Reviewerly/exHarmony huggingface dataset:
ESID: Exploring the Design and Development of a Visual Analytics Tool for Epidemiological Emergencies

Visual analytics tools can help illustrate the spread of infectious diseases and enable informed decisions on epidemiological and public health issues. To create visualisation tools that are intuitive, easy to use, and effective in communicating information, continued research and development focusing on user-centric and methodological design models is extremely important. As a contribution to this topic, this paper presents the design and development process of the visual analytics application ESID (Epidemiological Scenarios for Infectious Diseases ). ESID is a visual analytics tool aimed at projecting the future developments of infectious disease spread using reported and simulated data based on sound mathematical-epidemiological models. The development process involved a collaborative and participatory design approach with project partners from diverse scientific fields. The findings from these studies, along with the guidelines derived from them, played a pivotal role in shaping the visualisation tool.
Fast and Accurate Resume Parsing Method Based on Multi-Task Learning

With the growing demand for online recruitment, an efficient and highly accurate resume parsing method is needed. Existing resume parsing methods are basically two-stage approaches: resume segmentation and entity recognition. Some researchers apply deep learning to these two stages to improve the accuracy but at the cost of lower efficiency. In this article, we propose a multi-task deep learning model that completes resume segmentation and entity recognition at the same time, thus reducing the resume parsing time by nearly half. Moreover, our method can better parse the dual-list format resumes, which is usually ignored by other existing methods. We also use some post-correction rules to further improve the accuracy of resume parsing. Finally, compared with the baselines, we achieve state-of-the-art on both Chinese and English resumes.
Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming

Abstract We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing models. SLUG was shown to be successful on different types of classification tasks, achieving state-of-the-art results on the synthetic datasets produced by GAMETES, a tool for embedding epistatic gene–gene interactions into noisy datasets. SLUG has also been studied and modified to demonstrate that its two elements, wrapper and learner, are the right combination that grants it success. We report these results and test SLUG on an additional six GAMETES datasets of increased difficulty, for a total of four regular and 16 epistatic datasets. Despite its slowness, SLUG achieves the best results and solves all but the most difficult classification tasks. We perform further explorations of its inner dynamics and discover how to improve the feature selection by enriching the communication between wrapper and learner, thus taking the first step toward a new and more powerful SLUG.
Pengaruh Kualitas Sistem, Kualitas Informasi dan Kualitas Layanan Terhadap Kepuasan Pengguna Sistem Informasi Manajemen RS “X” Kota Palopo

The development of information systems and technology, which is currently increasing rapidly, is urgently needed by an organization, company or individual to process and obtain data or information more easily, quickly and accurately. "X" Hospital has many problems related to patient data and information, staff, schedules, medical records, queues, medicines, treatments, finances, number and effectiveness of employees and rooms, and so on. These problems include the process of entering data and searching, especially if the system does not use a computer, it will be difficult and take a long time. This type of research is quantitative where there are 3 independent variables and 1 dependent variable. The researcher conducted multiple linear regression tests to determine the effect of the independent variables on the dependent variable and found that system quality had a positive and significant effect on user satisfaction of the "X" Hospital Management Information System. This is evidenced by T count &gt; T table. Information quality has a positive and significant effect on user satisfaction of hospital management information systems. This is evidenced by T count &gt; T table. Service quality has a positive and significant effect on user satisfaction of the "X" Hospital management information system. This is evidenced by T count &gt; T table. Based on the results of the above research, it can be concluded that system quality, information quality, and service quality greatly influence the satisfaction of users of the hospital management information system (SIMRS).
Applying Dynamic Geometry Environment Software as a Visualization Tool for Teaching Planimetry Construction Tasks

The present study addresses a notable gap in the current pedagogical research by rigorously examining the impact of Augmented Reality (AR) on mathematics education, specifically focusing on two-dimensional (2D) plane geometry construction tasks. Our primary aim is to discern and articulate the unique contributions of AR as a pedagogical tool in this domain. Employing a quasi-experimental design, we engaged with 141 high school students, dividing them into two distinct groups: one receiving instruction through AR tools and the other via traditional teaching methods. Performance metrics were derived from pre- and post-construction tests as well as an attitudinal survey gauging students’ perceptions of AR. Our findings reveal that the AR-facilitated group showcased a marked improvement in post-test scores and exhibited a more positive attitude towards AR technology compared to the traditionally taught cohort. This point towards AR’s potential to significantly elevate comprehension and engagement levels in 2D geometry construction. The study further highlights the transformative potential of AR in bolstering students’ enthusiasm and curiosity in mathematical endeavors. The results presented herein emphasize the pressing need for extended research to gauge AR’s broader and sustained effects in mathematics education and its applicability across various mathematical disciplines.
Unlocking Metaverse Flow Experience Using Theory of Interactivity and User Gratification Theory

