Reviewerly/exHarmony huggingface dataset:
Generating real-world hypergraphs via deep generative models

Hypergraph generation has found wide applications in data analysis, social networks, and communication networks. Although various hypergraph generation methods have been proposed, many challenges remain. On the one hand, traditional hypergraph generation methods are time-consuming since they they rely on computing various statistics about hypergraph distributions prior to generation. On the other hand, they store many intermediate variables in the process of hypergraph generation, thus depleting a large number of memory resources, which could lead to memory explosion. To address these issues, we present a novel deep generative model for hypergraph generation (DGMH), which consists of a node selection module and a hyperedge size decision module. In DGMH, each hyperedge is treated as an embedding in a high-dimensional space, where values along specific dimensions represent the probabilities of node inclusion in the hyperedge. The node selection module captures the distribution of hyperedge embeddings and identifies other latent hyperedge embeddings within the space. In addition, the hyperedge size decision module determines the hyperedge size based on the hyperedge embedding. We sample a certain number of nodes from the categorical distribution parameterized by the embedding and assign these nodes to the hyperedge, an essential step in hyperedge generation. Once a sufficient number of hyperedges are generated, they are connected through common nodes to form a complete hypergraph. Experimental results demonstrate the effectiveness of DGMH in capturing the structural distribution of hypergraphs in a short time while maintaining a modest memory footprint during the generation process.
Automatic Recognition of Speaker Labels Using CNN-SVM Scheme

The most natural method of interaction is speech. To recognize the speaker, we employ a method known as “automatic speaker recognition,” which takes into account the speaker's voice's several characteristics, including pitch, timbre, tension, frequency, etc. Additionally, it communicates details regarding the manner in which voice is created and conveyed. The method is applicable to forensics, monitoring, and authenticity applications. The problem with the current approach is the vast number of features employed for voice recognition. In this work, we are planning to use support vector machine (SVM) as a classifier and reinforcement learning as a linear regression to recognize the speaker's voice purely based on a limited set of attributes selected from the convolutional neural network. Building a clear, unambiguous and delegated automatic spokesman identification organism is the major goal of this endeavor. We will only test our algorithm on a small voice dataset due to space restrictions. To increase accuracy, the system is trained and evaluated using a large number of numerical speech data samples. Here, 125 examples will be used to help identify each phrase. The NIST and ELSDSR speaker datasets were used to conduct an analysis of the hybrid network models that were suggested earlier. The findings of our experiments demonstrated that the CNN-SVM model that we proposed achieved a high recognition accuracy of 95% (avg). In the same vein, the findings show that the deep network model that was proposed is superior to other models in terms of its ability to correctly identify speakers.
Augment CAPTCHA Security Using Adversarial Examples With Neural Style Transfer

To counteract rising bots, many CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) have been developed throughout the years. Automated attacks [1], however, employing powerful deep learning techniques, have had high success rates over common CAPTCHAs, including image-based and text-based CAPTCHAs. Optimistically, introducing imperceptible noise, Adversarial Examples have lately been shown to particularly impact DNN (Deep Neural Network) networks. The authors improved the CAPTCHA security architecture by increasing the resilience of Adversarial Examples when combined with Neural Style Transfer. The findings demonstrated that the proposed approach considerably improves the security of ordinary CAPTCHAs.
FPGA-Based Hyperspectral Lossy Compressor With Adaptive Distortion Feature for Unexpected Scenarios

Lossy compression solutions have grown up during the last decades because of the increment of the data rate in the new-generation hyperspectral sensors, however linear compression techniques include useless information on regions of little interest for the final application and at the same time scarce information on areas of interest. In this paper, a transform-based lossy compressor, HyperLCA, has been extended to include a run-time adaptive distortion feature that brings multiple compression ratios in a same scenario. The solution has been designed to keep the same hardware-friendly feature, just like its previous version, specifically conceived to ease the deployment of the solution on reconfigurable hardware devices (FPGAs). The experiments demonstrate that the new version of the compressor is able to process 1024x1024 hyperspectral images and 180 spectral bands (377.5MB) in 0.935 seconds with a power consumption of 1.145 watts. In addition, experimental results also reveal that our architecture features high throughput (MSamples/s) and remarkable energy-efficiency (MB/s per watt) trade-offs, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6\times$</tex-math></inline-formula> greater than the best state-of-the-art solution, respectively.
Cyclic Lattices, Ideal Lattices, and Bounds for the Smoothing Parameter

