Reviewerly/exHarmony huggingface dataset:
Digital Death and Spectacular Death

Throughout human history, individuals, communities and societies have always had to confront and tackle the problem of death. Consequently, death remains a topic of social scientific relevance, highlighting the need for its study and for theorising around it. This article analyses the development of the social scientific study of death and dying, taking inspiration from Philippe Ariès’s historical stages to discuss the recent developments in the field, namely the study of digital death. The article begins with a discussion of the visibility of death in modern society in the context of spectacular death. The analysis emphasises its four dimensions: mediatisation, commercialisation, re-ritualisation and the revolution in end-of-life care. The article moves on to discuss the emergence of digital death as the current stage and reflects on its similarities to spectacular death and its transformation of public imaginaries around death in contemporary society. The article concludes with a reflection on future developments in the field, specifically the emergence and study of artificial intelligence (AI) in digitalised death culture.
Multi-object behavior recognition based on object detection for dense crowds

In recent years, research on multi-object behavior recognition methods based on pose estimation has made progress. However, in dense scenes, multi-object behavior recognition faces problems such as heavy occlusion, many small-scale objects, and an unbalanced distribution of object classes. Meanwhile, multi-object behavior recognition methods based on pose estimation are difficult to adapt to dense scenes. Therefore, we use the idea of object detection to complete the multi-object behavior recognition in dense scenes. However, compared with general object detection, the fine-grained classification and localization constrain each other due to the similarity of object classes, resulting in poor recognition performance. To solve the above problems, we propose a shallow convolutional neural networks (CNNs) module to extract task features separately in localization and classification task parallel branch structure, so that the two vision tasks avoid mutual constraints. Additionally, we propose a feature fusion mechanism to improve the detection performance of small objects by obtaining multi-scale high-level semantic information. To extract balanced and robust features, we exploit the number of objects in an image to generate dynamic class weights and difficulty weights. Experimental results on dense crowds in student classrooms demonstrated that the proposed method achieves superior performance compared to other state-of-the-art methods, with mAP50 of 88.72% and APs of 35.41%.
Linkage between Students’ Study Habits and their grades analyzed through Bayesian statistics

Abstract It is well documented that particular study habits and strategies, among other factors, can influence students' grades and contribute to college success, independent learning, and retention. To further explore such connections, we studied and analyzed distinctions between two groups, those reporting use of "good" study habits vs. "not so good" study habits. Results establish a link between the two groups and Exam 1 grades, coded as Pass/Fail. Logistic regression, Bayesian statistics, and Matlab programming language were utilized for analysis, indicating that study habits depicted in the group reporting use of study habits research indicates as more powerful are indeed more likely to obtain a passing grade compared with those who did not report employing the better study strategies. because Bayesian statistics are not commonly employed to analyze these effects, a brief is provided on Bayesian statistics to facilitate researchers in this field who might also wish to use Bayesian statistics in their work.
Research Experiences for Teachers Summer Program in Biologically-inspired Computing Systems

Abstract Funded by NSF Division of Computer and Network Systems, this grant establishes a new Research Experiences for Teachers (RET) Site at the University of South Alabama (USA). In 2021 summer, eight middle school and high school teachers from two local public-school districts spent six weeks engaged with research activities on biologically-inspired computing systems. They worked on discovery-based research projects and obtain transdisciplinary research experience on biologically-inspired computing systems spanning application (cancer detection), algorithm (Spiking Neural Networks), architecture and circuit (synaptic memory design), and device (memristor). The USA faculty mentors, curriculum development specialist from school districts, Instructional Coach from Science/Mathematics faculty at USA coached participants as they designed standards-compliant curriculum modules and conduct professional development activities. The implementation details of the summer program and the evaluations results are presented in this paper.
Can the COVID-19 pandemic boost collaborative online international learning (COIL) in engineering education? – A review for potential implementations

