Reviewerly/exHarmony huggingface dataset:
The Infiltration of Reasonable Reasoning in Primary School Mathematics

This paper focused on the infiltration of reasonable reasoning in primary school mathematics. It firstly introduced the definition of rational reasoning, then discussed the presentation of reasonable reasoning in the primary school mathematics teaching material with three examples, and finally analyzed the thinking characteristics of a primary school student and put forward three suggestions for teachers.
EKSPLORASI TERHADAP KEMUNGKINAN DESAIN PODCAST PENDIDIKAN TINGGI DI INDONESIA

This study aims to explore the possibilities of podcast designs that can be applied by higher education institutions in Indonesia. Podcast media has benefits in distance learning. However, there is no higher education in Indonesia that uses podcasts to deliver learning material. Therefore, this research synthesizes several models of podcasts that can be mixed and matched by teachers in creating learning podcasts. This article reviews Interest-Driven Learning Design, podcast types, and podcast types. IDLD reviews learning that accommodates students' interests outside the context of the educational environment; the types and types of podcasts review podcast characteristics that can be used as a guide when creating learning content through podcasts
How to Take Over Drones

The number of unmanned aerial vehicles (UAV) (hereinafter referred to as drone) is rising in both, private and commercial applications. This makes it necessary that a drone remains under full control of the owner at any time. Most drones are controlled wirelessly by protocols in the 2.4 GHz band. The most commonly used protocols are DSMX (Spektrum), ACCST D16 EU-LBT (FrSky), DEVO (Walkera) and S-FHSS (Futaba). While it has been known that the DSMX protocol is vulnerable to attacks, the security of the other protocols was an open question.
Teaching Quadruped to Walk Using Fault Adaptive Deep Reinforcement Learning Algorithm

Reinforcement learning holds the promise of enabling autonomous robots to learn behavioural skills with minimal human intervention. Reinforcement learning in robotics allows designing an algorithm that makes a robot learn a specific task by itself and arrive at an optimal policy that accomplishes its task without being specifically programmed to do so. This paper demonstrates the approach of using Deep reinforcement Learning in Teaching a quadruped robot how to walk, without programming it specifically to do so, and how to utilize the ability of self-learning robots in fault adaptive control algorithm based on reinforcement learning to teach the robot to adapt to faults and accomplish its mission.
Accurate Fall Localization for Patient based on GPS and Accelerometer Sensor in Outside the House

Falling of the elderly patients was increased every year and can cause head injury or broken bones. The elderly cannot move or making calls to the medical emergency center by himself after fall, which leads to increase the danger of fall injuries. This paper aimed to design and implemented a wearable fall detection system (FDs) which is used for detecting the falling for the elderly and helps them after a fall by making automatic communication between them and caregivers. A proposed system which includes the Bio-sensor, localization module based on GPS, controller unit, and GSM unit will alert the caregivers by sending messages contained his/her health status and geolocation (latitude and longitude coordinates) immediately when he or she passes a selected threshold of falling. The proposed system was validated by using statistical analysis based on mean absolute error. The fall detection system appeared the ability to distinguish between fall and normal daily activities of the elderly with an accuracy of 99.2% based on the proposed algorithm which compared acceleration magnitude and fall threshold values, called the FDB-FTAM algorithm. The result showed, the proposed system was satisfactory performance and more accurate when get the patient location with 1.938×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , and 1.327×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> of mean absolute error for latitude and longitude information respectively.
Perception of pramoedya on knowledge management in dutch colonial in Indonesia as its representation in Rumah Kaca

espanolEsta investigacion tiene como objetivo obtener una comprension de la percepcion de un novelista, a saber, Pramoedya Ananta Toer, sobre la practica de la gestion del conocimiento en el periodo colonial holandes en Indonesia, que esta representado en su obra literaria Rumah Kaca. Los hallazgos muestran que Pangemanann llevo a cabo procesos de gestion del conocimiento, a saber, la creacion, organizacion, transferencia y aplicacion del conocimiento, incluso espiar, archivar, buscar en los medios de comunicacion y difundir informacion, pero tambien manipulo el conocimiento que hizo que Minke fuera desterrado a la isla de Buru . Los funcionarios podrian sospechar facilmente de los nativos como oposicion al Gobierno de las Indias Holandesas, al monitorearlos a traves de los archivos. EnglishThis research aims to gain an understanding of the perception of a novelist, namely Pramoedya Ananta Toer on the practice of knowledge management in the Dutch colonial period in Indonesia, which is represented in his literary work Rumah Kaca. The findings show that Pangemanann carried out knowledge management processes, namely the creation, organization, transfer, and application of knowledge, even spying, archiving, searching newspaper media, and disseminating information, but also manipulated knowledge that caused Minke to be banished to Buru Island. Officials could easily suspect the natives as opposition to the Dutch Indies Government, by monitoring them through the archives.
Judging Pre-owned-bicycle Deals Type Using Machine Learning Methods

