Reviewerly/exHarmony huggingface dataset:
Algorithms and Lower Bounds for de Morgan Formulas of Low-Communication Leaf Gates.

The class FORMULA[s]∘G consists of Boolean functions computable by size-s De Morgan formulas whose leaves are any Boolean functions from a class G. We give lower bounds and (SAT, Learning, and pseudorandom generators (PRGs)) algorithms for FORMULA[n1.99]∘G, for classes G of functions with low communication complexity. Let R(k)G be the maximum k-party number-on-forehead randomized communication complexity of a function in G. Among other results, we show the following: •The Generalized Inner Product function GIPkn cannot be computed in FORMULA[s]° G on more than 1/2+e fraction of inputs for s=o(n2/k⋅4k⋅R(k)(G)⋅log⁡(n/e)⋅log⁡(1/e))2). This significantly extends the lower bounds against bipartite formulas obtained by [62]. As a corollary, we get an average-case lower bound for GIPkn against FORMULA[n1.99]∘PTFk−1, i.e., sub-quadratic-size De Morgan formulas with degree-k-1) PTF (polynomial threshold function) gates at the bottom. Previously, it was open whether a super-linear lower bound holds for AND of PTFs.•There is a PRG of seed length n/2+O(s⋅R(2)(G)⋅log⁡(s/e)⋅log⁡(1/e)) that e-fools FORMULA[s]∘G. For the special case of FORMULA[s]∘LTF, i.e., size-s formulas with LTF (linear threshold function) gates at the bottom, we get the better seed length O(n1/2⋅s1/4⋅log⁡(n)⋅log⁡(n/e)). In particular, this provides the first non-trivial PRG (with seed length o(n)) for intersections of n halfspaces in the regime where e≤1/n, complementing a recent result of [45].•There exists a randomized 2n-t #SAT algorithm for FORMULA[s]∘G, where t=Ω(n\√s⋅log2⁡(s)⋅R(2)(G))/1/2. In particular, this implies a nontrivial #SAT algorithm for FORMULA[n1.99]∘LTF.•The Minimum Circuit Size Problem is not in FORMULA[n1.99]∘XOR; thereby making progress on hardness magnification, in connection with results from [14, 46]. On the algorithmic side, we show that the concept class FORMULA[n1.99]∘XOR can be PAC-learned in time 2O(n/log n).
Time Series Analysis of Telecommunications Data Using Neural Networks

This paper shows a model of artificial neural networks (ANN) for the access to the advanced mobile service (AMS)of the mobile telephony of Ecuador to predict the demand of active lines by technology of mobile telephony of thedata obtained by the Agency of Regulation and Control of Telecommunications (ARCOTEL) in the period December2008 to August 2020, model that allows technically a better management of the social media that use the frequenciesof the radioelectric spectrum as are the carriers CONECEL S.A., OTECEL S.A. and CNT EP. The methodologyused is based on the analysis of the time series by means of processes of multilayer neural networks of the statisticalpackage SPSS. For this purpose, 70% of the data was used for training of the neural network and the remaining 30%as test data of the network already trained and later to make the predictions of the application of the ANN model.This allows carriers to know the demand and make the best decisions for the management of new technology in thefield of telecommunications.
Classification of Image Blood Cancer by Using Multi-Training RNN

new method presented in this research to classify bone marrow based on features classification that extracted by human body. Towards this end, new features derived from image based on blood taken by microscope used in proposed descriptor: also human pose Human pose plays important role in extracted features then using these features as the blood cancer input with classifier. In this paper we focused on using retrieval image processing techniques for divided into several steps that include image acquisition, features extraction, and classification. The Retrievable Neural Network (RNN) was used to classify the segmented cells into either normal or abnormal classes based on the features selected by the genetic algorithm (GA). As a result, the classification of cells achieved an accuracy of 98.4%. Subsequently, after the manual review of blood smears, the model will act as a second reader, and it would increase the diagnostic accuracy.
Some More Results in Inverse Total Domination on Intuitionistic Fuzzy Graphs

