Sputtered NbN films for ultrahigh performance superconducting nanowire single-photon detectors

Abstract: At the present time, ultrahigh performance superconducting nanowire single-photon detectors are the key elements in a variety of devices from biological research to quantum communications and computing. Accurate tuning of superconducting material properties is a powerful resource for fabricating single-photon detectors with desired properties. Here, we report on the major theoretical relations between ultrathin niobium nitride (NbN) film properties and superconducting nanowire single-photon detector characteristics, as well as the dependence of ultrathin NbN film properties on reactive magnetron sputtering recipes. Based on this study, we formulate the exact requirements for ultrathin NbN films for ultrahigh performance superconducting nanowire single-photon detectors. Then, we experimentally studied the properties of ultrathin NbN films (morphology, crystalline structure, critical temperature, and sheet resistance) on silicon, sapphire, silicon dioxide, and silicon nitride substrates sputtered with various recipes. We demonstrate ultrathin NbN films (obtained with more than 100 films deposition) with a wide range of critical temperature from 2.5 to 12.1 K and sheet resistance from 285 to 2000 Ω/sq and report a sheet resistance evolution of more than 40% within two years. Finally, we found out that one should use ultrathin NbN films with a specific critical temperature near 9.5 K and a sheet resistance of about 350 Ω/sq for ultrahigh performance state-of-the-art superconducting nanowire single-photon detectors at 1550 nm wavelength.


Multi-Level Transfer Learning for Optimal Features Prediction

Abstract: Abstract The quality of the final predictions in machine learning (ML) depends on a multitude of factors, and among these factors, the generalization of features within the input dataset plays a pivotal role. This generalization is particularly crucial when dealing with seismic data, as the complexity of the underlying geological structures and the seismic characteristics themselves exhibit substantial variability across different geological study fields. Such variations pose unique challenges when it comes to model training and achieving optimal predictions for specific seismic features. Considering these challenges, the research proposes a comprehensive approach that involves the application of three stages of transfer learning. This approach has been designed to facilitate the seamless convergence of the model towards an optimal level of adaptability for the extraction of seismic features. Each step of transfer learning in this method incrementally enhances the model's ability to accommodate the idiosyncrasies of seismic data from diverse study fields, ultimately leading to the desired seismic feature prediction. The research seeks to improve the overall accuracy and robustness of seismic feature prediction across varied geological contexts, thereby contributing to the advancement of seismic data analysis and geological interpretation.


Clinical risk prediction using language models: benefits and considerations

Abstract: Abstract Objective The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. Methods We propose two novel LM-based methods, namely “LLaMA2-EHR” and “Sent-e-Med.” Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes. Results Experiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt. Conclusion LMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration.


CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification

Abstract: Abstract Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class.


Selective Partitioned Regression for Accurate Kidney Health Monitoring

Abstract: Abstract The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.


Online Sequence-Based Deep Learning Approach for Metallic Debossed and Embossed Turbomachinery Blade Text Recognition Application

Abstract: Abstract Extracting the engraved characters of metal parts in the industrial sphere remains a challenging task due to low visual contrast, uneven illumination, corroded surfaces, and cluttered backgrounds of metal part images. Affected by these factors, generally existing methods could not identify low-contrast text areas very well. Furthermore, the task is complicated by the fact that the embossed and deposed text can have a wide range of appearances, including variations in curvature, orientation, and distortion, which makes it more difficult to accurately recognize the text. In this paper, we investigate a sequence-based approach consisting of detection and recognition deep learning models for blade text recognition. These models are integrated into a feedforward pipeline that segments, detects, and recognizes embossed and debossed imprinted text on metal surfaces. The text localization model utilizes a U-shaped network with a VGG-16 architecture for feature extraction. On the other hand, the text recognition model is an end-to-end convolutional neural network designed for image-based text recognition, capable of handling arbitrary sequence lengths and orientations.


Research and Application of Adaptive Ghost Wave Suppression Method in Time-Space Domain

Abstract: Summary Source ghost waves and cable ghost waves can generate frequency trapping effects, resulting in the loss of low-frequency and high-frequency information in seismic data, seriously affecting the imaging quality of seismic data, and increasing the difficulty of later seismic data interpretation and reservoir inversion. The ghost wave delay time can be expressed as a function of the depth of the detector. When the cable depth is accurate, the difference between the total wave field minus the primary wave field and the ghost wave energy should be minimized. Convert the automatic optimization problem of cable depth into a least squares problem, and obtain the true cable depth by solving the objective function. By scanning the depth of the cable and combining it with the matching subtraction algorithm, accurate cable depth can be obtained. Design an anti filter to effectively suppress ghost waves. By designing a single-layer reflection model for algorithm testing, the effectiveness of this method is verified. After removing ghost waves, the energy of the trap points has been greatly restored and improved, and the energy of the low-frequency and high-frequency ends of seismic data has been well improved. Adaptive ghost wave suppression technology can effectively suppress source ghost waves and cable ghost waves, achieving the goal of eliminating traps, expanding seismic frequency bands, and improving seismic data resolution. This method not only has good processing results in actual marine seismic data processing, but also has high processing efficiency and good applicability to seismic data. It can be well applied to conventional horizontal cable seismic data and oblique cable seismic data. Significant improvement in the quality of seismic data provides an important data foundation for reservoir research.


Case Study: Saturation Assessment in Law Resistivity Pay (LRP) 1d & 3d Modeling (DH-Libya)

Abstract: Abstract DH reservoir located in North Africa in the middle of Libyan dessert, Sirt Basin. This field is an immature carbonate reservoir with good Petrophysical properties, but with Low Resistivity Pay (LRP). The development of this field has encountered several obstacles to overcome the effect of micro-porosity (vugy porosity) measurements to estimate the water saturation. Standard electrical resistivity tools used widely on DH reservoir to compute the saturation with no clear vision of success. This issue effects directly the Water Saturation computation and ends up with High uncertainty of STOOIP calculation. The objectives of this study are to find a representative saturation profile and to better estimate the STOOIP for new development plans. The target of this paper is to show the wide range of the new fluid saturation assessment used to overcome the electrical bypass caused by the water bearing extents.