Abstract Several adverse consequences resulted from the COVID-19 pandemic. These have affected academia at many levels, including mandatory requirements for educators and students worldwide to switch to remote online learning or blended learning. This need exacerbated new technology and software learning to maintain proper communication and student engagement during online lectures. For instructors, these demands included learning how to use software to produce high-quality lessons, either in a pre-recorded format for asynchronous learning or through live online lectures for synchronous learning. For students, it meant rapid adaptability to learn video teleconferencing communication tools and a demand to keep up with multiple courses and learning management systems (LMS). Nonetheless, this accelerated learning of online communication tools bridges a technology gap that previously limited online international education. Students and instructors worldwide are now familiar with online connectivity tools; thus, it is essential to review potential opportunities to increase collaborative learning and education, particularly at an international level. In the past, Collaborative Online International Learning (COIL) programs provided stewardship to global learning experiences at many universities worldwide. Broadly, these should cover four significant aspects: (a) collaborations with students in other countries with other backgrounds and cultures, (b) engagement through online interactions for assignment completion or lectures, (c) development and assessment of students' global perspectives and competencies, and a (d) reflective aspect of learning and education. This study reviews previous COIL implementations in the literature, emphasizing chemical engineering and other STEM courses. To our knowledge, COIL implementations are widespread in social sciences; however, few reports highlight these practices in engineering courses. We review perspectives, methodologies, challenges, and resources needed for positive and long-lasting COIL implementations here. In addition, we offer a plan to implement a short-term COIL-based project in a chemical engineering course with faculty participants from the US and El Salvador. Any engineering educators planning to implement COIL activities can use these suggestions. Ultimately, this low-cost pedagogy could significantly aid in promoting cultural, technical, and global competence in engineering students in the next post-pandemic years.
Moving Towards Data-Driven Departmental DEI

Abstract Faculty and staff can and do influence the climate of a department and achievement of students. Research shows the positive effects of choosing to implement evidence-based teaching practices like active learning and inclusive teaching, and having a growth mindset in relation to the abilities of students. However, research also shows that the local climate in a department could cause students of color to be driven from STEM, or that a chilly climate could have a disproportionate impact on female students. And while the focus of Diversity, Equity, and Inclusion (DEI) efforts tends to be on women and under-represented minorities (URMs, defined as non-white, non-Asian), populations with representation at or above the demographics of the general population face their own challenges. In this paper, we describe recent efforts in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania to address these issues. Most of our initial efforts in this area have focused on the undergraduate population as well as their intersection with faculty and staff. We have started exploring the departmental structures and practices and have some initial demographic data on students and faculty. We are interested in exploring how retention, graduation, and achievement in general overlap and intersect with gender, race, and socio-economic status. We have also recently implemented a DEI Scholars program that further engages undergraduate and graduate students in this process. This initial work establishes baseline numbers and describes the first cohort we will track from acceptance through graduation. It is our aim that sharing these early efforts may encourage others to take on similar endeavors, and will also provide a reference point for future work of ours in this area.
Software Engineering and Security: Lessons Learned Creating a New Course in Security from a Software Engineering Perspective

Abstract Security is a rising concern for organizations hiring undergraduates out of college in computing disciplines. This is reflected in the emerging prominence of cybersecurity related courses, certificates, and degree programs, and reflected in the most recent curricular standards guidelines. Perspectives on security recognize it as both a system discipline, meaning the inclusion of hardware, software, and networking components, and a cross-cutting skill across the major phases on the software engineering lifecycle (requirements, design, verification & validation, construction, and evolution and maintenance). Our conjecture is that there are many open and available resources for the first perspective (system) but few for the second perspective (software engineering). In this paper we share experiences creating a new junior-level security course in secure software engineering as a required course in an undergraduate accredited software engineering degree program. Specifically, and aligned with the latter perspective, we share the challenges we faced when seeking curricular resources, including open courseware repositories and textbooks, to jump start the development. We reflect on these experiences by providing a map of curricular resources to cross-cutting software engineering lifecycle phases, examining popular open (and usually federally funded) courseware repositories including the SEED project from Syracuse University and the Cybersecurity Labs and Resource Knowledge-base (CLARK) initiative, as well as ad hoc resources.
Programming learners struggle as much in Python as in C++ or Java

