Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Characterization of High Temperature Black Body source in Microwave-Millimeter wave Spectrum for radiometric calibration

Electron Cyclotron Emission (ECE) radiometer diagnostics are broadband receivers that are widely used as standard microwave diagnostics for measurement of localized thermal plasma temperature profiles. Calibration of these diagnostics is usually done using high temperature sources that possess blackbody like properties. A silicon carbide based high temperature blackbody source is used to calibrate the E-band (60-90 GHz) radiometers at Institute for Plasma Research (IPR). To understand the characteristics of the designed silicon carbide calibration source in the microwave range, Sparameters are determined using ABmm wave VNA. This manuscript discusses the results obtained from the characterization over a wide frequency range starting from Kaband (26-40GHz) to D-band (140-160GHz) satisfying the varied frequency requirements from the tokamak experimentation end. Linearity test of the designed source is obtained using existing multi-channel radiometer that lies within acceptable limits while the temperature distribution characteristics are observed using an Infrared (IR) Camera. Experimental characterizations of black body source exhibit high absorption capabilities of $\gt60$ dB in $\gt50$ GHz frequency range with a reflection coefficient of $\leq$20dB.
A Reliability-based Strategy for Active Distribution Network Expansion Planning

The increase in the demand of the electrical loads is bringing challenges to power system reliability issues. Dealing with this, Distribution Network Expansion Planning (DNEP) is an approach used in Distribution Network (DN) to guarantee its reliability-related features. Another key solution for electricity provision is that the conventional distribution networks should be replaced by Active Distribution Networks (ADN) with Distributed Generations (DGs). The presence of these generation systems besides the DN, can change the rules of calculation of the reliability. This paper presents a DNEP-based framework while proposing a novel approach to identify the Expected Energy Not Supplied (EENS) in the ADN. A Mixed Integer Linear Programming (MILP) Framework is used to capture the optimal solution as a convex method. To guarantee the effectiveness of the presented approach, the IEEE 18 buses test case is used. The results verify that this model is cost-effective and can decrease the complexity of the calculation.
Evaluation of XAI Algorithms in IoT Traffic Anomaly Detection

Anomaly detection in network traffic, both in general computer networks and specifically in Internet of Things (IoT) networks, plays a crucial role in ensuring computer network security. Over the years, numerous machine learning and deep learning-based anomaly detection tools have been proposed, exhibiting high accuracy in identifying anomalous behavior. However, a significant challenge arises with most machine learning and deep learning algorithms, as they are often considered black-box models that lack interpretability. Consequently, explaining the reasons behind certain network behaviors being labeled as anomalous becomes a difficult task. To overcome this issue, we evaluate the combination of anomaly detectors and eXplainable Artificial Intelligence (XAI) algorithms in IoT traffic anomaly detection. Our research results demonstrate that XAI algorithms can consistently identify the most impactful network features of security anomalies. More specifically, (1) SHAP algorithm is the most robust and reliable in the four tested XAI algorithms for four types of supervised/unsupervised anomaly detection models, independent of two datasets including different anomalies. (2) Image-based XAI algorithms are not suitable for explainability of network anomaly detection.
Deep Learning Technique to Detect and Diagnose the Anomalous in Kidney

Several diseases that have had a impact on our society is kidney abnormalities, which rank as such ninth highest prevalent malignancy both in men and women globally. Premature KT identification has substantial advantages in terms of decreasing mortality rates, generating precautionary measures that lessen consequences, and eradicating the tumor. Deep learning (DL) automatic recognition techniques can speed up diagnostic, increase test precision, lower performance expenditures, and lighten the stress of physicians as contrasted to the laborious and time-consuming conventional diagnostic testing. This study established a deep ensemble model for identifying and categorizing different types of kidney ailments, including cyst, stone, tumor, and normal using computer tomography photographs. In addition, while executing layers in a neural network, the testing accuracy of the deep based pre-trained model reached a higher accuracy of 97.8% with 0.1 ms loss. With hybrid and other algorithms without normalizing pictures, several studies looked at how well 95.7% of kidney problems could be identified. However, the primary goal of our effort was to normalize the pictures in order to greatly increase computing efficiency and convergence speed.
Innovative Method of Predicting Crime Rate by CNN Model Compared with Naive Bayes Algorithm to Increase Accuracy

The objective is precisely determine rate of crime utilizing an enhanced methodology using innovative CNN model in comparison to NB. There are two groups made up of a total of 20 samples. Ten samples each were given. Group 1 is a member of the CNN algorithm, whereas Group 2 is a member of the Naive Bayes algorithm. Naive Bayes' accuracy (84%) is less than CNN's (86%).
Unsupervised Monocular Visual Localization Algorithm Based on Feature Enhancement Learning