Metaverse: Accelerates Upstream Growth with the Power of Immersive Collaboration

Abstract: Abstract The global transition towards sustainable future influenced industries to create a fundamental shift in the way they conduct the business to improve sustainability. This paper presents a comprehensive immersive platform that enables multidisciplinary teams and management to make decisions. It connects professionals to collaboratively demonstrate and share findings, run cross-functions business processes in an intuitive way capitalizing on artificial intelligence and cognitive capabilities. All of this is under a unified platform that we called the Upstream Metaverse. There are four pillars for the Upstream Metaverse: digital assets, real-time collaboration, intelligence, and venues. First digital assets, which include visualization of reservoir simulation static and dynamic properties (e.g., porosity, permeability, water, oil saturation, etc.), other datatypes such as core and well log data. Second is the collaboration and interactive capabilities that allows engineers to be immersed into their models, and run different scenarios. Third is the intelligent layer, which capitalizes on available Industrial Revolution 4.0 (IR4.0) solutions. Fourth is the collaboration venues represented by the Upstream collaboration centers to maximize the user experience. A synthetic model was used in this paper to demonstrate well placement process, which is one of the Upstream complex processes as it involves many stakeholders. Upstream Metaverse streamlined the process by integrating the key data, and workflows needed for the decision process in a single platform. It enables multiple intuitive visualization methods of well logs to address the complexity. In addition, it provides the visualization of structured data including reservoir models that can be cross-referenced with well logs. Moreover, it provides seamless integration with an intelligent voice assistant to enrich the user experience. These capabilities allow multidisciplinary teams to run different business scenarios, conduct effective field reviews, and collaboratively discuss their findings in an interactive and immersive platform. This allows real-time collaboration around the data for all participants. Furthermore, coupling Upstream Metaverse with digital transformation solutions provides a rich platform that is capable to effectively facilitate the meetings. Also, capitalizing on the integrated data sources, and cross-organization functions combined with streamlined IR4.0 processes enabled on spot decision making. The Upstream Metaverse established the main building blocks that are fully integrated with data repositories to address current and future challenges. This will directly increase the efficiency of conducting Upstream cross-functions operations through a single, unified, immersive, and collaborative platform. Upstream Metaverse is a sustainable environment that reduces the need for sequel meetings, supplemental silo efforts, and computation resources.


LLMs, Embeddings and Indexing Pipelines to Enable Natural Language Searching on Upstream Datasets

Abstract: Abstract Large Language Models (LLMs) are attracting an enormous amount of interest at the moment in many domains. Their general nature, and ability to "understand" natural language, has already stimulated multiple areas of research at our company. Here we successfully demonstrate a Natural Language querying system, which is able to search a large repository of unstructured exploration data. The system supports follow up querying on the returned results, plus automatic summarization of content. The system is integrated into our novel end-to-end data-mining platform, which continuously mines our unstructured exploration data for new changes and indexes the results. Important in our method are the enrichment processes that occur prior to use of the LLM. Our approach avoids usual "chunking" techniques, which in our experience results in inferior results, especially in the multiple domain areas of Exploration. By integrating our novel ontology-model AI in the enrichment of the initial Index, we drastically boost the performance of search resulting from the LLM steps. In order to perform the search, key parts of our unstructured data, plus the query itself, need to be transformed into a vector form. This is performed using the embedding feature of the LLM. For this work, we had around 500,000 embeddings to calculate. To improve performance these were indexed in a leading Analytics Engine as a vector object, allowing fast search via cosine or Euclidian similarity. A custom dashboard was made to allow fresh searches of the vector datastore to be returned for further analysis. Our current search time across 500,000 embeddings is under 20 milli-seconds. Our custom dashboard returns the top matches for further interrogation and analysis. This includes follow-up Natural Language question support on the returned matches for summarization tasks and other customised querying. Since our exploration-specific, ontology model is able to tag each piece of data with over 40 exploration-specific labels, we are able to cross-examine the LLM returned results with the tags. Agreement on a range of queries - ranging from targeted, highly specific questions to general, open-ended queries - was surprisingly good. Natural Language based querying of our unstructured data is opening a whole new approach to data discovery in our company. Tailoring it to the exploration domain has required specific domain expertise and a novel ontology-model be used to ensure relevant prompts and query results. Obtaining search results quickly has also required expertise and fine-tuning. Future directions include ingesting more data, scaling the support infrastructure and further capability enhancement.


Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Abstract: Abstract Geoscience datasets are fundamental for subsurface investigation. Paradoxically, they are sometimes exclusive and require subject-specific expertise to interpret and visualize. One such example is seismic interpretation. Geophysicists typically reconstruct ancient depositional settings by interpreting a myriad of seismic attributes and drawing analogs to the sedimentary process of the modern depositional environment (Posamentier et al. 2007; Vahrenkamp et al. 2019; Ramdani et al. 2021). Most of these interpretations will likely be reflection amplitude, frequency, impedance, or other geophysical attributes interpreted and "visualized" in the present-day geomorphology context (Posamentier et al. 2007; Warrlich et al. 2019; Ramdani et al. 2022b). The interpreter will then rely on verbal or written descriptions to convey their interpretation. Often, these descriptions are only well understood by fellow interpreters. Attempting to convey the same interpretation to a non-expert requires some degree of visual aid. Thus, a method to picture geophysical signals as a "depositional environment" is needed to bridge this gap. This study aims to leverage the application of generative AI as a tool for seismic interpretation. We propose a Conditional Generative Adversarial Network (CGAN)-based methodology capable of converting seismic attribute maps into photorealistic images of the modern satellite imagery analog as a visual aid for seismic interpretation.


Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies

Abstract: Abstract Lithology identification holds a pivotal role in the characterization of subsurface formations. In recent years, the advent of big data and the need for more precise lithology identification have spurred the growing adoption of machine learning algorithms. This paper's primary objective is to leverage unsupervised machine learning techniques for the identification and subsequent removal of anomalies inherent in complex datasets. The Isolation Forest model is the cornerstone of our approach for anomaly detection and elimination. To initiate this process, we employed the K-means algorithm to create clusters, followed by a evaluation using silhouette coefficients. Subsequently, we selected input data for each cluster and conducted exploratory data analysis both prior to and after the removal of outliers. The histograms portraying average anomaly scores for each cluster were thoughtfully presented. For real-time anomaly detection, we harnessed the power of the Isolation Forest model, which facilitated the creation of an Isolation Forest anomalies map by plotting neutron-porosity against bulk density. Our rigorous data analysis employed various statistical techniques, including data statistics, histograms, and cross plots between neutron-porosity and bulk density. This multifaceted approach successfully eradicated anomalies from the dataset, a fact vividly illustrated by the histograms, where anomalies were discernible through their negative scores. The Isolation Forest map conclusively demonstrated the effective removal of outliers from the dataset, underscoring the model's proficiency in identifying and mitigating these anomalies based on their negative scores. The Isolation Forest model has thus exhibited remarkable efficacy in the identification and elimination of data anomalies. Its versatility makes it a valuable asset for the detection and removal of outliers, deviations, or noise from datasets, rendering it particularly well-suited for anomaly detection and outlier mitigation in various analytical scenarios. Notably, the combination of the K-means and Isolation Forest algorithms emerges as a potent and advantageous approach, especially when dealing with extensive datasets and conducting comprehensive analyses.


