Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI PROGRESSIVE WEB APPLICATION PADA SISTEM INFORMASI PASAR LELANG KOMODITI PERTANIAN

Technology always has an impact on how products and services are designed. In recent years, there has been unprecedented growth in the web field. Therefore, it requires fast, reliable, attractive development and powerful applications. One of them is a product from Google, namely google sheets. The results of the data can be presented in full either in the form of sheets or graphic information, so that managers or admins do not need to process anymore and later will be able to be monitored on a web-based app system that uses Progressive Web Application (PWA) technology. In the development of information needs, it turns out that information needs have become very complex both online and offline, even today's technology can combine the transaction process on the local information system synchronized to the information system and or online data. The implementation of Progressive Web Application (PWA) makes it possible to distribute information more easily. The results of this research obtained modeling, designing and implementing local applications with data synchronized to google sheet media, data on google sheets will be accessed by different applications online using Progressive Web Application (PWA). it is also proven that with the implementation of Progressive Web Application (PWA), data after being uploaded and cached can still be accessed even if the internet communication line is interrupted. Because the application will always read the cache that has contained data as long as it is not synchronized data.
DeepQuic

In the complex 5G network environment, the number of multiple access devices increases sharply, which requires a more efficient multi-path scheduling algorithm. However, traditional multi-path heuristic algorithms can only work in static scenarios without time-varying path conditions due to their poor robustness. In this paper, we propose a learning-based multi-path scheduling algorithm, DeepQuic, to determine the multi-path scheduling for dynamic and heterogeneous 5G networks. DeepQuic works upon the multi-path QUIC (MPQUIC) protocol and focuses on increasing the transmission goodput of signaling in the 5G core network.
A deep regression convolutional neural network using whole image-based inferencing for dynamic visual crowd estimation

As intelligent surveillance system applications become ubiquitous, automated crowd counting solutions must be made continually faster and accurate. This paper presents an improved convolutional neural network (CNN) architecture for accurate visual crowd counting in crowd images. Multi-column convolutional neural network (MCNN) is widely used in previous works to predict the density map for visual crowd counting. However, this method has limitations in predicting a quality density map. Instead, the proposed model is architected using powerful CNN layers, dense layers, and one regressor node with whole image-based inference. Therefore, it is less computationally intensive and inference speed can be increased. Tested on the mall dataset, the proposed model achieved 2.01 mean absolute error and 8.53 mean square error. Moreover, benchmarking on different CNN architectures has been conducted. The proposed model shows promising counting accuracy and reasonable inference speed against the existing state-of-art approaches.
Earth and Space Science Open Archive Takes a Big Step Forward

The ESS Open Archive is moving to a new publishing platform where it will continue to accelerate the open discovery of Earth and space science research.
SEIR Model Analysis for The Spread of Covid-19 Disease Post New Normal Using Euler Method and Matlab-Assisted Heun Method

In early 2020 Corona Virus Disease 2019 (COVID-19) was declared by WHO as a pandemic and the Government of Indonesia based on Presidential Decree Number 11 of 2020 concerning the Determination of Public Health Emergency of Corona Virus Disease 2019 (COVID -19).On this basis, this study aims to find out the solution to the numerical solution of the SEIR epidemic on the rate of Coronavirus Disease (COVID-19) by using the Euler method and the Heun method and to find out the design of a mathematics lesson plan in high school that is in accordance with the concept of the numerical method.In analyzing the spread of the Covid-19 disease after the new normal, a mathematical model of the Covid-19 disease was developed using the SEIR type with the stages of Literature Study, Mathematical Modeling, Model Analysis, Simulation and Drawing conclusions.The target to be achieved in this research is to produce mathematical modeling from the results of the analysis that carried out, reports can be confirmed and report results can be published in international journals.This research will produce TKT types of social humanities and education with the results of the status of R&D results are important and significant for supporting decisions and policies.
Utilization of E-Voting as A Medium for Decisions Making on The Use of Village Funds

