Reviewerly/exHarmony huggingface dataset:
Using deep learning for effective simulation of ghost reflections

Stray light (SL) control is an important aspect in the development of optical instruments. Iterations are necessary between design and analysis phases, where ray tracing simulations are performed for performance prediction. This process involves trial and error, requiring to be able to perform rapid evaluation of SL properties. The limitation is that accurate SL simulations require sending many rays, which can be time consuming. In this paper, we use deep learning to improve the accuracy of SL maps even when obtained with very few rays. Two different deep learning methods are used, an autoencoder and an EAM. The training process is performed by generating a large database of artificial SL maps, with different noise levels reproduced with a Poisson distribution. Once the training completed, we show that the autoencoder performs the best and improves significantly the accuracy of SL maps. Even with extremely small number of rays, it recovers complex SL patterns which are not visible on raw ray traced maps. This method thus enables more efficient iterations between design and analysis. It is also useful for developing SL correction algorithms, as it requires tracing SL maps under large number of illumination conditions in a reasonable amount of time.
Constructions of correlation immnue S-boxes with high nonlinearity

S-boxes play a central role role in the design of symmetric cipher schemes. For stream cipher applications, a S-box should satisfy several criteria such as high nonlinearity, balanceness, correlation immunity, and so on. In this paper, by using disjoint linear codes, a class of S-boxes possessing high nonlinearity and 1st-order correlation immunity is given. It is shown that the constructed correlation immune S-boxes can possess currently best known nonlinearity, which is confirmed by the example 1st-order correlation immune (12, 3) S-box with nonlinearity 2000. In addition, two other frameworks concerning the criteria of balanced and resiliency are obtained respectively.
A bibliometric review of Internet of Things (IoT) on cybersecurity issues

Purpose In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses and individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy and data security. This study aims to provide a comprehensive understanding of the current research in this vital area, shedding light on research trends, gaps and emerging themes. Design/methodology/approach The study conducted a bibliometric analysis and systematic review of literature spanning over two decades (2013–2023). Bibliometric analysis is conducted using Biblioshiny which is R-software-based advanced analytical tool. Further, VOSviewer is used to conduct network analysis. The study highlights the evolving landscape of IoT cybersecurity, emphasizing interdisciplinary intersections and the ethical dimensions of IoT technologies. Findings The study uncovers crucial concerns related to IoT adoption, emphasizing the urgent need for comprehensive cybersecurity protocols. It identifies emerging themes such as artificial intelligence and blockchain integration, indicating a shift toward interdisciplinary solutions. Furthermore, the research highlights ethical gaps in current IoT discussions, emphasizing the importance of responsible innovation. Research limitations/implications Businesses can bolster their cybersecurity strategies, policymakers can craft informed regulations and researchers are encouraged to explore IoT’s ethical dimensions. Originality/value This study pioneers a nuanced analysis of IoT cybersecurity, filling a crucial gap in the existing business and management literature. By synthesizing a decade of scholarly work, it provides foundational insights for researchers, businesses and policymakers. The research not only informs academic discourse but also offers practical guidance for enhancing IoT security measures and fostering ethical innovation.
The design and use of a digital PMS, and the emergence of gaming logic: the experience of the Italian Ministry of Culture

The analysis of digital transformation processes by governments has increasingly attracted attention from academics and practitioners, but it has been less frequently investigated from an accounting perspective. This chapter investigates the complex process of designing and using a digital performance measurement system (PMS) in a central government, empirically exploring the case of the Italian Ministry of Culture, which enacted a reform to provide central and peripheral units with a system to monitor the reputation of state museums, leveraging real-time user-generated data (UGD). The study shows that the design and use of a digital PMS is characterised by the complexity of choosing and interpreting the algorithm to analyse UGD, which may stimulate gaming logic by the actors who are held accountable through the digital PMS. These findings contribute to a broader discussion on the accounting and accountability implications of adopting a digital PMS in the public sector.
Copyright and COVID

The rise of the COVID-19 pandemic revived a focus of scholars and policy makers on the issue of patents and access to medicines (and vaccines). But the COVID-19 pandemic displayed as well how other intellectual property rights, including copyright and trade secrets, may also complicate public health measures. This chapter documents many instances where non-patent forms of intellectual property created barriers to the global scale-up of access to vaccines, treatments, and the ability to contain the COVID-19 virus. It concludes by explaining some of the existing flexibilities in the multilateral intellectual property system that may be used to respond to these problems.
Cryptography in cloud: An in-depth investigation into encryption mechanisms for safeguarding cloud-based data

Cloud computing is one of the most significant computing models, which provides easy access of resources. It is an alternative for costlier data framework and networking models. The services are provided to the data users based on their requirements. More industries are depending on the cloud, because of its fast computations and robust nature. However, there are several security concerns and threats in cloud storages. Moreover, large amounts of data are shared between people and organizations through the cloud. The data owner doesn’t have control over the data in the cloud. To address the security and privacy issues in the cloud, this thesis aims at overcoming this trade-off, while considering security.
Cloud-Driven Transformation: Ensuring Security and Privacy in Data and Application Modernization

