Reviewerly/exHarmony huggingface dataset:
Style and content separation network for remote sensing image cross-scene generalization

Domain shift is the problem in which trained models fail to maintain their performance when they confront new test domains. Cross-scene classification, a technique developed to overcome this challenge, has attracted significant research interest in the field of remote sensing (RS). By exploring the correlation between the source domain and the target domain, relevant models can have better generalization ability regarding the target domain. Nevertheless, Domain Adaptation (DA), as the main technique for cross-scene classification, necessarily requires access to the target samples to assist the model training, a condition that is difficult to satisfy in real-world applications. Domain Generalization (DG) has attracted increasing research attention in recent years. Given one or several source domain(s), DG tends to learn models that can perform well when dealing with unseen (inaccessible) target domains. DG can better deal with out-of-distribution generalization with fewer restrictions, making it a good fit for cross-scene classification. Notably, little research has been conducted on domain generalization in the field of remote sensing. Thus, we refer to this type of method as cross-scene generalization. Recent studies have shown that convolutional neural networks have a strong bias towards recognizing textures rather than shapes. Accordingly, we proposed a Style and Content Separation Network (SCSN) for RS image cross-scene generalization in this paper, which can improve the generalizability and discriminative capability. The Style and Content Separation (SCS) module uses instance normalization to obtain the content information and thereby ensure better generalization ability. Moreover, the residual feature, which contains the style information, can supplement the feature representations after refinement. We further proposed a separation loss to constrain the style and content separation process. Experimental results and relevant analysis demonstrate the effectiveness of the proposed SCSN on cross-scene generalization tasks. Code is available at https://github.com/WHUzhusihan96/SCSN.
Indoor Positioning: Comparing Different Techniques and Dealing with a user Authentication use Case

Indoor positioning is a complex issue with many, heterogeneous application cases, each one presenting different requirements and environments.In such a complex ecosystem, an agile 1 taxonomy is needed to be able to select a proper solution for a given scenario, as well as practical recommendations for the most used solutions.Besides providing these tools, we analyze a real-world scenario and its requirements, selecting a practical solution and evaluating it together with its implications and consequences, providing a reference guideline for practical applications of indoor positioning.
Application of image recognition based on neural network algorithm in multimedia dance teaching

Neural network recognition algorithm is a deep learning-based convolutional neural network, which has been applied to target detection, image recognition, sound recognition and other fields. Although the recognition technology of convolutional neural network has been gradually stabilized, it is mainly implemented in single-machine serial mode. Therefore, there will be problems such as too long training time and insufficient memory capacity. According to the previous work, this paper has carried out related research and obtained some useful results. This paper focuses on the introduction of image recognition technology based on neural network algorithm. At the same time, this paper proposes a fast real-time image recognition algorithm, which has high recognition accuracy. In this paper, the existing algorithms mentioned in the paper are analyzed, the characteristics and shortcomings of all algorithms are summarized, and the appropriate combination of algorithms is selected for specific problems. On this basis, the acceleration method of artificial neural network is used to improve the algorithm of convolutional neural network image recognition technology. With the continuous development of education and the increase of the number of students year by year, the effect of classroom teaching becomes particularly important. Therefore, modern information technology is used to transfer the classroom teaching mode to the network and realize a new method of online remote online dance teaching. According to the actual situation of dance teaching, the advantages of multimedia dance teaching are analyzed. The software of multimedia dance teaching system is tested comprehensively, from the aspects of computer system memory occupancy rate and temperature, to test whether the activities of B/S mode multimedia dance teaching system meet the expected goals and requirements of each core module of multimedia dance teaching system.
Methodological Considerations for Assessing Automatic Brightness Control in Endoscopy: Experimental Study

