Reviewerly/exHarmony huggingface dataset:
A hyperspectral method of inverting copper signals in mineral deposits based on an improved gradient-boosting regression tree

In the process of mineral deposits formation, elements often show abnormal enrichment. Therefore, the inversion of element contents (the prediction of element contents in mineral deposits through hyperspectral data) has some indicative significance to the exploration of mineral resources. In order to solve the problem of nonlinear copper contents inversion using hyperspectral remote sensing, this paper put forward an improved Gradient Boosting Regression Tree (IGBRT) method to invert copper contents. The main innovations of this paper are: (1) replacing the simple average function with the k-nearest-neighbour weighted average function as the node prediction function to improve the accuracy; (2) using the adaptive reduction step instead of the fixed reduction step to improve the efficiency. At the end of the paper, taking the Altun region, Xinjiang province, China as the experimental area, the iron element which was high correlated with the copper was chosen as the intermediate variable, and the contents relationship between them was established to solve the problem of the small number of copper samples in the study area. Using the improved Gradient Boosting Regression Tree method to predict the iron contents, then the copper contents could be predicted through the relationship between them. The results showed that the IGBRT algorithm had the coefficient of determination (R2) of 0.744, which were 0.185 more than the traditional one, and the learning efficiency was increased by 39.4%. The results of this study can provide a reference for remote sensing inversion of copper contents in mineral deposits of uninhabited areas, and have some important significance for the delineation of copper prospects.
Fast Verification of Masking Schemes in Characteristic Two

We revisit the matrix model for non-interference (NI) probing security of masking gadgets introduced by Belaïd et al. at CRYPTO 2017. This leads to two main results. 1) We generalise the theorems on which this model is based, so as to be able to apply them to masking schemes over any finite field—in particular $$\mathbb {F}_2$$ —and to be able to analyse the strong non-interference (SNI) security notion. We also follow Faust et al. (TCHES 2018) to additionally consider a robust probing model that takes hardware defects such as glitches into account. 2) We exploit this improved model to implement a very efficient verification algorithm that improves the performance of state-of-the-art software by three orders of magnitude. We show applications to variants of NI and SNI multiplication gadgets from Barthe et al. (EUROCRYPT 2017) which we verify to be secure up to order 11 after a significant parallel computation effort, whereas the previous largest proven order was 7; SNI refreshing gadgets (ibid.); and NI multiplication gadgets from Groß et al. (TIS@CCS 2016) secure in presence of glitches. We also reduce the randomness cost of some existing gadgets, notably for the implementation-friendly case of 8 shares, improving here the previous best results by 17% (resp. 19%) for SNI multiplication (resp. refreshing).
Connecting geometry and performance of two-qubit parameterized quantum circuits

Parameterized quantum circuits (PQCs) are a central component of many variational quantum algorithms, yet there is a lack of understanding of how their parameterization impacts algorithm performance. We initiate this discussion by using principal bundles to geometrically characterize two-qubit PQCs. On the base manifold, we use the Mannoury-Fubini-Study metric to find a simple equation relating the Ricci scalar (geometry) and concurrence (entanglement). By calculating the Ricci scalar during a variational quantum eigensolver (VQE) optimization process, this offers us a new perspective to how and why Quantum Natural Gradient outperforms the standard gradient descent. We argue that the key to the Quantum Natural Gradient's superior performance is its ability to find regions of high negative curvature early in the optimization process. These regions of high negative curvature appear to be important in accelerating the optimization process.
Convolutional neural network of deep learning in computer vision and image classification problems

Abstract The article considers the possibilities of using the deep learning convolutional neural network ResNet in computer vision and image classification problems. The interpretation of the ResNet network and the datasets used for its training are presented, as well as a method for training a deep convolutional neural network with stochastic depth, which allows significantly reducing errors in the test sample.
On the Visualization of Hierarchical Multivariate Data

In this paper, we study the visual design of hierarchical multivariate data analysis. We focus on the extension of four hierarchical univariate concepts-the sunburst chart, the icicle plot, the circular treemap, and the bubble treemap-to the multivariate domain. Our study identifies several advantageous design variants, which we discuss with respect to previous approaches, and whose utility we evaluate with a user study and demonstrate for different analysis purposes and different types of data.
The Necessity and Difficulty of Navigating Uncertainty to Develop an Individual-Level Computational Model

