Reviewerly/exHarmony huggingface dataset:
Random vector functional link forests and extreme learning forests applied to UAV automatic target recognition

This paper proposes two novel machine learning algorithms, namely Random Vector Functional Link Forests and Extreme Learning Forests, to develop an improved unmanned aerial vehicles automatic target recognition system. Such models take advantage of the stochastic procedure followed by Random Forests, where random subsets of instances and features are selected to build diverse Decision Trees. However, different from the usual uni-variate split criterion from Decision Tree algorithms, we propose and employ the novel Random Vector Functional Link Tree or Extreme Learning Tree, where each decision split is performed using the fast non-linear mapping of multiple features provided by either Random Vector Functional Link or Extreme Learning Machines. To prove the efficacy of the novel algorithm, experiments are performed using 90 binary classification problems to compare the performance of the proposed algorithm against other state-of-the-art ensemble learning techniques. Statistical analysis indicates the success of the proposed algorithms in terms of both predictive performance and computational complexity. While the model with deeper trees outperforms classical ensembles in terms of predictive performance (1.41% error reduction) and has similar results to state-of-the-art ensemble models, the model with shallow trees outperforms all ensembles in terms of computational burden (at least 36% faster). Finally, the novel methods are applied to develop an automatic target recognition system for unmanned aerial vehicles, achieving a valuable trade-off in terms of area under receiver operating characteristic curve (0.9309), F1 score (0.8190), accuracy (0.8646), and computational time (4.14 s).
Geometric Variational Inference and Its Application to Bayesian Imaging

Modern day Bayesian imaging problems in astrophysics as well as other scientific areas often result in non-Gaussian and very high-dimensional posterior probability distributions as their formal solution. Efficiently accessing the information contained in such distributions remains a core challenge in modern statistics as, on the one hand, point estimates such as Maximum a Posteriori (MAP) estimates are insufficient due to the nonlinear structure of these problems, while on the other hand, posterior sampling methods such as Markov Chain Monte Carlo (MCMC) techniques may become computationally prohibitively expensive in such high-dimensional settings. To nevertheless enable (approximate) inference in these cases, geometric Variational Inference (geoVI) has recently been introduced as an accurate Variational Inference (VI) technique for nonlinear unimodal probability distributions. It utilizes the Fisher&ndash;Rao information metric (FIM) related to the posterior probability distribution and the Riemannian manifold associated with the FIM to construct a set of normal coordinates in which the posterior metric is approximately the Euclidean metric. Transforming the posterior distribution into these coordinates results in a distribution that takes a particularly simple form, which ultimately allows for an accurate approximation with a normal distribution. A computationally efficient approximation of the associated coordinate transformation has been provided by geoVI, which now enables its application to real-world astrophysical imaging problems in millions of dimensions.
Interpretability and Transparency in Artificial Intelligence

Abstract Artificial Intelligence (AI) systems are frequently thought of as opaque, meaning their performance or logic is thought to be inaccessible or incomprehensible to human observers. Models can consist of millions of features connected in a complex web of dependent behaviours. Conveying this internal state and dependencies in a humanly comprehensible way is extremely challenging. Explaining the functionality and behaviour of AI systems in a meaningful and useful way to people designing, operating, regulating, or affected by their outputs is a complex technical, philosophical, and ethical project. Despite this complexity, principles citing ‘transparency’ or ‘interpretability’ are commonly found in ethical and regulatory frameworks addressing technology. This chapter provides an overview of these concepts and methods design to explain how AI works. After reviewing key concepts and terminology, two sets of methods are examined: (1) interpretability methods designed to explain and approximate AI functionality and behaviour; and (2) transparency frameworks meant to help assess and provide information about the development, governance, and potential impact of training datasets, models, and specific applications. These methods are analysed in the context of prior work on explanations in the philosophy of science. The chapter closes by introducing a framework of criteria to evaluate the quality and utility of methods in explainable AI (XAI) and to clarify the open challenges facing the field.
Comparison Study of Novel Evolutionary Algorithms for Elliptical Shapes in Images

Shape recognition in digital image processing describes one of the difficult and hard-solving situations in artificial vision due to its nonlinear and stochastic structure. Traditional image processing methods have been commonly employed to solve this situation. Additionally, shape recognition considers evolutionary computation techniques. They have been exposed to better performance in terms of accurateness than traditional optimization methods.
Analysis and implementation of semi-automatic model for vulnerability exploitations of threat agents in NIST databases