Endoscopy is a critical application that requires adaptable illumination to adjust to varying imaging conditions. Automatic brightness control (ABC) algorithms ensure optimal brightness throughout the image with rapid but smooth response and render the true colours of the biological tissue under examination. To achieve good image quality, high-quality ABC algorithms are necessary. In this study, we propose a three-assessment method approach for objectively evaluating ABC algorithms based on (1) image brightness and its homogeneity, (2) controller response and response time, and (3) colour rendition. We conducted an experimental study to assess the effectiveness of ABC algorithms in one commercial and two developmental endoscopy systems using the proposed methods. The results showed that the commercial system achieved good, homogeneous brightness within 0.4 s, and its damping ratio was 0.597, indicating a stable system, but its colour rendition was suboptimal. The developmental systems had control parameter values that resulted in either a sluggish response (over 1 s) or a fast (about 0.3 ms) but unstable response with damping ratios above 1, causing flickers. Our findings indicate that the interdependency among the proposed methods can establish tradeoffs in the overall ABC performance better than single-parameter approaches. The study establishes that comprehensive assessments using the proposed methods can contribute to designing new ABC algorithms and optimising already implemented ones for efficient performance in endoscopy systems.
Wasserstein enabled Bayesian optimization of composite functions

Abstract Bayesian optimization (BO) based on the Gaussian process model (GP-BO) has become the most used approach for the global optimization of black-box functions and computationally expensive optimization problems. BO has proved its sample efficiency and its versatility in a wide range of engineering and machine learning problems. A limiting factor in its applications is the difficulty of scaling over 15–20 dimensions. In order to mitigate this drawback, it has been remarked that optimization problems can have a lower intrinsic dimensionality. Several optimization strategies, built on this observation, map the original problem into a lower dimension manifold. In this paper we take a novel approach mapping the original problem into a space of discrete probability distributions endowed with a Wasserstein metric. The Wasserstein space is a non-linear manifold whose elements are discrete probability distributions. The input of the Gaussian process is given by discrete probability distributions and the acquisition function becomes a functional in the Wasserstein space. The minimizer of the acquisition functional in the Wasserstein space is then mapped back to the original space using a neural network. Computational results for three test functions with dimensionality ranging from 5 to 100, show that the exploration in the Wasserstein space is significantly more effective than that performed by plain Bayesian optimization in the Euclidean space and its advantage grows with the dimensions of the search space.
Online motion planning based on swept volume search with replanning using sequential quadratic programming

This paper describes a method for obtaining a mobile manipulator's motion sequence by indicating a goal-hand pose. The proposed method entails recording various robot motions as a large number of motion sequences and associated swept volumes (SVs) and then selecting the most appropriate SV for the current situation. In addition, the motion sequences can be altered while the robot is in motion, and the transition motion is generated using sequential quadratic programming, enabling the robot to avoid collisions with obstacles found after it has begun to move. The proposed method's performance is verified by simulation to confirm the appropriate amount of data and the method's superiority.
A subjective interpretation of Liu–Liu’s credibility measures and expectations

Abstract We consider credibility measures introduced by Liu and Liu and the corresponding credibility expectations, obtained through the Choquet integral. In analogy with de Finetti’s approach to probability, we show that also for credibility measures and expectations, a subjective interpretation can be given through suitable notions of coherence. In detail, coherence can be expressed equivalently in terms of consistency, through a fair betting scheme and through a penalty criterion. Such notions of coherence rest upon the concepts of partially resolving uncertainty due to Jaffray, the notion of consonance due to Shafer and the notion of pessimism-optimism indifference introduced in this paper.
MGFNet: A Progressive Multi-Granularity Learning Strategy-Based Insulator Defect Recognition Algorithm for UAV Images

Due to the low efficiency and safety of a manual insulator inspection, research on intelligent insulator inspections has gained wide attention. However, most existing defect recognition methods extract abstract features of the entire image directly by convolutional neural networks (CNNs), which lack multi-granularity feature information, rendering the network insensitive to small defects. To address this problem, we propose a multi-granularity fusion network (MGFNet) to diagnose the health status of the insulator. An MGFNet includes a traversal clipping module (TC), progressive multi-granularity learning strategy (PMGL), and region relationship attention module (RRA). A TC effectively resolves the issue of distortion in insulator images and can provide a more detailed diagnosis for the local areas of insulators. A PMGL acquires the multi-granularity features of insulators and combines them to produce more resilient features. An RRA utilizes non-local interactions to better learn the difference between normal features and defect features. To eliminate the interference of the UAV images’ background, an MGFNet can be flexibly combined with object detection algorithms to form a two-stage object detection algorithm, which can accurately identify insulator defects in UAV images. The experimental results show that an MGFNet achieves 91.27% accuracy, outperforming other advanced methods. Furthermore, the successful deployment on a drone platform has enabled the real-time diagnosis of insulators, further confirming the practical applications value of an MGFNet.
Geochemical Evaluation for the Hydrocarbon Potential of Source Rocks in Central Sirt Basin, (concession -59), case study, Libya

