Reviewerly/exHarmony huggingface dataset:
Pengembangan E-Modul Discon Berbasis Android (E-Modul Disroid) pada Materi Cahaya bagi Siswa Sekolah Dasar

Changes from age to age in human life indirectly encourage humans to follow the times where it cannot be denied that all things are based on technology, including the world of education. The problem in this research is the change in the learning process carried out online with the help of development technology. The online learning process makes students difficult, especially in science learning, where learning is mostly carried out with practicum. The purpose of this study is to develop a valid and practical Android-based Discon E-Module. This research is a research and development (Research and Development) with a 4D model consisting of 4 stages, namely define, design, develop, and disseminate. The population in this study were fourth-grade students of Elementary School Inpres 1 Tondo. The research instrument used tests and media validation questionnaire sheets, material validation, and user validation. Based on the results of data analysis, it was obtained that the media validity was in the very good category, the material validity was in the good and very good categories, and user satisfaction was in the very good category. So it can be concluded that the Android-based E-Modul Discon (E-Modul Disroid) that was developed is included in the very valid and very practical criteria. So that this module can be used in science learning, especially for practical activities in grade IV elementary.
Geant4/CaTS/Opticks: optical photon propagation on a GPU

in quantum information processing, and fundamental investigations of the boundary between the macroscopic classical world and the microscopic world of the quantum.
Student financial support and sustainable recovery based on fuzzy-analytic hierarchy process comprehensive method

In the post-COVID-19 era, many university students face financial distress due to macroeconomic downturns. Helping college students overcome financial difficulties is an important aspect of sustainable recovery. Based on the theory of student satisfaction and the perspective of new financial support systems, this study constructs a performance evaluation system for financial support for financially distressed students in colleges and universities. To comprehensively evaluate financially distressed students for financial support from the perspective of satisfaction, an empirical study is conducted by taking the authors’ college as an example according to the fuzzy Analytic Hierarchy Process analysis and evaluation model. The results show that, according to the characteristics of students, the students' families’ characteristics should be considered and carefully sorted, and on this basis, the appropriate selection criteria for financially distressed students are selected.
Blockchain Technology Model Towards Smart Agriculture: A Proposed Model

The objective of this study is to examine the challenges created by blockchain technology to the agriculture stakeholders. Besides that, it is also noteworthy to understand that two important theories underlie the integration towards achieving the objectives of developing the blockchain technology for smart agriculture are the Grounded Theory and will conceptualise the Technology-Organisation-Environment (TOE) Theory. This study employs a qualitative approach because it can extract the complex description of the blockchain technology towards smart agriculture that involves various agriculture sector stakeholders. Semi-structured in-depth interviews will be conducted in six states: Perlis, Kedah, Selangor, Negeri Sembilan, Pahang and Sarawak as part of the research to get a more in-depth understanding of the profiles and better understanding on the challenges created by blockchain technology to the agriculture stakeholders. Drawing on the high value of smart agriculture principles, this study reconciles a comprehensive blockchain technology model and draw connections with the challenges faced by various stakeholders. This study expands knowledge on blockchain technology in supporting smart agriculture by looking into the blockchain technology attributes in terms of traceability, visibility, security and accessibility, as well as the challenges created by blockchain technology to the agriculture stakeholders. This study output will boost the modernisation process in the agriculture sector, managing the demand and supply of national food and agriculture supply. The underlying trend of agricultural research is discovered to be primarily embedded in scientific investigations from technologies’ perspectives. Researching in smart agriculture is crucial but remains scanty due to the challenges facing at the different levels in the supply chain. As such, the novelty fits within this research model are unavailable to the researchers’ best knowledge.
A Robust Method for Extracting Texture Features of Segmented Mammogram Images using M-ROI Technique

Radiologists generally uses mammogram images for extracting masses or cancer effected breast issues using texture features of the images by segmenting techniques. The most commonly used technique in this process is region of interest (ROI). But this method fails for large collection of mammogram image database. To address this issue this present paper proposed multi-ROI (M-ROI) technique. This method not only reduces limitations of ROI but also finds the very suitable texture features. This paper also evaluated the efficiency of the proposed M-ROI method using first order and second order statistical techniques.
Entangled Photon Generation From a Three-Level Laser with a Parametric Amplifier and Coupled to a Thermal Reservoir

