Reviewerly/exHarmony huggingface dataset:
Salvatore Florio and Øystein Linnebo.<i>The Many and the One. A Philosophical Study of Plural Logic</i>

Several natural languages such as English contain prima facie different kinds of referential and quantificational expressions. In particular, natural languages can contain singular forms of reference and quantification, as in, e.g., ‘Cecilia is Italian’ and ‘There is a basket of apples on the counter’. But they can contain also plural forms of reference and quantification, as in, e.g., ‘Cecilia and Francesca met in Cathedral Square’ and ‘There are some apples on the counter’. Famously, some sentences in natural language containing plural expressions cannot be formalized in first-order logic, as, e.g. the so-called Geach-Kaplan sentence: This kind of sentence requires at least the expressive power of second-order logic for its logical form to be captured appropriately. The very notions of plural reference and plural quantification are not new in the philosophical landscape.1 But it was from the mid-80s on, starting with the seminal work in [Boolos, 1998a; b], that so-called plural logic gained the attention of the community of logicians, philosophers in general, and philosophers of mathematics in particular.2 Ever since, more and more literature has been produced on it in several areas, e.g., logic and its philosophy, philosophical logic, and philosophy of mathematics, as well as philosophy of language, semantics, and linguistics.3
An improved stochastic gradient descent algorithm based on Rényi differential privacy

Deep learning techniques based on the neural network have made significant achievements in various fields of artificial intelligence. However, model training requires large-scale data sets, these data sets are crowd-sourced and model parameters will contain the encoding of private information, resulting in the risk of privacy leakage. With the trend toward sharing pretrained models, the risk of stealing training data sets through member inference attacks and model inversion attacks is further heightened. To tackle the privacy-preserving problems in deep learning tasks, we propose an improved Differential Privacy Stochastic Gradient Descent algorithm, using Simulated Annealing algorithm and Laplace Smooth denoising mechanism to optimize the allocation method of privacy loss, replacing the constant clipping method with adaptive gradient clipping method to improve model accuracy. we also analyze privacy cost under random shuffle data batch processing method in detail within the framework of Subsampled Rényi Differential Privacy. Compared with the existing privacy protection training methods with fixed parameters and dynamic privacy parameters in classification tasks, our implementation and experiments show that we can use less privacy budget train deep neural networks with the nonconvex objective function, obtain a higher model evaluation, and have almost zero additional cost in terms of model complexity, training efficiency, and model quality.
Cyber Security Legal Framework in India

Cybercrimes have become a global phenomenon due to the digital world. Cybercrimes in India are no exception. Because of this, it becomes very difficult to ensure cyber security. This adversely affects not only individuals but also companies, government, and society at large. As huge data is lost as a result of such crime, a law in India was enacted called the Information Technology Act, 2000 with an objective to prevent all types of crime relating to cyber security. Accordingly, other laws were amended like Indian Penal Code, 1860; The Negotiable Instrument Act, 1881; and the Indian Evidence Act, 1882. But, in spite of these stringent laws, the data of National Crimes Bureau shows an upward trend in every head relating to cybercrimes, thereby ensuring cyber security.
Emerging Computing Platforms for Solving Complex Engineering Problems

The basic utilities such as electricity, gas, and water are the types of services that can be commercialized and delivered at the doorstep of a user. The user is not concerned about the location of the service provider or the method of the service delivery. Computing is a new utility whose nature has been intensely transformed to a traditional utility where users can utilize the services as per their requirement and irrespective of their location. The significant advancement in information and communication technology needs a type of computing, which is required to meet the everyday demands of the generic computing class of users. There are many computing models such as cluster computing, grid computing, cloud computing, and many more which have been used in solving complicated problems of science and engineering. This chapter discusses the latest computing platforms which can provide fundamental computing services to users for fulfilling their computational needs.
Research into research

Conducting research into research. That is Daan Andriessen's primary task. At the Utrecht University of Applied Sciences, he has been researching the methodology of practical research for eight years. Design research – in all its variants – is an important movement within this research. In the chapter “Research Into Research,” Andriessen describes the development of design science research, which focuses on developing real solutions to pressing problems. In doing so, he asks himself how design can be used more effectively as a knowledge-generating activity. His personal challenge is to get design science research recognized as a scientific approach in the social sciences.
Application of 3D Animation Cluster System Based on Artificial Intelligence and Machine Learning

