Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable Importance Analysis in Imbalanced Datasets: A New Approach

Decision-making using machine learning requires a deep understanding of the model under analysis. Variable importance analysis provides the tools to assess the importance of input variables when dealing with complex interactions, making the machine learning model more interpretable and computationally more efficient. In classification problems with imbalanced datasets, this task is even more challenging. In this article, we present two variable importance techniques, a nonparametric solution, called <inline-formula> <tex-math notation="LaTeX">$mh$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula>, and a parametric method based on Global Sensitivity Analysis. The <inline-formula> <tex-math notation="LaTeX">$mh$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> employs a multivariate continuous response framework to deal with the multiclass classification problem. Based on the permutation importance framework, the proposed <inline-formula> <tex-math notation="LaTeX">$mh$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> algorithm captures the dissimilarities between the distribution of misclassification errors generated by the base learner, Conditional Inference Tree, before and after permuting the values of the input variable under analysis. The GSA solution is based on the Covariance decomposition methodology for multivariate output models. Both solutions will be assessed in a comparative study of several Random Forest-based techniques with emphasis in the multiclass classification problem with different imbalanced scenarios. We apply the proposed techniques in two real application cases in order first, to quantify the importance of the 35 companies listed in the Spanish market index IBEX35 on the economic, political and social uncertainties reflected in economic newspapers in Spain during the first quadrimester of 2020 due to the COVID-19 pandemic and second, to assess the impact of energy factors on the occurrence of spike prices on the Spanish electricity market.
Adaptive synchronization for the heterogeneous network of Hindmarsh-Rose neurons

This work is primarily aimed at studying the reasons for the formation of the synchronous regimes of functioning of neural populations in the central nervous system of human or animal. It is well known that the synchronization of large number of the neurons in the central nervous system plays a key role in the formation of the brain waves. It was also shown that the variety of diseases in the central nervous system are directly related to abnormal synchronization of some neural populations. Nowadays, methods based on the use of electrical impulses to suppress pathological synchronization in certain areas of the brain are being actively applied in the therapy of these diseases. However, such methods are still at an early stage of development and require a qualitative mathematical description, which determines the relevance of the study.
Ensemble Learning Based Robust Cooperative Sensing in Full-Duplex Cognitive Radio Networks

We propose an ensemble learning (EL) based cooperative sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. The FD communication further improves the spectrum awareness capability of the secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. In the meantime, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification attacks also degrade the cooperative sensing performance in practice. To alleviate the influence of interference and attacks, we design an EL framework that provides robust and accurate fusion performance with low time cost. In such a context, we analyse the spectrum waste and collision probabilities in the FD Listen-And-Talk (LAT) protocol to measure the performance. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of much lower and stable spectrum waste and collision probability for any number of trusted SUs.
Évaluation pronostique des anomalies du chromosome 1 dans le myélome multiple cohorte du CHRU de Brest de 2012 à 2017

Contexte : la valeur pronostique pejorative des anomalies du chromosome 1 dans le myelome multiple, notamment l'amplification du gene CKS1B en 1q21 et la deletion du gene CDKN2C en 1p32, est retrouvee dans la plupart des etudes, mais pas toutes. But : nous avons etudie en mono centrique la valeur pronostique, en survie sans progression et en survie globale, des deux anomalies susmentionnees et d’un critere composite, le ratio CKS1B/CDKN2C. Materiel et methodes : l’etude a porte sur une cohorte de 104 patients du CHRU de Brest, dont 48 eligibles a la greffe, pour lesquels etaient disponibles des donnees de FISH permettant de calculer le ration CKS1B/CDKN2C a partir du nombre de copies de chacun des deux genes. Resultats : les patients greffes voient leur survie sans progression impactee defavorablement par un âge superieur a 65 ans (p=0,005), les gains de CKS1B (p=0,001), un ratio CKS1B/CDKN2C ≥1,5 (p=0,006) et un statut ISS 3 (p=0,019). Leur survie globale etait significativement diminuee quand l'âge etait > 65 ans (p=0,044), le ratio CKS1B/CDKN2C ≥ 1,5 (p=0,049) et quand le myelome etait non secretant (p=0,018). Pour les sujets non eligibles a une intensification therapeutique, les facteurs pronostiques influencant negativement la survie sans progression etaient un taux de plaquettes inferieur a 150 G/L (p=0,020) et un ratio CKS1B/CDKN2C ≥ 3 (p=0,031). La survie globale etait influencee de facon defavorable par un taux de plasmocytes medullaires ≥ 60 % (p=0,018), le fait que le myelome soit a IgD (p=0,018) et un ratio CKS1B/CDKN2C ≥ 3 (p=0,026). Conclusion : les resultats de notre etude sont novateurs car il s'agit de la premiere etude ou les modifications chromosomiques du 1 sont evaluees sous la forme d'un ratio et surtout avec une significativite en termes de survie, qu'elle soit sans progression ou globale. Il est interessant de noter que ce ratio peut s’averer utile en pratique clinique, et que, si il etait valide, il pourrait servir a guider le choix de traitements adaptes au risque.
UD-based Linear Filtering for Discrete-Time Systems with Multiplicative and Additive Noises*

