Reviewerly/exHarmony huggingface dataset:
Spatial modelling of hydrothermal mineralization-related geochemical patterns using INLA+SPDE and local singularity analysis

The complexity of geochemical patterns in surficial media makes it necessary to consider the uncertainty when identifying geochemical anomalies in geochemical prospecting. In this contribution, the type of uncertainty related to spatial modelling of geochemical element distribution based on a limited number of observations is considered. A hybrid method combining the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE), commonly termed “INLA + SPDE”, is employed to simulate the spatial distribution of a geochemical variable. The local singularity analysis (LSA) is then performed on each realization simulated by INLA + SPDE. Based on the ensemble of local singularity exponent maps, geochemical anomalies can be evaluated by considering both the intensity and uncertainty of local singularity. A case study of processing 1:50, 000 stream sediment samples collected from the Fanshan district, Fujian province of China, further illustrates and validates the procedure, and enhance the knowledge of hydrothermal mineralization-related geochemical patterns in this region. The findings indicate that (1) INLA + SPDE offers a viable alternative to existing methods for simulating spatial distribution of geochemical element distributions, with advantages in quantifying uncertainties of model responses as well as model parameters, computational efficiency due to INLA, etc.; (2) it is an effective way to combine INLA + SPDE and LSA to facilitate identifying geochemical anomalies by considering both the intensity and uncertainty of geochemical patterns. The delineated geochemical anomalies based on the mean of local singularity exponents should be paid attention in further geochemical exploration, but those associated with high uncertainty should be taken care of and further verified by other evidences. The workflow that combines INLA + SPDE and LSA can also be used to obtain information from other types of geoscientific data, and can hence enrich the toolbox for further mineral exploration. • Uncertainty of geochemical patterns was modeled using INLA-SPDE. • A method for evaluating geochemical patterns by considering both the intensity and uncertainty was proposed. • Geochemical patterns associated with hydrothermal mineralization in Southern Fujian has been modeled.
Deep Rating and Review Neural Network for Item Recommendation

To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users' overall preference for the items, while the review text contains rich information about the users' preferences and the attributes of the items. In real life, reviews with the same rating may have completely opposite semantic information. If only the ratings are used for error backpropagation, the latent factors of these reviews will tend to be consistent, resulting in the loss of a large amount of review information. In this article, we propose a novel deep model termed deep rating and review neural network (DRRNN) for recommendation. Specifically, compared with the existing models that adopt the review text as the auxiliary information, DRRNN additionally considers both the target rating and target review of the given user-item pair as ground truth for error backpropagation in the training stage. Therefore, we can keep more semantic information of the reviews while making rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.
Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Semi-supervised learning (SSL) provides a way to improve the performance of prediction models (e.g., classifier) via the usage of unlabeled samples. An effective and widely used method is to construct a graph that describes the relationship between labeled and unlabeled samples. Practical experience indicates that graph quality significantly affects the model performance. In this paper, we present a visual analysis method that interactively constructs a high-quality graph for better model performance. In particular, we propose an interactive graph construction method based on the large margin principle. We have developed a river visualization and a hybrid visualization that combines a scatterplot, a node-link diagram, and a bar chart to convey the label propagation of graph-based SSL. Based on the understanding of the propagation, a user can select regions of interest to inspect and modify the graph. We conducted two case studies to showcase how our method facilitates the exploitation of labeled and unlabeled samples for improving model performance.
Efficient Execution of Deep Neural Networks on Mobile Devices with NPU

