Reviewerly/exHarmony huggingface dataset:
Teaching and research in plant health in times of COVID-19

&lt;p&gt;The current pandemic caused by the SARS-CoV-2 virus has led to radical changes in the teaching and research of plant health. Confinement forced us to make a rapid transition from the in-person model to virtual environments using different digital platforms such as Google, Microsoft Teams, Blackboard, Moodle. Research activities were planned based on a strict access schedule for students and academics when using research facilities. Theoretical subjects were taught in completely virtual settings, incorporating playful learning tools such as ‘flipped classroom’ and gamification. In theoretical-practical subjects, such as Introduction to Plant Pathology, new teaching schemes were designed. The most successful and innovative model was to take the laboratory to the student’s home. Students used conventional materials to isolate and identify phytopathogenic fungi through the Foldscope, a paper microscope with a resolution of up to two microns, priced less than 200 MXN. Teaching-learning innovation can improve the commonly adopted model of video conferences.&lt;/p&gt;
Salient Object Detection: An Accurate and Efficient Method for Complex Shape Objects

Deep learning-based salient object detection (SOD) methods have made great progress in recent years. However, most deep learning-based methods suffer from coarse object boundaries and expensive computations, especially in detecting objects with complex shapes. This paper presents an accurate and efficient SOD method that is based on a novel double-branch network that includes a body branch and an edge branch. To obtain an accurate edge, an edge profile enhancement module (EPEM) is embedded in the edge branch. In addition, a fusion feedback module (FFM) is embedded to integrate features from the two branches. To address the problem of expensive computations, channel attention module (CAM) is included to restrain redundant feature channels. Thus, the speed of the inference step can be improved with little reduction in the boundary accuracy. Experimental results on 9 datasets demonstrate that the proposed method performs favorably against 8 state-of-the-art methods in terms of both accuracy and efficiency. Additionally, our method achieves excellent detection results for objects with complex shapes.
An Improved Pedestrian Detection Algorithm using Integration of Resnet and Yolo V2

Pedestrian detection is one of the important tasks in object detection technology. The pedestrian detection algorithm has been used in applications like intelligent video surveillance, traffic analysis, and autonomous driving. In recent years, many pedestrian detection algorithms have been proposed but the key drawback is the accuracy and speed, which can be improved my integrating efficient algorithms. The proposed model improves the pedestrian detection algorithm by integrating two efficient algorithms together. The model is developed using the joint version of ResNet and YOLO v2, which preforms feature extraction and classification respectively. By using this model the efficiency of the system is increased by improving the accuracy rate so that it can be used with real-time applications. The model has been compared with existing models like SSD, Faster R-CNN and Mask R-CNN. Comparing with these models, the proposed model provides mAP value higher than these existing models with less loss function when tested on the INRIA dataset.
Alternative criteria for writing system typology

Abstract In essence, typologies of writing systems seek to classify the world’s diverse writing systems in principled ways. However, against backdrops of early, misguided assumptions (Gelb 1969 [1952]) and stubborn term confusions, most proposals have focused primarily on the dominant levels of representational mapping (i. e., morphemic, syllabic, or phonemic), despite their shortcomings as idealizations (Joyce 2016, forthcoming; Joyce and Borgwaldt 2011; Meletis 2018). In advocating for exploring a more diverse range of criteria, either as alternatives or complementary factors, this paper outlines a promising framework for organizing typology criteria (Meletis 2018; 2020), which consists of three broad categories; namely, (a) linguistic fit , (b) processing fit and (c) sociocultural fit . Linguistic fit concerns the match between a language and its writing system and, thus, relates closely to the traditional criterion of representational mapping. Processing fit pertains to the physiological and cognitive aspects of a writing system, such as word spacing. Finally, sociocultural fit addresses the communicative and social functions of writing systems, such as implementing orthographic reforms. In singling out a particular parameter from each category, the paper illustrates its potential application as a typology criterion with cross-linguistic observations from the German (GWS) and the Japanese writing systems (JWS).
Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation

This paper aims at ensuring an efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using an ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user’s context. It can, therefore, be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user’s location and time, user’s role, their preferences and experiences) are chosen as inputs to predict user’s situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user’s situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.
About the Essence of Intelligence – Will Artificial Intelligence (Ever) Cover Human Intelligence?

