Reviewerly/exHarmony huggingface dataset:
Active Learning for Testing and Evaluation in Field Robotics: A Case Study in Autonomous, Off-Road Navigation

Testing and evaluation of field robotic systems requires both experimentation in representative conditions and human supervision to effectively assess components, manage risk, and interpret results. Due to the complexity of robotic sys-tems, we argue this experimentation should be done adaptively by using insights gained from previous trials. Furthermore, we envision an advisory system that could assist experimenters with selecting trial configurations by learning and accounting for human preferences and risk tolerances; however, formal methods for human decision making in the context of field robotic experimentation remains an open question. In this work, we present and analyze a case study for how decisions were made during the testing and evaluation of an off-road, autonomous navigation system. From the perspective of active learning, we find that Bayesian Optimization is a promising mathematical framework for modeling human decision making in adaptive experimental design of field robotics and that a combination of the EI, KG, and PES acquisition functions would likely be useful for realizing an advisory system.
Learning-based Ellipse Detection for Robotic Grasps of Cylinders and Ellipsoids

In our daily life, there are many objects represented by cylindrical shapes and ellipsoids. The tops of these objects are formed by elliptic shape primitives. Thus, it is available for a robot to manipulate these objects by ellipse detection. In this work, we propose a novel approach to generating ground truth for training the model based on domain randomization. Using synthetic data generated in this manner, we build an end-to-end deep neural network with a detection backbone and then, combine multiple branches archived from the backbone for sharing the multiple-scale features; further, after employing active rotation filters, the features pass through the region proposal net to form the prediction branches of the box, orientation regression, and object classification; finally, these branches are fused to do ellipse detection, allowing robotic manipulations of cylinders and ellipsoids. To demonstrate the capabilities of the proposed detector, we show the comparison results with the state-of-the-art detector on synthetic and public datasets. The proposed model for ellipse detection and data generation pipeline based on domain randomization in a simulation are evaluated by a series of robotic manipulations implemented in real application scenarios. The results illustrate a high success rate on real-world grasp attempts despite having only been trained on a synthetic dataset. (A video of some robotic experiments is available on YouTube: https://youtu.be/Ueg1XSI2S98).
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity

Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices. We examine FL under two salient properties of contemporary networks: device-server communication delays and device computation heterogeneity. Our proposed StoFedDelAv algorithm incorporates a local-global model combiner into the FL synchronization step. We theoretically characterize the convergence behavior of StoFedDelAv and obtain the optimal combiner weights, which consider the global model delay and expected local gradient error at each device. We then formulate a network-aware optimization problem which tunes the minibatch sizes of the devices to jointly minimize energy consumption and machine learning training loss, and solve the non-convex problem through a series of convex approximations. Our simulations reveal that StoFedDelAv outperforms the current art in FL, evidenced by the obtained improvements in optimization objective.
A Survey of Deep Learning for Named Entity Recognition in Chinese Social Media

Named Entity Recognition is the research foundation of many Natural Language Processing sub-tasks. Named Entity Recognition for Chinese social media is to identify entity nouns such as person names, place names, and organization names in Chinese Social Media corpus. Due to the non-standardization of Chinese Social Media texts and the small size of the corpus, the accuracy of entity recognition will be affected. In this review, aiming at the above issues, we first introduce the historical development and research background of Chinese named entity recognition. Then, we investigate the latest improvement methods of Chinese named entity recognition for social media, and divide these improvement methods into methods to improve model recognition performance with external knowledge and methods to enhance internal knowledge to improve model performance. Finally, we summarize the challenges Chinese named entity recognition in social media based on deep learning, and propose the future development direction for these challenges.
Recommendation Model Based on Enhanced Graph Convolution That Fuses Review Properties

