Reviewerly/exHarmony huggingface dataset:
Ecolinguistic-Based English for Computer Learning Tools to Develop Students’ Creative Thinking Ability

Learning English is one of the important fundamental aspects that students must master from the lexicon/vocabulary learning process. Currently, lecturers only focus on giving assignments on test questions in general English books. This results in students not being confident in speaking English because they do not have a large collection of vocabulary. This study aims to produce an Ecolinguistic-based English for Computer learning tool. This research method is research development using the ADDIE model which consists of several steps, namely analysis, design, development, implementation, and evaluation. The subjects in this study consisted of 3 expert validation and 30 students in the information technology study program STKIP Yapis Dompu. The research instruments used in this research are Learning Device Validation Sheet, Learning Implementation Plan, Worksheet, Response Questionnaire, and Learning Outcome Test. The data analysis techniques in this study were expert validation analysis, test data analysis, and test analysis of learning outcomes. The result of this study shows learning tools of the material are presented well, the language used is easy to understand, and the questions given can be done by students. Textbooks are also effective in increasing the achievement of student learning outcomes because they are actively involved in finding material, finding relationships between materials, and being able to apply their knowledge so that students will be able to construct knowledge gained from their experiences. The resulting product is in the form of English course textbooks for students in the Information Technology Education Study Program.
Technologies of truth on the web

The spread of misinformation on the Internet today is a serious problem due to the vast impact on society of network information. The most obvious technology aimed at combating false information is fact-checking, which allows to identify the presence of facts in the message and compare them with the base of true information. Such technologies are applicable for tracking information distortions, but do not allow evaluating a random message. An alternative approach is to identify false messages based on indirect signs: its linguistic and paralinguistic features, as well as on the communicative history (author, creation and distribution) and other features. Database-trained artificial intelligence concludes that messages are false, without resorting to comparison with true judgments and logical procedures. The ability of a person to independently evaluate the truth of messages is limited by the economy of cognitive effort, and people are even able to generate their own memories that confirm false messages.
A dystopian narrative in Saramago's blindness

In this paper, the author analyses the dystopian narrative in Saramago's novel Blindness. The narrative in the novel shapes ideologies and identities of the characters and illustrates the dystopian outcome of white blindness, a mysterious disease known among the characters of the novel as the "white evil". Also, in this paper, the author analyses the disintegration of the social order as well as dehumanization in the modern world, caused by the epidemic of white blindness, since the white blindness epidemic inexplicably spreads, society disintegrates, and individuals, in the struggle for survival, openly project animalistic features. However, there still remain those individuals who make decisions that show altruistic sacrifice for the benefit of the others. The paper uses descriptive method and a text analysis method. The aim of this paper is to analyze the dystopian narrative in Saramago's Blindness.
An empirical study of the relationship between Foreign Direct Investments, Remittances, Political Stability and Economic Growth in Greece

This study attempts to shed light on the relationship between FDI, remittances, Gross Fixed Capital Formation, political stability, inflation and economic growth for the case of Greece, using time-series data of annual frequency, covering the period from 1980 to 2018; the end date is the last year before the COVID-19 and the 2022 Russian Invasion of Ukraine which caused a structural break on the data set.After establishing that the investigated variables are stationary in level or in their differences, and once the Johansen test and the residuals based approach to cointegration are employed in capturing the cointegrating relations between them, we proceed with a robust VEC model and an ARDL with cointegration bounds, depending on the level of integration of the time series, to capture both the short-run and the long-run dynamics.The overall findings of both models are jointly analyzed, revealing that according to the ARDL model, FDI and economic growth are positively related in the short-run, but negatively related in the long-run.Moreover, the model indicates a negative short-and long-run relationship between remittances and economic growth, as well as a positive link between political stability and economic growth in Greece, as expected.The negative short-run relationship between remittances and economic growth is further supported by the VECM.Finally, both the VECM and ARDL indicate that GFCF and economic growth are positively related in the short-run, while the ARDL model reveals that this relationship exists in the long-run too.
An Analysis of Students’ Speaking Anxiety at 7th Grade of Almumtaz Boarding School

