Reviewerly/exHarmony huggingface dataset:
On Normalized Rabotnov Function Associated with Certain Subclasses of Analytic Functions

In this paper, we investigate some sufficient conditions for the normalized Rabotnov function to be in certain subclasses of analytic and univalent functions.The usefulness of the results is depicted by some corollaries and examples.
Identification of possible sources for potentially toxic elements and polycyclic aromatic hydrocarbons and their spatially varying relationships in urban soils of Dublin, Ireland

Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) harm the ecosystem and human health, especially in urban areas. Identifying and understanding their potential sources and underlying interactions in urban soils are critical for informed management and risk assessment. This study investigated the potential sources and the spatially varying relationships between 9 PTEs and PAHs in the topsoil of Dublin by combining positive matrix factorisation (PMF) and geographically weighted regression (GWR). The PMF model allocated four possible sources based on species concentrations and uncertainties. The factor profiles indicated the associations with high-temperature combustion (PAHs), natural lithologic factors (As, Cd, Co, Cr, Ni), mineralisation and mining (Zn), as well as anthropogenic inputs (Cu, Hg, Pb), respectively. In addition, selected representative elements Cr, Zn, and Pb showed distinct spatial interactions with PAHs in the GWR model. Negative relationships between PAHs and Cr were observed in all samples, suggesting the control of Cr concentrations by natural factors. Negative relationships between PAHs and Zn in the eastern and north-eastern regions were related to mineralisation and anthropogenic Zn–Pb mining. In contrast, the surrounding regions exhibited a natural relationship between these two variables with positive coefficients. Increasing positive coefficients from west to east were observed between PAHs and Pb in the study area. This special pattern was consistent with prevailing south-westerly wind direction in Dublin, highlighting the predominant influences on PAHs and Pb concentrations from vehicle and coal combustion through atmospheric deposition. Our results provided a better understanding of geochemical features for PTEs and PAHs in the topsoil of Dublin, demonstrating the efficiency of combined approaches of receptor models and spatial analysis in environmental studies.
SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis

Abstract Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS .
A Reflection on Typology and Verification Flaws in consideration of Biocybersecurity/Cyberbiosecurity: Just Another Gap in the Wall

Verification is central to any process in a functional and enduring cyber-secure organization. This verification ishow the validity or accuracy of a state of being is assessed (Schlick, 1936; Balci, 1998). Conversely, breakdownin verification procedures is core to the interruption of normal operations for an organization. A key problemfor organizations that utilize biology as an interlock within their systems is that personnel lack sufficient abilityto verify all practically relevant biological information for procedures such as a nurse logging a blood draw, or amolecular biology technician preparing agar to culture microbes for study. This has several implications, one ofwhich is our diminished ability to approximate and defend against emerging biologically-linked cyberthreats.These could be in the form of mis- or dis-information, contaminants, or calculated threats to vital supplies.Two important questions to ask are: “What may be the implications of diminished ability to undergo strictverification measures (such as triple redundancy and technological distancing).” And “how does this impactour ability to anticipate and make changes for verification of biological processes?” This paper aims to discusskey areas where verification gaps exist and how to bridgethos gaps. Towards this, we cover data integrity,implications of the lack of verification, triple redundancy, technological distancing, biosafety concerns, andmore. All of this will factor into the ability of organizations with proximity to biosecurity to anticipate nationalchanges to biological processes that are nationally relevant.
The Relationship between Attention-Deficit/Hyperactivity Symptoms and Addiction Potential in Individuals with Substance Dependence: The Mediating Role of Executive Functions and Emotion Self-Regulation

The Relationship between Attention-Deficit/Hyperactivity Symptoms and Addiction Potential in Individuals with Substance Dependence: The Mediating Role of Executive Functions and Emotion Self-Regulation
Secure Digital Data Sharing on the Basis of Fractional-Order Chaotic Systems Under Noisy Channel

In recent years, many researchers have been interested in bandlimited digital chaos-based communication because in practice, all channels are indeed considered as band-limited ones (either low-pass or band-pass) and the channel bandwidth is limited by the maximum data rate. Therefore, several communication schemes based mainly on the synchronization of discrete-time fractional-order chaotic systems have been proposed. In this paper, we propose a new robust bandlimited communication scheme grounded basically on the synchronization of two fractional-order discrete chaotic systems. In order to improve the signal-to-noise ratio of the proposed scheme, an FIR type filter is connected to the input of the receiver. The results of proposed communication system simulation illustrate the performance of the proposed and studied approach.
Intertextuality as an Inherent Tool for the Composition and Interpretation of Texts: A Theoretical Reappraisal

