Reviewerly/exHarmony huggingface dataset:
Documentary principles in fiction in the light of the value-ontological approach

The purpose of the publication is to establish the reasons for the growth of the documentary principle in fiction for over the last one and a half centuries in connection with the transformation of the semantic and value powers that the author of the work possesses. The value aspect of the documentary principle in literature is about the role the fact plays as a link with being, when the writer’s ability and his right to authoritatively act as a subject coming into contact with the cumulative experience of mankind is called into question. In the presence of the documentary principle, the result of a creative act is not a narrowly understood aesthetic product, a phenomenon of an exclusively fictitious nature realized in “autonomous being”, taken outside the “reality of births and deaths” of the author and the reader. Reliance on the fact opens up the possibility for a creative act to be realized as an event, rooted in the space of its responsibility to true being. However, the creative activity of the artist as the founder and organizer of a meaning-generating event still remains an indispensable condition for a work of art. The dialectical unity of the truth of a fact (document) and the individual creative initiative of the author is preserved in literature of a predominantly documentary nature. The author confirms the authenticity of all stamps on the document, personally and artistically ratifying it and overcoming its subjective ontological fluctuation. The formative initiative of the author also remains of paramount importance due to the fact that a direct transfer of life to paper is impossible (either because of its inexpressiveness, or, more often, due to the intolerable traumatic experience that is ousted from the bright field of the individual’s consciousness). The author provides an opportunity for the other (the reader) to have access to the “bare life”, of which he was a witness and a participant, creatively building a subject-shaped structure that ensures this accessibility. The work of V. T. Shalamov serves as a support for the ideas presented in the article.
COPSEC: Compliance-Oriented IoT Security and Privacy Evaluation Framework

A rising number of Internet of Things (IoT) security and privacy threats have been documented over the last few years. However, IoT devices' domain designs are out-of-date and do not take into consideration the changing dangers associated with them. In this paper, we present COPSEC, a novel framework for evaluating whether IoT devices are compliant with security guidelines and privacy regulations. We extract metrics from existing guidelines and regulations and test them on a set of devices by performing hundreds of automated experiments. Our results indicate not only that these devices are not compliant with basic security guidelines, but also that their data collection operations may introduce privacy risks for the users that adopt them.
Cybercrime and Threats to the Electoral System

Cyberattacks have emerged as a growing threat in the context of elections worldwide, posing significant risks to the integrity, security, and trustworthiness of electoral systems in today's digital age where information technology plays a pivotal role. Drawing from documented cyberattack cases and security reports from government agencies and related organizations during elections, common attack types such as Denial-of-Service (DoS), Phishing, and Malware have been identified. Cybersecurity systems and data sovereignty form the bedrock of personal data protection. As technology advances, data has become a highly valuable commodity, and a nation's data sovereignty intersects with the private sector on a global scale in political and economic terms. The state's primary responsibility lies in crafting regulations for data protection and cybersecurity, ensuring citizens' rights to personal data protection, which necessitates enhancing their capacity and capabilities. Therefore, this qualitative research delves into the imperative of developing citizens' capacity and capabilities in building cybersecurity and data sovereignty, with a particular focus on safeguarding personal data in the Indonesian context, as books and journal articles serve as valuable sources for data collection. Bolstering citizens' capacity and capabilities is essential for preserving their personal data in the digital realm.
An Analysis of Teacher’s Strategy in Teaching Public Speaking at Aviation School of Nusantara Tjahja Cipta Sidoarjo

