Reviewerly/exHarmony huggingface dataset:
Image deblurring: comparison and analysis

Abstract Technological advancements and the advent of digital devices and media make images an important part of today’s social life. Image blurring is a common challenge that results from multiple factors such as object movement, camera shake, and raindrops, among others. Image deblurring has progressively become an important field of image restoration as directed by research findings. After research for more than five decades, significant research efforts have yielded useful technologies of image deblurring. This article provides an overview of the current knowledge on image deblurring technology by focusing on the classical methods and modern trends in the field. The article reviews the conventional methods and achievements made in past studies using evidence from 34 scholarly articles. The article also examines the application of algorithms in specific deblurring methodologies adopted in recent works. It covers the recent trend of learning-based models used to restore images and their effectiveness. They include Convolutional Neural Networks, Recurrent Neural Networks and Graph Convolutional Networks. Novel deep-learning deblurring techniques are also explored. Based on the findings, issues of concerns, opportunities and direction for future research are provided to advance image deblurring technologies.
Improved value iteration network for path planning

Abstract In this study, a series of improved path planning algorithms are designed for path planning tasks in autonomous control based on deep reinforcement learning. The Value Iteration Network (VIN) is used to deal with the path planning problem. Origin VIN performs well on small size maps, but when it comes to a bigger size of map on test set, the success rate decreased. In order to solve the problem that origin VIN lacks long-distance multi-step planning ability on large maps and generalization ability is insufficient, a three-step improvement was made. First of all, in view of the inconvenient data flow and the disappearance of gradients caused by the network being too deep, the jump connection structure is used to obtain the deeper VIN, in which the accuracy of the experiment is improved. Secondly, with the purpose of solving the problem that the complexity of the model is greatly increased due to the deepening of the network, Batch normalization is used to obtain a new network with dueling architecture plus batch normalization layer, which further accelerates the convergence speed of the network. Third, to deal with the global path planning problem on the big map, the hierarchical network structure is adopted for hierarchical value iteration, and the Hierarchical Structure VIN is obtained. In Hierarchical Structure VIN, the long-term planning ability and generalization ability of the algorithm have been significantly improved, and the algorithm could figure out the large-scale and complex path planning problem.
Intelligent Multi-agent Distributed System for Improving and Secure Travel Procedures: Al-Karama-King Hussein Bridge Study Case

Passports and other types of identification can take advantage of advanced technologies. Many national and international organizations are developing machine-readable solutions for biometric data. However, the selection of technologies remains questionable due to privacy and security concerns. The State of Palestine has recently started issuing biometric passports, and therefore such techniques had to be highlighted in depth to be employed at border bridges, especially et al.-Karama-King Hussein Bridge. This paper proposes an intelligent multi-agent distributed system for improving and securing travel procedures based on RFID technology and blockchain, replacing manual procedures et al.-Karama-King Hussein Bridge, and making travel mobility more secure. We implement agent-based and discrete event simulation for the proposed system and design an experimentation framework where we can reliably vary several parameters. The result from the experiments shows that the number of agents involved in the travel procedures, particularly in the early and last stages, and the optimal situation to decrease passenger overcrowding at the bridge and travel time demand a rapid reaction in the number of agents corresponding with the crossing’s capacity and available resources.
CNN combined with data augmentation for face recognition on small dataset

Abstract Faces have universal structures yet contain distinct features among individuals. Recognizing individuals based on their faces has always been a popular topic in pattern recognition, and computer vision and many traditional approaches have yielded satisfying results. In recent years, rapid growth in deep learning has encouraged researchers to use deep learning methods to solve authentication problems. Convolutional neural networks are one of the most popular deep neural networks with multiple layers and the ability to reduce parameters by using kernels to capture features from input. It has outstanding performance in pattern recognition due to its ability to extract features and take images as inputs. In machine learning, data augmentation is a technique to seemingly enlarge a dataset to avoid underfitting or overfitting problems caused by insufficient data. This paper uses convolutional neural networks to solve face recognition problems on a small dataset. It compares performance with traditional face recognition methods such as Principal Component Analysis and examines the impact on performance using data augmentation. Overall, data augmentation boosts the accuracy of the network but also results in an unsteady learning curve. The convolutional neural network performs well on pattern recognition and obtains an accuracy of 94% in an augmented dataset with only two convolutional layers.
A user-friendly Bees Algorithm for continuous and combinatorial optimisation

This paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems.
The Patentability of AI-Related Subject Matter According to the EPC as Implemented by the EPO

This chapter comments upon the necessity and the feasibility of patenting AI-related subject matter, considering AI’s distinguishing features, i.e., its technical complexity, autonomy, and self-evolving capacities. These issues are examined in light of the European legal order. The investigation endeavours to identify grounds advocating for a change of tack on the handling of AI patent applications within the European Patent System.
Legal Personhood for Autonomous AI: Practical Consequences in Private Law

The chapter examines the option of recognizing legal personality to autonomous AI machines from the perspective of private law, focusing on practical challenges. In particular, the chapter shall examine the scope and practical implications of recognizing legal personality to autonomous AI systems regarding: (a) the estate and the liability of the AI (including vicarious liability), (b) the right of personality and its protection, (c) the capacity to conclude binding and legally enforceable contracts as a contractual party and the application of contract law including the law of mistake and agency, (d) the question of identification and legal residence. Furthermore, it shall try to point out the interdependence between the legal status of AI and that of its user and the complexities arising from it.
International Perspectives on Regulatory Frameworks: AI Through the Lens of Patent Law

This chapter considers the approach to regulating artificial intelligence (AI) from a patent law perspective, exploring how existing forms of regulation can help inform our stance vis-à-vis AI. The focus is on contextualising the developments in software and pharmaceutical regulation through patent law, and then applying these lessons to AI. The EU, US and Japan all represent different approaches to the regulation of AI, and this diversity already impacts the existing relationship between AI and the patent system. The chapter concludes by recommending that self-regulation for AI inventions (but supported by robust systems of data protection) will be important in encouraging the commercial growth of AI. The patent system represents an important normative filter for AI, but the experience of software and pharmaceuticals in patent law highlights how it is more successful at excluding the most extreme iterations of a technology.
Review ArticleReview of R. Routley. <b>Ultralogic as Universal? The Sylvan Jungle – Volume 4</b> , edited by Z. Weber, with commentary essays by E. Mares, R. Brady, C. Mortensen. Synthese Library vol. 396. Cham, Springer, 205pp., € 90.94. ISBN 978-3-319-91973-7.

Click to increase image sizeClick to decrease image size Notes1 Parsons (Citation1983, 179) writes: ‘[Routley’s] discussion [in Ultralogic] is often provocative and almost always highly programmatic’.2 On a ‘turn’ in Routley’s philosophy in these years see Ficara Citation2021, § 3.3 During the 1960s the idea of relevant logic (or relevance logic in north America) takes shape with Anderson and Belnap but also, perhaps independently, with Routley. See Weber Citation2019, § 3.2. and Hyde Citation2014, 66, who underline the difference between Routleyanism and Andersonbelnapism in approaches to relevant logic – Burgess Citation1983, 45 states they are so dissimilar that it is misleading to apply a single label ‘relevantism’ to both.4 The longstanding friendship and collaboration between Routley and Priest began in the late 1970s, and led to the production of the joint seminal work on paraconsistency Essays on Paraconsistency (written in 1982 and published in-house under the title On Paraconsistency in the Philosophy Department’s Research Papers in Logic series in 1983, with a further edition reprinted the next year in the Research Series in Logic and Metaphysics, and finally in 1989 with Philosophia Verlag) in which the birth of dialetheism is sanctioned (Ficara Citation2021, 12).5 On Petrov’s influence on Routley’s dialectical turn see Ficara Citation2021, § 3.6 See the work done on logical pluralism (versus universalism) following Beall and Restall Citation2005: Griffiths and Paseau Citation2022 and Engel Citation2023 among others.7 On the aspirational character of the universality of ultralogic see also Weber Citation2019, § 7.
Methods and software for significant indicators determination of the natural language texts author profile

Methods for the formation and optimization of author profiles are presented. The author profile is an image – a vector in a multidimensional space, which components are author’s texts measurements by a number of methods based on 4-grams, stemming, recurrence analysis and formal stochastic grammar. The author’s profile is a model of his language, including vocabulary, sentence syntax features. A comparative analysis of each of the methods effectiveness is carried out. By means of the genetic algorithm, a reduced profile of the author is formed. Insignificant indicators are excluded, which allows to reduce their number by 20%. The reduced author’s profile contains attributes that are significant for this author and is an effective attribution of a particular author.
Experimental Quantification of Sensor-Based Stereocameras’ Extrinsic Parameters Calibration

