Reviewerly/exHarmony huggingface dataset:
The influence of elementary school students’ anthropomorphism of AI on the attitude and the career hope toward AI

Objectives This study intends to explore students’ anthropomorphism, attitude, and career hope about AI in order to get implications for AI literacy education.&#x0D; Methods This study conducted interviews with 27 elementary school students in the fourth to sixth grades who had the experience of AI education. We analyzed the interviews not only in a qualitative way but also in a quantitative way by quantifying the interviews and carrying out Fisher’s exact test in order to explore the relationships among anthropomorphism, attitude, and career hope about AI.&#x0D; Results Elementary school students were likely to perceive AI as creative as a human being although they somewhat disagreed that AI had emotion, self-willingness, and general intelligence. This study found individual differences in the anthropomorphism of AI, which influenced students’ attitude toward AI. Students, who believed that AI had emotion, were more likely to have a positive attitude toward the future life with AI. In addition, students with the positive attitude were more likely to have a career hope in the area of AI.&#x0D; Conclusions These findings imply that more research and education regarding the anthropomorphism of AI are necessary in elementary schools. Teachers need to help elementary school students to reflect on their previous beliefs and attitudes toward AI through authentic learning experiences with AI.
PregnancyLine: A Visual Analysis System for Pregnancy Care and Risk Communication

Pregnancy is a long and critical period for most women and a period that has significant impacts for newborns. Many pregnancies have an increased risk for complications for a variety of reasons. Fortunately, early prenatal care and regular examination can help women have healthy pregnancies and deliver with fewer complications. However, current research shows that many expectant mothers do not have a clear understanding of the pregnancy process due to the complexity of medical information and inadequate attention from caregivers. Therefore, in this work, we collected data on expectant mothers’ requirements and investigated the various medical treatments during pregnancy and the correlations between possible symptoms and newborn diseases. Based on the above knowledge, we then developed a visual analytic system named PregnancyLine, which presents a pregnancy care plan and information on the medical examinations at each pregnancy stage using a series of vivid visual metaphors. This system is designed to improve communication between pregnant women and doctors and help doctors identify patients with abnormalities and abnormal events. The results of the evaluation and case studies indicate that our system is effective in helping pregnant women understand pregnancy risk and the importance of examinations and that it is very useful in the detection and understanding of abnormalities.
Analysis and Application of the Business English Translation Query and Decision Model with Big Data Corpus

This paper aims to build an English translation query and decision support model using big data corpus and applies it to business English translation. Firstly, the existing convolutional network is improved by using depth-separable convolution, and the input statements are mapped to the depth feature space. Secondly, the attentional mechanism is used to enhance the expressive ability of input sentences in deep feature space. Then, considering the sequential relationship, use long short-term memory (LSTM) neural network as a decoder block to generate the corresponding translation of the input sentence. Finally, nonparametric metric learning module is used to improve the model in an end-to-end way. Wide range of experiments on the multiple corpora have shown the proposed model has better real-time performance while maintaining high precision in translation and query, and it has a certain practical application value.
Exploration on the Reform of College English Informatization Teaching from the Perspective of "Internet Plus"

Information technology is changing rapidly, "Internet Plus" thinking has penetrated into human life in all aspects, and human learning methods have undergone a fundamental change.Higher education shoulders to important historical responsibilities.This article finds through research methods of literature and logical analysis that college English teachers should effectively grasp the advantages of the integration of "Internet Plus " and college English teaching, and focus on college English information the main problems faced in the process of modernized teaching are to explore and construct a college English informationized teaching path based on the "Internet Plus" thinking, strengthen the "Internet Plus English education" informatized teaching concept, enrich the informatized teaching resources under the Internet conditions, and enhance informatization the advanced and diversified teaching methods can enhance the literacy of information technology, form an effective aid to traditional English classroom teaching, and meet the diverse needs of English teaching and talent training.
Reflections on the Discipline Construction Environment of World Literature and Comparative Literature in the Era of Big Data Analysis

