Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Beneficence: a Study of Pro-Social Attitudes in Comments of Online Social Media Users

Beneficence is a social phenomenon that has rarely been modeled computationally so far. In this paper, we propose to study the beneficence of online opinions and comments published on social media on essential topics for society. Our computational approach is based on measuring semantic similarity. We apply three measures to assess the beneficence of $\sim 41 K$ social media users: average Confidence, Normalized Google Distance, and Pointwise Mutual Information. As a use case, we analyze opinions on the topic of vaccinations on Facebook, where two distinct groups (Pro-Vax vs. Anti-Vax) are present. The results reveal a shared connection to beneficence among social media users, with both groups exhibiting similar levels of similarity and no significant clustering into echo chambers.
Classification of Balinese Herbal Plant Leaf Using Convolutional Neural Network

This research initiates the classification of Balinese herbal leaves based on Usada Taru Pramana Manuscript, one part of Balinese local cultural wisdom that contains knowledge about herbal plants and is used traditionally for treatment. The challenge in this research is that there is no study on herbal leaf image dataset based on Balinese herbal leaves available for research. Therefore, in this research we collected 50 types of Balinese herbal plants which taken from various regions in Bali. The classification method used relies on the Inception ResNet V2 CNN architecture as well as data augmentation techniques to increase the limited amount of training data. The training process uses 3000 images, while validation uses 200 images, and the testing process is performed on 200 images. The experimental results prove that this method produces good accuracy in the classification of herbal leaf types, which is 99.73% for training data, 67.71% for validation data, and 72.50% for testing data. The results of this research can be used to support the development of automation systems that help humans recognize herbs more precisely and quickly, as well as facilitate the preservation and utilization of cultural heritage in traditional medicine and botanical science.
A Compressive Study on Detection Accuracy Model for DoS Attack in SDN Using Ensemble Learning Techniques

The possible networking architecture known as a “Software-defined Network” (SDN) separates the information and management layers and offers polarized control over the network. This new approach considers responsibilities and empowers network administrators to electronically assign, manage, adjust, and monitor clan behaviour. One important advantage of SDN is its polarising power, which may occasionally cause a serious breach. The snitcher will have access to the complete framework if he is successful in getting to the controller’s core. The regulators are utterly powerless to combat Distributed Denial of Service (DDoS) attacks, which wear down the model and make the administrators of the regulations inaccessible. It’s crucial to identify potential dangers in the controllers early on. As a result, many algorithms and processes.
Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests. Invasive diagnostic technologies and expensive expenses are additional impediments to early diagnosis. An effective, accurate, and non-invasive method is required to detect anemia. In this study, the conjunctival image of the eye is analyzed as a non-invasive method of detecting anemia. Various model approaches were tested in an endeavor to categorize anemic and healthy patients as accurately as possible. The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the most effective plan. With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%. These findings show that the model can successfully identify healthy patients while accurately identifying anemic patients. This method offers a non-invasive means of detecting anemia early on, making it promising for use in clinical settings. The SVM+MobileNetV2 technique relies on images of the eye’s conjunctiva and can potentially improve healthcare by identifying people who may have had earlier anemia. This technique stands out as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificity are balanced.
How (not) to Evaluate Computational Empathy: Testing the Assumptions of the Evaluation Methods in a Use-Case

An increasing amount of successful contributions were made in the past two decades to model empathic behavior in artificial agents to enhance the interaction between humans and artificial agents in variety of contexts. However, this relatively novel research area still did not develop standard methods for evaluation. In this paper, we use an embodied conversational agent (ECA) with real time multi-modal interaction capabilities, to test the assumptions of the proposed evaluation method of computational empathy in a patient intake scenario of counselling services in university environments. Our findings show that our system was able to show more empathy compared to its human counterpart and that the proposed evaluation method was supported by the user study in determining the changes in users’ perception in empathic behavior by relying on feature-level metrics and other factors that are not directly related to empathy, which are shown to have a significant influence on the perception of empathic behavior. However, our detailed analyses showed poor reliability of the empathy questionnaire used for the system evaluation, suggesting the need for a metric that is tailored for artificial agents.
Forecasting Power Consumption of Home Appliances using LSTM