Many Deep Neural Network (DNN) based applications have been developed and run on mobile devices. Although these advanced DNN models can provide better results, they also suffer from high computational overhead which means long delay and more energy consumption when running on mobile devices. To address these problems, many companies have developed dedicated Neural Processing Units (NPUs) for mobile devices, which can process AI features. Compared to CPU, NPU can run DNN models much faster, but with lower accuracy. To address this issue, we leverage model partition techniques to improve the performance of DNN models on mobile devices with NPU. The challenge is to determine which part of the DNN model should be run on CPU and which part to be run on NPU. Based on the delay and the accuracy requirements of the applications, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraint, and Min-Time where the goal is to minimize the processing time while ensuring the accuracy is above a certain threshold. To solve these problems, we propose heuristic based algorithms which are simple but only search a small number of layer combinations (i.e., where to run which DNN model layers). To further improve the performance, we propose a Machine Learning based Model Partition (MLMP) algorithm. MLMP searches more layer combinations and considers both accuracy loss and processing time simultaneously. We also address many implementation issues to support model partition techniques on mobile devices with NPU. Experimental results show that MLMP outperforms the heuristic based algorithms and it can significantly improve the accuracy or reduce the processing time based on the application requirements.
The Neural Network Models Effectiveness for Face Detection and Face Recognition

The paper is devoted to estimation of the effectiveness of the use of modern convolutional neural networks for face detection and face recognition. On standard and custom datasets, learning of neural networks and comparison of the effectiveness of their functioning are carried out. An algorithm and recommendations are proposed regarding the practical application of the neural networks for detecting faces on digital photographs.
The SARS-CoV-2 pandemic: the race to trace: contact tracing scale-up in San Francisco—early lessons learned

Abstract In order to effectively control spread of coronavirus 2019 (COVID-19), it is essential that jurisdictions have the capacity to rapidly trace close contacts of each and every case. Best practice guidance on how to implement such programs is urgently needed. We describe the early experience in the City and County of San Francisco (CCSF), where the City’s Department of Health expanded contact tracing capability in anticipation of changes in San Francisco’s ‘shelter in place’ order between April and June 2020. Important prerequisites to successful scale-up included a rapid expansion of the COVID-19 response workforce, expansion of testing capability, and other containment resources. San Francisco’s scale-up offers a model for how other jurisdictions can rapidly mobilize a workforce. We underscore the importance of an efficient digital case management system, effective training, and expansion of supportive service programs for those in quarantine or isolation, and metrics to ensure continuous performance improvement.
Animated Presentation of Static Infographics with InfoMotion

Abstract By displaying visual elements logically in temporal order, animated infographics can help readers better understand layers of information expressed in an infographic. While many techniques and tools target the quick generation of static infographics, few support animation designs. We propose InfoMotion that automatically generates animated presentations of static infographics. We first conduct a survey to explore the design space of animated infographics. Based on this survey, InfoMotion extracts graphical properties of an infographic to analyze the underlying information structures; then, animation effects are applied to the visual elements in the infographic in temporal order to present the infographic. The generated animations can be used in data videos or presentations. We demonstrate the utility of InfoMotion with two example applications, including mixed‐initiative animation authoring and animation recommendation. To further understand the quality of the generated animations, we conduct a user study to gather subjective feedback on the animations generated by InfoMotion.
Circle-U-Net: An Efficient Architecture for Semantic Segmentation

State-of-the-art semantic segmentation methods rely too much on complicated deep networks and thus cannot train efficiently. This paper introduces a novel Circle-U-Net architecture that exceeds the original U-Net on several standards. The proposed model includes circle connect layers, which is the backbone of ResUNet-a architecture. The model possesses a contracting part with residual bottleneck and circle connect layers that capture context and expanding paths, with sampling layers and merging layers for a pixel-wise localization. The results of the experiment show that the proposed Circle-U-Net achieves an improved accuracy of 5.6676%, 2.1587% IoU (Intersection of union, IoU) and can detect 67% classes greater than U-Net, which is better than current results.
Embedding Probability Guided Network for Image Steganalysis

