Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Revolution of Multidimensional Learning Space in the Big Data Environment

Multiuser fair sharing of clusters is a classic problem in cluster construction. However, the cluster computing system for hybrid big data applications has the characteristics of heterogeneous requirements, which makes more and more cluster resource managers support fine-grained multidimensional learning resource management. In this context, it is oriented to multiusers of multidimensional learning resources. Shared clusters have become a new topic. A single consideration of a fair-shared cluster will result in a huge waste of resources in the context of discrete and dynamic resource allocation. Fairness and efficiency of cluster resource sharing for multidimensional learning resources are equally important. This paper studies big data processing technology and representative systems and analyzes multidimensional analysis and performance optimization technology. This article discusses the importance of discrete multidimensional learning resource allocation optimization in dynamic scenarios. At the same time, in view of the fact that most of the resources of the big data application cluster system are supplied to large jobs that account for a small proportion of job submissions, while the small jobs that account for a large proportion only use the characteristics of a small part of the system’s resources, the expected residual multidimensionality of large-scale work is proposed. The server with the least learning resources is allocated first, and only fair strategies are considered for small assignments. The topic index is distributed and stored on the system to realize the parallel processing of search to improve the efficiency of search processing. The effectiveness of RDIBT is verified through experimental simulation. The results show that RDIBT has higher performance than LSII index technology in index creation speed and search response speed. In addition, RDIBT can also ensure the scalability of the index system.
Carbamate pesticide residues analysis of potato tuber samples using high-performance liquid chromatography (HPLC)

Monitoring of pesticides is conducted globally to assess the environmental load of their residues. However, carbamate pesticides are among the most common used pesticides in potato protection in Egypt. Here, nine potato tuber samples were randomly collected from Assiut, Elminia, Kalubia, Cairo, and Giza cities in Egypt to detect the contamination of some carbamate residues using quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. Three methods were used in this study, viz., washed, washed with salted, and peeled. The results indicated that concentrations of kresoxim and thiodicarb residues were higher in all potato samples than other pesticides studied. However, the highest significant value was found in Assiut city (Alzahraa market) for both pesticides (0.18 and 0.038 mg/kg, respectively). Furthermore, peeled method was found to be the most effective method in reducing the carbamate pesticide residues. Thus, further investigation should be done to figure out the potential methods of reducing carbamate pesticide residues in vegetables and strict regulation should be applied in using pesticides. Key words: Carbamate, quick, easy, cheap, effective, rugged, and safe (QuEChERS), pesticide residues, high-performance liquid chromatography (HPLC), Potato tuber.
An Ultra-Short Measure of Positive and Negative Affect : The Reduced Affective Well-Being Scale ( RAWS )

In applied organizational research, where economy of scale is often a crucial factor in successful assessment, ultra-short measures are often needed. This study investigates the psychometric properties of the Reduced Affective Well-Being Scale (RAWS), an ultra-short measure of positive and negative affect in the workplace. This 6-item ultra-short version was compared with the original 12-item scale proposed by Segura and González-Romá (2003) in terms of internal consistency and criterion validity, using a sample of 1117 bank employees. In addition, longitudinal measurement invariance and within-subject reliability of the RAWS over time were assessed in a longitudinal sample of 458 employees at 12 time points. Results provide evidence that the RAWS is similar to the full scale in terms of reliability and validity. In addition, the RAWS shows satisfactory within-person reliability and factor loading invariance over time. In studies with intensive longitudinal designs that require repeated measures of affective well-being, the use of RAWS is a recommendable option.
RESIDUAL FACEBOOK DATA ANALYSIS IN BROWSERS