Under the influence brought by Covid-19, people who used to take bus for commuting and traveling are tending to cycle to the places they would like to go. So many of them choose to buy a pre-owned bike because of the cheap price. However, many of them complaint that they didn't get the lowest price they could get. The focus of this study is using logistic regression to build a classifier to determine if a pre-owned road bicycle deal is a good deal or not. Unlike new products with a fixed price, asymmetric information between the sellers and buyers before the transaction, lack of reliable transactional schemes due to frequent frauds and chaos during the transactions, and missing after-sales guarantee mechanism in pre-owned things deals. The result of the study could help people intending to buy a pre-owned road bicycle before they make the deal by letting them enter the key information of the bicycle they are about to buy and predict if the deal is a good deal or not, where "0" represent the bad deal while "1" represents the opposite.
Monitoring Health of IIOT Devices using Blockchain

Nowadays, we are encircled by countless IoT (Internet of Things) gadgets and sensors. These gadgets are intended to make life simpler and more agreeable. Blockchain innovation, particularly its mass application, is speedily developing into a profitable venture. A blockchain additionally called a circulated shared record, is a permanent information base of records by cryptography. It permits trade and capacity of computerized resources without the requirement for outsider oversight. Gadgets that download a setup record from an incorporated worker need to believe that power and in the event that it is undermined the gadget gets defenceless. With a blockchain, the requirement for focal authority is missing. Gadgets trade resources straight-forwardly between one another. Reception of blockchain into big business networks actually has a couple of provokes that should be handled. Using blockchain can bring expanded security and proficiency of gadget upkeep. The vital component of the blockchain, immutability, carries protection from unapproved alterations. The entire history of gadget arrangement changes is put away in the blockchain, consequently recuperation after occurrences is exceptionally direct. Observing of gadget execution and traffic assumes working organization gadgets and their interconnection. Gadget and interface statuses should be followed for picking up an extensive review of organization conditions. This paper elaborates on monitoring the organization data and the idea is dependent on blockchain innovation. Organization heads control network gadgets by implicating by recording the change of gadget arrangement into the blockchain. The critical curiosity of our answer is a circulated executives of setup documents of IoT gadgets in big business networks using blockchain innovation. This is basically improving security and capacity alternatives for arrangements in the blockchain.
Estimation of efficiency of measurement of digital camera photosensor noise by automatic segmentation of non-uniform target method and the standard EMVA 1288

In this paper important task of estimation of digital camera’s noise parameters is considered. Relation of accuracy of data obtained with digital camera and photosensor noise is discussed. Both standard European machine vision association EMVA 1288 and fast automatic segmentation of non-uniform target (ASNT) noise estimation methods are compared. Noise characteristics of machine vision PixeLink PL-B781F, scientific Retiga R6 and amateur mirrorless Canon EOS M100 cameras have been investigated. Accuracy of measurements, speed of calculation and experimental realization has been analyzed. Accuracy of temporal noise estimation by modified ASNT method is no less than that one for standard EMVA 1288. But the ASNT method can be implemented much faster than the standard EMVA 1288 even with additional frames for accuracy improvement.
Use of Naïve Bayes classifier algorithm to detect customers’ interests in buying internet token

Abstract Determining possible technology used in calculating customer buying interest is important for the company as it relates to marketing strategies and policies. The Naïve Bayes Classifier is a possible method to measure customer buying interest. This study aims to analyze the accuracy of this method in calculating customer buying interest of internet token. Data were obtained from past sales and then analyzed using the Naïve Bayes Classifier to predict future opportunities by defining the class of attributes. The classifications used were operator, internet quota, token active period, and product price. This study found that the Naïve Bayes Classifier was provably accurate in calculating customer buying interest as well as comparing the prediction with actual results aside the data training. Results showed that this method successfully classifies 10 products of 858 transactions as data training and 10 products of 115 transactions as data test. Here indicates that all data test and data training analysis showed eight of the tenth meaning that eight of ten predictions were correct or it had 80% accuracy. This study is expected becoming a reference in analyzing data sales and predicting future sales conditions, so it will help to determine appropriate strategy of product development.
Estimating Memory Retention Traces of Foreign Language Vocabulary from Reading Interaction Data

