Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Analysis of Deep Learning Methods for COVID-19 Detection and Classification

The progress of precise recognition techniques and the evaluation of genetic connections are required because of the worldwide spread of COVID-19. As conventional techniques for COVID-19 detection are time-consuming and require accurate variables in order to minimize or cut short errors that could possibly occur, the COVID-19 epidemic's necessity for early diagnosis and surveillance led to the deployment of Deep Learning (DL) technology for efficient evaluation. Deep learning is a branch of artificial intelligence (AI) and Machine learning (ML), and due to its remarkable ability to assimilate new information, DL technology has quickly risen to prominence. Many DL architectures, like RNN, CNN, and long-term memory, can be utilized for forecasting, perdition, and classification of COVID-19. The intention of this paper is to conduct a comprehensive analysis of various deep learning methods, for detecting and classifying the COVID-19 virus using diverse data sources, such as chest X-rays, CT scans, and genomic sequences, to highlight their exceptional accuracy in identification. We also explore DL's involvement in analyzing the virus's genetic code, providing insight into how it evolved. It aims to deliver a summary of earlier research and its COVID-19 applications.
From Probabilistic Occupancy Grids to versatile Collision Avoidance using Predictive Collision Detection

As robots are spreading in diverse environments, new collision avoidance challenges arise. Real world collision avoidance often includes dynamic, complex, unstructured and uncertain environments. While these constraints are rarely considered all together in perception and navigation research works, we present a broad integration from perception to navigation in a versatile collision avoidance approach that is designed to operate under these constraints. Our solution relies on a new Predictive Collision Detector that we propose as an interface between state-of-art grid-based perception and sampling-based planners. Unlike most other approaches, ours operates only on elementary spatial occupancy and does not use the concept of objects such that it captures the richness and versatility of modern occupancy grid perception. With simulated and experimental results on 2 robots, including a robotic car, we show that our solution generates advanced driving behaviors that compare with the related state-of-art and comply with safety standards even in complex scenarios. With this contribution, we hope to facilitate the use of probabilistic occupancy grids for collision avoidance and to enhance the connectivity of the state-of-art of perception and navigation.
Games Recommender System Using Singular Value Decomposition

The increasing number of games released each year and many platforms that distribute games such as Steam, Epic Games, Xbox Games, and others, can make it difficult for users to find games that suit their preferences. Furthermore, the recommender system plays an important role to make it easier for users to find games that match their preferences. Collaborative Filtering (CF) is a widely used and successful recommender paradigm. CF provides recommendations based on the similarity of preferences of other users. This paradigm can be divided into two categories such as memory-based CF and model-based CF. Model-based CF uses machine learning techniques such as matrix factorization (MF). Singular Value Decomposition (SVD) is one of the most relevant techniques for MF and can handle sparsity problems. This research aims to build a game recommender system using SVD to provide recommendations that match user preferences. In addition, this study evaluates the performance of SVD using cross-validate and compares it with non-Negative matrix factorization (NMF) using the RMSE evaluation metric. The results of the evaluation using cross-validate show that SVD outperforms NMF in the games domain with an average RMSE value of SVD is 0.36 and NMF is 0.47. In addition, SVD has a faster computation time, with the fastest time being 172 seconds, while NMF takes 231 seconds.
Review of precious metal exchange rates forecasts

Precious metals have always played a fundamental role in financial activities, being treated as a global currency, an attractive investment and a raw material for many industries. Effective forecasting of the trend in precious metals has long been of interest to researchers in many fields, and such forecasts can be very useful in regulating the risk of personal investments and in adjusting the national economy in times of strategic change. Nowadays, mainstream forecasting methods include machine learning methods, among which a number of regression models and deep learning algorithms are widely used and accepted. Recent developments in deep learning offer opportunities to extract valuable information and make forecasts using the vast financial and socio-economic data available. One of the biggest advantages of these methods is that they can learn significantly more complex relationships between input and output data, even those that are difficult to define or even unknown at first, so they can effectively help professionals make their predictions. Therefore, the main aim of this paper is to summarize and compare the results of the recent forecasting using different deep learning algorithms.
End-to-End Person Search Based on Content Awareness

