Reviewerly/exHarmony huggingface dataset:
Towards a context identification method for autonomous robots

The ability to precisely and accurately detect and evaluate environmental conditions with the help of sensors has strongly driven the development of autonomous robots in recent decades. This so-called context awareness and the identification of the context information relevant for a robot is already widespread in the areas of mobile computing and industrial applications and represents one of the most important enablers for autonomous behavior. Nevertheless, context identification still plays a minor role in the technical design phase due to higher development effort or lack of tools to deal with the inherent complexity. To promote context identification in an early design phase, this paper presents a method to analyze autonomous robots and identify context information that is relevant for realizing the desired behavior. The approach enables the integration of context awareness into autonomous robots and can be used in combination with all current design methods. The applicability of the method is demonstrated using a case study of an autonomous unmanned aerial vehicle.
An Image-Based Path Planning Algorithm Using a UAV Equipped with Stereo Vision

This paper presents a novel image-based path planning algorithm that was developed using computer vision techniques, as well as its comparative analysis with well-known deterministic and probabilistic algorithms, namely A* and Probabilistic Road Map algorithm (PRM). The terrain depth has a significant impact on the calculated path safety. The craters and hills on the surface cannot be distinguished in a two-dimensional image. The proposed method uses a disparity map of the terrain that is generated by using a UAV. Several computer vision techniques, including edge, line and corner detection methods, as well as the stereo depth reconstruction technique, are applied to the captured images and the found disparity map is used to define candidate way-points of the trajectory. The initial and desired points are detected automatically using ArUco marker pose estimation and circle detection techniques. After presenting the mathematical model and vision techniques, the developed algorithm is compared with well-known algorithms on different virtual scenes created in the V-REP simulation program and a physical setup created in a laboratory environment. Results are promising and demonstrate effectiveness of the proposed algorithm.
Why Blockchain: A Socio-technical Perspective on the Motives of Business Consortia Members to Engage with Blockchain Technology

Despite the large number of resources that blockchain has, and continues to mobilize, what makes the technology unique and why people engage with it is not yet fully understood. Hence, “Why blockchain?” is a question that many scientists and managers still ask themselves or must to answer when facing the technology's critics. While the question is undoubtedly justified, it cannot always be answered from a purely technical perspective. Thus, in this study, we apply a socio-technical information systems artifact perspective and analyze the reasons managers involved in the blockchain consortia have for using blockchain technology. Based on a multiple-case study of 19 blockchain consortia, including interviews with 53 stakeholders, we explicate 19 different motives that justify engagement with the technology in practice. Further, we identify the systemic character of tokenization and the importance of the socio-technical interplay of aspects like power decentralization that justify the necessity of blockchain.
Limitations on a device-independent key secure against a nonsignaling adversary via squashed nonlocality

We initiate a systematic study to provide upper bounds on device-independent keys, secure against a nonsignaling adversary (NSDI). We employ the idea of ``squashing'' on the secrecy monotones and show that squashed secrecy monotones are the upper bounds on the NSDI key. Our technique for obtaining upper bounds is based on the nonsignaling analog of quantum purification: the complete extension. As an important instance of an upper bound, we construct a measure of nonlocality called ``squashed nonlocality.'' Using this bound, we identify numerically a certain domain of two binary inputs and two binary outputs of nonlocal devices for which the squashed nonlocality is zero. Therefore one can not distill a secure key from these nonlocal devices via a considered (standard) class of operations. Showing a connection of our approach to the one in New J. Phys. 8, 126 (2006), we provide, to our knowledge, the tightest known upper bound in the (3,2,2,2) scenario. Moreover, we formulate a security condition, equivalent to known ones, for the considered class of protocols. To achieve this, we introduce a nonsignaling norm that constitutes an analogy to the trace norm used in the security condition of the quantum key distribution.
Quantum algorithm for calculating risk contributions in a credit portfolio

