Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Service Routing and Load Allocation Strategies for CloudSim-Enabled Visual Modeling Environment

Cloud computing has significantly transformed the deployment and management of applications. It has simplified the complexities of handling service demand, installation and maintenance. However, selecting the most suitable cloud service provider (CSP) can be challenging due to the diversity in pricing models, geographical coverage regions and service offerings. A tool named Cloud Analyst has been developed to tackle these diversities. This tool employs service broker and load balancing strategies to allocate user bases (UBs) to datacenters (DCs) and virtual machines (VMs), respectively. However, these strategies’ performance can be further improved by developing more efficient ones. This paper introduces efficient service routing (between UBs to DCs), VM provisioning and load allocation (UBs to VMs) strategies for a CloudSim-enabled visual modeling environment. Their performance is assessed in comparison to existing using three factors: overall response time (ORT), DC request servicing time (DCRST) and total cost (TC) through the Cloud Analyst tool. Our strategies reduce ORT by 68.82%, resulting in faster application delivery, DCRST by 41.19%, and yield a TC savings of 51.78% compared to the existing strategies. These findings demonstrate the effectiveness of our strategies in enhancing performance over existing strategies.
Hybrid CNN & Random Forest Model for Effective Turmeric Leaf Disease Diagnosis

This study dives into the classification of turmeric leaf diseases to enhance the field of agriculture and crop health management. Turmeric, a major agricultural commodity, is sensitive to a variety of illnesses, causing problems for both yield and quality. In turmeric production, accurate and prompt disease identification is critical for efficient disease management. The data analysis shows a meticulously developed categorization model that has produced impressive results. Precision, Recall, and F1-Score for a variety of diseases, including Leaf Spot, Bacterial Diseases, Fungal Diseases, Viral Diseases, Rust Diseases, Leaf Curling Diseases, or Wilting Diseases, routinely exceed 97%. These data highlight the model's outstanding ability to classify and diagnose turmeric leaf illnesses. Furthermore, the model's 99% accuracy rate emphasizes the model's suitability for realworld agricultural use. This research helps considerably to contribute to the advancement of efficient pest and disease control techniques by providing exact disease detection and classification, consequently improving crop protection and supporting sustainable turmeric production procedures. This work demonstrates the life-altering effects of advanced artificial intelligence (AI) techniques in tackling important agricultural difficulties, resulting in greater yields and improved health of turmeric crops.
Rain on Generative Seas

Following evidence derived in the field and from previous numerical experiments, we carry out wave modeling tests where the white‐capping and wind input source functions are modified to be functions of also the rain rate and the wind speed. We show the high sensitivity of the results to these parameterizations and how related physically sound, but purely intuitive, choices can reproduce almost perfectly the original results also in extreme conditions. We discuss the related physical and numerical implications.
MR-Sense: A Mixed Reality Environment Search Assistant for Blind and Visually Impaired People

Search tasks can be challenging for blind or visually impaired people. To determine an object’s location and to navigate there, they often rely on the limited sensory capabilities of a white cane, search haptically, or ask for help. We introduce MR-Sense, a mixed reality assistant to support search and navigation tasks. The system is designed in a participatory fashion and utilizes sensory data of a standalone mixed reality head-mounted display to perform deep learning-driven object recognition and environment mapping. The user is supported in object search tasks via spatially mapped audio and vibrotactile feedback. We conducted a preliminary user study including ten blind or visually impaired participants and a final user evaluation with thirteen blind or visually impaired participants. The final study reveals that MR-Sense alone cannot replace the cane but provides a valuable addition in terms of usability and task load. We further propose a standardized evaluation setup for replicable studies and highlight relevant potentials and challenges fostering future work towards employing technology in accessibility.
Deepwater dynamics and mixing processes during a major inflow event in the central Baltic Sea