While language-learning apps have empowered many users to learn languages inexpensively, the underlying technology limits the tasks they can offer to atomic, flashcard-like exercises. In this paper we present an algorithm that can effectively estimate memory traces from user interaction data that is generated in complex language learning tasks such as extensive reading. This technology can be applied to develop complex, intrinsically-motivating, high-value learning tasks based around long texts, television series or podcasts.
Unsupervised HDR Image Reconstruction Based on Over/Under-Exposed LDR Image Pair

This paper proposes an unsupervised high dynamic range (HDR) image reconstruction method based on an over/under-exposed low dynamic range (LDR) image pair. The framework includes two end-to-end branches: transferring an over-exposed image input to under-exposed images and transferring an under-exposed image input to over-exposed images. The LDR images with the same exposure from the two branches are averaged, and then reconstruct an HDR image by merging them. When training the model, we use the L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss of the same exposure image of the two branches and MEF-SSIM loss function as the objective function to ensure that the two branches get a similar visual effect at the same exposure, and use RGB loss and HSV loss to constrain the brightness and saturation of different exposure images. Experiments demonstrate that our unsupervised framework can generate comparable results with state-of-the-art supervised learning methods.
Discrete Fréchet distance for closed curves

The paper presents a discrete variation of the Fréchet distance between closed curves, which can be seen as an approximation of the continuous measure. A rather straightforward approach to compute the discrete Fréchet distance between two closed sequences of m and n points using binary search takes O(mnlog⁡mn) time. We present an algorithm that takes O(mnlog⁎⁡mn) time, where log⁎ is the iterated logarithm.
Construction and Research of Outside School Practice Base of Software Engineering Major

Software engineering major is a strong engineering major, the practice base outside the university has an important role in the training of software engineering professionals in undergraduate colleges. The article affirms the effect of off-campus practice base on the training of software engineering professionals, points out the problems faced in the construction and management of off-campus practice base, and puts forward corresponding suggestions and measures. The article believes that only through the reform and innovation to improve the management model of the practice base outside school, to achieve a win-win situation between schools and enterprises, can better provide technical and resource services for the training of software engineering professionals.
Extensions of IBE and Related Primitives

Identity-based encryption can be extended to various other primitives, such as identity-based key agreement protocol, fuzzy identity-based encryption, keyword search enabled public key encryption, threshold keyword search enabled public key encryption, wildcard identity-based encryption, identity-based conditional proxy re-encryption, and so on. Every extension has its own application in real world. In this chapter, we discussed all these extended primitives with their formal definitions and basic security requirements.
Mineralogy, alteration, geochemistry, and fluid inclusion studies of the Balazard Cu-Au prospect, southwest of Nehbandan

Mineralogy, alteration, geochemistry, and fluid inclusion studies of the Balazard Cu-Au prospect, southwest of Nehbandan
Amazon Blockchain Enabling Commercial and Governmental Applications

The COVID-19 pandemic created significant problems for everyone and every business, but a few enterprises thrived, and Amazon was at the top of the list. Through their AWS (Amazon Web Services), Amazon controls many companies' information flow and data of all sizes. But what the general public does not know is that Amazon has been working on several blockchain and AI initiatives for over a decade. This chapter will provide a detailed narrative about Amazon's activity, patents, databases, and services that will leverage blockchain technology. The many patents filed by Amazon will greatly benefit Amazon both now and in the future.
Research on development of a “VR + elderly care” system based on big data

The aging population in China is revealing the problems of the traditional elderly care system, including demand-supply imbalance, excessive burden of elderly care, and an immature elderly-care system. In this context, artificial intelligence (AI) finds its way into the elderly care industry, ushering a new era of elderly care, building a technical foundation for an AI-based smart elderly care mode that will combine emerging technologies including Internet, big data and virtual reality (VR). However, AI-based smart elderly care is still in the exploration stage. This study intends to use emerging technologies including VR to make up the defects of traditional elderly care systems, alleviate the burden of elderly care, and boost the development of a home-bound smart elderly care mode that is intelligent and economy-efficient.
SIMULATION MODEL OF THE SOFTWARE FOR WORKING WITH PERFECT CODES BASED ON HAMMING CODE AND GOLE CODE

This article presents an analytical review of noise-tolerant codes, namely, the perfect codes are considered. Ideal codes can be used to encode and decode information, as they have all the desired properties, namely, resistance to interference and ease of implementation. In conclusion, a simulation model of encoding and decoding the information word is developed.
Heterogeneous Graph Based Extractive Summarization Considering Discourse and Coreference Relations

