Reviewerly/exHarmony huggingface dataset:
Automatic knowledge generation for a persuasion dialogue system with enthymemes

Persuasion dialogues in multi-agent systems are required in situations in which agents debate to defend their viewpoint on a certain topic. In this work, we introduce a dialogue system for persuasion dialogues in which participants can exchange and complete enthymemes using structured argumentation as the underlying knowledge representation formalism. In our approach, the dialogue system provides the means to create arguments from enthymemes by generating assumptions based solely on the enthymeme's structure. Then, arguments are used to determine the dialectical status of the dialogue. We prove that under minimal restrictions, agents involved in a persuasion dialogue can reach an agreement successfully, and they can acquire knowledge such that their mental model is consistent with the outcome of the dialogue.
The RAI Way: A Technical Analysis and Design Method for Building Enterprise Semantic Layers

The enterprise-wide semantic layer is the Holy Grail of data management. Unlike a data lake or data warehouse, semantic layers promise to democratize access across an organization. Traditional implementations tailor to specific application domains and require significant upfront investment and development to associate semantics to the underlying data. In this paper, we present a new technical analysis and design method that standardizes and simplifies the development of enterprise semantic layers. Our approach provides semantics via an ontology that is modeled in the Object-Role Modeling language. The layer then conforms to this ontology and is populated by weaving source data from physical data assets into the ontology using a relational knowledge graph.
Artificial Intelligent Decoding of Rare Words in Natural Language Translation Using Lexical Level Context and Fuzzy Semantic Reasoning

Artificial intelligence based Machine Translation is a Natural Language Processing, attaining significant attention to fully computerize the system that can decode basis content into the target languages. The proposed method is a Neural Machine Translation, end-to-end system, with the sentence context for converting German sentences to English sentences. The Transformer based Machine Translation is used for translation integrated with fuzzy semantic representation for handling the occurrence of rare words in the sentence level context. The FSR organization uncommon phrases collectively and constitute sentence degree context as Latent Topic Representation. WMT En-De bilingual parallel corpus is used for translation handling the Out of Vocabulary words using clustering of tags. In the present approach there's a mismatch of translation however the proposed device is extra advanced because of the sentence context inclusion. The model performance is enhanced with hyper parameter optimization obtaining a BLEU score with a better translation of source to target language. Finally minimizing the TER score to attain a better translation rate
(Anti)-Intentional Harms: The Conceptual Pitfalls of Emotion AI in Education

‘Emotion AI’ is a subset of artificial intelligence (AI) technologies that claim to be able to detect the inner emotional states of individuals by collecting biometric information such as face scans, voice recordings, and traces of physical movement. Despite their growing popularity in education, these systems have the potential to produce serious harm. In this paper, we argue that a major concern with emotion AI technologies has to do with the theories of emotion that undergird them. Most emotion AI technologies are built on the foundations of anti-intentionalist theories of human emotion, which claim that emotions can be understood as discrete, universal states that arise as automatic physiological responses. Anti-intentionalists suggest that emotions are not directed at any object, or subject to cognitive reasons. In our work, we focus on the increasing use of these technologies in education to illustrate the ways in which these anti-intentionalist systems are problematic, as they dissolve the space for pushback against the judgements they make. We argue that their use thereby contributes to harms towards children broadly centered around student disempowerment, surveillance, and classification. We then consider three alternative policy approaches to emotion AI use in schools in light of their role with this political agenda of emotion commodification, assessing each of these options—interpretability, technical reform, and non-use—for their desirability and feasibility. In doing so, we underscore the conceptual harms produced by emotion AI systems in the context of education, and the criteria by which these technologies should be judged by educators and policymakers.
Blockchain-Based Health Information Systems to Improve Data Security

Healthcare services are dependent on health information systems (HIS), which enable health data collection and storage with improved management of healthcare service provision. However, several data security weaknesses in HIS have been identified by various studies in Tanzania. The majority are on data and information exchange leading to breaches of system security. In order to reduce security threats and accomplish security objectives (i.e., confidentiality, integrity, availability), disruptive technologies like blockchain must be used to address security breaches, attacks, and vulnerabilities of HISs. In this chapter, a problem-solving technique with digital innovative solutions for addressing real-world problems was used to arrive at the problem's solution. This chapter then shows how the data security weakness of HISs can be improved using the hyper-ledger blockchain. The system was virtually integrated with an existing HIS. Data storage security was attained through private data collection to meet security goals.
Digitising Standardised Educational Assessment in Africa Using Computerised Adaptive Testing

The experience of the COVID-19 pandemic has revealed a need for the educational sector in Africa to adopt innovative ways for assessment by leveraging technology with the increasing number of student enrollments at all levels of education. A systematic approach is also required to circumvent the challenges faced by developing countries transdisciplinary. Transdisciplinarity is still evolving and has been proved to be a viable tool for engagement, societal impact, new partnerships, fresh ideas, reality checks, new capacities, and systemic thinking leading to new career possibilities. This study conducted a documentary analysis of standardised examination administration in Africa and proposed a framework for action adopting a transdisciplinary approach to research for problem-solving. The study used a qualitative approach for collecting and analysing data from reports and documents supported by empirical findings from existing literature. The study created a framework for action through which the gains of the transdisciplinary approach can be translated to practice.
MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views