An Optimizing Business Process: A Comprehensive Analysis of Amazon Inc.’s Information Architecture

Abstract: This analysis studies Amazon Inc.’s information system design in detail using context, use cases, test cases, class, and sequence diagrams. Our research aims to improve business processes by better understanding the system’s interactions, functionality, and data structure. The representation of data structure and entity relationships in class diagrams provides insights into the system’s component organization. Sequence diagrams also provide a clear picture of the sequential interactions between system elements, showing potential bottlenecks and optimization opportunities. It provides unique insights into Amazon’s approaches to keeping a competitive edge in the e-commerce industry, and it may be used as a resource for firms looking to improve their own information systems and procedures.


<i>Noise as a Constructive Element in Music: Theoretical and Music-Analytical Perspectives</i>. Mark Delaere

Abstract: Journal Article Noise as a Constructive Element in Music: Theoretical and Music-Analytical Perspectives. Mark Delaere Get access Noise as a Constructive Element in Music: Theoretical and Music-Analytical Perspectives. Ed. by Mark Delaere. Pp. 232. Musical Cultures of the Twentieth Century ( Routledge, Abingdon and New York, 2022. ISBN 978-1-032-20039-2, £130.) Andy Hamilton Andy Hamilton Durham University, UK Search for other works by this author on: Oxford Academic Google Scholar Music and Letters, gcae022, https://doi.org/10.1093/ml/gcae022 Published: 26 February 2024 Article history Editorial decision: 15 February 2024 Received: 15 February 2024 Accepted: 16 February 2024 Corrected and typeset: 26 February 2024 Published: 26 February 2024


Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

Abstract: The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.


MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

Abstract: Abstract Stream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information. While efficient approaches for defining and updating clusters based on similarity criteria have been proposed, outliers and noisy data within stream clustering areas pose a significant threat to the overall performance of clustering algorithms. Moreover, the limitation of existing methods in generating non-spherical clusters underscores the need for improved clustering quality. As a new methodology, we propose a new stream clustering approach, MCMSTStream, to overcome the abovementioned challenges. The algorithm applies MST to micro-clusters defined by using the KD-Tree data structure to define macro-clusters. MCMSTStream is robust against outliers and noisy data and has the ability to define clusters with arbitrary shapes. Furthermore, the proposed algorithm exhibits notable speed and can handling high-dimensional data. ARI and Purity indices are used to prove the clustering success of the MCMSTStream. The evaluation results reveal the superior performance of MCMSTStream compared to state-of-the-art stream clustering algorithms such as DenStream, DBSTREAM, and KD-AR Stream. The proposed method obtained a Purity value of 0.9780 and an ARI value of 0.7509, the highest scores for the KDD dataset. In the other 11 datasets, it obtained much higher results than its competitors. As a result, the proposed method is an effective stream clustering algorithm on datasets with outliers, high-dimensional, and arbitrary-shaped clusters. In addition, its runtime performance is also quite reasonable.


AI Based Unstructured Document Geotagging Solution

Abstract: Abstract In the oil and gas industry, extracting relevant information for new projects often involves scrolling through a large unorganized or semi-organized unstructured electronic repository of unstructured documents inside an enterprise. However, the digital availability of these unstructured documents and their proper characterization with respect to E&P domain objects remain a significant challenge. To address this issue, we developed an - "AI-based unstructured document geotagging solution" that utilizes text mining, optical character recognition (OCR), natural language processing (NLP), and named entity recognition (NER) to extract entity relations for spatial E&P objects and document category (for example well completion, log data print, daily drilling report etc.) from documents content autonomously. We employed popular language models spaCy, Roberta-base, GPT2 and GPT3.5 and compared the results. The extracted information was then linked to a structured database that contained geo-locations. The solution was developed using the company specific data sciences platform but is capable of being deployed for an operator on-premises or in the cloud. The end-to-end solution can be integrated with any GIS-based dashboard solution, allowing user to select objects from the map and the model can predict related documents to the objects along with document category. This solution will allow user to obtain an organized list of related documents out of electronic repository with unorganized collection of uncharacterized documents, which will allow the user to prioritize and deploy only content of interest to be ingested to the insight and analytics system often hosted in cloud environment.


CCDA: A Novel Method to Explore the Cross-Correlation in Dual-Attention for Multimodal Sentiment Analysis

Abstract: With the development of the Internet, the content that people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or sentiments in different types of data. Currently, the realm of multimodal sentiment analysis faces various challenges, with a major emphasis on addressing two key issues: (1) inefficiency when modeling the intramodality and intermodality dynamics and (2) inability to effectively fuse multimodal features. In this paper, we propose the CCDA (cross-correlation in dual-attention) model, a novel method to explore dynamics between different modalities and fuse multimodal features efficiently. We capture dynamics at intra- and intermodal levels by using two types of attention mechanisms simultaneously. Meanwhile, the cross-correlation loss is introduced to capture the correlation between attention mechanisms. Moreover, the relevant coefficient is proposed to integrate multimodal features effectively. Extensive experiments were conducted on three publicly available datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The experimental results fully confirm the effectiveness of our proposed method, and, compared with the current optimal method (SOTA), our model shows obvious advantages in most of the key metrics, proving its better performance in multimodal sentiment analysis.


Efficient removal of 2,4,6-trinitrotoluene (TNT) from industrial/military wastewater using anodic oxidation on boron-doped diamond electrodes

Abstract: Abstract With growing public concern about water quality particular focus should be placed on organic micropollutants, which are harmful to the environment and people. Hence, the objective of this research is to enhance the security and resilience of water resources by developing an efficient system for reclaiming industrial/military wastewater and protecting recipients from the toxic and cancerogenic explosive compound—2,4,6-trinitrotoluene (TNT), which has been widely distributed in the environment. This research used an anodic oxidation (AO) process on a boron-doped diamond (BDD) electrode for the TNT removal from artificial and real-life matrices: marine water and treated wastewater. During experiments, TNT concentrations were significantly decreased, reaching the anodic degradation efficiency of above 92% within two hours and > 99.9% after six hours of environmental sample treatment. The presented results show the great potential of AO performed on BDD anodes for full-scale application in the industry and military sectors for TNT removal.


Inherently privacy-preserving vision for trustworthy autonomous systems: Needs and solutions

Abstract: Vision is an effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, identity, and activity of humans within that scene. This raises important questions about the reach, lifespan, and misuse of this information. This paper is a call to action to consider privacy in robotic vision. We propose a specific form of inherent privacy preservation in which no images are captured or could be reconstructed by an attacker, even with full remote access. We present a set of principles by which such systems could be designed, employing data-destroying operations and obfuscation in the optical and analogue domains. These cameras never see a full scene. Our localisation case study demonstrates in simulation four implementations that all fulfil this task. The design space of such systems is vast despite the constraints of optical-analogue processing. We hope to inspire future works that expand the range of applications open to sighted robotic systems.


