Reviewerly/exHarmony huggingface dataset:
The impact of an innovators group on the development of a culture of innovation in the use of educational technologies

Realising the full potential for educational technologies to improve the student experience is a challenge for higher education institutions. Although academic resistance is often blamed for poor dissemination of teaching technologies, recent literature has more satisfactorily framed the issue in terms of intransigent organisational culture. Features of higher education organisational culture that are conducive to innovation include senior executive support, freedom to experiment and permission to fail and interdisciplinary and cross-institutional engagement. One approach to changing organisational culture is to establish new organisational structures embodying positive traits. This study qualitatively evaluated a technology in teaching innovators group established in 2018 in a science, health and engineering faculty of an Australian university; it provides evidence that the group improved the culture of teaching technology innovation by creating a safe space for innovation, which members then disseminated into their departmental communities. The study also found that the group created a forum for teaching technology innovators to engage with stakeholders in the university’s learning and teaching and information and communication technology units, leading to improved outcomes for the university’s learning environment. Implications for practice or policy Teaching technology dissemination in higher education can be enhanced through a community of practice model. Higher education learning technology environments can be improved through an innovators group leading to greater cross-institutional collaboration. Educational technology staff may be able to overcome academic resistance to strategic projects by pursuing a community of practice approach.
Emora STDM: A Versatile Framework for Innovative Dialogue System Development

This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.
Digital Image Encryption Using Logistic Map

This study focuses on the application of the Logistic Map algorithm in the Python programming language for digital image encryption and decryption. It investigates the impact of image type, image size, and Logistic Map parameter values on computational speed, memory usage, encryption, and decryption results. Three image sizes (300px x 300px, 500px x 500px, and 1024px x 1024px) in TIFF, JPG, and PNG formats are considered. The Digital Image Encryption and Decryption process utilizes the Logistic Map algorithm implemented in Python. Various parameter values are tested for each image type and size to analyze the encryption and decryption outcomes. The findings indicate that image type does not affect memory usage, which remains consistent regardless of image type. However, image type significantly influences Decryption results and computation time. Notably, the TIFF image type exhibits the fastest computation time, with durations of 0.17188 seconds, 0.28125 seconds, and 1.10938 seconds for 300px x 300px, 500px x 500px, and 1024px x 1024px images, respectively. Additionally, the encryption results vary depending on the image type. The Logistic Map algorithm is unable to restore encryption results accurately for JPG images. Furthermore, the research highlights that higher values of x, Mu, and Chaos lead to narrower histogram values, resulting in improved encryption outcomes. This study contributes to the field by exploring the application of the Logistic Map algorithm in Python and analyzing the effects of image type, image size, and Logistic Map parameter values on computation time, memory usage, and Digital Image Encryption and Decryption results. Prior research has not extensively addressed these aspects in relation to the Logistic Map algorithm in Python
The Application of Data Analysis in A Web-Based Word Game: Worldle

We will analyze the game Worldle based on its data in 2022. First, we use the EEMD Model to analyze the trend of the number of players over time in this year.We need to forecast the number of players in the next 60 days and reduce the prediction error, we use the Moving Average and build Rolling Window to reduce the forecast error. We use both LSTM model and EEMD-LSTM for training and comparing the effects of the two and find EEMD-based LSTM model is better. Then, we come up with several properties of the solution words that we think would affect the difficulty of the puzzle, and after Correlation Analysis, we find that this is indeed the case and give some explanations. Second, we mainly use Deep Learning. To predict the associated percentages of (1, 2, 3, 4, 5, 6, X) for a given future solution word on a future date, we use a Genetic Algorithm and Back Propagation Neural Network (GA-BP Neural Network) to analyze the percentage distribution of given words. After that, the distribution of the number of answers for the example can be obtained by prediction as (1.1831,6.2839,21.3307,28.6500,24.7627,14.7354,3.8857). Third, how difficult is it to guess a word correctly? For this problem, we use a method belonging to Machine Learning - PSO Decision Trees. Before this, our group first used RSR and the Pearson Correlation Coefficient to divide the difficulty level of words.
Application of Accounting Information System for Sales and Cash Receipts in an Effort to Improve Internal Control