Content-adaptive steganography embeds information into the cover image adaptively under the guidance of embedding probabilities (also known as selection channel) of the image elements, which can also be obtained by the steganalyzer. Therefore, some adaptive steganalysis schemes with selection channel have been proposed to detect content-adaptive steganography. Existing selection-channel-aware CNN-based steganalyzers usually incorporate the embedding probability into the first convolutional layer. They may fail to make full use of embedding probability information because this information incorporated into the first layer may disappear when it propagates to deep convolutional layers. To address this issue, we propose embedding probability guided module to adaptively enhance the features from different levels of network. Moreover, these embedding probability guided features from different levels are progressively integrated to jointly make final decisions. Experimental results prove that our approach can outperform existing selection-channel-aware approaches in both the spatial domain and JPEG domain.
Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions.The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers' workflows.In this paper, we propose an ''evidencebased'' recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients.The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a ''social plane'' to jointly find answers to critical clinical questions to fight the pandemic.We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness.In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.
Real-time estimation of road slope based on multiple models and multiple data fusion

Although driving conditions of vehicles are complex, a single model is used in conventional slope estimation methods. This reduces the ability to track changes in actual road slopes in real time, resulting in low estimation accuracy. Furthermore, slope estimation based on a single estimation method are affected by braking, shifting, and sensor failure. This reduces the robustness and reliability of the slope estimation. To address these problems, we propose a slope estimation algorithm based on multi-model and multi-data fusion. First, for each estimation method (kinematics-based and dynamics-based), a two-layer interacting multiple model (TLIMM) slope estimation algorithm is developed. Second, the decision-level estimation fusion is based on the slope data estimated by the two methods. The experimental results show that the proposed TLIMM algorithm and multi-data estimation fusion method based on TLIMM can effectively improve the accuracy, robustness and reliability of the slope estimation.
Stealing Deep Reinforcement Learning Models for Fun and Profit

This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only from its interaction with the environment. Model extraction attacks against supervised Deep Learning models have been widely studied. However, those techniques cannot be applied to the reinforcement learning scenario due to DRL models' high complexity, stochasticity and limited observable information. We propose a novel methodology to overcome the above challenges. The key insight of our approach is that the process of DRL model extraction is equivalent to imitation learning, a well-established solution to learn sequential decision-making policies. Based on this observation, our methodology first builds a classifier to reveal the training algorithm family of the targeted black-box DRL model only based on its predicted actions, and then leverages state-of-the-art imitation learning techniques to replicate the model from the identified algorithm family. Experimental results indicate that our methodology can effectively recover the DRL models with high fidelity and accuracy. We also demonstrate two use cases to show that our model extraction attack can (1) significantly improve the success rate of adversarial attacks, and (2) steal DRL models stealthily even they are protected by DNN watermarks. These pose a severe threat to the intellectual property and privacy protection of DRL applications.
Monocular multi-person pose estimation: A survey

Multi-person pose estimation in unconstrained scenarios, with an unknown number of individuals, is a main step towards scene understanding and action recognition. Due to the recent advancements on the architecture of convolutional networks, body part detectors are now accurate and estimate poses in real-time for both single- and multi-person scenes. In contrast, assigning detected body parts to coherent human poses when there are multiple persons interacting is an arduous task. To name a few of the challenges that arise in such scenes: person-to-person occlusion, truncated body parts, and more sources for double counting. Recently, the community contributed towards solving most of them. Hence, it would be interesting to analyze and compile successful approaches from current literature into research trends, and identify possible gaps for future works. To the best of our knowledge, there is no up-to-date review on the main advancements in the field that target this particular set of challenges. This survey fills this gap by reviewing the main breakthroughs on multi-person pose estimation over the last decade and summarizing their impact on the state-of-the-art. Regarding scientific contributions, we propose a novel taxonomy that categorizes the reviewed methods according to their main contributions to the pose estimation pipeline, lists the main datasets and evaluation metrics to train new models, and provides insights on the best entries of publicly available benchmarks.
Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications

Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to realizing the sensing capability of multiple sensors via the signal from one sensor, which expanded the functionalities and sensing domains of traditional sensors. For the first time, we defined such sensing approach as “cross-sensing” and provided a comprehensive review on the cross-sensing towards wearable applications (i.e., human-machine interface, health services, and security). Specifically, this paper summarized the applied signal processing and machine learning algorithms, and discussed how cross-sensing would affect the development and innovation trends of wearable electronics.
A Novel Multi-Sensor Fusion Based Object Detection and Recognition Algorithm for Intelligent Assisted Driving

