Reviewerly/exHarmony huggingface dataset:
Downscaling Surface Albedo to Higher Spatial Resolutions With an Image Super-Resolution Approach and PROBA-V Satellite Images

For bifacial solar photovoltaic panels, surface albedo plays a crucial role in estimating the radiant energy. Since land surfaces are heterogeneous, the actual albedo of the surface where the solar photovoltaic panel is placed can vary widely and its temporality and sparsity present a significant challenge for renewable energy engineers. This paper develops a new image super-resolution deep learning model based on convolutional neural network to generate high resolution spatial representations of surface albedo from coarse resolution remote sensing-based data. For selected Australian locations, we generated a higher resolution surface albedo using imagery from PROBA-V/SPOT Earth Observation satellites. We proposed a Deep Downscaling Spectral Model with Attention (DDSA) with the capability of processing 10-day albedo images captured at a relatively low (≈ 1 km) resolution. The proposed DDSA was then applied to downscale observed surface albedo and generate predicted albedo at 500 m, 333 m and 250 m resolutions. The proposed model was benchmarked with alternative deep learning, super-resolution approaches: Super-Resolution Convolution Neural Network (SRCNN), Enhanced Deep Super-Resolution network (EDSR), Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Residual Dense Network (RDN). The results showed that the proposed DDSA model outperformed all comparative models in terms of the mean square error (MSE) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~0.0041$ </tex-math></inline-formula> , signal-to-noise ratio (PSNR) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~39.471$ </tex-math></inline-formula> , Structural Similarity Index (SSIM) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~0.999$ </tex-math></inline-formula> vs. an MSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> [0.0140-0.0387], PSNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> [29.761-33.850], SSIM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> [0.9994-0.999]). We also cross-validated the downscaled images with satellite imagery and ground-based observations, which reaffirmed the proposed DDSA model’s ability to produce high resolution surface albedo maps and its potential applications for granular scale tracking and mapping solar energy where bifacial solar photovoltaic panels are placed.
Libraries and Diminished Patron Privacy During the COVID-19 Health Crisis

The COVID-19 health crisis has ushered in an era of great change in the way individuals and institutions function. With the contagious and deadly nature of COVID-19, libraries geared their efforts to increase access to and use of digital collections. With the introduction of stay-home orders, the option of adopting full virtual services became the go-to solution as a way of protecting library staff and users. Collaboration with third-party vendors that provide online library services heightened like never before. As libraries reopened at a later point in the global pandemic, library staff were asked to conduct health screenings for patrons entering the library and actively monitor whether patrons were socially distanced in the library. The drastic change to library operations due to the pandemic resulted in the increased collection of patrons’ personal information ranging from health information to location data coupled with library records. Much as librarians are traditionally and professionally committed to protecting their patrons’ privacy, undertaking activities like health screening in the absence of data transparency practices can potentially impact library users’ privacy. In this study, we investigated whether the 25 Association of Research Libraries (ARL) members with the largest number of titles held developed or modified their privacy policies in response to the increase in data collection during the COVID-19 health crisis. Privacy policies are living documents that should evolve with the times and thus should be updated or modified to reflect the current realities. We also examined whether pre-pandemic privacy policies, if any, of the surveyed 25 ARL member libraries are aligned with the privacy requirements outlined in the American Library Association (ALA) Privacy Toolkit (ALA 2014). Our results show that of the surveyed 25 ARL libraries, none of them developed a new library privacy policy or modified an existing one to reflect their data practices with specific regard to the new realities presented by the COVID-19 pandemic. The disregard for such adjustments to their privacy policies violated the principle of data transparency. Our results also show that, at the time of the survey, 4 of the surveyed 25 ARL members did not have library privacy policies.
LAYANAN READERS’ ADVISORY DALAM MEWUJUDKAN LAYANAN REFERENSI YANG PRIMA PADA PERPUSTAKAAN

