Reviewerly/exHarmony huggingface dataset:
Characteristics of Blockchain Technology In Educational Development

The worldwide education implementation process has not been able to keep up with contemporary events. The education sector is now dealing with a number of issues, including the problem of verification and the presence of a third party in the management of certificates, which are micro and macro outputs of a company's learning process. The existence of a beacon of Blockchain technology, which is frequently referred to as a revolutionary solution to the current educational debate. In the education business, the merger of education with blockchain technology will usher in a new era. There are numerous reasons for this solution, but I don't believe they are sufficient to describe the features of blockchain in detail from the perspective of education utilizing blockchain, which examines the presence of blockchain for education based on scientific investigations. These features of blockchain will be highly valuable as a literary platform for scholars who will begin fundamental research and applications that will lead to blockchain for education in the future. Aside from that, there will be recommended solutions in the form of a framework for the cost, storage space, and speed of blockchain technology. As a result, this research can also serve as a basis for the new blockchain as a predecessor of technology-based governance blockchains.
A Two-Stage Line Matching Method for Multi-temporal Remote Sensing Images

Robust feature matching in multi-temporal remote sensing images is an essential but difficult task due to certain extent of appearance difference. In this paper, a two-stage line matching method is presented to match salient structures in multi-temporal remote sensing images. The proposed method consists of three main steps. In the first step, we extract the line structures from two corresponding images using the EDLines algorithm, and a line validation procedure is then implemented to remove meaningless and fragmented lines. In the second step, the detected lines are further divided into a set of line segments by a length threshold, and a new descriptor is computed to describe each line segment. After that, a coarse matching process is implemented to compute the projective transformation model. Finally, we adjust the length threshold according to the transformation model, and recompute the descriptors to further refine the matching process. We conduct the experiments on two pairs of multi-temporal remote sensing images, and the results demonstrate that the proposed approach can achieve favorable performance compared to common image matching methods.
A Study on the Needs for Career Decision and Curriculum Improvement of Military-Specialized High School Students

The purpose of this study is to find the influential factors of Military-specialized high school students on their career decision, and their needs for curriculum improvement, and thereby to propose an improvement plan.To this end, the researcher conducted a survey with 747 Military-specialized high school students.The data gathered from the survey were used for frequency analysis, t-test, and oneway ANOVA in SPSS Win 25.0.The results revealed that their career decision was highly influenced by teachers and parents.The motives for such a decision were to become a professional soldier, and to hope for occupational stability.Of the students, 68.3% wanted to work as a non-commissioned officer for a long time and then get discharged after their graduation.Their career satisfaction scored high at 4.05.Regarding career needs, teachers' offering of detailed information, secured occupational stability of non-commissioned officer, expansion of additional points for being appointed as a noncommissioned officer with long-term service, and fast track for an on-commissioned officer with longterm service were highly needed.With regard to needs for curriculum improvement, highly needed were operation of the curriculum specialized in line with the selection of long-term service, organization of the curriculum for enhancing competency in military specialties in association with the characteristics of local militaries, enhancement of professional subjects for raising advanced science & technology based soldiers, and the curriculum for enhancing relations between specialties and military service areas.Based on these findings, this study proposed a plan for supporting the career design of Militaryspecialized high school students and improving the quality of their curriculum.
Evaluación de resultados de aprendizaje mediante organizadores gráficos y narrativas transmedia

The objective of the article is to systematize two teaching experiences on the use of graphic organizers and transmedia narratives, as a strategy for evaluating learning outcomes in 63 undergraduate university students.The results of the pilot experience show that the students satisfactorily develop the different graphic organizers included in the evaluative activity, expressing a greater appreciation of the mind map and the comic as more appropriate methodologies to evaluate the achievement of the learning results of the contents studied.On the other hand, the participants of both training experiences describe the meme as a less pertinent method to implement evaluation processes in the university environment, despite being a very common cultural element for young people in the 21st century.In conclusion, the use of graphic organizers and transmedia narratives in the evaluation of learning outcomes offers an interesting alternative to be applied to the field of higher education.
Association and Conditional Independence

Conditional independence is a crucial concept in the network models discussed in this book, because the canonical pairwise Markov random field (PMRF) formulation of networks is defined in terms of this concept: nodes that are disconnected in the true PMRF are conditionally independent given the other nodes in the network. This chapter provides an introduction to the concept of conditional independence and illustrates its importance in reasoning about possible generative models. With the intuitive understanding of conditional independence provided in this chapter, readers will be able to grasp the essence of the estimation routines discussed in subsequent chapters.
Artificial Intelligence-Based Framework for Scheduling Distributed Systems Using a Combination of Neural Networks and Genetic Algorithms