Let G be anintuitionistic fuzzy graph. Let  be a minimum total dominating set of G. If  contains a total dominating set  of G, then  is called an inverse total dominating set with respect to  The inverse total domination number of G is the minimum cardinality of all inverse total dominating set of G.
Using Storytelling Technique To Teach Speaking For Tenth Graders Of Senior High School

English has a significant part to play in our lives. It is one of the essential means which people from different countries can communicate and express ideas. English is being given and taught to students today, from elementary to university. Essentially, individuals need correspondence with others. To do this, they need language to communicate their inclination, thoughts, and wants to other people. Learning English means learning four major skills: listening, speaking, reading, and writing. Besides four language skills, it also learns three important components such as pronunciation, vocabulary, and grammar. This research aimed to describe (1) how the implementation of storytelling technique in students speaking skills and (2) how student’s response toward the implementation of storytelling technique. The research take place in SMAN 1 Bangsal. The observation was done in the classroom. The data was taken from both verbal and non-verbal activities and then it was in the form of fieldnotes. The result of this research can be concluded that using storytelling technique is effective than memorization since it provides learning vocabulary and grammatical in context rather than memorizing words in isolation. Keywords: storytelling, speaking
Empirical Investigation of the impacts of Knowledge Management on Organizational Learning - A Case Study of Higher Education Institutions

Nowadays, knowledge management has become an inspiring catch-all in the eyes of researchers as it is a source of competitive advantage. Despite growing concern for organizational learning, less attention has been given to knowledge management. Therefore, this study sheds light on various dimensions of knowledge management that may help management get organizational learning benefits. The stratified simple random sampling technique was used to collect data from 255 faculty members of public and private universities of Pakistan. Due to the COVID-19 pandemic, the data were collected online. The data were analyzed through SmartPLS software v.3.0. The results revealed that knowledge acquisition, knowledge documentation, knowledge creation, and knowledge application positively influence organizational learning, whereas knowledge transfer has a non-significant effect on organizational learning. It is evident from this study that managers should focus more on knowledge management by valuing employee opinions and well-being. They should value their contribution, help employees when needed, and find more ways to enhance their learning attitude.
Modeling Simulation of BLDC Motor and HEV Motor System Using Adaptive Neuro Fuzzy (ANFIS) Inference Algorithm

The main concept of new adaptive neuro fuzzy inference system (ANFIS) along by means of supervisory learning algorithm isutilized for the regulation of speed that that is reduced by rise time and setting time of brushless DC (BLDC) that is hybrid motor motor system. Here the main advantage of the present algorithm is neural and fuzzy networks so in order to enhance the performance of electrical motor motor a well known adaptive neuro fussy ANN (ANFIS) technique that is based on supervisory learning algorithm is introduced. This method is deliberated in such a way to control the torque, startup power and also for improving the dynamic performance of the system. Here this technique follows minimum fuzzy rules and membership functions for implementation of system that is relatively easy and also it is compared by conventional fuzzy neural networks that are basically utilized for electrical motor applications. Here for demonstration and controlling of the proposed ANFIS they follow the speed reference by rejecting disturbance it perform simulation and compares that with conventional ANN manager.
AN INTEGERATED NAVIGATION SYSTEM FOR AUTONOMOUS MOBILE ROBOT IN DYNAMIC ENVIROMENTS

To enable an autonomous mobile robot to navigate safely in a dy- namic environment, the mobile robot must address four typical functional blocks of the navigation system including perception, localization, motion planning, and motor control. In this study, we present an integrated navigation system for the autonomous mobile robot in the dynamic environment by incorporating the techniques proposed in our previous studies, including object detection and tracking system, localization system and motion planning system, into a completed navigation system. In addition, we propose an extended timed elastic band (ETEB) technique for online trajectory planning, which allows the mobile robot to proactively avoid obstacles in the surrounding environment. We validate the effectiveness of the proposed model through a series of experiments in both simulated and real-world environments. The experimental results show that, our proposed motion model is capable of driving the mobile robots to proactively avoid dynamic obstacles, providing the safe navigation for the robots.
A Rational Protocol Treatment of 51% Attacks.