Comparative literature and world literature were originally two independent disciplines, but now they are merged into one. In the era of big data, building an efficient information management method is one of the important contents of university reform. Based on the construction of comparative literature and world literature, this paper applies the DSS (Decision Support System) structure based on data warehouse to the construction of comparative literature and world literature, and constructs the DSS structure of comparative literature and world literature. The related knowledge of DM (data mining) is used for research, and the application design and implementation process of DM are introduced into the subject quality evaluation system. The research shows that the experimental results of NB (Naive Bayes) algorithm will not take too long, about 56.08 s, and the compression ratio is 0.897, when the parameters are basically the same. This model can effectively help schools to analyze data and make decisions, and improve the level of information construction in schools.
Research on the Application of Computer-Assisted Translation Technology in Translation Teaching

In order to improve the quality of translation teaching, this study combines computer-aided translation technology to construct a translation teaching system, uses digital signal processing as a technical means to process waveform data online, and designs digital models for signal flow. Moreover, this study designs a digital filter to suppress waveform noise to improve the accuracy of amplitude measurement and uses a hardware algorithm for accumulating shift and averaging to correct the baseline stacking effect in real time. In addition, this study builds an intelligent translation software system with the support of algorithms in combination with translation requirements and conducts experimental verification of translation digital processing. Finally, the simulation experiment verifies the digital translation processing effect of the method in this study, and the application effect of computer-aided translation technology in translation teaching is verified through teaching evaluation research.
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets

Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space. The learning process is typically conducted using a two-stage training architecture, and it utilizes the contrastive loss (CL) for its feature learning. Contrastive learning has been shown to be quite successful in handling imbalanced datasets, in which some classes are overrepresented while some others are underrepresented. However, previous studies have not specifically modified CL for imbalanced datasets. In this work, we introduce an asymmetric version of CL, referred to as ACL, in order to directly address the problem of class imbalance. In addition, we propose the asymmetric focal contrastive loss (AFCL) as a further generalization of both ACL and focal contrastive loss (FCL). The results on the imbalanced FMNIST and ISIC 2018 datasets show that the AFCL is capable of outperforming the CL and FCL in terms of both weighted and unweighted classification accuracies.
An Analytical Review of Machine Learning and Deep Learning Approaches for Document Forgery Detection

Document forgery cases are widespread and have flourished in the modern world of gadgets and technology. The manual approaches of document verification are time consuming and destructive, paving the way for efficient and automated document verification systems. In this paper, we reviewed the work done by the researchers to detect document forgery through source printer identification, verifying identity documents and signature. This study analyzes the machine learning and deep learning approaches, results, datasets, and limitations used to detect document forgery for each domain. This review can help the researchers, digital document forensic, and analysis communities to provide them with a view of various types of document forgeries and future research prospects.
Link and Match : Sinkronisasi Pembelajaran Akuntansi Vokasi dengan Karir Akuntan Era Society 5.0

Vocational accounting education is education that prepares students andstudents to become reliable accountants in the future. By prioritizing the focus ofpractice-based learning rather than theory, vocational accounting graduatesboth at the vocational and diploma level are believed to make a majorcontribution to the progress of the nation. However, in its implementation thereare still many cases of learning in the field of accounting that do not support theneeds of the world of work, especially in the era of society 5.0. This study aimsto analyze the synchronization of vocational accounting learning with the needsof the workforce in the era of society 5.0 and the concept of vocationalaccounting learning that is in accordance with the needs of the workforce in theera of society 5.0. This research is a type of descriptive qualitative research. Thesubjects of this research are students majoring in accounting, while the object ofthis research focuses on the Special Region of Yogyakarta. The results of thisstudy are that there is a lack of synchronization of accounting learning inseveral vocational schools and universities with the needs of the businessworld/industrial world which are increasingly leading to the era of society 5.0.The government is now increasingly concerned with vocational education withthe link and match program initiated by the Director General of VocationalEducation so that there is a need for a learning curriculum that is closely relatedto the needs of the business world/industrial world.
MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation

The extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusion of several neural network outputs. One of the most widely used method to obtain such a consensus segmentation is the STAPLE algorithm. In this paper, we first demonstrate that the output of that algorithm is heavily impacted by the background size of images and the choice of the prior. We then propose a new method to construct a binary or a probabilistic consensus segmentation based on the Fréchet means of Jaccard distances which make it totally independent of the image background size. We provide a heuristic approach to optimize this criteria such that a voxel’s class is fully determined by its morphological distance, the connected component it belongs to and the group of raters who segmented it. We compared extensively our method on three datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than those methods. Codes are available at https://gitlab.inria.fr/dhamzaou/jaccardmap .
A Parallel Top-N Video Big Data Retrieval Method Based on Multi-features