In rating prediction research, how to capture user and item features from review text is a key to improving model prediction accuracy. The sparsity of review text and the accuracy of the description of items in the review text make it difficult to obtain accurate feature representations of users and items by modeling the text content alone. Therefore, it is important to evaluate the usefulness of the reviews at first because not all review texts are valuable. How to analyze the usefulness of a review is a key to modeling the review text. The way previous models use attention to inscribe semantic weights on the review text is not sufficient to indicate the degree of usefulness of a review, so we suggest adding property information to model reviews. Based on this, we propose an interaction recommendation model that is based on enhanced graph convolution and fuses review properties (PGIR), which incorporates property information into text modeling by different activations and matches useful property feature interaction pairs for review text in a self-supervised manner. This allows the model to obtain an accurate feature representation of the review text. In addition, we analyze the high-order connectivity among user–item pairs. Then, we design an enhanced graph convolution method to capture the collaborative signals between users and items and model the dynamic features of users and items on this basis. After extensive experiments conducted on five standard datasets based on Amazon, the results show that the PGIR model achieves a substantial improvement over existing state-of-the-art models in terms of rating prediction. In addition, we experimentally demonstrate the superiority of our proposed property activation method, which further improves the rating prediction performance of the PGIR model.
Hoope Project: User-Centered Design Process Applied in the Implementation of Augmented Reality for Children with ASD

In the present investigation, a little-known disciplinary field that integrates aspects of computer science is delved into: HCI human-computer interaction, usability, accessibility, and on the other hand, children with ASD. The scope of the research includes the analysis, design and implementation of software that uses augmented reality in a playful environment with the aim of reinforcing educational aspects of children diagnosed with ASD, it is in a stage of development and research, currently working on a proof of concept through the implementation of a prototype, the previous studies have been worked for a period of 4 years where aspects related to DCU were considered in a particular way,
How Related Are Designers to the Personas They Create?

In the world of User Experience (UX), personas are tools that represent what designers learn during research into potential customers and a population that might use a product, website, service, or represent a brand. They also serve to help designers and engineering teams focus on user values, as well as understand possible user constraints and limitations [1]. This study asks the question "how related are designers to the personas they create?" UX designers create for an ever-changing world and increasingly diverse audiences. It is important to ensure that their personas reflect evolving consumer base demographics. Our hypothesis grew out of studies discussing implications of innate human stereotyping behavior on the creation of personas [2]. However, to our knowledge, no previous studies directly examined how related UX design practitioners and the personas they create are. Turner and Turner [2] suggest, in their article "Is stereotyping inevitable when designing with personas?", that we tend to display our biases in the form of stereotypes. Without examining these biases, designers may fail to create representative personas that demonstrate empathy for the user's specific needs, values, and constraints, instead creating products around their own needs and vision [3]. Analyzing the data, we found most UX designers created personas that were closely related to themselves, with very little deviation from this norm. The sole exception was Black designers, who tended to produce a wider array of more diverse personas.
RHMD: A Real-World Dataset for Health Mention Classification on Reddit

People on social media share their thoughts and experiences using diseases and symptoms words other than to mention their health, which can introduce biases in data-driven public health applications. For the advancement of HMC research, in this study, we present a Reddit health mention dataset (RHMD), a new dataset of multi-domain Reddit data for the HMC. RHMD is composed of 10015 manually annotated Reddit posts that include 15 common disease or symptom terms and are labeled with four labels: personal health mentions (HMs), nonpersonal HMs, figurative HMs, and hyperbolic HMs. Empirical evaluation using recently proposed methods demonstrates the challenge of labeling user-generated text across these four types. Contributions to this work include the public release of a robustly annotated Reddit dataset (RHMD) for HM tasks and a comprehensive performance analysis of baseline methods. We expect the release of the dataset, and the evaluations will help facilitate the development of new methods for detecting HMs in the user-generated text. The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/usmaann/RHMD-Health-Mention-Dataset</uri> .
How Prepared Was the World for Emergency Distance Learning in K-12 Education? A Literature Review

The present study answers the question of how prepared the world was in school distance education. The situation of primary and secondary education is deepened around experiences in distance education focused on students, in the period that includes the decade between 2010 and 2019. Designs and modalities of distance education, types of interaction, recurring themes, and impact of reported experiences were analyzed. The results reveal a low preparation in distance school education, compared to the high demand for online education that has arisen since the COVID-19 pandemic. The blended learning design appears as the most used modality, while the type of asynchronous interaction was the most frequent before the pandemic. In addition, differences are recognized in the impacts reported in the analyzed literature, which allows establishing a type of impact that includes distance education as its independent variable, and another that considers it as a more contextual element while studying the relationships between other variables.
Exploring the Role of Culture-Centric Design Guidelines in the Design of mHealth Application for Senior Informal Caregivers: Lessons Learned in a Field Study

