Reviewerly/exHarmony huggingface dataset:
Clay minerals characterization of the Miduk ball mill output through spectral analysis

Porphyry copper deposits usually include typical alteration types, which are often composed of clay minerals. The mixing of clay minerals has a significant impact on their spectral absorption characteristics, and their identification and separation becomes complicated. One of the most accurate and widely used methods for identifying minerals is spectroscopy, which is a suitable alternative to traditional methods. Spectroscopy assists in identifying the mineral composition and classification. The aim of the following research is to quantitatively characterize clay minerals in mineral assemblage of ball mill output. Pure and composite samples were prepared through sampling from two alteration types and creating synthetic samples containing different percentages of clay minerals. Then they were subjected to spectral analysis in the range of 2500-350 nm and their output was subjected to necessary pre-processing. The results were used for classification by employing a hybrid algorithm. The analysis identified the following minerals in the Miduk deposit: hematite, carbonates, illite, biotite, kaolinite, chlorite and muscovite. Additionally, microscopic studies were conducted on the samples for further analysis. Finally, total and clay diffraction XRD analysis were used to validate the results.
Multi-Scale Semantic and Detail Extraction Network for Lightweight Person Re-Identification

Exploring multi-level information to obtain fine-grained features is the key factor to improve the performance of person re-identification (Re-ID). However, existing models for person Re-ID only focus on learning the high-level semantic information while neglecting the low-level detail information. To alleviate this issue, we propose a lightweight person Re-ID method termed Multi-Scale Semantic and Detail Extraction Network (MSDENet) to obtain robustness and discriminative feature representation for the Re-ID task. Specifically, we design a Series Channel-Spatial Attention (SCSA) and embed it into the lightweight backbone network to focus on the key parts of pedestrian images. Meanwhile, we propose a Multi-Scale Semantic and Detail Extraction (MSDE) method to extract multi-scale features of semantic information and detail information, which can effectively capture the feature diversity of pedestrian images. Furthermore, we design a Feature Enhancement Fusion (FEF), which enhances and fuses the fine-grained features of semantic extraction and detail extraction branches to better obtain the discriminative feature representation. Extensive experiments conducted on popular datasets Market1501, MSMT17, and CUHK03 demonstrate that the proposed MSDENet has competitive performance compared with the state-of-the-art methods.
3D detection transformer: Set prediction of objects using point clouds

Object detection in 3D scenes rely on two main methods: detection based on proposals (two-stage detectors) or detections based on anchors (single-stage detectors), similar to approaches for object detection in 2D. In this paper, we propose the 3DeTR framework that produces 3D detections without the use of anchors or proposals, allowing training of the entire neural network in an end-to-end manner. Raw point cloud scenes are augmented and input into distance-and-reflectiveness-based feature extractor to produce representative points. Then, a transformer encoder–decoder module learns the local object relations and global context to generate parallel detections, which are then passed to a set-based loss function to map predictions to the set of ground truth labels uniquely. The model's architecture produces 3D detections by regressing directly with the set of ground truths without the need for anchors or proposals, which are bottlenecks for object detection performances. We tested the framework on the KITTI Vision Benchmark Suite 3D object detection dataset, achieving results on par with the state-of-the-art: 80.37 AP on Cars (Moderate) class and 47.92 AP on Pedestrians (Moderate) class.
Making depthwise convolution SR-friendly via kernel attention injection

Despite the remarkable results achieved by deep convolutional neural networks (CNNs) in single image super-resolution (SISR), their computational cost increase exponentially as CNN models get deeper and wider. Consequently, depthwise separable convolutions (DSConvs) have emerged as the fundamental building basis for various contemporary lightweight network architectures. However, depthwise convolution is sub-optimal for restoring missing high-frequency details. In this paper, we commence with the vanilla depthwise convolution and progressively develop it to alleviate the above problem. Specifically, we introduce an effective method, kernel attention injection, to integrate intra-kernel correlation into the depthwise convolution layer by incorporating the learned kernel attention into the depthwise kernel. We find that the determinant is capable of capturing the correlation between diagonal elements of the depthwise kernel by summing the products of the diagonals. Therefore, we utilize the determinant as the kernel pooling method. Furthermore, we propose to remove the non-linear activation function behind the depthwise convolution (i.e., linear depthwise convolution) to preserve informative features for reconstructing faithful high-resolution (HR) images. Built on this recipe, we introduce kernel-attentive linear depthwise separable convolutions (KDSConvs), which exhibits promising super-resolution performance. Our experimental results underline the potential of depthwise convolution in super-resolution tasks by demonstrating that the proposed KDSConvs significantly outperforms DSConvs in terms of both quantitative measurements and visual quality. The code will be released at https://github.com/AwesomeHwang/KALDN.
Cyber Hygiene Practices from The Lens of Professional Youth in Malaysia

