Miracle-dz/UniFAR-FTUs huggingface dataset:
Image ordinal classification with deep multi‐view learning

Image ordinal classification has drawn substantial attention from the research community due to the ordering relation between image categories. Recent advancements towards image ordinal classification lie in applying deep neural networks [convolutional neural network (CNN)]. Nevertheless, the lack of ordinal training data prevents deep models from generalising to testing data. In this work, two multi-view learning approaches are proposed to tackle the insufficient data issue. On one hand, a multi-view ordinal classification with multi-view max pooling (MVMP) approach is proposed, in which each image is randomly blocked with some grids thus creating multiple views of the original data. All views are then used to train multi-view CNN for classification. On the other hand, in order to account for the ordinal relation, the authors propose a double-task learning on MVMP for classification and average pooling for regression. The task of regression benefits that of classification, mainly focusing on improving classification's recognition accuracy. The two proposed approaches are validated on Adience dataset, and show very compelling results. The code and models will be available online.
Molecular characterization Pinus merkusii native Kerinci revealed by rbcL

Pinus merkusii native Kerinci, also known as Sigi, shares the same taxonomic family as Pinus merkusii native Aceh but differs in morphology, anatomy, and physiology. Additionally, P.merkusii Kerinci has a high utilization potential but its population is decreasing and the entry of P.merkusii native Aceh around the Kerinci population can result in the erosion of the original genetic source (germplasm). The molecular characteristics of P.merkusii Kerinci and its differences with P.merkusii Aceh will be studied using rbcL. Molecular character of P.merkusii Kerinci will be compared with P.merkusii Aceh using rbcL marker in 4 individuals. The results obtained for the rbcL analysis are the percentage of conservative characters at 100%, and the four populations of P.merkusii Kerinci and Aceh are located in the same cluster and branch length. This indicates a large percentage of individual similarity between the five populations. These results tend to prove that P.merkusii Native kerinci and Aceh are located in the same taxa species and the rbcL marker cannot distinguish between Pinus merkusii native Kerinci and Aceh.
ECE 6960-021 Course Project Semi-Cyclic Gradient Descent Outperforms Federated Averaging with Suﬃciently Small Learning Rate

This report proposes the convergence analysis for the semi-cyclic gradient descent (SCGD) algorithm under the setting of distributed learning with heterogeneous data. For the strongly quasi-convex and smooth objective function, we show that given n devices, in order to achieve the (cid:15) error, the semi-cyclic gradient descent algorithm requires O ( n √ (cid:15) ) rounds of communications. It outperforms the performance of the traditional Federated Averaging (FedAvg) algo-rithm which requires O ( 1 (cid:15) ) rounds for the strongly convex and smooth objective function. Moreover, our theoretical results are consistent with our numerical experiments.
Do Islamic Values Impact Social Entrepreneurial Intention of University Students in Malaysia? An Empirical Investigation Into The Mediating Role of Empathy

Social entrepreneurship is considered a powerful means that provides sustainable solutions to existing problems, especially in developing societies. Since intention significantly affects the individuals’ involvement in social entrepreneurship, it is extremely important to support the intention of the young generation to move toward social entrepreneurial activities. To achieve this goal, the determinants of social entrepreneurial intention (SEI) need to be recognized and well understood. Literature suggests that Islamic values influence decision-making processes and individuals' intention to become a social entrepreneur. However, little research has been undertaken to understand social entrepreneurship from an Islamic perspective and the mechanism through which values impact social entrepreneurial intention. Using the theory of planned behavior, this study aims to examine the interrelationship between Islamic values, empathy, and SEI. The researchers employed structural equation modeling-partial least square technique to analyze the data. Based on a sample of 202 Muslim students selected from public and private universities in Malaysia, we found that empathy mediates the relationship between Islamic values and SEI. More specifically, Islamic religious values only affect SEI indirectly through empathy. Additionally, empathy positively affects individuals’ intention to establish a social venture. Bringing religion into play sheds light on the antecedents of SEI.
Leadership Training Program and Their Effect On Nursing Job Performance and Head Nurses Competency