Intrusions of large amounts of dense and oxygen-rich waters during so-called Major Baltic Inflows (MBIs) form an essential component of the Baltic Sea overturning circulation and deep-water ventilation. Despite their importance, however, detailed observations of the processes occuring in the central basins during an MBI are virtually lacking. Here, data from a long-term deployment of an autonomous profiling platform located in the center of one of the main basins are presented, providing the first direct and detailed view of the deep-water modifications and dynamics induced by one of the largest MBIs ever recorded (MBI 2014/2015). Approximately 21 Gmol of oxygen were imported during three distinct inflow phases with an unexpectedly large contribution of oxic intrusions at intermediate depth. Oxygen consumption rates during the stagnation period immediately following the inflow phase was found to be 87 g m−2 yr−1 with a dominant contribution of sedimentary oxygen demand. The most energetic deep-water processes (topographic and near-inertial waves) were only marginally affected by the inflow; however, sub-inertial energy levels associated with intrusions and eddies were strongly enhanced. Turbulence microstructure data revealed that the deep interior regions remain essentially non-turbulent even during the energetic conditions of an MBI, emphasizing the importance of boundary mixing. Warm intrusions frequently showed a temperature fine-structure with vertical scales of the order of 0.1∼m, without any signs of active turbulence. At the upper flanks of these intrusions double-diffusive staircases were often found to develop, suggesting an important alternative mixing process during inflow conditions.
Semantic Secrecy Assessment of Physical Layer Security in 5G NR Uplink Transmissions Under Fading Channel Conditions

This paper proposes a system architecture that embeds semantically-secure information-theoretic physical layer security (IT-PLS) non-intrusively into a 5G New Radio (NR) system in order to protect uplink transmissions via physical uplink control channels (PUCCH) against post-quantum eaves-dropping attacks. We implement a proof of concept of such system employing a code construction based on a modular coding scheme with a universal hash function that ensures semantic secrecy. We conduct link-level simulations of wiretap channels under different frequency-selective fading conditions and noise characteristics in order to evaluate the performance of such implementation for slow and fast fading scenarios. We model them using tapped delay line channel models involving rural and urban scenarios with line-of-sight (LOS) and non-LOS radio conditions, as specified by the 3GPP TR 38.901. We characterize such system by measuring the distinguishing error rate, block error rate, and secrecy outage probability under different time-varying fading channels. Our case study outlines how IT-PLS can be transparently embedded into future 6G systems as well.
Trap Contract Detection in Blockchain with Improved Transformer

Smart contracts are tailored software services that provide consistency and autonomy. The emergence of blockchain has powerfully facilitated the development of smart contracts but also brought dramatic challenges to their security and trustwor-thiness. Plenty of malicious traps are hidden in smart contracts, causing irreversible damage and obstructing the progress of this technology. Although researchers have gradually emphasized the identification of trap contracts, existing approaches suffer from a few concerns, viz., the shortage of an efficient detection model, the unbalanced categories of trap contracts, and the absence of a high-quality dataset with multi-trap contracts. In this paper, we propose an architecture called TrapFormer to intelligently detect trap contracts in the blockchain solely by leveraging the opcodes of smart contracts. We introduce a densely connected transformer that can segmentally extract opcode features and thus distinguish any potential traps. Furthermore, we implement an adaptive data augmentation method to alleviate the category imbalance of trap contracts. To demonstrate the feasibility of the proposed solution, we construct a multi-trap contract dataset from Ethereum. The experimental results reveal that the proposed solution can achieve superior performance for practical use.
A Comparative Study for Detecting Polycystic Ovary Syndrome Using A Machine Learning Framework

