Reviewerly/exHarmony huggingface dataset:
VR-mediated storytelling of experience in destination branding: hybrid 4D transformations

In COVID-19 era, destination branding faces the challenge of digitalization and virtual reality (VR) in particular. The fundamentals of VR-mediated storytelling in destination branding are in the process of being developed. There is a luck of research on immersive VR-mediated storytelling, scenarios, and messages in destination branding, especially realised with technologies of more complex - hybrid - immersivity (4D). The shift from 2D, 3D to 4D hybrid multisensory VR technologies is not only among the main technology developments - it provokes new research problems with VR-mediated destination branding and storytelling. The authors present the results of theoretical and empirical research of VR-mediated destination storytelling of a brand driven by the newest 4D hybrid multisensory technological approaches on the case of Switzerland. In Switzerland, VR-mediated projects in destination branding are developing actively last years but VR-mediated storytelling research in this field was not provided yet. In this regard, it was chosen 100 destination brand VR projects, presented in 2016-2022, to compare the parameters of VR-mediated storytelling of a brand. VR has to be included into brand storytelling paradigm, which must be rethought for this specific sphere. It was proved that it is more effective to combine different types of experience, virtual and physical both and make the VR-mediated brand storytelling hybrid. In terms of theoretical implications, this paper opened a specific research area by bridging theoretical and empirical ideas of destination branding, VR-mediated storytelling and digital media, technical and social communication.
First Encounter? [Turnstile]

I received the book <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Penguins, Pineapples and Pangolins</i> <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> as a Christmas gift in 2022. It is a fascinating compendium of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> encounters European travelers had during the Age of Exploration (the 15th to 17th century) with people, flora, and fauna in distant lands, all narrated in the explorers’ own voices. As the author, Claire Cock-Starkey, notes in the prologue <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> :
Towards Digital Forensics Investigation of WordPress Applications Running Over Kubernetes

Forensics investigation of WordPress applications hosted on containerized cloud environment is critically important due to their increased adaptation and an exponential increase in cybercrimes. Investigation of containerized environments is complex due to their volatile nature and dynamic allocation. This paper explores the forensic analysis of WordPress applications deployed inside a containerized environment. In this paper, we have used a log analysis approach to perform the digital forensics investigation of different attacks on WordPress applications running over Kubernetes. To investigate these events, we collected a large number of operational logs generated by these applications. Log filtrations techniques are applied to analyze only the relevant logs for a specific event to perform the forensic investigation efficiently. The experimental results confirm the proposed approach's effectiveness as we could trace all the malicious events to conduct the digital investigation on the WordPress applications running inside Kubernetes. The scheme successfully identified the attacker's information like incident time, incident source, duration of attack etc. Moreover, the proposed solution also provided forensics readiness for the WordPress applications running in a container-based cloud.
Metadial: A Meta-learning Approach for Arabic Dialogue Generation

Dialogue generation is the automatic generation of a text response, given a user’s input. Dialogue generation for low-resource languages has been a challenging tasks for researchers. However, the advancements in deep learning models have made developing conversational agents that perform the tasks of dialogue generation not only possible, but also effective and helpful in many applications spanning a variety of domains. Nevertheless, work on conversational bots for low-resource languages such as the Arabic language is still limited due to various challenges, including the language structure, vocabulary, and the scarcity of its data resources. Meta-learning has been introduced before in the natural language processing (NLP) realm and showed significant improvements in many tasks; however, it has rarely been used in natural language generation (NLG) tasks and never in Arabic NLG. In this work, we propose a meta-learning approach for Arabic dialogue generation for fast adaptation on low-resource domains, namely, Arabic. We start by using existing pre-trained models; we then meta-learn the initial parameters on high-resource dataset before finetuning the parameters on the target tasks. We prove that the proposed model that employs meta-learning techniques improves generalization and enables fast adaptation of the transformer model on low-resource NLG tasks. We report gains in the BLEU-4 and improvements in Semantic textual Similarity (STS) metrics when compared to the existing state-of-the-art approach. We also do a further study on the effectiveness of the meta-learning algorithms on the response generation of the models.
PENGARUH PENGGUNAAN ILUSTRASI PADA BUKU #88LOVELIFE TERHADAP PERSEPSI PEMBACA