This paper addresses the problem of constructing new linear filtering algorithms for discrete-time linear stochastic systems with multiplicative and additive noises, which are robust to machine roundoff errors. Two algorithms based on UDUT decomposition of covariance and information matrices are proposed. The first of these algorithms is the UD-based covariance filter, and the second one is the UD-based information filter. Both of these filters have an extended array form, and their computational schemes allow for updating all required filter quantities with the use of the numerically stable MWGS orthogonalization procedure. The results of numerical experiments show the robustness of the proposed algorithms against roundoff errors.
Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as airflow and electrocardiogram. However, the reduction of airflow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classifiers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classifiers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.
Exploiting Channel Sparsity for Beam Alignment in mmWave Systems via Exponential Learning

The large available spectrum in the millimeter wave (mmWave) band represents an attractive alternative for the congested sub-6 GHz spectrum. To overcome the difficult propagation conditions at high frequencies, directional communications via multiple antenna arrays and high-gain beams can be employed. Nevertheless, these beams need to be well aligned to reliably transmit data, which is a challenging task given the user mobility and the unpredictable changes of the wireless environment. In this paper, we propose a new distributed beam-alignment strategy relying on a single bit of feedback, which equals one if the signal-to-interference-plus-noise (SINR) reaches a predefined threshold. The novelty consists in a modified reward function, inspired from the sparse nature of the mmWave channel, coupled with the well-known exponential weights algorithm (EXP3). First, we show that our resulting adaptive policy comes with optimal theoretical guarantees in terms of sub-linear regret. Second, our numerical results demonstrate significant performance gains of our beam-alignment policy compared with the original EXP3 algorithm and other existing policies in a mmWave setting with user mobility.
A soft robotic glove for hand rehabilitation training controlled by movements of the healthy hand

Most stroke patients with hand dysfunction have normal function of one side of the body and their intact musculoskeletal systems are intact. Their hand function can be recovered through rehabilitation training. In this paper, a 3D-printed pneumatic-driven soft robotic glove is designed for hand rehabilitation training controlled by the movements of the healthy hand. Data glove is used to collect the motion data of the healthy hand that is then used to control the robotic glove. Characterization tests of the glove were carried out to prove the feasibility of the soft robotic glove. The experimental results show that the robotic glove can assist users to complete the rehabilitation training task.
Path-Balanced Logic Design to Realize Block Ciphers Resistant to Power and Timing Attacks

In this paper, binary decision diagram (BDD)-based dual-rail precharge logic circuit schemes have been developed to counter differential power analysis attacks, timing attacks, and early propagation attacks. Different precharge logic schemes (top precharging, top–bottom precharging, bottom precharging, and symmetric nMOS bottom precharging) are presented and evaluated. The hallmark of our circuit schemes is that an identical number of switchings is ensured on each circuit path. The transistors are interconnected to create pull-up and pull-down paths to outputs by way of binary decisions based on the input variables, so as to realize the required Boolean function. A simple synthesis algorithm for mapping a given Boolean function to such a BDD-based circuit is also presented. Experimentation has been carried out on two 4-, 16-, 32-, and 64-bits S-boxes to establish resistance of our circuits to power analysis attack and to highlight the low-power characteristics with the help of attributes such as peak power variance, average power, propagation delay, normalized standard deviation of power, and normalized energy deviation. Differential power attacks such as difference of mean and correlation power analysis have been carried out. Resilience to the early propagation effect is also demonstrated.
Distributed Model Predictive Formation Control for a Group of UAVs with Newton-Euler Dynamics*

