Reviewerly/exHarmony huggingface dataset:
Analyzing the effects of split-screen animated transition on user experience in In-Vehicle Infotainment Systems

Enhancing the user experience of IVIS GUI has become increasingly important in the automotive industry. The size of IVIS screen is getting larger and larger, and as time goes by, in the process of using IVIS, because the size of IVIS can usually allow different applications to be displayed at the same time, the screen needs to display the appropriate split-screen animated transition during the user opening split screen status. In this study, we designed quantitative experiments on three basic transition effects: fade, slide, and zoom, and manipulated (9 combinations of transition effects + no transition effects) 10 combinations of transition effects at 9 different interval times in sequence, with 31 users participating in our experiments. The results of the study show that the most suitable combination of transition motion is AzoomBfade, AzoomBslide, AzoomBzoom, and when B delay 150 ms interval is the most suitable interval for split-screen transition effect, and this time should not exceed 400 ms for A application and 430 ms for B. This study provides relevant guidelines for the field of motion design to improve user experience.
Cognitive friction measurement: Interaction assessment of interface information in complex information systems

The level of Cognitive Load (CL), which is used to react to the level of cognitive friction, is typically utilized in the study of user cognitive friction. Complex Information System (CIS) interactions include both informational and operational interaction behaviors, and Information interaction is regarded as a critical aspect in defining the quality and efficacy of system functionality. TIn order to assess Cognitive Friction (CF) and inform ensuing interface design improvement, this paper integrates numerous CL assessment aspects. First, a conceptual framework for the method is built by examining measuring metric differences and integrating them through literature research, which is then combined with the user research process in design. Second, using this framework as a reference, a comparative experiment on user CF measurement was carried out by fusing eye tracking with the cause and assessment factors of CL. Ultimately, the user was provided with the CF index under the multidimensional dimension using the Fuzzy Comprehensive Evaluation (FCE). The findings demonstrate that the Multidimensional User Cognitive Friction Measuring (MUCFM) approach may more accurately capture the degree of CF in the information interaction of CIS interfaces. The viability of the comparative integration method is confirmed by eye tracking tests.
Pengembangan Media Pembelajaran Berbasis Video Animasi pada Pembelajaran Tematik Muatan Bahasa Indonesia Kelas III SD

This research intends to develop media products in teaching and learning activities in the form of animated videos made for thematic learning of Indonesian language content for class III elementary schools that meet the requirements to assist teaching and learning activities in the 2013 curriculum. This research method is an RnD method with the model is ADDIE. The research data was obtained by involving 23 grade III students of 28 Pontianak City Public Elementary School, 1 class teacher, and 2 expert validators consisting of 1 media expert and 1 language and material expert. Techniques for collecting data in this study are observation and questionnaires. The results after the research show that the process of developing instructional media in the form of animated videos in Indonesian language content thematic learning activities in class III Elementary Schools has taken 3 stages of the ADDIE model research from the 5 stages. The validity of animated video products produces very good criteria with an average score of 95% for the design aspect, 94.4% for the language aspect, and 92.5% for the material aspect. So this animated video-based learning media in the thematic learning of Indonesian language content for class III Elementary School is feasible to use and try out at the next stage.
IMPLEMENTASI PENYELESAIAN PERSAMAAN BURGERS DENGAN METODE BEDA HINGGA DALAM BAHASA PEMROGRAMAN JULIA

Burgers equation is a partial differential equation used to modelling several events related to fluids. Burgers equation was firstly introduced by Harry Bateman in 1915 and later studied by Johannes Martinus Burgers in 1948. This study discusses solving Burgers equations with finite difference method. In this study, several parameters have been known for the Burgers equation and several cases of partitions are used in finite difference method. The result shows that the more partitions used, the numerical result obtained will be closer to the exact values. In this study, calculations are numerically carried out with the help of Julia programming language.
Description of knowledge levels about the maintenance of dental and oral health with the occurrence of dental caries in class VI at SDN Sindangbarang 2