Polycystic Ovary Syndrome (PCOS) is a widespread hormonal disorder affecting numerous women with ovaries, during their reproductive ages, often leading to various complications if undiagnosed or improperly managed. This research aims to improve PCOS detection by utilizing an interpretable machine-learning framework for diagnostic assistance. We utilized a publicly available dataset from Kaggle, featuring 541 samples and 44 attributes. Various preprocessing techniques and feature selection methods, such as Chi-Square, Mutual Information, and Correlation Coefficient, were employed here. Subsequently, we trained multiple machine learning algorithms and subjected them to K-fold cross-validation. Their performance is evaluated and compared by accuracy, precision, recall, and F1 score. Our findings reveal that the CatBoost algorithm, when paired with Chi-Square feature selection, achieved the highest accuracy rate of 93.9%. Our study focuses on investigating important features, comparing algorithm performance, and achieving high accuracy in PCOS detection. The research validates machine learning's effectiveness for non-invasive PCOS diagnosis, laying the groundwork for future medical diagnostics.
Diversity of nectariferous and polleniferous bee flora at Anjaneri and Dugarwadi hills of Western Ghats of Nasik district (M. S.) India

The present investigation was conducted to study the diversity of nectariferous and polleniferous bee flora and to develop a floral calendar for Anjaneri and Dugarwadi hills. The flowering plants were visited and observed for the presence and foraging activities of honeybees. Plants were scored as bee foraging species when at least three honeybees had visited to the flowers within the period of 10 minutes. The results revealed that 52 plant species were useful to honeybees, out of which 29 were agricultural crops and 23 wild plants. The identified flora was further grouped into pollen, nectar and both pollen and nectar yielding plants. Mid-December to February and mid-July to September were identified as honey flow periods and mid -April to midJune were the critical dearth periods during the year. Based on the availability, utility status and flowering duration of flora, floral calendar was developed for the study area.
Predictive Modeling for Heart Rate: A Comparative Analysis of LSTM, XGBoost, and LightGBM

This study evaluates the effectiveness of Long Short-Term Memory (LSTM) networks, XGBoost, and LightGBM in predicting heart rates across diverse time windows. Leveraging a dataset from 22 users across multiple observation windows, this research significantly broadens the current understanding of heart rate prediction in the field of personalized health monitoring and sports science. Performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Scatter Index (SI) were employed for assessment. Over intervals of 30, 60, and 180 seconds, both XGBoost and LightGBM significantly outperformed LSTM in terms of MAE, MSE, RMSE, and SI. These results suggest that XGBoost and LightGBM are superior options for high-accuracy heart rate prediction, underscoring their potential utility in healthcare and athletic performance monitoring applications.
Extended Binary Chirps Codebooks for Non-Coherent Communications

Binary chirps (BCs) are exponentiated 2nd-order Reed-Muller codes, which have interesting geometric and algebraic features, one of which is the close connection to the diagonal part of the Clifford group, which is the 2nd level of the Clifford hierarchy. We develop a novel transvection based method to analyze the diagonal Clifford hierarchy. Using this, we identify a connection of recently proposed generalized BCs with the 3rd level of Clifford hierarchy. Then, we propose two systematic extensions of the BC codebook to an arbitrary Clifford hierarchy level and find their minimum distances. In these extensions, the number of codewords grows exponentially with the hierarchy level. For decoding, we design a low-complexity decoding approach for the extended BCs, using the Howard algorithm for BC decoding as a component. Through simulations, we show that the performance of the proposed low-complexity decoder can achieve performance very close to the exhaustive search with significantly reduced complexity.
Electron holes in the outer radiation belt: Characteristics and their role in electron energization

Van Allen Probes have detected electron holes (EHs) around injection fronts in the outer radiation belt. Presumably generated near equator, EHs propagate to higher latitudes potentially resulting in energization of electrons trapped within EHs. This process has been recently shown to provide electrons with energies up to several tens of keV and requires EH propagation up to rather high latitudes. We have analyzed more than 100 EHs observed around a particular injection to determine their kinetic structure and potential energy sources supporting the energization of trapped electrons. EHs propagate with velocities from 1000 to 20,000 km/s (a few times larger than the thermal velocity of the coldest background electron population). The parallel scale of observed EHs is from 0.3 to 3 km that is of the order of hundred Debye lengths. The perpendicular to parallel scale ratio is larger than one in a qualitative agreement with the theoretical scaling relation. The amplitudes of EH electrostatic potentials are generally below 100 V. We determine the properties of the electron population trapped within EHs by making use of the Bernstein‐Green‐Kruskal analysis and via analysis of EH magnetic field signatures. The density of the trapped electron population is on average 20% of the background electron density. The perpendicular temperature of the trapped population is on average 300 eV and is larger for faster EHs. We show that energy losses of untrapped electrons scattered by EHs in the inhomogeneous background magnetic field may balance the energization of trapped electrons.
Hydrodynamic and dispersion studies in an estuary physical model incorporated with an idealized groundwater

