Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover

Abstract: Abstract Background The wide heterogeneity in the appearance of breast lesions and normal breast structures can confuse computerized detection algorithms. Our purpose was therefore to develop a Lesion Highlighter (LH) that can improve the performance of computer-aided detection algorithms for detecting breast cancer on screening mammograms. Methods We hypothesized that a Cycle-GAN based Lesion Remover (LR) could act as an LH , which can improve the performance of lesion detection algorithms. We used 10,310 screening mammograms from 4,832 women that included 4,942 recalled lesions (BI-RADS 0) and 5,368 normal results (BI-RADS 1). We divided the dataset into Train:Validate:Test folds with the ratios of 0.64:0.16:0.2. We segmented image patches (400 × 400 pixels) from either lesions marked by MQSA radiologists or normal tissue in mammograms. We trained a Cycle-GAN to develop two GANs, where each GAN transferred the style of one image to another. We refer to the GAN transferring the style of a lesion to normal breast tissue as the LR. We then highlighted the lesion by color-fusing the mammogram after applying the LR to its original. Using ResNet18, DenseNet201, EfficientNetV2, and Vision Transformer as backbone architectures, we trained three deep networks for each architecture, one trained on lesion highlighted mammograms (Highlighted), another trained on the original mammograms (Baseline), and Highlighted and Baseline combined (Combined). We conducted ROC analysis for the three versions of each deep network on the test set. Results The Combined version of all networks achieved AUCs ranging from 0.963 to 0.974 for identifying the image with a recalled lesion from a normal breast tissue image, which was statistically improved (p-value < 0.001) over their Baseline versions with AUCs that ranged from 0.914 to 0.967. Conclusions Our results showed that a Cycle-GAN based LR is effective for enhancing lesion conspicuity and this can improve the performance of a detection algorithm.


Elicitation and aggregation of multimodal estimates improve wisdom of crowd effects on ordering tasks

Abstract: We present a wisdom of crowds study where participants are asked to order a small set of images based on the number of dots they contain and then to guess the respective number of dots in each image. We test two input elicitation interfaces-one elicits the two modalities of estimates jointly and the other independently. We show that the latter interface yields higher quality estimates, even though the multimodal estimates tend to be more self-contradictory. The inputs are aggregated via optimization and voting-rule based methods to estimate the true ordering of a larger universal set of images. We demonstrate that the quality of collective estimates from the simpler yet more computationally-efficient voting methods is comparable to that achieved by the more complex optimization model. Lastly, we find that using multiple modalities of estimates from one group yields better collective estimates compared to mixing numerical estimates from one group with the ordinal estimates from a different group.


Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Abstract: The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.


Application of Deep Learning and Intelligent Sensing Analysis in Smart Home

Abstract: Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.


Synthetic data

Abstract: The rise of data-driven businesses poses a number of significant challenges for contemporary organisations. These include legal and ethical considerations arising from the use of personal data, the growing challenges of information security, and the difficulty managing the volume of data generated in business transactions of different kinds. The exponential growth of data continues unabated with global data volumes reaching 181 zettabytes by 2025, and with 90% of the world’s data generated in the last two years alone. This massive growth can be mainly attributed to data gathered by Internet of Things (IoT) and related sensory devices, in addition to data generated through the human use of digital tools and applications. Given this abundance of real-world data, in what context could synthetic data be necessary? This paper highlights the growing organisational use of synthetic data and explores where and how it can be optimally used. It examines the ethical aspects of synthetic data usage, the need to garner public perception and acceptance, and the key aspects of traceability, accountability and risk mitigation.


TdDS-UNet: top-down deeply supervised U-Net for the delineation of 3D colorectal cancer

Abstract: Automatically delineating colorectal cancers with fuzzy boundaries from 3D images is a challenging task, but the problem of fuzzy boundary delineation in existing deep learning-based methods have not been investigated in depth. Here, an encoder-decoder-based U-shaped network (U-Net) based on top-down deep supervision (TdDS) was designed to accurately and automatically delineate the fuzzy boundaries of colorectal cancer. TdDS refines the semantic targets of the upper and lower stages by mapping ground truths that are more consistent with the stage properties than upsampling deep supervision. This stage-specific approach can guide the model to learn a coarse-to-fine delineation process and improve the delineation accuracy of fuzzy boundaries by gradually shrinking the boundaries. Experimental results showed that TdDS is more customizable and plays a role similar to the attentional mechanism, and it can further improve the capability of the model to delineate colorectal cancer contours. A total of 103, 12, and 29 3D pelvic magnetic resonance imaging volumes were used for training, validation, and testing, respectively. The comparative results indicate that the proposed method exhibits the best comprehensive performance, with a dice similarity coefficient (DSC) of 0.805 ± 0.053 and a hausdorff distance (HD) of 9.28 ± 5.14 voxels. In the delineation performance analysis section also showed that 44.49% of the delineation results are satisfactory and do not require revisions. This study can provide new technical support for the delineation of 3D colorectal cancer. Our method is open source, and the code is available athttps://github.com/odindis/TdDS/tree/main.


Quantum-walk search in motion

Abstract: Abstract In quantum computing, the quantum walk search algorithm is designed for locating fixed marked nodes within a graph. However, when multiple marked nodes exist, the conventional search algorithm lacks the capacity to simultaneously amplify the marked nodes as well as identify the correct chronological ordering between the marked nodes, if any. To address this limitation, we explore a potential extension of the algorithm by introducing additional quantum states to label the marked nodes. The labels resolve the ambiguity of simultaneous amplification of the marked nodes. Additionally, by associating the label states with a chronological ordering, we can extend the algorithm to track a moving particle on a two-dimensional surface. Our algorithm efficiently searches for the trajectory of the particle and is supported by a proposed quantum circuit. This concept holds promise for a range of applications, from real-time object tracking to network management and routing.


A systematic literature review on the applications of recurrent neural networks in code clone research

Abstract: Code clones, referring to code fragments that are either similar or identical and are copied and pasted within software systems, have negative effects on both software quality and maintenance. The objective of this work is to systematically review and analyze recurrent neural network techniques used to detect code clones to shed light on the current techniques and offer valuable knowledge to the research community. Upon applying the review protocol, we have successfully identified 20 primary studies within this field from a total of 2099 studies. A deep investigation of these studies reveals that nine recurrent neural network techniques have been utilized for code clone detection, with a notable preference for LSTM techniques. These techniques have demonstrated their efficacy in detecting both syntactic and semantic clones, often utilizing abstract syntax trees for source code representation. Moreover, we observed that most studies applied evaluation metrics like F-score, precision, and recall. Additionally, these studies frequently utilized datasets extracted from open-source systems coded in Java and C programming languages. Notably, the Graph-LSTM technique exhibited superior performance. PyTorch and TensorFlow emerged as popular tools for implementing RNN models. To advance code clone detection research, further exploration of techniques like parallel LSTM, sentence-level LSTM, and Tree-Structured GRU is imperative. In addition, more research is needed to investigate the capabilities of the recurrent neural network techniques for identifying semantic clones across different programming languages and binary codes. The development of standardized benchmarks for languages like Python, Scratch, and C#, along with cross-language comparisons, is essential. Therefore, the utilization of recurrent neural network techniques for clone identification is a promising area that demands further research.


Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models

Abstract: Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.


Highly secured and effective management of app-based online voting system using RSA encryption and decryption

Abstract: This study pioneers an innovative approach to fortifying online voting systems, leveraging RSA (Rivest-Shamir-Adleman) encryption and decryption techniques for robust data protection. Through a comprehensive amalgamation of advanced security layers, including MobileFaceNet-driven face verification, device fingerprint matching, and multi-factor authentication, this system engenders a resilient shield against cyber vulnerabilities. By harnessing a Firebase database, user information is securely stored and authenticated, affirming their pivotal role in the democratic process. The symphony of RSA encryption and decryption orchestrates a formidable fortress around data transmission and storage, ensuring impregnable security against digital threats. This paradigm shift in voting technology strives to not only elevate security but also enhance accessibility and convenience, ultimately contributing to the evolution of online voting systems and fostering greater participation rates and reducing associated costs in the digital era.


How virtual reality, augmented reality and mixed reality facilitate teacher education: A systematic review

Abstract: Abstract Background Virtual reality (VR), augmented reality (AR) and mixed reality (MR) have sparked recently in improving the effectiveness of teacher education. However, there is a lack of review regarding the utilisation of these technologies in this field. These three technologies, namely VR, AR and MR, can be collectively referred to as extended reality (XR) (as mentioned in reference Tang et al., 2022). Objectives Remarkably, the utilisation of XR‐based technologies in teacher education needs to be explored. Moreover, research questions related to the training objectives, methodological features, and the effects of XR‐based teacher education remain unanswered. Methods To this end, the present study conducted a systematic review to analyse 52 articles from six databases (including Web of Science, Scopus, IEEE Xplore, ERIC, ScienceDirect, and ACM Digital Library). Results The results indicate that XR technologies have been primarily used to train teachers' procedural knowledge, for instance, classroom management. Furthermore, most studies have primarily focused on pre‐service teachers (PSTs) rather than in‐service teachers and utilised small sample sizes, with VR emerging as the most frequently employed tool. Finally, the majority of the studies reported that XR‐based training affected teachers positively. Conclusions It urges researchers and developers to consider theory‐driven training design, which increases the potential to better understand what features of XR promote in‐service teachers' and PSTs' learning and how they do so. This article additionally conducts a SWOT (strengths, weaknesses, opportunities, and threats) analysis of XR‐based teacher education to offer more insightful recommendations and foster further discussion.


A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Abstract: In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.


Blood cell image segmentation and classification: a systematic review

Abstract: Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc . Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.


CeCR: Cross-entropy contrastive replay for online class-incremental continual learning

Abstract: Aiming at the realization of learning continually from an online data stream, replay-based methods have shown superior potential. The main challenge of replay-based methods is the selection of representative samples which are stored in the buffer and replayed. In this paper, we propose the Cross-entropy Contrastive Replay (CeCR) method in the online class-incremental setting. First, we present the Class-focused Memory Retrieval method that proceeds the class-level sampling without replacement. Second, we put forward the class-mean approximation memory update method that selectively replaces the mistakenly classified training samples with samples of current input batch. In addition, the Cross-entropy Contrastive Loss is proposed to implement the model training with obtaining more solid knowledge to achieve effective learning. Experiments show that the CeCR method has comparable or improved performance in two benchmark datasets in comparison with the state-of-the-art methods.


Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview

Abstract: Abstract The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.


Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering

Abstract: This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state-space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input-output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples.


Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

Abstract: Prediction of adsorption via Adaptive Neuro–Fuzzy Inference System (ANFIS) can save the cost and time in practical applications. Chromium (VI) adsorption data obtained at different temperature, activated carbon dosage and pH values were evaluated by using MATLAB ANFIS. In order to achieve prediction of adsorption via ANFIS with acceptable error values, optimum membership function (MF) and optimum number of MF were determined by using Box-Behnken experimental design (BBD) method. In order to determine the optimum number of MF for each input, all combinations given in BBD matrix were examined via ANFIS, then, regression models for each MFs were developed between the root mean square error (RMSE) and MF numbers of each input. The most used five membership functions (triangular, trapezoidal, generalized bell shaped, Gaussian, Gaussian 2) were investigated. According to the analysis of variance (ANOVA), regression models developed for the test data with triangular and trapezoidal membership functions were significant in the 95 % confidence level. Predictions were employed via ANFIS by using optimum MF numbers of each inputs (6, 6, 3 for triangular MF and 8, 8, 2 for trapezoidal MF). Consequently, the best Cr(VI) adsorption percentage prediction (RMSE = 1.9084 and R2 = 0.992) was obtained by using triangular membership function with optimum MF numbers. Response surface plots, which gives the relationship between MF numbers and RMSE values for triangular MF were also evaluated. In this study, it was demonstrated that MF type and numbers, which are crucial for good prediction via ANFIS grid partition method, can be determined optimally by applying experimental design methodology.


RELATIONSHIP BETWEEN FEEDING AND ACADEMIC PERFORMANCE OF STUDENTS IN KITIMBWA SUB COUNTY KAYUNGA DISTRICT: A CROSS-SECTIONAL STUDY.

Abstract: Background This study aims to examine the relationship between feeding and academic performance of students in Kitimbwa Sub County Kayunga District. Methodology A cross-sectional survey design using both quantitative and qualitative methods was used to collect data from 202 students 30 teachers and 7 Headteachers. The qualitative study design investigated the possibility of the relationship between the independent and the dependent variables like feeding, sanitation, accommodation, and academic performance. Data on living conditions was gathered using a questionnaire with a standard Likert-type scale with closed-ranking items. Structured interview guides were used to collect views from the non-students. Data from questionnaires was analyzed using both descriptive and inferential statistics of Analysis of Variance (ANOVA) and the t-test while interview data was analyzed using frequency analysis by counting the number of times of responses had by the respondents. Results The majority 115 (56.9%) were female while 87 (43.1%) were male. This meant that girl child education has been given due consideration since girls were more than the boys in the A‟ level section unlike before when boys dominated the whole system of education. The results of the analysis showed a highly significant effect of feeding on academic performance. This was given by the computed value of the F- ratio of 8.26 while the corresponding p-value of 0.00 was less than the level of significance alpha 0.05. In schools where feeding was perceived as being good, the performance of students was higher than those in schools where feeding was moderate and poor. Conclusion In schools where feeding was poor, even students' academic performance was poor compared to schools where feeding was moderate and good. Recommendation Secondary schools in Kitimbwa Sub County should use their land to establish agricultural and poultry farms to supplement the food provided to balance the diet.


