Reviewerly/exHarmony huggingface dataset:
Design a Website-Based Building Materials Inventory Information System At Tb.Gilang Putra

TB. Gilang Putra is a business engaged in the sale of building materials. In the processing of building material supplies, Tb.Gilang Putra uses manual processing. Then Tb.Gilang Putra needs an optimal program to improve its performance. Design a building material inventory information system is a system that will be built to reduce problems contained in the building material inventory information system. The loss and destruction of some data that prevents the data from being inputted. The method used in this building material inventory information system is object orientation (OOAD) and uses the Rational Unified Processes (RUP) model and is supported by Unified Modeling Language (UML) for software engineering processes with good structure. The author draws the conclusion that the system in this design can be useful and alleviate the process of supplying building materials and is able to solve problems that occur so that the system that is run can be used anytime and anywhere.
Analysis of Observed and Perceived Climate Change and Variability in Gondar Zuria Woreda, Northwestern Ethiopia

Examining the spatiotemporal variability and trends of climate in the context of global climate changes particularly in countries like ours where rainfed agriculture is predominant is indispensable to suggest possible adaptation policies. Therefore, this study aimed at assessing the changes and variability of rainfall and temperature in Gonder Zuria Woreda, Northwestern Ethiopia. The study was conducted using quantitative research design. Climatic data which were collected from 1952 – 2016 were the data source of this study. Questionnaire was used to collect data from 100 local people about their perception on climate changes, and it was presented in frequency and percentage distributions. Coefficient of variation, anomaly index, Precipitation Concentration Index and Seasonality Index were used to analyze the variability of rainfall on annual and seasonal basis whereas Inverse distance weighting interpolation was used to show the spatial variability of rainfall. Furthermore, both non- Mann-Kendall tests and linear regression were used to detect the trends in time-series climatic data for the period of 1952-2016. The result revealed that there is intra-annual/seasonal and inter-annual variability of rainfall. Annual, summer and spring rainfall has decreased with a rate of 21.24, 7.2 and 1.85 mm per decade respectively. The minimum average and maximum rate of change of temperature was found to be 0.19, 0.25 and 0.3oC per decade respectively. The Mann-Kendall trend test result indicated that there is significant increasing trend for minimum, average and maximum temperatures. Most of the respondents (80%), perceived that temperature is increasing and rainfall is declining. Therefore, a sustainable climate risk management approach is recommended to adapt to the ongoing impacts ofclimate variability and climate change.
Preface

2023 12th International Conference on Information and Electronics Engineering was held during February 18-20, 2023. The conference ICIEE 2023 has been held in both online and onsite mode. The participants can choose to either attend the conference virtually, or join the event in person. ICIEE 2023 is sponsored by Jeju National University with technical support from Fukuoka Institute of Technology, Tokyo University of Science, CISCO Networking Academy. ICIEE 2023 continues a tradition of bringing together researchers, academics and professionals from all over the world, experts in information, communication and networks. Submitted papers were received and sent for preliminary and peer review conducted by the technical committees. Professional comments were given according to the aspects of originality, innovation, applicability, technical merit, organizing and writing, relevance to conference. After several rounds of review procedure, 12 excellent papers had been published in the conference proceedings. The proceedings contain a collection of research papers, organized as 3 chapters: smart image processing and application, digital communication network and signal analysis, Modern intelligent information system design and model calculation. We hope that this volume of conference proceedings will serve as a valuable reference for researchers, educators, and practitioners. We would like to thank the organization committees, the members of the program committees and technical committees. They have worked very hard in reviewing papers and making valuable suggestions for the authors to improve their work. At the same time, we would like to thank all the authors for their work, their interest in our conference, the quality of their presentations and their passion to communicate really pushed this conference into a series of successes. Hope you could benefit from this proceeding! ICIEE 2023 Conference Chair Yungcheol Byun Full Professor/Ph.D. Dept. of Computer Engineering, Jeju National University List of Committee is available in this Pdf.
The Role of Orthogonality and Compactness in Recovering 3D Symmetrical Shapes