This research was conducted due to several problems in speaking activity in the classroom, problems refer to the students’ feelings of anxiety to speak in the learning process. Based on the preliminary research, the researcher found that the students were anxious to speak in the classroom for some reasons like low of speaking aspects mastery and low of self-cofidence. In foreign language learning, students’ speaking ability can be affected by psychological factors. Thus, this research aimed to find out the factors that cause students’ speaking anxiety. This research was done in Al-Mumtaz Boarding School. The design of this research was descriptive qualitative research. In order to collect the data, the researcher used non-participative observation and semi-structured interview as the instruments. The population of this research was all the seventh grade students of Al-Mumtaz Boarding School which consists of four classes, one boy’s class and three girls’ classes. The researcher used purposive sampling to get the sample for the observation because all the populations had same characteristics. The observation’s sample consist of two classes, one boy’s class and one girl’s class. Then, the researcher used snowball sampling to get the sample for the interview. There were fourteen students who were interviewed in this research. The result showed that there were several factors that cause students’ speaking anxiety at seventh grade of Al-Mumtaz Boarding school, either on boy’s class or girl’s class. Those factors are fear of negative responses from the others, low of self-confidence, fear of making mistakes and low of speaking aspects mastery (pronounciation, comprehension and vocabulary). In conclusion, there were four factors that cause students’ speaking anxiety at seventh grade of Al-Mumtaz Boarding School.
Language-Independent Speaker Identification System-based Attendance System

The most convenient way of communication since ancient times is talking i.e., speaking to each other.Whenever we speak to someone, we convey information in the form of words or voice or to be prudent say speech as this project is related to it.Recognizing the speaker can simplify the task of authenticating or verifying the identity of a speaker as part of a security process as it enables access control of various services by voice.The development of language independent speaker Identification system for student attendance system is beneficial in many ways.It helps the lecturer in administrating the attendance of their student with efficiency.This is because students sometimes cheat with their attendance by signing on behalf of their friends who did not attend class.The proposed system intends to improve the process of taking attendance by enabling the lecturer to efficiently record attendance and prevent students from marking attendance for their friends.
Plasmon-Stimulated Synaptic Transistor

Abstract With the emerging transition from programmed to learning computing, traditional electronic ICs are unlikely to be able to match the massive perceptual data that will need to be processed in real-time. Neuromorphic engineering may surmount the von Neumann bottleneck by creating high-performance hardware for distributed and parallel processing with low power consumption. Nowadays, most of high-efficiency neuromorphic hardware is based on artificial synapsis and neuronal devices, utilizingelectrical or optical platforms and thus correspondingly limited in terms of bandwidth or footprint, respectively. In this work, we introduce a plasmon-stimulated synaptic transistor concept that may combine the high bandwidth of a photonic network with the compactness of electronic circuits. The actual synaptic characteristic, including excitatory postsynaptic current and paired-pulse facilitation, is achieved utilizing charge trapping effects to control the conductivity of a monolithic Al-Ge-Al Schottky barrier field-effect transistor. The population of traps is thereby controlled by hot electrons arising from the non-radiative decay of deliberately applied surface plasmon polariton spikes. The temporal dynamics of the trapping process and thus the plasticity of the synaptic transistor can be set by the geometry and operation mode of the synaptic transistor as well as the intensity and duration of the plasmon spikes.
Enhanced Software Risk Assessment in Software Development Lifecycle

Risk is a destructive and serious problem that may happen in the future of the software project.The definition of risk is an indicated negative thing.However, in software project development risk has two meanings.One is the opportunity for the developer and customer if it gives motivational consequence on the project objectives and the other one is a negative risk that affects the project objectives and may lead to the failure of the project.Improper assessment of negative risk is the main reason for the failure of SP.
Do we talk to a wall or pass on knowledge to students: The advantages and disadvantages of online learning

In the era of digitalization, boosted by Covid-19 pandemic, there is a constant need to measure the success of digital education, compare and reappraise its positive and negative outcomes, which are mainly dependent on the technical conditions available, as well as on the extent to which the previous knowledge of teachers has been adapted to practical and technical challenges. The aim of this paper is to present an analysis of the advantages and disadvantages of digitized education as perceived by students in a qualitative survey, conducted in 2021/22. The starting point were two hypotheses related to positive aspects and two hypotheses related to negative aspects of online learning. An in-depth interview was conducted among 20 high school and 20 university students. By analyzing the results obtained, and juxtaposing them with the data taken from a quantitative survey administered prior to the qualitative one, in 2020/21, it is possible to compare the changes that took place and gain a better insight into the entire process, which has undergone certain improvements, but which also leaves room for further enhancement, particularly in terms of students' and teachers' motivation to actively engage and interact. This would help students overcome their distrust of online education.
Optimizing the convolutional neural networks for resource-constraint hardwares