Each region in Indonesia has advantages and disadvantages, such as location, facilities, and infrastructure. These advantages and disadvantages give rise to social inequalities, especially in rural areas. To minimize social inequality, the government provides village funds that can be used to build facilities and infrastructure, training, and tourism development through Village-Owned Enterprises (BUMDes). The use of village fund assistance is discussed in village council meetings with the community. Some of the obstacles faced in the deliberations are the minimal attendance of participants, less than optimal participants getting information and giving opinions, and the corona virus-19 pandemic. Public concern about the COVID-19 pandemic has turned face-to-face meetings into virtual ones. In this study, public opinion obtained from village e-voting is processed using TF-IDF which aims to give weight to words as a reference in determining development priorities and empowerment needed by the community. After the system is completed, the system is tested with three characteristics of ISO 9126: usability, efficiency, and functional test. The results obtained from testing ISO 9126 are 86.5% for usability, 80% for efficiency, and 75.5% for functionality. TF-IDF generates requests for empowerment, improved facilities and infrastructure, and education. In the word cloud, empowerment becomes the most dominant, depicting it as the biggest. The results showed that the community's priorities and the results of the system were running well.
Lake Peigneur

In this case study, we look at what happens when two large industrial systems collide in an unexpected way. In this event, an oil drilling company and a coal mining company accidentally ended up trying to use the same block of physical space to carry out their operations. This resulted in the drainage of a large freshwater lake and the destruction of a salt mine. It is an interesting event because it highlights how catastrophic these “intersystemic” events can be. In other situations, intersystemic trade-offs may need to occur, but these are often more difficult than trade-offs that occur within systems because it is often unclear which system is responsible for managing them.
Generation of realistic simulated B-mode image texture with a GAN

The intima-media complex of the common carotid artery is considered the sentinel of a silent killer disease called atherosclerosis. Morphological biomarkers such as the intima-media thickness are already exploitable, but dynamic biomarkers, which reflect tissue deformation over the cardiac cycle, remain to be validated. Recent motion estimation methods seek to quantify compression, shear, and elongation coefficients, but their clinical applicability has not yet been well defined, and their actual accuracy is difficult to assess due to the absence of ground truth. This lack of reference also is the main limitation to explore fully supervised deep learning methods that have shown great potential in other applications. With this in mind, we propose a simulation pipeline to produce realistic in silico sequences, by combining a physics-based simulator with a post-processing based on a generative adversarial network.
Pelatihan Penggunaan Aplikasi Augmented Reality (AR) untuk Meningkatkan Literasi Digital Guru di SMP Negeri 3 BP Peliung

&lt;p class="8Abstractcontent"&gt;&lt;span lang="EN-US"&gt;In 2019 SMP Negeri 3 BP Peliung received assistance in the amount of 63 Gadget Units (Tablets) of the Evercross brand from the government with the aim that teachers can use them in learning. However, it has not been used properly. This is because the internet network at the school is not supported and the teacher's digital literacy is very low. The solution to this problem is to increase the ability of teachers in digital literacy in the use of Gadgets (Tablets) for learning media both offline and online by implementing Augmented Reality (AR) to optimize Gadgets (tablets) as learning media in the form of training and mentoring activities. The result of this training activity is that there is an increase in the understanding of the importance of teacher ICT literacy and understanding of AR as an innovative learning medium by 95.6%. Increased Teacher Digital Literacy by 87% and teacher skills in using AR by 78.2%.&lt;/span&gt;&lt;/p&gt;
Distant Supervision For Chinese Semantic Role Labeling

Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous works on SRL are mostly based on public datasets. However, existing public SRL datasets have limited annotated data, which restricts the generalization performance of the model and their downstream applications. Manually labeling data is time-consuming and labor-intensive. Inspired by the task of distant supervision for relation extraction, this paper brings in external language knowledge: collocation table and semantic dependency data, and finally obtained 82,182 high-quality synthetic data containing 319,302 predicate-argument-role pairs based on chunk dependency parsing and syntactic rules, and divided semantic roles into 27 types, the accuracy of the synthetic data is 89.33% by manual judgement, which provides new data and ideas for Chinese semantic role labeling tasks. Based on this data, use bert [1] to build a semantic role labeling model to further examine data quality and generalization performance.
A TWN Inspired Speaker Verification Processor with Hardware-friendly Weight Quantization

Speaker verification (SV) is not only a convenient biometric recognition technology but also an important method to ensure information security. Since SV systems are often deployed in mobile terminals, this places a higher demand on the trade-off between ensuring recognition accuracy and reducing system power consumption. Thus, this paper proposes an implementation of a speaker verification system based on a ternary weight network (TWN). First, we design a TWN structure for the SV system. Then a weight quantization scheme to reduce hardware storage overhead is adopted. After that, the hardware of the SV system is designed and simulated. The recognition accuracy of the proposed TWN is tested to be 83.3%@5dB, 87.9%@15dB, and 93.1%@clean, respectively. Using an industry of 22nm ULL process, the overall power consumption of the system is 16.3µW.
Structure and function

This chapter will draw together many of the concepts that have been introduced earlier in the book with the aim of presenting a revised and updated Viable Systems Model (rVSM). The rVSM is based on Stafford Beer's original VSM but is updated to reflect new research in neuroscience, particularly connectomics, ACT-R, algorithmic decision-making, two-mode decision making, and levels of performance. It also includes elements of the Cynefin framework. This chapter also goes into more detail about how the various parts of the structure will function under normal and abnormal conditions. It lays the groundwork of the three chapters that follow, which go deeper into the dynamics.
The rise of blockchain technology in healthcare for the preservation of patient-centric health records

Distributed systems are becoming more popular because of the advantages of using the benefits of blockchain technology including data authenticity, data privacy, data transparency, data scaling, and digital assets. Because of the digitization of physical documents, there has been a rise in the amount of healthcare data generated from a variety of sources, including hospital records, radiographic pictures, and sensors. That need to be collected and analyzed. Some of the information contained in the various databases pertaining to patients always remain confidential because of the healthcare record privacy. Blockchain technology, in contrast to more traditional approaches, permits the direct peer-to-peer exchange of electronic information stored in decentralized and distributed systems as a sequence of blocks or blockchain. Therefore, the implementation of blockchain technology in the healthcare sector is widely considered an efficient technique to ensure enhanced security in healthcare-related applications. Immutability, scalability, privacy built-in, and data traceability features of blockchain technology can be utilized to monitor the distribution of medications, drug delivery management, and appropriate supply chain patterns. Besides advancements, there are still various challenging issues, as blockchain technology has its own distinct limitations and constraints, such as mining incentives, mining threats, and key management. In this chapter, we provide an overview of the applications of blockchain technology in the healthcare industry. Particularly, healthcare data management, along with the exchange of medical records sharing, images, and reports, is the focus of the study. Finally, advantages and disadvantages are highlighted in order to help researchers in the field.
Modulation Recognition of BPSK/QPSK Signals based on Features in the Graph Domain

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR).Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study.First, the power spectrum of the squared candidate signal is truncated by a rectangular window.Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction.Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals.Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV).Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity.As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.
Estimation of Motion Sickness in Automated Vehicles using Stereoscopic Visual Simulation

Automation of driving leads to decrease in driver agency, and there are concerns about motion sickness in automated vehicles. The automated driving agencies are closely related to virtual reality technology, which has been confirmed in relation to simulator sickness. Such motion sickness has a similar mechanism as sensory conflict. In this study, we investigated the use of deep learning for predicting motion. We conducted experiments using an actual vehicle and a stereoscopic image simulation. For each experiment, we predicted the occurrences of motion sickness by comparing the data from the stereoscopic simulation to an experiment with actual vehicles. Based on the results of the motion sickness prediction, we were able to extend the data on a stereoscopic simulation in improving the accuracy of predicting motion sickness in an actual vehicle. Through the performance of stereoscopic visual simulation, it is considered possible to utilize the data in deep learning.
Emotion-aware Task Scheduling for Au-tonomous Vehicles in Software-defined Edge Networks