Nurses in the healthcare industry must work efficiently in order to fulfill their duties and tasks. As a result, in order to maintain this efficiency, firms must undergo employee training. It has been demonstrated that training is an important instrument in developing nursing professionals' job-related abilities. While this topic has been studied in the past, the variables of performance and skills connected to training have not been properly investigated, particularly in the Saudi Arabian context. As a result, the study was carried out to ascertain the impacts of training on the performance and competences of nurses. A quantitative correlational research design was used in this study. A straightforward sampling procedure was adopted for the 350 nurses that took part in the study. The data was collected using a three-part training questionnaire, including the nurse competence scale and clinical nurse performance instrument, and was analyzed using descriptive statistics and Pearson's correlation. The study's findings revealed that nurses were quite satisfied with their training and development (3.641.06). The competency level of nurses was moderate (3.130.72), whereas work performance was good (3.801.00). A statistically significant and favorably significant link was discovered between training for both competencies (r=0.88, p0.01) and nurse performance (r=0.79, p0.01). As a result of these findings, it is critical for healthcare administrations to undertake additional activities such as educational training due to its benefits. Nurses can broaden their skills for professional advancement as nursing professionals by following the study's recommended framework.
Multi-stability analysis of fractional-order quaternion-valued neural networks with time delay

This paper addresses the problem of multi-stability analysis for fractional-order quaternion-valued neural networks (QVNNs) with time delay. Based on the geometrical properties of activation functions and intermediate value theorem, some conditions are derived for the existence of at least $ (2\mathcal{K}_p^R+1)^n, (2\mathcal{K}_p^I+1)^n, (2\mathcal{K}_p^J+1)^n, (2\mathcal{K}_p^K+1)^n $ equilibrium points, in which $ [(\mathcal{K}_p^R+1)]^n, [(\mathcal{K}_p^I+1)]^n, [(\mathcal{K}_p^J+1)]^n, [(\mathcal{K}_p^K+1)]^n $ of them are uniformly stable while the other equilibrium points become unstable. Thus the developed results show that the QVNNs can have more generalized properties than the real-valued neural networks (RVNNs) or complex-valued neural networks (CVNNs). Finally, two simulation results are given to illustrate the effectiveness and validity of our obtained theoretical results.
Automating Extract Class Refactoring: a Novel Approach and its Evaluation

During software evolution the internal structure of the system undergoes continuous modiﬁcations. These continuous changes push away the source code from its original design, often reducing its quality, including class cohesion. In this paper we propose a novel approach for Extract Class refactoring aiming at improving class cohesion. The proposed approach analyzes the (structural and semantic) similarity between the methods in a class to identify chains of strongly related methods. The identiﬁed method chains are used to deﬁne new classes with higher cohesion than the original class. The proposed approach has been ﬁrst automatically evaluated in an artiﬁcial scenario, where classes with high cohesion of ﬁve open source systems have been merged to build classes with low cohesion and many responsibilities (i
Visualizing Research Evolution in E-Commerce Field: Year 2001 to 2020

: The paper aims to provide a comprehensive understanding of the evolution of the research themes and trends in e-commerce research based on the co-word analysis. 17,417 keywords were collected from 3,289 academic articles published in the selected seven leading journals in the field of e-commerce from 2001 to 2020 to examine the core research themes and trends over the two-time spans of years 2001-2010 and 2011-2022. Some research themes have kept their continuity in 20 years, indicating that the research themes in the e-commerce research have maintained temporal continuity. At the same time, the research themes in the e-commerce field have evolved with technology development and diffusion. This research provides useful suggestions for the future research direction for scholars in the e-commerce field.
A Short Review on the Impact of Graphene-Based Nanomaterials (Graphene oxide and Reduced Graphene Oxide) in the Biomedical Industry

