Ten Meter Walk Test with Mobile Devices: A dataset with accelerometer, magnetometer, and gyroscope

Abstract: This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status.


SOQCS: A Stochastic Optical Quantum Circuit Simulator

Abstract: Stochastic Optical Quantum Circuit Simulator (SOQCS) is a C++ and Python library which offers a framework to define, simulate and study quantum linear optical circuits in presence of various imperfections typically encountered in experiments. Quantum circuits can be defined from basic components, including emitters, linear optical elements, delays and detectors. The imperfections come from partial distinguishability of photons, lossy propagation media, unbalanced beamsplitters and non-ideal emitters and detectors for example. SOQCS also provides various simulator cores and tools to analyze the output. Furthermore, the configuration of detectors also includes postselection. SOQCS is developed using a modular approach in which different modules are applied in an automated easy to use manner. Furthermore, the modular approach allows for further extensions of the SOQCS capabilities in future.


A fitting model with optimal Multiple Image Hiding effect

Abstract: Image hiding is a field of research that focuses on covert storage and transmission techniques.It involves embedding a secret image within a container image to create a classified-carrying image that resembles a normal image.Nevertheless,the current image hiding methods based on Invertible neural networks suffer from a critical issue of information loss during the hiding process.This leads to a substantial degradation in the quality of the secret image extracted,thereby preventing the simultaneous achievement of secure transmission,high-capacity transmission,and high fidelity of the secret image in an insecure network environment.To address this issue,we introduce a novel image hiding architecture called FMIN (Fitting Models Based on Invertible Network).FMIN incorporates our innovative fitting model,which fits the loss information and generates a variable z simulating the loss information at the receiver side.This variable z is used as an input to the revealing process,enabling the high-quality extraction of multiple secret images from a single classified loaded image.Additionally,we introduce a novel decoupled training strategy aimed at enhancing the stability of image hiding model during training.Experimental results demonstrate that the image hiding method based on the proposed FMIN architecture in this paper significantly outperforms other SOTA image hiding methods for single-image and multiple-images hiding.


Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Abstract: Sentiment analysis is a popular natural language processing task that recognizes the opinions or feelings of a piece of text. Microblogging platforms such as Twitter are a valuable resource for finding such people’s opinions. The majority of Arabic sentiment analysis studies indicated that the data utilized to train machine learning algorithms is balanced. In this paper, we investigated the impact of sampling techniques and classification algorithms on an imbalanced Arabic dataset about people’s perceptions of COVID-19, with the majority of opinions reflecting people’s fear and stress about the pandemic, and the minority reflecting the belief that the pandemic was a hoax. The experiments concentrated on analyzing the imbalanced learning of Arabic sentiments using over-sampling and under-sampling techniques on seven single machine learning algorithms and two common ensemble algorithms from the bagging and boosting families, respectively. Results show that resampling-based approaches can overcome the difficulty of an imbalanced dataset, and the use of over-sampled data leads to better performance than that of under-sampled data. The results also reveal that using oversampled data from synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, or adaptive synthetic sampling with random forest classifier is the most effective in addressing this classification problem, with F1-score value of 0.99.


Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique

Abstract: While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.


Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment

Abstract: Networks consist of numerous resources; it is crucial not to overlook fault tolerance and consider it during planning. This is because errors during implementation can result in wasted time and effort, thereby squandering these resources. One solution to address this issue effectively is to implement the task on multiple resources to minimize the occurrence of failed tasks. However, employing an unspecified or fixed number of resources can lead to the depletion of network resources and the overall failure of the network. Replication plays a pivotal role in enhancing data availability in distributed systems. By storing data in multiple locations, users can still access it even if some copies are unavailable due to site failure. Many replication-based algorithms utilize a predetermined number of iterations per function, which may consume excessive network resources, even if the ongoing task does not require such abundant resources. This paper proposes task replication as a viable mechanism for an efficient and fault-tolerant scheduling system. We introduce an algorithm that dynamically selects the optimal and minimal number of replicas based on the network's failure history. This approach aims to minimize the failure rate during task execution.


Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Abstract: This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.


Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

Abstract: Differential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation operation using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scaling factor and the crossover rate values in a target vector to generate the corresponding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is increased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods.


Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Abstract: Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.


Diverse Structure-Aware Relation Representation in Cross-Lingual Entity Alignment

Abstract: Cross-lingual entity alignment (CLEA) aims to find equivalent entity pairs between knowledge graphs (KGs) in different languages. It is an important way to connect heterogeneous KGs and facilitate knowledge completion. Existing methods have found that incorporating relations into entities can effectively improve KG representation and benefit entity alignment, and these methods learn relation representation depending on entities, which cannot capture the diverse structures of relations. However, multiple relations in KG form diverse structures, such as adjacency structure and ring structure. This diversity of relation structures makes the relation representation challenging. Therefore, we propose to construct the weighted line graphs to model the diverse structures of relations and learn relation representation independently from entities. Especially, owing to the diversity of adjacency structures and ring structures, we propose to construct adjacency line graph and ring line graph, respectively, to model the structures of relations and to further improve entity representation. In addition, to alleviate the hubness problem in alignment, we introduce the optimal transport into alignment and compute the distance matrix in a different way. From a global perspective, we calculate the optimal 1-to-1 alignment bi-directionally to improve the alignment accuracy. Experimental results on two benchmark datasets show that our proposed method significantly outperforms state-of-the-art CLEA methods in both supervised and unsupervised manners.


The reality of backdoored S-Boxes—An eye opener

Abstract: The real-life incidents researched in academia have revealed that (possibly) state-level efforts are made to camouflage the intentional flaws in the mathematical layer of an S-Box for exploiting the information-theoretic properties, i.e., Kuznyechik. To investigate the common features in the intentionally weakened mappings, this research thoroughly examines the backdoored structures from the perspective of 24 cryptanalytic attack vectors available in the open literature. We have debunked the earlier claims by the backdoor engineers that the proposed designs are stealthy against statistical distinguishers. Although a backdoored architecture fulfils the notions of randomness, it lacks the strength to resist sophisticated cryptanalytic attacks. Our analysis has revealed that during the backdoor insertion phase, a malicious designer compromises vital cryptographic properties, prominently the algebraic degree, differential trails, avalanche characteristics and leaving the open ground for hybrid cryptanalytic attacks. It is also observed that these mappings attain the upper bound of BCT, FBCT and DLCT, paving the way for hybrid attacks with high probability and lower data complexity.


HITS-based Propagation Paradigm for Graph Neural Networks

Abstract: In this article, we present a new propagation paradigm based on the principle of Hyperlink-Induced Topic Search (HITS) algorithm. The HITS algorithm utilizes the concept of a “self-reinforcing” relationship of authority-hub. Using HITS, the centrality of nodes is determined via repeated updates of authority-hub scores that converge to a stationary distribution. Unlike PageRank-based propagation methods, which rely solely on the idea of authorities (in-links), HITS considers the relevance of both authorities (in-links) and hubs (out-links), thereby allowing for a more informative graph learning process. To segregate node prediction and propagation, we use a Multilayer Perceptron in combination with a HITS-based propagation approach and propose two models: HITS-GNN and HITS-GNN+. We provided additional validation of our models’ efficacy by performing an ablation study to assess the performance of authority-hub in independent models. Moreover, the effect of the main hyper-parameters and normalization is also analyzed to uncover how these techniques influence the performance of our models. Extensive experimental results indicate that the proposed approach significantly improves baseline methods on the graph (citation network) benchmark datasets by a decent margin for semi-supervised node classification, which can aid in predicting the categories (labels) of scientific articles not exclusively based on their content but also based on the type of articles they cite.


