Reviewerly/exHarmony huggingface dataset:
An Optimized Edward-elgamal Extreme Performance Signature Scheme Oedecas in Iot and Blockchain

Abstract Digital signatures (DS) are the modern-day guardians of authenticity and security in the digital realm. Even though the goal has stayed the same, the scope of its suitability and the boundaries at which it can be applied have necessitated perpetual improvements in speed, safety, and computation. Increased DS secure efficiency is in high demand, especially considering the Internet of Things (IoT), blockchain technology (BC), and cryptocurrencies. Signatures are generated using a digital signature scheme (DSS). To address the nonrepudiation problem of IoT over the Blockchain ecosystem, let us create this scheme. This article explores EdDSA (Edward Curve Digital Signature Algorithm), a popular DSA, along with a proposed hybrid signature, and how it can be implemented in the IoT based on BC. It also compares the Edward, Hyperelliptic, and Elliptic curve cryptography. Then, we compared the Edwards curve digital signature algorithm (EdDSA) to Elliptic Curve DSA(EcDSA) heuristically and Hyper Elliptic Curve DSA(HeDSA). The study results demonstrate that EdDSA is an improvement over ECDSA and HeDSA. From the study, the proposed scheme, Optimized Edward-El Gamal with Extreme performance Edward curve Accumulate signature (OEDECAS), provides more security and is evaluated to be used in the Blockchain and IoT sectors with positive results right away. It shows a 15% improvement in the transaction flow, a 15% improvement in block validation, and tamper-proof security for IoT, like the same system, implemented without OEDECAS.
Nonlinear filtering for the state of extended targets

This paper introduces a novel algorithm for multi–extended object tracking, which is based on probabilistic–statistical modelling, machine learning, and optimization theory. The proposed approach encompasses methods for modelling the state of extended objects, data association, and predicting and updating the state of extended objects at each time step. The performance of the algorithm was assessed and compared with other existing algorithms using simulation modelling in MATLAB. This simulation emulates scenarios in which extended objects are tracked while moving under various noise levels. Simulation results demonstrate that the algorithm can effectively detect and track the trajectories of multiple extended objects under different noise levels, utilizing both linear and non–linear measurements from an array of sensors. Furthermore, the algorithm estimates not only the number and kinematic states of the targets but also provides an approximate evaluation of their size.
The Role of Knowledge Management and Infrastructure in Developing Smart Universities in Saudi Arabia

Moving toward a smart university has become a necessity to bridge the gaps between research and industry.Against this background, most of studies focused on the technical approach of a smart university in developed countries.The purpose of this study is to examine effect of knowledge management (KM) and infrastructure on the development of a smart university in Saudi Arabia.Academic staff from top five universities in the country were the population of this study.A stratified sampling was used to collect the data from 202 academic staff.The data was analysed using a smart partial least square 4.0.The findings showed that the effect of KM and its practices, knowledge creation, knowledge sharing, and knowledge utilization, and continuous learning and professional development affected positively the development of a smart university.In addition, the infrastructure and its dimension physical and digital infrastructure have a positive effect on the development of a smart university.Infrastructure also mediated the effect of KM on the development of a smart university.More investment in the digital and physical infrastructure can enhance the utilization of KM and foster the development of a smart university.
KAJIAN TENTANG INDEKS KECOCOKAN MODEL DALAM PEMODELAN PERSAMAAN STRUKTURAL BERBASIS KOVARIAN D ALAM LISREL DAN AMOS SERTA BERBASIS PARTIAL LEAST SQUARE DALAM PLS SEM

The objective of this study is to review the goodness of fit (GOF) of Covariance and Partial Least Square Based Structural Equation Modeling using LSIREL, AMOS and Smart PLS. A method used is systematic secondary data search and meta - analysis. In this study, the present writer finds that there are some similarities and dissimilarities of the usage of each parameter used within the same parameter name; accordingly, users of those software must be able to distinguish each use of the parameter concerned in each software respectively in their researches&#x0D; Key Words : goodness of fit, SEM, LISREL, AMOS, SmartPLS &#x0D; &#x0D;
GazeGraphVis: Visual analytics of gaze behaviors at multiple graph levels for path tracing tasks

