Reviewerly/exHarmony huggingface dataset:
APT Cyber-attack Modelling: Building a General Model

The global community continues to experience an increase in the scale, sophistication, and successful perpetration of cyber-attacks. As the quantity and value of electronic information have increased, so too have the efforts of criminals and other malicious actors who have embraced the Internet as a more anonymous, convenient, and profitable way of carrying out their activities. The systems are attacked more and more by single or multiple hacktivists, state sponsored hackers, cyber criminals, cyber terrorists, cyber spies, or cyber warfare warfighters. The cyber security approach requires a balance of cyber threat intelligence, real time cyber-attack detection and especially the cyber early warning ability. Threats in cyberspace aredifficult to define, as it is hard to identify the source of attacks and the motives that drive them, or even to foresee the course of an attack as it unfolds. The identification of cyber threats is further complicated by the difficulty in defining the boundaries between national, international, public, and private interests. Because threats in cyberspace are global in nature and involve rapid technological developments, the struggle to respond them is ever-changing and increasingly complicated. Cyber-attack models describe the structure of an attack in different phases. They provide a means to conceptualize the different aspects and elements of an attack. However, it is important to understand that not all attacks must complete all phases to be successful, and the objective of the attack defines the structure of the attack. Different actors have built different cyberattack models. Modeling is used to understand the different goals of cyber attackers. Attack models are based on attack targets and attack objectives. This paper analyzes different APT cyber-attack models and presents a general cyber-attack model.
Detecting Fraud Job Recruitment Using Features Reflecting from Real-world Knowledge of Fraud

A common method for text-analysis and text-based classification is to process for term-frequency or patterns of terms. However, these features alone may not be able to differentiate fake and authentic job advertisements. Thus, in this work, we proposed a method to detect fake job recruitments using a novel set of features designed to reflect the behavior of fraudsters who present fake information. The features were missing information, exaggeration, and credibility. The features were designed to represent in the form of a category and an automatically generatable score of readability. Data from EMSCAD dataset were transformed in accordance with the designed features and used to train a detection model for fake job detection. The experimental results showed that the model from the designed features performed better than those based on the term-frequency approach in every applied machine learning technique. The proposed method yielded 97.64% accuracy, 0.97 precision and 0.99 recall score for its best model when used for classifying fake job advertisements.
Position-Aware Guided Hiding Data Scheme with Reversibility and Adaptivity for Dual Images

Reversible data hiding (RDH) in dual images is a technique that shares secret messages into two similar shadow images, while the secret messages and the cover image can be restored only when those two shadows are gathered simultaneously. In this paper, a novel turtle shell-based RDH hiding scheme based on the symmetric property is presented in order to increase the embedding capacity and maintain good visual quality in dual images under the guidance of position-aware. First, we classify each pixel pair into one of four types according to their locations and then determine a sunflower area centered around it in order to construct the combination of positions and the embedding table. Using the embedding table, the secret messages are concealed into a cover image by generating two shadow images. At the decoder’s side, the complete restoration of the secret messages and the cover image can be accomplished by identifying the position relationship between the two stego-pixel pairs. The experimental results confirmed that the proposed position-aware guided RDH scheme is superior to some of the relevant works on the aspects of embedding capacity or image quality. In addition, the proposed scheme provides a secure communication that can effectively resist attacks on the pixel value difference histogram, relative entropy, and regular singular analysis.
Impact of Standard Image Compression on the Performance of Image Classification with Deep Learning

Today, research in the field of artificial intelligence is entirely focused on deep learning, which has led to major advances in image and text processing. Potential application fields and use of deep learning are increasingly diverse since it exceeds various machine learning approaches in terms of performance. The implementation of deep learning in the cloud is the most common and typical method. Data collected by IoT devices is usually sent to the cloud for analysis and processing. However, storage and processing resources are limited due to the constraints on the size, energy, power and computing capacity of IoT objects. In addition, ensuring a reliable connection between the different IoT objects is difficult since the communication between these devices is mainly wireless. In this context, compression of collected data before transmission is crucial for saving bandwidth and energy. In this paper, we studied the impact of standard image compression on the classification performance of convolutional neural networks using a pre-trained model in the cloud. The obtained results showed that, on average, an image can be compressed by a quality factor of 10 and 20 for a JPEG and MozJPEG encoder, respectively, while maintaining a correct classification. Furthermore, the results showed that SSIM is more suitable than PSNR for evaluating the classification performance of convolutional neural networks with regard to the image degradation induced by lossy compression.
SBAR Standard and Mind Map Combined Communication Mode Used in Emergency Department to Reduce the Value of Handover Defects and Adverse Events