Three-dimensional digital image correlation (3D-DIC) has shown to be a powerful tool to extract full-field displacement and deformations of structures using a series of synchronized stereo images. Before performing 3D-DIC measurements, the stereovision system must be calibrated to determine the relative position of the two cameras. Traditionally, this has been done by taking pictures of a calibration target whose dimensions are well known. Because the calibration target must be as large as the inspected object to properly calibrate the entire measurement volume (i.e., large-area calibration), for large fields of view, the calibration procedure becomes a challenge. This research aims at reducing the complexity of large-area calibration by measuring the extrinsic parameters of the stereovision system using a suite of sensors. In particular, three inertial measurement units and a laser distance meter are used to measure the cameras’ relative position and orientation in space. In this chapter, the performance of the proposed sensor-based extrinsic calibration is compared with the traditional image-based calibration method. Laboratory tests show that the extrinsic parameters computed with the sensor-based method can be used for performing a 3D-DIC analysis that yields an error below 5% when the results are compared with the displacement retrieved using a traditional approach. The results of this study demonstrate that the proposed sensor-based calibration approach is a valid and a faster alternative to traditional image-based calibration methods. The sensor-based calibration has the potential to expand the use of 3D-DIC by making it more suitable for large-scale applications.
Effects of Image-Pair Processing Styles on Phase-Based Motion Extraction

Phase-based optical flow (PBOF) is a growing discipline in vibration extraction methods. Based on camera data, PBOF uses image gradients and image-pair differences to extract motion from video. Commonly, this technique is combined with the more ubiquitous digital image correlation, due in part to the restriction of PBOF to small, sub-pixel vibrations. In this chapter, work was done to delve deeper into the nominal upper bound and determine its relation to the chosen image-pair processing style. In particular, the upper bound was evaluated with respect to static (current frame-to-reference), dynamic (current frame-to-previous frame), and hybrid dynamic-static processing styles. Results include advantages of standard static and dynamic processing styles, leading to the presentation of the so-called region of goodness associated with the hybrid processing style. Discussion follows for how the hybrid processing style can be used for large-motion handling in simple structures, as well as modifications required for those that are heavily textured. The hybrid processing style is shown to extend the upper bound to the number of pixels between parallel edges within the structure, increasing the efficacy of PBOF to larger stationary vibrations.
Orthorectification for Dense Pixel-Level Spatial Calibration for Video-Based Structural Dynamics

Video-based structural dynamics techniques have shown great promise for applications such as monitoring the structural health of critical infrastructure such as locks and dams. Full-field approaches that make use of direct methods such as optical flow have the added attractive quality that they have demonstrated an ability to detect small damage on account of the high spatial density of pixels associated with imager measurements. For the case of inspecting critical infrastructure such as locks and dams, deformation measurements have also been shown to have utility. Imagers can be used to measure deformation; however, for the case of a complex 3D scene, every pixel can potentially have different sensitivity to deformation on account of the perspective transformation associated with pinhole camera photography. Telecentric lenses can be used to avoid perspective projection effects; however, telecentric lenses are large, expensive, and can only observe an area equal in size to their aperture greatly reducing their suitability for infrastructure inspection applications. In this work, we adapt techniques for orthorectification to attempt to address the issue of calibrating individual pixel deformations measurements across a scene. Orthorectification typically requires a height map information in the direction normal to the plane used to generate the orthophoto. The emergence of sensors such as time-of-flight imagers with high spatial resolution has made collecting these measurements more accessible for terrestrial infrastructure inspection applications. We demonstrate the ability to fuse subpixel motion measurements captured using a perspective camera, with 3D geometry data such as that which can be captured using a time-of-flight imager. This work focuses on the case of structures exhibiting planar geometry. We then show how these techniques impact the ability to perform video-based structural dynamics analysis.
Who Hacked My Car? Designing Autonomous Vehicles to Support Driver Response to Security Threats

Modern vehicles have evolved into intelligent and autonomous transportation systems, yet such computer-driven developments also create opportunities for unforeseen cyber-security attacks on self-driving cars. These threats may pose critical risks to human lives but there is a considerable lack of understanding in designing vehicle systems to support driver response during rare and hazardous security events. We conducted an interview with autonomous vehicle drivers to understand their perception of automotive threats and design needs for effective response during security events. Results show that drivers expect information relating to malfunction detection, trouble-shooting instructions, and log data from the vehicle. Moreover, drivers were more likely to act based on instincts the more the driver perceives the threat to be urgent, rather than process the situation during the initial stages of an attack. We thus propose a ‘Driver Response Phase’ and corresponding design implications for future vehicles that support user response to security risks.
The Transformation of English Interdisciplinary Education in the Context of Artificial Intelligence

