Reviewerly/exHarmony huggingface dataset:
Towards Purposeful Technology-Integration Practices

The purpose of this chapter is to assess the extent of technological pedagogical content knowledge of preservice teachers. Using the TPACK survey developed by Schmidt et al., participating preservice teachers self-assessed their TPACK knowledge. Participating preservice teachers were part of the first cohort required to take an instructional technology course in the undergraduate initial certification program due to revised program requirements. Results indicated that more than 80% of the survey participants agreed that they “can teach lessons that appropriately combine content area, technologies, and teaching approaches.” To further understand these self-reported data, TPACK constructs discussed in this exploration focus on three of the seven domains—TCK, TPK, and TPACK. Actionable considerations for preservice teachers are proposed, including a professional development learning module to build teachers' TPACK knowledge.
Engage Teacher Leaders to Design Inclusive and Inquiry-Based Practices

COVID-19 has presented new challenges for all teachers, especially teachers of students with special needs. Students have struggled with online learning environments with limited opportunities for social interaction and resources. The study investigated a co-design process to help teacher leaders to design inquiry-based learning with the integration of artificial intelligence (AI) technologies. The study utilized the convergent parallel mixed methods approach to analyze both qualitative and qualitative data to capture how teacher leaders introduced inquiry-based learning in the school community. The quantitative analysis revealed shifts in terms of teachers' ability to design inquiry-based projects that integrated AI. The qualitative analysis showed that the growth pattern of teacher leaders might be dependent upon (1) the usefulness of AI, (2) teachers' attitudes about the role of the teacher as a designer, and (3) perceived leadership support. The study provided a co-design process to engage teachers in designing inquiry-based projects during the pandemic times.
A Comprehensive Study on Gesture Detection

The advent of technologies made our lives more comfortable with their rapid spread in various sectors like business, education, productive industry, marketing, health sector, and many more. Computer vision being an interdisciplinary field has a large scope of applications, and among them, object detection carries a vital role and has wide spread in various fields of study. Object detection exhibits a potentiality in numerous applications like image and video annotation, gesture detection, etc. In this regard, a preliminary research is carried out on sign language gesture recognition to review and facilitate the road map of earlier research findings. The work is carried with a focus on the recent developments and the enormous techniques and tools that were introduced to handle gesture recognition varying from mathematical models to deep learning models with its advantages and limitations. Issues like computational complexity and accuracy with respect to the various verticals of the sign language interpretation are explored and are listed with their remarkable outcomes.
Distance Learning at Pedagogical Faculties of Universities in the Czech Republic

This study focuses on the topic of distance education at pedagogical faculties of universities in the Czech Republic during the time of COVID-19 and tries to map how theoretical and practical teaching was implemented during the Coronavirus pandemic. Based on the research goal, the following research questions were formulated.RQ1: Which tools were used to enable distance education at pedagogical faculties in the Czech Republic?RQ2: How were the defences of qualification theses and the state final examinations carried out?RQ3: How was the practical pedagogical training implemented?To answer the individual research questions, a content analysis of information provided on the websites of individual faculties and in the reports published on the Internet in the course of theoretical and practical teaching at pedagogical faculties in the Czech Republic during the Coronavirus pandemic was used. The research found that the distance teaching took place using various tools, frequently mentioned were the Moodle LMS, MS Teams, Zoom, Google Classroom or Meet. Practical subjects, which cannot be fully realised online, were taught in a full-time form in blocks at the end of the semester. The exams also took place online, the MS Teams or Zoom platform was used for oral examinations, and the Moodle LMS was used for tests. At the Faculty of Education of Palacký University Olomouc, the defences of qualification theses and state final examinations took place in the examination room of the university under strict hygienic conditions. At the University of Ostrava and Masaryk University, the defences of qualification theses and state final examinations took place online, using MS Teams. Practical teacher training also took place online, students participated in online teaching at individual faculty schools. Students helped teachers create materials for online teaching, participated in online teaching or led online teaching themselves. At the Faculty of Education of University of Hradec Králové, they performed the function of a teaching assistant or led tutoring.
A High-Precision Power Line Recognition and Location Method Based on Structured-Light Binocular Vision

To complete the wiring operation of the main transmission line stripped of its insulating skin in a live power distribution system, a structured-light binocular vision method is utilized to identify and locate the line. First, aiming at the interference of the background information, a depth threshold segmentation method is used to filter the background area. Second, a mean filtering method is proposed to filter out the mismatch noise of a binocular vision camera in an outdoor environment. The Canny algorithm is then utilized to extract the contour, the central axis of the main transmission line is fitted, and the difference in the neighborhood pixel value is used to recognize the stripping area. Finally, the spatial equation and attitude of the central axis of the fitting transmission line are obtained along with the central coordinates of the stripping area, guiding the robot to carry out the wiring.
Bayesians Still Don’t Learn from Conditionals

