Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Multiple-Instance Learning for Tongue Coating Classification

Tongue coating can provide valuable diagnostic information to reveal the disorder of the internal body. However, tongue coating classification has long been a challenging task in Traditional Chinese Medicine (TCM) due to the fact that tongue coatings are polymorphous, different tongue coatings have different colors, shapes, textures and locations. Most existing analyses utilize handcrafted features extracted from a fixed location, which may lead to inconsistent performance when the size or location of the tongue coating region varies. To solve this problem, this paper proposes a novel paradigm by employing artificial intelligence to feature extraction and classification of tongue coating. It begins with exploiting prior knowledge of rotten-greasy tongue coating to obtain suspected tongue coating patches. Based on the resulting patches, tongue coating features extracted by Convolutional Neural Network (CNN) are used instead of handcrafted features. Moreover, a multiple-instance Support Vector Machine (MI-SVM) which can circumvent the uncertain location problem is applied to tongue coating classification. Experimental results demonstrate that the proposed method outperforms state-of-the-art tongue coating classification methods.
Reconfigurable Intelligent Surface-Assisted Non-Orthogonal Multiple Access

Reconfigurable intelligent surface (RIS) is a revolutionary technology to achieve spectrum-, energy-, and cost-efficient wireless networks. This paper considers an RIS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the RIS. A combined-channel-strength based user-ordering scheme for NOMA decoding is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the non-convex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. For the single-antenna BS setup, the optimal power allocation at the BS and the asymptotically optimal phase shifts at the RIS are obtained in closed forms. For the multiple-antenna BS setup, it is shown that the rank of the SDR solution of the transmit beamforming design is upper bounded by two. Also, the proposed algorithms are analyzed in terms of convergence and complexity. Simulation results show that the RIS-assisted NOMA system can enhance the rate performance significantly, compared to traditional NOMA without RIS and traditional orthogonal multiple access with/without RIS.
Effect of Driving Frequency on Reduction of Radar Cross Section Due to Dielectric-Barrier-Discharge Plasma in Ku-Band

This study investigates the effect of driving frequency on the ability of a dielectric barrier discharge (DBD) plasma to reduce the radar cross section (RCS) in the Ku-band. Analysis based on the Drude model suggests that the electron density of the plasma will increase with the driving frequency, implying that the plasma will be more effective in terms of RCS reduction. Experimental results based on a multifingered DBD generator reveal that an RCS reduction of up to 4.1 dB is achieved at 18 GHz, which is a 1.3 dB increase due to increasing the driving frequency from 1 to 2 kHz. Finally, the electron density, which is extracted by fitting the simulated RCS results, increased by as much as approximately 330% due to the increase in the driving frequency from 1 to 2 kHz. As the driving frequency increases, the frequency of collision between plasma particles increases. Therefore, the ionization of gas molecules is enhanced, resulting in a higher electron density. The experimental results also suggest that enhancement in the RCS reduction is larger when the electric field intensity between the two electrodes of the DBD generator is greater. As a result, the plasma becomes electromagnetically more lossy and is more effective for reducing the RCS. Experimental results are provided and analyzed based on the electromagnetic parameters used for modeling the plasma.
EMFNet: Enhanced Multisource Fusion Network for Land Cover Classification

Feature extraction and fusion are two critical issues for the task of multisource classification. In this article, we propose an enhanced multisource fusion network (EMFNet) to address them in an end-to-end framework. Specifically, two convolutional neural networks are employed to extract features from two different sources. Each network is mainly comprised of three convolutional layers. For each convolutional layer, feature tuning modules are designed to enhance the extracted feature of one source by taking advantage of the other source. After getting the features of two sources, a weighted summation method is used to fuse them. Considering that fusion weights should vary for different inputs, a feature fusion module is designed to achieve this goal. In order to test the performance of our proposed EMFNet, we compare it with state-of-the-art fusion models, including the traditional models and the deep-learning-based models, on two real datasets. Experimental results show that the EMFNet can achieve competitive classification results in comparison with them.
Multi Domain Fake News Analysis using Transfer Learning