: The Internet is an essential tool for everyday tasks. Aside from common use, the option to browse the Internet privately is a desirable attribute. However, this can create a problem when private Internet sessions become hidden from computer forensic investigators in need of evidence. Our primary focus in this research is to discover residual artifacts from private and portable web browsing sessions. In addition, the artifacts must contain more than just file fragments and enough to establish an affirmative link between user and session. As social media applications such as Facebook become an integral part of our society, they are also becoming an important source of information in a digital (forensics) investigation. In this paper, we examine the potential to recover artifacts of forensic interest after three popular browsers, namely: Mozilla Firefox, Google Chrome and Internet Explorer, have been used to access Facebook. Findings from this research will hopefully contribute to a better understanding to mobile device and app forensics. Certain aspects of this topic have triggered many questions, but there have never been enough authoritative answers to follow. As a result, we propose a new methodology for analyzing private and portable web browsing artifacts. Our research will serve to be a significant resource for law enforcement, computer forensic investigators, and the digital forensics research community.
Outcome of Early Surgery in Gastric Ulcer Perforation at Rajshahi Medical College Hospital, Rajshahi

Background: The aim of this study was to look at how surgical treatment of early surgery gastric ulcer perforation has changed, as well as the outcomes. While it has been demonstrated that treating perforated gastric ulcers (GUP) is feasible and effective, its adoption into routine clinical practice has been gradual. Only a few studies have looked at its overall usefulness. Material and Methods: A population-based analysis of all patients undergoing surgery for GUP between 2018 and 2020 was conducted in Rajshahi Medical College Hospital as part of a multicentered non-randomized experimental prospective study. The demographics of the patients, as well as their medical assessment, management, and results, were assessed. The following are the outcomes: A total of 482 patients were included in the study, with a median age of 67 years (range, 20–100). Results: There were 482 patients in total, with a median age of 67 years (range, 20– 100). Women made up 284 (59%) of the total patients, and they were older (p0.001), had more comorbidities (p=0.002), and had a higher Boey risk score (p=0.036) than men. The position of the perforation was gastric/pyloric in 347 patients (72%), and duodenal in 135 patients (28%). A simple abdominal x-ray revealed pneumoperitoneum Thirty-one out of forty-one patients 361 patients (75%) had abdominal computerized tomography (CT) and 76 of 77 patients (98%) had abdominal computerized tomography (CT) (p0.001). 234 patients (42%) had laparoscopic surgery, which was completed in 361 patients (75 percent of attempted cases). During the study period, the rate of laparotomy care increased from 33222 patients (7%) to 46 percent (p=0.02). Patients handled by laparotomy had a shorter median operation period (70 minutes) than those treated by laparoscopy (82 minutes) or those transferred from laparoscopy to laparotomy (105 minutes; p=0.017). In 236 patients, postoperative complications occurred (49 percent). Overall, 77 patients died within 30 days of surgery (16 percent). Both open and surgical repair, there were no statistically significant differences in morbidity and mortality. Conclusion: The increased use of clinical evaluation with X-ray (CT) as the primary diagnostic method for PPU and repair in surgical care is demonstrated in this research. Changes in management are not linked to different results.
Effects of sodium and calcium salt stresses on strawberry photosynthesis

‘Darselect’,a strawberry variety （Fragaria ananassa Duch）, was hydroponically pot planted to study the effects of NaCl,CaCl_(2),Ca（AC）_(2) and Ca（NO_(3)）_(2) on its growth and photosynthetic features. It was shown that under the four types of stress it significantly decreased its biomasses, chlorophyll contents, transpiration rates and stomatal conductance and these effects became intensified as the concentrations increased, hs increments in net photosynthetic rate,transpiration rate and stomatal conductance decreased in the order of Ca（AC）_(2),Ca（NO_(3)）_(2),CaCl_(2), and NaCl. The strawberry reduced its root-shoot ratio under NaCI stress and increased its root-shoot ratio under calcium salt stresses compared with it under the control,and these effects became intensified as the concentrations of the salts increased. Calcium salt stresses affected the strawberry more strongly than NaCl stress and the strawberry suffer least from NaCl stress and most from Ca（AC）_(2) stress.
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays

We consider stochastic convex optimization problems, where several machines act asynchronously in parallel while sharing a common memory. We propose a robust training method for the constrained setting and derive non asymptotic convergence guarantees that do not depend on prior knowledge of update delays, objective smoothness, and gradient variance. Conversely, existing methods for this setting crucially rely on this prior knowledge, which render them unsuitable for essentially all shared-resources computational environments, such as clouds and data centers. Concretely, existing approaches are unable to accommodate changes in the delays which result from dynamic allocation of the machines, while our method implicitly adapts to such changes.
A Comparison of Results from Early and Late Laparoscopic Cholestectomies in Acute Biliary Pancreatitis Treatment

Aim In the treatment of acute biliary pancreatitis (ABP), a cholecystectomy is used to prevent acute pancreatitis recurrences. In this study, we aimed to compare the results of early and late-stage LC in patients hospitalized in our clinic with an ABP diagnosis. Material and Methods The patient group was comprised of 35 females (77.8%) and 10 males (22.2%). The 22 patients who underwent LC after ABP treatment were in Group 1, while the patients who were given a two-month interval after the ABP treatment and then underwent LC were in Group 2. Results The average age of the patients was 56 (range: 26–93) years. The average hospital stay was 13.18 days in Group 1 and 8.3 days in Group 2. The mean duration of LC was 57.8 minutes in Group 1 and 45.7 minutes in Group 2 (p < 0.01). The conversion to a conventional cholecystectomy was performed in three (13.6%) patients in Group 1 and two (8.7%) in Group 2. Postoperative complications were seen in four (18.1%) patients in Group 1 and four (17.4%) patients in Group 2. Acute pancreatitis was seen again in one (4.5%) patient in Group 1 and two (8.7%) patients in Group 2. Conclusion In this study, we showed that cholecystectomy surgery can be performed safely after the ABP picture regresses.
Phenomenological applications of the Weak Gravity Conjecture

In the present report the Weak Gravity Conjecture, its sharpened statements, and its application to phenomenology are discussed. In literature circular, spherical, and toroidal compactifications of the Standard Model were studied. We perform analitical calculation of the radion potential in S1/Z2 orbifold type of compactification for scalar lagrangian with kinetic and potential boundary terms that lead to non-trivial boundary conditions. Also we discuss calculation procedures in curved spacetime. Supervisors: Cristophe Grojean Oleksii Matsedonskyi Marc Montull
Diabetes and HIV: What Do We Know?

Context The acquired immunodeficiency syndrome (AIDS) has become one of the biggest pandemics since 1981. According to the Joint United Nations Program on HIV/AIDS (UNAIDS), in 2018, 38 million people worldwide were living with HIV. In parallel to this scenario, the prevalence and mortality of diabetes have increased in recent decades. Thus, it would be pertinent to know if the risk factors for the development of diabetes mellitus to which people with HIV/AIDS are exposed are the same as those of the non-infected population. According to autopsy studies conducted at the Sao Paulo Death Verification Service, University of Sao Paulo, HIV-infected patients in the pre-HAART era (Highly Active Antiretroviral Therapy) presented histological changes in the exocrine pancreas, although in the endocrine pancreas these were not significant. In the post-HAART era, the exocrine pancreas continued to present histological changes, but in this population, they also occurred in the endocrine pancreas and were particularly important in the islets of Langerhans. Objective We seek to address the relationship between the use of HAART for the treatment of HIV/AIDS and the possible impacts on the pancreas. Methods Horizontal review by the Prisma method, bibliographic search in PubMed and Google Scholar. Results Was observed an association between the development of diabetes mellitus and the use of HAART, mainly with the oldest protease inhibitors from 1997-2004. Conclusion Although diabetes mellitus is a multifactorial pathology, antiretrovirals have played an important role in increasing the prevalence of diabetes mellitus in the population undergoing HIV/AIDS treatment.
Investigating Factors that Affect Purchase Intention of Visitors of E-commerce Websites Using a High Scoring Random Forest Algorithm