Transformer-based text similarity and second language proficiency: A case of written production by learners of Korean

Abstract: The present study applies two transformer models (BERT; GPT-2) to analyse argumentative essays produced by two first-language groups (Czech; English) of second-language learners of Korean and investigates how informative similarity scores of learner writing obtained by these models explain general language proficiency in Korean. Results show three major aspects on model performance. First, the relationships between the similarity scores and the proficiency scores differ from the tendencies between human rating scores and the proficiency scores. Second, the degree to which the similarity scores obtained by each model explain the proficiency scores is asymmetric and idiosyncratic. Third, the performance of the two models is affected by learners’ native language and essay topic. These findings invite the need for researchers and educators to pay attention to how computational algorithms operate, together with learner language characteristics and language-specific properties of the target language, in utilising Natural Language Processing methods and techniques for their research or instructional purposes.


A Good View for Graph Contrastive Learning

Abstract: Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within contrastive learning, the selection of a “view” dictates the information captured by the representation, thereby influencing the model’s performance. However, assessing the quality of information in these views poses challenges, and determining what constitutes a good view remains unclear. This paper addresses this issue by establishing the definition of a good view through the application of graph information bottleneck and structural entropy theories. Based on theoretical insights, we introduce CtrlGCL, a novel method for achieving a beneficial view in graph contrastive learning through coding tree representation learning. Extensive experiments were conducted to ascertain the effectiveness of the proposed view in unsupervised and semi-supervised learning. In particular, our approach, via CtrlGCL-H, yields an average accuracy enhancement of 1.06% under unsupervised learning when compared to GCL. This improvement underscores the efficacy of our proposed method.


Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution

Abstract: The supervised super-resolution (SR) methods based on simple degradation assumptions (e.g., bicubic downsampling) have unsatisfactory generalization ability on real-world thermal images. To enhance the SR effect of real-world sceneries, we introduce an unsupervised SR framework for thermal images, incorporating degradation modeling and corresponding SR. Inspired by the physical prior that high frequency affects details and low frequency affects thermal contrast, we propose a frequency-aware degradation model, named TFADGAN. The model achieves image quality migration between thermal detectors of different resolutions by degrading different frequency components of the image from high-resolution (HR) to low-resolution (LR). Specifically, by adversarial learning with unpaired LR thermal images, the complex degradation processes of HR thermal images at low and high frequencies are modeled separately. Benefiting from the thermal characteristics mined from real-world images, the degraded images generated by TFADGAN are similar to LR thermal ones in terms of detail and contrast. Then, the SR model is trained based on the pseudo-paired data consisting of degraded images and HR images. Extensive experimental results demonstrate that the degraded images generated by TFADGAN provide reliable alternatives to real-world LR thermal images. In real-world thermal image experiments, the proposed SR framework can improve the peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM) by 1.28 dB and 0.02, respectively.


Trustworthy multi-view clustering via alternating generative adversarial representation learning and fusion

Abstract: Multi-view clustering (MVC) has attached extensive attention as it provides an effective approach to deal with the unlabeled data in real-world applications. To enjoy the strong feature extraction capacity of deep learning, traditional shallow MVC methods are further extended to deep version. Though achieve superiorities in many fields, most existing deep MVC models are still limited by their lacking assurance of the trustworthiness and robustness. This drawback also leads to the possible poor clustering performance. To tackle this drawback, we propose an alternating generative adversarial learning strategy based network, termed as Alternating Generative Adversarial Representation Learning (AGARL) network for multi-view clustering, aiming to drive different views to fall into the same semantic space for the further alignment and information fusion. Various from the traditional generative adversarial learning strategy, the proposed alternating adversarial representation learning strategy successes in further aligning the distributions of the view-specific representations and exploring the cross-view consistency credited by its setting each view as the reference alternately. Then a consensus representative latent representation can be learned through an attention-like sub-network for all views. Finally, a self-supervised clustering module is employed in the proposed network to guide the learning of cluster assignment. In addition, comprehensive experiments on numerous benchmark MVC datasets have been performed to demonstrate the effectiveness of the proposed network compared to other state-of-the-art methods.


Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence

Abstract: Building an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of continuous variable (CV) quantum key distribution (QKD) in challenging seawater environments. We consider the passive continuous variable (CV) measurement-device-independent (MDI) QKD in oceanic scenarios, since the passive-state preparation scheme offers simpler linear elements for preparation, resulting in reduced interaction with the practical environment. To provide a practical reference for underwater quantum communications, we suggest a prediction of transmittance for the ocean quantum links with a given neural network as an example of machine learning algorithms. The results have a good consistency with the real data within the allowable error range; this makes the passive CVQKD more promising for commercialization and implementation.


Uniform approximation of common Gaussian process kernels using equispaced Fourier grids

Abstract: The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponentials, with frequencies lying on a Cartesian equispaced grid. Here we provide rigorous error bounds for this approximation for two popular kernels—Matérn and squared exponential—in terms of the grid spacing and size. The kernel error bounds are uniform over a hypercube centered at the origin. Our tools include a split into aliasing and truncation errors, and bounds on sums of Gaussians or modified Bessel functions over various lattices. For the Matérn case, motivated by numerical study, we conjecture a stronger Frobenius-norm bound on the covariance matrix error for randomly-distributed data points. Lastly, we prove bounds on, and study numerically, the ill-conditioning of the linear systems arising in such regression problems.


Floating Offshore Photovoltaics across Geographies: An Enhanced Model of Water Cooling

Abstract: Solar photovoltaics (PV) continues to grow rapidly across the world and now accounts for a very considerable proportion of all non-fossil-fuel electricity. With the continuing urgency of greenhouse gas abatement, the growth of solar PV is inevitable. Competition with other land uses and the desire to optimize the efficiency of the panels by making use of water cooling are compelling arguments for offshore floating PV (OFPV), a trend that could also benefit from the existing infrastructure recently built for offshore wind farms. Building on our earlier work, we present a larger dataset (n = 82) located around the globe to assess global yield (dis)advantages while also accounting for a modified form of water cooling of the offshore panels. Using our results regarding the Köppen–Geiger (KG) classification system and using a statistical learning method, we demonstrate that the KG climate classification system has limited validity in predicting the likely gains from OFPV. Finally, we also explore a small subset of sites to demonstrate that economics, alongside geography and technology, impacts the feasibility of locating PV panels offshore.


