Miracle-dz/UniFAR-FTUs huggingface dataset:
VLAN Wireless Performance Analysis Of Central Access Point Management Topology According to the IEEE 802.11 Standard

Wireless network distribution is the right solution to disseminate network services in places and areas that are difficult to reach by cable networks. The effort to add a number of access points is intended to increase the coverage area of a network service so that the signal coverage can be even and broad . The development and improvement of wireless network users have made management and user management activities not easy, because of the way wireless technology works in the operational delivery of its packages, sorting and dividing the packages into 3 parts. Among them are packaged in the form of management, control, and data. Whereas if the management prioritizes data without considering management and control, the emergence of vulnerability in network distribution activities will occur when users are increasingly populated . To facilitate the flexibility and mobility of centralized wireless network users, this study utilizes VLANs that will be implemented at OSI Layer 2 to facilitate the classification of user-profiles, subnetting, securing, and roaming features between access point devices and their users. This research in its testing uses QoS parameters that refer to ITU G.165 / G.168 to monitor the quality of services provided. The parameters used in the measurement of interconnection include Throughput, Packet Loss Ratio, Latency, Jitter, and Delay. If the test is declared successful and complete, it is continued by analyzing and evaluating the results of the study in the hope that the system will successfully cluster the users based on the user characteristic dataset
Relations among participation, fairness and performance in collaborative learning with Wiki‐based analytics

Utilizing data analytics for supporting collaborative learning is under‐studied in secondary education. This study aims to evaluate the effectiveness of Wiki and Wiki‐based learning analytics in facilitating collaborative learning in a junior secondary school, in terms of students' participation, contribution, performance, and perception. A Wiki‐based learning analytic tool, Wikiglass, was employed for visualizing statistics of students' contributions and participation (e.g., revision counts) in Wiki, on both the group and individual levels. System log, student survey and performance data were collected from students involved in a Wiki‐supported inquiry project assignment. An Unfairness Index is proposed to measure students' group work distribution. Results of statistical analyses show that fairness of group work distribution was positively related to active participation in revisions on Wiki on the group level, and the number of sentences with higher‐order thinking was related to group performance scores. On the level of individual students, Wiki‐based analytics increased the visibility of work distribution and peers' work progress and contributions which might have changed students' collaborative behaviors.
Health Monitoring System for Individuals Using Internet of Things

Most of the elderly citizens are either living by themselves or locked up at home when the rest of the family members go to work. Health of the elderly deteriorates gradually with age, but people fail to notice these changes in everyday life. The elderly are at risk of not receiving attention immediately in the case of emergencies. Internet of things can be used to alert family members and health personnel immediately when an abnormality in the elderly person's health is sensed to prevent discovery of illness at an irrecoverable stage. Internet of things can monitor parameters like heart pulse rate, body temperature, body movement, position, and location, and raise an alert to take immediate preventive actions. Making this system portable is one of the most necessary requirements because it will be worn by the user. That introduces various conditions in itself. For instance, the system should not disturb the patient or be heavy.
A dissimilarity measure estimation for analyzing trajectory data

Quantifying dissimilarity between two trajectories is a challenging problem yet it is a fundamental task with a wide range of applications. Existing dissimilarity measures are computationally expensive to calculate. We proposed a dissimilarity measure estimate for trajectory data based on deep learning methodology. One advantage of the proposed method is that it can get executed on GPU, which can significantly reduce the execution time for processing a large number of data. The proposed network is trained using synthetic data. A trajectory simulator that generates random trajectories is proposed. We used a publicly available dataset to evaluate the proposed method for the task of trajectory clustering. Our experiments show the performance of the proposed dissimilarity estimation method is comparable with well-known methods while our method is substantially faster to compute.
Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers

Improving the accuracy of a neural network (NN) usually requires using larger hardware that consumes more energy. However, the error tolerance of NNs and their applications allow approximate computing techniques to be applied to reduce implementation costs. Given that multiplication is the most resource-intensive and power-hungry operation in NNs, more economical approximate multipliers (AMs) can significantly reduce hardware costs. In this article, we show that using AMs can also improve the NN accuracy by introducing noise. We consider two categories of AMs: 1) deliberately designed and 2) Cartesian genetic programing (CGP)-based AMs. The exact multipliers in two representative NNs, a multilayer perceptron (MLP) and a convolutional NN (CNN), are replaced with approximate designs to evaluate their effect on the classification accuracy of the Mixed National Institute of Standards and Technology (MNIST) and Street View House Numbers (SVHN) data sets, respectively. Interestingly, up to 0.63% improvement in the classification accuracy is achieved with reductions of 71.45% and 61.55% in the energy consumption and area, respectively. Finally, the features in an AM are identified that tend to make one design outperform others with respect to NN accuracy. Those features are then used to train a predictor that indicates how well an AM is likely to work in an NN.
Gender differences for peer influence on drug use among students from one university in Guyana: curriculum implications

