Online neuro-fuzzy model learning of dynamic systems with measurement noise

Abstract: Abstract Model identification of nonlinear time varying dynamic systems is challenging because the system behaviours may vary significantly in different operational conditions. If the changes are insufficiently captured by training data, the trained model is unable to capture the system response well when the operational condition changes. The model performance may also be deteriorated in real-time implementation due to the noise in sensors or the environment. This paper presents a self-adaptive Neuro-Fuzzy (NF) modelling framework to address these challenges. The NF model, trained offline based on experimental data, combines the Auto-Regressive with eXogenous (ARX) models and Gaussian activation functions to capture the nonlinear system behaviours. During online implementation, the ARX model parameters are updated using new data through a recursive generalised least squares method, which embeds a noise model to eliminate effects of the noise. The online updating algorithm has a provable convergence guarantee and enables the proposed NF model to adapt to changes in system behaviours automatically. Efficacy of the algorithm is verified through two numerical examples and an experiment on a commercial automotive engine.


Zk-SNARKs-Based Anonymous Payment Channel in Blockchain

Abstract: Payment channels serve as an effective solution to the scalability problem of cryptocurrencies, which significantly increase transaction rates by allowing users to conduct large-scale offline transactions off-chain without posting everything to the blockchain. However, the existing payment channels lack privacy protection for the transaction amount and the linking relationship between the two parties to the transaction. Therefore, in order to address the scalability and privacy issues of cryptocurrencies such as Bitcoin, this paper proposes a zk-SNARKs-based anonymous payment channel (zk-APC), which supports an unlimited number of off-chain payments between the payer and the payee and protects the privacy of the participants. Specifically, the proposed scheme achieves relational anonymity and amount privacy for both on-chain and off-chain transactions in the payment channel through utilizing zero-knowledge proof (zk-SNARKs) and commitment schemes. This paper proves that the proposed method is more effective than similar schemes through a performance evaluation.


Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data

Abstract: Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL) by learning from expert demonstrations in addition to interactions with the environment. In this paper, we propose a framework that combines techniques from search-based testing with RLfD with the goal to raise the level of dependability of RL policies and to reduce human engineering effort. Within our framework, we provide methods for efficiently training, evaluating, and repairing RL policies. Instead of relying on the costly collection of demonstrations from (human) experts, we automatically compute a diverse set of demonstrations via search-based fuzzing methods and use the fuzz demonstrations for RLfD. To evaluate the safety and robustness of the trained RL agent, we search for safety-critical scenarios in the black-box environment. Finally, when unsafe behavior is detected, we compute demonstrations through fuzz testing that represent safe behavior and use them to repair the policy. Our experiments show that our framework is able to efficiently learn high-performing and safe policies without requiring any expert knowledge.


Toward Automatically Completing GitHub Workflows

Abstract: Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood.


Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation

Abstract: Reliable effort estimation is of paramount importance to software planning and management, especially in industry that requires effective and on-time delivery. Although various estimation approaches have been proposed (e.g., planning poker and analogy), they may be manual and/or subjective, which are difficult to apply to other projects. In recent years, deep learning approaches for effort estimation that rely on learning expert features or semantic features respectively have been extensively studied and have been found to be promising. Semantic features and expert features describe software tasks from different perspectives, however, in the literature, the best combination of these two features has not been explored to enhance effort estimation. Additionally, there are a few studies that discuss which expert features are useful for estimating effort in the industry. To this end, we investigate the potential 13 expert features that can be used to estimate effort by interviewing 26 enterprise employees. Based on that, we propose a novel model, called Fine-SE, that leverages semantic features and expert features for effort estimation. To validate our model, a series of evaluations are conducted on more than 30,000 software tasks from 17 industrial projects of a global ICT enterprise and four open-source software (OSS) projects. The evaluation results indicate that Fine-SE provides higher performance than the baselines on evaluation measures (i.e., mean absolute error, mean magnitude of relative error, and performance indicator), particularly in industrial projects with large amounts of software tasks, which implies a significant improvement in effort estimation. In comparison with expert estimation, Fine-SE improves the performance of evaluation measures by 32.0%-45.2% in within-project estimation. In comparison with the state-of-the-art models, Deep-SE and GPT2SP, it also achieves an improvement of 8.9%-91.4% in industrial projects. The experimental results reveal the value of integrating expert features with semantic features in effort estimation.


RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness

Abstract: Deep Neural Networks (DNNs), an emerging software technology, have achieved impressive results in a variety of fields. However, the discriminatory behaviors towards certain groups (a.k.a. unfairness) of DNN models increasingly become a social concern, especially in high-stake applications such as loan approval and criminal risk assessment. Although there has been a number of works to improve model fairness, most of them adopt an adversary to either expand the model architecture or augment training data, which introduces excessive computational overhead. Recent work diagnoses responsible unfair neurons first and fixes them with selective retraining. Unfortunately, existing diagnosis process is time-consuming due to multi-step training sample analysis, and selective retraining may cause a performance bottleneck due to indirectly adjusting unfair neurons on biased samples. In this paper, we propose Responsible UNfair NEuron Repair (RUNNER) that improves existing works in three key aspects: (1) efficiency: we design the Importance-based Neuron Diagnosis that identifies responsible unfair neurons in one step with a novel importance criterion of neurons; (2) effectiveness: we design the Neuron Stabilizing Retraining by adding a loss term that measures the activation distance of responsible unfair neurons from different subgroups in all sources; (3) generalization: we investigate the effectiveness on both structured tabular data and large-scale unstructured image data, which is often ignored in prior studies. Our extensive experiments across 5 datasets show that RUUNER can effectively and efficiently diagnose and repair the DNNs regarding unfairness. On average, our approach significantly reduces computing overhead from 341.7s to 29.65s, and achieves improved fairness up to 79.3%. Besides, RUNNER also keeps state-of-the-art results on the unstructured dataset.


FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

Abstract: While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learning from compiler intermediate representations (IRs). Existing IR-based models typically utilize IR features such as instructions, control and data flow graphs (CDFGs), call graphs, etc. However, these methods confuse variable nodes and instruction nodes in a CDFG and fail to distinguish different types of flows, and the neural networks they use fail to capture long-distance dependencies and have over-smoothing and over-squashing problems. To address these weaknesses, we propose FAIR, a Flow type-Aware pre-trained model for IR that involves employing (1) a novel input representation of IR programs; (2) Graph Transformer to address over-smoothing, over-squashing and long-dependencies problems; and (3) five pre-training tasks that we specifically propose to enable FAIR to learn the semantics of IR tokens, flow type information, and the overall representation of IR. Experimental results show that FAIR can achieve state-of-the-art results on four code-related downstream tasks.


Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents

Abstract: The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users' perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of different XAI approaches on different types of decisions on the attitudes of (potential) applicants is scarce. Therefore, in this study, we use a quantitative, quota-representative study (n = 490) to assess the acceptance of CAs in recruiting. By applying an experimental within-subject design, we provide a more nuanced perspective on why and when providing explanations increases user acceptance. We also show that contextual factors such as the type of assessed skills are major determinants of this effect, and we conclude that XAI is not a "one-size-fits-all approach." Based on the insight that contextual factors of the decision problem are more important than the type of XAI approach itself, we argue that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user's characteristics and preferences.