BALQUE: Batch active learning by querying unstable examples with calibrated confidence

Abstract: Active learning alleviates labeling costs by selecting and labeling the most informative examples from an unlabeled pool. However, most existing active learning approaches estimate informativeness with uncalibrated confidence, resulting in unreliable informativeness estimation. These approaches generally ignored two significant issues caused by uncalibrated confidence methods. Firstly, the average uncalibrated confidence generated by modern neural networks is usually higher than the accuracy. Secondly, examples located near the decision boundaries are unstable during prediction when the target model updates parameters in the last several epochs, even throughout the training process. This phenomenon, caused by the forgetting characteristic of neural networks, has a significant impact on some specific models that estimate the informativeness by predicted probability vectors or pseudo labels. To address these issues, in this paper, we propose a novel active learning approach to reliably estimate informativeness with calibrated confidence. Specifically, we integrate the intermediate predictions for each unlabeled example, generated by the target model during the training process, to generate calibrated confidence. The calibrated confidence can capture a tendentious label from an indecisive subset of the class space. We show that the calibrated confidence with tendentiousness can maintain the ability of correct predictions. The empirical results demonstrate that our approach outperforms the state-of-the-art active learning methods on image classification tasks.


Introspective GAN: Learning to grow a GAN for incremental generation and classification

Abstract: Lifelong learning, the ability to continually learn new concepts throughout our life, is a hallmark of human intelligence. Generally, humans learn a new concept by knowing what it looks like and what makes it different from the others, which are correlated. Those two ways can be characterized by generation and classification in machine learning respectively. In this paper, we carefully design a dynamically growing GAN called Introspective GAN (IntroGAN) that can perform incremental generation and classification simultaneously with the guidance of prototypes, inspired by their roles of efficient information organization in human visual learning and excellent performance in other fields like zero-shot/few-shot/incremental learning. Specifically, we incorporate prototype-based classification which is robust to feature change in incremental learning and GAN as a generative memory to alleviate forgetting into a unified end-to-end framework. A comprehensive benchmark on the joint incremental generation and classification task is proposed and our method demonstrates promising results. Additionally, we conduct comprehensive analyses over the properties of IntroGAN and verify that generation and classification can be mutually beneficial in incremental scenarios, which is an inspiring area to be further exploited. The code is available at https://github.com/TonyPod/IntroGAN.


Digital simulation of convex mixtures of Markovian and non-Markovian single qubit Pauli channels on NISQ devices

Abstract: Abstract Quantum algorithms for simulating quantum systems provide a clear and provable advantage over classical algorithms in fault-tolerant settings. There is also interest in quantum algorithms and their implementation in Noisy Intermediate Scale Quantum (NISQ) settings. In these settings, various noise sources and errors must be accounted for when executing any experiments. Recently, NISQ devices have been verified as versatile testbeds for simulating open quantum systems and have been used to simulate simple quantum channels. Our goal is to solve the more complicated problem of simulating convex mixtures of single qubit Pauli channels on NISQ devices. We consider two specific cases: mixtures of Markovian channels that result in a non-Markovian channel (M + M = nM) and mixtures of non-Markovian channels that result in a Markovian channel (nM + nM = M). For the first case, we consider mixtures of Markovian single qubit Pauli channels; for the second case, we consider mixtures of Non-Markovian single qubit depolarising channels, which is a special case of the single qubit Pauli channel. We show that efficient circuits, which account for the topology of currently available devices and current levels of decoherence, can be constructed by heuristic approaches that reduce the number of CNOT gates used in our circuit. We also present a strategy for regularising the process matrix so that the process tomography yields a completely positive and trace-preserving (CPTP) channel. Key points This work simulates the convex mixtures of single qubit Markovian and non-Markovian quantum channels on NISQ devices provided by the IMBQE. The circuits used to implement the channels take into account the topolgy of the quantum device used as well as the number of CNOT gates used. We present a strategy for regularising the process matrix to ensure the quantum process tomography yields a CPTP channel. Something that is not correctly implemented in Qiskit. A method is outlined for finding mixtures of non-Markovian depolarising channels that yield a Markovian depolarising channel. It is also shown that, one cannot convexly mix two Markovian depolarising channels that leads to a non-Markovian depolarising channel.


Construction of a network intelligence platform for privacy protection and integrated big data mining

Abstract: Motivation: With the enhancement of people’s awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users’ privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. Objective: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users’ personal information and security, to enhance the user trust and use experience, to better meet the needs of users. Methods: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. Result: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. Conclusion: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy.


A hybrid stacking classifier with feature selection for handling imbalanced data

Abstract: Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%.


MTC-Net: Multi-scale feature fusion network for medical image segmentation

Abstract: Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic efficiency and accuracy. The segmentation task is made more difficult by the hazy lesion boundaries and uneven forms. Because standard convolutional neural networks (CNNs) are incapable of capturing global contextual information, adequate segmentation results are impossible to achieve. We propose a multiscale feature fusion network (MTC-Net) in this paper that integrates deep separable convolution and self-attentive modules in the encoder to achieve better local continuity of images and feature maps. In the decoder, a multi-branch multi-scale feature fusion module (MSFB) is utilized to improve the network’s feature extraction capability, and it is integrated with a global cooperative aggregation module (GCAM) to learn more contextual information and adaptively fuse multi-scale features. To develop rich hierarchical representations of irregular forms, the suggested detail enhancement module (DEM) adaptively integrates local characteristics with their global dependencies. To validate the effectiveness of the proposed network, we conducted extensive experiments, evaluated on the public datasets of skin, breast, thyroid and gastrointestinal tract with ISIC2018, BUSI, TN3K and Kvasir-SEG. The comparison with the latest methods also verifies the superiority of our proposed MTC-Net in terms of accuracy. Our code on https://github.com/gih23/MTC-Net.


A negative selection algorithm with human-in-the-loop for anomaly detection

Abstract: The existing negative selection algorithms can not improve their detection performance by human intervention during the testing process. This paper proposes a negative selection algorithm with human-in-the-loop for anomaly detection. It uses self-sample clusters to train detectors with a nonrandom strategy. Its detectors and self-sample clusters fully cover state space without overlapping each other. It locally adjusts detectors and self-sample clusters with human intervention to improve its detection performance during the testing process. Experiments were performed on two synthetic datasets and the Iris dataset from the UCI repository to assess its performance. The results show that it outperforms the other anomaly detection methods in most cases.