Book #88 Love Life by Diana Rikasari has a discussion on how to love yourself in living this life and also instill positive thoughts in yourself to deal with all life's problems.In this book Diana Rikasari invites an illustrator named Dinda Puspitasari to make illustrations for her book.Book # 88 Love Life uses illustrations on each page as an explanation of the pearls of wisdom in the book so that it is hoped that readers can catch the message that is in accordance with what Dinda Rikasari meant.The method used in this study is a quantitative method with a qualitative approach.This research was conducted to determine the effect of illustrations on readers' perceptions by using questionnaires as the main data of this study to determine the respondents' perceptions of illustrations in book #88 Love Life with the help of literacy from journals and articles.This research produces findings in the form of successful illustrations that affect the reader's perception so that the reader can have a perception that is in line with the author of the book.
A novel binary greater cane rat algorithm for feature selection

There is a surge in the application of population-based metaheuristic algorithms to find the optimal feature subset from high dimensional datasets. Many of these approaches cannot properly scale especially as they are expected to maintain two opposing goals: maximizing the accuracy of classification while at the same time minimizing the number of feature subsets selected. In this study, a novel binary greater cane rat algorithm (GCRA), inspired by intelligent nocturnal behavior of the GCR which significantly affects their foraging and mating activities. They leave trails to food sources, shelters, and water as they forage, and this information is kept by the dominant. Also, they split into male and female groups during mating season is during abundant food supply and near water source. This information is modeled into and effective method for selecting the optimal feature subset from high-dimensional datasets using two different approaches. Firstly, five variants of binary GCRA are developed using one each from the family of S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to binarize the GCRA. Secondly, the threshold which maps a variable to 0 or 1 is used to develop a variant of GCRA. The performance of the six (6) variants were evaluated using 12 datasets with different dimensionalities. The experimental results show the stability of all the proposed methods as they generally performed competitively. However, the threshold version known as BGCRA showed better performance in yielding the highest accuracy of classification on 9 of the 12 datasets utilized in the study and performed second in selecting the least number of important feature sets. It also showed superiority over other variants in yielding the least average fitness values in 11 of 12 (91.6%) of the datasets used. Hence, the BGCRA was utilized for further comparative analysis against 5 other popular feature selection (FS) algorithms with outstanding performance in terms of producing the highest mean accuracy of classification on 91.6% (11 of 12) of the datasets, 100% least average fitness values, and 91.6% in selecting the least average number of significant features. The results were also validated by statistical tests which showed that the BGCRA is significantly superior compared to other methods.
Conspiracy as ARG: media and game essence of QAnon

The results of a comparative analysis of the functional features of the QAnon theory and the conceptual signs of ARG (games in alternative reality) are displayed. The signs of ARG are designated and given as elements of the reference game model, the basis for comparison. The authors found that the QAnon conspiracy theory, the history of its emergence and development features is of a synthetic nature: ARG elements are present in modified form. They transform the roles of developers and participants. The QAnon quest structure was visualized, the gameplay based on experiencing the state of apophenia - a painful search for a connection between random phenomena or events - was described. The similarities of QAnon with religion and at the same time political ideology based on the mechanics of the game were identified, which explains its popularity. The relevance of the topic is due to the steady growth in the number of conspiracy theories and their followers since the beginning of the Covid-19 pandemic against the backdrop of audience distrust in the media. The authors demonstrate how QAnon, not being essentially a game in an alternative reality, but using game technologies and eschatological narratives about the Great Tribulation and Judgment Day, achieves high efficiency in shaping public opinion.
On the existence of special nonlinear invariants for round functions of XSL-ciphers

Abstract Nonlinear invariants of round transformations in XSL-schemes are studied. The emphasis is on invariants which may be found by means of the approach suggested at the conference ASIACRYPT 2016. Some known results on the inertia groups of decomposable functions are used to describe conditions on S-boxes and matrices of XSL-schemes which are necessary for the existence of such invariants. It is shown that for a number of schemes these conditions are not satisfied.
NODSTAC: Novel Outlier Detection Technique Based on Spatial, Temporal and Attribute Correlations on IoT Bigdata