Making sales to generate revenue is the prime aim of any business, including e-commerce. Research shows that one major barrier hindering the growth and expansion of e-commerce is the inability to effectively determine shoppers from browsers at e-commerce websites. This is attributed to the varying patterns in the shopping process of shoppers. Although there are predictive models based on e.g. naïve Bayes, multilayer perceptron and decision trees for predicting shopper purchase intention, predictive models based on large datasets with diverse features are better, as found in the study reported in this article. This research combines the classical random forest algorithm with hyperparameter tuning and the adaptive synthetic oversampling technique to predict the purchase intentions of shoppers at e-commerce websites whiles highlighting the importance of location in purchase decisions. The study uses a large dataset from the University of California Irvine Machine Learning Repository to build an effective machine learning model. The accuracy of the improved model is competitive to those of decision tree, support vector machine, multi-layer perceptron and classical random forest.
BEyond observation: an approach for ObjectNav

With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, control theory, path planning, Simultaneous Localization and Mapping (SLAM), scene reconstruction, and pattern recognition. In this work, we present our exploratory research of how sensor data fusion and state-of-the-art machine learning algorithms can perform the Embodied Artificial Intelligence (E-AI) task called Visual Semantic Navigation. This task, a.k. a Object-Goal Navigation (ObjectNav) consists of autonomous navigation using egocentric visual observations to reach an object belonging to the target semantic class without prior knowledge of the environment. Our method reached fourth place on the Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard Phase.
Learning to Explore, Navigate and Interact for Visual Room Rearrangement

Intelligent agents for visual room rearrangement aim to reach a goal room conﬁguration from a cluttered room con-ﬁguration via a sequence of interactions. For successful visual room rearrangement, the agents need to learn to explore, navigate and interact within the surrounding environments. Contemporary methods for visual room rearrangement display unsatisfactory performance even with state-of-the-art techniques for embodied AI. One of the causes for the low performance arises from the expensive cost of learning in an end-to-end manner. To overcome the limitation, we design a three-phased modular architecture (TMA) for visual room rearrangement. TMA performs visual room rearrangement in three phases: the exploration phase, the inspection phase, and the rearrangement phase. The proposed TMA maximizes the performance by placing the learning modules along with hand-crafted feature engineering modules—retaining the advantage of learning while reducing the cost of learning.
Inferring gender of Reddit users Masterarbeit

The content aggregator platform Reddit has established itself as one of the most popular websites in the world. However, scientific research on Reddit is hindered as Reddit allows (and even encourages) user anonymity, i.e., user profiles do not contain personal information such as the gender. Inferring the gender of users in large-scale could enable the analysis of gender-specific areas of interest, reactions to events, and behavioral patterns. In this direction, this thesis suggests a machine learning approach of estimating the gender of Reddit users. By exploiting specific conventions in parts of the website, we obtain a ground truth for more than 190 million comments of labeled users. This data is then used to train machine learning classifiers to use them to gain insights about the gender balance of particular subreddits and the platform in general. By comparing a variety of different approaches for classification algorithm, we find that character-level convolutional neural network achieves performance with an 82.3% F1 score on a task of predicting a gender of a user based on his/her comments. The score surpasses 85% mark for frequent users with more than 50 comments. Furthermore, we discover that female users are less active on Reddit platform, they write fewer comments and post in fewer subreddits on average, when compared to male users.
A Deep Reinforcement Learning Approach to Trafﬁc Management

—In this paper, we aim to investigate the applicability of deep reinforcement learning techniques to solve trafﬁc congestion in road intersections. This ranges from reviewing foundational reinforcement learning concepts, to the formulation of the intersection problem, and ﬁnally to extended investigations in the application of multi-agent deep reinforcement learning techniques. Current research in trafﬁc signal control that also applies multi-agent reinforcement learning train agents in speciﬁc, or even single, environments. Our goal is to train a system that can be practical in multiple settings, under multiple conditions. Overall, in this project, our goal is to apply the system to simulations of large-scale areas.
Identification de cibles intracellulaires de molécules à activité antiprion