Abstract Cyclic lattices and ideal lattices were introduced by Micciancio (2002), Lyubashevsky and Micciancio (2006), respectively, which play an efficient role in Ajtai’s construction of a collision resistant Hash function (see Ajtai (1996), Ajtai and Dwork (1997)) and in Gentry’s construction of fully homomorphic encryption (see Gentry (2009)). Let $$R=Z[x]/\langle \phi (x)\rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mi>Z</mml:mi> <mml:mo>[</mml:mo> <mml:mi>x</mml:mi> <mml:mo>]</mml:mo> <mml:mo>/</mml:mo> <mml:mo>⟨</mml:mo> <mml:mi>ϕ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> be a quotient ring of the integer coefficients polynomials ring, Lyubashevsky and Micciancio regarded an ideal lattice as the correspondence of an ideal of R , but they neither explain how to extend this definition to whole Euclidean space $$\mathbb {R}^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> </mml:math> , nor exhibit the relationship of cyclic lattices and ideal lattices. In this chapter, we regard the cyclic lattices and ideal lattices as the correspondences of finitely generated R -modules, so that we may show that ideal lattices are actually a special subclass of cyclic lattices, namely, cyclic integer lattices. In fact, there is a one to one correspondence between cyclic lattices in $$\mathbb {R}^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> </mml:math> and finitely generated R -modules (see Theorem 4). On the other hand, since R is a Noether ring, each ideal of R is a finitely generated R -module, so it is natural and reasonable to regard ideal lattices as a special subclass of cyclic lattices (see Corollary 7). It is worth noting that we use a more general rotation matrix here, so our definition and results on cyclic lattices and ideal lattices are more general forms. As an application, we provide a cyclic lattice with an explicit and countable upper bound for the smoothing parameter (see Theorem 5). It is an open problem that is the shortest vector problem on cyclic lattice NP-hard (see Micciancio (2002)). Our results may be viewed as a substantial progress in this direction.
The Practices of Digital Citizenship Among Undergraduates at Sultan Qaboos University in Oman During COVID-19

Abstract Digital citizenship (DC) has been an essential subject in educational research for the last few years. This study aimed at discovering the degree to which Omani undergraduate students practiced digital citizenship during the COVID-19 pandemic. The participants were 262 undergraduate students (116 males and 164 females) who took an online university elective course at Sultan Qaboos University (SQU) in Spring of 2021. The study adopted a quantitative approach to data collection to investigate students’ digital citizenship practices. An online situational judgment test was developed and conducted to address six dimensions of digital citizenship: digital identity, digital civic engagement, digital ethics, digital literacy, digital safety, and global digital communication. The findings show that the level of practicing digital citizenship dimensions ranged from intermediate to low. Additionally, the results show that the least practiced dimensions are digital civic engagement and digital identity. Further, the results indicated some gender differences in students’ practices of digital citizenship. Considering the results, more efforts are required from stakeholders to enhance the level of DC among undergraduates. The findings of the study are considered good indicators to be taken when designing and evaluating curricula at higher education institutions.
KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm

Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers’ preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models’ estimates with an accuracy of 95.94% and average F-measure of 95.93%.
A Study on Existing EHR Models Used for Validating the Clinical Records

EHR was a statistical database of electronic health records for individuals or demographics that can be accessed through diverse healthcare environments. Planning for long-term retention and handling of electronic health information is an important factor in the planning process. The majority of commercial EHR models were designed to integrate data from vast ancillary facilities like radiology, pharmacy, and laboratory with different clinical care components. Electronic health records (EHRs) were digital representations of hospital records that were renovated in real time. An (electronic health record (EHR) was a comprehensive statement on a person's health. EHRs relief in the provision of evidence-based care, the tracing of a patient's treatment development, and the enabling of improved healthcare judgments. This paper aims to review the comparison of different EHR models that are being used throughout the world. The usage of various machine learning approaches in existing EHR has also been explored in this study. A thorough introduction of EHR is provided initially, followed by a focus on some related work that explains the adoption principles employed by various authors in EHR. In addition, discuss the different EHR models and the methodologies they have used and challenges in existing EHR models, their advantages, need, and then finally the challenges of EHR. This paper can be concluded in a way that to create an effective framework of potential data exchange agreements between health services and patients more public dialogue around electronic health records is needed. Quantitative research, such as polls of a wide variety of customers and doctors, may also be performed in order to obtain better outcomes. In next paper, we intend to propose framework of such EHR model.
Detection of Cyber Bullying on Social Media

