Reviewerly/exHarmony huggingface dataset:
Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of the quarter that contains a tumor based on the centroid value of the cluster in this quarter, which is far from the centers of the remaining quarters. From the calculations conducted on several images' quarters, the experimental outcomes show that the centroid value of the cluster in each quarter was greater than 0.9 if this quarter did not contain a tumor while the value of the centroid value for the cluster containing a tumor was less than 0.4.For examples, in a quarter no.1 for STOMACH_1 medical image, the centroid value of the cluster was 0.973 while the value of the cluster centroid in quarter no.3 was 0.280. For this reason the tumor area was found in quarter no.(3) of the medical image STOMACH_1. Also, the centroid value of the cluster in a quarter no.2 was 0.948 for STOMACH_2 while, the value of the cluster centroid in quarter no.4 was 0.397. For this reason the tumor area was found in a quarter no.4 of the medical image STOMACH_2.
Smartphone-Based Virtual Reality as an Immersive Tool for Teaching Marketing Concepts

With the advent of Virtual Reality (VR) technology and the ubiquity of mobile devices, smartphone-based VR has become more affordable and accessible to business educators and millennial students. While millennials expect learning to be fun and prefer working with current technology, educators are constantly challenged to integrate new technology into the curriculum and evaluate the learning outcomes. This study examines the gain in learning effectiveness and students' intrinsic motivations that would result from the use of VR as compared to the use of traditional learning activity, namely think-pair and share. The results show that students who took part in the VR simulation demonstrated a better understanding of concepts and reported a better learning experience as compared to those who participated in the think-pair-share activity. In particular, the findings show evidence of higher intrinsic motivation and better learning outcomes.
Implementation of Data Security with Wallace Tree Approach Using Elliptical Curve Cryptography on FPGA

The growth of computing resources and parallel computing has led to significant needs for efficient cryptosystems over the last decade. Elliptic Curve Cryptography (ECC) provides faster computation over other asymmetric cryptosystems such as RSA and greater security. For many cryptography operations, ECC can be used: hidden key exchange, message encryption, and digital signature. There is thus a trade-off between safety and efficiency with regard to speed, area and power requirements. This paper provides a good ECC approach to encryption by replacing the Vedic multiplier (16 bit) with the Wallace tree multiplier with an improved output (128 bit). The proposed design processes data recurringly with less volume, less power consumption and greater velocity, in addition to improving efficiency. Using Xilinx 2015.2 software, the entire proposed design is synthesized and simulated and implemented on the ZYNQ FPGA Board. Compared with previous implementations, a significant improvement in field efficiency, time complexity and energy demand is demonstrated by the proposed design.
Feature Classification for Control System Devices

Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device’s memory.
FUZZY ADAPTIVE WHALE OPTIMIZATION ALGORITHM FOR NUMERIC OPTIMIZATION

Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the convergence of the algorithm to stack. Some known meta-heuristic approaches treat this issue by updating one or a predetermined number of dimensions in their update scheme. To improve the exploitation behavior of WOA, a fuzzy logic controller (FLC) based adaptive WOA (FAWOA) is suggested in this study. An FLC realizes the update scheme of WOA, and the proposed FLC determines the rate of the change in terms of dimension. The suggested FAWOA is evaluated using 23 well-known benchmark problems and compared with some other meta-heuristic approaches. Considering the benchmark problems, FAWOA achieves best results on 11 problem and only differential evaluation algorithm achieve best results on 10 problems. The rest of the algorithms couldn’t achieve the best results on not more than 5 problems. Besides, according to the Friedman and average ranking tests, FAWOA is the first ranked algorithm for solving the benchmark problems. Evaluation results show that the suggested FAWOA approach outperforms the other algorithms as well as the WOA in most of the benchmark problems.
Research on Face Recognition Algorithm Based on Improved Residual Neural Network

The residual neural network is prone to two problems when it is used in the process of face recognition: the first is "overfitting", and the other is the slow or non-convergence problem of the loss function of the network in the later stage of training.In this paper, in order to solve the problem of "overfitting", this paper increases the number of training samples by adding Gaussian noise and salt and pepper noise to the original image to achieve the purpose of enhancing the data, and then we added "dropout" to the network, which can improve the generalization ability of the network.In addition, we have improved the loss function and optimization algorithm of the network.After analyzing the three loss functions of Softmax, center, and triplet, we consider their advantages and disadvantages, and propose a joint loss function.Then, for the optimization algorithm that is widely used through the network at present, that is the Adam algorithm, although its convergence speed is relatively fast, but the convergence results are not necessarily satisfactory.According to the characteristics of the sample iteration of the convolutional neural network during the training process, in this paper, the memory factor and momentum ideas are introduced into the Adam optimization algorithm.This can increase the speed of network convergence and improve the effect of convergence.Finally, this paper conducted simulation experiments on the dataenhanced ORL face database and Yale face database, which proved the feasibility of the method proposed in this paper.Finally, this paper compares the time-consuming and power consumption of network training before and after the improvement on the CMU_PIE database, and comprehensively analyzes their performance.
SOCIAL MEDIA ANALYTICS USING SENTIMENT AND CONTENT ANALYSES ON THE 2018 MALAYSIA’S GENERAL ELECTION

