Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of emerging technology innovations on healthcare transformation in developing countries

This study aims to explicate the strategical to take up to change. Employment of digitization of health technologies in health units of greater significance as digital transformation in healthcare plays an essential role in the improving the healthcare, knowledge transfer, and Performance system especially in developing countries and in pursuing of Millennium Development Goals (MDGs). This aim of this paper propose the factors of emerging technology usage that affect the healthcare transformation. In this Technological Innovations efficiently controls the records for documenting processes and transaction. The swift pace of digital transformation in health sector leaving healthcare providers no choice if they want to remain competitive. In Mobility Application (MA), cloud computing (CC), Internet of Things (IoT), Artificial Intelligence (AI), Big Data (BD). It is important for those who have to use the technology to accept it and to develop a better understanding of what factors lead to successful impact of these systems to improve implementation method. Essential to the quality of care and health of the society. Thus, these rationales exert that currently today providers of healthcare are embracing digital transformation in healthcare units. The benefits of digital transformation in health units are extremely compelling. They include a reduction in costs associated with employee travelling; reduction in time spent away from the patients and reduced information system (IS) in healthcare sector times.
Single-molecule probes in organic field-effect transistors

The goal of this thesis is to study charge transport phenomena in organic materials. This is done optically by means of single-molecule spectroscopy in a field-effect transistor based on a molecular crystal. We present (in Chapter 2) a fundamental requirement for single-molecule spectroscopy concerning the energy levels of the guest molecule with respect to the ones of the host molecule. Following this constraint, we study (in Chapters 3 and 4) the photophysics of a new system for high-resolution spectroscopy at cryogenic temperatures, which consists of dibenzoterrylene molecules inserted in a crystal of anthracene. We then characterise (in Chapter 5) the electrical properties of the anthracene field-effect transistor with 'conventional' methods. In Chapter 6, we finally use the influence of an electric field on the spectroscopic properties of fluorescent molecules to investigate locally the charge carrier transport phenomena in a crystal of anthracene. The goal of this thesis is to study charge transport phenomena in organic materials. This is done optically by means of single-molecule spectroscopy in a field-effect transistor based on a molecular crystal. We present (in Chapter 2) a fundamental requirement for single-molecule spectroscopy concerning the energy levels of the guest molecule with respect to the ones of the host molecule. Following this constraint, we study (in Chapters 3 and 4) the photophysics of a new system for high-resolution spectroscopy at cryogenic temperatures, which consists of dibenzoterrylene molecules inserted in a crystal of anthracene. We then characterise (in Chapter 5) the electrical properties of the anthracene field-effect transistor with 'conventional' methods. In Chapter 6, we finally use the influence of an electric field on the spectroscopic properties of fluorescent molecules to investigate locally the charge carrier transport phenomena in a crystal of anthracene. The goal of this thesis is to study charge transport phenomena in organic materials. This is done optically by means of single-molecule spectroscopy in a field-effect transistor based on a molecular crystal. We present (in Chapter 2) a fundamental requirement for single-molecule spectroscopy concerning the energy levels of the guest molecule with respect to the ones of the host molecule. Following this constraint, we study (in Chapters 3 and 4) the photophysics of a new system for high-resolution spectroscopy at cryogenic temperatures, which consists of dibenzoterrylene molecules inserted in a crystal of anthracene. We then characterise (in Chapter 5) the electrical properties of the anthracene field-effect transistor with 'conventional' methods. In Chapter 6, we finally use the influence of an electric field on the spectroscopic properties of fluorescent molecules to investigate locally the charge carrier transport phenomena in a crystal of anthracene.
UML Modeling of Online Public Bus Reservation System in Egypt

Designing a proper public transportation system is a main concern for many countries with large population size like Egypt. Therefore, the online based transportation business for individuals is highly growing in Egypt and the concept of online reservation in the transportation field has arisen. In this paper, a model in the analysis phase for an online public bus reservation system (OPBRS) is proposed. The system is large, complex and includes many interrelated functions. Therefore, the object-oriented modeling approach is chosen for developing the system by using the industry standard Unified Modeling Language (UML). The basic functions of the system are defined. Hence, the functional and structural models are presented to provide a framework for a public transportation OPBRS in Egypt.
RotPredictor: Unsupervised Canonical Viewpoint Learning for Point Cloud Classification