Abstract One of the open questions in Bayesian epistemology is how to rationally learn from indicative conditionals (Douven, 2016). Eva et al. ( Mind 129(514):461–508, 2020) propose a strategy to resolve this question. They claim that their strategy provides a “uniquely rational response to any given learning scenario”. We show that their updating strategy is neither very general nor always rational. Even worse, we generalize their strategy and show that it still fails. Bad news for the Bayesians.
Pelatihan Pembuatan Cairan Serbaguna Eco-Enzyme dari Sampah Organik dan Cara Pemanfaatannya di Desa Gondangmanis, Bae, Kudus

&lt;p&gt;&lt;strong&gt;Training on Making Eco-Enzyme Multipurpose Liquids from OrganicWastes and How to Use in Gondangmanis Village, Bae, Kudus. &lt;/strong&gt;Eco-Enzyme (EE) is a multifunctional liquid produced from anaerobic fermentation of organic waste, especially from fresh fruit and vegetable wastes. Until now, most Indonesian citizens have not known about this enzyme, including the partner, residents of Gondangmanis Village, Bae District, Kudus Regency. This training aims to enable the partners to produce EE from organic wastes at the household level and its application. The direct benefit of this activity is to add value to household waste. The indirect benefit is to increase the volume of recyclable wastes towards zero waste. The used method consisted of lectures, practices of EE production, storage, and application. The EE production practice used a plastic drum with a capacity of 120 liter filled with 90 liter of water, 27 kg of organic wastes, and 9 kg of molasses. The result showed that total EE produced was 73 liter, smells good with light brown, and at a pH of 3.0. The partner used the EE for household cleaning, air purification, and floodwater purification. There is a percentage increase in participants' understanding of 90% who initially did not know EE to be able to make and apply it. Using organic waste for EE production can reduce as much as 50% of the amount of organic waste.&lt;/p&gt;
Tender Process for Transformers

This chapter describes how the tender process is organized to ensure that user expectations and requirements are correctly identified and consistently met and simultaneously that costs are controlled. Special emphasis is placed on internal preparations by the user, on supplier selection or qualification, and on tendering and contract awarding.
To Read Is the Challenge─Insights from 100 Days, 100 Papers Reading Challenge in Chemistry Education Research

The lack of time available for staying up to date with the literature is a common issue for scholars and practitioners in many disciplines. A recent challenge issued on Twitter with the goal to read 100 papers in 100 days attracted the attention of several members of the chemistry education research community. In this paper, we report the outcomes of this reading challenge, including insights into the group composition, its reading preferences, and challenges participants face. We also provide an overview of the themes covered in the group’s readings obtained via natural language processing of the abstracts of the papers read by the group. Common themes in the papers were generally centered around students, learning, and chemistry, with an emphasis on research validity and the role of technology in chemistry instruction. Differences between individual participants’ reading choices and those of the group as a whole are visualized using semantic network plots.
Cryptanalysis of DRPE using complex S-Box based on linear canonical transform

During recent decades, double random phase encoding grasped more attention for researchers. To achieve nonlinearity, it had been done with random S-Box. We exhibit this involvement that DRPE system is much vulnerable in the above methodology. Concatenating anything with DRPE needs an imaginary value, wherein S-Box unsuccessful in it. Used S-Box has been reformed into various sizes. Due to this scenario, S-Box values are replicating. So, complex S-Box has been employed and proposed size of the S-Box is similar to an input image. Numerical simulations such as performance analysis, histogram analysis and 3D plot analysis have been performed out to validate the practicability and trustworthiness of traditional DRPE system with complex S-Box. Moreover, in order to check the cryptanalysis, much other analysis done such as occlusion attack, noise attack, chosen plaintext analysis and sensitivity analysis also accomplished.
Multiparticle quantum walk–based error correction algorithm with two-lattice Bose–Hubbard model