The design of an individual-level computational model requires modelers to deal with uncertainty by making assumptions on causal mechanisms (when they are insufficiently characterized in a problem domain) or feature values (when available data does not cover all features that need to be initialized in the model). The simplifications and judgments that modelers make to construct a model are not commonly reported or rely on evasive justifications such as ‘for the sake of simplicity’, which adds another layer of uncertainty. In this paper, we present the first framework to transparently and systematically investigate which factors should be included in a model, where assumptions will be needed, and what level of uncertainty will be produced. We demonstrate that it is computationally prohibitive (i.e. NP-Hard) to create a model that supports a set of interventions while minimizing uncertainty. Since heuristics are necessary, we formally specify and evaluate two common strategies that emphasize different aspects of a model, such as building the ‘simplest’ model in number of rules or actively avoiding uncertainty.
DLFMNet: End-to-End Detection and Localization of Face Manipulation Using Multi-Domain Features

Recently, more and more realistic facial manipulation images and videos, known as DeepFakes, have been created and rapidly circulated in social media. Therefore, it is crucial to develop effective and efficient methods to detect the malicious DeepFakes. Previous approaches all adopt a two-step pipeline with multiple separate models, i.e., first face detection and then face forensics, and lacks robustness against compressed data. In this paper, we propose an end-to-end framework for detection and localization of face manipulation, named DLFMNet, which effectively integrates face detection and face forensics into one model, avoiding intermediate processes like image cropping and feature re-extraction. In addition, to capture richer and more robust manipulated clues, we exploit multi-domain features that takes advantages of two different but complementary domains (i.e., RGB and noise). The evaluations on FaceForensics++ dataset demonstrate the effectiveness of our proposed DLFMNet. https://github.com/LightningChan/DLFMNet.
People re-identification using depth and intensity information from an overhead camera

This work presents a new people re-identification method, using depth and intensity images, both of them captured with a single static camera, located in an overhead position. The proposed solution arises from the need that exists in many areas of application to carry out identification and re-identification processes to determine, for example, the time that people remain in a certain space, while fulfilling the requirement of preserving people’s privacy. This work is a novelty compared to other previous solutions, since the use of top-view images of depth and intensity allows obtaining information to perform the functions of identification and re-identification of people, maintaining their privacy and reducing occlusions. In the procedure of people identification and re-identification, only three frames of intensity and depth are used, so that the first one is obtained when the person enters the scene (frontal view), the second when it is in the central area of the scene (overhead view) and the third one when it leaves the scene (back view). In the implemented method only information from the head and shoulders of people with these three different perspectives is used. From these views three feature vectors are obtained in a simple way, two of them related to depth information and the other one related to intensity data. This increases the robustness of the method against lighting changes. The proposal has been evaluated in two different datasets and compared to other state-of-the-art proposal. The obtained results show a 96,7% success rate in re-identification, with sensors that use different operating principles, all of them obtaining depth and intensity information. Furthermore, the implemented method can work in real time on a PC, without using a GPU.
Privacy-preserving voluntary-tallying leader election for internet of things

The Internet of Things (IoT) is commonly deployed with devices of limited power and computation capability. A centralized IoT architecture provides a simplified management for IoT system but brings redundancy by the unnecessary data traffic with a data center. A decentralized IoT reduces the cost on data traffic and is resilient to the single-point-of-failure. The blockchain technique has attracted a large amount of research, which is redeemed as a perspective of decentralized IoT system infrastructure. It also brings new privacy challenges for that the blockchain is a public ledger of all digital events executed and shared among all participants. The decentralized IoT system relies on the leader election deeply to implement the decentralized communications among the distributed nodes. The conventional leader election must have a centralized authority, contrasting to the decentralization. As an alternative, self-tallying type schemes have been proposed in the literature for decentralized systems. These schemes suffer from adaptive and abortive issues. Also, some additional factors should be considered, such as the availability of candidate nodes. If the candidate node is unavailable after the voting phase due to being offline or ongoing tasks, the next available candidate should be elected. To accommodate such a need, in this paper, we propose a new leader election paradigm called voluntary-tallying leader election, which achieves the core requirements such as ballet secrecy, voter privacy and the additional feature of voluntary-tallying. We formalize the system and security models for this new election paradigm and present a secure and practical construction.
L0 regularization-based compressed sensing with quantum–classical hybrid approach

