Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation Voice Scrambling Model Based on Hybrid Chaotic Signals

One of the most common theories used in cryptography is chaos theory, as it has sufficient encryption properties. Such properties can be used to analyse diffusion and confusion processes, which is one of cryptography's key features; a speech scrambling is an important tool in wireless communication systems for enhancing data security. The main target is achieving security to resist malicious eavesdroppers and attacks. This paper presents a speech scrambling model using three various chaotic maps/flows (standard map SM, Unified system US and Chen system CS). The use of the chaotic keys in scrambling techniques are to enhance the transmitted data security, which is strong over the effects of AWGN. Five different tests were included in the evaluation measures: cepstral distance dCD, Linear predict coding distance dLPC, Mean Square Error MSE, Entropy H, and Correlation Factor Rc. The important criteria are residual intelligibility of speech signal between the (original and scrambling/descrambling signal) for the proposed models. The results are good enough to protect the encryption signal of the brute force attack, and when applying a model, the results are being evaluated. The offered model was carried out using SIMULINK (R2018b) by using MATLAB.
Retinal Blood Vessels Segmentation using ISODATA and High Boost Filter

Automatic segmentation of retinal vessels tree is a prerequisite step in the field of ophthalmology for computer-aided diagnosis. This paper describes a robust vessel segmentation algorithm that is efficient enough to segment retinal vessels tree automatically and accurately. The proposed method includes several steps. Initially, we splitted the color fundus image into three channels Red, Green and Blue channels. On Green channel, CLAHE is used for contrast enhancement and top-hat morphological operation are applied to remove noise. Meanwhile High Boost and Frangi filters are introduced to get the prominent vessels pixels. Finally, the ISODATA thresholding technique is applied to get final binarized output segmented image. The proposed method outperforms recently published methods in terms of statistical parameters, such as Accuracy, Sensitivity, and Specificity on DRIVE and STARE datasets. In addition, the proposed method showed a remarkable scientific value for future computer-aided diagnosis applications.
A Hybrid Encryption Approach for Secured Authentication and Enhancement in Confidentiality of Data

currently, data security issues are remaining as a major concern during digital communication. A large amount of crucial data is transmitted through the communication channel. There are many cryptographic algorithms available, which are used for providing data security during communication and storage process. However, the data needs to be decrypted for performing operations, which may lead to elevation of the privilege of data. The pin or passwords used for decryption of data can be easily identified using a brute force attack. This leads to losing the confidentiality of crucial data to an unauthorized user. In the proposed system, a combination of Homomorphic and Honey encryption is used to improve data confidentiality and user authentication problems. Thus, the system provides better data security for the issues related to outsourced databases.
Focusing Bistatic Forward-looking SAR Images use Omega-k algorithm based on Modified Hyperbolic Approximating

Forward-Iooking imaging has many potential applications, such as self-landing, navigation, etc. This paper shows a modified hyperbolic approximating method for bistatic forward-looking SAR imaging. Compared with the original hyperbolic approximating, the modified method can handle the bistatic forward-looking geometry better and make the equivalent range history more accurate. Based on the modified approximating method, a modified Omega-k imaging algorithm is deduced. Finally, numerical simulations are used to validate the proposed modified hyperbolic approximating and the imaging algorithm.
A Student-Centric Prototype for Courses Recommendation in Universities' Students Portal

The development of Recommender Systems in universities students’ portal provides a significant impact on the student study plan and grades. Such systems are more beneficial when they match the software interface usability goals. Henceforth, the students will be more likely to use them. To the best of our knowledge, there are no proposed prototypes neither research works for Courses Recommender Systems in universities students’ portal. Hence, this paper will present a prototype proposal for such a system. The proposed prototype evaluation achieved 4.8643 on the Likert scale reflecting a system with high usability.
Detection and Segmentation of Breast Tumor from MRI Images Using Image Processing Techniques

In medical image processing, segmentation plays an important role in the identification of breast tumors and has recently been performed with AI. But the same result can be achieved using different image processing tools in combined and successively. This proposed work advances an image processing based automated identification of breast tumors from MRI images. In the proposed method otsu’s thresholding segmentation is used with different image processing steps to identify the desired regions of interests’ from the MRI images. Results obtained in this research study prove that the accuracy rate has been increased compared to other existing approaches and human intervention is not necessary which means no calibration of processing parameters is essential.
Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning

In recent times Electronic Learning (E-Learning) and Massive Open Online Courses (MOOC) are more popular among the current generation of learners. Coursera, Edx, Simplilearn, Byjus and many other E-Learning service providers are available to deliver various courses. A recent study, in online courses, it has been found by Massachusetts Institute of Technology (MIT) that an astronomical dropout rate of about 96 per cent was found for the last five years. Educational researchers are attempting to decrease the dropout rate of E-Learning courses using various methods. Human Computer Interface (HCI) researchers are attempting to use Brain Computer Interface (BCI) to increase the efficiency of the E-Learning. Beta waves (14–30 Hz) are generated when the learners are more alert. Neil Fleming’s VARK (Visual, Auditory, Read and Write and Kinesthetic) questionnaires are used by many researchers to classify the learners. Carl Jung explored that Introverts and Extraverts are the personality traits among the humans. Soomin Kim’s study shows that for gathering of quantitative data, Chatbot may be a promising method. The proposed research work in this paper is to find out a correlation between Introvert and Extravert personality types and their learning styles. Initially, modified VARK questionnaires are implemented as a Chatbot to classify individuals as Introverts or Extraverts. After the classifications by the Chatbot, two minutes of visual and auditory contents are given to Introverts and Extraverts and learners’ Beta brain waves are recorded and a dataset is created at an interval of one second. The dataset is validated using Machine Learning (ML) algorithms, like Naïve Bayes, N48 and Canopy. The proposed method is found to improve the accuracy of classification of learners. Bio-Inspired learning style Brain Computing Interface (BIL-BCI) framework proposed in this paper is a recommendation system to increase the accuracy of the classification among the E-Learners.
The Effect of Perceived Interactivity and Social Norm to the Continuance Use of Mobile Wellness Apps

Usage of Mobile Wellness Applications (MWA) has been proven to be one of the ways to overcome obesity thorough the practice of self-monitoring. However, based on the literature, users have difficulties in prolonging the usage of MWA. Study of MWA continuance use seems to be one of the less explored and least examined topics in Information Systems (IS) domains. Also, most of the existing research regarding MWA are not guided by IS theories. Thus, this study aims to identify the factors that influence continuance use of MWA in the context of Malaysia by adapting the Information Systems Continuance Model (ISCM) as a post adoption IS theory. A pilot study was applied using the purposive sampling technique and data were collected from 30 MWA users who had undergone a wellness program. Partial Least Squares (PLS) method based on Structural Equation Modelling (SEM) was used for analyzing the survey data. The outcome of this study offered a theoretical model for encouraging the continuance use of MWA. This pilot study research reveals that Perceived Interactivity and Social Norm influence the Continuance Intention to use MWA and Satisfaction is found to be the strongest factor.
Feature-Improving Generative Adversarial Network for Face Frontalization

Face frontalization can boost the performance of face recognition methods and has made significant progress with the development of Generative Adversarial Networks (GANs). However, many GAN-based face frontalization methods still perform relatively weak on face recognition tasks under large face poses. In this paper, we propose Feature-Improving GAN (FI-GAN) for face frontalization, which aims to improve the recognition performance under large face poses. We assume that there is an inherent mapping between the frontal face and profile face, and their discrepancy in deep representation space can be estimated. The generation module of FI-GAN has a compact module named Feature-Mapping Block that helps to map the features of profile face images to the frontal space. Moreover, we produce a feature discriminator that can distinguish the features of profile face images from those of ground true frontal face images, which guide the generation module to provide high-quality features of profile faces. We conduct experiments on the MultiPIE, Labeled Faces in the Wild (LFW), and Celebrities in Frontal-Profile (CFP) databases. Our method is comparable to state-of-the-art methods under small poses and outperforms them on large pose face recognition.
A Review of Carbon Nanotubes Field Effect-Based Biosensors

Field effect-based biosensors (BioFETs) stand out among other biosensing technologies due to their unique features such as real time screening, ultrasensitive detection, low cost, and amenability to extreme device miniaturization due to the convenient utilization of nanoscale materials. Nanodevices pave the way for the detection of tiny biomolecules and minute concentrations of analytes as they are ultrasensitive to surface charge modulation, allowing for better point-of-care screening of various life-threatening infectious diseases. Semiconducting carbon nanotubes (sc-CNTs) are exceptionally promising for FET-channel integration to replace bulky silicon technology beyond the dimensions of the short channel effects for their 1D ultrathin structure, superior electronic features, and biocompatibility. However, performance of CNTFET biosensors is influenced by the inhomogeneous interface between sc-CNTs and metallic source and drain electrodes. This article reviews recent studies on CNTFET biosensors, morphology of these devices and the cause-and-effect of the interface issues between sc-CNTs and metallic electrodes. Finally, future outlook on suggested technology to improve the performance of such CNTFET devices is presented.
Energy Efficient Trust-Based Security Mechanism for Wireless Sensors and Unmanned Aerial Vehicles

Nowadays, security is one of the most debated topics due to the diversity and increased number of attacks. Researchers and industry try to come with solutions to be one step in front of attackers but they do not always succeed. Sensors are the preferred devices for attackers because of their limited capabilities. Traditional security mechanisms do not work on these and therefore, dedicated solutions need to be created. For this reason, this paper proposes a solution for mitigating internal attacks that affect wireless sensors. With the aid of a trust-based security mechanism, compromised sensors can be detected and excluded before they can make more damage in the environment. The novelty of the proposition comes from the fact that is developed for environments that uses unmanned aerial vehicles (UAV) to collect data. Current literature did not tackle with trust management in UAV-sensor communication and for this reason this paper can represent the first brick on a subject of great interest. The proposition uses both direct and indirect trust values for determining the final trust value. Results demonstrate that the proposition is energy efficient and can limit the damage produced by an attack.
Fast Deployment of Reliable Distributed Control Planes With Performance Guarantees

Current trends strongly indicate a transition towards large-scale programmable networks with virtual network functions. In such a setting, deployment of distributed control planes will be vital for guaranteed service availability and performance. Moreover, deployment strategies need to be completed quickly in order to respond flexibly to varying network conditions. We propose an effective optimization approach that automatically decides on the needed number of controllers, their locations, control regions, and traffic routes into a plan which fulfills control flow reliability and routability requirements, including bandwidth and delay bounds. The approach is also fast: the algorithms for bandwidth and delay bounds can reduce the running time at the level of 50x and 500x, respectively, compared to state-of-the-art and direct solvers such as CPLEX. Altogether, our results indicate that computing a deployment plan adhering to predetermined performance requirements over network topologies of various sizes can be produced in seconds and minutes, rather than hours and days. Such fast allocation of resources that guarantees reliable connectivity and service quality is fundamental for elastic and efficient use of network resources.
The suitability of the Galvanic Skin Response (GSR) as a measure of emotions and the possibility of using the scapula as an alternative recording site of GSR

Galvanic Skin Response (GSR) is a physiological marker that have been used to monitor the Electro-dermal activities (EDA) due to the change in the sweat glands activities. The autonomic nervous system is the responsible about most of the sweat glands activities; as a result, the GSR has been used to study the emotions and stress. This paper is focusing on three researching variables: firstly, examine the possibility of using the GSR as a physiological marker to monitor the emotions and stress of participants during presenting three groups of photos (sad, happy and neutral). Secondly, checking the reliability of a long term recording site rather than the hands; and finally, investigating the GSR signal while doing mild exercise (treadmill walking). The scapula has been chosen to record the GSR and to check the possibility of using it as long term recording site. Statistical analyses have shown that using the GSR signals to examine subjects’ emotions was not reliable when using emotional images as stimuli. On the other hand, correlations between the GSR signals from hands and scapula have shown high similarity between them with the correlation value (0.729). However, mild exercise has significantly altered the GSR signal making it difficult to interpret in the presence of stimuli.
Premises and Promises for Expansive Learning in Teacher Education

The study presented in the article focused on school-based development in lower secondary schools in Norway. School-based development represents a new practice not only for school leaders and teachers but also for teacher educators, who should assist schools in their development processes. The study was conducted within the framework of cultural historical activity theory (CHAT). The aim of the professional development project was twofold: to develop teaching practice in schools and also to evolve the teaching in the participating teacher education institutions. The problem formulation for the article is the following: How does teacher educators’ collaboration with schools contribute to learning in their own institutions? The purpose of the study was to find out how the teacher education institutions’ participation influenced the activity within the institutions and what factors could impede or support teacher educators’ actions and learning, and even expansive learning. Expansive learning means that a new collective practice or activity is developed in the institution. To answer the research question, a collective case study was conducted to understand the premises and promises for expansive learning in teacher education. The study found “Organizing of the work at the institutions,” “Teacher educators’ experiences and learning,” “Teacher educators as researchers,” and “Leadership and change” to be central categories that can describe teacher educators’ work and its premises and promises. The study concludes that leadership at the institutions is the main factor that can impede or enhance expansive learning and thus institutional development, and that an interplay between content, culture, and structure is necessary for expansive learning in teacher education.
Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19

We perform a statistical analysis for understanding the effect of the environmental temperature on the exponential growth rate of the cases infected by COVID-19 for US and Italian regions. In particular, we analyze the datasets of regional infected cases, derive the growth rates for regions characterized by readable exponential growth phase in their evolution spread curve and plot them against the environmental temperatures averaged within the same regions, derive the relationship between temperature and growth rate, and evaluate its statistical confidence. The results clearly support the first reported statistically significant relationship of negative correlation between the average environmental temperature and exponential growth rates of the infected cases. The critical temperature, which eliminates the exponential growth, and thus the COVID-19 spread in US regions, is estimated to be Tc = 86.1 ± 4.3 F.
Contribution of 5-HT2 Receptors to the Control of the Spinal Locomotor System in Intact Rats

Applying serotonergic (5-HT) agonists or grafting of fetal serotonergic cells into the spinal cord improves locomotion after spinal cord injury. Little is known about the role of 5-HT receptors in the control of voluntary locomotion, so we administered inverse agonists of 5-HT2 (Cyproheptadine; Cypr), 5-HT2A neutral antagonist (Volinanserin; Volin), 5-HT2C neutral antagonist (SB 242084), and 5-HT2B/2C inverse agonist (SB 206553) receptors intrathecally in intact rats and monitored their effects on unrestrained locomotion. An intrathecal cannula was introduced at the low thoracic level and pushed caudally until the tip reached the L2/L3 or L5/L6 spinal segments. Locomotor performance was evaluated using EMG activity of hindlimb muscles during locomotion on a 2 m long runway. Motoneuron excitability was estimated using EMG recordings during dorsi- and plantar flexion at the ankle. Locomotion was dramatically impaired after the blockage of 5-HT2A receptors. The effect of Cypr was more pronounced than that of Volin since in the L5/L6 rats Cypr (but not Volin) induced significant alteration of the strength of interlimb coordination followed by total paralysis. These agents significantly decreased locomotor EMG amplitude and abolished or substantially decreased stretch reflexes. Blocking 5-HT2B/2C receptors had no effect either on locomotion or reflexes. We suggest that in intact rats serotonin controls timing and amplitude of muscle activity by acting on 5-HT2A receptors on both CPG interneurons and motoneurons, while 5-HT2B/2C receptors are not involved in control of the locomotor pattern in lumbar spinal cord.
Going the Extra (Synaptic) Mile: Excitotoxicity as the Road Toward Neurodegenerative Diseases

Excitotoxicity is a phenomenon that describes the toxic actions of excitatory neurotransmitters, primarily glutamate, where the exacerbated or prolonged activation of glutamate receptors starts a cascade of neurotoxicity that ultimately leads to the loss of neuronal function and cell death. In this process, the shift between normal physiological function and excitotoxicity is largely controlled by astrocytes since they can control the levels of glutamate on the synaptic cleft. This control is achieved through glutamate clearance from the synaptic cleft and its underlying recycling through the glutamate-glutamine cycle. The molecular mechanism that triggers excitotoxicity involves alterations in glutamate and calcium metabolism, dysfunction of glutamate transporters, and malfunction of glutamate receptors, particularly N-methyl-D-aspartic acid receptors (NMDAR). On the other hand, excitotoxicity can be regarded as a consequence of other cellular phenomena, such as mitochondrial dysfunction, physical neuronal damage, and oxidative stress. Regardless, it is known that the excessive activation of NMDAR results in the sustained influx of calcium into neurons and leads to several deleterious consequences, including mitochondrial dysfunction, reactive oxygen species (ROS) overproduction, impairment of calcium buffering, the release of pro-apoptotic factors, among others, that inevitably contribute to neuronal loss. A large body of evidence implicates NMDAR-mediated excitotoxicity as a central mechanism in the pathogenesis of many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD), and epilepsy. In this review article, we explore different causes and consequences of excitotoxicity, discuss the involvement of NMDAR-mediated excitotoxicity and its downstream effects on several neurodegenerative disorders, and identify possible strategies to study new aspects of these diseases that may lead to the discovery of new therapeutic approaches. With the understanding that excitotoxicity is a common denominator in neurodegenerative diseases and other disorders, a new perspective on therapy can be considered, where the targets are not specific symptoms, but the underlying cellular phenomena of the disease.