In this article we study squeezing and statistical properties of light producing by a non-degenerate three-level laser with the parametric amplifier and coupled to a thermal reservoir. With the aid of master equation, we obtain stochastic differential equations. Applying solutions of resulting differential equations, we calculate quadrature variance, the mean and variance of photon number, the photon number correlation. However, the two-mode driving light has no effect on the squeezing properties of the cavity modes. On the other hand, parametric amplifier and thermal reservoir increase the mean and variance of photon number. Furthermore, employing the same solutions, we also obtain anti normally ordered characteristic function defined in the Heisenberg picture. For a linear gain coefficient of (A = 100), for a cavity damping constant of K= 0:8,μ = 0 and for thermal reservoir ¯ nth = 0 ,the maximum intra cavity photon entanglement is found at steady state and at threshold to be 60%.
Performance Analysis of Designed and Implementation Dual-Axis Solar Tracker with Monitoring System

Renewable energy has been the prime focus in recent times for mitigating the ever-rising energy demand. Because renewable energy doesn’t emit carbon into the environment, it is considered the cleanest form of energy source. Solar energy has always been the most popular renewable energy source in our country for its geological advantages. Solar cells need to use sunshine as effectively as possible. The continuous change in the relative angle of the sun concerning the earth reduces the watts delivered by the solar panel. At present, fixed arrangement of solar panels is used to harvest solar energy from the sun, which only receives few amount of its effectiveness from the sun because it encounters the sun for a limited period of time per day. In this context, the solar tracking system is the best alternative to increase the efficiency of the photovoltaic (PV) panel. Solar trackers move the payload toward the sun throughout the day. In this paper, we suggested a Light Dependent Resistor (LDR) based dual-axis solar tracking system to increase tracking movements as well as PV energy production by leveraging the benefits of sun radiation.
Asymmetric Group Message Franking: Definitions and Constructions

As online group communication scenarios become more and more common these years, malicious or unpleasant messages are much easier to spread on the internet. Message franking is a crucial cryptographic mechanism designed for content moderation in online end-to-end messaging systems, allowing the receiver of a malicious message to report the message to the moderator. Unfortunately, the existing message franking schemes only consider 1-1 communication scenarios. In this paper, we systematically explore message franking in group communication scenarios. We introduce the notion of asymmetric group message franking (AGMF), and formalize its security requirements. Then, we provide a framework of constructing AGMF from a new primitive, called . We also give a construction of based on the DDH assumption. Plugging the concrete scheme into our AGMF framework, we obtain a DDH-based AGMF scheme, which supports message franking in group communication scenarios.
Mortality Rate Prediction for Patients Admitted to Intensive Care Unit Based on APACHE IV

One of the important units of the hospital is the intensive care unit, which provides specialized services to patients with acute conditions. In this paper, we introduce an instrument that can improve ICU services to patients, differentiate critical patients, optimize ICU beds, and examine the severity of their disease. To predict the mortality rate, Physiont's database for computing in cardiology challenge (CinC) was used. The survival rate of patients hospitalized in the ICU was 85.33% out of 300. Among the patients, the acute physiology and chronic health evaluation (APACHE) IV scores ranged from 18 to 90, averaging 57.43±18.6. Using the APACHE IV tool, the predicted mortality rate was 24.19%, and the observed mortality rate was 35.67%. Calculations indicated that the average score of APACHE IV for the survivors was 46.84, and for the non-survivors, it was 69.35. As a result, APACHE IV proved to be a valuable tool for nurses and doctors to predict the future conditions of patients and how they will respond to the treatment process.
A stylometric study of two translators of Hwang Sun-won's short stories.