Enhancing online health consultations through fuzzy logic-integrated attribute-based encryption system

Abstract: Online health consultations are becoming more popular as a result of technological improvements. Patients routinely look for information about medical disorders online, which could jeopardize the privacy of medical records and increase the workload of healthcare professionals. Nonetheless, academics continue to be extremely concerned about issues related to the quality characteristics that relate to the current architectural models, such as energy consumption, latency, resource utilization, scalability, and packet loss. This method, however, also results in a significant strain being placed on medical experts who must sort through vast amounts of medical records to extract certain information. This paper presents a novel ciphertext policy attribute-based encryption method coupled with fuzzy logic to overcome these issues. This solution uses a hybrid structure of IPFS and blockchain to store data and enables complex bidirectional access control. Before being added to IPFS, medical records are encrypted. To ensure data integrity, related IPFS hash indexes are then added to the blockchain. Utilizing attribute-based technology, users’ data is encrypted to give them fine-grained bidirectional access control. A thorough security analysis proves the system’s resilience, especially when faced with chosen plaintext assaults inside the random oracle model. Tests for this study were conducted using 10–50 attribute sets. This paper’s technique solely makes use of a hash operation. All things considered; the study demonstrates that the proposed design is more efficient than earlier schemes. Thus, from the comparison study above, it can be concluded that the system presented in this work is more efficient. Results from simulations provide additional support for the suggested methodology by highlighting the improved computing efficiency of users as compared to baseline conventional systems. This study demonstrates how technological advancement and healthcare requirements can coexist harmoniously, paving the way for secure and effective online medical consultations that are powered by fuzzy logic.


Modeling the enablers of blockchain technology implementation for information management in healthcare supply chains

Abstract: Purpose Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains. Design/methodology/approach This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique. Findings The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized. Practical implications To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study. Originality/value This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.


A Lightweight Image Super-Resolution Reconstruction Algorithm Based on the Residual Feature Distillation Mechanism

Abstract: In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.


Understanding poetry using natural language processing tools: a survey

Abstract: Abstract Analyzing poetry with automatic tools has great potential for improving verse-related research. Over the last few decades, this field has expanded notably and a large number of tools aiming at analyzing various aspects of poetry have been developed. However, the concrete connection between these tools and traditional scholars investigating poetry and metrics is often missing. The purpose of this article is to bridge this gap by providing a comprehensive survey of the automatic poetry analysis tools available for European languages. The tools are described and classified according to the language for which they are primarily developed, and to their functionalities and purpose. Particular attention is given to those that have open-source code or provide an online version with the same functionality. Combining more traditional research with these tools has clear advantages: it provides the opportunity to address theoretical questions with the support of large amounts of data; also, it allows for the development of new and diversified approaches.


A metastable RNG using interleaved NAND- and NOR-based TEROs

Abstract: This study introduces a novel approach to address the limitations observed in existing random number generator (RNG) designs, focusing on the resolution for insufficiently covered problem statements and study purposes. In response to these challenges, we propose a high-throughput metastable-based RNG tailored for cryptography applications. Our design is featured with a two-layer transient effect ring oscillator (TERO) architecture, meticulously crafted to mitigate the locking phenomenon and enhance RNG randomness. Leveraging interleaved two-layer TEROs, realised through NAND and NOR SR latches, our RNG achieves unpredictable irregular sampling, crucial for cryptographic applications. To further enhance irregular sampling, the first layer of the RNG employs a multiplexer that selects inputs randomly. This multiplexer is governed by selection signals generated by the second layer of the RNG, ensuring superior randomness. Implemented using TSMC 40-nm CMOS process, our RNG passed the FIPS 140–1 randomness test at a clock frequency of 100 MHz, boasting a remarkable throughput of 50 Mbps. This study represents a significant advancement in RNG technology, addressing previous shortcomings and paving the way for high-speed communication systems' secure cryptographic applications.


Web Application Security: A Pragmatic Exposé

Abstract: Many individuals, organizations, and industries rely on web applications for the daily operations of their businesses. With the increasing deployment and dependence on these applications, significant attention has been directed towards developing more accurate and secure mechanisms to safeguard them from malicious web-based attacks. The slow adoption of the latest security protocols, coupled with the utilization of inaccurate and inadequately tested security measures, has hindered the establishment of efficient and effective security measures for web apps. This paper reviews recent research and their recommendations for web security over the last four years. It identifies code injection as one of the recent most prevalent web-based attacks. The recommendations presented in this paper offer a practical guide, enabling individuals and security personnel across various industries and organizations to implement tested and proven security measures for web applications. Furthermore, it serves as a roadmap for security developers, aiding them in creating more accurate and quantifiable measures and mechanisms for web security .


DCLGM: Fusion Recommendation Model Based on LightGBM and Deep Learning

Abstract: Abstract The recommendation system can mine valuable information according to user preferences, so it is widely used in various industries. However, the performance of recommendation systems is generally affected by the problem of data sparsity, and LightGBM can alleviate the impact caused by data sparsity to a certain extent. To this end, this paper proposes a fusion recommendation model based on the LightGBM and deep learning—CLGM model. The model is composed of LighGBM, cross network and deep neural network. First, the features in the dataset are fused and extracted through LightGBM, and the feature with the highest classification accuracy is selected as the input of the neural network layer; Then, using the cross network and the deep neural network, the linear cross combination feature relationship and nonlinear correlation relationship between high-order features are respectively obtained; finally, the results obtained by the pre-order network are linearly weighted and combined to obtain the final recommendation result. In this paper, AUC and Logloss are used as evaluation indicators to verify the model on the public dataset Criteo and dataset Avazu. The simulation experiment results show that, compared with the four typical recommendation models, the recommendation effect of this model is better.


Evaluating the Effectiveness of Bayesian Knowledge Tracing Model-Based Explainable Recommender

Abstract: Explainable recommendation, which provides an explanation about why a quiz is recommended, helps to improve transparency, persuasiveness, and trustworthiness. However, little research examined the effectiveness of the explainable recommender, especially on academic performance. To survey its effectiveness, the authors evaluate the math academic performance among middle school students (n=115) by giving pre- and post-test questions based evaluation techniques. During the pre- and post-test periods, students were encouraged to use the Bayesian Knowledge Tracing model based explainable recommendation system. To evaluate how well the students were able to do what they could not do, the authors defined growth rate and found recommended quiz clicked counts had a positive effect on the total number of solved quizzes (R=0.343, P=0.005) and growth rate (R=0.297, P=0.017) despite no correlation between the total number of solved quizzes and growth rate. The results suggest that the use of an explainable recommendation system that learns efficiently will enable students to do what they could not do before.


The use of blockchain technology in enterprise financial accounting information sharing

Abstract: This work intends to comprehensively analyze the application of blockchain technology in enterprise financial accounting information sharing and address prevalent issues such as information opacity, data tampering, and data security in the current practices. Therefore, it writes smart contracts based on the Ethereum platform to achieve the secure sharing of financial accounting information between enterprises. This work employs a randomized experimental design approach, using a computer-generated random number program to divide 100 enterprises into experimental and control groups, each comprising 50 enterprises. Enterprises in the experimental group share financial accounting information using smart contracts on the Ethereum platform during the experiment. The financial personnel of these enterprises upload reconciled data to the corresponding smart contracts using the enterprise’s digital signatures after each month’s accounting process. Enterprises in the control group continue to use traditional methods of financial accounting information sharing (such as email and web platforms) to share financial data files directly. Quantitative analysis is performed to compare the data between the experimental and control groups. Empirical results reveal a notable enhancement in information-sharing efficiency by 25.7%, a 19.8% improvement in data accuracy, and a 13.6% reduction in financial information-sharing costs within the experimental group compared to the control group. This work provides compelling evidence that adopting blockchain-based information-sharing methods can effectively elevate data trustworthiness and security. Supported by systematic empirical findings, this work validates the significant potential of blockchain technology in the realm of enterprise financial accounting information sharing.