In many living phenomena, the behavior of social animals (such as ants, fish stocks, and birds) has attracted great attention, and many theories and models have emerged to simulate the behavior of biological communities. These studies are important in theory and practice and have wide application potential in optimization methods and product design, which leads to the so-called cluster intelligence. Development of the 3D animation system can be closely combined with cluster intelligence, and the full application of cluster intelligence is conducive to continuously improve the stability of animation system design, for animation system design to bring continuous design inspiration. The purpose of the development platform is to promote the development of enterprise projects, record each stage of the animation process in 3D, form a complete industrial chain, ensure the traceability and resilience of the whole process, and provide a comprehensive and effective solution for 3D animation production.
Review of the Blockchain Technology and Consensus Algorithms for IoT-Based Banking

The internet of things (IoT) is a global network that connects devices and people, yet it still confronts various challenges. This chapter explores the challenges banks face and how blockchain technology (integrated with IoT devices) can help address them. The chapter has also conducted a review of the different consensus mechanisms employed by the blockchain systems to find out which consensus method should be deployed by IoT-based banks. The underlying consensus method was first created for permissionless blockchain on a trustless network model using proof-of-work, which is a mathematical challenge that demands a lot of computing power. The findings of the study revealed that considering the regulatory requirements such as permissioned blockchains are best suitable for banks due to know your client (KYC) and anti-money laundering (AML) regulatory requirements. Additionally, for such a blockchain network, the practical byzantine fault tolerance (PBFT) and proof of authority (PoA) consensus methods ensure business resiliency.
Ranking-based scores for the assessment of aesthetic quality in photography

Aesthetic quality assessment in photography has gained popularity in recent years, due to the huge volume of images generated by social networks and the variety of applications this task provides. Main proposals were dependent on the data available to learn from, where datasets are composed by images with ratings from different users. Most of these works reduce the problem to a binary classification [ snapshots , professional shots ] using rating average. Some articles indicate that the current forms to measure aesthetic quality present important weaknesses, since most of the ratings are concentrated in central values. This paper presents a novel solution by means of Aesthetic Ranking Value (ARV) and Weighted ARV (WARV). Both compute a score value from votes’ distribution able to determine, in an aggregated way, how (aesthetically) good an image is. In this scenario, ARV and WARV show better performance as aesthetic quality scores than the mean because they obtain more distributed and discriminatory information. A study using state-of-the-art frameworks is conducted and a new model based on the ranking relationships is presented. All models are tested on as many tasks as aesthetic scores are available, since each score represents a new ground-truth. Although it is not directly comparable, in this study distinct measurements and statistics support the hypothesis that ARV and WARV are more homogeneously distributed between the extremes. We would like to remark that the change in the ground-truth implied by the ranking-based scores allows perfectly the use of the techniques previously designed for aesthetics quality assessment. • This work presents two new ranking-based aesthetic quality scores: ARV and WARV. • These aesthetic scores are compared to the traditional approach using the mean of votes. • ARV and WARV prove to be more suitable as an aesthetic score by returning values that are better distributed over the entire range of values. • The most important frameworks that solve the aesthetic quality can be easily adapted to return the proposed aesthetic scores.
Distribution and provenance of heavy minerals from recent sediments of Green Lake, North Brazil, revisited

Geostatistical and multivariate statistical analyses were applied to heavy mineral data from an Amazonian fluvial-lake system near the Tapajós River mouth to investigate the spatial distribution and source-area of sediments. Twenty-one points were investigated, and the physical characteristics of the Green Lake deepest point were determined. Sand accumulates in the lake margins and mud quantity increases towards the lake center. Heavy mineral assemblage is composed of zircon, tourmaline, kyanite, rutile, staurolite, anatase, sillimanite, garnet, and spinel. Tourmaline, staurolite, and spinel are more abundant in the southeast area of the lake, while kyanite is dominant in the north area and zircon is in the whole lake except in its southeast area. Zircon - tourmaline and zircon - staurolite pairs are negatively correlated (r= -0.947 and -0.775, respectively), while tourmaline - staurolite and sillimanite - anatase pairs have a positive correlation (r= 0.628 and 0.675, respectively) which indicate different source rock types. Geostatistical analysis grouped the heavy minerals in three grups: Group 1 (tourmaline – staurolite – spinel - kyanite) and Group 2 (garnet – rutile – sillimanite - anatase) related to metamorphic source rocks ranging from medium to high grade, and Group 3 (zircon) related to acid igneous source rocks. The heavy mineral assemblage of Green Lake is analogous to the assemblage of the Alter do Chão Formation, indicating that this formation is the source of sediments of Green Lake.
Reflectance spectroscopic and geochemical characteristics of hydrocarbon microseepage-induced sediments from Assam–Arakan Fold Belt, India: Implications to hydrocarbon exploration