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry.Autonomous driving networks generate many intensive and delay-sensitive computing tasks.The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks.In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks.By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm.Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue.The algorithm uses task sentiment as a performance indicator to measure task scheduling.Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.
Topic selecting approach of academic journals based on research focus

The data of scientific research is important for decision making of sci-tech journals. At present more and more journals attach great importance to their impacts. As the data expands and artificial intelligence develops, it is feasible to help select topic for journals. In the paper we propose a topic selecting framework based on the research focus, which includes analysis of both research focus and paper topics, model training and topic recommendation. Compared with deep neural network, the proposed method could be more accurate with various periods and time intervals. We also predict different models for different kinds of paper. In this way, the model is more adaptable. The experiments demonstrate that our method is 69.8% better than benchmark algorithm in accuracy rate.
Correction: Song et al. New Results on Robust Synchronization for Memristive Neural Networks with Fractional Derivatives via Linear Matrix Inequality. Fractal Fract. 2022, 6, 585

In the original publication, there were two mistakes in Figure 1 and Figure 2, as published [...]
PELATIHAN KONVERSI SKRIPSI MENJADI ARTIKEL ILMIAH DALAM MENINGKATKAN KEMAMPUAN DAN MINAT MENULIS ARTIKEL MAHASISWA

The Community Partnership Program partners are students of the Digital Business Study Program, Faculty of Economics and Digital Business, Megarezky University Makassar. The purpose of this study is to provide solutions to the problems of: (1) the lack of students ability to write scientific articles, (2) the lack of student interest in writing scientific articles (3) the obligation of students to make scientific articles as one of the requirements for completing the thesis/final assignment. The external goal is that students can make scientific articles well and structured. The methods used in this research are lectures, discussions, and training/practice. The results achieved are (1) partners have knowledge and understanding of scientific articles, (2) partners have skills to convert thesis/final project to scientific articles that well and structured.
The Ultimate Transistor Timeline: The transistor's amazing evolution from point contacts to quantum tunnels

EVEN AS THE INITIAL sales receipts for the first transistors to hit the market were being tallied up in 1948, the next generation of transistors had already been invented (see “The First Transistor and How it Worked,” p. 24). Since then, engineers have reinvented the transistor over and over again, raiding condensed-matter physics for anything that might offer even the possibility of turning a small signal into a larger one.
A multi-wavelength approach to structured illumination

Optical measuring methods based on the principle of structured illumination frequently apply phase shift evaluation for optical spatial coding. While these approaches allow for efficient and high-precision coding in many applications, they reach their limits in particular when different signal components are superimposed, which may occur due to multiple reflections on the workpiece surface. Therefore, in the present contribution, a novel approach in the field of structured illumination is presented, which implements spatial coding by a pattern sequence with increasing spatial frequency, based on similar approaches from the field of absolute distance interferometry. In addition to basics of the method, a first experimental result is presented, which shows that the method allows for a high-quality separation of superimposed signals.
The paradox of safety within data-driven smart cities

Cities that have already embraced data-driven operations are reported to be experiencing a streak of safety on different dimensions as incidences are reported in real-time and appropriate actions taken in time. However, the aspect of data-driven smart cities being save is seen as a paradox, as there exists numerous concerns on how the security of data, and how it is exposing urban residents to compromise on their privacy, and the security of their private data. These concerns are particularly exacerbated by the active involvement of the private entities like the big tech companies that though they have huge capacities to handle massive data and has been always been contracted to offer data services for many local governments, their main agendas are profit maximization. This open a Pandora Box explored in this chapter, especially on whether such entities would guarantee the safety of both the urban fabrics as well as that of massive and sensitive data under their care.