Data mining involves examining vast quantities of data to uncover valuable insights that can be utilized for making informed decisions and driving business objectives. The study focuses on the task of finding relationships between features belonging to two different views using multi-view model, and proposes a novel approach called MARCMV. This approach extracts multi-view association rules from different views of the same data set using multi-clustering neural model. The study finds that MARCMV outperforms conventional symbolic methods in terms of association rule quality and running time.
Website-Based E-Pharmacy Application Development toImprove Sales Services Using Waterfall Method

An online shop is a way of trading and shopping online via the internet. Online sales in the business sector will significantly help reduce operational costs, especially for customer activities; in its contribution to commerce, online sales can also increase selling power and widely facilitate the marketing of a product. “Seger Waras” Pharmacy is an agency engaged inthe business of selling medicines and medical equipment. “Seger Waras” Pharmacy requires an online sales website or online store, which is intended to maximize service to existing or new customers. Therefore, the authors research the design of web-based drug sales systems at the “Seger Waras” Pharmacy. This study aims to increase the revenue of the relevant agencies and provide complete information about drug use to customers. Data collection methods use observation and interview methods for system modelsusing Data Flow Diagrams, Entity Relationship Diagrams, and Context Diagrams. The software used by MySQL as a database server, Apache as a web server, Sublime Text 3 for writing PHP and HTML programming languages, and Bootstrap as a framework. The result of the system that has been completed is an online store application that makes it possible to selland purchase drugs online; the online shop at the “Seger Waras” Pharmacy can increase sales turnover and promote business.
3D-shape formation of blood vessels based on computer aided design system

This paper proposes and tests a computerized approach for constructing a 3D model of blood vessels from angiogram images. The approach is divided into two steps, image features extraction and solid model formation. In the first step, image morphological operations and post-processing techniques are used for extracting geometrical entities from the angiogram image. These entities are the middle curve and outer edges of the blood vessel, which are then passed to a computer-aided graphical system for the second phase of processing. The system has embedded programming capabilities and pre-programmed libraries for automating a sequence of events that are exploited to create a solid model of the blood vessel. The gradient of the middle curve is adopted to steer the vessel’s direction, while the cross-sections of the blood vessel are formed as a sequence of circles lying in planes that are orthogonal to the gradients of the middle curves. The radii for the circles are estimated as a distance between the intersection points of the blood vessel edges with the orthogonal plane to the middle curve gradient. The system then uses these circles and the middle curve gradients to produce a solid volume that represents the 3D shape of the blood vessel. The method was tested and evaluated using different cases of angiogram images, and showed a reasonable agreement between the generated shapes and the tested images.
Be Cybersmart (DOS)

Federal Grants & ContractsVolume 47, Issue 14 p. 5-6 Grants Alerts Be Cybersmart (DOS) First published: 12 June 2023 https://doi.org/10.1002/fgc.33096Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue1415 June 2023Pages 5-6 RelatedInformation
A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.
TrendFlow: A Machine Learning Framework for Research Trend Analysis

As various research fields continue to evolve, new technologies emerge constantly, making it challenging for scholars to keep up with the latest and most promising research directions. To address this issue, we propose TrendFlow, a framework that leverages machine learning and deep learning techniques for analyzing research trends. TrendFlow first searches relevant literature based on user-defined queries, then clusters the searched literature according to the abstracts, and finally generates keyphrases of the abstracts as research trends for each cluster. Our experimental results highlight the superior performance of TrendFlow compared to traditional literature analysis tools. We have released the beta version of TrendFlow on Huggingface.
How to Quantify Software Quality Factors for Mobile Applications?: Proposed Criteria

The quality and reliability of any software product are the most important issues because the quality of any product directly depends upon branding. When one talks about software as a product then it is a big question of how to ensure every dimension of quality aspect. This issue becomes much bigger when moved from a desktop program to a mobile application, due to the inherent limitation of mobile due to mobility. Thus there is a requirement to help quality managers by customizing a suitable software quality model (e.g., ISO-9126, ISO-25010, etc.) standard to ensure software quality, in mobile environments. This paper proposed criteria to quantify the quality of a mobile application.
Medical Record Secured Storage Using Blockchain with Fingerprint Authentication

The significance of medical records in any given hospitals cannot be over-emphasized, as they are the main tool available for achieving the highest goals and are helpful to both patients and medical professionals. The majority of customers view electronic health records as ethereal because they don’t weigh anything because they are electronic. To resolve this issue, with the help of a specialist's approval and a unique finger impression, the administrator of this application can access the patient's prior clinical health information. Developing a system that is useful for both patients and professionals is likely to be one of the most pressing issues when dealing with this information. In this program, network updates are made while reports are being hashed. In this application, blockchain is executed while getting biometric access from patient. It keeps a decentralized and secure record of crypto exchanges. Hence, blockchain can ensure the constancy and security of information records.
Fashion Image Classification Using Machine Learning

