Reviewerly/exHarmony huggingface dataset:
Application of Convolutional Neural Network for Semantic Segmentation of Bandung Urban Scenes

Semantic segmentation is the process of classifying each pixel in an image into one of the predesignated classes. In its application for self-driving systems, semantic segmentation is used to identify objects and road conditions to make the right control decisions. This work uses the convolutional neural network method to perform semantic segmentation with the DeepLabV3+ architecture. Object of study is the urban scene dataset called Bandung Cityscapes collected around the city of Bandung, West Java, Indonesia and annotated by hand. Architecture testing is performed by combining the values of epoch, learning rate, and number of convolution filters to find the best mean intersection over union accuracy. The most optimal model achieved the highest average accuracy of 77.431% on the validation set, attained by the model with 128 convolutional filters, trained with 300 epochs and 0.0001 (10– <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) learning rate. This model maintained an average training accuracy of 87.194%, average validation accuracy of 75.274%, and average difference between the two of 11.919% throughout the training process.
Elastic Sample Filter: An FPGA-based Accelerator for Bayesian Network Structure Learning

proposed in 1985 by Judea Pearl [1],
$p$LPAQ: Accelerating LPAQ Compression on FPGA

In recent years, the demand for data storage space has increased dramatically due to the exponential growth of data volume. Data compression is of great significance since it saves data storage space and reduces data transfer demand. Compression algorithms based on statistical models have a much higher compression ratio than dictionary-based methods, but the high computational time cost of statistical modeling limits their wider application. In this paper, we introduce pLPAQ, an FPGA-based design of a powerful compression algorithm LPAQ based on statistical models. A novel hardware accelerator is proposed to speed up LPAQ by fully utilizing the parallelism of FPGA. Experimental results show that the proposed design can achieve a throughput of 12 MB/s on Xilinx Virtex Plus UltraScale XCVU9P card, 25x faster than executing on AMD Ryzen R7 4800U at 2.8 GHz and 80x faster compared with the naive FPGA implementation on average.
Library Development for Join Operation in Cassandra Database Management System

With the increasing need to handle and store data, NoSQL database management systems (DBMS) have grown in popularity, including Cassandra. Cassandra stores data in rows much like a relational database, but it does not provide join operations. The general solution for each problem that requires join on tables is to de-normalize the tables. Nevertheless, we argue that the need for join operations is still possible in the case of new and unexpected requirements beyond the database design. This research aims at developing a library that provides join operations for Cassandra. We start with understanding how Cassandra works internally, its data form, and how to retrieve data from Cassandra. The feasibility and performance of possible join algorithms are then analyzed to determine which algorithm to implement. We conclude that hybrid hash join and nested loop join algorithms are two feasible options to implement join in Cassandra. We then build the library for join operations in Cassandra. The join operations implemented library can perform both inner and outer join operations on either equi-join or non-equi-join. Based on performance testing, the hybrid hash join algorithm shows a good performance on small to large data, while the nested loop join algorithm shows a slower performance on large data.
Comparison of Key Point Detector Methods for Microcalcification’s ROI Identification on Breast Images: An alternative to SIFT

This document analyses different key point detectors: FAST (Features from accelerated segment test [1]), ORB (Oriented FAST and Rotated BRIEF [2]), MSER (Maximally stable extremal regions [3]), KAZE ( KAZE features [4] ), AKAZE (Accelerated-KAZE [5] ), STAR (CenSurE based STAR feature detector [6]), BRIEF (Binary Robust Independent Elementary Features [7]) and BRISK (Binary Robust Invariant Scalable Keypoints [8]) on mammography images from MIAS dataset [9] seeking for the better choice to extract ROI (regions of interest) with microcalcifications which will allow reducing noise and time of execution in microcalcification detection. An Image enhancement was implemented by filtering with a gaussian mask, thresholding to zero, and applying CLAHE to improve contrast. For ROI detection a K-Mean algorithm was implemented for grouping key points, the ROI’s area must include the microcalcification. An analysis was performed using 3 different parameters for each method: 1. if the microcalcification area is included in the ROI (called fitting), 2. time of execution, and 3. the mean number of points generated. The BRISK method had better performance, resulting in the best choice for mammography analysis, with a 100% of fitting, a time of execution of 0.38 seconds, and 408 points per image. This shows that a good Key point method selection may improve the future performance of an AI model for mammography analysis.
A Personality-Grounded Framework for Designing Artificial Intelligence-Based Product Appearance