This study analysed the political use of Twitter during the 2018 Malaysian General Election (GE14), using sentiment and content analyses to examine the patterns in online communication among urban Malaysians. Specifically, Naive Bayes, Support Vector Machine and Random Forest were used for sentiment analysis for the English tweets, with the results compared against two vectorization approaches. Content analysis involving human experts was used for the Malay tweets. Top trending hashtags were used to fetch tweets from April 15, 2018 to May 14, 2018, resulting in a curated corpus of 190 224 tweets. Naïve Bayes used along with Word2Vec outperformed all the other models with an accuracy of 63.7%, 66.8% and 64.9% for pre-GE14, GE14 and post-GE14, respectively. Generally, results indicate the majority of the sentiments to be positive in nature, followed by negative and neutral during pre-GE14, GE14 day and post-GE14 for the English speakers. Though similar sentiments were observed for the Malay speakers, the majority of their sentiments on election day were negative (i.e. 42%) as opposed to the English speakers (i.e. 31%).
Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).
The Role of Saudi Universities in Serving and Developing Society for Vision 2030: Evidence from Jouf University

This research aimed to study the effects of The Role of Jouf University in serving and developing the society for Vision 2030, by understanding the influence of training, continuous education, technical consultations, and applied researches on management development for employees and organizational development of institutions. For this purpose, a questionnaire was distributed to a sample size of 393 individuals in the Kingdom of Saudi Arabia. Further, to analyze the study data, the structural equations method (SEM) was used via the SmartPLS and statistical package for the social science (SPSS) 23 programs. After analyzing and testing the hypotheses of the study, it was found that there is a positive role of Jouf University in serving and developing the society for Vision 2030. There is a positive impact for applied researches and training on management development of employees, and a positive impact also for applied researches, training, and continuing education on organizational development of institutions.
EDUKASI ADAPTASI KEBIASAAN BARU DI LINGKUNGAN KAMPUS

The Covid-2019 pandemic is one of the most dangerous virus outbreaks and has shocked the world since the end of 2019. The virus is spreading very rapidly, affecting the way people live because people are required to remain productive. Therefore, new habit adaptations are compiled and carried out as an effort to reform and support the sustainability of the economy and the activities of various sectors including education. Politeknik Negeri Batam, which plays a role in providing services to the community, is included in the category of potential crowd. Therefore, we need a guide to educate each academician about the adaptation of new habits. One of the popular technologies for people during the Covid-19 virus pandemic is Motion Graphic. The solution built in this research is a video about Adaptation to New Habits in the campus environment in the form of a 2D Motion Graphic, as one of the educational media for the campus academic community. The video development method uses Luther Sutopo method. The resulting products are analyzed for their feasibility as a medium of education and information for academics at Politeknik Negeri Batam. Video eligibility is determined based on community perceptions through respondents using the EPIC model. The results of the research are: (1) Video in the form of motion graphics using MP4 format, with a duration of 03 minutes 07 seconds, can be run on various platforms, especially accessible via online media; (2) Based on the public perception of using the EPIC dimension, the average achievement is 90.7%, so that the video is considered very effective as an educational medium to inform about the use of elevators in the campus environment during the Covid-19 pandemic.
The impact of digital entrepreneurship on the environmental quality of agricultural companies: Evidence from agricultural companies in Jordan valley

The present study aimed to explore the impact of digital entrepreneurship on the environmental quality of agricultural companies. The population consists of 20 big and middle sized companies. The sample consists of 85 individuals. A questionnaire was used for data collection. SPSS program was used. The dimensions of digital entrepreneurship are: (digital knowledge management, digital business environment management, and electronic leadership skills). The dimensions of environmental quality are: (top management commitment, ongoing improvement, and team work). Several results were reached. For instance, it was found that agricultural companies practice digital knowledge management for improving the environmental quality. However, such companies have been facing many challenges. Such challenges include: the fluctuations in the cash flow. The researcher recommends providing talented employees in agricultural companies with incentives. That shall enable those companies to keep up with the latest development in the field.
Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation

&#x0D; &#x0D; &#x0D; Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many tasks. In this paper we present a transformer-based model for multi-turn dialog response generation. Our solution is based on a hybrid approach which augments a transformer-based generative model with a novel retrieval mechanism, which leverages the memorized information in the training data via k-Nearest Neighbor search. Our system is evaluated on two datasets made by customer/assistant dialogs: the Taskmaster-1, released by Google and holding high quality, goal-oriented conversational data and a proprietary dataset collected from a real customer service call center. Both achieve better BLEU scores over strong baselines.&#x0D; &#x0D; &#x0D;
You keep using that word: Slaying the dragon of reference desk statistics