Game-theoretic analyses of cryptocurrencies and—more generally—blockchain-based decentralized ledgers offer insight on their economic robustness and behavior when even their underpinning cryptographic assumptions fail. In this work we utilize the recently proposed blockchain adaptation of the rational protocol design (RPD) framework [EUROCRYPT ’18] to analyze 51% double-spending attacks against Nakamoto-style proof-of-work based cryptocurrencies. We first observe a property of the originally proposed utility class that yields an unnatural conclusion against such attacks, and show how to devise a utility that avoids this pitfall and makes predictions that match the observable behavior—i.e., that renders attacking a dominant strategy in settings where an attack was indeed observed in reality. We then propose a generic remedy to the underlying protocol parameters that provably deter adversaries controlling a majority of the system’s resources from attacks on blockchain consistency, including the 51% double-spending attack. This can be used as guidance to patch systems that have suffered such attacks, e.g., Ethereum Classic and Bitcoin Cash, and serves as a demonstration of the power of game-theoretic analyses.
Reversible Data Hiding in a Chaotic Encryption Domain Based on Odevity Verification

On the premise of guaranteeing the visual effect, in order to improve the security of the image containing digital watermarking and restore the carrier image without distortion, reversible data hiding in chaotic encryption domain based on odevity verification was proposed. The original image was scrambled and encrypted by Henon mapping, and the redundancy between the pixels of the encrypted image was lost. Then, the embedding capacity of watermarking can be improved by using odevity verification, and the embedding location of watermarking can be randomly selected by using logistic mapping. When extracting the watermarking, the embedded data was judged according to the odevity of the pixel value of the embedding position of the watermarking, and the carrier image was restored nondestructively by odevity check image. The experimental results show that the peak signal-to-noise ratio (PSNR) of the original image is above 53 decibels after the image is decrypted and restored after embedding the watermarking in the encrypted domain, and the invisibility is good.
Transformation of Nominal Variables for Classification Analysis

로지스틱회귀모형, 선형판별분류분석, 의사결정나무, 지지벡터기계 같은 다양한 방법으로 수행할 수 있는 분류 분석은 반응변수와 예측변수의 정보를 이용하여 개체를 특정 집단으로 분류하는 것이다. 일반적으로 분류 분석의 모형 추정은 예측변수가 연속형일 때 주로 정의 되어져 있다. 명목형 예측변수를 이용하기 위해 가변수 변환 후에 사용하고 있지만, 이는 반응변수 또는 명목형 예측변수의 범주가 많은 경우에 차원의 기하급수적 증가로 인하여 필요 모수 추정이 불안하여 비정칙 같은 문제점이 종종 발생한다. 적용 가능한 예측변수가 많아지고 다양해지면서 가변수 변환을 적용할 수 없는 경우도 발생한다. 따라서, 분류 분석에서 명목형 예측변수를 효과적으로 사용하기 위해 다른 변환 방법이 필요하다. 대안으로 반응변수의 정보를 반영하여 명목형 범주에 순서를 적절히 부여하는 CRIMCOORD 변환 방법을 소개한다. 이를 분류 분석에서 예측력을 가변수 변환과 비교 분석한다. 실제 자료를 훈령용와 검증용으로 분할하여 분류 분석 방법을 적용한 비교 실험 결과, CRIMCOORD 변환은 가변수 변환의 문제점을 보완해 주었다. 정보의 일부 손실에도 불구하고 손실을 최소하는 변환으로 분류 분석에서 예측력이 감소하지 않는 결과를 얻었다.Classification analysis, which can be performed by logistic regression, linear discriminant analysis, decision trees, support vector machine, utilizes the information of response and predictor variables. In general, model estimation is defined well when predictor variables are continuous. For nominal variables, dummy variable transformation is often employed; however, when the number of categories of response and predictor variables is large, the dimensionality is inflated dramatically and a singularity problem may occur because of unstable parameter estiomation. Various predictor variables may not be applicable for it. Therefore, alternative transformations are needed to utilize nominal variables efficiently. As an alternative, CRIMCOORD transformation is introduced and compared. As the result with train and test data partitions applied to classification analysis, CRIMCOORD remedies the problem of dummy variable transformation. In spite of somewhat loss of infromation, the prediction accuracy has not been decreased in classification analysis by minimizing the loss.
Sentiment Analysis of Cyberbullying in Social Media UsingDecision Trees

