Reviewerly/exHarmony huggingface dataset:
Research on the Interactive Relationship of Immersive Art

The theoretical discussion on immersive art is still in its infancy. With the rapid development of science and technology and the continuous progress of the times, immersive art is a contemporary form of "full sense art" that emphasizes participation, interaction, integration and penetration. This paper will discuss and interpret the concept definition, external supporting elements, internal characteristics, value connotation and sustainable development of immersive art by means of case study, field research and literature research. This paper holds that in the context of contemporary media integration, immersive art is characterized by interactivity, technicality and experience, and gradually constructs the structural relationship of technical interaction, environmental interaction and interpersonal interaction. In the dimension of time and space, immersive art gradually immerses the virtual into reality through technological upgrading, brings the most extreme sensory experience to the public, and realizes the economic goals in the consumer society. On the other hand, it quietly returns to the concern of "humanity", showing the artistic spiritual ideal of reshaping the realistic environment and regenerating the humanistic spirit.
One-Handed Character Input Method for Smart Glasses that Does Not Require Visual Confirmation of Fingertip Position

We have developed a way for users to enter characters with one hand without looking at their fingers. The user grabs the input device with one hand and taps or strokes with the thumb. The device is covered with a thin plate with holes. The plate limits the area that can be touched, and the boundaries of the holes allow the user to recognize the position of the finger by the tactile sensation of the finger. The text entered by the user is displayed on the smart glasses. Guide images that assist the user’s input are also displayed here. This guide shows the status of the input operation and what the user can do next. Enter one character in two steps. First, the stroke selects a group of characters. Then flick or tap to select one letter from the group. Ten groups are defined that can contain up to 5 characters. Therefore, up 50 characters can be assigned. This number is sufficient to allocate Japanese unvoiced hiragana. However, voiced, semi-voiced, and lowercase hiragana cannot be covered. These characters can be entered after tapping the button to change the selected group to that group. In the beginners experiment, the average input speed after inputting 50 words was 22.9 [CPM], and the error rate was 8%. In an experiment in which 10 words were entered daily for 30 days, the input speed of two participants was 35 [CPM] and the other participant was 25 [CPM].
Explanation of ensemble models

Ensemble learning is a type of machine learning, typically supervised learning, that combines the decisions of multiple individual models to improve the classification or regression accuracy. Since their introduction 2 decades ago, ensemble models have been widely used not only in academia but also in practical applications, particularly in data science competitions such as Kaggle because they excel with tabular and structured data. However, understanding the decision mechanisms of such large models is a challenge. In the explainable artificial intelligence (XAI) literature, several studies have attempted to make ensemble models more transparent. Although deep learning has recently attracted significant attention in XAI research, it is still important to introduce methods that help explain ensemble models. This chapter introduces the problem of explaining the ensemble models in detail. Two of the most popular ensemble approaches, bagging and boosting, are first introduced, and then the main factors that make ensemble models difficult to explain are discussed. Later, a taxonomy of ensemble interpretation methods and some representative techniques for each category is also presented.
D-WISE Tool Suite for the Sociology of Knowledge Approach to Discourse

Under the umbrella of the D-WISE project, manual and digital approaches to discourse analysis are combined to develop a prototypical working environment for digital qualitative discourse analysis. This new qualitative data analysis tool, called D-WISE Tool Suite, is built up in a process of close exchange by the two teams from humanities and informatics and focuses on developing central innovations regarding the availability of relevant Digital Humanities (DH) applications. Bridging the gap between structural patterns detected with digital methods and interpretative processes of human meaning making is at the core of the collaborative approach of anthropological studies and computer linguistics in the D-WISE project, which innovates both informatics technology of context-oriented embedding representations and hermeneutic methodologies for discourse analysis in the Sociology of Knowledge Approach to Discourse (SKAD). In this paper, the intertwining of the two paradigms Human-in-the-loop and AI-in-the-loop will be presented by outlining the concept of Human Computer Interaction (HCI) in the D-WISE Tool Suite with its AI-empowered features and established modes of feedback-loops and the supported functions for facilitating SKAD.
Creation and Significance of Database of Dictionary of Cognate Words

