Reviewerly/exHarmony huggingface dataset:
Nice is Different than Good: Longitudinal Communicative Effects of Realistic and Cartoon Avatars in Real Mixed Reality Work Meetings

We report a within-subjects study of the effect of realistic and cartoon avatars on communication, task satisfaction, and perceived sense of presence in mixed reality meetings. For 2 − 3 weeks, six groups of co-workers (14 people) held a recurring real work meeting using Microsoft HoloLens2 devices. Each person embodied a personalised full-body avatar with a realistic face and another with a cartoon face. Half the groups started in the realistic condition and the other half started in the cartoon condition; all groups switched conditions half-way. Initial results show that, overall, participants found the realistic avatars' nonverbal behaviour more appropriate for the interaction and more useful for understanding their colleagues compared to the cartoon one. Regarding the results over time, we identify different insights for cartoon and realistic avatars based on the type of avatar was embodied first. We discuss the implications of these results for mixed and virtual reality meetings.
ARRPNGAN: Text-to-image GAN with attention regularization and region proposal networks

Although text-to-image synthesis has shown remarkable success in generating high-resolution photorealistic images and semantic consistency, it still faces challenges in generating images with complex backgrounds. In this paper, we address this problem by proposing a novel generative adversarial text-to-image synthesis framework based on attention regularization modules and region proposal networks (ARRPNGAN). ARRPNGAN can precisely locate the keywords in text by exploiting attention model advantages and improving the accuracy in locating the subimage of target objects with the help of an RPN. Leveraging both attention regularization and the RPN a generative adversarial network (GAN) can obtain the most text description semantics and reduce the interference of complex background information. The results of extensive experiments on the Caltech-UCSD Birds and MS COCO datasets demonstrate that the proposed ARRPNGAN significantly outperforms other state-of-the-art text-to-image methods, especially in generating photorealistic images with complex backgrounds. Codes are available at: https://github.com/quanFN/ARRPNGAN . • Text-to-image synthesis combines textual and image features. • Generative adversarial network shows promising results in image synthesis. • Attention mechanism is effective at locating target objects and features. • Region proposal network can eliminate background interferences. • Proposing networks for synthesizing real-world images.
Blockchain and <scp>IPFS</scp>‐based reliable land registry system

Abstract Land registration is a process of keeping track of landowners of existing land. Registration of land helps maintain proof of ownership and prevents illegal land possession. Land‐related conflicts are common in developing countries like India due to inconsistent data and transparency. The land ownership transfers also take a longer time than expected. This article presents a land registry system using blockchain and InterPlanetary File System (IPFS) technology to overcome the mentioned problems. The land records will be immutable using blockchain, and nobody will forge them illegally. The system will be more transparent as all the users in the blockchain network can view the land and the owner's details before getting into ownership transfer. For maintaining the integrity of land and owner details, it will get stored in IPFS. The land transaction will be carried out via a smart contract. The smart contract checks whether all the entities agree with the transaction, and the money will get transferred within the described time limit. This proposed system will strengthen the land administration process by providing immutable and transparent land records. It will also speed up the registration process by a significant amount.
Finite-time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e529" altimg="si3.svg"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> control for interval type-2 fuzzy singular systems via switched fuzzy models and static output feedback

This paper is concerned with the finite-time H∞ control problems for T–S fuzzy singular systems. The interval type-2 fuzzy model is considered to better express the uncertainties in the membership functions. Based on the operating space partition and scale variation of membership functions, the fuzzy system is transformed into a switched one. A switched fuzzy controller which has linear membership functions is designed. By means of the singular value decomposition of singular systems, the fuzzy Lyapunov functions and the information of the linear membership functions, relaxed criteria are obtained to guarantee that the closed-loop systems to be regular, impulse-free, and singular H∞ finite-time boundedness. An algorithm is built to solve the conditions in the criteria. Two simulation examples are presented to show the effectiveness of the proposed methods.
Investigating Data Work Across Domains