The given paper describes modern approach to the task of sentiment analysis of cyberbullying in social media by using decision trees. These methods are based on statistical models, which are a machine learning algorithms. Using social media nowadays, age has grown significantly in our personal lives. People like to share their experiences in public social media sites with their friends. This has also risen the possibilities and advancement of security threats. This study uses data mining techniques from the views of users written ion Twitter to use in sentiment analysis of decision tree classification. The study used different keyword to extract users’ thoughts or feelings through their tweets. KNIME is also used to assist in making analysis sentiments by using three different datasets with the Decision Tree approaches.
DIALOGIC FEEDBACK TO PROMOTE DEEP LEARNING FOR EFL SPEAKING LEARNERS IN ONLINE LEARNING ENVIRONMENT: STUDENTS’VOICES

English as a foreign language (EFL) students, regardless of their ability to speaking English, encounter significant speaking challenges. They lack motivation and confidence to speak English in online learning environment, which is particularly during a global pandemic. Therefore, the present study aims to analyse the implementation of dialogic feedback in online learning for the students speaking skills and investigate the students’responses to the implementation of dialogic feedback in teaching speaking. The respondents of this study were tenth-grade students attending a private senior high school in Bangka Belitung. This study employed a qualitative approach with a case study design. Interviews and observation were employed as instruments. The findings of this study indicated that the majority of students were engaged in online speaking classes toward implementing dialogic feedback; they were able to clarify their confusions from the teacher's feedback during presentations and they were able to improve their errors in some aspects of speaking skills, such as pronunciation and intonation when telling a story.  Most students in an online class showed positive attitude toward dialogic feedback. They revealed that dialogic feedback is necessary for learners to improve their speaking abilities and enabled them to gain new knowledge through communication and sharing of new vocabulary. However, a few of the students stated technical issues and poor internet connection on the internet could be impediments during the class session. Keywords: Dialogic feedback, speaking skills, students’ perception.
Comparison of Convolutional Neural Networks Model Using Different Optimizers for Image Classification

Face detection technology and image classification are widely used in several industries that help humans in obtaining information and other related matters. In this paper, the utilization of the Computer Vision system uses the Convolutional Neural Network (CNN) algorithm to classify images by distinguishing the gender of the detected object. Architectural model through transfer learning by experimenting with three pre-trained models, namely VGG-16, Inception-V3, and MobileNet-V2 to determine the best architecture by using Optimizer Adam and RMSProp. To produce the best model and performance, experiments were carried out using several modules such as the data augmentation module and the re-indexing module. The Inception-V3 model got the best results in predicting Gender from the image with an accuracy and loss validation value of 0.9350, 0.1550, compared to VGG-16 and MobleNet-V2 with values 0.9320, 0.1660, and 0.8760, 0.3000.
MergeBERT: Program Merge Conflict Resolution via Neural Transformers.

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. In this paper, we introduce MergeBERT, a novel neural program merge framework based on the token-level three-way differencing and a transformer encoder model. Exploiting restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 64--69% precision of merge resolution synthesis, yielding nearly a 2x performance improvement over existing structured and neural program merge tools. Finally, we demonstrate versatility of our model, which is able to perform program merge in a multilingual setting with Java, JavaScript, TypeScript, and C# programming languages, generalizing zero-shot to unseen languages.
Selection of the Graphics Card to be used in Ethereum Mining with Linear BWM-TOPSIS