Today, businesses are advancing their IT strategies to leverage cloud environments providing greater scalability and efficiency in the changing digital environment. This indicates that it will be the modernization of data and apps that will lead the way as enterprises try to adapt themselves to the change brought about by the cloud. Even though they are not intentionally constructing a new entry medium that may be readily stolen, information providers in both the public and commercial sectors are also facing critical challenges regarding data security. At the same time as it satisfies the requirements of commercial customers as well as those of public sector organizations, this article provides the many methods that may be used to build cloud computing systems that protect the data of customers, guaranteeing that it is kept private. Also included in this are the methods for safely transferring client information from servers housed in your office building to the cloud, as well as the best practices that are prevalent within a certain industry and particulars that are applicable exclusively to a particular sort of company activity
AI-Assisted Programming Tasks Using Code Embeddings and Transformers

This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.
Ecological ICMS: environmental policy for the sustainable development of Rondônia

Emotional competence in teaching is a topic of great interest today, since it is extremely important for teachers to feel good about the tasks assigned to them in terms of work, and it is also very important for them to be emotionally successful in carrying them out efficiently. Due to the context of the COVID-19 Pandemic that we are all experiencing there have been several modifications to which the teachers had to adjust to provide good learning to the students and this is the reason for the conduct of this study, so the objective of this research is to know the emotional competence profile of the teachers of the Schools Groupings of the capital of Bragança district in the context of the COVID-19 Pandemic, making use of an anonymous and confidential questionnaire applied to the teachers of the Groupings of the capital of Bragança, divided into two parts: the first part, contemplating variables of sociodemographic and professional characterization and a second part consisting of "Scaling" the Veiga de Competência Emocional (EVCE) (Veiga-Branco, 2005, 2011, 2015, 2016, 2017, 2018). The results obtained show that there is no influence of sociodemographic or professional variables on the emotional competence profile of the teachers or on the dimensions of it.
A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8

Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network’s ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images.
Assessment of Student Satisfaction With the Quality of Education Using the Example of One of the Departments of the Kyrgyz-Russian Slavic University

To improve the quality of education at a university, analysis of the educational process and organization of effective interaction between teachers and students are crucial. The professional activity of a university teacher is multifaceted and covers many types of work, which are assessed during periodic certification. However, evaluation of a teacher’s performance is incomplete without taking into account the opinions of students, which can be identified using a survey. Therefore, the teacher should be an interesting person; hence, his subject will become an interesting science area. This article presents a comparative analysis of the students’ satisfaction at the Faculty of Medicine with the quality of taught disciplines, such as biology, physics, medical informatics and evidence-based medicine. These subjects are given in junior and senior courses at the Department of Physics, Medical Informatics and Biology at the Kyrgyz-Russian Slavic University. This analysis was carried out for the first time at the department in 2020 (297 students), again in 2023 (504 students), and showed that in general the average score of the department increased slightly, as well as the pedagogical qualities of the teacher in the rating of lecture courses. Nevertheless, the ratings for practical classes remained unchanged. An analysis of students’ feedback expressed in free form to the teacher showed the majority of students are grateful for the teachers’ work.
Do We Have a Duty to Mitigate the Deterioration of Democratic Communication?

Previous articleNext article No AccessDo We Have a Duty to Mitigate the Deterioration of Democratic Communication?Corrado FumagalliCorrado FumagalliPDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Just Accepted Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.1086/729957 HistoryAccepted October 16, 2023 © 2024 Southern Political Science Association. All Rights reserved.PDF download Crossref reports no articles citing this article.
Using Graphs for Network Analysis in ArcMap Environment

The emergence of powerful and fast computer technologies has led to the massive adoption of GIS in various fields. Modern GIS make it possible to quickly solve such problems in the mathematical analysis of network data. This work aims to limit the use of GIS modules in this area. The relevance of the article is the use of graphs to develop network analysis in the ArcMap environment.
A Comprehensive Review of Techniques Enabling OHGP Completions in Low Frac Window Environment

Abstract Openhole gravel packing has proven to be one of the best sand control completion methods in the industry with high reliability and low skin. As more and more wells are being drilled in challenging environment (long drain lengths, depleted formation, wellbore stability concerns, presence of faults, etc.) with low fracture window, the ability to fully pack the openhole sections constrains the wider use of the method. Many techniques have been used to overcome the low fracture window challenge both in alpha beta and shunted gravel packing. This paper will review the techniques used in both gravel packing approaches, highlight their working mechanisms, and provide a comparison of these methods. Techniques such as packing with friction reducer, BOP open, use of lighter carrier fluids, running diverter valves, wash-pipe less Bottom Hole Assembly (BHA), managed pressure gravel packing etc. have been successfully used depending on the available fracture margin, well configuration and gravel packing technique. The paper will briefly touch on the challenges leading to low fracture margin and the necessity to utilize one or a combination of the techniques to fully pack wells. It will then describe the working principle of each of the techniques, provide the advantages and disadvantages of the techniques. This will be complemented with several case histories showing how these wells were successfully completed despite the low fracture pressure window.
An efficient quantum circuit implementation of Shor’s algorithm for GPU accelerated simulation

