An automatic Darknet-based immunohistochemical scoring system for IL-24 in lung cancer

Abstract: Immunohistochemical (IHC) detection is of critical importance in the pathological diagnosis of lung cancer. Interleukin-24 (IL-24) is a significant predictive and prognostic marker in IHC detection, which can help to characterize the tumor and predict the clinical course. To determine the score of the IL-24 expression, pathologists will assess the positive area and staining intensity through a microscope and then use a semi-quantitative assessment method to assign a score for each IHC image of lung cancer. However, this process is a time-consuming, imprecise, and subjective process, which can result in inter- and intra-observer discrepancies. Meanwhile, The performance of computer-aided diagnosis (CAD) systems for IHC scoring depends on the quality of manually extracted features. For example, many advanced methods require precise extraction of features of cell nuclei and membranes. However, the complex background and overlapping cells in lung cancer IHC images present significant challenges for segmentation, which can affect the accuracy of the final system's predicted scoring results. In this paper, an automatic Darknet-based IHC scoring system for IL-24 in lung cancer is proposed. Firstly, the original IHC images of lung cancer are blocked and the features are concentrated by a "block attention mechanism, which can reduce the computational burden of the analysis of millions of pixels in IHC images. The blocked images are then inputted into a Darknet-based scoring network, which incorporates a novel feature extraction backbone and loss function to obtain the final scores. To the best of our knowledge, this is the first end-to-end system that directly outputs a clinical score using lung cancer IL-24 IHC images as input. We have constructed a dataset of 5000 manually annotated IL-24 IHC images of lung cancer obtained from the Institute of Life Science and Bioengineering at Beijing Jiaotong University. We will present experimental results to demonstrate the feasibility of our proposed method, which can greatly assist with the clinical diagnosis and treatment of lung cancer.


Autonomous hierarchy creation for computationally feasible near-optimal path planning in large environments

Abstract: Mobile robots have increasingly been used in warehouses, factories, open spaces, on roads, and urban areas to transport goods and people. Path planning in complex and large environments using classical graph-based search is computationally too intensive. One of the main challenges of mobile robotics is describing the large robot's workspaces with a reduction of graph complexity and improvement of path planning efficiency. A representation by a hierarchical graph (H-graph) facilitates graph creation and reduces the complexity of path planning. In this paper, we present an algorithm for autonomously generating a hierarchical graph of the environment from floor plans. The hierarchical abstraction depicts the environment in levels, from the most detailed to the most abstract representation of the environment, where pre-computed partial paths at the most detailed level are graph edges at a higher level. We use the E* algorithm to find partial paths in the most detailed abstraction level, and we propose the extraction of higher levels automatically from lower levels. We verified the proposed H-graph creation on three cases: our University premises as an example of a multi-building environment, a large warehouse with a lot of shelves, and an outdoor urban environment which is represented by a city map. All cases result in five abstract levels. The result shows that the path planning algorithm searches about 50 to 800 times fewer nodes when using the H-graph as the environment representation than when using a plain graph representing the entire environment as an occupancy grid map.


Evaluation method for the availability of solar energy resources in road areas before route corridor planning

Abstract: The full utilization of solar energy resources along the road is an effective method to solve the energy shortage in transportation. The key to this is an accurate evaluation of solar energy resources, which provides the rationale for the optimal location of road photovoltaic (PV) projects. However, determining the availability of solar energy resources in road areas before route corridor planning remains difficult. To address the issue, this paper developed a standardized method. Firstly, this paper analyzed the critical factors affecting the availability of solar energy resources. They were meteorology, topography, land use type, geology, and location. On that basis, we developed a four-level evaluation indicator system. Then, it proposed a multi-indicator evaluation method based on the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). It used AHP to calculate the weights of each indicator, with solar radiation being the most important. It used GIS to analyze and overlay the spatial distribution of multiple indicators to obtain the map of suitability grades, which could be used to route corridor selection for road PV projects. It also used a case study to demonstrate how the method is applied. The findings show that the method can accurately evaluate the availability of solar energy resources. Furthermore, it studied the appropriate division of evaluation units and it is advised that they shouldn't be larger than 30 m × 30 m. These results provide guidance as a reference for the application of road PV projects and site selection for route corridors worldwide. That will promote the integration of transportation and energy in the future.


Exploiting fuzzy rough entropy to detect anomalies

Abstract: Anomaly detection has been used in a wide range of fields. However, most of the current detection methods are only applicable to certain data, ignoring uncertain information such as fuzziness in the data. Fuzzy rough set theory, as an essential mathematical model for granular computing, provides an effective method for processing uncertain data such as fuzziness. Fuzzy rough entropy has been proposed in fuzzy rough set theory and has been employed successfully in data analysis tasks such as feature selection. However, it mainly uses the intersection operation, which may not effectively reflect the similarity between high-dimensional objects. In response to the two challenges mentioned above, distance-based fuzzy rough entropy and its correlation measures are proposed. Further, the proposed fuzzy rough entropy is used to construct the anomaly detection model and the Fuzzy Rough Entropy-based Anomaly Detection (FREAD) algorithm is designed. Finally, the FREAD algorithm is compared and analyzed with some mainstream anomaly detection algorithms (including COF, DIS, INFLO, LDOF, LoOP, MIX, ODIN, SRO, and VarE algorithms) on some publicly available datasets. Experimental results indicate that the FREAD algorithm significantly outperforms other algorithms in terms of performance and flexibility. The code is publicly available online at https://github.com/optimusprimeyy/FREAD.