Fake news detection is a significant problem where information is available from multiple sources across the internet. Most of the research on fake news has only targeted politics-related articles, but such models would not be robust enough to tackle fake news in the real world. To solve this problem, this research work incorporated transfer learning using attention-based transformers (BERT, RoBERTa, XLNet, DeBERTa, GPT2) and trained them on multi-domain datasets FakeNews AMT and Celebrity across different domains i.e. Politics, Entertainment, Sports, Business, Education and Technology. The proposed model has obtained state-of-the-art results while doing multi-domain and cross-domain testing, having beaten previous papers conformably. Also, the model has achieved a 99.3% accuracy on FakeNewsAMT and 84% accuracy on celebrity dataset. We believe the synergy of transfer learning in a multi-domain setting will make a robust model, which would be relevant in the real world. This idea originated from the fact that multi-domain research’s critical challenge is that data distribution is varying, and the key benefit of transfer learning is that it can perform well even when it is trained and tested on different data distributions.
Analysis of Cellular Vehicle-to-Everything Physical Layer Parameters

Intelligent transportation systems is one of the most popular research topics nowadays. Current IEEE standard is named IEEE 802.11 p. Its further enhancement 802.11 bd are now developed and addressed to eliminating of main 802.11 p drawbacks which are the poor scalability and performance in high mobility scenarios. The third V2X standard is 3GPP-NR-V2X which physical layer parameters is presented within this work. The link-level evaluation of the upcoming vehicular to everything standard NR-V2X is presented in the paper. The throughput measurement has been chosen as the quality parameter. The results compare with the Dedicated Short Range Communication and show that NR V2X outperform DSRC in throughput characteristic.
A Travelling-Wave-Fed Slot Spiral Antenna With Wide Axial-Ratio Bandwidth and Beamwidth for GNSS Applications

A travelling-wave-fed slot spiral antenna with wide axial-ratio (AR) bandwidth and beamwidth for GNSS applications is proposed in this paper. The evolution procedure of the proposed antenna can be classified into three steps. Firstly, 12 identical spiral slots are arranged concentrically in a compact area, and AR roundness in low elevation plane is improved by virtue of good rotational symmetry of slot radiators. Secondly, a two-turn travelling-wave feed line is sequentially coupled to the slots, and wideband response of desired magnitude ratio and phase difference of <inline-formula> <tex-math notation="LaTeX">$E_{\theta }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$E_{\varphi }$ </tex-math></inline-formula> is obtained by optimizing the number and growth rate of microstrip turns. Thirdly, by supporting the antenna with a modified metallic cavity, the CP radiation performance in low elevation plane is well improved, and thus a widened AR beamwidth is obtained. Finally, the proposed antenna with satisfactory performance is realized in a diameter of <inline-formula> <tex-math notation="LaTeX">$0.56\lambda _{0}$ </tex-math></inline-formula> and height of <inline-formula> <tex-math notation="LaTeX">$0.12\lambda _{0}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at center frequency 1.4 GHz. Measured bandwidth of <inline-formula> <tex-math notation="LaTeX">$|S_{11}|$ </tex-math></inline-formula> fully covers the entire GNSS bands, and the 3-dB AR bandwidth ranges from 1.14 to 1.67 GHz (37.7%). Moreover, the 3-dB AR beamwidth exceeds 140° at 1.4 GHz.
Gaseous Ionization Dependence of Surface Charge Pattern on Epoxy Insulator With Complex Surface Profile Under DC Voltage

