Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Corporate Credibility, Brand Awareness, Brand Image and Brand Loyalty on Purchase Intention in the Telecommunication Sector of Karachi

1Safeena Yaseen 2 Ibtesam Mazahir Abstract Over the past few decades, the telecommunication industry of Pakistan has shown a tremendous growth and competition, and its customer base is constantly increasing. Due to the increasing competition the identification of the predictors of purchase intention in the telecommunication industry has become important for the business development of the companies falls under the sector. Considering its significance, this study intends to measure the impact of corporate credibility, brand image, brand awareness and brand loyalty on the customers’ purchase intentions of in the telecommunication industry of Karachi. The population for this research includes all the customers in the telecommunication industry, and the sample size is 126. The data was collected through online survey. The developed model elucidated the impact of corporate credibility, brand awareness, brand image and brand loyalty on purchase intention, which is exhibited through the following equation: Purchase Intention=0.57+0.11*Corporate Credibility+0.06*Brand Awareness+0.41*Brand Image+0.25*Brand Loyalty+0.32. It was also concluded that the strongest predictor to purchase intention was brand image (R2 =0.52) followed by brand awareness (R2 =0.44), corporate credibility (R2 =0.43) and brand loyalty (R2 =0.41). This study has made and important contribution for marketing professionals and academic scholars.
Detecting Implementation Bugs in Graph Convolutional Network based Node Classifiers

Graph convolutional networks (GCNs) have achieved state-of-the-art performance on the task of node classification. However, the performance of GCNs is prone to implementation bugs that do not explicitly produce compile-time or run-time errors but degrade their effectiveness heavily. These bugs are hard to detect, since the way in which the node attributes and graph structures contribute to the outputs is complicated, non-transparent, and not traceable by humans. To address this issue, we propose a systematic approach with formal justifications to detect implementation bugs in GCN based node classifiers. Our approach is based on the idea of Metamorphic Testing, which does not check input-output relations for a single input, but for input-output pairs. To speed up our approach, we design a pipeline system, which synchronizes the workload on CPUs and GPUs adaptively and processes them simultaneously. Our empirical study shows that our approach is able to identify over 80% of the synthetic mutants and two real-world bugs in GCN implementations. In addition, our pipeline system can achieve more than $10\times$ speedup over the sequential system that leaves the CPU/GPU idle when using the other.
Remote Control Method with Tactile Sensation for Underwater Robot with Magnetic Coupling

This paper described the remote control method with the tactile sensation for the underwater robot. The remote control system for the underwater robot consists of the master system in the atmosphere and slave system in water. The wearable passive device which consists of the constant torque spring, and the powder brake is used as the master system. This device is designed based on the fingertip motion, and it is expected to improve the operability. On the other hand, the underwater manipulator with the module structure is used as the slave system. This manipulator has the water-proof property by using the magnetic coupling. The structure and driven system between the master and slave systems are different. In addition, the master device generates the only passive force like the braking force. Therefore, the only essential information for the realization of the remote control is extracted and used. In this paper, the position information is sent from the master system to the slave system, and the force information is transmitted from the slave system to the master system. From the experimental results, the validity of the proposed method was confirmed.
Energy Consumption Minimization using Data Compression in Mobile Edge Computing

In recent years, many researchers have done a lot of work on mobile edge computing (MEC). However, existing works on energy minimization of MEC systems mainly focus on comparing energy consumption between accomplishing tasks locally and computing tasks on MEC server, failing to explore energy optimal solutions for computing tasks on MEC server. In this paper, we consider a single-user-single-server MEC system, working under latency constraint. In order to reduce the energy consumption of mobile device and meet the latency constraint, we use computation offloading to accomplish large tasks. However, energy consumed by mobile devices (MD) for the transmitting process could be decreased further by compressing data transmitted from MD to mobile-edge server (MEC server). But energy consumed when compressing data on MD increases while the compression rate decreases. Therefore, we develop a solution to figure out the optimal compression rate to minimize the total energy for MD to accomplish one simple task and to meet the latency constraint at the same time. With the optimal compression rate, energy consumption for task-process could be reduced by 38.37% to 60.49% when the latency constraint varies from 0.1s to 0.8s.
Kinetic equations and asympotic theory

This book is focused on the recent developments on the mathematical theory of partial differential equations of kinetic type. During the last few years, this domain has received a lot of attention and has given rise to several developments. The use of general advanced mathematical tools (regularity theory, compactness averaging lemmas, dispersion lemmas) is described together with more introductory topics. They are used to analyze the Boltzmann equation and its various hydrodynamical limits: convergence towards the Euler equations of incompressible fluids, models or scallings which allow to recover parabolic or hyperbolic limits. The last part in this book concerns the derivation of kinetic equations in the limit of large systems of interacting particles. Here, the purpose is to justify rigorously the so-called Boltzmann-Grad limit which allows to recover kinetic equations from the BBGKY hierarchy.
Value-Approximation based Deep Reinforcement Learning Techniques: An Overview