Illumination variation robust circular target based on digital image correlation method

Abstract: Circular targets, as one of typical image features, are widely used in image-based measurement, such as photogrammetry, displacement sensing, and vibration monitoring. However, its positioning accuracy is usually sensitive to intensity changes in ambient lighting, particularly in outdoor applications and long-term monitoring scenarios. In this paper, a speckle-based circular target (SbCT) is introduced as a solution to the sensitivity of localization accuracy of traditional circular targets in illumination variation environment. By incorporating a speckle pattern on the circular target and utilizing digital image correlation method, the SbCT achieves insensitivity to illumination variations when an appropriate correlation function is chosen. This enables accurate and reliable measurements even in lighting varying environment. This paper initially outlines the design strategy of the SbCT and demonstrates its extraction procedure. Subsequently, simulations are conducted to analyze the positioning accuracy of SbCT under linearly varying illumination situations. Additionally, experiments are performed to study the accuracy and robustness of SbCT with the comparison to concentric circular targets under different lighting variation cases. The results from both simulation and experiment demonstrate the illumination robustness of SbCT, of which the stability is better than 0.01 pixel. According to the displacement tracking experiment, the relative error obtained by SbCT is smaller than 0.2%. Therefore, it can be concluded that SbCT is an illumination-robust feature, offering automatic recognition and accurate localization performance even in illumination varying situations.


Observations from the 2023 Kahramanmaraş earthquake sequence in Türkiye and Syria

Abstract: Early last year major earthquakes devastated parts of Türkiye and Syria, killing nearly 60 000 people and destroying 84 000 buildings. Seismic experts Ziggy Lubkowski and Kubilây Hiçyılmaz of Arup summarise the various international reports on the disaster and the lessons learned.


Dual Sparse Structured Subspaces and Graph Regularisation for Particle Swarm Optimisation-Based Multi-Label Feature Selection

Abstract: Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes.


Quantitative bounds to propagation of quantum correlations in many-body systems

Abstract: We investigate how much information about a quantum system can be simultaneously communicated to independent observers, by establishing quantitative limits to bipartite quantum correlations in many-body systems. As recently reported in Phys. Rev. Lett. 129, 010401 (2022), bounds on quantum discord and entanglement of formation between a single quantum system and its environment, e.g., a large number of photons, dictate that independent observers which monitor environment fragments inevitably acquire only classical information about the system. Here, we corroborate and generalize those findings. First, we calculate continuity bounds of quantum discord, which establish how much states with a small amount of quantum correlations deviate from being embeddings of classical probability distributions. Also, we demonstrate a universally valid upper bound to the bipartite entanglement of formation between an arbitrary pair of components of a many-body quantum system. The results confirm that proliferation of classical information in the Universe suppresses quantum correlations.


Enhancing sign language recognition using CNN and SIFT: A case study on Pakistan sign language

Abstract: Millions of people who have trouble hearing rely heavily on sign language as their primary means of communication. As a form of visual language, sign language is primarily used as features by the deaf and hard of hearing to communicate with one another. These features make it challenging for the average person to grasp what is being said. As a result, it becomes more difficult for deaf people to interact with the hearing community. In order to solve this communication barrier, scientists have been working on sign language recognition systems. The purpose of this research is to propose a hand gesture recognition framework for Pakistan Sign Language (PSL) by training a Convolutional Neural Network (CNN) on PSL gesture images obtained with the aid of a testbed built in the laboratory using the Kinect motion sensor for Urdu alphabets. Kinect images of hands were taken in this study under a variety of lighting conditions. A feature vector set was created from hand margin, size, shape, palm center points, and finger position using Scale Invariant Feature Transform (SIFT). Important features of PSL-based sign images were extracted using SIFT and then converted to vector points. The proposed improved CNN model was able to achieve an impressive accuracy of 98.74% on the PSL dataset, demonstrating an impressively low error rate of only 1.26%. A case study is demonstrated in the assessment of the system that has been provided. Finally, a comparison is performed between others and our work to show the efficiency of the proposed framework and legitimize the concept.


Abstract: Back to table of contents Previous article Next article Residents’ ForumFull AccessThere’s an App for That? Digital MH Should Be Integrated Into TrainingEliza DeCroce-Movson, M.D., German Velez, M.D.Eliza DeCroce-MovsonSearch for more papers by this author, M.D., German VelezSearch for more papers by this author, M.D.Published Online:19 Jan 2024https://doi.org/10.1176/appi.pn.2024.02.2.17As child and adolescent psychiatry fellows, we are often reminded of our work after our days are done. Even relaxing by watching a favorite reality television show makes us think of our training. A commercial may air for a mood medication that reminds us of the thorough education we’ve received from supervisors and mentors about prescribing, but we may also receive stark reminders of significant gaps in our medical training. Commercials advertising mobile apps designed to improve mood and sleep, for example, seem to run just as frequently as ads for medications approved by the Food and Drug Administration (FDA). Both medications and digital apps target similar symptoms and are marketed directly to consumers. Our patients could question us about either of these options during consultations, yet our training has prepared us to speak about only one of them.There are options for practicing psychiatrists who need guidance on mental health apps, such as APA’s App Advisor. But many residency and fellowship programs are missing a crucial opportunity to train the next generation of psychiatrists to guide their patients through the labyrinth of digital mental health care options available. It is time to commit to the development of curriculum, research, and advocacy to familiarize and train psychiatry residents and fellows to be competent in digital psychiatry.Many children face barriers to accessing traditional therapy, including time and financial constraints, long waiting lists, and a scarcity of trained professionals. This significant need for care among youth underscores the urgency to explore innovative therapeutic options, including digital solutions like the apps patients see advertised on television. As health care professionals, recognizing and adapting to these evolving treatment modalities are crucial to providing comprehensive care.The development of digital technologies is having a large impact on mental health care. Some are practical tools like medication reminders and symptom trackers. Others offer online consultations and advanced therapies like virtual reality and serious games (games that are designed to educate or change behaviors). The widespread use of smartphones makes app-based platforms a convenient treatment option for patients, regardless of their location or schedule. This was particularly important to many patients during the COVID-19 pandemic, when social distancing was essential. Despite advancements, the quality of digital mental health tools is varied. Some companies offer evidence-based therapies, while others present products that are less clearly so or are even ethically ambiguous.For many physicians, the term “digital health” is synonymous with telemedicine or electronic medical records, rather than including therapeutic apps or comprehensive treatment services accessible via a phone. Patients expect their doctors to guide them through the digital mental health landscape, not realizing that the physicians might be as unfamiliar with it as they.Today’s trainees are in a prime position to address this challenge. We have grown up with technology and are generally more adept with it than our senior colleagues. Many of us have personal experiences with mental health apps. We have the opportunity to use our training to develop expertise in digital psychiatry, benefiting our patients and contributing to the field. For this to happen, residency and fellowship programs need to prioritize education in digital psychiatry. We require mentorship, exposure, and structured training to confidently recommend digital health solutions to our patients, just as we do with pharmaceutical and lifestyle recommendations.This task is challenging, but vital. By embracing digital mental health in our academic training, we can better care for our patients and drive innovative change. If we fail to adapt, we risk being left behind in the rapidly evolving health care landscape.ResourceMore information on APA’s App AdvisorEliza DeCroce-Movson, M.D., and German Velez, M.D., are child and adolescent psychiatry fellows at Weill Cornell Medicine and the Columbia College of Physicians and Surgeons Department of Psychiatry. Velez is also APA’s resident-fellow member trustee. ISSUES New Archived


Lexicon-grammar tables standardization and implementation

Abstract: The lexicon-grammar approach is a very important linguistic approach in automatic natural language processing (NLP). It allows for the description of the lexicon of the language through readable and intuitive tables for human manual editing. However, the automatic use of the lexicon-grammar tables in the automatic NLP platforms remains difficult, given the incompatibility between the codes used to represent the properties in the lexicon-grammar tables and those used to represent the properties in the automatic NLP platforms. In this work, we present our method of standardizing the lexicon-grammar tables for the French language, since they constitute very rich lexical, syntactic, and semantic linguistic resources. First, we standardize their properties so that they can be compatible with those used in the NLP platforms. Then, to implement the standardized tables, we used a linguistic platform such as NooJ. For that, we describe the process of integrating these tables into this platform through the automatic generation of the dictionaries from these tables. Finally, to test the efficiency of the generated dictionaries, we create for some of them syntactic grammars that take into account all the grammatical, syntactic, and semantic information contained in the dictionaries.