It’s that time of the year again. You know, that time when we are chasing down our IPEDS/ACRL numbers for the dreaded annual reports. Even with some helpful changes over the past few years, I continue to have questions about the value of at least some of the information we are all asked to provide. Some important trends can be clearly tracked. Circulation of print/physical materials has been declining steadily overall and continues to do so. Why? Ask a roomful of librarians and you will undoubtedly get many different explanations, and the reasons are a combination of many of those. Expenditures on those print/physical items is also declining in many academic libraries.
ANALYSIS OF STUDENT’S DIFFICULTIES IN LEARNING ENGLISH AT SMA NEGERI 1 BATAUGA

The purpose of this study was to determine the difficulties of eleventh grade students in learning English at SMA Negeri 1 Batauga. The research design uses a mixed-method design, which is used to describe students' difficulties in learning English. The research subjects were 12 students. The place of this research is SMA Negeri 1 Batauga. The research data were collected through test and interview. Data were analyzed based on the type of analysis by Miles and Huberman—data reduction, presentation, and conclusion or verification. Based on the data obtained in the field, especially the results of test and interview, it can be concluded that the level of students’ learning achievement of SMA Negeri 1 Batauga is low. And the factor that affects the difficulty of students in learning English is the motivation or low interest of students in learning English. This is indicated by their indifference or reluctance to learn English. This reluctance has an impact on their mastery of English, especially English vocabulary and grammar as well as their reading and speaking comprehension skills. This is because students of SMA Negeri 1 Batauga rarely repeat English lessons at home and do not memorize English vocabulary gradually. The results of the interview also showed that the students' environment did not have a positive influence on students in learning. That includes English teacher and parental support.
Critique Generation to Increase Diversity in Conversational Recipe Recommender System

Conversational recommender systems help to guide users in exploring the search space in order to discover items of interest. During the exploration process, the user provides feedback on recommended items to refine subsequent recommendations. Critiquing as a way of feedback has proven effective for conversational interactions. In addition, diversifying the recommended items during exploration can help to increase user understanding of the search space, which critiquing alone will not achieve. Both aspects are important elements for recommender applications in the food domain. Diversity in diet has been shown to predict nutritional health, and conversational exploration can help to introduce new food items. In this paper, we introduce a novel approach that brings together critique and diversity to support conversational recommendation in the recipe domain. Initial evaluation in comparison to a baseline similarity-based recommender shows that the proposed approach increases diversity during the exploration process.
Dynamics of Fuzzy-Rough Cognitive Networks

Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools.
Reproducing the Sparse Huffman Address Map Compression for Deep Neural Networks

Deploying large convolutional neural networks (CNNs) on limited-resource devices is still an open challenge in the big data era. To deal with this challenge, a synergistic composition of network compression algorithms and compact storage of the compressed network has been recently presented, substantially preserving model accuracy. The proposed implementation, which we describe in this paper, offers different compression schemes (pruning, two types of weight quantization, and their combinations) and two compact representations: the Huffman Address Map compression (HAM), and its sparse version sHAM. Taken as input a model, trained for a given classification or regression problem (as well as the dataset employed, which is necessary for the fine-tuning of weights after network compression), the procedure returns the corresponding compressed model. Our publicly available implementation provides the source code, two pre-trained CNN models (retrieved from third-party repositories referring to well-established literature), and four datasets. This implementation includes detailed instructions to execute the scripts and reproduce the obtained results, in terms of the figures and tables included in the original paper.
The Formal Framework for Collective Systems

Automated reasoning is becoming crucial for information systems. Building one uniform decision support system has become too complicated. The natural approach is to divide the task and combine the results from different subsystems into one uniform answer. It is the basic idea behind the system approach, where one solution is a composition of multiple subsystems. In this paper, the main emphasis is on establishing the theoretical framework that combines various reasoning methods into a collective system. The system’s formal abstraction uses graph theory and provides a discussion on possible aggregation function definitions. The proposed framework is a tool for building and testing specific approaches rather than the solution itself.
PENERAPAN METODE PEMBELAJARAN BRAINSTORMING DALAM MENINGKATKAN HASIL BELAJAR MATA PELAJARAN BAHASA INDONESIA PADA SISWA KELAS V SDN 164319 TEBING TINGGI

The aim of this research is to improve learning result on Indonesian Language subject through brainstorming method. This classroom action research conducted by 2 cycles of the four phases: planning, implementation, observation, reflection. The subjects were students from grade V, SD Negeri 164319 Tebing Tinggi which amounted to 33 students. This study used a qualitative descriptive analysis technique. The results showed that the method of the brainstorming in Indonesian Language subject can improve student learning result characterized by increased mastery learning students, namely pre-cycle (69,7%), the first cycle (81,8%), cycle II (90,9 %) and complete learn the clasical equal to 90,9 %. Keywords: Brainstorming, Indonesian Language