Miracle-dz/UniFAR-FTUs huggingface dataset:
“Spanish Políglota”: an automatic Speech Recognition system based on HMM

The goal of this ASR system is to be able to recognize audio queries that request static translation of a given Spanish word into a specified language. We call this ASR system as the Spanish Políglota. The pronunciation dictionary for the language model is obtained by applying grapheme to phoneme conversion. It was developed via Festival Speech Synthesis Scheme scripts and the SPPAS Spanish lexicon. The possible audio queries are restricted by a BNF grammar we designed for this project. A triphone acoustic model was generated from a set of 1621 words audio recordings. This acoustic model is based on a N-gram model that estimates its probabilities based on the maximum likelihood estimation MLE. We evaluated the prediction of individual words, as well as of synthetic phrases. We generated 1577 synthetic phrases concatenating the words of our audio set. The performance was also measured over a new set of audio recordings from a different speaker. Evaluation of isolated word recognition achieved 77.91% of correct predictions. Nevertheless, the performance dropped when evaluating the synthetic phrases as well as the second speaker’s speech. We consider it is an initial step towards the development of a fully functional automatic speech recognition system.
Early Prediction of Epileptic Seizure Based on the BNLSTM-CASA Model

Epilepsy is one of the world’s most common neurological diseases. Reliable early prediction and warning of seizures can provide timely treatment for patients with epilepsy, and improve their quality of life. Compared with most hand-designed prediction methods, an automatic prediction model that can process the original electroencephalogram (EEG) signals directly and take into account the leads optimization problem is needed. In this paper, we proposed an end-to-end automatic seizure prediction model based on the Batch Normalization Long Short Term Memory networks (BNLSTM) and Channel and Spatial attention (CASA). Firstly, raw EEG signals without any preprocessing are used as the input to the system, which can reduce the computation amount. Secondly, BNLSTM and CASA retained the time and spatial information of the raw EEG data respectively. Channel attention (CA) achieved the automatic optimization of EEG full-lead data and improved the prediction accuracy. Spatial attention (SA) achieved the adaptive learning of feature parameters. Finally, a fully connected layer is applied to predict the seizures. The performance of the seizure prediction model we proposed is evaluated on the data of 14 patients with Area Under the Curve (AUC) of 0.986, accuracy (Acc) of 0.956, specificity (Spe) of 0.968, and sensitivity (Sen) of 0.942. In addition, the proposed method provided an accurate prediction for all 50 seizures of the other 5 patients in the generalization dataset. Experimental results show that the proposed model has a certain generalization performance, which can provide a reliable basis for early warning of epileptic seizures.
PTV Longitudinal-Lateral State Estimation Considering Unknown Control Inputs and Uncertain Model Parameters

The mixed driving of human-driven and automated vehicles is believed to be the most likely future transportation pattern, and this work studies longitudinal-lateral state estimation for human-driven preceding target vehicles (PTVs) in such situations, as this information cannot be directly obtained by the host vehicle (HV). For longitudinal state estimation, the constant turn rate and acceleration (CTRA) model is adopted and the unscented Kalman filter (UKF) is used for estimator construction. For lateral state estimation, a PTV lateral motion model constructed based on the bicycle model and PTV lateral motion relative to the road reference is used. However, the steering angle of the conventional human-driven PTV is unknown, and PTV parameters such as cornering stiffness are uncertain. To provide more accurate PTV lateral states, the design of a state estimator that handles both unknown control inputs and uncertain model parameters is investigated in this study. First, considering that the model parameters are unobservable with the conventional iteration-based method, a system model-based method that measures uncertain parameters via estimated states is proposed. With this method, the observability of uncertain model parameters is naturally established, as the system model fully characterizes the relationship between system states and model parameters. Then, the sensor measurements and estimated states are combined as measurements, and a state-input-parameter mixed Kalman filter (SIPMKF) is proposed to cope with the coexistence of uncertain model parameters and unknown control inputs for PTV lateral state estimation. The effectiveness of the proposed PTV longitudinal-lateral state estimation architecture is verified via simulations and experiments.
Guidelines for an Unmanned Aerial Vehicle-Based Measurement Instrument Design

