increase their efficiency. It may also be used to combat terrorism and ensure national security.
Future IoT Software in Healthcare Also Exploring IoT Industry Application

There has been a great deal of investigation into medical services ability and specialized advancements during the most recent 10 years. To state the obvious, Internet of Things (IoT) has demonstrated insure associating different clinical hardware, sensors, and medical services experts to give top-notch clinical consideration at a distant area. This has upgraded patient security, diminished medical care costs, expanded admittance to medical services benefits, and expanded functional adequacy in the medical care industry. Emerging technologies such as IoT have the potential to transform our lives in many ways. A smart ubiquitous framework can only be built using smart objects in the IoT system, which is its ultimate building pieces. This research surrenders an audit of potential IoT-based innovation applications in medical services conducted to date. This paper records the development of the use of the Healthcare Internet of Things (HIoT) in tending to different medical care worries according to the viewpoints of empowering innovation, medical care administrations, and applications. Besides, potential HIoT framework issues and issues are explored. The current research closes by giving a wellspring of comprehension on the various uses of HIoT with expectations of empowering future scholastics that are quick to chip away at and kick off something new in the field to have a superior handle of the subject. IoT innovation has helped medical care experts in checking and diagnosing an assortment of well-being concerns, estimating an assortment of well-being factors, and giving demonstrative capacities at far-off areas using these standards. The structure and implementation of a specific framework are the subject of this paper. This has moved the medical services industry’s concentrate away from clinics and toward patients.
Web-Based Sales Information System Design At Eco Farming Bukit Lawang

In Indonesia, internet users reach 150 million people with 56% penetration spread throughout the region. This number is only slightly different from the number of mobile internet users which amounted to 142.8 million people with a penetration percentage of 53%. E-Commerce is a process of buying and selling transactions carried out by buyers and sellers electronically from companies to other companies by utilizing communication and computer networks. At Eco Farming Bukit Lawang has great potential with the existence of excellent natural products, the surrounding community takes advantage of the results. Plants that exist in the Bukit Lawang area, but become in vain because the marketing of information on the results of these plants is not good.
Revolution of industry 4.0 and its influence on higher education in Vietnam today

The objective of this study was the revolution of industry 4.0 and its influence on higher education in Vietnam today. The method of this research is descriptive in which the researchers studied the subject using documents. The fourth industrial revolution has a strong impact on all areas of social life including higher education. The fourth industrial revolution has a direct impact on the production structure and human structure of the labor market, and therefore, higher education must also change to create human resources suitable for the current market and future employment. Based on an overview assessment of the impact of the fourth industrial revolution, a study of reports and documents on higher education evaluation, the article focuses on analyzing the impacts of the fourth industrial revolution on higher education and propose some solutions to improve the quality of higher education in Vietnam today.
Data augmentation for imbalanced blood cell image classification

Abstract Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achieve oversampling of the minority classes. We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.
ITSM Using AI Chat-Bot and Data Visualizers

Abstract: Information Technology Service Management is the process to plan, implement, deliver and support the IT services to cater the end-users’ needs. This project aims to study the existing ITSM systems and implement an improved system aided with technologies like chat-bot and data visualization. To resolve IT issues using quality ticket system, we need a robust service management framework based on ITIL framework. The system works in three distinct steps: Reporting, Managing and Resolving an issue. The aim of an ITSM system restore the undisturbed flow of the everyday business process after resolving the ticket. The chatbot caters to the smaller needs of the users without having a need to create a ticket in the ITSM system. Data visualization can help the management team as well as the users to visually keep the track of the tickets. Keywords: Information Technology Service Management, Information Technology Infrastructure Library, Chat-bot, Data Visualization, Natural Language Processing.
CosSinO: A Trigonometric Rule-based Deterministic Gaming Algorithm in a Multi-player Environment

Abstract Game Playing has been an attractive field of research in Artificial Intelligence (AI) domain. Different category of games are evolved since its inspection which are based on rules and heuristics and investigated under AI scope. In this article, we have presented a trigonometric rule-based, multi-player board game, named as CosSinO , with its graphical layout, playing rules and the deterministic algorithm. The auxiliary data structure needed for the algorithm is a Trigonometric Function Mapper (TFM) which is realized as a look-up table. The output of the game is stored in a score table which is basically an n x m matrix where n is the number of players and m is the number of mutually agreed trials among the players. The time and space complexity of the algorithm is proven to be polynomial and is O ( nm ). The game in its physical form has been tested exhaustively by exposing it to different group of players and it is proven to be an exciting game among trigonometry lovers
Mondrian Forest for Data Stream Classification Under Memory Constraints

Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases. However, in the Internet of Things, this assumption is unrealistic when data comes in the form of infinite data streams, or when learning algorithms are deployed on devices with reduced amounts of memory. In this paper, we adapt the online Mondrian forest classification algorithm to work with memory constraints on data streams. In particular, we design five out-of-memory strategies to update Mondrian trees with new data points when the memory limit is reached. Moreover, we design trimming mechanisms to make Mondrian trees more robust to concept drifts under memory constraints. We evaluate our algorithms on a variety of real and simulated datasets, and we conclude with recommendations on their use in different situations: the Extend Node strategy appears as the best out-of-memory strategy in all configurations, whereas different trimming mechanisms should be adopted depending on whether a concept drift is expected. All our methods are implemented in the OrpailleCC open-source library and are ready to be used on embedded systems and connected objects.
The Relationship between Indonesian Language Learning and the Environment

This paper is intended to provide the concept of environment-based language learning. Changes in the linguistic environment, of course also the social, humanitarian, and cultural environment, should be studied critically. Likewise, the development of media culture in particular, and the change in the "very global" values of life need to be examined, addressed, and re-evaluated more deeply. This life is always changing and evolving to a higher level higher civilization and culture.
A COMPARATIVE STUDY OF SEVERAL SLFN-BASED CLASSIFICATION ALGORITHMS FOR URBAN AND RURAL LAND USE

Abstract. In the study of urban sustainable development, accurate classification of land use has become an important basis for monitoring urban dynamic changes. Hence it is necessary to develop the appropriate recognition model for urban-rural land use. Although deep learning algorithms have become a research hotspot in image classification tasks in recent years, and many good results have been achieved. But other machine learning algorithms are not going away. Compared deep learning with machine learning, there are some advantages and disadvantages in data dependence, hardware dependence, feature processing, problem solving methods, execution time, and interpretability, etc. Especially in the classification for remote sensing images, the continuous research and development of traditional machine learning algorithms is still of great significance. In this paper, the performances of several SLFN-based classification algorithms were studied and compared, including ELM, RBF K-ELM, mixed K-ELM, A-ELM and SVM. Extreme Learning Machine (ELM) is a new algorithm for single-hidden-layer feedforward neural network (SLFN). It has simple structure, fast speed and is easy to train. In some applications, however, standard ELM is prone to be overfitting and its performance will be affected seriously when outliers exist. In order to explore the performance of ELM and its improved algorithm for urban-rural land use classification, comparative experiments between three improved ELM algorithms (RBF K-ELM, mixed K-ELM and A-ELM), ELM and SVM with image data from several study areas were performed, and the classification accuracy and efficiency were analysed. The results show that the three improved ELM algorithms perform better than the standard ELM and SVM both in overall accuracy and Kappa coefficient. However, it is worth noting that the computation efficiency of RBF K-ELM and mixed K-ELM decreases greatly with larger image, the time cost is much more than other algorithms. Compared with other algorithms, A-ELM has the advantages of higher Overall Accuracy and less classification time.
Static type checking without downcast operator

In the last couple of years several dynamically-typed, object-oriented programming languages have been equipped with optional static type checkers. This typically requires these languages to be extended with a downcast operator, which is a common operator in statically-typed languages but not in dynamically-typed languages. Our objective is to investigate an approach for static type checking of object-oriented languages that does not require such an additional downcast operator. We systematically weaken the rules for static type checking to avoid reporting errors that can be resolved using downcast operators. This leads to an approach similar to quasi-static typing that enables to make type annotations stricter in a gradual way. These static type checking rules can be applied to dynamically-typed languages by interpreting the dynamic type as the top of the subtype relation. Based on these ideas we have implemented a static type checker for the dynamically-typed, object-oriented language POOSL without introducing a downcast operator. Practical experiences with this type checker indicate that it is useful for early validation.
Peningkatan Prestasi Belajar Siswa pada Materi Kesebangunan dan Kekongruenan Melalui Pendekatan Saintifik di Kelas IX A SMP Negeri 2 Arut Selatan Tahun Pelajaran 2020/2021

In the pre-cycle, the number of students who completed learning reached 7 students. The average value has only reached 68, which means it is still below the KKM. The results of the observations showed a score of 39. This means that the activity of students in the class is not good. In the first cycle, the number of students who completed learning reached 19 students, the average value reached 78. This means that an effective scientific approach is used to improve student achievement in the material of similarity and congruence. In student observation activities, the teacher assesses that students listen to the material conveyed by the teacher, students look enthusiastic in participating in Mathematics learning material congruence and congruence, students can express their opinions, students ask questions about things they don't know and students can work in groups with other friends. The results of the observations showed a score of 85. It means that the activity of students in the class is quite good. However, in order to make it more conducive to learning Mathematics in terms of similarity and congruence, the researcher wants to carry out cycle II. In the second cycle, the number of students who completed learning reached 24 students, the average score was 85. This means that an effective scientific approach is used to improve student achievement in the material of similarity and congruence. Observation results show a score of 108. It means that the activity of students in the class is very good.