Efficient ray sampling for radiance fields reconstruction

Abstract: Accelerating the training process of neural radiance field holds substantial practical value. The ray sampling strategy profoundly influences the convergence of this neural network. Therefore, more efficient ray sampling can directly augment the training efficiency of existing NeRF models. We propose a novel ray sampling approach for neural radiance field that improves training efficiency while retaining photorealistic rendering results. First, we analyze the relationship between the pixel loss distribution of sampled rays and rendering quality. This reveals redundancy in the original NeRF's uniform ray sampling. Guided by this finding, we develop a sampling method leveraging pixel regions and depth boundaries. Our main idea is to sample fewer rays in training views, yet with each ray more informative for scene fitting. Sampling probability increases in pixel areas exhibiting significant color and depth variation, greatly reducing wasteful rays from other regions without sacrificing precision. Through this method, not only can the convergence of the network be accelerated, but the spatial geometry of a scene can also be perceived more accurately. Rendering outputs are enhanced, especially for texture-complex regions. Experiments demonstrate that our method significantly outperforms state-of-the-art techniques on public benchmark datasets.


An active semi-supervised transfer learning method for robot pose error prediction and compensation

Abstract: Robots are widely employed in industrial settings owing to their efficiency, flexibility, and extensive operational ranges. However, their application in high-precision scenarios is limited owing to their low absolute accuracies. Existing methods suffer from high measurement costs, and limited applicability and accuracy. To address these issues, an active semi supervised transfer learning method (ASTL) is introduced. The pose error prediction problem was modelled as a transfer learning paradigm for the first time. It leverages the proposed multi-stage greedy sampling (MGS) for informed sample labelling combined with coarse calibration and semi supervised transfer learning (STL) to embed theoretical knowledge for globally accurate predictions. The proposed method is compared with other prediction and compensation approaches for four robotic motion tasks. It significantly reduces the time consumption by approximately 89.3% compared with direct measurements and achieves a maximum reduction of approximately 90% in robot pose errors. This substantial enhancement in pose accuracy promotes high-quality applications of robots in the industrial sector.


DODFMiner: An automated tool for Named Entity Recognition from Official Gazettes

Abstract: Official gazettes are documents published by governments to publicize their actions, spanning long periods of time and making an important transparency mechanism. These documents have information on laws, contracts, and bidding processes, as well as on civil servants and their careers in public service. Automatic information extraction of these documents may contribute to public transparency, with two tasks being especially useful: the classification of the different segments of these documents, the so called acts; and the Named Entity Recognition (NER) within the acts. The variety of official gazettes and their patterns brings up the necessity of constructing different tools for specific gazettes. In this paper, we propose DODFMiner, a command-line interface tool to classify acts and extract named entities from the Official Gazette of the Federal District. The tool follows a 3-step approach: the pre-processing of the input data; text classification using rule-based systems with regular expressions; and NER with Machine Learning algorithms. It allows users to input JSON files and receive CSV as output, providing information that allows users to track government procurements through years, contracts duration and total amount, among others. We also propose a set of experiments to support the choice of models included in the tool, covering the classification and NER steps. Text classification achieved a mean F1-score of 0.778, while to the NER, we compared 3 different architectures, CRF with a mean F1-score of 0.851, CNN-biLSTM-CRF with 0.787 and CNN-CNN-LSTM with 0.841.


Convolution finite element based digital image correlation for displacement and strain measurements

Abstract: This work presents a novel global digital image correlation (DIC) method, based on a newly developed convolution finite element (C-FE) approximation. The convolution approximation can rely on the mesh of linear finite elements and enables arbitrarily high order approximations without adding more degrees of freedom. Therefore, the C-FE based DIC can be more accurate than the usual FE based DIC by providing highly smooth and accurate displacement and strain results with the same element size. The detailed formulation and implementation of the method have been discussed in this work. The controlling parameters in the method include polynomial order, patch size, and dilation. A general choice of the parameters and their potential adaptivity have been discussed. The proposed DIC method has been tested by several representative examples, including the DIC challenge 2.0 benchmark problems, with comparison to the usual FE based DIC. C-FE outperformed FE in all the DIC results for the tested examples. This work demonstrates the potential of C-FE and opens a new avenue to enable highly smooth, accurate, and robust DIC analysis for full-field displacement and strain measurements.


A novel efficient Rank-Revealing QR matrix and Schur decomposition method for big data mining and clustering (RRQR-SDM)

Abstract: Big Data is an emerging technology with enormous potential to develop business and its administration. Due to the enormous volume, efficient data mining and clustering methods are crucial to extracting meaningful insights and patterns from large-scale datasets. Problems may arise from the need to analyze, capture, share, store, and visualize the data. Several methods have already been proposed for mining knowledge from big data. It is practically inefficient or impossible to handle these massive data using the proposed methods in a single machine because big data are frequently acquired from dispersed locations and stored on several machines. Matrix decomposition is one of the critical strategies to retrieve knowledge from diverse, noisy, huge data generated by modern applications and stored in dispersed locations. This study proposes a novel approach called the Rank-Revealing QR Matrix and Schur Decomposition Method (RRQR-SDM) specifically designed for big data mining and clustering tasks. The RRQR-SDM is designed to reveal the rank of the data matrix in a computationally efficient manner by using a modified QR decomposition, eliminating the need for expensive Singular Value Decomposition (SVD) computations. The proposed RRQR-SDM method offers several advantages over existing approaches. Firstly, exploiting the inherent low-rank structure reduces the computational complexity associated with large-scale datasets. By revealing the rank of the input matrix, it enables dimensionality reduction and efficient data compression. Secondly, the Schur decomposition enhances the interpretability of the data by providing a clear separation between the relevant and irrelevant components. This feature makes the RRQR-SDM method particularly suitable for data mining and clustering tasks where identifying the most significant features is essential. To evaluate the performance of the RRQR-SDM method, extensive experiments were conducted on various big data datasets. The results demonstrate that the proposed method outperforms state-of-the-art computational efficiency and clustering accuracy techniques.


