Reviewerly/exHarmony huggingface dataset:
Deep Learning based Cervical Cancer Classification and Segmentation from Pap Smears Images using an EfficientNet

One of the most prevalent cancers in the world, cervical cancer claims the lives of many people every year.Since early cancer diagnosis makes it easier for patients to use clinical applications, cancer research is crucial.The Pap smear is a useful tool for early cervical cancer detection, although the human error is always a risk.Additionally, the procedure is laborious and time-consuming.By automatically classifying cervical cancer from Pap smear images, the study's goal was to reduce the risk of misdiagnosis.For picture enhancement in this study, contrast local adaptive histogram equalization (CLAHE) was employed.Then, from this cervical image, features including wavelet, morphological features, and Grey Level Co-occurrence Matrix (GLCM) are extracted.An effective network trains and tests these derived features to distinguish between normal and abnormal cervical images by using EfficientNet.On the aberrant cervical picture, the SegNet method is used to identify and segment the cancer zone.Specificity, accuracy, positive predictive value, Sensitivity, and negative predictive value are all utilized to analyze the suggested cervical cancer detection system performances.When used on the Herlev benchmark Pap smear dataset, results demonstrate that the approach performs better than many of the existing algorithms.
Hipotesis Penelitian Kuantitatif

The objective of this paper is to discuss the understanding of hypothesis writing, by reviewing some related literature. Hypothesis is an important part of research, which needs to be designed from the beginning of the study. Since the hypothesis is a temporary answer to a research question, which is expected to guide the research’s path. So that it requires an in-depth understanding of the meaning and types of directional and non-directional hypotheses, as well as the application of 1 tailed and 2 tailed t-test techniques. At the eventually, There are 3 conclusions, are the written hypothesis can be expressed in 3 forms, consists of positively directional, negatively directional and non-directional.&#x0D; Keywords: Hypothesis, t test, quantitative research
Application of fractal and textural analysis in medical physiology, pathophysiology and pathology

Fractal and textural analyses represent a rapidly developing class of computational and mathematical methods with potential wide applications in medicine and biology. In recent years, they have been successfully used for the evaluation of subtle alterations in cell and tissue morphology associated with various physiological and pathological processes. It has been shown that cells in early stages of apoptosis exhibit changes in chromatin fractal and textural features. Cellular senescence is also sometimes associated with changes in textural patterns in some cell populations. So far, artificial intelligence approaches based on co-occurrence matrix textural data were successfully implemented in predicting cell damage in in vitro conditions, with artificial neural networks achieving the best performance. In the future, several methodological issues and challenges related to the use of fractal and textural methods will have to be resolved before their introduction into contemporary clinical practice. This concise review focuses on the recent research on the application of fractal and textural methods in experimental physiology and related fields.
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition

Graph convolutional networks (GCN), which can model the human body skeletons as spatial and temporal graphs, have shown remarkable potential in skeleton-based action recognition. However, in the existing GCN-based methods, graph-structured representation of the human skeleton makes it difficult to be fused with other modalities, especially in the early stages. This may limit their scalability and performance in action recognition tasks. In addition, the pose information, which naturally contains informative and discriminative clues for action recognition, is rarely explored together with skeleton data in existing methods. In this work, we proposed pose-guided GCN (PG-GCN), a multi-modal framework for high-performance human action recognition. In particular, a multi-stream network is constructed to simultaneously explore the robust features from both the pose and skeleton data, while a dynamic attention module is designed for early-stage feature fusion. The core idea of this module is to utilize a trainable graph to aggregate features from the skeleton stream with that of the pose stream, which leads to a network with more robust feature representation ability. Extensive experiments show that the proposed PG-GCN can achieve state-of-the-art performance on the NTU RGB+D 60 and NTU RGB+D 120 datasets.
A Probabilistic Graphical Model Based on Neural-symbolic Reasoning for Visual Relationship Detection