Segmentary group-sparsity self-representation learning and spectral clustering via double <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e7481"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>21</mml:mn></mml:mrow></mml:msub></mml:math> norm

Abstract: With the rapid expansion of data dimensions, subspace representation learning, a method for mapping high-dimensional data samples to their corresponding underlying low-dimensional subspaces, has become an essential process for high-dimensional data clustering. Although the existing methods have achieved reliable data representation learning and precise clustering, few of them realized that the corrupted data points in the dataset will influence the linear representation of the others. When there are multiple heavily corrupted data in a dataset, the matrix of the self-representation coefficient would be influenced by these data. Therefore, this paper proposes the segmentary group-sparsity self-representation learning (SGSSL) and segmentary group-sparsity-based spectral clustering (SGSSC) models to eliminate their influence on representation learning and clustering results. We proposed that imposing varying degrees of row sparsity and column sparsity constraints on the representation coefficient matrix can prevent corrupted data from contaminating other data during the self-representation process, thus obtaining better spectral clustering results. Extensive experiments on several real datasets demonstrate that our proposed method can perform better than several related methods in recent years.


IoT Systems for Extreme Environments

Abstract: The deployment of Internet of Things (IoT) systems spans a large variety of applications, each with unique requirements. Many of these applications relate to the management of various cyber–physical systems, including road and rail traffic, electricity, water, food/goods transportation and storage, smart building management, crime/safety management, underwater systems, etc. Within this context, a growing concern is the deployment of IoT systems in extreme environments which may occur either because of the nature of the application or due to external factors. Some prominent examples of the former are 1) the IoT deployments in hazardous environments such as management of chemical or nuclear plants, management of underwater oil/gas infrastructure, mining operations, etc.; 2) IoT systems deployed specifically to manage accidents and disasters; and 3) IoT systems deployed in arctic/antarctic regions where they routinely experience extreme levels of changes in terms of temperature, wind conditions, sunlight availability, compression, etc. Such IoT systems generally are designed to specifically operate in the challenging environment they must operate in, and thus may be expected to be rather robust. However, the IoT systems designed to manage the physical infrastructures such as those in urban settings may also need to handle unprecedented and unexpected stresses due to the worldwide phenomena of aging physical infrastructure, demand that far exceeds the designed capacity, and increasingly extreme operating conditions due to climate change. This special issue covers all such scenarios and thus represents a vast and rich area for innovations.


On the Hotelling Game with Close Players

Abstract: The principle of minimum differentiation stated by Hotelling in his pioneer paper was proved to be invalid 50 years later, since there is a region where the players are so close that Nash equilibrium does not exist. Some authors have contributed with amendments to the original game in the aim of ensuring the existence of Nash equilibrium independently of the location of the players. However, this work analyzes the less frequently studied case of the Hotelling game with close players under different implementations of the game, considering elastic and inelastic demand (conventional Hotelling game, the Hotelling game with reservation cost and Hotelling-Smithies game). The study is supported by means of numerical simulation of the game.


A Fact-finding Survey and Improvement Plans for Mechanical Equipment Preconstruction Verification Application

Abstract: This paper presents the results of a fact-finding survey conducted among on-site mechanical equipment engineers. Additionally, it proposes improvement plans for completing the mechanical equipment preconstruction verification application. Currently, errors occur due to the unclear method of filling out the preconstruction verification application. This leads to a decrease in the quality of construction and undermines the purpose of enforcing the Machinery Equipment Act. Accordingly, this paper proposed a plan to improve the efficiency and accuracy of mechanical equipment construction. First, items for apartment housing information were added. Second, the types of registration certificates for mechanical equipment design, construction work, and supervision were specified to verify the qualifications of companies and personnel. Third, the technical level of the on-site mechanical equipment technician and the on-site mechanical equipment supervisor was required to be written so that the appropriate manpower arrangement could be confirmed. Through this, it is expected that the overall level of mechanical equipment construction can be improved by reducing errors in filling out preconstruction verification applications and increasing the transparency of construction.


Cui Bono? Software Professionals Should Always Ask “Who Benefits?”

Abstract: We suggest a straightforward approach to recognizing and managing ethical challenges that arise as software is designed, developed, and deployed. The approach features two “Cui bono?” questions: “Who benefits?” and “Who is at risk?” These questions can help computing professionals deal effectively and honorably with their responsibilities.


Using Machine Learning Algorithms in Determining the Stage of Breast Cancer from Pathology Reports

Abstract: Introduction: After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports.Material and Methods: A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score.Results: The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time.Conclusion: We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.


The Effect of Flashcards in Improving Vocabulary at the Eighth Grade Students of SMP Negeri 6 Palu

Abstract: Students face challenges in English communication primarily due to insufficient vocabulary. Consequently, teachers should actively implement strategic measures to enhance students' mastery of vocabulary, such as incorporating activities like the use of flashcard. The objective of this study is to investigate whether the utilization of flashcard media has a positive impact on enhancing vocabulary. This research is an experimental study with a pre post test design using 2 sample groups, namely the intervention group and the control group consisting of 32 people per group. In choosing the participants, the researcher used purposive sampling. This study was conducted in SMPN 6 Palu since the participants are the students of the eighth grade. The results were analyzed using t table and t calculations to determine the effect. The result showed that the mean score of pre-test of control group is 45.13 and that the mean score post test of control group was 53, whereas the mean score of pre-test of experimental group is 45.38, and the mean score of experimental group was 81.38. t-counted (14.57) is higher that t-table (1.9988). This implies that the research hypothesis has been validated. In summary, the utilization of flashcards proves to be effective in enhancing vocabulary among eighth-grade students at SMP Negeri 6 Palu. flashcard media could be used as one of media in the classroom, because it easy and interesting in applying for the students.


Performance Analysis of Various Image Feature Extractor Filters for Pothole Anomaly Classification

Abstract: Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the other hand, has an accuracy of roughly 87% when it comes to classifying potholes in low-light conditions. These findings are crucial in the selection of feature extraction filters for use by ML classifiers in the development of a robust autonomous pothole detection and classification system for improved navigation on anomalous roads and possible integration into self-driving cars.


Training iterated protocols for distillation of GHZ states with variational quantum algorithms

Abstract: We present optimized distillation schemes for preparing Greenberger-Horne-Zeilinger (GHZ) states. Our approach relies on training variational quantum circuits with white noise affected GHZ states as inputs. Optimizing for a single iteration of the scheme, we find that it is possible to achieve an increased fidelity to the GHZ state, although further iterations decrease the fidelity. The same scheme, acting on coherently distorted pure-state inputs, is effective only in certain special cases. We show that radically different results can be achieved, however, when one optimizes for the output after two iterations of the protocol. In this case, the obtained schemes are more effective in distilling GHZ states from inputs affected by white noise. Moreover, they can also correct several types of coherent pure-state errors.


Exploring the Spatial Distribution and Properties of Solar Bright Points in Different Solar Areas: A Statistical Analysis of Network and Internetwork Features

Abstract: Solar bright points (BPs) are small-scale magnetic features observed on the solar surface. They play a significant role in the transfer of energy and mass between the solar photosphere and the corona. This article presents a comprehensive statistical analysis of BPs in different solar areas: quiet Sun (QS), active regions (AR), and coronal holes (CH). We separate the BPs into two categories: network and internetwork. Our study aims to understand the spatial distribution and properties of BPs in these areas, which can help improve our knowledge of the Sun’s magnetic activity and energy transfer processes. Our research highlights the close relationship between the spatial distribution and characteristics of BPs and the solar magnetic field. Using machine learning, we achieved efficient and accurate BP detection, enabling analysis of a larger dataset. The prevalence of network BPs in the QS and internetwork BPs in CH suggests differences in magnetic field concentration. In AR, BPs are evenly distributed between network and internetwork areas, reflecting the complex nature of the magnetic field.