Survey of transformers and towards ensemble learning using transformers for natural language processing

Abstract: Abstract The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. Graphical Abstract


Enhancing Image Captioning and Auto-Tagging Through a FCLN with Faster R-CNN Integration

Abstract: In the realm of automated image captioning, which entails generating descriptive text for images, the fusion of Natural Language Processing (NLP) and computer vision techniques is paramount. This study introduces the Fully Convolutional Localization Network (FCLN), a novel approach that concurrently addresses localization and description tasks within a singular forward pass. It maintains spatial information and avoids detail loss, streamlining the training process with consistent optimization. The foundation of FCLN is laid by a Convolutional Neural Network (CNN), adept at extracting salient image features. Central to this architecture is a Localization Layer, pivotal in precise object detection and caption generation. The FCLN architecture amalgamates a region detection network, reminiscent of Faster Region-CNN (R-CNN), with a captioning network. This synergy enables the production of contextually meaningful image captions. The incorporation of the Faster R-CNN framework facilitates region-based object detection, offering precise contextual understanding and inter-object relationships. Concurrently, a Long Short-Term Memory (LSTM) network is employed for generating captions. This integration yields superior performance in caption accuracy, particularly in complex scenes. Evaluations conducted on the Microsoft Common Objects in Context (MS COCO) test server affirm the model's superiority over existing benchmarks, underscoring its efficacy in generating precise and context-rich image captions.


Assessment of differentially private synthetic data for utility and fairness in end-to-end machine learning pipelines for tabular data

Abstract: Differentially private (DP) synthetic datasets are a solution for sharing data while preserving the privacy of individual data providers. Understanding the effects of utilizing DP synthetic data in end-to-end machine learning pipelines impacts areas such as health care and humanitarian action, where data is scarce and regulated by restrictive privacy laws. In this work, we investigate the extent to which synthetic data can replace real, tabular data in machine learning pipelines and identify the most effective synthetic data generation techniques for training and evaluating machine learning models. We systematically investigate the impacts of differentially private synthetic data on downstream classification tasks from the point of view of utility as well as fairness. Our analysis is comprehensive and includes representatives of the two main types of synthetic data generation algorithms: marginal-based and GAN-based. To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic dataset generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness. Our findings demonstrate that marginal-based synthetic data generators surpass GAN-based ones regarding model training utility for tabular data. Indeed, we show that models trained using data generated by marginal-based algorithms can exhibit similar utility to models trained using real data. Our analysis also reveals that the marginal-based synthetic data generated using AIM and MWEM PGM algorithms can train models that simultaneously achieve utility and fairness characteristics close to those obtained by models trained with real data.


An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer's disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer's diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model's exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer's disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.


Simple but powerful interactive data analysis in R with R/LinekdCharts

Abstract: Abstract In research involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. For example, it might be helpful to have one chart showing a summary statistic for all samples, while a second chart provides details for points selected in the first chart. We present R/LinkedCharts, a framework that renders this task radically simple, requiring very few lines of code to obtain complex and general visualization, which later can be polished to provide interactive data access of publication quality.


Differential Privacy Preservation for Continuous Release of Real-Time Location Data

Abstract: Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability.


Cryptocurrency interest in geographical regions with high unemployment rates

Abstract: Purpose- In regions with high unemployment rates, individuals often face economic uncertainty and may seek alternatives to traditional financial systems. In such circumstances, some individuals may turn to alternative investments and financial instruments, with cryptocurrencies such as Bitcoin being among them. The value of these cryptocurrencies is often volatile due to their speculative nature, which can lead investors to take on more significant risks. While this situation can result in substantial gains, it can also lead to significant financial losses. The primary use of cryptocurrencies is generally for investment purposes. Since cryptocurrencies are digital, they operate entirely in a virtual environment. The loss or theft of the digital password in the virtual space means the loss of the wallet. Security in cryptocurrencies is very weak. The lack of government guarantees and legal infrastructure globally poses certain threats to investors, making cryptocurrencies carry a higher risk compared to other investment instruments. This study will investigate the regional relationship between cryptocurrency interest and unemployment rates. The aim is to determine whether individuals living in geographical areas with high unemployment rates show more interest in cryptocurrencies compared to those in other geographical regions. Methodology- In line with the research objective, Google search engine data for the past 10 years has been analyzed using content analysis methodology. A query has been conducted to determine in which regions of Turkey cryptocurrency names are most frequently searched, and a comparison has been made on a provincial level across all 81 provinces. Additionally, considering the unemployment data provided by the Turkish Statistical Institute (TUIK), regions with high and low unemployment rates have been identified. Subsequently, an investigation has been carried out to determine whether there is any correlation between regional Google searches and unemployment rates in these areas. Findings- When examining the findings from the perspective of unemployment, it has been observed that the regions where cryptocurrency names are most frequently searched align with areas having the highest unemployment rates, while the least searched areas correspond to regions with lower unemployment rates. Among the 10 provinces with the highest unemployment rates, 8 of them are included in the list of 10 provinces with the highest Bitcoin search frequency. Similarly, 8 out of 10 provinces with the lowest employment rates are also listed among the 10 provinces with the highest Bitcoin search frequency. Furthermore, among the 10 provinces with the lowest per capita gross domestic product (GDP), 7 of them are included in the list of 10 provinces with the highest Bitcoin search frequency. Conclusion- As a result of the relationship analyses conducted in the research, it has been observed that in Turkey, regions with high unemployment rates, low employment rates, and low Gross Domestic Product (GDP) have a higher frequency of Bitcoin searches compared to other regions. Keywords: Cryptocurrency, unemployment, bitcoin JEL Codes: P44, E24, E44


Emoji use in social media posts: relationships with personality traits and word usage

Abstract: Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples ( n = 76 and n = 245) in which we examined how emoji use on X (formerly Twitter) related to users’ personality traits and language use in posts. Personality traits were assessed from participants in an online survey. With participants’ consent, we analyzed word usage in posts. Word frequencies were calculated using the Linguistic Inquiry Word Count (LIWC). In both samples, the results showed that those who used the most emojis had the lowest levels of openness to experience. Emoji use was unrelated to the other personality traits. In sample 1, emoji use was also related to use of words related to family, positive emotion, and sadness and less frequent use of articles and words related to insight. In sample 2, more frequent use of emojis in posts was related to more frequent use of you pronouns, I pronouns, and more frequent use of negative function words and words related to time. The results support the view that social media users’ characteristics may be gleaned from the content of their social media posts.


