Reviewerly/exHarmony huggingface dataset:
Correlation of Her-2 Expression with Clinicopathological Characteristics in Invasive Ductal Breast Cancer Patients

Breast cancer is the most common cancer in the world and a leading cause of death in women. This cancer is the major cause of mortality and morbidity in Indonesian women. This study aims to investigate the correlation of Her-2 expression with a clinicopathological profile in invasive ductal breast cancer. This study was retrospective in design. Histopathological diagnosis and immunohistochemical ER, PR, and Her2 from 95 patients with invasive ductal breast cancer were included during the period January 2017 to June 2018.
User Provisioning in the Enterprise

User provisioning is the means by which user accounts are created and maintained in a system (e.g., database, SaaS app, operating system, etc.). When we say that a user-provisioning system maintains a user account, we mean everything from changes to attributes in the user account, changes of entitlements or privileges associated with the user account, locking and unlocking the user account, and even deletion of the user account. User provisioning is primarily an admin-time affair: a user account is created (or changed) based on an administrative action as opposed to a user’s action at the time of resource use. This article explores the uses and components of a user-provisioning system and focuses mainly on situations where user accounts are maintained in central repositories, typically enterprise and workforce settings.
Distance learning experience during the COVID-19 lockdown

The objective of this research was to carry out a diagnosis of the development of the distance education experience through the emergence of COVID-19, at the Universidad Autónoma de Santo Domingo (UASD), San Francisco de Macorís, Santiago and Puerto Plata campuses and at the La Vega Center. The methodology implemented corresponds to the quantitative, descriptive, bibliographic and field approach. Among the most important results, it can be mentioned that 62% of the students responded that they were only receiving distance teaching in some of the subjects selected in the 2020-1 semester, while 16% indicated that they were receiving teaching in all subjects. In conclusion, the university platform is not used due to the lack of knowledge that teachers and students have about its implementation. Among the recommendations is the creation and strengthening of technological units to support teaching and students at all campuses and centers.
Combinatorial optimization for low bit-width neural networks

Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based optimization in a two-stage train-and-compress setting or as a combined optimization where gradients are quantized during training. Such schemes require high-performance hardware during the training phase and usually store an equivalent number of full-precision weights apart from the quantized weights. In this paper, we explore methods of direct combinatorial optimization in the problem of risk minimization with binary weights, which can be made equivalent to a non-monotone submodular maximization under certain conditions. We employ an approximation algorithm for the cases with single and multilayer neural networks. For linear models, it has $\mathcal{O}(nd)$ time complexity where $n$ is the sample size and $d$ is the data dimension. We show that a combination of greedy coordinate descent and this novel approach can attain competitive accuracy on binary classification tasks.
A Hybrid Approach to Questionnaire Generation Using Las Vegas Algorithm and Naive Bayes Algorithm

Examination plays an important role in testing the student's knowledge.The conventional style of creating, preparing, and generating test questions manually is intriguing and cumbersome.In this study, we introduce an automated questionnaire generation system that uses a hybrid approach using Naïve Bayes (NB) and Las Vegas Algorithm (LVA).LVA is a randomized algorithm by Laslo Babai that always produces a correct result but has a running time that is based on a random value.While NB is one of the most well-known data mining algorithms for classification.
Comparative Between Neural Networks Generate Predictions for Global Solar Radiation and Air Temperature

Technology is becoming an increasingly important and indispensable tool in human life, making it necessary to develop various forms of renewable energies. However, over time it became necessary to improve this technology that becomes more advanced and efficient. The purpose of the research is to compare the results of three distinct AI algorithms, forecasting in two hours, using the database available by the Instituto Nacional de Meteorologia (INMET). The results indicate that the K-Nearest Neighbors Regression network proved to be more effective for estimating Global Solar Radiation (W/m²) and Multi-LayerPerceptron for forecasting Air Temperature (ºC).
[RETRACTED] Keanu Reeves CBD Gummies Shark Tank: Scam Exposed 2022 v1

[RETRACTED] Keanu Reeves CBD Gummies Shark Tank: Scam Exposed 2022
Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clustering

The movie recommender system has an important role in providing movie recommendations for users, but new users have difficulty choosing movies that are given by the recommender system because of the cold start problem. This study aims to overcome the cold start problem using a knowledge-based recommender system, i.e association rule mining using an apriori algorithm. The apriori algorithm aims to extract correlations between product itemsets, but the problem in the apriori algorithm is the large number of association rules that make the complex computation. To overcome this problem, we combine the apriori algorithm and k-means to produce more accurate recommendations, because the items are grouped before the recommendation process using the k-means algorithm. In this study, we use a dataset of movies and ratings from the Kaggle website. This study uses a minimum value of 0.5 confidence, and a minimum value of 4 lifts. To produce the best itemset in the form of antecedents and consequents of the Beauty and the Beast item with The Passion of Joan of Arc which has a value of 0.107981 support, 0.779661 confidence, 4.151695 lift
Application for Holographic Pyramids in Exhibition Spaces: An Expansion of the Teaching Model