This paper presents a distributed model predictive control (DMPC) algorithm for the formation control problem of a group of unmanned aerial vehicles (UAVs). Newton-Euler equations are used to model a generic UAV dynamics at quasi-stationary situations such as hovering or cruising. Based on the generic UAV model, a DMPC algorithm is introduced to solve the formation control problem. Simulations with parameters of a real-world quadrotor drone demonstrate the effectiveness of the proposed algorithm.
Towards Automatic Recognition of Sounds Observed in Daily Living Activity

An automated system is proposed to recognize different sounds from the daily living activity of humans. Such automated systems can assist the humans and caretakers to recognize any abnormal sound activity and take instant actions. The sound detection model is proposed, which recognizes sounds of the daily activity of an individual. Three Benchmark datasets are used to test our proposed model. The datasets used for our system are Real World Computing Partnership Sound Database in Real Acoustical Environment (RWCP-DB), Urban Sound8K and ESC10 data set. We used Linear Spectrogram, MFCC, Gamma tone Spectrogram as a base line for feature extraction using Convolution Neural Networks (CNN). We proposed two models based on CNN and CNN-SVM architecture and also trained Alex Net and Goggle Net using transfer learning. Our system performed well on different combinations of features and showed improved classification accuracy. Our system performed well in comparison with the other methods reported in literature.
Design, Control and Experiment of a Snake-like Robot with Gripper

In this paper, a novel snake-like robot is proposed by attaching a gripper on its head to enhance the exploration and operation abilities of the snake-like robots. First, the mechanical structures of the snake-like robot and the gripper are designed. Second, the physical prototype and its control system are built. Finally, the serpenoid, sidewinding, rolling gaits of our robot are designed based on the kinematics model, and the moving and gripping functions are verified through physical experiments. As a result, our robot has a high potential on moving in complex environments and performing a variety of tasks.
A Streamlined Continuous Improvement Process Based on Learning Analytics: Insights from the IT Program at King Saud University

Learning Analytics has gained significant attention in the domain of higher education. It involves dealing with the development of tools that harness educational data sets to enhance the learning process. It is a very active and promising research field. In this paper, we propose an insight by investigating how LA and its relevance can be used to improve the continuous improvement process. This can be considered as an initial step towards establishing a general framework for improving the educational outcomes, developing a real-time continuous improvement process, and enhancing students' learning experience in the Information Technology Bachelor Program at King Saud University.
Multi-Cue Semi-Supervised Color Constancy With Limited Training Samples

Color constancy is one of the fundamental tasks in computer vision. Many supervised methods, including recently proposed Convolutional Neural Networks (CNN)-based methods, have been proved to work well on this problem, but they often require a sufficient number of labeled data. However, it is expensive and time-consuming to collect a large number of labeled training images with accurately measured illumination. In order to reduce the dependence on labeled images and leverage unlabeled ones without measured illumination, we propose a novel semi-supervised framework with limited training samples for illumination estimation. Our key insight is that the images with similar features from different cues will share similar lighting conditions. Consequently, three graphs based on three visual cues, low-level RGB color distribution, mid-level initial illuminant estimates and high-level scene content, are constructed to represent the relationship among different images. Then a multi-cue semi-supervised color constancy method (MSCC) is proposed after integrating these three graphs into a unified model. Extensive experiments on benchmark datasets demonstrate that our proposed MSCC method outperforms nearly all the existing supervised methods with limited labeled samples. Even with no unlabeled samples, MSCC still obtains better performance and stableness than most supervised methods.
High-Power Machines and Starter-Generator Topologies for More Electric Aircraft: A Technology Outlook

