Reviewerly/exHarmony huggingface dataset:
The intelligibility of internet slangs between millennials and Gen Zers: A comparative study

The study focuses on the emergence of modern-day neologisms (i.e., internet slang) in social media and everyday conversations, with the aim of exploring the language gap between Millennials and Gen Z. These generations exhibit distinct linguistic characteristics compared to previous generations. The research investigates whether Millennials can comprehend and relate to the new emerging words or if they encounter confusion. This quantitative research was conducted at the local college in Opol, Misamis Oriental. The 100 respondents were selected based on registered quota sampling. The findings highlight a significant positive correlation between Millennials and Gen Z regarding modern-day net slang. However, Gen Z often understands metathesis forms of words to conceal syntactical features during the discourse more than millennials. This study recommends itself as a valuable source of data for further investigations into the proliferation of modern-day neologisms among Millennials and Gen Z on the internet.
Hur programmerare arbetar

Recension av: Espen Suenson 2015. How Computer Programmers Work – Understanding Software Development in Practice. Turku: Department of Information Technologies, Åbo Akademi. 287 pp. ISBN: 978-952-12-3225-1 ISSN: 1239-1883.
StegAI: detecting steganography with deep learning

Adversaries or internal threats wishing to exfiltrate sensitive data may choose a method that obfuscates the fact that a transfer is even taking place by camouflaging it inside of allowable data. This technique is known as steganography, which is the practice of concealing data (such as hidden communications or sensitive data of any kind) in an existing data transfer medium, such as images or video. Video streams may be targeted more readily than other media because of the bandwidth they offer compared to single images. Current methods for mitigating the risk associated with data exfiltration through steganography sanitization through unconditional alteration video frames. However, these approaches are limited as they do not detect if a steganography attack is occurring. Deep Neural Networks (DNNs) excel at pattern recognition, presenting an opportunity to leverage them for detection of steganography. Having the capability to detect steganography could prove extremely useful, with uses such as prompting security administrators to investigate further or to immediately halt a video stream. We investigate Least Significant Bit Steganography with both encrypted and cleartext payloads and show that machine learning can detect both.
A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns

The rise in adoption of cryptoassets has brought many new and inexperienced investors in the cryptocurrency space. These investors can be disproportionally influenced by information they receive online, and particularly from social media. This paper presents a dataset of crypto-related bounty events and the users that participate in them. These events coordinate social media campaigns to create artificial "hype" around a crypto project in order to influence the price of its token. The dataset consists of information about 15.8K cross-media bounty events, 185K participants, 10M forum comments and 82M social media URLs collected from the Bounties(Altcoins) subforum of the BitcoinTalk online forum from May 2014 to December 2022. We describe the data collection and the data processing methods employed and we present a basic characterization of the dataset. Furthermore, we discuss potential research opportunities afforded by the dataset across many disciplines and we highlight potential novel insights into how the cryptocurrency industry operates and how it interacts with its audience.
Saliency-Aware Generation of Adversarial Point Clouds

Deep learning for 3D point clouds has achieved excellent performance in some safety-critical applications, such as self-driving. The robustness of deep 3D models when facing adversarial attacks has attracted more and more attention. Existing 3D attack methods design a variety of perturbation constraints to improve the imperceptibility of the adversarial point clouds. However, these constraints generally lack consideration for the saliency degree of the points. We are the first to enhance the imperceptibility of adversarial samples by combining saliency maps of point clouds with the perturbation metrics. Moreover, we introduce Earth Mover’s Distance (EMD) to better measure the overall perturbation size of the point clouds. Finally, a novel saliency attack is proposed to combine the saliency-aware perturbation constraints and EMD. Experimental results show that our method can efficiently generate imperceptible 3D adversarial samples with tinny perturbations.
A Comparative Study on Crossover Operators of Genetic Algorithm for Traveling Salesman Problem

Genetic algorithm (GA) has been successfully employed to solve the traveling salesman problem (TSP). In GA, the crossover operator makes crucial influence on its optimization effectiveness and efficiency in solving TSP. Therefore, many kinds of crossover operators have been proposed successively in the literature, but a systematic investigation of these operators has not ever been conducted. To fill this gap, this paper systematically compares 10 widely used crossover operators. By conducting extensive experiments on different TSP instances of different sizes, we investigate the optimization effectiveness of the 10 crossover operators in helping GA solve TSP. Experimental results demonstrate that the sequential constructive crossover (SCX) and the zoning crossover (ZX) are the two best crossover operators for GA to solve TSP. Hopefully, this comparative study could provide a guideline for readers and facilitate them to choose a suitable crossover operator for GA to solve TSP effectively.
Reflections and conclusions

