Reviewerly/exHarmony huggingface dataset:
Performance enhancement of solar PV system introducing semi-continuous tracking algorithm based solar tracker

One common method of harnessing solar energy is via a Photovoltaic (PV) system. When sunlight strikes a PV panel surface at around ninety-degree angle, the system produces the most energy. The electricity generated by a PV system may be increased by using a solar tracker (ST). However, the ST has its own energy requirements and additional expenses. A semi-continuous single-axis ST is developed for this purpose. The “track and wait algorithm” is used to monitor the sun rather than constant tracking in the developed ST. While comparing the performance of a fixed PV system, a continuous single-axis solar tracker-based PV system, and a semi-continuous single-axis solar tracker-based PV system, the developed semi-continuous system is only slightly less efficient than the continuous system (by 1.12 %). Nevertheless compared to a PV system with continuous tracking, the semi-continuous tracking system reduces self-energy consumption by 34%.
Multi-level neural prompt for zero-shot weakly supervised group activity recognition

Group activity recognition aims at understanding the overall activity involved multi-person interaction with each other as a whole in the complex variations in both spatial and temporal transition in untrimmed video. While remarkably significance in applications, existing methods are often impractical for the following two reasons: (1) they rely on heavy annotations and off-the-shelf object detectors even in test stage; (2) they are trained to predict a closed-set predetermined group categories, which limits their generality to wider unseen categories. Motivated by this, we propose a novel zero-shot weakly supervised group activity recognition neural model, ZSTGroupCLIP, which is not only free from the restriction of heavy annotations, but also captures the generalizable elements that are vital for open-set exploration. Specifically, our model based on visual–textual joint representation learning seeks discriminative visual cues for aligning both the group vision and category text branches by multi-level neural prompt. Moreover, to further learn group-related contextual representations under weakly supervision, we learn layer-wise prompts across early layer of group encoder for progressive context modeling. Through extensive experimental validations and comparisons to several baseline methods on two benchmarks, Volleyball and NBA datasets, our method achieves outstanding performance.
Heterogeneous graph knowledge distillation neural network incorporating multiple relations and cross-semantic interactions

In recent years, the study of real-world graphs has revealed their inherent heterogeneity, prompting growing research interest in heterogeneous graphs. Characterized by diverse node and relation types, heterogeneous graphs have led to the development of heterogeneous graph neural networks, which possess the remarkable ability of modeling such heterogeneity. Consequently, researchers have embraced these networks, applying them in various domains. A prevalent approach is using meta-path based methods in heterogeneous graph neural networks. However, a significant limitation arises from the fact that such methods tend to overlook vital attribute information within intermediate nodes and disregard relevant semantics across various meta-paths. To address the above limitations, we propose a new model named HGNN-MRCS. Specifically, HGNN-MRCS incorporates three key components, i.e., a relation aware module to encapsulate the attribute information of the intermediate nodes; a meta-path aware technique to facilitate learning of semantic information of each meta-path and enable higher-order representation learning; and a knowledge distillation strategy to learn relevant semantics across meta-paths and fuse them. Experimental results on four real-world datasets demonstrate the superior performance of this work over the SOAT methods. The source codes of this work are available at https://github.com/ZZY-GraphMiningLab/HGNN-MRCS.
Chalcophile element degassing at an active continental arc volcano

Arc volcanoes are significant natural sources of trace chalcophile elements to the atmosphere via gas and aerosol plumes. Villarrica volcano, part of the Andean arc, erupts basaltic magmas and is characterised by a persistent volcanic gas plume and therefore presents an opportunity to quantify volcanic chalcophile processing in a subduction zone from slab to surface. Here we present geochemical data for olivine-hosted melt inclusions, as well as for the gas and aerosol plume. We show that melts erupted at Villarrica are enriched (over mid-ocean ridge basalts) in a suite of fluid-mobile elements comprising the large ion lithophiles, including Cs, and chalcophile elements W, Tl, Pb and Sb. Volcanic gas and aerosol samples show that the chalcophile elements, and Cs, are strongly enriched in the gas phase over the silicate melt, 103 to 106 times more so than the non-volatile Rare Earth Elements. Volatilities (the percentage of an element that degasses from a melt on eruption) reach ∼ 45 % for Tl, with Pb, Sn and Mo exhibiting volatilities of up to 0.3 % and Cu up to 0.08 %. Many of the chalcophile elements (e.g. Cu, Ag, Zn) have an affinity for chloride in the gas phase and we observe that the volatility of chloride-speciating trace metals is linked strongly to the availability of chlorine in volcanic plumes globally. Overall, we show that the trace element composition of the volcanic gas—and hence probably also the deeper, denser and more saline fluids in the subsurface—is sensitive to both the availability of chloride in the gas phase and the composition of the melt, which is controlled by the slab flux and may be variable between subduction zones.
Deep Learning-based POS Tagger and Chunker for Odia Language Using Pre-trained Transformers