Development of a standardized methodology for transfer learning with QSAR models: a purely data-driven approach for source task selection

Abstract: Transfer learning is a machine learning technique that works well with chemical endpoints, with several papers confirming its efficiency. Although effective, because the choice of source/assistant tasks is non-trivial, the application of this technique is severely limited by the domain knowledge of the modeller. Considering this limitation, we developed a purely data-driven approach for source task selection that abstracts the need for domain knowledge. To achieve this, we created a supervised learning setting in which transfer outcome (positive/negative) is the variable to be predicted, and a set of six transferability metrics, calculated based on information from target and source datasets, are the features for prediction. We used the ChEMBL database to generate 100,000 transfers using random pairing, and with these transfers, we trained and evaluated our transferability prediction model (TP-Model). Our TP-Model achieved a 135-fold increase in precision while achieving a sensitivity of 92%, demonstrating a clear superiority against random search. In addition, we observed that transfer learning could provide considerable performance increases when applicable, with an average Matthews Correlation Coefficient (MCC) increase of 0.19 when using a single source and an average MCC increase of 0.44 when using multiple sources.


Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

Abstract: The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.


A systematic analysis of deep learning in genomics and histopathology for precision oncology

Abstract: Abstract Background Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency. Methods Here, we present a systematic survey of the academic literature from 2010 to November 2023, aiming to quantify the application of DL for pathology, genomics, and the combined use of both data types. After filtering 3048 publications, our search identified 534 relevant articles which then were evaluated by basic (diagnosis, grading, subtyping) and advanced (mutation, drug response and survival prediction) application types, publication year and addressed cancer tissue. Results Our analysis reveals a predominant application of DL in pathology compared to genomics. However, there is a notable surge in DL incorporation within both domains. Furthermore, while DL applied to pathology primarily targets the identification of histology-specific patterns in individual tissues, DL in genomics is more commonly used in a pan-cancer context. Multimodal DL, on the contrary, remains a niche topic, evidenced by a limited number of publications, primarily focusing on prognosis predictions. Conclusion In summary, our quantitative analysis indicates that DL not only has a well-established role in histopathology but is also being successfully integrated into both genomic and multimodal applications. In addition, there is considerable potential in multimodal DL for harnessing further advanced tasks, such as predicting drug response. Nevertheless, this review also underlines the need for further research to bridge the existing gaps in these fields.


Remote Diagnosis on Upper Respiratory Tract Infections Based on a Neural Network with Few Symptom Words—A Feasibility Study

Abstract: This study aims explore the feasibility of using neural network (NNs) and deep learning to diagnose three common respiratory diseases with few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all three diseases. They revealed 90% accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients’ understanding of their conditions via a remote diagnosis. Unlike previous studies that have focused on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-processing.


Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma

Abstract: Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. A clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin–stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, we also assessed a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. We further assessed the hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. Our data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.


An ensemble novel architecture for Bangla Mathematical Entity Recognition (MER) using transformer based learning

Abstract:
Abstract
 Mathematical entity recognition is indispensable for machines to accurately explain and depict mathematical content and to enable adequate mathematical operations and reasoning. It expedites automated theorem proving, speeds up the analysis and retrieval of mathematical knowledge from documents, and improves e-learning and educational platforms. It also simplifies translation, scientific research, data analysis, interpretation, and the practical application of mathematical information. Mathematical entity recognition in the Bangla language is novel; to our best knowledge, no other similar works have been done. Here, we identify the mathematical operator, operands as numbers, and popular mathematical terms (complex numbers, real numbers, prime numbers, etc.). In this work, we recognize Bangla Mathematical Entity Recognition (MER) utilizing the ensemble architecture of deep neural networks known as Bidirectional Encoder Representations from Transformers (BERT). We prepare a novel dataset comprising 13,717 observations, each containing a mathematical statement, mathematical entity, and mathematical type. In our recognition process, we consider our proposed architectures using accuracy, precision, recall and f1-score as the performance metrics. The results have shown a satisfactory accuracy percentage of 97.98 with BERT and 99.76% with ensemble BERT.


A Failure in Decryption Process for Bivariate Polynomial Reconstruction Problem Cryptosystem

Abstract: In 1999, the Polynomial Reconstruction Problem (PRP) was put forward as a new hard mathematics problem. A univariate PRP scheme by Augot and Finiasz was introduced at Eurocrypt in 2003, and this cryptosystem was fully cryptanalyzed in 2004. In 2013, a bivariate PRP cryptosystem was developed, which is a modified version of Augot and Finiasz's original work. This study describes a decryption failure that can occur in both cryptosystems. We demonstrate that when the error has a weight greater than the number of monomials in a secret polynomial, p, decryption failure can occur. The result of this study also determines the upper bound that should be applied to avoid decryption failure.


Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study

Abstract: Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given code reviews. To conduct the study, we select the existing benchmark CodeReview and construct a new code review dataset with high quality. We use CodeReviewer, a state-of-the-art code review tool, as a baseline for comparison with ChatGPT. Our results show that ChatGPT outperforms CodeReviewer in code refinement tasks. Specifically, our results show that ChatGPT achieves higher EM and BLEU scores of 22.78 and 76.44 respectively, while the state-of-the-art method achieves only 15.50 and 62.88 on a high-quality code review dataset. We further identify the root causes for ChatGPT's underperformance and propose several strategies to mitigate these challenges. Our study provides insights into the potential of ChatGPT in automating the code review process, and highlights the potential research directions.


CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Abstract: Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To evaluate the effectiveness of these models, multiple existing benchmarks (e.g., HumanEval and AiXBench) are proposed, including only cases of generating a standalone function, i.e., a function that may invoke or access only built-in functions and standard libraries. However, non-standalone functions, which typically are not included in the existing benchmarks, constitute more than 70% of the functions in popular open-source projects, and evaluating models' effectiveness on standalone functions cannot reflect these models' effectiveness on pragmatic code generation scenarios (i.e., code generation for real settings of open source or proprietary code).


