Reviewerly/exHarmony huggingface dataset:
A Mobile Mesh-Network for Control a Swarm of Objects

The purpose of the article is to develop an adaptive wireless mesh-network architecture for managing technological processes, robotic devices and other objects. The article describes the architecture of a wireless mesh-network. The concept of a wireless mesh-network is described. The mesh-network is self-organizing, adaptive, and to be work autonomously able. The presented wireless mesh node controller architecture is the basis for small control systems for many objects and a swarm of objects. The interaction of network nodes is carried out using several transceivers. Multiple transceivers allow for the separation of control traffic and data traffic on different channels. The protocol stack is optimized for the functioning of the mesh network. The topology of a mesh network can change dynamically. That is, network nodes can move in space without losing communication with each other. The current state of the network is updated with a period that depends on the rate of change of the network node in the space. The mesh network configuration strategy and algorithms allow you to effectively build a network topology and establish interaction between network nodes. There can be many objects in the network that can act as a relay. A repeater can be a network node and a special dedicated repeater node. The low cost of network nodes makes it possible to create a low-cost network for performing various functions of object control and data transmission. The network can be used to manage a swarm of objects. Proprietary software is used to increase the efficiency of forming routing tables taking into account cluster structures in a mesh network. The mesh network can be used to control IoT objects, robotic objects, as well as a variety of objects and control systems for various technological processes.
Research on Robust Digital Watermarking Based on Reversible Information Hiding

The development of modern Internet communication technology and the popularization of multimedia technology have brought convenience to the sharing and storage of multimedia information such as images, videos, and audio. However, at the same time, it has brought about the problem of copyright theft of multimedia information, causing serious information security risks. Digital watermarking technology embeds copyright information in multimedia information in an invisible way, which can effectively realize copyright protection and traceability of infringement. Aiming at the problem that the existing learning model-based methods cannot fully extract and fuse the features of carrier images and watermark images, a robust digital watermarking method based on reversible information hiding is proposed. First, a watermark embedding model based on reversible information hiding is established, and the features of the download volume image and the watermark image in different dimensions are fully extracted and fused to generate a dense image with excellent visual quality. Then, a watermark extraction model based on reversible information hiding is established, and a noise layer is added between the embedding and the extraction model, and the attacked dense image is input to the watermark extraction model to extract the watermark. Under the constraint of the loss function, the network model learns to embed watermark information in the area that is more robust to the attack and is not easy to cause visual quality degradation, so as to optimize the comprehensive performance of the method. Experimental results show that the proposed method effectively improves the imperceptibility and robustness.
On Some Observations of the Fundamental Laws of Nature

The article describes an observed natural phenomenon, consideration of which hypothetically improves the accuracy of natural processes modeling.
Robust Watermarking Algorithm based on DCT Two-dimensional Histogram Shifting

In order to solve the robustness problem of existing video watermarking algorithms, we propose a robust watermarking algorithm based on DCT coefficients. The algorithm groups the DCT coefficients generated by H.264 video coding according to the embedding efficiency and embeds the watermark by a reversible method to achieve watermark extraction at the decoding end and lossless recovery of the original video. The experimental results show that the proposed algorithm access great robustness, and the normalized correlation coefficient of each carrier video is above 0.949.
Historical Document Image Segmentation Combining Deep Learning and Gabor Features

Due to the idiosyncrasies of historical document images (HDI), growing attention over the last decades is being paid for proposing robust HDI analysis solutions. Many research studies have shown that Gabor filters are among the low-level descriptors that best characterize texture information in HDI. On the other side, deep neural networks (DNN) have been successfully used for HDI segmentation. As a consequence, we propose in this paper a HDI segmentation method that is based on combining Gabor features and DNN. The segmentation method focuses on classifying each document image pixel to either graphic, text or background. The novelty of the proposed method lies mainly in feeding a DNN with a Gabor filtered image (obtained by applying specific multichannel Gabor filters) instead of an original image as input. The proposed method is decomposed into three steps: a) filtered image generation using Gabor filters, b) feature learning with stacked autoencoder, and c) image segmentation with 2D U-Net. In order to evaluate its performance, experiments are conducted using two different datasets. The results are reported and compared with those of a recent state-of-the-art method.
Framework for Digital Academic Records Management Using Blockchain Technology