Developing effective natural language processing (NLP) tools for low-resourced languages poses significant challenges. This article centers its attention on the task of Part-of-speech (POS) tagging and chunking, which pertains to the identification and categorization of linguistic units within sentences. POS tagging and Chunking have already produced positive results in English and other European languages. However, in Indian languages, particularly in Odia language, it is not yet well explored because of the lack of supporting tools, resources, and its complex linguistic morphology. This study presents the building of a manually annotated dataset for Odia phrase chunking task and the development of a deep learning-based model specifically tailored to accommodate the distinctive properties of the language. The process of annotating the Odia chunking corpus involved the utilization of inside-outside-begin labels, which were tagged by using designed Odia chunking tagset. We utilize the constructed Odia chunking dataset to build Odia chunker based on deep learning techniques, employing state-of-the-art architectures. Various techniques, such as Recurrent Neural Networks, Convolutional Neural Networks, and transformer-based models, are investigated to determine the most effective approach for Odia POS tagging and chunking. In addition, we conduct experiments utilizing diverse input representations, including Odia word embeddings, character-level representations, and sub-word units, to effectively capture the complex linguistic characteristics of the Odia language. Numerous experiments are conducted that evaluate the performance of our Odia POS tagger and chunker, employing standard evaluation metrics and making comparisons with existing approaches. The results demonstrate that our transformer-based tagger and chunker achieves superior accuracy and robustness in identifying and categorizing linguistic POS tags and chunks within Odia sentences. It outperforms existing work and exhibits consistent performance across diverse linguistic contexts and sentence structures. The developed Odia POS tagger and chunker have enormous potential for a variety of NLP applications, including information extraction, syntactic parsing, and machine translation, all of which are tailored to the low-resource Odia language. This work contributes to developing NLP tools and technologies for low-resource languages, thereby facilitating enhanced language processing capabilities in various linguistic contexts.
Efficient public auditing scheme for non-administrator group with secure user revocation

Cloud auditing, as an important part of cloud computing, ensures file security for users. To this end, many academic authors have conducted an enormous amount of research on cloud auditing. Unfortunately, existing schemes usually involve administrators who have greater power, and malicious administrators may abuse their authority. Thus, a large number of schemes have been designed to ensure users’ authority, such as implementing non-administrator groups. Whereas, it is difficult to achieve efficient user revocation in groups without administrators. This paper aims at solving the aforementioned problem. In particular, an efficient public auditing scheme is proposed with malicious users revocation and re-signature generation under the non-administrators model. First, the signature generation is executed by t users based on the threshold secret technique. Then, the proxy is responsible for aggregating t users’ partial keys and signatures. Moreover, the cloud, proxy and qualified users will collaborate to generate a new partial signature through proxy re-signature when a malicious user appears. Last, we give formal security analyses and conduct a number of experiments.
An intelligent identification and classification system of decoration waste based on deep learning model

Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional method of manual sorting for decoration waste is inefficient and poses health risks to sorting workers. It is therefore imperative to develop an accurate and efficient intelligent classification method to address these issues. To meet the demand for intelligent identification and classification of decoration waste, this paper applied the deep learning method You Only Look Once X (YOLOX) to the task and proposed an identification and classification framework of decoration waste (YOLOX-DW framework). The proposed framework was validated and compared using a multi-label image dataset of decoration waste, and a robot automatic sorting system was constructed for practical sorting experiments. The research results show that the proposed framework achieved a mean average precision (mAP) of 99.16 % for different components of decoration waste, with a detection speed of 39.23 FPS. Its classification efficiency on the robot sorting experimental platform reached 95.06 %, indicating a high potential for application and promotion. This provides a strategy for the intelligent detection, identification, and classification of decoration waste.
Graph-guided deep hashing networks for similar patient retrieval

