Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Remote Sensing Image Retrieval Under a Deep Image Captioning Perspective

The performance of remote sensing image retrieval (RSIR) systems depends on the capability of the extracted features in characterizing the semantic content of images. Existing RSIR systems describe images by visual descriptors that model the primitives (such as different land-cover classes) present in the images. However, the visual descriptors may not be sufficient to describe the high-level complex content of RS images (e.g., attributes and relationships among different land-cover classes). To address this issue, in this article, we present an RSIR system that aims at generating and exploiting textual descriptions to accurately describe the relationships between the objects and their attributes present in RS images with captions (i.e., sentences). To this end, the proposed retrieval system consists of three main steps. The first step aims to encode the image visual features and then translate the encoded features into a textual description that summarizes the content of the image with captions. This is achieved based on the combination of a convolutional neural network with a recurrent neural network. The second step aims to convert the generated textual descriptions into semantically meaningful feature vectors. This is achieved by using the recent word embedding techniques. Finally, the last step estimates the similarity between the vectors of the textual descriptions of the query image and those of the archive images, and then retrieve the most similar images to the query image. Experimental results obtained on two different datasets show that the description of the image content with captions in the framework of RSIR leads to an accurate retrieval performance.
Vulnerability Prediction From Source Code Using Machine Learning

As the role of information and communication technologies gradually increases in our lives, software security becomes a major issue to provide protection against malicious attempts and to avoid ending up with noncompensable damages to the system. With the advent of data-driven techniques, there is now a growing interest in how to leverage machine learning (ML) as a software assurance method to build trustworthy software systems. In this study, we examine how to predict software vulnerabilities from source code by employing ML prior to their release. To this end, we develop a source code representation method that enables us to perform intelligent analysis on the Abstract Syntax Tree (AST) form of source code and then investigate whether ML can distinguish vulnerable and nonvulnerable code fragments. To make a comprehensive performance evaluation, we use a public dataset that contains a large amount of function-level real source code parts mined from open-source projects and carefully labeled according to the type of vulnerability if they have any.We show the effectiveness of our proposed method for vulnerability prediction from source code by carrying out exhaustive and realistic experiments under different regimes in comparison with state-of-art methods.
Study on Charge-Enhanced Ferroelectric SIS Optical Phase Shifters Utilizing Negative Capacitance Effect

Si optical phase shifters are key components for efficient phase modulation utilizing carrier modulation. Among many structures for carrier modulation in them, a semiconductor-insulator-semiconductor (SIS) capacitor can achieve high phase modulation efficiency because of its large carrier accumulation. However, due to the limitation caused by relatively small plasma dispersion in Si, its size and voltage for the $\pi $ phase shift are large to integrate into conventional CMOS technology. To enhance its phase modulation efficiency, material engineering of a SIS capacitor is indispensable. Although semiconductor engineering has been proved to realize efficient phase modulation, there are few reports on the insulator engineering of a SIS capacitor for efficient phase modulation. Therefore, we have introduced new functionalities for a SIS optical phase shifter using insulator engineering. In this paper, we have investigated the charge enhancement effect in a SIS capacitor with ferroelectric (FE) materials utilized by the negative capacitance (NC) effect. We will discuss the design of an NC SIS optical phase modulator to enhance this effect for a new efficient phase modulation scheme.
Variation in the Plants Differentiated from the Tissue Culture of Pineapple

From the tissue cultures of syncarp, slip, crown and axillary bud of Ananas comosus (L.) Merr. , cultivar Smooth Cayenne, 448 plants were established. To exarnine the two possible merits of this technique on self-incompatible plants of vegetative propagation, mass-production of non-variants and induction of variants useful in breeding, the variations among the redifferentiated plants were investigated. Many variants regarding spine, leaf color, wax secretion on leaf surface and foliage density were found among the established plants. There were a few variarlts with narrow leaves or albino stripes. Organ-to-organ differences were found in the fre-quencies and the modes of distribution of these variations. Syncarps and a slip developed variants in high frequencies, while crowns and axillary buds did only in low frequencies. The variants from syncarps included the characters on leaf color, spine, wax and foliage density, but the ones from a slip, crowns and axillary bu.ds were almost confined in spine characters. From these present findings, it is considered possible to apply the tissue culture technique on the rapid propagation of non-variant plantlets and the induction of useful variants, if proper kinds of organs are chosen for the respective purpose.
Visible and Infrared Image Fusion Based on Convolutional Sparse Coding with Gradient Regularization