This study focuses on the pressing issue of cyberbullying on the internet, which negatively impacts both teenagers and adults, sometimes leading to tragic consequences such as suicide and depression. In order to address this problem, there is a growing need to establish regulations regarding content on social media platforms. To tackle this issue, the study aims to utilize data from two distinct forms of cyberbullying: hate speech tweets from Twitter and comments based on specific attacks from Wikipedia forums. The primary objective is to develop an effective model using Natural Language Processing and Machine Learning techniques to identify cyberbullying in textual data. The study explores three different approaches for feature extraction and evaluates the performance of four classifiers to determine the most effective method. The results of the study reveal that the developed model achieves an impressive accuracy of over 90% when applied to tweet data and over 80% when applied to Wikipedia data..
Spherical Image Inpainting with Frame Transformation and Data-Driven Prior Deep Networks

Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modeling, and medical imaging. It is nontrivial to extend an algorithm developed for flat images to the spherical ones. In this work, we focus on the challenging task of spherical image inpainting with a deep learning-based regularizer. Instead of a naive application of existing models for planar images, we employ a fast directional spherical Haar framelet transform and develop a novel optimization framework based on a sparsity assumption of the framelet transform. Furthermore, by employing progressive encoder-decoder architecture, a new and better-performed deep CNN denoiser is carefully designed and works as an implicit regularizer. Finally, we use a plug-and-play method to handle the proposed optimization model, which can be implemented efficiently by training the CNN denoiser prior. Numerical experiments are conducted and show that the proposed algorithms can greatly recover damaged spherical images and achieve the best performance over purely using a deep learning denoiser and a plug-and-play model.
Ethical issues for literary translation in the Era of artificial intelligence

Abstract Technological advancement has brought changes to many professions across the world. Furthermore, it has triggered a discussion about ethical issues. Machine Translation (MT) or Computer-Aided Translation (CAT) has vastly increased work efficiency and technology-related ethical issues are gaining academic attention these days. However, the discussion of ethical issues for literary translation against the backdrop of Artificial Intelligence (AI) is limited. After a quick review of the stages of MT, this paper will delve into literary translation’s emerging ethical issues, for example, the literary translator’s professional identity and copyright. Copyright ethics is an indispensable part of AI-enabled literary translation since training data and participatory NMT involve copyright issues. This study revealed that technological advancement will facilitate literary translation. However, no direct evidence exists that machine translation will replace human translators. Given the new working mode of “multi-players” or participatory translation, ethical issues arising from the human and machine interaction merit further academic inquiry.
Prototype Modeling and Performance Analysis of Transaction of Digital Intellectual Property in E-commerce

The ease of creation, modification, distribution, compatibility and above all, a large market opportunity drive the creator and the consumer to move towards the digital age to create and use their IP (Intellectual Property) digitally called Digital Intellectual Property (DIP). To guard the creators from the DIP infringements an attempt is made in this paper to design a DIP protection model in E-commerce. We have also analyzed the performance of proposed model using different object-oriented quality metrics, such as Chidamber & Kemerer and Metrics for Object Oriented Design (MOOD).
Local Completeness in Abstract Interpretation

Completeness of an abstract interpretation is an ideal situation where the abstract interpreter is guaranteed to be compositional and producing no false alarm when used for verifying program correctness. Completeness for all possible programs and inputs is a very rare condition, met only by straightforward abstractions. In this paper we make a journey in the different forms of completeness in abstract interpretation that emerged in recent years. In particular, we consider the case of local completeness, requiring precision only on some specific, rather than all, program inputs. By leveraging this notion of local completeness, a logical proof system parameterized by an abstraction A, called $$\textrm{LCL}_A$$ , for Local Completeness Logic on A, has been put forward to prove or disprove program correctness. In this program logic a provable triple $$[p]~ \textsf{c}~[q]$$ not only ensures that all alarms raised for the postcondition q are true ones, but also that if q does not raise alarms then the program $$\textsf{c}$$ cannot go wrong with the precondition p.
Federated Multi-Output Gaussian Processes

