Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Centralization of Internet Service Infrastructure in the Wild

The last decade has witnessed the rapid evolution of the Internet structure, one of which is centralization, that is, Internet core infrastructure has been constantly transferred into the hands of a few popular market participants. Researchers are trying to measure centrality and analyze its security impact from the perspective of traffic analysis. But the underlying distribution of service providers is still enveloped in mysterious veils. In order to address this problem and assess the security risk associated with such centralization. Firstly, we performed linear regression on the data of each kind of service provider in the Alexa Top 1M domains to study the current underlying distribution of various services for the first time. The results show that Zipf’s law is universal in various service providers’ market share, which proves that Internet service infrastructures are centralized. Secondly, we explored the security impacts of centralized infrastructures on the Internet. we conducted attack simulations on providers. Results show that intentional attacks on core providers can greatly downgrade the performance of the Internet. To make matters worse, the quantitative analysis of the provider’s infrastructures found that a considerable number of provider’s infrastructures have low diversity. In addition, we proposed an algorithm to calculate the dependencies between different types of service providers and carried out an evaluation of our datasets, and found the tendency for different services to depend on each other. Our results indicate that the Internet is facing huge security challenges, because the centralized infrastructure will impair service redundancy, and at the same time, it will also cause dependence between infrastructures, which in turn strengthens its centralization.
A WHOLE DELIVERY MEASURE OF COMPREHENSIVE SOCIAL SERVICE PROVISION

Various forces contribute to increasingly integrated social service delivery. In addition to an economic interest in creative responses that maximize resources, providers incorporate new knowledge to improve outcomes. For example, research on adverse childhood experiences (ACE) calls for movement beyond piecemeal interventions and fragmented services. In fact, research in - tegration has served as an alternative tactic to Integral Theory that engages mainstream provid - ers, policymakers, and researchers to consider comprehensive, whole-person service provision. The Restorative Integral Support (RIS) model, developed at the Committee on the Shelterless (COTS) to respond to ACE and other trauma, involves the intentional development of social networks within which evidence-supported interventions and emerging practices are integrated and brought to re- search. RIS highlights the role of leadership and policies/systems to facilitate social supports mo- bilizing resilience and recovery to enhance service impact. Recognizing that social service practice and theory developments can be advanced by creating and refining research measures, the Whole Delivery Measure (WDM) is offered as a multidimensional scale that captures ingredients involved in comprehensive, whole-person service delivery—leadership, culture, and systemic design. In ad- dition to assessing the extent to which service providers are RISing, a WDM is key to determining re- sults. For example, outcomes research using existing scales that measure only one or two quadrants (e.g., individual behaviors and systemic structures) without taking other quadrants into account is likely to find mixed results on "integration. "
Smart Evacuation Alert System using IoT

Fire protection is critical. In both residences and industries, a system for protecting people from leaking gas and fire is critical. The majority of existing fire safety systems is an effort by signalling with an alarm or words. This does not draw the attention of people who are not present at the scene of the incident. As a result, a smart evacuation alert system capable of overcoming this limitation is required. The proposed work performs multiple tasks which are detecting the flames, controlling the temperature, detecting the smoke or gas. The proposed work deals with a flame sensor that detects flame in the room show the signal through the relay, the MQ-2 gas sensor it detects the smoke in the room shows the signal through the relay, LM-35 temperature sensor it continuously monitors the temperature of the room by indicating through the relay. Every sensor has an individual bulb so if there is any problem, the bulb will automatically switch on with the help of a relay and immediately and the problem can be rectified which occurred respectively in the room. Arduino Board is used as the control circuit in this project. Relay acts as a switch for each sensor. Every sensor has its own respective function. Using the Arduino Board and all the sensors the work is completed and tested successfully. This is based on interfacing each sensor to the Arduino and making it useful to the society.
Automated Deep Learning based Disease Prediction Using Skin Health Records: Issues, Challenges and Future Directions

Due to recent breakthroughs in Artificial Intelligence (AI) and automation in the healthcare industry, people have been able to see an increased possibility for merging deep learning with healthcare. Automated deep learning and healthcare integration have been found as a feasible technique for enhancing disease prediction accuracy, which will aid in disease prevention and control. AI-enabled skin health data could be used as a unique technique for preliminary body health prediction by matching symptoms and influenced skin photos to a pool of current disease databases. The goal of this research is to offer an automated deep learning framework for predicting illness outcomes based on changes in skin characteristics. Various issues and challenges are also investigated, with the purpose of anticipating the integration of deep learning into healthcare in future medical research.
Exact component parameter agnostic QoT estimation using spectral data-driven LSTM in optical networks