This research was aimed to find out what are the teacher's strategies in teaching public speaking at Aviation School Nusantara Tjahja Cipta Sidoarjo with the qualitative descriptive method and used the Class observation documentation and Semi-Structured In-Dept Interview as the Instrumental data. Furthermore, the subject of this research was an English teacher. For the research results, the researcher concluded that in teaching public speaking, the English teacher used several strategies as follows: Pedagogical Principles (Experience-Based an Incremental Approach), Communication Apprehension Management, Practical, and Scoring. In the pedagogical Principle-strategy, the teacher implemented lecturing brainstorming activities to help students understanding the materials in detail and comprehensively. The second strategy that used was Communication Apprehension Management, in this kind of activity, the public speaking teacher implemented a variety of communication interactions by making a sharing session. Third, for the practical strategy, the public speaking teacher was giving the two kinds of assessments such as speaking groups of presentation and role-play dialogue conversation. The last in the strategy of scoring, the teacher evaluated and assessed regarding the students’ attendance and the presentations.
E-Learning Success Factors from Business Perspective

Increasing digitization trend in the business realms and the competitive market environment, e-Learning is overtaking traditional face-to-face classroom training style in their knowledge management system. However, it is hard to determine the future of e-learning in the workplace. While in all kinds of businesses, usage of elearning is encouraged by the time flexibility, teaching quality, the cost-effectiveness of the latest tech, social elearning processes that enhance knowledge and skills, the digital revolution, a spike in virtual classrooms, faster economic cycles, mergers, and more competitiveness. On the other hand, employee apprehension about using e-learning technologies concerns a lack of financing, a lack of business-relevant degree courses, concerns are limiting the business usage of these technologies. It is now not a matter of whether or not the Online can be utilized to change instruction in modern and viable ways. It's moreover not a question of whether or not firms ought to give the time, exertion, and assets required to fulfill their guarantee of making and developing new learning openings. The right question is, what is preventing businesses from using this incredible learning in their daily operations. It's probably time for businesses to band together and use the Internet's ability to learn from promise to practice. The emergence of e-learning is supposed to be considered an opportunity that arrives at a specific moment, which will be highly beneficial regarding organizational growth and development. Therefore, this paper will determine, analyze and represent the success factors of e-learning from the business perspective.
Teaching–Learning Optimization Algorithm Based on the Cadre–Mass Relationship with Tutor Mechanism for Solving Complex Optimization Problems

The teaching–learning-based optimization (TLBO) algorithm, which has gained popularity among scholars for addressing practical issues, suffers from several drawbacks including slow convergence speed, susceptibility to local optima, and suboptimal performance. To overcome these limitations, this paper presents a novel algorithm called the teaching–learning optimization algorithm, based on the cadre–mass relationship with the tutor mechanism (TLOCTO). Building upon the original teaching foundation, this algorithm incorporates the characteristics of class cadre settings and extracurricular learning institutions. It proposes a new learner strategy, cadre–mass relationship strategy, and tutor mechanism. The experimental results on 23 test functions and CEC-2020 benchmark functions demonstrate that the enhanced algorithm exhibits strong competitiveness in terms of convergence speed, solution accuracy, and robustness. Additionally, the superiority of the proposed algorithm over other popular optimizers is confirmed through the Wilcoxon signed rank-sum test. Furthermore, the algorithm’s practical applicability is demonstrated by successfully applying it to three complex engineering design problems.
A network model of referent identification by toddlers in a visual world task

Abstract We present a neural network model of referent identification in a visual world task. Inputs are visual representations of item pairs unfolding with sequences of phonemes identifying the target item. The model is trained to output the semantic representation of the target and to suppress the distractor. The training set uses a 200‐word lexicon typically known by toddlers. The phonological, visual, and semantic representations are derived from real corpora. Successful performance requires correct association between labels and visual and semantic representations, as well as correct location identification. The model reproduces experimental evidence that phonological, perceptual, and categorical relationships modulate item preferences. The model provides an account of how language can drive visual attention in the inter‐modal preferential looking task.
An image caption model based on attention mechanism and deep reinforcement learning

