Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and unified license plate recognition via lightweight deep neural network

: In this study, the authors are interested in building a unified deep learning framework to solve the recognition problem of both single-line and double-line car license plates. Most existing methods are designed for single-line license plate recognition. For double-line cases, detection and segmentation are usually adopted firstly to locate each line of characters. These methods are usually environmentally sensitive and will bring propagation of error between segmentation and recognition. To solve the problems, the authors propose a unified method that can recognise both single-line and double-line license plates in an end-to-end way without line segmentation and character segmentation. Specifically, an improved lightweight convolutional neural network is used to extract features efficiently. Then, the multi-task learning strategy is used to simultaneously perform license plate classification and character recognition. Finally, recognition task is treated as sequence labelling problems, which are solved by connectionist temporal classification directly. Experimental results indicate that the proposed method significantly outperforms the previous state-of-the-art methods on both public datasets and the synthetic dataset.
Residual-wider convolutional neural network for image recognition

: Recent works show that the performance of convolutional neural networks (CNNs) could be improved by making the network wider and introducing residual connections. For example, the Inception architecture is one of the classical models. However, the structure of the series of Inception is complex, and there are more convolution layers and existing redundant image feature information. In order to further improve the performance and observability of CNNs, a novel type of network structure based on two modules, wider module and residual module, is proposed to make full advantage of the information of the image and learn more abundant features in this study, which is called residual-wider network (R-WN). The structure of R-WN is easy to understand and adopts modular design method. Experiential results demonstrate that the proposed R-WN with optimal structure and much fewer convolution layers could achieve much better image recognition results on four classical open data sets (MNIST, CIFAR10, SVHN, and Oxford Flowers 17).
Vector ordering and regression learning-based ranking for dynamic summarisation of user videos

: Dynamic video summarisation (video skimming) is a process of generating a shorter video (video skim) as a summary of a given video, which helps in its easier and quicker comprehension. In this study, an efficient dynamic summarisation approach for user videos is proposed using vector ordering for ranking video units (frames/shots). User videos are casually shot unscripted videos, where skimming involves the selection of its interesting part(s) ignoring many uninteresting ones. The concept of R-ordering of vectors is employed to find a representative frame, which is used to perform relative ranking of the video frames. It is theoretically shown that significance is given to each element of a frame's feature vector while computing the importance scores that lead to the frame ranks used for skimming. Furthermore, the allocation of different weights to the features involved is also achieved using linear and Gaussian process regressions. Through extensive experiments considering several standard datasets with human-labelled ground truth, the proposed approach is demonstrated to be efficient and to perform better than the relevant state-of-the-art.
Method of Profanity Detection Using Word Embedding and LSTM

Department of Software Convergence Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea Department of Computer Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea IT Research Institute, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea Department of New Industry Convergence, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea
Social bat optimisation dependent deep stacked auto-encoder for skin cancer detection

: Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre-processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre-processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern-based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern-based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto-encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K -fold.
A Survey of Android Malware Static Detection Technology Based on Machine Learning

With the rapid growth of Android devices and applications, the Android environment faces more security threats. Malicious applications stealing usersʼ privacy information, sending text messages to trigger deductions, exploiting privilege escalation to control the system, etc., cause significant harm to end users. To detect Android malware, researchers have proposed various techniques, among which the machine learning-based methods with static features of apps as input vectors have apparent advantages in code coverage, operational efficiency, and massive sample detection. In this paper, we investigated Android applicationsʼ structure, analysed various sources of static features, reviewed the machine learning methods for detecting Android malware, studied the advantages and limitations of these methods, and discussed the future directions in this field. Our work will help researchers better understand the current research state, the benefits and weaknesses of each approach, and future technology directions.
Efficient image structural similarity quality assessment method using image regularised feature

: Image regularised features play a critical role in image processing domain, by integrating regularised feature and structural similarity, a new full-reference image assessment method (IRF_SSIM) is proposed in this study. As well known, the gradient operator always be used to capture the edge information of the image, while the total variational regularised features can be adopted to calculate the detailed change information of image contrast and texture, as well as noise removal and edge retention. Therefore, the IRF_SSIM method extends the gradient features into the image regularised features to measure the structural changes in the image. In addition, image quality is also affected by variations of luminance and contrast. For a more comprehensive image quality assessment, the IRF_SSIM method considers the changes in structure, luminance and contrast simultaneously. In other words, the total image quality is estimated by structural similarity calculated by integrating the effects of image structure, luminance and contrast changes. Comparing with the representative methods, the experimental results illustrate that the IRF_SSIM method is highly consistent with the subjective assessment results.
Iterative PET image reconstruction using cascaded data consistency generative adversarial network