Predicting the impacts of anthropogenic drivers on management scenarios using Bayesian belief network in the Zeribar freshwater wetland, Iran

Abstract: The Zeribar wetland in Iran’s western city of Marivan provides a valuable foundation for maintaining various aspects of social and cultural life, from water supply to recreational values, as well as for natural habitats. However, its different services have been put under strong anthropogenic pressure and urbanization processes. The present study is a part of ongoing efforts to improve understanding of the anthropogenic factors potential impact on management status of this vulnerable freshwater ecosystem, aiming to provide integrated management strategies using a Bayesian belief network (BBN) approach. A conceptual framework supporting the BBN was designed according to the main determining factors of the wetland’s management including “human-induced factors”, “economic factors and human capital”, “policy context” and “social and cultural issues”. The BBN model was parameterized using different data sources comprising literature review, face-to-face interviews, filed observation, model simulations, and experts’ knowledge. Stakeholders’ feedback on model accuracy and management strategies application was assessed in different workshops with representatives from locals, executives, and scientific communities. The results showed that the two subsystems of “policy context” and “economic factors and human capital” are the most important key factors influencing the target node, wetland management status. Then, fourteen alternative management scenarios (S2- S15) were generated using the improvement in prior probabilities of influencing factors, followed by evaluation and statistical comparison with the output of the current condition as the base-case scenario (S1). Under the S1 scenario, the probability of occurrence of the “Strong” state, in which the wetland is properly protected from anthropogenic disturbances, for the target node of the network was 25.8 %, while the most significant improvement in the wetland management status (66.4%) was observed after an improvement in all influencing variables was adopted (S15), suggesting a significant potential for improvement in the wetland management. According to the stakeholders’ feedback, the constructed BBN model, and the components included in the management scenarios are useful to fill a critical gap in the current management framework. The results of this work can assist decision-makers in managing the wetland more effectively by considering all socio-economic and anthropogenic dynamics.


Special Issue: Extended Reality in Design and Manufacturing

Abstract: Extended Reality (XR) is a collective term that contains Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and everything in between. VR is an immersive technology that allows users to interact within a computer-generated 3D virtual environment. AR enhances the user's situational awareness (SA) by superimposing virtual content onto a real scene. MR is a step beyond AR by combining physical reality with digital content in a way that enables real-time interaction with and among real and virtual elements. In the business world, a similarly captivating term, metaverse, indicates a single, universal, and immersive virtual world focused on social and economic connections facilitated by the use of XR technologies [1].XR has proven to be an effective communication medium for humans to instantly interact with other entities in shared spaces. As a result, it has received significant attention from both industry and academia. VR/AR is among the nine enabling technologies in the Industry 4.0 paradigm [2], serving as the only human-centric technology designed specifically for end users. This feature conforms well to the human-centric core value emphasized by the emerging Industry 5.0 [3]. With this advantage, XR could play a critical role in extending cyber-physical systems (CPS) into human-cyber-physical systems (HCPS), seamlessly integrating data, computational intelligence, and real-world situations through context-specific sensory cues for human interaction.With the recent advances in information and communication technologies (ICT), traditional manufacturing systems have evolved into “smart” ones in handling the ever-increasing complexity of tasks incorporating a high degree of autonomy and intelligence. Nevertheless, there are specific tasks on the shop floor that are either impractical or impossible to complete without human intervention. In such situations, the main purpose of smart manufacturing would be to facilitate manual operations, rather than to completely replace human involvement. Indeed, integrated with artificial intelligence (AI), 5G, cloud computing, and Internet-of-Things (IoT), XR applications have been successfully implemented and deployed to assist a diverse range of manual operations within the manufacturing sector [4,5]. The recent special issue titled “Challenges and Opportunities in Computing Research to Enable Next Generation Engineering Applications” published in the Journal of Computing and Information Science in Engineering (JCISE) also features three review articles focusing on extended reality [6–8]. In addition, the journal has published 16 papers with the words VR, AR, MR, XR, or Metaverse appearing in either the title or abstract between 2018 and 2023 [9–24]. This trend shows a growing interest in XR research in the JCISE community. A special issue focusing on the related fields would be necessary and timely to address this need.The purpose of this special issue is to solicit articles describing fundamental and applied research on XR techniques, solutions, and challenges in the area of design and manufacturing. This issue aims to cover all aspects of XR technology, including technical advances in software/hardware, algorithms, ergonomic analysis, system implementation, and novel empirical studies, for product design and manufacturing processes. In total, eight papers are included following rigorous review and revisions, and each is briefly introduced as follows.AR technology has made significant progress in recent years with applications successfully applied to assist manual tasks in various industries such as manufacturing, military, education, and entertainment. For instance, manual pointing and trajectory tracing are often used in robot programming in the manufacturing environment or during major surgical operations. Design guidelines are essential to develop assisted functions that can effectively enhance users’ situational awareness with instant sensory feedback through AR interfaces. For this purpose, Wang et al. conducted an experimental study on AR user interfaces with various sensory cues designed to assist in the manual tracing of simple and complex trajectories. The study compared the task performance under five sensory feedback conditions: central vision, peripheral vision, auditory sensation, force feedback, and no feedback. The experimental results showed that the feedback information should not only provide prompts for error status but also offer suggestions for correcting the actions of the subjects in complex trajectory tracing. The force feedback demonstrated better adaptation compared to visual and auditory cues. Furthermore, subjects tended to rely more on real-time force feedback to complete difficult tracing tasks than on the other cues. It was also suggested that the design of AR-assisted functions and instant sensory feedback should consider the potential limitations induced by the user's posture during a tracing task. These experimental findings could provide useful insights into the design of sensory feedback for AR-assisted functions developed for manual tracing tasks.Fewer studies have focused on the technical solutions for XR-based virtual footwear try-ons compared to those for garments and facial accessories. Therefore, Chou et al. introduced an approach based on machine learning that estimates the 6-DoF of a human foot using a single RGB image captured from a third-person view. Synthetic annotated data were generated by systematically adjusting the light source, foot geometry, background, camera position, as well as skin and background textures to ensure sufficient variation in information during the training process. Three CNN (Convolutional Neural Network) models (DOPE, DOPE2, and YOLO-6D) were trained using a proprietary dataset containing both synthetic and real foot images, following different training plans. Test results showed that training a CNN model for foot pose estimation performed better through a combination of pretraining and successive training with both proprietary and ImageNet datasets. In real use, YOLO-6D outperformed both the DOPE and simplified DOPE2 models in terms of prediction accuracy and computational speed.Natural human–computer interaction (HCI) remains an important research topic for industrial XR. Three articles featured in this special issue explore HCI from the perspectives of content authoring, cybersecurity, and human pose estimation. Content authoring for XR applications remains a challenge at scale. In response, Ipsita et al. developed VRFromX, an application-embedded workflow for in situ content creation and demonstrated its utility through a welding use case. Additional research is warranted to properly integrate XR technologies with minimal disruption into existing practical engineering workflows, such as the management of computer-numerical control (CNC) machines within a factory.Yang et al. addressed a potential cybersecurity risk existing in mid-air interactions commonly used in MR-based smart manufacturing applications. The authors developed a prototyping system for smart factory management using a video see-through MR HMD. This system allows users to input a password on a login interface, providing access to security/privacy-sensitive information through a single RGB camera capturing mid-air gestures in front of the user. A machine learning-based inference framework tracks hand gestures and estimates input passwords under assumed attack conditions. The test results in two scenarios suggested that potential preventive measures for cybersecurity attacks in MR applications during password input with hand gestures include using more complex characters, applying remote interaction methods, or entering the password while moving.The AR content presented by the head-mounted display (HMD) offers mobility, hands-free capabilities, and a first-person view, particularly useful in the context of industrial applications. The successful deployment of AR applications on these occasions depends on humans accurately interacting with and placing virtual objects in relation to the physical environment. This requires precise alignment and human perception of computer-generated graphics within the physical environment. Yang and Zhang resolved the misregistration issues occurring in video see-through AR HMDs that limit the potential for shared workspaces between humans and machines in shop-floor applications. They identified and analyzed the sources of errors within a comprehensive system framework. A mathematical model was proposed to characterize the influence and sensitivity of errors specifically in the HMD-to-camera transformation. This was achieved by examining three translational and three rotational components comprising the transformation matrix and simulating the resulting misregistration effects. An offline closed-loop calibration approach was applied to reduce the HMD-to-camera transformation error in a prototyping system. Both qualitative and quantitative evaluations demonstrated satisfactory global registration between virtual objects and the physical environment.Wu et al. implemented ImpersonatAR, a mobile-based application, that utilizes embodied demonstrations in the augmented environment to support the prototyping and evaluation of multi-scenario AR use cases. The application was developed based on a set of design considerations derived from interviews with AR expert designers to address the current limitations of AR prototyping tools. ImpersonatAR enables capturing events or steps through embodied demonstrations using avatars and 3D animations. It facilitates the organization of these events and steps to create a multi-scenario experience, allowing stakeholders to explore the scenarios through interactive role-play with the prototypes. Positive feedback from ten users validated that ImpersonatAR promotes the exploration of diverse design possibilities for multi-scenario AR use cases through embodied representations of different scenarios. This work contributes to human–computer interaction by providing an effective prototyping approach for AR use cases.Advancements in VR offer great opportunities to create effective digital learning and training environments. Multi-user distributed participation in VR facilitates collaborative problem-solving (CPS) and learning-by-doing, both of which are critical professional skills for solving real-world problems. In addition, VR applications integrating with sensors allow the collection of in-process user behavioral data for adaptation or correction. Kim et al. presented a sensor-based multiplayer VR manufacturing simulation game that improved virtual hands-on learning experiences and served as a behavioral modeling method for monitoring the collaborative problem-solving (CPS) skills of participants. The Virtual Learning Factory was proposed to facilitate low-cost implementation of virtual hands-on experiences as effective as their real-world equivalents. A dynamic network behavioral modeling approach was developed to extract time-varying patterns of player actions using action-log data using action-log data commonly available in digital learning environments. The heterogeneous-behavioral CPS score was defined by the graph similarity between players. Experimental results demonstrated that the score successfully monitored user-specific collaborative CPS dynamics between players, potentially leading to appropriate interventions to improve learning outcomes. This work enhances the effectiveness of virtual reality and gamification in engineering education.Nguyen et al. developed an augmented reality system for the human-in-the-loop registration of robot-sensor coordinates in production environments. The system efficiently records and visualizes the pose-dependent quality of computing the robot-sensor transformation, providing human operators with intuitive assessments of how changes in the configuration space affect registration and overall system performance. The metrics proposed to evaluate the quality of point measurements demonstrated a decrease in both the accuracy and precision of the registration as the points were measured farther from the robot base. The AR interface intuitively shows that the registration accuracy depends on the location of the sensor within the workspace. It may enable in situ manual re-sampling for better points to be used in registration. This technical brief highlights the potential of AR as a human-in-the-loop interface for metrology applications.The creation of this special issue indicates that extended reality-related studies have shifted from applications designed for various industrial sectors in earlier times to efforts aimed at improving practical values through the proposal of new techniques, algorithms, or assessment methods. It is worth noting that XR research must certainly be conducted from a human-centric perspective, given that its deployment or use is, after all, designed for people, not machines. Due to hardware device limitations, the positional precision of current VR/AR applications may fail to directly meet manufacturing requirements. However, these applications should function effectively in assisting manual tasks by enhancing human situation awareness and characterizing human behavior through the instant capture of their actions or intentions. This necessitates in-depth studies in human–computer interaction, particularly from ergonomic and UX/UI perspectives, in the context of engineering applications. We expect to see an increasing trend in using XR as an interfacing technology for humans to collaborate with computational intelligence (including artificial intelligence, simulation, analytics, and machines) in real time through a wide spectrum of technical elements such as sensors, IoT, cloud computing, and 5G/6G. This trend would transform Industry 4.0 into the emerging Industry 5.0 by fostering human–machine collaboration in a broader context. Especially, the intersection between XR and the recently booming large language model (LLM), as well as other multi-modality AI models, is expected to provide new affordances for new directions of innovation and advancement in broad areas of design and manufacturing. XR studies addressing sustainability and resilience issues could yield a higher degree of innovation and scientific impact along the transformation process.The guest editors would like to thank all contributing authors for their outstanding work. We want to express our sincere gratitude to the reviewers for dedicating their valuable time and efforts in providing constructive comments to the authors during the review process. In particular, we highly appreciate Ms. Amy Suski for her editorial assistance and timely reminders. Finally, we would like to extend our appreciation to Prof. S. K. Gupta and Prof. Y. Wang, the past and current Editor-in-Chief of JCISE, for offering the opportunity and their support for this special issue.Distribution A. Approved for public release: Distribution unlimited under AFRL-2024-0392 determined on January 23, 2024.


