Reviewerly/exHarmony huggingface dataset:
MC-RGN: Residual Graph Neural Networks based on Markov Chain for sequential recommendation

Sequential recommendation aims to predict the next item the user will interact with based on his/her historical interaction sequences. Existing sequential recommendation methods mainly use self-attention to capture the preferences with individual user–item historical interaction sequences, while ignoring the group preferences that are exhibited by all user–item interaction sequences. Besides, like general recommendation, data sparsity remains the main factor detrimentally affecting recommendation performance. To remedy the damage of data sparsity and improve the recommendation performance, we employ Residual connections and Graph Neural Networks based on Markov Chain to propose MC-RGN, which incorporates group preferences into a single user–item historical sequence. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted on four real-world datasets, MovieLens, Sports, Yelp and Beauty. Experimental results showed that the proposed method achieved an average improvement of 14.32% on Hit@10 and 27.17% on NDCG@10 compared to the baseline models. To showcase whether the proposed method alleviates the negative impact of data sparsity on recommendation performance, we compared the recommendation performance decline of the SASRec and the proposed method under various levels of data sparsity. The results indicate that the proposed method notably reduced the damage caused by data sparsity (0.07% vs 20.94%, 2.37% vs 23.70%, 6.51% vs 27.01%, and 22.78% vs 28.42% at data usage ratios of 80%, 60%, 40%, and 20%, respectively). Finally, our proposed model is available via https://github.com/crx1729/MC-RGN.
U.S. IT Faculty: Perceptions of Satisfaction and Stress

To better understand the U.S. IT baccalaureate landscape and aid in faculty recruiting efforts, a comprehensive survey of IT faculty was conducted in spring 2021 with a goal of revealing characteristics and perceptions of faculty teaching in IT baccalaureate programs in the U.S. A sample of 321 faculty responded to the online survey, and this paper focuses on the perceptions and satisfaction levels of full time IT faculty respondents. While most are generally satisfied with their work environment, there are areas for improvement that IT program leaders should consider. Some of these areas include salaries, workload, access to research and teaching assistants, and access to professional development opportunities.
Usability Study of a Learning Management System (LMS)

This paper presents the findings from a usability study of Canvas - a popular learning management system (LMS) used in higher education. The participants of this research are faculty users of Canvas LMS in a Research 1 University. The study included a survey, think-aloud interviews, and System Usability Scale (SUS) survey. In this paper, we discuss several usability issues that we uncovered through this research and provide design recommendations aimed at enhancing the LMS’s usability. We also propose that the approach employed in this research holds the potential for replication or adaptation in similar studies.
Panel: Better Team Projects

Team projects are common in many computing courses. They allow students to work on larger and more complex tasks, and give students an early experience with extended group work. But team projects can also be difficult to organize, monitor, and evaluate. This panel will provide an opportunity to share issues and approaches in support of successful team projects for IT and other computing students.
"Pedagogical Experiences from a Hands-on Cybersecurity Defense Course] \"It was a one of a kind experience:\" Student experiences and pedagogical design of a project-based hands-on cybersecurity pentesting course"

The selection of learning approaches, hands-on materials, and exercises for a hands-on cybersecurity defense course can be tricky and challenging. In this paper, we share the pedagogical design and student perceptions of a hands-on cybersecurity defense course. A hands-on cyber defense course was taught with the combination of lectures, guided penetration testing virtual labs, and an independent penetration testing project. To avoid potential pitfalls, the project goals were tied to the student learning outcomes, and student check-ins and mentoring were built in to the design of the project. The combination of guided labs and independent project allowed deep learning, increased student satisfaction and motivation, and taught important hands-on cybersecurity skills sought in the industry and higher level courses. Students found the learning experience in this course to be motivating and one of a kind. This paper will share the pedagogical design of the course and explore the student experiences and challenges afforded through the pedagogical choices made in the course. The paper contributes pedagogical knowledge to other faculty members looking for effective instructional strategies for hands-on, skill-based, or project-based learning. The insights may be useful in bridging the skills gap in the cybersecurity industry.
Integrating HCI in Web Technologies Course

Applying Human Computer Interaction (HCI) rules and principles in web design will improve the quality of the website and users’ satisfaction. In this paper, we propose to integrate HCI in the web design related courses. We create four categories of HCI web design rules, and design class projects for students to apply the HCI web design rules in design, implementation, and evaluation of their web pages. The survey results show that most students are confident in applying these rules in their web page design and implementation after taking the course.
Immersive gamification for education: No additional benefit gained from wearing a VR headset