A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections

Abstract: Abstract In this study, a hybrid traffic signal control (HTSC) system based on phase and time optimization was developed. The Flower Pollination Algorithm (FPA) approach was used for phase optimization, while Type-2 Fuzzy Logic, optimized with the Crow Search Algorithm (CSA), was utilized for time optimization. The hybrid system's performance was investigated using nine different traffic conditions and four different intersection geometries. The hybrid system was compared with three controller systems which are a fixed-time signal controller, a signal controller based on the FPA approach (FPA_TSC), and the optimized Type-1 fuzzy logic signal controller (Type-1 FL-TSC). The HTSC approach achieved the best performance with about 32% improvement over the fixed-time traffic controller and it showed 5% and 6% better performance than the FPA_TSC and Type-1 FL-TSC, respectively. Considering the performance of the new hybrid system, it is effective in minimizing delays and driver dissatisfaction occurring from signalization. It also contributes to the reduction of emissions and fuel consumption. The HTSC approach can be used as an alternative signal control method in the control of intersections with high traffic volume due to its fast and effective performance.


Density estimation based soft actor-critic: deep reinforcement learning for static output feedback control with measurement noise

Abstract: The state-of-the-art deep reinforcement learning (DRL) methods, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), among others, demonstrate significant capability in solving the optimal static state feedback control (SSFC) problem. This problem can be modeled as a fully observed Markov decision process (MDP). However, the optimal static output feedback control (SOFC) problem with measurement noise is a typical partially observable MDP (POMDP), which is difficult to solve, especially for the continuous state-action-observation space with high dimensions. This paper proposes a two-stage framework to address this challenge. In the laboratory stage, both the states and the noisy outputs are observable; the SOFC policy is converted to a constrained stochastic SSFC policy, of which the probability density function is generally not analytical. To this end, a density estimation based SAC algorithm is proposed to explore the optimal SOFC policy by learning the optimal constrained stochastic SSFC. Consequently, in the real-world stage, only the noisy outputs and the learned SOFC policy are required to solve the optimal SOFC problem. Numerical simulations and the corresponding experiments with robotic arms are provided to illustrate the effectiveness of our method. The code is available at https://github.com/RanKyoto/DE-SAC.


Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

Abstract: Abstract As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat .


Exploring Automatic Readability Assessment for Science Documents within a Multilingual Educational Context

Abstract: Abstract Current student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help to assist teachers, we explore the performance of natural language processing approaches when dealing with educational science documents for secondary education. Currently, readability assessment is mainly explored in English. In this work we extend our research to Basque and Spanish together with English by compiling context-specific corpora and then testing the performance of feature-based machine-learning and deep learning models. Based on the evaluation of our results, we find that our models do not generalize well although deep learning models obtain better accuracy and F1 in all configurations. Further research in this area is still necessary to determine reliable characteristics of training corpora and model parameters to ensure generalizability.


The art of personalization of education: Artificial Intelligence on the stages of special education

Abstract: This study investigated the use of artificial intelligence (AI) in special education, with the aim of personalizing teaching and promoting the inclusion of students with disabilities and special needs. The central problem addressed was the need to adapt teaching individually, taking into account the unique characteristics of each student. To achieve this objective, a literature review was conducted, analyzing studies, research and experiences related to AI in special education. The methodology included a literature review of reliable academic sources, such as scientific articles and research reports. The results of the analysis indicated that AI plays a key role in personalizing teaching, adapting content and resources according to the specific needs of students. Furthermore, AI contributes to promoting inclusion by creating accessible and equitable learning environments. However, ethical issues such as student data privacy and transparency in AI algorithms are identified that require careful consideration. Furthermore, practical limitations, such as the availability of technological resources and teacher training, represent challenges to be overcome in the successful implementation of AI in special education. Final considerations highlight the importance of AI as a promising tool for personalizing teaching in special education, as long as its application is conducted ethically and aware of practical limitations.


BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Abstract: Abstract Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.


Utilization of a blockchainized reputation management service for performance enhancement of Smart Grid 2.0 applications

Abstract: Blockchain has become the technology enabler in delivering modern Smart Grid 2.0 functionalities. Many services including Peer-to-Peer energy trading, distribution network management, financial settlements, and energy data management are catered through blockchain-enabled platforms. However, areas such as service quality-based pricing strategies, supply–demand balancing in distribution system to attain enhanced reliability and consumption-oriented rewarding mechanisms need improving in order to achieve the full benefits of the envisaged grid architecture. In response, this study proposes a novel Blockchain-as-a-Service for Energy Trading (BaaSET) platform, which offers reputation-based services, executed through smart contracts for smart grid applications. Reputation-based grid operations are automatically executed through smart contracts deployed onto a blockchain. The reputation is estimated using power quality and reliability indices, obtained through grid measurements. Further, tests have been conducted to evaluate the associated latency and the implementation cost of the proposed blockchainized service architecture. Test results signify the performance to be comparatively better considering the state-of-the-art. The results further suggest alternatives to improve the scalability of the architecture, to cater the increasing number of stakeholders in the SG 2.0 environment.


Low-cost MEMS accelerometers for earthquake early warning systems: a dataset collected during seismic events in Central Italy

Abstract: This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.


SPEC 2.0 Smart Pill Expert System

Abstract: The article throws light on the implementation of an enhanced idea of the smart pill expert system. The proposed system is used to automate the capability of dispensing the right dosage of medicines at a given day-time interval. The spec 2.0 can be used at one's home, workplace, at hospitals by a user of any age group, and then possibly expanding the functionalities to a wider audience. It mainly focuses on providing access control and monitoring management functionality through a mobile app on a daily basis with no monthly subscriptions required. There will be alerts and SMSs sent across to the user based on the pill dispensing event's result. The spec 2.0 mainly focuses on providing an “over-dosage” functionality so that the user won't have to consume the medication if he hasn't taken them at the allocated time interval, to prevent over consumption. The system has been tested and the results have been formulated on a daily basis.


Point-level feature learning based on vision transformer for occluded person re-identification

Abstract: Person re-identification is challenging due to the presence of variations in pose and occlusion, which significantly impact the matching of visual features across different camera views and pose considerable difficulty for accurate person re-identification. This paper proposes a novel method for occluded person re-identification by introducing point-level feature learning based on vision transformers. Our approach utilizes a pose estimator to detect the keypoints of the human body and employs these points to locate intermediate features. These intermediate features of keypoints are input to a pose-based transformer branch to learn point-level features. Then, we design a part-based transformer branch to learn part-level features that capture visual features of different image parts, further enhancing the discriminative power of the learned features. Additionally, we employ a global branch to learn the global-level feature by treating the person's image as a single entity. Finally, we integrate point-level, part-level, and global-level features to represent a person's features. The experimental results on occluded and partial person re-identification datasets demonstrate the effectiveness of our proposed approach in improving re-identification. Our approach shows potential for improving person re-identification in scenarios with occlusion and pose variations.


Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces

Abstract: We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative subspace while projecting out all previously identified ones, a combined subspace carrying all class-specific information can be found. This facilitates a detailed analysis of feature relevances, and enables improved low-dimensional representations and visualizations of labeled data sets. Additionally, the IRMA-based class-discriminative subspace can be used for dimensionality reduction and the training of robust classifiers with potentially improved performance.


