Reviewerly/exHarmony huggingface dataset:
Relationship between team learning profiles and outcomes in team project-based learning: a cluster analysis

ABSTRACTTeam project-based learning has become increasingly common in higher education. This study aimed to characterise and understand students’ team learning experiences in team project-based learning by considering various aspects, such as individual qualities, teamwork, task, and instructor support. K-means clustering analysis was performed using eleven variables: team learning preference, co-regulation, team efficacy, team climate, team trust, psychological safety, team interaction, perceived team loafing, task authenticity, the frequency of instructor feedback, and usefulness of instructor feedback. Data were collected from students who were involved in team project-based learning at a South Korean university in the 2nd semester of 2021, and 782 responses were used in the analysis. Consequently, six clusters were identified, each showing different patterns of individual team learning attitudes and skills, teamwork, task authenticity, and instructor feedback. Following the cluster analysis, one-way ANOVA was performed, finding significant differences in team performance and team learning satisfaction between clusters. Based on these findings, important factors in team learning are discussed, and implications of enhancing team project-based learning are presented.KEYWORDS: Team project-based learningcluster analysislearning outcomesteamworktask authenticityinstructor feedback Disclosure statementNo potential conflict of interest was reported by the author(s).
Nonce word evidence for the misinterpretation of implausible events

ABSTRACTGood-Enough Processing accounts posit a two-stream mechanism by which an algorithmic, bottom-up parse is simultaneously built alongside a heuristic, top-down parse that is prone, in real-time, to influences from real-world expectations, which sometimes leads to misinterpretations of implausible events. Post-interpretive accounts suggest the offline findings often used as evidence introduce interference due to the memory they require, favouring instead an algorithmic-only account of parsing. The current study uses self-paced reading, question answering, and sentence completions to provide converging evidence for these misinterpretations, using nonce-nouns as a baseline for increased working memory burden against which event plausibility can be compared. The findings reveal a pattern where implausible sentences rarely cause online processing difficulty compared to plausible sentences while at the same time resulting in higher rates of misinterpretation. The data favour a Good-Enough processing account and highlight the issues with relying solely on online methods for psycholinguistic inquiry.KEYWORDS: misinterpretationsimplausible eventsnoncanonical structuresnonce wordssentence processing Data availability statementThe data that support the findings of this study are openly available on OSF at https://osf.io/24ta7/. Identifier: doi:10.17605/OSF.IO/24TA7.Disclosure statementNo potential conflict of interest was reported by the author(s).
A Theory of Structured Propositions

This paper argues that the theory of structured propositions is not undermined by the Russell-Myhill paradox. I develop a theory of structured propositions in which the Russell-Myhill paradox doesn’t arise: the theory does not involve ramification or compromises to the underlying logic, but rather rejects common assumptions, encoded in the notation of the λ-calculus, about what properties and relations can be built. I argue that the structuralist had independent reasons to reject these underlying assumptions. The theory is given both a diagrammatic representation and a logical representation in a novel language. In the latter half of the paper I turn to some technical questions concerning the treatment of quantification and demonstrate various equivalences between the diagrammatic and logical representations and a fragment of the λ-calculus.
MODERNIZATION OF ACCOUNTING IN THE CONTEXT OF THE MODERN DEVELOPMENT OF THE DIGITAL ECONOMY

Purpose. The aim of the article is to substantiate the need for the development of digitalization of modern accounting of companies. Methodology of research. The research was conducted using a dialectical approach to the study of the current state of development of the digital economy. General scientific and special methods were used in the article, in particular: analytical - when reviewing normative sources; monographic - when studying literary sources on digitalization of accounting; system and analytical - when processing the received information. Findings. It was determined that in the conditions of digitalization of business processes, implementation and development of new business concepts, approaches to the methodology of accounting are changing, the need to create a new concept of accounting in companies under the auspices of total digitalization is increasing. It is substantiated that digitization and robotization contribute to changes in the functioning of company management and accounting, in particular. It was revealed that the result of the development of digital technologies will be the optimization of the organizational structure and management services of the accounting department, including due to the reduction of the staff and the review of the functionality, the introduction of new information and technological systems for processing primary documents and information, the modernization of accounting processes and accounting policy in general; strengthening requirements for professional skills of users. Originality. Directions for the development of robotization in the context of digitalization of the company's accounting service are proposed. The implementation of robotization in the activities of the accounting service of companies is substantiated. Practical value. The obtained research results will contribute to increasing the effectiveness of the formation and development of accounting robotization in the conditions of the digital economy and will become the basis for further scientific developments in this field. Key words: accounting, modernization, digitization, robotics, information technologies, business process.
GA Evolved Configuration Data for Embryonic Architecture with Built-in Self-test