Knowledge is the result of knowing someone about an object or something through the senses they have. Knowledge is very influential in shaping one's actions, one of which is in maintaining dental and oral health. One of the most common dental and oral health problems experienced by the community is dental caries. Dental caries is damage to the hard tissues of the teeth starting from the tooth surface (pits, fissures and interproximal areas) to extending to the dentin and pulp and can occur on one or more tooth surfaces. The purpose of this study was to describe the level of knowledge about the maintenance of dental and oral health with the occurrence of dental caries in class VI students at SDN Sindangbarang 2. This research is a descriptive study, the sampling technique used was total sampling technique. The results of research conducted on 38 students showed that the level of knowledge about dental and oral health maintenance which was categorized as good was 17 people (44.7%), medium category was 12 people (31.6%), and less category was 9 people ( 23.7%) and the average dental caries index in students was 1.26. The conclusion of this study is that the majority of students have a good level of knowledge about dental and oral health maintenance with 17 people (44.7%) and the average dental caries index in the low category with a value of 1.26.
Konsep Rancang Bangun Smart Home Base Berbasis IOT untuk Skala Perumahan

World energy demand continues to increase by 45% until 2030 based on projections of the International Energy Agency (IEA) with an average increase of 1.6% per year. About 80% of these energy needs supplied by fossil fuels. This increase occurred due to population growth, industrialization, lifestyle changes, and the use of increasingly complex technology. The household sector is one of the sectors that require energy to meet daily needs. Researchers conducted a study of research results related to rooftop Solar Power Plants, the use of thermoelectric as a producer of electrical energy, and the use of septic tanks for household gas with digesters that have been published in Indonesian-language journals and proceedings. The concept of a smart home base is implemented assuming a simple housing design, in this design than a technical calculation of the feasibility of a Smart Home Base is carried out which considers efficient methods in the development and provision of energy independently based on renewable energy for cost savings and supports government programs abaout Indonesia Ketahanan Energi 2030. Renewable energy applied to the smart home base concept utilizes Solar Power Plants which are hybrid with Thermoelectric Generators and biogas to replace of the LPG stove. Utilization of IoT-based technology to optimize energy savings at home. The housing concept is assumed with a simple housing design with a daily energy consumption of 5,507 Wh and 1,336 watts of power. The solar system used is 450 Wp for as many as 8 units of solar panels, 12 v 200 Ah battery capacity of as many as 8 units of batteries, and inverters with a power of 2000 Watts.
MTAD RF: Multivariate Time-series Anomaly Detection based on Reconstruction and Forecast

Anomaly detection in multivariate time series is an important research direction, which helps to improve the security of industrial systems by detecting abnormally unreliable devices. Multivariate time series (MTS) anomalies not only need to pay attention to the time correlation between different time series but also need to consider the abnormal changes in the relationship between different variables. Once the influence relationship between two variables that influence each other is ignored, it will likely lead to false positives or false positives. At the same time, the degree of influence between different time series or different features is also inconsistent, just like what happened recently have radically different influences on the present. Furthermore, most of the existing models are weak in detecting no abnormality. To tackle these issues, in this paper, we propose a new model of multivariate time series anomaly detection based on reconstruction and forecast, named MTAD RF. First, we capture the temporal and feature correlations of MTS through two parallel GAT layers, and at the same time distinguish the influence degree between different time series or different features based on attention coefficients. Second, we leverage the generative power of VAE and the single-step forecast power of MLP to jointly detect known and unknown anomalies based on reconstructed and predicted models. Major practical implications of the proposed approach is missing. Finally, anomalies are detected and explained based on temporal and feature anomaly scores. Experiments demonstrate that our model outperforms current state-of-the-art methods on 4 real-world datasets, with an average F1 score of about 95% and excellent anomaly diagnostic ability.
HUBUNGAN DISIPLIN BELAJAR DENGAN HASIL BELAJAR MATEMATIKA

Maulia Isnaeni. 037117042. The Correlation of Learning Discipline with Mathematics Learning Outcomes. This study adopts a correlational approach. The subjects of this research was the fourth-grade students from Class IV A and Class IV B at Pasir Putih 01 Elementary School, Depok City, comprising a total of 72 students with a sample size of 42 students. The data collection took place during the first semester of the academic year 2021/2022. The research findings indicate a significant correlation between learning discipline with mathematics learning outcomes, as evidenced by the statistical analysis yielding a correlation coefficient (ɼxy) of 0.53. Furthermore, the coefficient of determination (KD) is calculated as 0.2809 or 28.09%. These results imply that 71.91% of the variance in mathematics learning outcomes is accounted for by factors other than study discipline. The regression equation Ŷ = 7.80 + 0.59 x indicates that an increase of one unit in study discipline is associated with a 0.59 unit increase in learning outcomes. In light of these findings, it can be concluded that there is a positive and significant corellation between study discipline with mathematics learning outcomes among fourth-grade students at Pasir Putih 01 Elementary School, Sawangan District, Depok City, during the first semester of the academic year 2021/2022.
Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function