Recently, significant progress has been achieved in analyzing the 3D point cloud with deep learning techniques. However, existing networks suffer from poor generalization and robustness to arbitrary rotations applied to the input point cloud. Different from traditional strategies that improve the rotation robustness with data augmentation or specifically designed spherical representation or harmonics-based kernels, we propose to rotate the point cloud into a canonical viewpoint for boosting the following downstream target task, e.g., object classification and part segmentation. Specifically, the canonical viewpoint is predicted by the network RotPredictor in an unsupervised way and the loss function is only built on the target task. Our RotPredictor satisfies the rotation equivariance property in (3) approximately and the predication output has the linear relationship with the applied rotation transformation. In addition, the RotPredictor is an independent plug and play module, which can be employed by any point-based deep learning framework without extra burden. Experimental results on the public model classification dataset ModelNet40 show the performance for all baselines can be boosted by integrating the proposed module. In addition, by adding our proposed module, we can achieve the state-of-the-art classification accuracy with 90.2% on the rotation-augmented ModelNet40 benchmark.
Synthesis of novel arylamine containing perfluorocyclobutane and its electrochromic properties

A new electrochromic oligomer based on an arylamine compound containing a perfluorocyclobutane (PFCB) ring has been synthesized via 2 + 2 cyclodimerization. A new N,N,N′,N′-tetraphenyl-biphenyl-4,4′-diamine (TPD) with PFCB (TPD-PFCB) showed high thermal stability with a decomposition temperature (Td) of 435 °C. Cyclic voltammograms of TPD-PFCB films coated onto an indium–tin oxide substrate showed two reversible redox steps during a potential scan between 0.7 and 1.2 V. The spectroelectrochemical series of TPD-PFCB films exhibited color change from colorless to yellow and then to greenish blue during the oxidation reaction of TPD-PFCB. This oligomer exhibited a high electrochromic coloration efficiency (602 cm2/C) and a comparable response speed (coloring and bleaching time at 700 nm were 0.71 and 0.12 s, respectively).
Database-Assisted 3D Reconstruction and Printing of a Mechanical Key From a Single Photograph

Reverse engineering is a powerful tool for duplicating real objects from data acquired by tracking systems, industrial X-ray computed tomography (CT), laser scanners, and image captures. This work proposed a novel method using the three-dimensional (3D) reconstruction and 3D printing to duplicate a mechanical key based on only one single naturally-captured photograph. A reverse engineering system of the mechanical key was self-developed which includes functions of projective transformation, curve extraction, point clouds generation, and 3D geometry reconstruction. The database technology was introduced into the image-based 3D reconstruction process for the first time to store basic dimensional information of key blanks, which provided supplementary information during the 3D key reconstruction process. Five different types of duplicated keys verified the effectiveness of the proposed method. The maximum geometric deviation was less than 0.39 mm. The duplicating time cost was 5 minutes for 3D geometry reconstruction and 18.3 minutes for 3D printing.
A Study of T’nT and CRF Based Approach for POS Tagging in Assamese Language

Assamese is one of the official languages of India which is officially recognized among 21 other official languages which is verbalized by, around 20 million people in the Northeastern part of India. In the recent time, a large number of works related to Natural Language Processing is going on for the language. Different types of models and algorithms have been employed for tagging the part of speech in a sentence, which is done by tagging each word against a predicted tag by an algorithm or by a model based on some annotated trained corpus. Assamese is a morphologically rich language. The lexical ambiguity of the words in the language is extensive. In this paper, performances of two existing tagging techniques for Assamese language have been compared, that is, Conditional Random Field and Trigrams’nTag to study the efficiency of the models in tagging of Parts-of-speeches for the language and the study might also help the Natural Language Processing (NLP) researchers in getting an understanding and analyzing the performance standard and effectiveness of this two existent models for POS tagging task for other morphologically rich Indian languages.
Dimensionality Reduction for Cluster Identification in Metagenomics using Autoencoders

Metagenomics is the study of the genomic content of the microbial organisms extracted from a sample in their natural habitats. These unknown collections of genomic data are analyzed without any prior lab-based cultivation to avoid amplification bias. One of the vital aspects of metagenomics analysis is the visualization of the information that is derived from the genomic sequences of a microbiome sample. In a successful visualization, the congruent reads of the sequences should appear in clusters depending on the diversity and taxonomy of the microorganisms in the sequenced sample. In converting higher dimensional sequence data into lower dimensional data for visualization purposes, preserving the genomic characteristics is given the highest priority. In this process, the demand for precise and efficient methods of dimensionality reduction is crucial. Currently, Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are used for dimensionality reduction purposes in metagenomics, which are linear and nonlinear techniques respectively. Although the above-mentioned techniques are widely used, there are shortcomings in accuracy and efficiency in terms of visualizations. In this paper, we explore the possibility of using autoencoders, a deep learning technique, to get a rich dimensionality reduction, overcoming the prevailing impediments of PCA and t-SNE and outperforming them to achieve better metagenomic visualizations.
Children Semantic Network Growth: A Graph Theory Analysis