Abstract Finance is one of the promising field for industrial application of quantum computing. In particular, quantum algorithms for calculation of risk measures such as the value at risk and the conditional value at risk of a credit portfolio have been proposed. In this paper, we focus on another problem in credit risk management, calculation of risk contributions, which quantify the concentration of the risk on subgroups in the portfolio. Based on the recent quantum algorithm for simultaneous estimation of multiple expected values, we propose the method for credit risk contribution calculation. We also evaluate the query complexity of the proposed method and see that it scales as $\widetilde{O} (\sqrt{N_{\mathrm{gr}}}/\epsilon )$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mi>O</mml:mi><mml:mo>˜</mml:mo></mml:mover><mml:mo>(</mml:mo><mml:msqrt><mml:msub><mml:mi>N</mml:mi><mml:mi>gr</mml:mi></mml:msub></mml:msqrt><mml:mo>/</mml:mo><mml:mi>ϵ</mml:mi><mml:mo>)</mml:mo></mml:math> on the subgroup number $N_{\mathrm{gr}}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>N</mml:mi><mml:mi>gr</mml:mi></mml:msub></mml:math> and the accuracy ϵ , in contrast with the classical method with $\widetilde{O} (\log(N_{\mathrm{gr}})/\epsilon^{2} )$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mi>O</mml:mi><mml:mo>˜</mml:mo></mml:mover><mml:mo>(</mml:mo><mml:mo>log</mml:mo><mml:mo>(</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>gr</mml:mi></mml:msub><mml:mo>)</mml:mo><mml:mo>/</mml:mo><mml:msup><mml:mi>ϵ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:math> complexity. This means that, for calculation of risk contributions of finely divided subgroups, the advantage of the quantum method is reduced compared with risk measure calculation for the entire portfolio. Nevertheless, the quantum method can be advantageous in high-accuracy calculation, and in fact yield less complexity than the classical method in some practically plausible setting.
Sequence-to-Sequence Multi-Agent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment

Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales.
Factors affecting the blockchain application in construction management in China: an ANP-SWOT hybrid approach

As a decentralised ledger and data management technology, blockchain has attracted increasing interest and has been effectively applied in various areas. Despite its promising potential, there are few studies proposing effective strategies for promoting blockchain, particularly in providing a strategic analysis of blockchain in China's construction management. This study aims to investigate the external and internal environments of blockchain application and determine the roadmaps and strategies for blockchain application in construction management in China. A strength, weaknesses, opportunity, and threat (SWOT) analysis is employed to draw a panorama of the blockchain development status. Six roadmaps and strategies for blockchain application are proposed. By using the Analytic Network Process (ANP), a quantitative analysis was conducted to determine the ranking of SWOT sub-factors and roadmaps and strategies. The results show that 'traceability of information', 'high application threshold', 'national policy support', and 'low informatization level of the construction industry' are the most important sub-factors in strengths, weaknesses, opportunities, and threats respectively. The best roadmaps and strategies are 'improving the informatization level of the construction industry', followed by 'promoting the technological innovation of blockchain' and 'providing standard'. This study facilitates an in-depth understanding of the blockchain application in China's construction management. It will not only help researchers to conduct targeted blockchain research for more efficient project management, but also help practitioners to develop effective strategies for adopting blockchain.
Deep learning‐based scalable and robust channel estimator for wireless cellular networks

Abstract In this paper, we present a two-stage scalable channel estimator (TSCE), a deep learning (DL)-based scalable, and robust channel estimator for wireless cellular networks, which is made up of two DL networks to efficiently support different resource allocation sizes and reference signal configurations. Both networks use the transformer, one of cutting-edge neural network architecture, as a backbone for accurate estimation. For computation-efficient global feature extractions, we propose using window and window averaging-based self-attentions. Our results show that TSCE learns wireless propagation channels correctly and outperforms both traditional estimators and baseline DL-based estimators. Additionally, scalability and robustness evaluations are performed, revealing that TSCE is more robust in various environments than the baseline DL-based estimators.
Metallogeny of the state of Oaxaca, Mexico

The state of Oaxaca in southern Mexico includes copious metallic deposits of distinct types that were formed in several tectonothermal events since the Proterozoic. This work integrates a large database of mineral occurrences and deposits in Oaxaca. The 1:850,000 scale metallogenetic map depicts the location of deposits, mining regions, and mining districts on a lithological base, along with a summarized metallogenic history of the state and the distribution of the tectonostratigraphic terranes. We briefly describe the most relevant features of each mineralized area and the characteristics of the metallogenic epochs of Oaxaca. All the geolocation information was organized in GIS format.
NeuLighting: Neural Lighting for Free Viewpoint Outdoor Scene Relighting with Unconstrained Photo Collections