Researchers have recognized cyber hygiene as an essential factor in reducing cybersecurity breaches. Factors affecting cyber hygiene practices are cyber hygiene knowledge and demographic factors. Inconclusive research has been found to be concerned with the extent of expertise and demographic factors that may affect cyber hygiene practices. This current pilot study aims to diagnose the effect of knowledge and demographic factors on cyber hygiene practices among professional youth in Malaysia. Forty-one usable questionnaires were further analyzed. The result showed no significant differences between demographic factors and cyber hygiene practices; and no significant relationship between knowledge and cyber hygiene practices among professional youth in Malaysia.
A Deep Learning based Bangladeshi Vehicle Classification using Fine-Tuned Multi-class Vehicle Image Network (MVINet) Model

The classification of vehicles is an important task in many applications, including traffic management, surveillance, and law enforcement. In Bangladesh, vehicle classification has become increasingly challenging due to the rapid growth in the number of vehicles on the roads. In this study, a novel deep learning-based Bangladeshi vehicle classification model using fine-tuned Multi-class Vehicle Image Network (MVINet) based on DenseNet201 with four added layers has been proposed. Our approach uses convolutional neural networks (CNNs) to extract features from images of vehicles. To make our dataset, we employ a YOLO (You Only Look Once) model for initial vehicle detection, followed by manual filtering and augmentation of the dataset to improve classification accuracy. The proposed method demonstrates superior performance compared to manual data collection methods. We evaluated our approach using standard metrics such as accuracy, precision, recall, and F1 score. Our model achieved an accuracy of 97.06%, precision of 97.17%, recall of 97.24%, and F1-score of 97.12% indicating high performance in classifying vehicles. We also compared our approach with state-of-the-art techniques and found that the proposed deep learning-based approach outperformed them significantly. The proposed approach has the potential to improve traffic management and law enforcement in Bangladesh. It can be used to monitor traffic flow, detect traffic violations, and identify stolen vehicles. Overall, this study demonstrates the effectiveness of deep learning in Bangladeshi vehicle detection and classification and highlights the importance of using advanced technologies to address the challenges of a rapidly changing transportation landscape.
LLOD:A Object Detection Method Under Low-Light Condition by Feature Enhancement and Fusion

In this paper, we propose a novel approach to achieve our goal of implementing object detection under low-light conditions by incorporating several innovative components. First, we design a new feature fusion unit that enables semantic features to better align with target inspection characteristics. Second, we introduce a novel low-light enhancement encoder unit to augment the semantic features of low-light images. Third, due to the limited availability of large-scale datasets for low-light scenes, we train an enhancement model first, which can effectively assist object detection in low-light conditions through feature enhancement. Our method demonstrates promising results in addressing the challenges of object detection under poor lighting conditions, providing a valuable contribution to the field of computer vision and enhancing the performance of object detection tasks in low-light environments.
Automatic Generation of Network Micro-Segmentation Policies for Cloud Environments

Network segmentation is a technology that effectively defends against network threats, especially lateral movement attacks. However, the dynamics of the networks and the blurring of boundaries make it difficult for administrators to develop segmentation policies. In this paper, we propose a solution for the automatic generation of network micro-segmentation policies for cloud environments. We use dynamic and static analysis algorithms to obtain legitimate access relationships between services, which are used to construct network segments. Moreover, we design a policy engine to automatically convert network segments into low-level rules to achieve micro-segmentation policy recommendations. Finally, the results of our evaluation show that our scheme has a high level of accuracy and coverage.
Speech Recognition Method Based on Deep Learning of Artificial Intelligence