Although Unmanned Aerial Vehicles (UAVs) are used as mobile measurement platforms in several applications, no guidelines are provided to include the measurement uncertainty assessment in the design steps of the UAV platform. In this article, the steps required for the design of a UAV-based measurement instrument according to the application measurement target and the uncertainty assessment are delineated. In order to highlight the necessity of the proposed design guidelines, the case study of 3D reconstruction of archaeological sites is discussed.
Intelligent Wireless EEG Measurement System With Electrical and Optogenetic Stimulation for Epileptic Seizure Detection and Suppression

A two-channel real-time intelligent electroencephalography (EEG) measurement system with electrical and optical stimulations is presented for seizure detection and suppression. Based on software control through a wireless interface, this system provides closed- and open-loop feedback control with programmable waveform for electrical and optical stimulations. Matlab graphical user interface is used to monitor the intracranial EEG, perform the algorithm for seizure detection, and adjust the required intensity, frequency, duty cycle, and duration of electrical and optical stimulations. The whole hardware device is embedded on a printed circuit board with the size of 27.3 mm $\times23.6$ mm. C57BL/6 mice with Thy1-ChR2-YFP gene transfer and drug-induced seizure are used to demonstrate epileptic seizure suppression according to electrical and optogenetic stimulation in the proposed wireless EEG measurement system.
Design and Implementation of Visually Impaired Assistant system

The need for independent living is recognised in the case of visually impaired people who face a variety of issues, including social isolation.They are forced to live in strange environments with no means of assistance. Today’s tech world should supply them with this assistance for easier socialization. Our aim is to facilitate the life of visually impaired people who could lack the technology to help them in their lives. This project is proposed to focus on the development of a mobile application that uses voice commands and text to voice technology to enable users to interact with the mobile application. The user captures the item they want to identify using the smartphone’s camera and pick the image they want to upload for processing. The image chosen then will undergo through pre-processing techniques depending on the feature the user chose, the features include Money recognition CNN MobileNet architecture was used to analyze the Egyptian banknotes and Color and patterns recognition CNN resnet50 architecture was used to analyze the color and pattern of the clothing. As well, the application allows the user to register or login using facial recognition to further facilitate the experience for users. The application is designed to be effective and can be easily navigated by visually impaired users. ResNet50 architecture produce the highest accuracy in patterns and colors cloth recognition, Mobilenet architecture produce the highest accuracy in currency recognition.
TilGAN: GAN for Facilitating Tumor-Infiltrating Lymphocyte Pathology Image Synthesis With Improved Image Classification

Tumor-infiltrating lymphocytes (TILs) act as immune cells against cancer tissues. The manual assessment of TILs is usually erroneous, tedious, costly and subject to inter- and intraobserver variability. Machine learning approaches can solve these issues, but they require a large amount of labeled data for model training, which is expensive and not readily available. In this study, we present an efficient generative adversarial network, TilGAN, to generate high-quality synthetic pathology images followed by classification of TIL and non-TIL regions. Our proposed architecture is constructed with a generator network and a discriminator network. The novelty exists in the TilGAN architecture, loss functions, and evaluation techniques. Our TilGAN-generated images achieved a higher Inception score than the real images (2.90 vs. 2.32, respectively). They also achieved a lower kernel Inception distance (1.44) and a lower Fréchet Inception distance (0.312). It also passed the Turing test performed by experienced pathologists and clinicians. We further extended our evaluation studies and used almost one million synthetic data, generated by TilGAN, to train a classification model. Our proposed classification model achieved a 97.83% accuracy, a 97.37% F1-score, and a 97% area under the curve. Our extensive experiments and superior outcomes show the efficiency and effectiveness of our proposed TilGAN architecture. This architecture can also be used for other types of images for image synthesis.
An Ensemble-based Regularization Method for Multi-Head Attention

Multi-head attention is applied in Transformer to jointly attend to information from different representation sub-spaces at different positions. In this work, we are inspired by ensemble learning propose a new disagreement term regularization to ensure that the multi-head mechanism works effectively. Specifically, we treat each head as a base learner of emsemble model and make multi-head more diverse by the bias-variance-covariance decomposition. The test results show that our method is quite effective in Multi-30K Enlish⟹German and IWSLT16 Enlish⟹German translation tasks.
A Study on Global Monitoring Station Optimization Deployment Method Based on Navigation Satellite Quadruple Observing Coverage