In the wake of the hype around big data, artificial intelligence, and "data-drivenness," much attention has been paid to developing novel tools to capitalize upon the deluge of data being recorded and gathered automatically through IT systems. While much of this literature tends to overlook the data itself—sometimes even characterizing it as "data exhaust" that is readily available to be fed into algorithms, which will unlock the insights held within it—a growing body of literature has recently been directed at the (often intensive and skillful) work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming 'data-driven' or 'data-informed' can become real. Further, In some cases, increased demands for data work have led to the formation of new occupations, whereas at other times data work has been added to the task portfolios of existing occupations and professions, occasionally affecting their core identity. Thus, the evolving forms of data work are requiring individual and organizational resources, new and re-tooled practices and tools, development of new competences and skills, and creation of new functions and roles. While differences exist across the global North and the global South experience of data work, such factors of data production remain paramount even as they exist largely for the benefit of the data-driven system [21, 32]. This one-day workshop will investigate existing and emerging tasks of data work. Further, participants will seek to understand data work as it impacts: individual data workers; occupations tasked with data work (existing and emerging); organizations (e.g. changing their skill-mix and infrastructuring to support data work); and teaching institutions (grappling with incorporation of data work into educational programs). Participants are required to submit a position paper or a case study drawn from their research to be reviewed and accepted by the organizing committee (submissions should be up to four pages in length). Upon acceptance, participants will read each other's paper, prepare to shortly present and respond to comments by two discussants and other participants. Subsequently, the workshop will focus on developing a set of core processes and tasks as well as an outline of a research agenda for a CHI-perspective on data work in the coming years.
Shaping Textile Sliders: An Evaluation of Form Factors and Tick Marks for Textile Sliders

Textile interfaces enable designers to integrate unobtrusive media and smart home controls into furniture such as sofas. While the technical aspects of such controllers have been the subject of numerous research projects, the physical form factor of these controls has received little attention so far. This work investigates how general design properties, such as overall slider shape, raised vs. recessed sliders, and number and layout of tick marks, affect users' preferences and performance. Our first user study identified a preference for certain design combinations, such as recessed, closed-shaped sliders. Our second user study included performance measurements on variations of the preferred designs from study 1, and took a closer look at tick marks. Tick marks supported orientation better than slider shape. Sliders with at least three tick marks were preferred, and performed well. Non-uniform, equally distributed tick marks reduced the movements users needed to orient themselves on the slider.
A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet, and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems.
Language-parametric static semantic code completion

Code completion is an editor service in IDEs that proposes code fragments for the user to insert at the caret position in their code. Code completion should be sound and complete. It should be sound, such that it only proposes fragments that do not violate the syntactic and static semantic rules of the language. It should be complete, such that it proposes all valid fragments so that code completion can be used to construct all programs. To realize soundness and completeness, code completion should be informed by the language definition. In practice, the implementation of code completion is an additional effort in the implementation of a language. In this paper, we develop a framework for language-parametric semantic code completion for statically typed programming languages based on their specification of syntax and static semantics, realizing the implementation of a code completion editor service with minimal additional effort. The framework builds on the SDF3 syntax definition formalism and the Statix static semantics specification language. The algorithm reinterprets the static semantics definition to find sound expansions of predicates and solutions to name resolution queries in scope graphs. This allows a search strategy to explore the solution space and synthesize completion proposals. The implementation of the strategy language and code completion algorithm extend the implementation of the Statix solver, and can be used for any language defined in Statix. We demonstrate soundness and completeness of the completion proposal synthesis, and evaluate its performance.
Multi-Concept Representation Learning for Knowledge Graph Completion