Research indicates that Augmented Reality can contribute to the challenges of communicating complex research findings in an accessible language to a broader audience. This research will use the methodology of Design Science Research divided by the following: (1) Characterization of current knowledge on the subject based on an integrative literature review in the Scopus and Web of Science scientific databases; (2) Requirements and architecture of applicable hardware and software.
Cartesian Products over Interval Valued Intuitionistic Fuzzy Sets of Second Type

A = {〈x, MAL(x), NAL(x)〉|x∈X} and * 1 B = {〈y, MBL(y), NBL(y)〉| y ∈ Y} then * 1 × 1 * 1 = {〈〈, 〉, [ 2 (). 2 ()], [ 2 (). 2 ()]〉| ∈ , ∈ } …….. (2) from equation (1) and (2) we have * 1 ( × 1 ) = * 1 × 1 * 1 Similarly for = 2,3 & 4 we have * 2 ( × 1 ) = * 2 × 1 * 2 , * 3 ( × 1 ) = * 3 × 1 * 3 , * 4 ( × 1 ) = * 4 × 1 * 4 .(ii) First we prove the result for i = 2 × 2 = {〈〈, 〉, [ 2 () + 2 () - 2 (). 2 (), 2 () + 2 () - 2 (). 2 ()], [ 2 (). 2 (), 2 (). 2 ()]〉| ∈ , ∈ } Apply the operator * 2 in × 2 we have * 2 ( × 2 ) = {〈〈, 〉, [ 2 () + 2 () - 2 (). 2 ()], [ 2 (). 2 ()]〉| ∈ , ∈ }……(3) by the definition * 2 A = {〈x, MAL(x), NAU(x)〉| x ∈X} and * 2 B = {〈y, MBL(y), NBU(y)〉| y ∈Y} then * 2 × 2 * 2 = {〈〈, 〉, [ 2 () + 2 () - 2 (). 2 ()], [ 2 (). 2 ()]〉| ∈ , ∈ }……(4) from equation (3) and (4) we have * 2 ( × 2 ) = * 2 × 2 * 2 Similarly for = 1,3 & 4 we have * 1 ( × 2 ) = * 1 × 2 * 1 , * 3 ( × 2 ) = * 3 × 2 * 3 , * 4 ( × 2 ) = * 4 × 2 * 4 .
PENGGUNAAN METODE COLLABORATIVE FILTERING BASED UNTUK REKOMENDASI KENDARAAN BERMOTOR

Motorized vehicles are one of the main needs of every human being and also the most common form of transportation used by people. To choose a motorized vehicle, people should not choosing it in a hurry because it takes good consideration to choose the right brand, type, and the need for the vehicle. In making their choice, usually people will read the reviews from vehicle review sites such as Carmudi.co.id, OTO.com, KobaYogas.com, and so on. The purpose of this thesis is to help provide web-based vehicle recommendations using the values of rating and criteria selected by the user. User rating values are calculated with collaborative filtering. In addition to the rating value, users can also get vehicle recommendations by providing specifications of the vehicle needs. Rating values from the program users will be processed by using adjusted cosine similarity to determine their similarity score to the rating values from vehicle review sites and other users so the vehicle recommendations can be obtained according to the similarity of the other user ratings. Based on the results of User Acceptance Testing (UAT) from 21 respondents, the testing got an average score of 83.95% so the program can be categorized as “Very Good”.
The Application of Semantic Analysis Technology in the Analysis of Chinese and Korean Literature Mutual Translation

In order to improve the effect of the mutual translation of Chinese and Korean literature, this article combines the semantic analysis technology to analyze the mutual translation of Chinese and Korean literature. Moreover, this article explores the analysis of Chinese and Korean literature mutual translation through intelligent semantic analysis methods and analyzes the theoretical basis of densely connected networks based on the semantic analysis and verification methods of densely connected networks and multilayer attention. Finally, this article implements semantic matching verification through the densely connected BiLSTM structure and adds a multilayer attention mechanism to the network to better interact with sentences to obtain the semantic relationship of sentences. The simulation analysis shows that the Chinese and Korean literature mutual translation system based on semantic analysis proposed in this article has a good translation effect and can effectively promote Sino-Korean literature exchanges.
Transition to Modular System during the Pandemic: A Study from a Distant EFL Program