This study aims to determine the Application of Accounting Information Systems Sales and Cash Receipts in Efforts to Improve Internal Control at PT. Prime Success Prosperous. This research is a type of descriptive research with a qualitative approach. The data needed in this study include primary data and secondary data. The primary data of this study were obtained from direct interviews with parties involved in sales and cash receipts at PT. Prime Success Prosperous. While the secondary data of this research was obtained from the literature study conducted by the researcher. The results of this study indicate that the Accounting Information System of sales and cash receipts applied by PT. Perdana Sukses Makmur has been going well even though it is still manual in receiving cash. This is supported by the existence of procedures that have to do with sales and cash receipts. As well as the existence of various functions with their respective duties and responsibilities along with the completeness of documents and records in sales and cash receipts.
TokenFree: A Tokenization-Free Generative Linguistic Steganographic Approach with Enhanced Imperceptibility

&lt;p&gt;Since tokenization serves a fundamental preprocessing step in numerous language models, tokens naturally constitute the basic embedding units for generative linguistic steganography. However, token-based methods encounter challenges including limited embedding capacity and possible segmentation ambiguity. Despite existing character-level linguistic steganographic approaches, they neglect the problem of generating unknown or out-of-vocabulary words, potentially compromising steganographic imperceptibility. In this letter, we focus on both embedding capacity and imperceptibility for a tokenization-free linguistic steganographic approach. Firstly, we suggest that unknown words mainly stem from low-entropy distributions and rigid coding rules within candidate pools, and thus we propose an entropy-based selection approach to flexibly construct candidate pools. Further, we present a lexical emphasis approach, prioritizing characters within candidate pools capable of forming in-vocabulary words. Experiments show that, across a range of high embedding rates, our approaches achieve considerably higher imperceptibility and about 17% higher anti-steganalysis capacity than the baseline method without our approaches.&lt;/p&gt;
A binocular vision cooperative measurement method with line structured light

In line structured light vision measurement, due to the occlusion of the object, the complete information might not be collected. We propose a method to acquire the object’s all point data by dual-camera cooperative measurement. First, the transformation models between cameras and motion coordinates are constructed. Subsequently, the light plane is calibrated by special points from the images of the calibration plate which is raised 3 times with an appointed distance. Finally, the data points are fused and optimized with particular matrix. The results show that the average fusion error is reduced from 0.5668 mm to 0.1253 mm. This method improves the reconstruction accuracy greatly.
Text Content Summarization Technology Based on Machine Learning Methods

The article discusses a project aimed at recognizing objects by their descriptions and generating descriptions based on the Kensho Derived Wikimedia Dataset. The goal is to create a recognition model capable of recognizing objects by their descriptions. The text summarization model was found to be less successful than the text generation model, resulting in less satisfactory results. The project involved selecting a research topic, analyzing similar projects, identifying technical tasks, creating program code, creating a control example, and analyzing the program execution process.
A Generating System for Creating a Training Set to Convert Table Images into HTML Files

In our study, a software system has been developed for automatic generation of a training set in "image to HTML" pairs for table detection problem. In the system, table images with different designs reflecting real-world scenarios are randomly generated depending on various parameters. Depending on the parameters used, HTML files are first generated and then these HTML files are converted into table images using a web browser. Then a training set is created by matching these table images with appropriate HTML texts. This training set can be a useful tool for academic and industrial researchers and can be used in automatic document conversion processes.
Secure Electric Vehicle Dynamic Charging Based on Smart Contracts

Electric vehicles (EVs) have gained significant attention due to their environmental and energy-efficient benefits. Dynamic Wireless Charging (DWC) has emerged as one of the most promising EV charging methods because it overcomes the challenges associated with traditional charging stations. However, as EVs keep moving while charging, this requires specific authentication and payment methods, enabling EV mobility privacy and ensuring fair billing. To address these concerns, we propose in this paper a smart contract based solution to deal with privacy and fair billing for EVs dynamic wireless charging. The proposed solution includes a lightweight authentication mechanism enabling a continuous authenticate-and-charge process when EV is moving on the road.
Predicting VR cybersickness and its impact on visuomotor performance using head rotations and field (in)dependence