Currently, artificial intelligence products represented by ChatGPT are having a significant and far-reaching impact on school education. However, there are many education practitioners who are concerned about their negative impact on the education industry, and currently there are education departments and colleges and universities that have introduced a ban on the use of ChatGPT. However, it is better to block than to block, and how to deal with it properly should be the focus of teaching administrators and related organizations. For English education, we should not only teach students to use English to communicate, but also focus on cultivating students' national emotions and family values, so that they can grow up to be internationalized talents with both integrity and talent. English interdisciplinary theme learning as a new way of learning, in theory, it can provide path support for the implementation of students' core literacy through the cultivation of students' problem solving ability, but in practice, it is inevitable to encounter the problem that the English level of our primary and secondary school students is much lower than the cognitive level, how to grasp the opportunity of the times, the use of AI to empower English education, and to promote the transformation and upgrading of the teaching mode, it is worth exploring and thinking about. It is worthwhile for us to explore and think.
Characteristics of Storytelling in Epic Cinema

The core of epic film lies in telling a good story on the screen, in order to achieve this purpose, epic films are taken from history and claimed to be faithful to history, but these are not historical reality, and can only be called image reality. Based on this effect, the core of the story to be told in epic film is hidden behind the screen, and it is for the audience to mobilise their own deep psychological structure to reconstruct this again after watching the film, and the charm and vitality of this story is thus reflected.
On the Formation and Development of Hashimoto Grammar - Centered on Section and Connected Section

Hashimoto grammar is the theoretical foundation and source of "school grammar", and "school grammar" is the application and development of Hashimoto grammar in practical teaching. Therefore, based on previous research, this article discusses the formation and development of Hashimoto grammar. The main body of this article is divided into three parts. The first part summarizes the background and summary of the formation of Hashimoto grammar. The second part analyzes the core and development of Hashimoto grammar. Including section and connected section based on the "section theory". The third part attempts to correctly summarize the advantages and disadvantages of Hashimoto grammar. Thus, it can be argued that although Hashimoto grammar plays an important role in school teaching, it still has theoretical problems. Therefore, we should apply Hashimoto grammar correctly to better serve Japanese language education.
Analysis of Regional Differences in Teacher Resources in Secondary Vocational Schools in the Context of Beijing-Tianjin-Hebei Synergy

Under the background of beijing-Tianjin-Hebei coordinated development, the allocation of secondary vocational education resources has been deeply affected.Firstly, based on the relevant data of teachers' educational resources in Beijing, Tianjin and Hebei secondary vocational Firstly, based on the relevant data of teachers' educational resources in Beijing, Tianjin and Hebei secondary vocational schools in 2020, this paper defines each index of resource allocation in Beijing, Tianjin and Hebei secondary vocational schools. Secondly, the coefficient of variation method is used to calculate and analyze the related factors that affect the educational resources of secondary vocational schools. Finally, optimization strategies are proposed for the unbalanced resource allocation of teachers in Beijing-Tianjin-Hebei secondary In addition, the Joint Committee also recommends that the Government of the Republic of China (RDC), in cooperation with the Ministry of Education of the Republic of China and the Ministry of Education of the People's Republic of China (MEC), should consider the possibility of establishing a new system of education for teachers. reference for the establishment of modern vocational education system and optimization of resource allocation in first-class vocational education in The aim of this study is to provide a comprehensive overview of the development of vocational education in the Beijing-Tianjin-Hebei region.
Almost Injective and Invertible Encodings for Jacobi Quartic Curves

This paper introduces a novel encoding scheme for hashing values from the finite field $$\mathbb {F}_p$$ to points on Jacobi quartic curves. These curves possess efficient group law and are immune to timing attacks. The proposed encoding scheme achieves almost injective and invertible mappings of the input values into Jacobi quartic curves. When $$p \equiv 3 \mod 4$$ , our encoding saves $$ 2 \textbf{I} + \textbf{D} - 8 \textbf{M} - 4 \textbf{S}$$ compared to existing methods. This improvement amounts to approximately $$50\%$$ on average when compared to existing methods. The encoding scheme can be used in a variety of cryptographic applications that rely on elliptic curves, such as identity-based encryption schemes and private set intersection protocols.