: This study proposed a GAN-based reconstruction method-cascaded data consistency generative adversarial network (CDCGAN) to recover high-quality PET images from filtered back projection PET images with streaking artifacts and high noise. First, the authors embed defined data consistency layer (DC layer) in their generator network to constrain the reconstruction process and adjust accurately generated faked PET images. Second, to improve the accuracy of reconstruction on average, their generator network was built iteratively to achieve better performance with simple structures. They observed that the proposed CDCGAN allows the preservation of fine anomalous features while eliminating the streaking artifacts and noise. Experimental results show that the reconstructed PET images by their methods perform well comparably to other state-of-the-art methods but at a faster speed. A clinical experiment was also performed to show the validity of the CDCGAN for artifacts reduction.
Ordered and fixed-length bit-string fingerprint representation with minutia vicinity combined feature and spectral clustering

: The minutiae set defined by the ISO/IEC 19794-2 is one of the prevalent feature used in fingerprint recognition systems. Unfortunately, such characteristic of unordered and variable-sized minutiae information causes a restriction on the operation in some advanced template protection methods (e.g. fuzzy commitment), which usually require an ordered and fixed-length binary feature representation as the system input. In this study, in order to simultaneously extend the application of fingerprint recognition and provide satisfactory system performance, the authors propose a novel fixed-length bit-string conversion framework based on spectral clustering and the proposed newly designed discriminative fingerprint representation called minutia vicinity combined feature (MVCF). The proposed method consists of three stages: (i) the extraction of MVCF, (ii) bit conversion via the spectral clustering algorithm, and (iii) matching. Benefiting from feature invariance, fixed-length and bit-oriented coding, merits such as fast matching and decent accuracy are well guaranteed. The performance evaluation is conducted on six publicly available benchmark data sets: FVC2002 DB1, DB2, DB3 and FVC2004 DB1, DB2, DB3 confirms the superiority of the proposed method and suggests the promise of migrating to some other domains (e.g., template protection).
Current Research Trends in IoT Security: A Systematic Mapping Study

,e smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. ,is study aimed to provide comprehensive insights on IoTsecurity. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoTsecurity and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. ,e results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.
Image enhancement using convolutional neural network to identify similar patterns

: An image may be disturbed by impulse noise during transmission or acquisition. To effectively restore the disturbed image is important for the applications of image processing. This study aims at enhancing the disturbed images by using the convolutional neural network (CNN) to identify similar patterns for the restoration of noisy pixels. In the training phase, each noisy pixel is analysed and compared with the noise-free image to find the closest neighbouring pixels. The pixels in a local window form a micro-pattern. All the captured micro-patterns, whose centre pixel is noisy, become a dataset for the training of a position CNN. The closest neighbouring pixel of a noisy image to the centre one of the noise-free image at the same position of each micro-pattern is selected to be the target. In the enhancement phase, a noisy micro-pattern, where the centre pixel is noisy, is input into the trained position CNN. The top N pixels are recognised and averaged to replace the grey level of the centre pixel. An enhanced pixel is obtained. The experimental results show that the position CNN can well recognise the similar neighbouring pixels and effectively enhance the noisy pixels in an image disturbed by salt-and-pepper noise.
A FACTORIAL STUDY ON THE EFFECTS OF CYCLODEXTRINS, POLOXAMER 407 AND PVP ON THE SOLUBILITY AND DISSOLUTION RATE OF VALSARTAN Research Article