Graph visualization contributes to an efficient understanding of interconnected properties in graph data. However, the exponential growth of interconnections poses great challenges to the efficient visual cognition of graph data. Generation of expressive graph visualization requires investigations of the cognitive process of exploring graph visualization, which can be revealed through the analysis of gaze behaviors. In this paper, we propose GazeGraphVis, a visual analytics system, to analyze gaze behaviors for path tracing tasks. Specifically, GazeGraphVis visualizes gaze behaviors at multiple levels of graph, node and edge with overview + detail techniques to provide a comprehensive analysis of human cognitive processes when finishing path tracing tasks. The insights of gaze behaviors for path tracing tasks are revealed using an integrated multiple-view interface. Domain experts in visualization and eye tracking analysis gave high praise to GazeGraphVis for its capability of obtaining the overall search tendencies and deeply analyzing the factors that affect gaze behaviors.
CodeUP: A Web Application for Collaborative Question-answering System

The majority of collaborative learning and knowledge sharing (CLKS) platforms are built with numerous communication mediums, team and task management in mind. However, with the CLKS, the Question-Answering (QAs), User profile evaluation based on the quality of answers provided, and feeding of subject or project relevant data are all available. QAs are required for online or offline cooperation between team members or users. To that purpose, this paper presents a web application called CodeUP with features like QA system, Question similarity testing, and user profile rating for boosting communication and cooperation efficiency in CLKS for academic groups and small development teams. CodeUP is intended to be quickly established and step for academic or development groups to collaborate. As the CodeUP application supports the CLKS, it is also an ideal tool for academia and development teams to perform computer supported QA system and knowledge sharing in the sphere of work or study.
IntelliExtract: An End-to-End Framework for Chinese Resume Information Extraction from Document Images

Traditional document processing can be labor-intensive and time-consuming to manually extract and organize the information in a document. This manual process is often inefficient and error-prone. In order to improve processing efficiency and accuracy of document data, we develop IntelliExtract, an end-to-end framework designed for document information extraction. This is a comprehensive framework that includes image text detection and recognition, information extraction, and document intelligent question-answering. Some recent models and algorithms are employed, OCR models for converting scanned documents into machine readable text, layout analysis algorithms for understanding the spatial arrangement of document elements, and information extraction techniques for extracting structured data from unstructured documents. To evaluate the effectiveness of the framework, we conducted experiments by employing a Chinese Talent Resumes Dataset for visualizing the results. For named entity extraction, the confidence level of the extracted results from the text in the images is generally above 0.95. The proposed framework provides a powerful tool for enterprises, educational institutions, and other entities in processing document information, and holds promise for significant practical applications.
AN ANALYSIS OF STUDENTS’ ABILITY IN WRITING RECOUNT TEXT IN SMA JOSEPH KHATULISTIWA

Abstract: The research was carried out due to students having low ability in writing. The research aims are to describe students' ability in writing recount text and students' errors in using language features in writing recount text. This research used qualitative research. Data were collected using documentation techniques. Data collection tools are documents. The average of students' ability in content aspect is 35%, organization aspect is 23%, vocabulary aspect is 17%, grammar aspect is 20% and mechanic's aspect is 5%. The number of students' errors in writing recount text is 79 items. The omission error was 35 (44%), addition error was 13 (17%), misformation was 23 (29%) and misordering was 8 (10%). It was concluded the most mastered ability of students in writing recount text was content aspect and the lowest was in mechanic's aspect. The type of students' errors using language features was omission and the lowest in misordering.
Influence of mental model of GUI on usability

Applications operated on smartphones and other devices with a small user interface area on the screen and a large number of functions and contents will inevitably increase the number of operation procedures and make them more complicated. However, compared to the attention paid to information architecture and interaction in realizing intuitive user interfaces, less attention is paid to icons, and there are many examples where generic icons are used. Therefore, when icons and the functions they represent are different for each application, or when icons are unintelligible because of the distance between the user's mental model and the icons, the cognitive burden on the user is amplified, which may affect usability. We compared the usability of icons, one of the GUI elements, by focusing on their metaphors, and showed that the selection of metaphors that are in line with users' mental models has a positive effect on usability by improving the performance of task completion time and the number of errors. This study clarified the influence of the degree of congruence between the metaphor used and the user's mental model on the usability of icons, one of the GUI elements.
Applications of artificial intelligence systems