Aiming at the problems of dynamic target interference and occlusion in complex scenes of monocular visual localization system, a feature-enhanced visual localization algorithm based on attention mechanism is proposed. The image depth information and pose information are fused by the depth estimation network and the pose estimation network, and the tightly coupled training of the two-branch network is realized by using the view reconstruction loss. In order to further improve the feature representation ability of the network, the occlusion mask module and the attention mechanism module are respectively integrated, the binary mask is constructed using the depth information of adjacent images and the photometric loss is optimized, and the saliency of key features is improved by using channel attention and spatial attention. The experimental results show that the algorithm can effectively improve the feature representation ability of the network, alleviate the problem of scale error accumulation under long-term sequence tracking, and obtain higher localization accuracy.
ChatGPT for Visually Impaired and Blind

According to the World Health Organization (WHO), hundreds of million people have some type of visual disability. Vision impairment has a personal impact with lifelong consequences because more than 80 % of our perception, cognition, learning, and daily activities are mediated through vision. Moreover, in the era of rapid advancements in artificial intelligence (AI), visually impaired and blind people face challenges at work and in education because of inaccessibility to AI technologies. In this regard, we present an assistive mobile application with an intuitive user interface (UI) for visually impaired and blind people to interact with ChatGPT via natural conversation. The app employs automatic speech recognition (ASR), text-to-speech (TTS), keyword spotting (KWS), voice activity detection (VAD), and a convenient UI to interact with ChatGPT effortlessly. We have made the source code, pre-trained models, and VI publicly available at https://github.com/IS2AI/talk-llm to stimulate the development of assistive mobile applications.
Detecting Backdoors Embedded in Ensembles

Machine learning has experienced significant growth in recent decades, and ensemble learning, a powerful technique within this field, has also shown notable progress. However, as the adoption of machine learning grows, security concerns have emerged, with backdoor attacks (a.k.a., Trojan attacks) being a prominent example. While several studies have investigated backdoor attacks in various domains, including neural networks, transfer learning, and federated learning, research on backdoor attacks specifically targeting ensemble learning remains scarce, despite the heightened vulnerability. In this paper, we propose the first detection method designed to combat backdoor attacks in ensemble learning. Our focus lies on the modification attack, a potent and easily implemented technique achieved through the injection of a trigger into the training dataset. To counter such attacks, we leverage a meticulously designed test ensemble and analyze the magnitude of feature vectors to discern the benign nature of input models. Our approach effectively overcomes the limitations encountered by existing defenses when confronting backdoor attacks in ensemble learning, such as the dependence on clean datasets used for training the input models and practical cost considerations. We demonstrate that our scheme simultaneously achieves the aforementioned objectives and exhibits robust performance against advanced attacks.
Relaxed Gabriel Graphs

We study a new family of geometric graphs that interpolate between the Delaunay triangulation and the Gabriel graph. These graphs share many properties with skeletons for 2 [0,1] (such as sublinear spanning ratio) with the added benefit of planarity (and consequently linear size and local routability). A geometric graph is a finite graph whose vertices are points in the plane and whose edges are represented by straight line segments between their endpoints. We consider here a class of geometric graphs called proximity graphs [10]. Two points p and q in the vertex set S are deemed “close” to each other if some neighborhood of the segment pq is empty of other points in S. The corresponding proximity graph contains an edge between every such close pair of points. In this paper we study three well-known families of proximity graphs: Gabriel graphs, Delaunay triangulations, and -skeletons and combine the interesting aspects of each. The Gabriel graph [9] is a proximity graph where two vertices p and q are joined by an edge if and only if the disk with diameter pq has no other points of S in its interior. If the empty circle instead merely has to pass through p and q, and not necessarily have pq as its diameter, then the resulting graph is the Delaunay triangulation. The Delaunay triangulation was introduced by Delaunay in 1934 [6] and has been studied extensively to this day. (See [13] for a survey.) Finally, the -skeleton is a well-known proximity graph where the shape and size of the region that needs to be empty
Traffic Sign Board Prediction Using Ensemble Model of YOLOv8 And Detectron2

