Reviewerly/exHarmony huggingface dataset:
A Prior-Knowledge-Based Generative Adversarial Network for Unsupervised Satellite Cloud Image Restoration

High-quality satellite cloud images are of great significance for weather diagnosis and prediction. However, many of these images are often degraded due to relative motion, atmospheric turbulence, instrument noise, and other factors. In the satellite imaging process, the degradation also cannot be completely corrected. Therefore, it is necessary to further improve the satellite cloud image quality for real applications. In this study, we propose an unsupervised image restoration model with a two-stage network, in which the first stage, named the Prior-Knowledge-based Generative Adversarial Network (PKKernelGAN), aims to learn the blur kernel, and the second stage, named the Zero-Shot Deep Residual Network (ZSResNet), aims to improve the image quality. In PKKernelGAN, we propose a satellite cloud imaging loss function, which is a novel objective function that brings optimization of a generative model into the prior-knowledge domain. In ZSResNet, we build a dataset which contains the original satellite cloud image as high-quality images (HQ) paired with low-quality images (LQ) generated by the blur kernel learning from PKKernelGAN. The above innovations lead to a more efficient local structure in satellite cloud image restoration. The original dataset of our experiment is from the Sunflower 8 satellite provided by the Japan Meteorological Agency. This dataset is divided into training and testing sets to train and test PKKernelGAN. Then, ZSResNet is trained by the “LQ–HQ” image pairs generated by PKKernelGAN. Compared with other supervised and unsupervised deep learning models for image restoration, our model has a better performance. Extensive experiments have demonstrated that our proposed model can achieve better performance on different datasets.
Fluito: Towards Understanding the Design of Playful Water Experiences through an Extended Reality Floatation Tank System

Water's pleasant nature and associated health benefits have captivated the interest of HCI researchers. Prior WaterHCI work mainly focused on advancing instrumental applications, such as improving swimming performance, and less on designing systems that support interacting with technology in water in more playful contexts. In this regard, we propose floatation tanks as research vehicles to investigate the design of playful interactive water experiences. Employing somaesthetic design, we developed a playful extended reality floatation tank experience: "Fluito". We conducted a 13-participant study to understand how specific design features amplified participants' water experiences. We used a postphenomenological lens to articulate eight strategies useful for designers aiming to develop digital playful experiences in water, such as designing to call attention to the water and designing to encourage breathing and body awareness in water experiences. Ultimately, we hope that our work supports people to be playful and benefit from the many advantages of being in water.
Phishing detection on Ethereum via transaction subgraphs embedding

Abstract With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph‐based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models.
Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

Abstract One of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t ‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of &gt;97% in noisy data and an accuracy of &gt;98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%.
Public Health Calls for/with AI: An Ethnographic Perspective

Artificial Intelligence (AI) based technologies are increasingly being integrated into public sector programs to help with decision-support and effective distribution of constrained resources. The field of Computer Supported Cooperative Work (CSCW) has begun to examine how the resultant sociotechnical systems may be designed appropriately when targeting underserved populations. We present an ethnographic study of a large-scale real-world integration of an AI system for resource allocation in a call-based maternal and child health program in India. Our findings uncover complexities around determining who benefits from the intervention, how the human-AI collaboration is managed, when intervention must take place in alignment with various priorities, and why the AI is sought, for what purpose. Our paper offers takeaways for human-centered AI integration in public health, drawing attention to the work done by the AI as actor, the work of configuring the human-AI partnership with multiple diverse stakeholders, and the work of aligning program goals for design and implementation through continual dialogue across stakeholders.
Why should I bother to break the norm?: Exploring the Prospects of Adopting Technology-Driven Solutions by Indian Shrimp Farmers

