Reviewerly/exHarmony huggingface dataset:
‘Keeping our Faith Alive’: Investigating Buddhism Practice during COVID-19 to Inform Design for the Online Community Practice of Faith

Supportive digital technologies for the community practice of Faith remain relatively under-explored in Human Computer Interaction (HCI). We report on interviews with 12 members of a Buddhist community in the UK who self-organized and used video-conferencing tools to remain connected to their faith community during the COVID-19 pandemic, aiming to understand how they adopted online tools for their practice while shaping new collective experiences. Findings from Reflexive Thematic Analysis were combined with autoethnographic insights from the first author, also a community member. We evidence qualities of the practice that were valued by participants before and during the pandemic, and the limitations of existing tools and screen-based interactions. We contribute empirical insights on mediated religious and spiritual practice, advancing HCI discourses on Techno-Spirituality, Tangible Embodied Interaction, Soma Design and More-than-Human Worlds. We further develop design considerations for enriching spiritual experiences that are meaningful to practitioners in communities of faith.
Creating Inclusive Voices for the 21st Century: A Non-Binary Text-to-Speech for Conversational Assistants

As voice assistant usage continues to grow, their homogeneity becomes even more problematic with the UNESCO report, "I'd Blush if I could" showing that designing only feminine voice assistants encourages negative behavior, both with virtual assistants and with real people [3]. While masculine text-to-speech (TTS) voices exist, ones that cover the full range of gender presentations, such as non-binary or gender-ambiguous voices are largely missing. In this paper, we present a method of creating a non-binary TTS voice and an example voice, Sam, created with input from the non-binary and transgender communities. We have open-sourced the resulting voice, along with the process and data used to create it. Finally, we present results from a large-scale survey showing that non-binary individuals are more likely to prefer a non-binary voice assistant compared to cisgendered individuals and discuss differences across age and gender.
Privacy-Preserving Blockchain Framework for Supply Chain Management: Perceptive Craving Game Search Optimization (PCGSO)

The fierce competition in international markets and the rapid advancements in information technology result in shorter lead times, lower transportation capacity, and higher demand. The supply chain network is one of the most crucial areas of concentration in the majority of business circumstances. Blockchain technology is a promising option for safe information exchange in the supply chain network. Although preserving security at every level of the blockchain is somewhat important, cryptographic methodologies are frequently used in the existing works. The novel perceptive craving game search (PCGS) optimization algorithm is used to optimally generate the key for data sanitization, which assures the privacy of logistics data. Here, the original logistics data obtained from the manufacturer is sanitized with an optimal key generated by using the PCGS optimization algorithm, avoiding the risk of unauthorized access and data swarm that causes the system to lag. Moreover, the sanitized data obtained from the manufacturer is transmitted to the allowed parties via different sub-chains. The same generated key is used on the receiving customer side for reconstructing the original information from the sanitized data. The performance and results of the proposed blockchain-based privacy preservation model are validated using various parameters.
Collaborative Diffusion: Boosting Designerly Co-Creation with Generative AI

Visual communication in collaborative design provides common ground and a tangible trace of thoughts. Yet, it is often underused due to a lack of visualization skills and time needed to sketch and detail. Current visual co-creation tools can support this with existing images, whereas generative AI can produce entirely new images from textual prompts, allowing for highly specific, personalized results. In this paper, we explore how AI image generation can be used to enhance inspiration and communication throughout collaborative design. We introduce BrainFax, a tool that facilitates generating, editing, and sharing images with AI through a chat bot and online whiteboard. Through co-creation with designers in the field, we found that AI image generation can boost designerly co-creation, yet needs careful embedding into a workflow to leverage inspirational and communicative creations. We close with a critical perspective on the implications of this technology for design and discuss limitations and future work.
‘Treat me as your friend, not a number in your database’: Co-designing with Children to Cope with Datafication Online