Introduction: This exploratory study aims to participate in the development of the VR framework by focusing on the issue of cybersickness. The main objective is to explore the possibilities of predicting cybersickness using i) field dependence-independence measures and ii) head rotations data through automatic analyses. The second objective is to assess the impact of cybersickness on visuomotor performance. Methods: 40 participants completed a 13.5-min VR immersion in a first-person shooter game. Head rotations were analyzed in both their spatial (coefficients of variations) and temporal dimensions (detrended fluctuations analyses). Exploratory correlations, linear regressions and clusters comparison (unsupervised machine learning) analyses were performed to explain cybersickness and visuomotor performance. Traditional VR human factors (sense of presence, state of flow, video game experience, age) were also integrated. Results: Results suggest that field dependence-independence measured before exposure to VR explain ¼ of the variance of cybersickness, while the Disorientation scale of the Simulator Sickness Questionnaire predicts 16.3% of the visuomotor performance. In addition, automatic analyses of head rotations during immersion revealed two different clusters of participants, one of them reporting more cybersickness than the other. Discussion: These results are discussed in terms of sensory integration and a diminution of head rotations as an avoidance behavior of negative symptoms. This study suggests that measuring field dependence-independence using the (Virtual) Rod and Frame Test before immersion and tracking head rotations using internal sensors during immersion might serve as powerful tools for VR actors.
AI's Role in Sustainable Energy

This study overlook on the perspective role of AI in the energy sector, with main focus on improving efficiency and sustainability. The role is to establish a realistic benchmark for AI technology, facilitating global efforts, ambitions, novel applications, and industry obstacles. The study focuses three main areas: The application of AI in solar and hydrogen power creation. The utilisation of AI for contribution and demand management control of energy i. ii. iii. An overview of the latest techniques in AI technology This research explores how AI techniques outperform traditional models across various domains, including controllability, energy efficiency optimization, cyber-attack prevention, IoT (Internet of Things), big data handling, smart grid management, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as a pivotal tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within an increasingly competitive landscape. This research overlooks how AI techniques outperform traditional models across various domains, including Controllability, energy efficiency optimising, cyber-attack precaution, IoT (Internet of Things), big data handling, smart grid managing, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as an important tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within a rapid competitive landscap
Pengaruh Model Role Playing Terhadap Hasil Belajar Siswa

This study aims to see the effect of the Role Playing model on the Learning Outcomes of Class XI PAI at SMAN 1 Ulakan Tapakis. The sampling technique in this study used purposive sampling. Methods of data collection in this study using questionnaires and tests of student learning outcomes. The results of the validity of the questions in this study showed that 16 questions were valid out of the 25 questions tested. The reliability of the questions using Cronbach alpha obtained a value of 0.779 &lt;0.789 which means the questions have high reliability. Prior to testing the hypothesis, the normality test, homogeneity test, linearity test, and simple linear regression test were carried out. In the normality test for the experimental class, a sig value of 0.151&gt; 0.05 was obtained and in the control class, a sig value of 0.200&gt; 0.05 was obtained. In the homogeneity test, the sig value Fcount&lt;Ftable was obtained, namely 2.015&lt;4.012. In the linearity test, the sig value was 0.486 &gt; 0.05. Meanwhile, in the simple linear regression test, a sig value of 0.018 &lt;0.05 was obtained. The data in this research were analyzed using the independent sample T-test. The research results show: 1) the implementation of the Role Playing learning model is at 79.01% in the highest category; 2) The learning outcomes of experimental class students are in the very high category, it is known that in the 98-103 interval it has the highest frequency with 17 students or 51.52%. 3) The results of testing the hypothesis obtained Tcount = 6,955&gt; Ttable = 4,039.
Development of a Pellet Rotation System for Automated Visual Inspection