The application of blockchain to centralized systems is one of the trends of improving the privacy, security, and transparency of the centralized approach. Blockchain is especially meaningful in trustless systems, where stakeholders are susceptible to tampering and consensus is required. In this work, we propose a practical framework for academic records management so that stakeholders can exploit records effectively. Based on the framework, we also conduct feasibility testing. The experiment is conducted at the computer lab I1.501 in the Thu Dau Mot University (TDMU). The results show that it is possible to apply blockchain to the academic records management system. The source code is available in Github with an open source license to make sure that institute can freely use this framework or modify to make it more suitable ( https://github.com/vanhuudhsp/AcademicRecordsBlockChain ).
PENGARUH MODEL STUDENT FACILITATOR AND EXPLAINING (SFAE) TERHADAP KEMAMPUAN BERBICARA PADA MATA PELAJARAN BAHASA INDONESIA KELAS V SD NEGERI 88 PALEMBANG

This study aims to determine and describe whether or not there is a significant effect of the Student Facilitator And Explaining learning model on students' speaking skills in grade V Indonesian language learning at SD Negeri 88 Palembang. The research method used in this study is the experimental method of the Pretest-Posttest Control Group Design model by dividing the sample into two groups, namely the experimental group and the control group. The experimental group was class V.1 students and the control group was class V.2 students. The data collection technique used in this study is in the form of tests, the tests used in this study are the initial test and the final test in the form of an assessment component in speaking ability which assesses 5 aspects of assessment. The results of this study showed that the pretest (initial test) in the experimental class had an average of 62.45 with a sufficient category, after being given treatment using the SFAE model the results of the final test (posttest) in the experimental class showed a value of 80.00 with a good category. This shows that. obtained sig value (2-tailed) 0.000 &lt;0.05. Thus it can be said that Ho is rejected and Ha is accepted, meaning that there is a significant effect of the Student Facilitator And Explaining learning model on student speaking skills in grade V Indonesian language learning at SD Negeri 88 Palembang.
Drug Storage System based on Fuzzy Logic and the Internet of Things

Health-related issues are a top priority for anyone in the world, and if there is any issue related to health, then solutions should be found as soon as possible. Medicine is one of the most important causes of recovery, so storing medicines of the highest possible quality is necessary. Storing medicines is very important for the hospital because they are used for hormones, viruses, and ointments, which are of great value to the hospital's sustainability and must always be preserved. This study designed and implemented a system to continuously control and monitor sensitive atmospheric information for drug storage, such as temperature, humidity, and light exposure. The system starts by reading the environment sensors around the drug store by the microcontroller. Then the microcontroller passes the data to the fuzzy logic. Mamdani-type fuzzy control is implemented to control and monitor devices that are used on fuzzy rules. The system has the ability to send an alarm when any of the temperature and humidity-related parameters are higher or lower than normal. The data collected using this system is sent via two methods: the first is the website platform, and the second is GSM notification. Drug storage is designed and implemented using a microcontroller and IoT sensors. During the system analysis phase, the SWOT method was utilized to obtain user perspectives and preferences, which were then incorporated into the system output specifications for the development phase. This approach helped to identify the necessary system requirements. During the implementation phase of the system and to measure the capabilities of the system, the technique of measuring the system usability scale was used, with the contribution of 22 users of the system, and the percentage recorded as general satisfaction with the system was 91%. The new system improves the overall quality of drug storage, reduces the risk of drug deterioration to patients' health, and reduces the incidence of adverse events associated with drug mismanagement.
Analysis of Physical and Mechanical Properties of Rice Husk-based Particle Board

This study was conducted to determine the physical and mechanical properties of particleboard. Manufacture of particleboard utilizes raw materials of rice husk with epoxy resin. The composition variations of rice husk : Epoxy are, 82:18, 77:23, 72:28. The rice husk was soaked in 5% NaOH solution for 1.5 hours, then smoothed and filtered with a size of 20 mesh. After preparation, the rice husk and epoxy were mixed with their respective compositions. Then the hot forging process was carried out with a pressure of 25 kg/cm2 at a temperature of 140° C for 8 minutes. The results of density test was found from 0.703 - 0.712 g/cm3, the water absorption value from 7.12 % - 8.75% and the thickness expansion value from 11.5% - 19.4%. The compressive strength (MOR) values range from 56.105 -82.63 kgf/cm3 and the flexural strength (MOE) values range from 8695 kg/cm2 - 10470.49 kg/cm2.
Hate Speech and Institutionalized Society

