Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Linear Program for Multi-Type Inpatient Allocation Admission Scheduling Problem

This paper studies an inpatient allocation admission scheduling problem by considering the sharing of inpatient beds among patients from different medical departments. Hospital managers need to make admission control decision each day to minimize operational costs. Considering the uncertainty of patients' arrival and length of stay, we propose a Markov decision process (MDP) model with the objective to minimize patients' waiting cost, diversion cost and overcapacity cost. Due to the curse of dimensionality, the MDP is difficult to solve. To address the problem, an approximate linear programming (ALP) approach is developed by firstly transforming the MDP model into an equivalent linear program and then approximating the value function by a linear combination of basis functions with a set of parameters. The optimal parameters in the approximate value function can be obtained by solving the dual problem of the approximate linear program using column generation. Managers can make admission decision using the approximate value function. Numerical experiments show the effectiveness of ALP compared with other policies.
Application of digital enhancement techniques for groundwater exploration in a hard-rock terrain

Abstract Geomorphological and geological features, namely landforms, rock units and geological structures, have a definite relationship with the occurrence and movement of groundwater in hard-rock crystalline formations. Exploration and exploitation of this sub-surface resource depend upon the understanding of expected subsurface hydrological conditions through surface expressions. The different types of digital enhancement techniques using remotely sensed data help in the extraction of specific features that act as groundwater indicators and ultimately lead to the preparation of maps indicating groundwater prospective zones. In this study, different enhancement techniques, such as linear stretching, band combination, filtering and edge enhancement, have been employed for deriving groundwater-controlling features in typical hard-rock crystalline formations of parts of the Raichur district, Karnataka, India. The qualitative evaluation of each enhanced output for its feature exhibitance has narrowed down th.
SegFishHead: A Semantic Segmentation Approach for the identification of fish species in a Cluttered Environment

Identifying fish species, in general and in a cluttered environment in particular, is not an easy task for common people without the proper knowledge of fish taxonomy. This study proposes a challenging segmentation dataset consisting of fish images collected from different live fish markets situated in West Bengal, India. A total of five freshwater fish species named, Labeo catla, Labeo rohita, Cirrhinus mrigala, Labeo bata, and Hypophthalmichthys molitrix are considered in this study. A semantic segmentation-based fish head segmentation and identification in a cluttered environment is proposed in this study. Two popular deep learning-based segmentation networks, U-Net and PSPNet, are applied with two different pre-trained backbone networks, ResNet34 and InceptionV3. Using PSPNet with a ResNet34 pre-trained backbone, a best mean IoU of 0.76 is achieved by taking the background of the image as a class label. The fishing industries as well as their stakeholders will benefit from this proposed approach in a variety of contexts.
Automatic Workspace Calibration Using Homography for Pick and Place

For deploying any robotic solution, the most cumbersome yet essential task remains calibration. Almost every robotic system uses a camera to extract information to produce some output. In the case of the pick-and-place system, camera information is used to detect objects and estimate their position in real-world space. The accuracy of tasks like sorting and stacking the objects in the box depends on their estimated pose in the space, especially when they need to be arranged in a confined space. We propose the automatic calibration of the workspace using distinct markers. Our experimentation was carried out with loose packets of recycled paper, which consists of small parts inside. We evaluate our results on the basis of translation errors of the estimated centroid position of objects with respect to the position given by the calibrated 3D camera. In conclusion, we also assess the typical pick-and-place performance on the success rate of stacking the packets in a small box.
Relevant Tweets Identification from Disaster-related Tweets

Due to the growth in internet users, the information flow from social media platforms like Twitter is revving using micro-posts. These micro-posts share more information in less text to help handle natural crises for people stuck in natural disasters. The first job is to collect the tweets in real-time and classify them into disaster-victim-related tweets that need help and unrelated tweets efficiently. For this, we need to pre-process the tweets so that the problems in the text like ambiguity in words and different spelling for the exact words can be rectified. After that convert these tweets into tokens so that the proposed models can easily train and produce better accuracy.
Understanding Cloud Structures from Satellite Images using Deep Learning

