Reviewerly/exHarmony huggingface dataset:
“It Would Be Cool to Get Stampeded by Dinosaurs”: Analyzing Children's Conceptual Model of AR Headsets Through Co-Design

Children are being presented with augmented reality (AR) in different contexts, such as education and gaming. However, little is known about how children conceptualize AR, especially AR headsets. Prior work has shown that children's interaction behaviors and expectations of technological devices can be quite different from adults'. It is important to understand children's mental models of AR headsets to design more effective experiences for them. To elicit children's perceptions, we conducted four participatory design sessions with ten children on designing content for imaginary AR headsets. We found that children expect AR systems to be highly intelligent and to recognize and virtually transform surroundings to create immersive environments. Also, children are in favor of using these devices for difficult tasks but prefer to work on their own for easy tasks. Our work contributes new understanding on how children comprehend AR headsets and provides recommendations for designing future headsets for children.
Interrupting Merit, Subverting Legibility: Navigating Caste In ‘Casteless’ Worlds of Computing

Recent work in HCI has shed light on structural issues of inequality in computing. Building on this work, this study analyzes the relatively understudied phenomenon of caste in computing. Contrary to common rhetorics of 'castelessness,' we show how computing worlds in India and Indian diasporic communities continue to be shaped and inflected by caste relations. We study how, when and where Dalits (formerly 'untouchables') encounter caste in computing. We show how they artfully navigate these caste inscriptions by interpreting, interrupting and ambiguating caste and by finding caste communities. Drawing on the life stories of 16 Dalit engineers and anti-caste, queer-feminist and critical race theories, we argue that a dynamic and performative approach to caste, and other forms of inequality in HCI and computing, emphasizes the artfulness and agency of those at the margins as they challenge structural inequality in everyday life. Lastly, we suggest practical ways of addressing caste to build more open and inclusive cultures of global computing.
Exploring Intervention Strategies for Distracted Students in VR Classrooms

As one of the manifestations of virtual reality (VR) in education, virtual classroom allows students to enjoy a near-real classroom experience. VR class creates much better engagement and helps to stimulate interest and motivation in learning, making it an ideal solution to online teaching and learning activities, especially during the COVID-19 pandemic. Distraction is an unavoidable problem in immersive virtual classes, which has a great detrimental impact on learning. However, how to intervene in students’ distraction behaviors in immersive virtual environments has not been thoroughly investigated up to now. In this paper, inspired by teachers’ instructional techniques in real-life classes, we propose three intervention strategies, namely eye contact, verbal warning and text warning, and explore the intervening effects of these strategies on the inattention of students seated at the front or back of a virtual classroom via eye tracking. Our results show that all of the proposed intervention strategies have positive impacts on the attention of students. This research gives evidence that teachers’ instructional techniques in the real world can be transferred to the virtual class, which provides a new insight for the future design of educational VR.
The distributivity of extended uninorms over extended overlap functions on the membership functions of type-2 fuzzy sets

This paper mainly investigates the distributivity of an extended uninorm over an extended overlap function when the uninorm is continuous in (0,1)2. It first studies the distributivity of the extended representable uninorms over the extended overlap functions. Then, it presents the distributivity of the extended uninorms over the extended overlap functions when the uninorms belong to one of the usual classes Ucos,min, Ucos,max.
“There is no justice, just us”: Making mosaics of justice in social justice Human-Computer Interaction

The concept of social justice in Human-Computer Interaction has become an emergent domain of practice and research across the past decade. Work has included research efforts into meeting the needs of under-served populations, providing method blueprints for inclusion of marginalised identities, and a call for greater consideration on how positive impact is defined both in and beyond research engagements. While the number of justice-orientated works may have increased, new social forces question what is meant by the term justice in social justice initiatives; asking who is included in how justice is defined, what its goals are and how might we measure it. We offer this workshop as an opportunity to: (a) build conceptual and visual 'mosaics' of social justice works in HCI to map out the existing landscape; (b) build a supportive community of HCI researchers, practitioners, activists and designers who work with matters of in/justice to share vocabulary, approaches and expertise with likewise individuals; (c) facilitate critical conversations around meaningful justice-orientated action and practice, and how they might relate to wider justice frameworks.
Human-Computer Interaction and the Future of Work

Advances in computing technology, changing policies, and slow crises are rapidly changing the way we work. Human-computer interaction (HCI) is a critical aspect of these trends, to understand how workers contend with emerging technologies and how design might support workers and their values and aspirations amidst technological change. This SIG invites HCI researchers across diverse domains to reflect on the range of approaches to future of work research, recognize connections and gaps, and consider how HCI can support workers and their wellbeing in the future.
How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?