Abstract L0-regularization-based compressed sensing (L0-RBCS) has the potential to outperform L1-regularization-based compressed sensing (L1-RBCS), but the optimization in L0-RBCS is difficult because it is a combinatorial optimization problem. To perform optimization in L0-RBCS, we propose a quantum–classical hybrid system consisting of a quantum machine and a classical digital processor. The coherent Ising machine (CIM) is a suitable quantum machine for this system because this optimization problem can only be solved with a densely connected network. To evaluate the performance of the CIM-classical hybrid system theoretically, a truncated Wigner stochastic differential equation (W-SDE) is introduced as a model for the network of degenerate optical parametric oscillators, and macroscopic equations are derived by applying statistical mechanics to the W-SDE. We show that the system performance in principle approaches the theoretical limit of compressed sensing and this hybrid system may exceed the estimation accuracy of L1-RBCS in actual situations, such as in magnetic resonance imaging data analysis.
Natural Language Processing Enabled Cognitive Disease Prediction Model for Varied Medical Records Implemented over ML Techniques

Artificial Intelligence technologies are progressing into fields that were formerly believed to be still the domain of domain analysts. Thanks to recent advances in digital form of information processing, artificial learning, and computational resources, which helped personnel involved in healthcare services in various domains. In certain cases, AI and NLP can handle healthcare functions even well than individuals. One of the area in which machine learning along with natural language processing can work is psychological diseases. Anomalous thinking, emotions, and actions are characteristics of mental illnesses. Aberrant inner interactions and actions that defy social expectations could indicate the existence of a cognitive disease. This research paper proposes a cognitive disease prediction model for psychological disorders like, bipolar disorder, obsessive compulsive disorder, and schizophrenia. In the mainstream medical industry, providing cognitive health treatment entails new ways to treat individuals with mental illnesses. Proposed e-healthcare systems provide an extra layer which helps healthcare professional and patients by saving their time in early diagnosis.
Building a private cloud platform based on open source software OpenStack

OpenStack is a cloud operating system that can manage large pools of resources throughout the data center, including compute, storage, and network resources. Administrators can manage the entire system through the management desk and delineate resources for users through a web interface. It originated from a cloud computing platform project jointly developed by NASA and Rackspace, and is licensed open source under the Apache license. In this article, we'll go through the process of configuring each of the core OpenStack components and understand how they relate to each other. Finally, in this paper, we use SDN to create a virtual router to connect the public network with the private network created by OpenStack via the configured OpenStack, and then create an instance on the private network.
EIoU: An Improved Vehicle Detection Algorithm Based on VehicleNet Neural Network

Abstract The paper’s primary purpose is to optimize the performance (speed/accuracy) of vehicle detection. The vehicle dataset Vehicle2020 used in this paper is divided into ten different vehicle classes. Intersection over Union ( IoU ) is usually used as a standard to evaluate the accuracy of vehicle detection in a specific dataset. However, IoU as a performance of the object detection algorithm is still shortcomings. IoU is further improved and called a new algorithm EIoU . Finally, the neural network structure was redesigned, which was called VehicleNet. The experimental results show that EIoU as a performance evaluation algorithm used the vehicle detection framework can improve the performance of vehicle detection. Using the algorithm of this paper shows the performance superiority of vehicle detection.
Interpretable Generative Adversarial Networks With Exponential Function

As for Generative Adversarial Networks (GANs), its interpretability may be closely related to optimization objective functions, that is, information metrics play important roles in networks training and data generation. In terms of original GAN, the objective function based on Kullback-Leibler (KL) divergence has limitations on the performance of data training and generation. Therefore, it is significant to investigate objective functions for the optimization in GANs to bring gains on the efficiency of network learning from the perspective of metrics. In this paper, the objective function with exponential form, referred from the Message Importance Measure (MIM), is adapted to replace that with logarithm form in the optimization for adversarial networks. This approach named MIM-based GAN, may provide more hidden information in terms of interpretability on training process and probability events generation. Specifically, we first analyze the intrinsic relationship between the proposed approach and other classical GANs. Moreover, compared with the original GAN, LSGAN and WGAN, we discuss its advantages on training performance in theory including sensitivity, convergence rate and so on. In addition, we do simulations on the datasets to confirm why the MIM-based GAN achieves state-of-the-art performance on training process and data generation.
TDM: Trustworthy Decision-Making Via Interpretability Enhancement

Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust is an influential factor in determining the reliance on autonomy. However, it is not reasonable to trust systems that are beyond our comprehension, and typical machine learning and data-driven decision-making are black-box paradigms that impede interpretability. Therefore, it is critical to establish computational trustworthy decision-making mechanisms enhanced by interpretability-aware strategies. To this end, we propose a Trustworthy Decision-Making (TDM) framework, which integrates symbolic planning into sequential decision-making. The framework learns interpretable subtasks that result in a complex, higher-level composite task that can be formally evaluated using the proposed trust metric. TDM enables the subtask-level interpretability by design and converges to an optimal symbolic plan from the learned subtasks. Moreover, a TDM-based algorithm is introduced to demonstrate the unification of symbolic planning with other sequential-decision making algorithms, reaping the benefits of both. Experimental results validate the effectiveness of trust-score-based planning while improving the interpretability of subtasks.
-Score-Based Secure Biomedical Model for Effective Skin Lesion Segmentation Over eHealth Cloud