This research discusses how reader's advisory services create excellent reference services in libraries. This study uses a qualitative research design with data collection using literature. The readers' advisory service is a user-oriented service to assist users in choosing the best works and according to the needs of these users according to the librarian's knowledge. So that in this readers' advisory service librarians have extensive knowledge about the many collections and needs of users. From this research it can be seen that readers' advisory services are services that support the creation of excellent reference services. However, to create a readers' advisory service, a librarian is needed who is willing to always learn so that he can find collections that suit the needs of users.
RESMOTE: A New Approach for Class Imbalance Problem

Data mining is a growing domain. Wherever we are going, data mining Techniques are available to collect important information from the warehouse or from databases. In every sector, they need to keep data secure. Maintaining a balanced state of data is very crucial. Whenever a class tends to classify, the specimens in the class can be grouped as the majority group and the minority group. The majority group consists of higher number of data when compared with the data distributed in the minority group. This paper proposes the Randomized-Ensemble with Smote (RESMOTE) methodology to handle the class imbalance problem. This paper addresses imbalance issues and proposes a class imbalance solution using a sampling technique combined with certain classification metrics.
Deep Learning-based Attack Detector for Bilateral Teleoperation Systems

A teleoperation system is referred to as a plant that is controlled remotely, and it is often composed of a human operator, a local master manipulator, and a remote slave manipulator, all connected by a communication network. Bilateral teleoperation systems (BTOS) include transmissions in both the forward and backward directions between the master and slave. This paper discusses a class of (BTOS) focusing on the security of the system after modeling the master and slave robots mathematically. The false data injection attack is examined, where the attacker may inject false data into the states that are being exchanged between the master and slave robots. The vulnerability of BTOS, where the attack destabilizes the system, is presented. A deep learning-based detection technique is proposed to detect the presence of false data injection attacks. The deep learning model with convolution neural network structure is trained and tested with considering complex attacks where the attacker has full knowledge of the system and proficiency to emanate and control the target system. The proposed model achieves 96\% validation accuracy, and the efficacy of the proposed deep learning detector is demonstrated and tested into the BTOS.
PECULIARITIES OF FORMATION OF PROFESSIONAL MOTIVATION OF STUDENTS OF PEDAGOGICAL SPECIALTIES IN THE EDUCATIONAL PROCESS AT HIGHER EDUCATIONAL INSTITUTION

The article presents the results of an experimental study of the features of the formation of professional motivation of students of the pedagogical profession in the process of studying at a university. The formed professional motivation of students of pedagogical specialties forms a competent, mobile, responsible future school teacher with a stable system of spiritual and moral values. The formation of professional motivation of students of pedagogical specialties is a psychological and pedagogical process that requires methods of diagnosis, analysis and development. The purpose of our research is to develop a model and program for the formation of professional motivation of students of pedagogical specialties and to determine the psychological and pedagogical conditions for the formation of professional motivation. The pedagogical conditions of students of pedagogical specialties are theoretically determined: external (activation of activity) and internal (awareness of their own abilities, motivation for professional maturation, internal professional identity of the student; creative activity, readiness for professional self-improvement). The practical significance of the study lies in the fact that the results of the study can be used to diagnose and correct the professional motivation of students of pedagogical specialties, motivational training programs during training sessions. In order to form the professional motivation of students of pedagogical specialties, a model and a program for the formation of professional motivation in the form of a formative experiment have been developed and introduced into the educational process. Improving the efficiency of educational and professional activities of students of the experimental group, the formation of professional motivation of students, allows us to talk about one of the most important mechanisms for improving the professional development of students of pedagogical specialties. Keywords: pedagogical specialty, professional motivation, self-organization, psychological and pedagogical conditions, model, motivation structure.
Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair

Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users' needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms.
Analysis of College English Teaching Under the Network Teaching Platform