Surface charge accumulation on epoxy insulator installed in gas-insulated transmission line (GIL) and gas-insulated switchgear (GIS) has been recognized as a crucial factor for electric field distortion that results in the reduction of surface flashover voltage. This article reports on the gaseous ionization dependence of the charge accumulation pattern on a real-size epoxy insulator with multiarc surface profile by using various gas atmospheres. A coaxial electrode system was designed to investigate the charging behavior under dc voltages of −10, −20, and −30 kV. The insulator and electrodes were placed in a metal-enclosed test chamber, which was filled with 0.1 MPa air or 0.4 MPa SF6/N2 mixture to estimate the influence of the gas atmosphere on the charge accumulation behavior by a Kelvin-type probe. The accumulation mechanism and the electric field distortion induced by the charges were demonstrated based on numerical simulation. The results indicated that positive charges were accumulated in the shape of speckle on insulator surface in air, whereas both positive and negative charges were accumulated in SF6/N2 mixture and positive charge speckles appeared in SF6/N2 mixture only when the voltage raised up to −30 kV. It is considered that the charge accumulation behavior is determined by the competition result among the bulk, the gas and the surface conductions. As compared with the case in air, gaseous ionization in SF6/N2 mixture is more difficult to occur, which leads to the obvious difference in the charge accumulation pattern.
Analysis of Hardware Industry During COVID-19 Based on Fama-French Five Factor Model

COVID-19 has a huge impact on the global economy, especially the hardware industry, which is the cornerstone for the prosperity of each country. Based on the Fama-French Five-Factor Model and multiple linear regression, this paper analyzes the difference of the hardware industry before and after the outbreak of the epidemic. The data in this paper are selected from Kenneth R. French’s Web database and are obtained by the founder of Fama-French model based on relevant information of the U.S. stock market. The hardware industry data from 2019.7. 1 to 2020.10. 30 are adopted daily from 49 Industry Portfolios and divided equally into two groups for comparison. The coefficients are obtained by multiple linear regression. The results indicate that the outbreak of COVID-19 has not only a negative influence on the hardware industry but also some certain stimulative effects. Particularly, it concludes that the whole hardware market after the epidemic conforms to the CAPM model, leaving the four factors added recently insignificant.
Transfer Learning Aided Classification of Lung Sounds-Wheezes and Crackles

Nowadays, Lung diseases are the most life-threatening disease. In maximum belongings of lung disease are noticed once the illness is in the progressive stage, detection of lung disease can be supportive in to cure in early stages. Nowadays, advancement in technology plays an important role in healthcare. By using electronic stethoscopes, the lung sounds of the patients are recorded. Lung sounds carry important information related to lung diagnosis. The need for identifying lung disease using lung sounds is an active research area in the field of the healthcare domain. Transfer learning plays an important role in the medical system. Here, research paper represents various Machine Learning and Transfer learning approaches for the lung sounds classification. Further, Transfer learning model, the classification will be done on the basis of the RESNET-50 deep network and Mel spectrogram of lung sound signals. These classification models achieve 80% accuracy in lung sounds-wheezes and crackles classification which can be use lung disease diagnosis for future research.
Excitation Strategies for 3-D Electrical Capacitance Tomography Sensors

This article presents investigation of 3-D electrical capacitance tomography (ECT) sensors with different excitation strategies, including dual-electrode excitation in the same plane, in adjacent plane, in planes separated by one plane or two planes, and in the adjacent position or opposite position in the same or different planes. The results show that a 3-D ECT sensor with dual-electrode excitation in the opposite position provides higher mean capacitance and lower standard deviation than in the adjacent position. Dual-electrode excitation in different planes can increase the capacitance signal and decrease the dynamic range of capacitance measurements, and thus, decrease random errors and improve the quality of 3-D reconstructed images. A 3-D ECT sensor with dual-electrode excitation in planes separated by one plane and opposite position can provide small measurement uncertainty and high image quality.
Joint Active and Passive Beamforming for Reconfigurable Intelligent Surface Enhanced Symbiotic Radio System