The aim of the paper is to discuss the operational concepts and theory of intertextuality as a postmodern theory.Postmodern theory is a theory that emerged in the second half of the 1960s.This theory was born as a reaction to modernity and its ideals.By the 1970s, postmodern aesthetics, on which postmodern theory was based, began to be felt in almost every field of art, from architecture to painting, from literature to cinema.Intertextuality seems such a useful term because it foregrounds notions of relationality, interconnectedness, and interdependence in modern cultural life.In the Postmodern epoch, theorists often claim, it is not possible any longer to speak of originality or the uniqueness of the artistic object, be it a painting or novel, since every artistic object is so clearly assembled from bits and pieces of already existent art.An author or poet can use intertextuality deliberately for a variety of reasons.They would probably choose different ways of highlighting intertextuality depending on their intention.They may use references directly or indirectly.They might use a reference to create additional layers of meaning or make a point or place their work within a particular framework.A writer could also use a reference to create humour, highlight an inspiration or even create a reinterpretation of an existing work.The reasons and ways to use intertextuality are so varied that it is worth looking at each example to establish why and how the method was used.
A comparison of approaches to accessing existing biological and chemical relational databases via SPARQL

Abstract Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project.
Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination

Human activity recognition (HAR) performs a vital function in various fields, including healthcare, rehabilitation, elder care, and monitoring. Researchers are using mobile sensor data (i.e., accelerometer, gyroscope) by adapting various machine learning (ML) or deep learning (DL) networks. The advent of DL has enabled automatic high-level feature extraction, which has been effectively leveraged to optimize the performance of HAR systems. In addition, the application of deep-learning techniques has demonstrated success in sensor-based HAR across diverse domains. In this study, a novel methodology for HAR was introduced, which utilizes convolutional neural networks (CNNs). The proposed approach combines features from multiple convolutional stages to generate a more comprehensive feature representation, and an attention mechanism was incorporated to extract more refined features, further enhancing the accuracy of the model. The novelty of this study lies in the integration of feature combinations from multiple stages as well as in proposing a generalized model structure with CBAM modules. This leads to a more informative and effective feature extraction technique by feeding the model with more information in every block operation. This research used spectrograms of the raw signals instead of extracting hand-crafted features through intricate signal processing techniques. The developed model has been assessed on three datasets, including KU-HAR, UCI-HAR, and WISDM datasets. The experimental findings showed that the classification accuracies of the suggested technique on the KU-HAR, UCI-HAR, and WISDM datasets were 96.86%, 93.48%, and 93.89%, respectively. The other evaluation criteria also demonstrate that the proposed methodology is comprehensive and competent compared to previous works.
New Method for Reduced-Number IMU Estimation in Observing Human Joint Motion

Observation of human joint motion plays an important role in many fields. The results of the human links can provide information about musculoskeletal parameters. Some devices can track real-time joint movement in the human body during essential daily activities, sports, and rehabilitation with memory for storing the information concerning the body. Based on the algorithm for signal features, the collected data can reveal the conditions of multiple physical and mental health issues. This study proposes a novel method for monitoring human joint motion at a low cost. We propose a mathematical model to analyze and simulate the joint motion of a human body. The model can be applied to an Inertial Measurement Unit (IMU) device for tracking dynamic joint motion of a human. Finally, the combination of image-processing technology was used to verify the results of model estimation. Moreover, the verification showed that the proposed method can estimate joint motions properly with reduced-number IMUs.
Quantum neural network cost function concentration dependency on the parametrization expressivity

Although we are currently in the era of noisy intermediate scale quantum devices, several studies are being conducted with the aim of bringing machine learning to the quantum domain. Currently, quantum variational circuits are one of the main strategies used to build such models. However, despite its widespread use, we still do not know what are the minimum resources needed to create a quantum machine learning model. In this article, we analyze how the expressiveness of the parametrization affects the cost function. We analytically show that the more expressive the parametrization is, the more the cost function will tend to concentrate around a value that depends both on the chosen observable and on the number of qubits used. For this, we initially obtain a relationship between the expressiveness of the parametrization and the mean value of the cost function. Afterwards, we relate the expressivity of the parametrization with the variance of the cost function. Finally, we show some numerical simulation results that confirm our theoretical-analytical predictions. To the best of our knowledge, this is the first time that these two important aspects of quantum neural networks are explicitly connected.
Dentistry in the Digital Age: Embracing Blockchain Technology

Blockchain technology can revolutionize the dental industry by offering secure and efficient data management and patient care solutions. This editorial explores how blockchain can benefit dentistry, including enhancing data security and privacy, streamlining interoperability, facilitating supply chain management, and empowering patients. Blockchain ensures tamper-proof and authorized access to patient data, enabling dentists and patients to have peace of mind regarding data security. Interoperability between dental systems can be improved through blockchain, allowing seamless data exchange and care coordination. By leveraging the transparency and immutability of blockchain, supply chain management in dentistry can be enhanced to ensure the authenticity of dental products. Moreover, blockchain empowers patients by granting them control over their healthcare data, promoting transparency and trust in the dental industry. However, challenges such as scalability, energy consumption, regulatory compliance, and data governance need to be addressed for successful implementation. Collaboration among stakeholders, education, and the development of regulatory frameworks are crucial for maximizing the benefits of blockchain technology in dentistry. By embracing blockchain, the dental industry can create a future where patient care is secure, efficient, and patient-centric.
Simultaneous restoration and super-resolution GAN for underwater image enhancement

