Reviewerly/exHarmony huggingface dataset:
ASSESSING THE LEVEL OF THE STUDENTS’ READING COMPREHENSION ABILITIES AND ITS DIFFICULTIES

This study aimed to assess the student's reading comprehension level and the difficulties that the students faced in reading. The writer conducted this study at Senior High School 2 Pasuruan, especially the 11th-grade students in Language Programs. This study focused on the student's reading ability level and the difficulties that the students faced in reading. The writer used survey research designs in conducting this study. The participants of this study were 30 students. In collecting the data, the writer distributed closed-ended questionnaires, which consisted of 29 questions with a 4-point scale (always, sometimes, rarely, and never). The result showed that 47% of students faced many problems or difficulties understanding the reading text. While 37% of students sometimes found difficulties reading an English passage, 16% of the students who chose "rarely" indicated that the difficulty they faced was not challenging. In conclusion, it was found that many students still need help comprehending the text. The study implies that the influencing factors in reading must be addressed.Â Â
Visual Reasoning: From State to Transformation

Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e., transformation. They are solely defined to test how well machines understand concepts and relations within static settings, like one image. Such state driven visual reasoning has limitations in reflecting the ability to infer the dynamics between different states, which has shown to be equally important for human cognition in Piaget's theory. To tackle this problem, we propose a novel transformation driven visual reasoning (TVR) task. Given both the initial and final states, the target becomes to infer the corresponding intermediate transformation. Following this definition, a new synthetic dataset namely TRANCE is first constructed on the basis of CLEVR, including three levels of settings, i.e., Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Next, we build another real dataset called TRANCO based on COIN, to cover the loss of transformation diversity on TRANCE. Inspired by human reasoning, we propose a three-staged reasoning framework called TranNet, including observing, analyzing, and concluding, to test how recent advanced techniques perform on TVR. Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event, View, and TRANCO. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and new problems need to be investigated in this direction.
MODERN FORMATS OF STUDENTS' CREATIVITY DEVELOPMENT WITHIN THE FRAMEWORK OF SCHOOL-UNIVERSITY INTEGRATION

The article considers modern approaches to the development of students' creativity within the framework of school-university integration. The relevance of the students’ creativity development is substantiated on the basis of the renewed Federal State Educational Standards of Basic General Education. Best practices in development of creativity in educational institutions of Surgut and Astrakhan are highlighted. The article is intended for university teachers, teachers of secondary schools implementing students' creativity development programs.
Object Detection and Tracking: Exploring a Deep Learning Approach and Other Techniques

Object detection and tracking have a wide range of uses, for example, in security and surveillance systems to deter and investigate crimes, for traffic monitoring, and for communication through video sharing. In a smart city, data is continuously being obtained through various means. In terms of video data, the data collected through cameras and other digital devices need to be analyzed to derive useful information from it. Hence, the concept of object detection and tracking comes into play. This chapter looks into developing various frameworks for object detection and tracking in the context of video data. We will be working with a database of 1,939 pencil images. These images will be used to train a neural network that performs image classification tasks. Various object detection methods are implemented such as Convolution Neural Network (CNN) image classification, Canny edge detection, object detection using the Haar Cascade classifier, and background subtraction. The experiments are carried out with Python programming language, TensorFlow, and OpenCV library.
A Deep Learning-based Solution for Identification of Figurative Elements in Trademark Images

Labeling of trademark images with Vienna codes from the Vienna classification is a manual process carried out by domain experts by searching trademark image databases using specific keywords. Manual labeling is both a time-consuming and error-prone process. Therefore, in this paper, we investigate how deep learning techniques can improve and automate labeling of new unlabeled trademark images. Three different deep learning models, namely CNN, LSTM and GRU, are trained and tested on a collected dataset composed of 14,500 unique logos extracted from the European Union Intellectual Property Office Open Data Portal. A set of controlled experiments establishing baseline results on the dataset showed that CNN outperforms the other two models in terms of both accuracy and training time. The experimental results also suggest that deep learning models are an important tool that can be applied by Intellectual Property Offices in real-world applications to automate the trademark image classification task.
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Improved Image Caption Rating – Datasets, Game, and Model