This study explores the possibility that translators who have translated many works may develop their own translation styles rather than simply following the stylistic features of the source text. By examining the translation of Hwang Sun-won's concise literary style by two different translators, this study aims to identify the presence and aspects of their stylistic choices. An exploratory and stylometric approach was used to analyze a parallel corpus of 29 short stories by Hwang Sun-won and their translations by Bruce and Ju-chan Fulton and Edward W. Poitras, along with a corresponding reference corpus, at the lexical, syntactic, and textual levels, using stylistic markers such as STTR, average sentence length, syntactic complexity, readability, and document similarity. The results indicate that both the author's and the translators' styles contribute to the stylistic formation of translated texts, and the degree to which a translator's unique style is evident varies depending on the stylistic marker and the translator. These findings highlight the significant influence of stylistic choices made by translators on the final translated text, and emphasize the importance of considering both author's and translator's styles in translation production and analysis.
A mixed Harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the Harris’ hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris’ hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy.
Another scale-guided parallel transformer for image aesthetic assessment

Image aesthetic assessment (IAA) is a challenging task in computer vision fields, which aims to automatically evaluate image beauty by simulating human perception on image aesthetic. With the development of deep learning, although convolutional neural network (CNN)-based IAA approaches have achieved extraordinary progress, CNN experiences difficulty to capture long-distance relationships among visual elements. There is a strong correlation between image layout and image semantic information for image aesthetic. In order to solve this problem, an another scale-guided parallel transformer is proposed, including a multiscale local feature extractor (ME), a feature projection (FP), and an another scale-guided parallel feature fusion transformer (AST). The ME captures primary local features with classic ResNet at multiple scales. The FP performs dimension transformation on feature maps for each scale, which can obtain feature token and aesthetic token. The AST with two parallel transformer encoders is exploited to highlight the significant regions in the holistic image, in which the feature tokens and the aesthetic token from another scale are grouped together to obtain interscale guidance. The final score distribution is achieved by weighting multiple aesthetic tokens with learnable parameters for unified aesthetics assessment. Extensive experiments on two public datasets, including aesthetic visual analysis and aesthetics and attributes database, demonstrate that the proposed method outperforms the state-of-the-art methods across three different tasks.
Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Although the current face de-recognition algorithm can effectively protect the user's identity, it will change the original attributes of the image, destroy the visual effect, or change the characteristics of the image to varying degrees.Traditional methods such as pixelation and occlusion can effectively realize face de-recognition, but the processed image cannot be used for basic processing such as face statistics and expression recognition, which greatly destroys the usability of the image in the field of online new media.In order to preserve more attributes of the original image and reduce the changes of facial skin color and texture features caused by the above de-recognition algorithm's operation on the whole facial area, this paper proposes a privacy protection model of face image based on generative adversarial network and facial feature analysis.Our model realizes face recognition based on changing the original image as little as possible.Specifically, we identify and extract features from fixed parts in face images, including eyes, nose, mouth, eyebrows, and cheeks.Then, we designed a face privacy recognition and protection model based on conditional generative adversarial networks.We verify our proposed method on two most commonly used face datasets in face research, namely CelebFaces Attributes and Labeled Faces in the Wild, and the results show that our proposed method achieve better performance compared with other face privacy protection methods.
A Survey on Automated Student Attendance Management System Using Face Recognition

The old way of controlling attendance is obviously a time-consuming one that makes it tough for the person in charge to keep track of students' attendance.A more practical biometric student attendance system is required as we transition from manual to automated labour, which will lessen these challenges.The issue of manual labour can be solved using a variety of biometric approaches.Face recognition is projected to be employed in the student attendance system since it is seen to be the finest biometric method.This study's major goal is to comprehend the work that has already been done on a facial recognition-based student attendance system.The current study performed a literature review of earlier works based on face recognition-based student attendance management systems.The methods, strategies, and difficulties of face recognition are described in this work.Regarding research based on facial recognition, there are several difficulties.This research suggested a camera-based student attendance system that used facial recognition technology to address this problem.
Fuzzy Delphi Method (FDM) to Identify Elements, Constructs, and Items for Designing And Developing Contents for Pre-Braille Skills Module

On the basis of the discrepancy model, a need analysis questionnaire was distributed to 92 randomly selected special education teachers in Malaysia to determine if they required a specialised module for early braille learning lessons. The results of the survey indicate that teachers strongly agree that they need a guided module with detailed instructions and video demonstrations in teaching early braille lessons. Fuzzy Delphi Method (FDM) was chosen to determine the elements, constructs, and items required to design and develop the contents for Pre-Braille Skills Module. The Content Assessment of the Pre-Braille Skills Module questionnaire is used to collect data from seven authorised subject matter experts. The chosen elements, constructs, and items must satisfy three criteria of triangular fuzzy numbers: threshold value (d) ≤ 0.2, percentage of experts in agreement &gt; 75%, and fuzzy score with a minimum value of 0.5. The data analysis was conducted using an Excel template, called FDM
Selecting Tourism Site Using 6 As Tourism Destinations Framework Based Multi-Criteria Recommender System

