Reviewerly/exHarmony huggingface dataset:
Interpretation of hydrothermal alteration and structural framework of the Huize Pb–Zn deposit, SW China, using Sentinel-2, ASTER, and Gaofen-5 satellite data: Implications for Pb–Zn exploration

The Huize Pb–Zn deposit is one of the largest and high–grade Pb–Zn deposits in the world, containing more than 7 Mt Pb and Zn reserves at an ore grade of ∼30–35 % Pb + Zn. Therefore, brownfield exploration of additional Pb and Zn orebodies at Huize Pb–Zn deposit is of high priority. Hydrothermal alteration minerals and structural features are typically associated with Pb–Zn mineralization, which can be identified quickly and economically using a multi-source remote sensing satellite dataset. In this study, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Gaofen-5 (GF-5) satellite remote sensing imagery were employed for brownfield exploration of additional Pb–Zn orebodies in the Huize Pb–Zn deposit. The Sentinel-2 satellite remote sensing imagery was used to interpret the structure and map oxides/hydroxides by implementing the band ratio (BR) and principal components analysis (PCA) method; the ASTER satellite data were applied to map argillic, quartz, and carbonate hydrothermal alteration minerals using the PCA method; the GF-5 satellite data were utilized to map hematite, kaolinite, calcite, and dolomite minerals by the mixture tuned matched filtering (MTMF) method; and the mapped hydrothermal alteration information was verified by field reconnaissance and laboratory analysis. Finally, remote sensing geological exploration characteristics of the Huize Pb–Zn deposit were summarized by comprehensively analyzing the hydrothermal alteration minerals and structural characteristics, and three new brownfield exploration target zones of Pb–Zn mineralization were delineated. The study can provide a reference for the brownfields exploration in the vicinity of an existing Pb–Zn deposit around the world.
Video Moment Retrieval with Hierarchical Contrastive Learning

This paper explores the task of video moment retrieval (VMR), which aims to localize the temporal boundary of a specific moment from an untrimmed video by a sentence query. Previous methods either extract pre-defined candidate moment features and select the moment that best matches the query by ranking, or directly align the boundary clips of a target moment with the query and predict matching scores. Despite their effectiveness, these methods mostly focus only on aligning the query and single-level clip or moment features, and ignore the different granularities involved in the video itself, such as clip, moment, or video, resulting in insufficient cross-modal interaction. To this end, we propose a Temporal Localization Network with Hierarchical Contrastive Learning (HCLNet) for the VMR task. Specifically, we introduce a hierarchical contrastive learning method to better align the query and video by maximizing the mutual information (MI) between query and three different granularities of video to learn informative representations. Meanwhile, we introduce a self-supervised cycle-consistency loss to enforce the further semantic alignment between fine-grained video clips and query words. Experiments on three standard benchmarks show the effectiveness of our proposed method.
PassWalk: Spatial Authentication Leveraging Lateral Shift and Gaze on Mobile Headsets

Secure and usable user authentication on mobile headsets is a challenging problem. The miniature-sized touchpad on such devices becomes a hurdle to user interactions that impact usability. However, the most common authentication methods, i.e., the standard QWERTY virtual keyboard or mid-air inputs to enter passwords are highly vulnerable to shoulder surfing attacks. In this paper, we present PassWalk, a keyboard-less authentication system leveraging multi-modal inputs on mobile headsets. PassWalk demonstrates the feasibility of user authentication driven by the user's gaze and lateral shifts (i.e., footsteps) simultaneously. The keyboard-less authentication interface in PassWalk enables users to accomplish highly mobile inputs of graphical passwords, containing digital overlays and physical objects. We conduct an evaluation with 22 recruited participants (15 legitimate users and 7 attackers). Our results show that PassWalk provides high security (only 1.1% observation attacks were successful) with a mean authentication time of 8.028s, which outperforms the commercial method of using the QWERTY virtual keyboard (21.5% successful attacks) and a research prototype LookUnLock (5.5% successful attacks). Additionally, PassWalk entails a significantly smaller workload on the user than the current commercial methods.
Socio-technical capabilities for blockchain implementation by service providers: multiple case study of projects with transaction time reduction and quality improvement objectives