When the evolution of discrete time quantum walk is carried out for particles, the ramble state is prone to error due to the influence of system noise. A multiparticle quantum walk error correction algorithm based on the two-lattice Bose–Hubbard model is proposed in this study. First, two point Bose–Hubbard models are constructed according to the local Euclidean generator, and it is proved that the two elements in the model can be replaced arbitrarily. Second, the relationship between the transition intensity and entanglement degree of the particles in the model is obtained by using the Bethe hypothesis method. Third, the position of the quantum lattice is coded and the quantum state exchange gate is constructed. Finally, the state replacement of quantum walk on the lattice point is carried out by switching the walker to the lattice point of quantum error correction code, and the replacement is carried out again. The entanglement of quantum particles in the double-lattice Bose–Hubbard model is simulated numerically. When the ratio of the interaction between particles and the transition intensity of particles is close to 0, the entanglement operation of quantum particles in the model can be realized by using this algorithm. According to the properties of the Bose–Hubbard model, quantum walking error correction can be realized after particle entanglement. This study introduces the popular restnet network as a training model, which increases the decoding speed of the error correction circuit by about 33%. More importantly, the lower threshold limit of the convolutional neural network (CNN) decoder is increased from 0.0058 under the traditional minimum weight perfect matching (MWPM) to 0.0085, which realizes the stable progress of quantum walk with high fault tolerance rate.
Fair and Efficient Alternatives to Shapley-based Attribution Methods

Interpretability of predictive machine learning models is critical for numerous application contexts that require decisions to be understood by end-users. It can be studied through the lens of local explainability and attribution methods that focus on explaining a specific decision made by a model for a given input, by evaluating the contribution of input features to the results, e.g. probability assigned to a class. Many attribution methods rely on a game-theoretic formulation of the attribution problem based on an approximation of the popular Shapley value, even if the underlying rationale motivating the use of this specific value is today questioned. In this paper we introduce the FESP - Fair-Efficient-Symmetric-Perturbation - attribution method as an alternative approach sharing relevant axiomatic properties with the Shapley value, and the Equal Surplus value (ES) commonly applied in cooperative games. Our results show that FESP and ES produce better attribution maps compared to state-of-the-art approaches in image and text classification settings.
New Obfuscation Scheme for Conjunctions

Abstract Recently, there has been renewed interest in conjunction obfuscations. A conjunction, which is called pattern matching with wildcards sometimes, is associated with a pattern $\mathsf{pat}\in \{0,1,*\}^n$ where * is a wildcard. It accepts if and only if the input bits are the same as the pattern at all non-wildcard positions. The conjunction obfuscation starts to get noticed because it provides the ability to protect these sensitive patterns while preserving its functionality. It is meaningful when the conjunction obfuscation is applied in the pattern matching, biological recognition, resisting SQL injection attacks and so on. In this work, we propose a new candidate of conjunction obfuscation. It not only retains the simplicity of the intuitive scheme in BKM18, but also adds wildcards to the pattern. Besides, we also propose a conjunction obfuscation with multi-bit output. The second obfuscation has the same size of the obfuscated program as the first obfuscation. Both obfuscations provide the distributional virtual black-box security.
Dyslexia-friendly design features for tangible user interfaces: a systematic literature review

Purpose The aim of this paper is to explore, analyse and summarise the potential tangible user interface (TUI) design features for dyslexics learning to read and spell. Design/methodology/approach This study adopts a systematic literature review method through a manual search of published papers from 2011. This systematic literature review consists of three main phases starting with planning the review, conducting the review and studying the selection and reporting the results. Findings As the result, 10 papers were found that are considered most applicable as they met the review criteria. Based on the 10 papers, 11 design features are found in the current TUIs design features implemented in the tangible system dedicated for dyslexia and reading. The 11 design features are viewed to be fundamental and useful for the groundwork towards designing and developing a comprehensive TUIs design specifically and exclusively meant for dyslexic children learning to read and spell. Originality/value A critical reading of past literature relating to the TUIs concept reveals that this concept still appears to be under-explored even though it was introduced two decades ago. This systematic literature review highlights directions for future research, involving the dyslexic children early in the design process towards the evaluation of the prototype and finally examining the outcome of TUIs on children's learning.
Information Fusion and Construction of Evidence Collection

Information fusion is a holistic concept, and its direct technical goal is to replace single information with combined information as the input of the system and then obtain more accurate results for judgment. For urban transportation analysis technologies in the context of big data, the purpose of information fusion is to construct an effective evidence collection to support the judgment and decision-making of the problem.
Virtual portraits from rotating selfies

Abstract Selfies are a popular form of photography. However, due to physical constraints, the compositions of selfies are limited. We present algorithms for creating virtual portraits with interesting compositions from a set of selfies. The selfies are taken at the same location while the user spins around. The scene is analyzed using multiple selfies to determine the locations of the camera, subject, and background. Then, a view from a virtual camera is synthesized. We present two use cases. After rearranging the distances between the camera, subject, and background, we render a virtual view from a camera with a longer focal length. Following that, changes in perspective and lens characteristics caused by new compositions and focal lengths are simulated. Second, a virtual panoramic view with a larger field of view is rendered, with the user's image placed in a preferred location. In our experiments, virtual portraits with a wide range of focal lengths were obtained using a device equipped with a lens that has only one focal length. The rendered portraits included compositions that would be photographed with actual lenses. Our proposed algorithms can provide new use cases in which selfie compositions are not limited by a camera's focal length or distance from the camera.
Multi Lingual Smart Virtual Personal Assistance: A Step towards Improving Life of Specially Able Society