In this study, we aim to investigate how children’s language develops. To do so, we apply the network model of language and examine the graph-theoretic properties of Word2Vec semantic networks of children through development. The networks are made of words children learn prior to the age of 30 months as the nodes. The links in the word-embedding networks are built from the cosine vector similarity of words normatively acquired by children prior to 2 ½ years of age. By exploiting some graph measures such as the clustering coefficient and path length, the growth pattern of these semantic networks will be revealed. The small-world property allows for high amounts of local structure combined with global access. Within these semantic networks, there is a considerable local structure in the form of clusters of words. For global structure, some nodes act like bridges. They are actually the hubs of the network and connect the clusters which are semantically far-away. We explore the small-world property of these semantic networks and their changes through language development. The results demonstrate that the Word2Vec semantic networks of children show the small-world property from the early age of several months.
Ultra-Wideband Positioning Using Machine Learning Approaches

An ultra-wideband positioning system consists of at least three anchors and a tag. Via the ultra-wideband transceiver mounted on each device in the system, we can utilize the time-of-arrival technique and some classic algorithms to realize localization. However, in the real environment, the uncertain measurement of time and/or distance may bring incorrect positioning information. Hence, we reconsider this positioning issue by incorporating machine learning approaches with uncertain measurement in the real environment. Particularly, our method uses machine learning for overall consideration instead of using a deterministic model to evaluate uncertainty. The experimental results show some interesting properties of our algorithm in the practical experiment.
A Simple Positioning System for Large-Scale Indoor Patrol Inspection Using Foot-Mounted INS, QR Code Control Points, and Smartphone

A reliable and practical personnel positioning system is necessary for large-scale indoor patrol inspection. In this study, we designed a personnel positioning system that entails the implementation of a foot-mounted inertial module, a smartphone, and pre-determined sparse QR code points. Foot-mounted inertial navigation system (Foot-INS) is self-contained system that send real-time dead-reckoning positions to a smartphone via a built-in Bluetooth module. When the inspector arrives at a planned control point area marked by the QR code, the smartphone actively scans the QR code and obtains the three-dimensional (3D) coordinates of the control point. Then, a newly developed data fusion algorithm performs real-time and post-processing fusion of the relative foot position and 3D QR code control point coordinate data. The proposed algorithm was implemented on an Android smartphone and was evaluated by extensive experiments in an indoor office building and an outdoor open-sky area. The experimental results confirmed the effectiveness and feasibility of the proposed personnel positioning system. The designed system was proven to be decent, robust, and not susceptible to environmental changes. It is also low-cost, has low power consumption, is easy to operate, and is compatible with common smartphones. Moreover, the proposed personnel positioning system provides a practical application pattern of the Foot-INS.
A survey on graph-based methods for malware detection

The widespread presence of malware is causing economic loss to organizations and individuals. For this reason, detecting malware has gained great interest as part of a computer security topic. Most current malware detection software uses signature-based methods to identify threats. However, this syntactic approach fails to identify variants of known malware or previously undiscovered malware. Since graphs represent a strong tool for modeling the sophisticated behaviors of malware and it is harder for an attacker to radically change the behavior of malware than to morph its syntactic structure, many graph-based methods are proposed to overcome the disadvantages of the traditional approach. In this paper, we present a survey of graph-based approaches for malware detection.
Graphene patch array antenna at 60 GHz for 5G Millimeter-Wave Communications

In this paper, an improved Wave Concept Iterative Process method is used to simulate a graphene patch array antenna for future 5G millimeter-wave communication applications. The antenna array consists of twelve antenna elements which are fed by a 3-to-4 power divider and has a high gain of 14.47 dBi at 60.07 GHz with an excellent return loss. The proposed formulation has shown great efficiency and precision, and it has a shorter computation time than the CST and HFSS simulators.
Study of the Prediction of Way Weakening in Mobile Radio Service: Applied to a Part of the City of Batna-Algeria