India produces a large amount of shrimp every year and exports them globally. Yet, concerns have been raised about shrimps produced in India not qualifying the global safety standards. In this paper, we took an ethnographic approach to understand the traditional farming practices followed by shrimp farmers in India. We interviewed 29 shrimp farmers from Andhra Pradesh in India for eight months. We learned that most experienced and large-scale shrimp farmers in India prefer to follow traditional practices in their shrimp farms. They prefer to adopt technology-driven solutions in their farming only if absolutely needed. However, we observed that young, small-scale farmers are more open to trying new technologies in their farming. We discussed several areas where initiatives can be taken to encourage more Indian shrimp farmers to adopt new technology-driven solutions in their farming.
Selective Explanations: Leveraging Human Input to Align Explainable AI

While a vast collection of explainable AI (XAI) algorithms has been developed in recent years, they have been criticized for significant gaps with how humans produce and consume explanations. As a result, current XAI techniques are often found to be hard to use and lack effectiveness. In this work, we attempt to close these gaps by making AI explanations selective ---a fundamental property of human explanations---by selectively presenting a subset of model reasoning based on what aligns with the recipient's preferences. We propose a general framework for generating selective explanations by leveraging human input on a small dataset. This framework opens up a rich design space that accounts for different selectivity goals, types of input, and more. As a showcase, we use a decision-support task to explore selective explanations based on what the decision-maker would consider relevant to the decision task. We conducted two experimental studies to examine three paradigms based on our proposed framework: in Study 1, we ask the participants to provide critique-based or open-ended input to generate selective explanations (self-input). In Study 2, we show the participants selective explanations based on input from a panel of similar users (annotator input). Our experiments demonstrate the promise of selective explanations in reducing over-reliance on AI and improving collaborative decision making and subjective perceptions of the AI system, but also paint a nuanced picture that attributes some of these positive effects to the opportunity to provide one's own input to augment AI explanations. Overall, our work proposes a novel XAI framework inspired by human communication behaviors and demonstrates its potential to encourage future work to make AI explanations more human-compatible.
How Do Users Experience Moderation?: A Systematic Literature Review

Researchers across various fields have investigated how users experience moderation through different perspectives and methodologies. At present, there is a pressing need of synthesizing and extracting key insights from prior literature to formulate a systematic understanding of what constitutes a moderation experience and to explore how such understanding could further inform moderation-related research and practices. To answer this question, we conducted a systematic literature review (SLR) by analyzing 42 empirical studies related to moderation experiences and published between January 2016 and March 2022. We describe these studies' characteristics and how they characterize users' moderation experiences. We further identify five primary perspectives that prior researchers use to conceptualize moderation experiences. These findings suggest an expansive scope of research interests in understanding moderation experiences and considering moderated users as an important stakeholder group to reflect on current moderation design but also pertain to the dominance of the punitive, solutionist logic in moderation and ample implications for future moderation research, design, and practice.
Speculating with Care: Worker-centered Perspectives on Scale in a Chat-based Health Information Service

Seeking to address barriers to in-person care, governments and non-governmental organizations (NGOs) globally have been pushing for scaling chat- or phone-based information services that rely on care workers to engage with users. Despite theoretical tensions between care and scale and the essential role of care workers, workers' perspective on scale and its impact on care provision is rarely centered early on in decisions to scale. In this paper, we examine care and scale from the perspective of medical support executives (MSEs) who support a chat-based health information service for maternal and child health deployed across multiple states in India. We draw on observations of MSEs' work, interviews with MSEs, NGO staff who implement the service, and families who use the service, and speculative design sessions conducted with MSEs. We find that by centering MSEs' perspectives, we can differentiate between growth of the relationships and heterogeneity that enable social impact, versus scale-thinking that promotes the decontextualization of care. We leverage our findings to discuss implications for scale and automation in chat-based health information services, including the importance of human connection, place, and support for care workers.
Lane detection and tracking algorithm implementation for an Ackermann-steering mobile robot