Road safety is greatly enhanced and smart decision-making for self-driving cars and intelligent modes of transportation is made possible by the detection and classification of traffic signs. In order to attain cutting-edge performance, this study suggests an ensemble model for traffic sign board prediction that combines YOLOv8 with Detectron2. While Detectron2 offers accurate object boundaries using instance segmentation, YOLOv8 is renowned for its quickness and precision in object identification. The ensemble technique improves the overall accuracy and resilience of the traffic sign board prediction system by taking advantage of the complimentary characteristics of both models. Benchmark datasets like the Belgian Traffic Sign Dataset and the German Traffic Sign Recognition Benchmark are the subject of extensive experiments. The YOLOv8 and Detectron2 individual models are outperformed by the ensemble model about of accuracy, precision, recall, and F1 score. In addition, the ensemble model performs better in difficult settings like occlusions and various lighting levels. The results demonstrate the efficiency of ensemble models for traffic sign board prediction as well as their potential to improve both intelligent transportation systems and road safety.
3D Twist and Tilt Bipedal Model and Control Realization for Sit to Stand

The field of biomechanics for human voluntary motions in three dimensions encompasses a wide array of applications. The process of transitioning from a seated position to a standing position, referred to as sit-to-stand, is a basic motion that is frequently executed during various everyday tasks. The current investigation centers on enhancing 3D bipedal locomotion through the creation of a novel system utilizing a Twist and Tilt scheme to perform sit-to-stand (STS) movements. This mechanism, in contrast with previous models, does not consider any foot fixed for the whole profile. This type enables one foot to perform a twisting movement and the other foot to perform a tilting movement. The tilt foot schematic is built with two distinct schemes: one involving sliding tilt, the other with rotating tilt. This scheme is designed to study STS issues faced by neurologically deficient human beings while carrying out different joint positions. The twist and tilt models are designed using computer-aided design (CAD) software. They consist of an 8-part bipedal structure, which includes two feet, two calves / shanks, two thighs, one pelvis, and one HAT section. Both of these twist and tilt models are first produced in SOLIDWORKS Corp software. Subsequently, to facilitate control implementation, these models undergo linearization using SIMULINK / SIMSCAPE and MATLAB. By employing the analysis of Controllability and Observability, it is revealed that the design, which incorporates one foot linear tilt, consists of twelve states. Furthermore, it is determined that one foot linear model has full rank controllability and observability and is suitable to design controller.
A WEARABLE NEURAL INTERFACE FOR REAL TIME TRANSLATION OF SPANISH DEAF SIGN LANGUAGE TO VOICE AND WRITING

This paper describes a work related to the design and implementation of a communication tool for persons with speech and hearing disabilities. This tool provides to the user a Human-Computer interface capable of the capture and recognition of gestures belonging to the Mexican Spanish Sign Alphabet. To capture the manual expressions, a data-glove constructed to sense the position of fifteen articulations of one of the user’s hand is described. A location system that detects the position and movements of the hand with respect to the user’s body is also constructed. The data-glove and location system signals are processed by a pair of programmable automatons. The automaton’s outputs are sent to a personal computer that realizes the gesture recognition and interpretation tasks. Artificial neural network techniques are utilized to implement the mappings of the space of information generated by the instruments to the interpretation space, where the representation of the gestures are found. Once a gesture is captured and interpreted, it is presented in written form through a screen mounted in the clothes of the user, and in verbal form by a speaker. RESUMEN
Effect of repeated freeze–thaw cycles on geographically different populations of the freeze-tolerant worm Enchytraeus albidus (Oligochaeta)

Freeze-tolerant organisms survive internal ice formation; however, the adaptations to repeated freeze–thaw cycles are often not well investigated. Here we report how three geographically different populations of Enchytraeus albidus (Germany, Iceland and Svalbard) respond to three temperature treatments – constant thawed (0°C), constant freezing (−5°C) and fluctuating temperature (0 to −5°C) – over a period of 42 days. Survival varied between treatments and populations such that enchytraeids from arctic locations had a higher survival following prolonged freeze periods compared with temperate populations. However, enchytraeids from temperate locations had the same survival rate as arctic populations when exposed to repeated freeze–thaw events. Across all populations, metabolic rate decreased markedly in frozen animals (−5°C) compared with thawed controls (0°C). This decrease is likely due to the lower temperature of frozen animals, but also to the transition to the frozen state per se. Animals exposed to repeated freeze–thaw events had an intermediate metabolic rate and freeze–thaw events were not associated with pronounced excess energetic costs. Overwintering under either condition was not associated with a decrease in lipid content; however, during exposure to constant freezing and repeated freeze–thaw events there was a noticeable decrease in carbohydrate stores over time. Thus, animals exposed to constant freezing showed a decrease in glycogen stores, while both glucose and glycogen content decreased over time when the organisms were exposed to repeated freezing. The results therefore suggest that carbohydrate resources are important as a fuel for E. albidus during freezing whereas lipid resources are of marginal importance.
Performance analysis of Micro-blowers for the design of Micro Automatic Positive Airway Pressure System for Obstructive Sleep Apnea Treatment

