Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Low Resource NER using Assisting Language and Transfer Learning

Named Entity Recognition (NER) is a fundamental task in NLP that is used to locate the key information in text and is primarily applied in conversational and search systems. In commercial applications, NER or comparable slot filling methods have been widely deployed for popular languages. NER is utilized in applications such as human assets, client benefit, substance classification, and the scholarly community. This research study focuses on identifying name entities for low-resource Indian languages that are closely related, like Hindi and Marathi. This study uses various adaptations of BERT such as baseBERT, AlBERT, and RoBERTa to train a supervised NER model. The, compares multilingual models with monolingual models and establish a baseline. The results show the assisting capabilities of the Hindi and Marathi languages for the NER task. Also, the results show that the models trained using multiple languages perform better than a single language. However, this research study also observe that blind mixing of all datasets doesn't necessarily provide improvements and data selection methods may be required.
Mapping of Technostress Research Trends: A Bibliometric Analysis

The introduction of internet allowed for the quick evolution of new technologies, which in turn caused a considerable acceleration in the pace of both personal and professional life. On the other hand, it has been observed that digital technology is ubiquitous in both the work and life cycles. Technostress is a sickness that arises when someone experiences information overload and constant contact with most digital devices. It was highlighted in 1984 and is characterised by clinical features at the coronary circulatory, mental, and neurological levels. Technological stress affects the workplace and interpersonal relationships, leading to absenteeism, diminished professional effectiveness, conflict, and isolation. Although prior research has offered important insights into the many viewpoints, approaches, and ideas on social media addiction and workplace stress, there is a scarcity of bibliometric analysis accessible to explain the development and trends of the topic. The 510 technostress publications published up to February 2023 were evaluated using the following bibliometric techniques: citation analysis; co-authorship analysis, keyword analysis, cluster analysis; publication year & growth rate analysis, and documents type. Through this process, we look at the evolution of the technostress field over time, delineate the interrelated ideas constituting the discourse, and pinpoint the most significant works relying on the SCOPUS database.
MUI-TARE: Cooperative Multi-Agent Exploration With Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with the unknown initial poses of agents is a challenging problem. It requires both quickly exploring the environments and robustly merging the sub-maps built by the agents. Most existing exploration strategies directly merge two sub-maps built by different agents when a single frame observation is matched, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. In the meanwhile, some recent place recognition methods use sequence matching for robust data association. However, naively applying these sequence matching methods to multi-agent exploration may require one agent to repeat a large amount of another agent's history trajectory so that a sequence of matched observation can be established, which reduces the overall exploration time efficiency. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an adaptive manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a cooperative manner for agents whose sub-maps are merged, to improve exploration efficiency. Our experiments show that our approach is up to 50% more efficient than the baselines while merging sub-maps robustly.
Bald Eagle Search Optimization Algorithm For Economic Dispatch Problem With Renewable Energy Integration

Economic Dispatch (ED) problem is one of the most popular issues in power systems whose goal is to find the optimal power output combination from all generating units to minimize the total operational costs while satisfying all constraints of the system. Due to environmental concerns, renewable energy (RE) sources such as solar and wind are potential choices since fossil fuel supplies are depleted, fuel prices are rapidly rising and emissions are increasing. As a result, the globe is moving toward renewable energy sources to replace the old power plants. This paper presents a new and very recent nature-inspired meta-heuristics method, called the Bald Eagle Search (BES) optimization algorithm to solve the ED problem including RE sources. Moreover, three case studies (3, 6, and 10 thermal units) as well as a case that consider RE sources with three thermal units are used to ensure the effectiveness of the BES algorithm. Finally, the results of the proposed algorithm are compared to other optimization methods such as WOA, GA, PSO, and GSA. All the analysis is completed by using MATLAB R2017a.
Energy Constrained Sum-Rate Maximization in IRS-Assisted UAV Networks With Imperfect Channel Information

The focus of this article is maximizing the sum-rate of wireless unmanned aerial vehicle (UAV) networks with intelligent reflecting surfaces (IRS) in the presence of system practical limitations. More specifically, we consider that the phase compensation at the IRS is imperfect due to various factors such as device imperfections and channel estimation errors. Moreover, we consider that the IRS elements have limited switching frequency, which limits the possibility of being allocated to different UAVs over consecutive time slots when time-division multiple access is considered. To this end, we formulate an optimization problem, where the objective is to maximize the network sum-rate subject to total energy and quality-of-service constraints by optimizing the number of IRS elements and power allocated to each UAV. To solve the optimization problem, a low-complexity heuristic algorithm is proposed based on the quality of the estimated phase for each IRS element. The proposed approach is compared to benchmark techniques such as the uniform allocation process and genetic algorithm. The obtained results show that a significant sum-rate improvement of up to 45% can be gained using the proposed algorithm.
Latent Domain Generation for Unsupervised Domain Adaptation Object Counting