With the development of information technology and the increasing renewal and popularization of computer and global network technology in the twenty-first century, great changes have taken place in listening, speaking, reading, writing and translation in the process of College English learning. A single traditional teaching method can no longer meet the needs of students and society. The network has become a part of the daily life of contemporary college students. With the continuous improvement of the network teaching system, To some extent, it accelerates the transformation from traditional teaching mode to network mode. It is necessary and useful to use network resources to develop college English network teaching platform. College English teaching should make full use of multimedia and network technology, improve the single classroom teaching mode dominated by teachers, and gradually turn into a new teaching mode combining traditional teaching and network teaching. Many versions of College English online teaching platforms have been developed in China, and have been applied in many colleges and universities in China. Students can use the network to study independently. The network English teaching system provides a specific operation platform for students to learn English. This paper analyzes college English teaching based on network teaching platform.
Problems and research prospects on learning of new words: Fast mapping vs explicit encoding

This article reviews the problem of finding the most efficient strategy for new word acquisition. The authors analyse studies aimed at finding the differences between two learning strategies: fast mapping (FM), which is based on deduction and deriving meaning from context, and explicit encoding (EE), which implies learning new information through explicit instruction. While behavioural findings on these strategies’ efficiency (in terms of new word acquisition) are controversial, neurophysiological studies proved the existence of different neurophysiological mechanisms underlying the two strategies. The authors deem it necessary to shift focus of FM — EE differences research from comparing the efficiency of new word acquisition onto analysing of the two strategies’ additional characteristics, such as sensitivity to interference, speed of lexical and semantic integration of new words, memory consolidation during sleep. A new approach to explaining the differences between FM and EE is offered. It suggests a comparison of the two learning strategies’ mechanisms to neural mechanisms of the visual perception system — according to ascending and descending pathways of hierarchy (bottom-up and top-down signals into cortical activities). In particular, the authors suppose that FM activates bottom-up information processing, whereas EE activates top-down information processing. Future perspectives of research on this subject are discussed, such as exploring the effects of object or phenomena labelling on language learning occurring through FM or EE. The authors claim that it is important to conduct such studies with the use of an eye tracker in order to test the aforementioned suggestion about mechanisms, underlying learning strategies, and explaining the differences between them in terms of efficiency of language learning.
Improved Kmeans Algorithm Based on Privacy Protection

Privacy protection is a hot research topic in the field of network security. The traditional Kmeans clustering algorithm realizes the clustering of massive data by selecting the center point of the initial cluster, but privacy disclosure may occur in the clustering process and the publicity of the clustering results. This paper proposes an improved Kmeans algorithm with differential privacy protection, where density measurement is introduced into the original Kmeans to improve the in-class similarity of clusters and ensure that the selected centers are in relatively dense areas. The distance measure is introduced to reduce the similarity between clusters and improve the repellency of different cluster centers. The average maximum similarity between classes is applied to optimize the cluster number K, while the optimal initial intra-class center are dynamically programmed. To solve the problem of privacy disclosure, Laplace noise is introduced to protect information security. Experimental results show that the proposed algorithm has better clustering availability and data reliability than traditional algorithms.
PEMAPARAN APLIKASI KOMPUTER, MANAJEMEN KEUANGAN, DAN PERPAJAKAN DI SMK KOLESE TIARA BANGSA

This community service activity was carried out on students of SMK Kolese Tiara Bangsa Batam. The purpose of this service is to provide knowledge of mail-merge, financial management, and taxation to students. The devotion method is to use the counseling method for mail-merge material and the collaborative interactive lecture method for financial management and taxation material. Faculty and students are involved in this service. Service is carried out with 3 stages, namely the preparation stage, the implementation stage, and the evaluation stage. At the preparatory stage, observations are made to the object of devotion. At the implementation stage, the provision of bidding materials is carried out. At the evaluation stage, an assessment of the implementation of service is carried out. The results of the service show that students really need this material because they rarely get it in school except for taxation materials. The results of the evaluation show an increase in students' knowledge of the material presented. This service suggests that service activities should be carried out that are more devoted to the delivery of financial management materials.
Development and implementation of pixel art game based on human-computer interaction