The book’s combination of differing aspects of teaching IP. Our academic contributors share different styles and attitudes to the learning and teaching experience. They combine time honoured education approaches with cutting edge innovation. Our non-academic contributors, from practice and commerce, bring IP law to life with their insights derived from lived experience of managing their own IP or dealing with clients’ IP interests. Chapters are grouped to reflect discernible threads and that IP education has become much wider than just teaching IP law. IP education will continue to expand, so we include chapters that discuss using social media in the IP classroom, gaming as a teaching method and IP ethics, more broadly than just in its application to patents. In conclusion, we reflect that there are trends in technology development that are too new to have featured in significant IP teaching. Our book is published when the very future and nature of Higher Education is under the spotlight. Where IP rights might have been seen as rights pertaining to individuals, Gen Z increasingly sees its future enacting collaborative practice and collective thinking.
2022 Distributed Computer Museum Accreditation Ceremony

The Special Committee on History of the Information Processing Society of Japan (IPSJ) accredits annually several computer museums in Japan as a part of its distributed virtual museum. The accreditation had been withheld in the last three years because of COVID-19, however, it revived this year. On the afternoon of March 3, 2023, the second day of the National Convention of IPSJ held on the campus of the University of Electro-Communications, we celebrated two museums as the new members of our distributed museum and gave the certificates and plaques.
Adversarial attack on GNN-based SAR image classifier

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique in remote-sensing image recognition. The state-of-the-art Graph Neural Network (GNN) has achieved 99.09% accuracy on the MSTAR dataset. However, robustness is a critical concern when an adversary can add carefully crafted noise to the test image and mislead the classifier to give wrong output. In this work, we study the vulnerability of the state-of-the-art GNN-based SAR image classifier and show the feasibility of decreasing its accuracy by manipulating test images using the Fast Gradient Sign Method (FGSM). The intuition is that misclassifications are more likely to occur when the loss is high. For each SAR image, we add noise to every pixel, such that the perturbed image will be misclassified by the GNN model. For each pixel, the noise is determined by the gradient of the loss with respect to the pixel. Also, we guarantee that the noise values are sufficiently small such that the perturbation will be unnoticeable to human eyes. We conduct experiments by applying the attack on the 2425 test images from the MSTAR dataset. In our experiments, the accuracy of the target GNN drastically drops from 99.09% to 11.79% after we change each normalized pixel value (in [0, 1]) of the test images by less than 3/255.
Analyzing hybrid implementations of optimization algorithms

&lt;p&gt;Optimization problems can be found in almost every aspect of the practical world. They can range from something as complex as a wireless communication system or the stock exchange to something as mundane as scheduling a series of meetings inside a company. Almost every task in today’s world can be reconceived as a type of optimization problem. To solve these problems, entire fields in computer intelligence have been developed and continue to be the focus of extensive research today. Various algorithms have been proposed that effectively find solutions and do so in the shortest amount of time possible. However, the number of problems far exceeds the number of algorithms that have been developed to combat them. As such, each optimization algorithm is expected to produce optimal or at least near-optimal solutions in many optimization situations. Whilst this is certainly possible, various theorems state this to be unlikely. Thus, there is always a need to create new optimization algorithms. in recent years, a possible solution has been proposed; to combine the favorable characteristics of two optimization algorithms into a hybrid implementation.&lt;/p&gt; &lt;p&gt;This MRP investigates the performance of various hybrid implementations of optimization algorithms in various optimization scenarios. Utilizing thirteen benchmark functions, it compares the performance of seven algorithms; Genetic Algorithm (GA), Particle Swarm (PSO), Grey Wolf (GWO), Whale Optimization (WOA), Binary Bat Algorithm (BAT), a Binary Bat/Particle Swarm hybrid (BATPSO), and a Grey Wolf/Whale Optimization (GWOWOA) hybrid. This comparison is made over several case scenarios which tweak the general parameters found in these metaheuristic algorithms. To further quantify the results, statistical hypothesis testing techniques are utilized to differentiate between the performance of each algorithm. These tests determine the comparative gap in the performance samples gathered from each metaheuristic. Finally, it compares the performance of Genetic algorithm (GA) and Particle Swarm PSO) on a Fuzzy Inference System (FIS) tree application to gauge how optimization algorithms play a significant role in many real-world systems.&lt;/p&gt; &lt;p&gt;In its conclusion, it presents a summary of the findings made and recommends future courses of&lt;/p&gt; &lt;p&gt;action.&lt;/p&gt;
The role of human resource re-engineering in order to improve the quality of banking performance