The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in capabilities for self-repair. The embryonic cells have a complete genome, allowing the data to be replicated to a cell that is not defective in the event of a cell failure in the embryonic fabric. A novel, specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be notified by the proposed Built-in Self-test (BIST) module of the embryonic fabric. In this study, an effective centralized BIST design for a novel embryonic fabric is suggested. If the self-test mode is activated, the proposed BIST scans every embryonic cell. To optimize the data size, the genome or configuration data of each embryonic cell is decoded using the Cartesian Genetic Programming (CGP) format. This study evaluates the GA’s performance on 1-bit adder and 2-bit comparator circuits present in embryonic cells. Fault detection at every cell is made possible by the BIST module’s design. Additionally, the CGP format can offer gate-level fault detection. Customized GA and BIST are coupled with the novel embryonic architecture. The embryonic cell can perform self-repair through the process of scrubbing data for temporary defects.
A Survey on Text Processing Using Deep Learning Techniques

We report an experiment in which we attempted to determine emotion class at the phrase level. The method is based on a mixture of machine learning and key word analysis. A substantial annotated data set exists in which a statement was manually classified beyond the six fundamental emotions: joy, love, rage, fear, surprise and sadness. Using the annotated data set, create an emotion vector for the key word in the input sentence. Calculate the emotion vector using an algorithm of a phrase from the emotion vector of a word. The sentence was then classified into relevant emotion classes based on the emotion vector. In comparison to an individual method, the results are demonstrated and determined to be satisfactory. The goal of this article is to showcase many of the most significant text document categorization approaches and methodologies, as well as to raise awareness of some of the intriguing difficulties that remain unanswered, particularly in the fields of machine learning techniques and text representation.
SiamLBP: Exploiting Texture Discrepancies for Deepfake Detection

Advancement in technology to generate and synthesize digital media imposed both positive and negative impacts on the society from entertainment to malevolent faking of content. Progression in artificial intelligence (AI)-based synthesizing techniques, like deepfakes, provided more realism to the manipulated content. Though digital media has the ability to be utilized as proof of evidence, trust on these has diminished greatly. Considering the impact of deepfakes on the society, researchers have worked in the detection of synthesized media. This paper introduces a novel model, SiamLBP based on Siamese network that captures the texture dissimilarities between original and deepfaked faces. The proposed model is evaluated on FF++ and Celeb-DF datasets and provided a detection accuracy of 99% and 91%, respectively. In addition, deepfake dataset, focused on Indian celebrities, is generated using popular mobile applications such as FaceApp and FOM. The proposed model provided an average accuracy of 98% on generated dataset. Owing to its simplicity and efficient performance, the proposed SiamLBP outperforms other state-of-the-art detection models such as fCNN and iCaps-Dfake.
Meeting in the Metaverse: Language Learners’ Insights into the Affordances of Virtual Reality

The COVID-19 pandemic forced foreign language teaching and learning to occur online and educators to rethink how virtual learning environments can best serve students. While most educators used videoconferencing platforms like Zoom during the pandemic, virtual reality (VR) could allow physically distant learners to immerse themselves together virtually and better simulate in-person learning. Research has found that learners enjoy using VR for language learning. However, there is still a need for studies assessing learners’ opinions of how VR impacts their learning compared to other learning environments, particularly in the context of COVID-19. This chapter addresses this research gap by presenting findings from a study conducted during COVID-19 that examined the opinions of 38 foreign language French learners who completed 6 comparable speaking activities in VR, a traditional classroom, and on Zoom over a 12-week period. Upon completing all activities, participants partook in 15-to-25-minute semi-structured interviews where they gave their opinions on how the different environments enhanced or detracted from their learning. All interviews were transcribed and coded using thematic analysis. This chapter presents the findings from participants’ interviews.
Prediction of Colon Cancer Related Tweets Using Deep Learning Models

