Reviewerly/exHarmony huggingface dataset:
Keberagaman Kesadaran Belajar Sebagai Tolak Ukur Tingkat Motivasi Siswa PAUD dan SD di Kelurahan Setu

Education in Setu has been considered to have developed well, especially for Pendidikan Anak Usia Dini (PAUD) to Elementary School. However, the observation results showed that PAUD to Elementary School children still lacked awareness in learning. This article aimed to investigate and analyze the diversity of learning awareness as a measure of students’ motivation levels in Pendidikan Anak Usia Dini (PAUD) and Elementary School in Setu Subdistrict, South Tangerang City, Banten Province. Learning awareness is an affective, cognitive, and behavioral dimension that affects individual well-being in learning. This research was conducted on PAUD to Elementary School children with a total of 32 students, using a survey method with a questionnaire consisting of 18 written questions as a data collection technique. The collected data will be analyzed using descriptive analysis, which involves basic statistical calculations such as frequency, percentage, and average to summarize and explain the data obtained from the questionnaire. The results of this research are expected to provide a deep understanding of learning awareness among PAUD and Elementary School students in Setu Subdistrict, thus providing a foundation for improving student learning and well-being.
Special Issue on NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2023

We are very pleased to publish this Special Issue, NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2023, which includes three papers and one tutorial. The first paper, authored by Dhakal and Kunugi, presents the orientation changes that earthquake movements have caused in the accelerometers of a seafloor observation network for earthquakes and tsunamis along the Japan Trench (S-net). This study is expected to contribute to more accurate analyses of S-net data. The second and third papers are reports of research done on volcanoes. The paper by Tanada and Ueda presents the seismic activity of the Nasudake volcano through the use of NIED hypocenter data gathered from 1998 to 2022. Tanada and Ueda state that earthquakes have regularly occurred near the Chausudake lava dome and two craters and that, from the volcano disaster-prevention point of view, it is important to monitor the relationship between this seismic area and volcanic activity. The third paper, by Miyagi et al., reports on an experiment aimed at grasping the movement of climbers and sharing the level of disaster threat with climber-related organizations in real time. Their online visualization system for the movement of climbers helps to provide solutions to problems related to volcanic disaster prevention not only during disaster events but also in the periods between. The last paper is a tutorial by Hanashima and Usuda on “SIP4D-ZIP,” which is a standard for synthesizing and sharing common information from multiple organizations. This tutorial explains how to realize a versatile and automatic mutual data sharing and usage system for various kinds of necessary disaster information. In conjunction with the fourth 7-year mid/long term plan period 2016-2022, NIED has published seven special issues since 2017, including this 2023 issue. We believe that the articles in these seven issues can contribute to the advancement of science and technology for disaster risk reduction and resilience.
IMPACT OF IRRIGATION DEVELOPMENT ON SOCIO-ECONOMY AND REGIONAL DEVELOPMENT (CASE STUDY IN THE WAY TULUNG BUHO IRRIGATION AREA, TIYUH KIBANG YEKTI JAYA, LAMBU KIBANG DISTRICT, WEST TULANG BAWANG REGENCY)

Agricultural development aims to increase rice production to meet the food needs of the population. The development of irrigation networks is one of the efforts to increase rice production and create new rice fields carried out by the government, one of which is the Development of the Way Tulung Buho Irrigation Area, Tiyuh Kibang Yekti Jaya, Lambu Kibang District, West Tulang Bawang Regency. This study aims to examine the effect of irrigation development on agricultural, social and economic productivity, as well as the feasibility of irrigation development and development. This research was conducted by collecting data from all farmers who received Way Tulung Buho irrigation services in Tiyuh Kibang Yekti. The results showed that irrigation development had a positive and significant impact on increasing rice production, increasing the cropping index and being able to create new rice fields. The socio-economic impacts are in the form of the establishment of P3A organizations, increasing farmers' income and flooding problems that occur when irrigation is operating. Irrigation development is economically feasible and irrigation area development plans can be implemented.
David Lewis's сonvention and its contemporary perception