Deep Reinforcement Learning (DRL) combines the power of Deep Leaning and Reinforcement learning, and has started gaining a lot of attraction in various domains. Also, it is empowering the artificial intelligence based agents which could surpass human-level performance even the tasks which was earlier thought to be best performed by humans only. In Industry 4.0, DRL based agents are enabling applications ranging from autonomous fleets and automatic process control to dynamic scheduling for complex production lines. In this context, the study of value-approximation based DRL techniques gains significance as these techniques are instrumental in enabling the concept of General Artificial Intelligence. Therefore, to understand the value-based DRL and to apply it optimally, we present an overview of the value-approximation based DRL techniques and explained how DRL built on Markov Decision Process and the Bellman equation can further improve the general applicability of the DQN model for different applications and achieve better results over the existing ones. We also explain the setup of a Reinforcement learning problem and describe its environment, state, reward-function and agents.
Immunophenotypic Landscape and Prognosis of Diffuse Large B-Cell Lymphoma with MYC/BCL2 Double Expression: An Analysis of A Prospectively Immunoprofiled Cohort

Simple Summary Diffuse large B-cell lymphoma (DLBCL) with MYC/BCL2 double-expression (DE), a recently proposed poor prognostic group, can be easily identified by immunohistochemistry in routine clinical practice. However, clinical outcomes of DE-DLBCL patients vary immensely after R-CHOP immunochemotherapy and prognostic impact of MYC/BCL2-DE was conflicting according to the cell-of-origin, i.e., between germinal center-B-cell (GCB)- and non-GCB-DLBCLs. This implies the heterogeneity within DE-DLBCLs and emphasizes a need for proper risk stratification to select the patients who require more intensive therapy. By analyzing a prospectively immunoprofiled cohort of consecutively diagnosed DLBCL patients, we confirmed the poor prognostic value of MYC/BCL2-DE in DLBCL patients treated with R-CHOP irrespective of the cell-of-origin and international prognostic index. DE-DLBCLs with a concurrent risk factor, especially, elevated serum lactate dehydrogenase (LDH), had the worst survival and DE-DLBCL patients with normal LDH had clinical outcomes similar to those of non-DE-DLBCL patients. Risk stratification of DE-DLBCL based on serum LDH may guide clinical decision-making for DE-DLBCL patients. Abstract Diffuse large B-cell lymphoma (DLBCL) patients with MYC/BCL2 double expression (DE) show poor prognosis and their clinical outcomes after R-CHOP therapy vary immensely. We investigated the prognostic value of DE in aggressive B-cell lymphoma patients (n = 461), including those with DLBCL (n = 417) and high-grade B-cell lymphoma (HGBL; n = 44), in a prospectively immunoprofiled cohort. DE was observed in 27.8% of DLBCLs and 43.2% of HGBLs (p = 0.058). DE-DLBCL patients were older (p = 0.040) and more frequently exhibited elevated serum LDH levels (p = 0.002), higher international prognostic index (IPI; p = 0.042), non-germinal-center B-cell phenotype (p < 0.001), and poor response to therapy (p = 0.042) compared to non-DE-DLBCL patients. In R-CHOP-treated DLBCL patients, DE status predicted poor PFS and OS independently of IPI (p < 0.001 for both). Additionally, in DE-DLBCL patients, older age (>60 years; p = 0.017), involvement of ≥2 extranodal sites (p = 0.021), bone marrow involvement (p = 0.001), high IPI (p = 0.017), CD10 expression (p = 0.006), poor performance status (p = 0.028), and elevated LDH levels (p < 0.001) were significantly associated with poor OS. Notably, DE-DLBCL patients with normal LDH levels exhibited similar PFS and OS to those of patients with non-DE-DLBCL. Our findings suggest that MYC/BCL2 DE predicts poor prognosis in DLBCL. Risk stratification of DE-DLBCL patients based on LDH levels may guide clinical decision-making for DE-DLBCL patients.
Health education improves referral compliance of persons with probable Diabetic Retinopathy: A randomized controlled trial

