Reviewerly/exHarmony huggingface dataset:
Pembelajaran dan Pengajaran Tata Bahasa Berdasarkan Korpus

This study is prompted by the advancement of corpus-based methods in the realm of grammar education. The primary objective of this research is to assess the requirements and the perceived significance of integrating corpus linguistics into the methodologies of grammar instruction. Employing an experimental research approach, the study involves participants who are undergraduate students majoring in Indonesian Language and Literature Education as well as English Language Education. The research utilizes the Leipzig Indonesian Mix_2013 and English-corpus.org corpora. The outcomes of this investigation offer an insight into the teaching of grammar through the incorporation of corpora, drawing comparisons based on the outcomes prior to the experimental sessions. The findings from this study highlight that the implementation of corpus linguistics within grammar learning and teaching methodologies yields a favorable impact on students' comprehension of grammar rules. The analysis of needs underscores students' recognition of the importance of integrating corpus elements into grammar education, particularly given the diverse and evolving language contexts prevalent in daily life. This perception is further reinforced by the results of the experimental phase, which demonstrate a substantial enhancement in grammar comprehension within the group exposed to corpus-based instruction. The deeper understanding gained regarding the real-world application of grammar in communicative scenarios equips students with enhanced readiness to apply grammatical concepts effectively in practical contexts. In conclusion, this research underscores the pivotal role of corpus linguistics in augmenting the efficacy of grammar education. The study provides valuable insights into the potential advantages associated with the integration of corpus-driven methodologies into language instruction, considering the dynamic nature of language usage in contemporary society. Furthermore, the outcomes emphasize the significance of aligning teaching practices with the evolving landscape of language usage.
Visual Pursuit with Switched Motion Estimation and Rigid Body Gaussian Processes

We present in this article a pursuit controller with simultaneous data-based 3D target motion prediction and switching estimation by a 2D camera when a moving target may switch between multiple motion patterns. The switching target motion is modelled by a new Gaussian Process model for rigid body motions that can predict velocity fields based on observed target motion data and an online switching estimator. We further prove that the proposed methods for motion prediction and visual pursuit ensure stability and demonstrate the increased performance of pursuing a target by a camera-equipped drone in a Digital Twin simulator.
Real-Time System of Detection and Identification of Raised Hands and Persons by Deep Learning Algorithms for Teaching Support Systems

The authors have been developing a teaching support system by using neural network/deep learning technologies. This paper deals with detecting persons with or without a raised hand, tracking, and identifying individuals in a large classroom by using a camera. The algorithm consists of object detection by a deep learning algorithm YOLO and individual identification by cascading several neural networks. An experiment of automatically calling on a person among those with raised hands is also conducted in a real setting to avoid concentration to a very limited number of students. PTZ camera is shown to be essential in this system. In the real-time experiment, Jetson AGX Xavier, a portable GPU machine, is used to meet the requirement of computationally expensive task, and our system is shown to be useful.
Lyudmila Gogotishvili’s “Phenomenology of Speaking”: On the History of Unpublished Book (Based on Personal Archive Materials)

The article reconstructs the plot related to the unknown book by L. Gogotishvili “The Phenomenology of Speaking,” prepared for publication in 2009, but never published. A hypothesis is put forward according to which the book successively develops the main concept of Gogotishvili’s magnum opus, her only lifetime monograph “Indirect Speaking” (2006), while differing in its methodological guidelines and degree of completeness. On the basis of materials from the personal archive of Gogotishvili, the article provides with a comparison of the history of creation, variants of the title and structure of both books, and reveals conceptual similarities and differences between them. Particular attention is paid to the unfinished article “Analytical Ornaments of Symbolism and Symbolic Traceries of Analytism,” which was not included in the final composition of the second book. This example demonstrates the internal contradictions between the general idea and its implementation, which apparently influenced the author’s refusal to publish. The article presents one chapter of an unpublished book on the typology of symbolist discursive strategies (the paradigm of no-linear multi-layered “polyconcepts”), which contains the substantiation of the key idea of this concept, which was further developed in the late works of Gogotishvili.
Digital-Awareness Age and Foam Consciousness Cycle: A Model Essay on the New Form of Consciousness