This paper introduces the procedure of creating a database of Wang Li’s Dictionary of Cognate Words, including the conceptual data model design, the logical data model design, and the table structure design. Thus we can investigate information contained herein with the help of this database, and conducting quantitative researches on the Dictionary of Cognate Words in an efficient way. In detail, our main contributions can be summarized as follows: (i) It makes a comprehensive analysis of the Dictionary of Cognate Words and refutes some previous claims with wrong views. (ii) It demonstrates a distribution of Chinese cognate words and their relations. (iii) It provides samples for the subsequent digitization of dictionaries and further shows how to analyze large-scale corpus with database approaches.
Utility-Embraced Microaggregation for Machine Learning Applications

With access to vast amounts of data, privacy protection is more important than ever. Among various de-identification (anonymization) techniques, <i>k</i>-anonymous microaggregation has been widely studied since it enables us to balance between confidentiality and data utility. Despite plenty of microaggregation methods in the sense of reducing the information loss and/or computational complexity, machine learning (ML) models using the resulting aggregated data face the problem that they are not as effective as expected. Motivated by the fact that ML models can be heavily influenced by distorted training data (albeit slightly), we deliberate on the performance of microaggregation in terms of not only data privacy but also <i>data utility</i>. In this paper, we propose Util-MA, a new utility-embraced microaggregation framework for effective ML applications. Specifically, unlike prior studies that apply microaggregation techniques directly to raw data, we design a unified framework that can potentially enhance the data utility while preserving the <i>k</i>-anonymity through preprocessing steps including <i>dimensionality reduction</i> and <i>clustering</i>. By using real-world datasets, we empirically demonstrate the superiority of Util-MA over benchmark microaggregation methods in terms of classification accuracy. Moreover, we investigate the importance of preprocessing by measuring key performance indicators (KPIs) of clustering; the clustering stage of Util-MA leads to high performance on the classification when the clustering results substantially coincide with the ground truth labels. We also establish a close relationship between the KPIs of clustering and the classification accuracies, which tends to be revealed when there is a gain of Util-MA over the benchmark method is observed. Our framework is microaggregation-model-agnostic; thus, underlying microaggregation models can be appropriately chosen according to one&#x2019;s needs and ML tasks.
Engagements2 as ‘HCI Material’: Propagating Community Agency, Through Embedded Technologies

HCI has material attributes. As a sociotechnical assemblage, HCI mediates and/or translates technologies to public(s) and vice versa. It is malleable, ‘made’ and crafted and as a material media technology changes our relationships to ‘things’, each other and our surrounding world. Thinking through HCI as material allows us to unite disciplines with technologies, ensuring that how we conceptualise work is tangible and applicable. Working from this understanding of HCI, allows the authors to contextualise Engagements2 as an emerging ‘material’ space uniting art, design and other practices often fractured through disciplinary conventions. Traditionally, public engagement encompasses ways organisations engage with external parties. HCI contemporaries, Public Interest Technologies (PITs) empower public stakeholders and municipalities. PITs unravel intractable problems, through design, data, and delivery, thus providing user agency and yields wider societal benefit(s). We question how digital technologies can transition ‘public(s)’, to sustainable approaches. In time, Engagements2 will be commonplace as technologies (PITs, augmented reality, IoT sensing and more) are embedded into public environment(s), if engagement can be defined as a ‘craft-able’, material concern. The article unites contemporaries in: the public realm, social design, and public engagement methods to identify the: pitfalls, benefits, and opportunities. There is a need for creating a ‘best practice’ roadmap to creative, active engagement. These values go well beyond designing for inclusion and seek for more sustainable and integral interactions, impacts and culture creation.
Transformations to Improve the Approximation by a von Mises Distribution

Motivated by the success of the Box and Cox (J R Stat Soc Ser B Stat Methodol 26:211–243, 1964) [2] transformation to near normality, we consider the transformation of directional data to an approximate von Mises distribution. Variation about the central value of this von Mises distribution is symmetric and a single shape parameter controls the amount. The circular nature of directions requires the development of some novel transformations that are completely unrelated to transformations of the positive real line. We introduce a class of transformations indexed by two parameters. We verify the improvement of a von Mises approximation for three data sets that are well known to exhibit asymmetry. Then, we discuss the computational difficulties and give a proof of the consistency and asymptotic normality of the maximum likelihood estimators of all parameters.
Improving the Behaviour of Vision Transformers with Token-Consistent Stochastic Layers