Unsupervised cross-domain object counting has recently received great attention in computer vision, which generalizes the model from the source domain to the unlabeled target domain. However, it is an extremely challenging task because only unlabeled data is available from the target domain and the domain gap between two domains is implicit in object counting. In this paper, we propose a latent domain generation method to improve the generalization ability of unsupervised domain adaptation object counting by generating a latent domain. To this end, we propose a domain generator with random perturbations to learn a new latent distribution derived from the original source distribution. The latent domain generator can extract target information sampled in its stochastic latent representation, which preserves the original target information and enhances the diverse ability. Meanwhile, to ensure that the generated latent domain is consistent with the source domain in counting performance, we introduce a consistency loss to encourage similar output from latent and source domains. Moreover, to enhance the adaptation ability of the generated latent domain, we apply the adversarial loss to achieve alignment between the latent and target domains. The domain generator with the adversarial loss and consistency loss ensures that the generated domain is aligned to the target while also improving the robustness of the original source domain model. The experiment indicates that our framework can effortlessly extend to scenarios with different objects (crowd, cars). The experiments also demonstrate the effectiveness of our method on unsupervised realistic-to-realistic crowd counting problems.
Evaluation of Variation in Surface Solar Irradiance and Clustering of Observation Stations in Japan

AbstractVariation in surface solar irradiance is investigated using ground-based observation data. The solar irradiance analyzed in this paper is scaled by the solar irradiance at the top of the atmosphere and is thus dimensionless. Three metrics are used to evaluate the variation in solar irradiance: the mean, standard deviation, and sample entropy. Sample entropy is a value representing the complexity of time series data, but it is not often used for investigation of solar irradiance. In analyses of solar irradiance, sample entropy represents the manner of its fluctuation; large sample entropy corresponds to rapid fluctuation and a high ramp rate, and small sample entropy suggests weak or slow fluctuations. The three metrics are used to cluster 47 ground-based observation stations in Japan into groups with similar features of variation in surface solar irradiance. This new approach clarifies regional features of variation in solar irradiance. The results of this study can be applied to renewable-energy .
Tree species mapping in the Brussels Capital Region using deep learning and data fusion

A detailed tree inventory is necessary to accurately estimate the ecosystem contributions of urban forests. In this study, we evaluate a novel method for mapping of urban tree species. The method incorporates the fusion of (a) LiDAR data, (b) very-high resolution orthophotos and (c) multi-temporal PlanetScope data within a multi-modal deep learning framework. Early fusion was used to combine the LiDAR data with the orthophotos while intermediate fusion was used to combine both with the PlanetScope data. An ablation study was performed to assess the contribution of each image source. The proposed workflow reached an overall accuracy (OA) of 90.7%. The orthophotos contribute most to the accuracy of the model (80.9% OA) followed by the multi-temporal PlanetScope data (68.2% OA). The early fusion of the LiDAR data and the orthophotos did not prove effective and did not increase model accuracy any further.
Online Learning Student Engagement: RFM Model Perspective

Engagement in learning is seen as a key factor in figuring out how well online courses work. Perception-based surveys have been used to get a better idea of how students learn. The fact that a learning management system can keep track of log data from online learning activities opens up the possibility of adding data-driven measures to survey-based measures of student engagement. The purpose of this research is to investigate whether or not it is possible to construct a model of students' levels of engaged learning using the information taken from their online learning logs. Based on the findings of this study, a model of student engagement could be constructed using a marketing research methodology to analyze student online learning behavior. A fresh approach to determining how actively students are participating in their own education is provided by the methodology suggested in this research. What we already know about perception-based student learning engagement will be added to and improved by the information that can be gained from a new perspective on student learning engagement.
Computer statistics for three-body systems and mass determination in triplets of galaxies

The observations of triple galaxies were simulated numerically to obtain a proper way to use the virial theorem for mass estimations. The results are applied to the triple galaxies catalogued by Karachentsev I. D. (1987, Binary Galaxies, Nauka, Moscow). The dynamics was simulated using both the point-mass model and softened potential models. The results indicate that significant deviation from the point-mass model requires that the typical intergalactic distances are comparable to the half-mass radii of the galaxies
Smart Textiles for Biomedical Applications