Knowledge Graph Completion (KGC) aims at inferring missing entities or relations by embedding them in a low-dimensional space. However, most existing KGC methods generally fail to handle the complex concepts hidden in triplets, so the learned embeddings of entities or relations may deviate from the true situation. In this article, we propose a novel M ulti- c oncept R epresentation L earning (McRL) method for the KGC task, which mainly consists of a multi-concept representation module, a deep residual attention module, and an interaction embedding module. Specifically, instead of the single-feature representation, the multi-concept representation module projects each entity or relation to multiple vectors to capture the complex conceptual information hidden in them. The deep residual attention module simultaneously explores the inter- and intra-connection between entities and relations to enhance the entity and relation embeddings corresponding to the current contextual situation. Moreover, the interaction embedding module further weakens the noise and ambiguity to obtain the optimal and robust embeddings. We conduct the link prediction experiment to evaluate the proposed method on several standard datasets, and experimental results show that the proposed method outperforms existing state-of-the-art KGC methods.
Cauchy regularized broad learning system for noisy data regression

Recently, the Broad Learning System (BLS) has attracted much attention due to its efficiency in clean data regression tasks. However, the conventional BLS performs poorly in the noisy environment, because the least square regression based loss function it used for networking training is noise sensitive. To address this problem, the Cauchy Regularized BLS (CRBLS) is proposed in this paper for noisy data prediction. Specifically, the Cauchy loss function instead of the least square metric is introduced into the system to regularize the residues. Compared to the least square loss, the Cauchy loss can penalize the large noise terms owe to its nonlinearity and bounded influence function. This admits the proposed CRBLS to handle data corrupted by Gaussian noise and outliers with different noise levels. In addition, a new incremental learning algorithm is developed for fast model updating without retraining the whole network when additional training samples are added. To evaluate the feasibility of CRBLS, extensive experiments are conducted, and results show that the proposed CRBLS achieves robustness for noisy data predication.
”It Puts Life into My Creations”: Understanding Fluid Fiber as a Media for Expressive Display

Fluid fiber, with fluid flowing in a tube, is an attractive material to flexibly and dynamically display digital information. While it has recently attracted attention from the HCI field, there is currently little knowledge about this material, limited to controlling the position of the droplets to present representational information like letters and numbers. To develop a broader and deepened understanding of this material and its potential for display design, we conducted a study based on a design workshop where art and design practitioners engaged in creation practice with a toolkit we designed and developed. The toolkit includes hardware components for controlling bubbles and droplets and a GUI design tool for arranging the fluid layout. Our research reveals the structural and expressive affordance of such a fluid fiber for displaying information, highlighting the unique value of fluidity as an intuitive form to express life, emotion, movement and changes.
Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks

Microscopic image-based analysis has been intensively performed for pathological studies and diagnosis of diseases. However, mis-authentication of cell lines due to misjudgments by pathologists has been recognized as a serious problem. To address this problem, we propose a deep-learning-based approach for the automatic taxonomy of cancer cell types. A total of 889 bright-field microscopic images of four cancer cell lines were acquired using a benchtop microscope. Individual cells were further segmented and augmented to increase the image dataset. Afterward, deep transfer learning was adopted to accelerate the classification of cancer types. Experiments revealed that the deep-learning-based methods outperformed traditional machine-learning-based methods. Moreover, the Wilcoxon signed-rank test showed that deep ensemble approaches outperformed individual deep-learning-based models (p < 0.001) and were in effect to achieve the classification accuracy up to 97.735%. Additional investigation with the Wilcoxon signed-rank test was conducted to consider various network design choices, such as the type of optimizer, type of learning rate scheduler, degree of fine-tuning, and use of data augmentation. Finally, it was found that the using data augmentation and updating all the weights of a network during fine-tuning improve the overall performance of individual convolutional neural network models.
Helping Mobile Learners Know Unknown Words through their Reading Behavior

Vocabulary acquisition is a fundamental part of learning a new language. In order to acquire new vocabulary, words with meanings that are unknown to the learner must be added to the language learning process. When searching for material in the target language, it is useful to know how much of a document is made up of currently unknown words. One simple way to estimate the unknown words in a document is to use the frequency of occurrence, which indicates the difficulty of the word. However, this approach can lead to missed unknown words. In this study, we aim to improve the accuracy of unknown word estimation by using reading activity data obtained from smartphone sensors and taking into account the individual learner’s English reading behavior. We apply a novel user interface which allows us to improve estimation through reading behavior, without the use of eye-trackers.
Driving is believing: Using telepresence robots to access makerspace for teachers in rural areas