Within the domain of computer vision, person search stands out as a crucial and intricate undertaking. With the growth of urban population and the popularity of surveillance cameras, person search demonstrates its versatility in a multitude of applications, particularly within the realm of video surveillance, public safety and intelligent transportation. The person search task is comprised of two subtasks, namely person detection and person re-identification (ReID),the task of person detection concentrates on identifying similarities among diverse identities. In contrast, person ReID task centres on discovering dissimilarities among distinct identities. In order to reduce the influence of variations in tasks during the model training phase, we segment the ResNet50 network according to the subtasks and propose an end-to-end content-aware person search framework. Where the detection branch is built on top of shallow features and the person ReID branch is built on top of deep features. Meanwhile, we introduce gated deformable convolution module(GDCM)to sense the location of the person and further distinguish the background from the person. Finally, the effectiveness of the method is demonstrated on two authoritative datasets(CUHK-SYSU、PRW) .
Rock magnetic investigation and its geological significance for vein‐type uranium deposits in southern China

To characterize the metallogenic environment of a typical vein‐type uranium deposit, samples from diabase dykes, alteration zones including metamorphic diabase and uranium ore, and granites were systematically investigated for six boreholes from southeastern China. Rock magnetic results indicate that coarse‐grained magnetites (pseudosingle domain, PSD, and multidomain, MD) are dominant magnetic carriers in diabase. In contrast, the uranium ore is dominated by fine‐grained magnetites (superparamagnetic, SP, and single‐domain, SD). The concentration of magnetic particles in fresh granites is low. Magnetic properties of metamorphic diabases exhibit much greater variability of magnetic properties and higher degrees of sulfuration than unaltered diabase and granite, due to contact metasomatism and reduction effects close to the vein. Compared with diabase, magnetic remanence of the uranium ore is much lower, but displays much higher stability. The Koenigsberger ratio Q peaks in the uranium ore with a value of ∼1.00. Using the systematic rock magnetic results to constrain the interpretation, the contribution of the intersection zone of diabase dyke and silicified fault to magnetic anomalies was further modeled, and the effects of the ore body are significant for magnetic exploration. Overall, rock magnetic investigations of vein‐type uranium deposit provide a better understanding of the interactions between different rock types, and further facilitate regional magnetic surveys on the ground.
vLOFT: Variable Learning Rate for Online Federated Traffic Prediction

Deep learning (DL) models have established remarkable success in traffic state estimation (TSE); however, the widely-studied approaches' centralized nature presents deployment challenges in Intelligent Transportation Systems (ITS), especially regarding data ownership, privacy, and response times. Online models trained using Federated Learning (FL) approaches can harvest the rich real-time data streams in ITS for continuously updating the TSE models. Though, there are limitations during the online learning process, such as model drift. In this paper, we propose a variable learning rate (VLR) scheduling approach that integrates online FL with cooperative edge computing (CEC). Continuously collected local traffic data by roadside units (RSUs) and connected sensors train localized models, and only the updated model parameters are shared instead of raw data. VLR adjustments within the Adaptive Gradient Algorithm (ADAM) optimizer framework adjust the learning rate based on traffic patterns. We conduct extensive experimental analysis and compare example VLR strategies with a control model. The results indicate that the best-performing VLR strategy reduced the average mean absolute error (MAE) across all detectors by about 10.29%.
Understanding Antecedents Subscription Intentions for Video Streaming

This study aims to see the effect of Involvement, Moral Judgment, Perceived Risk and Theory of Planned Behavior (TPB) application on customer intention to use a paid version of the video streaming service. This study uses a survey model using a quantitative research approach with data instruments using a questionnaire. A total of 113 responses are collected in this study. Questionnaire data that has been collected is analyzed by Structural Equation Modeling (SEM) using SmartPLS 3.0 tools. The results of the analysis show that the TPB application (attitude, subjective norms, and perceived behavior control) can explain the factors that influence a person’s intention to enter subscription intentions of video streaming services. Apart from these three variables, it was also found that the intention was subscribed to by involvement, moral judgment, and perceived risk.
Observer Design for Takagi-Sugeno Fuzzy Systems Using Poly-Quadratic Lyapunov Function Based On Lipschitz Approach

In this work, a contribution to reduce the conservatism of the observers for Takagi-Sugeno fuzzy systems with unmeasurable premise variables (UPV) based on the Lipschitz assumption compared to previous work is proposed. In order to achieve this goal, the poly-quadratic Lyapunov function (PQLF) along with a good employment of the completion of squares property are used. The proposed method helps in reducing the conservatism that arises when the Lipschitz constant exceeds an admissible value and when the quadratic approach becomes infeasible. The stability conditions are presented as Linear Matrix Inequalities (LMIs), ensuring the asymptotic convergence of the state estimation error. A numerical illustration is provided to demonstrate the feasibility of the proposed work.
A novel 10T SRAM bit-cell with high static noise margin and low power consumption for binary In-Memory Computing