We propose NeuLighting, a new framework for free viewpoint outdoor scene relighting from a sparse set of unconstrained in-the-wild photo collections. Our framework represents all the scene components as continuous functions parameterized by MLPs that take a 3D location and the lighting condition as input and output reflectance and necessary outdoor illumination properties. Unlike object-level relighting methods which often leverage training images with controllable and consistent indoor illumination, we concentrate on the more challenging outdoor situation where all the images are captured under arbitrary unknown illumination. The key to our method includes a neural lighting representation that compresses the per-image illumination into a disentangled latent vector, and a new free viewpoint relighting scheme that is robust to arbitrary lighting variations across images. The lighting representation is compressive to explain a wide range of illumination and can be easily fed into the query-based NeuLighting framework, enabling efficient shading effect evaluation under any kind of novel illumination. Furthermore, to produce high-quality cast shadows, we estimate the sun visibility map to indicate the shadow regions according to the scene geometry and the sun direction. Thanks to the flexible and explainable neural lighting representation, our system supports outdoor relighting with many different illumination sources, including natural images, environment maps, and time-lapse videos. The high-fidelity renderings under novel views and illumination prove the superiority of our method against state-of-the-art relighting solutions.
Marginal Multiple Importance Sampling

Multiple importance sampling (MIS) is a powerful tool to combine different sampling techniques in a provably good manner. MIS requires that the techniques' probability density functions (PDFs) are readily evaluable point-wise. However, this requirement may not be satisfied when (some of) those PDFs are marginals, i.e., integrals of other PDFs. We generalize MIS to combine samples from such marginal PDFs. The key idea is to consider each marginalization domain as a continuous space of sampling techniques with readily evaluable (conditional) PDFs. We stochastically select techniques from these spaces and combine the samples drawn from them into an unbiased estimator. Prior work has dealt with the special cases of multiple classical techniques or a single marginal one. Our formulation can handle mixtures of those.
Lithium‐bearing Pegmatite Exploration in Western Altun, Xinjiang, using Remote‐Sensing Technology

Abstract Western Altun in Xinjiang is an important area, where lithium (Li)‐bearing pegmatites have been found in recent years. However, the complex terrain and harsh environment of western Altun exacerbates in prospecting for Li‐bearing pegmatites. Therefore, remote‐sensing techniques can be an effective means for prospecting Li‐bearing pegmatites. In this study, the fault information and lithologyical information in the region were obtained using the median‐resolution remote‐sensing image Landsat‐8, the radar image Sentinel‐1 and hyperspectral data GF‐5. Using Landsat‐8 data, the hydroxyl alteration information closely related to pegmatite in the region was extracted by principal component analysis, pseudo‐anomaly processing and other methods. The high spatial resolution remote‐sensing data WorldView‐2 and WorldView‐3 short‐wave infrared images were used and analyzed by principal component analysis (PCA), the band ratio method and multi‐class machine learning (ML), combined with conventional thresholds specified the algorithms used to automatically extract Li‐bearing pegmatite information. Finally, the Li‐bearing pegmatite exploration area was determined, based on a comprehensive analysis of the faults, hydroxyl alteration lithology and Li‐bearing pegmatite information. Field investigations have verified that the distribution of pegmatites in the central part of the study area is consistent with that of Li‐bearing pegmatites extracted in this study. This study provides a new technique for prospecting Li‐bearing pegmatites, which shows that remote‐sensing technology possesses great potential for identifying lithium‐bearing pegmatites, especially in areas that are not readily accessible.
User Experience of Virtual-Reality Interactive Interfaces: A Comparison between Hand Gesture Recognition and Joystick Control for XRSPACE MANOVA

This research intends to understand whether users would adopt the interactive interface of hand gesture recognition for XRSPACE MANOVA in the virtual-reality environment. Different from the traditional joystick control and external sensors, XRSPACE MANOVA’s hand gesture recognition relies on cameras built into the head-mount display to detect users’ hand gestures and interact with the system to provide a more life-like immersive experience. To better understand if users would accept this hand gesture recognition, the current experiment compares users’ experiences with hand gesture recognition and joystick control for XRSPACE MANOVA while controlling for the effects of gender, college major, and the completion time. The results suggest that users of hand gesture recognition have better perceptions of enjoyment, satisfaction, and confirmation, which means that they have a relatively fun and satisfying experience and that their expectations of the system/technology confirm their actual usage. Based on the parametric statistical analyses, user assessments show that perceived usefulness, perceived ease-of-use, attitude, and perception of internal control suggest that, in terms of operating performance, users are more accepting of the traditional joystick control. When considering the length of usage time, this study finds that, when hand gesture recognition is used for a relatively longer time, users’ subjective evaluations of internal control and behavioral intention to use are reduced. This study has, therefore, identified potential issues with hand gesture recognition for XRSPACE MANOVA and discussed how to improve this interactive interface. It is hoped that users of hand gesture recognition will obtain the same level of operating experience as if they were using the traditional joystick control.
State of the art of augmented reality capabilities for civil infrastructure applications