TRACED: Execution-aware Pre-training for Source Code

Abstract: Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency graphs, etc.). However, program semantics will not be fully exposed before the real execution. Without an understanding of the program execution, statically pre-trained models fail to comprehensively capture the dynamic code properties, such as the branch coverage and the runtime variable values, and they are consequently less effective at code understanding tasks, such as retrieving semantic clones and detecting software vulnerabilities.


Modularizing while Training: A New Paradigm for Modularizing DNN Models

Abstract: Deep neural network (DNN) models have become increasingly crucial components of intelligent software systems. However, training a DNN model is typically expensive in terms of both time and computational resources. To address this issue, recent research has focused on reusing existing DNN models - borrowing the concept of software reuse in software engineering. However, reusing an entire model could cause extra overhead or inherit the weaknesses from the undesired functionalities. Hence, existing work proposes to decompose an already trained model into modules, i.e., modularizing-after-training, to enable module reuse. Since the trained models are not built for modularization, modularizing-after-training may incur huge overhead and model accuracy loss. In this paper, we propose a novel approach that incorporates modularization into the model training process, i.e., modularizing-while-training (MwT). We train a model to be structurally modular through two loss functions that optimize intra-module cohesion and inter-module coupling. We have implemented the proposed approach for modularizing Convolutional Neural Network (CNN) models. The evaluation results on representative models demonstrate that MwT outperforms the existing state-of-the-art modularizing-after-training approach. Specifically, the accuracy loss caused by MwT is only 1.13 percentage points, which is less than that of the existing approach. The kernel retention rate of the modules generated by MwT is only 14.58%, with a reduction of 74.31% over the existing approach. Furthermore, the total time cost required for training and modularizing is only 108 minutes, which is half the time required by the existing approach. Our work demonstrates that MwT is a new and more effective paradigm for realizing DNN model modularization, offering a fresh perspective on achieving model reuse.


How do Developers Talk about GitHub Actions? Evidence from Online Software Development Community

Abstract: Continuous integration, deployment and delivery (CI/CD) have become cornerstones of DevOps practices. In recent years, GitHub Action (GHA) has rapidly replaced the traditional CI/CD tools on GitHub, providing efficiently automated workflows for developers. With the widespread use and influence of GHA, it is critical to understand the existing problems that GHA developers face in their practices as well as the potential solutions to these problems. Unfortunately, we currently have relatively little knowledge in this area. To fill this gap, we conduct a large-scale empirical study of 6,590 Stack Overflow (SO) questions and 315 GitHub issues. Our study leads to the first comprehensive taxonomy of problems related to GHA, covering 4 categories and 16 sub-categories. Then, we analyze the popularity and difficulty of problem categories and their correlations. Further, we summarize 56 solution strategies for different GHA problems. We also distill practical implications of our findings from the perspective of different audiences. We believe that our study contributes to the research of emerging GHA practices and guides the future support of tools and technologies.


Improving Testing of Deep-Learning Systems

Abstract: A combination of differential and mutation testing results in better test data.


Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Abstract: Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.


PonziGuard: Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph (CRBG)

Abstract: Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.


Neural Network-Based Forecasting of Student Enrollment With Exponential Smoothing Baseline and Performance Analysis

Abstract: His research interest cuts across the use of machine learning and data science in Computing Science Education to improve teaching and learning.Specifically, he is exploring


Work-in-Progress: Developing an Interactive, Immersive, 360-Degree Virtual Media for Enhancing Student Learning in Additive Manufacturing

Abstract: in South Carolina with a concentration in


Novel multimodal framework for embedding social justice education in technical engineering coursework

Abstract: Students enrolled in undergraduate Engineering programs are becoming more diverse while engineering science curricula remain unchanged and outdated.Educators and instructional designers of Engineering Science must transform courses so that they are relevant and engaging to students who vary in gender, age, race, sexual orientation, neurodiversity, disability, alterability, and socioeconomic status.Shifting instruction to serve the needs of diverse populations can benefit all students.If institutions fail to meet the academic needs of learners, students might instead choose the increasingly available alternatives to formal education such as vocational and trade schools, apprenticeships, and self-paced free and paid resources.To help instructional designers make the needed shift, we synthesized three known techniques into a novel framework for engineering science curriculum delivery.We utilize: Writing-to-Learn activities, Inclusive Magnification (InclusiveMag/GenderMag) methods, and Engineering for Social Justice criteria.This framework allows plenty of space for fundamental engineering science coursework and results in a deeper understanding of those engineering concepts through the lens of real-world collaboration and contexts.


Comparing the Effects of In-Person and Remote Learning on Student Performance in an Undergraduate Introductory Statics and Mechanics of Materials Course

Abstract: The COVID-19 pandemic in 2020-2021 compelled revolutionary changes in teaching methodologies for many conventional institutions of higher education.This study explores the implications of these changes on student performance in an undergraduate introductory mechanical engineering course; given that between the Fall of 2019 and the Spring of 2022, this course conducted normal classroom operations, transitioned to the virtual environment, conducted a full semester virtually, then transitioned back into our 'new normal' classroom environment.During this time the course content and grading methodologies remained relatively unchanged, facilitating comparison between semesters.In addition, this course is required for the entire undergraduate student population regardless of academic major.This provides a diverse cross-section of incoming students, academically and otherwise.With over 2600 students completing the course during these semesters, this study objectively compares performance on course graded events such as exams, lab reports, and group design projects.The ultimate goals of this study are to 1) compare student performance across remote and in-person classroom settings and 2) provide future instructors with data to help optimize the student learning environment for all classroom settings.The conclusions of this study indicate that semesters of transition between in-person and remote learning are the most challenging for students, but otherwise student performance was not significantly affected.


The Synergy of Intertwining Grant Activities: Cyber Up! and GenCyber Girls

Abstract: Excellence in Cyber Defense education and serves on multiple national


A Reflection on Action Approach to Teamwork Facilitation

Abstract: Abstract Working in teams has been recognized as an essential 21st-century skill. Introducing teamwork in the undergraduate classroom is crucial as it allows the students to work with individuals with diverse skillsets and learn from one another. It is important to note that just creating a team and allowing the students to work does not foster teamwork skills. Inculcating teamwork skills requires a consciousness on the part of the instructor and the teaching assistants. Pedagogies such as cooperative learning have been recognized as effective in helping students develop teamwork skills. We introduced a joint reflection on action approach to developing teamwork skills among novice students as part of a sophomore-level systems analysis and design course. In this evidence-based practice paper, we report on students' reflections regarding their perceptions of teamwork. This study approaches the following research questions: What are students' reflections about the role of communication while working in teams in a cooperative project-based learning environment? The guiding pedagogical framework for this course is cooperative learning. The course requires the students to work in teams in a semester-long software development project. To elicit reflection on action about their teamwork experience. Specifically, we exposed students to concrete experiences as part of their teamwork interactions, which became the basis for observations and reflections. For this, the semester-long project was complemented with one reflection-on-action activity. In the activity, students were asked to watch a video of secrets of successful teamwork and were asked to reflect on their perceptions about the role of communication within teams. The students' reflections on the activity were analyzed using qualitative inductive thematic analysis to understand the students' perceptions regarding teamwork and communication within teams. Keywords: cooperative learning, teamwork, team communication, undergraduate students