AI-based technology in our everyday lives is an emerging issue in business research with a lot of potential for businesses and users. However, there is a lack of frameworks that enable UX designers and product developers to create AI-based products with elements of appearance that induce desired emotions and institute effective human-machine communication. Drawing on the AI archetype concept, this research advances a four-stage personality-grounded framework for forming the specific AI-based product appearance with six dimensions that evoke specific emotions incorporating the stakeholders' input. The framework application is illustrated using a single classical case study of the rescue and accident robot. The profile of an AI-based rescue robot that elicits trust and is recognized by human unconsciousness is generated through in-depth interviews with United Arab Emirates citizens. The application of the proposed personality-grounded framework can be extended not only to robots helping in different contexts in airports, police stations, and response centers but to broader categories of AI-based products.
A systematic review on university instructors’ roles and competencies in online teaching environments

Online instruction is now a common practice in higher education. Due to the great demand for online instruction, roles and competencies of online instructors have received significant attention to be determined. This study is a systematic review of 11 years of research (2010–2020) on the higher education context's online instructor roles and competencies. The study aims to classify the roles and competencies of higher education instructors in online teaching and learning environments. Forty-three articles were selected from 9 major academic databases using search strings and manual screening protocols. The literature analysis emphasized a total of thirteen roles and eight competency categories. Communication, development, and management have been identified as top online instructors’ roles. While “planning, design, and organization”, “interaction”, and “assessment” have been found as top online instructors’ competencies. Further, the time trend analysis showed that communication and developing were the most repetitive roles, and two approaches to identify competencies were found.
Prescreening MCI and Dementia Using Shank-Mounted IMU During TUG Task

Detection of mild cognitive impairment (MCI) and dementia (DEM) is an important topic because, unless it is treated early, MCI can progress to DEM, which is an untreatable disease. This article proposes a timed-up-and-go (TUG) task features analysis and classification of MCI and DEM using inertial measurement units (IMUs) in wearable devices. Our goal is to create a generalized model that can be used for preclinical screening. As a result, rather than classifying only one subtype of DEM, such as Alzheimer’s disease (AD) or Parkinson’s disease (PD), we classify all subtypes as DEM. We also utilize feature selection methods on features from TUG tasks to optimize the MCI and DEM classification performance. From the results, our generalized model can outperform other works in normal control (NC)-MCI&DEM classification with an accuracy of 86.94% and sensitivity of 97.40%. For NC-DEM classification, the performance of our generalized model is slightly lower than that of specific-subtype models (e.g., NC versus AD). However, our generalized model can outperform the specific-subtype models when using a diverse variety of subtypes. It is a reasonable tradeoff, and it can be a good first step toward a future where the patient can preclinically self-screening the cognitive impairments using wearable devices in free-living environments. This could allow patients to notice the cognitive impairment early on and seek a comprehensive diagnosis from a doctor.
Value-addition, by Extraction of Critical Minerals — Cu, Co, Ni, Mo and REEs — from the Albitite Type U-deposits in Rohil and Adjoining Areas, Rajasthan, India

The albitite type of U-deposits have been established through detailed exploration and resource-evaluation by AMD in Rohil and adjoining areas of Rajasthan. Of these, 1,086.46 hectares in the Sikar district of Rajasthan has been recently issued a letter of interest (LOI), by the State Govt. of Rajasthan to start mining by the Uranium Corporation of India Ltd. (UCIL). Salient attributes of the deposits are given, with their U-ore containing many sulphide/oxide ore minerals from which critical minerals (CMs), viz., Cu, Co, Ni, Mo and REEs are recoverable. It is suggested in this communication that (i) these CMs, besides U, may be extracted from the ore under value-addition in an integrated cost-effective project with high returns of investment and (ii) simultaneous laboratory/pilot-plant scale mineral processing of bulk ore from the proposed Rohil exploratory mine along with mine construction/development of these deposits is preferable, as it saves some time in establishing a processing plant for the U-ore to recover its main product (U) and by-products (CMs).
Agile Development Methodologies and Natural Language Processing: A Mapping Review

