Reviewerly/exHarmony huggingface dataset:
Teaching Cybersecurity with Experiential Learning: The Case of the Phishing and Deviance Module in Social Science Courses

This article examines the impact of experiential pedagogy in integrating cybersecurity into the social science undergraduate curriculum of Historically Black University. The article is based on a National Science Foundation project, where twenty-six socio-cybersecurity modules were created, and some were integrated into Sociology and Criminal Justice courses. The impact of one of the modules, “Phishing and Deviance”, on cybersecurity learning outcomes is the focus of this article. The two research questions for this article are: 1) Was cybersecurity awareness increased after the infusion of the Phishing and Deviance Modules? 2) Were cybersecurity learning outcomes impacted by using experiential pedagogy? Vygotsky’s experiential learning theory was the grounding theory that guided the integration of the modules. The methodology included a pretest survey of students’ awareness of phishing, the module’s lecture and experiential activities, then a post-test survey of cybersecurity awareness. T-test analysis was performed on the data obtained from the quasi-experimental survey data. The results from the student responses demonstrate that experiential pedagogy was helpful and allowed them to showcase their new knowledge. This curriculum development project shows significant development in African American students’ awareness of cybersecurity at our institution. Post-research endeavors have expanded the socio-cybersecurity modules using constructivist experiential learning to other disciplines and institutions. Further research efforts can include developing more expansive socio-cybersecurity content like courses and programs and measuring their impacts through tracer studies and program evaluations.
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis

Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users’ ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users’ ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model’s decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users’ understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.
Breast Cancer Classification Using Support Vector Machines (SVM)

When compared to all other malignancies, breast cancer is one of the most common among women. It is the second leading cause of death from cancer in women. Early and accurate diagnosis enables timely treatment and enhances prognosis. For disease outcome, it is crucial to be able to detect whether the tumor is malignant or benign which the most experienced and educated physicians can do with 79%. The purpose of this research is to construct a computational model that exceeds this accuracy. For this purpose, machine learning algorithms were considered. They enable us to learn from data that has known classification (training set), test the model performance using more known data (testing set), and finally use the developed model to classify unknown data (validation set). We constructed two such computational models using Support Vector Machines (SVM) computational approaches. The models were tested on breast cancer data with a total of 569 rows (samples) and 32 columns (features) coming from the Wisconsin dataset. The achieved accuracy is 97% which well exceeds the accuracy of well-trained human professionals.
Efficient Training of Foosball Agents Using Multi-agent Competition

In this work, an efficient training concept for training striker and goalkeeper Foosball agents using Deep Reinforcement Learning is designed and implemented. Based on a literature review, individual components such as the observation and action space are defined and suitable configurations for efficient learning are determined by experiments. Furthermore, different aspects for the modeling of multi-agent training are discussed on the basis of further literature research and a concept is derived from this for the use in the Foosball domain. Using only two consumer laptops, it could be shown that the use of multiple agents is also suitable in resource constraint domains. Despite an imbalance in the difficulty of the tasks of striker and goalkeeper agents, both agents were able to learn individual strategies while taking the respective opponent into account.
Encounter-Based Density Approximation Using Multi-step and Quantum-Inspired Random Walks

In this paper we study encounter-based density estimation using different random walks and analyse the effects of the step-size on the convergence of the density approximation. Furthermore, we analyse different types of random walks, namely, a uniform random walk, with every position equally likely to be visited next, a classical random walk and a quantum-inspired random walk, where the probability distribution for the next state is sampled from a quantum random walk. We find that walks with additional steps lead to faster convergence, but that the type of step, quantum-inspired or classical, has only a marginal effect.
Can Mobile Device Use in the Classroom Facilitate Student Engagement in Higher Education

Bring Your Own Device (BYOD) has been heralded by many in academia and industry as a means of integrating new technology into existing processes, thus enhancing the learning experience but without the need for significant capital outlay or resources. One potential benefit of BYOD is an improvement in student engagement in the classroom. This research paper shall seek to identify if this is the case and if so by which means should this be applied. As education is an increasingly global enterprise the research shall be conducted from an international perspective to assess the impact of mobile technology across different countries. What can be alluded to from these findings is that from a student’s perspective there are surprisingly few differences between the reasons why and how they use their mobile devices in the classroom, just that they want to. What is notable however is the variation from an institutions outlook; those countries with more established HE provision are less receptive to the ideas of using mobile technology in their classrooms than their less established counterparts.
Deep Learning Based Shadow Removal: Target to Current Methodology Flaws