Abstract Many computer science instructors believe switching college-level introductory programming courses to Python, versus languages like C++, Java, or C, smooths the learning of programming, due in part to Python's syntax, dynamic types, and interpreted execution. Studies providing quantitative support for that belief are rare, in part due to the challenge of creating comparative studies without extensive confounding factors. However, today one of the top textbooks in C++, Java, and Python all happen to come from the same publisher and have similar style and content, have similar off-the-shelf programming assignments that instructors can use, incorporate a cloud-based development environment and auto-grader that records every run, and each is used by tens of thousands of students at hundreds of universities. As such, a unique opportunity exists to compare student performance data across those three languages. We obtained such data from the publisher for 56 courses that met specific criteria (18 Python, 16 C++, 22 Java) totaling 6,804 students. As a potential indicator of whether Python students had a smoother learning experience, we compared median time and number of attempts on nearly-identical off-the-shelf programming assignments across the three languages. Python students exhibited no reduction in time or attempts. Manual investigation into the students' programs suggest that problem understanding and programming logic, and not language features, may be the main hurdles to learning programming.
Leveraging the power of Python, Octave and Matlab for Machine Learning

Abstract The objective of this paper is to bring awareness, instigate interest, and promote the need of using AI and machine learning algorithms for information, engineering technology and premedical students. This paper will also attempt to review some of the widely used methods for supervised and unsupervised machine learning and the key issues that students may come across, suggest some discrete resolutions so as to provide optimal results on the accuracy and validity of Train and Test methodology. Artificial intelligence is the intelligence demonstrated by machines while machine learning is the ability of a computer to learn and make decisions the same as a human. Data science is the exploration and quantitative analysis of all available structured and unstructured data to develop understanding, extract knowledge, and formulate actionable results. Machine learning process may be divided into three main steps: data cleansing, feature extraction and optimization, and train/test system modeling. Models are evaluated based on statistics about the errors, or residuals, in the predicted values. Evaluating models is challenging since there is no testing data with labels to determine the correctness. PCA is used to evaluate clustering methods. Scikit-learn is a Python library that implements the various types of machine learning algorithms, such as classification, regression, clustering, decision tree, and more. Using Scikit-learn, implementing machine learning is now simply a matter of supplying the appropriate data to a function so that you can fit and train the model. The paper will explore selected programming tools, theoretical analysis of selected machine learning algorithms, demonstrate the main ML steps with examples and also explore the use of Matlab, Octave for ML besides python ML.
Do Undergraduate Data Science Program Competencies Vary by College Rankings?

Abstract The data science domain has increased exponentially due to advances in technology, including in infrastructure, storage, and analytical tools and techniques. The demand for data science technologies and skills is evident in various industries, such as retail, health care, finance, and all areas of economy and society. Data science careers are the top careers in the U.S. across many disciplines. The need for skilled workers in this domain will only continue to grow, requiring college graduates who can support this growth. Many colleges across the United States have already adopted undergraduate data science curriculum and programs. However, it is not clear whether undergraduate data science programs offered by colleges with different rankings provide similar or dissimilar data science competencies. The goal of this study is to compare program structure and competencies of undergraduate data science programs offered by colleges in the three US News ranking categories: National, Regional and National Liberal Arts. The research questions answered as a result of this study are: Do undergraduate data science program profiles vary by college ranking? Do undergraduate data science program competencies vary by college ranking?
Understanding and Promoting Earnest Completion in Online Textbooks

Abstract Digital textbooks are becoming more common in college-level computer, engineering, and science courses. For various reasons, some students quickly click on reading activities to earn completion points, without earnestly attempting the activities. We analyzed student earnestness in digital computer science and engineering textbooks across a semester. We found that student earnestness declines significantly as the semester progresses. We also found that earnestness levels at a semester's start have a tremendous impact on earnestness throughout the semester, namely that lower average earnestness at a semester's onset leads to a significantly more rapid decline. For example, one course with an initial 90% earnestness score fell to 76% by week 6, whereas another course with an initial 72% score dropped dramatically to just 32% by week 6. This finding emphasizes the importance of instructors ensuring students recognize the utility of the textbook activities from a course's start, and ensure that the activity workload is reasonable too, to start students with high earnestness. We close with recommendations for encouraging earnest completion early in the semester, which is crucial for maintaining earnest completion throughout the course.
Work In Progress: Side-Facing Cameras and Remote Proctoring Integrity