Image caption technology aims to convert visual features of images, extracted by computers, into meaningful semantic information. Therefore, the computers can generate text descriptions that resemble human perception, enabling tasks such as image classification, retrieval, and analysis. In recent years, the performance of image caption has been significantly enhanced with the introduction of encoder-decoder architecture in machine translation and the utilization of deep neural networks. However, several challenges still persist in this domain. Therefore, this paper proposes a novel method to address the issue of visual information loss and non-dynamic adjustment of input images during decoding. We introduce a guided decoding network that establishes a connection between the encoding and decoding parts. Through this connection, encoding information can provide guidance to the decoding process, facilitating automatic adjustment of the decoding information. In addition, Dense Convolutional Network (DenseNet) and Multiple Instance Learning (MIL) are adopted in the image encoder, and Nested Long Short-Term Memory (NLSTM) is utilized as the decoder to enhance the extraction and parsing capability of image information during the encoding and decoding process. In order to further improve the performance of our image caption model, this study incorporates an attention mechanism to focus details and constructs a double-layer decoding structure, which facilitates the enhancement of the model in terms of providing more detailed descriptions and enriched semantic information. Furthermore, the Deep Reinforcement Learning (DRL) method is employed to train the model by directly optimizing the identical set of evaluation indexes, which solves the problem of inconsistent training and evaluation standards. Finally, the model is trained and tested on MS COCO and Flickr 30 k datasets, and the results show that the model has improved compared with commonly used models in the evaluation indicators such as BLEU, METEOR and CIDEr.
Hand Gestures Replicating Robot Arm based on MediaPipe

A robotic arm is any variety of programmable mechanical devices designed to operate items like a human arm and is one of the most beneficial innovations of the 20th century, quickly becoming a cornerstone of many industries. It can perform a variety of tasks and duties that may be time-consuming, difficult, or dangerous to humans. The gesture-based control interface offers many opportunities for more natural, configurable, and easy human-machine interaction. It can expand the capabilities of the GUI and command line interfaces that we use today with the mouse and keyboard. This work proposed changing the concept of remote controls for operating a hand-operated robotic arm to get rid of buttons and joysticks by replacing them with a more intuitive approach to controlling a robotic arm via the hand gestures of the user. The proposed system performs vision-based hand gesture recognition and a robot arm that can replicate the user's hand gestures using image processing. The system detects and recognizes hand gestures using Python and sends a command to the microcontroller which is the Arduino board connected to the robot arm to replicate the recognized gesture. Five servo motors are connected to the Arduino Nano to control the fingers of the robot arm; These servos are related to the robot arm prototype. It is worth noting that this system was able to repeat the user's hand gestures with an accuracy of up to 96%.
Exact renormalization of wave functionals yields continuous MERA

The exact renormalization group (ERG) is a powerful tool for understanding the formal properties of field theories. By adapting generalized ERG schemes to the flow of wave functionals, we obtain a large class of continuous unitary networks, a special case of which includes a class of Gaussian continuous multiscale renormalization Ans\"atze (cMERAs). The novel feature of these generalized wave functional ERG schemes is allowing for modifications of the dispersion relation, which drastically changes the entanglement structure of the ultraviolet states. Through our construction, we demonstrate that cMERA can be derived from a more fundamental ``microscopic'' principle, which amounts to the usual RG principle of path integral independence, suitably adapted to quantum states of the field theory. The establishment of such a principle may provide a path forward for exploring cMERA beyond the free field regime, and for understanding the nature of entanglement renormalization intrinsically in the continuum.
Secure Transaction of Digital Currency through Fuzzy Based Cryptography

Objective: Internet technology is growing at a very fast rate around the globe and many of the countries are performing currency transaction through online mode. Due to increment of transaction of currency in an exponential manner, there is a need to include proper security features so that hackers or intruders could not hack the digital transaction carrying out between the two parties. Method: A new model using Unified Modeling Language (UML) is proposed which is developed in the hybrid mode with a combination of fuzzy rule-based computation, fingerprint authentication, Hash and Elgamal cryptography. The hybrid method is applied for secure transaction of the digital currency. The fuzzy rule based computation is used with Triangular Membership Function (TMF), which is based on current date and time. Fuzzy value creates a secret key and encrypts through Hash algorithm. Elgamal cryptosystem is used for encryption and decryption. The results obtained through hybrid method are tested through the concept of Finite State Machine (FSM) by generating the various test cases. Findings: The results obtained through hybrid method are tested through the concept of FSM by generating the various test cases. Novelty: In this work, mutual authentication as well as time stamp is also considered and the presented hybrid approach is based upon two-way authentication between client and user which is found to be very effective and may be used by the software industries for the customers especially related to the banking sectors. Keywords: Fuzzy; Hash Algorithm; Cryptosystem; FSM; Test Cases and Validation
Measles Disease Analysis in Bengkulu Province Using Zero Inflated Poisson Regression and Zero Inflated Negative Binomial Regression

