Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach for Semantic Image Annotation

In this study, a framework that generates natural language descriptions of images within a controlled environment is proposed. Previous work on neural networks mostly focused on choosing the right labels and/or increasing the number of related labels to depict an image. However, creating a textual description of an image is a completely different phenomenon, structurally, syntactically, and semantically. The proposed semantic image annotation framework presents a novel combination of deep learning models and aligned annotation results derived from the instances of the ontology classes to generate sentential descriptions of images. Our hybrid approach benefits from the unique combination of deep learning and semantic web technologies. We detect objects from unlabeled sports images using a deep learning model based on a residual network and a feature pyramid network, with the focal loss technique to obtain predictions with high probability. The proposed framework not only produces probabilistically labeled images, but also the contextual results obtained from a knowledge base exploiting the relationship between the objects. The framework’s object detection and prediction performances are tested with two datasets where the first one includes individual instances of images containing everyday scenes of common objects and the second custom dataset contains sports images collected from the web. Moreover, a sample image set is created to obtain annotation result data by applying all framework layers. Experimental results show that the framework is effective in this controlled environment and can be used with other applications via web services within the supported sports domain.
Encryption and Re-Randomization Techniques for Malware Propagation

Encryption, which is essential for the protection of sensitive information can also transform any malicious content to illegible form, which can then reside in any network, undetected. Encryption of malicious payload is used by malware authors to mask their code, however, the objective of hiding the malicious code can be further improved by techniques of re-randomization. The concept of re-randomization using asymmetric cryptography has been emerged as a new area of interest for malware designers. Re-randomizing is a technique which can prevent detection of source path of a malware and makes it indistinguishable. This article extends the idea of using asymmetric cryptography for re-randomization and has proposed a novel scheme using Pailliar’s asymmetric cryptosystem. Moreover, this research work illustrates the limitations of RSA for malware re-randomization. A comprehensive performance analysis of the re-randomization techniques for various malware payloads is also presented, which can be used for the detection of re-randomized malware effectively.
Experimental demonstration of a bat microresonator

The spatial oscillations in the eigenmode amplitude of microresonators have a characteristic length of the order of its eigenwavelength. However, this amplitude can be made uniform within the major eigenmode length for a microresonator called bat microresonator (BatMR) [1] . A BatMR is a special type of SNAP bottle microresonator created at the optical fiber with nanoscale radius variation resembling the profile of a bat (inset in Fig. 1(a) ). It can be formed at the uniform fiber segment by introducing "ears" at its edges. Potential applications of BatMRs include ultraprecise sensing of micro/nanoparticles, assembling of quantum emitters at the optical microresonator surface, and subnanometer accurate translation reference.
Design and control of NxN microphotonics switch array based on non-adiabatic theory

The development of linear programmable nanophotonics processors requires large number of couplers to route light through the photonic chip. This number scales nonlinearly with the number of inputs and outputs [1] , making the device increasingly complex to control, and bulky. Regarding the latter aspects, densely packed array of waveguides would be an elegant solution. In order to couple selectively the different –at first isolated- waveguides, we rely on the analogy between the equations governing the unidirectional propagation in optics and the time evolution in quantum physics [2] . Namely, as seen in fig. 1-a , if each waveguide can be considered as a photonic energy level, then a transition between discrete levels could be obtained providing the array is excited by the right spatial modulation. Such a strategy greatly differs from current design techniques that assume either adiabatic or diabatic transformations, hence without any transition between eigenstates. To the best of our knowledge, non-diabatic transition has been demonstrated in two-waveguide systems such as grating assisted contra-propagating couplers (subwavelength modulation), and PPLN (modulation caused by optical Kerr nonlinearity), and in long period gratings in fibers; but with little control and has never been generalized to waveguides array. Proper mathematical description involves the adiabatic theory which can be easily extended to the non-diabatic regime: it requires not only a modulation of the right periodicity [3] , but the parameters that are modulated must obey some symmetry rules.
Time-efficient object recognition in quantum ghost imaging

Ghost imaging utilises the optical correlations between two beams to form an image of an object. One beam interacts with the object, and the other possesses spatial information. It is not possible to reconstruct the image from either beam separately, but only via the correlations between them [1] . The origin of such correlations does not constitute a restriction, thus it has been demonstrated in the quantum and classical regimes [2] . Due to the low light levels and the possibility of non-degenerate photon correlations, quantum ghost imaging has great potential in areas such as biological imaging. This particular use has been hindered by the extensive image reconstruction times. Proposed solutions include the use of compressive sensing, novel reconstruction algorithms, and deep learning techniques. Here, we followed a three fold strategy to reduce the time required to reconstruct the image: optimizing the experimental parameters, performing image processing and introducing a recognition algorithm.
Aggregating Time Windows for Dynamic Network Extraction

Proper dynamic network extraction is a prominent problem for timely evolving systems' modelling. This problem is seen as finding the most proper window size for extracting the most informative and least noisy time series of snapshot features. Existing solutions suffer from using only network topological properties as snapshot features, applying subjective methodologies needed user-dependent data labeling, and extracting snapshots with equal sized windows. We propose a window aggregation strategy of intelligent snapshot extraction to overcome previous issues. The idea is to collect the snapshots that already have similar link structure. Thus, the result network has fewer snapshots with different duration. Experiments on Enron and Haggle Infocomm data sets reveal that our proposal extracts more informative snapshots than using constant window sizes. Moreover, as a complementary result, it is more effective in determining proper time interval for modelling.
Multi-Volume Data Visualization Using Bounding Shells

A method for visualizing multi-volume data and function-based surfaces using GPU is proposed. The method provides visualization of functionally defined surfaces, complex semi-transparent volumes, including intersections of volumes in structural solid modeling. Rendering different volumes at the same time is more difficult than rendering a single volume, because intersecting and blending operations are required. Function-based surfaces are used to visualize implants. The method uses bounding three-dimensional texture shells.
A Locust Recognition Method Based on Graph Convolution Neural Network

In recent years, due to frequent outbreaks of locust plagues around the world, causing catastrophic damages to a large number of food crops, locust identification is urgent. For common locust recognition methods, such as RCNN (Regions with CNN features) network training recognition, the recognition rate is lower. This paper proposes a Graph Convolutional Network(GCN)image recognition method, which is improved on the basis of ordinary convolutional neural network to train locust images to achieve higher recognition rate and ensure crops are not damaged.
Plant Disease Detection and Classification Using Deep Learning Model

Organic farming is becoming more common in many developing countries agricultural practices. There are a variety of issues that occur in plant growth due to various environmental factors. Crop diseases can result in a reduction in agricultural productivity and hence identification of crop diseases in the beginning stage will provide great advantages towards the field of agriculture. In the recent years, Identification of leaf disease is done through image processing technique where feature extraction plays important part since using the right features leads to better classification accuracy. Nowadays, there exists lot of machine learning techniques to perform plant disease detection and identification, the advancement of deep learning gained attention due to improved performance and accuracy. So this paper presents a model for detecting and identifying crop leaf disease using CNN based AlexNet model. This proposed model is compared with other CNN model (VGG-16 and Lenet-5) that shows our proposed AlexNet model is more accurate than VGG-16 and Lenet-5. The dataset considered for experimentations are collected from plant village repository with total number of 7070 diseased and healthy leaf images of corn blight, Corn Common Rust, Corn Gray Leaf Spot, Rice Bacterial leaf blight, Rice Brown spot, Rice Leaf smut, Tomato Bacterial spot, Tomato Early blight, Tomato Target Spot and Tomato mosaic virus. The proposed method will successfully identify the crop species with 96.76 % accuracy.
Interactive Volume Rendering Method Using a 3D Texture

Volume rendering is a method for converting scalar fields into images. In the GPU, three-dimensional scalar fields are stored as 3D textures. Recently, WebGL2 has added support for 3D textures, which allows you to implement elegant and fast volume rendering in the browser. In this paper, we propose a method of interactive volume rendering that works completely in the browser, implemented on WebGL2. The method takes into account the absorption and emission of light by the volume, as well as scattering effects. To create a physically realistic image from volume data, the absorption, emission, and scattering of light rays by the medium was simulated. Although modeling the propagation of light through the medium at this level produces spectacular and physically correct results, it is too expensive for interactive rendering, which is the goal of volume visualization. Since a fully physical model with scattering would be too expensive for interactive rendering, the method uses a simplified emission-absorption model. The proposed model does not calculate the cost effects of scattering and their approximation. The model of emission-absorption only is considered.
IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

The utmost advancements of artificial neural networks (ANNs), software-defined networks (SDNs) and internet of things (IoT) technologies find beneficial in different applications of the smart healthcare sector. Aiming at modern technology’s use in the future development of healthcare, this paper presents an advanced heuristic based on Morlet wavelet neural network for solving the mosquito release ecosystem in a heterogeneous atmosphere. The mosquito release ecosystem is dependent of six classes, eggs density, larvae density, pupae density, mosquitoes searching for hosts density, resting mosquito’s density and mosquitoes searching for ovipositional site density. An artificial neural network with the layer structure of Morlet wavelet (MWNN) kernel is presented using the global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The accurateness, reliability and constancy of the proposed MWNN-GA-ASA is established through comparative examinations with Adams method based numerical results to solve the proposed nonlinear system with matching of order 10−06 to 10−09. The accuracy and convergence of the proposed MWNN-GA-ASA is certified using the statistical operators based on root mean square error (RMSE), Theil’s inequality coefficient (T.I.C) and mean absolute deviation (MAD) operators.
Multi-Source Fusion Domain Adaptation Using Resting-State Knowledge for Motor Imagery Classification Tasks