We introduce token-consistent stochastic layers in vision transformers, without causing any severe drop in performance. The added stochasticity improves network calibration, robustness and strengthens privacy. We use linear layers with token-consistent stochastic parameters inside the multilayer perceptron blocks, without altering the architecture of the transformer. The stochastic parameters are sampled from the uniform distribution, both during training and inference.The applied linear operations preserve the topological structure, formed by the set of tokens passing through the shared multilayer perceptron. This operation encourages the learning of the recognition task to rely on the topological structures of the tokens, instead of their values, which in turn offers the desired robustness and privacy of the visual features. The effectiveness of the token-consistent stochasticity is demonstrated on three different applications, namely, network calibration, adversarial robustness, and feature privacy, by boosting the performance of the respective established baselines.
An Ontology-based Approach to Annotating Enactive Educational Media: Studies in Math Learning

Enactive media bring new possibilities for intelligent interactive educational systems, as they are capable of adapting to the user's emotional expressions and promote an advanced learning experience. This can motivate students and minimize learning problems, attracting and keeping students' attention by showing personalized content according to their emotional states. Annotations are necessary to recovery, adapt, compose and present educational content according to emotional expressions. Aiming at identifying and classifying enactive media, this article presents a novel ontology for semantic annotation. Our solution supports to annotate content (normal media) to be used by enactive (or linked to) educational media. The proposed ontology reuses and extends international standards for media annotation and promotes interoperability with existing models. Our investigation presents an evaluation in the context of teaching mathematics for Computer Science students. We illustrate the instantiation of the proposed ontology and its application in practice. In this study, professors execute scenarios of composition and use of media segments annotated according to the proposed ontology. Obtained results point to the potential contribution of the ontology in a classroom environment and research challenges to be addressed in further studies.
Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline

COVID-19 has been one of the most dominant discussion topics on Twitter since 2019. Users express their opinions representing public sentiment on the topic. This paper presents a sentiment timeline of Twitter users, regarding COVID-19 vaccines. This work raises concerns about the extracted information with regards to sentiment analysis, the dominance of each sentiment and its influential power. During the implementation of the analysis, several datasets were examined for the creation of the model. Various algorithms were employed with Random Forest performing best and therefore selected for training the model, achieving an accuracy of 91.5%. Our findings indicate that the majority of Twitter users are positive regarding COVID-19 vaccines and support WHO’s recommendations. Negative tweets comprising the minority of the tweets, appear to have a higher influential power with their retweet rates, outperforming positive and neutral sentiments.
Mixing Population-Based Metaheuristics: An Approach Based on a Distributed-Queue for the Optimal Design of Fuzzy Controllers

In this work, we present a distributed platform to execute multi-population metaheuristics. As proof of concept, we present an implementation using two metaheuristics: Genetic Algorithms, and Particle Swarm Optimization. We execute these multi-population algorithms asynchronously using a queue-based architecture. We optimize the parameters defining the membership functions of a rear-wheel fuzzy controller. We compare the results with a non-distributed sequential alternative and show the benefits of mixing the algorithms’ populations and integrating a migration process between them.
Artificial Intelligence Platform Construction and Integration Based on Multi-sensor Fusion

Sensor is an important source of data transmission, through the integration of mechanical radar, inertial measurement unit and other sensors, building an artificial intelligence platform, will effectively save data information. By taking advantage of sensor fusion, the data of gyroscope and accelerometer are transferred to the background, and then the time stamp of artificial intelligence platform is aligned to get useful and usable information. By combining the mechanical radar and millimeter wave radar, an artificial intelligence platform is built, which makes full use of the synergetic advantage of the related sensors, captures the data information independently and saves it according to the time stamp. This paper focuses on the construction and integration of artificial intelligence platform based on multi-sensor fusion.
Determining Column Numbers in Résumés with Clustering

In the recruitment process, the workload of manual résumé reviews is quite time consuming for the recruiters. This review process can benefit from Artificial Intelligent-aided intelligent systems to extract the actual meaning within the résumés and structure their forms. However, writing résumés has no standards, and the personalized structure of each received résumé makes this task highly challenging. This work is dedicated to tackling a part of this issue on structuring résumés. More specifically, we firstly focus on finding the column number of any résumé since once the main parts of the résumé are separated, the subdivisions can easily be analysed. This study, thus, formalizes the problem of finding columns of a résumé as a clustering problem. The experiments are performed on a data set of custom Turkish résumés having up to two-columns, on which we apply two algorithms: K-means and Density-based spatial clustering of applications with noise. As a result of the experiments, we observe that an optimal cluster size relates strongly to the valid column number. Our method is not limited to résumés but can be applied to any unstructured textual data.
On the Possibilities of Light Environment Art in Digital Scenes: From the Perspective of Metaverse Research