This paper aims to leverage symbolic knowledge to improve the performance and interpretability of the Visual Relationship Detection (VRD) models. Existing VRD methods based on deep learning suffer from the problems of poor performance on insufficient labeled examples and lack of interpretability. To overcome the aforementioned weaknesses, we integrate symbolic knowledge into deep learning models and propose a bi-level probabilistic graphical reasoning framework called BPGR. Specifically, in the high-level structure, we take the objects and relationships detected by the VRD model as hidden variables (reasoning results); In the low-level structure of BPGR, we use Markov Logic Networks (MLNs) to project First-Order Logic (FOL) as observed variables (symbolic knowledge) to correct error reasoning results. We adopt a variational EM algorithm for optimization. Experiments results show that our BPGR improves the performance of the VRD models. In particular, BPGR can also provide easy-to-understand insights for reasoning results to show interpretability.
Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method

Pneumonia is an illness that affects practically everyone, from children to the elderly.Pneumonia is an infectious disease caused by viruses, bacteria, or fungi that affect the lungs.It is quite difficult to recognize someone who has pneumonia.This is because pneumonia has multiple levels of classification, and so the symptoms experienced may vary.The multilayer perceptron approach will be used in this study to categorize Pneumonia and determine the level of accuracy, which will contribute to scientific development.The Multilayer Perceptron is employed as the classification method with hyperparameter learning rate and momentum, while SURF is used to extract the features in this classification.Based on the experiments that have been carried out, in general, the learning rate value is not very influential in the learning process, both at the momentum values of 0.1, 0.3, 0.5, 0.7, and 0.9.The best desirable accuracy value for momentum 0.1 is at a learning rate of 0.05.The best desirable accuracy value for momentum 0.3 is at a learning rate of 0.09.The most desirable accuracy value for momentum 0.3 is at a learning rate of 0.05 and 0.07.At a learning rate of 0.03 the highest ideal accuracy value is obtained.The best desirable accuracy value for momentum 0.9 is at a learning rate of 0.09.this research should be redone using the number of hidden layers and nodes in each hidden layer.The addition of a hidden layer, as well as variations in the number of nodes in the hidden layer, will affect computation time and yield more optimal accuracy results.
Event-related states in secondary predication

Analyses of secondary predication based on the aspectual properties of predicative complements are almost standard today. This paper shows that these approaches are not restrictive enough and make a large number of incorrect predictions. It also shows that the meaning of the main (or primary) predicate is essential to restrict the secondary one. Throughout the text, a pragmatically based analysis of the necessary restrictions is compared with one grounded on semantic paradigms, and several examples are proposed of how the latter may be articulated and developed. Event-related states in secondary predication are shown to be necessary, and are claimed to follow from a series if fine-grained semantic notions crucially related to the meaning of the primary verb.
Workshop: National Research Portal 101: The Dos and Don'ts of Building a Sustainable Portal

This paper summarises the half-day workshop on national and regional portals, organised by the RIS Synergy project for the CRIS2022 conference. The goal of the workshop was to discuss challenges and opportunities of national and regional research portals with representatives of six different portals and implementation projects. In an expert panel talk, participants shared their experiences with the initiation and implementation of their national/regional portal. Moderated questions also addressed various topics such as target groups, stakeholders, technical specifications and standards. The workshop contributes to the international collaboration of research portal operators and promotes the exchange of lessons learned.
Efficacy of Gamification on Introductory Architectural Education: a literature review

Due to their recent popularity and success in fields such as engineering and business, gamification and by extension game design principles demonstrate the ability to teach complex, multi-disciplinary skills in an engaging, entertaining, and effective way.Architectural education especially introductory architectural education is a foundational and fundamental part of a budding architecture student's career and oftentimes requires the understanding of dynamic systems, spatial reasoning, and experiential learning.The paper posits that gamification and game design principles can utilize certain components such as augmented reality, narrative design, and fun in order to create tools, gamify existing curriculum, and increase retention, engagement, and mastery of the difficult high-tech skillsets required of introductory architects.The paper focuses on reviewing and systematically analyzing research on gamification in education.In particular, it focuses on systematically reviewing and analyzing data from multiple relevant case studies chosen based on the application of technology such as augmented reality, the integration of game design, and the feasibility of gamification in educational environments.This data is examined based on feasibility, accessibility, and effects on information retention and the findings are outlined in a comparative table of methods, tools, and technologies organized based on their suitability.Ultimately, the paper aims to establish a framework for gamifying introductory modules in architectural education and hopes to create a future architectural augmented reality game meant to utilize gamification to help new architectural students.
Media and Resources Development of Android Based Interactive Digital Textbook in Nonformal Education