Les maladies a prions sont des maladies neurodegeneratives touchant l’homme ainsi que l’animal et dont l’issue est toujours fatale. Notre objectif etait d’utiliser la 6AP et le GA, deux drogues antiprion isolees au laboratoire comme des hamecons moleculaires pour identifier leurs cibles cellulaires afin de mieux comprendre leur mode d’action ainsi que les phenomenes de prionisation. Deux criblages, l’un genetique et l’autre biochimique ont revele que les chaperons de proteines et les ribosomes sont impliques. Les drogues antiprion testees ciblent specifiquement la fonction de repliement des proteines portee par le grand ARNr 23S/25S/28S. Ceci suggere que l’activite de chaperon de proteines du grand ARNr est necessaire pour le maintien et la propagation des prions. Ces molecules semblent aussi etre actives contre d’autres maladies liees a des problemes de repliement et d’agregation des proteines. Nos molecules ont donc un avenir potentiel dans le traitement de ces maladies.
Annotating the Corpus of Contemporary Serbian1

This article describes stages in annotation of the 113 million Corpus of Contemporary Serbian (preparation and implementation). There are several levels of annotation which have been conducted. Corresponding bibliographical information is attached to each corpus text. Part-of-speech (PoS) tagset is prepared, based on the electronic morphological dictionary of Serbian, as well as dictionary of possible annotations adapted for TreeTagger, the PoS tagging system. The Corpus of Contemporary Serbian has been automatically, morphosyntactically annotated with TreeTagger software, i.e. information about part of speech and lemma has been attached to each corpus word form. TreeTagger used manually tagged one million word corpus INTERA as a training set. Ten-fold cross-validation is used for evaluation of applied annotation procedure.
GENERALIZED SYMMETRIC MEIR-KEELER CONTRACTION FOR HYBRID PAIR OF MAPPINGS WITH APPLICATION

Abstract. We establish some common coupled fixed point theorems for hybrid pair of mappings under generalized symmetric Meir-Keeler contraction on a non-complete metric space, which is not partially ordered. It is to be noted that to find coupled coincidence point, we do not employ the condition of continuity of any mapping involved therein. Moreover, an example and an application to integral equations are given here to illustrate the usability of the obtained results. We improve, extend and generalize several known results.
Enhanced Self Care with Mobile Technology to Reduce Readmission in Congestive Heart Failure: A Pilot Study

Introduction: While people with congestive heart failure (CHF) are living longer, they often have limited ability to engage in treatment plans and self-management at home, thus resulting in increased hospitalizations. The use of novel digital technologies for improving self-management has been recommended for reducing CHF related hospitalizations. Objective: This pilot study compared a patient-centered enhanced self-care (ESC) intervention using a mobile system called “HeartMapp (HM)” (ESC HM) with an active wait listed control (WLC) group who received CHF education in reducing hospitalization rate and improving CHF outcomes. A total of 18 participants who met the inclusion criteria were block randomized to ESC HM (n=9) and active WLC group (n=9), both using similar mobile platform. Participants were followed for 30-days to examine the number of hospitalization and secondary outcomes including self-care, quality of life, and CHF knowledge. Results: Results showed that none of the participants randomized to ESC HM (n=9) were hospitalized during the study period of 30-days compared to two participants, both men (11%) in the WLC group. Additionally, clinically meaningful improvement in self-care management (t=3.38; p=0.006) and self-care confidence (6.7 vs 1.8; t=2.53; p=0.028) were observed in ESC HM group. Knowledge significantly improved in the ESC HM group while declined in the WLC group (t=2.37; p=0.037). Quality of life score declined by 2 points in the ESC HM group, while the WLC group score declined by 8 points (p=.08). Conclusion: Preliminary results of the pilot study demonstrated feasibility and potential efficacy of reducing hospitalization and improving CHF self-care; thus, warranting further evaluation in a well-designed large clinical trial.