Shadow removal, which converts images with shadow to images without shadow is currently an immature technology. When removing shadows, we need to consider various factors including illumination, environment etc. The traditional image processing method is complex and cumbersome, but the result is not ideal. Recently, with the rapid development of deep learning, various papers have proposed different methods to deal with this task, and the results have been greatly improved, but there are still some problems to be solved. At present, there are three problems in the shadow removal paper based on deep learning. First, the problem is color inconsistency. The shadow regions are difficult to restore the correct color and has a significant color difference from non-shadow regions after removal. Second, the shadow boundaries are clearly left in the image. Last, it is difficult to collect datasets in this field, resulting in a lack of training sets, making the model unable to adapt to various scenarios. We propose solutions to the above three problems respectively. The final model can effectively improve both the root mean square error (RMAE) and the structural similarity index (SSIM) of the shadow and non-shadow regions.
The Path to Autonomous Learners

In this paper, we present a new theoretical approach for enabling domain knowledge acquisition by intelligent systems. We introduce a hybrid model that starts with minimal input knowledge in the form of an upper ontology of concepts, stores and reasons over this knowledge through a knowledge graph database and learns new information through a Logic Neural Network. We study the behavior of this architecture when handling new data and show that the final system is capable of enriching its current knowledge as well as extending it to new domains.
Design and Implementation of a Postgraduate Micro-credential in Software Development

This article focuses on curriculum issues by describing the design of a micro-credential in software development, intended to provide a postgraduate, stackable, industry-relevant unit of study that provides a unique offering in the education marketplace. The key philosophy of this micro-credential was that it would bridge the often-siloed disciplines of academic study in areas of software development, drawing instead from the more integrated approaches of contemporary school curricula, and enable professionals from across technology-enabled organizations to participate in software development processes more effectively. The article describes the rationale and context for the micro-credential, reports on the outcomes of the consultation process with stakeholders, discusses the evolution of the design through this process, and then presents the final structure and indicative content of the micro-credential. It is hoped that this case study will contribute to the necessary ongoing debate around what role micro-credentials serve in the education ecosystem, and what design considerations are important when creating micro-credentials that can address the needs of both students and other stakeholders.
New Routines for Faster Balancing of AVL Trees

The longer running times of insert and delete operations of Adelson-Velskey & Landis (AVL) trees due to the complexity associated with its balancing technique and overhead workload, continues to impede its ubiquitous use and versatility compared to other binary search trees, like Red Black trees. In this paper, we present a non-geometrically inspired implementation for faster balancing of AVL trees. The performance in run-time of proposed routines is consistently better than four other publicly available algorithms of AVL trees.
Using Virtual Reality Learning Environments to Improve Success for Online Students

Dramatic changes have occurred in the postsecondary educational landscape since the COVID-19 virus compelled educators and researchers to innovate in order to continue serving learners in safe ways. As a result, the Metaverse has become increasingly popular. Virtual Reality (VR) platforms that provide students with immersive experiences in the Metaverse have been documented to improve learning outcomes via engaging simulations. Even beyond its potential for more experiential learning, the Metaverse landscape offers unprecedented opportunities to improve the quality of online STEM education. As such our university has launched a new initiative to study the impact of virtual reality in education.
Nature Inspired Metaheuristic Techniques of Firefly and Grey Wolf Algorithms Implemented in Phishing Intrusion Detection Systems

This paper presents the findings of a pilot study performed by applying metaheuristic optimized nature-inspired algorithms to detect phishing attacks via emails by the URLs encoded. Previous works by the authors are further expanded, updated, compared, and analyzed for significant contributions. Random forest and support vector machine (SVM) classifications are optimized with the firefly nature-inspired metaheuristic algorithm and then also with the grey wolf nature-inspired metaheuristic algorithm. Recent research utilizing NP-hard challenges like the traveling salesperson dilemma shows promise when implementing nature-inspired heuristics. Providing cyber security against threats is complex and falls into the same classification of NP-hard according to existing research. This updated study expands existing research of optimized nature-inspired metaheuristic algorithms with existing classifiers (random forest and SVM) for improved results of increased accuracy, increased F1-score, and decreased false negative results. The importance of this pilot study is the development of algorithms and systems with high precision and accuracy using nature-inspired and natural processes.
Stack-Based Buffer Overflow Implementation Using Python 3