The Sirt Basin important oil producing area in Libya is one of the richest regions in terms of hydrocarbon resources. Exploration studies of this area have shown that the oils are mixtures of several charges and may be from different source rocks particularly in the upper Cretaceous sediments this study based on organic geochemistry and organic petrographic analysis carried out on 179 cutting samples collected from four wells. From Cenomanian to upper Eocene succession within the central Sirt basin, the main aim of this study geochemical evaluation of source rocks, thermal maturation and estimate the other source rock in the area. The recorded geochemical values varied in these wells from one formation to another depending on lithology, paleoenvironment and tectonic events. Our results exposed that, The total organic carbon (TOC) values using Rock-Eval analysis showed that the Sirt Shale is considered the most important regard to hydrocarbon source potential, since it was found to reflect a high total organic carbon content 4.98 %. , (TOC) ranging (average 2.61), type–II kerogen (oil prone) and, with Hydrogen index (HI) mostly below 500. Rachmat average HI = 176 (kerogen types II, III), Organic facies analysis classified Sirt Shale in class B with dominantly amorphous high quality organic matter deposited in anoxic marine environment. Maturity evaluation result of Ro %, SCI, and Tmax oC, Formations are late immature to early mature stages.
THE USE OF ARTIFICIAL INTELIGENCE IN EDUCATIONAL DIVERSITY MANAGEMENT

The use of Artificial Intelligence (AI) has been steadily growing across various industries and sectors, and education is no exception.One area where AI has the potential to play a significant role is in diversity management in education.With the help of AI, educational institutions can create a more inclusive and diverse environment for students, teachers, and staff.
From Italian to Russian with the Superpower of Intercomprehension Inference in Graphical Decoding Across Different Alphabets

Intercomprehension is the ability to understand a language that is related to an already familiar one. It is based on the activation of previous knowledge and cognitive strategies, such as inference. In this article, we claim that intercomprehensive reasoning can also be applied across European linguistic families. We argue that by creating didactic materials based on the fundamentals of the intercomprehensive approach, it is possible to leverage the students’ natural ability for intercomprehension when approaching a completely unfamiliar language of Europe. In particular, we study if inference can be applied to graphical decoding of familiar and authentic words in an unfamiliar European language with a different alphabet. A small-scale empirical study conducted in Rome with Italian speakers shows that they were able to autonomously decipher the Russian alphabet through a specifically created task.
Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN

Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be difficult to identify. In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators.
Improving Norwegian Translation of Bicycle Terminology Using Custom Named-Entity Recognition and Neural Machine Translation

The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging for online translators, such as Google. For this reason, local companies outsource translation or translate product descriptions manually, which is cumbersome. In light of the Norwegian B2B bicycle industry, this paper explores transfer learning to improve the machine translation of bicycle-specific terminology from English to Norwegian, including generic text. Firstly, we trained a custom Named-Entity Recognition (NER) model to identify cycling-specific terminology and then adapted a MarianMT neural machine translation model for the translation process. Due to the lack of publicly available bicycle-terminology-related datasets to train the proposed models, we created our dataset by collecting a corpus of cycling-related texts. We evaluated the performance of our proposed model and compared its performance with that of Google Translate. Our model outperformed Google Translate on the test set, with a SacreBleu score of 45.099 against 36.615 for Google Translate on average. We also created a web application where the user can input English text with related bicycle terminologies, and it will return the detected cycling-specific words in addition to a Norwegian translation.
All-Weather Pedestrian Detection Based on Double-Stream Multispectral Network