Mobile health (mHealth) applications help reduce the burden of informal caregivers. However, limited mHealth applications are fully adopted by informal caregivers. Research has suggested that cultural tendency may affect the motivations and willingness of informal caregivers to adopt technological tools. Our study aims to explore how we can incorporate Hofstede's five cultural dimensions and Aaron Marcus's matrix of culture-centric interface design guidelines into mHealth application design for senior informal caregivers in Taiwan. Through participatory design sessions, we examined senior informal caregivers' perceptions of our mHealth app user interface (UI) design. Our findings include lessons learned from participatory process with senior informal caregivers, translating Aaron Marcus's guidelines to mHealth app UI elements and design considerations of mHealth app. We conclude by offering suggestions for future research aiming to incorporate culture-centric design guidelines into mHealth app design for senior informal caregivers from specific cultural background.
First Person vs. Third Person Perspective in a Persuasive Virtual Reality Game: How Does Perspective Affect Empathy Orientation?

Persuasive games, which use immersive technology to address social issues, appeal to player empathy by making them feel and understand the suffering of others. However, it remains ambiguous what type of empathy the player feels and for whom when experiencing a specific situation. We hypothesized that visual information regarding a distressed person obtained by the player influences the player’s empathic orientation towards the person, whereby we note that other-oriented empathy is a more useful psychological state for developing prosocial attitudes than self-oriented empathy. The purpose of this study was to preliminarily investigate which player perspective (first-person perspective vs. third-person perspective) is more effective in promoting other-oriented empathy towards a virtual character in persuasive VR games and to provide insight into the design of such games. In a between-subjects experiment (N = 12), participants played a persuasive VR game from each perspective, and their empathy orientation was investigated using a questionnaire. The results show that there was no significant difference in empathy orientation between the two conditions. The explanation may be that self-oriented empathy is mixed with attitude as a player, such as paying attention to the progress of the game.
A Novel Transformer-Based Model for Dialog State Tracking

Dialog state tracking (DST) is a core component in the task-oriented dialog systems. Many previous approaches regard DST as a classification task for a set of predefined slot-value pairs, but these approaches can not handle the situation of dynamic ontology. Other methods consider slot as span-based, while these methods are insufficient when the target slot-value cannot be found as a word segment in the dialog context. To mitigate these problems, we propose a Transformer-based model for DST. The proposed method can achieve the DST task by extracting the slot-value from the dialog context or classifying for the slot with limited values. Experimental evidence shows that our proposed model can achieve competitive performance on the WOZ 2.0 dataset while being 20 $$\times $$ faster than the state-of-the-art model. To demonstrate the effectiveness of our proposed model on multi-domain, we experiment with the recently released MultiWOZ-2.0 dataset. The experiment shows that our model can achieve promising result.
Girls4Cyber: A Game to Promote Awareness and Innovation in Cybersecurity

Privacy is a fundamental human right. However, cybersecurity attacks are growing in number and sophistication. There is the need for more cybersecurity professionals, more specifically, women professionals, otherwise we might miss 50% of the world’s population experiences, ideas and innovation, to help building a more cyber secure society. The objective of this work is to develop a mobile serious game for young Portuguese girls (10/11 years old) which could inform and motivate them regarding this area. To better understanding the population needs, we followed a user centered design (UCD) approach informed by: i) anonymous online surveys; ii) a workshop; and iii) a cultural probe. Collected data helped defining a persona as well as the main character and scenarios for the game. Low-fidelity mockups are presented and future work will focus on developing a full functioning prototype to be tested with the target population.
AWATO: A Serious Game to Improve Cybersecurity Awareness