Proactive security plays a vital role in preventing the attack before entering active mode. In the modern information environment, it depends on the vulnerability management practitioners of an organization in which the critical factor is the prioritization of threats. The existing models and methodology follow the traditional approaches of a Common Vulnerability Scoring System (CVSS) to prioritize threats and vulnerabilities. The CVSS is not able to provide effectiveness to the security of the business of an organization. In contrast, the vulnerability analysis needs a model which can give significance to the prioritization policies. The model depends on the CVSS score of threats and compares various features of vulnerability like threat vectors, inputs, environments used by threat agent's groups, and potential outputs of threat agents. Therefore, the research aims to design a semi-automatic model for vulnerability analysis of threats for the National Institute of Standards and Technology (NIST) database of cyber-crime. We have developed a semi-automatic model that simulates the CVE (Common Vulnerabilities and Exposures) list of the NIST database between 1999 and 2021, concerning the resources used by the threat agents, pre-requisites input, attack vectors, and dormant results. The semi-automatic approach of the model to perform the vulnerability analysis of threat agent groups identified in a network makes the model more efficient and effective to addresses the profiling of threat agents and evaluating the CTI (Critical Threat intelligence feed). Our experimental results imply that the semi-automatic model implements the vulnerability prioritization based on the CVSS score and uses the comparative analysis based on the threat agent's vectors identified. It also provides potency and optimized complexity to an organization's business to mitigate the vulnerability identified in a network.
Comparison of Metaheuristics for Chaotic Systems Estimation

In recent years Parameter Estimation (PE) has attracted the attention of the scientific community.
AI Recruitment System Using EEG to Explore the Truth of Interviewers

COVID-19 pandemic is behind the implementation of the “AI recruitment system.” The number of companies trying to introduce AI recruitment systems is increasing because the non-face-to-face method is recommended due to the COVID-19 pandemic and the management change of the organization comes with the development of IT technology. Behind the positive evaluation that the development of AI technology improves the efficiency of work, the demand for fair and transparent recruitment procedures has been increasing as controversy over fairness and objectivity has increased due to various hiring irregularities. This study aimed to approach in a more systematic and scientific way to maximize the effect of recruiting talent. In the previous study, voice and video were identified based on ML. In situations where the problem of truth and falsehood is raised, this study conducted EEG-based biological experimental studies with a deep learning method to explore more objectively. Also, the experimental design applied biological experiments between brain activity patterns and brain regions as signals from EEG-based 14 channels to explore the truth/false authenticity of the experimenters. As a result of the experiment, the best performance and effect were shown in the CNN model with an accuracy of 91% truth and 89% false among the comparative analysis of Decision Tree, Random Forest, and CNN.
A Comparative Approach for Two-Dimensional Digital IIR Filter Design Applying Different Evolutionary Computational Techniques

The two-dimensional Infinite Impulse Response (2D-IIR) filters design has attracted attention in many fields of engineering due to its wide range of applications. Incorporating a user-defined filter in the 2D-IIR structure can be represented as an optimization problem. Nevertheless, considering that 2D-IIR filters can easily generate unstable transfer functions, they produce multimodal error surfaces which are complex to optimize. On the other hand, Evolutionary Computation (EC) techniques methods with the ability to explore complex search spaces for suitable solutions.
Classification of Adaptive Fuzzy Controllers

Since the early 1990s, adaptive fuzzy control has actively been under research.
Natural language processing of spatially crowdsourced data in petroleum revenue management

Abstract It has been over a decade of petroleum revenue utilisation in Ghana. Yet, there is a dearth of research on Ghanaians’ sentiments on petroleum revenue management in Ghana. However, research indicates that investigating citizens’ sentiments and addressing their grievances could prevent conflicts and promote better revenue utilisation in natural resource-rich countries. So, this study investigated Ghanaians’ sentiments about petroleum revenue management and its contribution to the Free Senior High School (SHS) programme in Ghana through an online survey. The study employed the quantitative approach in which the data was gathered through an online survey questionnaire and analysed using natural language processing techniques. The results show that the participants had negative sentiments about petroleum revenue management and the Free SHS programme in Ghana. However, they trust the managers and anticipate better revenue management in the future. The study recommends that the government should consult broadly with all stakeholders regarding petroleum revenue management to avoid potential conflicts. The article concludes that petroleum revenue managers can combine spatial crowdsourcing and natural language processing to extract citizens’ opinions at specific locations for better revenue management.
A Practical Guide to Chinese Word Segmenters and POS taggers