We explore the role of geometrical projections from 3D to 2D as well as the role of compactness, symmetry, and orthogonality constraints in monocular 3D shape recovery. This recovery is an ill-posed inverse problem, but our prior work has shown that subjects’ performance is quite reliable. On each trial, a static image is shown on the left side of a computer screen, and on the right side of the screen a user-adjustable 3D shape is shown rotating. The subject adjusts either one or two parameters of the 3D shape on the right until it is as close as possible to their 3D percept produced by the static 2D image on the left. The images are either orthographic or perspective projections of the 3D shapes. To test the role of orthogonality constraint, we generated three classes of objects: (i) synthetic polyhedral objects composed of boxes which have no 90deg angles; (ii) similar polyhedral objects but with a number of 90deg angles; and (iii) symmetrical or approximately symmetrical, real-world objects selected from the ModelNet-40 dataset. Many of these real-world objects have a high degree of orthogonality. The computational model uses regularization to perform 3D shape recovery. The cost function has terms that penalize departure from orthogonality and from 3D compactness of the 3D shape’s convex hull. Mirror symmetry is an implicit constraint. Perspective images lead to more accurate recovery than orthographic images. This is related to the fact that perspective images determine a unique 3D interpretation of symmetrical shapes. Monocular recovery from perspective images is as good as binocular recovery reported in prior studies. Recovery of natural objects is more accurate than recovery of synthetic polyhedral objects. This result suggests that familiarity plays a role. We will discuss the role of familiarity with individual objects and familiarity with categories of objects.
Local texture manipulation further illuminates the intrinsic difference between CNNs and human vision

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance on a wide range of visual tasks and have provided currently the best computational models of visual processing in the primate brain. However, CNNs are strongly biased towards textures rather than shapes, which may harm object recognition. This is rather surprising as human vision benefits from textures and shapes as complementary and independent cues for recognizing objects (e.g., humans can reliably recognize an apple by either its texture or shape alone). Considering this stark contrast, it is important to understand how CNNs use the two cues, object texture and shape, for object recognition. Here we address this very question. To this end, we compare multi-label image-classification accuracy when the models are trained on either original (intact), object (local), or scene (global) texture-manipulated datasets. We then evaluate the models’ ability to generalize to other unseen datasets. We tested CNNs and Transformers known to have a strong shape bias. A psychophysical experiment was also conducted to evaluate human performance. We employ images from the COCO dataset containing natural scenes with multiple objects. Local textures are manipulated by replacing each object's texture with a random, artificial texture chosen from the DTD dataset. Global textures are manipulated by using image style transfer with a random texture. We find noticeable differences in the models’ ability to generalize to untrained datasets. Specifically, both CNNs and Transformers trained on the original dataset show a sharp decrease in accuracy when tested on texture-manipulated datasets. However, CNNs, but not Transformers, trained on local texture-manipulated datasets perform well on both the original and global texture-manipulated datasets. As expected, human observers show difficulty recognizing local texture-manipulated images. Our findings suggest that, unlike humans, CNNs do not use texture and shape independently. Instead, textures appear to be used to define object shape per se.
How to ensure that animated data visualizations respect visual capacity limits

Data visualizations help people understand numbers that describe our world, across research, public policy, education, and business. Computer screens allow for the animation of visualizations, which can effectively show how data change over time, and they can be engaging to watch. For example, countries could be shown as points in a scatterplot, with GDP and life expectancy on the X and Y axes, and the points could move to show changes in those values over time. But animated data visualizations can easily overwhelm visual processing capacity, and designers rarely have clear rules that help them see when and why that might occur. To produce these rules, we reviewed examples of real-world animations, and created a taxonomy of perceptual tasks that those animations tend to demand. The identified tasks include holistic judgments, such as extracting optic flow or ensemble statistics, or evaluating changes to the shape envelope surrounding a set of points, and tracking tasks across individual objects, such as tracking objects with a common feature, noticing the removal/addition of objects, and tracking objects without (easier) or with (tougher) remembering their features. We then described the known processing limits of each of these perceptual tasks, so that designers can create animations that respect those limits. We catalog design techniques for preventing information overload, such as making a subset of objects more salient, or animating only a subset of objects at once, and explain why they work. Together, the resulting guidelines will help designers engage and explain with animations while avoiding or mitigating viewer overload.
None to rule them all? No generalization of saliency models across age

Human gaze behavior provides rich insights into the innermost states of our minds, reflecting perception, attention, and even intentions. Saliency maps are models that predict fixation locations, created to generalize across a wide range of visual scenes but validated using only few participants. Generalisations across individuals are generally implied. Here, this implied generalisation across people, not images, was focused on by assessing gaze behavior of 8,325 participants, including children. Using a single, yet feature-rich image, shortcomings were found in the ability to predict over this diverse sample in each of 21 models. Strikingly, models performed well on participants aged 18-35, but poorly for other age groups, with children in particular. Modelling and understanding gaze behavior beyond college-student like samples thus requires an approach which incorporates knowledge on differences in gaze behavior across the lifespan or at least validation on (age)-diverse samples.
Uncovering the dynamics of visual memory representations over time