The objectives of this paper are to analyse how blockchain can help in transaction time reduction and quality improvement in supply chains. It also aims to identify the social and technical capabilities needed by the service providers for blockchain implementation and how those capabilities vary between the projects. To achieve the above mentioned objectives, a multiple case study approach is followed, and data are collected from the service providers that have implemented blockchain technology with their customers. We conduct task-technology fit (TTF) analysis to assess the suitability of blockchain to address the tasks to be completed, considering customer needs. The TTF analysis shows that some additional technological solutions related to communication support and user experience design need to be implemented together with the blockchain platform. We then identify common social and technical capabilities, such as empathising with customers and system design, to facilitate implementation and contingent capabilities that vary across different types of blockchain implementation projects. We also develop a process model, a generalisable framework for blockchain implementation and related propositions. The findings from this study will provide guidance to the blockchain service providers to emphasise social and technical capabilities for quality improvement and transaction time reduction from blockchain implementation.
COVID-19 contact tracing apps and the governance of collective action: social nudges, deliberation, and solidarity in Europe and beyond

During the COVID-19 pandemic, Digital Contact Tracing (DCT) tools were deployed by governments in Europe and beyond as a novel mobile technology to assist traditional manual contact tracing to track individuals who have come in close contact with an infected person. The public debate on this topic focused strongly on the protection of individual privacy. While this debate is important, it fails to address important governance questions – such as, for example, that DCT tools took on the role of social nudges, namely, tools of soft regulation that calibrate information flows so as to “push” people to act in ways that promote collective purposes. Social nudges include a range of norms and values that, however, are built into the technological and social features of the nudge, rather than rendering them open to public scrutiny and debate. Although the use of contact tracing apps is being phased out, the digitization of contact tracing can be seen as a case study of the broader trend towards digitization of the provision of health services. Debates of their governance thus have broader implications for the governance of data driven tools deployed for public health purposes in times of crisis.
Non-parametric mixture modeling of cognitive psychological data: A new method to disentangle hidden strategies

In a wide variety of cognitive domains, participants have access to several alternative strategies to perform a particular task and, on each trial, one specific strategy is selected and executed. Determining how many strategies are used by a participant as well as their identification at a trial level is a challenging problem for researchers. In the current paper, we propose a new method - the non-parametric mixture model - to efficiently disentangle hidden strategies in cognitive psychological data, based on observed response times. The developed method derived from standard hidden Markov modeling. Importantly, we used a model-free approach where a particular shape of a response time distribution does not need to be assumed. This has the considerable advantage of avoiding potentially unreliable results when an inappropriate response time distribution is assumed. Through three simulation studies and two applications to real data, we repeatedly demonstrated that the non-parametric mixture model is able to reliably recover hidden strategies present in the data as well as to accurately estimate the number of concurrent strategies. The results also showed that this new method is more efficient than a standard parametric approach. The non-parametric mixture model is therefore a useful statistical tool for strategy identification that can be applied in many areas of cognitive psychology. To this end, practical guidelines are provided for researchers wishing to apply the non-parametric mixture models on their own data set.
Waveform level adversarial example generation for joint attacks against both automatic speaker verification and spoofing countermeasures

Adversarial examples crafted to deceive Automatic Speaker Verification (ASV) systems have attracted a lot of attention when studying the vulnerability of ASV. However, real-world ASV systems usually work together with spoofing countermeasures (CM) to exclude fake voices generated by text-to-speech (TTS) or voice conversion (VC). The deployment of CM would reduce the capability of the adversarial samples on deceiving ASV. Although additional perturbations against CM may be generated and put on the crafted adversarial examples against ASV to yield new adversarial examples against both ASV and CM, those additional perturbations would however hinder the examples’ adversarial effectiveness on ASV. In this paper, a novel joint approach is proposed to generate adversarial examples by considering attacking ASV and CM simultaneously. For any voice from TTS, VC or a real-world speaker, our crafted adversarial perturbations will turn its original labels on CM and speaker ID to bonafide and some target speaker ID, correspondingly. In our approach, a differentiable front-end is introduced to replace the conventional hand-crafted time–frequency feature extractor. Perturbations can thus be estimated by updating the gradients of the joint objective of ASV and CM on the waveform variables. The proposed method has demonstrated a 99.3% success rate on white-box logical access attacks to deceive ASV and CM simultaneously, which outperforms the baselines of 65.3% and 36.7%. Furthermore, transferability on black-box and physical settings has also been validated.
Transformer-based moving target tracking method for Unmanned Aerial Vehicle