Objective Lack of awareness about Diabetic Retinopathy (DR) is the most commonly cited reason why many persons with type 2 diabetes are non-compliant with referral instruction to undergo retinal screening. The purpose of this study was to evaluate the efficacy of a culturally, geographically and socially appropriate, locally adapted five-month-long health education on referral compliance of participants. Method A prospective randomized, open-label parallel group study was conducted on persons with type 2 diabetes who underwent basic eye screening at a diabetes hospital between September 2017 and August 2018. Participants who were noncompliant with referral instruction to visit a hospital for advanced DR management were randomly divided into health education intervention group (n = 143) and control group (n = 156). Both groups received information regarding DR and referral instruction at the diabetes hospital. The intervention group was provided personalized education followed by telephonic reminders. The primary endpoint was ‘increase in referral compliance’ and the secondary endpoint was ‘increase in knowledge of DR’. Multivariate logistic regression model was used to identify significant predictors of compliance to referral. Results A total of nine participants dropped and 290 completed the post intervention survey. The compliance rate in intervention group was found to be significantly higher than the control group (64.3% vs 28.2%; OR 4.73; 95% CI 2.87–7.79; p<0.001). Participants in the intervention group acquired better knowledge on DR (p<0.05). Apart from intervention, referral compliance rate was also found to be significantly associated with participants’ self-perception of vision problem (OR 2.02; 95% CI 1.02–4.01; p = 0.045) and participants’ income (OR 1.24; 95% CI 1.06–1.44; p = 0.008). Discussion Our results suggest that intensive health education on DR should be integrated with diabetes education as it may result in significantly improved referral compliance. Outcomes may be sustainable if interventions are institutionalized at referral point. Trial registration Clinical Trials.gov (Registration # NCT03658980); https://clinicaltrials.gov/ct2/show/NCT03658980.
Effect of the Geometry of Cathode Microprotrusions on the Parameters of the Explosive Emission Processes

This report presents results of a numerical simulation of the electrical explosion of a cathode microprotrusion initiated by explosive emission current. The microexplosion parameters (the pre-explosion time and the specific current action integral) have been estimated in relation to the geometry (current density) of the microprotrusion and the type (direct-current and high-frequency) voltage across the diode. The variations in the main parameters of the cathode material (temperature and density) during such a microexplosion are investigated for tungsten and copper cathodes.
A Generic Log Analyzer for automated troubleshooting in container orchestration system

Containerization becomes the proven technology for modernization of any application. Hence the adaptability of the container orchestration platform also increases widely across the industry. Many orchestration systems are available in the market, which could deploy and manage containerized application. Log analyzes in such environment become complex and challenging as a large number of components been involved. Troubleshooting is also a difficult task in such environment because of the dependency of each system or component. In this paper we propose a generic log analyzer which could analyses the log and automates the process of troubleshooting in an orchestration environment.
Performance comparison of CNN and LSTM algorithms for arrhythmia classification

One of the critical CVDs is cardiac arrhythmia and has caused significant fatalities. Recently, deep learning models are utilized for the classification of arrhythmia disease through electrocardiogram (ECG) signal analysis. Among the existing deep learning model, convolutional neural network (CNN) and long short-term memory (LSTM) algorithms are extensively used for arrhythmia classification. However, there is a lack of study that analyzes the performance comparison of CNN and LSTM algorithms for arrhythmia classification. In this paper, the performance of CNN and LSTM algorithms for arrhythmia classification is compared for a publicly available dataset. Specifically, the MIT-BIH arrhythmia dataset is used and the performance is measured in terms of area under the curve (AUC) and receiver operating characteristic (ROC) curve. Analyzing the performance of these algorithms will further assist in the development of an enhanced deep learning model that offers improved performance.
English Learners’ Perceptions of Video Games as a Medium for Learning and Integration into the English Curriculum 1

This study explores L2 learners’ perceptions of different dimensions of current commercial video games as a medium to foster English learning. Moreover, it looks into L2 learners’ attitudes toward the integration of gaming into the English curriculum. To that end, a quantitative approach to data collection and analysis was employed. Responses were elicited using an online questionnaire that consisted of 29 items from 101 Arabic speaking students enrolled in an English and Translation bachelor program at a Saudi public university. The findings of the descriptive statistics (means and standard deviations) indicate that the participants positively perceive English learning through the medium of video games. According to students’ views, several factors and elements conducive to English learning are recognized in commercial video games. These include knowledge of some aspects of English that are not taught in educational settings, predisposition to use English, more opportunities for practicing English, long-term retention of encountered-then-learned language points, independent learning, motivation to learn and deal with English, a comfortable environment for using English, and familiarity with vernacular Englishes. Meanwhile, the findings reveal that learners have positive attitudes toward the integration of gaming into the English curriculum, and they sanction the place of gaming in formal education, provided that current commercial video games need modifications to render optimal L2 learning outcomes.
QoS-aware Metamorphic Testing: An Elevation Case Study

Elevators are among the oldest and most widespread transportation systems, yet their complexity increases rapidly to satisfy customization demands and to meet quality of service requirements. Verification and validation tasks in this context are costly, since they rely on the manual intervention of domain experts at some points of the process. This is mainly due to the difficulty to assess whether the elevators behave as expected in the different test scenarios, the so-called test oracle problem. Metamorphic testing is a thriving testing technique that alleviates the oracle problem by reasoning on the relations among multiple executions of the system under test, the so-called metamorphic relations. In this practical experience paper, we report on the application of metamorphic testing to verify an industrial elevator dispatcher. Together with domain experts from the elevation sector, we defined multiple metamorphic relations that consider domain-specific quality of service measures. Evaluation results with seeded faults show that the approach is effective at detecting faults automatically.
A Mobility Aware Clustering Scheme Based on Swarm Intelligence in FANETs