Contrastive completing learning for practical text-image person ReID: Robuster and cheaper

Abstract: Text-image person re-identification (TIReID) seeks to leverage textual descriptions for the retrieval of target pedestrians. Due to its versatility, TIReID has gained increasing attention. However, manual annotation of textual descriptions and identity labels can be time-consuming and costly, limiting its scalability in practical settings. Privacy concerns and poor data storage can lead to data loss or ineffectiveness, further exacerbating challenges in real-world scenarios. To address these limitations, we propose for the first time incomplete Text-image person re-identification (iTIReID), which comprises a small amount of complete pairwise data and a large amount of incomplete data, where all identity labels are unavailable. We introduce a novel Contrastive Completing Learning (CCL) framework for iTIReID, consisting of two stages: Pure Contrastive Learning (PCL) and Feature Completion Contrastive Learning (FCCL). In PCL, only complete pairwise data is utilized for training, which serves as a preliminary improvement of the model’s capacity and prepares for the upcoming feature completion stage. In FCCL, available features are used to complete missing modality features and facilitate effective training with incomplete data. During this process, Cross-modal Semantic Measure (CSM) is proposed to leverage intra-modality similarity to measure cross-modal similarity and filter out features with the highest semantic similarity, thereby circumventing modality discrepancy. Semantic-Weighted Generation (SWG) is proposed to generate approximate features based on the semantic similarity weight of the similar features. To fully leverage pairwise data for label-free training, we introduce the contrastive CMPM (CCMPM) loss for contrastive learning to achieve weakly supervised training. Experimental results verify the effectiveness of our proposed methods and demonstrate competitive performance compared to fully supervised methods using complete data.


On Size-Independent Sample Complexity of ReLU Networks

Abstract: We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously [GRS20] obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.


TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision

Abstract: End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on region-of-interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with a transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multitask modeling. It allows for mutual training and optimization of classification, segmentation and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an adaptive global aggregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the suboptimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on the TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.


QoS‐enhanced load balancing strategies for metaverse‐infused VR/AR in engineering education 5.0

Abstract: Abstract The evolution of augmented reality (AR) and virtual reality (VR) technologies has ushered in a new era of immersive experiences, with applications ranging from entertainment to education. The proposed framework introduces a fog layer with an innovative, improved geographic load‐balancing algorithm. It optimizes load distribution and provides quality of service (QoS) parameters that are important for enhancing user experiences for AR/VR applications. The iFogSim toolkit experimentally validates the framework in electroencephalogram‐based VR/AR gaming applications. Also, the proposed framework is tested in a diverse range of scenarios. Results show that the proposed algorithm improves QoS for AR/VR applications with noticeable improvements on average latency, jitter, and packet loss. Future research should aim to address these limitations for a more comprehensive understanding of the proposed framework's practicality and effectiveness. The development of immersive technologies will continue to expand in multiple sectors, and future research will focus on energy efficiency, security, and real‐world applications.


Building causal models for finding actual causes of unmanned aerial vehicle failures

Abstract: Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause–effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause–effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause–effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.


Backpropagation through Back Substitution with a Backslash

Abstract: .We present a linear algebra formulation of backpropagation which allows the calculation of gradients by using a generically written "backslash" or Gaussian elimination on triangular systems of equations. Generally, the matrix elements are operators. This paper has three contributions: (i) it is of intellectual value to replace traditional treatments of automatic differentiation with a (left acting) operator theoretic, graph-based approach; (ii) operators can be readily placed in matrices in software in programming languages such as Julia as an implementation option; (iii) we introduce a novel notation, "transpose dot" operator "\(\{\}^{T_\bullet }\)" that allows for the reversal of operators. We further demonstrate the elegance of the operators approach in a suitable programming language consisting of generic linear algebra operators such as Julia [Bezanson et al., SIAM Rev., 59 (2017), pp. 65–98], and that it is possible to realize this abstraction in code. Our implementation shows how generic linear algebra can allow operators as elements of matrices. In contrast to "operator overloading," where backslash would normally have to be rewritten to take advantage of operators, with "generic programming" there is no such need.KeywordsbackpropagationAutoDiffbackslashoperatorJuliagraphMSC codes05C50


Incorporating a Teach for Mastery System in a Computer Science Course

Abstract: Teaching for Mastery is the idea that students should progress through material when they have conquered the previous material it depends on and each student may progress at their own pace.The paper documents the process and results of incorporating a teach for mastery system into a Computer Science Programming Language Design course.The course is aimed at junior and senior undergraduate students and its goal is to introduce them to the four main language families and to show them how to teach themselves a programming language.This teach for mastery system caused both student learning and student satisfaction to improve in a course that was already well received before these changes.Other benefits of this system include a marked decrease in the amount of time spent grading and a straightforward way to identify which subjects need to be re-thought and which are being learned to the intended depth.The system does come with an increase in the amount of contact time spent with students which is both a positive in that you develop a closer relationship with the students and a negative in that it is a large amount of contact time.Contributions include the structural changes and teaching artifacts needed to implement teaching for mastery in a course.The structural changes include flipping the classroom and replacing traditional sit-down exams with short one-topic take-when-ready exams.The teaching artifacts produced include a concept map.Students leave the course with a clear list of which topics they mastered and which they are still working on.This model still permits room for traditional laboratory and project components.


Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining

Abstract: Abstract A vast body of research demonstrated the learning benefits and importance of collaborative learning (i.e., pedagogical approaches involving students learning collectively by working on collaborative assessments that require joint intellectual effort). Such benefits include increasing student retention, higher-level thinking skills, improved communication skills, responsibility for students' own learning, and more. Acknowledging these benefits, more instructors are integrating collaborative learning into their curriculum (Järvenoja et al., 2020). With this increased adoption of collaborative learning approaches, instructors must understand their students' problem-solving approaches during collaborative learning activities to better design their class activities. Among the multiple ways to reveal collaborative problem-solving processes, temporal submission patterns is one that is more scalable and generalizable in Computer Science education. In this paper, we provide a temporal analysis of a large dataset of students' submissions to collaborative learning assignments in an upper-level database course offered at a large public university. This research uses sequential pattern mining techniques to identify temporal patterns that capture students' problem-solving strategies when working on collaborative assignments. We study the log data collected from an online assessment and learning system, which contains the timestamp data of each student's submissions to a problem on the collaborative assignment. Each submission was labeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter or longer than the 25th and 75th percentile. Our analysis revealed seven submission patterns after applying two compacting rules: Q, Q-LNG, M, M-LNG, QM-LNG, S, and S-LNG, where "LNG" dictates that the compacting rule was applied. The compacting rule indicates that there were at least three consecutive submissions of the same pattern (i.e., Q-LNG is a compaction of Q-Q-Q). We then investigated how submission patterns link to become discriminating sequences by computing the transition probabilities with L* metrics. We identified transition pairs that include the same submission patterns in different transition positions (i.e., transit from and transit to) to construct more extended transition patterns by combining transition pairs. For example, the transition pairs M-LNG to S-LNG and S-LNG to Q have a correlation of 0.782 (p<0.001), so we can generate a three-pattern transition: M-LNG to S-LNG to Q. With these transition patterns, we can then better pinpoint collaborative learning problem-solving behaviors among students.