The objective is to explore the mind map communication mode used in the emergency department combined with the SBAR standard to reduce the occurrence of handover defects and adverse events. 180 cases of emergency treatment and patient observation from January to June 2021 were selected and studied. According to the time of admission, the selected patients were divided into observation group and control group (90 cases). The control group adopts the traditional handover mode, and the observation group thinks. The map is combined with the SBAR standard communication mode to handover; compared; and observed the two groups of nurse's handover quality scores, handover problems and adverse events, handover defects, mastery of the patient's condition and understanding of critical illness, and nursing satisfaction. The quality scores of nurses in the control group were significantly higher than those in the observation group; the incidence of adverse events in the observation group was 8.9%, and the incidence of handover problems was 2.2%, which was significantly lower than that of the control group. The mastery score of the observation group was significantly higher than that of the control group; the nursing satisfaction of the observation group was 90% significantly higher than that of the control group. The handover defect rate of 56.7% in the control group was significantly higher than that in the observation group. The nurses in the observation group had a 98.9% understanding of the critically ill patients' condition than in the control group. All the above items are statistically significant, P < 0.05. The combined communication mode of SBAR standard and mind map used in the emergency department can improve the quality of handover, reduce adverse events and handover problems, clear patient conditions, higher patient satisfaction, and lower handover defect rate, and nurses' critically ill patients have a higher understanding of the condition, and this communication method is worthy of clinical promotion.
De Jure and De Facto: An Overview on the Italian Measures on Compulsory Vaccination

The outbreak of the Covid-19 pandemic has forced States to take restrictive measures to contain the growing number of infections. Among these measures, after the approval of vaccines by the EMA, the possibility of introducing a compulsory vaccination has become a plausible and attractive prospect. In Italy, Covid-19 compulsory vaccination is implemented by a succession of Decrees concerning specific categories of workers and, only recently, a section of the population with the recent Decree of 7 January, 2022, no. 1. However, if we look back at the most critical restrictions implemented in the country, we realise that a de facto obligation was already in place before establishing a de jure obligation. The following article traces the most important profiles of the vaccination obligation implemented de jure and de facto by the Italian government.
An Analysis on the Satisfaction and Perception of Performance Outcomes of the University Information Disclosure System

The purpose of this study is to analyze the satisfaction and perception of performance outcomes of the university information disclosure system.This is to examine whether the university information disclosure system provides users with customized information and if it satisfies the public's right to know.To this end, this study surveyed a group of active users of the university information disclosure system (high school teachers, university professors, and university administrators) regarding the purpose of use, satisfaction, and perception of its performance.The survey analysis results show the highest-ranking purpose of use was to acquire various information about the university and to acquire information necessary for university research and evaluation.The overall satisfaction with the university information disclosure system is generally high.Upon analyzing satisfaction according to the purpose of use, the performance perception of the university information disclosure system was also high.However, the satisfaction of users with college admission information is relatively low.Also, compared to other outcomes, satisfaction in providing an opportunity for university selection was the lowest.The approach to enhance the enterprise agility of the university information disclosure system in the future needs to meet the information demands of two major groups.It is necessary to establish and implement a strategy that can meet the information needs on university admissions, of whom teachers are representative, and the information demands of consumers who need to select a university.
Distance Field-Based Convolutional Neural Network for Edge Detection

