Reviewerly/exHarmony huggingface dataset:
Steering Stories

In this paper, we problematize popular narratives of driving automation. Whether positive or negative, these propagate simplistic assumptions about human abilities and reinforce technocratic approaches to mobility innovation. We build on narrative approaches to participatory research and adversarial design, to explore how design-led confrontation can create opportunities for reflection on implicit assumptions and narratives that stakeholders may refer to when discussing and making decisions about automated driving technologies. Specifically, we discuss the results of four focus groups where we used contestational artifacts to promote critical discussions and confront taken-for-granted beliefs among stakeholders. We reflect on the results to distill methodological insight and design recommendations for conducting adversarial participatory design research as a way towards confronting dominant narratives. Together with the methodological approach, the main contribution of this work, we also provide a set of narrative tensions that can be used to question common beliefs surrounding automated driving futures.
Programming without a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming

Existing tools for writing prompts for language models (known as "prompt programming") provide little support to prompt programmers. Consequently, as prompts become more complex with the addition of multiple input/output examples ("few-shot" prompts), they can be hard to read, understand, and edit. In this work, we observe that prompts are often used to solve complex problems, but lack the strict grammar of a traditional programming language. We describe methods for extracting the semantically meaningful structure of natural language prompts (e.g., regions of the prompt representing a preamble or input/output examples) in the absence of a rigid formal grammar, and demonstrate a range of editor features that can leverage this information to assist prompt programmers. Finally, we relate initial feedback from design probe explorations with a set of domain experts and provide insights to help guide the development of future prompt editors.
Quantum Bilinear Interpolation Algorithms Based on Geometric Centers

Bilinear interpolation is widely used in classical signal and image processing. Quantum algorithms have been designed for efficiently realizing bilinear interpolation. However, these quantum algorithms have limitations in circuit width and garbage outputs, which block the quantum algorithms applied to noisy intermediate-scale quantum devices. In addition, the existing quantum bilinear interpolation algorithms cannot keep the consistency between the geometric centers of the original and target images. To save the above questions, we propose quantum bilinear interpolation algorithms based on geometric centers using fault-tolerant implementations of quantum arithmetic operators. Proposed algorithms include the scaling-up and scaling-down for signals (grayscale images) and signals with three channels (color images). Simulation results demonstrate that the proposed bilinear interpolation algorithms obtain the same results as their classical counterparts with an exponential speedup. Performance analysis reveals that the proposed bilinear interpolation algorithms keep the consistency of geometric centers and significantly reduce circuit width and garbage outputs compared to the existing works.
Towards 5G, 6G and 7G Sustainable and Potential Applications Using Blockchain: Comparative Analysis and Prospective Challenges*

The Internet of Things (IoT) turns pervasive computing into a reality of connected everything. IoT connects billions of devices for rapid information sharing, specifically in 5G and 6G but it creates significant issues in privacy, transparency, and security. Blockchain has proved to be adaptive and useful for many applications, including those in the automobile sector. The aim of Industry 4.0 is to improve performance while sustaining quality under cost. The paper presents the existing survey of 5G, 6G, and 7G along with blockchain. The study proposes the architecture of 6G and an overview of the challenges and possible applications of blockchain using 5G, 6G, and 7G
Build a Smart Sustainable Windhoek: An AR game

The City of Windhoek is dedicated to become a Smart, Sustainable City, in alignment with sustainable development goal 11. Local officials strive to include residents to guide smart and sustainable initiatives and strategies, but lack tools and techniques to engage those unfamiliar with smart city concepts. We proposed a Build a Smart Sustainable Windhoek game, which educates and raises awareness while sharing citizen needs and desires to inform smart and sustainable city strategies. It is an interactive hybrid game combining physical interactions with boxes and augmented reality. The game was co-designed with unemployed youth from an informal settlement in the capital. It was played by 8 teams, each consisting of three Windhoek residents from different backgrounds, during a half-day smart city event. The survey results show that participants enjoyed the role of a town planner and building their own city having been teamed up with fellow residents from different suburbs. Moreover, they considered the game to be most informative in creating awareness on Smart City topics.
ObjGen: Constructing Objects with Digital Genetic Information