Evaluating the Impact of a Multimodal Cybersecurity Education Approach on High School Student Cybersecurity Learning

Abstract: Abstract Introduction: The need for cybersecurity is growing as we become more dependent on digital tools and programs to run our daily lives, including the sharing and storing of personal data. According to research reports, a successful security awareness program is one of the critical milestones in boosting and bolstering cybersecurity [1]. The rise in cyberattacks shows that the conventional methods of education and awareness still need to be improved to develop the required cybersecurity competencies [2]. This project aims to increase the understanding and abilities of high school students in cyber security. The primary goal of this project is to give students a complete understanding of cybersecurity by using an interactive visualization tool, explaining cybersecurity through lectures, lab/experimental sessions, and smartphone app development exercises. To improve high school students' understanding of cybersecurity, Purdue University Northwest and the University of Toledo conducted a week-long summer camp, collected relevant data, and analyzed the results to see the impact. Goal and Objective: This project aims to expand high school student's knowledge about cybersecurity. Therefore, we offered a free summer camp whose main goal was to educate high school students on the necessity of cybersecurity and the many cyberattacks they can encounter daily, like phishing, scareware, and ransomware. The morning sessions began with a straightforward explanation of cybersecurity principles, followed by interactive lab sessions in the afternoon. Through these lab sessions, students also understood how phishing, scareware, and ransomware operate in the real world. Students learned how cyber-attacks might occur through this. Utilizing MIT App Inventor, students also learned how to design applications. To accomplish the goals of our study, we completed a survey that included 6 pre- and 6 post-surveys, which we then distributed to the students. Our survey findings show that the summer camp successfully taught cybersecurity to students irrespective of gender, race, or previous background in the domain. Research Methodology and Activities: We engaged the students in several activities to help them learn about cybersecurity, such as (i) Interactive visualization tool, (ii) MIT App Inventor (for smartphone app development), (iii) Lecture sessions with thorough explanations, (iv) lab/hands-on sessions, and (v) exposure to various types of malware. The interactive visualization tool was created using the Unity gaming engine, a platform for game-based programming [3]. Students gained practical knowledge using this application in cryptography, malware, network security, and web security. A quiz is included in each module to assess the student's knowledge of the content. In MIT App Inventor, the elements of an app are represented as blocks, and an app is created by arranging the blocks in the correct order. So, the students don't need any technical knowledge. The research questions used during the summer camp are listed below. 1. What curriculum components are most effective in teaching the content? a. What are the characteristics of each module that interest students? b. Can these characteristics be employed to make other modules more interesting? 2. How effective are interactive animated visualization modules more interesting? a. Are the unique differences based on gender and/or race? b. Does student perception of cybersecurity concepts improve? References: [1]. Y. Peker, L. Ray. Online Cybersecurity Awareness Modules for college and High school Students. [2]. H. Qusa, J. Tarazi. Cyber-Hero: A Gamification framework for Cyber Security Awareness for High School Students. [3]. Unity.(2022)Unity scenes. Available: https://docs.unity3d.com/2022.1/Documentation/Manual/CreatingScenes.html Accessed: November 7 2022.


Identifying the Static and Dynamic Nature of Course Content: Focus on Construction

Abstract: Abstract In the US, there is a variety of programs that produce students ready to be employed in the construction industry. These programs are generally accredited by ABET or ACCE. ABET accredited programs are reviewed by a variety of commissions, such as the Engineering Accreditation Commission, the Engineering Technology Accreditation Commission, and the Applied and Natural Science Accreditation Commission, and have descriptions such as "Construction Management", "Construction Engineering", "Construction Engineering Technology", etc. ACCE accredited programs are more streamlined, and their descriptions are in general "Construction Management". In any case, these programs have curricula that are similar in various ways, and this research aims to identify these similarities. Research presented in this paper shows results from a survey of faculty from these programs that aimed to identify: 1) courses/topics with content that does not dramatically change over time and does not need to be constantly updated, 2) courses/topics that need constant updating and for what reasons, 3) courses/topics that are novel and represent material that is in the forefront of construction engineering/management technology, and 4) topics that would be appearing in curricula in the near future. The results of this investigation will help construction programs plan their future curricula and help faculty ensure an even distribution of their workload.


Central bank digital currencies and the future of monetary sovereignty

Abstract: Initiatives to develop central bank digital currencies (CBDCs) have accelerated dramatically across the world in the last few years. What is their significance for longstanding scholarly debates about the fate of monetary sovereignty in the digital age? Public officials themselves state that a central reason for these initiatives is to defend monetary sovereignty against threats emanating from the growth of private digital currencies, foreign CBDCs, and the displacement of state issued cash by private digital payments instruments. They (and others) highlight how CBDCs could even strengthen monetary sovereignty by bolstering financial inclusion as well as enhancing the state's capacity to monitor and control monetary transactions and conduct monetary policy. In these ways, CBDC initiatives cast doubt on arguments that suggest the digital currency revolution necessarily challenges monetary sovereignty. However, critics of that line of argument also need to be cautious because CBDCs are already attracting much political opposition. Even if that opposition is overcome, CBDCs may be implemented in constrained ways or be unsuccessful in meeting their goals for other reasons. In short, CBDCs may hold the potential to defend and even strengthen monetary sovereignty, but it is far from clear whether this potential will be realised.


Smarter and resilient smart contracts applications for smart cities environment using blockchain technology

Abstract: Our daily activities revolve around various technologies, the smart city technologies and the services they offer have all influenced modern living conditions. The purpose of a smart city is to enhance people's quality of life and provide possibilities to address social and environmental issues. As a developing technology, blockchain is beneficial for enhancing smart city services including food tracking, supply-demand matching, the security of connected cars, and regulatory compliance. We propose a new framework for describing how blockchain technology is used in smart contracts to improve security, dependability, and many other positive outcomes in a smart city environment. We propose smarter and resilient smart contracts using blockchain technology to manage real estate information. We propose a framework with tamperproof functionality to store the data and retrieve the data. Smart contract mathematical computations like overhead rate, execution time, mean computational cost, standard deviation, throughput, and resource utilization are evaluated and the results are compared. This paper focuses on real-world rental file management scenarios to demonstrate the benefits of blockchain technology and how it is used to address the issues that currently exist in developing smart contracts and services while exchanging real land and other properties in a smart city environment.


Community-Driven, Participatory Engineering Design Frameworks to Shape Just, Liberatory Health Futures

Abstract: Abstract Engineering education regularly overlooks people it is supposed to serve, especially those historically and systemically marginalized by technology. To address this, we must teach students of engineering to engage critically with who they are designing and work directly with communities living closest to systemic problems. This can be done through community-driven, participatory design where community-based lived experiences inform the development of technical solutions. This work is vital for identifying what problems need technical solutions, and the limitations of technical solutions in addressing systemic challenges. I will investigate what this means in my field of medical technology, rife with racism, sexism, ableism, due to most technologies being developed by those in power, that being white, cis, able-bodied men. I argue that systemically marginalized populations receive worse medical care because of technology and how it was designed. Embedding myself in the narrative, I detail my own experiences living in a chronically ill body as I experience the severe limitations of current technologies, and how that impacts my own health. Tracing legacies of resistance to dominant systems of power within biomedicine, I uncover the stories of lay experts challenging the existing "politics of knowledge" to democratize biomedical innovation for their own benefits. Weaving together theories of black feminism, queer liberation, disability justice, and embodiment with design justice and participatory design, I outline principles for engineering liberation through health innovation. These five principles include 1) understand the system shaping inequity 2) realize your positioning and power, at the intersections of race, gender, sexual orientation, class, and (dis)ability 3) establish relationships with those closest to health disparities to root out root causes and stay accountable to potential harms 4) build technologies that create value for all parties while remaining "safe to fail" and 5) connect the innovation to a greater political strategy for achieving equity and liberation. This work in progress paper ends with a call to action for engineers to choose a side: do we serve as architects of the visions of the powerful, or the visions of the public? As architects of medical technology, our decisions shape who lives and thrives and who suffers and dies.