By crossing a path between the transmitter and the receiver, the transmitted signal is likely to be experienced by many phenomena having a deteriorating effect on the quality of the signal. This degradation is reflected in practice by errors in the received messages which cause loss of information for the user or the system. For this Prediction plays a fundamental role in the design of mobile radio systems. Indeed, it allows us to learn about the boundaries of cells, the optimal location of base stations and the choice of equipment. In addition, it is an invaluable way to reduce the silence. The purpose of the present work is the study of the prediction of propagation losses in terrestrial mobile radio service related to the Algerian reality. The approach followed can be summarized in the study and choice of predictive models that can be used, comparison of these models, as well as the taking of a campaign of measures for the adoption or possibly adjustment of one or more modeled.
How are you feeling? Multimodal Emotion Learning for Socially-Assistive Robot Navigation

We present a real-time algorithm for emotion-aware navigation of a socially-assistive robot among pedestrians. Our approach estimates time-varying emotional dynamics and behaviors of people from multiple modalities (i.e., their faces and trajectories) using a combination of deep-learning, and affective features from the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used to predict pedestrian movement with proxemic constraints. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33parcent. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot.
A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions

The high prevalence of chronic pain in society raises the need to develop new digital tools that can automatically and objectively assess pain intensity in individuals. These tools can contribute to an optimisation of clinical resources, as they offer cost-effective solutions for early detection, continuous monitoring, and treatment personalisation by utilising Artificial Intelligence techniques. In this work, we present our contribution to the Pain Intensity Estimation from Facial Expressions task of the EMOPAIN 2020 Challenge. Specifically, we compare the performance of Recurrent Neural Networks trained with standard or Curriculum Learning (CL) approaches to predict the pain intensity level of individuals reported in an 11-point scale from facial expressions. The results obtained using the test partition support the use of CL-based approaches in the automatic prediction of pain from facial features. The best model trained using a CL approach achieved a Concordance Correlation Coefficient (CCC) of 0.196 in the test partition, while the model trained using a standard approach, without CL, achieved a CCC of 0.174. In terms of CCC, these results respectively represent an improvement of 0.136 and 0.114 on the best results of the baseline system reported by the Challenge organisers using the test partition.
Review of Mutual Coupling Reduction in Microstrip Patch Antenna Array for MIMO Applications

Mutual coupling drastically reduces the system performance and radiation patterns in MIIMO (multiple input multiple output) antennas. This reduction in MIMO antenna system is a major challenging issue and many works are carried to minimize the mutual coupling between adjacent antenna components. This paper provides theoretical review on recently proposed works and hybrid techniques for reduction of mutual coupling in antenna array. The increase in mutual coupling loss downgrades the radiation efficiency of antenna system and which can be improved by advanced hybrid techniques, but there are tradeoffs between different performance parameter metrics. Therefore, this paper proposes the different advance techniques, which are used in reducing the mutual coupling and comparative analysis of the same is carried out. The scope for future research work is suggested corresponding to the other performance parameters and indoor wireless applications.
Digital Design Techniques for Dependable High Performance Computing

As today’s process technologies continuously scale down, circuits become increasingly more vulnerable to radiation-induced soft errors in nanoscale VLSI technologies. The reduction of node capacitance and supply voltages coupled with increasingly denser chips are raising soft error rates and making them an important design issue. This research work is focused on the development of design techniques for high-reliability modern VLSI technologies, focusing mainly on Radiation-induced Single Event Transient. In this work, we evaluate the complete life-cycle of the SET pulse from the generation to the mitigation. A new simulation tool, Rad-Ray, has been developed to simulate and model the passage of heavy ion into the silicon matter of modern Integrated Circuit and predict the transient voltage pulse taking into account the physical description of the design. An analysis and mitigation tool has been developed to evaluate the propagation of the predicted SET pulses within the circuit and apply a selective mitigation technique to the sensitive nodes of the circuit. The analysis and mitigation tools have been applied to many industrial projects as well as the EUCLID space mission project, including more than ten modules. The obtained results demonstrated the effectiveness of the proposed tools.
Explaining Any Time Series Classifier

We present a method to explain the decisions of black box models for time series classification. The explanation consists of factual and counterfactual shapelet-based rules revealing the reasons for the classification, and of a set of exemplars and counter-exemplars highlighting similarities and differences with the time series under analysis. The proposed method first generates exemplar and counter-exemplar time series in the latent feature space and learns a local latent decision tree classifier. Then, it selects and decodes those respecting the decision rules explaining the decision. Finally, it learns on them a shapelet-tree that reveals the parts of the time series that must, and must not, be contained for getting the returned outcome from the black box. A wide experimentation shows that the proposed method provides faithful, meaningful and interpretable explanations.