Constructing Quadratic Equations modulo N to emphasize the differences between real and modular arithmetic operations

Abstract: Cadets majoring in Cybersecurity at the United States Coast Guard Academy are typically enrolled in the department's Introductory Cryptography course during the Fall of their Junior year.Many of these students struggled to understand the need to use modular operations to solve quadratic equations modulo N.This was because homework assignments requiring the solution of these equations typically employed equations for which regular real operations were perfectly satisfactory.Quadratic equations modulo N are assigned to help the students recognize the similarities between modular and real arithmetic while also helping them see the differences.These equations are also introduced to prepare the students for the study of elliptic curves.Three specific differences between modular quadratic equations and real arithmetic quadratic equations were identified.Modular arithmetic equations can fail to have any solutions if the modulus for an otherwise acceptable equation does not permit a multiplicative inverse required for solution.Modular quadratic equations can also have solutions where real quadratic equations would fail to have any solutions (complex solutions).And finally, modular quadratic equations with composite moduli can have many more than two solutions.All three of these differences can be exploited to prevent the students from solving a quadratic equation using real arithmetic methods and thus compel them to use modular techniques.It was observed on the homework assignments that a significant fraction of the students failed to use modular techniques to solve the quadratic equations that were assigned and it was suspected that many of those that did were unsure of why they needed to employ them.After constructing quadratic equations that took advantage of the peculiar properties of modular arithmetic the students were unable to use real arithmetic techniques and had to resort to modular methods thus reinforcing the need to use those methods to solve the modular equations.


TreeVisual: Design and Evaluation of a Web-Based Visualization Tool for Teaching and Learning Tree Visualization

Abstract: theoretic algorithms, graph-based techniques, and deep learning solutions for big data analytics.


Sustainable Boutique Research Services in a Mid-sized STEM Library: A Preliminary Study

Abstract: Information seekers' increasing comfort in leveraging research technology has reduced demand for traditional models of reference services, while highlighting the role of expert assistance when navigating complex information needs.Librarians at the Colorado School of Mines (Mines) are exploring how to provide meaningful research support to faculty, postdocs and graduate students on campus while maintaining some aspects of traditional reference services that focus on undergraduates.This study explores the concierge or boutique model that provides individualized, flexible, value-added services within the larger scope of the library.A literature review, internal scan of the Mines institutional context and an outward environmental scan are combined to evaluate feasibility of designing boutique research services in a mid-sized STEMfocused library.Lessons learned in this exploration will influence the design of a pilot program to be implemented in the Mines library.


Cluster2Former: Semisupervised Clustering Transformers for Video Instance Segmentation

Abstract: A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture, with similarity-based constraint loss to handle partial annotations efficiently. We demonstrate that despite using lightweight annotations (using only 0.5% of the annotated pixels), Cluster2Former achieves competitive performance on standard benchmarks. The approach offers a cost-effective and computationally efficient solution for video instance segmentation, especially in scenarios with limited annotation resources.


Virtual Reality Field Trip of a Steel Building Under Construction

Abstract: The objective of this study is to determine if a virtual reality field trip can provide a valuable educational experience to undergraduate students.The format of this virtual reality field trip was a tour of a steel-framed skyscraper under construction narrated by a lead structural engineer, who took the participants to eight stops around a typical floor and explained various steel framing concepts with illustrations.The tour was provided to the students using standalone virtual reality headsets in a classroom setting.The assessment method consisted of pre-and post-tour quizzes to quantify the comprehension improvement of specific structural steel framing concepts and to gauge the impact of the virtual reality tour on inspiring students' interest in the subject.The assessment data was supplemented by additional post survey data on students' opinions of the experience.It was found that students were excited to interact with the virtual site visit and left the experience with a better understanding of structural steel framing.Lessons learned from this experience are discussed along with student recommendations for improvements in the educational module.


Modularity Analysis of Makerspaces to Determine Potential Hubs and Critical Tools in the Makerspace

Abstract: Layton's research is an expert in bio-inspired system design, with a focus on the use of biological ecosystems as inspiration for achieving sustainability


Model-Based 3D Gaze Estimation Using a TOF Camera

Abstract: Among the numerous gaze-estimation methods currently available, appearance-based methods predominantly use RGB images as input and employ convolutional neural networks (CNNs) to detect facial images to regressively obtain gaze angles or gaze points. Model-based methods require high-resolution images to obtain a clear eyeball geometric model. These methods face significant challenges in outdoor environments and practical application scenarios. This paper proposes a model-based gaze-estimation algorithm using a low-resolution 3D TOF camera. This study uses infrared images instead of RGB images as input to overcome the impact of varying illumination intensity in the environment on gaze estimation. We utilized a trained YOLOv8 neural network model to detect eye landmarks in captured facial images. Combined with the depth map from a time-of-flight (TOF) camera, we calculated the 3D coordinates of the canthus points of a single eye of the subject. Based on this, we fitted a 3D geometric model of the eyeball to determine the subject’s gaze angle. Experimental validation showed that our method achieved a root mean square error of 6.03° and 4.83° in the horizontal and vertical directions, respectively, for the detection of the subject’s gaze angle. We also tested the proposed method in a real car driving environment, achieving stable driver gaze detection at various locations inside the car, such as the dashboard, driver mirror, and the in-vehicle screen.


Development of a Security Engineering Curriculum for a Distance Learning Environment

Abstract: In this paper, we describe the ongoing development of the undergraduate security engineering program at the University of Central Florida.While a clear need for security education exists, the goals of such a program are not always so clear.Security, like much of engineering, is a discipline that is applied to other fields.Therefore, it is necessary to educate students not only in the technical aspects of security, but also in the situationdependent way that it is applied.Such an education would provide students with the skills needed to enter the field and the in-depth understanding to be upwardly mobile.To this end, the curriculum is being developed to provide a mix of both technical skills and broad knowledge.This is present both in the course material, and our commitments to utilizing the strengths UCF has to offer (National Center for Forensic Studies, Public Safety Technology Center, etc.) and developing tight integration with other departments (Criminal Justice, Business, etc.).Additionally, all of the courses in this program are made available through the FEEDS distance learning system and must be developed to support remote students.The hands-on, applied coursework being developed must be compatible with distance learning.The four primary educational goals of this program are as follows: Technical knowledge (ready-to-apply skills); Broad knowledge (ability to apply current knowledge to unknown future situation); Business and management procedures; and Legal and ethical issues.Rather than have a course, or set of courses, devoted to each of these areas, we believe that each and every course should cover these four concepts as they apply to the specific subject being taught.The end goal is to provide the students a substantial body of knowledge while avoiding pitfalls such as too little applicable knowledge, too much vendor-specific training, and not enough depth in specific areas.