— Graphene is a highly densely packed sp2 hybridized single layer carbon atom two-dimensional honeycomb structure. Due to its chemical, physical, physicochemical, and biocompatible properties day by day use of graphene oxide (GO) and reduced graphene oxide (RGO) has been increased in biomedical, biotechnology, and pharmaceutical research fields. Graphene oxide is used as an energy storage device (battery and supercapacitors), biosensors, photocatalysis and biomedicine purposes, etc. Not only GO but its modified nanomaterials like RGO have applications in medical biotechnology. RGO nanocomposites are broadly used in drug delivery systems to deliver insoluble drugs, nucleic acids, and other molecules into cells for bioimaging. Due to its antimicrobial and cytotoxic properties, its uses have been increased as a therapeutic agent. For therapeutic effect, it should be noticed its size range and dosage. Hybrid materials like gold-based GO and RGO sheets have excellent properties as catalytic biosensing agents. Though graphene-based nanomaterials like GO and RGO has several novel applications in biomedical, biotechnology as well as a pharmaceutical research field, these have several drawbacks for the environment and human health also. But proper synthesis procedures could reduce its adverse effect on the environment. In this review, we studied the different methods of synthesis of RGO from GO like green synthesis, hydrothermal method, solvent assistant thermal reduction, etc., and their applications in the biomedical field as well as health care also.
Modelling and Assessing the Spatiotemporal Changes to Future Land use and Climate Change Scenarios in the Mellegue Basin

The watershed of Mellegue, which covers 10500 km 2 , is a common area for Algeria and Tunisia. The catchment was chosen to predict future Land Use Land Cover (LULC) changes using GIS and remote sensing. Two future LULC scenarios have been generated using the Multi-Layer Perceptron (MLP) Markov Model of Idrisi. Afterwards we have tried to link the future response of climate changes to the predictable LULC scenarios. With the assistance of Python, we have produced four predictive weather change models. Forecasted models were based on the historical data generated from AquaCrop software. The predictive results have revealed a significant alteration of climate as a response for the great change that occurs in all of the LULC patterns.
Experimenting with Purchase History Based Price Discrimination: a Comment

Brokesova, Deck and Peliova [Int. J. Ind. Organ. 37 (2014) 229-237] have shown that comparative static results from two-period behavior-based pricing models hold in laboratory experiments, but they observed signiﬁcant diﬀerences from point predictions. We report ﬁndings in conformity with these point predictions throughout a uniform pricing benchmark, a replication of Brokesova, Deck and Peliova’s behavior-based pricing treatment and a follow-up experiment. Reference dependence seems to shift participants’ second-period pricing behavior upwards. A post hoc analysis shows that considering myopic consumers instead of strategic consumers explains a downward shift of ﬁrst-period prices and rationalizes the ﬁndings of Brokesova, Deck and Peliova. Volatile price levels aﬀect price-based welfare measures such as sellers’ proﬁts and customers’ total costs. We show that transport costs serve as a robust welfare measure, alleviating the impact of distorted prices. These ﬁndings are relevant for the design of experiments and when assessing the eﬃciency of experimental markets. cutoﬀs. There we do not observe divergence of second-period prices. The most puzzling discrepancy is the diﬀerence in ﬁrst-period prices between the second case of BDP and our behavior-based pricing treatment. Unlike BDP we observe higher prices and a peak in the distribution at the theoretical point prediction. The most evident explanation for this diﬀerence is that BDP actually implemented myopic instead of strategic consumers and subjects use experimentation rather than deduction in their pricing decisions in the experiment. We show that assuming myopic consumers leads to a theoretical prediction, which is in line with the observed prices in BDP’s second case.
SMART BUILDINGS: USE CASE FOR MIDDLEWARE FOR DATA VISUALIZATION AND DATA ANALYTICS