Abstract Many in academia perceive that remotely proctored exams allow more opportunities for a student to cheat than in-person testing. We introduced the use of the side-facing camera arrangement to increase the integrity of remote proctored exams by expanding the view of the examinee's environment and therefore reducing the opportunities to cheat. The side-facing camera arrangement displays the examinee's head, torso, hands, and keyboard and therefore should make behaviors consistent with cheating more obvious. This alternate camera arrangement serves as an environment camera in addition to monitoring the student throughout the exam. This paper is a report on the continued work to assess the efficacy of the side-facing camera arrangement. We followed up on our successful deployment of the side-facing camera in a large online class by examining how the attitudes and perceptions of students vary between the two camera arrangements. In Spring 2021, we asked students to complete pre- and post-surveys from three different classes and found that most students were not negatively impacted by the camera arrangement used. We qualitatively coded the students' responses to identified themes among them. Most of the respondents surveyed didn't have any preference towards the camera arrangement used in remote testing. After the post-survey, more students expressed that they felt there was a higher sense of integrity when using the side-facing camera arrangement. Students also reported that they had a difficult time setting up the side-facing camera arrangement, but all students who were required to use the side-facing camera arrangement were able to do so. Students also expressed some concern about their physical privacy when using the side-facing camera. Our ongoing work seeks to address the concerns we identified in our assessment of the surveys while increasing the integrity of remote proctored exams as well as the comfortability of students.
Inexpensive solar garden light provides valuable energy system learning tool

Abstract Solar powered residential garden lights, also called landscape lights, are ubiquitous and provide an inexpensive and convenient means of low-power outdoor illumination. A typical solar garden light fixture consists of a small solar cell, rechargeable battery, white LED, and controlling electronics all contained within a weatherproof enclosure mounted on top of a short plastic stake that can be pressed into the ground. The solar cell charges the battery during daylight hours and the battery powers the LED during the evening. The battery charge/discharge cycle is carried out daily under the control of an application-specific integrated circuit. This small system contains the same functional components as many large-scale solar energy systems (residential, utility-scale, satellites, etc.) and thus provides an excellent tool for demonstrating the concepts of energy generation, storage, and efficiency. First-year students in an electro-mechanical engineering technology program have used the garden lights in an introductory electrical systems course laboratory. Prior to working with the garden lights, the students have become familiar with basic circuit analysis techniques (Ohm's law and Kirchhoff's laws), energy, power, and efficiency as well as basic diode and solar cell operation. These concepts come together and are solidified by the solar garden light laboratory exercises. Most students have seen the garden lights outside of class but are not familiar with their components and operation. Several manufacturers and models of garden lights were investigated, and all were found to be easily disassembled without damage and modified as necessary to allow access to the required system parameters (voltages and currents). This paper describes the operation of the garden lights and how they can be modified to provide various laboratory exercises. Examples of laboratory exercises are presented, and experimental results are discussed.
A Challenge-based Teaching model for Structural Analysis Courses with Strategic Industry Partners

Abstract In this paper we present a challenge based teaching model for Structural Analysis courses within the framework of our education model, namely (blinded for review) in which some courses are taught with the participation of an industry partner. The content of this course is usually complex and some principles and concepts are difficult for students to associate with a real life application. Contents include Energy Methods, Stiffness and Flexibilities Methods and an Introduction to the Finite Element Method. The main component of the model is the solution of a challenge for which, students work in teams. The challenge presented to students at the beginning of an intense five week period consisted on the design of the structure of an industrial warehouse in a given location. Students have to present a solution applying the concepts seen in the course as well as the local building code requirements. The course is taught by two or three professors and the participation of people from a chosen industry partner, that for the case of this course, it is a professional and chartered structural engineering firm. For the solution of the challenge, the participation of such an industry partner was crucial, as they provided information, advice and recommendations to students in several meetings throughout the five week period. The results included in this paper are from two different implementations of the model in two different campuses of our university system. Two different industry partners participated, one for each location and the challenge was similar in both cases. Some results on student satisfaction and the type of activities that they enjoyed the most are also presented. This work can be used to design new courses related to Structural Analysis that can benefit from the participation of industry partners, as we consider that this type of model can close the gap between those theoretical courses and the design courses that are further ahead in the curricula of Civil Engineering students.