Agile software development is one of the most important development paradigms these days. However, there are still some challenges to consider to reduce problems during the documentation process. Some assistive methods have been created to support developers in their documentation activities. In this regard, Natural Language Processing (NLP) can be used to create various related tools (such as assistants) to help with the documentation process. This paper presents the current state-of-the-art NLP techniques used in the agile development documentation process. A mapping review was done to complete the objective, the search strategy is used to obtain relevant studies from ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Willey. The search results after inclusion and exclusion criteria application left 47 relevant papers identified. These papers were analyzed to obtain the most used NLP techniques and NLP toolkits. The toolkits were also classified by the kind of techniques that are available in each of them. In addition, the behavior of the research area over time was analyzed using the relevant paper found by year. We found that performance measuring methods are not standardized, and, in consequence, the works are not easily comparable. In general, the number of related works and its distribution per year shows a growing trend of the works related to this topic in recent years; this indicates that the adoption of NLP techniques to improve agile methodologies is increasing.
Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined Indicator

This study has developed a combined indicator to evaluate the performance of different search engines. Documentary analysis, survey, and evaluative methods are employed in the present study. The research was conducted in two stages. First, a combined indicator was designed to measure search engines. To this end, 72 criteria for measuring the performance of search engines were identified, out of which 22 criteria were selected. Accordingly, 10 criteria were selected in six general classes through a survey of subject matter experts. Validation of our proposed combined indicator was obtained by Delphi method and using the opinions of experts in the fields of information science and information system. Second, web search engines were evaluated based on the proposed combined indicator. The statistical population of this part of the research consisted of two categories: (1) general web search engines, and (2) general subjects. The sample size of the first category contained four search engines Yahoo, Google, DuckDuckGo, and Bing, and the second category involved 40 search terms under 10 general categories. The results showed that the combined indicator had six general criteria: (1) relevance, (2) ranking, (3) novelty ratio, (4) coverage ratio, (5) ratio of unrelated documents, and (6) proportion of duplication hits. According to this indicator, Google is at the top, followed by Bing. This study proposes a new indicator for evaluating search engine performance, which can measure the efficiency of search engines. Therefore, its use to measure the performance of search engines is recommended to researchers and search engine developers.
A Case Study on the Grow path and Continuous Learning Activities of SMEs innovative technical manpower

Objectives The purpose of this study is to investigate the effectiveness of programs at high vocational schools and universities in a view of technical manpower mastering their entry jobs, and the growth path and people who gradually attempted innovative learning activities in and out of the organization and the local consortium. The learning performance researched according to the Kirkpatrick Model. Methods Individual interviews with college professors, Delphi surveys to develop performance indicators, and questionnaires with high school students, college students, teachers, professors, graduates, and corporate official were conducted. Being participated in the business meeting, it recorded the document, opinions and debates presented by public officer and researchers. Results In the individual learning process, job identity education was important for long-term employment in SMEs. To get certificates was effective in cultivating learning habits, the professional way of thinking was naturally acquired in close contact and communication with the professor. Although the role of companies does not meet the students and school officials’ expectation, employment contract decreased the energy waste and make student concentrate on preparing entry job. Group self-efficacy by observing alumni for a long time affect investment plans for R&amp;D personnel growth. SMEs is easy to acquire experience as a president with undifferentiated job duties. To note, there is less social prejudice against technicians from vocational high schools and disadvantage to get qualified by post learning. Conclusions It analyzed the overall project evaluation, insufficient to view the learning that can be classified as an innovative technical manpower in this sample. A follow-up study is requested on how technical personnel occupy a high power position in technology, management, and education fields out of view that consortium unit as organizational learning.
The Relationship between Major Choice Decision, Intention to Change Major, Major Satisfaction and Major Commitment recognized by college students and graduates in Seoul

