Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF THE ELECTRE METHOD IN DETERMINING THE DIRECTOR OF THE SUMATERA BLIND FOUNDATION

A director is a position held by someone in an agency or institution. The head of an institution engaged in special education, namely the Extraordinary School, is responsible for managing the school, so many criteria must be met and selected. Determination of the director requires a method to be more efficient, and the results are more valid. This study aims to implement the ELECTRE method to elect administrators at blind education schools at the Sumatra Blind Education Foundation. The ELECTRE method can be used when alternatives that do not fit the criteria are removed and the appropriate alternative is selected. The criteria for being a director are having ability in visual impairment, ability as a leader, psychological test results, ability to speak English and the results of interviews with HBM Germany. Implementing the ELECTRE method provides a sequence of choices for each alternative; if the number of ecl = 1 is more significant, the alternative is better. The results showed that of the four alternatives as director candidates, alternative A1 was the better choice, while alternatives A2, A3 and A4 were eliminated.
Complexities of Capturing Large Plastic Deformations Using Digital Image Correlation: A Test Case on Full-Scale Pipe Specimens

Abstract This paper identifies the challenges associated with using digital image correlation (DIC) measurements for full-scale bending tests that result in large deformations, and it describes techniques to address these challenges. Full-scale bend tests experimentally determine the strain capacity of line pipe, which is a critical parameter in strain-based design and assessment. DIC is a preferred measurement technique for these tests as it provides non-contact, full-field measurements of the pipe deformation over time. The primary challenges identified in using DIC for full-scale, large-deformation pipe testing relate to the speckle pattern, imaging system, and DIC performance measurement. A series of procedure and equipment improvements have been developed to address each of these challenges and to help improve the overall accuracy of the measurement system. The successful implementation of the DIC technique is illustrated through a recent full-scale pipe bend test of a 4.45-metre-long section of 24-inch diameter, X65 pipe. The pipe was subjected to severe bending (up to a 51° bend angle), resulting in the formation of a self-contact buckle. The test was monitored using four DIC systems (each system consisting of stereo cameras) incorporating the recommended procedure and equipment improvements identified herein. The DIC results were validated against conventional strain gauges.
Application of The Fuzzy Grid Partition-Based Mamdani Method to Decision Support System for Determining Salary Increase Eligibility

Fuzzy grid partition has been used to produce appropriate and optimal output. The output of several fuzzy inference methods such as the Tsukamoto method and the Mamdani method have been improved by applying grid partitions. This study aims to apply the fuzzy grid partition-based mamdani method to determine the feasibility of increasing employee salaries. The stages of the method are carried out starting with determining the number of partitions, forming fuzzy sets, carrying out the process of implicit rules, carrying out rule composition by selecting the maximum value of feasible and infeasible decisions, and finally carrying out the defuzzification process to obtain the calculation of the crips value. Attributes or features for selecting the eligibility for a salary increase are employee status, class status, years of service and benefits received. The results of the research get a decision for each employee whether it is appropriate to receive a salary increase or not. From one sample data tested on sample X7, the result of defuzzification (Z) is 5. Based on the table of feasible and inappropriate decisions, the value of Z = 5 is in a feasible decision
Quantum Illumination with Symmetric Non-Gaussian States

Quantum illumination (QI) is a technique that uses entangled states to illuminate a target region surrounded by a bright thermal bath to detect low-reflectivity objects. While QI has been extensively studied in the context of Gaussian systems, we propose a new QI protocol that utilizes symmetric non-Gaussian states to probe the target. We analyze the sensitivity of the target using the quantum Fisher information (QFI) of the symmetric non-Gaussian states under various squeezing parameters and the sensitivity of the measurement using the signal-to-noise ratio (SNR). Our results show that under certain conditions, symmetric non-Gaussian states can outperform Gaussian states in QI.
An Extensive Study on Lattice-Based Cryptography and its Applications for RLWE-Based Problems

Lattice-based cryptography has emerged as a powerful paradigm for constructing secure cryptographic primitives, offering resistance to quantum attacks and providing a versatile framework for building post-quantum cryptographic systems. This research paper provides an in-depth exploration of lattice-based cryptography, focusing specifically on its applications for problems based on Ring Learning with Errors (RLWE). We analyze the fundamental concepts of lattice theory, delve into the RLWE problem, and highlight the security properties and challenges associated with lattice-based schemes. Furthermore, we discuss various real-world applications of lattice-based cryptography, demonstrating its potential for secure communication, privacy-preserving protocols, and post-quantum cryptography.
The Planning &amp; Designing of Urban Building Water Supply and Drainage Engineering in AI Era