Because of its relative ease of implementation, the buffer overflow attack is one of the most common attacks today. This paper explains the concept of stack-based buffer overflow using a step-by-step approach to exploit the program, Vulnserver, while monitoring its logical memory using a debugger to understand the seemingly unusual behavior of the program. Windows 10 (64-bit) is the victim host, while Kali Linux (64-bit) is the attacker in a virtualized environment. The detailed explanation of the steps involved in stack-based buffer overflows demonstrated in this paper is intended to help researchers visualize how modern buffer overflows are implemented using Python 3 and how to protect programs against these attacks. Furthermore, knowledge from this paper can also help software developers and researchers validate and reproduce buffer overflow attacks posted on popular websites, such as Exploit Database, with relative ease and offer practical solutions against them.
De-anonymising Individuals Through Unique Patterns in Movement Data

Recent developments have accelerated the need to track individual contact behaviours to counter the pandemic of COVID-19. Various solutions have been implemented to support contact tracing activities with respect to disease propagation. In particular, digital contact tracing approaches range from manual QR scanning to automated Bluetooth handshakes. However, a problem that all the contact tracing methods face is the issue of how to protect individuals’ privacy while also dealing with the threat of a global disease. Approaches such as IMSI-catching surface the data privacy issue of an absence of key rotation, which are aimed at minimising the potential risk of de-anonymisation. Rotating keys, however do not fully protect individual movement patterns or behaviour routines that can be inferred from contact tracing data. In this paper, we show that individual movement patterns in contact tracing data can be used to discover behaviour routines by finding routines and patterns that are representable as quasi-identifiers, and that such quasi-identifiers can enable re-identification. We demonstrate the practicality of our proposed de-anonymisation approach via a series of three (3) experiments based on both semi-synthetic and real-world datasets, to search for quasi-identifiers that reveal unique observation patterns. Since widely popular contact tracing apps employ similar movement and contact patterns, we discuss the risk of location based behaviour pattern tracking in spite protection mechanisms such as rotating keys that are currently used to protect handshake identifiers.
Designing an Interactive Learning Suite for Children: Results from a Usability Study with a Multidisciplinary Research Team

This research project is the culmination of multiple years of research aimed at creating modular, interactive learning environments that establish the basis of spatial and computational thinking in young learners. Throughout each stage, multidisciplinary teams of college faculty, undergraduate, and graduate students collaborated to create each design iteration and improve upon the existing prototypes. This research paper describes year-long research and testing of the final prototype aptly named U-Design. During this timeframe, the research team created an in-depth literature review that explored education and teaching frameworks that relate to cyber-physical learning and knowledge application within STEAM fields. Team members used these principles to support the design and manufacturing process of an effective tool that can explain complex topics to children while teaching methods of creating systematic, efficient solutions to real-world problems. The paper reports on usability studies and heuristic evaluations of the prototype at key intervals throughout the process to ensure design goals were met.
Sağlık Bilimlerinde Öğrenim Gören Öğrencilerin Mobil Öğrenmeye Yönelik Tutumları

Objective: This study aimed to examine the attitudes of health sciences students towards mobile learning. &#x0D; Methods: This descriptive and cross-sectional study was carried out in the departments of Nursing, Physical Therapy and Rehabilitation, Pharmacy, and Medicine in the Faculty of Health Sciences of a university located in the western Black Sea region of Turkey between December 2020 and May 2021. The study data were collected with the "Student Information Form" consisting of 10 questions and the "Mobile Learning Attitude Scale." The online survey method was used to manage the research data, and the answering time of the survey was 7-8 minutes on average. &#x0D; Results: The study determined that the students had a moderate average score for their attitudes towards mobile learning. The highest average (3.11±0.45) score in its sub-dimensions was in usability in mobile learning.&#x0D; Conclusion: It has been determined that the health science students' attitudes towards mobile learning are moderate. It was observed that the m-learning attitudes of the students who found the effectiveness of online education at a "low" level in theoretical and clinical/practical courses were also at a "low" level.
A COMPARATIVE STUDY BETWEEN INDONESIAN UNDERGRADUSTE AND GRADUATE STUDENTS’ PERCEIVED ANXIETY AND SELF-EFFICACY IN CRITICAL WRITING