A study on hydrodynamic and dispersion has been undertaken in an estuary physical model. The model represented the Severn Estuary in the UK which has the second largest tidal range in the world. The scale of the model was 1:125 vertically and 1:25,000 horizontally. Experiments in nine sample points showed that water level was different in those points. Velocity measurements showed that the velocity in the physical model coild be analysed using the wave resonance theory. The velocity profile measurements showed that the balance between ebb and flood tide, and this balance were different in different sampling points. The experiments were then compared to the computer modelling results. The water level model showed accordance with the experimental results. The velocity model results were then calibrated using several parameters, namely n-Manning, COED, and momentum correctioncoefficient (β). The calibration was then continued by the refinement of boundary condition and bathymetry. Studies on dispersion in the estuary physical model revealed that longitudinal dispersion coefficient affected the dispersion significantly, while the lateral turbulent diffusion affected the dispersion less significantly.Constant dispersion coefficient was compared with the variable dispersion coefficient, and the results showed that the constant dispersion coefficient gave a better representation of the dispersion process. Studies were also undertaken in an idealized groundwater which was linked to the estuary physical model. The static and dynamic conditions of the groundwater were studied. For static condition which was also undertaken to calibrate the water level probes, the change of water level in the groundwater clearly followed the changes in the estuary water levels, with a delay of water level according to the distance between the borehole and the estuary boundary. Dynamic condition of the groundwater as a results of tide in the estuary clearly shows the phase difference and the tide range at each borehole according to their distance to the estuary boundary. The effect between pump and tide was studied, and this showed clearly the effects of each component on groundwater level. Through Darcy’s analysis it was proved that the groundwater hydrodynamics follow the Darcy’s principle. Finally the dispersion in the groundwater were studied using two scenarios, namely open-open boundary and close-open boundary. Analysis results showed that open-open boundary gave lower dispersion coefficient than the close-open boundary.
Enhancing Carrier Transport and Injection of Ga2O3 Deep-Ultraviolet Schottky Photodiode by Introducing Impurity Energy Level

In this letter, a Ti/Sn-Ga2O3/Ni Schottky photodiode device was achieved by a rarely-reported PECVD technology. Benefitting from the introduction of Sn impurity energy level, which provides extra paths for carriers’ generation and helps create a stronger built-in electric field to facilitate carriers’ separation, the carrier transport and injection efficiency of device are collaboratively enhanced. Under 254 nm deep-ultraviolet (DUV) light, the device displays an ultrahigh responsivity (<inline-formula> <tex-math notation="LaTeX">${R}{)}$ </tex-math></inline-formula> of 3508A/W, outperforming the corresponding Ti/Ga2O3/Ni device (14.7 A/W) and many other Sn-doped Ga2O3 photodetectors (PDs). Meanwhile, the device exhibits the low dark current of −6.6pA/4.79nA (~720 rectification ratio) under dark conditions, together with a remarkable external quantum efficiency of <inline-formula> <tex-math notation="LaTeX">${1}. {72}\times {10} ^{{6}}$ </tex-math></inline-formula>%, an outstanding detectivity of <inline-formula> <tex-math notation="LaTeX">${4}. {9} \times {10} ^{{14}}$ </tex-math></inline-formula> Jones at 5 V bias under DUV illumination. Furthermore, the device can operate in self-powered mode with an <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> of 100 mA/W as well, and this work demonstrates the huge potential of high-performance PECVD-grown elemental-doped Ga2O3 film-based PDs.
Object Detection using PointPillars with Modified DarkNet53 as Backbone