In this paper, we first propose an accurate edge detector using a distance field-based convolutional neural network (DF-CNN). In recent years, CNNs have been proved to be effective in image processing and computer vision. As edge detection is a fundamental problem among them, we try to improve the accuracy of edge detection based on the deep learning framework. The proposed network combines a feature extraction backbone that can fully exploit the multiscale and multilevel information of the edge with the supervised training of the distance field branch to realize the accurate end-to-end object edge detection. The distance field branch is applied to predict the Euclidean distance from nonedge points to the nearest edge point in the feature maps. And the distance information embedded in the predicted distance field map can effectively improve the accuracy of edge detection. The network is trained to minimize the weighted sum of the distance field branch loss and the cross-entropy loss. Our experimental results show that the proposed edge detector achieves better performance than previous approaches and the effectiveness of the proposed distance field branch.
Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm

Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this algorithm tends to convergence to local optimum of the model parameters. Therefore, optimizing HMM parameters remains a crucial and challenging work. In this paper, a Variable Neighborhood Search (VNS) combined with Baum-Welch algorithm (VNS-BWA) is proposed. The idea is to use VNS to escape from local minima, enable greater exploration of the search space, and enhance the learning capability of HMMs models. The proposed algorithm has entire advantage of combination of the search mechanism in VNS algorithm for training with no gradient information, and the BWA algorithm that utilizes this kind of knowledge. The performance of the proposed method is validated on a real dataset. The results show that the VNS-BWA has better performance fifinding the optimal parameters of HMM models, enhancing its learning capability and classifification performance.
Zero Trust and Advanced Persistent Threats: Who Will Win the War?

Advanced Persistent Threats (APTs) are state-sponsored actors who break into computer networks for political or industrial espionage. Because of the nature of cyberspace and ever-changing sophisticated attack techniques, it is challenging to prevent and detect APT attacks. 2020 United States Federal Government data breach once again showed how difficult to protect networks from targeted attacks. Among many other solutions and techniques, zero trust is a promising security architecture that might effectively prevent the intrusion attempts of APT actors. In the zero trust model, no process insider or outside the network is trusted by default. Zero trust is also called perimeterless security to indicate that it changes the focus from network devices to assets. All processes are required to verify themselves to access the resources. In this paper, we focused on APT prevention. We sought an answer to the question: "could the 2020 United States Federal Government data breach have been prevented if the attacked networks used zero trust architecture?" To answer this question, we used MITRE's ATT&amp;CK® framework to extract how the APT29 threat group techniques could be mitigated to prevent initial access to federal networks. Secondly, we listed basic constructs of the zero trust model using NIST Special Publication 800-207 and several other academic and industry resources. Finally, we analyzed how zero trust can prevent malicious APT activities. We found that zero trust has a strong potential of preventing APT attacks or mitigating them significantly. We also suggested that vulnerability scanning, application developer guidance, and training should not be neglected in zero trust implementations as they are not explicitly or strongly mentioned in NIST SP 800-207 and are among the mostly referred controls in academic and industry publications.
Individual Differences in Cyber Security

Christopher's belief in the importance of education stems from his roots as a first-generation college student.This passion for education is what sprouted his interest in obtaining a PhD in psychological research.His research interests include: motivation, self-efficacy, personality, performance, behavior and end-users in cyber security.Understanding how things work and improving the lives of others is what drives Christopher's research.Working as a communications intern for the County of Santa Cruz Human Services Department has given Christopher professional experience in organizational development.
A Model for the Development of the Key Competence of Specialists of a Network Self-learning University

Modern challenges and increasing requirements for the effectiveness of higher education organisations actualise the search for new solutions.The authors see such a solution in developing the key competence of specialists of the innovative network self-learning university.The research aims at theoretical substantiation and development of a predictive model for developing the key competence of specialists of a network self-learning university.A new approach is proposed to improve the effectiveness and competitiveness of the university through the continuous development of the key competence of its specialists on a motivational and value basis in the process of creating a network self-learning university.As the key competence of university specialists, we consider their key managerial competence, which depends on the solution of complex professional tasks and the successful management of their professional activities.The methodological basis was competence-based and axiological approaches.Based on the results of the analysis of scientific literature, the results of the experiment, the professional tasks of three groups of specialists of the network self-learning university were determined, and the key universal professional tasks for them were identified.As a modelling result, the structure of the key managerial competence of specialists of a network self-learning university and a model for developing this competence has been developed.The essence of the concepts of "network self-learning university", "key managerial competence of university specialists" is revealed; the structure of key managerial competence of specialists is substantiated, and the predictive model of continuous development of this competence is characterised, which reflects this development process in the conditions of the motivational and value basis of ongoing formal, informal and informational professional education.
Do Small and Medium Enterprises (SMEs) need Government Financial Support to Successfully Implement Cloud ERP for Better Performance?