The role of human factors in cybersecurity is an under-explored area that has a lot of potential towards mitigating attacks. As a result, an SLR that explored human factors in cybersecurity, focusing on phishing, revealed five key human factors that were persistent with phishing related attacks or issues. Based on the results of the SLR, further explorations into threat modelling were conducted to determine how to classify human factor related behaviour and the decisions that are likely behind them or lead towards human error. From here, this information was used to develop a human factor-centred threat model called STRIDE-HF that was implemented into a game called Another Week at the Office (AWATO). The results of further testing of AWATO revealed that is an effective tool for improving users awareness of good cybersecurity practices.
Effect of Self-efficacy on Open Card Sorts for Websites

Card sorting is a popular way for creating website information architectures based on users' mental models. This paper explores the effect of participants' self-efficacy on card sorting results. A two-phase study was carried out. The first phase involved 40 participants rating their self-efficacy on a standardized scale, followed by an open card sort experiment. The median self-efficacy score was used to split the open card sort data into two groups: one for low and one for high participants' self-efficacy. These two datasets were analyzed following state-of-the-art techniques for open card sort data analysis, which resulted in two information architectures for the eshop. In the second phase, two functional prototypes were first created for the eshop, one for each information architecture of the first phase. Subsequently, 30 participants interacted with both prototypes in a user testing study. This paper found that users interacting with the information architecture produced by open card sort participants with low self-efficacy made statistically significantly more correct first clicks, significantly less time to find content items, rated the tasks as significantly easier, and provided higher perceived usability ratings compared to when they interacted with the information architecture produced by users with high self-efficacy.
Mapped Polynomials and Discontinuous Kernels for Runge and Gibbs Phenomena

In this paper, we present recent solutions to the problem of approximating functions by polynomials for reducing in a substantial manner two well-known phenomena: Runge and Gibbs. The main idea is to avoid resampling the function or data and relies on the mapped polynomials or “fake” nodes approach. This technique turns out to be effective for stability by reducing the Runge effect and also in the presence of discontinuities by almost cancelling the Gibbs phenomenon. The technique is very general and can be applied to any approximant of the underlying function to be reconstructed: polynomials, rational functions or any other basis. A “natural” application is then quadrature, that we started recently to investigate and we propose here some results. In the case of jumps or discontinuities, where the Gibbs phenomenon appears, we propose a different approach inspired by approximating functions by kernels, in particular Radial Basis Functions (RBF). We use the so called Variably Scaled Discontinuous Kernels (VSDK) as an extension of the Variably Scaled Kernels (VSK) firstly introduced in Bozzini et al. (IMA J Numer Anal 35:199–219, 2015). VSDK show to be a very flexible tool suitable to substantially reducing the Gibbs phenomenon in reconstructing functions with jumps. As an interesting application we apply VSDK in Magnetic Particle Imaging which is a recent non-invasive tomographic technique that detects super-paramagnetic nanoparticle tracers and finds applications in diagnostic imaging and material science. In fact, the image generated by the MPI scanners are usually discontinuous and sampled at scattered data points, making the reconstruction problem affected by the Gibbs phenomenon. We show that VSDK are well suited in MPI image reconstruction also for identifying image discontinuities.
A Novel Metaheuristic Algorithm: The Team Competition and Cooperation Optimization Algorithm

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions. In order to evaluate the performance of the proposed algorithm, this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm (GOA), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA) and Sparrow Search Algorithm (SSA). Experiment results of 30 test functions commonly used in the optimization field indicate that, compared with these current advanced metaheuristic algorithms, TCCO has strong competitiveness in both solution accuracy and convergence speed.
Follow the Blue Butterfly – An Immersive Augmented Reality Museum Guide

We present our concept and prototypical implementation of an avatar guide that helps museum visitors to navigate to chosen points of interest. The prototype shows an animated butterfly with interactive behavior that is controlled by a state machine. The avatar's path can be pre-defined at an authoring stage, by walking the space with the head-mounted display and by placing path nodes interactively, or it can be generated at runtime, in which case the whole area needs to be scanned and processed beforehand. As the app is iteratively redesigned and tested with first-time users for accessibility, we report on first lessons learnt.