The number and development of ground stations are one of the most important factors, which infect the determent of satellite orbit parameters and clock correction precise, so to study a method to meet the needs of navigation satellite's observing coverage will play a vital role in BeiDou System's monitoring and assessment. This paper firstly described the definition of observing coverage performance, and established a station observation model; secondly, it proposed an innovative method of quadruple observing coverage deployment, and gave a global deployment scheme of 18 stations base on BeiDou System, to meet the needs of full orbit segment quadruple observing coverage to BeiDou MEO satellite; finally, it simulated two deployments which are 18 stations and iGMAS stations, and each deployment's observing coverage performance to BeiDou MEO satellite.
Anomaly Detection Mechanism Based on Hierarchical Weights through Large-Scale Log Data

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery.
The MMX Sampler for Phobos Sample Return Mission

The Martian Moons Exploration (MMX) mission of the Japan Aerospace Exploration Agency (JAXA) is scheduled to launch in 2024 and aims to be the world's first mission to return a sample from Phobos. For this, JAXA is developing the MMX sampler. There is a special interest in developing the corer shooting mechanism (C-Sampler) to acquire regolith, a robotic arm to position the core sampler and transfer the acquired regolith, and a sample transfer mechanism to move the regolith to a sample return capsule. This paper reports the system design of the MMX Sampler as well as the results of the tests for the corer sampling mechanism and the regolith conditions needed for effective penetration.
A Failure Prediction Model for Large Scale Cloud Applications using Deep Learning

Many cloud service providers face significant challenges in preventing hardware and software failure from occurring. Due to the large scale and heterogeneous nature of cloud computing, cloud services continue to experience failures in their components. A significant proportion of previous studies have focused on the characterization of failed jobs and understanding their behavior, while a few studies have focused on failure prediction, with a focus on increasing the accuracy of failure prediction models. This paper presents the development and implementation of a failure prediction model using a deep learning approach. The proposed model can identify and detect failed tasks early on before they occur. The key feature of the failure prediction model is to improve the performance of cloud applications by reducing the number of failed jobs. In order to investigate the behavior of failure and apply the prediction of failure to the large-scale environment, we used three different traces, namely Google Cluster Trace, Mustang and Trinity. Moreover, we have evaluated the proposed model performance using different evaluation metrics to ensure that the proposed model provides the highest accuracy of predicted values. The proposed model is designed and implemented to achieve high accuracy for failure prediction, regardless of whether the model uses a large or small trace size. The evaluation results show that our proposed model achieved a high precision, recall and f1 score.
Adaptive Control of a Lower Limb Exoskeleton Based on Fuzzy Compensation

A novel adaptive fuzzy approach is proposed for the control system which is used for a lower limb exoskeleton developed by us. In this study, the fuzzy compensation is used to approximate the human-exoskeleton interaction, uncertainties and unmodeled items of the exoskeleton system. Above all, a detailed dynamic model of swing leg is constructed with a consideration of the actuators, and also the human-exoskeleton interaction and uncertainties are taken into account in the dynamic modeling. Then, an adaptive controller with fuzzy compensator which can adjust the control law through the output is designed for the lower limb exoskeleton with a stability analysis. The simulation results showed that the proposed method can effectively help the swing leg to track the desired trajectory with little error. With the presented control strategy, it is able to mitigate the effects of uncertainties, imprecise model and human-exoskeleton interaction to the control of lower limb exoskeleton.
Robust Multiview Subspace Clustering of Images via Tighter Rank Approximation

In this paper, we focus on the multi-view subspace clustering problem under the framework of third order tensor. Recently, tensor nuclear norm (TNN) has been widely used in the multi-view subspace clustering problem. It is known that TNN is a convex surrogate of the tensor rank. However, TNN is linearly proportional to the sum of the singular values. Thus solving the TNN minimization problem will over-penalize the large singular values and lead to a sub-optimal solution. To address this issue, in this paper, a novel tighter tensor log-determinant (TLD) regularizer is proposed to better approximate the tensor rank. Although the proposed method is non-convex, a closed-form solution has been deduced via solving the Euler-Lagrange equation. A corresponding algorithm associated with augmented Lagrangian multipliers is established and the constructed convergent sequence to the Karush-Kuhn-Tucker (KKT) critical point solution is mathematically validated in detail. Extensive experiments indicate that the proposed model achieves significant improvements compared to the state-of-the-art convex subspace clustering models.
Asset Allocation Based On Reinforcement Learning