This paper mainly explores the possibility of light environment art in digital scenes from the perspective of Metaverse research. Firstly, the concept of Metaverse is summarized and condensed on the basis of existing research results. The digital scene in the Metaverse is a digital society with a high degree of integration between virtual and reality. Continuous presence and a highly immersive experience. The light environment art, as the art of painting with light as the language, plays an irreplaceable role in both the real world and the digital scene. The light environment art stated in this article mainly refers to the virtual light environment art that plays a role in the digital scene. From the perspective of Metaverse research, this paper summarizes the three major characteristics of light environment art: hypermedia information transmission, surreal space construction of light images, and nonlinear light language and painting narrative mode; and further analyzes the light environment in the Metaverse space. The application of art includes: the symbol designation of the light environment in the virtual architecture, the visual presentation of the light environment in the virtual architecture, and the shaping of the light environment space in the virtual architecture.
Anchor-free Siamese Network Based on Visual Tracking

The Visual tracking problem can usually be solved in two parts. The first part is to extract the feature of the target and get the candidate region. The second part is to realize the classification of the target and the regression of the bounding box. In recent years, Siameses network in visual tracking problem has always been a frontier research hotspot. In this work, it applies two branches namely search area and tracking template area for similar learning to track. Some related researches prove the feasibility of this network structure. According to the characteristics of two branch shared networks in Siamese network, we also propos a new fully convolutional Siamese network to solve the visual tracking problem. Based on the Siamese network structure, the network we designed adopt a new fusion module, which realizes the fusion of multiple feature layers at different depths. We also devise a better target state estimation criterion. The overall structure is simple, efficient and has wide applicability. We extensive experiments on challenging benchmarks including generic object tracking-10k (GOT-10K), online object tracking benckmark2015 (OTB2015) and unmanned air vehicle123 (UAV123), and comparisons with state-of-the-art trackers and the fusion module commonly used in the past, Finally, our network performed better under the same backbone, and achieved good tracking effect, which proved the effectiveness and universality of our designed network and feature fusion method.
Human Cognitive Constraints on the Separation Frequency and Limit of Separable Words

Based on the CCL corpus, the paper employs Java to statistically analyze the Chinese characters and words inserted between the AB morphemes of the separable words. The conclusion is as follows: (1) In terms of average value, considering frequency, the top five average values of inserted Chinese characters and words are about 4 ± 2, and regardless of frequency, the top five average values of inserted Chinese characters and words are about 8 ± 3; (2) In terms of frequency distribution, about 2 ± 1 Chinese characters or words can be inserted, and the maximum capacity of Chinese characters and words can be inserted is about 7 ± 2. The separated form of separable words can be regarded as gestalt, closely related to human working memory and information processing ability. It has psychological reality: the limit of human processing information is about ‘seven’, and about ‘four’ is the sensitive point of information easier to process.
Research Status and Trends of the Gamification Design for Visually Impaired People in Virtual Reality

This paper explores the current research status, issues, and future research trends of gamification of educational content related to spatial ability training in virtual environments for cognitive characteristics of people with visual impairment (PVI). Based on the Web of Science core collection, 29 related papers, including 20 gamification design cases, were selected and analyzed using the document content analysis method. The education content, techniques, and design in these previous work showed that: the current training tasks in gamification design mainly focus on navigation, orientation, and mobility training; sensory augmentation and sensory substitution with spatial audio and tactile interface are popular in interaction design for PVI, and it is controversial whether the information of different modalities complement each other or interfere with each other. Future research trends may include strategies of multimodal information for PVI, personalized dynamic adjustment of content and tasks, and standardized effectiveness evaluation methods.
Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents a method for analyzing healthcare information that uses in future prediction. The major goal is to take a variety of data while improving efficiency and minimizing process time. The suggested method employs a hybrid method that is divided into two stages. In the first stage, it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight, opposition-based learning, and distributor operator. In the second stage, CSVM is used which combines the benefits of convolutional neural network (CNN) and SVM. The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources. For improved economic flexibility, greater protection, greater analytics with confidentiality, and lower operating cost, the suggested approach is built on fog computing. Overall results of the experiments show that the suggested method can minimize the number of features in the datasets, enhances the accuracy by 82%, and decrease the time of the process.