The study aims to examine and identify the important antecedents and consequences of Metaverse flow experiences, leading to user gratifications and purchases on the Metaverse platform. The study used the theory of interactivity integrated with user gratification theory to investigate purchases on Metaverse. 379 responses received through the online survey were analysed through structural equation modeling. The study found that interactivity made of Metaverse design characteristics and User interactivity, Metaverse augmentation made of Wearable devices and Avatars lead to a flow experience. The study also validated that metaverse flow induced hedonic gratification for metaverse purchases. However, the study refuted the influence of flow experience on utilitarian gratification and purchase. The study makes a novel input to the existing literature on consumer behaviour concerning metaverse purchases. Marketers can gain insight from our study regarding the metaverse flow factors that stimulate hedonic and utilitarian gratification, thereby increasing purchases through the metaverse platform.
G+G: A Fiat-Shamir Lattice Signature Based on Convolved Gaussians

We describe an adaptation of Schnorr’s signature to the lattice setting, which relies on Gaussian convolution rather than flooding or rejection sampling as previous approaches. It does not involve any abort, can be proved secure in the ROM and QROM using existing analyses of the Fiat-Shamir transform, and enjoys smaller signature sizes (both asymptotically and for concrete security levels).
Self-adaptive credit-based framework for blockchain-based IoT (BIoT)

&lt;p&gt;The Internet of Things (IoT) connects and improves crucial global technologies like sensor nodes. The Internet is evolving from a human-centric network to one that enables inanimate things to wirelessly communicate with one another. The lifespan of an IoT network may be affected by the energy requirements of its routing protocol. Data is transmitted through the internet, and it may compromise the security of the data. An attacker can access the data and modify the data in order to break the security of the network. Although various solutions are available, such as cryptography and steganography-based approaches, none provide secure data transmission in large-scale networks with low energy consumption. Blockchain technology plays a vital role in the prevention of network malware. In this paper, an attempt has been made to propose a credit-based mechanism for secure data transmission in an efficient manner with low energy consumption. In order to achieve optimal results, the proposed framework uses blockchain for data security and credit distribution to avoid delays. The proposed framework has been simulated using the Contiki Cooja (CC) simulator. The efficiency of the proposed framework is measured by comparing its performance with state-of-the-art techniques.&lt;/p&gt;
Blockchain and Onion Routing-Based Secure Data Management Framework for Healthcare Informatics

Healthcare is undeniably a paramount concern for both individuals and nations alike. Its significance extends beyond just individual well-being, as the state of healthcare plays a pivotal role in the overall growth and stability of a nation’s economy. Recognizing this, industry and academics have embarked on various pioneering initiatives aimed at improving the quality of healthcare decisions and services. These efforts have remarkably transitions in healthcare frameworks, progressing from Healthcare 1.0 to Healthcare 5.0. Nowadays, wearable devices make the healthcare analysis of any individual easy. Doctors/hospitals can take the data, analyze it and suggest the diagnosis. But, this is not easy as we think. It is because of security issues where attackers can tamper with the wearable device message before reaching the concerned doctor. The modified message can misguide the doctor with an accurate diagnosis. This leads to a more severe cause for the patent and can endanger the patient’s life. Existing frameworks are mainly focusing on traditional cryptographic and blockchain solutions, which offer some level of security but no anonymity. Motivated by this, we proposed an artificial intelligence (AI), blockchain and onion routing-based secure and anonymous healthcare data management framework for wearable devices in this paper. Onion routing offers high security and anonymity with multi-layer encryption, whereas AI is used to distinguish malicious non-messages from malicious ones. Blockchain is used to authenticate the intermediate nodes of the onion network.
Development Local Culture Story Book to Teach Reading Comprehension for Junior High School