How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. What is a good caption? We investigate this problem by focusing on image-caption ratings and by generating high quality datasets from human feedback with gamification. We validate the datasets by showing a higher level of inter-rater agreement, and by using them to train custom machine learning models to predict new ratings. Our approach outperforms previous metrics - the resulting datasets are more easily learned and are of higher quality than other currently available datasets for image-caption rating.
SLEPLET: Slepian Scale-Discretised Wavelets in Python

Wavelets are widely used in various disciplines to analyse signals both in space and scale.Whilst many fields measure data on manifolds (i.e., the sphere), often data are only observed on a partial region of the manifold.Wavelets are a typical approach to data of this form, but the wavelet coefficients that overlap with the boundary become contaminated and must be removed for accurate analysis.Another approach is to estimate the region of missing data and to use existing whole-manifold methods for analysis.However, both approaches introduce uncertainty into any analysis.Slepian wavelets enable one to work directly with only the data present, thus avoiding the problems discussed above.Applications of Slepian wavelets to areas of research measuring data on the partial sphere include gravitational/magnetic fields in geodesy, ground-based measurements in astronomy, measurements of whole-planet properties in planetary science, geomagnetism of the Earth, and cosmic microwave background analyses.
An Exact Algorithm to Solve Multi-objective, Multi-Constrained Shortest Path Problems with Forbidden Paths

Over the years, shortest path (SP) algorithms have been extended to (i)~optimize multiple objective functions, (ii)~take into account graph-related constraints or (iii)~forbidden paths. These extensions arose from the need to adapt SP computation to real world domains or to use it as inner-procedure for the resolution of more complex problems. These adaptations have been proposed as separate contributions. However, in several cases all such aspects are to be considered altogether, and algorithms to jointly handle them are missing.In this paper we adapt previous contributions (and fix a bug in one of them) from the literature to obtain a simple exact algorithm that computes one-to-all multi-objective SPs with budget constraints and forbidden paths. We show its computational performance under different scenarios. Compared to existing off-the-shelf solutions, our algorithm can avoid looping paths, which was not possible before. This enables us to solve a case-study on a specialized multicommodity flow problem, which we could not do before.
The research of functions and the construction of their graphs

In the article, the algorithm of the research of elementary functions is proposed, used to solve various problems, which will contribute to the more effective acquisition of mathematics in gymnasiums and high schools.
An examination of SOAR strategies for college students writing from multiple sources: Soaring to Successful Synthesis Writing

Writing assignments at the collegiate level that emphasize synthesis are quite often assigned. In reading-to-writing tasks, students must choose, organize, and integrate facts from a variety of sources in order to produce an original piece of writing. This might be challenging for certain students. Consequently, it could be challenging for college students to construct a synthesis that has a distinct structure and is tightly connected to their own ideas. This research aimed to get a deeper understanding of the SOAR strategy system, which assists students in selecting, arranging, linking, and controlling various forms of information. This was accomplished in full compliance with the requirements for the synthesis writing process. In the first piece of study, the value of the educational tools that SOAR has made available to its users was investigated. Reading four separate books—four that can be finished with or without the SOAR supplements, depending on the student's preference—helps college students prepare to write synthesis essays. These four readings can be completed with or without the SOAR supplements. Students who did not utilize SOAR as their guide were able to write essays that were less comprehensive (in terms of the selection of relevant materials), less categorically organized (in terms of structuring), and had fewer intertextual relationships than students who did use SOAR as their guide (connecting). The second experiment investigated the degree to which SOA is trainable and how the quality of synthesis writing is affected by student-created SOAR tools. Students in higher education were tasked with completing their first assignment, an essay in the form of basic synthesis. After that, individuals either elected to participate in the SOAR program or decided against it and relied on their methods instead. They finished yet another writing synthesis project as their very final assignment for the day. Students who had participated in SOAR instruction could produce better-structured essays and covered more ground than those who had not participated in the program. During the dialogues, a broad variety of topics were discussed. Some of these topics included the underlying cognitive mechanisms, the limits of the study, prospective directions that future research would pursue, and the practical ramifications.
Using text-based indices, one can predict how lexically rich short texts in French, German, and Portuguese will be perceived