With the rapid growth and widespread application of electronic health records (EHRs), similar patient retrieval has become an important task for downstream clinical decision support such as diagnostic reference, treatment planning, etc. However, the high dimensionality, large volume, and heterogeneity of EHRs pose challenges to the efficient and accurate retrieval of patients with similar medical conditions to the current case. Several previous studies have attempted to alleviate these issues by using hash coding techniques, improving retrieval efficiency but merely exploring underlying characteristics among instances to preserve retrieval accuracy. In this paper, drug categories of instances recorded in EHRs are regarded as the ground truth to determine the pairwise similarity, and we consider the abundant semantic information within such multi-labels and propose a novel framework named Graph-guided Deep Hashing Networks (GDHN). To capture correlation dependencies among the multi-labels, we first construct a label graph where each node represents a drug category, then a graph convolution network (GCN) is employed to derive the multi-label embedding of each instance. Thus, we can utilize the learned multi-label embeddings to guide the patient hashing process to obtain more informative and discriminative hash codes. Extensive experiments have been conducted on two datasets, including a real-world dataset concerning IgA nephropathy from Peking University First Hospital, and a publicly available dataset from MIMIC-III, compared with traditional hashing methods and state-of-the-art deep hashing methods using three evaluation metrics. The results demonstrate that GDHN outperforms the competitors at different hash code lengths, validating the superiority of our proposal.
Colorectal cancer detection based on convolutional neural networks (CNN) and ranking algorithm

With the development of targeted therapies, many treatments are based on molecular studies, which require sampling tumor tissue from paraffin blocks for sequencing. An automated solution could potentially reduce the workload of pathologists by acting as a screening device and may reduce the subjectivity in diagnosis. In tissue-based diagnostics, most of the work still needs to be done manually by a pathologist using a microscope to examine stained slides. The foundation of such tasks is to accurately distinguish cancer/malignant cells from normal/benign cells. However, the determination of tumor content is poorly reproducible with significant variation. As the size of tumor regions can be very small, pathologists are often required to use high magnification for detecting tumor cells. This requirement significantly increases the workload for pathologists. As digital pathology datasets have become publicly available and have opened up the possibility of evaluating the feasibility of applying deep learning techniques to improving the efficiency and quality of histologic diagnosis. The model proposed in this work is an application to detect colorectal cancer based on the Convolutional Neural Network and Ranking algorithm. Based on the performance evaluation, it is found that the proposed model is yielding better results in diagnosis of Colorectal Cancer than the existing methods in terms of Recall, Precision and Accuracy.
Pre-training Question Embeddings for Improving Knowledge Tracing with Self-supervised Bi-graph Co-contrastive Learning

Learning high-quality vector representations (aka. embeddings) of educational questions lies at the core of knowledge tracing (KT), which defines a task of estimating students’ knowledge states by predicting the probability that they correctly answer questions. Although existing KT efforts have leveraged question information to achieve remarkable improvements, most of them learn question embeddings by following the supervised learning paradigm. In this article, we propose a novel question embedding pre-training method for improving knowledge tracing with self-supervised Bi -graph Co -contrastive learning ( BiCo ). Technically, on the basis of self-supervised learning paradigm, we first select two similar but distinct views (i.e., representing objective and subjective semantic perspectives) as the semantic source of question embeddings. Then, we design a primary task (structure recovery) together with two auxiliary tasks (question difficulty recovery and contrastive learning) to further enhance the representativeness of questions. Finally, extensive experiments conducted on two real-world datasets show BiCo has a higher expressive power that enables KT methods to effectively predict students’ performances.
Exploratory machine learning with unknown unknowns

In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labels. This paper studies a new problem setting in which there are unknown classes in the training data misperceived as other labels, and thus their existence appears unknown from the given supervision. We attribute the unknown unknowns to the fact that the training dataset is badly advised by the incompletely perceived label space due to the insufficient feature information. To this end, we propose the exploratory machine learning, which examines and investigates training data by actively augmenting the feature space to discover potentially hidden classes. Our method consists of three ingredients including rejection model, feature exploration, and model cascade. We provide theoretical analysis to justify its superiority, and validate the effectiveness on both synthetic and real datasets.
Multi-phase quantitative compositional mapping by LA-ICP-MS: Analytical approach and data reduction protocol implemented in XMapTools