With the advancement of remote sensing technology, many high-resolution satellite images have been produced that can be used in a variety of disciplines, including assessing crop yields and monitoring land cover and pollution in the ocean and on land. However, the hit back to this occurs when thick clouds block the path of identification of ground level objects. Around 65% of the earth’s outer surface is covered with clouds hence this has become a major problem. To encounter this issue, we discuss some of the deep learning techniques like convolution neural networks (CNN), for the detection of cloud shapes. The semantic segmentation technique namely U-Net was chosen in this paper as the base architecture as it works well on very fewer training samples and provides a better result. Also, the computation cost is very less which is a major advantage compared to other segmentation models as the non-availability of freely available data owing to many security and investment issues. The paper introduces a new architecture for this problem statement which is an improvement to the U-Net architecture which majorly uses the concept of altering the skip connections in different to improve the way the information is passed on. The dataset used here is a derived version of NASA Worldwide view data which is widely used for cloud segmentation. The evaluation results show significant improvement in terms of accuracy.
Photorealistic Simulation Approach for Verification of an Aerial Digital Photogrammetry System

In this work, a photorealistic virtual simulator is developed to simulate the flight dynamics of an unmanned aerial vehicle (UAV - quadcopter drone) with a camera embedded, whose photographing process can be also emulated to gather image and flight data that can be further used to point cloud generation and 3D reconstruction as in digital photogrammetry process. The system is intended to simulate the UAV-based digital photogrammetry of large structures (industrial structures, small buildings, residences). To accomplish this goal, the mathematical modeling of the dynamics of a commercial-of-the-shelf drone was developed and a flight controller was designed and verified in Matlab. Finally, the simulator is verified, generating a descriptive point cloud of an inspection mission that is virtually simulated. The 3D reconstruction of the object of analysis was properly performed in the photorealistic environment.
Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network

Extended back electromotive force (EEMF) has been widely used as speed estimation of permanent magnet synchronous motors (PMSMs) due to its excellent performance at medium and high speeds. However, at low speed, EEMF method has low estimation accuracy. Several sensorless methods have combined EEMF with different accurate low-speed estimation techniques to attain a satisfactory estimation performance in the whole speed range. However, the transition between EEMF and low-speed estimation methods is a crucial step that requires two separate speed estimators with different structures. This leads to increased design complexity. Therefore, in this paper, a single sensorless full-speed control is proposed using an artificial neural network (ANN). The proposed ANN method does not require any transition method compared with hybrid full-speed sensorless methods. Simulation results show that proposed ANN can estimate rotor position for full speed compared with EEMF which estimates the rotor speed at medium and high speed.
Challenges in the Implementation of Information and Communication Technology for Effective Education Dissemination Post Covid-19

Information and communication technologies (ICT) have played a critical role in disseminating education worldwide. The coronavirus disease of 2019 (COVID-19) made every institution further realize the usefulness of ICT in the dissemination of education at every level. Besides primary, secondary, and higher education, research conferences are now also conducted in hybrid mode. ICT is now vigorously used in the educational sector. From educators to students to administrators, everyone is relying on ICT for the teaching-learning process. With new variants of the coronavirus emerging periodically, there is no option for the institutions but to adopt ICT continuously for the dissemination of education. This calls for ICT tool training and development for educators, which will enable them to implement ICT tools for the dissemination of education in a seamless manner. This is a descriptive study in an attempt to discover the challenges faced by institutions in ICT implementation. The present data from Statista.com shows that the internet penetration rate in India still hovers around fifty percent, with an expected six hundred and fifty million internet users by 2023, which is almost half of the present Indian population. The findings of the study suggest that educators are unwilling or partially willing to upgrade their knowledge about ICT tools. Another finding suggests that higher education institutions are unwilling to spend money on upgrading the digital infrastructure.
Research on Key Technology and Application of Epidemiological Survey Using Knowledge Graph

Epidemiological survey of infected people, infected suspects, and their close contacts (hereinafter referred to as epidemiological survey) is the core and key link in the health and quarantine work at ports. However, the work of ports epidemiological surveys mainly depends on the way of manual or simple remote connection. The quality of epidemiological surveys is greatly affected by the staff's technical level and working status. There are deficiencies and hidden dangers in fixing the risk of infection, the type of epidemic disease, the diagnosis of follow-up treatment measures, and the infection of the staff in the flow survey. In this paper, we designed an epidemiological survey system using modern high-tech information technology such as knowledge graph, machine learning, artificial intelligence, data mining, speech recognition, and natural language processing and applies it to the ports entry epidemiological surveys. It effectively lessened human participation, quickly completed the survey, and screening of suspicious cases, which provides technical support and guarantee for improving the overall defense and control capability and level of national security and building smart ports.
Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy

Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in AI systems. This approach addresses a key criticism of current generation systems, namely, their inability to generate human-understandable explanations for their outcomes and ensure safe behaviors, especially in scenarios with unknown unknowns (e.g., cybersecurity, privacy). The integration of neural networks, which excel at exploring complex data spaces, and symbolic KGs, which represent domain knowledge, allows AI systems to reason, learn, and generalize in a manner understandable to experts. This article describes how applications in cybersecurity and privacy, two of the most demanding domains in terms of the need for AI to be explainable while being highly accurate in complex environments, can benefit from neurosymbolic AI.
Comparing Short-Term Net Load Forecasting Methods for Solar Homes

As the prevalence of residential PV systems rises, operational decision making with relation to these resources becomes increasingly important. Forecasting is one of the most important methods of informing these decisions and while both short-term load and PV generation forecasting have been extensively studied, net load forecasting for solar homes features far less in the literature on the topic. This is despite the importance of aggregated load and PV behaviour for both homeowners and network operators. This paper provides a comparison of net load forecasting techniques across three categories: classical statistical techniques, artificial intelligence, and configurable forecasting tools, looking at both integrated and additive net load forecasting. It shows that the Prophet tool is a promising candidate for short-term net load forecasting of solar homes despite it being omitted from similar studies in literature to date, with a 20% performance improvement over daily average profiles in this study. It should be noted that forecasting accuracy of a particular method is highly dependent on the underlying dataset.
Application of Facial Emotion Recognition Based on Multi-Scale Fusion in Smarter Education

The traditional teaching mode has little interaction between teachers and students, and teachers can not grasp students' emotional changes in time. Therefore, how to timely and effectively feed back students' emotional state to teachers in class has become an urgent problem to be solved in current classroom teaching. This paper takes the classroom as the application scenario and uses the deep learning technology to propose a residual attention network and multi-scale fusion student emotion recognition model. The model uses image preprocessing to improve the quality of the input image, uses residual attention module to guide the network to focus on local salient features, uses multi-scale fusion module to strengthen the information interaction between different levels and expand the receptive field, and obtains high accuracy on FER2013 data set. Using this method to identify the students' attention state in class and judge the teacher's teaching effect and students' classroom listening attitude can correct students' listening attitude, improve teachers' teaching efficiency and promote the improvement of teaching quality.
A Novel Airfare Dataset To Predict Travel Agent Profits Based On Dynamic Pricing

Profit prediction in the aviation industry is an im-portant process to increase competitiveness in business. However, it is challenging because it relies heavily on good quality datasets. A good quality dataset can increase the linear correlation between features and can improve the coefficient of determination is the proportion of variation in the dependent variable that can be predicted from the independent variable. The purpose of this research is to create a novel flight ticket dataset where we add three new features to the general flight ticket dataset. We add NetPembayaran, SalesAgent and Profit features to our dataset. The approach used in making this dataset is to collect flight ticket sales transaction data, flight schedule lists, and flight ticket sales history data from various available sources, and integrate them into one comprehensive dataset. Our novel dataset successfully increases the linear correlation between features and can also increase the coefficient of determination. We also evaluated the model we used for profit prediction using RMSE.
Calligraphy's Reflective Expression: Intelligent Chinese Calligraphy Affective and Style Recognition

Calligraphy expresses people's inner mood and reflects their self-cultivation. Style and emotion plays a key role in calligraphy artistry. Consequently, we applied intelligent technology to analyze the emotion and artistic style recognition of Chinese calligraphy artworks. A dataset of 5,000 calligraphy images was established, including art works over seventy renowned Chinese calligraphists. We compared several deep-learning methods to build the best model. The results show that the optimal model achieves an accuracy of 0.9319 with MobileNet in calligraphy emotion recognition, while it achieves the best accuracy of 0.972 in calligraphy font style recognition, and the highest classification accuracy of 0.943 in calligraphy artist's style recognition respectively using ResNet50. Finally, an intelligent Chinese calligraphy emotion and style exploration application are developed based on the optimal model.
Three-dimensional tidal flow around headlands