Modular systems, which involve several self-contained modules where skills and knowledge are developed in separate units throughout the program, are gaining popularity in teaching English as a foreign language (EFL). This qualitative study aimed to reveal language instructors’ and administrators’ experiences and opinions about an online modular teaching system adopted newly due to the pandemic in an English preparatory program of a tertiary level of a state university in Turkey. Data were gathered through individual semi-structured interviews from with five instructors and three administrators and observations of online classrooms. Thematic analysis was utilized to form systematic patterns from the data. The results revealed that although the modular system provided clear goals and objectives for both instructors and learners, a number of challenges and obstacles were experienced, stemming primarily from the online implementation of the modular system, such as learners’ lack of engagement in the absence of physical classroom environment, online assessment, and instructors’ increased workload. These issues led to confusion for instructors regarding the true nature of the modular system. It could be concluded that it would be more productive to implement the modular system as a hybrid model, allowing for some face-to-face interaction and assessment, rather than relying solely on the online mode.
Students’ Responses to QuizWhizzer Educational Game to Strengthen Mathematical Concept Understanding Ability

Understanding concepts is an important part of mathematics because through this ability students can understand and distinguish symbols, concepts, operations, words, and signs. However, students' ability to understand mathematical concepts is still relatively low. For this reason, learning media are needed that can strengthen these abilities, such as the educational game QuizWhizzer. QuizWhizzer is a learning media in the form of an interesting, interactive game, prioritizing cooperation and communication, and can create positive interactions between students through games in the learning process. Therefore, the purpose of this study was to determine students' responses to the learning media in the form of the educational game QuizWhizzer to strengthen the ability to understand concepts. This study is a descriptive study about the level of student satisfaction in learning using the educational game QuizWhizzer. The instruments used in this study were educational games and student response questionnaires. The population in this study were students of class VIII SMP Negeri 8 Yogyakarta, while the samples were students of class VIII-H and VIII-I. This study concludes that the use of the QuizWhizzer which contains indicators of the ability to understand concepts in the small-scale trial class and large-scale trial class obtains a positive response from students. This result is evidenced by the average score in the small-scale class is 55, while in the large-scale class it is 57. The score is in the Good classification. For this reason, the use of the QuizWhizzer educational game is stated to be practical to use for learning. The results of this study need to be developed on a larger scale considering the different characteristics of students.
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection

Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting, or invades data privacy by revisiting used exemplars. Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data). The absolute memorization is demonstrated in the sense that class-incremental learning using ACIL given present data would give identical results to that from its joint-learning counterpart which consumes both present and historical samples. This equality is theoretically validated. Data privacy is ensured since no historical data are involved during the learning process. Empirical validations demonstrate ACIL's competitive accuracy performance with near-identical results for various incremental task settings (e.g., 5-50 phases). This also allows ACIL to outperform the state-of-the-art methods for large-phase scenarios (e.g., 25 and 50 phases).
TOOLS FOR DEVELOPING THE CREATIVE ABILITIES OF PRESCHOOL CHILDREN

What gives human creativity? First and foremost, it is the power that allows a person to rise to the highest level on their own and to have unique abilities and capabilities by nature. Through creative thinking, everyone is able to discover new, original ways of solving problems, types of actions, not by understanding and applying the knowledge they already know. Why does the development of the creative abilities of preschool children play such an important role? Creative play and art are an important part of development and help nurture the feelings of your precedent. You can help your preschoolers by creating a supportive environment and devoting a lot of time to the free development of creative activity.
Impact of Moral Disengagement on Counterproductive work behaviours in IT Sector, Pakistan

This research examines the role of moral disengagement towards counterproductive work behaviour in the information technology sector of Pakistan. Furthermore, research is also focused on the mediating effect of information security awareness (Attitude &amp; knowledge) and information security awareness behaviours. The target population consisted of public sector I.T. departments of Punjab, Pakistan. A convenience sampling technique is utilized. Data collection has been done through a survey questionnaire from technical and non-technical staff currently employed in the Public sector I.T. departments of province Punjab. Statistical software PLS-SEM is used for analysis. This study highlights the role of the information technology sector staffing level of engagement that affects the employee’s counterproductive work behaviour and information security awareness behaviour. Moreover, the study proposes that management should take the initiative for the implementation of strategies that may be helpful to get awareness about information security amongst employees.
Desired cybersecurity skills and skills acquisition methods in the organizations