Under the influence of information, network and intelligent high-speed development situation, China's intelligent technology and other aspects have made great progress and achievements, derived a lot of advanced artificial intelligence technology, machine learning technology and deep learning technology, etc., to promote the development of intelligence and information in major fields. Artificial intelligence deep learning is the fusion of artificial intelligence technology and machine learning technology, which lays the foundation for the reform and innovation of artificial voice intelligent recognition technology and intelligent robot technology. So in order to improve the application level of intelligent speech recognition technology, it is necessary to continuously optimize the speech recognition method based on AI deep learning. In this regard, according to the relevant literature, this paper addresses the problem that phoneme features of varying duration are generated during the propagation of speech signals, and these features affect the correct rate of speech recognition, and the phoneme features of different lengths are standardized based on the deep learning research mentioned in this paper with BLSTM-CTC as an example. By evaluating the model on the Thchs30 and ST-CMDS datasets, the results show that the MCFN-based BLSTM-CTC speech recognition model has a reduced recognition word error rate compared with the traditional speech recognition model.
Deep Joint Source-Channel Coding for Image Transmission With Visual Protection

Joint source-channel coding (JSCC) has achieved great success due to the introduction of deep learning (DL). Compared to traditional separate source-channel coding (SSCC) schemes, the advantages of DL-based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and relief of “cliff effect”. However, it is difficult to couple existing secure communication mechanisms (e.g., encryption-decryption mechanism) with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of this emerging technology. To this end, our paper proposes a novel method called DL-based joint protection and source-channel coding (DJPSCC) for images that can successfully protect the visual content of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is to use a neural network to conduct visual protection, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJPSCC method learns: 1) deep neural networks for image protection and image deprotection, and 2) an effective DJSCC network for image transmission in the protected domain. Compared to existing source protection methods applied with DJSCC transmission, the DJPSCC method achieves much better reconstruction performance.
Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches

In the elderly population, falls are one of the leading causes of fatal and non-fatal injuries.Fall detection and early alarms play an important role in mitigating the negative effects of falls, especially given the growing proportion of the elderly population.Due to their non-intrusive nature, data availability, and low deployment costs, RGB videos have been used in many previous studies to detect falls.The RGB data, however, can be affected by background environment changes, resulting in non-recognition.To overcome these challenges, many researchers propose extracting skeleton data from RGB videos and using it for fall detection.Although there have been multiple surveys on fall detection, most of them focus on assessing fall detection systems using different kinds of sensors, and a comprehensive evaluation of skeleton-based fall detection in RGB videos is lacking.In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms.Further, we present several skeleton-based fall detection techniques and their performance results on various benchmark datasets, along with challenges and future directions in this field.
Extreme Weather and PV Performance

The impact of extreme weather events on photovoltaic (PV) performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the National Renewable Energy Laboratory's PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1% of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60% loss, showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events above 90 km/h, by comparing PLR before and after these events. Similarly, hail events caused higher PLR for hail sizes of and above 25 mm indicating that more stringent hail testing for PV modules is required. In addition, very high snow loads may also cause increased PLRs, but more data are required to better quantify the impact. These findings illustrate the substantial risk extreme weather events pose short- and long-term to fielded PV installations.
Improved Denclue Outlier Detection Algorithm With Differential Privacy and Attribute Fuzzy Priority Relation Ordering

Outlier detection is an important method in data mining.Although Denclue algorithm is particularly good at finding clusters of arbitrary shape and detecting outliers, it does not protect the user's privacy well in the operation process.In this paper, differential privacy technology is introduced into Denclue algorithm to ensure the privacy security in the application of Denclue algorithm and outlier detection.Firstly, the differential privacy technology is used to add the Laplacian noise to the density to realize the sensitive information hiding between data objects.Secondly, in order to compensate for the decrease of outlier detection accuracy caused by noise, the information entropy weight distance was introduced to amplify the influence of important attributes in the algorithm, and the density function of entropy weight distance was used to calculate each data point.Finally, through the method of ordering fuzzy priority relation, a new measure index is defined by analogy to measure the degree of outliers between attributes.According to the measure index, the attributes are reordered and the weight distance of information entropy is improved.A differential privacy the Denclue outlier detection algorithm based on attribute fuzzy priority ordering (EAF-DP-Denclue) is proposed.The numerical results of the experiment show that the performance of EAF-DP-Denclue is more than that of traditional algorithms, and the identification process of EAF-DP-Denclue protects sensitive privacy information, and is better than that of the DP-DBScan outlier detection algorithm.
Journey to the Center of Software Supply Chain Attacks