The development of Android-based interactive digital textbooks is one of the means which is expected to help improve students' understanding of concepts in the learning process. Interactive digital books can be used as a tool by lecturers in the learning process in the classroom so that lecturers do not only use conventional learning methods in the learning process. In line with the times, many lecturers, students and even the public generally access all information via digital. This paper aims to create an Android-based interactive digital textbook as media and resources for Nonformal Education (PLS) learning tools. The ADDIE Model (Branch, 2009) is used in this study. It consists of five stages: analysis, design, development, implementation, and evaluation. The research starts with a literature review and need`s assessment of 47 students for an Android-based interactive digital textbook, followed by a textbook draft. After compiling the Android-based interactive digital textbook, it is validated by three experts: content/material expert, presentation expert, and Indonesian language expert. After being evaluated by three experts, the draft of an Android-based interactive digital textbook is revised to accommodate individual and small group testing. In the outstanding category, interactive digital media textbooks and PLS learning resources based on Android received a total score of 146 (91.25%) from three expert validators. This research shows that interactive digital textbooks are very practical, with a practicality percentage of 83.33% for one-to-one and 83.16% for small groups. The android-based PLS media and learning resources are valid and valuable. Due to the lack of availability of media teaching materials and learning resources in out-of-school education, the presence of Android-based textbooks that can be accessed anywhere and at any time can help both lecturers and students understand media materials and learning resources outside of school.
Enhanced-KNN (M-KNN) based outlier detection and sensor data aggregation for large data streams in the IoT-cloud

IoT is a technology that facilitates several applications of the modern age. The pervasive deployment of Sensor Nodes (SN) affects the sensor data acquisition. The IoT-data requires utilisation of Dimensionality Reduction (DR) and classification techniques. An advanced Data Aggregation (DA) technique decreases the number of total data transmissions and facilitates data accuracy. Here, a data analysis framework is suggested for DA and Outlier Detection (OD) by employing a modified K-Nearest Neighbour (M-KNN) algorithm. Further, the recovery error rate as well as Energy Consumption (EC) of proposed methodology is analysed and compared with the existing Recursive Principal Component Analysis (R-PCA) technique. The techniques are compared by varying the cluster size and the results depict that the proposed M-KNN attains the lowest relative error and lowest EC for variable number of cluster size for both Intel and NDBC-TAO data. The proposed model is crucial for exploring the sensor data and predicting future events based on observed sensor data analysis.
A Non-Convex Activation and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

In this paper, a zeroing neural network model with properties of non-convex activation and noise suppressing (NANSZNN) is proposed for finding the square root of a time-varying matrix.In comparison with the existing zeroing neural network models, the proposed NANSZNN model relieves the limit of convex constraint and makes a breakthrough in noise-suppressing.Furthermore, theoretical analyses and strict proofs are provided in detail for the global convergence and noise-suppressing performance of the proposed NANSZNN model under the circumstances of various noises by using Lyaponov stability theory.Finally, numerical experiments and comparative analyses are offered to further illustrate the availability, effectiveness, and robustness against different noises of the proposed NANSZNN model.
USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS

This article aims at studying the behavior of different types of crossover operators in the performance of Genetic Algorithm. We have also studied the effects of the parameters and variables (crossover probability (pc), mutation probability (pm), population size (pop-size) and number of generation (NG)) for controlling the algorithm. This research accumulated most of the types of crossover operators these types are implemented on evolving weights of Neural Network problem. The article investigates the role of crossover in GAs with respect to this problem, by using a comparative study between the iteration results obtained from changing the parameters values (crossover probability, mutation rate, population size and number of generation). From the experimental results, the best parameters values for the Evolving Weights of XOR-NN problem are NG = 1000, pop-size = 50, pm = 0.001, pc = 0.5 and the best operator is Line Recombination crossover.
KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS

The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.
Comparative Analysis of Data mining Methods to Analyze Personal Loans Using Decision Tree and Naïve Bayes Classifier

The data mining classification techniques and analysis can enable banks to move precisely classify consumers into various credit risk group.Knowing what risk group a consumer falls into would allows a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued on terms commensurate with the risk of default.So research en for classification and prediction of loan grants.The attributes are determined that have greatest effect in the loan grants.For this purpose C4.5, CART and Naï ve Bayes are compared and analyzed in this research.This concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.
Terrain-Aware Learned Controllers for Sampling-Based Kinodynamic Planning over Physically Simulated Terrains

This paper explores learning an effective controller for improving the efficiency of kinodynamic planning for vehicular systems navigating uneven terrains. It describes the pipeline for training the corresponding controller and using it for motion planning purposes. The training process uses a soft actor-critic approach with hindsight experience replay to train a model, which is parameterized by the incline of the robot's local terrain. This trained model is then used during the expansion process of an asymptotically optimal kinodynamic planner to generate controls that allow the robot to reach desired local states. It is also used to define a heuristic cost-to-go function for the planner via a wavefront operation that estimates the cost of reaching the global goal. The cost-to-go function is used both for selecting nodes for expansion as well as for generating local goals for the controller to expand towards. The accompanying experimental section applies the integrated planning solution on models of all-terrain robots in a variety of physically simulated terrains. It shows that the proposed terrain-aware controller and the proposed wavefront function based on the cost-to-go model enable motion planners to find solutions in less time and with lower cost than alternatives. An ablation study emphasizes the benefits of a learned controller that is parameterized by the incline of the robot's local terrain as well as of an incremental training process for the controller.
Low-Code Versus Code-Based Software Development: Which Wins the Productivity Game?

Software is becoming increasingly larger and complex, and companies should be aware of which technologies and platforms allow for higher productivity, that then translates into lower costs, shorter development times, and fewer required specialized resources. This article presents the results of an experiment carried out to compare the productivity of two low-code/code-based software development technologies. The results show that the development and maintenance of software with low-code technology is significantly faster, thus supporting the high potential of this technology. The key lesson is that low-code technology needs to be seriously taken into account by companies because of the potential productivity increase it represents for the development of management information systems.
RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora

Text detoxification is the task of rewriting a toxic text into a neutral text while preserving its original content. It has a wide range of applications, e.g. moderation of output of neural chatbots or suggesting less emotional version of posts on social networks. This paper provides a description of RUSSE-2022 competition of detoxification methods for the Russian language. This is the first competition which features (i) parallel training data and (ii) manual evaluation. We describe the setup of the competition, the solutions of the participating teams and analyse their performance. In addition to that, the large-scale evaluation allows us to analyse the performance of automatic evaluation metrics.
Integration of artificial intelligence in robotic vehicles: A bibliometric analysis

Abstract Robots are rapidly growing technologies in simple words. Robots are artificial living things in this world that are created by humans for reducing human involvement in the top domains such as agriculture, medicine, and industries. Based on the requirements of work, robots are classified into six major categories. Robotic vehicles come under the division of autonomous mobile robots and automated guided vehicles. These vehicles are more popular in factories for supporting humans. Integration of artificial intelligence (AI) in a robotic vehicle gives the brain to the vehicle that can sense the surrounding environment and work accordingly without human operation. This article describes bibliometric research on the integration of AI in robotic vehicles. During the research, 1,196 documents are extracted from the Scopus database between 2015 till now. The types of publications included in this bibliometric analysis are articles, conference papers, reviews, book chapters, books, and short surveys that help understand the global research domain. The pictorial representation was made with the help of open-source platforms such as VOSviewer, GPS visualizer, Gephi, ScienceScape, and word cloud. This analysis helps understand the research gap in this domain.