Do Engineering Students Learn Ethics From an Ethics Course?

Abstract: The goal of the present research is to develop machineassisted methods that can assist in the analysis of students' written compositions in ethics courses.As part of this research, we analyzed Social Impact Assessment (SIA) papers submitted by engineering undergraduates in a course on engineering ethics.The SIA papers required students to identify and discuss a contemporary engineering technology (e.g., autonomous tractor trailers) and to explicitly discuss the ethical issues involved in that technology.Here we describe the ability of three machine tools to discriminate differences in the technical compared to ethical portions of the SIA papers.First, using LIWC (Language Inquiry and Word Count) we quantified differences in analytical thinking, expertise and selfconfidence, disclosure, and affect, in the technical and ethical portions of the papers.Next, we applied MEH (Meaning Extraction Helper) to examine differences in critical concepts in the technical and ethical portions of the papers.Finally, we used LDA (Latent Dirichlet Allocation) to examine differences in the topics in the technical and ethical portions of the papers.The results of these three tests demonstrate the ability of machine-based tools to discriminate conceptual, affective, and motivational differences in the texts that students compose that relate to engineering technology and to engineering ethics.We discuss the utility and future directions for this research.


Deep learning automation of radiographic patterns for hallux valgus diagnosis

Abstract: Artificial intelligence (AI) and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine. Deep learning has already been utilized for automated detection of pneumonia from chest radiographs, diabetic retinopathy, breast cancer, skin carcinoma classification, and metastatic lymphadenopathy detection, with diagnostic reliability akin to medical experts. In the World Journal of Orthopedics article, the authors apply an automated and AI-assisted technique to determine the hallux valgus angle (HVA) for assessing HV foot deformity. With the U-net neural network, the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior high-resolution radiographs. The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability. The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency. Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies, this work adds to the growing evidence supporting AI as a powerful diagnostic tool.


Applying Agile Business Solutions as a Graduate Capstone Project

Abstract: The Department of Engineering Leadership and Program Management at the Citadel designed and implemented a graduate course to teach the fundamentals of Agile project management as part of the MS Technical Project Management curriculum.The course was designed to provide a solid foundation in Agile methodologies for project management practice to enable graduates to execute appropriate Agile techniques to plan, and deliver quality results that frequently deliver value to customers.During the course students are introduced to Agile processes which include developing user stories and story maps, estimate and plan for sprints, track project execution and releases using burn-down charts and conduct retrospective sessions.This paper discusses how a graduate student was able to successfully apply the Agile project management principles and processes learned in the graduate program to an Independent Study project designed to provide students with the opportunity to apply their knowledge and experience gained during the curriculum to addressing and solving a real business problem.In this case the student chose to develop and implement an Agile framework solution to a software implementation and performance problem his business organization was facing.


How Sonata Forms: A Bottom-Up Approach to Musical Form Yoel Greenberg New York: Oxford University Press, 2022 pp. xi + 264, ISBN 978 0 197 52628 6

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An Analysis of Speech Acts: Request in Daily Conversation

Abstract: In daily conversation, people frequently communicate implicitly as an issue that is covered in illocutionary acts. Illocutionary acts have five types, they are representative/assertive, directive, commissive, expressive and declaration. This research was aimed to analyze the used of request in daily conversation in the sentences form. The request speech acts were analyzed based on the Yule theory and the type of sentences based on Frank theory. The data were taken from the daily conversation between the researcher and the speaker in daily life. The subject of this research was researcher’s friend who doing talk with the researcher. This research employed qualitative research. The result of this research showed that there are three types of sentences, namely; statement sentences, interrogative sentences, and imperative sentences. From the total data 25 sentence, the researcher found that there are 18 sentences of imperative (72%), 5 sentences of interrogative (20%), and 2 sentences of statement (2%). Therefore, the most dominant type of sentence used by the speaker is imperative sentence. Meanwhile, the least type of sentence used by speaker is statement sentence. The speaker often used imperative sentence because of its function to get the hearer to do something. Imperative sentence can be used for directive speech act because the speaker wants the hearer or target to do what the speaker says.


Virtual Reality Lessons in Undergraduate Introductory STEM Courses

Abstract: Abstract The availability of low-cost virtual reality (VR) hardware and software provides opportunities to utilize its unique characteristics in higher education. Lessons designed for immersive learning in VR have to carefully take into consideration the affordances of virtual reality. Thus, appropriately designed VR lessons would positively impact student engagement. This paper provides details of the development of VR lessons for undergraduate students in introductory courses in five STEM disciplines. These developed lessons were implemented at an HBCU. The hardware and an off-the-shelf software for developing the VR lessons are shared. The participant attitudes and motivation to learn with the VR technology were investigated using items from validated instruments. The perceptions of the students about VR usability were also measured. Analysis of data indicates a positive impact on student attitudes and engagement with the content. This project is supported by NSF grant # XXXXXX


ReQUSA: a novel ReRAM-based hardware accelerator architecture for high-speed quantum computer simulation

Abstract: Abstract Quantum computers are currently regarded as an emerging computing technology that can solve problems more quickly than classical computers. However, since constructing a general quantum computer is technically difficult, quantum computer simulation has been used instead of real quantum computers. Simulating quantum computers on classical computers is challenging because the time and resources required for the vectormatrix multiplication (VMM) increase exponentially with the number of qubits. This paper proposes a new accelerator architecture called ReQUSA that leverages resistive random access memory (ReRAM) to accelerate the quantum computer simulation. The ReQUSA employs a ReRAM crossbar array structure, which is specialized for implementing the VMM, and a realized state method for reduced VMM operation. To the best of our knowledge, ReRAM-based accelerators for quantum computer simulators has not been previously reported. Here, we describe the hardware design of the architecture and compare the performances (hardware resource, simulation time, and accuracy) of our accelerator with those of current quantum computer simulators (QuEST, QPlayer, and Qiskit). On average, our proposed architecture reduced the simulation times by factors of ×10 4 and ×10 3 (×10 2 ) on average from those of QuEST and QPlayer (also Qiskit), respectively. In addition, our architecture achieved 99% accuracy in 16-bit fixed-point data representation.