At present, a large amount of video data has been generated at an amazing speed through social media, industrial Internet and high-tech visual devices. When videos are retrieved, since the traditional video retrieval method often requires a lot of execution time, and takes a little account of the multi-source heterogeneous characteristics of videos, it is difficult to fully meet the needs of people’s daily life. Therefore, it is necessary to study a retrieval method for video big data to address the above issues. At present, there are few studies on video big data retrieval methods. The existing studies have an issue that the retrieval efficiency of video big data is low due to the sorting of all data nodes in the shuffle stage. To address this issue, this paper proposes a parallel top-N video big data retrieval method based on multi-features. First, the low-level and high-level semantic features of each video are extracted distributed. Second, a local sensitive hashing algorithm is introduced to partition and compress the video features to improve the efficiency of similarity matching for video data. Finally, a parallel top-N algorithm is constructed to quickly sort and summarize the similarity matching results of videos, so as to detect the approximate video data in the video big data. In this paper, experiments are carried out on Stanford I2V dataset. The results show that compared with the existing methods, the proposed method can effectively improve the efficiency of video big data retrieval while ensuring the accuracy of video big data retrieval.
Characteristics of Accuracy Function on Multiclass Classification Based on Best, Average, and Worst (BAW) Subset of Random Forest Model

This study aims to determine the effect of percentage of training data and number of features on average accuracy of classification model. Characteristics of the accuracy function of 2 datasets with different feature data types were also studied. All possible random forest models were calculated for accuracy based on some percentage level of training data and number of features. The accuracy function was constructed based on subsets of best accuracy (Best), average accuracy (Average), and worst accuracy (Worst) using polynomial regression. There were no significant difference in the average accuracy of various percentage levels of training data. However, there were significant difference in the average accuracy based on the number of features. The best polynomial model for average accuracy function of the 2 datasets had R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 99.50% and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 96.99%, respectively. Based on individual testing (with a 95% confidence level), there was a linear effect of the percentage of training data on the average accuracy, and there were linear and quadratic effects on the number of features on the average accuracy. In the first dataset, classification model using 7 features obtain classification accuracies ranging from 96.9% – 97.6%. In the second dataset using 7 features, the classification accuracies range from 74.0% – 75.0%. Characteristics of the Best, Average, and Worst accuracy (BAW) functions could be an important description of the accuracy function characteristics and to determine the number of features.
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise

Angluin's L* algorithm learns the minimal (complete) deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approximatively correct (PAC) version substitutes an equivalence query by a large enough set of random membership queries to get a high level confidence to the answer. Thus it can be applied to any kind of (also non-regular) device and may be viewed as an algorithm for synthesizing an automaton abstracting the behavior of the device based on observations. Here we are interested on how Angluin's PAC learning algorithm behaves for devices which are obtained from a DFA by introducing some noise. More precisely we study whether Angluin's algorithm reduces the noise and produces a DFA closer to the original one than the noisy device. We propose several ways to introduce the noise: (1) the noisy device inverts the classification of words w.r.t. the DFA with a small probability, (2) the noisy device modifies with a small probability the letters of the word before asking its classification w.r.t. the DFA, and (3) the noisy device combines the classification of a word w.r.t. the DFA and its classification w.r.t. a counter automaton. Our experiments were performed on several hundred DFAs. Our main contributions, bluntly stated, consist in showing that: (1) Angluin's algorithm behaves well whenever the noisy device is produced by a random process, (2) but poorly with a structured noise, and, that (3) almost surely randomness yields systems with non-recursively enumerable languages.
A new frog leaping algorithm-oriented fully convolutional neural network for dance motion object saliency detection