Drug use in our society seems to be a growing concern. Hence the concern of the ES/CICAD and CAMH to sponsor Multicentric Research projects for which this is one. This study therefore sought to determine gender differences for peer influence on drug use among students from one university in Guyana. A survey was applied to 263 university students selected by a purposive sampling. Mean, percentage, cross-tab, t- test and Spearman correlation were used for data analysis. Drug use by male and female participants was minimal. Gender was not significantly different in the level of peer influence. But it was significantly different in the use of illicit drug and in its association with the relationship between peer influence and drug use. The result of this study had curriculum implications. Based on the sampling technique, it was recommended that a similar study be carried out in a wider community outside of the university. DESCRIPTORS: Gender. Narcotics. Students. Curriculum.
Effects of therapeutic ultrasound for contour asymmetry after liposuction, a case study

This is a case report involving a novel setting of sonographic scan for thickness of superficial abdominal fat and a non-invasive approach for reduction of the fat thickness. A 38-year-old woman complained of her abdominal contour asymmetry after the laser-assisted liposuction performed two years ago. Compression-free sonographic scanning confirmed that her superficial abdominal fat was thicker in left side than that of right side. After 9 sessions of 40KHz therapeutic ultrasound, the contour asymmetry was reduced.
Wireless Sensor Network using LoRa for Smart Agriculture

: Presently the climatic conditions are not same and not unsurprising. In addition, the Wireless Sensor Network (WSN) cut way in numerous applications. The pertinence of WSN are gigantic. The utility of WSN is for gathering the detected information, putting away or preparing the detected information and the transmitting information to the proper focal station. In WSN, LoRa innovation allows the information correspondence on substantial zones, permitting to dispense with multi-jump information transmission that requires an extremely exact synchronization. Because of these points of interest it is being utilized increasingly more in practical solution. Agriculture is one of the fields which have as of late deflected their examination to WSN. There are numerous manual techniques to develop a solid harvest. In any case, it requires a great deal of labor included which is difficult nowadays. So as to make it keen, straightforward and give right contribution to the yield, here we are structuring a remote sensor arrange for savvy agribusiness. Smart agriculture (or Precision agriculture) is applying right contributions at the correct time to get more development with less power and work. This plan gives genuine contribution as indicated by the environment; one can transmit the continuous information rapidly with in the blink of an eye. The constant information depends on the few attributes of climate like temperature, humidity and so on. This structure utilizes Arduino as the centre part. Here a sensor network is planned; every hub has a gathering of sensors (sensor hub) associated with the Arduino and LoRa (Sender/Base Station). The sensors utilized are dampness and temperature sensors. Humidity sensor senses the soil dampness content and temperature sensor sense the warmth in the air. The values which are estimated by the sensors are transmitted to an incorporated gadget which is LoRa (Receiver/Central Station). After the values are gotten by LoRa, as per those values exact choice will be taken.
SIDERA - A Simulation Model for Time-Triggered Distributed Real-Time Systems

SIDERA (SImulation model for DEpendable Real-time Architectures) is a simulation model for time-triggered distributed real-time systems. It is based on the Time-Triggered Architecture TTA and allows the simulation of large-scale real-time systems. SIDERA provides simulation of various real-time protocol services like system startup, communication, clock synchronization, membership service and protocol error detection and handling. A failure simulation module allows testing the stability of the systems under investigation in the presence of node failures. This paper is about basic concepts of time-triggered distributed systems and the implementation of these concepts in a simulation environment. Further, it provides a case study of clock synchronization in distributed systems and presents an approach that significantly improves the synchronism in a distributed system while reducing the need for high-quality oscillators.
Educational Data Mining-Students Performance Prediction

: Educational Data Mining is a prediction technique which extracts information of students from educational institutes like schools, colleges, universities. The whole idea of focusing on educational sector is the one related to discover new reasons or factors involved which are affecting their education. In this research, we conducted feature selection to select high impact characteristic and use techniques of data mining: Linear regression with the help of SPSS. Naïve Bayes, decision tree with the help of WEKA. This research is hold on 206 students by using 17 attributes in Madhav institute of technology and science, Gwalior. The result achieved with the help of the decision tree and naïve Bayes is able to predict the total number of students whose are outstanding, excellent, very-good, good, average, poor. The outcomes of feature selection method showed that 10 th percentage and 12 th Percentage were ranked top from all features and the result obtained by Naïve Bayes is better because it has a higher accuracy rate. The result also provides step to improve students’ performance who were predicted to poor. With the help of this prediction, we will identify the students whose are weak and help to gain better CGPA.
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade

For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models that transcribe with automatic speech recognition (ASR), then trans- late with machine translation (MT). A major cause of the performance gap is that, while existing AST corpora are small, massive datasets exist for both the ASR and MT subsystems. In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets. Simple data augmentation by translating ASR transcripts proves most effective on the English–French augmented LibriSpeech dataset, closing the performance gap from 8.2 to 1.4 BLEU, compared to a very strong cascade that could directly utilize copious ASR and MT data. The same end-to-end approach plus fine-tuning closes the gap on the English–Romanian MuST-C dataset from 6.7 to 3.7 BLEU. In addition to these results, we present practical rec- ommendations for augmentation and pretraining approaches. Finally, we decrease the performance gap to 0.01 BLEU us- ing a Transformer-based architecture.
An assessment of Information disclosures by Pharmaceutical Industry: Evidence from India

The information disclosed by the companies in their annual reports reveals much about company’s performance and prospects. Investors take the information as base for decision for investment. Under such circumstance, companies choose to disclose beyond what is mandatorily required. Theories like agency theory, capital need theory and signaling theory support the need of voluntary disclosure. This study is about investigating the extent of Voluntary disclosure in pharmaceutical sector of India which is 3rd in World in terms of Volume of Trade.Objective: To investigate the extent of voluntary disclosure practices prevailing in pharma sector of India, for the year 2010-11 to 2017-18.Significance of the study: This study aims to explore the corporate aspect of pharmaceutical sector. Any growing avenue is a potential opportunity for investors looking for parking their money to get adequate returns. Thus, Indian Pharma sector has come up in flying colors as an avenue for investors to place their money owing to its 100% FDI . Investors have been looking for more and more information from this sector to ensure the safety of funds. Thus the extent of disclosures is worth studying to place a suggestion for the policymakers to introduce the changes in the present set of disclosure practices in pharmaceutical sector.Research Methodology: To understand the extent of voluntary disclosure, a disclosure checklist is constructed and descriptive statistics are carved out to reach the results. The checklist consists of 55 items which are not mandatory by law. The checklist is based on dichotomous scale of ‘1’ and ‘0’ representing presence and absence of the checklist item respectively. The cross sectional analysis is carried out to investigate the year wise and company wise disclosure for eight years.Findings: Though the study observes an increasing trends in the disclosure scores, but the findings are alarming to state that the highest score attained by any company throughout the period of 8 years was 37 (out of 55) not even meeting 80% of the total checklist score. This shows that pharmaceutical sector is not so friendly at disclosures. The probable reasons for such startling results are discussed in the study.
Denoising in spatial particle tomography on multi-layer holography reconstruction by deep learning

Spatial particle distribution can be recorded by holography technology and can be constructed from multi-layer hologram. Due to the influence of holographic recording and reconstruction process, each tomography of multi-layer reconstruction from holography also contains noise in addition to containing spatial particle distribution information. How to denoise each tomography is a key problem. The existing methods either have a long operation time or the noise reduction effect is not obvious. In order to solve the above problems, we proposed a denoising method based on deep learning in this paper. A deep neural network is built to train and test with simulated spatial particle tomography on multi-layer holography reconstruction. According to the simulation results, the method proposed in this paper is effective in denoising the reconstruction results of spatial particles. The proposed method has the advantages of rapidity and high efficiency.
Blended learning in emergency medicine: implementing the e-learning component

Blended learning (BL) refers to the combination of approaches to learning and teaching using a variety of delivery modes. It is playing an increasingly prominent role in medical education. Its strategy includes the combination of traditional face to face learning with e-learning, multimedia videos, online discussions and computer-based assessments. This paper discusses the steps undertaken in implementing an e-learning model through the utilization of a Blackboard system. The phases involved in the implementation include: 1) Training and Familiarisation: introductory learning session with hands-on trial and explanation of the system and its capabilities; 2) Deepening Understanding: formal training with real time trials and demonstration, as well as more complex problem handling; 3) Feedback: trainees and supervisors present and share experience of using the system and how to improve/ enhance it; 4) Assimilation and integration: final stage where knowledge and skills learnt are blended into clinical applications in daily practice. In combining e-learning with other forms of learning under the umbrella of BL, there could be a notable shift from a teacher-oriented model to an active learnercentred one. The whole process emphasizes active learning, with collaboration between learners and supervisors, for the eventual delivery of best clinical care to patients.
Fuzzy Logic Control on Solid State Servo Voltage Stabilizer