Polarization-based all-optical logic gates using diffractive neural networks

Abstract: Abstract Optical logic operations are an essential part of optical computing. The inherent stability and low susceptibility of polarization to the external environment make it a suitable choice for acting as the logical state in computational tasks. Traditional polarization-based optical logic devices often rely on complex cascading structures to implement multiple logic gates. In this work, by leveraging the framework of deep diffractive neural networks (D 2 NN), we proposed a uniform approach to designing polarization-encoded all-optical logic devices with simpler and more flexible structures. We have implemented AND, OR, NOT, NAND, and NOR gates, as well as High-order Selector and Low-order Selector. These polarization-based all-optical logic devices using D 2 NN offer passive nature, stability, and high extinction ratio features, paving the way for a broader exploration of optical logic computing in the future.


Correction: Developing a patient flow visualization and prediction model using aggregated data for a healthcare network cluster in Southwest Ethiopia

Abstract: [This corrects the article DOI: 10.1371/journal.pdig.0000376.].


Quantum filter routing of single photons

Abstract: A quantum router takes the central role in an optical quantum network. However, how to route the expected photons with different frequencies to the targeted output ports of the quantum network is still a basic challenge. Here, we propose an effective approach, by setting the proper cavity-atom and photon-cavity chiral interactions, to realize quantum filter routing of single photons with different frequencies in a multi-channel quantum network. With the frequency serving as the signpost, the photons can be effectively routed to the targeted output ports by modulating the detunings between the cavities and the auxiliary atoms. Hopefully, this technique can play an important role in the construction of a highly efficient optical quantum network.


Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

Abstract: Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball’s trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking.


Board 336: Math to Makerspace: Evolution of a Bridge Program to Support Cohort Development

Abstract: Jill's teaching, service, and research activities focus on enhancing the first year student experience by providing the foundational technical skills, student engagement opportunities, and professional skill development necessary to improve success in the major, with emphasis on


Response Process Validity of the CBE Adaptability Instrument When Used with Engineering Instructors

Abstract: Her role in the College of Engineering at UNL is to lead the disciplinary-based education research initiative, establishing a cadre


Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

Abstract: Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, Bayesian approach for approximation of variances in measurement noise with KALMAN filter is applied for estimation of the dynamical state and measurement data in discrete dynamical system. Detection of uncertainty and estimation of those can be done simultaneously with adaptive KALMAN filter. This algorithm at each step time estimates noise variance and state of system with KALMAN filter. Then, approximation is formed at each step separately and at each step sufficient statistics of the state and noise variances are computed with a fixed-point iteration of a KALMAN filter. For showing influence of variance in measurement data on algorithm different simulations is applied. First, effect of variance and its distribution on detection performance is simulated in KALMAN filter without Bayesian formulation. Then simulation is applied to KALMAN filter with ability of variance tracking of measurement data.in these simulations, influence of distribution of measurement data in each step is estimated and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.


A Multi-strategy Enhanced Arithmetic Optimization Algorithm and Its Application in Path Planning of Mobile Robots

Abstract: Abstract A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the Arithmetic Optimization Algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.


Detection of centroblast cells in H&E stained whole slide image based on object detection

Abstract: Introduction Detection and counting of Centroblast cells (CB) in hematoxylin &amp; eosin (H&amp;E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells and compared with the World Health Organization (WHO) guideline that organizes lymphoma into 3 grades. Spotting and counting CBs is time-consuming and labor intensive. Moreover, there is often disagreement between different readers, and even a single reader may not be able to perform consistently due to many factors. Method We propose an artificial intelligence system that can scan patches from a WSI and detect CBs automatically. The AI system works on the principle of object detection, where the CB is the single class of object of interest. We trained the AI model on 1,669 example instances of CBs that originate from WSI of 5 different patients. The data was split 80%/20% for training and validation respectively. Result The best performance was from YOLOv5x6 model that used the preprocessed CB dataset achieved precision of 0.808, recall of 0.776, mAP at 0.5 IoU of 0.800 and overall mAP of 0.647. Discussion The results show that centroblast cells can be detected in WSI with relatively high precision and recall.


Optimal Realization of Yang–Baxter Gate on Quantum Computers

Abstract: Abstract Quantum computers provide a promising method to study the dynamics of many‐body systems beyond classical simulation. On the other hand, the analytical methods developed and results obtained from the integrable systems provide deep insights on the many‐body system. Quantum simulation of the integrable system not only provides a valid benchmark for quantum computers but is also the first step in studying integrable‐breaking systems. The building block for the simulation of an integrable system is the Yang–Baxter gate. It is vital to know how to optimally realize the Yang–Baxter gates on quantum computers. Based on the geometric picture of the Yang–Baxter gates, the optimal realizations of two types of Yang–Baxter gates with a minimal number of controlled NOT (CNOT) or gates are presented. It is also shown how to systematically realize the Yang–Baxter gates via the pulse control. The different realizations on IBM quantum computers are tested and compared. It is found that the pulse realizations of the Yang–Baxter gates always have a higher gate fidelity compared to the optimal CNOT or realizations. On the basis of the above optimal realizations, the simulation of the Yang–Baxter equation on quantum computers is demonstrated. These results provide a guideline and standard for further experimental studies based on the Yang–Baxter gate.


Augmented Botswanan Learning Experience

Abstract: The Augmented Botswana Learning Experience (ABLE) is an international collaboration between two STEM institutions that engages engineering students in solving real-world problems using a problem-based learning approach and service-based learning pedagogy.The ABLE project aims to create a collaborative learning environment for engineering students in Botswana and the US by utilizing augmented reality and Onshape technologies to develop their visualization and 3D modeling skills.The project provides an eight-week co-curricular program focusing on problem-based learning approaches to address authentic real-world problems, particularly in Botswana.To accomplish the ABLE project's goals, six high-performing students from an introductory engineering graphics course in the US will serve as consultants for design teams in Botswana.The emphasis is on creating an immersive experience that utilizes mixed reality for the students using AR technology, enabling them to participate in activities that involve decision-making, object design, basic assembly and the mentality of "modify and redefine".The ABLE project represents an innovative approach to engineering education that leverages technology and cross-cultural collaboration to prepare students for real-world problem-solving, using 3D Modeling along with AR technology.Deliverables for the project include a 3-D model of the original device, 3-D model of modified model utilizing AR technology, 3-D printed examples of final products and a 2-page write up detailing the modifications and describing its presumed increased functionality.