Nowadays artificial intelligence is a rapidly developing technology that encompasses the development of intelligent algorithms and machines capable of learning. Therefore, it is relevant and timely to examine the topic. These artificial intelligence algorithms and machines have the ability to perform tasks that traditionally relied on human intelligence in the past. This study provides an in-depth exploration of artificial intelligence systems and their key components. It examines various aspects of artificial intelligence systems, including natural language processing, machine learning, detection and pattern recognition, and knowledge representation and other form of artificial intelligence systems. Natural language processing enables machines to understand and generate human language, while machine learning empowers systems to learn from data and improve their performance over time. Detection and pattern recognition allow artificial intelligence systems to interpret and understand complex sensory inputs, while knowledge representation enables the storage and utilization of information. Furthermore, other form of artificial intelligence systems will be also discussed. This study sheds light on the fundamental elements of artificial intelligence systems, paving the way for their practical applications and advancements.
Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Javanese script is one of Indonesia's cultural heritages that are increasingly rarely used today. The difficulty of recognizing the shapes of letters, let alone writing them, is the main obstacle in using the Hanacaraka script. This research offers an alternative to Hanacaraka script recognition using a combination of image feature extraction and machine learning, where we utilize a pre-trained SquzeeNet model and Multilayer Backpropagation algorithm. Of the 18 models built using ReLu, Sigmoid, and Tanh activation functions, we found that the Tanh activation function, using the combination of 50-50-100 neuron configuration and 25 epochs, was the most optimal function used to classify the training data with accuracy, precision, and recall values of 93.8%. Meanwhile, the Tanh activation function, using the 50-100-50 neuron configuration and 50 epochs, is the most optimal function to classify the testing data, with accuracy, precision, and recall values of 89.1%, 89.5%, and 89.5%. All built models show a training and testing performance ratio below 10%. From this result, we conclude that all models have good reliability in the training and testing classification process.
Attribute-based Single Sign-On: Secure, Private, and Efficient

A Single Sign-On (SSO) system allows users to access different remote services while authenticating only once. SSO can greatly improve the usability and security of online activities by dispensing with the need to securely remember or store tens or hundreds of authentication secrets. On the downside, today's SSO providers can track users' online behavior, and collect personal data that service providers want to see asserted before letting a user access their resources. In this work, we propose a new policy-based Single Sign-On service, i.e., a system that produces access tokens that are conditioned on the user's attributes fulfilling a specified policy. Our solution is based on multi-party computation and threshold cryptography, and generates access tokens of standardized format. The central idea is to distribute the role of the SSO provider among several entities, in order to shield user attributes and access patterns from each individual entity. We provide a formal security model and analysis in the Universal Composability framework, against proactive adversaries. Our implementation and benchmarking show the practicality of our system for many real-world use cases.
Converged Security and Information Management System as a Tool for Smart City Infrastructure Resilience Assessment

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers the practical application of the theoretical principles of converged security presented in the publication to a functional sample, deployed and tested in practical conditions of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the context of resilience assessment is defined. In the beginning, the general principles of security/ safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment method is presented for better understanding. Subsequently, PSIM and SIEM systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.
Accessibility and Inclusiveness of New Information and Communication Technologies for Disabled Users and Content Creators in the Metaverse