Abstract To explore how to improve the quality of urban building water supply and drainage engineering and further promote the economic construction of urban buildings, this paper combines artificial intelligence with water supply and drainage engineering of urban buildings. A detailed analysis of water supply and drainage engineering is unfolded, and the combination of BP neural network algorithm and particle swarm algorithm is used to improve the convergence accuracy of the algorithm and network generalization ability to optimize the BP neural network algorithm and construct the artificial intelligence algorithm model. Through experiments in the artificial intelligence algorithm model, the actual output quality parameter is 82, and the difference with the desired output quality parameter of 81 is 1, while in the ordinary design, the actual output quality parameter is 70, and the difference with the desired output quality parameter is 11, get in the artificial intelligence model the desired output and the expected output close. It shows that combining artificial intelligence with urban building water supply and drainage engineering can improve the quality of the project, which is more accurate and superior to the ordinary design and reduce the project cost.
Hear The Images Talk - Visual Literacy in The Face Of Today's World

The changing world of arts has its foundational pillars on 7 types art. They are Painting, sculpture, literature, film, theater, architecture, and the world of the mesmerizing music. Some of the Art Styles are Modern Art, Contemporary Art, Pop Art, typography, Still Life and Abstract Art. We have beautiful Art everywhere and the most prominent being the Visual Arts. We all love to chat using emoji, share memes, et up beautiful profile pictures and share "edited" photographs on social media and thus our ability to successfully do so indicates our catch on visual literacy skills. In simple word visual literacy is the ability to read, write and create images. We use these skills for thinking, understanding, visualizing, communicating and expressing our thoughts and ideas. The drawings on the prehistoric caves, the signs and symbols of the hieroglyphs, tells us how long of a process it has been. Considering the idea that visual images are a language, some authors consider visual literacy to be more of a metaphor, relating imagery interpretation to conventional literacy, than a teachable skill. A human being can develop these skills by seeing the imagery and at the same time having and integrating other sensory experiment. The development of these skills is fundamental to the normal human learning. It is remarkable that this field is credited with the development of educational programs that enhance students' abilities to interpret and create visual messages, as well as improvement of reading and writing skills through the use of visual imagery. In this paper we would see a broad overview of the concept of visual literacy, focusing on its interdisciplinary nature, learning how to develop visual literacy skills, promoting visual literacy and teaching these skills to children without them even realizing it!
MDBF: Meta-Path-Based Depth and Breadth Feature Fusion for Recommendation in Heterogeneous Network

The main challenge of recommendation in a heterogeneous information network comes from the diversity of nodes and links and the problem of semantic expression ambiguity caused by diversity. Therefore, we propose a movie recommendation algorithm for a heterogeneous network called Meta-Path-Based Depth and Breadth Feature Fusion(MDBF). Using a random walk for depth feature learning, we can extract a depth feature meta-path that reflects the overall structure of the network. In addition, using random walks in adjacent nodes, we can extract a breadth feature meta-path, preserving the neighborhood information of a node. If there is some auxiliary information, it will be learned by its own meta-paths. Then, all of the feature sequences can be fused and learned by the Skip-gram algorithm to obtain the final feature vector. In the recommendation process, based on traditional collaborative filtering, we propose a secondary filtering recommendation. The experimental results show that, without external auxiliary information, compared to the existing state-of-the-art models, the algorithm improves each index by an average of 12% on MovieLens and 22% on MovieTweetings. The algorithm not only improves the effect of movie recommendation, but also provides application scenarios for accurate recommendation services through auxiliary information.
Sharing Health Records in Senegal Using Blockchain

Electronic Health Records offer real advantages for accessing and storing patient health information, which can improve the management of patient care. However, the attractive features of electronic records (accessibility, portability, and portability of patient health information) also present privacy risks. Organizations need to share person-specific health data without disclosing the privacy of their subjects. Current mechanisms for effective management and protection of health records in Senegal have proven insufficient. In this paper, we propose a system that addresses the issue of sharing health data between hospitals in a trustless environment based on the Consortium Blockchain to improve the quality of care and the efficiency of the health system in Senegal. After a brief introduction, we present some characteristics of Blockchain as well as the different types and securing of Blockchain using cryptographic algorithms. Then an overview of related work is conducted. Finally, we presented the preliminaries of sharing health records with the Blockchain in Senegal. Our work ends with the description of the functioning of our medical record management mechanism with the Blockchain in Senegal and its implementation.
Improved Javanese script recognition using custom model of convolution neural network