Objectives This study analyzed the correlation between the decision factors of the first major Choice of university students and graduates and the intention to change major, and further verified the mediating effect of major satisfaction and major commitment in this process. Methods For this purpose, 299 copies were used for the final statistical analysis except 11 copies of the total 310 copies of the questionnaires, which were missing or repeated the same responses, and frequency analysis, exploratory factor analysis, correlation and multiple regression analysis, and mediating effect analysis were conducted using SPSS statistical analysis program. Results First, personal factors and future factors among the sub-factors of major choice decision factors had a significant positive effect on major satisfaction, major commitment, and major change intention. This means that as the factors of major choice decision are higher in personal factors or future factors, it can act as a positive psychological mechanism that lowers unnecessary and negative major change intentions. Second, major satisfaction and major commitment had a significant negative effect on major change intention. Third, among the sub-factors of major choice decision factors, major satisfaction and major commitment played a full mediating role between personal factors, future factors and major change intentions. The higher the degree of major satisfaction and commitment, which are parameters, the more it can be inferred that it is ultimately related to the effect of lowering the negative major change intention. Conclusions The implications of this study are as follows: First, it is possible to use the core variables necessary to increase the satisfaction and immersion level of the majors recognized by actual university students and graduates and to minimize the intention to change the majors, and to be used as basic data for effective majors, careers, and future design. Second, the necessity of attracting talented people due to the recent decrease in the number of college entrance age population and the necessity of establishing practical policies to improve the satisfaction and immersion of college students in order to enhance university competitiveness and economic efficiency.
A Study on the Summary Writing Education of College Students: Focused on the case of S University

Objectives This study proposes summary writing education standardized in this subject in connection with S University's liberal arts compulsory &lt;Critical Reading and Writing&gt; class, and analyzes students' summary cases to examine qualitative performance. In particular, since summary writing is operated in connection with the writing clinic, an extra academic activity, the results of summary writing education using it will also be examined. Methods To this end, the researcher conducted a qualitative study using a summary of students who took Critical Reading and Writing from the first semester of 2020 to the second semester of 2021. First, the summary submitted by a total of 230 students in 10 classes over four semesters was first reviewed, and eight of them were selected for case analysis. In addition, learners' perception of summary writing education were reviewed based on the results of a survey of participants in the writing clinic. Results As a result of the study, through text-based summary writing education, students' summarization and transcription skills were greatly improved every semester, and reconstruction ability to structure overview and content was also cultivated. And as a result of the survey, students' satisfaction with summary writing education was high, and students' perceptions were also very positive. Conclusions Through this, it was found that the summary writing education in connection with the writing clinic, a comparison program, greatly contributes to cultivating the summary ability of college students. This study is meaningful in that it sought learner-centered writing education through one-on-one mentoring by linking summary writing education required in university education with extra academic activities.
A Correlation between Sentiment Expression and Writing Level of High School Student’s Persuasive Text

Objectives In this study, the problem was that the current writing evaluation did not take into account the emotional level of student writing. In order to examine the correlation between the sentiment level of high school students' editorial text and the level of writing, an persuasive sentiment lexicon was prepared, the sentiment level was measured, and the correlation between the sentiment level and the evaluation score was analyzed. Methods A corpus was created by collecting 132 student essays on two subjects, and the emotional dictionary of the corpus was constructed by determining the emotional level of the vocabulary as the expert's rating. By measuring the emotional level with the editorial sentiment dictionary, the correlation with the writing evaluation score was examined, and specifically, it was confirmed whether there was a difference in the correlation according to the upper/lower level. Results In both the KNU sentiment dictionary and the editorial sentiment dictionary, the degree of emotion and the evaluation score were positively correlated, and the correlation degree of the editorial sentiment dictionary (0.543) was higher than the KNU sentiment dictionary (0.368). Although the degree of correlation was different for each topic, both the emotional level and the writing level showed a positive correlation, especially the number of positive and negative words and the highest correlation. As a result of examining the texts by dividing them into upper, middle and lower levels, the correlation between the lower level text and the emotional level was high (0.571), and the upper level had no significant correlation with the emotional level. Conclusions It was found that the higher the emotional level of the editorial text, the higher the writing level, and the lower-level writing had a higher correlation with the emotional level. It was confirmed that it is necessary to use emotional words appropriately in order to effectively convey one's argument to the other party, rather than exclude the degree of emotion from the editorial text. For students with low writing ability, since the amount of words has a great influence on their writing level, it is necessary to learn various words including emotional words.