Reviewerly/exHarmony huggingface dataset:
Identification method of typical defects in transmission lines based on YOLOv5 object detection algorithm

In order to realize the automatic identification of typical defects in transmission lines, a typical defect identification method for transmission lines based on You Only Look Once (YOLO)v5 algorithm is proposed, which has the advantages of low missing detection rate and false detection rate. Firstly, a transmission line defect image dataset is constructed for three typical defects: bird's nest defect, insulator defect and shakeproof hammer defect, and the corresponding image labeling strategy is formulated according to the defect characteristics. Secondly, this paper introduces the network structure of YOLOv5 in detail. For the sake of further improving the neural network's ability to recognize the visual defects of transmission lines, the attention mechanism and small target detection layer are introduced into YOLOv5 neural network. Finally, this paper design transmission line defect recognition cases based on three kinds of typical defects, and the proposed method is compared with the recognition methods based on Faster Region based Convolutional Neural Network (RCNN) and Single Shot MultiBox Detector (SSD) in terms of the proposed performance indexes (missing detection rate and false detection rate). The results show that the missing detection rate and false detection rate of the YOLOv5-based identification method are much lower than those of the Faster RCNN and SSD-based identification methods, and the improved YOLOv5 network recognition rate is higher, which is significant for reducing the workload of transmission line inspection personnel.
Comparing Dwell time, Pursuits and Gaze Gestures for Gaze Interaction on Handheld Mobile Devices

Gaze is promising for hands-free interaction on mobile devices. However, it is not clear how gaze interaction methods compare to each other in mobile settings. This paper presents the first experiment in a mobile setting that compares three of the most commonly used gaze interaction methods: Dwell time, Pursuits, and Gaze gestures. In our study, 24 participants selected one of 2, 4, 9, 12 and 32 targets via gaze while sitting and while walking. Results show that input using Pursuits is faster than Dwell time and Gaze gestures especially when there are many targets. Users prefer Pursuits when stationary, but prefer Dwell time when walking. While selection using Gaze gestures is more demanding and slower when there are many targets, it is suitable for contexts where accuracy is more important than speed. We conclude with guidelines for the design of gaze interaction on handheld mobile devices.
Felt Experiences with Kombucha Scoby: Exploring First-person Perspectives with Living Matter

Designing with living organisms can offer new perspectives to design research and practices in HCI. In this work, we explore first-person perspectives through design research with Kombucha Scoby, a microbial biofilm. We began with a material design exploration, producing digitally fabricated and crafted samples with Scoby. As we noticed our felt experiences while growing and working with Kombucha Scoby, we shifted towards a reflective autoethnographic study. Through reflective writings, we followed sensory experiences such as hearing the Kombucha fermentation, touching the Scoby while harvesting it, and watching the slow growth of layers over time. Subsequently, we designed "sensory engagement probes”: designed experiments that bring forward new connections and communicate our process, motivations, and tensions that emerged while engaging with the organism. Lastly, we discuss how such design research can inform material design with living matter by creating space to contemplate "life as shared experience" and more-than-human design perspectives.
Collaborative Online Learning with VR Video: Roles of Collaborative Tools and Shared Video Control

Virtual Reality (VR) has a noteworthy educational potential by providing immersive and collaborative environments. As an alternative but cost-effective way of delivering realistic environments in VR, using 360-degree videos in immersive VR (VR videos) received more attention. Although many studies reported positive learning experiences with VR videos, little is known about how collaborative learning performs on VR video viewing systems. In this study, we implemented two collaborative VR video viewing modes based on the way of group video control, synchronized or shared (Sync mode) and non-synchronized or individual (Non-sync mode) video control, against a conventional VR video viewing setting (Basic mode). We conducted a within-subject study (N = 54) in a lab-simulated remote learning environment. Our results show that collaborative VR video modes (Sync and Non-sync mode) improve users’ learning experiences and collaboration quality, especially with shared video control. Our findings provide directions for designing and employing collaborative VR video tools in online learning environments.
No Pie in the (Digital) Sky: Co-Imagining the Food Metaverse

Human behaviour and habits co-evolve with technology, and the metaverse is poised to become a key player in reshaping how we live our everyday life. Given the importance of food in our daily lives, we ask: how will our relationships with food be transformed by the metaverse, and what are the promises and pitfalls of this technology? To answer this, we propose a co-design study that reveals the important elements people value in their daily interactions with food. We then present a speculative catalogue of novel metaverse food experiences, and insights from discussing these ideas with food designers, anthropologists and metaverse experts. Our work aims to provide designers with inspirations for building a metaverse that: provides inclusive opportunities for the future of food; helps re-discover the forgotten or lost knowledge about food; facilitates the exploration, excitement and joy of eating; and reinvigorates the ways that food can soothe and heal.
Re-Evaluating VR User Awareness Needs During Bystander Interactions

Virtual reality (VR) users are often around bystanders, i.e. people in the real world the VR user may want to interact with. To facilitate bystander-VR user interactions, technology-mediated awareness systems have been introduced to increase a user's awareness of bystanders. However, while prior works have found effective means of facilitating bystander-VR user interactions, it is unclear when and why one awareness system should be used over another. We reviewed, and selected, a breadth of bystander awareness systems from the literature and investigated their usability, and how they could be holistically used together to support varying awareness needs across 14 bystander-VR user interactions. Our results demonstrate VR users do not manage bystander awareness based solely on the usability of awareness systems but rather on the demands of social context weighted against desired immersion in VR (something existing evaluations fail to capture) and show the need for socially intelligent bystander awareness systems.
CoilCAM: Enabling Parametric Design for Clay 3D Printing Through an Action-Oriented Toolpath Programming System

Clay 3D printing provides the benefits of digital fabrication automation and reconfigurability through a method that evokes manual clay coiling. Existing design technologies for clay 3D printing reflect the general 3D printing workflow in which solid forms are designed in CAD and then converted into a toolpath. In contrast, in hand-coiling, form is determined by the actions taken by the artist's hands through space in response to the material. We theorized that an action-oriented approach for clay 3D printing could allow creators to design digital fabrication toolpaths that reflect clay material properties. We present CoilCAM, a domain-specific CAM programming system that supports the integrated generation of parametric forms and surface textures through mathematically defined toolpath operations. We developed CoilCAM in collaboration with ceramics professionals and evaluated CoilCAM's relevance to manual ceramics by reinterpreting hand-made ceramic vessels. This process revealed the importance of iterative variation and embodied experience in action-oriented workflows.
Obstacle Avoidance Strategy for Mobile Robot Based on Monocular Camera

This research paper proposes a real-time obstacle avoidance strategy for mobile robots with a monocular camera. The approach uses a binary semantic segmentation FCN-VGG-16 to extract features from images captured by the monocular camera and estimate the position and distance of obstacles in the robot’s environment. Segmented images are used to create the frontal view of a mobile robot. Then, the optimized path planning based on the enhanced A* algorithm with a set of weighted factors, such as collision, path, and smooth cost improves the performance of a mobile robot’s path. In addition, a collision-free and smooth obstacle avoidance strategy will be devised by optimizing the cost functions. Lastly, the results of our evaluation show that the approach successfully detects and avoids static and dynamic obstacles in real time with high accuracy, efficiency, and smooth steering with low angle changes. Our approach offers a potential solution for obstacle avoidance in both global and local path planning, addressing the challenges of complex environments while minimizing the need for expensive and complicated sensor systems.
FlowAR: How Different Augmented Reality Visualizations of Online Fitness Videos Support Flow for At-Home Yoga Exercises

Online fitness video tutorials are an increasingly popular way to stay fit at home without a personal trainer. However, to keep the screen playing the video in view, users typically disrupt their balance and break the motion flow — two main pillars for the correct execution of yoga poses. While past research partially addressed this problem, these approaches supported only a limited view of the instructor and simple movements. To enable the fluid execution of complex full-body yoga exercises, we propose FlowAR, an augmented reality system for home workouts that shows training video tutorials as always-present virtual static and dynamic overlays around the user. We tested different overlay layouts in a study with 16 participants, using motion capture equipment for baseline performance. Then, we iterated the prototype and tested it in a furnished lab simulating home settings with 12 users. Our results highlight the advantages of different visualizations and the system's general applicability.
Into the Metaverse: Technical Challenges, Social Problems, Utopian Visions, and Policy Principles

The metaverse holds a prominent place in debates over the future direction of digital networks. Proponents claim that advances in virtual and augmented reality will shape every facet of social life. This article defines the metaverse, explores the state of the technology, and addresses its public policy significance. It makes use of a political economic perspective focusing on the concepts of commodification and spatialisation. Specifically, it considers how major platform and gaming companies plan to use the metaverse to expand market share. The article also addresses the cultural dimensions of the metaverse as the latest in a series of utopian visions of a digital sublime. It proceeds to take up the social problems associated with the metaverse and concludes by describing the essential policy principles that should guide public authorities in the regulating the metaverse. These principles include acknowledging that current concerns over implementation do not limit future deployment. Moreover, public policy should start by recognising that the metaverse is a public space and not the private property of the major platforms. Finally, policy must address specific social problems deepened by the arrival of the metaverse including crime, privacy, the impact on climate, and data ownership.
Extrapolation and AI transparency: Why machine learning models should reveal when they make decisions beyond their training

The right to artificial intelligence (AI) explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which can reveal whether a model is making inferences beyond the boundaries of its training. We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency, accountability, and fairness. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our commentary accompanies practical clauses useful to include in AI regulations such as the AI Bill of Rights, the National AI Initiative Act in the United States, and the AI Act by the European Commission.
U-YOLOv7: A network for underwater organism detection

Detecting and monitoring underwater organisms is very important for sea aquaculture. The human eye struggles to quickly distinguish between aquatic species due to their variety and dense dispersion. In this paper, a deep learning object detection algorithm based on YOLOv7 is used to design a new network, called Underwater-YOLOv7 (U-YOLOv7), for underwater organism detection. This model satisfies the requirements with regards to both speed and accuracy. First, a network combining CrossConv and an efficient squeeze-excitation module is created. This network increases the extraction of channel information while reducing parameters and enhancing the feature fusion of the network. Second, a lightweight Content-Aware ReAssembly of FEatures (CARAFE) operator is used to obtain more semantic information about underwater images before feature fusion. A 3D attention mechanism is incorporated to improve the anti-interference ability of the model in underwater recognition. Finally, a decoupling head using hybrid convolution is designed to accelerate convergence and improve the accuracy of underwater detection. The results show that the network proposed in this paper obtains an improvement of 3.2% in accuracy, 2.3% in recall, and 2.8% in the mean average precision value and runs at up to 179 fps, far outperforming other advanced networks. Moreover, it has a higher estimation accuracy than the YOLOv7 network.
Smart contract swarm and multi-branch structure for secure and efficient BIM versioning in blockchain-aided common data environment

Managing versions of data for building information modeling (BIM) data is critical for design collaboration, especially with multiple disciplines involved where each team has specific data requirements and design procedures. However, existing version control approaches are still inefficient for two limitations: (1) lacking an efficient data structure for managing version dependencies among multi-disciplinary BIM models and (2) risking data manipulation due to a centralized versioning architecture that may lead to reworking, losing design traceability and raising disputes. Blockchain technology is an emerging and promising solution for version management as it provides a decentralized, immutable, and traceable database paradigm. Hence, this paper proposes a blockchain-aided solution for secure and efficient BIM versioning with three major innovations. Firstly, a two-layer container common data environment (TLCCDE) model integrating blockchain and Interplanetary File System (IPFS) is developed to illustrate an overall logic for BIM versioning in a distributed environment. Secondly, a smart contract swarm (SCS) is developed to automate versioning actions in the TLCCDE. Thirdly, a novel multi-branch structure (MBS) with efficient algorithms is designed to simultaneously manage version change continuity, issue attachment, and dependency compliance. The proposed TLCCDE model is evaluated and validated in design scenarios based on a real-world project. Results show that: (1) the TLCCDE model is workable in BIM versioning; (2) TLCCDE computing performance metrics, including SCS latency and throughput, as well as MBS latency and scalability, are all validated to be practical; and (3) the TLCCDE outperforms existing versioning approaches by augmenting dependency automation and versioning cybersecurity.
Graph Representation Learning and Its Applications: A Survey

Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.
Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model's superior performance.
High-resolution remote sensing images semantic segmentation using improved UNet and SegNet

Semantic segmentation for high-resolution (HR) remote sensing (RS) images under the condition of a small number of training samples with imprecise labels is a challenging task. In this paper, we propose a novel method based on improved UNet and SegNet. First, a batch normalization layer is introduced into the original UNet to accelerate the convergence speed and an ELU activation function is selected instead of ReLU to avoid the neuron death. Second, to enhance the conventional SegNet, the encoder is reconstructed and a skip connection is designed to reuse the deep features, which is also beneficial for the network convergence. Third, a joint model is constructed by combining the improved UNet and SegNet and meanwhile a voting strategy is proposed to compute the final results. Experiments on the real-world HR RS images verify the effectiveness and superiority of the proposed method.
A Novel Data Poisoning Attack in Federated Learning based on Inverted Loss Function

Data poisoning attack is one of the common attacks that decreases the performance of a model in edge machine learning. The mechanism used in most of the existing data poisoning attacks diverges the gradients to a minimal extent which prevents models from achieving minima. In our approach, we have come with a new data poisoning attack that inverts the loss function of a benign model. The inverted loss function is then used to create malicious gradients at every SGD iteration, which is almost opposite to that of minima. Such gradients are then used to generate poisoned labels and inject those labels into the dataset. We have tested our attack in three different datasets, i.e. MNIST, Fashion-MNIST, and CIFAR-10, along with some preexisting data poisoning attacks. We have measured the performance of a global model in terms of accuracy drop in federated machine learning settings. The observed result suggests that our attack can be 1.6 times stronger than the targeted attack and 3.2 times stronger than a random poisoning attack in certain cases.
When interacting with a service robot is (not) satisfying: The role of customers’ need for social sharing of emotion

Service robots (SRs) are fast becoming the source of automated service delivery processes and personalized frontline customer experiences in service firms. This study investigates the impact of customers' emotional state on how they evaluate service experiences after interacting with an SR, which remains underexplored. Drawing on the theory of constructed emotion and social sharing of emotions, it conducts three online scenario-based experiments. Study 1 shows that customers in a neutral emotional state are more satisfied with the service provided by SRs than are those in a negative emotional state. The mechanisms underlying this dissatisfaction are perceived relatedness and perceived appropriateness, as shown by Study 2. Study 3 demonstrates how SRs' sympathetic capabilities moderate this mediating relationship. The findings were consistent across different emotion manipulation methods, negative emotional states, and sources of emotional states. Given the increasing prominence of SRs on organizational frontlines, this study has numerous theoretical implications for SRs' interaction with emotional customers and practical implications for employing SRs.
The use of autoencoders for training neural networks with mixed categorical and numerical features

Abstract We focus on modelling categorical features and improving predictive power of neural networks with mixed categorical and numerical features in supervised learning tasks. The goal of this paper is to challenge the current dominant approach in actuarial data science with a new architecture of a neural network and a new training algorithm. The key proposal is to use a joint embedding for all categorical features, instead of separate entity embeddings, to determine the numerical representation of the categorical features which is fed, together with all other numerical features, into hidden layers of a neural network with a target response. In addition, we postulate that we should initialize the numerical representation of the categorical features and other parameters of the hidden layers of the neural network with parameters trained with (denoising) autoencoders in unsupervised learning tasks, instead of using random initialization of parameters. Since autoencoders for categorical data play an important role in this research, they are investigated in more depth in the paper. We illustrate our ideas with experiments on a real data set with claim numbers, and we demonstrate that we can achieve a higher predictive power of the network.