This article analyzes the related histories of institutionalization and hate speech in order to address the issue of hate speech as an accumulated result of structural violence, often stimulated by the state. Institutionalization, as an analytic framework, refers to a social mechanism that produces “unproductive” and “abnormal” citizens, or non-citizens, and incarcerates them both within and outside of society, all in the name of protecting the “society”. Individuals who can’t get their social position in legal or family structures found themselves outside of society's protective structures. I criticize how the contemporary tendency confines the criticism of hate speech to the rhetoric of “freedom of speech” and conflict between individuals and how it obscures, and thus perpetuates, the structural violence that facilitates hate speech. By addressing the concept of “institutionalization” as an analytic framework, this article highlights the linkage between institutionalized society, state power, and hate speech. In doing so, I argue that the rethinking of “human rights” can be a useful method to cut off the structural reproduction of hate speech.
Neutrosophic MCDM Model for Assessment Factors of Wearable Technological Devices to Reduce Risks and Increase Safety: Case Study in Education

This study investigates the feasibility of using wearable technologies in education to improve safety. This article explores how wearables may be used to improve school safety and wellness, as well as their advantages, disadvantages, and future potential. The article covers a wide range of wearable gadgets and their respective safety-related features, from smartwatches to location trackers to panic buttons and biometric sensors. Privacy issues, data security, user acceptability, and ethical considerations are only some of the problems and hazards discussed in this research on wearables in education. This study the neutrosophic set to deal with uncertain data. The neutrosophic set is integrated with the multi-criteria decision-making (MCDM) CRITIC method. The CRITIC method is used to compute the weights of factors and rank it. There are 15 factors used in this study. The case study is applied in the education field. Educators, technologists, and legislators all need to work together to guarantee the safe and effective use of wearable devices in schools, as shown by the study's findings. The article reiterates the importance of wearables and their potential to enhance safety measures in education before making the case for more studies, pilot programmers, and policy development to fully realize their promise.
Fully Digitalized Fingerprint and RFID-Based Voting System

In the current ongoing times, most of the nation’s conduct their elections using electronic voting machines (EVM’s), where your vote is stored electronically and registered using electronic devices from which little to no wastage of ballot paper takes place. As security is one of the major parameters to be considered during an election that no individual can vote twice or misuse their right to vote, this can be prevented using fingerprint and RFID technology, as the minutiae feature on the finger of each person is dissimilar, the elector can be easily identified and distinguished from others. The machines permit the person to cast the vote with his fingerprint; the fingerprint of the voter is used to uniquely and distinctively identify the person using the fingerprint module; also the system will only allow the user to use the machine once. If the voter tries to use the machine again, it will indicate an error. An admin is assigned to a group of voters standing for the election, the operator only will have the access to delete or add the voters’ photo and name. Admin will also authenticate the details of the candidates by proving the voter’s identity, and after verification, operator will give the identity card so that the voter can cast his vote at the election booth.
Cognitive Assistant for Physical Exercise Monitoring in Hand Rehabilitation

This paper introduces a novel, affordable companion robot that has been designed for rehabilitation purposes among the elderly population. The robot is equipped with a camera that records exercises, and an animation screen that delivers clear and easy-to-follow instructions and feedback. To evaluate the device, a machine learning algorithm was used on a dataset of therapy exercises. The results indicate that the robot effectively recognizes gestures and accurately identifies the exercises being performed. This study presents a groundbreaking and cost-effective solution for elderly rehabilitation and has the potential to revolutionize the industry with its cutting-edge technology.
Fuzzy Logic Function as a Post-hoc Explanator of the Nonlinear Classifier

Pattern recognition systems implemented using deep neural networks achieve better results than linear models. However, their drawback is the black box property. This property means that one with no experience utilising nonlinear systems may need help understanding the outcome of the decision. Such a solution is unacceptable to the user responsible for the final decision. He must not only believe in the decision but also understand it. Therefore, recognisers must have an architecture that allows interpreters to interpret the findings. The idea of post-hoc explainable classifiers is to design an interpretable classifier parallel to the black box classifier, giving the same decisions as the black box classifier. This paper shows that the explainable classifier completes matching classification decisions with the black box classifier on the MNIST and FashionMNIST databases if Zadeh’s fuzzy logic function forms the classifier and DeconvNet importance gives the truth values. Since the other tested significance measures achieved lower performance than DeconvNet, it is the optimal transformation of the feature values to their true values as inputs to the fuzzy logic function for the databases and recogniser architecture used.
Perancangan Sistem Deteksi Plagiarisme Skripsi (Judul Dan Abstrak) Berbasis Matlab Menggunakan Algoritma Winnowing