This research was used to develop a local culture story book to teach reading comprehension for grade 8 at SMPI Hasanuddin Kesamben Blitar. The subjects used in this study were 20 8th grade students. Based on the results of the needs analysis, it was found that 100% of students' answers did not have any special books that support their learning apart from materials from the government. Furthermore, based on these findings, the research developed a local culture storybook to teach reading comprehension for eighth graders at SMPI Hasanudin Kesamben. The method used in this study was adopted from Borg and Gall by taking some steps that includes: (1) observation and data collection, (2) Planning , (3) Product Development, (4) Validation the Product, (5) Revision stage I, (6) Trials Product, (7) Revision Stage II, (8) Product Improvement. In this study, there are two kinds of validation used to assess the product. The first validation is a material expert who got the result of 80%, its mean that the product is valid category. The second, the validation of media experts got the results of 83,75%, meaning that the product is valid. After the product was validated, then the product was tested on students and obtained student satisfaction responses with the total of 90% which was categorized that the product was Good/Valid.
Propagation of Subspaces in Primitives with Monomial Sboxes: Applications to Rescue and Variants of the AES

Motivated by progress in the field of zero-knowledge proofs, so-called Arithmetization-Oriented (AO) symmetric primitives have started to appear in the literature, such as MiMC, Poseidon or Rescue. Due to the design constraints implied by this setting, these algorithms are defined using simple operations over large (possibly prime) fields. In particular, many rely on simple low-degree monomials for their non-linear layers, essentially using x ↦ x3 as an S-box.In this paper, we show that the structure of the material injected in each round (be it subkeys in a block cipher or round constants in a public permutation) could allow a specific pattern, whereby a well-defined affine space is mapped to another by the round function, and then to another, etc. Such chains of one-dimensional subspaces always exist over 2 rounds, and they can be extended to an arbitrary number of rounds, for any linear layer, provided that the round-constants are well chosen.As a consequence, for several ciphers like Rescue, or a variant of AES with a monomial Sbox, there exist some round-key sequences for which the cipher has an abnormally high differential uniformity, exceeding the size of the Sbox alphabet.Well-known security arguments, in particular based on the wide-trail strategy, have been reused in the AO setting by many designers. Unfortunately, our results show that such a traditional study may not be sufficient to guarantee security. To illustrate this, we present two new primitives (the tweakable block cipher Snare and the permutation-based hash function Stir) that are built using state-of-the-art security arguments, but which are actually deeply flawed. Indeed, the key schedule of Snare ensures the presence of a subspace chain that significantly simplifies an algebraic attack against it, and the round constants of Stir force the presence of a subspace chain aligned with the rate and capacity of the permutation. This in turns implies the existence of many easy-to-find solutions to the so-called CICO problem.
A Distributed Optimization Approach for Collaborative Object Lifting Using Multiple Aerial Robots

In the past decade, multi-robot collaborative object transport has garnered significant attention, with the majority of research targeting transport strategies. This study recasts the collaborative object lifting challenge into an optimization problem framework. Within this setup, each robot leverages a local evaluation function to determine its lifting location. Collectively, these robots strive to optimize a unified evaluation function. An intertwined equation constraint is embedded within the optimization schema, ensuring that the system’s mass center remains stable throughout the lifting process. Furthermore, we impose local feasibility constraints, thereby delimiting the optimal lifting location to a specified region. This research introduces several algorithms, differentiated based on the constraints applied to robot velocity. By harnessing these algorithms, robots can autonomously pinpoint the most apt lifting location that aligns with predetermined criteria. This methodology necessitates a robot to engage in exchanges of auxiliary variables solely with its immediate peers. Noteworthily, parameters such as location, velocity, and mass are accessed in a localized manner, reinforcing data privacy and reducing communication burdens. The paper concludes with a robust mathematical validation that underscores asymptotic convergence to the exact optimal lifting location, underpinned by numerical simulations which attest to the potency of the proposed algorithms.
Semi-automated Software Requirements Categorisation using Machine Learning Algorithms