The modern Era is a boon to humanity with ever-changing technology, which makes it possible to do the things that can make our world smart and sustainable. God is a great creator, and along with so many variations, he has also created people with a unique abilities. Accidently or eventually, due to old age, a healthy person may be helpless and then suffer from depression due to sustainability issues in this fastmoving and emotionless world. However, with cognitive computing technologies and machine learning, Artificial Intelligence (AI) is a miracle specifically in the form of Virtual Personal assistants. Many instances are available in the recent literature about the same. This chapter presents a simple, mobile-based user-friendly application that can be operated with a multi-lingual voice signal to enable the person to perform regular tasks. This concept gives an intelligent platform to automate our tasks with ease, comfort, and accuracy. The idea of a smart and sustainable world motivated us to develop a Personal Helper with brilliant intelligent powers, interacting with the objects in our surroundings, mobile services, and PC services just by one human voice. The hardware device receives the audio request through a voice input device like a microphone. The proposal is then processed so that the device can execute the task. The primary objective of the virtual assistant is to help the disabled, specially-abled people, do their work by giving single voice commands in their natural voice. The chapter demonstrates the concept of significantly reducing impaired or elderly persons' physical efforts and living a 'smart' quality life.
An Empirical Study on the Occurrences of Code Smells in Open Source and Industrial Projects

Background: Reusing source code containing code smells can induce significant amount of maintenance time and cost. A list of code smells has been identified in the literature and developers are encouraged to avoid the smells from the very beginning while writing new code or reusing existing code, and it increases time and cost to identify and refactor the code after the development of a system. Again, remembering a long list of smells is difficult specially for the new developers. Besides, two different types of software development environment - open source and industry, might have an effect on the occurrences of code smells. Aims: A study on the occurrences of code smells in open source and industrial systems can provide insights about the most frequently occurring smells in each type of software system. The insights can make developers aware of the most frequent occurring smells, and researchers to focus on the improvement and innovation of automatic refactoring tools or techniques for the smells on priority basis. Method: We have conducted a study on 40 large scale Java systems, where 25 are open source and 15 are industrial systems, for 18 code smells. Results: The results show that 6 smells have not occurred in any system, and 12 smells have occurred 21,182 times in total where 60.66% in the open source systems and 39.34% in the industrial systems. Long Method, Complex Class and Long Parameter List have been seen as frequently occurring code smells. The one tailed t-test with 5% level of significant analysis has shown that there is no difference between the occurrences of 10 code smells in industrial and open source systems, and 2 smells are occurred more frequently in open source systems than industrial systems. Conclusions: Our findings conclude that all smells do not occur at the same frequency and some smells are very frequent. The short list of most frequently occurred smells can help developers to write or reuse source code carefully without inducing the smells from the beginning during software development. Our study also concludes that industry and open source environments do not have significant impact on the occurrences of code smells.
Characterizing the Usage of CI Tools in ML Projects

Background: Continuous Integration (CI) has become widely adopted to enable faster code change integration. Meanwhile, Machine Learning (ML) is being used by software applications for previously unsolvable real-world scenarios. ML projects employ development processes different from those of traditional software projects, but they too require multiple iterations in their development, and may benefit from CI. Aims: While there are many works covering CI within traditional software, none of them empirically explored the adoption of CI and its associated issues within ML projects. To address this knowledge gap, we performed an empirical analysis comparing CI adoption between ML and Non-ML projects. Method: We developed TraVanalyzer, the first Travis CI configuration analyzer, to analyze the CI practices of ML projects, and developed a CI log analyzer to identify the different CI problems of ML projects. Results: We found that Travis CI is the most popular CI tool for ML projects, and that their CI adoption lags behind that of Non-ML projects, but that ML projects which adopted CI, used it for building, testing, code analysis, and automatic deployment more than Non-ML projects. Furthermore, while CI in ML projects is as likely to experience problems as CI in Non-ML projects, it has more varied reasons for build-breakage. The most frequent CI failures of ML projects are due to testing-related problems, similar to Non-ML and OSS CI failures. Conclusion: To the best of our knowledge, this is the first work that has analyzed ML projects’ CI usage, practices, and issues, and contextualized its results by comparing them with similar Non-ML projects. It provides findings for researchers and ML developers to identify possible improvement scopes for CI in ML projects.