Underwater images are generally of low quality, limiting the performance of subsequent perceptual tasks, such as underwater object detection and recognition. However, only a few methods can improve the quality of underwater images by simultaneously restoring and super-resolving underwater images. In this paper, we propose an end-to-end trainable model based on generative adversarial networks (GANs) called Simultaneous Restoration and Super-Resolution GAN (SRSRGAN) to obtain clear super-resolution underwater images automatically. In particular, our model leverages a cascaded architecture with two stages of carefully designed generative adversarial networks to restore and super-resolve corrupted underwater images in a coarse-to-fine manner. The major advantages of SRSRGAN are twofold. First, it is a unified solution that can simultaneously restore and super-resolve images. Second, SRSRGAN is not limited by the prior experience of the types and levels of underwater degraded images but can perform the inference using only observed corrupted images. These two advantages enable SRSRGAN to enjoy better flexibility and higher practicability in realistic underwater scenarios. Extensive experimental results demonstrate the superiority of SRSRGAN in underwater image restoration, super-resolution, and simultaneous restoration and super-resolution.
IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography

Deaf and hearing-impaired people always face communication barriers. Non-invasive surface electromyography (sEMG) sensor-based sign language recognition (SLR) technology can help them to better integrate into social life. Since the traditional tandem convolutional neural network (CNN) structure used in most CNN-based studies inadequately captures the features of the input data, we propose a novel inception architecture with a residual module and dilated convolution (IRDC-net) to enlarge the receptive fields and enrich the feature maps, applying it to SLR tasks for the first time. This work first transformed the time domain signal into a time–frequency domain using discrete Fourier transformation. Second, an IRDC-net was constructed to recognize ten Chinese sign language signs. Third, the tandem CNN networks VGG-net and ResNet-18 were compared with our proposed parallel structure network, IRDC-net. Finally, the public dataset Ninapro DB1 was utilized to verify the generalization performance of the IRDC-net. The results showed that after transforming the time domain sEMG signal into the time–frequency domain, the classification accuracy (acc) increased from 84.29% to 91.70% when using the IRDC-net on our sign language dataset. Furthermore, for the time–frequency information of the public dataset Ninapro DB1, the classification accuracy reached 89.82%; this value is higher than that achieved in other recent studies. As such, our findings contribute to research into SLR tasks and to improving deaf and hearing-impaired people’s daily lives.
Cloud Computing Security Issues and Countermeasure: A Comprehensive Survey

Cloud computing has drawn attention in the current technological world due to its many benefits in contrast to traditional information technology (IT) infrastructure.Nowadays, many companies moved to the cloud instead of hosting computing resources internally in a privately owned cloud data center.As a result, the use of cloud computing has allowed companies to focus more on their business instead of developing and maintaining their own information technology (IT) equipment.In addition, cloud computing solves the limited resources problem; it intends to minimize the cost of services by sharing valuable resources which can be accessed remotely from anywhere among multiple users.Currently, organizations depend on cloud computing to effectively manage and store their data.However, storing organizational data raises many concerns and vulnerabilities; researchers have experimented with and used a variety of data protection and prevention strategies and techniques to reinforce the security barrier.However, gaps still need to be addressed by improving and strengthening these methods.This paper offers a survey of current relevant work that focuses on security concerns and protective measures in various aspects.These considerations must be taken into account when applying security to cloud computing infrastructure.Moreover, the paper proposes a new classification of the literature review following the CIA common model, which forms the basis for the systems security development.
Learning Shape-Invariant Representation for Generalizable Semantic Segmentation

Semantic segmentation assigns a category for each pixel and has achieved great success in a supervised manner. However, it fails to generalize well in new domains due to the domain gap. Domain adaptation is a popular way to solve this issue, but it needs target data and cannot handle unavailable domains. In domain generalization (DG), the model is trained without the target data and DG aims to generalize well in new unavailable domains. Recent works reveal that shape recognition is beneficial for generalization but still lack exploration in semantic segmentation. Meanwhile, the object shapes also exist a discrepancy in different domains, which is often ignored by the existing works. Thus, we propose a Shape-Invariant Learning (SIL) framework to focus on learning shape-invariant representation for better generalization. Specifically, we first define the structural edge, which considers both the object boundary and the inner structure of the object to provide more discrimination cues. Then, a shape perception learning strategy including a texture feature discrepancy reduction loss and a structural feature discrepancy enlargement loss is proposed to enhance the shape perception ability of the model by embedding the structural edge as a shape prior. Finally, we use shape deformation augmentation to generate samples with the same content and different shapes. Essentially, our SIL framework performs implicit shape distribution alignment at the domain-level to learn shape-invariant representation. Extensive experiments show that our SIL framework achieves state-of-the-art performance.
Plane curves giving rise to blocking sets over finite fields

In recent years, many useful applications of the polynomial method have emerged in finite geometry. Indeed, algebraic curves, especially those defined by Rédei-type polynomials, are powerful in studying blocking sets. In this paper, we reverse the engine and study when blocking sets can arise from rational points on plane curves over finite fields. We show that irreducible curves of low degree cannot provide blocking sets and prove more refined results for cubic and quartic curves. On the other hand, using tools from number theory, we construct smooth plane curves defined over $$\mathbb {F}_p$$ of degree at most $$4p^{3/4}+1$$ whose points form blocking sets.