Multi-output Gaussian process (MGP) regression plays an important role in the integrative analysis of different but interrelated systems/units. Existing MGP approaches assume that data from all units is collected and stored at a central location. This requires massive computing and storage power at the central location, induces significant communication traffic due to raw data exchange, and comprises privacy of units. However, recent advances in Internet of Things technologies, which have tremendously increased edge computing power, pose a significant opportunity to address such challenges. In this article, we propose FedMGP, a general federated analytics (FA) framework to learn an MGP in a decentralized manner that uses edge computing power to distribute model learning efforts. Specifically, we propose a hierarchical modeling approach where an MGP is built upon shared global latent functions. We then develop a variational inference FA algorithm that overcomes the need to share raw data. Instead, collaborative learning is achieved by only sharing global latent function statistics. Comprehensive simulation studies and a case study on battery degradation data highlight the superior predictive performance and versatility of FedMGP, achieved while distributing computing and storage demands, reducing communication burden, fostering privacy, and personalizing analysis.
Optimization of Controller Parameters for the Proposed DCALS Using Particle Swarm Optimization (PSO) Technique

GA is a well-established algorithm with many versions and many applications. PSO is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. PSO is similar to the GA in the sense that these two evolutionary heuristics are population-based search methods. In other words, PSO and the GA move from a set of points (population) to another set of points in a single iteration with likely improvement using a combination of deterministic and probabilistic rules.
Perspectives on Enhancing Foreign Language Vocabulary Learning Through Mobile Apps

One of the crucial elements of any language used for communicating the language fluently is vocabulary. It is considered a major issue in second or foreign language learning. The growing popularity and development of technology have provided potential advantages of mobile devices for learning all over the world in general, and in second/foreign language learning in particular. The mobile technology not only has provided valuable resources for learners, but also by supporting learning with their multimedia capabilities, portability, connectivity, and flexibility. The present study by exploring the effect of mobile-facilitated concept-mapping vocabulary learning strategy on EFL learners' online and face-to-face vocabulary learning and retention aims not only to enrich the body of literature but also hopes to peruse its major significance by equipping EFL learners with effective strategies such as concept mapping to develop their vocabulary knowledge and assist them to easily access this knowledge through modern technologies.
The royal manuscript collection revisited by the work of the librarian Luís Joaquim dos Santos Marrocos in the nineteenth century

Abstract: During the establishment of the Portuguese court in Brazil, at the beginning of the nineteenth century, much crossed the Atlantic, including printed papers and manuscripts. In this text, we discuss the royal manuscript collection through the work of the librarian Luís Joaquim dos Santos Marrocos. The main subjects examined are: a) the experience of the enlightened subject, assistant to a librarian at the Royal Library of Rio de Janeiro in the universe of letters, analyzing his dimension as a cultural mediator; and b) his daily work in the Manuscript Room, including the production of a systematic map of the manuscript collection. These propositions are situated on the basis of his letter writing practice and the production of map in question. This is ongoing research focused on privileged documentation to make considerations about the universe of written culture of the period and its developments in contemporary times.
Influence of e-auditing on credibility and reliability of financial information within public shareholding companies

The current study aimed to find out the role of electronic auditing (e-auditing) in the positive impact on the reliability and credibility of financial information by adopting the dimensions and specifications of e-auditing (relevance, competence, sufficiency, and timeliness) and demonstrating the ability of these specifications to impart reliability and credibility to the financial information. The quantitative approach was adopted for this purpose and a questionnaire was distributed to a sample of 117 financial managers in public shareholding organizations in various sectors in Jordan. Relying on Statistical Package for the Social Sciences (SPSS), the study concluded that all e-audit variables have a positive impact on increasing the reliability and credibility of financial statements, and the most influential was timeliness with a value of R = 0.851. The study recommended intensifying the possibility of using external expertise with better knowledge in the field of software and technology, if there is a need to do so, and focused on the need for a periodic and permanent update of the accounting audit systems. Further recommendations were presented in the study.