Abstract This study explored two different ways for K‐12 school teachers to access educational makerspace through virtual fieldtrips. K‐12 school teachers from rural areas of the Southeast USA were divided into two groups. The experimental group ( n = 48) drove telepresence robots to take their fieldtrip, while the comparison group ( n = 23) watched the same fieldtrip through a recorded video. Analyses of the quantitative and qualitative data, collected through surveys and written reflections, showed that the experimental group reported significantly higher scores in embodiment, social presence and engagement (ie, behaviour, emotion and cognition) than the comparison group, and that actual driving the robots for the virtual fieldtrips was highlighted as a favored experience. Educational significance and implications are discussed. Practitioner notes What is currently known about this topic With makerspaces gaining increasing attention in K‐12 education, professional development (PD) opportunities are needed to help teachers understand makerspaces and related technologies. Teachers in rural areas are faced with the challenges of accessing makerspace resources and opportunities, especially during the COVID‐19 pandemic. Little research has explored teacher makerspace PD in rural areas. Maker resource deficiency in rural areas and the COVID‐19 pandemic call for an alternative approach to the makerspace PD, particularly for teachers in rural areas. What this paper adds Explored the use of telepresence robots for rural teachers to access an authentic makerspace through virtual fieldtrips. Examined the effects of virtual fieldtrip to access makerspaces on K‐12 teachers' embodiment, social presence and engagement. Examined the aspects of the telepresence fieldtrip that were most valuable to K‐12 teachers. Investigated the factors that contribute to teachers' engagement in a virtual fieldtrip. Implications for practitioners Telepresence robot was an effective tool that enabled teachers in rural areas to explore authentic makerspaces through a virtual trip over distance. To help teachers conceptualize the idea of makerspace, it is important to allow teachers to move in the physical makerspace and interact with the environment. To design an engaging fieldtrip that triggers active learning, more attention should be given to how to develop the emotional aspect of a fieldtrip experience. Embodiment plays an important role in virtual fieldtrips. When teachers have a higher level of embodiment, they are more likely to be emotionally and cognitively engaged in a fieldtrip experience.
Cross-project defect prediction based on G-LSTM model

Cross-project defect prediction (CPDP) is currently a hot research direction in the field of software reliability. Traditional CPDP methods cannot capture the semantic and contextual information of programs by handcrafted features, which affects the prediction performance. In this paper, we apply technology in the NLP domain to solve it. We first extract token vectors from the abstract syntax tree (AST) of source and target code files, and then convert them into numerical vectors by the word embedding algorithm of continuous bag-of-word model (CBOW) as the input of the proposed deep learning model named Generative Adversarial Long-Short Term Memory Neural Networks (G-LSTM). The model integrates generative adversarial network (GAN) and bidirectional long-short term memory networks (BiLSTM) with attention mechanism to automatically learn semantic and contextual features of programs. Specifically, GAN is used to eliminate the differences in data distribution between source and target projects, and BiLSTM is the feature extraction encoder. We compose five projects of the PROMISE dataset into 20 source-target project pairs and conduct comparison experiments on them. The experimental results demonstrate that our method outperforms some traditional and state-of-the-art CPDP methods in terms of the evaluation metrics of AUC and Acc.
An automatic modulation classification network for IoT terminal spectrum monitoring under zero-sample situations

Abstract Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment we live. However, the proliferation of IoT devices has led to the misuse of spectrum resources, and many IoT devices occupy the frequency band without permission. As a consequence, the spectrum regulation has become an essential part of the development of IoT. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. Generally, deep learning (DL)-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. The accuracy of one unseen class and two unseen classes exceeds 90% and 76%, respectively. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model.