Deep learning models for image classification suffer from dangerous issues often discovered after deployment. The process of identifying bugs that cause these issues remains limited and understudied. Especially, explainability methods are often presented as obvious tools for bug identification. Yet, the current practice lacks an understanding of what kind of explanations can best support the different steps of the bug identification process, and how practitioners could interact with those explanations. Through a formative study and an iterative co-creation process, we build an interactive design probe providing various potentially relevant explainability functionalities, integrated into interfaces that allow for flexible workflows. Using the probe, we perform 18 user-studies with a diverse set of machine learning practitioners. Two-thirds of the practitioners engage in successful bug identification. They use multiple types of explanations, e.g. visual and textual ones, through non-standardized sequences of interactions including queries and exploration. Our results highlight the need for interactive, guiding, interfaces with diverse explanations, shedding light on future research directions.
What Could Possibly Go Wrong When Interacting with Proactive Smart Speakers? A Case Study Using an ESM Application

Voice user interfaces (VUIs) have made their way into people's daily lives, from voice assistants to smart speakers. Although VUIs typically just react to direct user commands, increasingly, they incorporate elements of proactive behaviors. In particular, proactive smart speakers have the potential for many applications, ranging from healthcare to entertainment; however, their usability in everyday life is subject to interaction errors. To systematically investigate the nature of errors, we designed a voice-based Experience Sampling Method (ESM) application to run on proactive speakers. We captured 1,213 user interactions in a 3-week field deployment in 13 participants' homes. Through auxiliary audio recordings and logs, we identify substantial interaction errors and strategies that users apply to overcome those errors. We further analyze the interaction timings and provide insights into the time cost of errors. We find that, even for answering simple ESMs, interaction errors occur frequently and can hamper the usability of proactive speakers and user experience. Our work also identifies multiple facets of VUIs that can be improved in terms of the timing of speech.
Assisting Teaching Assistants with Automatic Code Corrections

Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs' ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.
FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.
Investigating the Effects of Different Levels of User Control on the Effectiveness of Context-Aware Recommender Systems for Web-Based Search

Recommender systems assist users by providing recommendations based on some filtration criteria to reduce information overload. Embedding context-awareness allows recommender systems to use context information around the user, situation, and system to adapt and provide more efficient, relevant, and personalized recommendations. However, embedding context-awareness into recommender systems inherently limits the users‘ control over the systems due to reduced interactivity from automatic adaptations. This may potentially impact users’ use and perception of the systems. Control can be purposefully designed to be given to the user in context-aware recommender systems at different levels. Our work investigates the effects of different levels of user control on the effectiveness and understandability of context-aware recommender systems (CARS) within the scenario of learning through web-based search (called ‘Search-As-Learning’). To enable our study, we implemented a CARS that supports web-based search by recommending users a link using context such as browsing history. Our study found that participants used more recommendations from the CARS with high control compared to no control and some control. In conclusion, higher control in a recommender system for web-based search is preferred by the user despite control manipulation taking more time possibly due to explicit user needs.
Solar Radiation Prediction Model for the Yellow River Basin with Deep Learning

Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the highest temperature Tmax, lowest temperature Tmin, average temperature Tavg, wind speed U, relative humidity RH, sunshine duration H and maximum sunshine duration Hmax as input variables to construct a deep learning prediction model of solar radiation in the Yellow River Basin. It is compared with the recommended and corrected values of the widely used Å-P method. The results show that: (1) The correction results of the Å-P equation are better in the upstream and downstream of the Yellow River Basin but worse in the midstream. (2) The prediction result of the deep learning model in the Yellow River Basin is far better than that of the Å-P equation using the FAO-56 recommended value. It is the best in the downstream of the Yellow River Basin: R2 increases from 0.894 to 0.934; MSE, RMSE and MAE decrease by 43.12%, 27.73% and 25.80%, respectively. The upstream prediction result comes in second: R2 increases from 0.888 to 0.921; MSE, RMSE and MAE decrease by 33.27%, 20.02% and 19.04%, respectively. The midstream result is the worst: R2 increases from 0.869 to 0.874; MSE, RMSE and MAE decrease by −0.50%, 0.07% and 3.82%, respectively. (3) The prediction results of the deep learning model in the upstream and downstream of the Yellow River Basin are far better than those of the Å-P equation using correction. The R2 in the upstream of the Yellow River Basin increases from 0.889 to 0.921. MSE, RMSE and MAE decrease by 22.11%, 11.84% and 8.94%, respectively. R2 in the downstream of the Yellow River Basin increases from 0.900 to 0.934, and MSE, RMSE and MAE decrease by 13.21%, 11.40% and 5.55%, respectively. In the midstream of the Yellow River Basin, the prediction results of the deep learning model are worse than those of the Å-P equation using correction: R2 increases from 0.870 to 0.874, but MSE, RMSE and MAE decrease by −24.93%, −10.83% and −11.56%, respectively.
Easy balanced mixing for long-tailed data

In long-tailed datasets, head classes occupy most of the data, while tail classes have very few samples. The imbalanced distribution of long-tailed data leads classifiers to overfit the data in head classes and mismatch with the training and testing distributions, especially for tail classes. To this end, this paper proposes an easy balanced mixing framework abbreviated EZBM to fit the long-tailed data and match training and testing distributions. The proposed EZBM utilizes a two-stage learning strategy to conduct feature extraction and classification hyperplane adjustment. In the first phase, EZBM utilizes ResNet as a backbone to map the input data into a new feature space and a fully connected layer as a classifier to conduct the feature extracting process. In the second phase, EZBM combines each training sample with another sample from a random class in the feature space to generate a mixed sample close to the head class. Then, EZBM adjusts the classification hyperplane to be close to mixed samples. In this way, EZBM biases the classification hyperplane to the head class, which is suitable for recognizing tail samples. Experiments on long-tailed datasets demonstrate the effectiveness of EZBM.
A Blockchain-Based Security Model for IoT Systems

The current Internet of Things (IoT) technology has entered a relatively mature development stage, and more and more IoT devices can readily access the Internet. However, along with this, the IoT system still faces fragile security of device nodes, easy data tampering, and low system stability. To this end, this paper proposes a smart contract-based security model for IoT systems. The proposal is based on the super ledger Fabric blockchain platform having decentralised, tamper-proof, and programmable features. These features achieve credible authentication of IoT device nodes on the one hand and tamper-proof data storage on the other hand. Further, with these features, we gain a trustworthy environment for enhancing the security of the whole IoT system.
Focusing for Interaction Design: An Introspective Somatic Method

Attending to the challenges of describing first-person experience, this article illustrates different uses of the Focusing method in interaction design and HCI, offering a systematic way of accessing the subtle qualities of lived experiences for design use. In this approach, the implicit bodily knowledge -or felt sense- becomes the material capture of aesthetic experiences used to inform data collection, ideation and prototyping. We offer a high-level, yet systematic coverage of Focusing applied to two case studies, informing both a set of instructions to use the method and a series of design considerations to adopt this understudied tool of introspection in interaction design research and practice.
Extracting Prerequisite Relations among Concepts From the Course Descriptions

Nowadays, online learning is becoming more and more popular. Various online learning platforms provide a huge amount of learning resources for learners around the world. When choosing or sorting learning resources, learners often need to know what important knowledge concepts are addressed in each learning resource. Exploring the prerequisite relations among concepts is of great significance to educational planning. In this paper, we extracted concepts from the content of course descriptions and proposed a new approach that uses both course-based features and Wikipedia-based features to discover the prerequisite relations between knowledge concepts. Experiments on both English and Chinese datasets show that the proposed method outperforms existing baselines.
Fostering Digital Media-Realted Competences of Student Teachers