Convolutional neural networks (CNNs) play an important role in many computer vision applications such as object classification and recognition. To achieve high recognition rate, these neural networks are usually implemented on high-performance computing platforms with high processing speed and large memory. This is a big obstacle for deploying these models on devices with limited hardware resources such as embedded computers. For convolution layers, it requires a lot of multiply-accumulation operations to extract useful features from input images. Furthermore, multiplication of floating-point numbers has long latency and demands a big hardware overhead. In this paper, we analyze and identify the causes that limit the performance of CNNs. Then a method for implementing convolutional networks on hardware with limited resources is presented. Performance evaluation in terms of power, execution time as well as recognition rate is presented in detail. Experimental results on both the FPGA hardware platform and the ARM Cortex-A embedded processor indicate that CNNs using the XNOR-popcount approach can be optimized to achieve a 1000-fold increase in computational performance and approximately a 24-fold reduction in power consumption compared to the tranditional implementation of CNNs on common embedded computer systems.
Penggunaan Alat Peraga untuk Meningkatkan Motivasi dan Hasil Belajar Matematika Peserta Didik Kelas VI SD Inpres Lisabheto

The method used in this study is Classroom Action Research (CAR) using teaching aids to find out whether there is an increase in learning outcomes and the motivation of students in class VI SD Inpres Lisabheto in mathematics, especially material building. Based on the pre -survey results, explaining that many student learning outcomes in pre -cycles have not yet reached the KKM, as well as the low motivation to learn students. This can be seen at the first meeting with an average learning outcome of 65.85 completeness level of 50% and the second meeting averaged 75.78 disjolized 1 so that the completeness level of 68.75%. Because there are learning outcomes that have not yet reached the KKM, they will be continued. On average in the second cycle the first meeting was 90.01 completeness level of 100% and at the second meeting an average of 95.63 with a completeness level of 100%. In this second cycle it has reached KKM with a value of ? 66. While the motivation of student learning in the first cycle with an average of 49.9 and the second cycle reaches 62.8. Based on the average learning motivation in cycle 1 and cycle 2 it can be concluded that the media of teaching aids can affect student motivation so that it provides maximum learning outcomes after evaluating class VI at SD Inpres Lisabheto Academic Year 2021/2022
SMART MEDICINE REMINDER AND DISPENSER

Because of our specialized understanding in our profession, engineers have a higher duty to address societal problems as they arise. Many individuals today all around the world suffer from common disorders like high blood pressure, diabetes, etc. People with chronic illnesses should take their medications on a regular basis to treat their conditions, but in today's hectic society, many people forget to do so. Elderly people frequently forget to take their medications or take the incorrect medication at the wrong time. The project that we selected is entirely for the health of the elderly and those who regularly take medication. Our solution combines a medication dispenser and a reminder to dispense the right medication at the right time. The whole control activity of the smart medicine reminder is regulated by this system's Arduino UNO board. The voice alarm used by the system to remind the patient to take their medication can be adjusted by the patient or their caretakers. The close relatives of the patient, like son, daughter, wife, husband, mother, or father, can be the voice used in this system. To keep the date and time accurate for the system, we use a real-time clock module. Also, we use a 1x5 keypad to set alarms, and an LCD display is used to show the current time and date as well as to set alarms. The dispensing section uses servo motors to provide the medication efficiently. To remind hospital patients to take their medications on time, this approach can also be utilized in hospitals. This Smart medicine dispensers and reminders are highly helpful in a variety of scenarios, including patient homes, hospitals, COVID wards, and quarantine areas. It may also be incorporated into the system in the patient monitoring system.
Towards Respecting Systems of Systems in IT Project Quality Management

Project quality management in general and IT projects quality management in particular has been a research topic for several decades.However, project quality management is usually considered a subpart of project management per se, which includes such activities as project initiation, team formation, responsibilities allocation, and others.In this position paper, we abstract from the abovementioned activities and focus on only one perspective in project management, which, to the best of our knowledge, has not been intensively studied.We propose to focus on the perspective of Systems of Systems in project quality management paying more attention to the systems that are related or belong to a particular IT project.For this purpose, the questions for further research are defined regarding three aspects of IT project quality management, namely, product, process, and inputs of an IT project.The questions are derived based on common features of systems of systems and the enterprise architecture model represented in ArchiMate.
Breast Cancer Cell Data Analysis and Visualization Using R