High-performance distributed computation has emerged as a new development in which demand for access to resources over the Internet is being delivered to distributed servers that are dynamically scalable. One of the important research issues that must be considered in order to achieve efficient performance is timing. The goal is to map tasks to the right resources and optimize one or more subgoals so that users get the most out of their resources as quickly as possible. Therefore, in this study, in order to find a suitable solution to the scheduling problem and reduce the total time of task completion, a hybrid approach is proposed by neural networks and genetic algorithms. In this hybrid algorithm, the predictive power of artificial neural networks is used to improve scheduling problem task in order to predict the time of tasks completion on resources and the meta-heuristic of genetic algorithm is used in order to find optimal resources for tasks. Experimental results show that the performance of the proposed method is improved in Makespan compared to other methods available in the journals and can adhere to the principles of service quality.
Sustainable digital pedagogy in language teacher education: Perception of teachers in Ekiti state government colleges

This study investigated the perception of English language teachers concerning digital pedagogy in teaching oral English in Ekiti state government Colleges. The underpinning objective encompasses a survey of teachers’ perception concerning digital instructional style. This study is descriptive research of the survey type. The population of the study consisted of 78 English language teachers in Ekiti state Government Colleges. Using multistage sampling procedure, four government colleges were randomly selected comprising forty English language teachers from the four Ekiti state government colleges. Questionnaires were administered on the sample of the study. Descriptive statistics was used in analysing data. The findings of this study revealed that 65% of the teachers could distinguish between digital and conventional pedagogy while 65% experienced no significant challenges with digital pedagogy during oral English lessons. Furthermore, majority of the English language teachers admitted that students feel bored when taught oral English with charts. These findings explicate the need to sustain digital pedagogy for teaching oral English. It was recommended that English language teachers should be retrained and trained in digital skills for teaching oral English in government colleges in Ekiti state.
Preclustering Algorithms for Imprecise Points

We study the problem of preclustering a set B of imprecise points in $${\mathbb {R}}^d$$ : we wish to cluster the regions specifying the potential locations of the points such that, no matter where the points are located within their regions, the resulting clustering approximates the optimal clustering for those locations. We consider k-center, k-median, and k-means clustering, and obtain the following results. Let $$B := \{b_1,\ldots ,b_n\}$$ be a collection of disjoint balls in $${\mathbb {R}}^d$$ , where each ball $$b_i$$ specifies the possible locations of an input point $$p_i$$ . A partition $$\mathcal {C}$$ of B into subsets is called an $$(f(k),\alpha )$$ -preclustering (with respect to the specific k-clustering variant under consideration) if (i) $$\mathcal {C}$$ consists of f(k) preclusters, and (ii) for any realization P of the points $$p_i$$ inside their respective balls, the cost of the clustering on P induced by $$\mathcal {C}$$ is at most $$\alpha $$ times the cost of an optimal k-clustering on P. We call f(k) the size of the preclustering and we call $$\alpha $$ its approximation ratio. We prove that, even in $${\mathbb {R}}^1$$ , one may need at least $$3k-3$$ preclusters to obtain a bounded approximation ratio—this holds for the k-center, the k-median, and the k-means problem—and we present a (3k, 1) preclustering for the k-center problem in $${\mathbb {R}}^1$$ . We also present various preclusterings for balls in $${\mathbb {R}}^d$$ with $$d\geqslant 2$$ , including a $$(3k,\alpha )$$ -preclustering with $$\alpha \approx 13.9$$ for the k-center and the k-median problem, and $$\alpha \approx 193.9$$ for the k-means problem.
Unsupervised anomaly detection via dual transformation‐aware embeddings

Unsupervised anomaly detection refers to the discovery of unconventional images that are globally or locally different from the training set. Recently, reconstruction-based anomaly detection methods have made great progress. However, most of the existing methods take reconstructing the original image as the goal of latent feature learning. Due to lack of effective semantic guidance, latent features have intrinsic characteristics which retain redundant details of spatial structure. Such information is too general and cause over-expression problem. To solve this problem, in this paper, dual transformation-aware embeddings are coined which aims to achieve a stable model to learn high-level latent features in a self-supervised manner. To be more specific, the authors try to extract transformation-detectable feature embeddings for both structure and content views which explore the regular pattern under different transformations in normal situations. In addition, the relationship between the original feature and the transformed feature is established. Based on such relationship, the latent feature of generated image to predict transformation parameter is extracted. Then, a transformation-consistency regularization is proposed to constrain decoder to generate high-quality image with high-level consistency and achieve a more stable model. Experiments on MVTec-AD and CIFAR10 datasets prove the effectiveness and robustness of the proposed method.
Robot grasping target detection method based on YoLov3