Query reformulation system based on WordNet and word vectors clusters

Abstract: With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly dependent on the entered keywords. Casual users may enter a vague query due to lack of knowledge of the domain-specific words. We propose a query reformulation system that determines the context of the query, decides on keywords to be replaced and outputs a better-modified query. We propose strategies for keyword replacements and metrics for query betterment checks. We have found that if we project keywords into the vector space of word projection using word embedding techniques and if the keyword replacement is correct, clusters of a new set of keywords become more cohesive. This assumption forms the basis of our proposed work. To prove the effectiveness of the proposed system, we applied it to the ad-hoc retrieval tasks over two benchmark corpora viz TREC-CDS 2014 and OHSUMED corpus. We indexed Whoosh search engine on these corpora and evaluated based on the given queries provided along with the corpus. Experimental results show that the proposed techniques achieved 9 to 11% improvement in precision and recall scores. Using Google’s popularity index, we also prove that the reformulated queries are not only more accurate but also more popular. The proposed system also applies to Conversational AI chatbots like ChatGPT, where users must rephrase their queries to obtain better results.


Hybrid image encryption using advanced least significant bit algorithm, chaotic maps and DNA encoding for digital healthcare

Abstract: Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted.


Explainer on GNN-based segmentation networks

Abstract: Abstract Graph Neural Networks (GNN) are powerful tools for deep learning. Similar to other neural networks, GNNs are complex models, in which humans can’t understand the decision-making procedures of the models. Therefore, it brings the need to explainability of GNNs. Explainability is critical for deep learning to support its predictions. In this paper, we will investigate the Grad-Cam and Integrated-Gradients explaining methods. The Grad-Cam applies a global average pooling over the feature activation mapping, and then which was followed by a ReLU activation to obtain an attribution. The Integrated-Gradients explains models by taking a line integral between the baseline image (a black image) and the source image. We demonstrate how Grad-Cam and the Integrated-Gradients methods explain the graph-deep model in semantic segmentation tasks over the Cityscapes dataset. FCN and LRASSP-MobileNet are used as a comparison to the DualGCN in the experiment to show the explaining effect.


Using nonclassical states to explore the superradiant phase transition in the quantum Rabi model

Abstract: The ground state of the quantum Rabi model (QRM) exhibits rich nonclassical states including the squeezed state, cat state, and entangled state in different parameter regimes. In this paper, we first use the polaron picture with high precision and fidelity to figure out the process of the superradiant phase transition (SPT) in view of the nonclassical states. The squeezing effect is considered to be a result of the higher-order excitations and important in order to understand the fundamental quantum physics of the SPT in the QRM. According to the polaron wave functions, the squeezed state extends across the entire range of parameters. Based on the squeezing and superradiance, a classification of the coupling strength for the nonclassical states containing the ground state is presented; the system sequentially goes through the vacuum states, the squeezed vacuum states, the squeezed cat states, and the cat states as the coupling strength increases. The process of the SPT is also demonstrated by the photon number distribution in Fock space, and then the novel behavior of parity chains for the SPT is revealed. Additionally, the nonclassical states can be intuitively characterized by the Wigner functions with analytical expressions in the polaron picture, and the Wigner negativity is evaluated to measure the nonclassicality. We find that a small but nonzero weighted antipolaron is necessary to form novel semicat states. Our paper provides guidance for preparing nonclassical states in experiments based on the light-matter interaction.


Risk management in the beverage production industry using FMEA and fuzzy cognitive map in an uncertain environment

Abstract: Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach.


An improved DDPG algorithm based on evolution-guided transfer in reinforcement learning

Abstract: Abstract Deep Reinforcement Learning (DRL) algorithms help agents take actions automatically in sophisticated control tasks. However, it is challenged by sparse reward and long training time for exploration in the application of Deep Neural Network (DNN). Evolutionary Algorithms (EAs), a set of black box optimization techniques, are well applied to single agent real-world problems, not troubled by temporal credit assignment. However, both suffer from large sets of sampled data. To facilitate the research on DRL for a pursuit-evasion game, this paper contributes an innovative policy optimization algorithm, which is named as Evolutionary Algorithm Transfer - Deep Deterministic Policy Gradient (EAT-DDPG). The proposed EAT-DDPG takes parameters transfer into consideration, initializing the DNN of DDPG with the parameters driven by EA. Meanwhile, a diverse set of experiences produced by EA are stored into the replay buffer of DDPG before the EA process is ceased. EAT-DDPG is an improved version of DDPG, aiming at maximizing the reward value of the agent trained by DDPG as much as possible within finite episodes. The experimental environment includes a pursuit-evasion scenario where the evader moves with the fixed policy, and the results show that the agent can explore policy more efficiently with the proposed EAT-DDPG during the learning process.


Sketching of interactive VoIP traffic with multivariate statistical learning-based classification

Abstract: Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today’s network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system’s high accuracy, consistently around 98-99% . These findings affirm the system’s suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management.


Multi-CNN based logical reasoning system for facial expression recognition on small-sample datasets

Abstract: Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms.


Improving polygon image segmentation by enhancing U-Net architecture

Abstract: Abstract The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model’s capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer.


Impact of oxymoron features and deep learning techniques in the detection of sarcastic contents

Abstract: Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms (SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model.


Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

Abstract: Abstract With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.


Empowering Micro-Credentials Using Blockchain and Artificial Intelligence

Abstract: The recognition and transfer of credits is becoming essential for students, as an increasing number of them are studying at different institutions, often at the same time online, in both traditional and unconventional settings. Micro-credentials can aid in this process by providing easily accessible and transparent evidence of skills or knowledge, certified by an authority, based on small units of learning. The development of blockchain technology holds promise of becoming a useful enabler for supporting the storage and dissemination of micro-credentials on a global scale. Because of its immutability, blockchain can be used to attest to students' accomplishments securely and privately. Artificial intelligence (AI) can facilitate the maintenance and dissemination of micro-credentials, while ensuring that access is readily available for students under their control. So, AI can play a role in supporting blockchain-enabled micro-credentials. For educators, a basic understanding of the development of all three technologies is becoming essential.


Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR

Abstract: Abstract The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model’s significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset’s SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research.


Markovian noise modelling and parameter extraction framework for quantum devices

Abstract: Abstract In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.


Estimating many properties of a quantum state via quantum reservoir processing

Abstract: Estimating properties of a quantum state is an indispensable task in various applications of quantum information processing. To predict properties in the postprocessing stage, it is inherent to first perceive the quantum state with a measurement protocol and store the information acquired. In this paper, we propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs. A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties, while most of the previous methods need an exponentially increasing number of measurement settings. To estimate $M$ properties simultaneously, the size of the classical approximation scales as $lnM$. Moreover, this estimation scheme is extendable to higher-dimensional systems and hybrid systems with nonidentical local dimensions, which makes it exceptionally generic. We support our theoretical findings with extensive numerical simulations.