Computers were originally developed for executing complex calculations fast and effectively. The intelligence of computer was based on arithmetic capabilities. This has been the mainstream in the development of computers until now. In the middle of 1950s a new application area, Artificial Intelligence (AI), was introduced by researchers. They had interest to use computers to solve problems in the way intelligent beings do. The architecture, which supported calculations, were conquered to perform tasks associated with intelligence beings, to execute inference operations and to simulate human sense. Artificial intelligence has had several reincarnation cycles; it has reappeared in different manifestations since this research area became interesting for the researchers. All the time a lot of discussion about intelligence of these systems has been going on – are the AI based systems and robots intelligent, what is the difference of human and machine intelligence, etc. Abilities related to intelligence cover ability to acquire and apply knowledge and skills, as well as ability to learn. AI provides different manifestations to the term “intelligence”: the human intelligence is a wide variety of different types of intelligence, as well as the meaning of artificial intelligence has varied over time. In our paper we will look to this term, especially to provide means for comparing human and artificial intelligence and have a look to the learning capability related to it.
Prediction of Global Solar Radiation from Sunrise Duration Using Regression Functions

The paper estimates the global solar radiation in Dubai and Abu Dhabi meteorological stations in the United Arab Emirates. Theoretical models based on Angstrom-Prescott have been applied on data of sunshine hours for the period from 2010 to 2019. The models are developed using 2010 - 2018 data and validated by comparing to the 2019 data. Theoretical solar radiation estimates the maximum of global solar radiation to be in June for both stations, which is in good agreement with the actual data. The performance of the model is tested using statistical indicators such as the coefficient of determination R2 and the Mean Root Square RMSE. The results show that the models were effective enough to describe the global solar radiation with overall R2 of 0.8874 and 0.8706, RMSE of 0.0258 and 0.0241, MBE of 0.0412 and 0.0376, and MPE of 0.2371 and 0.2318, for Abu Dhabi and Dubai meteorological stations, respectively.
OntoInfoG++: A Knowledge Fusion Semantic Approach for Infographics Recommendation

As humans tend to improvise and learn on a constant basis, the need for visualizing and recommending knowledge is increasing.Since the World Wide Web is exploded with a lot of multimedia content and with a growing amount of research papers on the Web, there is a potential need for inferential multimedia like the infographics which can lead to an ultimate new level of learning from most viable information sources on the Web.The potential growth and future of technology have called for the need of a Web 3.0 compliant infographic recommendation system in order to be able to visualize, design and develop aesthetically.The trend of the Web has asked for better infographic recommendations in the attempt of technological exploration.This paper proposes the OntoInfoG++ which is a knowledge centric recommendation approach for Infographics that encompasses the amalgamation of metadata derived from multiple heterogenous sources and the crowd sourced ontologies to recommend infographics based on the topic of interest of the user.The user-clicks are taken into consideration along with an Ontology which is modeled using the titles and the keywords extracted from the dataset comprising of research papers.The approach models user topic of interest from the Query Words, Current User-Clicks, and from standard Knowledge Stores like the BibSonomy, DBpedia, Wikidata, LOD Cloud, and crowd sourced Ontologies.The semantic alignment is achieved using three distinct measures namely the Horn's index, EnAPMI measure and information entropy.The resultant infographic recommendation has been achieved by computing the semantic similarity between enriched topics of interest and infographic labels and arrange the recommended infographics in the increasing order of their semantic similarity to yield a chronological order for the meaningful arrangement of infographics.The OntoInfoG++ has achieved an overall F-measure of 97.27 % which is the best-in-class F-measure for an infographic recommendation system.
PECULIARITIES OF ORGANIZATION OF MOTOR ACTIVITY OF STUDENTS IN THE CONDITIONS OF DISTANCE LEARNING