In this paper, a novel 10T SRAM bit-cell with a high read/write static noise margin and low power dissipation is presented to be utilized in binary in-memory computing. The proposed SRAM bit-cell employs a conventional 6T SRAM along with four extra transistors to decouple read and write operation paths and improve the stability of the bit-cell. The two transistors M7 and M8 in the proposed structure act as switches to enable binary operation of the dot product in IMC mode. To conduct a comparative analysis, the various SRAM bit-cell designs were simulated using the Cadence design framework in the 65 nm technology. The proposed SRAM bit-cell not only presents a higher read stability value among the considered cells but also has a proper read delay value. The read noise margin of the proposed circuit is 1.91× as compared to a conventional 6T SRAM bit cell. Furthermore, the read energy of the proposed 10T SRAM has the lowest value of 28 fJ at a supply voltage of 1V compared to all the considered SRAM bit cells.
Movement Patterns of Pedestrians and Cyclists at Signalized Segregated Crosswalks: A Case Study in Nanjing, China

Interactions and conflicts between vulnerable road users, mainly pedestrians and cyclists, are frequently observed on crosswalks, especially in urban areas with relatively high traffic volume. To alleviate the potential safety risks, one possible measure is to adopt the segregated crosswalk to provide separate crossing space for pedestrians and cyclists. However, the crossing behaviours of pedestrians and cyclists at segregated crosswalks have rarely been investigated. To fill the gap, this study aims to investigate the movement patterns of pedestrians and cyclists at signalized segregated crosswalks. Field observations and data collection are conducted at a typical intersection with segregated crosswalks in Nanjing CBD, China. In total, trajectory data of 659 pedestrians and 1,212 (e-)cyclists is collected and analysed. The route choice and crossing speed of pedestrians and cyclists are explored, and multiple influencing factors are analysed. The research findings indicate that road users, especially cyclists, in the minor direction violate the segregation rule more frequently compared to road users in the major direction. It is recommended to adopt the segregated crosswalks mainly for unidirectional pedestrian and cyclist flow.
Evaluation of Traffic Signal Control at Varying Demand Levels: A Comparative Study

The topic of traffic signal control (TSC) for urban intersection networks has been a key research area for many years, but comprehensive comparisons among state-of-the-art methodologies remain limited. This calls for a more exhaustive approach to benchmarking TSC techniques. This study builds upon previous TSC benchmarking work by evaluating the performance of both reinforcement learning (RL) and model-based TSC algorithms across a spectrum of demand levels. We adapt new metrics for in-depth simulation analysis and ground our investigation in a real-world urban arterial scenario. Our results unveil a noteworthy observation: the performance of tested algorithms is inconsistent when evaluated under diverse demand levels and across different metrics. This highlights the importance of implementing multidimensional evaluations in future TSC studies for a more nuanced understanding of their performance.
Overview of the Starkey Project: mule deer and elk research for management benefits.

Managers have long been concerned about the welfare of mule deer (Odocoileus hemionus) and elk (Cervus elaphus) on public lands in the western United States. These two species generate millions of dollars annually to state wildlife agencies from sales of hunting licenses, and elk viewing generates millions of additional dollars to local and regional economies (Bolon 1994, Bunnell et al. 2002). By contrast, the potential for elk and mule deer to compete with livestock, to damage agricultural crops and to modify plant succession make the two species obvious sources of controversy among private and public land managers.
A Case for Monte Carlo Tree Search in Adaptive Traffic Signal Control: Modifiability, Interpretability and Generalization

Adaptive Traffic Signal Control methods based on Reinforcement Learning have been applied successfully to reach state-of-the-art results in simulation. However, most recent works in the area use model-free methods, which learn value and/or policy functions directly through environment in-teractions without a dynamics model. Model-free methods have several potential drawbacks: (1) difficulties in generalization to dynamics not seen in training, as well as difficulties in transfer to different intersection geometries; (2) low sample-efficiency, resulting in high resource requirements for training; (3) often, reliance on difficult-to-interpret function approximators such as neural networks. In this work, we demonstrate experimentally that Monte Carlo Tree Search (MCTS) with a simple queue and platoon-propagation dynamics model can reach lower delay on a single intersection than the standard model-free Deep Q-Networks (DQN) algorithm. Moreover, we provide empirical results that illustrate that model-based receding-horizon planning can alleviate many of the drawbacks of the model-free methods listed above: better generalization characteristics; more direct and less resource-demanding transfer to different intersection geometries and phasing schemes; and simpler visualization and interpretation, aiding modification and debugging.
Dune stabilization with PANICUM AMARUM along the North Carolina coast