Using VR (Virtual Reality) Technology to Teach Fall Safety Topics to Students: Simulation Outcomes and Student Learnings

Abstract: VR (Virtual Reality) which is a new technology, has been used to simulate training that is too expensive and time-consuming to practice in real life.This study aims to find students' learning improvement on Fall related safety by using low-cost VR technology.This will also help practicing how to survive Fall hazard techniques.In the current study, a well-known VR software was used to introduce participants to Fall safety scenarios.Rather than students being passive viewers where the instructor/trainer only demonstrates, the students were given full control to navigate through the virtual environment and to interact with virtual objects using a headset and a handheld controller.Students identified a simple climbing and Fall hazard example before going to more rigorous training in the virtual environment.Results indicated that students were more engaged by the new game-like learning environment and that they reported that they found the experience fun, interesting, and thought-provoking.Their enhanced enthusiasm for what is relatively standard Fall-safety training demonstrates the promise of using game-based virtual environments for vital but otherwise tedious Fall-safety skills training for students.


Using Deep Learning and Augmented Reality to Improve Accessibility: Inclusive Conversations Using Diarization, Captions, and Visualization

Abstract: Abstract The problem of diarization - identifying different speakers in a conversation stream - has not been sufficiently addressed for deaf and hard-of-hearing students in learning communities such as student design teams in engineering and related STEM disciplines. Though the accuracy of the latest automated real-time speech-to-text systems is now approaching usable low word error rates, the generated text output is an incomplete representation of a multi-party conversation; In short, it solves the "what" but not the "who." This creates barriers to our ideal of an inclusive and equitable learning community. Thus students who are deaf or hard of hearing are further marginalized and excluded from multi-party peer discussions with non-deaf participants because it is hard to visually follow who is speaking. To address these communication barriers, we utilized the Human Centered Engineering Design framework to identify a set of features that overcomes the above barriers. This paper explores computerized diarization techniques that utilize a wide set of algorithms and audio metrics to assist in speaker identification. These techniques include mel-frequency cepstrum coefficients (MFCC), volume, fundamental frequency identification, and deep learning of voice prints. For the goals described in this paper, a subset of existing algorithms that respected privacy and legal constraints was selected and evaluated for the purposes of identifying speakers using a live audio stream. Several visualization methods were also designed and evaluated. These included visualization of embedding mel-frequency cepstrum, speaker identifier, pitch, volume, and other voice characteristics into a live caption stream. Both diarization and visualization were integrated into a live captioning tool, ScribeAR, previously introduced in ASEE regional proceedings, and rendered using a lightweight Augmented Reality display. In order to facilitate captioning services in areas with limited network connectivity, whisper.cpp, a derivative of OpenAI's Whisper project, was also incorporated into the application. Links to the open source project are included so that other educators may adopt this inclusive practice. Some accessibility-related opportunities that could be used as motivating design projects for engineering students are described.


Clustering of Animation View Times in an Interactive Textbook for Material and Energy Balances

Abstract: Abstract Data science tools can help elucidate trends from clickstreams and other interactions generated by students actively using interactive textbooks. Specifically, data generated when using animations, which are multi-step visuals with text captions, will be presented in this work. Each animation step divides content into appropriate chunks, and so aligns with tenets of cognitive load theory. Both the quantity and timing of students' clicks record provide large data sets when examining students across hundreds of animations and multiple cohorts. Specifically, an interactive textbook for a chemical engineering course in Material and Energy Balances will be examined and build upon data presented previously. While most of the previous data focused on very high reading completion rates (>99% median) compared to traditional textbooks (20-50%), a deeper examination of how long students take when watching animations will be explored. With over 140 unique animations and tens of thousands of completed views over five cohorts, a spectral clustering algorithm applied to students' animation view times distinguished several types of animation watching behavior as well as monitor changes in this animation watching behavior over the course of a semester. After examining different numbers of clusters, two or three clusters in each chapter captured the animation usage. These clusters usually correspond to a group of students who watched animations at 1x speed (longer), another group who watched at 2x speed (shorter), and a third group, when present, who watched irregularly, including skipping animations. Overall, more students belonged to the belonged to the cluster with longer view times, with 63% of students aggregated over all cohorts and chapters compared to 35% of students in the cluster with shorter view times. The remaining 2% of students belonged to the irregular cluster, which was present in less than one quarter of the chapters. Many students stayed in the same cluster between chapters, while a smaller fraction switched between the longer and shorter clusters.


Utilizing Virtual Reality to Fortify Professional Skills in Engineering Education

Abstract: Several skills are needed and, most of the time, are required by the accreditation bodies as part of the preparation for entering the professional practice of engineering.Academic institutions require vision, planning, and time and budget allocation to achieve these objectives.Students are required to produce solutions to meet the specific consideration.For that reason, practical experience in engineering principles is the key to enhancing student knowledge in engineering education.Virtual reality (VR) technology can be implemented to simulate the real workplace and for educational purposes.Applying VR can encourage students to advance their understanding of the course content.And also, the student should be able to actively engage in the learning process.Implementing this technology makes it possible to improve the effectiveness of education by simplifying what is normally challenging to understand in physical classrooms.In the case of the exploitation of energy resources to differentiate the operation of offshore oil and gas rigs, VR technology can be applied to enhance the understanding and visualization of the requirement and components of the types of equipment.This paper presents real-world petroleum facilities to the classroom with the help of VR technology in the Middle East campus.As part of the innovative teaching methods in engineering courses, the main objective of applying VR is to overcome the challenge of the limited real trips, improve interest, and augment the student learning experience by applying innovative teaching methods.VR technology connects formal and informal learning processes with actual experiences.Several engineering concepts can be easily introduced thru the technology.Feedback from students shows their excitement and engagement in the learning process.Also, it supports promoting an active learning process for the new generation and engineering education.


Undergraduate Research in Quantum Computing: Lessons Learned from Developing Student Researchers

Abstract: At Loyola she has taught all levels of lectures and labs


Survey of Research in Engineering Librarianship, 2015-2019

Abstract: Abstract This work-in-progress research study aims to examine what research was conducted pertaining to engineering librarianship from 2015-2019 (pre-pandemic) with a particular focus on methodology. Peer-reviewed articles published between 2015-2019 were surveyed to identify who is performing research in engineering librarianship, what research methods are being used, if the studies are repeatable, and if the methods chosen are appropriate for the research question under investigation. Each paper that matched the inclusion criteria was examined and information was extracted relating to authorship, journal published in, research design, method, and analysis, as well as research question and instrument (when relevant). Simple statistical analysis will be conducted to derive patterns around research papers in engineering librarianship during this 5-year period. This paper will report the in-progress results of this comprehensive literature review.