This paper presents the implementation of a lane detection and tracking algorithm for the autonomous navigation of an Ackermann-steering mobile robot. The proposed implementation employs an RGB camera mounted on the robot, the image information is processed through the lane detection and tracking algorithm to define the robot’s present and future position within the lane. This information is used to determine the orientation of the wheels required to steer the robot within the lane. The implementation employs a Raspberry Pi as the primary logic controller to process the image received from the RGB camera. The Ackermann-steering mobile robot performs steering and navigation with a proportional-integral-derivative controller that manages the orientation of the steering. Experimental results are presented to validate the implementation considering a physical implementation of the Ackermann-steering mobile robot.
Social Wormholes: Exploring Preferences and Opportunities for Distributed and Physically-Grounded Social Connections

Ubiquitous computing encapsulates the idea for technology to be interwoven into the fabric of everyday life. As computing blends into everyday physical artifacts, powerful opportunities open up for social connection. Prior connected media objects span a broad spectrum of design combinations. Such diversity suggests that people have varying needs and preferences for staying connected to one another. However, since these designs have largely been studied in isolation, we do not have a holistic understanding around how people would configure and behave within a ubiquitous social ecosystem of physically-grounded artifacts. In this paper, we create a technology probe called Social Wormholes, that lets people configure their own home ecosystem of connected artifacts. Through a field study with 24 participants, we report on patterns of behaviors that emerged naturally in the context of their daily lives and shine a light on how ubiquitous computing could be leveraged for social computing.
"Contradiction pushes me to improvise": Performer Expressivity and Engagement in Distanced Movement Performance Paradigms

The virtualization of traditionally in-person experiences has altered the workflows of performing arts communities, resulting in modifications to performance venues and alterations to expressiveness and interaction. 25 professional movement-based performers who have participated in both live and online performances were interviewed in order to determine how virtualization processes affected their practices and how they adapted to these changes. We discovered that performers viewed online performances as time-limited, non-interactive film recordings. Instead of avoiding distant venues, performers adapted to new limitations, inventing improvisation strategies in distracting environments and using time and technological constraints as creative constraints. To investigate how a distanced paradigm affects the live-action workflow of dancers, we staged a performance in which a dancer interacted with a robot in a remote location. The case study demonstrated that the performer modified her rehearsal techniques to work with remote technology and adapted to live interaction with a remote audience by visualizing unseen interactions. This study provides guidance for the design of interactive technology for virtual performances, taking into account the adaptation strategies that performers are currently employing to circumvent limitations of time, location, and absence.
ConvEx: A Visual Conversation Exploration System for Discord Moderators

Moderators are at the core of maintaining healthy online communities. For these moderators, who are often volunteers from the community, filtering through content and responding to misbehavior on time has become increasingly challenging as online communities continue to grow. To address such challenges of scale, recent research has looked into designing better tools for moderators of various platforms (e.g. Reddit, Twitch, Facebook, and Twitter). In this paper, we focus on Discord, a platform where communities are typically involved in large, synchronous group chats, creating an environment with a faster pace and a lack of structure compared to previously studied platforms. To tackle the unique challenges presented by Discord, we developed a new human-AI system called ConvEx for exploring online conversations. ConvEx is an AI-augmented version of the standard Discord interface designed to help moderators be proactive in identifying and preventing potential problems. It provides visual embeddings of conversational metrics, such as activity and toxicity levels, and can be extended to visualize other metrics. Through a user study with eight active moderators of Discord servers, we found that ConvEx supported several high-level strategies in monitoring a server and analyzing conversations. ConvEx allowed moderators to obtain a holistic view of activity across multiple channels on the server while guiding their attention towards problematic conversations and messages in a channel, helping them identify important contextual information to obtain reliable information from the AI analysis while also being able to pick up on contextual nuances which the AI missed. We conclude with design considerations for integrating AI into future interfaces for moderating synchronous, unstructured online conversations.
Breaking the Silence: Investigating Which Types of Moderation Reduce Negative Effects of Sexist Social Media Content