In driving economic development, small and medium enterprises (SMEs), which are the economic pillars of many countries receive support from the government for various technological innovations such as cloud enterprise resource planning (ERP).In Malaysia, government financial support (GFS) for cloud ERP implementation in SMEs has been encouraging.However, despite numerous GFS, SMEs are hesitant to implement the technology.Given cloud ERP is a low-cost technology coupled with SMEs' challenges in accessing GFS, the question of whether GFS is required for SMEs to successfully implement cloud ERP for improved performance arises.While the impact of government support on cloud ERP implementation has been well documented, the precise impact of GFS on cloud ERP implementation success and how this affects performance remains unclear.The goal of this research is to determine the impact of GFS on cloud ERP implementation success and how this affects the financial performance of SMEs.The study's model was validated using data from 204 Malaysian manufacturing SMEs.The results of partial least square structural equation modelling (PLS-SEM) show that GFS has a positive influence on cloud ERP implementation success, which in turn improves financial performance.The findings can be used by policymakers and practitioners to develop strategies for improving cloud ERP implementation and economic development.
Semantically Enriched Augmented Reality Applications: A Proposed System Architecture and a Case Study

With a view to creating a mixed reality that combines coexisting real and virtual objects and to providing users with real-time access to information in an interactive manner, augmented reality enriches users’ physical environment by incorporating digital and real objects and rendering them in the physical environment in the proper time and spatial framework. Due to its nature, augmented reality can be combined with and exploit other innovative technologies in order to improve its efficiency and potentials. Some such technologies are semantic web, knowledge graphs and deep learning. The study main purpose and contribution is to showcase the benefits of developing semantically enriched augmented reality applications and to present a system architecture for developing such applications as well as to showcase and assess an augmented reality application developed following the proposed architecture. The specific application aims at facilitating end-users’ day-to-day activities, enhancing the learning and informing process and increasing user experience (UX). The results of this study showcase that such applications have the potential to be applied and positively affect various sectors and that they can be a useful, flexible, interactive and informative tool for end-users.
Counterfactual-Based Action Evaluation Algorithm in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) algorithms have made great achievements in various scenarios, but there are still many problems in solving sequential social dilemmas (SSDs). In SSDs, the agent’s actions not only change the instantaneous state of the environment but also affect the latent state which will, in turn, affect all agents. However, most of the current reinforcement learning algorithms focus on analyzing the value of instantaneous environment state while ignoring the study of the latent state, which is very important for establishing cooperation. Therefore, we propose a novel counterfactual reasoning-based multi-agent reinforcement learning algorithm to evaluate the continuous contribution of agent actions on the latent state. We compute that using simulation reasoning and building an action evaluation network. Then through counterfactual reasoning, we can get a single agent’s influence on the environment. Using this continuous contribution as an intrinsic reward enables the agent to consider the collective, thereby promoting cooperation. We conduct experiments in the SSDs environment, and the results show that the collective reward is increased by at least 25% which demonstrates the excellent performance of our proposed algorithm compared to the state-of-the-art algorithms.
Sentiment Analysis on PeduliLindungi Application Using TextBlob and VADER Library

The Covid-19 virus has become a global pandemic, including Indonesia. Various efforts have been made by the Government to reduce the negative impact by this pandemic, one of which is through the PeduliLindungi application. The research was conducted to obtain public sentiment towards the application by using twitter data. The data collection period is from August 31 to September 7, 2021, this period was chosen due to the emergence of news regarding vaccine data leaks associated with data leaks in the PeduliLindung application. Sentiment analysis is carried out using the TextBlob and VADER libraries. The results of this sentiment analysis are sufficient to display public opinion and it is hoped that decision makers can improve applications based on these opinions. Then, it was found that the VADER library can be said to be better in conducting sentiment analysis in research because the lexicon approach used is based on social media.