In the present day, social media has become an influential tool in society; therefore, recent research has been directed to monitor, analyze and predict the reactions resulting from the cumulative data of these means. Thanks to modern technology and the spread of social media, it is possible for people to leave their comments on various topics, including the aspect related to disease issues. In this research, people's reactions to Colon cancer were analyzed and benefited from them to predict the future of this disease; colon patient reviews and data sets were used in conjunction with data collected from Twitter. In this work, we proposed three Deep Learning (DL) models; these models Long Short-Term Memory (LSTM), Gated recurrent units (GRU), and convolutional neural network (CNN). The results showed that GRU model performance gives more stable results than LSTM and CNN models in terms of accuracy.
DTFTCNet: Radar Modulation Recognition With Deep Time-Frequency Transformation

Existing work on modulation classification of radar signals widely employs two-stage algorithms of time-frequency transform (TFT) and convolutional neural network (CNN). This paper explores the vulnerability of the time-frequency representation (TFR) to noise in the traditional TFT-CNN framework. It proposes a deep TFT classification network (DTFTCNet) that does not require explicit TFT during testing. The network reformulates the cooperation scheme between TFT and CNN, proposes a deep TFT built into the neural network to replace conventional TFT, and obtains classification results and ideal TFR without TFT during testing, realizing an end-to-end deep learning framework. Experiments show that DTFTCNet outperforms state-of-the-art modulated recognition networks and conventional TFT-CNNs in nine radar signal datasets at all signal-to-noise ratio (SNR) nodes (−14 dB to 8 dB), and DTFTCNet can mimic various traditional TFT styles. The clarity of the TFR output from DTFTCNet is significantly better than the comparison method. This paper also aims to inspire researchers to explore cooperation between conventional TFTs and deep learning.
What's Next for Crypto?

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KCReqRec: A Knowledge Centric Approach for Semantically Inclined Requirement Recommendation with Micro Requirement Mapping Using Hybrid Learning Models

Software requirement recommendation is one of the most important strategies that is required when first building a new system. It provides a baseline of what exactly the software will do and how it is expected to perform, along with this it also describes how the product will fulfil all the stakeholder's needs. In this paper, a model to predict the requirement specifications has been proposed. The techniques used were micro requirement mapping using hybrid learning models such as K-Means Clustering hybridized with Support Vector Machines and Cosine Similarity which yielded 80.17% average precision, 82.69% average recall, 81.43% average accuracy, 81.41% of the average measure of harmonic means and 0.1983 false discovery rate and also the proposed KCReqRec system which yielded 94.27% average precision, 96.39% average recall, 95.33% average accuracy, 95.31% of the average measure of harmonic means and 0.0573 False Discovery Rate. These models were run on the Kaggle Software Requirement Dataset integrated with data from the PURE (PUblic REquirements) dataset.
Person Re-Identification With Visual Semantic Representation Mining and Reasoning

Person re-identification aims to match images of the same person in different scenarios. The challenge of this task is how to extract discriminative features from person images with complex background noise, severe occlusions and large pose variations. Recently, some studies have attempted to apply human semantic parsing or attention mechanisms to help capture human parts or important object regions. Despite the performance improvements, these methods either require the introduction of additional prior knowledge or ignore the connections between the human body parts. To solve the above problem, we propose a novel Visual Semantic Representation Mining and Reasoning (SRMR) block to capture more discriminative semantic features of person images. Specifically, to get rid of the restriction of the external model, we propose to directly mine the clustering relationship between each local feature in the global structure and obtain the discriminative region in the person image by voting. Then, to establish the relationship between person body parts, we utilize Graph Convolutional Networks (GCN) to effectively construct the correlation between body parts. Extensive ablation studies demonstrate that our SRMR block can significantly improve the feature representation power and achieve state-of-the-art performance on several popular benchmarks.
Evaluation of Convolution Primitives for Embedded Neural Networks on 32-Bit Microcontrollers

Deploying neural networks on constrained hardware platforms such as 32-bit microcontrollers is a challenging task because of the large memory, computing and energy requirements of their inference process. To tackle these issues, several convolution primitives have been proposed to make the standard convolution more computationally efficient. However, few of these primitives are really implemented for 32-bit microcontrollers. In this work, we collect different state-of-the-art convolutional primitives and propose an implementation for ARM Cortex-M processor family with an open source deployment platform (NNoM). Then, we carry out experimental characterization tests on these implementations. Our benchmark reveals a linear relationship between theoretical MACs and energy consumption. Thus showing the advantages of using computationally efficient primitives like shift convolution. We discuss about the significant reduction in latency and energy consumption due to the use of SIMD instructions and highlight the importance of data reuse in those performance gains. For reproducibility purpose and further experiments, codes and experiments are publicly available ( https://gitlab.emse.fr/b.nguyen/primitive_of_convolution ).
Semi-supervised Learning Enabled Fault Analysis Method for Power Distribution Network Based on LSTM Autoencoder and Attention Mechanism