In the past, video games were functionally limited to displaying monochrome pixels on an 8-bit screen. As the technology has evolved, this form of expression has been preserved for its unique nostalgic aesthetic and evolved into pixel art. Many excellent teams and independent developers have adopted the pixel art style and produced amazing works at low cost. In this paper, we develop a pixel art game based on human-computer interaction. The layered tetrad method is taken as the game design principle and realizes the interactions in this game. In terms of gameplay, this game will also take the knowledge in the introduction to gaming as the guiding theory, so that the works can bring better interactive experience to players. Throughout the game, players will experience immersive adventures, combat interactions based on mathematical models, and a well-acted story with a fragmented narrative style.
Analiza doniesień prasowych z wykorzystaniem technik przetwarzania języka naturalnego

The dynamic development of natural language processing results in a growing number of products utilizing so-called speech and language technologies. On the one hand this refers to the possibility of interacting with a computer using a language that people naturally use in speech and writing; one the other – making the information contained in all sorts of texts accessible for a computer. We present how methods for gathering and extracting information can be applied to news releases, to possibly reduce the overhead generated by republishing the same news by numerous internet information portals. We present how web syndication can be used to gather press releases; how to process those texts in order to determine mutual similarity; and how to visualize those. We present preliminary results of an experiment with application of the above-mentioned methods to selected Polish internet portals.
Special issue on ‘Software Engineering and Formal Methods’

High-quality labeled textual data are reported as an important type of research data in data-driven requirements engineering (RE), especially in automatic mining and analysis of massive textual data produced by software systems. Several tools have been designed to facilitate manual labeling of textual data at different levels of granularity. However, these tools neither aim to provide visualized statistics and analysis of labeled textual data, nor support collaboration among the coders to reduce the time cost in manual labeling and enhance the quality of labeling results. Besides, these tools seldom explicitly serve RE researchers. In this paper, we developed a Web-based labeling tool named CoolTeD (available at http://williamsriver.cn) for collaborative labeling of the textual datasets for RE purposes. Specifically, CoolTeD can be used to: (1) label textual data with the tag category based on ISO 25010 or other user-defined tag categories in a collaborative way; (2) review the labeling results with different confidence levels and contradictory labels, (3) identify contradictory labels and disagreements online; (4) automatically calculate the Cohen's Kappa coefficient of multiple coders, and (5) visualize the labeling results. The tool demo is available at https://youtu.be/KTVrLLenvLE.
High-speed multi-Line structured-light 3-D scanning system for accurate surface profilometry

is caused by height change.
Method of power grid operation rules information recognition based on deep learning

In order to improve the online application ability and auxiliary decision-making ability of power grid dispatching operation rules, a method for identifying key information of dispatching operation rules based on Bidirectional Long Short-Term Memory network-Conditional Random Field (BiLSTM-CRF) is proposed. A method for feature extraction and feature labeling of dispatching operation rule text is proposed, and BiLSTM is used to learn nonlinear characteristics and implicit timing information between regular text features, and CRF is used to optimize bidirectional network encoding labels globally. The recognized rule text information is combined according to the definition level to form a knowledge graph of dispatching operation rules. Through the verification of the power grid operation rules of a control center, compared with other methods, the proposed model recognizes the average precision rate, recall rate and F1 value of 98.10%, 98.19% and 98.10%, respectively, which can more accurately support the refined retrieval and positioning of the text of the dispatching operation rules.
On the Use of Clonal Selection Principle in Cartesian Genetic Programming for Designing Combinational Logic Circuits

Evolutionary techniques have been used in the design and optimization of combinational logic circuits. This procedure is called evolvable hardware and Cartesian Genetic Programming (CGP) is the evolutionary technique with the best performance in this context. Despite the good results obtained by CGP techniques, its search procedure usually evolves a single candidate solution by an evolution strategy and this approach tends to be trapped in local optima. On the other hand, clonal selection techniques in general, and CLONALG in particular, were designed to avoid converging to a low-quality local optimum. Thus, we propose here using the representation of CGP with the search procedure of a Clonal Selection Algorithm to minimize the number of transistors of combinational logic circuits. Furthermore, a parameter sensitivity analysis is performed. The results are assessed considering a benchmark from the literature and showed a reduction in the number of transistors when compared to the baseline ESPRESSO.