Obstructive Sleep Apnea (OSA) is a condition, in which during the sleep the upper airway is obstructed due to the throat muscles relaxation. The conventional systems used for treatment of OSA are Continuous positive airway pressure (CPAP) and Automatic positive airway pressure (APAP) devices which deliver required air pressure to open the airway obstruction. The major drawback of these systems is the presence of tubing that tangles around the patient’s neck causing discomfort and curbs the freedom of sleep position during sleep. This research work aims at analysing and selecting Micro-Electro-Mechanical System (MEMS) based micro-blowers for the design of an APAP system. It is planned to design a hoseless APAP system which embeds the APAP system within a face mask. Five different micro blowers such as Murata MZB1001T02, Delta BSB01703HA300CAJ, TF029B-1000F, TF037F-2000F, Sunon UB5U3-700 have been identified based on weight, flow rate, pressure and operating voltage. The performance metrics such as air pressure, flow specifications, current and power consumption of these 5 different micro-blowers are analysed for the effective use in the hoseless APAP system. Two micro-blowers namely, TF029B-1000F and UB5U3-700 were found to deliver the required pressure and air flow as per the conventional APAP systems. TF029B micro-blowers were used for the design of hoseless APAP device as it weighs less compared to TF037F micro-blower. This hoseless APAP device does not disturb the patients when changing their sleep position. It is acting as a user comfortable sleep aiding device compared to existing CPAP/APAP devices available in the market.
Evidence on the occupational segregation of older people with disabilities in Europe

Purpose. To analyse the existence of occupational segregation between older people without and with disabilities in an European context. Particular attention is paid to the positive relationship between female participation and occupational segregation among older workers with disabilities. Method. Using data from the first two waves (2004 and 2007) of the Survey of Health, Ageing and Retirement in Europe, we compare the distribution of workers (disabled vs. non-disabled) across occupations for three different European regions in order to identify the percentage of older workers with disabilities who are concentrated in certain occupations and underrepresented in others. Also, we calculate the Duncan and Duncan Index of Dissimilarity to measure whether there is occupational segregation between disabled and non-disabled workers and the trend that European regions have followed during the last years. Results. Comparing occupational distribution for European regions, we can identify a similar distribution in the Scandinavia and Central area, wherein there is an overrepresentation in favour of non-disabled workers in the most qualified occupation, while disabled workers are overrepresentated in services workers and elementary occupations. In Mediterranean area, disabled workers are overrepresentated in technicians and also in elementary occupations. In terms of occupational segregation the results show that older workers with disabilities suffer this problem someway in all European regions, especially in the Mediterranean countries wherein the Duncan and Duncan Index of Dissimilarity is higher. Nevertheless, the gap detected in the Duncan and Duncan Index of Dissimilarity between non-disabled and disabled older workers has reduced between 2004 and 2007. Conclusions. The analysis of the occupational segregation among older people with disabilities across Europe becomes an important area of further research and potential policy implications. This knowledge can help policy makers and government to improve their labour conditions, social integration and levels of well-being.
Variations in the Pattern of Pubertal Changes in Boys

Mixed longitudinal data on the physical changes at puberty in 228 normal boys are presented together with normal standards for stages of genital and pubic hair development. The genitalia began to develop between the ages 9½ years and 13½ years in 95% of boys (mean = 11.6 ± 0.09) and reached maturity at ages varying between 13 and 17 (mean = 14.9 ± 1.10). The age at which pubic hair first appeared was not accurately determined, but its development through the later stages was studied. It reached the equivalent of an adult female distribution at a mean age of 15.2 ± 0.01 years. On average the genitalia reached the adult stage 3.0 years after they first began to develop; but some boys completed this development in as little as 1.8 years while others took as much as 4.7 years. Some boys complete the whole process in less time than others take to go from Stage G2 to Stage G3. The genitalia begin to develop before pubic hair is visible in photographs in practically all boys. The 41 boys in whom it could be studied reached their maximum rate of growth (peak height velocity) at a mean age of 14.1 ± 0.14 years. Very few boys (about 5%) reached peak height velocity before their genitalia were in Stage 4 and over 20% did not do so until their genitalia were adult. Peak height velocity is reached, on the average, nearly 2 years later in boys than in girls, but the boys' genitalia begin to develop only about 6 months later than the girls' breasts. Pubic hair appears about 1½ years later in boys than in girls.