We will discuss doubtlessly tough troubles going through the e-commerce enterprise. One of them is related to the trouble that sellers face when they upload snapshots of merchandise on the sales platform and then manually edit them. This outcomes in misclassification, which ends up in not performing in the outcomes of the questions. Another trouble relates to the ability bottleneck while putting orders, wherein the purchaser may not recognize the proper key phrases, but they have got an affect of the visual image. A photo-based search algorithm can release the genuine capability of e-trade by permitting shoppers to click on an object's image and search for associated products while not having to kind text. In this newsletter, we explore gadget gaining knowledge of algorithms that may help solve both of those problems.
Comparing Ensemble Techniques for Bilingual Multiclass Classification of Online Reviews

Understanding customers and their need is how a business run today. Gone are the days when customers went to shops for buying the goods. With emerging technology, another revolutions are in terms of providing the goods to the customers as per their convenience which leads to things like online shopping, home delivery, and getting feedback on services. Feedback or comment provided by customer plays a critical role in understanding the customer needs and helps in building the future sales based on recommendation and sentiments. Dealing with global world and customers, the language of these comments can be bilingual, multilingual and can be in English or non-English languages. Understanding the language on a single platform for different categories is a time-taking manual process which may lead to delay in getting the advantage from the reviews. Here, in this paper, two non-English languages are being considered with multiple product categories and their categorization using Natural Language Processing and machine learning by comparison of ensemble techniques to bring out hidden insights on different platforms for given language. Various retail and CPG companies have great customers’ base, and providing best services is new goal. The languages considered are French and Spanish with 20 product categories written in English language. Comparing various ensemble techniques, the model with highest performance got selected with an accuracy of 99.84% for test sample. The methodology used is highly effective, easy to use and fast with great accuracy.
Petrographic and geochemical properties of the ignimbrites around Hatunsaray (Konya)

The study area is located around the town of Hatunsaray, approximately 40 km away from the south-west of Konya. In this study, the petrological and geochemical characteristics of the Bulumya ignimbrite, Detse ignimbrite and Sadıklar ignimbrite observed in the region were revealed. The study area contains Upper Miocene - Lower Pliocene aged Güneydere formation and overlying Bulumya ignimbrite, Detse ignimbrite, Sadıklar ignimbrite and Quaternary alluviums. All these units were formed in the Upper Miocene - Lower Pliocene aged fluvial and lake environment and have a lateral vertical transition with carbonate, clastic units. The gray-colored Bulumya ignimbrite contains andesite-dacite rock fragments and large pumice grains. The Detse Ignimbrite is yellow in color and shows a well sorted lapilli tuff composition. The Sadıklar Ignimbrite, on the other hand, contains agglomeratic levels with yellow colored slightly fused lenses and wedge geometry. All ignimbrite samples have porphyric texture and were classified as “crystal-vitric tuff” and “crystal lithic-vitric tuff” in the glass-crystal-rock fragment classification. Petrographic investigations of ignimbrites show that major components are quartz, plagioclase, plagioclase microliths, biotite, amphibole, opaque minerals and rock fragments. Geochemical data shows that all ignimbrite samples are subalkaline, trachy-andesite, andesite-basaltic andesite and calc-alkaline in character. When the main oxide, trace and rare earth elements in ignimbrites are evaluated, fractional crystallization controlled by feldspar minerals is observed. In addition, the high K and Rb content in the spider diagrams indicate crustal contamination. The distribution of the ignimbrite samples in the Rb/Y-Nb/Y diagram suggests that the magma source forming the samples is enriched by subduction and/or crustal components.
Exploring multivariate generalized gamma manifold for color texture retrieval

This work proposes a novel method for color-textured image retrieval on a Multivariate Generalized Gamma Distribution manifold (MGΓD). Thanks to the Gaussian copula theory, we define the expression of MGΓD, which efficiently models the statistical dependence structure between dual-tree complex wavelet transform (DTCWT) of the color components. The major contribution of this paper is to provide a geometric perspective to the MGΓD by treating it as a Riemannian manifold while proposing the geodesic distance (GD) as a measure of Riemannian similarity on it. Based on information geometry tools, we conduct a geometrical study of the MGΓD manifold, allowing us to derive two suitable approximations of the GD. The experiments are performed on five well-known color texture databases, considering the content-based image retrieval (CBIR) framework and using the RGB color space. The obtained results demonstrate the efficiency of the geometric interpretation through the proposed GD as a natural and intuitive similarity measure on the studied statistical manifold.
Federated learning‐based private medical knowledge graph for epidemic surveillance in internet of things

Abstract With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance.