Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Augmentation on Limited Biometric Data Set for M2M Authentication Model Testing

—Examining the performance of artiﬁcial intelligence (AI)-based model for machine to machine (M2M) authentication needs a large number of data. The accessibility of open biometric data is restricted by privacy regulations such as general data protection regulation (GDPR). This is because the data is commonly obtained by sensors embedded in personal wearable devices. Thus exploring and developing a system with biometric data as the main parameter is difﬁcult to do. As data augmentation is a technique to increase the size of the data set by producing derived data from the original data, its usage in AI is popular especially for computer vision. Incorporating data augmentation in the development of AI-based authentication model could solve the shortage of database. Therefore this study proposes a data augmentation model and analyzes its training and augmenting performance.
Cu-ZnO Nanostructures Synthesis and Characterization

5wt% copper doped zinc oxide (Cu-ZnO) nanostructures were prepared via the hydrothermal technique at different temperatures of 70, 100, 130, 160 and 190 o C. UV spectroscopy, FE-SEM microscopy, XRD crystallography, and EDS measurements were used for nanostructure characterization. UV spectroscopy indicated a red shift for the absorption peaks, and hence a blue shift for the energy gap values, as temperature increased from 70 to 190 o C. FE-SEM microscopy showed an increase in the average lengths and diameters of the nanostructures following a similar increase in temperature. XRD crystallography indicated decent structural patterns for Cu-ZnO nanostructures with an increase in crystallite size upon temperature increase. Interestingly, three unprecedented extra indices appeared in the structural pattern at 190 o C, which might indicate a configuration of hexagonal crystallite with three extra planes. EDS measurements indicated the sole presence of Cu, Zn and O.
Hippocampal Astrocyte Response to Melatonin Following Neural Damage Induction in Rats

Introduction: Brain injury induces an almost immediate response from glial cells, especially astrocytes. Activation of astrocytes leads to the production of inflammatory cytokines and reactive oxygen species that may result in secondary neuronal damage. Melatonin is an anti-inflammatory and antioxidant agent, and it has been reported to exert neuroprotection through the prevention of neuronal death in several models of central nervous system injury. This study aimed to investigate the effect of melatonin on astrocyte activation induced by Traumatic Brain Injury (TBI) in rat hippocampus and dentate gyrus. Methods: Animals were randomly divided into 5 groups; Sham group, TBI group, vehicle group, and melatonin-treated TBI groups (TBI+Mel5, TBI+Mel20). Immunohistochemical method (GFAP marker) and TUNEL assay were used to evaluate astrocyte reactivity and neuronal death, respectively. Results: The results demonstrated that the astrocyte number was reduced significantly in melatonin-treated groups compared to the vehicle group. Additionally, based on TUNEL results, melatonin administration noticeably reduced the number of apoptotic neurons in the rat hippocampus and dentate gyrus. Conclusion: In general, our findings suggest that melatonin treatment after brain injury reduces astrocyte reactivity as well as neuronal cell apoptosis in rat hippocampus and dentate gyrus.
AUDIO CAPTION GENERATION FROM IMAGES USING DEEP LEARNING

Visually impaired individuals face various types of difficulties as they cannot visualize the natural environment. To overcome this problem, the proposed system would automatically generate captions for an input images and convert the generated caption to an audio format so that visually impaired individuals can listen to the generated captions. Captioning is performed using Deep Learning algorithm Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory.
The WORC* database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies

The WORC database consists in total of 930 patients composed of six datasets gathered at the Erasmus MC, consisting of patients with: 1) well-differentiated liposarcoma or lipoma (115 patients); 2) desmoid-type fibromatosis or extremity soft-tissue sarcomas (203 patients); 3) primary solid liver tumors, either malignant (hepatocellular carcinoma or intrahepatic cholangiocarcinoma) or benign (hepatocellular adenoma or focal nodular hyperplasia) (186 patients); 4) gastrointestinal stromal tumors (GISTs) and intra-abdominal gastrointestinal tumors radiologically resembling GISTs (246 patients); 5) colorectal liver metastases (77 patients); and 6) lung metastases of metastatic melanoma (103 patients). For each patient, either a magnetic resonance imaging (MRI) or computed tomography (CT) scan, collected from routine clinical care, one or multiple (semi-)automatic lesion segmentations, and ground truth labels from a gold standard (e.g., pathologically proven) are available. All datasets are multicenter imaging datasets, as patients referred to our institute often received imaging at their referring hospital. The dataset can be used to validate or develop radiomics methods, i.e., using machine or deep learning to relate the visual appearance to the ground truth labels, and automatic segmentation methods. See also the research article related to this dataset: Starmans et al., Reproducible radiomics through automated machine learning validated on twelve clinical applications, Medical Image Analysis, Submitted.
Multi-channel Feature Extraction and Super-Resolution Reconstruction of Remote Sensing Images

Super-resolution reconstruction is an imaging method to improve image resolution. It refers to reconstruct a clear high-resolution image from a low-resolution image. High-resolution remote sensing images can provide more detailed information and higher density, but in the field of remote sensing, because of the limitation of the hardware and vast distances, the remote sensing images are fuzzy sometimes. To facilitate subsequent tasks, this paper proposes a multi-channel feature extraction generative adversarial remote sensing image reconstruction method. According to the characteristics of remote sensing image, a generator is designed, which adds Laplace operator to enhance the edge information of the image, and uses multi-channel feature extraction, which not only enhances the ability of feature extraction but also reduces the number of parameters. In this paper, the super-resolution reconstruction task is carried out based on the 2X magnification factor, and the experimental results are evaluated on SET5/14 and NWPU-RESISC45 dataset. The experimental results show that the images generated by this method have a higher detailed texture and better super-resolution reconstruction effect of remote sensing images.
Comparative Assessment of Existing Meaningful Image Encryption Techniques

In recent trends, digital images are commonly used for communication, storage, medical imaging, etc. These images may contain confidential data. Image encryption and steganography are popular techniques to provide information security. In image encryption, a secret image is encoded using an encryption key, and in image steganography, a secret image is embedded in some cover media. Encrypted images are visually recognizable as these are noise-like, but stego images are visually unrecognizable. In this concern, some visually secure encryption techniques are developed, known as Meaningful Image Encryption (MIE). It includes the advantages of both image encryption and steganography. This paper provides a thorough review of existing MIE techniques. A comparative assessment based on various performance parameters is presented. Further, this paper provides the main application areas of MIE techniques. Moreover, the future research perspective to enhance the existing MIE techniques are discussed in detail.
Compressing Biosignal for Diagnosing Chronic Diseases

Electrocardiogram, electromyogram, electroencephalogram are the foremost required vital signs for diagnosing chronic diseases like sleep disorder, mood disorder, epilepsy etc., which demands long-term monitoring. A sensor based wearable system which is enabled with internet technology, supports the continuous recordings of these vital signs without troubling the patient’s daily activities. And the wearable hub is responsible for collecting the readings of bio signals from multiple micro-sensor nodes deployed around the body which creates the short range of communication and forward to the observer. These continuous monitoring increases the signal transmission cost and declines the battery life of wearables. So, the observed multiple bio signals can be compressed jointly than individually before sending, at an edge level. This paper proposes transfer learning based multimodal convolutional denoising autoencoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with better quality score than by randomly initialized weights. The proposed work achieves compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in case of consuming multiple sensors. And the experimental result proves that the computation cost is low in multimodal compression than in unimodal compression.
A service caching method for mobile edge computing

Mobile Edge Computing (MEC) is considered as a key technology to support 5G networks. It can push cloud computing functions to the edge of the network, and achieve the purpose of reducing latency and reducing energy consumption through computing offloading and edge caching. In the MEC system, by caching computing services from the cloud center to the network edge, MEC can provide users with more services, and also be able to make greater use of network resources and improve system performance. However, which services to cache at edge nodes and how to cache these services have become an important challenge of the research. In order to solve the aforementioned problems, in this paper, we analyze the popularity of each cached object firstly, and propose a novel service caching algorithm, which aims to reduce system delay and improve resource utilization. In addition, we design a novel cache replacement algorithm that successfully predict future requests by analyzing historical data, thereby greatly improves the cache hit rate. Finally, experiments prove that our algorithms have greatly improved system performance and hit rate.
Research on Feature Enhancement for Small Object Detection

Small object detection is a challenging research direction in the field of computer vision, due to the low resolution and restricted information of small objects. At present, the general detectors only use appearance features to classify and locate objects, but they are prone to failure under the interference of background noise. On the other hand, the detector based on deep neural network has excellent performance on large scale, but it is difficult to extract enough information of small objects. This paper proposes a feature enhancement network (FENet), which contains two modules. The Residual feature enhancement (RFE) module combines residual learning and sub-pixel convolution to improve the resolution of input small objects and remove image noise. The Attention Feature Pyramid (AFP) module integrates the feature pyramid and attention mechanism, which can extract context information and filter redundant context information. At the same time, considering the imbalance of the contribution of large and small objects to the loss function during the training process, a feedback-driven function is introduced to solve the problem of uneven loss under multiple scales. Experimental results show that compared with the existing small object detection methods, our method has better performance.
Multi-objective optimization configuration of wind-solar-storage microgrid based on NSGA-III

Reasonable allocation of the capacity of micro power supply such as wind turbine, photovoltaic and battery is the premise to ensure the economic, reliable and environmental protection operation of microgrid. Aiming at the capacity allocation problem of grid connected microgrid, this paper establishes a multi-objective optimal allocation mathematical model of grid connected microgrid considering the economy, reliability and environmental protection of microgrid, and uses the genetic algorithm NSGA-III based on the reference point selection mechanism to obtain the multi-objective Pareto solution set of micro source capacity allocation. The simulation results show that NSGA-III can not only optimize the three objective functions better than NSGA-II, but also improve the distribution uniformity of Pareto solution set and the diversity of population. The simulation results show that NSGA-III algorithm is more suitable for multi-objective optimal configuration of microgrid.
Analytical measurements of contact resistivity in two-dimensional WSe2 field-effect transistors

It becomes clear that, in two-dimensional (2D) materials-based devices, sheet resistances underneath electrodes change due to a metallic contact, leading to substantial errors in determining a transfer length. Thus, the extraction of transfer length and corresponding contact resistivity must be revisited to assess the performance of 2D devices. In this study, we present the three different approaches of determining the contact resistivity in 2D WSe2 field effect transistors for the first time by theoretical analysis using the resistive network model as well as electrical measurements using the contact-end resistance and transfer length methods, based on the followings: (a) contact resistance multiplied by transfer length ( RcfW⋅LTk ), (b) integrated contact resistance ( ∫0LR(x)⋅Wdx ), and (c) contact resistance raised to a constant power RcfWα ). These different extraction methods give rise to almost identical transfer length and contact resistivity, validating our model and its accuracy from the results obtained by using various contact metals and plasma doping conditions. This work serves as a foundation for future research on the determination of physical parameters responsible for the carrier transport at the metallic contact interface in 2D semiconductor devices.
Fast detection method of transmission line defects and faults based on airborne laser LiDAR

In view of the fact that it is difficult for transmission line operators to find transmission line defects and accurate location information in time, and in the face of the discovered transmission line defects, they are unable to quickly and accurately return detailed defect information to provide urgent repair to provide rectification solutions, etc., this paper proposes a fast transmission line defect fault detection method based on airborne laser LiDAR, and compares and analyzes it by using laser point cloud data in different periods. It is found that there are some defects such as the interruption of the transmission line channel and the sinking of the tower foundation. Using laser point cloud data, the tilt detection and deformation of poles and towers are found, and large-scale topographic maps are quickly generated to quickly restore the three-dimensional channel environment of transmission lines, so as to provide auxiliary decision-making for transmission line defects and fault rectification schemes. The digital and scientific management of transmission line defects and equipment fault detection are realized through laser point cloud.
Routing in Delay-tolerant network Based on Nodes’ Sociality

To solve the problem of low ratio success delivery, long latency caused by the rapid dynamic change of network topology, limited node buffer, large amount of data transmission and large user population density in delay-tolerant network (DTN). This paper propose a routing algorithm in DTN based on nodes’ sociality. In this paper, contact history was used to determine the node link quality; Markov model was used to predict the node position based on the nodes’ movement track; according to nodes’ social similarity to divide community, then calculated node betweenness centrality. Finally, the best forwarding nodes for message transmission were determined. Besides, to increase the ratio of successful delivery, message delivery probability in the relay node’s buffer was calculated. Experimental results show that compared with traditional routing algorithm, the proposed algorithm performs better on the delivery ratio and average end-to-end delay.
Decision Trees-based Anomaly Detection in Computer Assessment Results

A survey of the current software in the area of computer assessments of students at a university is done. This work shows ways to improve quality of computer assessments and educational management. It suggests options to build a computer system for detecting anomalous assessment results of individual students and entire disciplines. This information could then be used to amend lectures and instructional material or management and better intercommunication to students. This work suggests feature sets for the analysis of student assessment results and demonstrates their availability in the current university software. After surveying known artificial intelligence systems this work proves the choice of decision trees for this problem. It suggests methods and algorithms to improve positive prediction rate of decision trees based on ideas of bootstrap method. A software tool was developed that implements the suggested algorithms.
COMPARISON OF PHENOLIC ACIDS AND ANTIOXIDANT ACTIVITIES OF YOUNG AND MATURE LEAVES OF TECTONA GRANDIS L F

Objective: The present study aims in comparison of phenolic acids and its antioxidant potentialities of young and mature leaf extract of Tectona grandis L f.Methods: Various phenolic acids present in methanolic leaf extract of young and mature leaves of T. grandis were analyzed by reversed-phase high-performance liquid chromatography analysis. The antioxidant potentiality of the extracts was determined by various analytical methods such as 2,2-diphenyl-1-picrylhydrazyl, ferric reducing/antioxidant power, and metal chelating activity.Results: Methanolic leaf extracts of young and mature leaf of T. grandis showed a pool of phenolic compounds such as gallic, vanillic, p-hydroxybenzoic, ferulic, chlorogenic, sinapic, p-coumarate, and cinnamic acids. Irrespective of the methods used for analyzing antioxidant capacity, young leaf extract showed potent antioxidant potentiality when compared with the mature leaf extract.Conclusion: The methanolic extract of teak leaves is rich source of many phenolic compounds, and these compounds attribute to the antioxidant capacity of the leaves. Meanwhile, by comparing the young and mature leaves, the young leaves showed much more potential than the other. It is, therefore, concluded that the young teak leaves can be used as a good source of natural antioxidant.
Review of Pulverized Combustion of Non-Woody Residues

The intense use of wood-derived fuels in (co)firing processes results in an enormous pressure on the forest. In order to alleviate this pressure and to proceed with the CO2 emissions reduction process, it is necessary to increase the use of non-woody residues, in particular herbaceous materials and agricultural residues. (Co)firing using such residues can cause a number of problems due to the presence of alkali metals, chlorine, and other ash-related impacts as well as corrosion of metallic surfaces and particulate matter emissions. This may limit the variety of biomass residues that can actually be used in (co)firing processes. This review aims to summarize recent developments in the combustion of pulverized non-woody residues and includes experimental and numerical studies of single particle combustion and combustion in small- and large-scale furnaces. The review provides an overview of the properties of non-woody residues, describes the existing research facilities to study the subject, and summarizes .
RELATIONSHIP BETWEEN BODY IMAGE DISSATISFACTION AND DISORDERED EATING: MEDIATING EFFECT OF SELFESTEEM AND DEPRESSION AMONG FEMALE UNIVERSITY STUDENTS

The objectives of this study were to examine the relationship between body dissatisfaction and disordered eating and to examine whether depression and selfesteem mediate the relationship. The sample consisted of 186 undergraduate and post graduate female university students. Data collection tools were the Body-Image Ideals Questionnaire (BIQ), the Eating Attitude Test (EAT-26), the Rosenburg Self-Esteem Scale (SES) and the Beck Depression Inventory (BDI). The Linear Structural Relations (LISREL) software and Structural Equation Modeling (SEM) were used for the analysis of data. The findings of this study showed a significant positive correlation between body dissatisfaction and disordered eating. In addition, body dissatisfaction had an indirect effect through depression on disordered eating. On the contrary, selfesteem did not have a mediating effect. This study highlights the potential role of depression in the prediction of disordered eating. Thus, it informs counselors and therapist in clinics to assess for depressive symptoms after assessing for body dissatisfaction. In so doing, this will help to identify and prevent at risk female students from engaging in disordered eating attitudes. it is recommended that female students in the UAE, should be educated on the dangers involved in trying to model the Western ideology of thinness portrayed as beauty and attractiveness.
Chapter 5 Switching the Isotype of Monoclonal Antibodies

Isotype switch variant selection is an important adjunct to monoclonal antibody technology. Through the production of hybridomas, we can now harness the specificity of the humoral immune response to generate virtually unlimited amounts of monoclonal antibody of desired specificity. Unfortunately, a selected hybridoma may fail to secrete specific immunoglobulin with an isotype of desired functional activity. This problem may be alleviated through the selection of isotype switch variants that secrete antibody of a different immunoglobulin isotype but with preserved antigen-binding specificity. In addition, by selecting hybridomas that produce monoclonals differing from one another in isotype only, comparative studies can be performed on the functional activities of the different murine immunoglobulin isotypes. Such studies may increase our understanding of potential uses of monoclonal antibodies in cell biology and medicine. This chapter will review current concepts of immunoglobulin isotype switching, discussing several advances that facilitate the selection of switch variant hybridomas.