Plagiarism is an act of plagiarizing the work of others who will then acknowledge the work as one's own work without mentioning the source of the work. This research aims to create a plagiarism detection system using the winnowing algorithm in MATLAB to prevent plagiarism in the final project of the Mathematics Department students. In order to get the best k-gram value and window size that will be used in the system, a testing process is carried out between document I (100% data) and document II (80% data) by using variations in k-gram values ​​and window sizes. The test results show that the best k-gram ​​and window size are 12 and 4.
The Possibility of Distinguishing Between Content-neutral Regulations of Election Campaign: Focusing on Recent Decisions of the Constitutional Court of Korea

Regulation of speech can be categorized into content-based regulation and content-neutral regulation. This classification is widely accepted by both the Constitutional Court of Korea and the U.S. Supreme Court. However, distinguishing between the two types is not always clear in actual legislation. Therefore, a flexible approach is necessary when conducting constitutional review. In particular, regarding content-neutral regulation - even if a certain regulation is deemed to be content-neutral on its face -, it is necessary to consider the legislative purpose and the secondary effects caused by the legislation in order to determine whether the regulation functions as a form of censorship that prohibits expression itself or its content. Prior to such a discussion, it should be determined whether the regulation focuses on restricting actions rather than the expression itself, which may require a relaxed intensity of constitutional review. Based on the above, the Restriction on Use of Loudspeaker Systems clause in Public Official Election Act can be considered as a form of conduct-regulation rather than speech-regulation. The Prohibition of Installing of Facilities and Prohibition of Unlawful Distribution or Posting of Documents and Pictures in Public Official Election Act require alternative measures to minimize the restriction of political speech. The Restriction on Various Assemblies in Public Official Election Act is considered unconstitutional. In this regard, the recent decisions of the Constitutional Court of Korea are considered justifiable.
A Novel U-Shaped Hybrid Network for Single Image Dehazing

Image dehazing is a challenging problem due to its ill-posed parameter estimation. Despite the significant success of Convolutional Neural Network (CNNs), the inherent locality of CNNs remains a bottleneck for dehazing performance. Though Transformers mitigate the shortcomings of CNNs and have demonstrated promising performance in high-level vision task, the inherent computational complexity makes them infeasible for low-level vision task. In this work, an efficient U-shaped Convolution and Transformer hybrid network, called UCPformer, is proposed. Specifically, Channel Enhanced Transformer (CET) and Efficient Pixel Enhanced Transformer (EPET) is designed in this paper for efficient encoding and decoding of hazy image features. The CET inherits the local representation capability of CNN and general architecture of Transformer, extracting local information efficiently and treating different channels unequally. The EPET inherits the global context modeling capability of Transformer, treating different pixels unequally with linear complexity. Experiments demonstrate the proposed UCPformer achieve superior performance against other dehazing methods.
Optimized Deep Learning Model for Pose and Expression Invariant Face Recognition in an IoT-Cloud Environment

Face recognition from massive data in Internet-of-Things (IoT)-cloud environments is challenging with limitations in learning the pose and facial expression variations. An intelligent Pose and Expression Invariant Face Recognition Model (PEIFRM) is developed in this paper by extracting the local features of face pose and expression variations using Temporal Stacked Convolutional Denoising Autoencoder (TSCDAE) and Optimized Siamese Convolutional Ladder Networks (OSCLN) for recognition. TSCDAE acquires the local informative features of persons' facial pose and expression variations through different color components. OSCLN is an integration of Siamese neural networks (SNN), semi-supervised ladder form of convolutional neural networks (CNN) and Artificial Lizard Search Optimization (ALSO) to improve the training speed and reduce the error rate with local and global feature fusion to improve the recognition accuracy. Experimental results comparisons showed that the proposed PEIFRM model achieved 98.95% and 99.75% accuracies for LFW and ORL datasets, respectively.