We propose the use of spectral data-driven LSTM-based machine learning to improve generalized signal-to-noise ratio (gSNR) quality-of-transmission estimation in component parameter-agnostic network scenarios. We show gSNR estimation improvements up to 1.1 dB for unseen networks. © 2021 The Author(s)
A Survey on Autonomous Damage Detection on Aircraft Surfaces using Deep Learning Models

The employment of contemporary technology in aircraft maintenance and breakdown operations has increased in the aviation sector in recent years, as the application area of artificial intelligence has expanded. Surface faults produced by dent, corrosion and cracks, and stains from oil spills, grease, and soil deposits, are all detected during an aircraft surface examination. The traditional way for evaluating aircraft skin is human visual inspection, which is time-consuming and wasteful, but robots with onboard vision systems can scan the aircraft’s skin safely, quickly, and correctly. In aviation maintenance, deep learning can be utilized to automate visual exams. This can help improve damage detection accuracy, save aircraft downtime, and prevent Inspection errors. This work uses deep learning to investigate the visual detectability of various types of in-service damage in laminated composite aviation structures, based on a review of more than five studies. The application of deep learning-based visual inspection systems to aviation maintenance and repair is examined. This discovery will facilitate future research and development in the field of autonomous visual inspection of composite structures, potentially lowering costs, eliminating health and safety concerns, and reducing downtime associated with integrity assessment.
Boundary Element Method for Fast Solution of Acoustic Problems: Active and Passive Noise Control

This thesis presents boundary element formulations for three-dimensional acoustic problems of active (ANC) and passive (PNC) noise control. A new boundary element strategy, referred to as RABEM (Rapid Acoustic Boundary Element Method), has been formulated and implemented for acoustic problems. The assembly time for both the system matrix and the right hand side vector is accelerated using a Hierarchical-matrix approach based on the Adaptive Cross Approximation (ACA). Two di erent H-matrix-GMRES solvers (one without preconditioners and one with a block diagonal preconditioner) are developed and tested for low and high frequency problems including noise emanated by aircraft approaching an airport. A new formulation for solving the ANC based on attenuating the unwanted sound in a control volume (CV) rather than cancelling it at a single point is presented. The noise attenuation is obtained by minimising the square modules of two acoustic quantities the potential and one component of the particle velocity within the CV. The two formulations presented include a single and a double secondary source, respectively. Several examples are presented to demonstrate the e ciency of the proposed technique. A new approach, based on sensitivity analysis, for determining the optimum locations of the CV and the optimum location/orientation of the secondary source is presented. The optimisation procedure is based upon a rst order method and minimises a suitable cost function by using its gradient. The procedure to calculate the cost function gradients is explained in detail. Finally, a PNC strategy applied to the interior of an aircraft cabin is investigated. A lower noise level is achieved through the introduction of a new textile with a higher noise absorbing coe cient than a conventional textile, especially at low frequencies. The so-called bubble concept , which consists of adding cap insertions at the sides of the passenger head, is also investigated.
Online Supervision and Smart Punch Card System in Campus Based on Fingerprint Recognition

Online supervision and smart punch card system in campus based on fingerprint recognition is designed and also implemented in this manuscript. Fingerprint matching is usually based on minutiae matching. This article has conducted an indepth study on the minutiae problem. A set of the fingerprint minutiae features are extracted from the refined fingerprint point-line map. Biological characteristics are not easy to copy, and the biometric recognition technology has some of strong anti-counterfeiting and anti-counterfeiting capabilities. Considering these features, this paper proposes the novel methodology of the system to achieve the online supervision goal. The proposed system is implemented with the hardware model and the performance is tested which obtained the nice results.
Dimensionality Reduction Procedure for Bigdata in Machine Learning Techniques

In the present field of software applications, the prominently employed parameters for parameters control are the kinds of models such as cloud computing, machine learning, and big data analytics. So, in the current scenario, these are in high demand and are on-line with the trends for future decades as well. Nevertheless, as mentioned earlier, these models can access very low data and process speed. It is well known that the storage equipment’s for day-to-day monitoring serves at a higher cost and has hardware complexity, further leading towards rapid increment in dimensionality. Therefore, for the higher rate of dimensional data, the optimization approach of any variety would consume time to a greater extent. The concern issues are mostly related to the dimensionality with high data space instead of the low data space. A dimensional dropped approach is proposed in this paper in combinational with the Logistic regression (L.R.) version. The proposed technique is well known and applicable for the problems of clustering and dimension reduction. The size of the dimensional data to the LRML method has diminished, and the efficiency achieved at the rate of 95.5% and the reduction ratio is 34.89%.
Sign Language Recognition and Translation into Pashto Language Alphabets

Sign language is mandatory and mostly used by huge number of individuals (particularly disabled) in society and it is another way of communication; The form of the hand, the contour of the movement, and the posture of the hand, face, and body parts are all distinct in each sign language; as a result, visual sign language identification is a challenging area of computer vision research; various academics have suggested several models in recent years that have been greatly improved using deep learning approaches. This study looked at the vision-based sign language recognition models suggested utilizing deep learning approaches in the last five years. Although the suggested model’s general trend reveals that sign language recognition accuracy has substantially increased, specific problems still have to be overcome. There are various and multiple languages in the world, people from different languages, have their alphabets and sign to communicate with each other. Many language speakers have developed a way to communicate with disabled people like Indian sign Language, French sign language several more; In this research paper, a novel Pashto sign language has been worked on. The most spoken language in southern Afghanistan is the Pashto language, and it has its grammar and history; it has approximately 40 million population in the world. The aim is to make a system by which disabled people of this language can communicate with the society. The system will take the signs and gestures of disabled people to change them into the Alphabet of Pashto language. 2500 dataset images are prepared and CNN model i s a ppl i e d to recognize the hand gestures and have executed the model with an accuracy of 98%.
Automation Detection of Malware and Stenographical Content using Machine Learning

In recent times, the occurrence of malware attacks are increasing at an unprecedented rate. Particularly, the image-based malware attacks are spreading worldwide and many people get harmful malware-based images through the technique called steganography. In the existing system, only open malware and files from the internet can be identified. However, the image-based malware cannot be identified and detected. As a result, so many phishers make use of this technique and exploit the target. Social media platforms would be totally harmful to the users. To avoid these difficulties, Machine learning can be implemented to find the steganographic malware images (contents). The proposed methodology performs an automatic detection of malware and steganographic content by using Machine Learning. Steganography is used to hide messages from apparently innocuous media (e.g., images), and steganalysis is the approach used for detecting this malware. This research work proposes a machine learning (ML) approach to perform steganalysis. In the existing system, only open malware and files from the internet are identified but in the recent times many people get harmful malware-based images through the technique called steganography. Social media platforms would be totally harmful to the users. To avoid these difficulties, the proposed Machine learning has been developed to appropriately detect the steganographic malware images (contents). Father, the steganalysis method using machine learning has been developed for performing logistic classification. By using this, the users can avoid sharing the malware images in social media platforms like WhatsApp, Facebook without downloading it. It can be also used in all the photo-sharing sites such as google photos.
Epileptic Seizure Detection using Two-Layer Feature Extraction and Hyper-Parameter Optimization

Epileptic seizures happen owing to anarchy in intellect functionality that can influence patient's physical condition. Finding of epileptic seizures inception is fairly valuable for medication and emergency alerts. Machine learning techniques and computational methods play a key part in detecting epileptic seizures from Electroencephalograms (EEG) signals. The main objective of this work is to provide an ANN framework with optimized performance related to seizure detection. Here, a machine learning framework is employed for seizure detection where the two-layer feature extraction with ANN classifiers are used to categorize seizure and non-seizure data. To get better performance, the best parameters related to ANN with the dataset are identified through bayes-optimization method. This model affords a trustworthy feature extraction and optimization for training a detection model. The proposed model is evaluated using the popular public dataset CHB-MIT.
FPGA based Silicon on Chip using Flexible Digital Pre-Distortion for Software Defined Radio Transceiver

Reconfigurable FPGA based silicon on chip is designed using flexible digital pre-distortion for software defined radio transceiver in this proposed system. In the Silicon on Chip (SoC) platform the Flexible Digital Pre-distortion (FDP) is implemented along with the generation of real time baseband signal. The model named recollection multinomial constructed FDP explanation is realized for linearization of Radio Frequency (RF) Power Amplifiers (PA) here which makes system more cost effective and dynamic. On basis of the gain and phase coefficients of the Look Up Tables (LUT) the power amplifier behavior is identified. The non-linear coefficients are extracted using memory polynomial model. Therefore the non-delayed normalized mean square error can be achieved through the calculation of system performance and the LUT coefficients are updated. Finally the pre-distorted signal is generated for software defined radio transceiver.
SecQSA: Secure Sampling-Based Quantile Summary Aggregation in Wireless Sensor Networks