The objective of the study is to evaluate the individual main effects and combined (or interaction) effects of cyclodextrins (βCD and HPβCD), surfactant (Poloxamer 407) and PVP on the solubility and dissolution rate of valsartan in a series of 23 factorial experiments. The solubility of valsartan in eight selected fluids containing CDs, Poloxamer 407 and PVP as per 23 factorial study was determined. The solubility of valsartan was marginally enhanced by βCD (1.23 fold) and HPβCD (1.378 fold). Whereas PVP gave a moderate enhancement (4.58 fold) and Poloxamer 407 gave markedly higher enhancement (20.58 fold) in the solubility of valsartan. Combination of CDs with Poloxamer 407 and PVP resulted in a much higher enhancement (4.70-15.68 fold) in the solubility of valsartan than the CDs alone. Solid inclusion complexes of valsartan-CDs (βCD and HPβCD) were prepared with and without Poloxamer 407 and PVP by kneading method as per 23-factorial design. ANOVA indicated that the individual main effects of CDs (βCD and HPβCD), Poloxamer 407 and PVP and their combined effects in enhancing the solubility and dissolution rate (K1) were highly significant (P < 0.01). βCD alone and in combination with Poloxamer 407 and PVP gave only a marginal increase (1.37-1.64 fold) in the dissolution rate of valsartan. HPβCD alone and in combination with PVP gave a marked enhancement (5.54-6.62 fold) in the dissolution rate of valsartan. Though Poloxamer 407 has given highest enhancement (20.58 fold) in the solubility of valsartan, it gave only a marginal increase in the dissolution rate, K1 (1.96 fold) and DE30 (1.34 fold). Combination of CDs with PVP has markedly enhanced the solubility and dissolution rate of valsartan, a BCS class II drug than is possible with them individually.
Analyzing Barriers, Innovating Pedagogy: Applying Universal Design for Learning in a Teacher Residency

Abstract Over the last two decades, Universal Design for Learning (UDL) has emerged as a pedagogical framework for inclusive teaching and learning. UDL builds on learner variability as a starting point for instructional and curricular design, and aims to minimize barriers in the learning environment. In addition to its widespread use in K12 settings and its endorsement in federal education policy, UDL has also gained momentum in teacher education research and practice. Although a number of scholars have explored the use of UDL in preservice teacher education curricula, there are limited descriptions of how teacher educators apply concepts of UDL in their own pedagogy and practice. The purpose of this paper is to describe the process of applying UDL as a pedagogical innovation in a teacher residency (preparation) program. We share examples of how turning the lens on our own practice as teacher educators has allowed us to redesign multiple aspects of our curriculum, instructional practice, and program at large. We conclude by highlighting the iterative nature of UDL application, and by making recommendations for future research and inquiry on UDL in teacher education.
Modification of plant communities by bison in Riding Mountain National Park

ABSTRACT Plains bison (Bison bison bison) are suspected to have played an important role in maintaining and structuring the plant communities throughout the Aspen Parkland. We examine the effects of a fenced bison herd on grassland plant communities in Riding Mountain National Park, Manitoba, Canada. We focus on the relationship between bison and woody plant encroachment along the forest-grassland ecotone, and on the effects that short- and long-term occupation by bison have on Festuca hallii grassland communities including the effects of bison reintroduction to a grassland area ungrazed by bison for 165 years. We found limited impacts of bison on the shrub community at the forest grassland edge. Bison rarely entered areas with dense, tall shrub understories, and thus bison cannot be a major factor controlling the ecotone. Shrub stem mortality due to fire may be a necessary precursor to bison effectively mitigating woody encroachment in the Aspen Parkland. We found limited short-term impacts of bison on grassland communities, with reduced sward heights but no change in composition. The long-term (>80 year) presence of bison was associated with greater species richness, reduced litter accumulation, and a distinct plant community composition compared to areas without bison impacts.
Evidence for passive dispersal of ground beetles (Coleoptera: Carabidae) in the Nearctic boreal forest

ABSTRACT Passive transport has likely contributed to the post-glacial dispersal of species in temperate regions. However, identifying such processes from patterns can be obscured by confounding environmental conditions. We studied the distribution of ground beetles in Ontario’s Far North, a vast (450,000 km2) and largely intact region, to identify mechanisms that aid in species dispersal when confounding factors, such as temperature, are controlled. We tested a model of passive, riverine dispersal using recent records of flightless and flighted ground beetles from 34 sites across the region. The number of species declined with distance from main watershed rivers, as predicted from our model at the site level. Contrary to expectations, this pattern was evident with flighted species but not flightless species. The opposite pattern, with flightless species displaying a decrease with increasing distance from river, was evident at a regional level. These patterns are consistent with ground beetles carried along rivers since glacial retreat. We surmise that northward dispersal of these invertebrates into the boreal forest has been aided by vegetation and other debris flowing down rivers, including soil from eroded banks in which larvae and pupae reside. Biogeographic inferences, although often subtle, can be supported by broad-scale studies of intact landscapes.
Thermal-induced modification of the contact mechanics of adhering liposomes