As the consumption of energy in the world rises in means of both households, business organizations and industries, it is becoming paramount for having sophisticated and efficient sustainable energy management practices. In these recent years it has been uplifting in grabbing the attention towards the exploitation of machine learning techniques which can precisely prognosticate the consumption of power, with the aim of minimizing the usage of energy, cost reduction and develop sustainability. While antecedent studies have laid the foundation, this research distinctively approaches the challenge by aggregating insights gleaned from prior investigations. In alignment with the continuum of prior research, the proposed forecasting approach culminates in a novel perspective. This paper aims to address this challenge by exploring the use of sensors to collect user-specific data for developing customized models that can provide accurate power consumption forecasts. The dataset for the study is developed by assimilating Split Core Transformer (SCT-013) and Zero Mode Plate Transformer (ZMPT-101B) sensors with the Micro Controller Unit (Node MCU ESP32) obtaining the power consumption values. The sensor-based forecasting approach was carried out and the proposed Long Short-Term Memory (LSTM) model achieved a Mean Squared Error (MSE) value of 5.81e-08 and a Mean Absolute Error (MAE) value of 0.000197, indicating its effectiveness in accurately forecasting power consumption. Additionally, the high R-squared score of 0.98 recommends a well defended association between the prognosticated and actual power consumption data. In essence, this study reframes the trajectory of power consumption forecasting. It amalgamates previous insights into a novel framework that harnesses sensor-derived data, poised to redefine the landscape of energy management with tailored accuracy and unwavering reliability.
Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy and Cardiac Amyloidosis: From Clinical Management to Catheter Ablation Indication

Atrial fibrillation (AF) is the most common arrhythmia in patients affected by cardiomyopathies. Reports estimate a prevalence of 27% in patients with hypertrophic cardiomyopathy (HCM) and 40% in patients with cardiac amyloidosis (CA). The presence of AF typically results in progressive functional decline, an increased frequency of hospitalizations for heart failure, and a higher thromboembolic risk. Medical management using mainly beta-blockers or amiodarone has produced variable outcomes and a high rate of recurrence. Catheter ablation reduces symptom burden and complications despite a moderate rate of recurrence. Recent evidence suggests that an early rhythm control strategy may lead to more favorable short- and long-term outcomes. In this review, we summarize contemporary data on the management of AF in patients with cardiomyopathy (HCM and CA) with particular reference to the timing and outcomes of ablation procedures.
Design of The Ultra-Low-Power Driven VMM Configurations for μW Scale IoT Devices

Operating IoT devices by supplying power from an energy harvester and installing AI accelerators in IoT devices are required. Nevertheless, conventionally selected architectures for AI processing require a large amount of power, making it difficult to operate in low-power bands such as IoT devices or even impossible to operate in the first place. Therefore, driving AI accelerators with power that energy harvesters can supply is an issue. However, there has been no past exploration of AI accelerators in the driven of the μW scale. In this paper, we analyze the configuration of Vector Matrix Multiplier, mainly used for AI accelerators, and show the effective configuration for μW scale IoT devices. Using 180nm CMOS to synthesize the four architectures of various sizes, we characterize device performance and analyze energy consumption and circuit area. As a result of the analysis, it shows that the configuration in which all calculations are deployed on the circuit can have the lowest energy consumption. In addition, we found that when there is a limit on circuit area, a configuration in which some calculations are performed in the time domain by lowering the voltage is suitable.
Treatment and Prevention of Post-dural Puncture Headaches: A Systematic Review

Post-dural puncture headache (PDPH) is occasionally an inevitable side effect of neuraxial anesthesia, which can happen after spinal anesthesia or if an accidental dural puncture (ADP) happens during epidural anesthesia. The treatment and prevention options for PDPH differ widely from one institution to another. The management of PDPH is heterogeneous in many institutions because of the absence of clear guidelines and protocols for the management of PDPH. This study aimed to summarize all articles published during the past decade that discussed the treatment or prevention of PDPH. From 2013 to 2023, 345 publications were filtered for all treatment and prevention approaches used for PDPH patients. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020 guidelines were followed for conducting this systematic review, and 38 articles were included for analysis and review. Existing data come from small randomized clinical trials and retrospective or prospective cohort studies. This review supports the effect of oral pregabalin and intravenous aminophylline in both treatment and prevention. Intravenous mannitol, intravenous hydrocortisone, triple prophylactic regimen, and neostigmine plus atropine combination showed effective and beneficial outcomes. On the other hand, neither neuraxial morphine nor epidural dexamethasone showed promising results. Consequently, the use of neuraxial morphine or epidural dexamethasone for the prevention of PDPH remains questionable. Regarding the posture of the patient and its consequences on the incidence of the headache, lateral decubitus is better than a sitting position, and a prone position is better than a supine position. Smaller non-cutting needles play a role in avoiding PDPH. Minimally invasive nerve blocks, including sphenopalatine ganglion or greater occipital nerves, are satisfyingly effective. Epidural blood patches remain the more invasive but the gold standard and ultimate solution in patients resisting medical therapy. This study highlights the need for larger research to define the best approach to prevent and treat PDPH.
Real-Time Co-Simulation for the Analysis of Cyber Attacks Impact on Distance Relay Backup Protection