Joint group and pairwise localities embedding for feature extraction

Abstract: Many practical applications generate high-dimensional data, which poses challenges in terms of computational time and storage. To address this issue, feature extraction has become a popular research topic. Learning by way of graph embedding is useful for discovering potential intrinsic low-dimensional structures and has thus gained wide attention. However, traditional embedding models typically utilize only one graph or one type of multiple graphs to capture local relationships, which may be insufficient for high-dimensional data that may exhibit different kinds of local relationships. To overcome this drawback, we developed a joint embedding framework that incorporates multiple types of graphs. Under this framework, a novel joint group and pairwise locality embedding model (GPE) is proposed. The GPE model has the following distinctive merits: (1) it simultaneously incorporates simple graphs, hypergraphs, and probabilistic hypergraphs, enabling the use of not only one type of multiple graphs but also multiple types of multiple graphs; (2) it can leverage both group relationships and pairwise graphs to discover local information during feature extraction; and (3) its objective function can be solved using an alternating optimization strategy, which involves solving eigenvalue problems and quadratic programming problems, resulting in very fast convergence. Finally, we arranged classification tasks and clustering tasks on several high-dimensional real-world datasets, and the experimental results prove that the identification capability of GPE is encouraging.


Preserving data privacy in machine learning systems

Abstract: The wide adoption of Machine Learning to solve a large set of real-life problems came with the need to collect and process large volumes of data, some of which are considered personal and sensitive, raising serious concerns about data protection. Privacy-enhancing technologies (PETs) are often indicated as a solution to protect personal data and to achieve a general trustworthiness as required by current EU regulations on data protection and AI. However, an off-the-shelf application of PETs is insufficient to ensure a high-quality of data protection, which one needs to understand. This work systematically discusses the risks against data protection in modern Machine Learning systems taking the original perspective of the data owners, who are those who hold the various data sets, data models, or both, throughout the machine learning life cycle and considering the different Machine Learning architectures. It argues that the origin of the threats, the risks against the data, and the level of protection offered by PETs depend on the data processing phase, the role of the parties involved, and the architecture where the machine learning systems are deployed. By offering a framework in which to discuss privacy and confidentiality risks for data owners and by identifying and assessing privacy-preserving countermeasures for machine learning, this work could facilitate the discussion about compliance with EU regulations and directives. We discuss current challenges and research questions that are still unsolved in the field. In this respect, this paper provides researchers and developers working on machine learning with a comprehensive body of knowledge to let them advance in the science of data protection in machine learning field as well as in closely related fields such as Artificial Intelligence.


Anomaly detection of aviation data bus based on SAE and IMD

Abstract: To detect remote terminal (RT) spoofing attacks on MIL-STD-1553B data bus and prevent the network paralysis of integrated avionics system (IAS) caused by misjudgment, an anomaly detection method of aviation data bus based on the combination of sparse autoencoder (SAE) and integrated mahalanobis distance (IMD) is proposed. Aiming at the communication traffic training set with only normal data, an unsupervised learning algorithm SAE is used to train a model that only represents normal behavior. To combine the feature information of each layer within SAE, the IMD, which can measure the similarity between data characteristics, is used to obtain the anomaly score of test data, and the comprehensive anomaly score (CAS) is obtained by considering the reconstruction error between SAE input and output. To solve the problem that data distribution and detection requirements were not considered in a single threshold, a heuristic multi-threshold selection method is proposed, which maximizes the performance of the classifier by considering the accuracy, youden index (YI), and F1. The experimental results demonstrate the effectiveness and feasibility of the method.


Causal attribution of agricultural expansion in a small island system using approximate Bayesian computation

Abstract: The extent and arrangement of land cover types on our planet directly affects biodiversity, carbon storage, water quality, and many other critical social and ecological conditions at virtually all scales. Given the fundamental importance of land cover, a key mandate for land system scientists is to describe the mechanisms by which pertinent cover types spread and shrink. Identifying causal drivers of change is challenging however, because land systems, such as small-scale agricultural communities, do not lend themselves well to controlled experimentation for logistical and ethical reasons. Even natural experiments in these systems can produce only limited causal inference as they often contain unobserved confounding drivers of land cover change and complex feedbacks between drivers and outcomes. Land system scientists commonly grapple with this complexity by using computer simulations to explicitly delineate hypothesized causal pathways that could have resulted in observed land cover change. Yet, land system science lacks a systematic method for comparing multiple hypothesized pathways and quantifying the probability that a given simulated causal process was in fact responsible for the patterns observed. Here we use a case study of agricultural expansion in Pemba, Tanzania to demonstrate how approximate Bayesian computation (ABC) provides a straightforward solution to this methodological gap. Specifically, we pair an individual-based simulation of land cover change in Pemba with ABC to probabilistically estimate the likelihood that observed deforestation from 2018 to 2021 was driven by soil degradation rather than external market forces. Using this approach, we can show not only how well a specific hypothesized mechanism fits with empirical data on land cover change, but we can also quantify the range of other mechanisms that could have reasonably produced the same outcome (i.e. equifinality). While ABC was developed for use in population genetics, we argue that it is particularly promising as a tool for causal inference for land system science given the wealth of data available in the satellite record. Thus, this paper demonstrates a robust process for identifying the emergent landscape-level signatures of complex social-ecological mechanisms.