Asset allocation is an important task in wealth management. In this paper, reinforcement learning(RL) is applied in asset allocation task. Firstly, we propose an Advanced Proximal Policy Optimization(Ad-PPO) algorithm and compare its performance with other algorithms. The empirical results show that Ad-PPO algorithm has a better performance in asset allocation than PG algorithm, PPO algorithm, and equal-weight policy. Secondly, we find the RL algorithms may overfit easily in asset allocation task. To solve this problem, we control the training episodes, simplify the policy network structure and take random initialization on the environment. By these treatments, RL algorithms can be applied in asset allocation tasks effectively.
Optimization of Deep Reinforcement Learning with Hybrid Multi-Task Learning

As an outcome of the technological advancements occurred within artificial intelligence (AI) domain in recent times, deep learning (DL) has been established its position as a prominent representation learning method for all forms of machine learning (ML), including the reinforcement learning (RL). Subsequently, leading to the evolution of deep reinforcement learning (DRL) which combines deep learning’s high representational learning capabilities with current reinforcement learning methods. Undoubtedly, this new direction has caused a pivotal role towards the performance optimization of intelligent RL systems designed by following model-free based methodology. optimization of the performance achieved with this methodology was majorly restricted to intelligent systems having reinforcement learning algorithms designed to learn single task at a time. Simultaneously, single task-based learning method was observed as quite less efficient in terms of data, especially when such intelligent systems required operate under too complex as well as data rich conditions. The prime reason for this was because of the restricted application of existing methods to wide range of scenarios, and associated tasks from those operating environments. One of the possible approaches to mitigate this issue is by adopting the method of multi-task learning. Objective of this research paper is to present a parallel multi-task learning (PMTL) approach for the optimization of deep reinforcement learning agents operating within two different by semantically similar environments with related tasks. The proposed framework will be built with multiple individual actor-critic models functioning within each environment and transferring the knowledge among themselves through a global network to optimize the performance.
A Comprehensive Review of Fault Diagnosis and Tolerant Control in DC-DC Converters for DC Microgrids

As the power interface between the energy storage devices (ESD) and the dc link of DC Micro-grids, dc-dc converters, which have been regarded as the heart of many critical applications such as electrical vehicles, data center, and aerospace power systems, have gained more and more attentions recently. The fault diagnosis and tolerant control in dc-dc converters become essential to ensure a reliable and robust power system especially for critical applicants, where a sudden stoppage, loss of functionality, or degradation of performance might result in a catastrophe. This paper focuses on different types of faults in dc-dc converters by analyzing main failure spots and mechanism, covering catastrophic fault and parametric fault from component to system. A comprehensive review of the up-to-date fault mode analysis, fault diagnosis signals and algorithms, and fault tolerant control in various dc-dc topologies has been presented. This research will provide a useful design guideline for the reliability improvement in all forms of dc-dc converters.
Square-root filtering method for continuous identification of industrial systems

The article deals with the possibility of using the square-root filtering methods to guarantee numerical stability of continuous identification during long-term monitoring of time-varying industrial systems. The main emphasis is being placed on long-term deployment, therefore the process of recursive computation of the covariance matrix $P_{(k)}$ in the form of regression analysis is discussed in detail. The application of regression analysis in form of a generalized least-squares method for the purpose of weighting a posteriori information is solved by using the square-root filtering methods, since the methods guarantee, that the difference between two positive definite matrices and the result are positive definite matrices. Also, the square-root filtering suppresses the negative effect of errors caused by insumcient length of computers bit word, rounding, etc., which leads to the loss of numerical stability of the algorithm and also to the loss of positive definiteness of the matrices. The described methodology is being used to create continuous identification forgetting and weighting algorithms. These algorithms are used for long-term identification and creation of mathematical models of the monitored industrial systems. The obtained models are then being used for various applications, mostly for applications which require predictive control.
Around-the-Corner or Through-the-Wall? Classification of Non-Line-of-Sight Conditions

It is well known that location accuracy is low in non-line-of-sight (NLOS) scenarios. Several techniques have been proposed to mitigate ranging errors to improve location accuracy. These techniques play an important role in accurate localization for many applications which target safety and productivity in harsh industrial environments. In some cases, these mitigation techniques target specific NLOS scenarios, therefore such scenarios have to identified before mitigation can be applied. In this paper, we study identification of two types of NLOS scenarios that are common indoors: Through-the-Wall (TTW) and Around-the-Corner (ATC). We conduct a measurement campaign in various indoor TTW and ATC scenarios, and show that these two types of NLOS scenarios can be differentiated with high accuracy using channel statistics from ultra-wideband radios.