Miracle-dz/UniFAR-FTUs huggingface dataset:
Indonesian Students’ Acceptance of Google Classroom

Online learning platforms have become popular due to the COVID-19 pandemic outbreak. Many educators still use the online platform even after the pandemic ended. Google Classroom is one of the platforms used for conducting online learning. The aim of this study was to evaluate Indonesian students’ acceptance of Google Classroom using the technology acceptance model. A total of 452 students participated in the study. Structural Equation Modelling was performed using SPSS AMOS software. The findings of this study showed that students’ behavioral intention positively affects students’ use of Google Classroom. Whereas students’ intention is positively influenced by the platform’s usefulness and students’ attitude toward using Google Classroom. Students’ attitude toward using itself is positively affected by the ease of use and usefulness of Google Classroom.
Blood Pressure Estimation from Single Photoplethysmogram via Multi-modal Data Generation and Feature Fusion using Deep Learning

With the increasing prevalence of cardiovascular diseases, accurate prediction and monitoring of human blood pressure have become increasingly important. In the context of continuous interdisciplinary advancements, non-invasive blood pressure monitoring techniques have also seen rapid development. However, most existing research relies on conventional computational or machine learning algorithms for feature engineering and is validated only on a small number of test sets, resulting in limited generalization performance and a lack of universality. In this study, we propose an innovative approach that combines multimodal data generation and feature fusion for model construction and training, utilizing the MIMIC dataset. This approach enables us to achieve an accurate estimation of human blood pressure using single PPG as input. Testing the trained model on a dataset that contains more than 60,000 sample segments produced encouraging experimental results. Additionally, through ablation experiments, we substantiated the efficacy of the data generation and feature fusion approaches advanced within this study. Furthermore, using a customized dataset as well as the University of Queensland Vital Signs Dataset, we carried out personalized calibration experiments for each participant. After the calibration procedure, the Mean Absolute Error (MAE) of SBP and DBP showed improvements of 9.60 mmHg and 4.32 mmHg, respectively. The result underscores the practicality of utilizing deep learning for blood pressure estimation while also highlighting the imperative nature of individualized calibration techniques.
Optimal Kinodynamic Trajectory Planner for Mobile Robots in an Unknown Environment Using Bézier Contours

Trajectory planning in the field of mobile robotics involves the generation of a trajectory to navigate a robot from a start state to a goal state. One widely employed technique involves a two-step approach: a path planner generates a path made up of piecewise linear segments with sharp turns, which are then smoothed in the trajectory generation step. In contrast, this work formulates trajectory generation as an optimization problem based on the Bézier curve, denoted as ‘BTP’, to generate the robot’s trajectory in one step. It uses a weighted objective function of trajectory length and navigation time to suit different optimization strategies while considering the robot’s kinematics and dynamics limitations. BTP adopts matrix-based formulations for all mathematical operations to enable dynamic adjustment of the degree of the Bézier curve during the optimization process, if convergence is not obtained with the current degree. Additionally, BTP guarantees that the robot’s trajectory is always within the open space identified by the robot’s sensors. The efficacy of BTP has been evaluated through simulations and real-world experimentation, including soccer games and cluttered environment scenarios. Finally, the performance is benchmarked against some of the existing trajectory planners. BTP reduced the robot’s navigation time by a minimum of 11% up to 55% compared to other tested trajectory planners, ensuring $C^{2}$ continuity rather than just $C^{1}$ continuity. Furthermore, it consistently achieved precise goal configuration, unlike the tested trajectory planners, which exhibited deviations of up to 0.6 meters.
SINDy-CRN: Sparse Identification of Chemical Reaction Networks from Data

This work considers an important problem of identifying the dynamics of chemical reaction networks from time-series data. We propose an approach to identify complex chemical reaction networks (CRN) from concentration data using the concept of sparse model identification. Particularly, we demonstrate challenges associated with the application of the sparse identification of nonlinear dynamics (SINDy) and its variants to data obtained from CRNs. We develop a SINDy-CRN algorithm based on the properties of CRNs for identifying governing equations of a CRN. The proposed algorithm is illustrated using a numerical simulation example.
A Highly-Scalable Area-Efficient ADC-Direct Neural Recording Front-End with Proportional-Integral Single-Bit Feedback