&lt;div align="center"&gt;&lt;span lang="EN-US"&gt;Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.&lt;/span&gt;&lt;/div&gt;
Symbolic Regression Applied to Cosmology: An Approximate Expression for the Density Perturbation Variance

Computations of cosmological properties, such as the density perturbation variance, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma$</tex> , are computationally expensive. In this work we propose the application of Symbolic Regression to generate analytical expressions to approximate these quantities. We created simulated data for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma$</tex> using a Boltzmann solver, CAMB. These simulations cover seven parameters; the five cosmological parameters and the redshift and mass of dark matter halos. We then apply a Symbolic Regression engine, TuringBot, to this simulated data and obtain an analytical equation to approximate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma$</tex> . The resulting mathematical expression has a mean accuracy of ≈ 98.96% over the entire domain, and is five orders of magnitude faster than using simulated data from CAMB, demonstrating the applicability of Symbolic Regression to accelerate cosmological inference.
Practicing the Extension in Software Engineering Education: an Experience Report

University Extension is an educational, cultural, and scientific process that establishes the exchange between academic and popular knowledge. Project-Based Learning (PjBL) consists of allowing students to confront the issues and problems of the world through a real project. PjBL has been adopted in Software Engineering (SE) teaching to motivate and engage students in studying and applying processes and methods. In this work, the university extension was integrated with the PjBL in teaching SE in a postgraduate course to bring students closer to real practices of software development, meeting a demand from the State of notorious value for society. Interviews were conducted with stakeholders, artifacts were delivered, and students received feedback from the professor and the stakeholders. Through a questionnaire, the students provided feedback about the practical activity, the learning method, and their perceptions regarding their evolution and dedication to group work. We can highlight the following findings: (i) the use of PjBL together with extension and the development of a real project was promising for the evolution of learning; (ii) greater motivation of the students, who evaluated: (iii) the learning method as satisfactory, and (iv) the practical activity and their evolution of learning as good and excellent. Our main contribution is the lessons learned that might guide future experiences that adopt PjBL in SE teaching and extension.
Exploring Psychological Safety in Software Engineering: Insights from Stack Exchange

[Context] Interpersonal relationships are as important as processes and technology mastery in software engineering. These relationships can impact the quality of the software product and the software teams’ quality of life. Understanding how and which aspects impact interpersonal relationships is essential for a psychologically safe environment. However, little is known about which aspects may affect the maintenance of a psychologically safe environment within the software engineering context. [Aims] This study aims to investigate interpersonal conflict scenarios and their potential indicators of psychological insecurity from the perspective of software practitioners. [Method] This article identified and qualitatively analyzed discussions related to psychological safety on Stack Exchange, an important question-and-answer site used by software professionals in their daily activities. [Results] We identified 11 scenarios concentrated on four types of interpersonal relationships: with the client, with team members, with leadership, and with other departments. In addition, we investigated whether there were previous attempts to resolve conflicts. Lastly, we found seven potential indicators of psychological insecurity in the conflicts exposed. [Conclusion] We organized the set of scenarios and their potential indicators of psychological insecurity in a Sankey diagram. It can support researchers and practitioners in understanding how to define a healthier and more productive environment for software projects.
Item Deletions Based on Difficulty Values and Discriminating Values

Background: Deletion of items from MCQ tests or Likert type scales may be necessary due to various reasons. Methods: Considering entire data the paper gives new measures of difficulty and discriminating value of items as well as test along with their relationships including relationship with test reliability (. Discriminating value of test () and item ( are expressed as coefficient of variation (CV) of test scores and item scores respectively. Results: Non-linear relationship between and derived. As number of persons giving correct answer to an item increases, curve increases and curve decreases and intersect at a point (say. Items lying outside the interval [ where SD is standard deviation of or can be deleted. Choosing acceptance region as [] may result in discarding too few items. For Likert scale, items with high values of CV may be deleted. Relationship of reliability and discriminating values helps to find effect of such deletions. Conclusions: Proposed method of item deletions based on difficulty values and discriminating values offers significant benefits and is recommended. However, the approach may be compared with deletion of items by “alpha if the item is deleted”. Future studies suggested.
The role of visual, auditory, and tactile cues in the perception of illusory self-motion (vection) in younger and older adults