The ability to accurately retrieve visual details of past events is a fundamental cognitive function relevant for daily life. While a visual stimulus contains an abundance of information, only some of it is later encoded into long term memory. However, an ongoing challenge has been to objectively define and isolate what representations of past visual experiences are maintained in memory over time. To address this question, we leveraged the hierarchal structure of convolutional neural networks (CNNs), and its correspondence to human visual processing. Participants were recruited from the Amazon Mechanical Turk platform and performed the task online. They first encoded a set of images and were then tested via a two alternative forced choice recognition memory test at different encoding-retention time intervals (immediate, 24 hours, 7 days). Importantly, to objectively isolate different levels of visual processing, distractors were selected based on their similarity to the target along specific network layers of the CNN (VGG-16) hierarchy. Accordingly, distractors were assigned based on sharing high similarity in early, intermediate, or late network layers with the target. Preliminary results suggest that high-level representations (corresponding to late network layers) are better retained over time, while lower-level representations (corresponding to early network layers) decay faster. This experimental approach and consequent findings provide novel insights into the dynamics of different levels of visual memory representations over time.
Metode Economic Order Quantity dalam Sistem Manajemen Stok Bandar Barang Bekas (SiBandar)

Junk goods are no longer piles of worthless goods for businessmen who are keen to see the economic value by taking this opportunity by becoming a pelapak company to buy and sell junk goods purchased from collectors to be sold to buyers. The problems found with Pelapak include the manual recording of sales administration which has the potential to cause recording errors. Pelapak does not know for sure the stock of junk they own and those owned by collectors because the locations of the collectors are far apart. Orders of junk goods between pelapak and collectors are still via telephone so pelapak does not know which collectors are in the process of sending goods. Problems also occur on the part of collectors including unprofessional sales transactions. Based on these problems, a used goods stock management system is needed using the Economic Order Quantity (EOQ) method. The purpose of this study is to build a used goods stock management information system that can monitor the availability of used goods owned by collectors and pelapak. System development uses the prototype method and designs with concepts. The results of the research are that the system created can help find out the stock and the amount in each collector, recap sales reports, provide accurate information, facilitate business transactions and fulfill sales targets to buyers.&#x0D;
Sistem Informasi Posyandu Remaja Dalam Perkembangan Kesehatan Gizi dan Konseling Remaja Menggunakan Framework Codeigniter

Genius Youth Posyandu is the smallest health unit in the RW 14 area of ​​Sidanegara Subdistrict, Cilacap Regency which has the task of providing nutrition and health services to adolescents. So far, all series of Posyandu Adolescent activities are recorded conventionally in a book. The emergence of time inefficiency due to the limited number of cadres and the large number of young members of the Youth Posyandu. The difficulty of monitoring the development of nutritional health and nutrition counseling that has been carried out has also become a concern of cadres in the process of reporting Posyandu Adolescent activities to the Kelurahan level. system using blackbox testing. After testing this system, it was proven that all system functions were running well so that it made it easier for cadres to manage adolescent Posyandu anthropometric data, cadres to identify adolescent nutrition trends, and youth to view Posyandu data and offer counseling online.
Single Sign On Using Keycloak Integrated Public Key Infrastructure for User Authentication In Indonesia’s Electronic Based Government System

The government in carrying out its function as a public administration servant is regulated in law of the Republic of Indonesia number 25/2009 on public services. In this regulated about electronic government (e-government), many individuals use various web applications that require users to authenticate themselves to access each application. Many entities require various web- based applications for operational activities. This makes centralized access management for web-based applications very much needed. Currently, access management is often implemented using Single Sign On (SSO) with password authentication method. Security considerations arise against the use of passwords. This is because passwords have a vulnerability to brute forcing using a password list, and human nature often uses repeated or uncomplicated passwords. There is an alternative authentication method, namely Mutual TLS which utilizes Public Key Infrastructure (PKI). Users authenticate with X.509 digital certificates, so the authentication factor becomes something you have. This research aims to implement an integrated PKI SSO system and RBAC access automation. The approach of this project is research, design, implementation, and testing. The entire system is built with open-source software and implemented on a cloud infrastructure. The system has three subsystems, namely registration, login and RBAC access automation. All subsystems are tested according to the specified flow. The test results show that the registration subsystem has been successfully carried out as evidenced by the success of filling in personal data, approval flow, and downloading of certificates. The login subsystem was also successfully implemented, as evidenced by the existence of mTLS authentication with certificate validation. In testing the RBAC access automation subsystem, it is shown that the script created can perform access checks and access remediation if needed.
Post-Editing Machine Translation (PEMT) as the preference method for university students in Indonesia