Unmanned Aerial Vehicle (UAV) moving target tracking is one of the fundamental implementations in remote sensing and has been widely applied in monitoring, search and rescue, pursuit-escapes, and other fields. Currently, most UAV tracking algorithms merely establish the local relationship between the template and search region without fully using the global context information, leading to problems such as target loss and misclassification, and imprecise bounding boxes. This paper innovatively proposes a UAV tracker, TransUAV, overcoming the above challenge by a feature correlation network based on the self-attention mechanism. The method efficiently combines global features between the search region and the template to reduce the influence of external interference, enhancing the precision and robustness of the tracking algorithm. Moreover, the global spatio-temporal features are acquired by learning query embedding and temporal update strategies to make predictions, enhancing the adaptability to rapid changes in the appearance of target object. There is no proposal or predetermined anchor in this method to satisfy the requirements of onboard operational speed, therefore, no post-processing procedure is required, and the entire approach is end-to-end. The superiority of the proposed TransUAV is verified by an exhaustive evaluation of six challenging target tracking video datasets benchmarks, and the accuracy and robustness of the proposed TransUAV are compared with state-of-the-art methods.
Performance Analysis of YOLO Algorithms for Real-Time Crowd Counting

Real-time head detection with counting based on crowded scenes is a very challenging and computationally complex task in the case of lengthy surveillance videos. Existing head detection methods suffer from slow detection and a high rate of missed detection, especially in the case of congested crowd regions as well as occluded heads. In this work, we performed performance analysis of various YOLO (You Look Only Once) architectures for real-time head detection and counting. We evaluated different YOLO architectures on standard datasets like SCUT_HEAD_A, SCUT_HEAD_B, and the Brainwash dataset. After experimental analysis, it is found that YOLOR outperforms by providing an mAP value of 0.91, 0.92, and 0.95 on the SCUT_HEAD_A dataset, SCUT_HEAD_B dataset, and Brainwash dataset, respectively..
Animated Vega-Lite: Unifying Animation with a Grammar of Interactive Graphics

We present Animated Vega-Lite, a set of extensions to Vega-Lite that model animated visualizations as time-varying data queries. In contrast to alternate approaches for specifying animated visualizations, which prize a highly expressive design space, Animated Vega-Lite prioritizes unifying animation with the language's existing abstractions for static and interactive visualizations to enable authors to smoothly move between or combine these modalities. Thus, to compose animation with static visualizations, we represent time as an encoding channel. Time encodings map a data field to animation keyframes, providing a lightweight specification for animations without interaction. To compose animation and interaction, we also represent time as an event stream; Vega-Lite selections, which provide dynamic data queries, are now driven not only by input events but by timer ticks as well. We evaluate the expressiveness of our approach through a gallery of diverse examples that demonstrate coverage over taxonomies of both interaction and animation. We also critically reflect on the conceptual affordances and limitations of our contribution by interviewing five expert developers of existing animation grammars. These reflections highlight the key motivating role of in-the-wild examples, and identify three central tradeoffs: the language design process, the types of animated transitions supported, and how the systems model keyframes.
Enhancing scene understanding based on deep learning for end-to-end autonomous driving

Efficient understanding of the environment is a crucial prerequisite for autonomous driving, but explicitly modeling the environment is hard to come true. In contrast, imitation learning, in theory, can arrive at the direct mapping from visual input to driving command, but the inscrutability of scene representation in imitation learning is still a challenging problem. In this paper, we propose to enhance the abstract representation of visual scene from two aspects for better scene understanding, i.e. Visual Guide path and Driving Affordances path. For Visual Guide path, we leverage semantic information as visual priors to learn the intuitive state of the environment, e.g. the spatial semantic occupation of the visual scene. For Driving Affordances path, several driving affordance indicators reflecting the relationship between environment and vehicle behavior are learned as the global guidance to guide the driving system to learn safe and efficient driving policies. With the complementarity of these two paths, a Bilateral Guide Network is designed to realize the complete mapping from visual input to driving command. Our method is evaluated on the CARLA simulator with various scenarios to demonstrate the effectiveness. Besides, comparative analyses are made with some state-of-the-art methods to justify the performance of our method in the aspect of autonomous driving.
An Adaptive Dynamic Multi-Template Correlation Filter for Robust Object Tracking