Abstract Augmented reality (AR) is a technology superimposes interactional virtual objects onto a real environment. Since the beginning of the millennium, AR technologies have rapidly grown, with significant research publications in engineering and science. However, the civil infrastructure community has minimally implemented AR technologies to date. Civil engineers face one of the challenges when understanding and using AR is the lack of a classification of AR in the context of capabilities for civil infrastructure applications. Like most engineering fields, practitioners in civil infrastructure prioritize understanding a new technology's maturity level before considering its adoption and field implementation. This article compares the capabilities of 16 AR head‐mounted devices (HMDs) available in the market since 2017, ranking them in terms of performance for civil infrastructure implementations. Finally, the authors recommend a development framework for functional AR interfaces with civil infrastructure and operations.
Tracking changes in touch desire and touch avoidance before and after the COVID-19 outbreak

Touch is essential for survival, social bonding, and overall health. However, the COVID-19 pandemic calls for an abrupt withdrawal from physical contact, and the prolonged lockdown has left many people in solitude without touch for months. This unprecedented dissociation from touch has cast a shadow on people's mental and physical well-being. Here we approached the issue by examining COVID-19's impact on people's touch attitudes. We analyzed people's desire and avoidance for animate and inanimate targets based on large-scale Japanese Twitter posts over an 8-year span. We analyzed the impact of the COVID-19 outbreak with the difference-in-differences estimation method, which can estimate the impact while accounting for other changes over time such as seasonality or long-term effects. As a result, we found that people's desire for touching the human body and pet animals increased significantly after the COVID-19 outbreak and remained high afterward. In contrast, the avoidance of touching everyday objects (e.g., doorknobs and money) increased immediately after the outbreak but gradually returned to the pre-COVID-19 levels. Our findings manifest the impact of COVID-19 on human touch behavior. Most importantly, they highlight the sign of "skin hunger," a public health crisis due to social distancing, and call attention to the trend that people are becoming less aware of infection control as COVID-19 persists.
Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network

Due to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detection is still a great challenge in SAR images. To cope with these problems, a novel approach called adaptive deformable network (ADN) combined with peak feature fusion (PFF) is proposed for aircraft detection. The PFF is designed for taking full advantage of the strong scattering features of aircraft, which consists of peak feature extraction and fusion. To fully exploit the strong scattering features of the aircraft in SAR images, peak features are extracted via the Harris detector and the eight-domain pixel detection of local maxima. Then, the saliency of aircraft under multiple imaging conditions is enhanced by multi-channel blending. All the PFF-preprocessed images are fed into the ADN for training and testing. The core components of ADN contain an adaptive spatial feature fusion (ASFF) module and a deformable convolution module (DCM). ASFF is utilized to reconcile the inconsistency across different feature scales, raising the characterization capabilities of the feature pyramid and improving the detection performance of multi-scale aircraft further. DCM is introduced to determine the 2-D offsets of feature maps adaptively, improving the geometric modeling abilities of aircraft in various shapes. The well-designed ADN is established by combining the two modules to alleviate the problems of the multi-scale targets and attitude sensitivity. Extensive experiments are conducted on the GaoFen-3 (GF3) dataset to demonstrate the effectiveness of the PFF-ADN with an average precision of 89.34%, as well as an F1-score of 91.11%. Compared with other mainstream algorithms, the proposed approach achieves state-of-the-art performance.
Online Interactions: Mobile Text-Chat as an Educational Pedagogic Tool

The reactions of education systems to the global lockdowns implemented during the COVID-19 epidemic highlighted that there remain questions regarding how everyday technologies might be used to support mass education. This paper draws on Conversation Analysis in online textual communication to study key features of mobile text communication by analysing book discussions among adult students of an online reading programme. We captured and analysed three patterns of interaction (i.e., single linear conversations; intertwined conversations; trunk-branch conversations) as to their affordances for educational communication. This study shows that synchronous text has distinctive communicative features, including short text exchanges and various turn-taking patterns, which are different to the elaborated forms of discourse expected in schools. Though "disorder" and "messiness" accompanied the interactions, we take them as opportunities rather than challenges of education and suggest that appropriate pedagogic design may enable teachers to utilise this distinctiveness to develop various learning environments.