AnnotationIntroduction.One of the main factors in ensuring the full life of modern man, preserving and strengthening his health is a rationally organized physical activity.Unfortunately, scientists note that the population of industrialized countries is insufficiently engaged in various types of physical activity, which provides the necessary minimum energy expenditure.This fully applies to students, as their physical activity during admission to higher education is sharply reduced due to increased workload.Insufficient physical activity leads to reduced efficiency, increased fatigue, poor health.The forced transition to distance learning under quarantine restrictions has exacerbated the problem.The transition of students to distance learning using distance learning technologies can cause adverse changes in functional status due to low motor component and significant mental stress.This situation requires specialists in physical education to develop recommendations for the rational organization of the motor regime of students in distance learning.The hypothesis of the study was that the rational organization of the motor mode of students, based on the integrated use of various forms of exercise, will help to improve their physical condition and well-being.The purpose of the study: to develop a model of specially organized motor activity of students in the conditions of distance learning and to investigate the features of the influence of the proposed motor mode on the indicators of the physical condition of the subjects.Material and methods of research: analysis, generalization and systematization of data of scientific and methodical literature; pedagogical observation, sociological (questionnaires, surveys); modeling; method of functional tests, method of indices, methods of mathematical statistics.The research was conducted on the basis of Oles Honchar Dnipro National University.The study involved 120 applicants for higher education -60 boys and 60 girls.Results.Analysis of the results of the survey shows that the transition to distance learning has led to a decrease in motor activity of students, increasing the length of their stay at the computer.This is accompanied by a deterioration in the well-being of students.To optimize the motor mode of students, we developed a model of weekly specially organized physical activity, which included: a list of forms of physical education, the number of classes per week, their estimated energy value and determine the content of each form of activity and was aimed at achieving appropriate norms of physical activity and energy expenditure.The introduction of the proposed model in the lifestyle of students helped to increase the functionality of students, improve their well-being.Conclusions.The obtained results testify to the expediency of introducing the model of specially organized motor activity developed by us into the mode of life of students in the conditions of distance learning.
ELEMENTS OF SCHOOLCHILDREN’S COMPETENCE MODEL IN RELATION TO THE COMPONENTS OF FUNCTIONAL LITERACY: CONTENT ANALYSIS OF THE FEDERAL STATE EDUCATIONAL STANDARD

The article examines the problems of including the concept of “functional literacy” in the Russian educational field, reveals the interrelationships of basic ideas about the formation of functional literacy of a school graduate in the international PISA monitoring and the requirements of the Federal State Educational Standard of basic general education for subject, meta-subject and personal education results.
A Statistical Method of Detecting Bioremediation

Hydrocarbon contaminated soils result from pipeline ruptures, petroleum manufacture spills, as well as storage and transportation accidents (Bossert and Bartha (1984)).The cost of removal of the contaminated solids followed by incineration or by disposal in a landfill is prohibitive.Bioremediation -the use of microorganism populations to eliminate hydrocarbon contaminations from the environment -is the most acceptable technology for hydrocarbon cleanup (Bossert and Bartha (1984)).It can be argued that a decrease of the oil concentration in soil is not due to biodegradation but due to sorption.If this were the case, since mass transfer of sorption is a gradual process, a slow decrease in the oil recovery rate may be observed after a spill.However, a rapid or sudden decrease in the oil concentration during the incubation should exclude sorption as the primary mechanism contributing to the observed hydrocarbon loss.A Bayesian procedure is given to detect a change of the linear relationship between the oil concentration (the dependent variable) and the time in days since the addition of the oil (the independent variable).The advantage of this procedure is that it does not need to assume that the variance of the error before the change is equal to that after the change.The implementation of this procedure is straightforward.
SERS-based ssDNA composition analysis with inhomogeneous peak broadening and reservoir computing

Surface-enhanced Raman spectroscopy employed in conjunction with post-processing machine learning methods is a promising technique for effective data analysis, allowing one to enhance the molecular and chemical composition analysis of information rich DNA molecules. In this work, we report on a room temperature inhomogeneous broadening as a function of the increased adenine concentration and employ this feature to develop one-dimensional and two dimensional chemical composition classification models of 200 long single stranded DNA sequences. Afterwards, we develop a reservoir computing chemical composition classification scheme of the same molecules and demonstrate enhanced performance that does not rely on manual feature identification.
QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