This letter considers a symbiotic radio (SR) system enhanced by a reconfigurable intelligent surface (RIS). The active transmit beamforming of the base station (BS) and the passive beamforming of the RIS are jointly designed to minimize the BS’s transmission power, subject to the signal-to-interference-plus-noise ratio constraints for decoding backscatter signal, and the rate constraint for primary communication. To solve the non-convex problem, we propose an efficient iterative algorithm based on alternating optimization and semi-definite relaxation. The algorithm’s convergence and complexity are analyzed. Numerical results show that the RIS-enhanced SR achieves lower transmission power than SR without RIS.
Incentive-Based Coded Distributed Computing Management for Latency Reduction in IoT Services—A Game Theoretic Approach

This article studies distributed computing (DC) mechanisms on heterogeneous mobile devices (MDs) for latency reduction (LR) in Internet-of-Things (IoT) services by mitigating the effect of straggling MDs. We propose novel coded DC mechanisms with two different incentive distributions that consider the time-discounting value of processed results and the amount of the workload computed by MDs. Specifically, we consider distributed gradient descent computing with coding when a task publisher (TP) with a limited amount of budget offers incentives to encourage MDs’ participation in the computation. To analyze a hierarchical decision-making structure of the TP and MDs, we formulate a strategic competition between them as a Stackelberg game. In the case that the MDs are the leaders, we design a CPU-cycle frequency control scheme to balance each MD’s computing speed and energy consumption for obtaining its maximum utility with the incentive mechanisms. As the follower, the TP aims at minimizing latency of the DC, and it follows the MDs’ decisions to determine the load allocation for each MD. Then, we design an algorithm achieving the Stackelberg equilibrium, which is shown to be a unique Nash equilibrium of the game. The performance evaluation results show that the proposed mechanisms achieve 39% of LR on average compared to the benchmark mechanism. Furthermore, the results corroborate the efficiency of the proposed mechanisms in terms of the MDs’ social welfare.
Solid-phase microextraction for analysis of mould cheese aroma

Solid-phase microextraction coupled with gas chromatography was used for the analysis of volatile aroma compounds in Niva cheese. The extraction conditions were very mild, which minimises thermal, mechanical, or chemical modification of the sample; the method is rapid, simple, and cheap. In total, 54 compounds were identified in Niva cheese using this method: 3 hydrocarbons, 5 aldehydes, 11 ketones, 18 alcohols, 3 esters, 10 fatty acids, and 4 sulphur compounds. These aroma compounds were quantified and subsequently the changes in the concentrations of them were studied throughout the ripening period. Most of the volatile compounds identified were present at all stages of the cheese ripening, their amounts changing significantly, however, in most cases the final concentration in the ripe cheeses was similar to the initial concentration in the unripe cheese.
OERFF: A Vehicle Re-Identification Method Based on Orientation Estimation and Regional Feature Fusion

Vehicle re-identification (re-id) is an important issue in the transportation and vehicle tracking area. The existing FastReID framework has made significant improvements in data preprocessing, model structure and parameter configuration of re-id. However, the FastReID did not consider the directional differences between vehicle images, which makes it difficult to balance the image pairs with different directional differences during calculating the similarity between vehicles. To make a further improvement based on the FastReID, this paper proposes an Orientation Estimation and Regional Feature Fusion method, named OERFF. In OERFF, the orientation estimation model is trained to judge the orientation and the main region of a vehicle in an image. Then the dedicated region feature model is utilized to extract the regional feature of a vehicle according to its proper orientation. Finally, a feature fusion strategy is applied with weighted distance according to the orientation difference of vehicles to further improve the identification accuracy. Extensive experiments are conducted based on three workbench datasets and results show that the proposed method can improve the mAP by 2.5% on the VeRi-766 dataset and 3.2% on the VERI-Wild dataset. The accuracy of rank-1 is improved by 2.4% on the VehicleID dataset.
Effects of Virtual Environments and Self-representations on Redirected Jumping