Aim to clarify the concept of social convention and the philosophical ideas associated with it as proposed by David Lewis and his contemporary critics, as well as to analyze the applicability of these ideas to the study of implicit rules of human behavior.&#x0D; Material and methods. We analyzed the works of D. Lewis "Convention: A Philosophical Study", "Languages and Language", as well as the works of contemporary authors who critically start from the concept of convention by Lewis in their research: R.G. Millikan, V. Zachnk, M. Matczak, I.F. Deviatko, B. Skyrms, B. Celano and others.&#x0D; Results. One of the main problems with understanding everyday behavior and speech as based on social convention by Lewis is the problem of determining the regularities of behavior. Lewis believes that conventions emerge from the preferences of some people based on the knowledge that others understand language and the world in general in the same way they do. But if each native speaker has in mind some own unique meaning, this does not prevent the appearance of an illusion of understanding, which in a sense leads to true understanding. It is the same for behavior: it is not very important how different participants of the interaction understand the rules of behavior, some result will still take place and everything will look as if the participants have assumed in advance according to what rules they themselves and their partners are going to act.&#x0D; Conclusion. The idea of conventionality can be used to explain the grammar of behavior, if supplemented with the understanding that real natural signs are always "ill defined". A realistic theory of conventions should assume that signaling systems, languages, ideas about possible situations and behaviors coincide only partially among representatives of the population in which the convention is maintained, which does not prevent either communication or other coordinated behavior.
SISTEM PAKAR DIAGNOSA JENIS KULIT WAJAH MENGGUNAKAN METODE FORDWARD CHAINING (STUDI KASUS : DITUJUKAN PADA KONSUMEN WARDAH)

The role of facial beauty experts is very important to identify the type of facial skin and provide solutions in the form of treatment recommendations using the right skincare products for each type of facial skin. The thing that is often complained of by the public when performing treatments is the small number of beauticians because not every region has a beauty clinic and the limited time the beautician doctors cause a long queue when going to a beauty clinic. From this, an expert system is needed to help provide solutions by building an expert system that can identify the type of facial skin by including treatment solutions. The Fordward Chaining method carries out tracking that starts with input information, and then tries to draw conclusions. From this research will produce a web-based application to diagnose skin types and solutions to any skin problems and display the name of the product recommended for treatment.
A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This includes the high costs of procurement and lack of backup techniques if communication with weather data services fail. These practical issues are not widely considered yet in the current literature. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Similar studies have shown satisfactory accuracies, but this study proposes a novel data preprocessing technique—cyclical features encoding—that we hypothesized would boost model accuracy significantly. We used ML techniques to predict power production in a 24-hour time horizon, using input data of the past 48 hours of power production. The Random Forest model offered the best results, with a Pearson Correlation Coefficient of 0.97 (11% higher than previous studies), Mean Absolute Error of 0.0266 (60% better than previous studies), and Root Mean Squared Error of 0.0773 (38% better than previous studies). These results are comparable to state-of-the-art weather models in the field. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.
Effects of different synthetic training data on real test data for semantic segmentation

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train. These huge datasets often are lacking in many fields such as specific weather events and climates. They also lack variety as most datasets are of city and street scenes as opposed to more rural scenes. Synthetic-to-real domain adaptation can be used to fill those gaps. Although there has been prior work on how to generate virtual data, in this paper we present insight on how features of the training data can affect the model and ultimately, the outcome. They can also provide insight on the dataset selection process. We hypothesized that different synthetic training data on real test data will affect the outcome of semantic segmentation. By testing real-world data on two iterations of a model trained by two separate virtual datasets using Python and deep learning models, we compared the results and analyzed the differences and similarities the datasets yielded for semantic segmentation. We unearthed critical insights that shed light on the dataset selection process, enabling researchers and practitioners to make more informed decisions when choosing the appropriate dataset type for semantic segmentation tasks. We contributed valuable findings, unveiling the limitations of substituting real datasets with virtual counterparts and offering guidance for dataset selection.
Blockchain databases: Encrypted for efficient and secure NoSQL key-store