This article discusses open source software supply chain attacks and proposes a general taxonomy describing how attackers conduct them. We then provide a list of safeguards to mitigate such attacks. We present our tool Risk Explorer for Software Supply Chains to explore such information, and we discuss its industrial use-cases.
Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine

Abstract Objective Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. Materials and methods Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. Results The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. Discussion Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. Conclusion This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.
Image Matching and Localization Based on Fusion of Handcrafted and Deep Features

With the popularity of vision sensors and the advancement of image processing technology, machine vision tasks based on image matching have received widespread attention. Matching methods based on handcrafted features have strong interpretability, but they are susceptible to noise in complex environments. In addition, deep learning has shown potential in improving matching performance, but at the cost of large amounts of specific samples and computing resources. In recent years, there have been efforts to explore how to combine handcrafted features and deep features to enhance the performance, but most of them only consider their sequential relationship. To address these challenges, we propose a novel image matching scheme with a fine-grained decision-level fusion (DLF) method that fully considers the judgments of handcrafted features and deep features for potential correspondence points, effectively combining handcrafted and deep features in a novel way. We also train generic features on public datasets to handle unseen scenes. We use RootSIFT as prior knowledge to guide the extraction of deep features, significantly reducing computational overhead. In addition, we carefully design preprocessing steps by incorporating device attitude information. We conduct image matching experiments on public datasets and our own captured dataset. Compared to the existing methods, as illustrated in our experimental results, the proposed scheme achieves an overall 2.5– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\times $ </tex-math></inline-formula> more correct matches with improved robustness. Finally, we design a target localization algorithm incorporating an unmanned aerial vehicle (UAV) to achieve accurate and efficient localization using the proposed matching scheme and weighted least squares (WLS) estimation algorithm, demonstrating the versatility of the proposed image matching scheme.
Partial multi-label feature selection via subspace optimization

Feature selection is an effective way to improve the model learning performance while being a challenging problem in the Partial Multi-label Learning (PML). Different from multi-label learning, PML is closer to reality, which means the annotators may assign irrelevant labels at least one in the set of candidate labels for each instance. However, previous PML researches only design the model on label disambiguation but ignore to consider the problem as point of finding discriminative features in the noisy scenario. In this paper, a novel feature selection method named PML-FSSO is proposed which utilizes the theory of linear weighted to jointly consider the label subspace and feature subspace. The influence of noisy labels is eliminated by decomposing the label matrix into the low-dimensional space, and we call the low-dimensional space as the label subspace. Then, the feature matrix is not only designed to be the denoising matrix as the feature subspace, but also to steer the direction for label disambiguation. Finally, the coherent subspace is constructed through the shared coefficient weight matrix which is used in the linear weighted method. Extensive experiments clearly validate the effectiveness of PML-FSSO in improving the state-of-the-art feature selection methods in the field of partial multi-label learning.
Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration

Harnessing commonsense knowledge poses a significant challenge for machine comprehension systems. This paper primarily focuses on incorporating a specific subset of commonsense knowledge, namely, script knowledge. Script knowledge is about sequences of actions that are typically performed by individuals in everyday life. Our experiments were centered around the MCScript dataset, which was the basis of the SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. As a baseline, we utilized our Three-Way Attentive Networks (TriANs) framework to model the interactions among passages, questions, and answers. Building upon the TriAN, we proposed to: (1) integrate a pre-trained language model to capture script knowledge; (2) introduce multi-layer attention to facilitate multi-hop reasoning; and (3) incorporate positional embeddings to enhance the model’s capacity for event-ordering reasoning. In this paper, we present our proposed methods and prove their efficacy in improving script knowledge integration and reasoning.