Mastery Grading Approach in a Calculus Course

Abstract: Abstract Calculus I students at our engineering school are at a wide range of preparedness levels. Many struggles with this course and the pandemic in the past two years only exacerbated the situation. In fall 2021, 33 out of 183 students (18%) in Calculus I at our engineering school received D/fail/withdraws. Students in this getaway course (first course in engineering and in college) need much more support than students in other courses. They feel anxious about their grades and lack confidence, and a sense of belonging in the classroom [1]. In cases where students were not prepared because they did not have access to academic support and educational resources before coming to college, their performance towards the beginning and throughout the semester will not be accurate and will affect their overall course grade. It is also important to remember that not all students learn the material at the same pace, and some will need a longer time to grasp the concepts. The traditional grading approach reinforces the inequities among students. Consequently, it becomes crucial to rethink how we teach these students and how we align our grading practices to their learning and well-being. To encourage students to prioritize learning over grades, to reduce their anxiety, and to improve and grow throughout the semester, we implemented the mastery grading approach in our Calculus I classes, which impacted about 90-100 first-year engineering students who take Calculus I during their first semester. There is a wide variety of practices in alternative grading systems, such as "Mastery-based Testing (MBT)" [2], "Standards-Based Grading (SBG)" [3], or "Specifications Grading (Spec)" [4]. "Mastery grading" is used as an umbrella term for these alternative grading approaches. This paper will discuss the main elements of the mastery grading design in a Calculus I class, as well as preliminary results on 1) how students' performance differs between mastery and traditionally graded sections, 2) how mastery grading affects students' stress, anxiety, mindset, and beliefs about their math ability. This study is ongoing, and data is being collected in the Fall of 2022. Students' performance will be compared using their placement test scores and their common final exam. The pre-and post-surveys will be administered to assess students' stress, anxiety, and beliefs about their math ability. The analysis will be done after the fall semester concludes.


Changing the Landscape of the Digital Workforce and DEI: A Call to Action for Engineering Education

Abstract: Abstract Digital transformation has been implemented across a wide range of industries. Advanced technologies (e.g., artificial intelligence, robotics, cloud, internet of things etc.) and data are used to digitally connect organizations, automate processes, and represent each aspect of complex systems, products, and services. While digital transformation has changed the landscape of the engineering workforce, engineering programs of today remain largely based on 20th-century needs. Due to the interdisciplinary nature and interconnectedness of digital transformation, the traditionally siloed engineering disciplines require innovation to keep pace with the workforce demands. This paper explores the digital transformation workforce demands and digital competencies that are needed now and for the future of the engineering workforce. These skills extend beyond the technical, but also encompass social, emotional, and higher cognitive skills. Further, digital transformation will have tremendous impact on diverse communities - with the opportunity to either create access or create further disparity due to access to engineering education and opportunities beyond. The paper also reports on the experiences and advice of participants in the invited panel that was held at the 2023 ASEE Southeastern Regional Conference where panelists addressed the need to modernize engineering education in the era of Digital Transformation and the strategies they envision or have implemented to address opportunities and risks for diversity, equity, and inclusion.