Image saliency detection is an important research topic in the field of computer vision. With the traditional saliency detection models, the texture details are not obvious and the edge contour is not complete. The accuracy and recall rate of object detection are low, which are mostly based on the manual features and prior information. With the rise of deep convolutional neural networks, saliency detection has been rapidly developed. However, the existing saliency methods still have some common shortcomings, and it is difficult to uniformly highlight the clear boundary and internal region of the whole object in complex images, mainly because of the lack of sufficient and rich features. In this paper, a new frog leaping algorithm-oriented fully convolutional neural network is proposed for dance motion object saliency detection. The VGG (Visual Geometry Group) model is improved. The final full connection layer is removed, and the jump connection layer is used for the saliency prediction, which can effectively combine the multi-scale information from different convolution layers in the convolutional neural network. Meanwhile, an improved frog leaping algorithm is used to optimize the selection of initial weights during network initialization. In the process of network iteration, the forward propagation loss of convolutional neural network is calculated, and the anomaly weight is corrected by using the improved frog leaping algorithm. When the network satisfies the terminal conditions, the final weight is optimized by one frog leaping to make the network weight further optimization. In addition, the new network can combine high-level semantic information and low-level detail information in a data-driven framework. In order to preserve the unity of the object boundary and inner region effectively, the fully connected conditional random field (CRF) model is used to adjust the obtained saliency feature map. In this paper, the precision recall (PR) curve, F-measure, maximum F-measure, weighted F-measure and mean absolute error (MAE) are tested on six widely used public data sets. Compared with other most advanced and representative methods, the results show that the proposed method achieves better performance and it is superior to most representative methods. The presented method reveals that it has strong robustness for image saliency detection with various scenes, and can make the boundary and inner region of the saliency object more uniform and the detection results more accurate.
Blockchain Technologies in Construction

The purpose of this volume is to inform the reader on the state of affairs of a new subset of construction informatics, Blockchain (B) and Decentralized Ledger Technologies (DLT)s. The initial motivation for writing and editing this volume was that we noticed the emergence of a range of ideas as a loose “body of work”, produced from a variety of lenses and research stances in the informatics of construction.
Will metaversive technologies help writers to reclaim tacit knowledge?

This article challenges the assumption that traditional genres of academic writing are as appropriate for practice-based students of art, drama or design as they were for book-centred disciplines, such as the humanities or sciences. It argues that scholarly writing diminished the importance of embodied and situated aspects of human ‘knowledge’ within mainstream university art school courses, such as visual and performative arts. In the traditional book-centred disciplines, scholarly writing was useful for encoding declarative knowledge (e.g. ‘knowing that’) but is less effective for the kinds of procedural knowledge (e.g. ‘knowing how’) that are vital in creative, studio-/practice-based learning. Now that academic writing is aided by technologies offering automatic spelling and grammar checks, global text search, cut-and-paste this has further widened the gaps between the knowledges pertaining to head, heart and hand. Soon, however, the combined benefits of 5G, virtual reality, artificial intelligence and ambisonic technologies look likely to make ‘immersive’ and ‘experiential’ technologies almost ubiquitous. Given the appropriate research and development, the ‘metaverse’ could encourage students to think in ways that are more presently situated, relational, embodied and multidimensional.
Writing the paradigm

Citing a recently published book on metadesign the article asks whether designers could learn to change paradigms. Defining metadesign as an extensible superset of adaptable and comprehensive practices, it suggests that we also need to include the many ‘conditions’ of design (e.g. education and training, evaluative frameworks, international standards and protocols, etc.) that shape the outcomes of practice. Given the complexity and scope of this idea, it seems likely that we will need to develop genres of writing that are judged by outcomes, rather than by style, veracity or logic. The article outlines a diagnostic tool that enables users to register, evaluate and map the critical relationships that co-sustain a given paradigm. Mapping enables them to be evaluated holistically. Modifying individual parts of the map might help to change the paradigm as a whole. The article also draws upon gastronomic and chemical analogies to bring non-serial clusters of critical elements together in effective ways. These would be designed, for example, to ‘emulsify’ team relations or to ‘catalyse’ active engagement within groups.
Multi-qubit doilies: Enumeration for all ranks and classification for ranks four and five

For N≥2, an N-qubit doily is a doily living in the N-qubit symplectic polar space. These doilies are related to operator-based proofs of quantum contextuality. Following and extending the strategy of Saniga et al. (Mathematics 9 (2021) 2272) that focused exclusively on three-qubit doilies, we first bring forth several formulas giving the number of both linear and quadratic doilies for any N>2. Then we present an effective algorithm for the generation of all N-qubit doilies. Using this algorithm for N=4 and N=5, we provide a classification of N-qubit doilies in terms of types of observables they feature and number of negative lines they are endowed with. We also list several distinguished findings about N-qubit doilies that are absent in the three-qubit case, point out a couple of specific features exhibited by linear doilies and outline some prospective extensions of our approach.