Work in Progress: Physiological Assessment of Learning in a Virtual Reality Clinical Immersion Environment

Abstract: Abstract Personalized learning is one of the 14 grand challenges for engineering in the 21st century identified by the National Academy of Engineering. Education is shifting from a one-size-fits-all approach to a personalized process in which learning is tailored to a student's individual needs and learning abilities. In addition to the need for personalized education, the CHIPS and Science Act has listed relevant and experiential training as a strategic objective for US educational institutions. In biomedical engineering programs, this includes the need to perform clinical immersion, or learning from synergistic transactions between people and the environment within a healthcare setting. However, due to a lack of access to nearby medical centers, access for non-essential personnel enter hospitals, and increasing engineering class sizes, the ability for engineering students to shadow physicians has become difficult. To be able to allow students to perform clinical immersion without physically entering a hospital, the Department of Biomedical Engineering at the the University of California Irvine (UCI) has developed a virtual reality clinical immersion platform for student learning of unmet clinical needs and medical device design. This platform and the corresponding learning modules were piloted in Spring of 2022 as a junior-level unmet needs finding course, where students utilized the virtual reality clinical procedures to identify and screen unmet clinical needs using learning methods adopted from the Stanford BioDesign Process, and determine a proposed innovation for the problem that can be designed and validated in their senior capstone course the following year. A virtual reality platform was utilized over video recordings for the program, as prior research suggests that virtual reality for student learning can have a higher positive effect on knowledge transfer and self-efficacy than video recordings. To assess whether knowledge transfer is occurring within the virtual reality clinical immersion platform, and further develop the efficacy of the virtual reality clinical immersion experiences, the authors are developing a phenomenological framework that has been previously utilized in other virtual reality research applications. To this end, the developed framework analyzes the attention to interactions between the users and the virtual environment they are immersed in. To test the framework, student volunteers enrolled in the Spring of 2023 offering of the unmet clinical needs finding course will complete questionnaires regarding their experience engaging with the virtual content before, during, and after taking the course alongside participating in one-on-one interviews (UCI IRB Exempt No. 904). To analyze their phenomenological responses to the virtual environments, the students will also perform the virtual reality experiences while their visual attention data is collected through eye tracking within a virtual reality headset (HP Omnicept Reverb G2). This information will be used to detect what and for how long the student is looking at in the environments. In addition, electroencephalogram (EEG) recordings (Neuroscan 19-Channel Quik-Cap, Biopac MP 160 Data Acquisition System) will be synchronized with the eye tracking information to assess whether there are neurological correlates of learning occurring within the environment using known EEG correlates of learning based on prior research findings. By comparing the physiological correlates of attention and learning against the course learning outcome assessments, such as a laboratory innovation notebook, the authors hope to be able to develop real-time interventions based on the individual user's physiological patterns of learning to improve innovation and unmet clinical needs finding skills within virtual reality environments.


Cybersickness in Virtual Reality: The Role of Individual Differences, Its Effects on Cognitive Functions and Motor Skills, and Intensity Differences during and after Immersion

Abstract: Background: Given that VR is used in multiple domains, understanding the effects of cybersickness on human cognition and motor skills and the factors contributing to cybersickness is becoming increasing important. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills. Methods: 30 participants, 20–45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR. During immersion, they were exposed to a roller coaster ride. Before and after the ride, participants responded to the CSQ-VR and performed VR-based cognitive and psychomotor tasks. After the VR session, participants completed the CSQ-VR again. Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness. Pupil dilation emerged as a significant predictor of cybersickness. Experience with videogaming was a significant predictor of cybersickness and cognitive/motor functions. Cybersickness negatively affected visuospatial working memory and psychomotor skills. Overall the intensity of cybersickness’s nausea and vestibular symptoms significantly decreased after removing the VR headset. Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness. Pupil dilation appears to be a cybersickness biomarker. Cybersickness affects visuospatial working memory and psychomotor skills. Concerning user experience, cybersickness and its effects on performance should be examined during and not after immersion.


The State of the Practice Integrating Security in ABET Accredited Software Engineering Programs

Abstract: Abstract Within the software engineering discipline, concerns related to security continue to grow. Since the early 2000's, the number of cyberattacks against deployed software systems has significantly grown. In 2014, recognizing this concern, a modification to the ABET EAC program accreditation criteria for software engineering was made, explicitly requiring topical coverage of security for accredited programs. Since taking effect in 2016, all programs in software engineering have been required to demonstrate appropriate coverage of the topic as part of the accreditation process. While the criteria requires that the topic of security be covered, the implementation has been left open to individual programs. This article serves two purposes. First and foremost, it provides an updated status on the demographics of accredited software engineering programs. In doing so, it also provides a snapshot of the state of the practice of how security is integrated into program curricula by analyzing the 37 domestic ABET accredited bachelors' programs in software engineering. The article will identify at a high level the topics that are covered in the programs, as well as provide an overview of other aspects of the institutions which impact the depth and breadth of security coverage available to undergraduate students.


Decision support model to leverage extended reality technologies to augment manufacturing education

Abstract: Amit Jariwala develops and maintains industry partnerships to support experiential, entrepreneurial, and innovative learning experience within the academic curriculum of the school.He is a Woodruff School Teaching Fellow and strives to enhance education by developing classes, workshops, and events focused on implementing hands-on, collaborative learning through solving real-world problems.He directs the operations of the Institute-wide Georgia Tech Capstone Design Expo, which highlights projects created by over 2000 Georgia Tech seniors graduating students on an


Board 423: Using Agile Principles for Cohort Building in a Graduate Software Engineering Program

Abstract: This report describes an approach to building a cohort of students in a graduate software engineering program supported by the Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) Program of the National Science Foundation.We used many agile principles for building and sustaining the cohort, which is scaffolded around the students' academic studies and their simultaneous work on an externally sourced software development project.We discuss how the agile principles were applied in practice in this S-STEM project, how they helped build a cohesive student cohort, and how


A deep learning approach for the comparison of handwritten documents using latent feature vectors

Abstract: Abstract Forensic questioned document examiners still largely rely on visual assessments and expert judgment to determine the provenance of a handwritten document. Here, we propose a novel approach to objectively compare two handwritten documents using a deep learning algorithm. First, we implement a bootstrapping technique to segment document data into smaller units, as a means to enhance the efficiency of the deep learning process. Next, we use a transfer learning algorithm to systematically extract document features. The unique characteristics of the document data are then represented as latent vectors. Finally, the similarity between two handwritten documents is quantified via the cosine similarity between the two latent vectors. We illustrate the use of the proposed method by implementing it on a variety of collections of handwritten documents with different attributes, and show that in most cases, we can accurately classify pairs of documents into same or different author categories.


WIP: Toward a Free-Body Diagram Mobile Application

Abstract: No skill is more important for a student of mechanics than the ability to draw a complete and accurate free-body diagram (FBD).A good FBD facilitates proper accounting of forces when writing the balances that lead to governing equations in statics, solid mechanics, and dynamics.Because this skill is essential, educational approaches that improve the ability of students to draw correct FBDs are critical for maximizing the potential of the next generation of engineers.Traditionally, learning to draw FBDs involves classroom instruction followed by homework practice consisting of problems drawn from a textbook.Homework as practice does not serve all students well, because it does not scaffold the process of drawing FBDs in terms of distinct tasks (e.g., isolating the body, considering support reactions) nor does it offer immediate feedback, which students often need to avoid falling into the same error repeatedly.To address these shortcomings, we embarked on the design, implementation, and testing of a mobile application (app) that offers an alternative venue for FBD practice.The app provides students with asynchronous opportunities for training, varied tasks that target specific FBD issues, and several levels of immediate feedback.We hypothesize that the gamified environment and puzzle-based gameplay will improve student skill and self-efficacy in drawing FBDs, particularly for women, who may feel less confident in their spatial skills.Data collected to describe student experiences may also provide additional insight into how to improve FBD instruction generally.In this paper, we detail the process for designing and implementing the app and provide initial data regarding student impressions and use.The app was piloted in Fall 2022 in a large Introduction to Statics course as a non-graded study activity; all students except one (n=97) participated in an evaluation of its design features and user experiences.Approximately half (54%) of students indicated they had played half or more of the available games.When commenting about how the FBD app did, or did not, help their learning, 49% of respondents appreciated that the app allowed additional opportunities for practice.Students used these opportunities to further develop several skills, such as visualizing the system and setting up accurate diagrams, which strengthened their confidence and reviewed key concepts.While describing the value of practicing through the app, 21% of students called out how the app provided feedback.They specifically mentioned the positive experiences of receiving feedback that is immediate, that explains boundary connections, and that deepens learning after mistakes are made.These and other findings from the pilot study are discussed with corresponding next steps for development.