Abstract : This study was conducted to determine the dune stabilizing and dune building potential of Panicum amarum (bitter panicum) along the North Carolina coast. Experimental plantings with emphasis on bitter panicum together with Ammophila breviligulata (American beachgrass) and Uniola paniculata (sea oats) were made at two coastal locations. On a sandy site about 300 meters inland where there was relatively little sand moving, bitter panicum contributed more of the estimated cover through two growing seasons than either of the other two grasses. Over a 24-month period at a foredune site, bitter panicum accumulated about one-fourth the sand accumulated by either three selections of American beachgrass or mixed species plantings involving all three grasses. Results indicate that the principal values of bitter panicum are in stabilizing sandy coastal areas and in mixed species plantings to build and stabilize coastal foredunes in the southeastern United States.
Information Management in Business: Effect of Social Media Use on Job Performance

Social media use is expanding dramatically in practically every aspect of life with over 53% of users worldwide, including in interactions between employees in an organization or company. However, unwise use of social media can negatively impact employee productivity, disrupt focus, and negatively impact the company's image. High social media usage can positively improve business performance financially and non-financially. Ineffective social media use may have an impact on workplace productivity. Consequently, this study is to explore the effects of social media use for both personal and professional reasons on employee performance. We conducted a comprehensive study involving employees of productive age (1855 years) at the Fertilizer Company and observed the field phenomena that occur. The study found that employees may engage in two types of usage behaviors that have an impact on job performance: use of social and work-related social media on both internal and external social media. We discovered that the interaction between work-related use and social-oriented social media has a favorable effect on employee job performance. The use of internal social media for work purposes can improve employee performance. Based on the findings, we conclude that in contrast to socially oriented social media, increased social media usage for social issues has no discernible impact on employees' performance at work.
An Efficient Convolutional Multi-Scale Vision Transformer for Image Classification

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks.
Aerosol‐driven increase in Arctic sea ice over the middle of the twentieth century

Updated observational data sets without climatological infilling show that there was an increase in sea ice concentration in the eastern Arctic between 1950 and 1975, contrary to earlier climatology infilled observational data sets that show weak interannual variations during that time period. We here present climate model simulations showing that this observed sea ice concentration increase was primarily a consequence of cooling induced by increasing anthropogenic aerosols and natural forcing. Indeed, sulphur dioxide emissions, which lead to the formation of sulphate aerosols, peaked around 1980 causing a sharp increase in the burden of sulphate between the 1950s and 1970s; but since 1980, the burden has dropped. Our climate model simulations show that the cooling contribution of aerosols offset the warming effect of increasing greenhouse gases over the midtwentieth century resulting in the expansion of the Arctic sea ice cover. These results challenge the perception that Arctic sea ice extent was unperturbed by human influence until the 1970s, suggesting instead that it exhibited earlier forced multidecadal variations, with implications for our understanding of impacts and adaptation in human and natural Arctic systems.
Calculation of the Air-Gap Field Distribution in Surface-Mounted Permanent-Magnet Synchronous Motors Using the Finite Difference Method

A quick calculation of the air-gap held distribution in surface-mounted permanent-magnet synchronous motors (PMSMs) is significant. Knowing the flux density distribution, one can calculate quantities such as magnetic loading, static torque, and inductances. This paper aims to provide a fast numerical procedure to calculate the air-gap field in surface PMSMs using the finite difference method (FDM), being a good tool before running comprehensive non-linear finite element simulations. Several analytic methods exist in the literature, and most of these, center on the separation of variables and Fourier series. That makes their formulations cumbersome. The proposed method in this work centers itself on the FDM with a straightforward implementation. We can use this with the finite element method (FEM) for the optimal design of surface PMSMs. A 10-pole/12-slot fractional slot surface-mounted PMSM is selected as a benchmark. We calculate its field distribution due to both magnets (no-load condition) and armature excitation using the proposed method and FEM. Results corresponding to each case are presented, and we show that the proposed method has enough accuracy compared with the finite element analysis.