In the field of computer vision and robotics, scholars use object tracking technology to track objects of interest in various video streams and extend practical applications, such as unmanned vehicles, self-driving cars, robotics, drones, and security surveillance. Object tracking is a mature technology in the field of computer vision and robotics; however, there is still no one object tracking algorithm that can comprehensively and simultaneously solve the four problems encountered by tracking objects, namely deformation, illumination variation, motion blur, and occlusion. We propose an algorithm called an adaptive dynamic multi-template correlation filter (ADMTCF) which can simultaneously solve the above four difficulties encountered in tracking moving objects. The ADMTCF encodes local binary pattern (LBP) features in the HSV color space, so the encoded features can resist the pollution of the tracking image caused by illumination variation. The ADMTCF has four templates that can be adaptively and dynamically resized to maintain tracking accuracy to combat tracking problems such as deformation, motion blur, and occlusion. In this paper, we experimented with our ADMTCF algorithm and various state-of-the-art tracking algorithms in scenarios such as deformation, illumination variation, motion blur, and occlusion. Experimental results show that our proposed ADMTCF exhibits excellent performance, stability, and robustness in various scenarios.
Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph

Facing the massive tourism data, the recommendation system mines the user’s interest to provide a personalized information service. The Knowledge Graph is introduced into a recommendation system, as auxiliary information can effectively solve the problems about data sparse and cold-start. Therefore, this paper proposes a new algorithm of personalized Chinese tourism recommendation based on the Knowledge Graph. First of all, because lack of the public Chinese tourism Knowledge Graph, a complete Chinese tourism Knowledge Graph is built. Secondly, a new B-TransD (Bernoulli-TransD) knowledge representation model is proposed to reduce the probability of false negative triples. Finally, the method of user interest model based on the attribute information of users and tourist attractions is proposed to improve the performance of the recommendation system. Experiments are conducted on a data set containing 9100 tourist attractions. The experimental results demonstrate that the proposed algorithm achieves significant improvement over the existing algorithms.
The Quantum Governance Stack: Models of Governance for Quantum Information Technologies

Abstract The emergence of quantum information technologies with potential application across diverse industrial, consumer and technical domains has thrown into relief the need for practical approaches to their governance. Technology governance must balance multiple objectives including facilitating technological development while meeting legal requirements, normative expectations and managing risks regarding the use of such technology. In this paper, we articulate a variety of idealised governance models and approaches for synthesising these complementary and sometimes competing objectives. We set out a comparative analysis of quantum governance in the context of existing models of technological governance. Using this approach, we develop an actor-instrument model for quantum governance, denoted the ‘quantum governance stack’, across a governance hierarchy from states and governments through to public and private institutions. Our model sets out key characteristics that quantum governance should exhibit at each level in the stack, including identification of stakeholder rights, interests and obligations impacted by quantum technologies and the appropriate instruments by which such impacts are managed. We argue that quantum governance must be responsive based on (a) the state of technology at the time; (b) resource and economic requirements for its development; and (c) assessments and estimates of the near-term and future impacts of such technology. Our work provides a pragmatic introduction to quantum governance by (a) specifying a taxonomy of governance actors and instruments and (b) providing examples of how different stakeholders within the stack might implement governance responses to quantum information technologies. It is intended for use by stakeholders in government, industry, academia and civil society to help inform their governance response to the quantum technology revolution.
Statistical Validation of Synthetic Data for Lung Cancer Patients Generated by Using Generative Adversarial Networks

The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned.
Deep Residual Network and Transfer Learning-based Person Re-Identification

Person re-identification (person re-id) is a field in which a person is recognized from different views, which plays a significant role in surveillance. One of the major problems in-person re-id is the unavailability of large labeled datasets, which has bounded the performance of the deep learning models. In this study, different variations were experimented with of deep residual networks trained with transfer learning, and then fine-tuning was done on the pre-trained model. These models are pre-trained on a larger dataset, ImageNet, to learn the visual features better. The proposed model is based on deep residual networks because of its better understanding and performance of the visual features, which leads to better classification. The proposed model was evaluated on the Market-1501 dataset, which consists of 1501 identities collected from 6 cameras. In this paper, the effect of hyperparameters is studied for better accuracy. The model has achieved the highest mAP score of 68.4%, with an improvement of 5.3%, and 96.0% of Rank-1 with 12.4%. The proposed model has outperformed all the existing models trained in a supervised manner on the Market 1501 dataset. The results have proved that this model could help better person re-id classification problems even when there is no availability of large labeled datasets and could be used for better security and surveillance.