Datafication refers to the practices through which children's online actions are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in ways including behavioural engineering and monetisation. Previous research has shown that not only do children care significantly about various aspects of datafication, but they demand a chance to take action. Through 10 co-design sessions with 53 children, we examined how children in the UK want to be supported to cope with the datafication practices. Our findings provide insights for creating age-appropriate support for children's algorithmic literacy development, highlighting and unpacking the importance of no one-size-fitting-all designs to support children's coping with datafication. We contribute a first understanding of how children aged 7–14 would like to be supported with datafication and what future data-driven digital experiences should be like for them, who demand a shift of the current data ecosystem towards a more humane-by-design and autonomy-supportive future.
Counterventions: a reparative reflection on interventionist HCI

Research in HCI applied to clinical interventions relies on normative assumptions about which bodies and minds are healthy, valuable, and desirable. To disrupt this normalizing drive in HCI, we define a "counterventional approach" to intervention technology design informed by critical scholarship and community perspectives. This approach is meant to unsettle normative assumptions of intervention as urgent, necessary, and curative. We begin with a historical overview of intervention in HCI and its critics. Then, through reparative readings of past HCI projects in autism intervention, we illustrate the emergent principles of a counterventional approach and how it may manifest research outcomes that are fundamentally divergent from dominant approaches. We then explicate characteristics of "counterventions" – projects that aim to contest dominant sociotechnical paradigms through privileging community and participants in research inquiry, interaction design, and analysis of outcomes. These divergent research imaginaries have transformative implications for how interventionist HCI might be conducted in future.
Making Energy Matter: Soma Design for Ethical Relations in Energy Systems

There is a need to reframe our relationship to energy, particularly in Western energy contexts, where we have plentiful access and no meaningful barriers to use. This paper outlines a first-person engagement with energy systems and shows how somaesthetic design is one possible means to cultivate and design for new ways of ethical being with energy systems. Early autobiographical design work focused on designing for 'sustainability' revealed a trajectory of fatalism and restriction. A turn towards enacting material relations, co-performed with others, opened into a more holistic relationship with energy. Reflecting on how this process unfolded, we argue that sustainability is not, in itself, a somaesthetic sensibility, and remains constrained within rational framings. We develop this argument to contribute to a new understanding of how we somatically relate to energy and how relational ethics in interaction design research and practice can encourage a felt sense for the materiality of energy.
AeroRigUI: Actuated TUIs for Spatial Interaction using Rigging Swarm Robots on Ceilings in Everyday Space

We present AeroRigUI, an actuated tangible UI for 3D spatial embodied interaction. Using strings controlled by self-propelled swarm robots with a reeling mechanism on ceiling surfaces, our approach enables rigging (control through strings) physical objects’ position and orientation in the air. This can be applied to novel interactions in 3D space, including dynamic physical affordances, 3D information displays, and haptics. Utilizing the ceiling, an often underused room area, AeroRigUI can be applied for a range of applications such as room organization, data physicalization, and animated expressions. We demonstrate the applications based on our proof-of-concept prototype, which includes the hardware design of the rigging robots, named RigBots, and the software design for mid-air object control via interactive string manipulation. We also introduce technical evaluation and analysis of our approach prototype to address the hardware feasibility and safety. Overall, AeroRigUI enables a novel spatial and tangible UI system with great controllability and deployability.
Exploration of Bare-Hand Mid-Air Pointing Selection Techniques for Dense Virtual Reality Environments

Target selection in dense virtual reality (VR) environments is challenging. Prior work has explored different controller-based raycasting techniques to assist target selection in such environments. However, limited research has focused on selection via mid-air barehand, which represents another major input metaphor for immersive environments. In this paper, we first review the existing raycasting selection techniques for dense VR environments. Based on this, we propose and develop two freehand pointing selection techniques—HandDepthCursor and HandConeGrid, and implement MultiFingerBubble, a recently-proposed technique. We then conduct a user study to compare and evaluate their performance and experience in a target selection task in dense VR environments. Our results suggest that HandDepthCursor and HandConeGrid led to significantly faster and more accurate selection performance, and lower perceived workload and arm fatigue. In addition, HandConeGrid showed a distinct advantage in high-density environments.
Improving Multiparty Interactions with a Robot Using Large Language Models

Speaker diarization is a key component of systems that support multiparty interactions of co-located users, such as meeting facilitation robots. The goal is to identify who spoke what, often to provide feedback, moderate participation, and personalize responses by the robot. Current systems use a combination of acoustic (e.g. pitch differences) and visual features (e.g. gaze) to perform diarization, but involve the use of additional sensors or require overhead signal processing efforts. Alternatively, automatic speech recognition (ASR) is a necessary step in the diarization pipeline, and utilizing the transcribed text to directly identify speaker labels in the conversation can eliminate such challenges. With that motivation, we leverage large language models (LLMs) to identify speaker labels from transcribed text and observe an exact match of 77% and a word level accuracy of 90%. We discuss our findings and the potential use of LLMs as a diarization tool for future systems.
Explainable hybrid word representations for sentiment analysis of financial news

Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are their long-term benefits. However, it is challenging to analyze the sentiments of texts related to the financial domain, given the enormous amount of information available. The existing approaches are unable to capture complex attributes of language such as word usage, including semantics and syntax throughout the context, and polysemy in the context. Further, these approaches failed to interpret the models' predictability, which is obscure to humans. Models' interpretability to justify the predictions has remained largely unexplored and has become important to engender users' trust in the predictions by providing insight into the model prediction. Accordingly, in this paper, we present an explainable hybrid word representation that first augments the data to address the class imbalance issue and then integrates three embeddings to involve polysemy in context, semantics, and syntax in a context. We then fed our proposed word representation to a convolutional neural network (CNN) with attention to capture the sentiment. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of financial news. The experimental results also show that the proposed model outperforms several baselines of word embeddings and contextual embeddings when they are separately fed to a neural network model. Further, we show the explainability of the proposed method by presenting the visualization results to explain the reason for a prediction in the sentiment analysis of financial news.
The Role of Data Science in Closing the Implementation Gap

Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinicians to create unbiased applications that are integrated into the clinical workflow.
A semi-supervised generative adversarial network for amodal instance segmentation of piglets in farrowing pens

Occlusions, such as farrowing pens in piggeries, hinder computer vision applications for automated animal monitoring. Amodal instance segmentation (AIS), aiming to predict a complete mask of an occluded target, is a promising solution. However, AIS usually requires amodal datasets, which are challenging to create and limit the application of AIS. To solve this problem, we proposed a novel semi-supervised generative adversarial network (GAN) for AIS, denoted “the AISGAN”. Our AISGAN only requires a regular modal dataset and generate amodal samples by random occlusions, making the AIS method more applicable. A corresponding segmentation loss was added to overcome mode collapse of GAN. The results showed that the AISGAN achieved a mean Intersection of Union (mIoU) of 0.823 and outperformed the mIoUs of Mask RCNN, Raw, and Convex Hull (0.801, 0.780, and 0.778, respectively). As a semi-supervised method, the mIoU of our AISGAN was further enhanced (by 0.6%) when we fine-tuned it with unlabeled new data, showing its extensibility to new unseen scenarios. The visualization demonstrates that the AISGAN can produce realistic masks of piglets, including details of their noses and legs, even under heavily occluded conditions. With the AISGAN, we achieved an occlusion-resistant spatial distribution analysis of the piglets in farrowing pens. Thus, the AISGAN is a promising tool to manage occlusion problems for automated animal monitoring in complex housing environments.
CLAHE-Based Low-Light Image Enhancement for Robust Object Detection in Overhead Power Transmission System