: Industry has been craving for a Servo Voltage Stabilizer with no moving parts, high efficiency, fast correction and a tight line and load regulation. This can be catered in two ways as per the application. One is by using a fixed voltage boost transformer with error correction PWM on the line voltage. The other is with a Power Factor Corrector and SPWM generated over the power delivered by PFC. For both the methods fuzzy logic in the feedback loop serves very fast correction of voltage across the load. In transformer method power switching and correction takes place in the low voltage secondary which gets in series with the line voltage. Weight of the system is higher but much less than a conventional servo voltage stabilizer. Switching effect does not fall on the load reducing di/dt. This is not the situation in PFC delivered power control. Power is switched at a high frequency then filtered and delivered to the load. Since this does not employ bulky transformers, system weight is low and compact in size.
Impacto de LoRaWAN no Desempenho de Plataformas de IoT baseadas em Nuvem e Névoa Computacional

LoRaWAN is a new technology that has been consolidating as a key data communication component to send data in IoT-based systems, due to its ability to send data over long distances with low energy costs. However, literature considers only wireless aspects, disregarding its computational aspects and its integration with IoT platforms, as well as ignoring the deployment possibilities that involve cloud and fog computing. In order to understand the computational impacts of the LoRa architecture we performed a careful performance evaluation study in a complex IoT scenario, exploring cloud and fog computing scenarios and integrating with the IoT FIWARE platform. The results show that the LoRaWAN architecture is scalable, but it has impacts on system performance.
Reconstruction tridimensionnelle calibrée à partir d'images ultrasonographiques: Applications à la mesure volumique des caillots in vitro

Objectif : Application de la reconstruction tridimensionnelle ultrasonographique calibree avec acquisition a main libre a l'etude volumique des caillots veineux in vitro. Methodologie : L'acquisition des coupes 2 D natives necessaire a la reconstruction tridimensionnelle etait menee a main libre, les coordonnees spatiales de chaque coupe etant delivrees en temps reel par un positionneur electromagnetique. En nous placant dans des conditions d'examen ultrasonore standard, nous avons teste sur un plan quantitatif la reconstruction 3D calibree. Une quantification volumique a ete realisee sur 5 caillots veineux in vitro de taille croissante avec une segmentation manuelle en double aveugle. realisee par deux operateurs independants. Resultats : La bonne concordance entre les volumes reels et les volumes mesures, comme en temoignent les tests de comparaison sur les droites de regression inter-operateurs, a confirme la coherence de la chaine de reconstruction et la faisabilite de la technique dans un environnement medical de routine. La variabilite intraoperateur etait de 7 et 11 %, la variabilite inter-operateur de l'ordre de 16,91%. Conclusion : Cette reconstruction calibree s'avere compatible avec la mesure de volume de caillots veineux in vitro, et les incertitudes de mesure rendent applicable cette technique au suivi evolutif de la tete des thrombi veineux profonds proximaux in vivo. La technique s'averera plus fiable lorsque des processus de segmentation semi-automatiques, voire automatiques seront disponibles.
Performance of Standard and Customized Search Interfaces

The research conducts a performance comparison between standard search interfaces available in modern search engines with a customized search interface that allows the user to change the way the information is presented on the screen. The tested hypothesis is that customized search interface results in a decrease in total search time and increase (or the same amount) of successful searches. Customizable options included font, background, help, tool tips, highlights, preview text and Boolean operators. Based on the performed study customized interfaces allowed users to increase the speed of obtaining correct search results by 10.2%. The benefit comes with a cost of time that users spend to configure the interface. Further research is recommended to both test the results on wider audiences, and test additional customization options.
MET AND UNMET PALLIATIVE CARE NEEDS FOR PEOPLE LIVING WITH HIV/AIDS IN SELECTED AREAS IN RWANDA.

ii DECLARATION iv DEDICATION v ACKNOWLEDGEMENTS vi TABLE OF CONTENTS vii LIST OF FIGURES xiv LIST OF TABLES xv ABBREVIATIONS xvii APPENDICES xviii CHAPTER ONE: INTRODUCTION 1 1.1. BACKGROUND OF THE STUDY 1 1.2. THE PANDEMIC OF HIV/AIDS IN RWANDA 2 1.2. 1. Efforts to address HIV/Aids 2 1.2. 2. Impact of HIV/Aids on Health Systems 3 1.3. THE CONCEPT OF PALLIATIVE CARE 4 1.3. 1 Current status of Palliative care and HIV/Aids in Rwanda 5 1.3. 2. Home-based care for people living with HIV/Aids 5 1.4. MOTIVATION FOR THE STUDY 7 1.5. AIM OF THE STUDY 7 1.6. OBJECTIVES OF THE STUDY 8 1.7. RATIONALE FOR THE STUDY 8 1.8. DEFINITIONS OF TERMS USED IN THE THESIS 9 1.9. SUMMARY OF CHAPTERS 10 viii CHAPTER TWO: LITERATURE REVIEW 11 2.