This study aims to process the private medical data over eHealth cloud platform. The current pandemic situation, caused by Covid19 has made us to realize the importance of automatic remotely operated independent services, such as cloud. However, the cloud servers are developed and maintained by third parties, and may access user’s data for certain benefits. Considering these problems, we propose a specialized method such that the patient’s rights and changes in medical treatment can be preserved. The problem arising due to Melanoma skin cancer is carefully considered and a privacy-preserving cloud-based approach is proposed to achieve effective skin lesion segmentation. The work is accomplished by the development of a Z -score-based local color correction method to differentiate image pixels from ambiguity, resulting the segmentation quality to be highly improved. On the other hand, the privacy is assured by partially order homomorphic Permutation Ordered Binary (POB) number system and image permutation. Experiments are performed over publicly available images from the ISIC 2016 and 2017 challenges, as well as PH <?TeX $^2$?> dataset, where the proposed approach is found to achieve significant results over the encrypted images (known as encrypted domain), as compared to the existing schemes in the plain domain (unencrypted images). We also compare the results with the winners of the ISBI 2016 and 2017 challenges, and show that the proposed approach achieves a very close result with them, even after processing test images in the encrypted domain. Security of the proposed approach is analyzed using a challenge-response game model.
The Computational and Latency Advantage of Quantum Communication Networks

This article summarizes the current status of classical communication networks and identifies some critical open research challenges that can only be solved by leveraging quantum technologies. Until now, the main goal of quantum communication networks has been security. However, quantum networks can do more than just exchange secure keys or serve the needs of quantum computers. In fact, the scientific community is still investigating the possible use cases/benefits that quantum communication networks can bring. Thus, this article aims to point out and clearly describe how quantum communication networks can enhance in-network distributed computing and reduce the overall end-to-end latency, beyond the intrinsic limits of classical technologies. Furthermore, we also explain how entanglement can reduce the communication complexity (overhead) that future classical virtualized networks will experience.
Earthquake Early Warning System in Israel—Towards an Operational Stage

The Geological Survey of Israel has upgraded and expanded the national Israeli Seismic Network (ISN), with more than 110 stations country-wide, as part of the implementation of a governmental decision to build a national Earthquake Early Warning (EEW) system named TRUAA. This upgraded seismic network exhibits a high station density and fast telemetry. The stations are distributed mainly along the main fault systems, the Dead Sea Transform, and the Carmel-Zfira Fault, which may potentially produce M w 7.5 earthquakes. The system has recently entered a limited operational phase, allowing for initial performance estimation. Real-time performance during eight months of operation (41 earthquakes) matches expectations. Alert delays (interval between origin-time and Earthquake Early Warning alert time) are reduced to as low as 3 s, and source parameter errorstatistics are within expected values found in previous works using historical data playbacks. An evolutionary alert policy is implemented based on a magnitude threshold of Mw 4.2 and peak ground accelerations exceeding 2 cm/s 2 . A comparison between different ground motion prediction equations (GMPE) is presented for earthquakes from Israel and California using median ground motion prediction equations values. This analysis shows that a theoretical GMPE produced the best agreement with observed ground motions, with less bias and lower uncertainties. The performance of this GMPE was found to improve when an earthquake specific stress drop is implemented.
Depth-Guided AdaIN and Shift Attention Network for Vision-And-Language Navigation

Visual Language Navigation (VLN) is the grand goal of AI, which enables the agent to act by the language instructions from humans. In VLN task, the agent learns to search for a specific region described by the instructions in the training environments, and performs the navigation in the unseen environments. Normally, there exists a large domain gap be-tween the seen and unseen environments. Numerous works have been put on data augmentation and designing new loss in such a multi-task navigation setting. However, as a spatial and temporal searching task, a valuable signal source for the navigation – depth has not yet fully explored and thus been ignored in previous efforts. Typically, the current models lack the ability to capture the relative spatial directions to the grounding view. To address these issues, we propose an environment adaptive method based on a Depth-guided Adaptive Instance Normalization (DG-AdaIN) module to adjust the RGB features in term of the depth features, and develop a shift attention module to model the relative direct information in the attention map. Extensive experiments have validated the efficacy of our method on the benchmark dataset.