Virtual reality (VR) technologies have been increasingly gaining attention for use in a variety of applications. Yet, wider adoption is impeded by the discomfort of headsets, costs of adopting the technology, and the computational power required for rendering. Instead, much of VR’s benefit can be achieved with simple a “desktop VR” application using a computer monitor. However, it is not known whether this semi-immersive VR approach is as effective. In this paper, we compare the experience of VR using a headset versus on the desktop, to test the effectiveness of an immersive 3D gamification approach for education of K through 12 grades and postsecondary students. In our survey of 73 general education college students from diverse backgrounds, we found no statistically significant gains from wearing a VR headset in learning computer programming concepts. To investigate further, we followed up with a secondary study of 46 middle school students, where we confirmed our findings. Additional attitude questions in the follow-up study showed that motion sickness and wearing glasses were the most important reasons for avoiding the headset. We conclude that while immersive gamification is effective for education, we recommend the inexpensive and more convenient desktop VR approach, which is also remote-learning friendly.
Teaching IT Software Fundamentals: Strategies and Techniques for Inclusion of Large Language Models

This paper argues for the inclusion of tools that utilize Artificial Intelligence (AI) Large Language Models (LLMs) in information technology (IT) undergraduate courses that teach the fundamentals of software. LLM tools have become widely available and disrupt traditional methods for teaching software concepts. Learning objectives are compromised when students submit AI-generated code for a classroom assignment without comprehending or validating the code. Since LLM tools including OpenAI Codex, Copilot by GitHub, and ChatGPT are being used in industry for software development, students need to be familiar with their use without compromising student learning. Incorporating LLM tools into the curriculum prepares students for real-world software development. However, students still need to understand software fundamentals including how to write and debug code. There are many challenges associated with the inclusion of AI tools into the IT curriculum that need to be addressed and mitigated. This paper presents strategies and techniques to integrate student use of LLM tools, assist students’ interaction with the tools, and help prepare students for careers that increasingly use AI tools to design, develop, and maintain software.
An IoT-based System of Converting Handwritten Text to Editable Format via Gesture Recognition

Despite the prevalence of online learning or e-learning, the evolution of traditional classrooms has never stopped. Electronic classrooms, where classrooms equipped with projectors, computers, and sound systems, have become dominant in the last decade. The next step of evolution for electronic classrooms is smart classrooms. One of the most popular features of an electronic classroom is capturing video/photos of lecture content and extracting handwriting for note-taking. Various techniques have been implemented in order to extract handwriting from the video/photos of the lecture. However, there are still deficiencies in the existing techniques that prevent us from turning an electronic classroom into a smart classroom. This paper presents a real-time Internet of Things (loT)-based system to convert handwritten text into editable format by implementing Hand Gesture Recognition (HGR) with Raspberry Pi. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. For text conversion, the recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, graphs, and figures is included with the recognition of words, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT technologies, students can access the editable format of lecture notes via a desktop application, which helps students to edit and share notes and images according to their necessity. Implementation details and comprehensive evaluations of the system are summarized in the paper.
Comparison of Password Hygiene for Computer Science and Non-Computer Science Undergraduates

In today’s digital world, it is imperative that the next generation of workers is prepared to be successful working online. Students graduating from a technology curriculum may be prepared for secure digital work due to the nature of their coursework, but students in non-technical fields will also require strong cybersecurity awareness as all professions continue to move more online. Adding cybersecurity education to non-technical fields may assist in this endeavor, but it is unclear the degree to which cybersecurity knowledge correlates with realized cybersecurity behavior. This paper describes an analysis of the results of a survey of 119 students in various fields of study at a large public university. Students’ cybersecurity knowledge and password hygiene were assessed, with password hygiene serving as an example of cybersecurity behavior. It was found that Computer Science (CS) students performed only marginally better than their non-CS counterparts on a general cybersecurity knowledge survey. However, the CS students’ password hygiene was significantly better. This suggests that some element other than just knowledge of cybersecurity topics is important in encouraging actual strong cybersecurity behavior among students.
The art of deep learning and natural language processing for emotional sentiment analysis on the academic scholars' peer review process.