Breast cancer is the most frequently occurring cancer in women. If the cancer is diagnosed and treated at an early stage, the patient has a survival rate of 99% after 5 years, but it significantly drops to 29% when it reaches a distant stage. Thus, it is very important to detect ‘positive cancer cells’ in the early stage, so I analyzed the 569 breast cancer cell data provided by the University of Wisconsin using R-Studio. Through this program, I visualized the relationships between 10 different cell characteristics, and researched the kind of relationship between radius and positive cancer cells by utilizing graphs. Also, I created a predictive model that can detect positive cancer cells based on logistic regression and training original 569 data. Finally, I recommended the adequate age (35-39) for the breast cancer examination by analyzing the breast cancer statistics. Through this research, I derived effective breast cancer predicting model and deeply explored big data analysis method and data-mining on R-Studio.
Website Analysis and Design Using Iconix Process Method: Case Study: Kedai Lengghian

Kedai Lengghian is one of the culinary businesses that has not applied technology to support its business processes. In the era of globalization, information technology is developing rapidly, allowing for business development. The development of information technology has an impact on the tight competition in the culinary business. Researchers provide solutions so that the managers and employees of Kedai Lengghian can increase the effectiveness and efficiency of business processes there. This is done by analyzing and designing a website-based information system that can later help implement system creation. Before creating an information system that suits the needs of its users, it is necessary to analyze and design software. Researchers use the Iconix Process method because the concept of building a system that is run focuses on the needs of its users. The Iconix Process has four stages: requirement, analysis, preliminary design, detailed design, and implementation. Kedai Lenghian can use information system technology to become a reference at the level of system implementation. In addition, this website-based information system is expected to increase the effectiveness and efficiency of the store's business processes and become an attraction for Kedai Lengghian consumers. The result of Website Analysis and Design Using the Iconix process is an object-oriented design that can then be coded. Also, its produced UML design gives Kedai Lengghian a picture of website making based on user needs, system needs, and system design.&#x0D;
Lucas-Kanade Optical Flow Machine Learning Implementations

Optical flow is an effective measurement to gauge motion in a scene, which allows for the computation of pixel-by-pixel motion in a frame pair. This paper aims to address the ambiguity with determining how to gain optical flow results for a given sequence. Due to varying speeds and nuances of a sequence, where it’s set, how fast it’s moving, a different amount of blur radius, i.e., the extent to which the image is blurred, may have to be applied to gain realistic flow maps. Furthermore, this paper touches on the many variables that can impact the efficacy of the flow outputted by an optical flow algorithm. Thus, we aim to determine whether the composition of results obtained through different blur values provides for more ground-truth flow outputs.
Joint Label Enhancement and Label Distribution Learning via Stacked Graph Regularization-Based Polynomial Fuzzy Broad Learning System

Label distribution learning (LDL), leveraging the label significance (LS), is more appropriate for solving label ambiguity problems than multilabel learning (MLL). However, directly obtaining the LS of LDL is extremely expensive and challenging. Thus, label enhancement (LE) algorithms are effectively proposed to acquire inherent LS from MLL for training the LDL models. Nevertheless, most existing LE models will suffer from low accuracy and low efficiency with following issues: ignoring mapping relationship between feature and label space, resulting in inaccurate enhanced data; designing independently apart from LDL models, resulting in an inability of unified LE-LDL learning; and requiring to optimize numerous parameters iteratively, resulting in worse training efficiency. Consequently, a novel unified LE-LDL learning framework, namely stacked graph-regularized polynomial-based fuzzy broad learning system (SGP-FBLS), is proposed by following three innovations: polynomial-based fuzzy system is introduced to enhance feature mapping ability while improving the learning performance effectively; graph regularized-based optimization objective function (GP-FBLS) is presented by considering interinstance correlation and label correlation to mine potential LS, thereby improving the accuracy of subsequent LDL tasks; and a weight stacked strategy is innovatively proposed to directly transmit LS and weighted parameters from GP-FBLS to SGP-FBLS without retraining, achieving the most satisfactory performance while significantly improving the training efficiency. Finally, comparative studies on 19 practical datasets demonstrate the effectiveness and superiority of proposed methods.
Clustering Courses Based On Student Grades Using K-Means Algorithm With Elbow Method For Centroid Determination

Students who have taken courses will receive grades from a performance index with a weight of 0 to 4. The amount of historical student data, particularly on course grades, has the potential to discover new insights. Still, course grades are closed data and are only for academic and management purposes. The research aims to a grouping of courses with high average grades. In this research, the clustering of courses using the k-means clustering algorithm using the elbow method to determine the centroid. Based on the Sum of Squares calculation, the optimal number of clusters with k=2 was obtained. The clustering results produced cluster 1 with a centroid value of 2.686 and 15 members and cluster 2 with a centroid value of 3.245 and 40 members. It can be concluded from this research that the members of cluster 2 are a group of courses with high average grades.&#x0D;