This study aimed to investigate students’ perception on their writing anxiety and how they employed their writing self-efficacy as well as the relationship between writing anxiety and writing efficacy. This research was conducted toward 29 students from undergraduate and graduate EFL students in Yogyakarta who were taking critical writing course. This study used mixed-method research employing a close-ended questionnaire and interview to gather the data. The writing anxiety questionnaire statements were adopted from Cheng (2004), while the writing efficacy statements were adopted from Eby (2018). The data results were analysed using SPSS version 25 then described statistically, whilst the interview data were analysed using thematic analysis. The results showed that both undergraduate and graduate students have a high self-efficacy and anxiety in writing. A correlation test was conducted and described in the discussion section to see the relationship between writing anxiety and writing efficacy indicates that all aspects of writing anxiety had a negative correlation but not significant with writing ability and behavior. However, not all aspects of writing anxiety such as somatic anxiety, avoidance behaviour, and cognitive anxiety had a negative correlation with writing ideas, writing conventions and self-regulation in writing. The interview result showed that the students who had good self-efficacy in writing felt anxiety as a means to explore themselves and foster a positive mindset to continue progressing in writing. The implication of this research is to expand knowledge related to writing practice in EFL context and educational psychology.
Using WhatsApp as a medium of postgraduate instruction in a private university in Nigeria: fall-outs of the Covid-19 Pandemic

The Covid-19 pandemic resulted in a global lockdown and the cessation of all academic activities on-site. Higher education in Nigeria was not exempt, as a paradigm shift was required for university officials to explore non-conventional teaching methods to prevent students from wasting time and to keep them intellectually engaged. During the pandemic, our research examines how a private institution in Ede, Osun State, Nigeria, utilised the social networking platform WhatsApp for Ph.D. student seminars. The study obtained data from ten doctoral students in the Department of Library and Information Science at Adeleke University via online interviews. The difficulties faced by the students and the benefits of utilising ICT were highlighted. Due to the constant insecurity and poor road network in Nig eria, as well as the risk of long-distance travel, especially for postgraduate students who are not always resident on campus, we recommend the continued use of E-learning platforms in Higher Education Institutions as a solution to reducing transportation costs.
A Speed Estimation for Reliable Performance of Field Oriented PMSM Control using Adaptive Neuro-Fuzzy Inference System

This paper discusses the high-performance exigencies using field oriented control (FOC) resolver. To overcome permanent magnet synchronous motor (PMSM) under dynamic speed and load disturbance, the drive control features space vector pulse width modulation (SVPWM) is proposed. An adaptive neuro-fuzzy inference system (ANFIS) controller is used in the system to enhance speed performance. A novel efficient PMSM control is verified with digital implementation.
Parameter-efficient Tuning of a Pre-trained Model via Prompt Learning in Cross-modal Retrieval

One effective approach to improving the performance of cross-modal retrieval is to fine-tune a pre-trained cross-modal model. However, conventional fine-tuning approaches usually require plenty of computational resources. To alleviate such a requirement, we propose a parameter-efficient tuning method of a pre-trained model via prompt learning for cross-modal retrieval. Obtaining inspiration from the prompt learning technique in natural language processing, our method constructs a multidimensional vector as a prompt for the cross-modal retrieval, and the prompt with a few parameters is optimized to achieve better retrieval performance. We conducted experiments on the open dataset, and the results verify that our proposed method is effective and parameter-efficient.
Using Machine Learning to Predict <i>TP53</i> Mutation Status and Aggressiveness of Prostate Cancer from Routine Histology Images

Despite years of progress, we still lack reliable tools to predict the aggressiveness of tumors, including in the case of prostate cancer. Biomarkers have been developed, but they often suffer from poor accuracy if used alone due to tumor heterogeneity. Nevertheless, some mutations, notably TP53 mutations, are highly correlated with progression. In their work in this issue of Cancer Research, Pizurica and colleagues implemented a machine learning-based model applied to routine histology and trained with prior information on TP53 mutation status. Their model output provides a quantitative prediction of TP53 mutation status while having a strong correlation with aggressiveness, showing promise as a prognostic in silico biomarker. See related article by Pizurica et al., p. 2970.
Depth from motion parallax: Deictic consistency, eye contact, and a serious problem with Zoom

The dynamics of head and eye gaze between two or more individuals displayed during verbal and nonverbal face-to-face communication contains a wealth of information and is used for both volitionary and unconscious signaling. Current video communication systems convey visual signals about gaze behavior and other directional cues, but the information they carry is often spurious and potentially misleading. I discuss the consequences of this situation, identify the source of the problem as a more general lack of deictic consistency, and demonstrate that using display technologies that simulate motion parallax are both necessary and sufficient to alleviate it. I then devise an avatar-based remote communication solution that achieves deictic consistency and provides natural, dynamic eye contact for computer-mediated audiovisual communication.