The physiochemical properties of the phospholipid bilayer directly modulate the interaction between adhered cells and extracellular matrixes. The thermotropic transition of phospholipid bilayer has been shown to be highly coupled with the mechanical properties of liposomes (Heimburg, T. Biochim. Biophys. Acta 1998, 147, 1415). However, there is currently a lack of understanding of the thermal-induced effect on the adhesive contact mechanics of liposomes adhering on non-deformable substrates. In this study, high-resolution reflectance interference contrast microscopy, phase contrast microscopy, and contact mechanics modeling are applied to probe the interfacial adhesion contacts of phospholipid vesicles on fused silica substrates during gel to liquid crystalline transition of the bilayer. It is shown that the contact area, contact angle, and adhesion energy of adhering liposome increase during the thermotropic transition. The trend of adhesion energy against temperature is dictated by the complex interplay.
Detection of OH Radicals Formed at PEFC Electrodes by Means of a Fluorescence Probe

A fluorescence probe method, which enables highly sensitive detection of OH radicals (·OH) by forming a stable fluorescent product, was applied to a model reaction system of polymer electrolyte fuel cells (PEFCs). 'OH was successfully detected under open-circuit condition with a homemade test cell having reservoirs of a coumarin probe solution at both anode and cathode electrodes. The experiments with H 2 O 2 and Fe 2+ in the probe solution suggested that the ·OH is produced via electrode reduction and oxidation of H 2 O 2 at the anode and cathode, respectively.
Low-Band-Gap Polymers That Utilize Quinoid Resonance Structure Stabilization by Thienothiophene: Fine-Tuning of HOMO Level

Compared with the dominant donor−acceptor approach to construct low-band-gap polymers, the quinoid strategy has been much less explored. However, a new series of polymers based on a prequinoid structure, thieno[3,4-b]thiophene (TT), and a comonomer, benzo[1,2-b:4,5-b′]dithiophene (BnDT), have recently achieved over 7% power conversion efficiency in bulk heterojunction (BHJ) polymer solar cells (PSC). In order to further explore the utility of the thienothiophene (TT), we studied a library of six polymers by varying the electronic properties of the comonomers (NDT, naphtho[2,1-b:3,4-b′]dithiophene, QDT, dithieno[3,2-f:2′,3′-h]quinoxaline, and BnDT) and those of the thienothiophene (TT and fluorinated TT (FTT)). It was discovered that the thienothiophene unit predominantly decides the low-band-gap characteristic of these polymers; however, the highest occupied molecular orbital (HOMO) energy level of these polymers can be tuned, depending upon the electronic properties of the comonomer and the substitution .
5-Hydroxytryptamine2B Receptor Function Is Enhanced in the N ω-Nitro-l-arginine Hypertensive Rat

Arterial hyperresponsiveness to serotonin (5-hydroxytryptamine, 5-HT) is observed in experimental models and human forms of hypertension. Presently, we test the hypothesis that the 5-HT2B receptor is up-regulated and necessary for maintaining elevated blood pressure in a rat made hypertensive by the nitric-oxide synthase inhibitor N ω-nitro-l-arginine (LNNA; 0.5 g/l). After 2 weeks of treatment, thoracic aorta were removed from LNNA hypertensive (systolic blood pressure = 189 ± 5 mm Hg) and sham normotensive rats (121 ± 1 mm Hg), denuded, and mounted into isolated tissue baths for measurement of isometric contraction. In sham tissues, 5-HT-induced contraction was mediated by the 5-HT2A receptor as evidence by a parallel rightward shift in response to 5-HT by the 5-HT2A/2C receptor antagonist ketanserin (10 nM) and lack of shift by the 5-HT2B receptor antagonist 6-methyl-1,2,3,4-tetrahydro-1-[3,4-dimethoxyphenyl)methyl]-9H-pyrido[3,4-b]indole hydrochloride (LY272015) (10 nM). In contrast, LY272015 produced a 4-fold rightward shift to 5-HT in aorta from LNNA hypertensive rats, and blockade by ketanserin did not occur at low concentrations of 5-HT. Maximal contraction to the 5-HT2B receptor agonist 1-[5-(2-thienylmethoxy)-1H-3-indolyl]propan-2-amine hydrochloride was significantly greater in LNNA hypertensive rats (percentage of phenylephrine contraction in sham = 7 ± 4, and in LNNA = 61 ± 7%). 5-HT2B receptor protein was present in aortic homogenates from sham and LNNA rats, but the density of 5-HT2B receptor protein in LNNA homogenates was 300% that in sham. Importantly, the 5-HT2B receptor antagonist LY272015 reduced blood pressure of the LNNA hypertensive but not the sham normotensive rats. Thus, these data suggest that the up-regulated 5-HT2B receptor in the LNNA hypertensive rats is physiologically activated to maintain elevated blood pressure.