Defining and visualizing process execution variants from partially ordered event data

Abstract: The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions that are control flow equivalent. Such variants are an integral concept for process mining and are used, e.g., for data exploration and filtering. We propose high-level and low-level variants covering different levels of abstraction and present corresponding visualizations. Compared to existing variant visualizations, we support partially ordered event data and allow for heterogeneous temporal information per event, i.e., we support both time intervals and time points. We evaluate our contributions using automated experiments showing practical applicability to real-life event data. Finally, we present a user study indicating significantly improved usefulness and ease of use of the proposed high-level variant visualization compared to existing variant visualizations for typical analysis tasks.


Augmented reality guided autonomous assembly system: A novel framework for assembly sequence input validations and creation of virtual content for AR instructions development

Abstract: In recent years, industries are striving to adapt immersive technologies in the real-time assembly shop floor setting to achieve effective outcomes in terms of enabling the worker to handle the complex cognitive assembly task execution/training circumstances. The manufacturers are presently facing difficulties with the process of Augmented reality (AR) instruction creation for the required product assembly system. The existing AR instruction development process demands highly skilled experts and more time consumption. Hence, the manufacturers are presently relying on third parties to acquire the required AR instructions by providing the necessary assembly particulars as input. Where, the cost of developing AR instruction is another big challenging concern. Predominantly, the significance of providing exact assembly input is vital for the experts to develop the appropriate AR content without any errors. An automated approach is developed in this study to resolve the difficulties with assembly sequence input for the AR instructions development process. The automated approach affords four different provisions that include validation of an assembly sequence input prior to the AR instructions development stage, suggestions for assembly sequence input generation, simulation of validated assembly sequence in the Virtual reality (VR) environment for user perception, and generation of virtual content for AR visualizations. Subsequently, the generated digital contents from the automated approach are imparted into the AR Unity platform to develop the required AR instructions for the product assembly system. Notably, this study explores the AR customized marker technique to visualize the AR content in the user's view of the physical context. Finally, the AR guidelines are built as apps for the selected 11-part assembly and deployed on the shop floor for the user to perceive the AR content through visualization devices for execution of assembly operations in a real-time context. In addition, the effectiveness of the automated approach is analyzed with the existing approach, and the consequences are explored through the process of receiving feedback from the experts about the error rate, cognitive level, and creation time.


Brain-inspired GCN: Modularity-based Siamese simple graph convolutional networks

Abstract: In graph representation learning, Graph Convolutional Networks (GCNs) and their variants have received much attention. However, GCNs encounter oversmoothing as the models get deeper, limiting their ability to aggregate node representations within high-order neighborhood. Inspired by the modular structure of the brain network, we propose Modularity-based Siamese Simple Graph Convolution (MS-SGC), a Siamese network architecture that incorporates the characteristics of brain modular structure into graph convolutional networks. Spectral clustering is leveraged to detect the modular structure in the graph, and then the weight of cross edges between modules is lowered. Siamese network is adopted to combine the modularity-preserved graph representation with the original graph representation, improving classification performance and reducing oversmoothing. Furthermore, a graph convolution method that functions as a linear low-pass graph filter is elaborated through spectral analysis to preserve the similarity of nodes within the same module and further alleviate the oversmoothing problem. We validate the effectiveness of MS-SGC on citation networks and extend our experimental analysis to various downstream tasks. Extensive experiments demonstrate that MS-SGC outperforms state-of-the-arts while reducing the time and computation complexity for node classification. Moreover, MS-SGC achieves competitive performance compared to other state-of-the-arts in node clustering, community prediction and text classification tasks.


Grassmannians of codes

Abstract: Consider the point line-geometry Pt(n,k) having as points all the [n,k]-linear codes having minimum dual distance at least t+1 and where two points X and Y are collinear whenever X∩Y is a [n,k−1]-linear code having minimum dual distance at least t+1. We are interested in the collinearity graph Λt(n,k) of Pt(n,k). The graph Λt(n,k) is a subgraph of the Grassmann graph and also a subgraph of the graph Δt(n,k) of the linear codes having minimum dual distance at least t+1 introduced in [9]. We shall study the structure of Λt(n,k) in relation to that of Δt(n,k) and we will characterize the set of its isolated vertices. We will then focus on Λ1(n,k) and Λ2(n,k) providing necessary and sufficient conditions for them to be connected.


Mobile robot’s path-planning and path-tracking in static and dynamic environments: Dynamic programming approach

Abstract: One of the main issues in robotics systems is planning and tracking a safe path in diverse environments. This paper addresses an optimal methodology for generating the desired path and thereafter forces the mobile robot to follow the designed reference path. The proposed technique has the potential to tackle the inherent challenges and intricacies of the environment, enabling the robot to navigate both static and dynamic workspaces. Several simulations are exploited to verify the captured theoretical results. Three cases were examined in a static environment, achieving average performance metrics for reference signals in the X- and Y-directions, and heading tracking of 99.62%, 99.64%, and 95.08%, respectively. For the dynamic environment, two cases were studied, with average performance in following the Y-direction and heading angle recorded as 97.58% and 86.79%, respectively. Simulation results show that the controller calculated the optimal signal with an average computational time of 7 ms per iteration for static environments and 21.3 ms per iteration for dynamic environments.