Deep learning and natural language processing has emerged as modern machine with state-of-the-art modern learning and applicable techniques that help academicians, students, and teachers to identify, evaluate and validate documents and text features. Most educational systems have incorporated deep learning and natural language processing in fulfilling tasks except publishers and editorial boards, whom they handle important aspects of academic achievement in the decision-making for both journals and conferences. The aim is to introduce a standardized systematic peer review process based on factual justification, amendment, and recommendation approach on scholarly works free from emotional sentiments, politicization, bias, criticism, and condemnation. The study uses peer review feedback from journal XXXX to identify, evaluate and validate emotional sentimental aspects. A Confusion Metrix model was examined in the study to justify and validate the emotional sentiment pros and cons of the reviewer. Also, behavior-oriented drive and influential functions were used to convert text to value and rank the sentiment score. Each reviewer's text was converted into value using a behavior-oriented drive and influential function were used to examine the emotional sentiment level of the reviewers. Text mining and extraction of data uses a natural language process approach to identify sentiments from the reviewer's comments. Based on the confusion matrix and behavior-oriented drive, and influential function, the findings revealed limited significant emotional sentiment involved during the peer review process. The study concluded that the reviewers didn't perfectly validate the manuscript content due to minor emotional sentiments. The reviewer's evaluation of paper #Journal XXXX based on the findings exercise, few emotional sentiments were attracted during the review process. The study recommended editorial boards, journals, conferences, and publishing houses implement this approach to avoid emotional sentiments, politicization, and bias impact on academic evaluation, and validation.
U.S. Master's Level Cybersecurity Programs: An Analysis of the Diversity of Content

As the popularity of Cybersecurity-related curricula grows both among students and institutions at the higher education level, there exist many questions regarding how degree programs should be structured and what specific content should be taught in them. Many areas of study that were once thought to be somewhat removed from a networking or information security focus have created new courses, or adapted existing ones, to create degree programs based on some aspect of Cybersecurity. Whether the program is titled Information Security, Information Governance, Business Systems Security, Network Security, or a host of other variations and terms, the heart of all of them is Cybersecurity. In other words, all of these programs revolve around the securing of data, systems, networks, and their physical footprints in organization. This work seeks to review the various types of master's level programs related to Cybersecurity in the U.S.
Hybrid agreement with English quantifier partitives

This paper presents a novel case study of a 3/4 agreement pattern typically found with hybrid nouns. This case study involves agreement and binding with Quantified NPs in English. I propose an analysis that relies on different classes of agreement targets agreeing at different times and couple this with a condition on the access to semantic agreement features. This new analysis can account for the novel data presented here as well as the data from the literature. This paper hence broadens both our empirical knowledge of 3/4 patterns as well as refines our theory of features and agreement that underlie such patterns.
CGAPP: A continuous group authentication privacy-preserving platform for industrial scene

In Industry 4.0, security begins with the workers’ authentication, which can be done individually or in groups. Recently, group authentication is gaining momentum, allowing users to authenticate as group members without the need to specify the particular individual. Continuous authentication and federated learning are promising techniques that might help group authentication by providing privacy, by its own design, and extra security compared to traditional methods based on passwords, tokens, or biometrics. However, these techniques have not previously been combined or evaluated for authenticating workers in Industry 4.0. Thus, this paper proposes a novel continuous group authentication privacy-preserving (CGAPP)platform that is suitable for the industry. The CGAPP platform incorporates statistical data from workers’ smartphones and employs federated learning-based outlier detection for group worker authentication while ensuring the privacy of personal data vectors. A series of experiments were performed to measure the framework’s suitability and address the following research questions: (i) What is the cost of using FL compared to full data access in industrial scenarios? (ii) How robust is federated learning against adversarial attacks, specifically, how much malicious data is required to deceive the model? and (iii) How much noise is required to disrupt the authentication system? The results demonstrate the effectiveness of the CGAPP platform in the industry since it provides factory safety while preserving privacy. This platform achieves an accuracy of 92%, comparable to the 96% obtained by traditional approaches in the literature that do not address privacy concerns. The platform’s robustness is tested against attacks in the second and third experiments, and various countermeasures are evaluated. While the CGAPP platform exhibits certain vulnerabilities to data injection attacks, straightforward countermeasures can alleviate them. Nevertheless, the system’s performance experiences a notable impact in the event of a data perturbation attack, and the countermeasures investigated are ineffective in addressing this issue.
Title Pending 8850