We investigated how accurately computerized calculations of the lexical features of the texts might predict readers' perceptions of the lexical richness of short texts. This was done by looking at the lexical characteristics of the texts. Over 150 indices were constructed from the writings of 3,060 children between the ages of 8 and 10 written in French, German, and Portuguese. They were assessed for their lexical richness by 3 and 18 raters who had not been educated. The writings ranged in length from 9 to 284 words. We observed that the precision with which the ratings of shorter texts could be expected was similar to that of longer texts and that the ratings could be predicted mainly based on these indices. However, models with fewer predictors based on a 6-dimensional framework of lexical richness perceptions or even just a straightforward predictor, Guiraud's index, performed slightly worse than models with more predictors. The most potent predictors for French and German were opaque models with scores of predictors.
Mapping the spectrum of case analysis genres in a program for information systems

Students generally find it difficult to adopt the academic/learner and professional/mock consultant roles necessary for the case study. The case study is a well-liked literary device commonly employed in business and information systems (IS) education. Conflicting demands are placed on these obligations to be kept to the abovementioned situation. We concluded that it would be useful to develop a continuum for case studies covering the spectrum from educational to professional contexts at our institution, which offers four different information systems programs based on the findings of design-based research. Taking advantage of the study's findings helped us complete this project successfully. This section examines the expectations for each of these four courses, how the case analysis assignments fit into the continuum, and whether or not they promote overall healthy student development. According to the results of our study, students are typically helped by expanding our educational institution when they move from academic to professional duties. However, the trajectory may be improved by increasing the volume of help offered with producing case studies, which is in the middle of the continuum. Finally, we offer some suggestions for how academic programs should use the benefits offered by the developmental trajectory to enable a seamless transition across a student's four-year college experience. This is being done to ensure that the student's educational experience evolves naturally.
A Study of Global Solar Radiations Measurement in Java Island, Indonesia

Solar energy is well-known renewable energy that has been widely used nowadays.Given that Solar energy is green, inexhaustible, pollution-free and simple to use, it has received significant interest all over the world including in Indonesia.Therefore, the main purpose of the present study is to evaluate and compare the potential of solar energy in six stations on Java Island in Indonesia.The methodology has used the renowned Angstrom-Prescott mathematical method while estimating the global solar radiation in the six stations on java island as a potential to utilize solar energy to be a source of electricity generation.The meteorological data used in this assessment is recorded in daily format for five consecutive years starting from 2015 until 2019.The results show that the area that has the highest global solar radiation potential is Banten city with an average value of 5.43 kWh/m 2 while the city with the lowest global solar radiation potential is Yogya city with an average value of only 5.40 kWh/m 2 .The highest clearness index value among the six provinces is in Jeteng with a value of 0.52 and the lowest is in the province of Jabar with a value of 0.50.Whereas the difference in the daily ratio of the duration of sunlight between one province and another province is very small.Moreover, each province has extraterrestrial solar radiation values increasing from January until June and then decreasing from July until December.Thus, in this study, it is expected that it can raise awareness to reduce conventional energy by utilizing domestic renewable energy.
Path Planning Method for Underground Survey Robot in Dangerous Scenarios

Over the past 20 years, there has been a growing demand for autonomous exploration of underground environments such as tunnels, urban underground corridors, and complex cave environments. With the development of robotics, dangerous tasks that used to be done by humans have been replaced by underground survey robots. To avoid obstacles in the process of robot work, path planning technology has become one of the important components of underground survey robot. The underground survey robot not only needs to move in the plane, but also needs to climb along the pipe. However, the existing research is mostly two-dimensional plane, which cannot be directly applied to the underground exploration robot. Firstly, this paper compares the existing research and chooses Dijkstra algorithm for three-dimensional implementation. After that, the environment map is established, and the position of obstacles faced by the robot was set for simulation verification. Simulation results show the effectiveness of the proposed algorithm.
Artificial Intelligence and Human Workers Interaction

Artificial intelligence are one of the representative technologies of the fourth industrial revolution, which promote the transformation and upgrading of manufacturing enterprises to automated and intelligent production. As changes of traditional work scenarios and organizational models are happening, understanding how robots will influence human worker in various aspects is becoming more and more important for the reason that companies need to ensure a healthy and efficient relationship between human and robots. In particular, it is essential to figure out how human workers will be influenced. This question has attracted a lot of attention from academic and practical circles. However, the impact of the use of robots on employees and the mechanism of its generation have not yet been systematically reviewed and summarized. In this paper, a detailed summary is presented on the topic focusing on how robots will influence human workers in two separate ways: mental state and job performance. The whole summary will be developed from a basic understanding of the current stage of robotics application to the psychological and behavioral responses of employees induced by use of robots. The theories involved in the existing studies are also sorted out and reviewed, with an ultimate production of a research framework and future research directions.
Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

The dataset in the application scenario of existing fault diagnosis methods is often balanced, while the data collected under actual working conditions are often imbalanced. Directly applying existing fault diagnosis methods to this scenario will lead to poor diagnosis effect. In view of the above problems, we proposed a method called dual generative adversarial networks (DGANs) combining conditional assistance and feature enhancement. The method uses data augmentation as a basic strategy to supplement imbalanced datasets by generating high-quality data. Firstly, a new generator is designed to build the basic framework by sharing the dual-branch deconvolutional neural networks, and combining the label auxiliary information and the coral distance loss function to ensure the diversity of generated samples. Secondly, a new discriminator was designed, which is based on deep convolutional neural networks and embedded with auxiliary classifiers, further expanding the function of the discriminator. Thirdly, the self-attention module is introduced into both the generator and the discriminator to enhance deep feature learning and improve the quality of generated samples; finally, the proposed method is experimentally validated on datasets of two different testbeds. The experimental results show that the proposed method can generate fake samples with rich diversity and high quality, using these samples to supplement the imbalanced dataset, the effect of imbalanced fault diagnosis has been substantially improved. This method can be used to solve the problem of fault diagnosis in the case of sample imbalance, which often exists in actual working conditions.
The role of motivational interviewing training in supporting the practice of COVID-19 contact tracers

Purpose To explore how motivational interviewing (MI) training might benefit the practice of COVID-19 contact tracers. Design/methodology/approach Following co-production of a MI training package, with a United Kingdom (UK) track and trace organisation, training was delivered virtually to 101 volunteer participants involved in contact tracing. Data were captured via an online survey, incorporating questions from recognised measures of occupational self-efficacy and workplace wellbeing, prior to the training. Open data fields were used to gather feedback about participants' reasons for attending, and views about the training afterwards. Findings Although the contact tracers reported high occupational self-efficacy and workplace wellbeing, both quantitative and qualitative data suggested participants saw practitioner value and utility in MI. Research limitations/implications The sample was self-selecting and typically involved contact tracers from UK local authorities. The study did not measure impact on compliance with self-isolation guidance and/or providing details of contacts, and larger-scale research would be needed to establish this. This was not a pre-post-test evaluation study, and measures of occupational self-efficacy and workplace wellbeing were gathered to give insight into the sample and to test the feasibility of using this survey for a future large-scale study. The research was conducted during the height of the pandemic. While UK COVID-19 contact tracing services have since been reduced, there are potential implications for infection control more generally. Practical implications MI is potentially a useful approach for enhancing contact tracing practice. However, implementation factors should be carefully considered, to ensure effective and sustainable practice. Social implications Improved practice in contact tracing could have potential benefits in infection control, through improving compliance with central guidance, although this requires more widespread investigation. Originality/value This is the first empirical study to investigate how MI training could benefit COVID-19 contact tracing practice.
Securing the Internet of Things (IoT) with Blockchain: A Proof-of-Concept Implementation and Analysis

The Internet of Things (IoT) has revolutionized the way we interact with everyday objects, enabling devices to collect and share data seamlessly. However, this increased connectivity has also increased the security risks associated with these devices, as they often lack the necessary security mechanisms to prevent malicious attacks. To address this issue, we propose using blockchain technology to secure IoT devices. In this paper, we present a proof-of-concept implementation of a blockchain-based IoT security system and analyze its effectiveness. Our system leverages blockchain's distributed ledger technology to ensure data integrity, decentralization, and transparency, making it more resilient to attacks. We evaluate our system's performance and compare it with other existing IoT security solutions. Our results show that our blockchain-based approach outperforms traditional security measures and is a viable solution for securing IoT devices. Finally, we discuss the limitations of our study and suggest future research directions for improving the security of IoT devices.