Board 343: Native American Student Research Experiences in IoT-Enabled Environmental Monitoring Technologies: An Analysis of North Dakota Tribal Student Experiences in Beijing, China and Mobile, Alabama

Abstract: Abstract Native American students were recruited annually from five Tribal Colleges and two research universities in North Dakota to participate in a five-week, National Science Foundation (NSF)-funded International Research Experiences for Students (IRES) summer program (2019-2022). In 2019, Native American student participants traveled to Beijing, China to learn Internet of Things (IoT)-enabled technology at the Beijing University of Technology (BJUT). In 2020 and 2021, training for student participants was placed on hold due to the Covid-19 pandemic. In 2022, public limitations related to Covid-19 had eased in the United States due to vaccine availability; however, fluctuating global travel restrictions created the need to domestically relocate the IRES training program to Mobile, Alabama. In 2022, a cohort of 12 students – 5 new Native American students from North Dakota, 5 traditionally underrepresented engineering students from southern Alabama, and 2 previous Native American students from the 2019 Cohort (serving as senior mentors) – were trained on wireless sensor network design, energy harvest design, edge-AI design, and new technologies, such as memristors, for IoT-enabled environmental monitoring systems at the University of South Alabama (USA) in Mobile, Alabama. Engineering-based learning content in the program remained consistent with instructional material taught to the 2019 cohort, apart from technical updates; however, changes in sociocultural learning experiences were adjusted for the new program location. Learning activities and dialogues were expanded to incorporate aspects of the south Alabama culture – comprising of a multilayered history involving White Southern, Black Southern, and Poarch Band of Creek Indian populations – with Chinese and the Northern Great Plains Native Americans cultures. Program training effectiveness and student experiences were evaluated through student skill assessments, student engagement observations, formative and summative participant surveys, training observations, feedback from group discussions, and individual interviews. Results indicate that student participants grasped key quantitative and technical skills through the combination of instructional guidance, peer-mentoring, and hands-on experiences. Nearly all students experienced positive and unexpected encounters in cultural learning and perceived the overall experience as positively impacting their academic training, career development, and cultural understanding. Changing an academic training program from being in a 'study-abroad' to a 'study-domestic' setting because of a global pandemic has unique challenges; however, gaining unexpected insight into how diverse student populations navigate, adapt, and experience different training environments and cultures is invaluable. This NSF-funded IRES program revealed essential concepts that make Native American students feel welcomed, valued, and heard. Listening and adapting to student needs through a flexible, genuine, and culturally-aware curriculum empowers students and is essential for lifelong learning and successful integration of underrepresented students into engineering-based disciplines.


Creation of a Mobile Science and Engineering Road Show for Texas A&M University at Qatar: Multicultural STEM Education and Entertainment (Resource Exchange)

Abstract: Abstract While active learning environments within pre-college have become increasingly common, the impact and novelty of the science demonstration cannot be overlooked. For many students, the spectacle of a well-presented science demo, full of gratuitous sparkle and showmanship, is the catalyst that creates initial interest in chemistry, physics, and engineering. Demonstrations can never supplant active learning or cumulative lessons, but they can act as reinforcement in situations where hands-on learning is still in its infancy. The educational systems of Qatar have undergone radical changes in the past twenty years with a rapidly expanding population, influx of international school systems, and an increased focus on teacher training and modern pedagogical ideas. But many of Qatar's students, for a variety of reasons, had no exposure to classroom demonstrations or laboratory experiments to be able to witness scientific phenomena. Texas A&M University at Qatar created the Science and Engineering Road Show to mediate these issues. Based upon the successful Chemistry Road Show and the Physics Festival at Texas A&M University in College Station, USA, the Road Show has performed demonstrations since 2017, traveling across Qatar reaching out to over 25,000 students. A wide variety of topics related to chemistry, physics, and engineering are discovered using audience participation and eye-catching approaches. As part of the Road Show, comprehensive risk assessments, safety and language surveys, and infrastructure methods were created and instituted. Obstacles such as language barriers, gender separation, and cultural differences were resolved via discussions and planning with multiple stakeholders across schools, ministries, and corporate sponsors. The Road Show has become a national institution; invited to schools, public events, and festivals to educate and entertain Qatar children and families. Details regarding the preparation, planning, safety aspects, and organizational structure of such a mobile demonstration platform are included in this paper as a model for others to start their own local road show.


Skilling for and Acculturation to Integrated Photonics Industry Using VR Simulations, Game-Based Learning, and Augmented Reality Games

Abstract: Abstract The integrated photonics and fiber optics industries are rapidly expanding and innovating, respectively, to enable transformative new integrated circuit, AI, datacom, wireless, sensing and imaging systems for cloud and mobile computing, automobile and aircraft, display, medical, and energy industries. This 21st century advanced manufacturing sector is in dire need of a massive increase in its photonics engineer and technician workforce, over the next decade. To support this near-term workforce demand, a modular library of digital simulations (sims) and blended (digital and hands-on) learning content are needed to supplement current university or community college curricula, professional training workshops, and also nourish a K-12 pipeline of future industry talents. A multi-university education research team led by MIT, Clemson University, and The University of Arizona, has completed an integrated photonics and fiber optics industry education roadmap, and created a mix of (i) desktop Virtual Reality (VR) tool-training sims, (ii) photonics device visualization sims, and (iii) application-focused educational games, for both online MOOC learning and blended learning in training bootcamps. In addition, (iv) an Augmented Reality Game (ARG) has been created for K-12 engagement. Collectively, these education assets can facilitate the upskill of photonics-adjacent industry incumbent and incipient workers; the reskill of legacy photonics industry incumbent workers; and acculturate a next-generation workforce to evolving photonics careers. We review examples of (i)-(iii), including an optical fiber preform lathe and fiber draw tower sim; a photonic chip die bondersim; passive and active microphotonic device sims; and games that instruct in the operational trade-offs of photonics-enabled hyperscale data centers, on-chip chemical gas sensors, mmWave wireless drones, and LiDAR imaging vehicles. Such a cumulative curricular instruction is anticipated to fortify learner motivation by interconnecting the procedural skilling of manufacturing tools, with the scaffolding of photonics device function intuition, and constraint-analysis of complex real-world engineering systems. Results from summative A/B testing in a MOOC course and formative assessment interviews during a blended learning bootcamp demonstrate the capacity of these interactive digital tools to both enhance (technician and engineer-level) learner retention, and reduce the preparatory overhead and cognitive load of on-site instructors. Early results from the testing of the ARG may indicate an engagement impact in K-12 introduction to integrated photonics, by highlighting causal links between the operation of complex photonics engineering systems and fundamental optics concepts, such as total internal reflection and light scattering. Collectively, these educational strategies suggest disruptive new methods to enhance interstitial learning experiences and sustain life-long learning practices for engineers and technicians pursuing microelectronics and integrated photonics careers.


Shifting Identity Trajectories within a Scholarship Program: Local Community Practices that Shape Computing Careers

Abstract: Abstract As computer science seeks to diversify, the act of becoming in computing will be an important avenue of investigation, and the connections students make to specific careers in the field may support identity development that moves beyond the role of student towards future professional identities. This study is part of a three-year multi-method study of a program serving students in a rural computer science department at a Hispanic Serving Institution. The situative learning theory was employed to understand whether and how students developed interest and agency in a cybersecurity career path as they participate in a federally funded scholarship program for high achieving low income students. Evidence from the case study suggest that structured, mandatory participation in competitions fueled interest in cybersecurity, as did regular opportunities to discuss contemporary events in the field with students across grade levels and professional interests. Access to a national conference with a hackathon as well as a career fair supported career pathways in the field—the iterative nature of the participation in competitions and conferences gave an opportunity to see growth over time in cybersecurity. The study of community practices and discursive language development may support other educators intending to develop career-focused co-curricular activities that emphasize mutual engagement with other students across grade levels and intent on a joint enterprise, or shared goal. With time and through engagement with more capable peers, students in the study developed shared repertoires of technical language and technical skill specific to a potential career. Focusing on cybersecurity as a subfield shifted the emphasis of identity from one of major choice to one of future possibilities and connection to career. This shift may support future identity work in other subfields of computing, as well as in other elements of computing education, such as graduate study.