Transfer learning is the method that makes use of knowledge from other fields to solve problems in related fields. It has been shown that it can deal with the problem of insufficient labeled data for new users or new tasks in the brain-computer interface. Domain adaptation is one of the transfer learning methods which is widely used for its excellent performance. Here, the offline cross-subject EEG signal classification is mainly focused on. The unlabeled EEG trials of the new user are classified by using the EEG trials with labels from source subjects. In this paper, a novel transfer learning method called multi-source fusion adaptation regularization (MFAR) is proposed. MFAR preprocesses the EEG signal by aligning the motor imagery trials to their resting state trials, and can reduce the differences among subjects. It also defines a learning framework by combining weighted balanced distribution adaptation (W-BDA), source empirical risk, and manifold regularization to further reduce the variation between source and target domains. We validated the method on two BCI Competition IV datasets for motor imagery tasks. In the absence of labeled EEG trials of the target subject, compared with the excellent counterparts, the classification accuracy increases by 9.28% and 11.73%. After the alignment algorithm is added, the accuracy of the MFAR is improved by 9.36% and 4.17% on the basis. The experimental results show that our learning framework outperformed several state-of-the-art transfer learning algorithms. Even when the training data from the new user are sufficient, the proposed approach achieves good performance.
Full-field Real-Time Measurement of Ultrafast Soliton Fission

The real-time characterization of ultrafast dynamics is essential to the understanding of the underlying physics and in recent years there has been major interest into the measurement of complex instabilities. One important example of instability is associated with modulation instability, a key physical process underlying the emergence of localized structures such as the Peregrine soliton. It is only very recently that it has been possible to precisely characterize these dynamics in the laboratory with the development of real-time measurement techniques. All previous experimental studies have been made for the case of ideal modulation instability excited by a near continuous wave input but it is in fact much more common for modulation instability to develop under non-ideal conditions in a regime where the dynamics present mixed multiscale features of both nonlinear envelope evolution and modulation instability dynamics on the pulse envelope itself. Here, combining a broadband reference phase-stable field generated by self-phase modulation with Fourier-transform spectral interferometry (FTSI) [1] , [2] we characterize for the first time modulation instability dynamics of an ultrafast pulse leading to fission into its fundamental soliton constituents. Our experiments can characterize the associated complex field in real-time with 20 fs resolution, allowing to resolve the individual soliton structures emerging from the initial stage of modulation instability.
Explicit Size-Reduction of Circularly Polarized Antennas Through Constrained Optimization With Penalty Factor Adjustment

Modern communication systems of high data capacity incorporate circular polarization (CP) as the preferred antenna radiation field configuration. In many applications, integration of the system circuitry with antennas imposes size limitations on CP radiators, which makes their development process a challenging endeavor. This can be mitigated by means of simulation-driven design, specifically, constrained numerical optimization. Majority of the performance-related constraints are expensive to evaluate, i.e. require full-wave electromagnetic (EM) analysis of the system. Their practical handling can be realized using a penalty function approach, where the primary objective (antenna size reduction) is complemented by contributions proportional to properly quantified constraint violations. The coefficients determining the contribution of the penalty terms are normally set up using designer’s experience, which is unlikely to render their optimum values in terms of the achievable miniaturization rates as well as constraint satisfaction. This paper proposes a procedure for automated penalty factor adjustment in the course of the optimization process. Our methodology seeks for the most suitable coefficient levels based on the detected constraint violations and feasibility status of the design. It is validated using two CP antenna structures. The results demonstrate a possibility of a precise constraint control as well as superior miniaturization rates as compared to the manual penalty term setup.
Segmentation integration in multivariate curve resolution applied to coherent anti-Stokes Raman scattering

Coherent anti-Stokes Raman scattering (CARS) is a vibrational spectroscopy method based on a coherent χ (3) nonlinear interaction between light and matter through four-wave mixing. CARS process has been extensively used as a microscopy technique in biology. However the shape of CARS spectra is generally complex because of the combination of both resonant and non-resonant contributions, which limits the development of CARS microspectroscopy applications. In this context, phase retrieval methods (PRM) like the maximum entropy method (MEM) [1] are commonly used to extract the vibrationally resonant signal, corresponding to Im[χ (3) ], and thus to provide Raman-like spectra, which are more suitable for interpretation.
Experimental Evaluation of RF Waveform Designs for Wireless Power Transfer Using Software Defined Radio

The possibility to harvest energy from ambient radio-frequency (RF) sources has intrigued humankind for the past several decades. In this context, there has been a tremendously growing research interest in the field of wireless power transfer (WPT) using the RF range of the electromagnetic (EM) spectrum. In this paper, we experimentally investigate the aspect of real-time energy harvesting (EH) via different types of waveform designs such as orthogonal frequency division multiplexing (OFDM), square, triangular, sinusoidal, and sawtooth. We make use of a Software Defined Radio (SDR) and a Powercast P21XXCSR-EVB EH module to carry out the experiments on a practical device to assess performance. Specifically, we are interested in obtaining some insights based on the comparison between the aforementioned waveform designs from the perspectives of the separation distance between the USRP and P21XXCSR-EVB EH module, and power emission via USRP. In this vein, we perform additional subsequent experiments after reporting the practical effectiveness of the OFDM waveform, which also follows our intuitive analysis. Correspondingly, we study the effect on WPT with variable USRP transmit power, the separation distance between the USRP and EH antennas, number of OFDM sub-carriers, and multipath setting. As an application of OFDM, the effectiveness of fifth generation-new radio (5G-NR) and long-term evolution (LTE) waveforms are also tested for the WPT mechanism. The demonstration of the EH is provided in terms of the above-mentioned investigation metrics while seeking the best waveform to support WPT.
Detection of a Levitated Nanoparticle’s Position via Self-Homodyne

The efficiency with which the position of a levitated nanoparticle is detected is one factor that sets the minimum temperature of the particle’s center-of-mass motion that can be achieved by feedback cooling. Typically, the detection is based on the interference of the light scattered by the particle with a reference Gaussian beam in a homodyne setup [1] - [3] . This detection scheme is limited by the wavefront mismatch between the scattered and the reference fields and by the numerical aperture (NA) of the lens that collects the scattered light. As a result of these limitations, temperatures close to the ground state have only been achieved in experiments exploiting high NA objectives (> 0.8), namely, optical-tweezer-based experiments [1] , [2] . Here, we demonstrate how the detection efficiency can be improved in a setup based on homodyne detection, by improving the mode-matching conditions and by increasing the sensitivity of the interference pattern to the displacement of the particle.
Direct Bilirubin Detection Using Surface-Enhanced Raman Spectroscopy

Biliary atresia is the most common cause of childhood liver disease and is the main cause of liver replacement. The use of an effective early screening technique can efficiently improve the therapeutic effect on children with biliary atresia, especially those in the remote rural areas. This study introduces a novel method for detecting direct bilirubin by Raman spectroscopy using composite surface-enhanced Raman spectroscopy (SERS) chips. These chips were created by spreading silver nanowires on the nanostructure surface of an anodic aluminum oxide substrate. Experimental results demonstrated that the detection limit of direct bilirubin concentration vastly improved far below the disease criteria of bilirubin in adults and infants. Meanwhile, using only $10~\mu \text{L}$ of direct bilirubin, a wide detection range from 10−7 to 10−4 M was obtained, covering the clinic reference range with R2 of 0.98336 at the peak value of 1.616 cm−1. Furthermore, the results revealed that the proposed composite SERS substrates possessed good statistical relative standard deviation and reproducibility (N = 3) of 4.6% and 2.2%, respectively. Thus, the composite SERS chip combined with the Raman spectroscopy approach can be integrated with portable Raman spectrometer for the effective clinical detection of direct bilirubin.
Downscaled Correction of Temperature Forecast Algorithm with Encoder-Decoder over the Hengduan Mountains

Global climate models (GCMs), a primary tool of Numerical weather prediction (NWP) has an irreplaceable position in weather forecasting. Because of the inevitable model error and the coarse resolution, it is necessary to develop the downscaled correction method to reduce the model prediction error and improve the forecast result on the basis of the existing model, especially in the mountains. The Hengduan Mountains (HMs) belongs to the monsoon region accompanied by the highly complex mountainous topography, leading to the accuracy of GCMs limited in this region. The study aims to propose a more accurate deep learning algorithm than the downscaled correction model based on the deep learning method before. In this study, downscaled correction of temperature forecast algorithm with Encoder-Decoder (DCTA-ED) is proposed for downscaling the GCMs results of 2m temperature from coarse grid data to meteorological station point forecast over HMs. The proposed algorithm extracts the interaction between multiple meteorological data and time features of temperature, wind speed, air pressure and humidity from previous position observation, to make the downscaled correction prediction results more consistent with the characteristics of positions. The experimental results demonstrate that the proposed algorithm exceeds most of the downscaled correction models for weather stations with different terrains.