Buildings are becoming more intelligent and in the same time more complex, in due time with the advance in IoT developments and innovations. This means more interconnected devices and more challenges to enhance tenant comfort, to reduce energy consumption and to assert the financial well-being of the space. Therefore, research on Smart Buildings is relevant to address opportunities for IoT innovation in intelligent spaces, increase operational performance and improve efficiency. This work presents data platforms for dashboards and predictions trends, emphasizing multiple provocations in the field. Our objective is to build a middleware for scalable implementation to explore and visualize spatial and temporal data across buildings. We propose the understanding of the meaning behind data - using an open source backend tool as TICK stack with the front end of Chronograf or even Grafana which runs as a web application to create smart buildings sensing service.
Application of Computer-Aided Translation in Interpretation Learning under the New Liberal Arts Horizon

: In the context of the development and prosperity of economic globalization, translation, as a means of information transmission in intercultural communication, plays an increasingly important role. The translation market has an increasing demand for talents, which requires higher quality of talents. However, the training speed of translation talents in colleges and universities is relatively slow. This paper mainly studies the application of computer-aided translation in interpreting learning from the perspective of new liberal arts. This paper first introduces the new arts and computer-aided translation related content, and then discusses the application of computer-aided translation in practical teaching. Through the comparison of the results of pre-test and post-test, it can be seen that the experimental class which applied computer-aided translation in interpreting learning has made great progress, showing a difference with the control class.
Wireless Sensor/Actuator Networks in Precision Agriculture: Recent Trends and Future Directions

: Agricultural production and water has critical importance for socio-economic development of the societies. Unfortunately, the underground water level is slowly falling down and forests are being cut which reduces the rainfall as well. Technological advances on sensor technology and wireless communication are leading to the appearance of wireless sensor/actuator networks (WSANs) in a variety of commercial, industrial and military applications. There is no doubt merging wireless sensor technology into agricultural facilities will make farming activities much easier. In this paper, we look at the role of WSANs in agricultural production. We also investigate the communication architecture of WSAN based large scale irrigation management system and explain the key issues that are faced in the system design. Thanks to the easy installation and maintenance of WSANs, lots of water can be saved by giving timely feedback from field to improve the agricultural irrigation efficiency. This kind of solution can greatly help farmers to monitor the amount of water applied to a field.
Online Coresets for Parametric and Non-Parametric Breg-man Clustering

We present algorithms that create coresets in an online setting for clustering problems based on a wide subset of Bregman divergences. Notably, our coresets have a small additive error, similar in magnitude to the gap between expected and empirical loss Bachem et al. (2017a), and take update time O ( d ) for every incoming point where d is the dimension of the point. Our first algorithm gives online coresets of size ˜ O (poly( k, d, ϵ, µ )) for k -clusterings according to any µ -similar Bregman divergence. We further extend this algorithm to show the existence of non-parametric coresets, where the coreset size is independent of k , the number of clusters, for the same subclass of Bregman divergences. Our non-parametric coresets also function as coresets for non-parametric versions of the Bregman clustering like DP-Means. While these coresets provide additive error guarantees, they are significantly smaller for high dimensional data than the (relative-error) coresets obtained in Bachem et al. (2015) for DP-Means— for the input of size n our coresets grow as O (log n ) while being independent of d as opposed to O ( d d ) for points in R d Bachem et al. (2015). We also present experiments to compare the performance of our algorithms with other sampling techniques.
An Anthropological Study of Security Operations Centers to Improve Operational Efficiency