Work in Progress: Development of an AutoCAD Plugin to Increase Student’s Spatial Visualization Skills

Abstract: Abstract Spatial visualization of 3D objects based upon 2D drawings is a fundamental skill for engineers, contractors, and architects, and a lack of spatial visualization skills has been shown to result in lower scores in math, science, and engineering courses as well as lower persistence to graduation. Although BIM (Building Information Modelling) technology has grown in adoption throughout the AEC (Architect, Engineering, and Construction) industry, 2D drawings are still a fundamental deliverable that require correct interpretation for accurate construction. Most universities introduce AutoCAD in their lower-level classes to teach 2D drawings in their AEC curriculum, and then include 3D visualization courses in their upper-level classes. Students, however, still find difficulty interpreting 2D lines in AutoCAD as 3D objects, especially when they have poor spatial visualization skills. In addition, students often do not know why they are taught to use different line types when drafting, such as what the difference between a solid and dashed line from a 2D drawing represents in the physical structure. This work-in-progress project is testing a custom plugin developed in AutoCAD to help undergraduates improve their spatial visualization ability, by facilitating the connection between 2D sketches and 3D objects. This custom AutoCAD plugin has been developed that allows students to self-verify their 2D drawings by checking the 3D versions of what they have drafted. Students could then engage in what-if scenarios to visualize what would be the interpretation of 2D objects in a 3D space by changing their drawings and the properties of the assigned line types. As a pilot test of this plugin, this study will collect feedback on the usability and training tutorial for the plugin from students who are enrolled in AutoCAD classes in the construction science and management program in a large, Hispanic Serving Institution in the South-Central United States. This pilot study is guiding the refinement of the plugin tutorial so that the full test of the plugin's efficacy on students' spatial visualization skills can run smoothly. To get this feedback on the tutorial, students enrolled in an AutoCAD course will watch the tutorial video in class and then try out the plugin's features during an in-class exercise. Clarifying questions from the students will be noted as areas to improve upon. Further, students will be asked to complete a survey after the exercise for their feedback and suggestions. The instructor will also be interviewed for the same. This paper will present details about the AutoCAD plugin, the tutorial video, and results from the pilot study.


Sense of Belonging in the Cybersecurity Field of Study

Abstract: Abstract ABSTRACT Because cybersecurity professionals are crucial to increased and continuing national security, public safety, and economic prosperity, employment opportunities in cybersecurity continue to increase. To meet the public and private sectors' need for cybersecurity professionals, universities are adding academic programs in Cybersecurity. One large, mid-Atlantic, land-grant R1 University with a vibrant cybersecurity program that offers a B.S. degree, academic minor, and an Area of Emphasis in Cybersecurity has received an NSF S-STEM grant to increase the number and diversity of highly qualified cybersecurity graduates by offering scholarships to high-achieving and economically challenged undergraduate students. Past research has focused on grit and motivation of the NSF S-STEM scholars from this initiative, their retention and persistence through their educational program, and what elements of the S-STEM program they found most helpful in supporting their development as cybersecurity professionals [1]. This research explores evidence of cybersecurity students' feelings of inclusion or sense of belonging within their major. Because many of the benefits of the NSF S-STEM program are available to all cybersecurity professionals, we will consider the level of engagement of all cybersecurity students and the effect of that engagement on an indication of their sense of belonging. This research is important because sense of belonging is one characteristic that has been linked with increased retention [2] – and lack of sense of belonging has been linked with attrition – within many STEM majors, which struggle to serve a student body that is as diverse as the broader population of college-age people in the U.S. [3]. References [1] Paper presented at the2022 ASEE Annual Conference at the 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. [2] Sophia Krause-Levy, William G. Griswold, Leo Porter, and Christine Alvarado. 2021. The Relationship Between Sense of Belonging and Student Outcomes in CS1 and Beyond. In Proceedings of the 17th ACM Conference on International Computing Education Research (ICER 2021), August 16–19, 2021, Virtual Event, USA. ACM, New York, NY, USA 13 Pages. https://doi.org/10.1145/3446871.3469748 [3] The STEM Labor Force of Today: Scientists, Engineers, and Skilled Technical Workers. National Science Foundation and National Science Board, Science & Engineering Indicators. August 2021. https://ncses.nsf.gov/pubs/nsb20212/participation-of-demographic-groups-in-stem. Accessed 11.16..2022.


Application of Product Lifecycle Management in the University Classroom and Laboratory

Abstract: Abstract Despite widespread acknowledgement of PLM technologies' pivotal role in supporting modern industry, undergraduate STEM programs often struggle to include instruction on PLM topics due to the difficulty of covering the preponderance of discipline-specific concepts inherent in traditional curricula within the allocated credit hours. This paper discusses the establishment and operation of a new center at with directives to prepare collegiate students in utilizing PLM technologies through a variety of extracurricular activities and PLM resources. These include short-course lectures, guided software workshops, semester-length projects, support for research activities, and provision of PLM software in an open-access computer lab. To access the effectiveness of these educational efforts, the analysis of student surveys can help to tailor the activities to better meet learning needs. This model of supporting traditional curricula with extracurricular activities shows potential in preparing students for career roles driven by modern, digital processes.


Rock, Paper, Scissors, Code! Laying a Foundation for Writing Algorithms (Resource Exchange)

Abstract: Abstract This ~50 minute decontextualized introductory coding activity will challenge students' critical thinking, communication, and collaboration skills while developing a robust understanding of the foundations of algorithms (sequence, selection, and repetition). First, students individually develop written instructions for how to play "Rock, Paper, Scissors." Next, students swap instructions with a partner and attempt to play the game exactly as written. Inevitably, students will have issues with instructions (e.g., unclear, not repeatable) and will work together to develop improved game instructions. Throughout this lesson, the instructor will utilize effective questioning strategies to help students develop conceptual understanding of algorithms, sequencing, selection, and repetition by connecting concepts back to their Rock, Paper, Scissors instructions. After these concepts are developed as a whole class, small groups rotate instructions and help one another critique and modify instructions, promoting collaboration and exchange of ideas. Finally, students will apply their understanding of the foundations of algorithms by developing algorithms for a well-known game of their choosing. In addition to coding concepts, the instructor will incorporate Nature of Engineering ideas (e.g., Engineering solutions may vary as there may be more than one solution to the same problem). Instructors should actively evaluate student understanding through effective questioning throughout the activity and can use the final application (new game write-up) as an assessment of conceptual understanding. This activity is structured using the Learning Cycle and involves small group and whole group discussions. Goals for students include critical thinking and problem-solving, communication and collaboration, as well as a deep understanding of the foundations of algorithms (i.e.,sequence, selection, and repetition). This introductory activity can be utilized for middle or high school students to teach foundational coding concepts.


A Literature Review to Explore a Relationship: Empathy and Empathy and Mindfulness in Design Education

Abstract: Abstract Learning to design in undergraduate engineering is a higher-order mentality that entails creative, conceptual, and methodological capabilities. One of the core principles of design thinking is its focus on human values at every stage of the process, including empathy for the eventual recipients or users of the tangible outcomes of the designs. Learning to be empathetic has a wide range of benefits, such as supporting effective teamwork and communication; supporting ethical decision-making; and informing the design process. The existing literature provides not enough guidance on how to foster empathy in undergraduate engineering programs, despite the recognized benefits. However, a growing body of research on the benefits of mindfulness has begun to show its potential as an intervention strategy to improve empathy in design. In this theory/method paper, we present an extended review that explores the relationship between how empathy may be supported through mindfulness practice. Based on the content of the studies, we organized publications in terms of the research focus. While some reported on the relationship between empathy and mindfulness practice, other publications focused on specific effects on individuals. These specific effects clustered around three major groups important to design education: (1) transition between analytical and affective mindsets (2) subjective understanding of the profession by focusing the mind on how we conceptualize engineering expectations, and (3) increased behaviors to support sustainability mindsets. Through our extended literature review on these topics, we suggest that promoting mindfulness practice in engineering design courses, workplaces and elsewhere encourages whole-bodied processes in professional decision-making and empathetic designs. Keywords: empathy, mindfulness, engineering design