This paper introduces and explains in detail the overall information and tutorial about the commonly used Chinese morphological analyzers (e.g. ICTCLAS, Jieba, Stanford CoreNLP) which are employed in Chinese preprocessing tasks of Chinese Word Segmentation (CWS) and Part-of-speech tagging. In particular, the usability of the tools was enhanced by developing simple executables distributed to linguistic researchers unfamiliar with coding, along with rich execution examples in GUI and CLI environments. Plus, by introducing the unique features and functions of each morphological analyzer, it was recommended the most suitable analyzer tailored to the needs of individual researchers. As a guide for Chinese morphological analysis, which is inevitably accompanied by data-driven quantitative research, this study presents practical tools and useful guidelines for Chinese text preprocessing to researchers who want to expand their research interests to corpus linguistics, computational linguistics, and natural language processing.
Capturing Features and Performing Human Detection from Human Gaits Using RFID

Recently, radio frequency identification (RFID) sensing has attracted much attention due to its contact-free nature, low cost, light weight and other advantages. RFID-based person detection has also become a hot research topic, but there are still some problems in the existing research. First, most of the current studies cannot identify numerous people at a time well. Second, in order to detect more accurately, it is necessary to evaluate the whole-body activity of a person, which will consume a lot of time to process the data and cannot be applied in time. To solve these problems, in this paper we propose RF-Detection, a person detection system using RFID. First of all, RF-Detection takes step length as the standard for person detection, divides step length into specific sections according to the relationship between step length and height, and achieves high accuracy for new user detection through a large amount of training for a specific step length. Secondly, RF-Detection can better identify the number of people in the same space by segmenting continuous people. Finally, the data collection was reduced by expanding the data set, and the deep learning method was used to further improve the accuracy. The results show that the overall recognition accuracy of RF-Detection is 98.93%.
Models of Future Teachers’ Adaptation to New Post-Pandemic Digital Educational Scenarios

The aim of this study was to determine the post-pandemic learning adaptation scenarios from the perspective of university students from the Faculty of Education Science of the University of Seville (Spain) as a function of the competencies identified in the context of digital transformation. This was a non-experimental, descriptive study that used a short version of the Scale of Attitudes on the Perceptions of Future Teachers toward the New Post-pandemic Educational Scenarios (SANPES). The sample consisted of 972 students of the University of Seville (Spain) (72% women, 28% men), registered in the academic year 2021–2022. A cluster analysis was performed, using a hierarchical procedure (dendrogram), followed by a non-hierarchical procedure (k-means algorithm). The results show significant differences in the responses of the university students. Conclusions: progressive models or scenarios of adaptation to post-pandemic learning based on some student competencies, such as motivation, collaboration, self-learning and digital methodology: (a) initial adaptation model, (b) moderate adaptation model, and (c) advanced adaptation model.
3D Metrology Using One Camera with Rotating Anamorphic Lenses

In this paper, a novel 3D metrology method using one camera with rotating anamorphic lenses is presented based on the characteristics of double optical centers for anamorphic imaging. When the anamorphic lens rotates -90° around its optical axis, the 3D data of the measured object can be reconstructed from the two anamorphic images captured before and after the anamorphic rotation. The anamorphic lens imaging model and a polynomial anamorphic distortion model are firstly proposed. Then, a 3D reconstruction model using one camera with rotating anamorphic lenses is presented. Experiments were carried out to validate the proposed method and evaluate its measurement accuracy. Compared with stereo vision, the main advantage of the proposed 3D metrology approach is the simplicity of point matching, which makes it suitable for developing compact sensors for fast 3D measurements, such as car navigation applications.
Jointly Modeling Aspect Information and Ratings for Review Rating Prediction

Although matrix model-based approaches to collaborative filtering (CF), such as latent factor models, achieve good accuracy in review rating prediction, they still face data sparsity problems. Many recent studies have exploited review text information to improve the performance of predictions. The review content that they use, however, is usually on the coarse-grained text level or sentence level. In this paper, we propose a joint model that incorporates review text information with matrix factorization for review rating prediction. First, we adopt an aspect extraction method and propose a simple and practical algorithm to represent the review by aspects and sentiments. Then, we propose two similarity measures: aspect-based user similarity and aspect-based product similarity. Finally, aspect-based user and product similarity measures are incorporated into a matrix factorization to build a joint model for rating prediction. To this end, our model can alleviate the data sparsity problem and obtain interpretability for the recommendation. We conducted experiments on two datasets. The experimental results demonstrate the effectiveness of the proposed model.
A Study on the Development Pattern of Simulated Instruction of Pre-service Chinese Teachers.