Requirement engineering is a mandatory phase of the Software development life cycle (SDLC) that includes defining and documenting system requirements in the Software Requirements Specification (SRS). As the complexity increases, it becomes difficult to categorise the requirements into functional and non-functional requirements. Presently, the dearth of automated techniques necessitates reliance on labour-intensive and time-consuming manual methods for this purpose. This research endeavours to address this gap by investigating and contrasting two prominent feature extraction techniques and their efficacy in automating the classification of requirements. Natural language processing methods are used in the text pre-processing phase, followed by the Term Frequency – Inverse Document Frequency (TF-IDF) and Word2Vec for feature extraction for further understanding. These features are used as input to the Machine Learning algorithms. This study compares existing machine learning algorithms and discusses their correctness in categorising the software requirements. In our study, we have assessed the algorithms Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Neural Network (NN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) on the precision and accuracy parameters. The results obtained in this study showed that the TF-IDF feature selection algorithm performed better in categorising requirements than the Word2Vec algorithm, with an accuracy of 91.20% for the Support Vector Machine (SVM) and Random Forest algorithm as compared to 87.36% for the SVM algorithm. A 3.84% difference is seen between the two when applied to the publicly available PURE dataset. We believe these results will aid developers in building products that aid in requirement engineering.
ANALISA INDIKATOR KINERJA TEROWONGAN ANGIN RANGKAIAN TERBUKA (OPEN CIRCUIT WIND TUNNEL) TIPE SUBSONIC

Fluid is a substance that can change its shape continuously due to the acting tangential shear force. Aerodynamics is a branch of fluid dynamics that studies specifically the forces acting on an object contained in a fluid flow. Wind Tunnel is a tool used in aerodynamics research to study airflow characteristics. Wind tunnel performance indicators include flow uniformity, flow and pressure distribution, aerodynamic forces acting on test objects in the wind tunnel. By using unsymmetrical airfoils, the impact of speed variations of 2 m/s, 4 m/s, 6 m/s, 8 m/s, 10 m/s and increasing angles of attack ranging from 0°, 5°, 10° on fluid flow can be determined. Tests were conducted in a section of the wind tunnel measuring 30 cm x 30 cm x 40 cm. Overall, the open-type wind tunnel has not been able to create uniform flow. The Reynolds number is at Re&gt;4,000, so the wind tunnel under study is turbulent. The results of the speed variation and angle of attack increase also have an impact on the lift and drag coefficient values of the airfoil. At the maximum speed variation of 10 m/s with an angle of attack of 10°, the lift coefficient is 0.14350 and the drag coefficient is 0.15162.
Exploring Cybersecurity Awareness Among Students from Two Latin American Universities: An Empirical Analysis

In the contemporary digital landscape, safeguarding the digital future has become a matter of paramount importance. Cybersecurity, a critical concern in the present interconnected world, highlights the vulnerability of students to various cyber threats due to a lack of awareness. This empirical study delves into the realm of cybersecurity awareness and cyber hygiene practices among university students in Latin America. Employing the Cyber Hygiene Inventory (CHI), a comprehensive analysis of cybersecurity practices and perceptions is undertaken. A randomized sample of 53 students from two Latin American universities constitutes the study cohort. The research unfolds in three principal stages: questionnaire design, data collection, and data analysis. The survey in the questionnaire evaluates the five dimensions of cyber hygiene. The outcomes unveil a spectrum of responses, spanning from highly conscious students exhibiting commendable cyber hygiene practices to those evincing limited comprehension and suboptimal practices. The significance of maintaining hygienic device storage, ensuring secure information exchange, cultivating responsible social media usage, and implementing robust authentication mechanisms is underscored. Notably, this study accentuates the exigency of cybersecurity education and awareness initiatives within Latin American universities. The assessment of satisfaction levels with cyber hygiene and cybersecurity knowledge yields a continuum from Very Dissatisfied (5.66%) to Dissatisfied (18.86%), Neutral (47.17%), Satisfied (26.42%), and Very Satisfied (1.89%). The recommendation emerges for the implementation of comprehensive programs that nurture awareness, illuminate potential threats, and advocate sound cyber hygiene practices. These findings contribute to the comprehension of cybersecurity awareness among Latin American university students and underscore the perpetual significance of research and proactive measures in this swiftly evolving domain.
AI-powered Internet Traffic Classification: Past, Present, and Future

Traffic classification (TC) is pivotal for network traffic management and security. Over time, TC solutions leveraging Artificial Intelligence (AI) have undergone significant advancements, primarily fueled by Machine Learning (ML). This paper analyzes the history and current state of AI-powered TC on the Internet, highlighting unresolved research questions. Indeed, despite extensive research, key desiderata goals to product-line implementations remain. AI presents untapped potential for addressing the complex and evolving challenges of TC, drawing from successful applications in other domains. We identify novel ML topics and solutions that address unmet TC requirements, shaping a comprehensive research landscape for the TC future. We also discuss the interdependence of TC desiderata and identify obstacles hindering AI-powered next-generation solutions. Overcoming these roadblocks will unlock two intertwined visions for future networks: self-managed and human-centered networks.