The research to identify the impact of human resource re-engineering and its dimensions in improving the quality of banking performance in commercial banks. The dimensions related to human resource engineering, represented by (technological capabilities, human resources, and organizational components) and the dimensions to improving the quality of banking performance, represented by (effectiveness, efficiency, correlation or suitability) in commercial banks. So the research population represents Jordanian commercial banks, and six Jordanian commercial banks were selected and the research was applied to a sample of 200 individuals that includes (bank managers, department heads and employees with experience and competence), the questionnaire was used as the main tool for collecting data and information from the respondents, and interviews, to identify the relationships and the effect between the research variables to obtain answers related to the research problem. The study concluded that there is a correlation of a coherent nature between the re-engineering of human resources and the realization and development of its dimensions, and the quality of banking performance is improved.
Selecting Favourite Majors at Sari Mulia University Using SAW Method

A decision support system, also known as a decision support system (DSS), is an interactive information system that offers data, models, and information. DSS is used as a decision aid in semi-structured and unstructured situations where there is no clear decision-making procedure. Determining the preferred major is one of the challenges in universities. The purpose of determining the most popular major is to improve the quality and services provided to students in each department, which is a crucial objective for universities. Currently, Universitas Sari Mulia determines the most popular majors based on qualitative data, which makes the determination of the most popular majors themselves inaccurate; therefore, a method capable of managing data on the selection of the most popular majors is necessary. In this study, the Simple Additive Weighting (SAW) technique will be utilized. This method is used to compare each criterion with one another in order to determine the most popular majors at Sari Mulia University and to evaluate each department.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Morressier and easychair • Number of submissions received : 86 • Number of submissions sent for review : 78 • Number of submissions accepted : 62 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 72.1 • Average number of reviews per paper : 1 • Total number of reviewers involved : 15 • Contact person for queries : Name : Pipit Wijayanti Email : pipitwijayanti@staff.uns.ac.id Affiliation : Universitas Sebelas Maret
Multi-Factor Authentication for Improved Enterprise Resource Planning Systems Security

Universities across the globe have increasingly adopted Enterprise Resource Planning (ERP) systems, a software that provides integrated management of processes and transactions in real-time. These systems contain lots of information hence require secure authentication. Authentication in this case refers to the process of verifying an entity’s or device’s identity, to allow them access to specific resources upon request. However, there have been security and privacy concerns around ERP systems, where only the traditional authentication method of a username and password is commonly used. A password-based authentication approach has weaknesses that can be easily compromised. Cyber-attacks to access these ERP systems have become common to institutions of higher learning and cannot be underestimated as they evolve with emerging technologies. Some universities worldwide have been victims of cyber-attacks which targeted authentication vulnerabilities resulting in damages to the institutions reputations and credibilities. Thus, this research aimed at establishing authentication methods used for ERPs in Kenyan universities, their vulnerabilities, and proposing a solution to improve on ERP system authentication. The study aimed at developing and validating a multi-factor authentication prototype to improve ERP systems security. Multi-factor authentication which combines several authentication factors such as: something the user has, knows, or is, is a new state-of-the-art technology that is being adopted to strengthen systems’ authentication security. This research used an exploratory sequential design that involved a survey of chartered Kenyan Universities, where questionnaires were used to collect data that was later analyzed using descriptive and inferential statistics. Stratified, random and purposive sampling techniques were used to establish the sample size and the target group. The dependent variable for the study was limited to security rating with respect to realization of confidentiality, integrity, availability, and usability while the independent variables were limited to adequacy of security, authentication mechanisms, infrastructure, information security policies, vulnerabilities, and user training. Correlation and regression analysis established vulnerabilities, information security policies, and user training to be having a higher impact on system security. The three variables hence acted as the basis for the proposed multi-factor authentication framework for improve ERP systems security.
Comparative education the curriculum of the discipline – 011 Educational, pedagogical sciences