People are becoming more health conscious after pandemic and as a result, smart textile based biomedical devices are becoming popular among people. By incorporating electrical components into the textile items humans use every day, a new hybrid area called “smart textiles for biomedical applications” has been created. Along with conventional textile apparel, these wearable e-textiles provide user-defined applications. One of the main application areas for these new hybrid goods is the medical industry, and rather developed items may be seen both in the lab and on the market. These solutions are made to meet both the specific needs of individual patients as well as continuous patient monitoring in large healthcare facilities. These goods are also being utilised to substitute traditional ways and for complicated medical treatments in the interim. Major focus areas are the smart textiles that are able to measure Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), Electrooculography (EOG), Respiration measurement, Body temperature measurement, Blood pressure measurement, Blood glucose measurement, Sleep analysis. Huge number of researchers across the globe are continuously working for advancement in smart textile based biomedical devices but due to huge manufacturing costs, most of the proposed products are not practically feasible. Another major issue is effectiveness of the sensors after washing as most of such devices are wearable e-textiles, where textile fibres are turned in to conductive materials by using various type of coatings. More dedicated interdisciplinary efforts are required form electronics, medical and textile experts for reduction of manufacturing cost and making the products practically feasible and affordable.
A bias reduction theorem with applications in nonparametric regression

A general approach is derived for reducing the bias of estimators in a parameter estimation problem. The technique is particularly well suited for certain types of bias that arise in nonparametric regression analysis. This is illustrated by deriving new methods for the removal of edge effects from kernel regression, smoothing spline and trigonometric series estimators. An application to estimation in partially linear semiparametric models is also presented.
Bubbles, Drops, and Particles in Non-Newtonian Fluids

Preface Preface to the First Edition Acknowledgements About the Author INTRODUCTION, SCOPE, AND ORGANIZATION NON-NEWTONIAN FLUID BEHAVIOR Definition of a Newtonian Fluid Non-Newtonian Fluids: Definition, Examples of Different Types, Mathematical Models Dimensional Considerations in the Mechanics of Viscoelastic Fluids Experimental Techniques: Rheometry RIGID PARTICLES IN TIME-INDEPENDENT FLUIDS WITHOUT A YIELD STRESS Governing Equations for a Sphere Spherical Particles in Newtonian Fluids Spheres in Shear-thinning Fluids Spheres in Shear-thickening Fluids Light Spheres Rising in Pseudoplastic Media Pressure Drop due to a Settling Sphere Non-Spherical Particles RIGID PARTICLES IN VISCOPLASTIC FLUIDS Static Equilibrium of Particles Flow Field: Shape and size of flow zones Drag Force Role of Values of Yield stress used in correlations Time Effects RIGID PARTICLES IN VISCOELASTIC FLUIDS Flow over a sphere Flow over a cylinder Other Studies Involving Interactions Between Non-Newtonian Characteristics, Particle Shape, Flow Field, etc. FLUID PARTICLES IN NON-NEWTONIAN MEDIA Formation of Fluid Particles Shapes of Bubbles and Drops in Free Rise or Fall Terminal Velocity-Volume Behavior in Free Motion Drag Behavior of Single Particles Bubble and Drops Ensembles in Free Motion Coalescence of Bubbles and Drops Breakage of Drops Motion and Deformation of Bubbles and Drops in Confined Flows NON-NEWTONIAN FLUID FLOW IN POROUS MEDIA AND PACKED BEDS Porous Medium Definition, Examples and Characterization Flow of Newtonian Fluids Flow of Non-Newtonian Fluids Miscellaneous Effects Two Phase Gas/Liquid Flow FLUIDIZATION AND HINDERED SETTLING Two-Phase Fluidization Sedimentation or Hindered Settling Three Phase Fluidized Beds MOMENTUM, HEAT AND MASS TRANSFER IN BOUNDARY LAYER FLOWS Boundary Layer Flows Viscoelastic Effects in Boundary Layers Mass Transfer from Bubbles Mass Transfer from Drops Mass Transfer from Ensembles of Bubbles and Drops Heat and Mass Transfer in Fixed Beds Heat and Mass Transfer in Fluidized Beds Heat and Mass Transfer in Three Phase Fluidized Beds Heat Transfer from Tube Bundles WALL EFFECTS Definition For Rigid Spheres For Non-Spherical Particles For Drops and Bubbles FALLING OBJECT RHEOMETRY Falling Ball Method Rolling Ball Method Rotating Sphere Viscometer Falling Cylinder Viscometer *All Chapters contain Introduction, Summary and Nomenclature sections References Subject index Author index
Gaussian Process Regression for Learning Environment Impacts on Localization Accuracy of a UAV with Respect to UGV for Search Planning