The emerging Flying ad-hoc Network (FANET) has been used in many applications and services ranging from military to emergency rescue. Unmanned Aerial Vehicle (UAV) clustering is a significant technology in FANETs. In each cluster, the cluster head (CH) is responsible for cluster management. However, due to the frequent movement and limited energy of UAVs, one of the foremost challenges in FANETs is unstable communication performance of cluster. Hence, to cope with this issue, we propose a Mobility Aware Clustering scheme based on Swarm Intelligence (MACSI) for FANETs, which is enlightened by animal collective behavior. More specifically, the modified glowworm swarm optimization (GSO) algorithm based on chaos strategy is put forward for CH selection and cluster formation. It aims to help UAVs cooperate with each other to conduct missions and change their positions accordingly. In addition, a stable CH selection mechanism depends on energy, position and the movement relationship. Our proposal has been evaluated with respect to metrics as energy consumption and cluster life time. The experimental results obtained validate that, the proposed scheme has less energy consumption and extended cluster life time compared with existing clustering schemes.
The Effect of Non-Ideal Operating Conditions on Reliability of Inverters in Microgrids

Microgrids are becoming increasingly popular due to modern trends in power consumption and distribution. Power converters are essential in order to ensure the desired function of a microgrid. As with traditional distribution systems, reliability is a major concern since it is one of the main functions of a microgrid [1]. A lot of literature has been published related to microgrid specific reliability. However, literature on the reliability of converters under non-ideal operating conditions is scarce. In this paper, the reliability in terms of failure rate of a three-phase converter operating under non-ideal conditions is investigated. The studied microgrid is derived from the CIGRE LV benchmark system. The study shows that unbalanced conditions have a particularly detrimental effect on converter reliability expressed as its failure rate. Nonlinear conditions also have a negative impact and even more so than the impact of increased RMS currents due to low power factor (pf) loads. In addition, the ambient temperature has a large impact on the system reliability. Temperature swings in the power electronic devices caused by harmonics and imbalanced currents could also cause large failure rate deviations. The results show that failing to include the effect of expected non-ideal operating conditions could lead to a wrong estimation of the system reliability as an effect of worsened failure rates in the power electronic components.
Machine Learning based Learning Disability Detection using LMS

This paper highlights an E-learning system created using Moodle which is an open-source Learning Management System (LMS) that enables a better learning environment between the tutors and students. This system detects two learner profiles i.e. students with Learning Disability (LD) and without Learning Disability (Non-LD) using dedicated courses designed on the basis of various aspects of an LD student. This work also multiple stages of our approach for informal testing used to capture the learning parameters for Dyslexic students. The first stage i.e. data collection has two approaches where the first approach pertains to a smaller age group of 8-10 years with limited parameters whereas the second approach pertains to the age group 11-13 years i.e. grades 6-8 with more parameters. Natural Language Processing (NLP) has been used to perform Speech-to-Text (STT) conversion on the audio responses of the users. The analysis of these responses have been performed in python language. To detect whether the user has LD (Dyslexia in this case) or not, Machine Learning (ML) is used. Two ML algorithms namely Logistic Regression (LR) and Support Vector Machine (SVM) are used to perform binary classification with LD (1) and Non-LD (0) as the two classes of the dataset. The results are shown for both the approaches and comparative analysis shows that the dataset generated in the final approach for capturing parameters involving NLP is better and more robust. LR algorithm for ML shows better results as compared to SVM for performing detection based on the generated dataset.
Evaluation of RESTful frameworks under soft errors

RESTful frameworks provide a platform for easy deployment, and management of enterprise-level microservices in a scalable and maintainable manner. Like any computer system and its components, RESTful frameworks are susceptible to soft errors, a subset of transient hardware faults that are caused by cosmic rays and package impurities, which can lead to unexpected behaviour from the services that use the frameworks. Failures in these platforms can cause unavailability and unreliability which can lead to major damages including financial or reputation losses to the service providers, and frustration to users who rely on the service provided. Despite soft errors and their impact being a well-studied problem in some fields, such as aeronautics and safety-critical systems, their effect on service frameworks is still uncharacterized. This paper employs fault injection and fuzzing to evaluate how 5 different frameworks behave when affected by soft errors. The obtained results show that using a framework increases the probability of experiencing a failure by an amount that varies from framework to framework and suggest that most failures pose an issue for service availability, which can be relatively easily handled by standard fault tolerance techniques.