More electric aircraft (MEA) architectures consist of several subsystems, which must all comply with the settled safety requirements of aerospace applications. Thus, achieving reliability and fault-tolerance represents the main cornerstone when classifying different solutions. Hybrid electric aircraft (HEA) extends the MEA concept by electrifying the propulsive power as well as the auxiliary power, and thereby pushing the limits of electrification. This paper gives an overview of the high-power electrical machine families and their associated power electronic converter (PEC) interfaces that are currently competing for aircraft power conversion systems. Various functionalities and starter-generator (S/G) solutions are also covered. In order to highlight the latest advancements, the efficiency of the world’s most powerful aerospace generator (Mark 1) developed within the E-Fan X HEA project is graphically represented and assessed against other rivaling solutions. Motivated by the strict requirements on efficiency, power density, trustworthiness, as well as starting functionalities, supplementary considerations on the system-level design are paramount. In order to highlight the MEA goals and take advantage of all potential benefits, all subsystems must be treated as a whole. It is then shown that the combination of PECs, aircraft grid and electrical machines can be better adapted to benefit the overall system. This survey outlines the influence of these concerns and offers a view of the future technology outlook, as well as covering the present challenges and opportunities.
Confidence Calibration for Incremental Learning

Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks. At a task transition, we observe an unintentional imbalance of confidence or likelihood between the classes of the past and the new task. We argue that the imbalance aggravates a catastrophic forgetting for class incremental learning. We propose a simple yet effective learning objective to balance the confidence of classes of old tasks and new task in the class incremental learning setup. In addition, we compare various sample memory configuring strategies and propose a novel sample memory management policy to alleviate the forgetting further. The proposed method outperforms the state of the arts in many evaluation metrics including accuracy and forgetting <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> by a large margin (up to 5.71% in <inline-formula> <tex-math notation="LaTeX">$A_{10}$ </tex-math></inline-formula> and 17.1% in <inline-formula> <tex-math notation="LaTeX">$F_{10}$ </tex-math></inline-formula>) in extensive empirical validations on multiple visual recognition datasets such as CIFAR100, TinyImageNet and a subset of the ImageNet.
Hardware Implementation and Analysis of Gen-Z Protocol for Memory-Centric Architecture

With the increase in memory-intensive applications, a memory-centric architecture has been proposed in which the central processing units (CPUs) access a pool of fabric-attached memory. This architecture eliminates the dependency of system components and provides benefits for achieving an independent upgrade cycle and fine-grained resource control. However, developing a memory-centric architecture requires new hardware and software for achieving the low-latency and high-bandwidth communication between the memory and the CPU. This paper presents a hardware prototype of a memory-centric architecture using Gen-Z, which is a new universal system interconnect optimized for ultralow latency and ultra-high bandwidth. The Gen-Z hardware prototype was designed according to the core specification 1.0a and implemented in two types of host interfaces. In this study, we measured the performance of the Gen-Z hardware prototype, i.e., the latency and throughput, and compared it with of the solid-state drive (SSD) and local memory. The experimental results indicated that the performance of remote memory access for a specific write request that utilizes the Gen-Z protocol was better than that of the SSD and local memory. Further, we discussed methods for improving the performance of the Gen-Z prototype.
Energy Efficient Smart Street Lighting System in Nagpur Smart City using IoT-A Case Study

Street Lighting is an important factor for ensuring safety in the city and instilling a sense of security in the mind of the citizens. But the cost associated with it is quite high which is borne by the local municipalities. Recent development in the lighting devices has seen a paradigm shift in the types of devices used which has led to little savings in energy consumption, but a lot remains wasted. Smart street lighting is a cost-effective solution in an urban environment which compromises of advance wireless communication techniques, low cost LED lights and additional sensors which controls the intensity of light. This new system has significant cost and energy savings over the conventional switching ON-OFF method. This paper presents a case study of smart lighting system in Nagpur smart city where one of the goals was to reduce the carbon footprint by reducing the energy consumption. This was achieved by replacing the outdated 320 street luminaries and integrating additional 63 LED lights with motion detection smart lighting system. The deployed intelligent street lighting system of Nagpur smart city has drastically reduced the energy consumption by approximately 55 % per month, while maintaining the lighting norms for the pedestrians and vehicles.