&lt;p&gt;One of the most critical components to an immersive experience in Virtual Reality (VR) is vection, defined as the illusion of self-motion. Vection is a multisensory phenomenon, relying on cues from a multitude of sensory modalities such as visual, auditory, and tactile. As a natural result of aging, the sensory systems which detect and process these cues decline. The objective of the present study was to investigate vection in adults of different ages, to test how age-related changes may influence the perception of vection. Twenty-four younger adults and 10 older adults were recruited to participate in a study at Toronto Rehabilitation Institute’s StreetLab. Participants were seated in a rotatable chair in StreetLab and exposed to revolving visual, auditory, and tactile stimuli inducing the illusion of circular vection. Results indicated older adults experienced notably longer durations overall, as well as an audio by tactile interaction regarding vection intensity. &lt;/p&gt;
Liver Diseases’ Stage Detection Using Hybrid Technique of Supervised Machine Learning Classifiers

The danger of liver disease, one of the deadliest illnesses in the world, has been rapidly increasing among humans over the past several decades. For researchers, one of the most difficult problems is predicting the disease using massive medical information. To solve this issue, machine learning techniques like classification and clustering have been created. The primary aim of this study is to use classification algorithms to figure out a patient’s risk of getting liver disease. The stage of the liver disease is also indicated, including cirrhosis liver, liver fibrosis, fatty liver, and healthy liver. As a result, the classification accuracy and processing speed of the proposed Hybrid Classifier (RF, SVC, XGBoost) and the methods NB, SVM, LOR, RF, DT, KNN, and RBTC are assessed. Using After considering these performance factors, the Hybrid Classifier is determined to be the best classifier with 99% accuracy.
Air-to-air quantum key distribution with boundary layer effects

Airborne platforms offer a potential solution to overcome the challenges of quantum key distribution (QKD) over long distances and in free space. However, the presence of boundary layer around high-speed flying vehicles can introduce unpredictable shifts and disturbances to transmitted photons, affecting their mode. In this article, we propose a performance evaluation scheme for air-to-air QKD with boundary layer effects. Our analysis compares the effects of the boundary layer at the receiver and at the transmitter on QKD performance, as well as an evaluation of two possible air-to-air QKD models. The results indicate that in the co-directional flight model, a particular azimuth can significantly increase the QBER when both ends are affected by the boundary layer, and photon deflection angles are larger when the transmitter is at a relatively low altitude.In the counter-directional flight model, the boundary layer around the aircraft may result in a maximum reduction in secure key rate by approximately 79.6% and introduce average channel loss of around 3.45 dB for transmitted photons. These findings highlight the importance of considering the boundary layer effects in air-to-air QKD and provide insights for the design and optimization of QKD systems in practical scenarios.
DIGITALISASI ARSIP DALAM MENDUKUNG PELAKSANAAN E-GOVERNMENT

E-Government is a government-issued policy to increase access to open information for digital-based communities. Higher education through the Higher Education Archives Institute seeks to implement an e-government system by joining the National Archival Information Network (JIKN) and the National Archival Information System (SIKN). On the other hand, to implement this, Higher Education Archives Institutions need to digitize their previously conventional archives. Old media has become digital, the results of which can be accessed by the public. Digitizing archives is one of the tasks of the Higher Education Archives Institution to provide information on the track record of higher education, starting from its founding to its development. This study aims to analyze the management of digitization of archives carried out by the Archives Office of the University of North Sumatra in supporting the implementation of e-Government and the utilization of an information network system that is used as an archive access portal. The research method used is descriptive with a qualitative approach which aims to obtain an overview of archive digitization management activities at the Archives Office of the University of North Sumatra. The results showed that digitizing the archives of the University of North Sumatra was carried out by transferring vital historical archives contained in photographs, cassette tapes, and VHS tapes, Betamax (old media). Then the University of North Sumatra Archives became a member of JIKN and SIKN to support the implementation of e-Government. This study recommends that archival research researchers start to create a grand design for digitizing archives with high value as a source of knowledge. Then, this research will be helpful for policymakers and all stakeholders to manage the digitization of university archives.