The purpose of visible and thermal infrared scene fusion is to generate a synthetic image, in which clear thermal target and pleasant visual background can be obtained simultaneously. Standard convolutional sparse coding is an effective method to solve the problem of detail conserve and sensitivity to registration errors in sparse domain fusion method. However,partial infrared-visible fusion results based on standard convolution sparse coding have lower contrast as different imaging modalities of infrared-visible images.The gradient regularization of convolutional sparse coefficient graph is introduced into convolutional sparse coding and a new visible-infrared image fusion method is proposed. Experimental results demonstrate that our method can achieve clearly fusion performance in terms of both objective and visual.
SUPRASPECIFIC CLASSIFICATION IN THE SCROPHUL ARIACEAE : A REVIEW

The present status of our knowledge of the classification of the Scrophulariaceae, a family of perhaps 250 genera and 5000 species (Thieret, 1959), reflects the general lack of detailed information regarding intraand extra-familial relationships of many of the families of flowering plants. New and detailed research on the family, using whatever tools are available, and a re-evalution of past research are necessary before a logical taxonomie treatment of the family can be realized. The necessity of such research and such re-evaluation is attested to by the essentially pre-evolutionary, century-old treatment of the family found in the majority of the world's floras. The present paper is meant as a summary of major taxonomie problems presented by the Scrophulariaceae and of especially significant past taxonomie research that has been carried out on the supra-specific level in the family. My interest in the family was awakened a number of years ago when I began a study of gross internal and external morphology of scrophulariaceous seeds in relation to supra-specific classification of the family. It soon became evident that the taxonomy of this family is in a sorry state indeed, as the following pages will show. Wettstein (1935) briefly but pointedly characterized the current state of the classification of the Scrophulariaceae when he wrote (p. 897): "Eine naturliche Systematik innerhalb der Familie ist zur Zeit noch nicht erreicht. " Indeed, even the limits of the family itself are not agreed upon (compare, for example, the treatments of Wettstein, 1891, and Hallier, 1903).
Analytical Review on Various Aspects of Educational Data Mining and Learning Analytics

Over the last decade, there is a tremendous increase in online learning. The use of electronic devices has paved a path for creating a learning gateway beginning from lower classes to higher class alltogther i.e. from preschool to postgraduate level. These mechnisms are being used enormously to improve the quality of education. The online mode of education has also provided researchers with mechanism to collect data and analyze learning patterns which would to path breaking results in ananlyzing the learning habits of a child and help us provide him with personlized inputs for the best results. The educators and management would also be benifitted from the result of analytics. Everyone around the globe is willing to adopt the EDM and LA to frame strategic policies and understand student behaviours. The Educational Data Mining and Learning Analytics helps us look for the new patterns in data and helps us predict the instructional systems. The implementation of EDM and LA is largely helped by advancement in the last decade in e-learning implementations in education and training as well as of research and development work in areas such as educational data mining, web analytics and statistics. The analytical requirement would be implemented various data mining and visualization tools. Learning analytics is an emergent field of research that is growing fast.
Cotton Leaf Disease Classification using Deep Convolution Neural Network for Sustainable Cotton Production

The economic gainful of the country largely join on the crop production quality and quantity. The production profit can be improved by leaf disease detection in correct time. Many image processing methods have been developed for leaf disease detection. Technological improvement allows the task easier and faster at very early stage. Leaf disease is one of the major issue in the agriculture field. The Cotton leaves are affected by the disease named Cercospora, Bacterial blight, Ascochyta blight, and Target spot. General observation by farmers may time consume, expensive and sometimes inaccurate. For this, we introduce Deep Convolutional Neural Network based approach for identifying Cotton leaf diseases automatically. As per our understanding, Deep Convolutional Neural Network is used for the first time to detect cotton leaf disease. We train our data and assess the experimental result which displays the average accuracy of 96 %.
Acuity of Echolocation in Collocalia hirundinacea (Aves: Apodidae), With Comments on the Distributions of Echolocating Swiftlets and Molossid Bats

The acuity of echolocation in Collocalia hirundinacea was studied by observing the birds' performances in an obstacle course constructed 10 m inside the mine they used as a roost. Birds flying into the mine missed 10 mm diameter rods significantly more often than rods 4 or 1.5 mm in diameter (73 % vs. 67% or 55%, respectively), but avoided even the 1.5 mm diameter rods. Birds flying through the obstacle course on their way out of the mine apparently, on some occasions, used vision to detect the rods, for they generally hit fewer rods than birds flying in. The orientation clicks of C. hirundinacea and their performances in the obstacle course were very similar to previous reports for the closely related Collocalia vanikorensis. The benefits of echolocation for Collocalia and the coincident distribution of Collocalia and a depauperate molossid bat fauna are discussed. Occupation of a cave or mine by the swiftlets does not preclude use of the same site by bats. THE ABILITY TO ECHOLOCATE is known from two groups of birds, the caprimulgid Steatornis caripensis (Griffin 1954) and at least four species in the apodid genus Collocalia (Griffin and Suthers 1970). In all of these birds the highest energy components of the orientation vocalizations are of low frequency ( < 10 KHz) relative to the orientation cries of microchiropteran bats, and consequently the acuity of their echolocation would not be expected to be as good as that of the Microchiroptera. The genus Collocalia includes from 12 to 20 species, depending upon which classification one follows (Mayr 1937; Peters 1964; Thomson 1964), and of these, C. brevirostris (Novick 1959), C. maxima (Medway 1959), C. fuciphaga (C. salangana; Medway 1967), and C. vanikorensis (Griffin and Suthers 1970) echolocate, while C. esculenta does not. To date there appear to be no other published data on orientation sounds, or their lack, in the remaining species. This study was undertaken to determine if Collocalia hirundinacea, a species very similar and perhaps closely related to C. vanikorensis, produced orientation sounds, and if it was capable of echolocation.
The home range ghost