Collective writing as survival tool: Mechanisms of reflexivity against neoliberal academia

Abstract: This paper introduces an innovative method for enhanced researcher reflexivity: the use of synchronous collective writing as a space to collaboratively reflect on experiences of subjectification within the contemporary academy. We explore how, despite its apparent importance to contemporary research, the neoliberalisation of academia leaves little room for meaningful reflexivity. The authors in this paper – ranging from Master’s student to postdoctoral researcher – wrote collaboratively in real-time to organically develop a method of collective reflexivity. Through auto-ethnographic vignettes that act as raw data, and a critical analysis of how we came to experience the events showcased in these vignettes, we analyse how our positionalities shape both our subjection to, and perpetuation of, systems of symbolic violence in neoliberal academic institutions. Through this method, we explore experiences of the contemporary university as patriarchal, intensively marketised, and as a space where the prevalence of ‘weak’ reflexivity has negative impacts on research ethics. We argue that the affect of collaborative writing spaces acts as a resistance against our experiences of loneliness, competition and individualism. We also argue our new approach fosters research that is more responsive to the socio-material conditions to which it attends, and enables a deeper engagement with affect-led methodologies and slow-research.


Abstract: HomeRadiologyVol. 310, No. 2 PreviousNext Reviews and CommentaryEditorialEmojis and Radiology: The Power of Shared LanguageSanjeev Bhalla Sanjeev Bhalla Author AffiliationsFrom the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110.Address correspondence to the author (email: [email protected]).Sanjeev Bhalla Published Online:Feb 27 2024https://doi.org/10.1148/radiol.233089MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Yow WQ, Lim TZM. Sharing the same languages helps us work better together. Palgrave Commun 2019;5(1):154. Crossref, Google Scholar2. Bankier AA, MacMahon H, Colby T, et al. Fleischner Society: glossary of terms for thoracic imaging. Radiology 2024;310(2):e232558. Google Scholar3. Curtis AM, Smith GJ, Ravin CE. Air crescent sign of invasive aspergillosis. Radiology 1979;133(1):17–21. Link, Google Scholar4. Kim SJ, Lee KS, Ryu YH, et al. Reversed halo sign on high-resolution CT of cryptogenic organizing pneumonia: diagnostic implications. AJR Am J Roentgenol 2003;180(5):1251–1254. Crossref, Medline, Google Scholar5. Paredes A. The WIRED Guide to Emoji. https://www.wired.com/story/guide-emoji/. Published February 1, 2018. Accessed July 1, 2023. Google ScholarArticle HistoryReceived: Nov 12 2023Revision requested: Dec 1 2023Revision received: Dec 4 2023Accepted: Dec 6 2023Published online: Feb 27 2024 FiguresReferencesRelatedDetailsRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 310, No. 2 Metrics Altmetric Score PDF download


Character sums over sparse elements of finite fields

Abstract: Abstract We estimate mixed character sums of polynomial values over elements of a finite field with sparse representations in a fixed ordered basis over the subfield . First we use a combination of the inclusion–exclusion principle with bounds on character sums over linear subspaces to get nontrivial bounds for large . Then we focus on the particular case , which is more intricate. The bounds depend on certain natural restrictions. We also provide families of examples for which the conditions of our bounds are fulfilled. In particular, we completely classify all monomials as argument of the additive character for which our bound is applicable. Moreover, we also show that it is applicable for a large family of rational functions, which includes all reciprocal monomials.


Design for performability: a proposal for democratic redesign of the classical guitar

Abstract: Mastery of a musical instrument, including an ability to reach virtuosity in expression and performance, is commonly achieved through a lifelong commitment to practise. The specific instrument played has a crucial role in this journey. This paper reports on design research that aimed to determine the potential for redesigning the classical guitar, whether iteratively or radically, from the perspective of key stakeholder groups: luthiers, composers, and performers. A total of 25 interviews were conducted and analysed to identify shared and unique concerns amongst the stakeholder groups. The findings are used to conceive and develop a new design-related musical term ‘performability’, which builds upon the established term ‘playability’ but has much stronger interactional and experiential dimensions. Furthermore, the paper provides insights into ways in which an industrial designer can contribute to classical guitar design as a fourth stakeholder. Industrial design expertise is proposed to provide a mediating and catalysing role for democratic (i.e. multi-stakeholder, multi-perspective) instrument design or redesign. The paper concludes with a proposal for a ‘design for performability’ framework suited to co-design sessions aiming to achieve a highly performable classical guitar.


Instance-level Adversarial Source-free Domain Adaptive Person Re-identification

Abstract: Domain adaption (DA) for person re-identification (ReID) has attained considerable progress by transferring knowledge from a source domain with labels to a target domain without labels. Nonetheless, most of existing methods require access to source data, which raises privacy concerns. Source-free DA has recently emerged as a response to these privacy challenges, yet its direct application to open-set pedestrian re-identification tasks is hindered by the reliance on a shared category space in existing methods. Current source-free DA approaches for person ReID still encounter several obstacles, particularly the divergence-agnostic problem and the notable domain divergence due to the absent source data. In this paper, we introduce an Instance-level Adversarial Mutual Teaching (IAMT) framework, which utilizes adversarial views to tackle the challenges mentioned above. Technically, we first elaborately develop a variance-based division (VBD) module to segregate the target data into instance-level subsets based on their similarity and dissimilarity to the source using source-trained model, implicitly tackling the divergence-agnostic problem. To mitigate domain divergence, we additionally introduce a dynamic adversarial alignment (DAA) strategy, aiming to enhance the consistence of feature distribution across domains by employing adversarial instances from the target data to confuse the discriminators. Experiments reveal the superiority of the IAMT over state-of-the-art methods for DA person ReID tasks, while preserving the privacy of the source data.


A heuristic for the non‐unicost set covering problem using local branching

Abstract: Abstract In this paper, we present a heuristic for the non‐unicost set covering problem using local branching. Local branching eliminates the need to define a problem specific search neighbourhood for any particular (zero‐one) optimisation problem. It does this by incorporating a generalised Hamming distance neighbourhood into the problem, and this leads naturally to an appropriate neighbourhood search procedure. We apply our approach to the non‐unicost set covering problem. Computational results are presented for 65 test problems that have been widely considered in the literature. Our results indicate that our heuristic is better than six of the eight other heuristics we examined, slightly worse than that of one heuristic, but that there is a single heuristic that outperforms all others. We believe that the work described here illustrates that the potential for using local branching, operating as a stand‐alone matheuristic, has not been fully exploited in the literature.