The object detection and recognition algorithm based on the fusion of millimeter-wave radar and high-definition video data can improve the safety of intelligent-driving vehicles effectively.However, due to the different data modalities of millimeter-wave radar and video, how to fuse the two effectively is the key point.The difficulty lies in the data fusion methods such as insufficient adaptability of image distortion in data alignment and coordinate transformation and also the mismatching of information levels of the data to be fused.To solve the problem of data fusion of millimeter wave radar and video, this paper proposes a decision-level fusion method of millimeter-wave radar and high-definition video data based on angular alignment.Specifically, through the joint calibration and approximate interpolation, projected to polar coordinate system, the radar and the camera are angularly aligned in the horizontal direction.Then objects are detected by a deep neural network model from video data, and combined with those detected by radar to make the joint decision.Finally, object detection and recognition task based on the fusion of the two kinds of data is completed.Theoretical analysis and experimental results indicate that the accuracy of the algorithm based on the two data fusion is superior to that of the single detection and recognition algorithm on the basis of millimeter-wave radar or video data.
Image retrieval for Structure-from-Motion via Graph Convolutional Network

Conventional image retrieval techniques for Structure-from-Motion (SfM) are limited in their ability to effectively distinguish symmetric or repetitive textured patterns and cannot guarantee an accurate generation of pairwise matches without costly redundancy. In this paper, we formulate the image retrieval task as a node binary classification problem with graph data: if a candidate node is marked as positive, it is believed to share the same scene with the query image. The key idea of our approach is that the local context in the feature space around a query image contains abundant information about the matchable relation between the image and its neighbours. By constructing a subgraph surrounding the query image as input data, we adopt a learnable Graph Convolutional Network (GCN) to determine whether nodes in the subgraph have overlapping regions with the query photograph. Experiments demonstrate that our method performs remarkably well on a challenging dataset of highly ambiguous and duplicated scenes. Furthermore, compared with state-of-the-art matchable retrieval methods, the proposed approach significantly reduces unnecessary attempted matches without sacrificing the accuracy and completeness of reconstruction.
Institutional Aspects of Integrated Quality Assurance of Engineering Study Programs at HEI Using ICT

Informatization of Ukrainian higher education institutions is a key priority of the state education policy, one of the directions to improve HEI's IT infrastructure. The paper aims to develop an integrated quality assurance information system to enhance institutional aspects of higher education institutions' study programs. Therefore, there is an urgent need for all stakeholders to develop, at the university level, a service-oriented architecture to assure the quality of higher education with mandatory elements, for example, student survey results, student ratings, university teachers' rating, educational programs, results of uniqueness verification for scientific research and qualifying papers, review of educational programs for higher education applicants etc. It will allow to unify the approaches to electronic resource management and accelerate the integration of multi-level HEI quality assurance resources into a single portal. The paper presents a quality assurance information system, which allows processing students' feedback to provide HEI's authorities' decision making.
Scattered subspaces and related codes

After a seminal paper by Shekeey (Adv Math Commun 10(3):475-488, 2016), a connection between maximum h-scattered $${{\mathbb {F}}}_{q}$$ -subspaces of $$V(r,q^n)$$ and maximum rank distance (MRD) codes has been established in the extremal cases $$h=1$$ and $$h=r-1$$ . In this paper, we propose a connection for any $$h\in \{1,\ldots ,r-1\}$$ , extending and unifying all the previously known ones. As a consequence, we obtain examples of non-square MRD codes which are not equivalent to generalized Gabidulin or twisted Gabidulin codes. We show that, up to equivalence, MRD codes having the same parameters as the ones in our connection come from an h-scattered subspace. Also, we determine the weight distribution of codes related to the geometric counterpart of maximum h-scattered subspaces.