Despite the proliferation of Blockchain Metaverse projects, the inclusion of physically disabled individuals in the Metaverse remains distant, with limited standards and regulations in place. However, the article proposes a concept of the Metaverse that leverages emerging technologies, such as Virtual and Augmented Reality, and the Internet of Things, to enable greater engagement of disabled creatives. This approach aims to enhance inclusiveness in the Metaverse landscape. Based on the findings, the paper concludes that the active involvement of physically disabled individuals in the design and development of Metaverse platforms is crucial for promoting inclusivity. The proposed framework for accessibility and inclusiveness in Virtual, Augmented, and Mixed realities of decentralised Metaverses provides a basis for the meaningful participation of disabled creatives. The article emphasises the importance of addressing the mechanisms for art production by individuals with disabilities in the emerging Metaverse landscape. Additionally, it highlights the need for further research and collaboration to establish standards and regulations that facilitate the inclusion of physically disabled individuals in Metaverse projects.
Blockchain-Based Smart Farm Security Framework for the Internet of Things

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies, the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms.
The Self-Anti-Censorship Nature of Encryption: On the Prevalence of Anamorphic Cryptography

As part of the responses to the ongoing crypto wars, the notion of Anamorphic Encryption was put forth. The notion allows private communication in spite of a dictator who is engaged in an extreme form of surveillance and or censorship, where it asks for all private keys and knows and may even dictate all messages. The original work pointed out efficient ways to use two known schemes in the anamorphic mode, bypassing the draconian censorship and hiding information from the all-powerful dictator. A question left open was whether these examples are outlier results or whether anamorphic mode is pervasive in existing systems. Here we answer the above question: we develop new techniques, expand the notion, and show that the notion of Anamorphic Cryptography is, in fact, very much prevalent. We first refine the notion of Anamorphic Encryption with respect to the nature of covert communication. Specifically, we distinguish Single-Receiver Encryption for many to one communication, and Multiple-Receiver Encryption for many to many communication within the group of conspiring users. We then show that Anamorphic Encryption can be embedded in the randomness used in the encryption, and we give families of constructions that can be applied to numerous ciphers. In total the families cover classical encryption schemes, some of which in actual use. Among our examples is an anamorphic channel with much higher capacity than the regular channel. In sum, the work shows the very large extent of the potential futility of control and censorship over the use of strong encryption by the dictator (typical for and even stronger than governments engaging in the ongoing crypto-wars): While such limitations obviously hurt utility which encryption typically brings to safety in computing systems, they essentially, are not helping the dictator. While the actual implications of what we show here and what it means in practice require further policy and legal analyses and perspectives, the technical aspects regarding the issues are clearly showing the futility of the war against Cryptography.
Verifiable Distributed Aggregation Functions

The modern Internet is built on systems that incentivize collection of information about users. In order to minimize privacy loss, it is desirable to prevent these systems from collecting more information than is required for the application. The promise of multi-party computation is that data can be aggregated without revealing individual measurements to the data collector. This work offers a provable security treatment for "Verifiable Distributed Aggregation Functions (VDAFs)", a class of multi-party computation protocols being considered for standardization by the IETF. We propose a formal framework for the analysis of VDAFs and apply it to two constructions. The first is Prio3, one of the candidates for standardization. This VDAF is based on the Prio system of Corrigan-Gibbs and Boneh (NSDI 2017). We prove that Prio3 achieves our security goals with only minor changes to the draft. The second construction, called Doplar, is introduced by this paper. Doplar is a round-reduced variant of the Poplar system of Boneh et al. (IEEE S&amp;P 2021), itself a candidate for standardization. The cost of this improvement is a modest increase in overall bandwidth and computation.
On the Role and Form of Personal Information Disclosure in Cyberbullying Incidents

Disclosing personal information significantly increases the likelihood of incidents of cyberbullying. This highlights the significance of investigating the relationships between various stakeholders in cyberbullying incidents. Our objective is to gain insight into the roles of the stakeholders, types, and typical paths of personal information in cyberbullying incidents. To achieve this, we conducted a large-scale survey with a representative sample of internet users from the United States and Nigeria (N = 1555). Our findings indicate that cyberbullying is often fueled by personal information that becomes known, directly or through social media, to other stakeholders. Cyberbullying incidents involve more than just attackers and victims; they can involve other stakeholders as third-parties 'disclosers' Both strangers and friends typically engage in such activities. Cyberbullying incidents are twice as common in Nigeria as in the United States. Our findings have implications for design, social-media literacy programs, and policy.