By upgrading the existing distribution network fault statistical analysis system of Shanghai Institute of Electrical Science and Technology, comprehensive research is needed to investigate the hardware configuration and system functions of the system, optimize the system structure, meet the needs of the company’s distribution network business department, and provide strong support for the daily operation and management of the distribution network. In this paper, we present a semi-supervised learning enabled fault analysis method for power distribution network based on LSTM autoencoder and attention mechanism. We make the LSTM autoencoder’s loss function more robust so that it may be affected by both labeled and unlabeled input. Next, by minimizing the loss function, we can learn how the distribution of both types of data is distributed. We also added an attention mechanism to make the model performance more stable as the weight of the marked data changes. We apply the improved experience of LSTM autoencoder to the LSTM prediction model and realize the LSTM prediction model under semi-supervised learning. The proposed algorithm can effectively solve the problems of strong dependence of time series data and high cost of marking, so as to obtain better fault detection results of power distribution network.
Human Activity Detection Using Attention-Based Deep Network

Nowadays, digital surveillance devices are widely implemented to collect massive volumes of data indefinitely, necessitating human monitoring to identify various activities. The requirement for smarter surveillance in this era is for artificial intelligence and computer vision technology to automatically identify normal and aberrant actions. We present a long short-term memory (LSTM)-based attention mechanism based on a pre-train convolutional neural network (CNN) that focuses on the most important characteristics in the source frame to distinguish human activities in videos. We employ the DenseNet layers to extract the prominent spatial features from frames. We input these characteristics into an LSTM to learn temporal features in video; after that, an attention technique is used to enhance performance and calculate more high-level selected activity-related patterns. The presented system was evaluated on UCF11 datasets and achieved recognition rates of 97.90%, demonstrating a substantial advancement over the current state-of-the-art (SOTA) method.
Signature and Log-Signature for the Study of Empirical Distributions Generated with GANs

In this paper, we address the research gap in efficiently assessing Generative Adversarial Network (GAN) convergence and goodness of fit by introducing the application of the Signature Transform to measure similarity between image distributions. Specifically, we propose the novel use of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) Signature, along with Log-Signature, as alternatives to existing methods such as Fréchet Inception Distance (FID) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Our approach offers advantages in terms of efficiency and effectiveness, providing a comprehensive understanding and extensive evaluations of GAN convergence and goodness of fit. Furthermore, we present innovative analytical measures based on statistics by means of Kruskal–Wallis to evaluate the goodness of fit of GAN sample distributions. Unlike existing GAN measures, which are based on deep neural networks and require extensive GPU computations, our approach significantly reduces computation time and is performed on the CPU while maintaining the same level of accuracy. Our results demonstrate the effectiveness of the proposed method in capturing the intrinsic structure of the generated samples, providing meaningful insights into GAN performance. Lastly, we evaluate our approach qualitatively using Principal Component Analysis (PCA) and adaptive t-Distributed Stochastic Neighbor Embedding (t-SNE) for data visualization, illustrating the plausibility of our method.
Revolutionizing Academic English Writing through AI-Powered Pedagogy: Practical Exploration of Teaching Process and Assessment

Artificial intelligence (AI) is revolutionizing various fields, including education. This study aims to explore the practical implementation of AI-powered pedagogy in the teaching of academic English writing. Specifically, the study examines the impact of an AI-powered writing tool on the teaching process and assessment in an English as a Foreign Language (EFL) classroom setting. A pre-test/post-test experimental design was employed to measure the effectiveness of the tool on students' writing skills. Results showed that the AI-powered writing tool positively impacted students' writing proficiency, as evidenced by significant improvements in writing scores from the pre-test to the post-test. In addition, the tool was found to enhance the teaching process by providing timely and individualized feedback, promoting student engagement, and increasing efficiency in grading. The findings highlight the potential of AI-powered pedagogy in transforming traditional English writing teaching and offer practical implications for teachers, students, and educational technology developers.