This paper presents the design and implementation of a DC-coupled continuous-time-input ADC-direct neural recording front-end architecture. The architecture incorporates a single-bit variable-step DAC to create a proportional-integral feedback path. This makes the design needless of conventionally-used area-inefficient nonlinear high-resolution multi-bit DACs for realizing 2nd-order noise shaping, significantly improving its area efficiency, linearity, and scalability. Additionally, thanks to employing a differential-difference gm stage, the feedback is subtracted from the inputs without the need for connection to the same node, allowing for the realization of a high-ZIN DC-coupled chopping-friendly input structure. The front-end achieves a maximum SNDR of 58dB and tolerates input differential DC offsets/artifacts up to ±350mV. The design is realized using a 130nm standard CMOS technology, occupies 0.021mm2, and consumes 5. 6μW. It is verified through system and circuit-level simulations, and boasts a >800MΩ differential input impedance over frequency band up to 500Hz, and an IRN-PSD of $161nV/\sqrt {Hz} $, all while maintaining a minimum use of passives in its structure.
AP-1 Network Shared in Pathways of Rheumatoid Arthritis Subtyped Liver-kidney Deficiency Characterize the Pathology

Differentially expressed genes (DEGs) shared among different yet associated pathways on rheumatoid arthritis (RA) may shed light on its pathology study. In this study, DEGs of RA subtyped liver-kidney deficiency (a syndrome in traditional Chinese medicine) were collected from the GSE205962 dataset toward the roles they played among RA-associated pathways. GWAS (genome-wide association study) and enrichment analysis demonstrated that the AP-1 transcription factor-associated genes are the top DEGs shared by the RA-associated pathways. As a result, the AP-1 delivers complex regulations on immune responses e.g. activating T cells via up-regulating JUN, FOS, MAPKs, and NFKB1B while inhibiting IKBKB, LCK, NFKB1, CD3D, et al. The protein networks of DEGs shared in the T cell receptor signaling pathway and Th17 cell differentiation, together with the AP-1-centered network, were proposed.
Improving efficiency in rationale discovery for Out-of-Distribution molecular representations

Graph Neural Networks (GNNs), as the dominant approach in Molecular Representation Learning (MRL), have exhibited remarkable efficacy in diverse tasks, including molecular property prediction and drug discovery. Considering the dynamic and diverse nature of test molecules in real-world contexts, the previous independently and identically distributed (i.i.d.) assumption for training and test molecules is not align with the requirements of practical applications in chemistry. Rationalization has been proposed to enhance the Out-of-Distribution (OOD) generalization of GNN, but a critical concern remains unexplored: low efficiency in rationale discovery. We consider this bottleneck is due to large search space and overly flexible modeling. Here, we introduce a framework called Molecule Stratifier and Invariant Rational Improver (MSIRI) to overcome the challenge. Specifically, MSIRI adopt vector quantization to obtain invariant rationales and the search space is narrowed by substructure and two assumptions based on subgraph matching. Experimental results on ten datasets demonstrate that our approach both achieves significant improvements across various GNN backbones and outperforms other six Out-of-Distribution method, clearly demonstrating its effectiveness in addressing the OOD challenges in MRL.
Tug of Peace: Distributed Learning for Quality of Service Guarantees

Consider $N$ players, where the action of player $n$ is a number in the interval $[0,\ B_{n}]$ that is interpreted as its “pull”. Each player has a reward function that depends on all actions. We define Tug-of-War (ToW) games where increasing the action of one player decreases the rewards of all others. Tug-of-War games can model networking scenarios such as transmission power control and activation in sensor networks. We propose Tug-of-Peace algorithm, a simple stochastic approximation, and prove that in Tug-of-War games, it converges to a equilibrium that satisfies a target feasible Quality of Service reward vector for the players. Moreover, with high probability it converges to the “minimal pull” equilibrium. Our algorithm uses infrequent 1-bit communication between the players, but we also propose a fully distributed modification that does not require any communication at all and achieves almost the same guarantees. We then simulate our algorithms in the power control and sensor activation scenarios.
Pure Pursuit Strategy Enhanced with Defense Margin under Noisy Measurements for Protective Drone Missions

This paper investigates the problem of protecting a safe zone against rogue drone intrusion when the defender has noisy observations. The conventional strategies were not sufficient to achieve high mission success rates, prompting the introduction of a concept called defense margin. The proposed strategy improves upon the Pure Pursuit (PP) strategy by incorporating the defense margin strategy, offering better performance compared to using either strategy alone. Simulation results demonstrate the effectiveness of the proposed strategy, resulting in higher mission success rates.
miRcorrNetPro: Unraveling Algorithmic Insights through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis

High throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of microRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://github.com/Miray-Unlu/miRcorrNetPro.
Prediction of the Caenorhabditis elegans body posture and strains

Caenorhabditis elegans is a model organism with a simple and well-connected nervous system. Phenotype is the manifestation of nematodes in various environments, and the accurate extraction of phenotypes is of great significance for the study of locomotion behavior, physiological changes and life expectancy of C. elegans. Here, we propose an efficient, low-cost and automated nematode phenotypic feature extraction method based on shape mathematical representation and image processing technology, and combine with nematode movement models to analyze the movement differences of different strains. We also proposed a posture prediction model based on the mathematical representation of nematode shape and deep learning method (Time2vec-LSTM-Attention). Through the results of experiment, it can be seen that our method can achieve good performance.
Cyber-Immunity at the Core: Securing Biomedical Devices through Hardware-Level Machine Learning Defense

Healthcare cybersecurity is crucial for protecting hospitals’ networks and computing systems from malicious cyber-attacks. With the increasing motivation and capability of cyber attackers, it is necessary to secure the software and hardware infrastructure of biomedical computing systems used in healthcare. Due to the increasingly digitized nature of modern biomedical devices used in healthcare systems, effective cybersecurity solutions have become more essential. Traditional security protocols focused on software-level protection, with the underlying hardware assumed to be secure. However, recent studies have revealed vulnerabilities in hardware that attackers can exploit to compromise computing systems. To address this, in this paper a bottom-up approach is proposed, delegating security to the underlying hardware to enhance the security and cost-efficiency with less visibility to the attacker. This paper aims to develop effective intelligent countermeasures for hardware-assisted cyber-security at run-time in biomedical devices. It leverages processors’ performance events via Hardware Performance Counters (HPCs) combined with Machine Learning (ML) techniques to fortify biomedical devices at the hardware level. To achieve this objective, we introduce a tailored and cost-effective hardware monitoring framework, complemented by a machine learning-enabled methodology. This approach enhances the accuracy and efficiency of malware detection and identification using real-time data from biomedical processors’ hardware events. The experimental results demonstrate that the XGBoost model, using 4 hardware events, excels in malware detection, boasting 95% detection rate (F-measure and Accuracy), efficient resource utilization, and low inference latency. Additionally, the ExtraTree classifier achieves 87% F-measure and exhibits swift performance in classifying malware types in real-time.
Current Perspectives and Virtualisation solutions with SDN for IoT

During the past years, IoT has acquired a lot of consideration since it incorporates intelligent gadgets which empower many applications that work in our day-to-day existence. Due to this the rising number of clients and the interest in more different and specific applications have prompted a tremendous expansion in the network traffic. Managing different traffic requests from various applications is a difficult task for the current traditional networking architecture. Therefore, the paper provides a thorough analysis of the SDN and various other technologies-based network virtualization methods as well as current perspectives for the IoT. The representation provides the working of various up-going technologies such as Machine Learning, Edge Computing, and Virtualization with SDN to betray the performance of the SDN applications in today’s world.
Incentive Based Plastic Waste Segregation

The issue of waste management is a pressing environmental concern that requires immediate action. In recent years, automated recycling bins and incentive-based recycling systems have gained popularity as effective solutions to address this issue. The project consists of three main components: sensing, mechanical, and incentivizing for proper waste disposal and segregation. Recycling is vital for successful waste management and lessening the sheer amount of junk dropped in landfills . However, low participation rates in household recycling operations remain a serious challenge in densely populated areas with fast-paced consumer lifestyles and inconvenient facilities. This research proposes a smart incentive-based garbage recycling system that employs data analysis and Internet of Things (IoT)technology to enhance the design process [5]. The system includes four intelligent features, such as information sharing, pricing adjustment suggestions, collection amount forecasting, and amount pattern detection.
Class-Balanced by SMOTE & Filtering Mechanism Combined with XGBoost Algorithm for Classifying Imbalanced Data