Language translating process by Indonesian students is particularly suffered from many errors. Its defined strategy should be carefully watched due to completing its translating perfection. Thus, this study identifies the problematic translation ways commonly referred and used by university students in Indonesia. The mixed method using embedded design between qualitative and quantitative data with a case study approach was carried out to find the preferences of the translation method used by the students. The respondents are 50 English Department students from different levels of studying years. The result shows that Post-Editing Machine Translation (PEMT) was preferred among other translating methods. The reasons are related to the ease and quality of the translation work. 76.90% of the students responded and chose to have PEMT as the preference. However, there were only 53.8% of them implementing PEMT as the translating process, and 38.5% were familiar with Machine Translation (MT) without post-editing instead. It indicates an inconsistency in the use of the translation methods as each of them experienced and chose different type of methods. However, PEMT was placed as the most referred translating method used by the students. This suggests that the presence of technology is well utilized while still paying attention to the quality of the translation through the post-editing process.
Komparasi Model Analisis Sentimen Pada Twitter Terhadap Kemahalan Minyak Goreng dengan Metode Naive Bayes dan Support Vector Machine

At the end of 2021, people are shocked by the drastically reduced supply of cooking oil and high prices. This makes people talk about it a lot through social media like Twitter. Freedom on Twitter raises many responses from the public. The number of negative and positive responses on Twitter makes comparisons between the two responses difficult to observe. This study aims to determine the comparison of positive responses and negative responses. Machine learning with the naïve Bayes method and support vector machine is able to overcome this problem. The research conducted examines how the comparison between positive responses and negative responses and which method has higher accuracy. The data used is 10,000 Indonesian language tweets. Model testing was carried out with 1839 test data. the Naive Bayes method gets an accuracy of 74.06% with the results of predicting two positive tweets and 1837 negative tweets. The SVM method was tested on linear, polynomial, RBF, and sigmoid kernels. The kernel with the highest accuracy value is the sigmoid kernel with an accuracy of 81.8% with the predicted results of 266 positive tweets and 1573 negative tweets.&#x0D;
Impediments and Enablers to Implement Digital Learning: A Focus Group Discussion of Malaysian School Educational Technology Leaders in The Wake of Covid-19

It appears that digital learning will serve as the primary vehicle for education given the rapid increase of technology in this digital era. However, despite numerous national plans made, school educational technology leaders still had to deal with many challenges. In the wake of Covid-19, impediments and enablers of digital learning seemed to accrue. Hence, this study attempts to identify the latest impediments and enablers in implementing digital learning. Focus group discussion (FGD) was conducted by using purposive sampling, where seven (7) school educational technology leaders who had teaching experience of more than ten (10) years and led digital learning in their schools were selected. Results of the discussion were then analysed using Atlas.ti. The content analysis of the focus group yielded seven (7) major themes of impediments, which were accessibility issues, time management, digital learning as a new norm, teachers’ roles and skills, parental involvement, school administrations, and physical, emotional and mental health of the teachers, students, and parents. Meanwhile, for the digital learning enablers, four (4) major themes were identified, which were accessibility, empathetic learning, leadership, and the learning culture. This study draws implications for the digital learning guidelines or frameworks for the school educational technology leaders in planning any digital learning program which are more appropriate for K12 education.
Awareness and Perception of certified librarians of Nigeria towards the use of robotic technologies in the libraries

The study adopted the social survey technique and the population of the study comprised 452 certified librarians of Nigeria libraries that were certified by the Librarians’ Registration Council of Nigeria (LRCN) on November, 24th, 2021. The librarians who took part in the survey were chosen using a random sampling method. One out of every three librarians being admitted on the day was given the questionnaire. The researcher used Statistical Package for the Social Sciences (SPSS version 20.0) application software with simple percentages chosen for easier analysis and interpretation. The findings of this study revealed that certified librarians are averagely aware of the existence of robotic technologies usage for library services and the anxiety of employee retrenchment is the major anxiety, despite they are aware that, the innovative apparatuses will increase global recognition and enhance library services. It shows that certified librarians are faced challenges towards the use of robotic technologies such as potential job loss, inadequate funding and epileptic among others. The study recommended that certified librarians should keep up with modern trends in information dissemination by learning fundamental skills that are technologically compatible among others.