Three-dimensional (3D) object detection using LIDAR data is a crucial aspect in building autonomous vehicles. This study focuses on investigating the effect of modifying the backbone network for deep convolutional neural networks (DCNN) on object detection accuracy and total number of learnable parameters. The PointPillars network is employed in our experiments, which boasts a straightforward design, fast processing capabilities, and the flexibility to easily incorporate additional features. The 2D CNN network used as backbone in the benchmark PointPillars model has been replaced by a variant of Darknet53 in the proposed work. It is demonstrated that by modifying the original backbone network of the PointPillars model, there is significant improvement in the detection accuracy for higher confidence thresholds of 75% and 95% with almost half the number of learnable parameters.
Mobile-Based Deep Convolutional Networks for Malaria Parasites Detection from Blood Cell Images

This paper uses a novel mobile-embedded and parameter-efficient convolutional network architecture to detect malaria parasites, a life-threatening disease, from the microscopic blood smear images. Recent research on malaria parasite detection has used computer-aided 2D convolutional neural networks (CNNs) with and without transformers and attention-based networks. Many mobile-based network architectures have also been analyzed for the publicly available microscopic blood smear image dataset. Like other computer-aided CNNs and mobile-embedded network architectures, we introduce a novel but straightforward, parameter-efficient, mobile-supported, and deep-learning architecture on this malaria image dataset to detect malaria parasites. Here, we apply the Squeeze-and-Excitation block with a spatial 1D CNN layer. Our deep learning-based proposed model performs with a testing accuracy of 99.52% on detecting malaria parasites from blood cell microscopic image modified dataset. Extensive evaluation was tested on the original malaria image dataset, where our proposed model shows 96.78% testing performance. Our proposed model with 2.2M trainable parameters and 12.6M FLOPS achieves state-of-the-art accuracy for the original and modified malaria detection image datasets.
Content-Aware Quantization Index Modulation for Physical Layer Steganography

Conventional Quantization Index Modulation (QIM) has been tailored for diverse data hiding applications, yet it lacks the ability to achieve minimal distortion in physical layer steganography. To address this, we introduce an enhanced QIM scheme, Content-Aware QIM (CA-QIM). By adapting the codebook to the probability distribution of embedded messages and host signals, CA-QIM excels at handling non-uniformly distributed messages, resulting in significantly reduced distortion. Our approach outperforms other QIM variants in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), and percentage residual difference (PRD), as demonstrated through simulation results. CA-QIM offers a promising solution for achieving improved perceptual transparency and robustness in steganography applications.
Machine Learning for Estimating the Impact of Adding New Mobile Network Cells

In the context of densifying the mobile network in urbanized areas, it is a common practice to increase the capacity by updating existing cell sectors. This paper studies upgrades consisting in adding new cellular frequencies on existing sectors. Using historical data aggregated at sector level spanning over 18 months, we proposed methods to i) following a sector upgrade, evaluate the trending growth rates of resource availability and of the proportion of resource usage per user of already existing cells, ii) use machine learning to predict this trend given the sector existing frequencies, planned frequencies to be added, previous performance indicators and urban fabrics, iii) prioritize cell deployments by computing a predictive score based on the previous predictions. To evaluate the efficiency of machine learning methods, gradient boosting, random forest and SVM models were compared with a baseline, using several metrics. The baseline model was designed to predict the traffic activity evolution as the mean evolution of the same added and existing configurations seen in the training set. This baseline was beaten by all the models trained with our methods. The ranking made by the best ML models had an average precision higher than the baseline by 0.19, a reciprocal rank higher by 0.11 and normalized discounted cumulative gain higher by 0.12.