Human consciousness is a complex phenomenon that involves various activities such as perception, interpretation, positioning, and development. However, these activities are influenced by the cultural zeitgeist, available tools, and existing consciousness infrastructure. In the digital age, people are faced with a new awareness contract, which requires us to find new ways of engaging with the consequences of these interactions, rather than relying on fixed working principles of the human mind. To achieve this, we need a new model that can explain how human consciousness is shaped by the big data of the digital age. This model should also describe how significant mechanisms manipulate human consciousness and prevent us from questioning them and developing systematic answers. To develop this model, I conducted a qualitative study that involved face-to-face interviews with 17 participants. The study enriched two models: a "foam consciousness cycle diagram" and a complicated consciousness algorithm cycle. These models are based on image epistemology and incorporate philosophical, sociological, and psychological perspectives. In summary, the digital age requires us to rethink our understanding of human consciousness and develop new models that can help us navigate the complex interactions between technology and society.
Scientific Results Using Statistical Data on Economic Studies

This article deals with economic scientific research. In many practice studies, the scientists use practice ways to improve students' skills and students achievements. They are also trained in using scientific research and publishing statistical economic results. This paper gives some results using economic statistical data. Using the constructive method we focused on economic acquisition, as well as the research practices. Statistically, we show how the practical methods achieved their goals. This effective practice method requires students to understand what they know and need to learn practices, and research, with great effect in terms of scientific economics results. This article shows the methods of evaluating the success of implementing the practice method, as well as shows that many research ways need to use several assessment parameters. In our research, we give tabular results to compare the achievement of economic studies in a theoretical and practical way. Instead of developing as a modern study of international research, it is allowing the gap between students of international economics and students of international politics to grow their research wider and deeper and more impassable than ever. The results and conclusions of our paper show that their studies and scientific practice are probably explained by the opportunities for math analysis.
Geobody-oriented interpretable velocity fusion modeling in depth domain with seismic facies informed segmentation method

Full-layer high-resolution velocity modeling in depth domain is essential for imaging deep complex oil and gas reservoirs in China. However, limited by economic cost, especially in land exploration areas, seismic survey usually uses big observation grid and short offset distance, resulting in insufficient illumination to the subsurface. Therefore, it is difficult to reveal velocity structures with small scale, large buried depth or hidden characteristics by conventional wavefield inversion methods. With geobody identification as the core theory and velocity fusion as the tool, this paper proposes a geobody-oriented velocity modeling framework on the attribute side to realize full-layer high-resolution velocity modeling in depth domain. Using the combined method of chaotic fixed-point classifier and K-means clustering, all geobodies in the entire strata from the surface to the subsurface are simultaneously identified and then classified as different geological labels. The separated geobodies are independent of each other and can be easily manipulated individually, which makes it possible to interpret the velocity values on each of geobodies so that velocity fusion processing becomes more flexible and interpretable. Taking the data of Sichuan basin in China as an example, three strategies illustrate how to use geobody-oriented velocity fusion to improve modeling resolution. These examples also demonstrate that integrating geophysical data into geology will effectively improve the reliability of the depth-domain velocity model.
Chaos Cryptography

Exploring chaos cryptography, this chapter introduces a cryptographic paradigm using chaotic systems to design secure algorithms. Fundamental principles of chaotic systems and their application in cryptography are explained. Theoretical foundations, applications, and quantitative assessments of chaos cryptography are presented. The chapter concludes with discussions on potential vulnerabilities, sensitivity considerations, and the pros and cons of chaos-driven unpredictability, key sensitivity, and computational overhead.
The Convolutional Neural Network Combing Feature-Aligned and Attention Pyramid for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) heavily relies on partial localization and part-based discriminant feature learning. The current methods mainly focus on extracting information from high-level features, while ignoring the influence of low-level features on FGVC. Based on this, this paper integrates low-level detailed information and high-level semantic information to improve the model performance by enhancing the feature representation and accurately locating the discriminant part. This paper proposes an end-to-end convolutional neural network combining feature-aligned and attention pyramid (FAAP-CNN), which consists of three main modules: 1) Feature pyramid: transmits high-level semantic information in a top-down path. Meanwhile, the semantic gap and information loss in information transmission are reduced through feature alignment and feature selection, and the integrity and reliability of high-level feature information are maintained. 2) Attention pyramid: pass the detailed information of low-level features in a bottom-up path to enhance the feature representation; 3) ROI feature refinement: dropblock and zoom-in are used for feature refinement to effectively eliminate background noise. The experimental results on three publicly FGVC datasets show that FAAP-CNN has excellent performance.
Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review

In this paper, a systematic literature review was provided that investigated the present evidence regarding extremist words in Arabic opinion mining methods. This study aimed to perform a Systematic Literature Review (SLR) in order to detect, evaluate, and synthesize the existing evidence regarding opinion mining techniques for extremist Arabic text. From the SLR, it is evident that opinion-mining techniques have several opportunities for detecting extremism in the Arabic text. Over the past few years, multimedia sentiment analysis has gained traction as visual content is becoming more incorporated into social media networking. Opinion mining is the process of identifying, extracting, and categorizing views about anything. It is a sort of Natural Language Processing (NLP) used to track public sentiment about a certain law, policy, or marketing, for example. It entails the creation of a method for collecting and analyzing comments and opinions concerning legislation, regulations, policies, and so on that are posted on social media. The process of information extraction is critical since it is both a beneficial tool and a difficult undertaking. In this article, we have examined the recent and advanced methodologies to extract sentiment from a web-wide item, opinion-mining methods must be automated. Also, we have analyzed the novel Artificial Intelligence and lexical-based algorithms for sentiment analysis. These methodologies find better applications in the customer feedback analysis of any organization.
Seismic data compression by variational autoencoder with hyperprior

The evolution of digitization and advancements in seismic data acquisition technology have led to an exponential increase in data volumes within the seismic industry. This surge in data has introduced significant challenges in storage, transmission, and computation. Despite the attempts of previous studies to explore seismic data compression, achieving a balance among the compression ratio, quality of decompressed data, and computational efficiency continues to pose a challenge. This study capitalizes on the recent advancements in deep learning to utilize a variational autoencoder incorporated with a hyperprior. The aim is to simultaneously optimize the reconstruction accuracy and compression ratio in a comprehensive, end-to-end approach. Our experimental analyses demonstrate the efficacy of the introduced compression model on both pre-migration and post-migration seismic datasets.
Automation of Analysis and Voice-Based Search of University Resolutions Based on Artificial Intelligence Algorithms

Detecting topics in text is a challenge in Artificial Intelligence (AI) and Natural Language Processing (NLP). This detection is a method for organizing, understanding, and summarizing large amounts of textual information and can be described as a technique for finding in a collection of words (topics) those that best represent the information in the collection.
Interpreting the results of InterCriteria Analysis: Pareto principle at work

The present short note aims to propose a new, alternative, way to interpret the results of the intuitionistic fuzzy sets-based method for multicriteria decision support named InterCriteria Analysis. Given an &lt;math&gt;m \times n&lt;/math&gt; dataset of multiple (''m'') objects evaluated numerically against multiple (''n'') criteria, the ICA method generates an &lt;math&gt;n \times n&lt;/math&gt; table of intuitionistic fuzzy pairs &lt;math&gt;\langle \mu_{i,j}, \nu_{i,j} \rangle, \ i, j \in {1, 2, \ldots, n}&lt;/math&gt; where the given pair indicates the extent of relation between the corresponding pair of criteria &lt;math&gt;C_i, C_j&lt;/math&gt;. Traditionally, the interpretation of these intuitionistic fuzzy pairs regarding the extent of positive or negative dependence between two criteria (or, respectively, the lack of such) requires that two threshold values, both in the [0,1] interval too, are used. Now we propose to use only one such threshold value belonging to the [0,1] interval, for instance a minimal threshold of the degree of membership, while the other threshold {would} be essentially related to the size of the subset of intercriteria pairs being shortlisted for interpretation, rather than their degree of non-membership. We justify that the proposed approach, inspired by the Pareto Principle, in certain cases yields better results than the traditionally used one..
What's law got to do with IT: an analysis of techno-regulatory incoherence

As the law has sought to gain greater control over the internet, this developing techno-regulatory coherence has rendered the law increasingly incoherent when applied to emergent technologies. Developing technological architectures may therefore be allowed to privately regulate themselves, rather than undermine the functionality of our current regulatory web. Yet, the denser this web becomes, the more readily it can be exploited by design, with multiple fringe architectures co-existing with functionalities intentionally abstracted from legal regulatory models. We risk entrenching a centralised framework, which distracts from a peripheral landscape wherein fragmentation and legal incoherence abounds. Is such a future improbable, fundamentally limited by our inability to develop technological architectures of requisite utility fast enough to create such confusion? Or will the inherent utility of incoherence be exploited? If the law is to maintain its regulatory sovereignty over the future, it must disentangle itself from the architectures of the present.
Analysis of Automated Music Generation Systems Using RNN Generators

Deep Learning Algorithms has advanced drastically, finding their way in almost every field of where automation can be done or is required such as Computer vision, Natural Language Processing, Speech recognition, Robotics and many other. Most of these tasks are limited to unimaginative or less creative tasks in society. Generating Music is one of the challenging tasks for a Deep learning algorithm as it requires content generation, but it finds its application in co-creative tools. Thus, Artificial Music generation is one of the fields where Deep Learning is being applied, generating new compositions based on historical data. Our report “music generation using deep learning” is based on LSTMs – long short-term memory to generate music using Maestro dataset. Our main goal is to generate music using Deep learning algorithm i.e., LSTMs based on previous notes.
A Blockchain Based System for Product Tracing and Tracking in the Supply Chain Management

Collection of information, resources, people, activities and organizations intended to transfer a service or a product from supplier to buyer is known as the supply chain. The purpose is to maintain the characteristics of the product or service. Therefore, fast and reliable tracking and tracing of the goods right from the source to the destination is needed. Most of the existing solutions depend on the centralized architecture and therefore have a single-point of collapse. Blockchain, being a distributed ledger as a decentralized system helps create applications that preserve data integrity and are secure. In this paper, the author proposes a blockchain-based management system to find any forged items that may be present in the supply chain.
A Mobile Application for Currency Denomination Identification for Visually Impaired

Inspite of availability of various payment apps, currency notes are playing a prominent role as primary mode of exchange for many day to day transactions. For a person with good vision denomination identification is a straight forward task, whereas visually impaired it becomes a challenge. Each currency note is embossed with unique symbols, identification becomes much tougher to them to differentiate between currency notes. This paper mainly focuses on extracting distinctive features from an Indian Currency note using Oriented FAST and Rotated BRIEF (ORB) algorithm. This system also recognizes currency notes with different orientations. Apart from identifying currency denominations, it also calculates total amount available at hand. As now-a-days smart phone is carried by everyone, therefore a mobile application is developed for currency detection to aid visually impaired, which provides detected currency notes information through audio.
A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming

The combination of genetic programming with federated learning could solve the computational distribution while promoting a collaborative learning environment. This paper proposes a federated learning configuration that enables the use of genetic programming for its global model. In addition, this paper also proposes a new aggregation algorithm that enables the collaborative evolution of genetic programming individuals in federated learning. The case study uses flexible genetic programming, an existing and successful algorithm for image classification, integrated into a federated learning framework. The results show that the use of genetic programming with federated learning achieved a classification error rate of 1.67%, better than the scenario without federated learning, that had an error rate of 3.33%, considering a configuration with three clients with different datasets each.
356: ENHANCING PREDICTIVE MODELS FOR PE IN ICU PATIENTS USING PRETRAINED GENERATIVE ADVERSARIAL NETWORKS

Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
393: MACHINE LEARNING MODELS FOR PREDICTING MORTALITY IN SEPSIS: A SYSTEMATIC REVIEW

Kobayashi, Hirotada1; Amrein, Karin2; Lasky-Su, Jessica3; Christopher, Kenneth4 Author Information
Example Patterns

One of the more valuable uses for Enterprise Canvas is that it provides a consistent pattern to guide a basic completeness check for service designs. As with the core principles for Enterprise Canvas, we can apply this kind of completeness check at any level, from whole-organization to detail-level service. This chapter shows five brief examples of this completeness-check review as applied to various types of service:
1362: UTILIZING LARGE LANGUAGE MODELS FOR DISEASE PHENOTYPING IN OBSTRUCTIVE SLEEP APNEA

Khurram, Ifrah1; Zamora-Resendiz, Rafael2; Morrow, Destinee2; Crivelli, Silvia2 Author Information
Blockchain Framework for Healthcare Data Management System in Clinical Trials

The concept of information management involves the identification and organization of data into distinct sectors, sometimes referred to as silos, in order to anticipate and address future situations. One of the primary obstacles encountered in the administration of clinical trials pertains to the presence of information silos among the several stakeholders involved in the process. The improvement of cooperation and integration may be achieved by processing information in real-time and implementing a comprehensive system. This manuscript evaluates the viability of implementing the blockchain model for auditing clinical trials. It also proposes a blockchain-based solution that has the potential to enhance the overall quality of the practiced solutions and establish sustainable approaches for conducting clinical trials auditing. The suggested model focuses on the development of a grid-based system for organizing the many stages of a clinical trial. This involves creating distinct blocks for each phase and then constructing a complete network of these blocks to be shared across the consortium responsible for managing the trial data. The suggested architecture has substantial potential for enhancing security and adaptability, hence facilitating systematic improvements to information systems in the context of clinical trials.