Abstract An outlier in the Internet of Things is an immediate change in data induced by a significant difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause the decision-maker to make incorrect judgments about data analysis. Hence it is essential to detect outliers in any discipline. The detection of outliers becomes the most crucial task to improve sensor data quality and ensure accuracy, reliability and robustness. In this research, a novel outlier detection technique based on spatial, temporal correlations and attribute correlations is proposed to detect outliers (both Errors and Events). This research uses a correlation measure in the temporal correlation algorithm to determine outliers and the spatial correlation algorithm to classify the outliers, whether the outliers are events or errors. This research uses optimal clusters to improve network lifetime, and malicious nodes were also detected based on spatial–temporal correlations and attribute correlations in these clusters. The experimental results proved that the proposed method in this research outperforms some other models in terms of accuracy against the percentage of outliers infused and detection rate against the false alarm rate.
Unravelling interlanguage facts via explainable machine learning

Abstract Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e. that of analysing the internals of an NLI classifier trained by an explainable machine learning (EML) algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena ‘give a speaker’s native language away’. We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e. guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners’ essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker’s L1; our experiments indicate that the most discriminative features are the lexical ones, followed by the morphological, syntactic, and statistical features, in this order. We also present two case studies, one on Italian and one on Spanish learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s; we show that the traits identified as most discriminative well align with our intuition, i.e. represent typical patterns of language misuse, underuse, or overuse, by speakers of the given L1. Overall, our study shows that the use of EML can be a valuable tool for the scholar who investigates interlanguage facts and language transfer.
Meaning of Female Literacy Learners’ Experience in Participation of Group Art Healing Program and Exhibition

This study aims to explore the meaning and essence of the experience in which female literacy learners participated in group art healing program and exhibition. To this end, in-depth interviews were conducted with six female literacy learners at S Mother School who participated in the group art healing program and exhibited their work. An analysis was conducted based on the application of phenomenological research method. As a result, 40 converted semantic units, 14 themes, and seven central meanings were derived within three categories. First, the meaning of the experience of the group art healing program showed that they felt the burden of self-opening and drawing, but actively enjoy art activities away from psychological atrophy while feeling the joy of attempting new challenges in the midst of encouragement and expectations. Second, in the meaning of the experience of the exhibition, it could be seen that their works hung in an unfamiliar exhibition hall became the pride of their lives and that the value of their self was newly recognized. Third, the integrated meaning of group art healing program and exhibition experience, they came to have a positive view of themselves and the world, and they were able to restore their confidence and discover new possibilities for themselves. On the basis of these results, it confirms that group art healing program and exhibition could be meaningful interventions for the psychological support of female literacy learners, and suggested that they could be extended beyond the individual level of female literacy learners to a practical method of subjective interaction within the community.
A Basic Study on the Selection of Required Operational Capability for Attack Drones of Army TIGER Units Using AHP Technique

The importance of each warfighting function for Army TIGER unit attack drones is measured using the AHP technique. As a result, the importance of attack drones is high in the order of maneuver, firepower, intelligence, command/control, protection, and operation sustainment, but the importance of maneuver, firepower, and intelligence are almost similar. In addition, it is analyzed that attack drones capable of carrying out day and night missions by being equipped with an EO/IR sensor and being commanded/controlled in conjunction with the C4I system to eliminate threats with small bombs or aircraft collisions is needed. Finally, based on the results of this study, a virtual battle scenario for attack drones is proposed.
The Intercorrelation between Zero trust, Dark web, and its Implications in the field of Digital Forensics

The integral question, regarding how the data connected to an individual or an organization can be securely stored, transferred and updated has been under the hammer for quite a while. With respect to the advancements of technology and the varying trends in the usage of data, there has been attempts to make data as secure as possible, but the undeniable fact that there has been an acute rise in the number of data breaches that's happens world-wide, clearly indicates the need for a much deeper analysis that connects the origin, the methodology, the demand for breached data, the possible solutions and various other micro-factors. This is where developments like zero trust can help in deriving solutions that might help in the reduction of such breaches. The Analytics of Dark web, will give a broader picture of what happens to the breached data and how digital forensics can be more effective in this analysis.
Virtual User Interface for Differently Abled People Using Vision Transformer Model

We are aware that using computers has become commonplace. But using digital devices like laptops, pcs and tablets is too challenging for those with varied abilities (those without hands and those who are paralyzed). Our goal is to use a webcam to read real-time video while integrating various face emotions with the corresponding mouse events. In order to imitate mouse motions and order, we used pyautoGUI in conjunction with OpenCV for image processing and computer vision tasks. Image processing is mainly used to identify faces and facial expressions. For key board related functions, we integrated speech to text conversion. Speech is transformed to text whenever user speaks and it will be displayed according to the user's wish like when the cursor is over a search box or a micro soft word. By doing this, all the computer tasks can be accomplished without the usage of hands.
Design and development of information management system using QR code based technique

Today's university students, staff and visitors expect an intelligent, digitally connected campus that improves security, supports integrated learning experiences and simplifies student services. It is very common for a visitor to get lost in the large universities searching for a particular department or information. To prevent this, the paper discusses a QR code based solution to help visitors of the campus to provide campus information at one point. Although QR code is used for product tracking, item identification, time tracking and document management, its usefulness can be explored in information management too. This paper discusses the digitization of the university campus by using simple technological tools. Every university has a website that contains detailed information such as faculty information, placement statistics, student information and a complete virtual tour of the entire college campus. This site is linked to QR Codes that will be displayed in front of each department in the campus. People can use a QR code scanner and obtain the information quickly instead of surfing the university website. The main objective is to provide appropriate information efficiently at one place obviating the need to type in the URL of the website.
Application of Blockchain in Digital Healthcare

To protect personal patient information and comply with regulations, data security is a necessary aspect of the healthcare business. Systems in the healthcare business that are based on the block chain are beneficial to both medical professionals and patients. The use of block chain technology in the healthcare industry as a useful tool for decentralizing patient health information, monitoring pharmaceutical goods, and increasing payment options is swiftly gaining support. Because patient data is written on paper and stored in filing cabinets, it is simple to protect and secure it. However, all data is now saved digitally, such as patient records, which are now stored on computers, servers, and storage devices. Poor wireless network security, data breaches, legacy system use, malware, viruses, and other hostile attacks are all risk considerations in health care. When compared to other businesses, one of the key reasons to safeguard health care data is that healthcare companies gather and keep extremely detailed patient records, including patient names, dates of birth, addresses, and social security numbers. This paper aims to discuss the issues and solutions to achieving digital healthcare using blockchain technology.
Semantic Analysis of Auto-generated Sentences using Quantum Natural Language Processing

Quantum computing has been highly promising and is advancing quickly in the field of computing. Natural language processing is not harnessed to its full potential. The behavioural pattern of quantum hardware helps in the fast processing of linguistic tasks. The lack of quantum computing machines has slowed the process of conducting extensive research on quantum-based applications. This paper contains the quantum circuits used for semantic interpretation of autogenerated sentences using the Qasm simulator and IBM quantum experience. Our main objective is to apply semantic interpretation.
Design and Implementation of Synthesizable Two-Level Cryptosystem for High-Security enabled Applications

With a greater reliance on digital data in the most recent technological era, maintaining its security is also a crucial task. The power of information in this day and age can be abused to commit immoral acts. Data must therefore be kept secure. In order to achieve double encryption, we have updated the Elliptic Curve Cryptographic (ECC) and Diffe-Hellman (DH) algorithms in this study. ECC is preferred primarily because it offers higher security with reduced key sizes. It uses left to right point multiplication, which requires less computing time than conventional techniques. The suggested changes result in a cypher text transmission that is half as large, resulting in an increase in transmission speed and a halving of memory consumption. As a result, when compared to a conventional ECC, we have improved security while also reducing computation time.