We design experiments to measure the perception (detection thresholds for gains, presence, embodiment, intrinsic motivation, and cybersickness) and physical performance (heart rate intensity, preparation time, and actual jumping distance) of redirected jumping (RDJ), under six different combinations of virtual environments (VEs) (low and high visual richness) and self-representations (SRs) (invisible, shoes, human-like). Results suggested that both VEs and SRs influence users’ perception and performance in RDJ, and have to be taken into account when designing locomotion techniques.
Assessment of genetic variability in tomato (Solanum lycopersicum L.) for yield and yield attributing traits

The genetic parameters were studied to elucidate the genetic variability, heritability, genetic advance and genetic advance as per cent over mean in tomato (Solanum lycopersicum L.). Sixty diverse genotypes were evaluated during the year 2019-20 at experimental research farm of the Haweli campus, College of Horticulture, Bagalkot, and Karnataka. The analysis of variance revealed highly significant difference among 60 genotypes for all 24 characters under study. Phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for majority of the characters studied indicating influence of the environment on the trait expression. High heritability with high genetic advance as per cent of mean was observed for plant spread (98.2% and 35.76%), equatorial diameter (95.40% and 42.68%), polar diameter (96.00% and 38.97%), pericarp thickness (66.10% and 35.27%), fruit color (79.80% and 37.70%), fruit pH (91.40% and 31.99), lycopene (75.90% and 60.388%), titrable acidity (74.50% and 44.38%), ascorbic acid (75.40% and 26.80), fruit firmness (94.30% and 26.24%), average fruit weight (94.30% and 72.04), number of fruits per plant (63.40% and 57.23%). High heritability combined with high genetic advance indicates that additive gene action plays a major role in governing these traits and these traits can be improved by simple selection.
Pt-Ni Aerogels as Unsupported Electrocatalysts for the Oxygen Reduction Reaction

The commercial feasibility of polymer electrolyte fuel cells (PEFCs) passes by the development of Pt-based, O2-reduction catalysts with greater activities and/or lower Pt-contents, as well as an improved stability. In an effort to tackle these requirements, unsupported bimetallic Pt-Ni nanoparticles (NPs) interconnected in the shape of nanochain networks (aerogels) were synthesized using a simple one-step reduction and gel formation process in aqueous solution. The products of this novel synthetic route were characterized by X-ray absorption spectroscopy to elucidate the materials’ structure. Using electrochemical experiments, we probed the surface composition of the as-synthesized aerogels and of equivalent materials exposed to acid, and concluded that a Ni-(hydr)oxide side phase is present in the aerogel with a larger Ni-concentration. Regardless of this initial surface composition, the Pt-Ni aerogels feature a ≈3-fold increase of surface-specific ORR activity when compared to a commercial platinum-on-carbon catalyst, reaching the mass-specific requirement for application in automotive PEFCs. © The Author(s) 2016. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. [DOI: 10.1149/2.0251609jes] All rights reserved.
Optimized Torque Assistance During Walking With an Idealized Hip Exoskeleton

The hip muscles account for a great percentage of the total human energy expenditure during walking and many wearable devices have been developed in assisting the hip joint to reduce the metabolic Cost Of Transport (COT) for walking. However, the effectiveness of assisting the hip in only one direction (either flexion or extension) or both directions has not been systematically studied and the underlying muscle mechanics and energetics affected by the assistance are not well understood. In this study, human-exoskeleton simulation based optimizations were performed to find optimized hip assistance torque profiles for (1) unidirectional flexion assistance, (2) unidirectional extension assistance, and (3) bidirectional flexion and extension assistance. Our results show that the bidirectional assistance is the most effective in reducing the COT of walking (22.7% reduction) followed by flexion (19.2%) and extension (11.7%). The flexion assistance resulted in more COT saving than the output of its net work by 35.9%, which indicates that the negative work done (42.2% of its positive counterpart) also played an important role in reducing the COT. The bidirectional assistance also reduced the activations of the hip extensors to a great extent and shifted the activation pattern of the hip flexor (ilipsoas). These results can provide valuable information for optimal hip actuation (timing and profiles) and help exoskeleton designers make informed decisions.