In the modern era, the classification dilemma of Imbalanced Datasets (IDs) has received a sustained interest, both in practical and theoretical aspects. One widely used Oversampling (OS) approach to handle Imbalanced Classification (IC) is the "Synthetic Minority Over-sampling Technique (SMOTE)". Over the past decade, researchers have proposed various SMOTE-based approaches, such as Borderline SMOTE (B-SMOTE), GSMOTE, SVM SMOTE, and among others. In this paper, we propose the Class-Balanced by SMOTE & Filtering combined with XGBoost (BSF-XGBoost) approach to address highly imbalanced datasets. This prototype simultaneously tackles feature selection with different filtering mechanisms and the class imbalance dilemma. The XGBoost approach offers several hyperparameters that focus more on the Minority Class (Minc) misclassification through the training stage. To evaluate the performance of the BSF-XGBoost algorithm, we conducted experiments on 10 highly imbalanced binary-class datasets, employing existing classification models, and comparing the results with various OS approaches. Finally, the findings indicate that our proposed algorithm exhibits slightly better performance compared to other existing classification approaches and variants of SMOTE-based methods.
Implementing Lean Process Improvement in the Sterile Processing Department at the Academic Medical Center

Sterile Processing Departments (SPD) within hospitals are using lean methods to improve process efficiency. In this project, the team utilized the Lean A3 problem solving methodology to examine the root causes of missing instruments at The Academic Medical Center. The team developed several countermeasures through 5S sub-projects, focused on inventory management, facility layout and process flow. The team recommended updating the inventory management system, posting visual work standards and reducing kit variety.
An Improved Unsupervised Convolutional Neural Networks for Detection of Steel Wire Defects

The detection of defects on steel surfaces in the industry has been a difficult problem for quality control for years. Steel wire rods are a raw material used in the production of bolts in the industry, and undetected defects in this component cause poor-quality product production. Although image processing-based approaches have been used for the detection of defects on steel surfaces, deep learning-based methods have received great attention in recent years. The developed deep learning-based approaches generally treat the defect detection problem as object detection or segmentation. However, these approaches require the availability of defective data and the need for labeling. Due to the lack of defective data in the industry, reliable results cannot be obtained. In this study, an unsupervised convolutional neural network is proposed for the detection of defects in steel wire rods. Our approach uses only healthy image samples in the training phase. In the testing phase, defective areas are marked according to defective and healthy data samples. Resnet18 and EfficientNet were used as backbones for feature extraction. Experimental results show that the EfficenNet model gives more accurate and near real-time results, showing that pixel-level defect detection achieves a 98.7% performance.
Almost-Bayesian Quadratic Persuasion with a Scalar Prior

In this article, we consider a problem of strategic communication between a sender (Alice) and a receiver (Bob) akin to the now-traditional model of Bayesian Persuasion introduced by Kamenica & Gentzkow, with the crucial difference that Bob is not assumed Bayesian. In lieu of the Bayesian assumption, Alice assumes that Bob behaves “almost like” a Bayesian agent, in some sense, without resorting to any specific model. Under this assumption, we study Alice's strategy when both utilities are quadratic and the prior is scalar. We show that, contrary to the Bayesian case, Alice's optimal response may be more subtle than revealing “all or nothing. ” More precisely, Alice reveals the state of the world when it lies outside a specific interval, and nothing otherwise. This interval increases (and the amount of information shared decreases) as Bob further departs from Bayesianity, much to his detriment.
Transformers for Detection of Distressed Cardiac Patients with an ICD Based on Danish Text Messages

Cardiac patients with implantable cardioverter defibrillator devices frequently exhibit signs of anxiety and depression (termed "Distressed"). Early detection of these patients is vital for evaluation, intervention, and prevention against relapse. Considering the growing datasets relevant to distress, coupled with the evolution of machine learning methodologies, there exists a promising prospect to develop intelligent systems for the detection of distressed cardiac patients through written materials. In this context, data from two sources were collected: a questionnaire and text communication messages, acquired through the randomized ACQUIRE-ICD study of 168 participants. These textual messages were labelled as either Distressed or Non-Distressed based on questionnaire responses. Following preprocessing, the dataset facilitated the development of transformer-based classification models, including mBERT, XLM-RoBERTa, ÆLÆCTRA, and RøBERTa, as well as a hard voting ensemble method to classify patients into Distressed and Non-Distressed categories. To address imbalances in class distribution and dataset scarcity, a data augmentation method was employed. Results indicated the superior performance of the proposed hard voting ensemble, recording weighted metrics of 80% precision, 67% recall, 73% F1-score, and 75% accuracy. Notably, this ensemble correctly identified 67% of Distressed samples, while the most efficient base transformer, mBERT, identified 63% of Distressed samples.