vi CHAPTER 1: INTRODUCTION 1 CHAPTER 2: RESEARCH METHODOLOGY 6 2.1 Observation Strategy 7 2.2 Ethics and Participant Safety 8 CHAPTER 3: SOC HUMAN CAPITAL MANAGEMENT 9 3.1 Fieldwork Setup 11 3.2 SOC Demography 12 3.3 Gaining Trust and Acceptance 12 3.4 Data Collection 12 CHAPTER 4: GROUNDED THEORY APPROACH TO DATA ANALYSIS 13 4.1 Open Coding 13 4.2 Axial Coding 14 4.3 Selective Coding 15 CHAPTER 5: A MODEL FOR SOC ANALYST BURNOUT 17 5.1 Human Capital 17 5.1. 1 Skills 18 5.1. 2 Empowerment 19 5.1. 3 Creativity 20 5.1. 4 Growth 21 5.1. 5 Burnout Trajectory and Avoidance 22 5.2 Automation 23 5.3 Operational Efficiency 26 5.4 Metrics 27 5.5 Burnout Model Validation 30 5.6 Fieldwork Setup 31 5.7 Burnout Symptoms 31 5.8 Automation 31 5.9 Empowerment 32 5.10 Metrics 34
Parkour as a Visual Urban Subculture: An Aesthetic Understanding of its Performative Scope

Parkour is a relatively recent urban sport that has been anchored in a complex yet co-constituting media environment. While media representations of the sport fascinate outsiders, they also generate certain uneasiness for parkour’s practitioners, who know these representations are more the result of mediated effects than actual renderings of their own interactions. And yet, many traceurs keep on producing these materials themselves. This article analyzes how this tension reveals parkour’s underlying philosophy working on a two-tiered structure, seemingly contradictory at times but actually serving a complementary function. In that sense, the analysis aims to show how this structure is articulated. Once the social world of parkour is described, both on its interactional and its media levels, an analysis of its aesthetic narratives and representations shows how an “inexact” or “complementary” series of depictions actually serve a supplementary objective, a task that fulfills both an organizational as well as a symbolic need.
Machine Learning Stacking Ensemble Model for Predicting Heart Attacks

— To mitigate the extent of one of the world’s leading causes of death, heart attacks, there needs to be an improvement in the technological aspect to predict this disease more accurately. Machine learning methods have come very far in increasing prediction accuracy based on patient data. Ensemble methods have exhibited improvement compared to individual classifier models. For this study, the goal is to develop a Machine Learning model to reach a very high level of accuracy for predicting myocardial infarction, otherwise known as a heart attack. A stacked ensemble model is used in this study and combines a group of three base-level classifiers such as Naïve Bayes, Random Forest, and Extreme Gradient Boosting (XGBoost). This model will help identify those who are at risk and prevent heart attacks, therefore, lowering the mortality rate globally. Diversity among strong classifiers used in this model will be a more effective way to achieve the highest accuracy. The metrics used to evaluate the prediction performances are accuracy, Area Under the Curve (AUC), specificity, precision, and sensitivity. This process is carried out using RStudio and the results indicate that the proposed stacked ensemble method had a better performance under every evaluation metric compared to the individual base-level classifiers that were utilized.
Development of Parkinson's Dementia Prediction Model Using Regression with Optimal Scale

: The objectives of this study were to develop a model for predicting Parkinson's disease Parkinson's disease with dementia (PDD) based on a neuropsychological test using a nationwide survey conducted by Korea Centers for Disease Control and Prevention and present baseline data for detecting Parkinson's dementia earliest. This study targeted 289 patients with Parkinson's disease (110 PDD and 179 MCI) who were 65 years or older. Regression with optimal scale was used to identify the independent relationship between each neuropsychological test and PDD. The analysis results were presented with a regression coefficient, standard error by bootstrap (n=1,000), quantification index (HAYASI I score), odds ratio, and 95% CI. In the regression with optimal scale analysis, K-MMSE(b=-0.52, SE=0.16) and H&Y staging (b=0.44, SE=0.19) were significantly (p<0.05) effective models to distinguish PDD from PD-MCI even after adjusting for all tests (Parkinson's motor symptoms and neuropsychological tests). The results of this study showed that the number of optimal categories was 10 for K-MMSE and 7 for H&Y staging. The results of this study suggested that the optimal classification scores of MMSE-K and H&Y staging, among various neuropsychological tests, could be utilized as an effective screening test for the early discrimination of PDD from PD-MCI.