In order to improve the detection accuracy of robot visual grasping target, and overcome the problems that the traditional target detection technology has low recognition accuracy and can not meet the actual needs due to the disordered placement position, mutual occlusion, small scale and other factors. This paper adopts the popular real-time target detection algorithm YoLov3, and collects a large number of image information samples according to the robot grabbing the target, and labels them into a training set. Through many training and continuous adjustment of parameters, the best capture target detection model is obtained. The experimental results on VOC datasets show that this method has achieved good detection results for different captured target images, and can well meet the needs of practical application.
Towards increased interoperability of paleoenvironmental observation data

Data is an important foundation of scientific progress.It allows us to contrast hypotheses with observational evidence.
Impact Assessment for Data Network's Rehabilitation. Case of Ecuador

Access to the Internet is necessary to ensure equity for the right to education. However, about 75&#x0025; of school-age children in rural areas of the world do not have access to the Internet at home. In the Ecuadorian context, only 5.1&#x0025; of the rural population uses the Internet in public/state schools. Therefore, actions are necessary to reduce the digital gap to improve the quality of education in rural communities in Ecuador. An initiative of IEEE Ecuador, with the support of IEEE SIGHT and the Municipality of Nab&#x00F3;n, allowed the rehabilitation of a data network in 40 schools in the Nab&#x00F3;n community in 2020. This paper examines the impact of the project, through of the collection of quantitative and qualitative data through stakeholder surveys, to evaluate the impact of data network rehabilitation in the community during the first year of operation. The results show that the Internet in schools has improved the quality of education for students, has allowed children and young people to have access to online education during the COVID-19 pandemic, and has decreased the dropout rate in the benefited schools. The Internet service in schools has also allowed families to save money and entertain themselves. The paper also reports on the evaluation of the social impact of the project, through a Social Return On Investment (SROI) analysis.
An Automatic Light Control System for Camera Barcode Reader

The aim of the present paper is to develop a novel light control system for the camera barcode reader. Barcodes are printed on several products and applications which create reading problems when a conventional camera barcode reader is used to scan these barcodes. This problem emerges as a result of uneven light and reflection caused by the barcode surface. To overcome this problem, an automatic light control system is prominent. The study carried out in this paper is to design a unique system that will automatically control the light intensity of the camera back light unit based on the barcode conditions that is being scanned. The system has been designed in such a way that, the camera lens captures the barcodes and sends an appropriate signal to the light control circuit to perform suitable actions. In this study, an Arduino Uno board is used as a controller unit to implement light control strategies. Laboratory experiments had been conducted to validate the efficiency of the designed automatic light control circuit.
Stabilization of Altitude Control for Multirotor Flying Vehicles with Respect to Propeller Actuator Dynamics

To design motion control system for drones and multirotor vehicles, the traditional approach has relied on the dynamical equations of the vehicle body. However, almost of the studies neglected the existence of the propeller actuators. Considering the altitude control problem, this paper shows that it is essential to address the following property of the actuator to properly ensure system stability. It is the nonlinear relationship between the thrust force and the rotational speed of the propeller. To reduce the complexity of stability analysis, this paper shows that the altitude control system can be generally represented by the feedback connection of two parts: (i) the linear part with rank-1 physical interaction; (ii) the nonlinear part which belongs to certain sector bound. Based on this modelling, this paper derives the condition for the controller to ensure the absolute stability of the control system. The condition is independent of the number of propeller actuator, and it allows graphical test which can be performed conveniently. Moreover, it can easily address the change of battery voltage in accordance with the state of charge. The effectiveness of the proposed approach is demonstrated via numerical simulation and experiments with a quadrotor vehicle.
Supporting Prototyping of Novel Interfaces Using Laser Cut Clear Perspex