Databases have revealed many vulnerabilities in which hackers can penetrate and gain access to the data. Blockchains have been proven useful to protect against fraud and data miners, exemplified in cryptocurrencies. In this paper, we propose a blockchain database framework as a non-relational database structure that integrates the properties of blockchains and databases. The independent variables when testing this database were the Append, Delete, Query, and Update functions in the database framework. We predicted that the Append, Query, and Update functions would have a logarithmic runtime and linear throughput; while the Delete function would have a constant runtime and throughput. The database was tested with about 150,000 operations with the server and client locally running. We observed the runtime was slightly positively correlated while the Delete was not positively correlated with the number of operations in the Append, Query, and Update functions. The throughput of the Append, Query, and Update functions were significantly negatively correlated with the number of operations. The blockchain database framework displayed end-to-end encryption and feasibility with this experiment. One possible application of the database framework is in the financial industry, as there is a one-to-one correspondence with user and transaction.
Solving the Schrödinger equation computationally using the Lanczos algorithm

The Schrödinger equation is a fundamental equation in quantum mechanics that describes the behavior of particles in terms of wave functions. These wave functions provide a probabilistic description of where the particle is likely to be found, making them a crucial tool for understanding how particles interact with their environment. Methods for solving the Schrödinger equation analytically tend to be very mathematically complex, leading us to look for other ways to solve the Schrödinger equation. Unfortunately, in most cases, the Schrödinger equation cannot be solved for an exact solution. There are methods, however, for approximate solutions. In this paper, we investigated the Lanczos algorithm, a computational method that can solve the 2D Schrödinger equation in cases where finding an analytical solution would not be feasible. We discretized the Schrödinger equation, then ran a Python program using the Lanczos algorithm to solve for the ground state wave function. We hypothesized that the Schrödinger equation could be solved computationally for the ground state wave function using the Lanczos algorithm. We found that this method efficiently solved the Schrödinger equation for complicated 2D potentials. Additionally, we verified the accuracy of the method by comparing the results with wave functions from problems with known, exact solutions.
Pengamanan Aplikasi Menggunakan Algoritma Camelia

&#x0D; Safeguarding Applications Using the Camelia Algorithm is a software that can assist in securing or locking an application file, where the file can no longer be opened by anyone except the owner of the file. In this research the locking and reopening process of the application file is performed using the Camelia Algorithm , So that it produces a software that can lock and open an application file.&#x0D;
A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. Furthermore, extenuating weather conditions such as rain, snow, and fog exponentially increase the likelihood of accidents by reducing visibility and increasing the time for detection. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison. To do so, the model employs multiple classical deep learning techniques in two separate sub-modules: a Visibility Correction Module (VCM) and an Object Detection Module (ODM). Firstly, the model employs image classification techniques and masking to identify environmental factors frame-by-frame within an image, and then uses a novel dimensionality reduction network to remove said effects. Next, corrected images are analyzed to classify and label objects within frames. The proposed algorithm achieved an average accuracy of 89.72%, and outperformed industry alternatives in mean accuracy and time for detection, demonstrating the validity and efficiency of utilizing dimensionality reduction to improve object detection.
Identifying Neural Networks that Implement a Simple Spatial Concept

Modern artificial neural networks have been remarkably successful in various applications, from speech recognition to computer vision. However, it remains less clear whether they can implement abstract concepts, which are essential to generalization and understanding. To address this problem, we investigated the above vs. below task, a simple concept-based task that honeybees can solve. We hypothesized that neural networks would successfully solve this task, and that performance would vary substantially between network architectures. Specifically, we predicted that the convolutional neural network (CNN), a prototypical architecture well known for its ability to classify objects accurately, would perform better than the single-layer and multi-layer perceptrons (SLP and MLP, respectively). In the first task (Experiment 1), a visual target was presented above or below a black bar; in the second (Experiment 2), a visual target was presented above or below a reference shape. We found that all networks achieved 100% testing accuracy on Experiment 1. In contrast, the networks’ accuracy differed substantially in Experiment 2. The SLP had only a 50% testing accuracy, and the CNN outperformed the MLP (98% vs. 81% accuracy, respectively). Further analysis of the connection weights and distances between shapes suggested that the MLP may not evaluate relative spatial relationships in Experiment 2. Instead, the network seemed to partition the image into upper and lower zones, which appears inconsistent with the concept of relative locations. These findings indicate different capacities of network architectures, offer insight into their mechanisms, and motivate work on how neural systems implement conceptual knowledge.
Rubik’s cube: What separates the fastest solvers from the rest?