Characterizations of hydrocarbon microseepage-induced surface sediments are essential to utilize remote sensing and geochemistry as effective petroleum exploration tools. However, only few researchers have attempted spectroscopic and geochemical characterizations of microseepage-affected sediments and that too are focussed on arid or semi-arid climatic regions. This article aims to delineate the spectral, mineralogical and geochemical characteristics of the microseepage-affected sediments in Assam–Arakan Fold Belt (AAFB), NE India, dominated by intense precipitation. The analytical approach followed consists of diffuse reflectance spectroscopy, X-ray diffraction (XRD), X-ray fluorescence (XRF), and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) analyses. Reflectance spectroscopy reveals that microseepage-induced sediments are enriched in illitic clay and deficient in goethite than the unaffected regions. XRD studies support the presence of illitic clays in the sediments. Major element analyses indicate higher Al2O3 and K2O and lower Na2O and SiO2 in the altered sediments. Trace element patterns point out enrichment of V, Cu, Zn, Ga, Zr, and Mo and depletion of Li, Cr, Co, Ni, Rb, Sr, Sc, and Y in the microseepage-affected sediments. Normalized rare earth element (REE) patterns are similar for both the altered and unaltered sediments, but the former are deficient in REE contents. Though based on limited sample analysis, the study indicates microseepage affected and unaffected sediments differ in spectral and geochemical characters. This may help in strengthening the microseepage model for hydrocarbons in high precipitation areas and thereby may help in reducing exploration risks in the basin.
Data Privacy Policy

In an era dominated by technology and trade, the business processes have started looking forward to innovations and ever-changing applications. This paradigm shift in the thought processes is not limited to any industry per se. Thus, the cyber practices have developed various tools, practices, as well as avenues for applications in real-time situations. With the advancement of these technological platforms, various gadgets in the form of smart phone, etc. have also complimented to the use of the same. Retail processes like any other business practices have also seen the changes due to these technological interventions. As a process responsible for creating direct customer touch points, retail at its operational level needs to be vigilant about the threats, insecurity, loss of privacy, and challenges coming because of the technological adoptions. Therefore, a systematic and well-designed data privacy policy with all possible components incorporated can lead to a secure environment.
Subjective quality assessment of synthesized videos for windowed six degrees of freedom video system

For synthesized videos, view synthesis and compression are two main types of artefacts influencing the perceived quality. Moreover, vertical disparity co-exists with horizontal disparity in windowed six degrees of freedom (6DoF) system, and the viewers have some possible movement when exploring the content. This letter constructs a windowed 6DoF synthesized video quality database involving 128 distorted videos, using four sequences, four levels of compression, and four rendering schemes. The impact of two navigation trajectories on the perceived quality of windowed 6DoF videos is studied subjectively. The authors also evaluate the performances of several objective quality metrics on the proposed database; the results reveal the effectiveness and necessary of our database.
Chapter 12. Differential object marking in heritage and homeland Italian

We examine variable patterns of use of differential object marking (DOM) in conversational Italian recorded in Toronto, Canada, and Calabria, Italy. An exhaustive sample of 366 direct objects, produced by Homeland and three generations of Heritage speakers, shows retention of the DOM system. Successive generations have lower rates of DOM, but this is because they don’t produce enough tokens of certain syntactic and semantic types (e.g., left-dislocated or indefinite pronouns). Thus, they have less opportunity to use DOM: token distributions account for their lower rates. In contexts with sufficient tokens, significant contrasts emerge, indicating that all generations retain the conditioning of relevant factors (Definiteness, Referent of Object, Verb Type, Dislocation). No effects of social network or linguistic practices emerged.
Student T-Based Maximum Correntropy Unscented Kalman Filter for UAV Target Tracking

Considering that the Student T distribution has heavy-tailed non-Gaussian property, the heavy-tailed non-Gaussian noises induced by strong maneuvering target are modeled as the Student T distribution, and a cost function based on the Student T distribution as the kernel function is designed. On this basis, the Student T-based Maximum Correntropy Unscented Kalman filter (TMCUKF) is proposed based on the designed Student T distribution cost function together with the maximum correntropy criterion. In addition, the convergence condition and proof of the proposed method are also given. This algorithm has strong suppression ability to the heavy-tailed non-Gaussian noise, and has the ability to improve the tracking accuracy.
Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks

Digitalization brings challenges and new opportunities to the development of landscape gardening, "smart gardening," which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts "digital landscape design logic" to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools.
3D Reconstruction of Traditional Handicrafts Based on Binocular Vision

As an indispensable and essential material part of China's traditional culture, traditional handicrafts have played a more important role in modern life. It is not only the crystallization of the wisdom of the Chinese people of all nationalities but also an excellent platform to display China's long-standing culture. However, the protection and reconstruction of traditional handicrafts are relatively lagging. To realize the reconstruction of traditional handicrafts, this paper proposes an improved AD-Census stereo matching algorithm of a binocular measurement system for object positioning and three-dimensional reconstruction. Methods based on the principle of binocular vision measurement include firstly, histogram equalization, adaptive threshold Canny edge extraction, and expansion are used for image preprocessing; Secondly, the calibration method is used to complete camera calibration, remove camera distortion through stereo correction, and improve the AD-Census stereo matching algorithm based on gradient division of weak texture and edge area. Finally, the parallax map generated by the improved algorithm is used to realize three-dimensional reconstruction. The experimental results show that this method can control the error standard deviation within 0.5 mm, can realize the reconstruction of traditional handicrafts, have high accuracy, and can be widely used in practice.
Instrumental Interaction and Subversion in John Cage’s <i>Concert for Piano and Orchestra</i>

This article examines the dynamic nature of instrumental interaction in indeterminate music, using John Cage’s Concert for Piano and Orchestra (1957–58) as a case study. Performing the Concert requires instruments to be dismantled, detuned, and destabilised, and within the parts themselves techniques are often stretched or combined to the point of complete breakdown. Drawing on interviews and observational studies undertaken with the experimental music ensemble Apartment House, I explore how the indeterminacies of the instrumental parts are enacted and negotiated in performance. The article suggests the ways in which indeterminacy is not an abstract compositional device, but is distributed across musicians, their instruments, and their environments. More broadly, it shows how a reading of indeterminacy through performance both underlines and complicates the relationships between individuals, objects, and the kinds of agency that are enacted and animated in creative work.
Privacy-Preserved Federated Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans

Semantic segmentation over three-dimensional (3D) intra-oral mesh scans (IOS) is an essential step in modern digital dentistry. Many existing methods usually rely on a limited number of labeled samples as annotating IOS scans is time consuming, while a large-scale dataset of IOS is not yet publicly available due to privacy and regulatory concerns. Moreover, the local data heterogeneity would cause serious performance degradation if we follow the conventional learning paradigms to train local models in individual institutions. In this study, we propose the FedTSeg framework, a federated 3D tooth segmentation framework with a deep graph convolutional neural network, to resolve the 3D tooth segmentation task while alleviating data privacy issues. Moreover, we adopt a general privacy-preserving mechanism with homomorphic encryption to prevent information leakage during parameter exchange between the central server and local clients. Extensive experiments demonstrate that both the local and global models trained with the FedTSeg framework can significantly outperform models trained with the conventional paradigm in terms of the mean intersection over union, dice coefficient, and accuracy metrics. The FedTSeg framework can achieve better performance under imbalanced data distributions with different numbers of clients, and its overall performance is on par with the central model trained with the full dataset aggregated from all distributed clients. The data privacy during parameter exchange of FedTSeg is further enhanced with a homomorphic encryption process. Our work presents the first attempts of federated learning for 3D tooth segmentation, demonstrating its strong potential in challenging federated 3D medical image analysis in multi-centric settings.
Improved marine predators algorithm with hybrid-strategy

Aiming at the shortcomings of marine predators algorithm, such as insufficient operation accuracy and easy to fall into local optimization, an improved marine predators algorithm with hybrid-strategy is proposed. Chebyshev chaotic map is introduced in the initialization stage of the algorithm to homogenize the population distribution and reduce the randomness of the operation results. As the algorithm enters the later stage of iteration, the degree of individual assimilation gradually increases, and the Fish Aggregating Devices effect of marine predators algorithm is not enough to help the algorithm break away from local optimization. The Cauchy mutation strategy is introduced as the perturbation term to help the algorithm identify the local optimal solution more clearly. The experimental results show that the improved marine predators algorithm with hybrid-strategy improves the overall performance of the algorithm.