Sexist content is widespread on social media and can reduce women's psychological well-being and their willingness to participate in online discourse, making it a societal issue. To counter these effects, social media platforms employ moderators. To date, little is known about the effectiveness of different forms of moderation in creating a safe space and their acceptance, in particular from the perspective of women as members of the targeted group and users in general (rather than perpetrators). In this research, we propose that some common forms of moderation can be systematized along two facets of visibility, namely visibility of sexist content and of counterspeech. In an online experiment (N = 839), we manipulated these two facets and tested how they shaped social norms, feelings of safety, and intent to participate, as well as fairness, trustworthiness, and efficacy evaluations. In line with our predictions, deletion of sexist content - i.e., its invisibility - and (public) counterspeech - i.e., its visibility - against visible sexist content contributed to creating a safe space. Looking at the underlying psychological mechanism, we found that these effects were largely driven by changes in what was perceived normative in the presented context. Interestingly, deletion of sexist content was judged as less fair than counterspeech against visible sexist content. Our research contributes to a growing body of literature that highlights the importance of norms in creating safer online environments and provides practical implications for moderators for selecting actions that can be effective and accepted.
AIMEE: An Exploratory Study of How Rules Support AI Developers to Explain and Edit Models

In real-world applications when deploying Machine Learning (ML) models, initial model development includes close analysis of the model results and behavior by a data scientist. Once trained, however, models may need to be retrained with new data or updated to adhere to new rules or regulations. This presents two challenges. First, how to communicate how a model is making its decisions before and after retraining, and second how to support model editing to take into account new requirements. To address these needs, we built AIMEE (AI Model Explorer and Editor), a tool created to address these challenges by providing interactive methods to explain, visualize, and modify model decision boundaries using rules. Rules should benefit model builders by providing a layer of abstraction for understanding and manipulating the model and reduces the need to modify individual rows of data directly. To evaluate if this was the case, we conducted a pair of user studies totaling 23 participants to evaluate AIMEE's rules-based approach for model explainability and editing. We found that participants correctly interpreted rules and report on their perspectives of how rules are beneficial (and not), ways that rules could support collaboration, and provide a usability evaluation of the tool.
Tensions in Data Journey Activities: Mobilising, Processing, Producing, and Re-purposing Data in Environmental Assessment Practice

Digital data shape work practices as they travel through space, time, and social situations. In turn, workers shape the materiality of data through different data-oriented activities. In this paper, we draw on theory of data journeys to develop and present a conceptual framework of four data-intensive activities: Producing, mobilising, processing, and re-purposing. Together, those activities capture entanglements of data transformations and data-intensive practices. We develop the framework through a qualitative study of digitisation in Environmental Assessment (EA) practice. EA practitioners reflect on their experiences working with digital environmental data through ten semi-structured and two interactive interviews. The paper offers two main contributions. First, we develop a data journey theoretical framework and show that it is useful as a unit of analysis for uncovering tensions in data-oriented activities. Second, we use these insights to outline a set of tensions for future design activities in the area of data work practices.
Form to Flow: Exploring Challenges and Roles of Conversational UX Designers in Real-world, Multi-channel Service Environments

Conversational agents are widely used in today's multi-channel service environments. However, little is known regarding the challenges faced by user experience (UX) designers. This study explores the challenges faced by conversational UX designers working in multidisciplinary teams for understanding the design process involved in the transformation of conventional graphical interfaces to conversation flows. In-depth interviews with UX designers working in industries reveal the key challenges in the form-to-flow transformation process. Moreover, collaborative work with various stakeholders in complex work environments involves conversational artificial intelligence-engendered gap-filling work phenomena wherein UX designers tend to work across role boundaries. Our results indicate the need for added support for CUX designer including tools and guidelines to suppport form-to-flow design, tools for testing, and defining extended roles for CUX designers with collaborative perspectives for designing conversational agents in multi-channel service environments.