Smart Grid is a cyber-physical system that incorporates Information and Communication Technologies (ICT) into the physical power system, which introduces vulnerabilities to the grid and opens the door to cyber attacks. Wide area protection is one of the most important smart grid applications that aims at protecting the power system against faults and disturbances, which makes it an attractive target to cyber attacks, aiming at compromising the reliability of the power system. Understanding the interaction between the cyber and physical components of the smart grid and analyzing the damage that cyber-attacks can do to wide area protection is very important as it helps in developing effective mitigation approaches. This paper evaluates the impact of cyber attacks on a wide area distance relay backup protection scheme in real-time, through the development of a co-simulation platform based on Real Time Digital Simulator (RTDS) and network simulator 3 (NS3) and using the IEEE-14 bus power system model.
Foreground and Text-lines Aware Document Image Rectification

This paper aims at the distorted document image rectification problem, the objective to eliminate the geometric distortion in the document images and realize document intelligence. Improving the readability of distorted documents is crucial to effectively extract information from deformed images. According to our observations, the foreground and text-line of the original warped image can represent the deformation tendency. However, previous distorted image rectification methods pay little attention to the readability of the warped paper. In this paper, we focus on the foreground and text-line regions of distorted paper and proposes a global and local fusion method to improve the rectification effect of distorted images and enhance the readability of document images. We introduce cross attention to capture the features of the foreground and text-lines in the warped document and effectively fuse them. The proposed method is evaluated quantitatively and qualitatively on the public DocUNet benchmark and DIR300 Dataset, which achieve state-of-the-art performances. Experimental analysis shows the proposed method can well perform overall geometric rectification of distorted images and effectively improve document readability (using the metrics of Character Error Rate and Edit Distance). The code is available at https://github.com/xiaomore/Document-Image-Dewarping.
Testing the Antecedents of Attitudes towards Organic Food (An Extended of the Theory of Planned Behaviour)

The primary purpose of this study was to examine purchase intention and behavior of organic food based on the theory of planned behavior. People do not always use their intentions to behave even though intentions are powerful. The novelty of this study is to add two antecedent variables of attitude, namely knowledge about organic food and attention to health. The sampling technique used was purposive sampling. This research was conducted through two studies. Study I was conducted to test the extent of TPB, and Study II tested buying behavior. The sample size is 187 respondents for Study I and 153 respondents for Study II. Data were analyzed using Structural Equation Modeling (SEM) for Study I and Logistic Regression for Study II. The study results show that the overall hypothesis proposed is supported and that intention has a substantial impact on buying behavior of organic food. Based on this research, producers or marketers can develop better marketing strategy models to increase consumer understanding of the benefits of organic food consumption.
Underwriting Knowledge Graph Construction, Maintenance and Its Application on Explainable Exclusion

The current underwriting rule engine (URE) that our industry partner and many other companies have implemented is a rule-based system that streamlined the underwriting manual into a customisable question tree flowchart with question branches leading to exclusion codes as the final result. As the hardcoded process proceeds, the task becomes sub-optimal and loses the customisation aspect for the customers. To address these challenges, we propose constructing an Underwriting Knowledge Graph (UKG) that incorporates an overview of customer risk profiles using explainable exclusion. The UKG leverages interconnected information related to customer attributes, historical data, and exclusion codes to provide valuable insights into risk profiles and potential associations with specific exclusion codes. Our investigation into domain-specific requirements leads to the first-ever UKG, based on real-life underwriting data from an insurance company in Australia. We also present a novel method for UKG maintenance that facilitates multi-label classification. The UKG offers a comprehensive view of the insurance ecosystem, aids risk profile visualization, and enables explainable exclusion classifications. With potential applicability across industries, the UKG promises to improve risk assessment and decision-making for other highly personalized services beyond life insurance.
Health Index Modeling for Trustable Electronic Sensor Systems in an Autonomous Application