Territorial Differential Meta-Evolution (TDME) is an efficient, versatile, and reliable algorithm for seeking all the global or desirable local optima of a multivariable function. It employs a progressive niching mechanism to optimize even challenging, highdimensional functions with multiple global optima and misleading local optima. This article introduces TDME and uses standard and novel benchmark problems to quantify its advantages over HillVallEA, which is the best-performing algorithm on the standard benchmark suite that has been used by all major multimodal optimization competitions since 2013. TDME matches HillVallEA on that benchmark suite and categorically outperforms it on a more comprehensive suite that better reflects the potential diversity of optimization problems. TDME achieves that performance without any problem-specific parameter tuning.
PENGARUH HASIL BELAJAR PENGANTAR AKUNTANSI TERHADAP TINGKAT PEMAHAMAN AKUNTANSI MAHASISWA FAKULTAS EKONOMIKA DAN BISNIS DI UNIVERSITAS KRISTEN SATYA WACANA DENGAN MOTIVASI BELAJAR SEBAGAI VARIABEL MODERASI

The purpose of this study is to determine the role of moderation of learning motivation in the influence of introductory accounting learning outcomes on the level of understanding of accounting. The sample taken was students of the Faculty of Economics and Business who had taken 10 courses of the Quality Earth Peg (PBMt). The results of data analysis are known that simultaneously all free variables together do not have a significant influence on bound variables, where the results of introductory accounting learning, learning motivation and interaction between the two together have no influence on the level of understanding of accounting. While partially obtained, (1) the results of learning introduction to accounting do not affect the level of understanding of accounting, (2) there is no influence of the results of learning introduction to accounting on the level of understanding of and there is no influence of learning motivation results on the level of understanding of accounting,(3) introductory accountinglearning outcomes,learning motivation and interaction between the two have no effect on the level of understanding of accounting.
Fortifying the Cloud: Unveiling the Next-Generation Security Model of AWS

Amazon Web Services (AWS) is a renowned provider of cloud computing services and infrastructure for businesses of all kinds. As organizations shift crucial data and apps to the cloud, security becomes paramount. This abstract describes AWS's security model and efforts to secure client data and prevent unauthorized access. The AWS security model protects client assets with many layers. AWS data centers include tight access controls, surveillance systems, and numerous levels of protection. AWS also meets worldwide security requirements and best practices. The shared responsibility approach governs AWS and customer security. AWS secures the infrastructure, while clients secure applications, data, and user access. AWS offers identity and access management, network security, encryption, and threat detection to help businesses meet security requirements. AWS security relies on identity and access management (IAM) to govern user access to AWS resources. IAM lets organizations set fine-grained access controls, implement multi-factor authentication, and link with identity systems, improving security. AWS protects data in transit and at rest with strong network security. Network access control lists (ACLs) and security groups provide clients granular control over inbound and outgoing traffic in Virtual Private Cloud (VPC) environments. AWS data is protected by several encryption solutions, including key management services for secure key storage and access. AWS monitors and logs to detect and mitigate security risks. AWS CloudTrail, Config, and Guard Duty let clients monitor and audit their AWS environments, detect configuration changes, and detect security breaches and criminal behavior. In conclusion, AWS security protects client data and applications. AWS secures cloud installations through physical, network, data, and identity and access management. Organizations must understand their shared obligations and use security services and features to secure their AWS resources.
Flood Management Strategy Based on Community Perception in Rajabasa Area, Bandar Lampung City