Wireless sensor networks are widely expected to play a key role in the emerging Internet of Things (IoT)-based smart cities in which a large number of resource-constrained sensor nodes collect data about our physical environment to assist intelligent decision making. Since blindly forwarding all the sensed data to the base station may quickly deplete sensor nodes’ limited energy, secure data aggregation has been considered as a key functionality in wireless sensor networks that allow the base station to acquire important statistics about the sensed data. While many secure data aggregation schemes have been proposed in the literature, most of them target simple statistics such as Sum, Count, Min/Max, and Median. In contrast, a quantile summary allows a base station to extract the $\phi-$ quantile for any 0 < $\phi$ < 1 of all the sensor readings in the network and can provide a more accurate characterization of the data distribution. How to realize secure quantile summary aggregation in wireless sensor networks remains an open challenge. In this paper, we fill this void by first evaluating the impact of a range of attacks on quantile summary aggregation using simulation and then introduce a novel secure quantile summary aggregation protocol for wireless sensor networks. Detailed simulation studies confirm the efficacy and efficiency of the proposed protocol.
Efficient Thyroid Disease Prediction using Features Selection and Meta-Classifiers

Prediction of Thyroid is a complex axiom in medical research. Machine learning methods are more powerful and compact for the healthcare industry to handle the massive amount of healthcare records. The methods in machine learning provides the facility to use different kind of data values which are used for prediction. Data cleaning techniques are used for enhancing the dataset to provide accurate results. The noisy and missing values are handled using data pre-processing methods. In this work, Adaboost and Bagging techniques are used for thyroid classification. The methods are executed, and the results are compared to show the effective method for thyroid prediction.
Role of Computational Intelligence Techniques in Diagnosing Alzheimer’s disease at Early Stages: A Systematic Literature Review

Computer aided disease diagnosis with computational intelligence research has shown that several intelligent models have been developed to assess medical data and make assumptions about illness severity and early diagnosis. Many researchers find it difficult to anticipate and diagnose the deadly neurodegenerative disorders that fall under the category of dementia using medical image data. Alzhiemers is now the most common dementia, affecting around a third of all people. Many machine learning models have been developed, but there is still a lot of room for further improvement in the field of Alzheimer's disease detection and prevention. This article includes a thorough literature evaluation of machine learning approaches that have been developed to detect Alzheimer's disease at an earlier stage. As a result of these studies, budding Alzheimer's disease researchers will be able to better understand the extent of study in forecasting the illness via the use of artificial neural networks, support vector machines, and deep learning-based ensemble models.
HSCAD:Heart Sound Classification for Accurate Diagnosis using Machine Learning and MATLAB

With the rise in technology AI (Artificial Intelligence) is being used in healthcare at a wider level. Also, for health care automation deep learning and medical image processing is being proven as a boon for capturing and tracking accurate patient record and disease prediction. Heart sounds play a significant role in screening for heart disease. Because the signal-to-noise ratio (SNR) is low; it is a problem that separates experts and takes time to estimate correct heart waves. Also, ECG verification is being done prominently for every kind of disease and abnormality, which is why monitoring accurate ECG and heart rate is very essential in proper diagnosis. Distinguishing the purpose of the heart is therefore essential. Here in the study, we combined conventional feature engineering methods with deep learning to automatically distinguish between normal and abnormal heart sounds. We proposed a one-dimensional convolution neural network (CNN) model that divides the ECG into abnormal heart sounds. We have used multilayer perceptron (MLP) for the classification of heart waves. Differentiation of heart sounds is implemented using the MATLAB framework.
Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid‐19 epidemic in British local authorities

The spatio-temporal pattern of Covid-19 infections, as for most infectious disease epidemics, is highly heterogenous as a consequence of local variations in risk factors and exposures. Consequently, the widely quoted national-level estimates of reproduction numbers are of limited value in guiding local interventions and monitoring their effectiveness. It is crucial for national and local policy-makers, and for health protection teams, that accurate, well-calibrated and timely predictions of Covid-19 incidences and transmission rates are available at fine spatial scales. Obtaining such estimates is challenging, not least due to the prevalence of asymptomatic Covid-19 transmissions, as well as difficulties of obtaining high-resolution and high-frequency data. In addition, low case counts at a local level further confounds the inference for Covid-19 transmission rates, adding unwelcome uncertainty. In this paper we develop a hierarchical Bayesian method for inference of transmission rates at fine spatial scales. Our model incorporates both temporal and spatial dependencies of local transmission rates in order to share statistical strength and reduce uncertainty. It also incorporates