Characterising Modal Formulas with Examples

Abstract: We study the existence of finite characterisations for modal formulas. A finite characterisation of a modal formula φ is a finite collection of positive and negative examples that distinguishes φ from every other, non-equivalent modal formula, where an example is a finite pointed Kripke structure. This definition can be restricted to specific frame classes and to fragments of the modal language: a modal fragment \(\mathcal {L} \) admits finite characterisations with respect to a frame class \(\mathcal {F} \) if every formula \(\varphi \in \mathcal {L} \) has a finite characterisation with respect to \(\mathcal {L} \) consisting of examples that are based on frames in \(\mathcal {F} \) . Finite characterisations are useful for illustration, interactive specification, and debugging of formal specifications, and their existence is a precondition for exact learnability with membership queries. We show that the full modal language admits finite characterisations with respect to a frame class \(\mathcal {F} \) only when the modal logic of \(\mathcal {F} \) is locally tabular. We then study which modal fragments, freely generated by some set of connectives, admit finite characterisations. Our main result is that the positive modal language without the truth-constants ⊤ and ⊥ admits finite characterisations w.r.t. the class of all frames. This result is essentially optimal: finite characterizability fails when the language is extended with the truth constant ⊤ or ⊥ or with all but very limited forms of negation.


Greater usage and positive mood change for users of a dynamic VR app before and after the COVID-19 pandemic onset

Abstract: Americans reported an increase in stress during the novel coronavirus disease 2019 (COVID-19). Virtual reality (VR) apps have been shown to distract users from stressors in the environment, but little is known about the efficacy of specific content features to reduce stress or improve mood for consumer users during a pandemic. The present study investigated secondary archival data to explore how mood and usage behavior changed before and after the onset of COVID-19 for consumer users of a VR app with dynamic, interactive content. Study findings indicate that the COVID-19 pandemic had significant effects on user behavior and mood. Users created more accounts and used app content more often during the pandemic, while reporting increased negative mood states. This suggests that users were motivated to use the content to cope with pandemic stressors. Users also experienced a greater positive mood change after using the content during the pandemic than before, which implies that elements related to the VR app content met users’ psychological needs. Passive content with less interactivity resulted in a greater positive mood state after the COVID-19 onset, likely related to its capacity to reduce stress, facilitate restoration, and improve persistent affective states in stressful environments. This study offers a vital window into how consumer users respond to psychosocial pandemic stressors outside of a controlled environment as well as the prospective for VR app content to serve as a valuable mental health intervention during similar stressful events.


FPGA-Based Acceleration of K-Nearest Neighbor Algorithm on Fully Homomorphic Encrypted Data

Abstract: The suggested solution in this work makes use of the parallel processing capability of FPGA to enhance the efficiency of the K-Nearest Neighbor (KNN) algorithm on encrypted data. The suggested technique was assessed utilizing the breast cancer datasets and the findings indicate that the FPGA-based acceleration method provides significant performance improvements over software implementation. The Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme is used for the computation of ciphertext. After extensive simulation in Python and implementation in FPGA, it was found that the proposed architecture brings down the computational time of KNN on ciphertext to a realistic value in the order of the KNN classification algorithm over plaintext. For the FPGA implementation, we used the Intel Agilex7 FPGA (AGFB014R24B2E2V) development board and validated the speed of computation, latency, throughput, and logic utilization. It was observed that the KNN on encrypted data has a computational time of 41.72 ms which is 80 times slower than the KNN on plaintext whose computational time is of 0.518 ms. The main computation time for CKKS FHE schemes is 41.72 ms. With our architecture, we were able to reduce the calculation time of the CKKS-based KNN to 0.85 ms by using 32 parallel encryption hardware and reaching 300 MHz speed.


Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence

Abstract: Abstract On 18 September 2022, the M W 6.9 Chihshang earthquake struck the south half of the Longitudinal Valley, Taiwan, and caused severe damage. A precise and rapid report for the distribution of aftershock sequence after a devastating earthquake provides key information for deciphering the seismogenic structure in the source region. The utilization of deep-learning methodologies for earthquake event detection offers a significant acceleration in data analysis. In this study, we use SeisBlue, a deep-learning platform/package, to extract the whole earthquake sequence from September to October 2022, including the M W 6.5 Guanshan foreshock, the M W 6.9 mainshock, over 14,000 aftershocks, and 866 foal mechanisms from two sets of broadband networks. After applying hypoDD for earthquakes, the distribution of aftershock sequence clearly depicts not only the Central Range Fault and the Longitudinal Valley Fault but also several local, shallow tectonic structures that have not been observed along the southern Longitudinal Valley.


Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach

Abstract: Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques.


Marginal effects for non-linear prediction functions

Abstract: Abstract Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either as derivatives of the prediction function or forward differences in prediction due to changes in feature values. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a general model-agnostic interpretation method for machine learning models. This may stem from the ambiguity surrounding marginal effects and their inability to deal with the non-linearities found in black box models. We introduce a unified definition of forward marginal effects (FMEs) that includes univariate and multivariate, as well as continuous, categorical, and mixed-type features. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for FMEs. Furthermore, we argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to average homogeneous FMEs within population subgroups, which serve as conditional feature effect estimates.


Nonlinear erasing of propagating spin-wave pulses in thin-film Ga:YIG

Abstract: Nonlinear phenomena are crucial for magnon-based information processing, but the nonlinear interaction between two spin-wave signals requires their spatiotemporal overlap, which can be challenging for directional processing devices. Our study focuses on a gallium-substituted yttrium iron garnet film, which exhibits an exchange-dominated dispersion relation and, thus, provides a particularly broad range of group velocities compared to pure YIG. Using time- and space-resolved Brillouin light scattering spectroscopy, we demonstrate the excitation of time-separated spin-wave pulses at different frequencies from the same source, where the delayed pulse catches up with the previously excited pulse and outruns it due to its higher group velocity. By varying the excitation power of the faster pulse, the outcome can be finely tuned from a linear superposition to a nonlinear interaction of both pulses, resulting in a full attenuation of the slower pulse. Therefore, our findings demonstrate the all-magnonic erasing process of a propagating magnonic signal, which enables the realization of complex temporal logic operations with potential application, e.g., in inhibitory neuromorphic functionalities.


Detecting SQL injection attacks by binary gray wolf optimizer and machine learning algorithms

Abstract: Abstract SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application's database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset's smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features.


A novel image multitasking enhancement model for underwater crack detection

Abstract: Remotely Operated Vehicles (ROVs) carrying vision systems provide an efficient solution for the underwater crack search. However, the degradation of underwater images severely limits the prognosis of cracks. For the problem of ROV image multiple degradation in complex underwater environments, a robust and accurate multitask enhancement method for underwater crack images is proposed, which can simultaneously enhance the color, brightness. and deblurring of images. In the model, we propose a depth-residual encoder–decoder and feature calibration module to address low-level feature loss. Meanwhile, we propose a simulation method to construct paired training data. Experiments show that our model outperforms existing methods in image enhancement and provides significant enhancements for downstream tasks. The model has been successfully applied to practical engineering and shows good adaptability, which can well assist ROVs for underwater crack detection. In future work, we will continue to improve the robustness of the ROV crack detection system under more complex noise scenarios.