Are virtual reality applications effective for construction safety training and education? A systematic review and meta-analysis

Abstract: Virtual reality (VR) gains attention in construction safety training because it allows users to simulate real activities without the risks of real activities. However, a literary work comprehensively describing the effectiveness of VR in construction safety training and education (CSTE) is lacking.This study provides a systematic review of the research related to VR applications for CSTE over the past decade using meta-analysis techniques. Standardized mean differences between traditional training methods and VR training were grouped by measurement. Potential moderators possibly affecting the effectiveness of VR in CSTE were analyzed.Results showed that VR is significantly more effective in construction training and education than traditional methods. The effectiveness of VR was 0.593, 0.432, and 0.777 higher than that of traditional methods for behaviors, skills, and experience measurements, respectively. The training context and mean work experience of trainees were two important moderators that significantly affected the effectiveness of VR in CSTE (p < 0.001).The presented results suggested the need for targeted development and management of VR technology in the construction industry and the early promotion of VR for general safety training among young, inexperienced construction workers.


Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain

Abstract: VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey's (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA. Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (~2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (~400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data. The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities.


Geothermal Play Fairway Analysis, Part 2: GIS methodology

Abstract: Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.


RepDehazeNet: Dual subnets image dehazing network based on structural re-parameterization

Abstract: In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images.


A supervised parallel optimisation framework for metaheuristic algorithms

Abstract: A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption - Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.


A transition from porphyry Mo to vein-style Zn-Pb mineralization: Insights from hydrothermal fluid evolution of the Chalukou deposit, NE China

Abstract: To reveal the hydrothermal evolution from porphyry Mo to vein-style Zn-Pb mineralization, the Chalukou deposit in NE China with these two typical mineralization overprinted is selected for detailed research. From early to late hydrothermal veins, the quartz cathodoluminescence intensities change from bright to dark and the textures also evolve. The Ti concentrations of quartz in these veins change from high to moderate and then to low values. The fluid inclusion petrography, microthermometry and stable isotope analyses indicate intermediate-density magma-derived fluids (δ18Owater = +4.7 to + 6.2‰, δD = −132 to −136‰) exsolved at high temperatures (560° to 600 °C) with low to moderate salinity (2.4 to 8.0 wt% NaCl equiv) containing minor CO2 (<5.7 mol %). Early minor molybdenite occurred under lithostatic conditions at high temperatures (540° to 580 °C) during fluids pH change and early magnetite precipitation. Later main molybdenite precipitation at moderate temperatures (350° to 430 °C), result from fluid cooling and involving of meteoric water under the transition from lithostatic to hydrostatic conditions. The precipitation of pyrite in transitional veins at moderate temperatures (330° to 420 °C), and the late sphalerite-galena from a lower-temperature fluid (180° to 300 °C) is consistent with the cooling of magmatic fluid, and gradually dilution with meteoric water under hydrostatic conditions. It is deduced the transition from porphyry Mo to vein-style Zn-Pb mineralization is likely driven by progressive decrease in temperature of the magmatic fluids and the increasing influx of meteoric water.


3D face reconstruction from a single image based on hybrid-level contextual information with weak supervision

Abstract: Currently, deep learning-based 3D face reconstruction methods have shown promising results. However, they ignore the contextual information of the face, which is a topologically unified entirety. This paper proposes a 3D face reconstruction approach based on hybrid-level contextual information. Firstly, we suggest a regression network with contextual modeling capability at the feature level, PPR-CNet, which adopts a preferential parameter regression to regress the 3DMM parameters dynamically based on their various impacts on the reconstructed 3D face. Furthermore, we design a contextual landmark loss to constrain the face geometry at the landmark level. We introduce a differentiable renderer combined with multiple loss functions for weakly-supervised training. Quantitative experiments on two benchmarks show our method outperforms several SOTA methods. Extensive qualitative experiments indicate that our method performs efficiently in realism, facial proportion, and occlusion.


Trust in a ‘trust-free’ system: Blockchain acceptance in the banking and finance sector

Abstract: Blockchain is a disruptive technology that revolutionizes traditional business models and revamps relationships between financial transaction counterparties. While the volume of literature on blockchains has been rapidly growing in recent years, research on the relationship between trust and blockchain acceptance at the individual level is underexplored. Our study aims to fill this research gap by investigating the determinants of blockchain acceptance in the banking and finance sector from the affordance theory. We extend UTAUT model with trust-related constructs (trust in technology and trust among communities), advocate a research framework to examine the impact of trust on blockchain acceptance. Quantitative data were collected from 218 employees at Chinese banks, and structural equation modeling was applied to analyze the data. Our empirical analysis revealed that these trust relationships are positively associated with the intention to accept blockchain technology. The results of polynomial regressions and the response surface support the significance of the trust congruence effect. We make a theoretical contribution by extending the UTAUT with two trust relationships in a Fintech context. The results can also be used as a guideline for the practitioners, inspiring them to consider the critical determinants of blockchains acceptance in blockchain implementation.


Robust weapon detection in dark environments using Yolov7-DarkVision

Abstract: Currently, weapon detection through video surveillance has been extensively studied using deep learning techniques by researchers. However, limited attention has been given to the detection of weapons in night time or dark scenarios. This paper aims to address this gap by proposing a novel approach for weapon detection specifically modified for low-light conditions. The authors demonstrate accurate and robust detection of weapons in challenging nighttime environments by modifying the deep learning model YOLOv7. The YOLOv7-DarkVision model has been developed by combining a brightening algorithm with the advanced image processing techniques and architecture of YOLOv7. A dataset of 15,367 images were collected for training of the model, along with five dark videos from various sources for performance evaluation. The derived detection model, which has a precision score of 95.50% and an F1-Score of 93.41%, performs absolutely well as a weapon detector.


SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification

Abstract: The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI.


Decentralised and collaborative machine learning framework for IoT

Abstract: Decentralised machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralised and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.


Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?

Abstract: As a crucial component of the model chain, which facilitates irradiance-to-power conversion during solar resource assessment and forecasting, separation modeling continues to draw attention in both academia and industry. However, when evaluating even the best separation model today, one can quickly recognize its limited accuracy compared to other energy meteorology models such as transposition models. The task of separating global horizontal irradiance into diffuse and beam components does not seem soluble by any derivative effort aimed at tweaking the existing semi-physical models. As a result, an appealing alternative is to consider end-to-end data-driven models, which have demonstrated predictive capability in scenarios where the volume of data is substantial and the interaction among variables is complex. This work discusses the separation of 1-min irradiance from a data-driven perspective. In this preliminary study, a total of 10 representative data-driven separation models are developed and compared to the state-of-the-art semi-physical models, using a comprehensive 1-min irradiance database that spans five years and covers numerous climate types. The average error of the data-driven models is found to be 15.2% to 22.6% lower than that of the semi-physical models for training locations and 7.9% to 17.6% lower for completely unseen locations. Data-driven models also have significantly lower standard deviations (up to 87.2% even for completely unseen locations), highlighting their robustness. In addition, this work provides a guideline for choosing between data-driven and semi-physical models based on data availability, application needs, computational resources, interpretability, and model adaptability. Furthermore, the study underscores the challenges in accurately predicting the diffuse fraction using available input features and indicates that the incorporation of additional weather-related variables and domain knowledge could enhance the performance of data-driven separation models.


Ensemble fluid simulations on quantum computers

Abstract: We discuss the viability of ensemble simulations of fluid flows on quantum computers. The basic idea is to formulate a functional Liouville equation for the probability distribution of the flow field configuration and recognize that, due to its linearity, such an equation is in principle more amenable to quantum computing than the dynamic equations of fluid motion. After suitable marginalization and associated closure, the Liouville approach is shown to require several hundreds of logical qubits, hence calling for a major thrust in current noise correction and mitigation techniques.


GSD-YOLOX: Lightweight and more accurate object detection models

Abstract: We present an enhanced YOLOX model for vehicle detection, addressing issues of slow detection speed, high parameter counts, and complex computations. Our model significantly improves inference speed while maintaining high detection accuracy. We introduce two lightweight modules, DG and DS, to reduce model size and enhance detection speed. The DG module eliminates redundant features during feature extraction, and the DS module optimizes the performance of DG, enhancing feature extraction efficiency. We utilize the CIoU loss function for accurate bounding box regression. Additionally, we introduce the Chinese Chongqing Vehicle Detection Benchmark (CCVTDB) dataset to address dataset limitations. Our lightweight model achieves an impressive 83.36% detection accuracy on CCVTDB at 65 FPS with 4.4 million parameters. Compared to the original model, our approach improves detection speed by 30% and reduces model size by 51%, while maintaining substantial detection performance.


Defect detection enhancement using Gaussian Sparse Models

Abstract: Detecting defects is critical in industrial fabrication, such as pipe welding, where radiography testing (RT) is the gold standard as a non-destructive method for monitoring weld quality and weld corrosion. The extraction of seamless information from radiographic images is critical for this approach. Image processing techniques can improve the quality of radiographic images by enhancing image contrast, especially in flawed regions. In this study, a method based on Gaussian mixture models was implemented and applied to radiographs of welded objects to improve visualization and detectability. In the Sparse Coding and Gaussian Scale Mixture method (SSC-GSM), the local image patches are described as a mixture of Gaussian distributions. Given the different levels of noise in the individual images, the background was determined and subtracted from each original image. The results show that the proposed techniques can maintain and improve the edge information in radiographs and defective regions. The density of pixels along the analyzed profile lines has yielded enhancement by a factor of approximately two for the reconstructed images as compared to the original images. We tested the SSC-GSM method on GD-Xray database with 68 radiographs of the weld. In total, the SSC-GSM technique can improve the contrast of radiography image by back ground removal method and show the defects better that exist in weld radiographs, compared to traditional method for the detection of weld defects.


Image smoothing combining edge-consistency with region-piecewise flatting

Abstract: Image smoothing constitutes a fundamental task within the realm of computer vision. Effectively preserving the structural integrity of edges during the image smoothing process represents an arduous challenge. Current methodologies persistently encounter issues in adequately exploiting and harnessing the intrinsic value of edge information within images, while simultaneously suffering from the limitation of employing overly simplistic constraints for achieving desirable smoothing outcomes. To solve these problems, we propose a new Edge Consistency and Region Piecewise Flatting Network (ECRPF-Net). To circumvent the impact of the convolution process on edge-preserving properties, our network adopts the approach of superimposing significant edge information onto the feature layer, while also incorporating a weak structure reinforcement module. To efficiently preserve the edge structure, we incorporate an edge consistency module (ECM), which utilizes the edge-response consistency relationship between the input and output images to ensure the preservation of edges. To enhance the quality of image smoothing and mitigate the occurrence of edge artifacts, we introduce the Region Piecewise Flatting Module (RPFM). This module partitions the image into different regions based on the similarities and differences in edge response features, and applies a flexible sub-region smoothing approach to constrain the final output, ensuring superior smoothing results. Experimental results demonstrate the excellent performance of the ECRPF-Net in preserving the prominent edge structure of the image, achieving more visual-appealing smoothing outcomes, and surpassing the majority of existing methods on public datasets.