Zero Inflated Poisson regression (ZIP) and Zero Inflated Negative Binomial (ZINB) regression were used if there was overdispersion and no multicollinearity in the data. This study aims to analyze measles in Bengkulu Province using the ZIP and ZINB regression models. Among them are selecting the best model, seeing the influential variables from the best model, and predicting the results of the best model. The data used is one dependent variable, namely the number of measles cases (Y) in each puskesmas and six independent variables (X) namely the percentage of measles immunization, the amount of malnutrition, the percentage of exclusive breastfeeding, the percentage of vitamin A, the percentage of proper sanitation, and the percentage of healthy house. The results of this study, the ZIP regression model formed is a discrete model for , namely ln()=-5.042-0.007X1-0.014X3+0.094X4 and a zero inflation model for , namely logit()= -3.656+0.101X4-0.054X6, while the ZINB regression model formed is a discrete model for , namely ln()=-9,289+0.120X4 and a zero inflation model for , namely logit()=- 17.841+0.205X4. The AIC value of the ZINB regression model is 255.249, which is smaller than the AIC value of the ZIP regression model of 331.467, so the ZINB regression model is better to use. The influential variable in this study is the percentage of vitamin A administered. There is not much difference between predicted results and the actual data.
Verifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics

With the development of information technology, massive and heterogeneous consumer electronic products can access the network. These products may engage third-party servers for federated learning and generating more accurate models, where they can monitor, collect and aggregate various data from households almost in real-time. Even though federated learning can update participant parameter data without collecting their raw data, prior research revealed that the shared gradients still retain sensitive information from the training set. Meanwhile, malicious third-party aggregation servers may return forged aggregated gradients, and lightweight execution of the entire solution needs to be ensured during the aggregation process. This paper demonstrates a verifiable federated learning scheme supporting secure data aggregation without using bilinear groups (FLVA) to address these issues. Particularly, to solve the issue of private key leakage in the gradient aggregation process on electronic product data, a three-party key negotiation protocol is developed. The private gradients are uploaded and aggregated in ciphertext format, which ensures the privacy of the electronic product gradient. Security analysis showed that our FLVA system can effectively protect the security and privacy of the private gradients. Finally, the experimental results showed that compared with existing solutions, the proposed scheme is more efficient and practical.
Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

Abstract Identifying, analyzing, and evaluating cybersecurity risks is essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems is lacking. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.
Transforming education: Perceptions and challenges of technology-enabled teacher education programmes

The education landscape is undergoing a significant transformation due to technological advancements and shifting pedagogical paradigms. Technology-enabled teacher education programs are embracing digital tools to enhance pedagogy, provide adaptive learning experiences, foster global connectivity, and promote data-driven decision-making. These programs prepare educators for 21st-century classrooms, equipping them with the skills needed for 21st-century classrooms. However, concerns about overreliance on technology persist, emphasizing the importance of striking a balance between digital tools and traditional teaching methods. Unequal access to technology and the digital divide poses significant challenges, necessitating infrastructure development and equitable access initiatives. Ensuring technological literacy among educators and students is crucial, as these programs demand a certain level of digital proficiency. Balancing theory with practice in these programs is essential for their continued success. Understanding learner perceptions and addressing challenges is key in shaping the evolution of teacher education programs that effectively prepare educators for the dynamic demands of modern classrooms. Balancing theory and practice are essential for the success of these programs.