Field measurements of tidal flow around a headland indicate secondary circulation induced by flow curvature. The secondary flow, defined to be the flow in the plane normal to the direction of the vertically averaged current, is directed toward the headland near the bottom and seaward near the surface, consistent with theoretical predictions. The strength of the secondary flow varies from 5 to 15% of the streamwise flow. It is strongest when the water column is stratified because of both the enhanced shear of the streamwise flow and the reduced frictional damping of the secondary flow. The transverse exchange accomplished by the secondary flow significantly influences the structure of the streamwise flow, causing a broadening of the transverse shear and changing the horizontal and vertical distributions of momentum. It may also have a significant influence on horizontal dispersion in the vicinity of headlands.
Semantic Range Image for Loop Closure of 3D Lidar SLAM

The purpose of SLAM loop closure detection is to reduce the cumulative error in the system by identifying the same scene that the robot passed through. Traditional loop detection methods detect loops based on physical information. These methods use basic physical features to identify loop closures and pay less attention to the role of semantic information as high-level information in loop closure detection. This paper regards semantic information as one of the criteria for confirming loop closure, so that semantic information can be deeply involved in the process of loop closure detection. We construct a global descriptor based on range image and semantic information, which is called SeRI. The descriptor encodes the semantic and geometric features of the surrounding environment. Loop closure candidates are determined by matching between pairs of semantic range images. We evaluate our method on the KITTI dataset. Experiments have shown that the proposed method can effectively identify loop closure candidates correctly with high precision and recall rate.
A Survey on Visible Light Communication for 6G: Architecture, Application and Challenges

One of the factors contributing to provide data transmission in 6G based on visible-light communications (VLC) is the rising demand for high-speed indoor wireless connectivity. This article classifies the technical influences that the transition from 5G to 6G requires. The purpose of this paper is to speculate on the possible applications of visible light communications (VLC) for a 6G technology platform. Aspects of increasing data rates and reducing interference are among the research topics that need more investigation. The establishment of suitable spectrally efficient multiple-access (MA) techniques facilitates the effective management of multiple users, specifically when working with the restricted modulation bandwidth of the light-emitting diodes, in relatively large space indoor environments. The paper concentrates on the suggested specifications of applications, the network architecture and its significant performance indicators for VLC aided applications in 6G. Finally, some potential directions for future study in VLC for 6G are examined.
Estimation of D-Q Inductances and Initial Rotor Position Considering Voltage Distortions in High-Speed Motors for Air Compressors of Fuel-Cell Systems

It is difficult to use a position sensor for high-speed motors because of mechanical limitations such as vibration and heat. When the initial position of the motor is unknown, there is a risk of initial start failure or temporary reverse rotation. Reverse rotation would cause slight damage to an airfoil bearing in high-speed applications, degrading its durability. Therefore, an initial position estimation algorithm is required for high-speed motor drives. Additionally, to implement current controllers and sensorless control algorithms, it is necessary to know the electrical parameters. This study proposes a method for estimating the dq-axis inductances and initial position. The inductances are estimated considering the voltage distortion caused by the inverter nonlinearity; thus, the inductance values are identified with less variation regardless of the injected voltage levels. Moreover, because the inductances can be estimated even at a low voltage, there is low risk of rotor movement during the estimation process. Simulation and experimental results for a 120-krpm high-speed motor for fuel-cell systems verified the effectiveness of the proposed method.
Quantitative Profiling of Lysosomal pH Heterogeneity using Fluorescence Lifetime Imaging Microscopy

Lysosomes play crucial roles in maintaining cellular homeostasis and promoting organism fitness. The pH of lysosomes is a crucial parameter for their proper function, and it is dynamically influenced by both intracellular and environmental factors. Here, we present a method based on fluorescence lifetime imaging microscopy (FLIM) for quantitatively analyzing lysosomal pH profiles in diverse types of primary mammalian cells and in different tissues of the live organism Caenorhabditis elegans. This FLIM-based method exhibits high sensitivity in resolving subtle pH differences, thereby revealing the heterogeneity of the lysosomal population within a cell and between cell types. The method enables rapid measurement of lysosomal pH changes in response to various environmental stimuli. Furthermore, the FLIM measurement of pH-sensitive dyes circumvents the need for transgenic reporters and mitigates potential confounding factors associated with varying dye concentrations or excitation light intensity. This FLIM approach offers absolute quantification of lysosomal pH and highlights the significance of lysosomal pH heterogeneity and dynamics, providing a valuable tool for studying lysosomal functions and their regulation in various physiological and pathological contexts.