The area (A) covering a sample of non-autocorrelated animal relocations (n) is generally thought to increase asymptotically towards a true home range size with increasing sample size. This should be the case for any home range with a stable centre, whether applying a minimum convex polygon, box counting or some more sophisticated method for area demarcation. We show by simulation that the rate of increase of A is expected to decrease significantly from one m-fold increase of n to the next m-fold increase, for n as low as 100-200 relocations. For larger n the rate of increase of A is expected to be close to zero for last m-fold increase of a subsample of n. This central null hypothesis in ecology is not supported by an extensive, and assumed representative, sample of (n,A) literature data. Even after adjusting for small-n expected underestimates of A, A increases approximately in proportion with the square root of n. Literature examples are given where no area asymptote appeared in sample sizes passing thousands of relocations. The results support an alternative model for home range area use, the multiscaled home range model (MHR).
Electrodegradation of Root Exudates to Mitigate Autotoxicity in Hydroponically Grown Strawberry (Fragaria ×ananassa Duch.) Plants

Root exudates from strawberry plants are known to cause autotoxicity and benzoic acid (BA), a potent growth inhibitor, was found in the exudate. BA (400 μM) was easily decomposed by electrodegradation (ED) treatment at 10.0 V and 2.0 A after the addition of BA into nutrient solution. The growth of strawberry plantlets in nutrient solution containing BA (400 μM) or in control nutrient solution varied significantly depending on the use of ED treatment. The growth inhibition of plantlets in the BA-containing nutrient solution or in nutrient solution used for strawberry culture was significantly ameliorated by ED treatment. This growth recovery resulting from ED treatment was most pronounced in terms of the yield of strawberry fruit. These results suggest that ED treatment of the culture nutrient solution could result in the decomposition of toxic root exudates, including BA from strawberry plants, and mitigates the autotoxicity observed in closed hydroponic culture. In addition, the supplementation of Fe-EDTA is recommended after ED treatment as a result of the rapid decomposition of Fe-EDTA by ED.
Small Boat Detection for Radar Image Datasets with YOLO V3 Network

Small boat detection under the influence of sea clutter is usually difficult, especially for the low-resolution pulse Doppler Radar, as the amplitude of the boat is covered by sea clutter in the time domain, and the spectrum of the boat and sea clutter overlap in the frequency domain. This paper proposes a new method for the detection task based on time-frequency analysis and YOLO V3 network. This method can automatically extract the characteristics of sea clutter and boat in time-frequency images and complete the classification task. The accuracy of the classification is 94.89 percent, which has an improvement of 14.39% than the accuracy of LeNet-5. The measured data verified the method.
LSTM-based Network Attack Detection: Performance Comparison by Hyper-parameter Values Tuning

Network attacks have been around since the beginning of the Internet and they are still relevant due to the numerous attempts of independent hackers, cybercrime organizations and state-sponsored hacking squads to intrude into others computer networks. Similarly, the industry and academia have been providing constantly evolving solutions to try to thwart the ever-sophisticated network attack techniques. Machine learning (ML) and deep learning have shown promising results for detecting and preventing network attacks. In this paper, we investigate a Long Short-Term Memory (LSTM)-based network attack detection. Our goal is to find the optimal values for network attack detection by fine-tuning different LSTM hyper-parameters: optimizers, loss functions, learning rates and activation functions, and by comparing their performance by using the CICIDS2017 labeled dataset. Our results show that LSTM can effectively detect the network attacks with high accuracy and reasonable detection rates. With the optimized hyper-parameter values, the proposed LSTM model can detect DoS attacks with an effective detection accuracy of 99.08% and a detection rate of 0.93. We achieved BoT, DDoS, and port scan attacks detection accuracy of 99.54% and detection rate of 0.84.
Two High Stepped up Continuous Input Current Active Switched-Inductor Quasi-Z-Source Inverters

This study proposes two new structures for active switched-inductor quasi-Z-Source inverters. The proposed topologies use the switched-inductor structure to extend the inverter voltage gain. The first proposed inverter can be named the active switched-inductor quasi ZSI (ASL-qZSI). The second topology is the active switched-capacitor/inductor quasi ZSI (ASCL-qZSI). These inverters have the advantage of increased voltage gain per smaller duty cycle and both of them have continuous source current. Compared with conventional enhanced boost qZSI with two switched impedance network (EB-qZSI) the proposed inverters provide high boost ability by one extra switch with less number of components (two capacitors and one inductor are reduced in proposed ASL-qZSI, one capacitor, one diode and one inductor are reduced in proposed ASCL-qZSI). Compared with conventional modified switched-capacitor Z-source inverter the proposed ASCL-qZSI has high boost factor with fewer components in Z-source network (with one extra switch). The experimental results of the proposed inverters with 48 Vdc input voltage are presented to verify the merits of the proposed inverters.
A Performance Analysis of Neural Network Models in HRRP Target Recognition

In recent years, deep learning method represented by neural network has made great progress in the field of image recognition, but it has not been widely used in the field of high range resolution profile (HRRP) target recognition. On this basis, representative autoencoder (AE), convolutional neural network (CNN) and their derived network structures are applied to HRRP target recognition. Self-built data set is used to identify four types of ship targets. According to the identification accuracy, loss curve, model size and operation time, the comprehensive performance of different models was compared and analyzed, which verified the superiority of neural network model in HRRP target identification, and provided reference for the follow-up work in this field.
The life cycle of Ciona intestinalis (L.) f. typica in relation to the environmental temperature

Investigations of Scandinavian populations of Ciona intestinalis f. typica and a survey of the literature concerning other areas evidences a clear relation between age, growth, spawning and embryonic development, on the one hand, and the environmental temperature conditions, on the other. In sub-arctic regions with temperatures constantly below about 50C, the individual Ciona seems to attain an age of several years, in boreal regions an age of about one year and in the Mediterranean, subtropical and tropical regions an age of less than one year. Growth is more rapid at high temperatures than at low and the number of yearly generations which succeed each other in the warmer seas seems to be at least three and possibly four. In boreal regions there are two generations per year in restricted and sheltered water, while populations in more open waters and at somewhat greater depths, about 15-30 m, have one generation per year. In deeper waters (below 50-60 m), in which temperatures of 6-90C prevail all the year round, each generation lives for at least two years and in the colder, sub-arctic waters even longer. The greater the difference between the summer and winter temperatures, the more distinct the spawning periods. In areas with an even temperature all the year round, it seems that reproduction may take place, broadly speaking, throughout the year. Experiments show that on the coasts of Europe embryonic development from the fertilized egg
Increasing sensitivity to drugs in leukemias by modulation of microRNA expression

miRNAs are small, single stranded, non-coding RNAs that regulate gene expression at a post-transcriptional level. They are known to be involved in several biological processes and diseases such as cancer. Indeed, their abnormal expression alters the regulation of various cancer-related genes. The role of miRNAs in leukemia has been thoroughly studied but the function of specific miRNAs in the pathogenesis of the disease is not fully understood. The work performed in this thesis aimed at further understanding the role of some miRNAs in leukemia. The specific aims were: i) further understand the role of miR-21 in chronic myeloid leukemia (CML) cell lines; ii) investigate if miR-21 is present in various extracellular vesicles from CML cells and iii) study the role of miR-128 in an acute myeloid leukemia (AML) cell line. The overexpression of miR-21 has been observed in many cancers, including leukemia. The involvement of this miRNA in cancer related processes, such as chemoresistance, has been shown but its correlation with autophagy had never been addressed. In this thesis it is shown that by downregulating mR-21 expression with antimiRs, in K562 (CML) cells, there was a decrease in cellular viability and a decrease in cellular proliferation. miR-21 downregulation also caused increased programmed cell death and a decrease in the expression levels of Bcl-2 protein, although PARP cleavage was not observed. Thus, the possible involvement of autophagy was analysed. It was verified that miR-21 downregulation increased the expression of the autophagy related proteins Beclin-1, Vps34 and LC3-II. Accordingly, an increase in autophagic vacuoles was seen, both by monodansylcadaverine and acridine orange staining as well as by transmission electron microscopy. In addition, downregulation of miR-21 expression increased the sensitivity of both K562 and KYO-1 (CML) cells to etoposide and doxorubicin, which was reverted by pre-treating cells with an autophagy inhibitor, 3-MA. Therefore, these results described for the first time autophagy induction via miR-21 downregulation and its involvement in drug sensitivity. Extracellular vesicles (EVs) have recently emerged as important intermediates of intercellular communication. These EVs, shed by donor cells, may contain proteins and both coding and non-coding RNAs, which can be transferred to recipient cells. In particular, it has become evident that EVs may carry miRNAs that are responsible for drug resistance, from resistant to sensitive cells. However, it is not known if miR-21 is