Nuclear power plants use uranium fuel, consisting of solid ceramic pellets of cylindrical shape with 18 mm diameter and 14 mm height, which are manufactured by various metallurgical processes. The pellets are placed on specially designed trays after grinding and pellet washing operations. These pellets are manufactured in large quantities and must confirm very stringent quality requirements usually prevalent in nuclear engineering applications. As part of quality checks on the fuel pellets, the operators manually check defects like fits, cracks, etc. occurring on the cylindrical surface. With the help of a machine vision system, the pellets were inspected by turning each pellet in its location itself while observing its surface for defects. The main equipment involves transferring system of pellets, a pellet rotation system, a vision system, a pellet retrieval system, and a system of rejection and recirculation of defective pellets. This paper mainly deals with the pellet rotation system. The design equipment has the provision to receive 5 pellets at 80 mm pitch, such that the axis of all the pellets is in the horizontal plane. They are rotated in their position in steps of 30 degrees such that in 12 steps the entire cylindrical surface will cover precisely. The cameras of the vision system were placed above each pellet with proper illumination. The programmable logic controller is used to control the pneumatic cylinders with proper intervals of time to accept or rejection of the pellet by the vision system.
Correction: Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology

[This corrects the article DOI: 10.1371/journal.pone.0286862.].
Katılımcı Sözlüklerde Mizojinik Görünümler

Like other forms of marginalization, misogyny feeds on negative stereotypes associated with the targeted group. These stereotypes are dominant in society, perpetuated through discourse, and often perceived as normal. Participatory dictionaries, popular sources of information for people from childhood onward, are one of the platforms where online misogyny is produced. Given the influence of these dictionaries, investigating and revealing misogyny in them may require rethinking and questioning the social assumptions about the female sex in the online environment. Though misogyny does not always result in direct violence, it mentally feeds violence against women. Investigating it in online environments is therefore vital, especially in a world where individuals are increasingly becoming socialized and learning judgments about their sex identities in digital spaces. This study aims to uncover discriminatory and misogynistic discourses against women in participatory dictionaries. For this purpose, its examination is conducted using the oldest and most popular dictionaries in Turkey—namely, Ekşi Sözlük (1999), Instela (2004 at ITU-2015 at Instela), Uludağ Sözlük (2005), and İnci Sözlük (2009). A total of 684 entries containing misogynistic elements under the topic of women are analyzed in this study. In line with the purpose of this study, these entries are analyzed using van Dijk’s ideological square technique. Our analysis reveals that discriminatory language is used against women, describing them as creatures or non-human entities through the use of negative adjectives. In addition to direct misogyny, such as insults and belittlement, indirect misogyny is also found among the entries, including victimization and objectification.
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing

Abstract Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. Previous work has primarily considered silver-standard data augmentation or zero-shot methods; exploiting few-shot gold data is comparatively unexplored. We propose a new approach to cross-lingual semantic parsing by explicitly minimizing cross-lingual divergence between probabilistic latent variables using Optimal Transport. We demonstrate how this direct guidance improves parsing from natural languages using fewer examples and less training. We evaluate our method on two datasets, MTOP and MultiATIS++SQL, establishing state-of-the-art results under a few-shot cross-lingual regime. Ablation studies further reveal that our method improves performance even without parallel input translations. In addition, we show that our model better captures cross-lingual structure in the latent space to improve semantic representation similarity.1
Picture Processing Optimization Technology Based on Mask R-CNN Algorithm

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages.
PENGEMBANGAN MEDIA POWTOON TERHADAP KEMATANGAN KARIER SISWA KELAS X

This study has the aim of developing a powtoon media on student career maturity. In this study using the Research and Development (R&amp;D) method using a model from Brog and Gall. The subjects in this study were students of class X. The results of the study were 1. Product development stage a) Phase I Preliminary study. b) Phase II includes product design, product validation, product revision, product testing. 2. The eligibility of material experts, media experts, and practitioners gets a score in the "Very Eligible" category. 3. Responses from students in the limited and broad trials got scores in the "Very Eligible" category. 4. Product feasibility from material experts, media experts, practitioners and students that powtoon media is very feasible and can be used. It can be concluded that the powtoon media on the career maturity of class X students is very feasible and can be used.