In this study, we introduce a novel implementation of Shor’s algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode.
Disorder enhanced relative intrinsic detection efficiency in NbTiN superconducting nanowire single photon detectors at high temperature

The intrinsic detection performance of superconducting nanowire single photon detectors (SNSPDs) is highly dependent on the superconducting properties of underlying thin films. This report outlines the enhancement of detection performance for single telecom wavelength photons in disordered NbTiN SNSPD at 4.2 K. By increasing the nitrogen content and deposition pressure, the NbTiN films show suppression in critical temperature and an increase in sheet resistance. Notably, the resulting SNSPDs display a broader saturation plateau at 2.2 K, leading to superior detection performance at 4.2 K. With the disordered 7-nm-thick NbTiN films, we fabricated SNSPDs with system detection efficiency up to 83% for 1550 nm photons at 4.2 K. Moreover, these devices also show saturated intrinsic detection efficiency for 2000 nm photons. With the features outlined, the devices can be integrated into the idle 4.2 K stage of the dilution refrigerator for applications in optical quantum information processing or utilize for detecting laser radar signals in airborne platforms.
Model selection over partially ordered sets

In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be specified as the number of variables/edges that are incorrectly included or excluded in an estimated model. However, there are several other problems such as ranking, clustering, and causal inference in which the associated model classes do not admit transparent notions of false-positive and false-negative errors due to the lack of an underlying Boolean logical structure. In this paper, we present a generic approach to endow a collection of models with partial order structure, which leads to a hierarchical organization of model classes as well as natural analogs of false-positive and false-negative errors. We describe model selection procedures that provide false-positive error control in our general setting, and we illustrate their utility with numerical experiments.
Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application

We evaluated the impact of training set size on generative adversarial networks (GANs) to synthesize brain MRI sequences. We compared three sets of GANs trained to generate pre-contrast T1 (gT1) from post-contrast T1 and FLAIR (gFLAIR) from T2. The baseline models were trained on 135 cases; for this study, we used the same model architecture but a larger cohort of 1251 cases and two stopping rules, an early checkpoint (early models) and one after 50 epochs (late models). We tested all models on an independent dataset of 485 newly diagnosed gliomas. We compared the generated MRIs with the original ones using the structural similarity index (SSI) and mean squared error (MSE). We simulated scenarios where either the original T1, FLAIR, or both were missing and used their synthesized version as inputs for a segmentation model with the original post-contrast T1 and T2. We compared the segmentations using the dice similarity coefficient (DSC) for the contrast-enhancing area, non-enhancing area, and the whole lesion. For the baseline, early, and late models on the test set, for the gT1, median SSI was .957, .918, and .947; median MSE was .006, .014, and .008. For the gFLAIR, median SSI was .924, .908, and .915; median MSE was .016, .016, and .019. The range DSC was .625-.955, .420-.952, and .610-.954. Overall, GANs trained on a relatively small cohort performed similarly to those trained on a cohort ten times larger, making them a viable option for rare diseases or institutions with limited resources.
Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

&lt;p&gt;Human Action Recognition (HAR) is a progressive research area in the field of computer vision and machine learning. Earlier methods on HAR were based on single sensor modality, either vision-based sensor or wearable inertial sensor. Both of these modalities have some limitations that prevent widespread adoption of HAR; e.g. visual sensors typically require elaborate hardware setup and are limited to a small operating area, where inertial sensors are prone to drift. The solution is to fuse the information from different modalities. In this dissertation, we present novel multimodal sensor fusion frameworks that overcome the limitations of single sensor modality. In these frameworks, we convert all data streams to images through innovative signal to image conversion schemes and feed them to Convolutional Neural Networks (CNN), thus enabling extraction of higher-level features that CNNs are proven to be capable of especially from images. Moreover, we propose fast and robust multilevel fusion schemes by extracting features from multiple layers of the CNNs and employing statistical methods such as Canonical Correlation Analysis and gated fusion, instead of the more popular single stage fusion. We applied these fusion frameworks for HAR using depth and inertial sensors. At the input of each fusion framework, we transform depth and inertial sensor data into images called Sequential Front view Images (SFI) and Signal Images (SI). The SFI and SI images are then fused through our proposed multilevel frameworks for more accurate HAR while maintaining computational speed. We evaluate the proposed frameworks on three public multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTDMHAD Kinect V2 and achieved accuracies of 99.3%, 99.85% and 99.8% respectively.&lt;/p&gt; &lt;p&gt;While the proposed frameworks were developed with HAR as a target application area, they can be applied to other fusion problems as well. We show the generalizability of frameworks by applying them to a different domain, where ECG (1D time series) data is converted to multimodal images and fed through our fusion frameworks for arrythmia classification and stress assessment. Preliminary results in these applications are encouraging, further strengthening the significance of the proposed frameworks.&lt;/p&gt;