Batu City is a place with many types of tourism and had many tourists in 2019. However, there was an imbalance of tourist attractions visited from the total number. Tourists are only fixated on famous tourist spots. Therefore, a recommendation system is needed that can provide recommendations for tourists. In this study, we use the Multi-Criteria Recommender System (MCRS) method based on the rating value between users to obtain recommendations from the system regarding selecting tourist destinations. The authors use the 6 As Tourism Destinations (6AsTD) framework for user assessment criteria in this study. The framework consists of six indicators that assess the success of tourism destinations, including attractions, accessibility, amenities, support services, activities, and available packages. The six components are considered the key to the success of a tourist destination under the marketing approach. This study aimed to obtain a recommendation system for selecting tourist destinations using the multi-criteria concept based on the 6AsTD framework. Based on the experimental results, the proposed method has an accuracy rate of up to 72%.
Attribute Relevance

A random forest study can help the investigator determine the degree of relevance of some attribute set that is under scrutiny, thus leaving the investigative study with a greater degree of insight into where ultimately that compass actually points towards. This chapter performs a random forest study with the data sets that were derived in previous chapters, and they are leveraged so that under a supervised learning model a degree of relevance over the attribute set may be ascertained. By following this process, it becomes feasible to determine the similitude that may exist with additional bodies of work and to ultimately be able to ascertain a goodness of fit for posterior models. This process of affirmation helps to guide the discussion to an ultimate state of truth, which is what is the only matter of concern for the scientist.
Case Study 2

The ability to integrate micro-distributed applications into a content management system such as WordPress demonstrates the flexibility that is afforded through MDAs. In this case study, the challenge is undertaken to define the requirements that would be needed in order to create a restaurant table reservation system that would integrate directly into WordPress, thus giving the solution an immediate benefit to existing restaurant websites that utilize the WordPress engine. This case study illustrates the leverage that may be created for the author's of MDAs such as the one undertaken here whereby the benefit of indexing becomes immediately afforded to these creators and whereby the dynamic lends itself to help elevate the status of the authoring domain with the search purveyors. The solution presented here demonstrates a seamless integration that affords a consistent experience to the client, a benefit from current practice.
A New Perspective for Multivariate Time Series Decision Making through a Nested Computational Approach Using Type-2 Fuzzy Integration

The integration of key indicators from the results of the analysis of time series represents a constant challenge within organizations; this could be mainly due to the need to establish the belonging of each indicator within a process, geographic region or category. This paper thus illustrates how both primary and secondary indicators are relevant for decision making, and why they need to be integrated by making new final fuzzy indicators. Thus, our proposal consists of a type-2 fuzzy integration of multivariate time series, such as OECD country risk classification, inflation, population and gross national income (GNI) by using multiple type-1 fuzzy inference systems to perform time series classification tasks. Our contribution consists of the proposal to integrate multiple nested type-1 fuzzy inference systems using a type-2 fuzzy integration. Simulation results show the advantages of using the proposed method for the fuzzy classification of multiple time series. This is done in order so the user can have tools that allow them to understand the environment and generate comparative analyses of multiple information sources, and finally use it during the process prior to decision making considering the main advantage of modeling the inherent uncertainty.
Echo State Network-Based Adaptive Event-Triggered Control for Stochastic Nonaffine Systems with Actuator Hysteresis

This paper studies the problem of the event-triggered control of nonaffine stochastic nonlinear systems with actuator hysteresis. The echo state network (ESN) is introduced to approximate an unknown nonlinear function. The command filtering technology is used to avoid the derivation of the virtual controller in the controller design process and tries to solve the problem of complexity explosion in the traditional method. Based on Lyapunov’s finite-time stability theory, the proposed method verifies the stability of non-affine stochastic nonlinear systems. It is proved that the proposed controller method can guarantee that all of the signals in the closed-loop system are bounded, and the tracking error can converge to a minimal neighborhood of zero even if there exists an actuator hysteresis. The effectiveness of the proposed method is demonstrated by the simulation example. The simulation results show that the proposed method is effective.