The work programme of the discipline “Comparative education” is designed to train the applicants for higher education of the third (educational and scientific) level, educational degree “Doctor of Philosophy”, field of knowledge 01 Education / Pedagogy, specialty: 011 “Educational, pedagogical sciences”. The aim of the discipline “Comparative education” is the formation of comparative education competence of a researcher – a qualified specialist who has a high level of ability and readiness for professional activity in the field of home, European and world science and education using the comparative education methodology. For teachers and applicants for higher education of the third (educational-scientific) level Educational degree “Doctor of Philosophy”, Field of knowledge 01 Education / Pedagogy, Specialty: 011 “Educational, pedagogical sciences”.
USING AUTHENTIC MATERIALS TO IMPROVE ENGLISH - MAJOR STUDENTS’ READING COMPREHENSION: AN EXPERIMENTAL RESEARCH PROJECT AT A VIETNAMESE COLLEGE

This experimental research study aims at investigating the effectiveness of authentic materials (AM) in reading comprehension. The participants include 30 English - majored students, evenly divided into two groups: a control group using only coursebook and an experimental group using AM in eight weeks. Both groups took a pre-test and a post-test, and then the experimental group was interviewed. The findings showed that the experimental group exposed to authentic materials achieved better results at reading comprehension. They also mentioned some benefits of authentic materials such as updated contents, interesting writing stylse, various cultural values, global knowledge and cooperative skills. Some recommendations were provided, including teachers’ implementation of various AM genres with the communicative teaching method, and students’ involvement in reading activities such as discussing reading texts of interest with their peers.
TREND ANALYSIS OF GREENHOUSE GASES (GHGs) EMISSIONS OVER NIGERIA FROM 2000 - 2021

The global climate is changing with attendant devastating consequences on human livelihood and socioeconomic activities.This change has been related to greenhouse gas (GHG) emissions, wh ich primarily include carbon dioxide (C O2), methane (CH4), and nitrous oxide (N2O) and are characterised by spatiotemporal fluctuations over the planet.The objective of the research is to analyse temporal variation of greenhouse gases emissions over Nigeria from 2000 -2021.Data used were obtained from the archive of the Emissions Database for Global Atmospheric Research (EDGAR).This database provides time-series emissions as national totals using international statistics and a consistent Intergovernmental Panel on Climate Change (IPCC) methodology.Monthly data for a period of 21 years covering 2000-2021 were obtained for Nigeria.Trend analysis was carried out on a monthly, seasonal and annual basis to define the h istorical emissions trend path over Nigeria using descriptive statistics and the Mann -Kendall test.Results of the monthly analysis from January to December for the given h istorical years demonstrated steady upward trends for two gases namely CO2 and N2O which are significant a t the 0.05 alpha levels.On the contrary, the CH 4 gas emission indicated different trends for different months which are not significant at the 0.05 degree of alpha.Furthermore, similar scenarios of significant upward trends of CO2 and N2O emissions, but not for CH4 are noticeable with respect to the seasonal and annual time-series.As a result, Nigeria's Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change (UNFCCC) have not resulted in a positive commitme nt to achieve net-zero emissions by 2060.
Small sample data augmentation and abundances inversion of minerals hyperspectral

Abstract： Using deep learning methods to retrieve mineral abundance requires numerous labeled hyper• spectral data samples.Thus， a method based on the Hapke mixed model with filling factor is proposed for data augmentation of small mineral samples， to generate a large number of labeled datasets.First， five kinds of common mineral powders were mixed by multiple elements in the laboratory according to the weight mixing ratio， and the spectra of mixed minerals were measured.Subsequently， mixing spectra were simulated considering the corresponding weight proportion of the five mixing models， including the linear mixing model.The simulated spectra of the augmented data using the original Hapke and the Hapke mixing model with filling factors of 0. 1， 0. 2， and 0. 3 were compared with the measured spectra.Final•
Media Pembelajaran Pendidikan Agama Islam “E-Course” Melalui Penerapan Model Ashyncronous

This study is the application of e-course learning media through the asynchronous model in Islamic Religious Education subjects at SMK Negeri 2 Lumajang which aims to determine the application of e-course learning media through the asynchronous model in subjects and to find out its implications. This research uses qualitative methods, namely by conducting observations, documentation, and interviews. The results showed that with the application of the e-course learning media through the asynchronous model that students preferred it because the e-course contained varied teaching materials, not only presented literacy material, but also in the form of interactive learning videos connected to social media accounts, and can be accessed at any time. Implications in the application of e-course learning media through asynchronous models, being able to foster creativity, hone teacher skills in assimilation with digitalization of education, in an effort to overcome student boredom during online learning. On the other hand, in the affective aspect, the decrease in the inner bond between teachers and students and teachers who have not grown awareness to be ready to collaborate with technology feel burdened in preparing materials while online (In the Network).