In this article, we present a path planning algorithm for a team of an Unmanned Ground Vehicle and an Unmanned Aerial Vehicle (UAV) that leverages Gaussian process regression to plan a path that meets information gathering objectives while reducing the UAV's localization uncertainty by learning to compensate for outlier measurements or missed expected sensor measurements over the trajectory. Simulation results are compared to approach that also compensates for belief space planning but is incapable of handling outliers or unexpected degradation from the environment1.
Study on the Optimization Method of Unit Commitment Model based on Induction Technology

To improve the efficiency of unit commitment (UC) solution, this paper studies and proposes a unit commitment model optimization method based on induction technology. On the one hand, an optimization strategy of induction weight coefficient is proposed to improve the conservation of the weight coefficient, and the influence of the induced error rate is taken into account in the setting of the weight coefficient to improve the speed of UC solution. On the other hand, an optimization strategy of the induced variable range is proposed to achieve the range control of the induced variables by constructing the range function, thus improving the quality of UC solution. Finally, simulation analysis based on 36–200 gens systems verifies the effectiveness of the proposed method. Compared with the existing methods, the proposed method can effectively improve the efficiency and quality of UC solution.
Flower Classification using a Transfer-based Model

The flower classification problem involves identifying the species of a given flower image. There were several challenges faced by existing technologies for flower classification like overfitting, computational complexity, limited accuracy, and parameter tuning. In this research, a deep learning model based on Xception architecture is proposed to solve this problem. The proposed model consists of multiple Xception blocks, each of which has a convolutional layer followed by a residual connection and a series of other operations. The output of the final Xception block is fed into a fully connected layer to obtain the final classification. The model was trained on a large dataset of flower images and achieved high accuracy on the test set. The proposed model also conducted experiments to evaluate the performance of the model under various conditions, such as different input resolutions and different amounts of training data. The results show that the proposed model outperforms state-of-the-art methods on the flower classification task. It demonstrates the accuracy of 99.48% and the effectiveness of using the xception architecture in deep learning for image classification tasks and highlights the importance of proper data pre-processing and augmentation techniques in achieving good performance.
Brain Tumor Classification using Transfer Learning on Augmented Data and Visual Explanation using Grad-CAM

Brain tumors are irregular growths of cells within the brain, and some of them can be cancerous. MRI is the typical diagnostic tool for identifying brain tumors. MRI images can reveal any anomalous tissue regeneration in the brain. Applying novel algorithms to MRI images, predicting a tumor becomes much faster and more accurate to help deliver treatments to patients. These predictions also help radiologists make quick decisions. In the proposed work to enhance diversity in the dataset, data augmentation techniques are utilized. The presence of brain tumors is detected using VGG19 and InceptionV3 neural networks, with the performance visualized through Grad CAM.
Analysis of Automatic Emergency Braking System Performance Insufficiency Based on System Theory Process Analysis

Automatic emergency braking (AEB) has been widely deployed in new passenger cars. It is a typical application of intelligent assisted driving technology and is of great significance for improving vehicle driving safety. Recently, the ISO/PAS 26448 is proposed to deal with unintended safety issues caused by interactions with surrounding environments. This paper first analyzes the working principle and components of the AEB system, and then uses the system theory process analysis method to assess the risk of the AEB system, analyzes the possible hazards in the AEB system, and proposes corresponding safety goals for the trigger time. Aiming at the influence of pavement information on the AEB system, the performance limitations of the AEB system are analyzed. By applying STPA methods to the AEB system, new design constraints and safety requirements can be implied for product improvement. The analysis process helps the development of a safer AEB system and can be conducted in any AEB product development steps.
An Overview of Diagnostic and Management Strategies for Talaromycosis, an Underrated Disease

Underrated and neglected, talaromycosis is a life-threatening fungal disease endemic to the tropical and subtropical regions of Asia. In China, it has been reported that talaromycosis mortality doubles from 24 to 50% when the diagnosis is delayed, and reaches 100% when the diagnosis is missed. Thus, the accurate diagnosis of talaromycosis is of utmost importance. Herein, in the first part of this article, we provide an extensive review of the diagnostic tools used thus far by physicians in the management of cases of talaromycosis. The challenges encountered and the perspectives which may aid in the discovery of more accurate and reliable diagnostic approaches are also discussed. In the second part of this review, we discuss the drugs used to prevent and treat T. marneffei infection. Alternative therapeutic options and potential drug resistance reported in the contemporary literature are also discussed. We aim to guide researchers towards the discovery of novel approaches to prevent, diagnose, and treat talaromycosis, and therefore improve the prognosis for those afflicted by this important disease.