Recently, advanced driver assistance systems (ADAS) have attracted wide attention in pedestrian detection for using the multi-spectrum generated by multi-sensors. However, it is quite challenging for image-based sensors to perform their tasks due to instabilities such as light changes, object shading, or weather conditions. Considering all the above, based on different spectral information of RGB and thermal images, this study proposed a deep learning (DL) framework to improve the problem of confusing light sources and extract highly differentiated multimodal features through multispectral fusion. Pedestrian detection methods, including a double-stream multispectral network (DSMN), were used to extract a multispectral fusion and double-stream detector with Yolo-based (MFDs-Yolo) information. Moreover, a self-adaptive multispectral weight adjustment method improved illumination–aware network (i-IAN) for later fusion strategy, making different modes complimentary. According to the experimental results, the good performance of this detection method was demonstrated in the public dataset KAIST and the multispectral pedestrian detection dataset FLIR, and it even performed better than the most advanced method in the miss rate (MR) (IoU@0.75) evaluation system.
An Event Extraction Approach Based on a Multi-Round Q&amp;A Framework

Event extraction aims to present unstructured text containing event information in a structured form to help people quickly mine the target information. Most of the traditional event extraction methods focus on the design of complex neural network models, which rely on a large amount of annotated data to train the models. In recent years, some researchers have proposed the use of machine reading comprehension models for event extraction; however, the existing methods are limited to the single-round question-and-answer model, ignoring the dependency relation between the elements of event arguments. In addition, the existing methods do not fully utilize knowledge such as a priori information. To address these shortcomings, a multi-round Q&amp;A framework is proposed for event extraction, which extends the existing methods in two aspects: first, by constructing a multi-round extraction problem framework, the model can effectively exploit the hierarchical dependencies among the argument elements; second, the question-and-answer framework is populated with historical answer information encoding slots, which are integrated into the multi-round Q&amp;A process to assist in inference. Finally, experimental results on a publicly available dataset show that the proposed model achieves superior results compared to existing methods.
Optimization of the Join between Large Tables in the Spark Distributed Framework

The Join task between Spark large tables takes a long time to run and produces a lot of disk I/O, network I/O and disk occupation in the Shuffle process. This paper proposes a lightweight distributed data filtering model that combines broadcast variables and accumulators using RoaringBitmap. When the data in the two tables are not exactly matched, the dimension table Key is collected through the accumulator, compressed by RoaringBitmap and distributed to each node using broadcast variables. The distributed fact table data can be pre-filtered on the local server, which effectively reduces the data transmission and disk reading and writing in the Shuffle phase. Experimental results show that this optimization method can reduce disk usage, shorten the running time and reduce network I/O and disk I/O for Spark Join tasks in the case of massive data, and the effect is more obvious when the two tables have a higher incomplete matching degree or a fixed matching degree but a larger amount of data. This optimization scheme has the advantages of being easy to use, being easy to maintain and having an obvious effect, and it can be applied to many development scenarios.
A Variational Retinex Model With Structure-Awareness Regularization for Single-Image Low-Light Enhancement

Low-light image enhancement (LLIE) is a method of improving the visual quality of images captured in weak illumination conditions. In such conditions, the images tend to be noisy, hazy, and have low contrast, making them difficult to distinguish details. LLIE techniques have many practical applications in various fields, including surveillance, astronomy, medical imaging, and consumer photography. The total variational method is a sound solution in this field. However, requirement of an overall spatial smoothness of the illumination map leads to the failure of recovering intricate details. This paper proposes that the interaction between the global spatial smoothness and the detail recovery in the total variational Retinex model can be optimized by adopting a structure-awareness regularization term. The resultant non-linear model is more effective than the original one for LLIE. As a model-based method, its performance does not rely on architecture engineering, super-parameter tuning, or specific training dataset. Experiments of the proposed formulation on various challenging low-light images yield promising results. It is shown that this method not only produces visually pleasing pictures, but it is also quantitatively superior in that the calculated full-reference, no-reference, and semantic metrics are beyond most of state-of-the-art methods. It has a better generalization capability and stability than learning-based methods. Due to its flexibility and effectiveness, the proposed method can be deployed as a pre-processing subroutine for high-level computer vision applications.