Mapping of trace element signatures is an expanding tool in geoscience and material sciences, which allows the study of solid materials, and processes that may not be captured by major elements. Developments in laser-ablation inductively-coupled-plasma mass-spectrometry (LA-ICP-MS) capabilities in the last decade now provide the necessary spatial resolution for in situ element mapping. The acquisition of two-dimensional, fully quantitative and geologically meaningful data with LA-ICP-MS is still a challenging task, and a particular obstacle is the calibration of inhomogeneous phases, such as chemically zoned minerals. This work presents a novel approach to data reduction and image generation for multi-element mapping employing LA-ICP- quadrupole MS (LA-ICP-QMS), implemented in the free and open-source software XMapTools. Three geological applications are presented to illustrate the benefits of the procedures. Garnet from an eclogitic sample (Latto Hills, Togo) and plagioclase, k-feldspar, biotite from a migmatite sample (El Oro Complex, Ecuador) were mapped multiple times at different spatial resolutions to test the calibration quality and chemical detection capabilities. Rutile in a metapelite sample (Val Malenco, Italian Alps) was mapped, and Zr-in-rutile thermometry shows a temperature range of 510 to 550 °C within a single grain. The accuracy of the LA-ICP-MS method was verified by comparison with zoned major and minor element maps (garnet, plagioclase) and Ti-in-biotite geothermometry maps obtained by electron probe microanalysis (EPMA). A spatial resolution of up to 5 μm is achieved with LA-ICP-QMS, which is similar to the resolution reported for LA-ICP time-of-flight mass spectrometry (LA-ICP-TOFMS), albeit at significantly lower acquisition speed. Maps with lower spatial resolution offer better chemical detection power as demonstrated by lower per-pixel limit of detection (LOD) map calculation. Moreover, such maps are also recorded faster. The pixel allocation strategy and the instrumental conditions also have a direct impact on map quality. We recommend that maps are interpolated to square pixels, where a pixel consists of multiple sweeps to gain an improved detection power. Benchmarks using an emulated LA-ICP-MS mapping show that the spot size, together with scan direction, can lead to a shift in composition depending on the feature size of chemical patterns. This is verified by mapping a thin <50 μm annulus in garnet visible in REE and such compositional shifts can have a significant impact on e.g., diffusion modelling. The new software solution provides a multi-standard and variable composition calibration of LA-ICP-MS maps with single pixel LOD filtering at 95% confidence, allowing the user to quantify inhomogeneous materials of major and trace elements simultaneously with improved accuracy.
Human image animation via semantic guidance

Image animation creates visually compelling effects by animating still source images according to driving videos. Recent work performs animation on arbitrary objects using unsupervised methods and can relatively robustly perform motion transfer on human bodies. However, the complex representation of motion and unknown correspondence between human bodies often lead to issues such as distorted limbs and missing semantics, which make human animation challenging. In this paper, we propose a semantically guided, unsupervised method of motion transfer, which uses semantic information to model motion and appearance. Specifically, we use a pre-trained human parsing network to encode the rich and diverse foreground semantic information, thus generating fine details. Secondly, we use a cross-modal attention layer to learn the semantic region's correspondence between human bodies to guide the network in selecting appropriate input features, prompting the network to generate accurate results. Experiments demonstrate that our method outperforms state-of-the-art methods in motion-related metrics, while effectively addressing the problems of semantic missing and unclear limb structures prevalent in human motion transfer. These improvements can facilitate its applications in various fields, such as education and entertainment.
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows

We propose a novel modular inference approach combining two different generative models — generative adversarial networks (GAN) and normalizing flows — to approximate the posterior distribution of physics-based Bayesian inverse problems framed in high-dimensional ambient spaces. We dub the proposed framework GAN-Flow. The proposed method leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs to define a low-dimensional data-driven prior distribution. Once a trained GAN-prior is available, the inverse problem is solved entirely in the latent space of the GAN using variational Bayesian inference with normalizing flow-based variational distribution, which approximates low-dimensional posterior distribution by transforming realizations from the low-dimensional latent prior (Gaussian) to corresponding realizations of a low-dimensional variational posterior distribution. The trained GAN generator then maps realizations from this approximate posterior distribution in the latent space back to the high-dimensional ambient space. We also propose a two-stage training strategy for GAN-Flow wherein we train the two generative models sequentially. Thereafter, GAN-Flow can estimate the statistics of posterior-predictive quantities of interest at virtually no additional computational cost. The synergy between the two types of generative models allows us to overcome many challenges associated with the application of Bayesian inference to large-scale inverse problems, chief among which are describing an informative prior and sampling from the high-dimensional posterior. GAN-Flow does not involve Markov chain Monte Carlo simulation, making it particularly suitable for solving large-scale inverse problems. We demonstrate the efficacy and flexibility of GAN-Flow on various physics-based inverse problems of varying ambient dimensionality and prior knowledge using different types of GANs and normalizing flows. Notably, one of the applications we consider involves a 65,536-dimensional inverse problem of phase retrieval wherein an object is reconstructed from sparse noisy measurements of the magnitude of its Fourier transform.