The genome of each living organism encodes its phenotype. Inspired by this phenomenon, we have designed a new method that allows embedding digital genetic information in everyday objects, which we call the ObjGen. These embeddings encode detailed information on how each object is built as well as additional metadata (e.g., manufacturing instructions, assembly instructions, or user's manual). By scanning only a small fragment of an object, the whole object can be recreated and its metadata read. We show how this workflow can be implemented for different manufacturing processes: laser cutting, CNC milling, and molding. Our method creates digital genetic codes in the form of digital textures incorporated into the object's surface. The embedded construction code can be decoded using standard digital cameras. We demonstrate this workflow on a variety of different objects.
OurStrategy: Employee Voice In Transnational Strategy Development

The rich detail generated by qualitative research is essential for understanding workplace contexts and processes for organizational development. This can be a highly involved and extensive process, thus limiting the number of people who can normally be allowed to fully participate. This is especially problematic when it comes to workplace collaboration and development, as it means that a large number of voices are not considered and heard. In this paper, we provide and implement a novel semi-automated approach that enables large-scale qualitative research. This was done within the context of developing a new organizational strategy for a large and diverse multi-lingual international development organization. We included over 150 different stakeholders whilst enabling their views to be collected and analyzed in depth. This case study demonstrates how to effectively implement qualitative interview research at scale with an application to the employee voice setting, contributing the tools and means to achieve this.
Personal Predictors of Online Teaching - Experiences of The Teachers in Serbia in The Covid Regime

The pandemic of the COVID-19 virus initiated a rapid organizational transformation of teaching at all levels of education and its implementation in a virtual environment. In this context, teachers’ perceptions of the predictors of online teaching largely determined the quality of its current implementation, but also the character of its further application in the future. Using the survey method, research was carried out with the aim of examining the attitudes of teachers in Serbia about personal predictors of online teaching and their determinants, in the past two school years. Personal predictors of online teaching are operationalized through the cognitive and affective competence of the teachers, as well as through organizational and technical support. Six hundred and seven teachers, who teach in the first three cycles of education, from all districts of the Republic of Serbia, participated in the research. Regarding all personal predictors considered, teachers evaluate cognitive competence as the most important predictor of the implementation of online teaching, then affective competence and finally, organizational and technical support. As the results of the research suggest, a significant influence of the dominant online experience, of the applied online platform, work experience and educational cycle on teachers’ perceptions of affective competence as a predictor of online teaching were determined, while the perceptions of organizational and technical support were significantly influenced by the place where the school was located and the applied online platforms. The obtained results may have implications for pedagogical practice and professional development of teachers in terms of consideration and awareness of the role of various personal predictors required for the successful use of modern educational technology.
Using Virtual Reality and Co-Design to Study the Design of Large-scale Shape-Changing Interfaces

Large-scale shape-changing interfaces (SCIs) such as shape-changing walls offer opportunities for enhancing user experiences within buildings, e.g., for navigation. However, due to the embryonic nature of SCI technologies, designing and explaining the shape features that are beneficial to users is challenging. Previous work used virtual platforms (2D video or Projected Augmented Reality) to design SCI. This paper explores how Virtual Reality (VR) can provide an immersive experience that can help in designing large-scale SCI. We follow a co-design approach in which we use VR to obtain users’ impressions of shape-changing walls. Then, we conduct co-design sessions to understand how shape-changing walls can be designed to become ambient and blend with the environment. We report our results to guide the design of shape-changing walls as well as discuss how our approach can provide valuable insights into how a VR experience, prior to design, and can help in the design process.
Reach Prediction using Finger Motion Dynamics

The ability to predict the object the user intends to grasp or to recognize the one she is already holding offers essential contextual information and may help to leverage the effects of point-to-point latency in interactive environments. This paper investigates the feasibility and accuracy of recognizing un-instrumented objects based on hand kinematics during reach-to-grasp and transport actions. In a data collection study, we recorded the hand motions of 16 participants while reaching out to grasp and then moving real and synthetic objects. Our results demonstrate that even a simple LSTM network can predict the time point at which the user grasps an object with 23 ms precision and the current distance to it with a precision better than 1 cm. The target’s size can be determined in advance with an accuracy better than 97%. Our results have implications for designing adaptive and fine-grained interactive user interfaces in ubiquitous and mixed-reality environments.
Three recommendations to engage At-Risk Students in Critical Reflection on Intelligent Technologies through Remote Learning