This study conducted a simulated instruction for Pre-service Chinese teachers, and analyzed the development pattern of simulated instruction based on the self-reflection reports prepared by the Pre-service Chinese teachers. The self-reflection report consists of diagnostic evaluation and reflection evaluation. The components of diagnostic evaluation are introduction, motivation, progression, class composition, question-and-answer, voice, body movement, and media utilization, and the reflective evaluation elements are class preparation, progress, and evaluation. As a result of this study, the development pattern of simulated instruction could be classified into 6 types(development type, distributed type, incomplete type, stagnant type, regressive type and other type), and differences and commonalities were found in the simulated instruction.
Isochromatic-Art: A Computational Dataset for Digital Photoelasticity Studies

The importance of evaluating the stress field of loaded structures lies in the need for identifying the forces which make them fail, redesigning their geometry to increase the mechanical resistance, or characterizing unstressed regions to remove material. In such work line, digital photoelasticity highlights with the possibility of revealing the stress information through isochromatic color fringes, and quantifying it through inverse problem strategies. However, the absence of public data with a high variety of spatial fringe distribution has limited developing new proposals which generalize the stress evaluation in a wider variety of industrial applications. This dataset shares a variated collection of stress maps and their respective representation in color fringe patterns. In this case, the data were generated following a computational strategy that emulates the circular polariscope in dark field, but assuming stress surfaces and patches derived from analytical stress models, 3D reconstructions, saliency maps, and superpositions of Gaussian surfaces. In total, two sets of ‘101430’ raw images were separately generated for stress maps and isochromatic color fringes, respectively. This dataset can be valuable for researchers interested in characterizing the mechanical response in loaded models, engineers in computer science interested in modeling inverse problems, and scientists who work in physical phenomena such as 3D reconstruction in visible light, bubble analysis, oil surfaces, and film thickness.
A Video-Based Real-Time Tracking Method for Multiple UAVs in Foggy Weather

Aiming at the real-time tracking problem of multiple unmanned aerial vehicles (UAVs) based on video under fog conditions, we propose a multitarget real-time tracking method that combines the Deepsort algorithm with detection based on improved dark channel defogging and improved You Only Look Once version 5 (YOLOv5) algorithm. The contributions of this paper are as follows: 1. For the multitarget tracking problem under fog interference, a multialgorithm combination method is proposed. 2. By optimizing dark channel defogging, the complexity of the original algorithm is reduced from On2 to On, which simplifies the processing time of the defogging algorithm. 3. The YOLOv5 network structure is optimized so that the network can synchronously reduce the detection time while maintaining high-precision detection. 4. The amount of algorithm processing through image size compression is reduced, and the real-time performance under high-precision tracking is improved. In the experiments conducted, the proposed method improved tracking precision by 36.1% and tracking speed by 39%. The average time of tracking per image frame was 0.036s, satisfying the real-time tracking of multiple UAVs in foggy weather.
A linear algorithm for the restricted subtraction games

The subtraction game is a well-known problem in the field of game theory, which is often called a one-heap Nim game. There are two players, a heap of tokens, and a strategy matrix, in this game. It is called a restricted subtraction game if some constraints are imposed on the strategy matrix. The subtraction game could be solved by a classical algorithm with a query complexity no less than O ( N 2 ) and computed by an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mi>O</mml:mi><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:msup><mml:mi mathvariant="normal">l</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mi>N</mml:mi></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> quantum query algorithm with an error probability <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mi>ϵ</mml:mi><mml:mo>≤</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:mfrac></mml:math> . The restricted subtraction game proposed by Huang et al. can be computed by an exact quantum algorithm with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mi>O</mml:mi><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> queries. They also proved that the classical exact query complexity is Θ( N 2 ). In this study, we use an array and an adjacency list to replace the strategy matrix and propose another more general subtraction game than the one introduced by Huang et al. Based on dynamic programming and the replaced game strategy, two linear classical query algorithms are designed to solve the restricted subtraction game proposed by Huang and this study, respectively.