In this paper, based on an analysis of the scope properties of objects in Japanese, I argue that&amp;nbsp;(i) an entire phase is transferred when a higher phase is completed, and (ii) the movement of&amp;nbsp;objects and adjuncts is constrained by anti-locality. I propose that in the potential construction,&amp;nbsp;the movement of accusative objects is more constrained than the movement of nominative&amp;nbsp;objects owing to the interaction between phasal transfer and anti-locality, which leads the&amp;nbsp;accusative and nominative objects to exhibit distinct scope behaviors. This analysis leads us to&amp;nbsp;conclude that negation in Japanese is an adjunct. Furthermore, the analysis correctly predicts&amp;nbsp;the movement of objects in simple transitive sentences and the non-movement of nominative&amp;nbsp;phrases in certain cases.
Manifold Learning for Adversarial Robustness in Predictive Process Monitoring

In recent years, many predictive models have been successfully applied to predictive process monitoring, enabling tasks such as predicting the next activity, remaining time, or the future state of a process instance (case). However, recent developments have shown the vulnerability of these models to adversarial attacks, causing algorithms to make incorrect predictions. This paper addresses this issue by leveraging adversarial examples to evaluate the predictive performance of predictive process monitoring models in the face of adversarial threats. Although augmenting training data with adversarial examples has proven effective in defending against specific adversarial attacks, it often remains insufficient in mitigating vulnerabilities to other types of attacks. Our proposed approach explores the use of manifold learning techniques to restrict these examples within the range of data on which the model is trained. By learning from these specifically engineered (hidden) attacks, we seek to develop models that maintain accuracy on new, unseen data while effectively improving adversarial robustness against potential threats.
Measuring the Stability of Process Outcome Predictions in Online Settings

Predictive Process Monitoring aims to forecast the future progress of process instances using historical event data. As predictive process monitoring is increasingly applied in online settings to enable timely interventions, evaluating the performance of the underlying models becomes crucial for ensuring their consistency and reliability over time. This is especially important in high risk business scenarios where incorrect predictions may have severe consequences. However, predictive models are currently usually evaluated using a single, aggregated value or a time-series visualization, which makes it challenging to assess their performance and, specifically, their stability over time. This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring. The framework introduces four performance meta-measures: the frequency of significant performance drops, the magnitude of such drops, the recovery rate, and the volatility of performance. To validate this framework, we applied it to two artificial and two real-world event logs. The results demonstrate that these meta-measures facilitate the comparison and selection of predictive models for different risk-taking scenarios. Such insights are of particular value to enhance decision-making in dynamic business environments.
Effects of the Exciton Fine Structure Splitting on the Entanglement-Based Quantum Key Distribution

The reliable transmission of secure keys is one of the essential tasks to be efficiently accomplished by quantum information processing, and the use of entangled particles is a very important tool toward that goal. However, efficient production of maximally entangled states is still a challenge for further progress in quantum computing and quantum communication. In the search for optimal sources of entanglement, quantum dots have emerged as promising candidates, but the presence of dephasing in the generated entangled states raises questions about their real usefulness in large-scale quantum networks. In this work, we evaluate the effects of the exciton fine structure splitting, present in most quantum dot samples, on the fidelity of the BBM92 protocol for quantum key distribution. We find that the protocol’s performance is heavily impacted by such splitting and establish an upper limit for the product between the energy splitting and the exciton lifetime to have a dependable distributed key.
Quantifying Grover speed-ups beyond asymptotic analysis

Run-times of quantum algorithms are often studied via an asymptotic, worst-case analysis. Whilst useful, such a comparison can often fall short: it is not uncommon for algorithms with a large worst-case run-time to end up performing well on instances of practical interest. To remedy this it is necessary to resort to run-time analyses of a more empirical nature, which for sufficiently small input sizes can be performed on a quantum device or a simulation thereof. For larger input sizes, alternative approaches are required.In this paper we consider an approach that combines classical emulation with detailed complexity bounds that include all constants. We simulate quantum algorithms by running classical versions of the sub-routines, whilst simultaneously collecting information about what the run-time of the quantum routine would have been if it were run instead. To do this accurately and efficiently for very large input sizes, we describe an estimation procedure and prove that it obtains upper bounds on the true expected complexity of the quantum algorithms.We apply our method to some simple quantum speedups of classical heuristic algorithms for solving the well-studied MAX-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-SAT optimization problem. This requires rigorous bounds (including all constants) on the expected- and worst-case complexities of two important quantum sub-routines: Grover search with an unknown number of marked items, and quantum maximum-finding. These improve upon existing results and might be of broader interest.Amongst other results, we found that the classical heuristic algorithms we studied did not offer significant quantum speedups despite the existence of a theoretical per-step speedup. This suggests that an empirical analysis such as the one we implement in this paper already yields insights beyond those that can be seen by an asymptotic analysis alone.