A flood is an event that occurs when an excessive volume of water flow submerges land. Bandar Lampung is one area that has the potential for flooding. Floods that occur in the Rajabasa area are caused by disturbances to the balance of nature due to human activities. In this case, this study aims to determine the coping strategy before the flood occurred in the Rajabasa area, to find out the coping strategy when a flood occurs in the Rajabasa area and the last is to find out the disaster management strategy after the flood in the Rajabasa area. The research method used was literature review such as journals and articles about flooding as a source of research carried out by collecting data and information about flooding and survey activities by distributing questionnaires to the public using the Google form as well as short and focused interviews with flood-affected communities. The results of this study indicate that the flood management strategy in the Rajabasa area of Bandar Lampung City, based on the community's point of view, is in the medium category, that is, the community already understands the coping strategy before the flood occurs, during the flood and after the flood occurs.
Effect of Smartphone-Assisted Jigsaw Cooperative Learning on Students’ Mathematics Self-Efficacy

Smartphones have become handy tools in the hands of 21st-century teenagers owing to their necessity for communication and socialization. These devices could therefore possess underlying benefits for mathematics education. This quasi-experimental study probed the consequence of smartphone-assisted Jigsaw cooperative learning on senior secondary school students’ mathematics self-efficacy with recourse to how gender and smartphone efficacy could moderate this effect. The study involved five research questions and hypotheses. Two schools from the Educational District IV, Lagos State were selected by purposive sampling technique from which five hundred and thirty four (534) students drawn from intact classes were assigned into experimental and control groups. Smartphone Assisted Learning Package (SALP) served as the intervention while a valid and reliable Mathematics Self-Efficacy Questionnaire (r=0.90) was employed for data collection. Statistical tools deployed for descriptive and inferential analysis include mean, standard deviation and Analysis of covariance (ANCOVA). Outcome of data analysis portrayed a momentous effect of treatment on students’ mathematics self-efficacy. Smartphone efficacy and gender were found to possess substantial influence on participants’ mathematics self-efficacy. These findings led to the recommendation that teachers should expose mathematics students to smartphone-assisted Jigsaw cooperative learning strategy to promote active learning and improve students’ mathematics self-efficacy.
A New Face Image Authentication Scheme based on Bicubic Interpolation

&nbsp;Nowadays, image manipulation algorithms are increasingly being used even by people who do not have any deep knowledge of technology, and one can easily find an application to manipulate face image for various purposes. After the development of image processing algorithms, the research community highlighted the need to develop a technology that detects image manipulation, but it did not highlight the recovery of the facial region after manipulation localization, which would be very useful in practical applications. In this paper, a new face image modification detection and recovery scheme based on image watermarking and bicubic interpolation algorithm is presented. Several experiments were conducted to evaluate the performance of the proposed scheme, which proved its effectiveness in generating high-quality watermarked images, detecting different types of manipulation, localizing the manipulated blocks in the face region, and restoring the face region with good visual quality. A comparison with the latest detection techniques demonstrates the superiority of the proposed scheme.
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models

With the advent of deep learning applications on edge devices, researchers actively try to optimize deep learning model deployment on low-power and restricted memory devices. There are established compression methods such as quantization, pruning, and architecture search that leverage commodity hardware. Apart from conventional compression algorithms, one may redesign the operations of deep learning models, leading to more efficient hardware implementation. To this end, we propose EuclidNet, an efficient computing method designed to be implemented on hardware that replaces multiplication, with squared difference. We show that EuclidNet is aligned with matrix multiplication and can be used as a measure of similarity in the case of convolutional layers. Furthermore, we show that under various transformations and noise scenarios, EuclidNet exhibits the same performance compared to the deep learning models designed with multiplication operations.
Small Missional Church as an alternative in the period of Church Decline in Korea

This article examines the causes and circumstances of the decline of Korean churches since the 2000s. After briefly sketching the history and critical events of the growth of the Korean church, it pays attention to one of the leading reasons people leave thechurch and a reason that the Korean church has lost its reliability in the view of much of Korean society. After that, the author introduces two phenomena in the process of the slow growth and decline of the Korean church: the phenomenon of “believing without belonging” and the increase in numbers of bi-vocational ministers. This is a time when the missional church has begun to emerge as a new type of church, different from the traditional church, and at the same time non-institutional churches have emerged as a missional approach to believing without belonging. Furthermore, the author argues that the small missional church is a helpful alternative and required paradigm in the context of the decline of the Korean church by using several case studies, including shared worship space, bi-vocational ministry, and local missional church.
Complex Text Detection Algorithm Based on Edge Attention Mechanism

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas.