Reviewerly/exHarmony huggingface dataset:
Where Should We Put It? Layout and Placement Strategies of Documents in Augmented Reality for Collaborative Sensemaking

Future offices are likely reshaped by Augmented Reality (AR) extending the display space while maintaining awareness of surroundings, and thus promise to support collaborative tasks such as brainstorming or sensemaking. However, it is unclear how physical surroundings and co-located collaboration influence the spatial organization of virtual content for sensemaking. Therefore, we conducted a study (N=28) to investigate the effect of office environments and work styles during a document classification task using AR with regard to content placement, layout strategies, and sensemaking workflows. Results show that participants require furniture, especially tables and whiteboards, to assist sensemaking and collaboration regardless of room settings, while generous free spaces (e.g., walls) are likely used when available. Moreover, collaborating participants tend to use furniture despite personal layout preferences. We identified different placement and layout strategies, as well as the transitions in-between. Finally, we propose design implications for future immersive sensemaking applications and beyond.
A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.
Towards Collaborative Learning in Virtual Reality: A Comparison of Co-Located Symmetric and Asymmetric Pair-Learning

Pair-learning is beneficial for learning outcome, motivation, and social presence, and so is virtual reality (VR) by increasing immersion, engagement, motivation, and interest of students. Nevertheless, there is a research gap if the benefits of pair-learning and VR can be combined. Furthermore, it is not clear which influence it has if only one or both peers use VR. To investigate these aspects, we implemented two types of VR pair-learning systems, a symmetric system with both peers using VR and an asymmetric system with one using a tablet. In a user study (N=46), the symmetric system statistically significantly provided higher presence, immersion, player experience, and lower intrinsic cognitive load, which are all important for learning. Symmetric and asymmetric systems performed equally well regarding learning outcome, highlighting that both are valuable learning systems. We used these findings to define guidelines on how to design co-located VR pair-learning applications, including characteristics for symmetric and asymmetric systems.
Improving Agricultural Product Traceability Using Blockchain

Most traditional agricultural traceability systems are centralized, which could result in the low reliability of traceability results, enterprise privacy data leakage vulnerabilities, and the generation of information islands. To solve the above problems, we propose a trusted agricultural product traceability system based on the Ethereum blockchain in this paper. We designed a dual storage model of "Blockchain+IPFS (InterPlanetary File System)" to reduce the storage pressure of the blockchain and realize efficient information queries. Additionally, we propose a data privacy protection solution based on some cryptographic primitives and the Merkle Tree that can avoid enterprise privacy and sensitive data leakage. Furthermore, we implemented the proposed system using the Ethereum blockchain platform and provided the cost, performance, and security analysis, as well as compared it with the existing solutions. The results showed that the proposed system is both efficient and feasible and can meet the practical application requirements.
Towards risk-aware artificial intelligence and machine learning systems: An overview

The adoption of artificial intelligence (AI) and machine learning (ML) in risk-sensitive environments is still in its infancy because it lacks a systematic framework for reasoning about risk, uncertainty, and their potentially catastrophic consequences. In high-impact applications, inference on risk and uncertainty will become decisive in the adoption of AI/ML systems. To this end, there is a pressing need for a consolidated understanding on the varied risks arising from AI/ML systems, and how these risks and their side effects emerge and unfold in practice. In this paper, we provide a systematic and comprehensive overview of a broad array of inherent risks that can arise in AI/ML systems. These risks are grouped into two categories: data-level risk (e.g., data bias, dataset shift, out-of-domain data, and adversarial attacks) and model-level risk (e.g., model bias, misspecification, and uncertainty). In addition, we highlight the research needs for developing a holistic framework for risk management dedicated to AI/ML systems to hedge the corresponding risks. Furthermore, we outline several research related challenges and opportunities along with the development of risk-aware AI/ML systems. Our research has the potential to significantly increase the credibility of deploying AI/ML models in high-stakes decision settings for facilitating safety assurance, and preventing systems from unintended consequences.
Debiased Contrastive Learning of Unsupervised Sentence Representations

Recently, contrastive learning has been shown to be effective in improving pre-trained language models (PLM) to derive high-quality sentence representations. It aims to pull close positive examples to enhance the alignment while push apart irrelevant negatives for the uniformity of the whole representation space.However, previous works mostly adopt in-batch negatives or sample from training data at random. Such a way may cause the sampling bias that improper negatives (false negatives and anisotropy representations) are used to learn sentence representations, which will hurt the uniformity of the representation space.To address it, we present a new framework DCLR (Debiased Contrastive Learning of unsupervised sentence Representations) to alleviate the influence of these improper negatives.In DCLR, we design an instance weighting method to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space.Experiments on seven semantic textual similarity tasks show that our approach is more effective than competitive baselines. Our code and data are publicly available at the link: bluehttps://github.com/RUCAIBox/DCLR.
RagRug: A Toolkit for Situated Analytics

We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualizations with data streams from the Internet of Things using distributed dataflow. To this end, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.
A New Age of AI: Features and Futures

By reviewing the 70 years of AI, this article summarizes and discusses the paradigm transformations from the age of AI before the year 2000 to the new age of AI from the year 2000 onward. It reviews the AI thinking and features of various AI generations and paradigms during these two ages of AI and their transformations. The paper further summarizes several AI Formulas from the AI vision, system, goal, task, and process perspectives. Several important areas are highlighted in developing AI Futures: shrinking the gaps between human, natural and social AI, and developing human-like/level AI, meta AI, reflective AI, metasynthetic AI, data-driven AI, beyond ‘IID AI,’ actionable AI, and sustainable AI. In the new age of AI, we encourage your deep thinking of AI futures.
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization

In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal representations for video summarization. Most existing video summarization methods extract image-level features with ImageNet pre-trained deep models. Differently, our method exploits object-level and relation-level information to capture spatial-temporal dependencies. Specifically, our method builds spatial graphs on the detected object proposals. Then, we construct a temporal graph by using the aggregated representations of spatial graphs. Afterward, we perform relational reasoning over spatial and temporal graphs with graph convolutional networks and extract spatial-temporal representations for importance score prediction and key shot selection. To eliminate relation clutters caused by densely connected nodes, we further design a self-attention edge pooling module, which disregards meaningless relations of graphs. We conduct extensive experiments on two popular benchmarks, including the SumMe and TVSum datasets. Experimental results demonstrate that the proposed method achieves superior performance against state-of-the-art video summarization methods.
Eye-gaze control of a wheelchair mounted 6DOF assistive robot for activities of daily living

Abstract Background Building control architecture that balances the assistive manipulation systems with the benefits of direct human control is a crucial challenge of human–robot collaboration. It promises to help people with disabilities more efficiently control wheelchair and wheelchair-mounted robot arms to accomplish activities of daily living. Methods In this study, our research objective is to design an eye-tracking assistive robot control system capable of providing targeted engagement and motivating individuals with a disability to use the developed method for self-assistance activities of daily living. The graphical user interface is designed and integrated with the developed control architecture to achieve the goal. Results We evaluated the system by conducting a user study. Ten healthy participants performed five trials of three manipulation tasks using the graphical user interface and the developed control framework. The 100% success rate on task performance demonstrates the effectiveness of our system for individuals with motor impairments to control wheelchair and wheelchair-mounted assistive robotic manipulators. Conclusions We demonstrated the usability of using this eye-gaze system to control a robotic arm mounted on a wheelchair in activities of daily living for people with disabilities. We found high levels of acceptance with higher ratings in the evaluation of the system with healthy participants.
ggpubfigs: Colorblind-Friendly Color Palettes and ggplot2 Graphic System Extensions for Publication-Quality Scientific Figures

Clear and effective figures are central to successfully communicating scientific data. Here, we present ggpubfigs, an R package with colorblind-friendly color palettes and extensions of the ggplot2 graphic system, which helps make publication-quality scientific figures from quantitative data; ggpubfigs is an open-source and user-friendly tool that is available from https://github.com/JLSteenwyk/ggpubfigs.
A Self-Organized Method for a Hierarchical Fuzzy Logic System Based on a Fuzzy Autoencoder

In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent the distribution characteristics of the input data, which improve the accuracy and the rationality. A fuzzy autoencoder is used to train the HFS layer by layer, which can not only ensure the effectiveness of the fuzzy system's hidden layer variables but also provide interpretability. Compared with the traditional fuzzy logic system, the HFS reduces the total number of rules and the complexity. The proposed HFS is tested on three different regression datasets. The experimental results illustrate that the hierarchical self-organized fuzzy system still performs better in terms of regression accuracy indicators than the self-organized fuzzy system.
Unsupervised person re-identification with multi-label learning guided self-paced clustering

Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations across disjoint camera views. In this paper, we address the unsupervised person Re-ID with a conceptually novel yet simple framework, termed as Multi-label Learning guided self-paced Clustering (MLC). MLC mainly learns discriminative features with three crucial modules, namely a multi-scale network, a multi-label learning module, and a self-paced clustering module. Specifically, the multi-scale network generates multi-granularity person features in both global and local views. The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank. After multi-label training for several epochs, the self-paced clustering joins in training and assigns a pseudo label for each image. The benefits of our MLC come from three aspects: i) the multi-scale person features for better similarity measurement, ii) the multi-label assignment based on the whole dataset ensures that every image can be trained, and iii) the self-paced clustering removes some noisy samples for better feature learning. Extensive experiments on three popular large-scale Re-ID benchmarks demonstrate that our MLC outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID.
Theoretical and practical applications of blockchain in healthcare information management

A primary objective of blockchain technology is to address information security and efficiency issues related to existing information sharing systems. For the sharing of health records, little is known about the application of blockchain in management information systems. There are strict regulations for sharing health information due to insecure systems and privacy concerns. To significantly and effectively improve medical diagnosis, it is beneficial to have efficient, reliable, and accurate accessibility to a patient's full medical history. Due to concerns with the security of health systems and privacy concerns, medical histories are not always accessible to healthcare providers. To help increase accessibility options, this research proposes a blockchain-based model that facilitates sharing medical records in a manner that is beneficial to both healthcare providers and patients. Social exchange theory provides the theoretical support for the conceptual model presented. Experimental findings based on 151 participants revealed that the blockchain technology can provide a secure information system and increase patient motivation to share medical records. To show the applicability of the proposed use of blockchain from a practical managerial perspective, we show feasibility by developing an Android software application.
Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms

Sheet metal forming tools, like stamping presses, play an ubiquitous role in the manufacture of several products. With increasing requirements of quality and efficiency, ensuring maximum uptime of these tools is fundamental to marketplace competitiveness. Using anomaly detection and predictive maintenance techniques, it is possible to develop lower risk and more intelligent approaches to maintenance scheduling, however, industrial implementations of these methods remain scarce due to the difficulties of obtaining acceptable results in real-world scenarios, making applications of such techniques in stamping processes seldom found. In this work, we propose a combination of two distinct approaches: (a) time segmentation together with feature dimension reduction and anomaly detection; and (b) machine learning classification algorithms, for effective downtime prediction. The approach (a)+(b) allows for an improvement rate up to 22.971% of the macro F1-score, when compared to sole approach (b). A ROC AUC index of 96% is attained by using Randomized Decision Trees, being the best classifier of twelve tested. An use case with a decentralized predictive maintenance architecture for the downtime forecasting of a stamping press, which is a critical machine in the manufacturing facilities of Bosch Thermo Technology, is discussed.
STNet: An End-to-End Generative Framework for Synthesizing Spatiotemporal Super-Resolution Volumes

We present STNet, an end-to-end generative framework that synthesizes spatiotemporal super-resolution volumes with high fidelity for time-varying data. STNet includes two modules: a generator and a spatiotemporal discriminator. The input to the generator is two low-resolution volumes at both ends, and the output is the intermediate and the two-ending spatiotemporal super-resolution volumes. The spatiotemporal discriminator, leveraging convolutional long short-term memory, accepts a spatiotemporal super-resolution sequence as input and predicts a conditional score for each volume based on its spatial (the volume itself) and temporal (the previous volumes) information. We propose an unsupervised pre-training stage using cycle loss to improve the generalization of STNet. Once trained, STNet can generate spatiotemporal super-resolution volumes from low-resolution ones, offering scientists an option to save data storage (i.e., sparsely sampling the simulation output in both spatial and temporal dimensions). We compare STNet with the baseline bicubic+linear interpolation, two deep learning solutions ( SSR+TSF, STD), and a state-of-the-art tensor compression solution (TTHRESH) to show the effectiveness of STNet.
Effect of the temperature difference between land and lake on photovoltaic power generation

The rapid development of photovoltaic plays an important role in achieving the carbon-neutral goal. How to improve the conversion efficiency and power generation of solar photovoltaic has always been a focus issue. However, more attention is paid to the impact of photovoltaic panel working temperature on the performance of photovoltaic power generation, and how air temperature affects photovoltaic power generation has been ignored. This paper compared and analyzed the impact of the difference in air temperature between lake and land on the revenue of photovoltaic power generation, and established the functional equation between photovoltaic power generation, air temperature and solar radiation, and revealed the relationship between air temperature, solar radiation, and photovoltaic power generation.
CrowdFL: Privacy-Preserving Mobile Crowdsensing System Via Federated Learning

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good scalability and low deployment cost but raises privacy concerns. In this paper, we propose a privacy-preserving MCS system called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdFL</small> by seamlessly integrating federated learning (FL) into MCS. At a high level, in order to protect participants’ privacy and fully explore participants’ computing power, participants in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdFL</small> locally process sensing data via FL paradigm and only upload encrypted training models to the server. To this end, we design a secure aggregation algorithm ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecAgg</monospace> ) through the threshold Paillier cryptosystem to aggregate training models in an encrypted form. Also, to stimulate participation, we present a hybrid incentive mechanism combining the reverse Vickrey auction and posted pricing mechanism, which is proved to be truthful and fail. Results of theoretical analysis and experimental evaluation on a practical MCS scenario (human activity recognition) show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdFL</small> is effective in protecting participants’ privacy and is efficient in operations. In contrast to existing solutions, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdFL</small> is 3× faster in model decryption and improves an order of magnitude in model aggregation.
SoK

Blockchain, a potentially disruptive technology, advances many different applications, e.g., crypto-currencies, supply chains, and the Internet of Things. Under the hood of blockchain, it is required to handle different kinds of digital assets and data. The next-generation blockchain ecosystem is expected to consist of numerous applications, and each application may have a distinct representation of digital assets. However, digital assets cannot be directly recorded on the blockchain, and a tokenization process is required to format these assets. Tokenization on blockchain will inevitably require a certain level of proper standards to enrich advanced functionalities and enhance interoperable capabilities for future applications. However, due to specific features of digital assets, it is hard to obtain a standard token form to represent all kinds of assets. For example, when considering fungibility, some assets are divisible and identical, commonly referred to as fungible assets. In contrast, others that are not fungible are widely referred to as non-fungible assets. When tokenizing these assets, we are required to follow different tokenization processes. The way to effectively tokenize assets is thus essential and expecting to confront various unprecedented challenges. This paper provides a systematic and comprehensive study of the current progress of tokenization on blockchain. First, we explore general principles and practical schemes to tokenize digital assets for blockchain and classify digitized tokens into three categories: fungible, non-fungible, and semi-fungible. We then focus on discussing the well-known Ethereum standards on non-fungible tokens. Finally, we discuss several critical challenges and some potential research directions to advance the research on exploring the tokenization process on the blockchain. To the best of our knowledge, this is the first systematic study for tokenization on blockchain.