This article presents a model to evaluate a relevant health index of an autonomous lane keeping robot in order to make the associated electronic sensing system more trustable. The system's functional operation relies on the quality of the image data from a mounted camera sensor. In order to emulate the relevant hardware and environmental faults of the sensor, faults with different intensities are injected to the image data. Based on the faults and the associated quality of the lane keeping performance, the presented model evaluates and assigns a suitable risk index for a given fault. The risk value is then used to determine the necessary control action to ensure the safe operation of the system. The presented concept can be easily adapted to different autonomous applications.
Memory-efficient Edge-based Non-Neural Face Recognition Algorithm on the Parallel Ultra-Low Power (PULP) Cluster

Resource-constrained edge computing nodes observed scarcity in terms of memory and computation power. It is inevitable to use computer vision (CV) algorithms on the edge for feature extractions. However, it is challenging to implement compute-intensive and resource-hungry convolution neural network (CNN) algorithms on the edge. Hence, this work targets a non-neural Eigenfaces-based face recognition (FR) algorithm to reduce the memory footprint and latency on the edge. Furthermore, the effective implementation of computations related to artificial neural networks (ANN) and machine learning (ML) algorithms has recently seen the development of quantization as an important and very active area of research. Quantization of Eigenface-based FR algorithm’s training data from 32-bit floating-point to 8-bit fixed-point offers 93% accuracy on octa-core PULP platform with 26.31× less model size compared to state-of-the-art (SoA) deep learning (DL) SqueezeNet1.1-based FR on GAP8 platform. Quantization and parallelization were performed on Eigenface-based FR to improve the performance in terms of response time. Additionally, the quantized model was transferred from HyperRAM (L3) to cluster memory (L1) using direct memory access (DMA) via fabric control memory (L2) with double DMA transfer (D2T ), which reduces the number of execution cycles by 82×. With this technique, 120K faces can be recognized per second on the multi-core RISC-V PULP cluster at an accuracy of 93%. Compared to the non-DMA non-quantized (NDNQ) version of the Eigenfaces-based face recognition system, our implementation reduces the recognition time by 1742.85× and 917.43× for single- and octa-core, respectively. Quantization improves the recognition rate due to the reduction in execution cycles, instructions, and load operations by 82×, 25.37×, and 4658.74× respectively, compared to NDNQ versions of FR on the PULP cluster.
MASSE: A Multi-Agent Based Air Ticketing Service Selection Approach

Online travel agencies (OTAs) rely on the global distribution system (GDS) to provide air ticket query and booking services. Air companies put their tickets onto a number of ticket counters for sale and OTAs provide value-added services to customers relying on air ticketing services provided by ticket counters. When OTAs query these services, the returned ticket information is redundant. Therefore, OTAs should choose some air ticketing services from GDS to query the latest air ticket information. At the same time, the quality (i.e., the prices of tickets provided) of air ticketing services differs because their tickets come from a relatively fixed set of air companies, who have their specific ticket pricing strategies. Since the quality of an air ticketing service is related to the ticket query it processes , to select appropriate services from all air ticketing services in terms of the content of the ticket query is a difficult task, which can not be solved by current service selection approaches. Therefore, we propose a Multi-agent-based Air ticketing Service SElection approach (MASSE). MASSE is based on a multi-agent reinforcement learning so that it can capture the dynamics of quality of air ticketing services. Extensive experiments show MASSE can reduce invalid queries to air ticketing services while retaining the quality of query results.
Sentiment Analysis and Fake Amazon Reviews Classification Using SVM Supervised Machine Learning Model

—This project attempts to conduct sentiment analysis of short and long Amazon reviews and report their effects on the supervised learning Support Vector Machines (SVM) model, to bridge for fake reviews classification. Firstly, the SVM model was evaluated by comparing its performance against Naive Bayes, Logistic Regression, and Random Forest models and proved to be superior (second assumption) based on the accuracy (70%), precision (63%), recall (70%), and F1-score (62%). Hyperparameter tuning improved the SVM model for sentiment analysis (accuracy of 93%), then altering the review length affected the model’s performance, which validated that review length affects the classifier (first assumption). Secondly, conducted fake reviews classification on the fake reviews’ dataset yielded 88% accuracy, while the merged subsets of the two datasets yielded 84% accuracy.