Modeling the relations between text spans in a document is a crucial yet challenging problem for extractive summarization. Various kinds of relations exist among text spans of different granularity, such as discourse relations between elementary discourse units and coreference relations between phrase mentions. In this paper, we utilize heterogeneous graphs that contain multiple edge/node types to model the input document as well as the various relations among text spans in it. Also, we propose a heterogeneous graph based model for extractive summarization that considers the heterogeneity of the document graph. Experimental results on a benchmark summarization dataset verify the effectiveness of our proposed method.
Quantum Asymmetric Encryption Based on Quantum Point Obfuscation

Quantum obfuscation means encrypting the functionality of circuits or functions by quantum mechanics. It works as a form of quantum computation to improve the security and confidentiality of quantum programs. Although some quantum encryption schemes have been discussed, any quantum asymmetric scheme based on quantum obfuscation is not still proposed. In this paper, we construct an asymmetric encryption scheme based on quantum point function, which applies the advantages of quantum obfuscation to quantum public-key encryption. As a start of the study on applications of quantum obfuscation to asymmetric encryption, our work will be helpful in the future quantum obfuscation theory and will therefore promote the development of quantum computation.
Design of Network Distance Education System Based on ASP under the Background of Massive Open Online Course

Massive open online course is a large-scale online open course, and massive open online course can spread all the teaching resources in the world through network channels, so that all students can learn online through the network. The asp-based network distance education system under the background of massive open online course is constructed, the parameter set of network distance education information distribution under the background of massive open online course under the hybrid cloud architecture system is designed, the graph model of network distance education information distribution under the background of massive open online course is obtained by using the method of directed graph structure feature decomposition, and the network distance education information scheduling model under the background of massive open online course is constructed by combining the methods of associated node scheduling and resource balanced allocation. The embedded protocol scheduling and PCI bus control methods are adopted to analyze the communication interface and database module of network distance education system under the background of massive open online course, to realize the resource feature matching and open source information fusion in the process of network distance education under the background of massive open online course, and to realize the optimized design of network distance education under the background of massive open online course under the hybrid cloud architecture and ASP architecture. The simulation results show that the output performance of the network distance education system design under the background of massive open online course is better and the system reliability is improved.
Investigating Reading Comprehension Problems Encountered by Sudanese Secondary School Students: Teachers perspective

This study aimed to investigate reading comprehension problems encountered by Sudanese EFL secondary school students. To achieve the purpose of the study, the researcher used an analytical descriptive method and a questionnaire as a tool. The sample of the study consisted of 30 teachers who were randomly selected from Kenana secondary schools in White Nile State, and responded to the items of the questionnaire. The findings of the study showed that Sudanese Secondary School students face problems in reading and answering comprehension questions. The main causes of these problems were attributed to students’ lack of vocabulary and motivation. English language curriculum and lack of teachers’ training were also considered as causes of this difficulty. The researcher suggested some recommendations for both teachers and syllabus designers. As a recommendation for both sides; teachers and students, the researcher recommended that English language teachers should be trained to improve students’ levels in reading comprehension. Teachers should also motivate their students to have more interest in learning English language. In addition to that, they should encourage their students to read extensively so as to improve their level of comprehension, and syllabus designers should provide good reading material with adequate exercises besides English language literature.
Jumpgate

Network-connected accelerators (NCA), such as programmable switches, ASICs, and FPGAs can speed up operations in data analytics. But so far, integration of NCAs into data analytics systems required manual effort.
Duration-Squeezing-Aware Communication and Computing for Proactive VR

Proactive tile-based virtual reality video streaming computes and delivers the predicted tiles to be requested before playback. All existing works overlook the important fact that computing and communication (CC) tasks for a segment may squeeze the time for the tasks for the next segment, which will cause less and less available time for the latter segments. In this paper, we jointly optimize the durations for CC tasks to maximize the completion rate of CC tasks under the task duration-squeezing-aware constraint. To ensure the latter segments remain enough time for the tasks, the CC tasks for a segment are not allowed to squeeze the time for computing and delivering the subsequent segment. We find the closed-form optimal solution, from which we find a minimum-resource-limited, an unconditional and a conditional resource-tradeoff regions, which are determined by the total time for proactive CC tasks and the playback duration of a segment. Owing to the duration-squeezing-prohibited constraints, the increase of the configured resources may not be always useful for improving the completion rate of CC tasks. Numerical results validate the impact of the duration-squeezing-prohibited constraints and illustrate the three regions.