The lighting quality of the unmanned aerial vehicle (UAV) images is the key factor that affects the intelligent operation and maintenance efficiency in power system. When captured in low-light condition, the UAV images suffer from loss of details, leading to a decrease in object detection accuracy. However, the research is lacking with respect to low-light image enhancement for UAV-based overhead power transmission system images. In this letter, we propose a low-light image enhancement method based on Contrast Limited Adaptive Histogram Equalization (CLAHE) to robustify object detection against low-light condition. We first separate the luminance and chrominance channels of the UAV image via color space conversion so that CLAHE is used to enhance luminance contrast without introducing chrominance shift. Moreover, we optimize the contrast limit parameter in CLAHE to balance contrast enhancement and noise suppression, which further boosts object detection accuracy. Experimental results validate the effectiveness of the proposed scheme in detail enhancement without noise over-amplification, leading to a magnificent increase of insulator detection accuracy from 58.0% to 82.0%.
Semantic Representations With Attention Networks for Boosting Image Captioning

Image captioning has shown encouraging outcomes with Transformer-based architectures that typically use attention-based methods to establish semantic associations between objects in an image for caption prediction.Nevertheless, when appearance features of objects in an image display low interdependence, attention-based methods have difficulty in capturing the semantic association between them.To tackle this problem, additional knowledge beyond the task-specific dataset is often required to create captions that are more precise and meaningful.In this article, a semantic attention network is proposed to incorporate general-purpose knowledge into a transformer attention block model.This design combines visual and semantic properties of internal image knowledge in one place for fusion, serving as a reference point to aid in the learning of alignments between vision and language and to improve visual attention and semantic association.The proposed framework is validated on the Microsoft COCO dataset, and experimental results demonstrate competitive performance against the current state of the art.
From Code Complexity Metrics to Program Comprehension

Understanding code depends not only on the code but also on the brain.
Highly efficient and ultra‐compact micro‐LED pico‐projector based on a microlens array

Abstract Pico‐projection displays are the main developing direction for future projection displays, but the balance between the optical efficiency and the system size is still a big concern. This paper presents a pico‐projector design based on micro‐LED (μLED) light sources. Firstly, based on the analysis of the characteristics of μLED, a highly integrated microlens array is designed to improve the system efficiency by reshaping the spatial light distribution. In addition, the matching relationship between the aperture angle of projection lens and the divergence angle of μLED is studied, and on this basis, a set of four‐piece spherical lens group is proposed to achieve compact size. Simulation results show that the μLED equipped with the microlens array can concentrate about 86% of the light energy within a dispersion angle of ±20°, thus increasing the light energy utilization by about 3.5 times and reducing the size of the pico‐projector to 30.18 mm 3 . The designed pico‐projector has high performance and compact size with great potential for future applications.
Acid Mine Drainage Discrimination Using Very High Resolution Imagery Obtained by Unmanned Aerial Vehicle in a Stone Coal Mining Area

Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems.
Impact of ICT diffusion and financial development on economic growth in developing countries

Purpose The present study aimed to examine the relationship between information and communication technologies (ICT) diffusion, financial development and economic growth in the panel of developing countries for 2005–2019. Design/methodology/approach The study employed the principal component analysis (PCA) to extract the index of ICT diffusion. First-generation panel unit root tests such as Levine Lin Chu (LLC), Im Pesaran Shin (IPS), Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) were employed to check the stationarity of the variables. Pedroni and Kao co-integration techniques were used to examine the existence of the long-run relationship, and co-integration coefficients were estimated using FMOLS and dynamic ordinary least squares (DOLS). The panel Granger causality approach examined the short-run and long-run causality. Findings The results confirmed that ICT diffusion, financial development and trade openness accelerate growth, whereas inflation dampens economic growth. Further, the causality test showed bidirectional causality between ICT growth and financial development growth but a unidirectional causality from financial development to ICT diffusion in developing countries. Originality/value The study recommends synchronizing public and private sector investment for a synergistic effect on ICT infrastructure and adequate investment in the financial sector to increase the growth rate in developing countries. Economic policies should be adopted toward incentives and subsidies to ensure affordable ICT services for disadvantaged communities. Also, training programs focussing on enhancing digital literacy to enable all segments of the population to use digital platforms for financial services are recommended.