Abstract:Key personnel and their competences play important roles in continuity management and improving resilience of cybersecurity in organizations. Researchers have addressed many topics and studies in the cybersecurity domain. However, relevant cybersecurity skills and acquisition of them in expertise development, have only been partially touched. If designed systematically and properly, cybersecurity training can improve cybersecurity expertise to ensure better performance in complex cybersecurity situations. More through study on the acquisition of cybersecurity skills, and work-life needs are needed. The research three questions of this study are: How do work-life representatives see cybersecurity? How do work-life representatives see cybersecurity related skills? How do work-life representatives see methods for skills acquisition in the organizations? The work is multi-method, as it builds on both a literature review on skills acquisition in cybersecurity, and on empirical findings of a questionnaire study on cybersecurity skills desired by the work-life representatives. The findings show that cybersecurity is seen important in the organizations. The demanded skills from the employees focus especially on communication and situational awareness. There is a specific need for training with Cyber Ranges (CR) to ensure skills acquisition on cybersecurity. These results can be used to plan and design training and education for future professionals. This study aims to promote constructive discussion on skills and their acquisition in the cybersecurity domain.
Mitigating Dataset Bias by Using Per-sample Gradient

The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes having a strong correlation with the target attribute are present, the trained model can provide unintended prejudgments and show significant inference errors (i.e., the dataset bias problem). Various methods have been proposed to mitigate dataset bias, and their emphasis is on weakly correlated samples, called bias-conflicting samples. These methods are based on explicit bias labels involving human or empirical correlation metrics (e.g., training loss). However, such metrics require human costs or have insufficient theoretical explanation. In this study, we propose a debiasing algorithm, called PGD (Per-sample Gradient-based Debiasing), that comprises three steps: (1) training a model on uniform batch sampling, (2) setting the importance of each sample in proportion to the norm of the sample gradient, and (3) training the model using importance-batch sampling, whose probability is obtained in step (2). Compared with existing baselines for various synthetic and real-world datasets, the proposed method showed state-of-the-art accuracy for a the classification task. Furthermore, we describe theoretical understandings about how PGD can mitigate dataset bias.
PSO-RBNN Based Control Design for Trajectory Tracking

Inspite of so universally accepted, control performance by NN depends on many of the varying factors such as output weights. To ensure the functional accuracy of the NN, it is required to have an defined value of these performance effecting factors. Control scheme proposed in this paper uses an emerging optimization technique naming, PSO to get the optimal value of the parameters, naming spread factor and weights of output layer in RBNN. Thus, this hybrid controller possesses the advantageous qualities of RBNN and PSO both. For the further improvement in the basic PSO algorithm, inertia weight factor of PSO is made adaptive.This projected controller has been verified by comparing it with a basic PSO and the basic RBNN controller for the trajectory tracking control of a 2-DOF remotely driven robotic manipulator. To check the robustness of the controller its performance has been checked by incorporating uncertainties naming payload masses and friction. Appropriate conclusions have been drawn in last.
Generation of MBSE models from system requirements

Cancellations of DoD acquisition programs have resulted in billions of dollars of losses annually, which reduces resources for new capabilities. One area identified is a lack of proper requirement scoping, which is prohibitively complex when tracing architectures for large systems. MBSE was developed to address these issues and provide a common framework to rapidly represent, convey, and synchronize information. This research explores the maturity of NLP and ML methodology needed to automatically generate and trace requirements in MBSE models, thus providing the tools to rapidly generate models from requirement documents and reducing the risk of program cancellation due to requirement scoping problems.
A Computer in Digital Forensic: Computer Forensic

The report focuses on computers' roles in digital forensics. All of these industries are implementing digitalization into their operations as the globe progresses toward digitalization. As a result of digitalization, working methods are becoming more productive and convenient. To employ digitalization, all we need is a computer or a system. Some forensics software tools are available, but they must be loaded on computers in order to complete the operation. It gives us the advantage of being able to operate quickly. Crimes committed have grown increasingly frequent in electronic or digital worlds, particularly cyber. Technology is being used by criminals to commit crimes, providing new obstacles for law enforcement agents, lawyers, magistrates, army members, and security experts.
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning

A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes to address this issue by re-using knowledge from previously learned tasks. However, determining which source task qualifies as the most appropriate for knowledge extraction, as well as the choice regarding which algorithm components to transfer, represent severe obstacles to its application in reinforcement learning. The goal of this paper is to address these issues with modular multi-source transfer learning techniques. The proposed techniques automatically learn how to extract useful information from source tasks, regardless of the difference in state-action space and reward function. We support our claims with extensive and challenging cross-domain experiments for visual control.
Speaker Identification using Speech Recognition

The audio data is increasing day by day throughout the globe with the increase of telephonic conversations, video conferences and voice messages. This research provides a mechanism for identifying a speaker in an audio file, based on the human voice biometric features like pitch, amplitude, frequency etc. We proposed an unsupervised learning model where the model can learn speech representation with limited dataset. Librispeech dataset was used in this research and we were able to achieve word error rate of 1.8.