Digital fabrication technologies such as laser cutters have been widely used for supporting prototyping of interactive devices as they are able to work with a wide range of materials. However, the majority of laser cut prototype components are often unable to support interaction or visualisation capabilities within themselves (e.g., functionality embedded within the material). Often materials that are laser cut do not have functional properties aside from serving as enclosures for interactive components. Our work explores how optical properties of clear Perspex material can be exploited to support interaction and visualisation capabilities for interface prototyping. Our proposed fabrication approach demonstrates the potential to support the development of novel displays devices that do not require expensive or complex circuitry and electronics. We produce a light-sensitive button and a seven-segment display which are combined into a wearable watch prototype demo. We also discuss design implications and future direction for this work.
HMT: A Hardware-Centric Hybrid Bonsai Merkle Tree Algorithm for High-Performance Authentication

Bonsai Merkle tree (BMT) is a widely used data structure for authenticating data/metadata in a secure computing system. However, the predominantly recursive andsequential nature of traditional BMT algorithms make them challenging to implement with Field-Programmable Gate Array (FPGA) in modern heterogeneous computing platforms. In this work, we introduce HMT, a hardware-friendly implementation methodology for BMT that enables the verification and update processes to function independently, as well as saves additional write-backs by making the update conditions more flexible compared to previous algorithms. The methodology of HMT contributes both novel algorithm revisions and innovative hardware techniques to implementing BMT. Our empirical performance measurements have demonstrated that HMT can achieve up to 7x improvement in bandwidth and 4.5x reduction in latency over the baseline.
Haar Wavelet Transform and Multiobjective Cost Function for Video Watermarking

Generally, Watermarking is the process of hiding the concealed message into multimedia sources, like image, video and audio.Video watermarking is mostly concentrated in the robustness of the system rather than other steganography.In this paper, the multiobjective cost function is proposed for video watermarking.At first, the cover image (video frame) is subjected into cost function computation.Subsequently, the cost function is recently proposed and modeled by various constraints, like energy, intensity, coverage, edge, as well as brightness.Then, the Haar Wavelet Transform is applied to the original frame, which attains a wavelet coefficient on the basis of the video frame.Concurrently, by exploiting the bit plane technique the concealed message is partitioned into binary images.In the embedding phase, the message bit is embedded into the wavelet coefficients according to the cost value.The concealed message is retrieved in the extraction phase.At last, the simulation results are examined, and performance is evaluated by exploiting metrics like Peak Signal Noise Ratio (PSNR) and correlation coefficients.
Review on Functional Architecture and Key Technologies of Intelligent Substation

Intelligent substation is the key link of building Strong-Smart Grid and power Internet of Things. The key architectural composition of intelligent substation, the structure of the perception layer and the upper application platform are introduced in this paper. The typical function characteristics and application of intelligent substation are analyzed. Finally, in terms of the intellisense terminals, the intelligent analysis algorithm, and the network real-time communication ability, the restriction of the existing key problems and its research direction suggestion are discussed. There is a certain reference value of this paper to improve and develop the function of the intelligent substation.
Testing equivalence of clustering

In this paper, we test whether two data sets measured on the same set of subjects share a common clustering structure. As a leading example, we focus on comparing clustering structures in two independent random samples from two deterministic two-component mixtures of multivariate Gaussian distributions. Mean parameters of these Gaussian distributions are treated as potentially unknown nuisance parameters and are allowed to differ. Assuming knowledge of mean parameters, we first determine the phase diagram of the testing problem over the entire range of signal-to-noise ratios by providing both lower bounds and tests that achieve them. When nuisance parameters are unknown, we propose tests that achieve the detection boundary adaptively as long as ambient dimensions of the data sets grow at a sublinear rate with the sample size.
Data Visualization on Breast Phantom Mammogram Images Using Kernel Performance of SVM

Breast phantoms developed with nanotechnology are used as experimental models for evaluating and screening parameters while using mammographic screening. Nanotechnology is the emerging trend in manufacturing human organs to handle the difficult challenges of diseases like cancer. BI-RADS Atlas maintains a database for mammogram images and is used for analysis in this work. The affected area in breast phantoms is determined by using the region-growing technique and using the appropriate kernel function for categorizing cancer-affected areas. Data visualization is an attempt to understand data by putting it in a digital context in order to reveal models, patterns and associations that may not otherwise be identified. Python provides many large graphic libraries filled with several features. The classification accuracy for different types of kernels is determined by calculating sensitivity, specificity and classification accuracy. The analysis proves that the polynomial kernel of fourth degree has the highest classification accuracy compared to other types of kernels for this breast-phantom-based cancer classification.