Blockchain technology is becoming more and more important and new usage areas are emerging every day. However, the most fundamental one of these usage areas is cryptocurrencies, which led to the emergence of blockchain technology. Cryptocurrency transfers are made possible with mining. Although there are many cryptocurrencies available today, a lot of them use Ethereum-based blockchain technology. The choice of the most optimal graphics card (GPU; Graphics Processing Unit) in cryptocurrency mining is very important for the efficiency and profitability of the mining operations to be performed. Since this decision problem depends on more than one criterion, it should be handled using Multiple-Criteria Decision-Making Methods (MCDM). Accordingly, the study focused on the mining of Ethereum-based cryptocurrencies and the selection of the optimal GPU to be used in mining with linear BWM-TOPSIS.  As a result of the study, a model is presented in which miners can choose the most efficient GPU for them and the optimal GPU as of January 2020 has been determined.
Comparative Analysis of Public Cloud Security Based Schemes and Cryptographic Algorithms

Cloud computing has been established as the next renewable technology in the IT world. While numerous cloud protection assessments are now prevalent, there is still some disparity between the accurate mapping of problems and their related solutions. Cloud data storage is fast, but the information we store must be safe, and the information stored in the cloud-managed the cloud service providers or the cloud caregiver. The data owner is worried about the integrity and trustworthiness of data stored in the cloud. In this article, a detailed comparative examination of different schemes and “cryptography algorithms” used to protect information over the public cloud will be carried out and provided a clear review of various methods and their limitations in tabular format.
Reading Proficiency, Comprehension Level Of Grade 8 Students And Readability Of Afro Asian Learning Module

This study is conducted to identify the reading proficiency and comprehension level of the Grade 8 students on the different literary texts such as short story, drama and poetry, and the readability of Afro-Asian module. There are 566 respondents randomly selected from the City Schools of Quezon City. The instruments used are MAZE test which consists of a 25-item reading test to assess the reading proficiency and Mc Call Crabb test which consists of a 54- item comprehension test on different literary texts which identifies the comprehension level of the respondents. The Fry Readability Graph is also used to validate the reading level of the Afro-Asian module. From the results of the data gathered, the researcher arrives at the conclusion that most of the respondents are identified frustrational readers based on the MAZE reading test and Mc Call Crabb test on varied literary texts. Using the Fry Readability Graph, Afro-Asian short story and drama texts are in the higher level and the poetry texts are lower than the expected readers. Descriptive-correlation method is used in the study describing the reading proficiency and comprehension level on different literary texts such as short story, poetry and drama of the respondents. The results reveal that reading proficiency and comprehension of the students need to be enhanced in schools to minimize the number of frustrational readers, and the Afro-Asian module should be revised to match the readability of the intended readers
Proposed PowerPoint-based tests administering approach for large classrooms with limited access to technology

The use of information communication technologies (ICTs) in test administration has replaced the traditional paper-based test (PBT) at various educational levels. Among the key benefits of the modern test approaches is the elimination of printed question papers, applicability on a larger number of candidates and transferability to technologically challenging environments. However, teachers in developing countries struggle with large classrooms with limited technological support. Studies have shown different approaches devised by these teachers in delivering lectures and testing learners. This study proposed a PowerPoint-based test (PPBT) especially for testing candidates with minimal computing skills and for environments with limited access to ICTs for the computer-basedtest. Furthermore, the effect of PPBT on students’ performance was explored using the empirical data generated from two years’ pair of tests in both PBT and PPBT. One paired samples t-test, Person correlation, and descriptive statistical techniques of mean, and standard deviation were used during the data analysis process. The results indicated significant differences in students’ performance with higher performance in PPBT. This study demonstrates the efficacy of using PPBT by teachers under settings with limited access to ICTs.
Comparative Study of Enhancement Techniques on Different Types of Images.

The technique of image enhancement is critical in digital image processing since it allows an image to be used for a variety of applications. A digital image is being processed here in order to produce a higher quality image. Time and frequency domain approaches will be used to improve the digital images. Image enhancement is the practice of altering an image to make it more suitable for a given use than it was before. The word specific is significant since it indicates that enhancement strategies are problem oriented from the start.