New Course Development for Internet of Things

Abstract: Abstract IoT is a fast growing technology sector that is estimated to be valued at 100's of billions in US dollars for 2023. The number of IoT connected devices is growing at an annual rate of 20%/year with billions of devices connected. Accordingly, there is much need for foundational IoT engineering courses in our educational institutions to prepare engineers for this technology sector. The challenge for course developers is that IoT technical foundations are exceedingly broad - ranging from smart sensors to low power computing to cloud infrastructure. Most universities focus on one or two aspects of IoT technical foundations, specifically those associated with the computing aspects of IoT. We have developed a novel approach for an IoT course by segmenting the course into three fundamental technology areas. These areas are respectively (1) foundations of sensing, (2) IoT communication and networking, and (3) IoT computing models and architectures. Each module is taught by a different professor with technical expertise in that module. This paper will focus on course objectives, design, and outcomes. We use the Module 2 IoT communication and networking as an example of the type of content. In this module, we cover fundamental knowledge of networking including layered-architecture, protocols, wireless communication and propagation. Then we introduce two common communication protocols, i.e., Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT), which are widely used for IoT systems. Further, Node.js programming is taught and students learned how to use node.js for HTTP and MQTT implementation. Hands-on experiments using Arduino WiFi1010 board and grove sensors are designed to help deepen students' understanding for developing a small-scale IoT system as well. The term project of building an IoT-enabled smart Heating, Ventilation, and Air Conditioning (HVAC) will be introduced. Instructor observations and anecdotal student feedback on the course design and delivery are presented as well. Lessons learned will be discussed and modifications are proposed for future improvement.


The Impact of Immersive Virtual Reality on Undergraduate STEM Students

Abstract: Abstract Virtual reality (VR) holds great potential for increasing undergraduate student learning outcomes. However, its effective integration to enhance the learning process requires recognizing and leveraging the unique affordances of a VR environment. The process of development of a well-integrated virtual reality lesson requires several steps. These steps include not only aspects of traditional instructional design but also include identification of content appropriate for utilizing the unique characteristics of VR, user interface and user experience. This paper provides details of an exploratory study of the integration of VR lessons in aerospace engineering, biology, math, and physics introductory level courses at an HBCU. The paper includes information about the software and hardware choices, and the process of development of the lessons. Data was collected to measure usability, effectiveness, engagement, and impact of the lessons. Students reported that the VR lessons were engaging and helped them getting a better overview of the content. A comparison between the student responses to implementation in a non-immersive (computer display) and immersive (with VR headsets) environment is also included.


Board 386: Sketchtivity, an Intelligent Sketch Tutoring Software: Broadening Applications and Impact

Abstract: Abstract Sketching is an essential part of engineering education. Engineering students gain various competencies from learning to sketch, which includes improvement in spatial visualization, problem-solving, communication, and idea generation. Although learning to sketch benefits students in various ways, teaching them to sketch and providing individualized feedback to students is challenging. To address this, we developed SketchTivity, an intelligent tutoring system, to provide students a place to practice sketching fundamentals and two-point perspective with randomly generated exercises. Our tool measures line quality (e.g., line smoothness and straightness), drawing speed, and geometric and perspective correctness of sketches. Sketch recognition algorithms are robust to the myriad of ways students can draw, allowing them to draw with one or more strokes. Feedback at the end of exercises aligns with the sketching techniques taught in classrooms to reinforce good habits. The main goals of this project are to improve engineering students' sketching skills and to study the implications of learning to sketch in the context of idea generation, creativity, and engineering design self-efficacy. Sketching assessment is a challenging task that directly depends on the goal of sketching lessons. To better understand the state of sketching assessment in engineering and design, we conducted a systematic literature review to investigate sketching assessment methods and topics. The results of the systematic review will benefit the engineering education community with a summary of prevalent strategies. Informed by the systematic literature review and expert consultation, an Object Assembly Sketching skills Test was developed to benefit engineering educators. This poster will present our Object Assembly Sketching skills test rubric. Through this poster, we present our recent findings that indicate the need for more in-depth training on qualitative skills like spatial reasoning and sketching in engineering education. Our team conducted a study over several semesters that tracked students' development of sketching abilities and spatial skills in an entry-level design and graphics course. We examined how those skills influenced the use of sketching in future design project courses. We found that strong spatial skills in an entry-level course positively correlated with sketching frequency in a capstone design course (r(119) = 0.260, p < .01). We found that initial sketching skill in the entry-level course positively correlated with using sketching for a variety of purposes in a capstone design course (r(107) = 0.193, p < .05). These findings suggest the need for more frequent and high-quality sketching instruction in engineering classrooms to improve design skills and spatial reasoning. To summarize, through this poster, we would like to expand the reach of our free Intelligent Tutoring system that could be used in any university to improve sketching skills in engineering students. We will also present our most recent findings since ASEE 2022 up until the next few months based on our ongoing research.


Student Experiences of an Intentionally Embedded Computer Science and Cybersecurity Pathway in U.S. High Schools

Abstract: Abstract Research Problem: K-12 school systems are racing to implement Computer Science (CS) education within classrooms across the United States. Prior research on education reform movements suggests that without rigorous research, combined with careful technical support for implementation, we should expect wide variation across districts in how they choose to implement computer science education as well as extreme inequality in which districts provide equitable opportunities to learn CS, with the most underserved students fairing the worst (Ahn & Quarles, 2016; Bryk, Gomez, Grunow, & LeMahieu, 2015; Carlson, Borman, & Robinson, 2011; Gordon and Heck, 2019). It stands to reason that these same challenges are at play in the CS subfield of cybersecurity. Research Question: In what ways does the JROTC-CS experience impact the cognitive (e.g. knowledge and skills) and non-cognitive factors (e.g. social and emotional behaviors) of cadets in high school? Methodology: We used a qualitative study using a semi-structured interview protocol with JROTC cadets attending the schools involved in the intervention (n=17). The interview protocol focused on the types of cognitive and non-cognitive impacts the cadets experienced when participating in CS and Cybersecurity learning experiences. Data Collection: We conducted interviews with 17 cadets and coded the transcripts using a priori codes. Findings: Sixteen of the students reported an increase in their knowledge and skills through self-reported grades and self-perceived knowledge gained through the CS and cybersecurity experiences. While all of the students indicated that the courses and extracurricular activities were beneficial and interesting, only two of the students indicated they wanted to have a career in the computer science or cybersecurity field. However, the findings indicated a lack of school personnel support, specifically at the guidance counselor level. Finally, all of the students reported a strong sense of belonging in their CS and cybersecurity experiences leading to increased peer collaboration and support. Implications: Based on other evidence collected during the intervention, the intervention had multiple successes in expanding equitable experiences for cadets in the schools involved in the study. Guidance counselors and other personnel who are in a position to influence the future career choices of cadets may need more professional development; however, more research is needed to understand the ways in which they currently influence cadets.


Board 57: WIP - A Web-based Face Recognition Application for Better In-Person Learning

Abstract: Abstract WIP - A Web-based Face Recognition Application for Better In-person Learning It is widely agreed that students gain more confidence and build stronger relationships when their instructor addresses them by their names. However, it is often difficult for instructors to recognize and remember all the names of students in their lectures when the lecture size becomes relatively large. A traditional approach is to ask the students to bring a name tag. However, name tags can be tacky and difficult to manage. During the covid pandemic, educational institutions utilized web meeting technologies in place for in-person meetings. When the instructors want to interact with other participants, they can call out their names without knowing them priorly by just looking at their name tags. Using face recognition algorithms, we want to bring the convenience of web meeting technologies to in-person education environments. This abstract shall introduce the proposed solution as a Work-In-Progress (WIP). Drawing inspiration from the name tag feature, we are motivated to design a digitalized solution that relieves the pressure on instructors from remembering names. The proposed solution is a web-based application that will capture faces from videos/pictures through a User Interface that operates on web browsers and passes the data to a face recognition AI mechanism. User management systems will also be implemented to protect user privacy. With the Real-time Face Recognition Application, the course instructors can quickly recognize their students and address them by their names. This creates a more engaged and confidence-inspiring learning environment for students. We are conducting surveys and collecting feedback from instructors and students while testing our application in lectures. The students' survey focus is on their comfortability-in-learning scale and degree of agreement on an enhanced and increased classroom learning experience. On the other hand, the focus of the instructor's survey and feedback is on students' engagement and their willingness to engage in asking and answering questions during lectures.