Beyond controversy, identifying and using the potential of digital media for teaching requires media-related skills of future teachers. The paper presents in a first step a framework for digital media-related competencies. Within this framework, different areas are identified in which teachers need media pedagogical (didactical), subject-specific and technological competencies related to the instructional design of digital media. In a second step, an educational approach to support student teachers to develop these competencies is presented. This approach is based on project learning and encompasses a media-based teaching project and a media development project. Hereby, different support structures are implemented, i.e. blended learning proposals and self-learning materials, personal support, and learning analytics. Finally, the project evaluation with formative and summative elements, which guides the iterative process of implementing the educational approach into the existing teacher education programme, is presented.
HCI Across Borders: Navigating Shifting Borders at CHI

Human–Computer Interaction (HCI) research has led to major innovations used by large and diverse audiences in different parts of the world. However, a recent meta-analysis [25] found that research at CHI is still highly (73%) concentrated in western contexts. HCI Across Borders (HCIxB) has gathered a diverse audience by conducting workshops and symposia since CHI 2016, aiming to expand borders within CHI. For CHI 2022, we expect to regroup for a virtual workshop to reflect on shifting boundaries from CHI's past and emerging challenges in HCI research, education, and practice in recent years.
Introduction to Data-Enabled Design

During the Introduction to Data-Enabled Design (DED) course attendees will learn about designing for intelligent ecosystems while using data as a creative material. We will take the course participants through both major steps, contextual and informed, of this method by means of data collected (upfront) from the attendee's personal context. This course offers a careful balance between hands-on work and DED theory. The learning outcomes focus on topics of (physical) prototyping, (remote) data collection and analysis, using data as a creative material, and designing remote interventions.
Expressive Auditory Gestures in a Voice-Based Pedagogical Agent

In this paper, we explore how expressive auditory gestures added to the speech of a pedagogical agent influence the human-agent relationship and learning outcomes. In a between-subjects experiment, 41 participants assumed the role of a tutor to teach a voice-based agent. The agent used either: expressive interjections (e.g.,“yay”, “hmm”, “oh”), brief expressive musical executions, or no auditory gestures at all (control condition), throughout the interaction. Overall, the results indicate that both gestures can positively affect the interaction, but in particular, interjections can significantly increase feelings of emotional rapport with the agent and enhance motivation in learners. The implications of our findings are discussed as our work adds to the understanding of conversational agent design and can be useful for education as well as other domains in which dialogue systems are used.
Mask wearing detection algorithm based on improved YOLOv4

Abstract Since the outbreak of COVID-19, the domestic situation has gradually improved, but small-scale outbreaks are still frequent, the current situation of COVID-19 cannot be eased up. Using target detection algorithms to identify mask wearing to remind people to pay attention to the epidemic, can reduce the probability of cross infection in public places. YOLOv4 is a real-time target detection algorithm with fast detection speed and small size, but it has problems such as insufficient positioning of the bounding box and easy to miss the occluded targets. Based on this, we improve the algorithm and integrates the CBAM attention module into the backbone network. At the same time, the depthwise over-parameterized convolution (DO-Conv) is used to replace the ordinary convolution, which improves the network fitting ability without adding additional parameters. The experimental results show that the improved algorithm has improved the positioning accuracy of the bounding box and detection accuracy.
Methodological Aspects of Linguistic Expert Analysis of Communications on Sexual Topics with Minors

The article is devoted to the linguistic aspect of examinations on crimes against sexual integrity and sexual freedom. Based on the consistent legal, expert and linguistic analysis, the author observes the formation of a new type of forensic linguistic and comprehensive psychological and linguistic examinations, the object of which is the communication on sexual topics with minors in social networks and messengers. He also analyzes criminal law provisions covering crimes against a person’s sexual inviolability and sexual freedom. Subsequently, the expert concepts of “sexual acts”, “motivation” and “threat”, which are advisable to establish during the examination, are developed and brought into correlation with the linguistic knowledge. Finally, the author proposes methodological approaches to solving expert linguistic tasks when examining messages to the minors on sexual topics.