The agenda of Computational Empowerment points towards the need for inclusive approaches for supporting all students in learning about technology and the development of digital literacy. This paper aims at exploring how to develop remote learning activities in technology education during a pandemic for at-risk students. We present a case study with 23 primary students (11-12 years) who we involved in online and offline learning activities about both how intelligent technologies work but also on the implications that these technologies bring to our society. Our findings showed potential challenges to engage at-risk students in the agenda of Computational Empowerment. We propose three recommendations and future directions to scaffold at-risk students' learning about intelligent technologies in remote contexts: (1) support the development of agency through the engagement of contexts-for-action; (2) reduce the complexity of remote communication through creative and bodily engagement; (3) reflect upon forces that marginalize oneself in a digitized society.
Multilayered logical qubits and synthesized quantum bits

Abstract Quantum error correction is likely to be key in obtaining near term quantum advantage. We propose a novel method for providing multiple logical qubits in the correction of quantum errors using classical computers. The core idea of our work is built upon two main pillars: dividing the Hilbert space into reduced Hilbert spaces with individual logical qubits and synthesizing the reduced Hilbert spaces through a mathematical collaborating between classical bits and logical quantum states. We demonstrate that our method supports at least 20 logical qubits in a surface code with a code distance of 3. Furthermore, we generate entangled states of multiple logical qubits from lattice surgery-based surface codes using only physical qubit operations. This approach enables classical computers to support a larger number of logical qubits using less memory and perform faster simulations.
AF‐TigerNet: A lightweight anchor‐free network for real‐time Amur tiger (<i>Panthera tigris altaica</i>) detection

Abstract Monitoring the critically endangered Amur tiger ( Panthera tigris altaica ) is crucial for the conservation of the natural environment. This paper proposes a novel lightweight deep‐learning network for detecting Amur tigers in mobile and resource‐constrained environments, like those using unmanned aerial vehicles. The network uses an anchor‐free mechanism with modified CSPNet and cross stage partial (CSP)‐path aggregation network (PAN) structures, which improves the model's feature extraction capabilities. Label assignment strategy is also improved to stabilize the model training process. Additionally, the random Mosaic and Mixup data augmentation strategy is utilized to address the overfitting issue due to insufficient data in the data set. The model achieves 55.5% mean average precision (mAP [0.5:0.95]) with only 0.617 million parameters and 73.58 frames per second on mobile CPU with pixels input. Results show that the model is an accurate, fast, and practical detector of the Amur tiger, serving as a reference for wildlife detectors of other species.
Privacy preserving federated learning for full heterogeneity

Federated learning is a novel distribute machine learning paradigm to support cooperative model training among multiple participant clients, where each client keeps its private data locally to protect its data privacy. However, in practical application domains, Federated learning still meets several heterogeneous challenges such data heterogeneity, model heterogeneity, and computation heterogeneity, significantly decreasing its global model performance. To the best of our knowledge, existing solutions only focus on one or two challenges in their heterogeneous settings. In this paper, to address the above challenges simultaneously, we present a novel solution called Full Heterogeneous Federated Learning (FHFL). Firstly, we propose a synthetic data generation approach to mitigate the Non-IID data heterogeneity problem. Secondly, we use knowledge distillation to learn from heterogeneous models of participant clients for model aggregation in the central server. Finally, we produce an opportunistic computation schedule strategy to exploit the idle computation resources for fast-computing clients. Experiment results on different datasets show that our FHFL method can achieve an excellent model training performance. We believe it will serve as a pioneer work for distributed model training among heterogeneous clients in Federated learning.
Authentication Challenges in Customer Service Settings Experienced by Deaf and Hard of Hearing People

Customer services are important for answering questions, providing information, and handling issues. Often people want to connect with a customer service representative, yet, this can be an accessibility barrier for Deaf and Hard of Hearing People (DHH) when voice calls are the only method. However, little is known about the challenges experienced by DHH people in solely voice-based customer service settings (e.g., authenticating themselves over the phone to their banks). To address this, we interviewed 18 DHH people to understand the challenges when remotely authenticating themselves with customer service. Though DHH people are not unique in their attitudes and behaviors toward mobile authentication, we found that voice-based authentication and services are challenging. Furthermore, DHH people can often be put in positions where they must trust third-party support (e.g., human interpreters) when discussing sensitive information to authenticate themselves through voice-based authentication and services. We present several avenues of future work needed to address these challenges.
Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR

With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and restoration of environments in oil-producing areas. In this study, the content of petroleum hydrocarbon and the hyperspectral data of soil samples collected from an oil-producing area were measured. For the hyperspectral data, spectral transforms, including continuum removal (CR), first- and second-order differential (CR-FD, CR-SD), and Napierian logarithm (CR-LN), were applied to eliminate background noise. At present, there are some shortcomings in the method of feature band selection, such as large quantity, time of calculation, and unclear importance of each feature band obtained. Meanwhile, redundant bands easily exist in the feature set, which seriously affects the accuracy of the inversion algorithm. In order to solve the above problems, a new method (GARF) for hyperspectral characteristic band selection was proposed. It combined the advantage that the grouping search algorithm can effectively reduce the calculation time with the advantage that the point-by-point search algorithm can determine the importance of each band, which provided a clearer direction for further spectroscopic research. The 17 selected bands were used as the input data of partial least squares regression (PLSR) and K-nearest neighbor (KNN) algorithms to estimate soil petroleum hydrocarbon content, and the leave-one-out method was used for cross-validation. The root mean squared error (RMSE) and coefficient of determination (R2) of the estimation result were 3.52 and 0.90, which implemented a high accuracy with only 8.37% of the entire bands. The results showed that compared with the traditional characteristic band selection methods, GARF can effectively reduce the redundant bands and screen out the optimal characteristic bands in the hyperspectral data of soil petroleum hydrocarbon with the method of importance assessment, which retained the physical meaning. It provided a new idea for the research of other substances in soil.
A Collective Approach to Providing Digital Skills Training Among U.S. Public Housing Residents

Addressing the digital divide would support access to essential activities such as healthcare, employment, and education among under-resourced communities in the United States (U.S.). However, half of the adults in the U.S. lack confidence and preparedness to use digital tools for learning. We developed and piloted an intervention to train public housing residents as intermediaries to provide digital support to their community members to address this gap. Collaborating with community partners, we developed a cohort-based basic digital skills training program consisting of online courses and offline social learning support. We trained nine public housing residents and present best practices of collective training and the challenges the trainees faced. Preliminary results suggest an increase in trainees' self-efficacy in basic digital skills. Our approach aims to increase digital literacy and minimize barriers to online learning among traditionally-excluded populations. Our work extends prior interventions that only provide device and Internet access.
Spatiality and Semantics - Towards Understanding Content Placement in Mixed Reality

Mixed Reality (MR) popularizes numerous situated applications where virtual content is spatially integrated into our physical environment. However, we only know little about what properties of an environment influence the way how people place digital content and perceive the resulting layout. We thus conducted a preliminary study (N = 8) examining how physical surfaces affect organizing virtual content like documents or charts, focusing on user perception and experience. We found, among others, that the situated layout of virtual content in its environment can be characterized by the level of spatial as well as semantic coupling. Consequently, we propose a two-dimensional design space to establish the vocabularies and detail their parameters for content organization. With our work, we aim to facilitate communication between designers or researchers, inform general MR interface design, and provide a first step towards future MR workspaces empowered by blending digital content and its real-world context.
Tacit Knowledge Elicitation for Shop-floor Workers with an Intelligent Assistant

Many industries face the challenge of capturing workers' knowledge to share it, particularly tacit knowledge. The operation of complex systems such as a manufacturing line is knowledge-intensive. Considering this knowledge's breadth and dynamic nature, existing knowledge-sharing solutions are inefficient and resource intensive. Conversational user interfaces are an efficient way to convey information that mimics how humans share knowledge; however, we know little about how to design them specifically for knowledge sharing, especially regarding tacit knowledge. In this work, we present an intelligent assistant that we have developed to support the elicitation of tacit knowledge from workers through systematic reflection. The system can interact with workers by voice or text and generate visualizations of shop floor data to support reflective prompts.
Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

The railway system of Japan plays a vital role in the national transportation network. A key issue in public transport safety is anomaly detection in railways. Lately, developing robust algorithms and methods for anomaly detection has become the premier task in this field. Recently introduced approaches based on convolutional neural networks, generative adversarial networks, and vision transformers (ViTs) have remarkably improved the research in anomaly detection. Our work proposes a high-performance module for the anomaly detection of signal bonds. First, we present an overview of the proposed module; then, the object detection model and the proposed hybrid classification model based on texture-biased convolutional neural networks and edge-biased ViTs are introduced. Finally, the proposed anomaly detection module is evaluated for accuracy using the dataset from the East Japan Railway Company and the receiver operating characteristic curve. The results show that our proposed module achieves a better performance on real-life data than the two methods that were combined to generate it, raising hope toward possible usage in other areas as well.