An enhanced adaptive differential evolution algorithm with dual performance evaluation metrics for numerical optimization

Abstract: In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer.


Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measure

Abstract: In recent years, Unsupervised Feature Selection (UFS) methods have attracted considerable interest in different research areas due to their wide application in problems where unlabeled data appears. Nevertheless, few UFS methods can process datasets described by both numerical and non-numerical features (mixed data). This paper introduces a new filter UFS method based on a new feature correlation measure for mixed data to select a relevant and non-redundant feature subset. The proposed method addresses the feature selection problem in two stages through a strategy that combines Spectral Feature Selection to identify relevant features and a Pair-wise Redundancy Analysis to remove those features with a high correlation with others. According to our experiments, the proposed feature selection method based on the introduced feature correlation measure provides better quality results than previous filter UFS methods for mixed data reported in the literature.


FedBoosting: Federated learning with gradient protected boosting for text recognition

Abstract: Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protection. The Federated Learning (FL) framework enables the collaborative learning of a shared model without necessitating the centralization or sharing of data among the data proprietors. Nonetheless, in this paper, we demonstrate that the generalization capability of the joint model is suboptimal for Non-Independent and Non-Identically Distributed (Non-IID) data, particularly when employing the Federated Averaging (FedAvg) strategy as a result of the weight divergence phenomenon. Consequently, we present a novel boosting algorithm for FL to address both the generalization and gradient leakage challenges, as well as to facilitate accelerated convergence in gradient-based optimization. Furthermore, we introduce a secure gradient sharing protocol that incorporates Homomorphic Encryption (HE) and Differential Privacy (DP) to safeguard against gradient leakage attacks. Our empirical evaluation demonstrates that the proposed Federated Boosting (FedBoosting) technique yields significant enhancements in both prediction accuracy and computational efficiency in the visual text recognition task on publicly available benchmarks.


KI-MAG: A knowledge-infused abstractive question answering system in medical domain

Abstract: Abstractive question-answering (QA) has emerged as a prominent area in Natural Language Processing (NLP) due to its ability to produce concise and human-like responses, particularly with the advancement of Large Language Models. Despite its potential, abstractive QA suffers from challenges like the need for extensive training data and the generation of incorrect entities and out-of-context words in the responses. In safety–critical domains like medical and clinical settings, such issues are unacceptable and may compromise the accuracy and reliability of generated answers. We proposed KI-MAG (Knowledge-Infused Medical Abstractive Generator) model, a novel Knowledge-Infused Abstractive Question Answering System specifically designed for the medical domain. KI-MAG aims to address the aforementioned limitations and enhance the correctness of generated responses while mitigating data sparsity concerns. The KI-MAG system produces more precise and informative answers by incorporating relevant medical entities into the model’s generation process. Furthermore, we adopt a synthetic data generation approach using question–answer pairs to overcome the challenge of limited training data in the medical domain. These synthetic pairs augment the original dataset, resulting in better model generalization and improved performance. Our extensive experimental evaluations demonstrate the effectiveness of the KI-MAG system. Compared to traditional abstractive QA models, our approach exhibits a substantial increase of approximately 15% in Blue-1, Blue-2, Blue-3, and Blue-4 scores, indicating a remarkable improvement in answer accuracy and overall quality of responses. Overall, our Knowledge-Infused Abstractive Question Answering System in the Medical Domain (KI-MAG) presents a promising solution to enhance the performance and reliability of abstractive QA models in safety–critical medical applications where precision and correctness of answers are of utmost importance.


CDSC: Causal decomposition based on spectral clustering

Abstract: Causal discovery is pivotal across numerous scientific and engineering disciplines. General causal discovery methods often falter in accuracy and efficiency when dealing with high-dimensional data due to the curse of dimensionality. While recent advancements in recursive constraint-based methods offer some improvements for high-dimensional causal discovery, their reliance on high-order conditional independence (CI) tests in causal decomposition hinders efficiency and accuracy. To address this challenge, we introduce a novel causal decomposition method based on spectral clustering, called CDSC, tailored for enhanced high-dimensional causal discovery. Concretely, CDSC first constructs the Laplacian matrix of the original variable set based on correlation distance. Then, CDSC decomposes the original variable set into multiple subsets without CI tests and recovers the causal structure of each subset. After determining these causal structures, CDSC merges these sub-results into a complete causal structure. A notable advantage of CDSC over other causal decomposition methods lies in its use of simplified spectral clustering. CDSC preserves the connectivity of the subsets to avoid destroying the causality in partitioned subsets. Moreover, we analyze the validity and time complexity of CDSC theoretically. Exclusive experiments on various real-world causal structures show that CDSC outperforms the state-of-the-arts both in accuracy and efficiency significantly.


Linear time manageable edge-aware filtering on complementary tree structures

Abstract: Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from the input image. Each pixel on the MST only connects to a sub-set of pixels in the 8-connected neighborhood, resulting in piece-wise constant output with fake edges among sub-trees for the unbalanced information propagation along eight directions. In this paper, we propose two complementary spatial trees to incorporate information from the entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distances on each tree. We introduce an efficient algorithm to recursively compute the output and the normalization constant on each tree with linear time complexity. For each pixel, we first calculate the outputs from eight subtrees and then fuse them to obtain the result on each tree structure. The final filtering output of our method is the weighted average of the results from two complementary spatial trees. Moreover, we present a distance mapping scheme to adjust the intensity distance between neighboring pixels, enabling our method to filter out a manageable degree of low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifact removal, tone mapping, detail enhancement, and colorization, demonstrate the effectiveness and versatility of our method.


