Reviewerly/exHarmony huggingface dataset:
Quantitative estimation of rare earth element abundances in compositionally distinct carbonatites: Implications for proximal remote-sensing prospection of critical elements

Rare earth elements (REE) became a strategic raw material in the 21st century and carbonatite-related deposits frequently carry potentially economic levels of these critical metals. To determine concentrations of major and trace elements (including REE) conventional analysis of bulk rock geochemical samples by means of inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) is mostly applied. However, these analytical methods are labor-intensive, costly and time-consuming. In contrast, modern Remote Sensing (RS) – e.g. proximal remote sensing or imaging spectroscopy – has become a novel tool for detecting and quantifying geological materials as they offer an efficient, non-destructive and cost-effective way not only to identify different minerals and/or chemical elements but also model their abundances. In this study, we investigated whether Partial Least Squares Regression (PLSR) models linking the laboratory sample reflectance to mineral/geochemical property could be employed for quantitative predictions of bulk LREE and HREE concentrations. The suite included samples from four different geographic regions in India, East Africa and the USA. Our results showed that for such samples the quantitative approaches have some limitations. First, our models required to exclude samples with high modal abundance of hematite and samples with too low REE contents (LREE < 500 μg/g, HREE < 100 μg/g). Furthermore, it was revealed that the sample mineralogy has a significant impact on the PLSR predictions. To achieve reliable models the sample suite had to be divided into two datasets following their mineralogy and geochemistry. The first dataset comprised rocks with increased amounts of strontium (Sr, DatasetSr), where REE were predominantly bound in Sr-carbonates. On the other hand, REE mainly bound in Ca–REE-carbonates characterized the second dataset (DatasetOther). Following this division, we were able to construct valid prediction models for bulk LREE and HREE concentrations using PLSR. However, the detected spectral assignments associated with the REE presence indicated that the predictions were mainly indirect, based on the present mineral phases rather than direct absorptions related to LREE and HREE. This illustrates the difficulty and limitations for further model generalization and the ability to be further transferred to other lithologically diverse carbonatite sites. We posit that this topic requires future systematic investigations using the extended carbonatite datasets – samples having a wide range of REE abundances – collected from lithologically diverse regions. This would enable further validation and re-calibration of the constructed prediction models.
Full-duplex strategy for video object segmentation

Abstract Previous video object segmentation approaches mainly focus on simplex solutions linking appearance and motion, limiting effective feature collaboration between these two cues. In this work, we study a novel and efficient full-duplex strategy network ( FSNet ) to address this issue, by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding stage. Specifically, we introduce a relational cross-attention module (RCAM) to achieve bidirectional message propagation across embedding sub-spaces. To improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings, we adopt a bidirectional purification module after the RCAM. Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios (e.g., motion blur and occlusion), and compares well to leading methods both for video object segmentation and video salient object detection. The project is publicly available at https://github.com/GewelsJI/FSNet .
Dynamic Diffuse Global Illumination Resampling

Ray traced global illumination can be partitioned into direct contributions of the light sources that reflect to the camera after one bounce and indirect contributions that scatter for multiple bounces. We propose a new real-time solution called dynamic diffuse global illumination resampling that computes direct and indirect illumination accurately and with low noise. The key idea is to derive a new, unified algorithm from the principles of the state of the art ReSTIR many-lights direct shadowing [Bitterli et al. 2020] and the DDGI indirect light probes [Majercik et al. 2019] real-time algorithms. By this unification, global illumination resampling achieves higher quality than the combination of its two components at real-time framerates. At the cost of little bias, our technique also outperforms hardware accelerated path tracing in both runtime and noise.
A Scalable Two-Tier PBFT Consensus for Blockchain-Based IoT Data Recording

The use of blockchain for Internet-of-Things (IoT) data recording necessitates an efficient and scalable consensus mechanism. In this paper, we describe a two-tier architecture in which IoT data is packaged in batches or blocks, approved by a low-tier cluster first and a top-tier cluster second, before being added to the replicated blockchain ledger. Both tiers use PBFT-like consensus enhanced with multiple-entry point operation using bandwidth reservation. This approach eliminates the dependence on a single primary leader that is characteristic for PBFT-like protocols, and allows the system to be deployed in geographically wide area. We provide a detailed probabilistic analysis of the proposed architecture using a discrete time Markov chain, and show that system performance depends on the number of ordering nodes in each cluster and the number of low-tier clusters.
Emotion Classification of COVID-19 Chinese Microblogs Based on the Emotion Category Description

Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself, without combining the semantics of emotion categories for modeling. Emotion classification of microblogs is a process of reading the content of microblogs and combining the semantics of emotion categories to understand whether it contains a certain emotion. Inspired by this, we propose an emotion classification model based on the emotion category description for COVID-19 Chinese microblogs. Firstly, we expand all emotion categories into formalized category descriptions. Secondly, based on the idea of question answering, we construct a question for each microblog in the form of 'What is the emotion expressed in the text X?' and regard all category descriptions as candidate answers. Finally, we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual and category semantics, the model can better understand the emotion of microblogs. Experiments on the COVID-19 Chinese microblog dataset show that our approach outperforms many existing emotion classification methods, including the BERT baseline.
Design and Development of Smart Airbag Suit for Elderly with Protection and Notification System

Human fall is the second leading cause of death by accidental or unintentional injury which leads to coma, paralysis, and brain trauma. The existing method has a hip protection airbag suit which prevents to get an injury in the trochanteric region and prevent to get fractures. The drawback of this methodology, it protects only the hip region of patients, without protecting important parts like the head, thoracic cavity. After a fall - related accident, senior patients mentally, emotionally week and getting `fear of repeated fall'. To rectify those problems, our innovation presents the use of an airbag suit with a certain sensor to protect the life of elders and also gives an alarm message to care-takers. After identifying hazardous slant, the micro controller must start a sign which can enact the engine driver to expand an airbag consciously positioned kind of a suit. The airbag garment is stretchable fabric that is made up of nylon or polyamide cloth filled with nitrogen or argon gas which will inflate in case of sudden fall, to avoid severe injury in the head, thoracic region, vertebrate column, and pelvic region. With help of tri-axis accelerometer sensor and through GSM the emergency notification sends to the care-taker.
Recommending Faulty Configurations for Interacting Systems Under Test Using Multi-objective Search

Modern systems, such as cyber-physical systems, often consist of multiple products within/across product lines communicating with each other through information networks. Consequently, their runtime behaviors are influenced by product configurations and networks. Such systems play a vital role in our daily life; thus, ensuring their correctness by thorough testing becomes essential. However, testing these systems is particularly challenging due to a large number of possible configurations and limited available resources. Therefore, it is important and practically useful to test these systems with specific configurations under which products will most likely fail to communicate with each other. Motivated by this, we present a search-based configuration recommendation ( SBCR ) approach to recommend faulty configurations for the system under test (SUT) based on cross-product line (CPL) rules. CPL rules are soft constraints, constraining product configurations while indicating the most probable system states with a certain degree of confidence. In SBCR , we defined four search objectives based on CPL rules and combined them with six commonly applied search algorithms. To evaluate SBCR (i.e., SBCR NSGA-II , SBCR IBEA , SBCR MoCell , SBCR SPEA2 , SBCR PAES , and SBCR SMPSO ), we performed two case studies (Cisco and Jitsi) and conducted difference analyses. Results show that for both of the case studies, SBCR significantly outperformed random search-based configuration recommendation ( RBCR ) for 86% of the total comparisons based on six quality indicators, and 100% of the total comparisons based on the percentage of faulty configurations (PFC). Among the six variants of SBCR, SBCR SPEA2 outperformed the others in 85% of the total comparisons based on six quality indicators and 100% of the total comparisons based on PFC.
Adaptive Counter Clock Gated S-Box Transformation Based AES Algorithm of Low Power Consumption and Dissipation in VLSI System Design

Abstract Advanced Encryption Standard (AES) is an effective algorithm trending today. According to the key length, it works efficiently. Here three types of key lengths are used such as 128, 192, and 256 bits. This paper introduced an Adaptive Counter-Clock (ACC) S-Box algorithm. This algorithm rectifies the error during data encryption and secure data from hackers. In this paper, we have done power consumption, power dissipation, and reduced the area size. Field Programmable Gate Array (FPGA) is utilized for hardware execution in the encryption process. Inverse Mix Column, Inverse Sub Bytes (S-Box) and Inverse Shift Row stages takes place in the decryption, so that the receiver can read the message in plain text which was sent by the sender. For this the receiver should have the same key used by the sender for encryption process. RTL coding is done using Verilog HDL, Xilinx ISE 14.7 FPGA will be used in the implementation.
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation

Variational inference is a powerful framework, used to approximate intractable posteriors through variational distributions. The de facto standard is to rely on Gaussian variational families, which come with numerous advantages: they are easy to sample from, simple to parametrize, and many expectations are known in closed-form or readily computed by quadrature. In this paper, we view the Gaussian variational approximation problem through the lens of gradient flows. We introduce a flexible and efficient algorithm based on a linear flow leading to a particle-based approximation. We prove that, with a sufficient number of particles, our algorithm converges linearly to the exact solution for Gaussian targets, and a low-rank approximation otherwise. In addition to the theoretical analysis, we show, on a set of synthetic and real-world high-dimensional problems, that our algorithm outperforms existing methods with Gaussian targets while performing on a par with non-Gaussian targets.
Geometry from Thin Air: Theremin as a Playful Learning Device

This paper presents a proof of concept of a playful learning device aimed at the enhancement of geometry knowledge acquisition for young children. A novel form of interaction through hand movement has been employed using a theremin-based device. Following a low-cost and energy efficient approach, the presented prototype consists of a digital CMOS ICs, 8×8 LED matrix and Arduino platform. Motivated by the possible IoT utilization in an educational context, a use case scenario has been presented demonstrating potential advantages for the improvement of users' learning abilities and fine motor skills. This device could provide innovative ways for children to play and learn, fostering imagination while engaging them in STEM-related activities.
Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study

Background Asthma hospital encounters impose a heavy burden on the health care system. To improve preventive care and outcomes for patients with asthma, we recently developed a black-box machine learning model to predict whether a patient with asthma will have one or more asthma hospital encounters in the succeeding 12 months. Our model is more accurate than previous models. However, black-box machine learning models do not explain their predictions, which forms a barrier to widespread clinical adoption. To solve this issue, we previously developed a method to automatically provide rule-based explanations for the model’s predictions and to suggest tailored interventions without sacrificing model performance. For an average patient correctly predicted by our model to have future asthma hospital encounters, our explanation method generated over 5000 rule-based explanations, if any. However, the user of the automated explanation function, often a busy clinician, will want to quickly obtain the most useful information for a patient by viewing only the top few explanations. Therefore, a methodology is required to appropriately rank the explanations generated for a patient. However, this is currently an open problem. Objective The aim of this study is to develop a method to appropriately rank the rule-based explanations that our automated explanation method generates for a patient. Methods We developed a ranking method that struck a balance among multiple factors. Through a secondary analysis of 82,888 data instances of adults with asthma from the University of Washington Medicine between 2011 and 2018, we demonstrated our ranking method on the test case of predicting asthma hospital encounters in patients with asthma. Results For each patient predicted to have asthma hospital encounters in the succeeding 12 months, the top few explanations returned by our ranking method typically have high quality and low redundancy. Many top-ranked explanations provide useful insights on the various aspects of the patient’s situation, which cannot be easily obtained by viewing the patient’s data in the current electronic health record system. Conclusions The explanation ranking module is an essential component of the automated explanation function, and it addresses the interpretability issue that deters the widespread adoption of machine learning predictive models in clinical practice. In the next few years, we plan to test our explanation ranking method on predictive modeling problems addressing other diseases as well as on data from other health care systems. International Registered Report Identifier (IRRID) RR2-10.2196/5039
Pseudoreference Subaperture Images and Microlens Image-Based Blind Light Field Image Quality Measurement

Light field image (LFI) records the radiation intensity and direction information of light in scene, offering powerful capabilities for computer vision. It is found that the distortion caused by LFI processing is obviously reflected in the two representations of LFI, that is, subaperture images (SAIs) and microlens images (MLI). Specifically, on the one hand, pseudoreference SAI (PSAI) of a distorted SAI, which is rendered with the adjacent distorted SAIs, can be used to measure the distortion of the involved SAIs. On the other hand, macropixel blocks (MPBs) of MLI can be adopted to measure LFI global angular distortion. Therefore, a PSAIs- and MLI-based blind LFI quality measurement (PM-BLFIQM) method is proposed in this article. First, the local light field (LLF) is defined to generate PSAI of the central distorted SAI in each LLF of LFI, and then a pseudo-full-reference quality measurement metric based on PSAIs is presented. Second, to measure the angular consistency of the distorted LFI, an MLI-based feature extraction scheme is proposed for MPBs and their mean removal version by using discrete cosine transform and singular value decomposition. Finally, the quality of the distorted LFI is predicted with the features extracted from LLFs and MLI. Experimental results demonstrate that both PSAI and MPB are effective for measuring the quality of distorted LFI. In addition, the results on three LFI databases show that the proposed method achieves higher Spearman rank-order correlation coefficient (SROCC) and Pearson linear correlation coefficient (PLCC) values, so it outperforms the state-of-the-art methods in terms of consistency with human visual perception.
Gap volume prediction for aircraft wing assembly