Research Data Sharing in Engineering: A Report on Faculty Practices and Preferences Prior to the Tri-Agency Policy

Abstract: Abstract The Tri-Agencies Council of Canada that includes the Natural Sciences and Engineering Research Council of Canada (NSERC) is implementing its Research Data Management (RDM) Policy in the Spring of 2023 [1]. The policy requires Canadian post-secondary institutions to develop an Institutional RDM Strategy to support and guide researchers funded by one or more of the Tri-Agencies. Researchers will be required to provide a Data Management Plan (DMP) and deposit their research data into a repository at the time of publication in order to fulfill funding obligations. This paper describes the survey results conducted at a U15 research institution in Canada asking engineering faculty about their research data sharing practices and preferences. The purpose of the survey was to answer the following questions: 1. How well prepared are engineering researchers for data deposit, 2. Are engineering researchers willing to share their data, and 3. What barriers exist for sharing engineering data? Results demonstrate knowledge of and acceptance of open access (OA) practices but when it comes to data, engineering researchers are more reluctant to share their data widely and may need guidance on RDM best practices. Subject librarians can prepare to provide assistance to faculty and educate students by gaining an understanding of engineering data management and sharing behaviors. Faculty may benefit from RDM support through all stages of the data life cycle and students may benefit from RDM literacy introduced into their curriculum. The described survey results in this paper aim to help the subject librarian identify where they might best offer support for faculty and students. [1] Government of Canada, "Tri-Agency Research Data Management Policy," Mar. 15, 2021. https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy (accessed Oct. 28, 2022).


A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

Abstract: Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.


The Development, Assessment, and Advancement of a Student-Centered Cyber Risk Management Course

Abstract: Managing risk is a key component to achieving a strong cyber security posture.Practitioners must consider a combination of human, technology, and policy-based factors when establishing their postures.To help students understand these interdependencies, we designed and developed a course to apply, analyze, synthesize, and evaluate these in a real-world environment.Students have gained an appreciation for various technical and managerial concepts related to cyber security and demonstrated the knowledge gained by developing submissions to the annual Maritime Cyber Risk Symposium student poster contest.This paper describes the multi-year developmental journey in standing up a Cyber Risk Management course linked to National Security Agency/Department of Homeland Security National Center of Academic Excellence in Cyber Education knowledge units, ABET outcomes for cybersecurity computing programs, and the National Institute of Standards and Technology framework and guidance.It includes studentcentered individual classroom learning, project-based team learning, and team development of scholarly work for submission and external evaluation.The efficacy of this approach after two offerings is based upon student feedback, ABET and NCAE assessment, and external results.The paper concludes with how we see the course evolving as the cyber systems major evolves.Teaching cyber-focused concepts in an interdisciplinary manner is critical and, in that spirit, the teaching of this course is moving from the School of Engineering and Cyber Systems to the School of Leadership and Management.


Mobile-assisted language learning and pronunciation instruction: A systematic literature review

Abstract: Abstract In recent years, learners worldwide have expressed increasing interest in mobile learning and the utilization of mobile devices for language learning purposes. However, few research synthesis studies have focused on pronunciation development in relation to employing MALL (mobile-assisted language learning). This review article contributes to the discourse on how mobile learning aids in acquiring and practicing EFL (English as a foreign language) pronunciation since it is this language system that is both a crucial and frequently neglected aspect of English language learning. With the emergence of e-generation, it is necessary to investigate the influence of MALL on pronunciation acquisition. This paper provides a systematic literature review of the findings of 15 empirical studies published between 2015 and 2022, which is based on PRISMA 2020. The aim of the review is threefold: to investigate the effect of mobile devices on L2 learners’ pronunciation performance, examine learners’ attitudes toward employing mobile learning for pronunciation practice and development, and identify which mobile devices and apps are present in the process. The results indicate that overall, mobile learning exerts a beneficial impact on L2 pronunciation and that participants in the reviewed studies demonstrate positive attitudes toward MALL. Moreover, smartphones appear to be at the center of attention as the primary mobile device, and it was revealed that a variety of mobile apps and platforms have been employed in the studies. The article concludes with pedagogical implications and recommendations for potential research exploration in the future.


Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

Abstract: In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.


Pushing Ethics Assessment Forward in Engineering: NLP-Assisted Qualitative Coding of Student Responses

Abstract: Abstract Recent headlines have featured large language models (LLMs), like ChatGPT, for their potential impacts throughout society. These headlines often focus on educational impacts and policies. We posit that LLMs have the potential to improve instructional approaches in engineering education. Thus, we argue that as an engineering education community, we should aim to leverage LLMs to help resolve challenges in engineering education. This study takes up one aspect of instructional design: valid assessment of students' learning outcomes in engineering ethics. In this study, we present a method for engineering educators to implement NLP in open-ended ethics assessments (here, written responses to an ethics case scenario). Grading such open-ended responses has challenges: it requires a non-trivial time commitment and attention to consistency. To mitigate these challenges, we developed an NLP approach based on open-source, transformer-based LLMs. We applied and evaluated our NLP approach for coding students' responses to an open-ended ethics case scenario in a first-year engineering course. The results showed that our NLP approach labeled 380 out of 472 sentences accurately. Conversely, only 8% (37 out of 472 responses) were inaccurately labeled. Overall, our NLP approach provides a step toward analyzing written responses to scenario-based assessments in a scalable manner. However, it is not perfect. One current downside of our NLP approach is that it requires a large upfront time investment in setting up the system. Our future work aims to lower that barrier to entry, thereby making it more accessible to a larger group of potential users.


Performing a Knowledge Audit Within a South African Chemical Manufacturer

Abstract: Despite the increased efforts of organisations to implement knowledge management (KM) initiatives, many fail. Performing a knowledge audit before embarking on KM activities increases the probability of success. In this interpretive case study, the authors applied a modified version of the knowledge audit methodology developed by Perez-Soltero et al. to a South African (SA) veterinary medicine, fine chemical, and pharmaceutical manufacturer. The authors engaged members of the organisation in focus group sessions and individual interviews to identify knowledge assets related to core processes within the organisation. They used the data from the focus groups and individual interviews to identify and articulate many of the knowledge assets at the core of the organisation's current success. In addition, the process of conducting a knowledge audit and making the steps explicit while adjusting for context, may inform researchers and practitioners in terms of knowledge audit approach.


Board 174: Stakeholder Views in Building a Sustainable Engineering Learning Ecosystem: Afterschool Green Energy, Robotics, and Automation (Work in Progress)

Abstract: Abstract This research was part of the first year of a National Science Foundation funded project aimed at promoting high school students' interest in green energy, robotics, automation and post-secondary engineering and technology study. High school teachers, undergraduate majors in STEM areas, and community based non-profit organizations were involved in this afterschool engineering program for high school students with the goal of broadening participation among minoritized groups in engineering and engineering technology. This study investigated how these different stakeholders' views aligned and diverged about (1) the characteristics of STEM engagement, and (2) the factors that influence the development of engineering identities. The purpose of this investigation was to uncover the relationships between community members' viewpoints, community assets, and the positionality of the project personnel.