Multimodal fusion hierarchical self-attention network for dynamic hand gesture recognition

Abstract: Recent improvements in dynamic hand gesture recognition have seen a shift from traditional convolutional architectures to attention-based networks. These attention networks have been proven to outclass CNN + LSTM architectures, showing higher accuracy as well as reduced model parameters. Especially, skeleton-based attention networks have been shown to outperform visual-based networks due to the rich information from skeleton-based hand features. However, there is an opportunity to introduce complementary features from other modalities like RGB, depth, and optical flow images to enhance the recognition capability of skeleton-based networks. This paper aims to explore the addition of a multimodal fusion network to a skeleton-based Hierarchical Self-Attention Network (MF-HAN) and test for increased model effectiveness. Unlike traditional fusion techniques, this fusion network uses features derived from other sources of multimodal data in a reduced feature space using a cross-attention layer. The model outperforms its root model and other state-of-the-art models on the SHREC’17 track dataset, especially in the 28 gestures setting by more than 1 % in gesture classification accuracy. The experimentation was tested on the DHG dataset as well.


Adv-4-Adv: Thwarting changing adversarial perturbations via adversarial domain adaptation

Abstract: Whereas adversarial training can be useful against specific adversarial perturbations, they have also been proven ineffective in generalizing towards attacks deviating from those used for training. However, we observe that this ineffectiveness is intrinsically connected to domain adaptability, another crucial issue in deep learning for which adversarial domain adaptation appears to be a promising solution. Consequently, we propose Adv-4-Adv as a novel adversarial training method that aims to retain robustness against unseen adversarial perturbations. Essentially, Adv-4-Adv treats attacks incurring different perturbations as distinct domains, and by leveraging the power of adversarial domain adaptation, it aims to remove the domain/attack-specific features. This forces a trained model to learn a robust domain-invariant representation, which in turn enhances its generalizability. Extensive evaluations on Fashion-MNIST, SVHN, CIFAR-10, and CIFAR-100 demonstrate that a model trained by Adv-4-Adv based on examples crafted by simple attacks (e.g., FGSM) can be generalized to more advanced attacks (e.g., PGD), and the performance exceeds state-of-the-art proposals on these datasets.


A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition

Abstract: As an important development direction of natural language processing, emotion recognition in conversation (ERC) remains a challenge in sentiment analysis. Given the large-scale dialogue datasets and their wide application in the fields of recommendation systems and human–machine dialogue systems, researchers have begun to pay more attention to the issue of ERC. In recent research, the task of ERC has been largely based on the graph structure to model the speaker level. However, most existing studies simply splice multimodal features, and the heterogeneity of multimodal features tends to be overlooked. Hence, this paper proposes a multivariate messaging framework to embed heterogeneous information into multimodal relational graphs. In the process of aggregating graph node information, we take into account the homogeneity of nodes and assign different weights to different nodes so as to better aggregate semantic information. In order to improve the robustness of the model, we utilize the mechanism of sharing weights among neighbors to reduce the number of network parameters and improve the generalization ability of the model. In so doing, the node information is aggregated through the constructed graph network, and the final semantic vector representation is obtained. Experiments over two benchmark datasets for ERC show that our proposed model achieves improved performance in accuracy and F1 value.


A novel and efficient model pruning method for deep convolutional neural networks by evaluating the direct and indirect effects of filters

Abstract: Deploying deep convolutional neural networks (DCNNs) on devices with low memory resources or in applications with strict latency requirements remains a challenge. The weight-based filter pruning is an effective technique that has been widely applied to DCNNs compression and acceleration due to its lighter computation consumption and better flexibility. However, many existing methods select the filter to be pruned by evaluating the direct effect of the filter on the current pruning layer, resulting in insufficient performance of the pruned model. In this paper, we point out that the key issue for the filter level pruning criteria to improve performance is how to evaluate the importance of filters and propose a new weight-based filter pruning method. The proposed method comprehensively considers the direct and indirect effects of filters, which can better reflect the filter importance, allowing the safe removal of unimportant filters. Extensive experiments demonstrate that the proposed weight-based method achieves a better performance than previous works, which are reaching the level of data-based methods.


Performance enhancement of solar PV system introducing semi-continuous tracking algorithm based solar tracker

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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.


Autoencoder-based joint image compression and encryption

Abstract: Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network as the backbone. After extracting semantic features from the learning network, encryption layers are added to introduce end-to-end learning to image encryption. And for a higher level of visual security, the parameters of the synthesis network are replaced by a new parameter matrix based on a logistic map controlled by a secret key. The encryption key of the system is derived from the image content, which will be embedded in the deep feature vectors to save the cost of sending the key for different images. Another secret key, which will be shared among other images, is used for controlling the embedding process. And to learn the entropy model from the scrambled feature maps, an attention scheme is exploited in estimating parameters to achieve more effective compression. Experimental results and the security analysis using Kodak dataset show that the proposed scheme can achieve a sufficiently high-security level with high compression efficiency.


Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

Abstract: The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences.Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials.Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view -cross sectional analysis -of these complex structures during assembly and curing.In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality.Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave.(2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD).This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies.(3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD).EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal.The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods.This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application.The code and dataset will be made publicly available.