In this work, two methods for predictive shimming based on laser scan technology and point cloud processing software have been presented. The first one is direct measurement method in PolyWorks, and the second one is feature-based modelling method in SolidWorks. Firstly, both component-level and assembly-level scans are collected by a laser scanner. Then, the point clouds are post-processed and registered in PolyWorks. Thirdly, gap information (shape and thickness) is obtained either in PolyWorks or in SolidWorks. Results show that the proposed methods can obtain gap information successfully based on current hardware and software.
Ethics and Facial Recognition Technology: An Integrative Review

This integrative review will synthesize current studies and research on the ethics of facial recognition technology to raise awareness and reflection among engineering practitioners, teachers, and students. Engineers as developers of this software-and the professional communicators who help test and translate them for the public-could use ethical insights to discuss and implement user-centered design leading up to and after deployment more meaningfully. The integrative review will focus on academic research and studies conducted in the past ten years about ethical issues in facial recognition to synthesize findings and insights toward a framework for guidelines and best practices.
Improved Faster-RCNN Algorithm for Mask Wearing Detection

In order to salve the problem that it is difficult to detect the face objects wearing masks in the natural environments, a detection method based on improved Faster-RCNN was proposed. Based on the original Faster-RCNN as the overall framework, the ResNet-50 combined with the feature pyramid network (FPN) was used to replace the VGG-16 for feature extraction. K-Means algorithm and Soft-NMS algorithm were used to optimize the anchors to locate the targets efficiently. At the same time, imbalance problem between positive and negative samples was solved by introducing the Focal Loss function. Self-made face wearing diverse masks dataset were made to train the network and evaluate the network. The results of the experiment show that, comparing with the original networks, the mean average precision (mAP) of the optimized network improves by 6.4%.
Resource Optimal Executable Quantum Circuit Generation Using Approximate Computing

Quantum Computing is an emerging technology that combines the principles of computer science and quantum mechanics to solve computationally challenging problems significantly faster than classical computers. In this paper, we present a proof-of-principle procedure for generating hardware-executable quantum circuits for Noisy Intermediate-Scale Quantum (NISQ) devices that follows the paradigm of approximate computing.Our approach starts from the reference circuit and trans-forms it into an executable circuit with tuneable parameters by replacing the high-level quantum operations by approximate decompositions into hardware-native gates. An inner optimization loop over the rotation gates’ angles ensures that the so-created circuit behaves in the same way as the reference one in terms of its expectation-value landscape. This technique is complemented by compiler-based optimizations to further reduce or aggregate gate groups of the optimized circuit. This three-step procedure is embedded into an outer genetic algorithm framework that inspects many different circuit designs with placements of single- and multi-qubit gates according to the hardware’s lattice structure, and returns a set of approximate quantum circuits that can be executed on NISQ devices directly.We have validated our approach for superconducting quantum systems from IBM and Rigetti for various benchmark algorithms. In nearly all cases, our approach outperforms the vendors’ quantum-compiler frameworks and produces significantly smaller circuits with up to 50% reduction in the number of gates.
Learning residual coding for point clouds

Recent advancements in acquisition of three-dimensional models have been increasingly drawing attention to imaging modalities based on the plenoptic representations, such as light fields and point clouds. Since point cloud models can often contain millions of points, each including both geometric positions and associated attributes, efficient compression schemes are needed to enable transmission and storage of this type of media. In this paper, we present a detachable learning-based residual module for point cloud compression that allows for efficient scalable coding. Our proposed method is able to learn the encoding of residuals in any layered architecture, and is here implemented in a hybrid approach using both TriSoup and Octree modules from the G-PCC standard as its base layer. Results indicate that the proposed method can achieve performance gains in terms of ratedistortion when compared to both base layer alone, which is demonstrated both through objective metrics and subjective perception of quality in a rate-distortion framework. The source code of the proposed codec can be found at https://github.com/mmspg/learned-residual-pcc.