To optimize the QoS of optical burst switching networks, a QoS routing optimization algorithm based on a multi-objective genetic algorithm is proposed. A Bayesian network model is used to locate the fault of optical burst switching network and obtain the fault location of the transmission link of optical burst switching network; In this position, the routing optimization algorithm based on a multi-objective genetic algorithm transforms the multi constrained network quality of service routing optimization problem into a constrained multi-objective routing optimization problem. Under multiple constraints, the best path of optical burst switching network service is obtained to realize the optical burst switching network quality of service routing optimization. The results show that after applying the proposed algorithm, the average delay of video, text and picture transmission in an optical burst switching network is less than 400ms. The proposed algorithm can improve the packet delivery rate of information transmission in an optical burst switching network, reduce the transmission delay, blocking probability and use cost of an optical burst switching network, and optimize the service quality of an optical burst switching network.
Implementasi Kahoot dalam Pembelajaran Bahasa Prancis pada Masa Pandemi Covid-19 di SMK DKI Jakarta

The information technology in learning during this pandemic situation is very important, because we did online courses, while not all teachers, including French teachers, are ready to do online courses. In learning stage there are preliminary, core and closing stages. In the preliminary stage contains information about Core Competencies (KI) and Basic Competencies (KD) that will be discussed, motivating students, apperception, etc. The core activity stage is the stage of making students understand the material in various ways and various media. The closing stage is to provide an evaluation, exercise, or test. This research focuses on the closing stage, which using Kahoot in the evaluation stage. Because not all French teachers know about Kahoot, this Kahoot implementation training becomes needed. The results are Kahoot documents made by French vocational schoolteachers. Kahoot is made according to their needs. There are 8 Kahoot documents with various KI and KD.
READING DIFFICULTIES AMONG MALAYSIAN UNDERGRADUATE ESL LEARNERS: A PRELIMINARY STUDY

Reading deficiency in reading English texts or materials among ESL undergraduate students has been one of the most significant problems in Malaysia. Educators in Malaysia are currently facing the problem as it reflects on their students’ academic performance. This study aims to investigate Malaysian ESL undergraduate students’ reading difficulties in reading English texts. 25 Malaysian students who are taking advanced English courses in a university, were selected to participate in this study. A quantitative method was employed for this study. The questionnaires were designed to fit the purpose of this study. Data analysis was done using SPSS to analyse the data collected. The result shows that students are weak in grasping the main idea when they are reading, having poor vocabulary, lack of reading habit and interest in reading English texts, and are pressured with the lack of time when reading. It can be concluded that reading deficiency affects their academic performance and their English proficiency. In overcoming their difficulties, students must equip themselves with some reading strategies to help them to comprehend the English texts that they are reading.
STEAM Oriented Science Learning Management During The COVID-19 Pandemic

This research is motivated by the importance of experimentation in science learning which cannot be done directly in online learning during the pandemic. The application of STEAM is one solution to problems that arise in the management of science learning in elementary schools during the pandemic. The subjects of this study were 20 grade 6 students in an elementary school in Surakarta. The method used in this study was descriptive qualitative with a case study design. The recruited informants consisted of students and their parents. The techniques used in this research are interviews, observation and documentation. Data analysis using data collection techniques, data reduction, presentation of power and drawing conclusions. The data validity technique is technique triangulation and source triangulation. The results of this study indicate that (1) the application of online learning changes the way students learn (2) In online learning experiments cannot be done directly (3) During the pandemic, teachers are required to innovate in the teaching and learning process (4) The STEAM approach can provide a meaningful experience for students (5) STEAM can encourage student development for the better. The implications of this research are: First, more and more teachers are implementing the STEAM approach in the teaching and learning process as an alternative so that students can learn optimally. Both parents are willing to take the time to observe and assist their daughter's online learning process. This study describes the application of the STEAM approach in online learning during a pandemic
Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

Smart contract has greatly improved the services and capabilities of blockchain, but it has become the weakest link of blockchain security because of its code nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system. Oriented to Ethereum smart contract, the study solves the problems of redundant input and low coverage in the smart contract fuzz. In this paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a dangerous operation database is designed to identify the dangerous input, and genetic algorithm is used to optimize the code coverage of the input, which construct the fuzzing framework for smart contract together. Finally, by comparing Oyente and ContractFuzzer, the performance and efficiency of the framework are proved.