The Rubik’s Cube is a 3D combination puzzle. Speedcubing is the activity of solving Rubik’s Cubes as fast as possible. In this paper, we identified and quantified key factors that enable some speedcubers to be faster than others. Knowledge of these factors could allow speedcubers to focus their practice deliberately into specific areas, accelerating their improvement. We hypothesized that a low fraction of pause times, low regrip frequency, and low move counts would show the strongest correlations with faster solves. To test this, we analyzed 69 solve videos frame by frame across a wide variety of ability levels, as well as survey data collected from 1,385 speedcubers. To our knowledge this study represents the most comprehensive factor study of speedcubing to date. We discovered that the following factors correlate most strongly with solve times: frequency of regrips and rotations, fraction of time spent pausing, cross move count, number of algorithms known, turn speed, duration of pause in transition between cross and first two layers (F2L) steps, and orientation of the last layer (OLL) and permutation of the last layer (PLL) recognition time. Based on our findings, we generated specific recommendations for how speedcubers of different ability levels might most efficiently improve their solve times.
Decreasing heart rate after consuming caffeine

In this study, we sought to answer the question “how can we decrease heart rate after drinking caffeine?” To find ways to help/boost the heart rate recovery after drinking caffeine. Hypotheses tested are taking a walk (1), deep breathing (2), and drinking water (3). After testing each hypothesis, we found that all methods result in a decrease in heart rate. However, only deep breathing was able to reduce heart rate while also decreasing side effects (dizziness, lightheadedness, etc). People who drank caffeine and faced either heart rate increase or side effects (dizziness/ lightheadedness, etc) could perform the methods on themselves to prevent either heart rate increase or side effects. Results of each method were different, and the recommended method is taking a walk since after applying, it decreased heart rate without side effects.
Design and implementation of a cryptographically secure electronic voting infrastructure

Cyber security is the application of technologies, processes, and controls to protect against attacks on confidentiality, integrity, and availability. Cryptography maintains confidentiality by securing communications from being intercepted, provides integrity by preventing unauthorized modification of data, and provides availability by allowing data to be transmitted securely. There is currently limited to no application of cryptographic controls at election sites in today's voting environment due to the use of legacy systems and paper systems that do not support the technology required for encryption. This paper proposes an electronic voting solution to mitigate risk through the design and implementation of a secure, electronic voting app and infrastructure. Here, we present evidence, using a thorough National Institute of Standards and Technology (NIST) risk assessment, that removing human interaction remediates vulnerabilities within today’s infrastructure and mitigates overall risk. We also extract multiple NIST Special Publication 800-53 family controls to analyze the vulnerabilities in today’s voting infrastructure. Using our proposed secure electronic infrastructure, we mitigate the risk inherent in today’s election environment, and we propose a model to secure our democracy and the future prospect of voting electronically.
The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. It plays a key role in biomedical analysis tasks like cell characterization, cancer cell identification, and gene expression measurement. There has been a recent proliferation of deep neural network-based object detection techniques for cell segmentation. However, previous literature does not suitably address the proper understanding of the different methods and the merits of the various neural architectures proposed. One key design point relates to the learning of segmentation masks and how neural methods go about detecting, segmenting, and extracting nuclei from unseen images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. We investigate whether for cell nuclei segmentation, learning the nuclei masks in parallel with boundary detection features of the image provides superior performance and significantly cleaner separation of nuclei from images. We further explore the trade-off between increased accuracy, achieved through more complex deep learning models, and the heavy requirements imposed on both computational resources and training times. We believe this paper establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.
Propagation of representation bias in machine learning

Transfer learning is an emerging paradigm in machine learning that involves reusing existing pretrained models to develop new machine learning applications. As machine learning grows in importance, keeping new models and applications clear of biases is of paramount importance. While previous works focus on the development of bias free models, they fail to address the mechanism of bias propagation in transfer learning. Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.